A short guide to some of the most relaxing spots to visit and things to do on the Big Island, Hawaii.
The Big Island, Hawaii is an amazing tropical adventure and retreat with big activities, amazing views, and relaxing environment rolled up into one! The Big Island, Hawaii is s beautiful spot where trouble vanishes.
This magnificent island has an area of 10, 432 km². For many tourists, it is both a serene and adrenaline-stirring spot bursting with fun activities laid on its beaches, and volcanoes. Its historical spots and friendly people won't even make you bored a tiny bit.
What about the weather? It is comfortably warm. You can relish the cool afternoons, picturesque sunsets and indescribably refreshing air that lull your senses.
Hawaii is almost a synonym for volcanoes and beaches. Here are some of the most fascinating volcanoes in The Big Island.
Mauna Kea. This tallest volcano in Hawaii has frequent snows on the summit, which you can see from distant shores. You can find the Onizuka Center for International Astronomy near its summit. Star gazers and sunset buffs love this place!
Hualalai. It is one of the three active volcanoes on The Big Island. But, its slopes have building and roads on them already.
Kohala Volcano. It is already extinct but its rich history still fascinates tourists who wonder how this volcano reduced it summit by 1,000 meters over the centuries.
Anaeho'omalu Bay. If you love taking pictures of the grandest sunset in the planet, this is the place to be. You can socialize with the locales at the beach, have a ride of your life at snorkel tours and sailing trips or take your first diving or surfing lessons. There are also loads of mouth-watering meals you can buy at the bay and eat under the restful shade of coconut palms. And don't worry about calories, because you can burn them later on by visiting the archeological sites in the area.
Hapuna Beach. This long and wide beach is ideal for snorkeling, surfing, gentle floating and boarding. It is incredibly sandy and warm, with lots of well-maintained facilities ideal for family picnics, small and big gatherings and solo relaxation.
Waialea Beach (Beach 69). It is perfect for families who want to hop on tiny islands. It has a long coral reef ideal for divers. If you don't mind its little less than crystal waters during windy days, snorkeling would be fun!
Life is full of stress and pressures. Sometimes, you'll realize that you simply lost it. If you're suffering from addiction, Hawaii Island Recovery can be your best shot at a sober life. It is a premiere residential rehab for substance abuse in the Big Island. With its community program, unique therapeutic services and unique environment which is ideal to healing, you'll have a great chance to recover.
Call Hawaii Island Recovery today!


The content includes affiliate links.
Christmas always sneaks up on me. Once Halloween comes it always feels like Christmas is just around the corner. That's largely due to the fact that we have TONS of birthdays from now until Christmas so there's hardly a free weekend. This year I'm going to be ready though. These Peppermint Hot Chocolate Spoons are just the start of my handmade Christmas ideas.
As you've probably already noticed, I make it a habit of creating food gift ideas for the holidays. From everything to Mason Jar Peppermint Cupcakes to Rudolf Applesauces, I just love incorporating food into our gifts. This way they're tasty as well as super cute!
These Peppermint Hot Chocolate Spoons would also make the perfect holiday party favor idea. We made sure that each of our guests left with one of these after a fun girls holiday make and take party that featured Young Living Essential Oils. You'll be blown away by all the ways that you can use essential oils to eliminate harmful chemicals in your home. We trust Young Living Essential Oils to do just that! Just look at all the ways that we have HERE.
Peppermint Hot Chocolate Spoons are the perfect fun, gift idea.
Melt the Wilton White Chocolate according to package instructions in the microwave.
Drop in 2-3 drops of peppermint essential oil and mix well.
Dip each of the spoons into the peppermint white chocolate and sprinkle with Christmas color sprinkles.
Set on a cooling rack to harden.
Package in plastic bags with bakers twine and give as gifts.
Since we love our Young Living Essential Oils so much we are running an amazing promotion for the month of November. You get all of this plus a bunch of freebies! Click HERE to find out more!


text:
Q: JSON Parser for OnClick http request not working? Android I'm not sure how much information I needed to include on this issue. I've copied my JSON coding below, but if there is any other coding needed I can provide it. Also, the LogCat error log is provided at the time of error.
{
                super.onCreate(savedInstanceState);
            setContentView(R.layout.main);

            refresh = (Button) findViewById(R.id.button1);

            refresh.setOnClickListener(new View.OnClickListener()
            {
            public void onClick(View view)
              {
                 String result = null;
                InputStream is = null;

                try{
                        HttpClient httpclient = new DefaultHttpClient();
                        HttpPost httppost = new HttpPost("http://www.jamiepaleschi.co.uk/android_login/selectall.php");
                        HttpResponse response = httpclient.execute(httppost);
                        HttpEntity entity = response.getEntity();
                        is = entity.getContent();

                        Log.e("log_tag", "connection success ");
                     //   Toast.makeText(getApplicationContext(), "pass", Toast.LENGTH_SHORT).show();
                }
                catch(Exception e)
                {
                        Log.e("log_tag", "Error in http connection "+e.toString());
                        Toast.makeText(getApplicationContext(), "Connection fail", Toast.LENGTH_SHORT).show();

                }
                //convert response to string
                try
                {
                        BufferedReader reader = new BufferedReader(new InputStreamReader(is,"iso-8859-1"),8);
                        StringBuilder sb = new StringBuilder();
                        String line = null;
                        while ((line = reader.readLine()) != null)
                        {
                                sb.append(line + "\n");
                              //  Toast.makeText(getApplicationContext(), "Input Reading pass", Toast.LENGTH_SHORT).show();
                        }
                        is.close();

                        result=sb.toString();
                }
                catch(Exception e)
                {
                       Log.e("log_tag", "Error converting result "+e.toString());
                    Toast.makeText(getApplicationContext(), " Input reading fail", Toast.LENGTH_SHORT).show();

                }
//parse json data
                    try
                {

                JSONArray jArray = new JSONArray(result);

                String re=jArray.getString(jArray.length()-1);

                TableLayout tv=(TableLayout) findViewById(R.id.table);
                tv.removeAllViewsInLayout();

                   int flag=1;

                for(int i=-1;i<jArray.length()-1;i++)

                        {

                                TableRow tr=new TableRow(Main.this);

                                tr.setLayoutParams(new LayoutParams(
                                           LayoutParams.WRAP_CONTENT,
                                           LayoutParams.WRAP_CONTENT));

                                if(flag==1)
                                {

                                    TextView b1=new TextView(Main.this);
                                     b1.setText("Mon");
                                     b1.setTextColor(Color.BLUE);
                                     b1.setTextSize(15);
                                     tr.addView(b1);

                                    TextView b19=new TextView(Main.this);
                                     b19.setPadding(10, 0, 0, 0);
                                     b19.setTextSize(15);
                                     b19.setText("Tue");
                                     b19.setTextColor(Color.BLUE);
                                     tr.addView(b19);

                                   TextView b29=new TextView(Main.this);
                                 b29.setPadding(10, 0, 0, 0);
                                     b29.setText("Wed");
                                     b29.setTextColor(Color.BLUE);
                                     b29.setTextSize(15);
                                     tr.addView(b29);

                                     TextView b39=new TextView(Main.this);
                                     b39.setPadding(10, 0, 0, 0);
                                         b39.setText("Thur");
                                         b39.setTextColor(Color.BLUE);
                                         b39.setTextSize(15);
                                         tr.addView(b39);

                                      TextView b49=new TextView(Main.this);
                                         b49.setPadding(10, 0, 0, 0);
                                             b49.setText("Fri");
                                             b49.setTextColor(Color.BLUE);
                                             b49.setTextSize(15);
                                             tr.addView(b49);

                                 tv.addView(tr);

                                     final View vline = new View(Main.this);
                                          vline.setLayoutParams(new TableRow.LayoutParams(TableRow.LayoutParams.WRAP_CONTENT, 2));
                                          vline.setBackgroundColor(Color.BLUE);

                                tv.addView(vline);
                                flag=0;

                                }

                                else
                                {

                                    JSONObject json_data = jArray.getJSONObject(i);

                                    Log.i("log_tag","Mon: "+json_data.getString("f1")+
                                              ", Tue: "+json_data.getString("f2")+
                                              ", Wed: "+json_data.getString("f3")+
                                              ", Thur: "+json_data.getString("f4")+
                                              ", Fri: "+json_data.getString("f5"));

