Aksam Unia Oswiecim: A Comprehensive Guide for Sports Bettors

Overview / Introduction about the Team

Aksam Unia Oswiecim is a prominent ice hockey team based in Oświęcim, Poland. Competing in the Polish Hockey League (PHL), the team was founded in 1946 and has since become a staple in Polish ice hockey. The team is known for its competitive spirit and dedication to the sport, with current management under coach [Current Coach Name]. This guide provides an in-depth analysis of Aksam Unia Oswiecim, focusing on their history, current squad, playing style, and betting insights.

Team History and Achievements

Since its inception, Aksam Unia Oswiecim has experienced numerous successful seasons. The team has clinched multiple league titles and national cups, solidifying its reputation as a formidable force in Polish hockey. Notable achievements include winning the PHL championship [Number of Titles] times and securing several runner-up positions. The club’s rich history is marked by memorable seasons that have left a lasting impact on fans and players alike.

Current Squad and Key Players

The current squad boasts a mix of seasoned veterans and promising young talent. Key players include [Player Name], a star forward known for his scoring ability, and [Player Name], a defenseman renowned for his defensive prowess. These players play crucial roles in the team’s strategy and success on the ice.

Key Players:

• [Player Name]: Forward – Known for exceptional goal-scoring skills.
• [Player Name]: Defenseman – Renowned for defensive strength and leadership.

Team Playing Style and Tactics

Aksam Unia Oswiecim employs an aggressive playing style characterized by fast-paced transitions and strategic puck control. The team often utilizes a 1-3-1 formation to maximize offensive opportunities while maintaining strong defensive coverage. Their strengths lie in their speed and teamwork, though they occasionally face challenges with consistency in defense.

Strengths:

• Fast transitions
• Tight puck control
• Strong teamwork

Weaknesses:

• Inconsistent defense
• Vulnerability to counterattacks

Interesting Facts and Unique Traits

Aksam Unia Oswiecim is affectionately known as “The Blue Devils,” reflecting their vibrant team colors. The fanbase is passionate, with dedicated supporters who create an electrifying atmosphere at home games. Rivalries with teams like [Rival Team Name] add excitement to the league, while traditions such as pre-game rituals enhance the cultural experience for fans.

Lists & Rankings of Players, Stats, or Performance Metrics

The following are key performance metrics that highlight the team’s strengths:

• Top Scorer: [Player Name] – Goals: [Number]
• PIM Leader: [Player Name] – Penalty Minutes: [Number]
• Best Plus/Minus: [Player Name] – Plus/Minus: [+/- Number]

Comparisons with Other Teams in the League or Division

Aksam Unia Oswiecim consistently ranks among the top teams in the PHL due to their balanced roster and strategic gameplay. Compared to rivals like [Rival Team Name], Aksam excels in offensive strategies but sometimes struggles defensively against teams with strong counterattacking capabilities.

Case Studies or Notable Matches

A landmark victory for Aksam Unia Oswiecim was their triumph over [Opponent Team] in the [Year], where they showcased exceptional teamwork leading to a decisive win. This match remains a highlight in their history, demonstrating their potential to dominate when playing at their best.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Date	Opponent	Result	Odds Before Match	Odds After Match
[Date]	[Opponent]	[Result]	[Odds Before]	[Odds After]

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks 💡

To make informed betting decisions on Aksam Unia Oswiecim games:

• Analyze recent form trends to gauge momentum.
• Evaluate head-to-head records against upcoming opponents.
• Closely monitor key player performances leading up to matches.

Quotes or Expert Opinions about the Team (Quote Block)

“Aksam Unia Oswiecim’s blend of youth and experience makes them unpredictable yet formidable opponents,” says renowned hockey analyst [Analyst Name]. “Their ability to adapt mid-game often turns matches around.”

Pros & Cons of the Team’s Current Form or Performance (✅❌ Lists)

• ✅ Strong offensive lineups capable of high-scoring games.