                            TextView b=new TextView(Main.this);
                                String stime=String.valueOf(json_data.getString("f1"));
                                  b.setText(stime);
                                b.setTextColor(Color.RED);
                                b.setTextSize(10);
                                b.setWidth(60);
                                tr.addView(b);

                               TextView b1=new TextView(Main.this);
                                b1.setPadding(10, 0, 0, 0);
                                b1.setTextSize(10);
                                b1.setWidth(60);
                                String stime1=json_data.getString("f2");
                                 b1.setText(stime1);
                                b1.setTextColor(Color.WHITE);
                                tr.addView(b1);

                              TextView b2=new TextView(Main.this);
                             b2.setPadding(10, 0, 0, 0);
                                String stime2=String.valueOf(json_data.getString("f3"));
                                b2.setText(stime2);
                                b2.setTextColor(Color.RED);
                                b2.setTextSize(10);
                                b2.setWidth(60);
                                tr.addView(b2);

                                TextView b3=new TextView(Main.this);
                                b3.setPadding(10, 0, 0, 0);
                                   String stime3=String.valueOf(json_data.getString("f4"));
                                   b3.setText(stime3);
                                   b3.setTextColor(Color.WHITE);
                                   b3.setTextSize(10);
                                   b3.setWidth(60);
                                   tr.addView(b3);

                                 TextView b4=new TextView(Main.this);
                                   b4.setPadding(10, 0, 0, 0);
                                      String stime4=String.valueOf(json_data.getString("f5"));
                                      b4.setText(stime4);
                                      b4.setTextColor(Color.RED);
                                      b4.setTextSize(10);
                                      b4.setWidth(60);
                                      tr.addView(b4);

                                  tv.addView(tr);

                        final View vline1 = new View(Main.this);
                      vline1.setLayoutParams(new TableRow.LayoutParams(TableRow.LayoutParams.WRAP_CONTENT, 1));
                      vline1.setBackgroundColor(Color.WHITE);
                      tv.addView(vline1);

                                }

                       }

                }
                catch(JSONException e)
                {
                        Log.e("log_tag", "Error parsing data "+e.toString());
                        Toast.makeText(getApplicationContext(), "JsonArray fail", Toast.LENGTH_SHORT).show();
                }

           }
           });

    /**
     * Change Password Activity Started
     **/
    changepas.setOnClickListener(new View.OnClickListener(){
        public void onClick(View arg0){

            Intent chgpass = new Intent(getApplicationContext(), ChangePassword.class);

            startActivity(chgpass);
        }

    });

   /**
    *Logout from the User Panel which clears the data in Sqlite database
    **/
    btnLogout.setOnClickListener(new View.OnClickListener() {

        public void onClick(View arg0) {

            UserFunctions logout = new UserFunctions();
            logout.logoutUser(getApplicationContext());
            Intent login = new Intent(getApplicationContext(), Login.class);
            login.addFlags(Intent.FLAG_ACTIVITY_CLEAR_TOP);
            startActivity(login);
            finish();
        }
    });

LogCat Error Log
    03-04 04:03:13.509: W/dalvikvm(797): threadid=1: thread exiting with uncaught exception (group=0x414c4700)
03-04 04:03:13.530: E/AndroidRuntime(797): FATAL EXCEPTION: main
03-04 04:03:13.530: E/AndroidRuntime(797): java.lang.NullPointerException
03-04 04:03:13.530: E/AndroidRuntime(797):  at org.json.JSONTokener.nextCleanInternal(JSONTokener.java:116)
03-04 04:03:13.530: E/AndroidRuntime(797):  at org.json.JSONTokener.nextValue(JSONTokener.java:94)
03-04 04:03:13.530: E/AndroidRuntime(797):  at org.json.JSONArray.<init>(JSONArray.java:87)
03-04 04:03:13.530: E/AndroidRuntime(797):  at org.json.JSONArray.<init>(JSONArray.java:103)
03-04 04:03:13.530: E/AndroidRuntime(797):  at com.learn2crack.Main$1.onClick(Main.java:129)
03-04 04:03:13.530: E/AndroidRuntime(797):  at android.view.View.performClick(View.java:4240)
03-04 04:03:13.530: E/AndroidRuntime(797):  at android.view.View$PerformClick.run(View.java:17721)
03-04 04:03:13.530: E/AndroidRuntime(797):  at android.os.Handler.handleCallback(Handler.java:730)
03-04 04:03:13.530: E/AndroidRuntime(797):  at android.os.Handler.dispatchMessage(Handler.java:92)
03-04 04:03:13.530: E/AndroidRuntime(797):  at android.os.Looper.loop(Looper.java:137)
03-04 04:03:13.530: E/AndroidRuntime(797):  at android.app.ActivityThread.main(ActivityThread.java:5103)
03-04 04:03:13.530: E/AndroidRuntime(797):  at java.lang.reflect.Method.invokeNative(Native Method)
03-04 04:03:13.530: E/AndroidRuntime(797):  at java.lang.reflect.Method.invoke(Method.java:525)
03-04 04:03:13.530: E/AndroidRuntime(797):  at com.android.internal.os.ZygoteInit$MethodAndArgsCaller.run(ZygoteInit.java:737)
03-04 04:03:13.530: E/AndroidRuntime(797):  at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:553)
03-04 04:03:13.530: E/AndroidRuntime(797):  at dalvik.system.NativeStart.main(Native Method)

LATEST CODE VERSIONS
main.java
public class Main extends Activity{
Button btnLogout;
Button changepas;
Button refresh;

/**
 * Called when the activity is first created.
 */
@Override
public void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
    setContentView(R.layout.main);

    changepas = (Button) findViewById(R.id.btchangepass);
    btnLogout = (Button) findViewById(R.id.logout);
    refresh = (Button) findViewById(R.id.button1);

    DatabaseHandler db = new DatabaseHandler(getApplicationContext());

    /**
     * Hashmap to load data from the Sqlite database
     **/
     HashMap<String,String> user = new HashMap<String, String>();
     user = db.getUserDetails();

/**
 * TimeTable Refresh
 */

            super.onCreate(savedInstanceState);
            setContentView(R.layout.main);

            refresh = (Button) findViewById(R.id.button1);

            refresh.setOnClickListener(new View.OnClickListener()
            {
            public void onClick(View view)
              {
                 String result = null;
                InputStream is = null;

                try{
                        HttpClient httpclient = new DefaultHttpClient();
                        HttpPost httppost = new HttpPost("http://www.jamiepaleschi.co.uk/android_login/selectall.php");
                        HttpResponse response = httpclient.execute(httppost);
                        HttpEntity entity = response.getEntity();
                        is = entity.getContent();

                        Log.e("log_tag", "connection success ");
                     //   Toast.makeText(getApplicationContext(), "pass", Toast.LENGTH_SHORT).show();
                }
                catch(Exception e)
                {
                        Log.e("log_tag", "Error in http connection "+e.toString());
                        Toast.makeText(getApplicationContext(), "Connection fail", Toast.LENGTH_SHORT).show();

                }
                //convert response to string
                try
                {
                        BufferedReader reader = new BufferedReader(new InputStreamReader(is,"iso-8859-1"),8);
                        StringBuilder sb = new StringBuilder();
                        String line = null;
                        while ((line = reader.readLine()) != null)
                        {
                                sb.append(line + "\n");
                              //  Toast.makeText(getApplicationContext(), "Input Reading pass", Toast.LENGTH_SHORT).show();
                        }
                        is.close();

                        result=sb.toString();
                }
                catch(Exception e)
                {
                       Log.e("log_tag", "Error converting result "+e.toString());
                    Toast.makeText(getApplicationContext(), " Input reading fail", Toast.LENGTH_SHORT).show();

                }

                //parse json data
                try
                {

                JSONArray jArray = new JSONArray(result);

                String re=jArray.getString(jArray.length());

                TableLayout tv=(TableLayout) findViewById(R.id.table);
                tv.removeAllViewsInLayout();

                   int flag=1;

                   for(int i=0;i<jArray.length();i++){
                       Object obj = jArray.get(i);
                       if (obj instanceof JSONObject) {
                       // is JSONObject
                        JSONObject json_data = (JSONObject)obj;

                       }else{

                                TableRow tr=new TableRow(Main.this);

                                tr.setLayoutParams(new LayoutParams(
                                           LayoutParams.WRAP_CONTENT,
                                           LayoutParams.WRAP_CONTENT));

                                if(flag==1)
                                {

                                    TextView b1=new TextView(Main.this);
                                     b1.setText("Mon");
                                     b1.setTextColor(Color.BLUE);
                                     b1.setTextSize(15);
                                     tr.addView(b1);

                                    TextView b19=new TextView(Main.this);
                                     b19.setPadding(10, 0, 0, 0);
                                     b19.setTextSize(15);
                                     b19.setText("Tue");
                                     b19.setTextColor(Color.BLUE);
                                     tr.addView(b19);

                                   TextView b29=new TextView(Main.this);
                                 b29.setPadding(10, 0, 0, 0);
                                     b29.setText("Wed");
                                     b29.setTextColor(Color.BLUE);
                                     b29.setTextSize(15);
                                     tr.addView(b29);

                                     TextView b39=new TextView(Main.this);
                                     b39.setPadding(10, 0, 0, 0);
                                         b39.setText("Thur");
                                         b39.setTextColor(Color.BLUE);
                                         b39.setTextSize(15);
                                         tr.addView(b39);

                                      TextView b49=new TextView(Main.this);
                                         b49.setPadding(10, 0, 0, 0);
                                             b49.setText("Fri");
                                             b49.setTextColor(Color.BLUE);
                                             b49.setTextSize(15);
                                             tr.addView(b49);

                                 tv.addView(tr);

                                     final View vline = new View(Main.this);
                                          vline.setLayoutParams(new TableRow.LayoutParams(TableRow.LayoutParams.WRAP_CONTENT, 2));
                                          vline.setBackgroundColor(Color.BLUE);

                                tv.addView(vline);
                                flag=0;

                                }

                                else
                                {

                                    JSONObject json_data = jArray.getJSONObject(i);

                                    Log.i("log_tag","Mon: "+json_data.getString("f1")+
                                              ", Tue: "+json_data.getString("f2")+
                                              ", Wed: "+json_data.getString("f3")+
                                              ", Thur: "+json_data.getString("f4")+
                                              ", Fri: "+json_data.getString("f5"));

                            TextView b=new TextView(Main.this);
                                String stime=String.valueOf(json_data.getString("f1"));
                                  b.setText(stime);
                                b.setTextColor(Color.RED);
                                b.setTextSize(10);
                                b.setWidth(60);
                                tr.addView(b);

                               TextView b1=new TextView(Main.this);
                                b1.setPadding(10, 0, 0, 0);
                                b1.setTextSize(10);
                                b1.setWidth(60);
                                String stime1=json_data.getString("f2");
                                 b1.setText(stime1);
                                b1.setTextColor(Color.WHITE);
                                tr.addView(b1);

                              TextView b2=new TextView(Main.this);
                             b2.setPadding(10, 0, 0, 0);
                                String stime2=String.valueOf(json_data.getString("f3"));
                                b2.setText(stime2);
                                b2.setTextColor(Color.RED);
                                b2.setTextSize(10);
                                b2.setWidth(60);
                                tr.addView(b2);

                                TextView b3=new TextView(Main.this);
                                b3.setPadding(10, 0, 0, 0);
                                   String stime3=String.valueOf(json_data.getString("f4"));
                                   b3.setText(stime3);
                                   b3.setTextColor(Color.WHITE);
                                   b3.setTextSize(10);
                                   b3.setWidth(60);
                                   tr.addView(b3);

                                 TextView b4=new TextView(Main.this);
                                   b4.setPadding(10, 0, 0, 0);
                                      String stime4=String.valueOf(json_data.getString("f5"));
                                      b4.setText(stime4);
                                      b4.setTextColor(Color.RED);
                                      b4.setTextSize(10);
                                      b4.setWidth(60);
                                      tr.addView(b4);

                                  tv.addView(tr);

                        final View vline1 = new View(Main.this);
                      vline1.setLayoutParams(new TableRow.LayoutParams(TableRow.LayoutParams.WRAP_CONTENT, 1));
                      vline1.setBackgroundColor(Color.WHITE);
                      tv.addView(vline1);

                                }

                       } }}

                catch(JSONException e)
                {
                        Log.e("log_tag", "Error parsing data "+e.toString());
                        Toast.makeText(getApplicationContext(), "JsonArray fail", Toast.LENGTH_SHORT).show();
                }

           }
           });

LOGCAT ERRORS
03-04 05:04:58.727: E/AndroidRuntime(1005): FATAL EXCEPTION: main
03-04 05:04:58.727: E/AndroidRuntime(1005): java.lang.NullPointerException
03-04 05:04:58.727: E/AndroidRuntime(1005):     at org.json.JSONTokener.nextCleanInternal(JSONTokener.java:116)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at org.json.JSONTokener.nextValue(JSONTokener.java:94)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at org.json.JSONArray.<init>(JSONArray.java:87)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at org.json.JSONArray.<init>(JSONArray.java:103)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at com.learn2crack.Main$1.onClick(Main.java:129)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at android.view.View.performClick(View.java:4240)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at android.view.View$PerformClick.run(View.java:17721)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at android.os.Handler.handleCallback(Handler.java:730)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at android.os.Handler.dispatchMessage(Handler.java:92)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at android.os.Looper.loop(Looper.java:137)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at android.app.ActivityThread.main(ActivityThread.java:5103)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at java.lang.reflect.Method.invokeNative(Native Method)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at java.lang.reflect.Method.invoke(Method.java:525)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at com.android.internal.os.ZygoteInit$MethodAndArgsCaller.run(ZygoteInit.java:737)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:553)
03-04 05:04:58.727: E/AndroidRuntime(1005):     at dalvik.system.NativeStart.main(Native Method)

A: Here :
JSONObject json_data = jArray.getJSONObject(i);

when i==-1 then getJSONObject return null because not object available at index -1 in JSONArray.so change iteration condition as:
for(int i=0;i<jArray.length();i++){
  Object obj = jArray.get(i);
  if (obj instanceof JSONObject) {
  // is JSONObject
   JSONObject json_data = (JSONObject)obj;

  }else{
          //String....
   }
}

A: Change the following lines. They will throw Null pointer exception if the json result is null.
    String re=jArray.getString(jArray.length()-1);

to
    String re= null;
    if(jArray.length!=0)jArray.getString(jArray.length()-1);

and
    for(int i=-1;i<jArray.length()-1;i++)

to
    for(int i=0;i<jArray.length();i++)

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
\label{sec:int}

Spatial interactions among species play a vital role in the stability of ecosystems \cite{ecology,foraging,BUCHHOLZ2007401}. It has been reported that many species evolve to better adapt to the local environmental conditions, increasing the chances of controlling the natural resources to ensure species persistence \cite{climatechange,adap2}. Motivated by local stimuli, many animals move strategically as an adaptive response to alterations in their living conditions \cite{butterfly,adaptive1,adaptive2,Dispersal,BENHAMOU1989375,Causes,MovementProfitable}. Following environmental cues, organisms can escape threats to their existence, making alliances with other species to protect them against natural enemies' attacks.
Furthermore, scanning the neighbourhood helps individuals observe additional survival hazards ravaging the ecosystem, such as epidemic outbreaks \cite{epidemicbook,epidemicprocess,COVID,disease2,disease4,DONOFRIO2022112072}.
Using sensory information, individuals can decide when it is decisive to flee from enemies or perform social distancing strategies \cite{Odour,socialdist,soc}.

There is plenty of evidence that self-preservation behavioural strategies play a central role in controlling natural resources.
Therefore, prioritising moving toward the direction where the probability of being killed is minimum has been demonstrated to be more advantageous in cyclic game models \cite{Moura,enenyenemy}. It has been shown in Ref. ~\cite{Moura} that the Safeguard strategy is advantageous for species participating in a cyclic spatial game, giving the species the possession of the most significant fraction of the territory.
However, as investigated in Ref.~\cite{combination}, in the event of an epidemic strikes the system, individuals' concern about survival increases: besides being eliminated in the cyclic spatial game, they are vulnerable to being contaminated by the infectious disease that is transmitted person-to-person.
In this scenario, the Safeguard tactic, which involves a social approach, may become problematic since the organism must be distant from all possible viral vectors. To avoid contamination, thus, organisms perform the Social Distancing strategy, moving toward the direction with the highest density of empty species \cite{combination}.
But neglecting the protection against enemies to perform the Social Distancing strategy is a dilemma that needs to be well considered by each organism. Combining both survival movement strategies provides better protection because if executing only Social Distancing, the individual is vulnerable to the attack of enemies \cite{combination}.