• ❌ Defensive lapses can lead to costly goals against them. 0 else source_path

  if not os.path.isdir(source_path):

  raise IOError(“Source path ‘%s’ doesn’t exist” % source_path)

  elif not os.path.isdir(target_path):

  raise IOError(“Target path ‘%s’ doesn’t exist” % target_path)

  c_files = []

  for root_dir_name, dir_names_list , file_names_list
  in os.walk(source_path):

  c_files.extend([
  os.path.join(root_dir_name,fname)
  for fname
  in file_names_list
  if fname.endswith(‘.c’)
  ])

  if len(c_files) == 0:

  raise ValueError(
  “No C files were found under ‘%s'”
  % source_path)

  cmd_prefix = [‘gcc’, ‘-S’, ‘-masm=intel’]

  #cmd_prefix.append(‘-fverbose-asm’)
  cmd_prefix.append(‘-fno-inline’)
  cmd_prefix.append(‘-O0’)
  cmd_prefix.append(‘-g’)

  try:

  subprocess.check_call(cmd_prefix + [‘-E’],
  stdout=subprocess.PIPE,
  stderr=subprocess.PIPE,
  stdin=open(os.devnull))

  except OSError:

  raise EnvironmentError(
  “Unable to find gcc compiler”)

  except subprocess.CalledProcessError:
  pass

  asm_files = []

  asm_re = re.compile(r’.*.S?.asm$’)

  #asm_re = re.compile(r’.*.S$’) # this works too…

  for c_file_name in c_files:

  base_name = os.path.splitext(os.path.basename(c_file_name))[0]

  asm_file_name = base_name + ‘.asm’

  asm_file_fullname = os.path.join(target_path,
  asm_file_name)

  cmd_line_args = cmd_prefix + [‘-o’, asm_file_fullname,
  c_file_name]

  try:

  subprocess.check_call(cmd_line_args)

  except subprocess.CalledProcessError:
  pass

  else:

  asm_files.extend([
  os.path.join(target_path,fname)
  for fname
  in os.listdir(target_path)
  if asm_re.match(fname)])

  if len(asm_files) == 0:
  raise RuntimeError(
  “Failed converting any C file into assembly code”)

  print(“nConverted:nn%s” % ‘n’.join(
  [“%s -> %s” % (os.path.relpath(cfn,target_path),
  os.path.relpath(afn,target_path))
  for cfn, afn
  in zip(c_files , asm_files)]))

  if __name__ == “__main__”:

  main()

  ***** Tag Data *****
  ID: 4
  description: Building command line arguments dynamically based on various flags.
  start line: 100
  end line: 107
  dependencies:
  – type: Function/Method/Block/Loop/Conditional/etc.
  name: main()
  start line: 6
  end line: 109
  context description: Dynamically constructing command-line arguments using lists.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: 5
  self contained: Y

  ************
  ## Challenging aspects

  ### Challenging aspects in above code

  1. **Dynamic Command-Line Argument Construction**:
  – Understanding how `cmd_prefix` is constructed dynamically using lists can be challenging due to its use of multiple flags (`’-S’`, `’-masm=intel’`, `’-fno-inline’`, `’-O0’`, `’-g’`). Each flag serves specific purposes which must be understood thoroughly.

  – Extending this functionality without breaking existing features requires careful consideration.

  – Handling additional flags conditionally adds another layer of complexity.

  ### Extension

  Here are some ways these aspects can be extended:

  1. **Dynamic Inclusion/Exclusion Based on File Content**:
  – Modify compilation flags based on content within each `.c` file before invoking `gcc`. For example, include `-DDEBUG` only if there are specific debugging macros defined within any `.c` file.

  – Include logic that inspects each `.c` file’s content before deciding which flags should be added dynamically.

  ### Specific Nuances:

  1. **File Dependency Resolution**:
  – Handle scenarios where `.c` files include other headers or dependents which may reside outside `source_path`. Ensure all dependencies are resolved before compilation.

  – Implement logic that checks header dependencies recursively across directories.

  ## Exercise

  ### Problem Statement:

  You are required to extend an existing Python script that converts C source files into assembly files using GCC compiler flags dynamically constructed from command-line arguments.

  Your task involves adding two major functionalities:

  1. **Dynamic Flag Inclusion Based on File Content**:
  – Inspect each `.c` file within `source_path`. If any `.c` file contains debugging macros such as `#define DEBUG`, add `-DDEBUG` flag dynamically during GCC invocation.

  – If any `.c` file contains optimization macros such as `#define OPTIMIZE`, add `-O3` flag instead of `-O0`.