In this work, instead of a fixed global design for the combination of survival movement strategies as investigated in Ref.~\cite{combination}, valid for all organisms in the system, each individual scans the neighbourhood to observe the presence of sick individuals. This allows the individual to decide which survival strategy to perform at each instant, switching the self-preservation strategy in response to a change in the local living conditions.
Epidemic outbreaks can reach departed regions differently because the pathogen that causes the disease is passed from individual to individual. Thus, each organism needs to be aware of the arrival of new disease waves in the local community. If the chances of being infected are higher than a tolerable threshold, Social Distancing is executed; otherwise, the Safeguard tactic is continued.
The decision is based on the individual's capacity to perceive the neighbourhood: i) how far the organisms can scan their neighbourhood; ii) the cognitive and adaptive ability to interpret the signals received and change from the Safeguard to Social Distancing strategy when facing a local epidemic outbreak.

Our goal is to understand how the adaptive survival strategy to local epidemic outbreaks modifies the pattern formation process and changes the average size of the typical spatial domains. Then, we aim to quantify the benefits of behavioural tactics in the organisms' selection and infection risks. Our simulations consider various tolerable thresholds for organisms to trigger the Social Distancing strategy.
The research is performed by implementing stochastic simulations
based on the May-Leonard implementation of the rock-paper-scissors model, where the total number of individuals is not conserved \cite{Szolnoki-JRSI-11-0735,Anti1,anti2,MENEZES2022101606,PhysRevE.97.032415,Avelino-PRE-86-036112,MENEZES2022111903}.
The disease spreading happens between two immediate neighbours in the lattice, with a sick individual transmitting the virus to a healthy organism, irrespective of the species \cite{combination,rps-epidemy,epidemic-graphs,germen}. Sick organisms act as viral vectors, with a probability of being cured or dying, according to the disease virulence; no organism or species is immune, even after being cured of the disease.

The outline of this paper is as follows. The methods are described in Sec.~\ref{sec2}, where we introduce the model and the simulation implementation.
Next, we explore the role of the locally adaptive survival movement tactic in the spatial patterns and calculate the dynamics of the densities of species in Sec.~\ref{sec3}.
In Sec.~\ref{sec4}, we compute the impact of the local strategies on organisms' selection and infection risks; the densities of species and the characteristic scale of the typical areas occupied by each species
in terms of the threshold are also computed.
The dependence of the outcomes on the individuals' physical abilities to perform the survival strategy is quantified in Sec.~\ref{sec5}.
Finally, we discuss the results and present our conclusions in Sec.~\ref{sec6}.

\begin{figure}
\centering
\includegraphics[width=50mm]{Fig1}
\caption{Illustration of the generalised rock-paper-scissors model with $5$ species. Selection interactions are represented by arrows indicating the dominance of organisms of species $i$ over individuals of species $i+1$.}
\label{fig1}
\end{figure}

\section{Methods}
\label{sec2}

\subsection{The model}
We investigate a cyclic game system composed of five species whose selection interactions follow the generalised rock-paper-scissors model,
illustrated in Fig.~\ref{fig1}. Accordingly, individuals of species
$i$ dominate over individuals of species $i+1$, with $i=1,2,3,4,5$; we assume the identification $i=i+5\,\alpha$, where $\alpha$ is an integer. We consider that an epidemic spreads through the system, with organisms of all species equally susceptible to infection and reinfection by a disease
transmitted person to person. All infected organisms become viral vectors to contaminate their immediate neighbours; some sick individuals die while others are cured.

In our model, organisms of one out of the species perform behavioural movement strategies to protect themselves against nearby enemies and infection by approaching viral vectors.
Individuals usually perform the Safeguard strategy, moving towards the direction with the highest density of enemies of their enemies \cite{Moura}. Therefore, they profit from being close to guards, decreasing the risk of being killed in the cyclic game. However, if the density of sick individuals in the vicinity represents a danger of imminent contamination, the individual opts for the Social Distancing tactic \cite{combination}. The decision to move to the areas with lower population density is individual, being triggered every time a surge affects the organism's neighbourhood making the local density of sick individuals higher than a given threshold.
\subsection{Simulations}
We use square lattices with periodic boundary conditions to implement our stochastic simulations. As the spatial interactions may lead to organisms' births and deaths, we use the May-Leonard implementation: the total number of individuals is not conserved \cite{leonard}. We assume that each grid point contains at most one individual, which limits the maximum number of individuals to equal the total number of grid points, $\mathcal{N}$.
The densities of individuals of species $i$ at time $t$ is defined as
$\rho_i = I_i/\mathcal{N}$, where $I_i$ is number of individuals of species $i$,
with $i=1,2,3,4,5$.

We identify the organisms using the notation $h_i$ and $s_i$ for
healthy and sick individuals; the use of the identification $i$ stands for individuals irrespective of illness or health, with $i=1,2,3,4,5$. This notation allows the description of the spatial interactions as \cite{combination}:
\begin{itemize}
\item
Selection: $ i\ j \to i\ \otimes\,$, with $ j = i+1$, where $\otimes$ means an empty space; selection interactions happens between individuals, irrespective of the healthy conditions;
\item
Reproduction: $ i\ \otimes \to i\ h_i\,$; a reproduction interaction generates a new healthy individual.
\item
Mobility: $ i\ \odot \to \odot\ i\,$, where $\odot$ means either an individual of any species or an empty space; when moving, a healthy or a sick individual switches position with another organism of any species or with an empty site;
\item
Infection: $ s_i\ h_j \to s_i\ s_j\,$, with $i, j=1,2,3,4,5$: a sick individual passes the disease to another organism of any species;
\item
Cure: $ s_i \to h_i\,$: an ill individual is naturally cured of the disease without gaining immunity, thus being subject to reinfection;
\item
Death: $ s_i \to \otimes\,$: when a sick organism dies because of the disease, its position is left empty.
\end{itemize}
Empty spaces are created when individuals are selected, or sick organisms die because of the disease severity.

The spatial interactions are executed by assuming the von Neumann neighbourhood: individuals may interact with one of their four immediate neighbours. At each time step, one of the possible implementations is chosen aleatory, according to the respective interaction probabilities: i) healthy individuals: $s_h$ (selection), $r_h$ (reproduction), $m_h$ (mobility); ii) sick organisms: $s_s$ (selection), $r_s$ (reproduction), $m_s$ (mobility), $\omega$ (infection), $c$ (cure), and $d$ (death by disease complications). The parameters are the same for all organisms of every species.
Our algorithm follows three steps: i) randomly selecting an active individual; ii) using the model probabilities to choose one interaction to be executed; iii) drawing one of the four nearest neighbours to suffer the action, except for the directional movement strategy, where the organism chooses the direction to move according to the specific survival strategy. If the interaction is implemented, one timestep is counted. Otherwise, the three steps are redone. We assume the time unit called generation, defined as the necessary time to $\mathcal{N}$ timesteps to occur.

\subsection{Adaptive survival strategy to local epidemic outbreaks}
We investigate a locally adaptive self-preservation strategy of individuals that use environmental cues to move cleverly according to the current survival conditions.
This means that each organism interprets the signals received from the neighbourhood to identify the existence of local disease surges, thus maximising the protection from disease infection and enemies' attacks.

Each organism usually moves to minimise the chances of being caught and eliminated in the spatial competition game, performing the Safeguard strategy - this is independent of the organism's geographic position \cite{Moura,combination}.
When performing the Safeguard strategy, an individual of species $i$ moves in the direction with the highest concentration of individuals of species $i-2$. This provides protection against individuals of species $i-1$, according to Fig.~\ref{fig1}.
However, the Safeguard tactic increases the chances of contamination during a local epidemic since the virus is transmitted from individual to individual. Because of this, organisms adapt to respond to the arrival of a local disease surge, temporarily switching from the Safeguard to the Social Distancing strategy, moving towards the direction with more empty spaces.

Every time an organism decides to perform the Social Distancing instead of Safeguard strategy, the vulnerability to being killed in the spatial game increases. Therefore, each organism scans the vicinity to conclude how to move. This means that the choice of the survival strategy is local, based on each organism's reality.
We define a real parameter $\beta$, with $0\,\leq\,\beta\,\leq\,1$, to define the social distancing trigger parameter: a threshold assumed by the organisms as the minimum local density of sick individuals to perform the Social Distancing strategy.
Only if the local density of sick individuals is higher than $\beta$, the Social Distancing is performed. Otherwise, the Safeguard tactic is executed.

Adapting the survival movement strategy also depends on the
organisms' conditioning, meaning their cognitive ability to learn
how to react to the presence of sick individuals.
We then introduce the adaptation factor, $\lambda$, a real parameter, $0 \leq \lambda \leq 0$,
which indicates the organism's conditioning to respond to a local surge appropriately. For $\lambda=0$, the organism cannot identify the presence of sick individuals, performing only the Safeguard strategy. In the limit $\lambda=1$, the organism is fully conditioned to react to the local surges correctly.

The numerical implementation of the strategic movement follows the steps \cite{MENEZES2022101606,combination}:
\begin{enumerate}
\item
We define the perception radius $R$, measured in lattice spacing, as the maximum distance an individual can scan the environmental cues to use the information to decide what strategic movement is suitable at the moment.
\item
We implement a circular area of radius $\mathcal{R}$ centred in the active individual, defining the total grid points the organism is able to analyse.
\item
We calculate the density of sick organisms within the
disc of radius $R$ surrounding the active individual. In case of the local density is below the threshold $\beta$, the organism decides to perform the Safeguard strategy; otherwise, the Social Distancing tactic is chosen.
\item
We separate the organism's perception area
into four circular sectors in the directions of the nearest neighbour. Thus, we count the number of empty spaces and organisms within each circular sector; individuals on the borders are assumed to be in both circular sectors.
\item
We define the movement direction according to the organism's strategy choice: the direction with the larger number of organisms of species $i-2$ ($h_{i-2}+s_{i-2}$) for Safeguard or the direction with
more empty spaces for Social Distancing. If more than one direction is equally attractive, a draw is done.
\end{enumerate}

\subsection{Spatial Autocorrelation Function}

To study the effects of the adaptive survival strategy to local epidemic outbreaks in the spatial patterns, we calculate the characteristic length scale of the typical spatial domains occupied by each species. For this purpose, we compute the spatial autocorrelation function $C_i(r)$, with $i=1,2,3,4,5$, in terms of radial coordinate $r$. Defining
the function $\phi_i(\vec{r})$ to describe the position $\vec{r}$ in the lattice occupied by individuals of species $i$, we use the mean value $\langle\phi_i\rangle$ to find the Fourier transform
\begin{equation}
\varphi_i(\vec{\kappa}) = \mathcal{F}\,\{\phi_i(\vec{r})-\langle\phi_i\rangle\},
\end{equation}
and the spectral densities
\begin{equation}
S_i(\vec{k}) = \sum_{k_x, k_y}\,\varphi_i(\vec{\kappa}).
\end{equation}

The normalised inverse Fourier transform allows us to find the autocorrelation function for species $i$ as
\begin{equation}
C_i(\vec{r}') = \frac{\mathcal{F}^{-1}\{S_i(\vec{k})\}}{C(0)},
\end{equation}
which we write as a function of $r$ as
\begin{equation}
C_i(r') = \sum_{|\vec{r}'|=x+y} \frac{C_i(\vec{r}')}{min\left[2N-(x+y+1), (x+y+1)\right]}.
\end{equation}
Defining the threshold $C_i(l_i)=0.15$, we define
the characteristic length scale for spatial domains of species $i$ as $l_i$, with $i=1,2,3,4,5$.

\subsection{Selection and Infection Risks}

The impact of the locally adaptive organisms' response to their survival is computed by employing two death risks:
a) Selection Risk, $\zeta_i(t)$: the probability of an organism of species $i$ being killed by an individual of species $i-1$ at time $t$; b) Infection Risk, $\chi_i(t)$: the probability of a healthy organism of species $i$ being infected by an ill individual of any species at time $t$.

The numerical implementation of $\zeta_i(t)$ and $\chi_i(t)$ follows the algorithm:
\begin{enumerate}
\item
We count the total number of healthy and sick individuals of species $i$ when each generation commences;
\item
We calculate how many individuals of species $i$ are selected (killed by individuals of species $i-1$) during the generation;
\item
We compute the number of healthy organisms infected during the generation;
\item
The selection risk is the ratio between the number of selected individuals and the total number of organisms;
\item
The infection risk is the ratio between the number of infected individuals and the total number of healthy organisms.
\end{enumerate}
\begin{figure*}
\centering
    \begin{subfigure}{0.24\textwidth}
        \centering
        \includegraphics[width=33mm]{Fig2a}
        \caption{}\label{fig2a}
    \end{subfigure} %
    \begin{subfigure}{0.24\textwidth}
        \centering
        \includegraphics[width=33mm]{Fig2b}
        \caption{}\label{fig2b}
    \end{subfigure} %
       \begin{subfigure}{0.24\textwidth}
        \centering
        \includegraphics[width=33mm]{Fig2c}
        \caption{}\label{fig2c}
    \end{subfigure} %
           \begin{subfigure}{0.24\textwidth}
        \centering
        \includegraphics[width=33mm]{Fig2d}
        \caption{}\label{fig2d}
    \end{subfigure} %
               \begin{subfigure}{0.24\textwidth}
        \centering
        \includegraphics[width=33mm]{Fig2e}
        \caption{}\label{fig2e}
    \end{subfigure} %
   \begin{subfigure}{0.24\textwidth}
        \centering
        \includegraphics[width=33mm]{Fig2f}
        \caption{}\label{fig2f}
    \end{subfigure} %
    \begin{subfigure}{0.24\textwidth}
        \centering
        \includegraphics[width=33mm]{Fig2g}
        \caption{}\label{fig2g}
    \end{subfigure} %
       \begin{subfigure}{0.24\textwidth}
        \centering
        \includegraphics[width=33mm]{Fig2h}
        \caption{}\label{fig2h}
    \end{subfigure} %
    \caption{
Spatial patterns captured from simulations in lattices with $500^2$ grid points of the generalised rock-paper-scissors game. Figures ~\ref{fig2a}, ~\ref{fig2b}, ~\ref{fig2c}, and ~\ref{fig2d} show snapshots of the spatial configuration after $20$, $100$, $250$, and $2000$ generations. The colours follows the scheme in Fig.~\ref{fig1}; empty spaces appear in white dots.
Figures ~\ref{fig2e}, ~\ref{fig2f}, ~\ref{fig2g}, and ~\ref{fig2h} highlight the organisms of species $1$, with orange and purples indicating individuals performing Social Distancing and Safeguard tactics, respectively. The social distancing trigger is $\beta=0.6$ and the perception radius is $R=3$. The dynamics of the spatial patterns during the entire simulation are shown in videos https://youtu.be/tRxooHOroZo and https://youtu.be/FYH24c7A1Uc.
}
  \label{fig2}
\end{figure*}

\begin{figure}
\centering
\includegraphics[width=93mm]{Fig3}
\caption{Temporal variation of the densities of organisms of species $i$ in the realisation in Fig.~\ref{fig2}. The grey line depicts the variation of the density of empty space,
while red, blue, pink, green, and yellow stand for species $1$, $2$, $3$, $4$, and $5$, respectively.}
\label{fig3}
\end{figure}

\subsection{Initial conditions, parameters and figures}

Throughout this paper, all outcomes presented were obtained in simulations with $\mathcal{N}=500^2$ grid points, starting from random initial conditions and running for a timespan of $5000$ generations.
The initial conditions are elaborated by allocating an individual of a random species at each point.
The number of individuals of each species is the same at the beginning of the simulation, $I_i=\mathcal{N}/5$, with $1\%$ being sick - there are no empty spaces in the initial state.
All simulations ran for
the set of probabilities: $s_h = r_h=m_h=1/3$, $s_s=r_s= 5/31$, $m_s=9/31$, $\omega = 10/31$, and $c=d=1/31$. However, having repeated the simulations for other parameters, we verified that our conclusions are valid for other sets of interaction probabilities.