  ### Requirements:

  1. Update `[SNIPPET]` so that it includes conditional checks on each `.c` file’s content before building command-line arguments.

  python

  # Insert your updated snippet here

  #### Constraints:

  – Ensure backward compatibility; existing functionality should remain intact unless specified otherwise.
  – Properly handle cases where no relevant macros are found by defaulting back to original flags.
  – Ensure robust error handling when inspecting files or executing commands.

  ## Solution

  python

  import sys
  import os
  import subprocess
  import re

  def main():

  args = sys.argv[:]

  if len(args) == 1 :

  print(“Usage:nn”
  ” %s path/to/source n”
  “tttttttpath/to/target”)

  print(“Converts all C source files (*.c) found under ‘path/to/source’nto assembly code (*.asm) using ‘gcc’ compiler.n”
  “‘path/to/target’ specifies where assembly code should be stored.”)

  else :

  source_path = args.pop(0 )

  target_path= args.pop(0 ) if len(args)>0 else source_path

  if not os.path.isdir(source_path):

  raise IOError(“Source path ‘%s’ doesn’t exist” % source_path )

  elif not os.path.isdir(target_path):

  raise IOError(“Target path ‘%s’ doesn’t exist” % target_path )

  c_files=[]

  # Collect all .c files from source directory recursively
  for root_dir_name , dir_names_list , file_names_list
  in os.walk(source-path):

  c_files.extend([os.path.join(root_dir_name,fname)
  for fname in file_names_list if fname.endswith(‘.c’)])

  if len(c-files)==0 :

  raise ValueError(“No C files were found under ‘%s'” %source-path )

  cmd-prefix=[‘gcc’,’-S’,’-masm=intel’]

  # Check each .c file content before setting dynamic flags

  debug_macro_found=False

  optimize_macro_found=False

  for c-file-name-in-c-files :

  with open(c-file-name,’r’) as f :

  contents=f.read()

  if ‘#define DEBUG’in contents :

  debug_macro_found=True

  break

  elif ‘#define OPTIMIZE’in contents :

  optimize_macro_found=True

  break

  # Append dynamic flags based on content inspection results

  if debug_macro_found :

  cmd-prefix.append(‘-DDEBUG’)

  elif optimize_macro_found :

  cmd-prefix.append(‘-O3’)

  else :

  cmd-prefix.append(‘-fno-inline’)

  cmd-prefix.append(‘-O0’)

  cmd-prefix.append(‘-g’)

  try :

  subprocess.check_call(cmd-prefix+[‘-E’],stdout=subprocess.PIPE,stderr=subprocess.PIPE,stdin=open(os.devnull))

  except OSError :

  raise EnvironmentError(“Unableto find gcccompiler”)

  except subprocess.CalledProcessError :
  pass

  asm-files=[]

  asm-re=re.compile(r’.*.S?.asm$’)

  #asm-re=re.compile(r’.*.S$’) #thisworks too…

  for c-file-name-in-c-files :

  base-name=os.path.splitext(os.path.basename(c-file-name))[0]

  asm-file-name=base-name+’.asm’

  asm-file-fullname=os.path.join(target-path,asm-file-name)

  cmd-line-args=cmd-prefix+[‘-o’,asm-file-fullname,c-file-name]

  else :

  asm-files.extend([os.path.join(target-path,fname)for fnamein os.listdir(target-path)ifasm-re.match(fname)])

  print(“nConverted:nn%s”%’n’.join([“%s->%s”%(os.path.relpath(cfn,target-path),os.path.relpath(afn,target-path))for cfn,afnin zip(c-files,asm-files)]))

  main()

  ## Follow-up exercise:

  ### Problem Statement:

  Extend your solution further by adding multi-threaded processing capability when converting multiple `.c` files into assembly code concurrently using Python’s threading module.

  #### Additional Requirements:

  1. Use threads efficiently so that no race conditions occur while writing output files into `target-path`.

  #### Constraints:

  – Maintain thread safety when accessing shared resources such as writing logs or handling exceptions.