The outcomes presented in Fig.~\ref{fig2} and \ref{fig3} were obtained for $R=3$, $\lambda=1.0$, and $\beta=0.6$.
The statistical analyses whose results appear in Figs.~\ref{fig4} to ~\ref{fig7} were realised by performing a series of $100$ simulations, starting from different initial conditions; in each case, the standard deviation is represented by error bars. The parameters were assumed as follows: i) Figs.~\ref{fig4} and ~\ref{fig5}: $0 \leq \beta \leq 1$, with intervals of $\Delta \beta = 0.1$ (for fixed $R=3$ and $\lambda=1.0$); ii) Fig.~\ref{fig6}: $0 \leq R \leq 5$, with intervals of $\Delta R = 1$ (for fixed $\beta=0.6$ and $\lambda=1.0$); iii) Fig.~\ref{fig7}: $0 \leq \lambda \leq 1$, with intervals of $\Delta \lambda = 0.1$ (for fixed $R=3$ and $\beta=0.6$).

In Figs.~\ref{fig2a} to \ref{fig2d}, organisms of species $i$ are
depicted by the respective species colours, following the scheme in Fig.~\ref{fig1}, where red, blue, pink, green, and yellow stand for species $1$, $2$, $3$, $4$, and $5$, respectively; white dots show the empty sites. The organisms of species $1$ present in Figs.~\ref{fig2a} to \ref{fig2d} are shown in Figd.~\ref{fig2e} to \ref{fig2h}, with
purple and orange dots indicating the individuals performing Safeguard and Social Distancing, respectively; grey areas are empty or occupied by organisms of species $2$, $3$, $4$, $5$.
The colours in Figs.~\ref{fig3} to ~\ref{fig7} also follow the scheme in Fig.~\ref{fig1}; the grey lines represents empty spaces.

\section{Spatial patterns}
\label{sec3}

To compute the effects of the adaptive survival strategy to local epidemic surges on spatial patterns, we first ran a single simulation considering that organisms react to sick organisms' presence in case the local density of ill individuals is higher than the $60\%$ ($\beta=0.6$). Snapshots of the individuals' spatial distribution after $20$, $100$, $250$, and $2000$ generations are shown in Figs. ~\ref{fig2a}, ~\ref{fig2b}, ~\ref{fig2c}, and ~\ref{fig2d}, respectively. White dots represent empty spaces, whereas organisms are depicted with the respective species' colour in Fig.~\ref{fig1}. The spatial configuration during the whole simulation is shown in the video https://youtu.be/tRxooHOroZo, whereas the temporal dynamics of the densities of species are depicted in Fig.~\ref{fig3}.

As soon as the simulation commences, organisms of species $1$ cleverly move toward the direction where the death risk is minimum, while all individuals of the other species walk randomly. The advantage of species is reflected in rapid population growth, as observed in the high magnitude in the density of species $1$ in Fig.~\ref{fig3}; the areas with red dots dominate the snapshot in Fig.~\ref{fig2b} ($t=100$ generations).
The high abundance in the density of species $1$ favours organisms of species $5$, that take control of the areas of species $1$ and grow, as shown by the yellow line in Fig.~\ref{fig3} ($t=250$ generations). After the initial stage of high fluctuations in the densities of species (the pattern formation process), spiral waves spread on the lattice. The results show that the cyclic dominance symmetry inherent in the rock-paper-scissors model is broken because deaths of organisms of species $1$ are rarer than fatalities that affect other species. Cleverly adapting the survival movement strategy, organisms guarantee the control of the largest fraction of territory, leading the species $1$ to predominate in the cyclic game, as shown in Fig.~\ref{fig3}.

In addition, Figs. ~\ref{fig2e}, ~\ref{fig2f}, ~\ref{fig2g}, and ~\ref{fig2h} highlight the spatial distribution of organisms of species $1$ in Figs. ~\ref{fig2a}, ~\ref{fig2b}, ~\ref{fig2c}, and ~\ref{fig2d}, respectively.
When the snapshots were captured, organisms shown in orange were performing the Social Distancing tactic, while the individuals depicted by purple dots were executing the Safeguard tactic. Grey areas are occupied by individuals of species $2$, $3$, $4$, $5$, and empty spaces. Video https://youtu.be/FYH24c7A1Uc shows the dynamics of the organisms of species $1$ executing each survival strategy during the entire simulation.

The results highlight the ability of organisms of species $1$ to
adjust the survival directional tactic in response to local
disease surges.
After an initial proportion of sick individuals is $1\%$, the disease spreads through the lattice, causing local outbreaks. Because of this, the proportion of individuals performing the Social Distancing tactic (orange dots) grows significantly in the first stage of the simulations, as shown in Figs.~\ref{fig2e} and \ref{fig2f}.
However, as each organism performs the Social Distancing strategy only if more than $60\%$ of its neighbours are
infected, many of them continue executing the Safeguard strategy
(purple dots). This guarantees the species to profit with the protection against selection and virus infection.
Therefore, changing the directional movement responding to localised concentrations of infected organisms benefits the species whose population grows, as depicted in Fig.\ref{fig3}.
\begin{figure}
\centering
\includegraphics[width=93mm]{Fig4}
\caption{Characteristic length scales of the typical spatial domains occupied for species $i$ in terms of the social distancing trigger parameter.
The results were averaged from sets of $100$ simulations; the error bars indicate the standard deviation. The colours follow the scheme in Fig.~\ref{fig1}.}
\label{fig4}
\end{figure}
\begin{figure}[t]
\centering
    \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig5a}
        \caption{}\label{fig5a}
    \end{subfigure} %
       \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig5b}
        \caption{}\label{fig5b}
    \end{subfigure} \\%
           \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig5c}
        \caption{}\label{fig5c}
    \end{subfigure} %
    \caption{Infection risk (Figure a), selection risk (Figure b), densities of species (Figure c) in terms of the social distancing trigger parameter.
The outcomes were averaged from sets of $100$ simulations; the error bars show the standard deviation.}
  \label{fig5}
\end{figure}

\section{The role of the social distancing trigger parameter}
\label{sec4}

\subsection{Characteristic length scales of typical spatial domains}

The spatial patterns observed in Fig.~\ref{fig2} show that the asymmetry in the
species segregation results from the local interactions where each organism of species $1$ moves strategically, optimising the results of the behavioural survival strategy according to its own reality. To quantify the characteristic length scale of the typical spatial areas occupied by each species, we ran a set of $100$ simulations for various values of $\beta$. The mean value of characteristic length scale $l_i$, with $i=1,2,3,4,5$ is depicted in Fig.~\ref{fig4}; the error bars represent the standard deviation.

If organisms of species $1$ exclusively execute the Social Distancing tactic, independent of the perception of the local density of sick individuals ($\beta=0.0$), the average size of the areas occupied by them is larger than the others. In this limit case, individuals of species $5$ form the second-largest groups; the characteristic length scale of regions dominated by species $3$ is the shortest. The results are approximately the same for $0.0\,\leq\,\beta\,\leq\,0.3$. However, as the social distancing trigger grows (individuals live with less concern about the disease contamination), the difference in the average sizes of the regions of each species decreases. For $\beta \geq 0.8$, organisms of species $4$ are
limited to the smallest areas; species $1$ occupies spatial domains with the second shortest characteristic length scale.

\subsection{Selection, infection risks, and densities of species}

Performing a series of simulations, starting from different initial conditions, we computed the social distancing trigger parameter
interferes with the organisms' infection, selection risks, and densities of species.
Figures \ref{fig5a}, \ref{fig5b}, and \ref{fig5c} depict $\zeta_i$, $\chi_i$, and $\rho_i$ as functions of $\beta$ - the simulations parameters are the same as in the previous sections.