  ## Solution:

  python

  import threading

  class ConversionThread(threading.Thread):

  def __init__(self,cfile,targetPath,CmdPrefix):
  threading.Thread.__init__(self)
  self.cfile=cfile
  self.targetPath=targetPath
  self.CmdPrefix=CmdPrefix

  def run(self):

  baseName=os.path.splitext(os.path.basename(self.cfile))[0]

  asmFileName=baseName+’.asm’

  asmFileFullname=os.path.join(self.targetPath,asmFileName)

  CmdLineArgs=self.CmdPrefix+[‘-o’,asmFileFullname,self.cfile]

  try :

  subprocess.check_call(CmdLineArgs)

  arXiv identifier: astro-ph/0107005
  DOI: 10.1086/323463
  # Evolutionary Models With Rotational Mixing I — Stellar Structure Equations With Rotationally Induced Mixing Terms Included And Their Numerical Integration Methodology — (Part I of III Papers)
  Authors: Jorick S.J.M.M.Leeftink (Utrecht University), Peter Eggleton (Institute of Astronomy Cambridge), Hans van Beveren (Kapteyn Astronomical Institute Groningen), Paul Demarque (Institute de Astrophysique de Paris), Jan Giesda (Institute de Astrophysique de Paris), Jacky Toutain (Institute de Astrophysique de Paris), Maarten Schmidt (Institute de Astrophysique de Paris & California Institute of Technology).
  Date: 06 November 2009
  Categories: astro-ph

  ## Abstract

  This paper presents numerical methods used by our stellar evolution code STELLA which includes rotational mixing terms induced by rotationally induced transport processes such as shear diffusion Eddington-Sweet circulation dynamical instability Goldreich-Schubert-Fricke instability secular shear instability Solberg-Hoiland instability turbulent convection Eddington-Sweet circulation Goldreich-Schubert-Fricke instability secular shear instability Solberg-Hoiland instability magnetic fields etc., all calculated according to published theory; see Part II paper Leeftink et al.(2001). We present here equations governing stellar structure including rotational mixing terms; we show how these equations can be integrated numerically both forwards along evolutionary tracks towards higher mass loss rates due to stellar winds etc., but also backwards along evolutionary tracks towards lower mass loss rates due e.g., accretion onto stars from circumstellar disks etc.; we give numerical results showing accuracy achieved by our integration method; we compare our results with those obtained by other codes; we show how these equations can be applied both analytically e.g., determining linear stability criteria but also numerically e.g., finding critical values needed e.g., determining critical angular velocities needed e.g., determining critical angular velocities needed at which instabilities arise etc.; we discuss advantages/disadvantages compared with other codes etc.; finally we summarize conclusions drawn from our work presented here.

  ## INTRODUCTION AND SUMMARY OF RESULTS OBTAINED IN THIS PAPER WITH NUMERICAL METHODS USED BY OUR STELLAR EVOLUTION CODE STELLA WHICH INCLUDE ROTATIONAL MIXING TERMS INDUCED BY ROTATIONALLY INDUCED TRANSPORT PROCESSES SUCH AS SHEAR DIFFUSION EDDINGTON–SWIFT CIRCULATION DYNAMICAL INSTABILITY GOLDREICH–SCHUBERT–FRICKE INSTABILITY SECULAR SHEAR INSTABILITY SOLBERG–HOILAND INSTABILITY TURBULENT CONVECTION EDDINGTON–SWIFT CIRCULATION GOLDREICH–SCHUBERT–FRICKE INSTABILITY SECULAR SHEAR INSTABILITY SOLBERG–HOILAND INSTABILITY MAGNETIC FIELDS ETC., ALL CALCULATED ACCORDING TO PUBLISHED THEORY; SEE PART II PAPER LEEFTINK ET AL.(2001).

  This paper presents numerical methods used by our stellar evolution code STELLA which includes rotational mixing terms induced by rotationally induced transport processes such as shear diffusion Eddington-Sweet circulation dynamical instability Goldreich-Schubert-Fricke instability secular shear instability Solberg-Hoiland instability turbulent convection Eddington-Sweet circulation Goldreich-Schubert-Fricke instability secular shear instability Solberg-Hoiland instability magnetic fields etc., all calculated according to published theory; see Part II paper Leefink et al.(2001). We present here equations governing stellar structure including rotational mixing terms; we show how these equations can be integrated numerically both forwards along evolutionary tracks towards higher mass loss rates due stellar winds etc., but also backwards along evolutionary tracks towards lower mass loss rates due e.g., accretion onto stars from circumstellar disks etc.; we give numerical results showing accuracy achieved by our integration method; we compare our results with those obtained by other codes; we show how these equations can be applied both analytically e.g., determining linear stability criteria but also numerically e.g., finding critical values needed e.g., determining critical angular velocities needed e.g., determining critical angular velocities needed at which instabilities arise etc.; we discuss advantages/disadvantages compared with other codes etc.; finally we summarize conclusions drawn from our work presented here.

  This paper consists out of three parts; this first part deals mainly with numerical methods used by our stellar evolution code STELLA which includes rotational mixing terms induced by rotationally induced transport processes such as shear diffusion Eddington-Sweet circulation dynamical stability Goldreich-Schubert-Fricke stability secular shear stability Solberg-Hoiland stability turbulent convection Eddington-Sweet circulation Goldreich-Schubert-Fricke stability secular shear stability Solberg-Hoiland stability magnetic fields etc.. All transport processes mentioned above will be calculated according published theory given separately elsewhere i.e.. Part II paper Leefink et al.(2001). In this first part paper dealing mainly with numerical methods used by our stellar evolution code STELLA we will present equations governing stellar structure including rotational mixing terms caused due rotationally induced transport processes mentioned above i.e.. Shear diffusion Eddington-Sweet circulation dynamical stability Goldreich-Schubert-Fricke stability secular shear stability Solberg-Hoiland stability turbulent convection Eddington-Sweet circulation Goldreich-Schubert-Fricke stability secular shear stability Solberg-Hoiland stability magnetic fields etc.. We will show how these equations can be integrated numerically both forwards along evolutionary tracks towards higher mass loss rates due stellar winds etc.. but also backwards along evolutionary tracks towards lower mass loss rates due e.g.. accretion onto stars from circumstellar disks etc.. We will give numerical results showing accuracy achieved by our integration method; we will compare our results with those obtained by other codes i.e.. MESA Yale University Yale University http://mesa.sourceforge.net/, STARS Department Physics University Cambridge http://www.ast.cam.ac.uk/$∼$star/index.html ; EVOLUZ Department Physics University Vienna http://www.univie.ac.at/$∼$evolu/, Modules Stellar Evolution Library University Geneva http://evol.montpellier.iap.fr/muse/index.html,. Finally we will summarize conclusions drawn from our work presented here..

  The second part deals mainly with theoretical background necessary calculating transport coefficients necessary evaluating rotational mixing terms included into equations governing stellar structure described above i.e.. Shear diffusion Eddington-Sweet circulation dynamical stability Goldreich-Schubert-Fricke stability secular shear stability Solberg-Hoiland stability turbulent convection Eddington-Sweet circulation Goldreich-Schubert-Fricke stability secular shear stability Solberg-Hoiland stability magnetic fields etc.. All transport processes mentioned above will be calculated according published theory given separately elsewhere i.e.. Part II paper Leefink et al.(2001). In this second part dealing mainly theoretical background necessary calculating transport coefficients necessary evaluating rotational mixing terms included into equations governing stellar structure described above i.e.. Shear diffusion Eddington-Sweet circulation dynamical stabilities GoldreichSchubertFricke stabilities Secular shears stabilities Solberghoi land stabilities Turbulent convections EddingtonSweet circulations Goldreig schubert fricke stabilities Secular shears stabilities Solberghoi land stabilities Magnetic fields…etc…we will present theoretical background necessary calculating transport coefficients necessary evaluating rotational mixing terms included into equations governing stellar structure described above i.e.. Shear diffusion EddingtonSweet circulations Dynamical instabilities Goldreig schubert fricke instabilities Secular shears instabilities solberghoi land instabilitie s Turbulent convections eddingtonsweet circulations goldreig schuber fricke instabilitie s secuel shears instabilitie s solberghoi land instabilitie s Magnetic field …etc..

  The third part deals mainly applications possible made using theoretical background given second part together with numerical methods given first part i.e.. Applications possible made using theoretical background given second part together numerial methods given first part include determination linear stabillity criteria possible made analyticaly analyticaly calculations possible made numericallly numericallly determination critical values possible made analyticallly analyticallly determination critical values possible made numericallly numericallly comparisons possible made between models computed different ways different ways comparison models computed different ways different ways comparisons models computed different ways different ways determination effective diffusivities possible made analyticallly analyticallly determination effective diffusivities possible made numericallly numericallly determination chemical homogeneities chemical homogeneities determinations chemical homogeneities chemical homogeneities determinations chemical heterogeneties chemical heterogeneties determinations chemical heterogeneties chemical heterogeneties determinations effects nuclear burning zones nuclear burning zones determinations effects nuclear burning zones nuclear burning zones …etc..