Overall, the results show a crossover between scenarios where organisms perform exclusively one out of two survival movement strategies: the Social Distancing ($\beta \leq 0.3$) and Safeguard ($\beta \geq 0.9$) tactics.

\begin{itemize}
\item
If the social distancing trigger is low, the organisms are more careful against disease contamination. For $\beta\,\leq\,0.3$, the fear of being sick is too high that all organisms forget the risk of being killed by enemies for executing exclusively social distancing, as shown in Fig.~\ref{fig5a}.
The consequence is that the infection risk reaches the minimum value while the selection risk is maximum for $\beta\,\leq\,0.3$: the more careful the organisms are against viral infection, the more exposed to being killed by individuals of species $5$.
\item
Conversely, as $\beta$ grows, the organisms are more tolerant of the presence of viral vectors, concentrating more on approaching the enemies of their enemies. Thus, the selection risk decreases while infection risk grows for larger $\beta$. According to Fig.~\ref{fig5b}, for $\beta \geq 0.9$, the individuals' negligence in avoiding disease contamination leads to the maximum infection risk. This happens because although the protection received from organisms of species $4$ against individuals of species $5$ is maximum,
there is a chance of part of the guards being sick, thus, infecting the individuals of species $1$.
\end{itemize}
The results depicted in Fig.~\ref{fig5c} show that the benefits of the adaptive survival strategy for species $1$ are maximised for $\beta=0.6$. In this case, species $1$ predominates in the cyclic game, with the density of organisms of species $1$ reaching the maximum.

\section{The influence of the perception radius}
\label{sec5}
To investigate the role of the organisms' physical ability to scan the neighbourhood, we performed simulations for various values of $R$ considering $\beta=0.6$, which brings the most significant benefit in terms of population growth for species $1$, as depicted in Fig.~\ref{fig5c}.
Figures \ref{fig6a} and \ref{fig6b} show the organisms' infection and infection risks, respectively; the average densities of individuals of each species appear in Fig.~\ref{fig6c}.

We found that, for short-range perception,
the signals captured provide imprecise information about the neighbourhood.
Therefore, organisms neither properly detect the danger of contamination by nearby ill organisms nor accurately choose the direction to walk when performing the directional movement strategy. However, even for $R=1$, the locally adaptive strategy is advantageous for species $1$, which dominates in the cyclic game, as depicted in Fig.~\ref{fig6c}.

As $R$ increases, the environmental cues improve, giving the organisms more precise knowledge of the existence of a local epidemic outbreak: organisms' reaction is more accurate, correctly switching the movement strategies to maximise protection against disease contamination and enemies. The results show that for $R \geq 2$, the reduction in the infection and selection risks accentuates, as depicted in Figs.~\ref{fig6a} and Fig.~\ref{fig6b}. As the number of deaths of individuals of species $1$ decreases for larger $R$, the population rises, as shown in Fig.~\ref{fig6c}.

\begin{figure}[t]
\centering
    \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig6a}
        \caption{}\label{fig6a}
    \end{subfigure} %
       \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig6b}
        \caption{}\label{fig6b}
    \end{subfigure} \\
    \centering
           \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig6c}
        \caption{}\label{fig6c}
    \end{subfigure} %
    \caption{Infection risk (Figure a), selection risk (Figure b), and densities of species (Figure c) as functions of the organisms' perception radius.
The results were averaged from $100$ simulations for $\beta=0.6$; the error bars indicate the standard deviation.}
  \label{fig6}
\end{figure}
\begin{figure}[t]
\centering
    \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig7a}
        \caption{}\label{fig7a}
    \end{subfigure} %
       \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig7b}
        \caption{}\label{fig7b}
    \end{subfigure} \\
    \centering
           \begin{subfigure}{.47\textwidth}
        \centering
        \includegraphics[width=73mm]{Fig7c}
        \caption{}\label{fig7c}
    \end{subfigure} %
    \caption{Infection risk (Figure a), selection risk (Figure b), and densities of species (Figure c) as functions of the organisms' adaptation factor.
The results were averaged from $100$ simulations for $\beta=0.6$; the error bars indicate the standard deviation.}
  \label{fig7}
\end{figure}
\section{The impact of the organisms' adaptation factor}
\label{sec5}
Finally, we study how the organisms' conditioning to interpret the environmental cues and react correctly to a local disease surge
controls the benefits of the locally adaptive survival movement strategy. For this, we ran sets of $100$ simulations for many values $\lambda$, as depicted in Fig.~\ref{fig7a} (infection risk),
\ref{fig7b} (selection risk), and \ref{fig7c} (density of species).
Following the outcomes in Fig.~\ref{fig5c}, we have chosen $\beta=0.6$ that maximises the benefits of territorial control for species $1$.

Fig.~\ref{fig1} shows that organisms of species $1$ are riskier of being infected than the others. This happens because only organisms of species $1$ perform the Social Distancing tactic, approaching individuals of species $4$, while organisms of other species move randomly.
The outcomes show that if the fraction of organisms of species $1$ conditioned to perceive the presence of a local epidemic outbreak and start the Social Distancing tactic is small, $\chi_1$ grows instead of decreasing. This counterintuitive result holds for $\lambda \leq 0.7$; however, if more than $70\%$ of individuals learn to identify local surges, the infection risk of organisms of species $1$ decreases. The results show that, for $\lambda >0$, organisms of species $2$ and $3$ also benefit from dropped infection risk - although they do not perform any survival movement strategy.

Regarding the selection risk, the results depicted in Fig.~\ref{fig7b} show that the risk of organisms of species $1$ being killed in the cyclic game rises as $\lambda$ grows. This happens because as the proportion of individuals of species $1$ learning to avoid social grows, the vulnerability to being caught by an enemy increases. However, Fig.~\ref{fig7c} shows that the balance is positive for species $1$, which occupies the greater fraction of the grid as $\lambda$ increases. The results show that the benefits for species $1$ are maximised if $90\%$ of individuals are conditioned to adjust the survival movement strategy in response to local epidemic outbreaks.

Fig.~\ref{fig7c} reveals that as the proportion of organisms of species $1$ learning to change the survival strategy when facing a local disease surge grows, the species $5$ also profits more with population growth. In the case of species $4$, the spatial density increases for $\lambda \leq 0.8$, with the best results obtained if $\lambda \approx 0.4$. In  contrast, the population decline of species $2$ and $3$ increase as $\lambda$ grows;
the density of empty remains approximately constant with the adaptation factor.

\section{Discussion and Conclusions}
\label{sec6}

We explored a generalised rock-paper-scissors model with a disease being transmitted between neighbours, irrespective of the species. We consider that organisms of one out of the species execute a behavioural movement strategy, where each organism moves in the direction with a higher density of individuals that can protect them against enemies. However, the alliance with enemies of enemies is not advantageous when a local surge represents an increased infection risk. In this case, if an organism is apt to recognise the existence of a local epidemic outbreak, the Safeguard strategy is avoided. Instead, the Social Distancing tactic is performed, with the individual moving in the direction with the highest density of empty spaces. In contrast with the model investigated in Ref.~\cite{combination}, where a fixed global design for the combination of survival strategies is assumed by any organism (independent of what is happening in its neighbourhood), here, the choice of the movement tactic is individual. Therefore, each organism is autonomous in taking the more appropriate decision according to the current circunstances, with the Social Distancing tactic substituting the Safeguard strategy only when the local epidemic outbreak reaches the organism's neighbourhood.

The unevenness introduced by the behavioural movement strategy impacts the pattern formation, with the social distancing trigger determining the scales of the spatial regions inhabited by each species and controlling the level of protection against selection and infection. Our findings show a crossover between a regime where organisms focus only on the Social Distancing strategy (low social distancing trigger) and a scenario where they are exclusively interested in protecting themselves against death in the cyclic game (high social distancing trigger). We found that the profit in terms of population growth is maximised if both survival strategies are executed, with organisms using the Social Distancing strategy only when a local surge appears in the neighbourhood.

The organisms' perception radius plays a central role in the local adaptation of the behavioural strategy. The results show that the further the organisms can scan the vicinity, the easier the detection of a forthcoming disease surge and
more accurate is the decision of the best direction to move.
This means a long-range perception radius produces a more significant reduction in the risks of individuals being killed by an enemy and contaminated by disease. The consequence is the rise of the species population for a large perception radius.

Our outcomes also show that the organisms' adaptation factor influences population dynamics. We found that,
independent of the proportion of individuals conditioned to move adaptively, the species prevails in the cyclic game.
However, the fraction of territory controlled by the species increases as the adaptation factor increases.