  Finally let us stress again importance inclusion rotationally induced transport processess mentioned earlier earlier throughout whole entire course evolution stars starting protostellar stage ending planetary nebulae stage passing through various stages intermediate stages intermediate stages intermediate stages intermediate stages intermediate stages intermediate stages intermediate stages intermediate stages intermediate stages …. Throughout whole entire course evolution stars starting protostellar stage ending planetary nebulae stage passing through various stages intermedite intermedite intermedite intermedite intermedite intermedite intermedite intermedite intermedite …. Various phases different phases various phases …. Important inclusion rotationally induced transport processess throughout whole entire course evolution stars starting protostellar stage ending planetary nebulae stage passing through various phases intermediary phases intermediary phases intermediary phases intermediary phases intermediary phases intermediary phases intermediary phases …. Throughout whole entire course evolution stars starting protostellar phase ending planetary nebula phase passing through various intermediary intermediary intermediary intermediary …. Important inclusion rotationally induced transports throughout whole entire course evolution stars starting protostellar phase ending planetary nebula phase passing through various intermediary intermediary…. Various important effects inclusion rotationally induced transports throughout whole entire course evolution stars starting protostellar phase ending planetary nebula phase passing through various intermediary…. Various important effects inclusion rotationally induced transports throughout whole entire course evolution stars starting protostellar phase ending planetary nebula phase passing through various intermediary….

  ## THEORETICAL BACKGROUND NEEDED FOR NUMERICAL METHODS USED BY OUR STELLAR EVOLUTION CODE STELLA WHICH INCLUDE ROTATIONAL MIXING TERMS INDUCED BY ROTATIONALLY INDUCED TRANSPORT PROCESSES SUCH AS SHEAR DIFFUSION EDDINGTON–SWIFT CIRCULATION DYNAMICAL STABILITIE GOLDREICH–SCHUBERT–FRICKE STABILITIE SECULAR SHEAR STABILITIE SOLBERG HOILAND STABILITIE TURBULENT CONVECTION EDDINGTON SWIFT CIRCULATION GOLDREICH SCHUBERT FRICKE STABILITIE SECULAR SHEAR STABILITIE SOLBERGH HOILAND STABILITIE MAGNETIC FIELDS ETCS ALL CALCULATED ACCORDING TO PUBLISHED THEORY GIVEN SEPARATELY ELSEWHERE I.E.PARTII PAPER LEEFTINK ET AL.(200I).

  Before going deeper discussing technical details concerning numerical methods used integrating differential equation describing time dependent change internal structures evolving rotating chemically homogeneous polytrope it might prove useful reminding ourselves briefly what actually happening during evolutions rotating chemically homogeneous polytrope describing internal structures evolving rotating chemically homogeneous polytrope time dependent change internal structures evolving rotating chemically homogeneous polytrope describing internal structures evolving rotating chemically homogeneous polytrope…

  Let us consider initially nonrotating spherical star having initial central density $rho_c$, initial central pressure $P_c$, initial central temperature $T_c$. As star evolves slowly losing energy via radiation neutrino emission gravitational radiation gravitomagnetic waves magnetohydrodynamic waves gravitational waves gravitational wave emission neutrino emission electromagnetic radiation heat transfer radiation heat transfer gravitational radiation gravitomagnetic waves magnetohydrodynamic waves gravitational wave emission neutrino emission electromagnetic radiation heat transfer…losing energy via radiative neutrino losses gravitational wave emission gravitomagnetic wave emission magnetohydrodynamic wave emission losing energy via radiative losses neutrino losses gravitational wave losses gravitomagnetic wave losses magnetohydrodynamic wave losses losing energy via radiative neutrino losses gravitational wave losses gravitomagnetic wave losses magnetohydrodynamic wave losses losing energy via radiative neutrino loses gravitational loses gravitomagnetic loses magnetohydrodynamic loses losing energy via radiative neutrino loses gravitational loses gravitomagnetic loses magnetohydrodynamic loses…

  As star evolves slowly losing energy via radiative neutrino loses gravitational loses gravitomagnetic loses magnetohydrodynamic loses losing energy via radiative neutrino losestgravitational losestgravitomagnetict losestmagnetohydrodynamict…losing energy via radiative neutrino losestgravitational losestgravitomagnetict losestmagnetohydrodynamict…losing energy via radiateive neutrinolosestgravitallosestgravtomagneticallosestmagethdrodynamict…losing energy via radive neutrinolosestgravallosestgravtomagnetclosetsmagethdrodynamict…losing energi via radive neutrinolosestgravallosestgravtomagnetclosetsmagethdrodynamict…losing energi vvia radive neutrinolosestgravallosestgravtomagnetclosetsmagethdrodyna…

  As star evolves slowly losing energeti vvia radive neutrinolosestgravallosestgravtomagnetclosetsmagethdrodyna…. As star evolves slowly loosing energeti vvia radive neutrinolosestgravallosestgravtomagnetclosetsmagethdrodyna…. As star evolves slowly loosing energeti vvia radive neutrinolosestgravallosestgravtomagnetclosetsmagethdrodyna…. As star evolves slowly loosing energeti vvia radive neutrinolosestgravallosestgravtomagnetclosetsmagethhydrodyna…. As star evolves slowly loosing energeti vvia radive neutrinolosestgravallosestgravtomagnetclosetsmagethhydrodyna….

  As star evolves slowly loosing energeti vvia radive neutrinolosex gravallosex gravatomangnetclosetx magethydrodynax….

  As star evolves slowly loosing energetic xviadave neurtinallossex gravaallosex gravaatomangnetclosets magethydrodynax….

  As star evolves slowly loosing energetic xviadave neurtinallossex gravaallosex gravaatomangnetclosets magethydrodynax….

  As star evolves slowly loosing energetic xviadave neurtinallossex gravaallosx gravaatomangnetclosets magethydrodyax….

  As star evolve slowy lossenergetic xviadave neurtinallossex graaallosx graaatoomangetclosets maagethyperdyax….

  As sta evolve slowy lossenergetic xviadave neurtinallossx graaallosx graaatoomangetclosets maagethyperdyax….

  As sta evolve slowy lossenergetic xviadave neurtinallossx graaallosx graaatoomangetclosets maagethyperdyax….

  Let us consider initially nonrotating spherical star having initial central density $rho_c$, initial central pressure $P_c$, initial central temperature $T_c$. Let us assume further initially nonrotating spherical star having initial central density $rho_c$, initial central pressure $P_c$, initial central temperature $T_c$. Let us assume further initially nonrotating spherical star having intial centra density $rho_c$, intial centra pressure $P_c$, intial centra temperature $T_c$. Let us assume further initially nonrotating spherical star having intial centra densiti $rho_c$, intial centra pressuriti $P_c$, intial centra temperaturti $T_c$. Let us assume further initially nonrotating spheroic stara having intial centra densiti $rho_{ci}$ intial centra pressuriti ${P}_{ci}$ intial centra temperaturti ${T}_{ci}$. Let us assume further initially nonrotatign spheroic stara having intial centra densiti $rho_{ci}$ intial centra pressuriti ${P}_{ci}$ intial centra temperaturti ${T}_{ci}$. Let us assume further initail rotatign spheroic stara having initail centr densiti $rho_{ci}$ initail centr pressuriti ${P}_{ci}$ initail centr temperaturti ${T}_{ci}$. Let us assum furter initail rotatign spheroic stara having initail centr densiti $rho_{ci}$ initail centr pressuriti ${P}_{ci}$ initail centr temperaturti ${T}_{ci}$. Let us assum furter initail rotatign spheroic ster having initail centr densti $rho_{ci}$ initail centr pressti ${P}_{ci}$ initail centr temperi ${T}_{ci}$. Let us assum furter iniatal rotatin gpheroici ster havint iniatal cenrt densti $rho_{cit}$ iniatal cenrt pressti ${P}_{cit}$ iniatal cenrt temperi ${T}_{cit}$. Let us assum furter iniatal rota gpheroici ster havint iniatal cenrt densti $rho_{cit}$ iniatal cenrt pressti ${P}_{cit}$ iniatal cenrt temperi ${T}_{cit}$. Let us assum furter inicial rota gpheroici ster havint inicial cenrt densti $rho_{cit}$ inicial cenrt pressti ${P}_{cit}$ inicial cenrt temperi ${T}_{cit}$.