Our simulations assume that each individual can distinguish sick from healthy neighbours; for example, symptoms of the disease are identified as a result of the infection. However, we can extend our conclusions for the case where neither all ill organisms present symptoms. As the individuals are not fully aware of the local surges, the level of precaution should be increased. Suppose that the level of symptomatic disease is $\eta$, where $\eta$ is a real number ($0\,\leq\,\eta\,\leq\,1$). In this case, one has $\beta = \eta\,\tilde{\beta}$, where $\tilde{\beta}$ is the minimum local density of ill organisms with symptoms triggering the Social Distancing tactic.

\section*{Acknowledgments}
We thank CNPq, ECT, Fapern, and IBED for financial and technical support.

\section*{References}
\bibliographystyle{iopart-num}

redpajama_set_name: RedPajamaArXiv


text:
Biografia
Nato nel 1944 a Chiomonte, in provincia di Torino, a 23 anni ha partecipato ai Giochi olimpici di , uscendo nelle qualificazioni nello slalom (3º nel primo turno, non arrivato al traguardo nel 2º), arrivando invece 11º nello slalom gigante con il tempo totale di 3'34"52.

Nel 1966 è stato campione italiano nello slalom gigante.

L'anno successivo (1967) si è piazzato 44º in classifica generale nella prima edizione della Coppa del Mondo di sci alpino e 22º nella graduatoria di slalom gigante, ottenendo un piazzamento nei primi dieci (decimo nel prestigioso slalom gigante di Adelboden del 9 gennaio 1967.

Dopo il ritiro è diventato maestro di sci.

Palmarès

Coppa del Mondo
 Miglior piazzamento nella Coppa del Mondo di sci alpino: 44º nel 1967.
 Miglior piazzamento nella Coppa del Mondo di slalom gigante: 22º nel 1967.

Note

Collegamenti esterni

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June 21, 2017, 2:06 p.m.
June 13, 2017, 1:47 p.m.
June 15, 2017, 1:59 p.m.
Are you fascinated by the biological world around you? Are you intrigued by the mystery of our existence? These amazing topics and more can be explored in Ecology and Evolution.
This class is cool because it attempts to discover why life is the way it is, both visibly and microscopically. Whether you're a science buff or not, you'll have fun exploring why certain life forms exist and why they exhibit certain characteristics.
Discussions will include what may have occurred leading up to the appearance of the first organisms, how these theories have been tested in laboratories and why some suggestions about the appearance of life are not possible. Projects will also include the study of DNA and how gene duplication relates to evolution.
Through the study of "The Beak of the Finch" by Jonathan Wiener, one of the best case studies of evolution in motion, you'll see in real time how natural selection has played out in finch populations in the Galapagos islands.
Another highlight of this course—a custom edition textbook is available that includes only the articles and chapters you'll need for the course. This will cost you about half of what you'd pay if you purchased the regular textbooks.
Upon completion of the course, you'll be able to define and discuss topics related to ecology, evolution and macroevolution and make the connection between what you learn and your own life experiences. You'll know more about the life around us and be able to share this insight with your friends and family.
See what else is offered by the College of Professional and Continuing Studies.
Tami Althoff holds a bachelor's degree in journalism. She is a reporter with more than 20 years' experience working for newspapers, including The Oklahoman. She has covered everything from breaking news to local music and art. She loves sports, especially OU football and basketball games, where she often embarrasses her children by yelling too loudly.
March 12, 2019, 11:24 a.m.
Jan. 15, 2019, 12:48 p.m.
Dec. 19, 2018, 11:33 a.m.
Oct. 9, 2018, 2:52 p.m.


Went last night and checked the surface where we attempted the set up. It was perfectly level.
This weekend will pull trailer back up there and use measurements to level the trailer rather than a placing a level on the floor of the trailer.
I will attempt the set up (raising the ball) as many times as necessary to remove the squat. Then I will head to the scales to check GVW, tongue weight, axle weights.
I read a post on an RV message board that said to be prepared to adjust the hitch more than once, as you rarely get it right the first time. Set up instructions are to get you close, then you have to tweak the set up to get it where it needs to be.
Sound right to any seasoned vets?
Well, looks like I finally got it right. The article I mentioned on the RV message board said a couple of things that really helped.
First, the tilt of the hitch is what pulls the front end back down. Once you get the front end where it needs to be (with WD bars) the rear will take care of itself. I had to add back a couple of washers that I had removed previously.
Then, adjusting the ball height levels the trailer.
I now have about 1/2" rise in the front, about a 1.5" drop in back and the trailer is perfectly level (by measuring the frame to the ground front and back).
With the way it tows now, I can see that the ride is good and bags would not have solved the problem.
Sometimes it just takes me a while to think things through.
Thanks for everyone's input and helping me think it through.
Ah, forgot about to mention the tilt of the hitch. Glad you got it worked out.
Quick Reply: Are bags the answer?


text:
Conquest Film Review
Lucio Fulci's Sword & Sorcery Epic is as Trippy and Bizarre as You'd Expect, and it's Great
While legendary Italian filmmaker Lucio Fulci is mostly known for delivering some killer classics within the horror genre, he would sometimes dive into other genre's, like this Sword & Sandal's fantasy from 1983, a year after directing two of his most well known classics (Manhattan Baby and The New York Ripper). I was convinced to give this one a shot by a buddy of mine, even though I had never even heard of it until now. While certainly not one of Fulci's more well-known films, and not an easy film to come by generally speaking, I was lucky to discover that Blue Undergrounds 2016 DVD release is pretty affordable and to date, the best release you're going to find of this. So let's dig in.
A young man, armed with a magic bow and arrow, joins forces with a barbarian outlaw and embarks on a mystical journey to take down an evil sexy witch who plans on stealing the magical bow and arrow for evil.
First thing's first. Conquest is hot garbage. It's the kind of film you could easily tear and pick apart, with plenty of issue's we could spend hours going over. But guess what? You don't go into these for high art. You dig into these Italian Trash films because they're fun, and that's exactly what you get here. A fun, glorious, trippy ride that seems to throw so many random sequences together that you never know what will happen next. What Conquest lacks in budget, director Lucio Fulci more than makes up for in visual brilliance as he makes this Sword & Sorcery epic look unlike anything you've ever seen before.
Filmed entirely through a lens filter that gives the film an eternal hazy quality (sort of like a soap opera), Conquest is chock full of nudity, gore, swords and sorcery, and enough WTF? moments to make you question your sanity. Yet despite, or perhaps because of all of these things, Conquest still manages to entertain in a way I hadn't expected. For a guy who's made a name for himself in the horror genre, his ability to turn the Sword & Sorcery genre on it's head is impressive to say the least.
But make no mistake, Conquest is not a good film, not by any stretch of the imagination. It's a fun film, made all the better with Fulci's very special brand of filmmaking and some pretty outstanding visuals to boot. It's in all of it's low-budget unintentional campiness that makes it a great slice of 80's unhinged goofy weirdness. Throw in a ton of nudity and some out of left field gore, and you have one of the most original barbarian films I've ever seen. I can certainly understand why people might not like it, but I enjoyed the hell out of it. It carries a very specific quality that I wish most of these low-budget genre films would possess. But it's definitely an acquired taste. It almost reminded me of a live-action version of Bakshi's Fire and Ice.
Ultimately, Conquest makes no apologies. It's crude, violent, magical, sexual and utterly silly, yet so much fun and strange at the same time. There are some truly outstanding moments full of impressive eye candy, mixed together with some surreal, bizarre, fantastical moments that only Lucio Fulci could conjure up. It is what it is and whether you love or hate it, you can't say that it wasn't fun.
Currently there's no HD or Blu Ray release of this here in the states, and boy could it ever use a solid cleanup job. The best version we have over here would be Blue Undergounds 2016 DVD release, and really, that's not saying much.
Because of Fulci's strong use of filters throughout, the picture is hazy, grainy and of really poor quality - almost as if you're watching it on VHS, especially in the night sequences. Yet it's still just a tad better than VHS quality. Here's to hoping someone will give this a proper restoration and new transfer some day. If there is any film from Lucio Fucli's cannon that could benefit from a strong new transfer, it would be this one. I would also love to hear about some of the history regarding this film, like what his inspirations were when making it, and how the hell they came up with this crazy story to begin with.
90's Action Attack!: Moon 44
90's Action Attack!: Street Hunter
90's Action Attack: McBain
80's Action Attack: American Ninja III: Blood Hunt...
Maximum Overdrive: Stephen King's 80's Trash Maste...
90's Action Attack!: Demolition High (1996)
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