AVL stats & predictions

Volleyball AVL AUSTRIA: Tomorrow's Exciting Matches and Expert Betting Predictions

The Austrian Volleyball League (AVL) is gearing up for another thrilling round of matches tomorrow. Fans and bettors alike are eagerly anticipating the games, with expert predictions already in the spotlight. This article will delve into the key matchups, provide insights into team performances, and offer expert betting predictions to guide your wagers.

Key Matchups to Watch

Tomorrow's schedule features several high-stakes matches that promise to captivate volleyball enthusiasts. Here are the standout games:

• Team A vs. Team B: This clash between two top contenders is expected to be a nail-biter. Both teams have shown exceptional form this season, making this matchup a must-watch.
• Team C vs. Team D: Known for their strategic gameplay, Team C faces off against the defensively strong Team D. The outcome of this match could significantly impact league standings.
• Team E vs. Team F: With both teams fighting for playoff positions, this game is crucial for their aspirations. Expect intense competition and strategic plays from both sides.

Team Performances and Analysis

Analyzing team performances provides valuable insights into potential outcomes. Here's a breakdown of some key teams:

Team A

Team A has been in stellar form, boasting an impressive win-loss record. Their powerful offense and solid defense make them a formidable opponent on any given day.

Team B

Despite facing challenges earlier in the season, Team B has rebounded strongly, thanks to strategic adjustments by their coach. Their resilience will be tested against Team A.

Team C

Known for their tactical acumen, Team C has consistently outmaneuvered opponents with well-executed plays. Their ability to adapt during matches gives them an edge over many rivals.

Team D

With a focus on defense, Team D has managed to keep their goals minimal while capitalizing on counterattacks. Their defensive prowess will be crucial against Team C's offensive strategies.

Betting Predictions: Expert Insights

Betting enthusiasts can rely on expert predictions to make informed decisions. Here are some insights from top analysts:

• Prediction 1: Analysts predict a close match between Team A and Team B, with a slight edge towards Team A due to their recent form.
• Prediction 2: For the game between Team C and Team D, experts suggest betting on an underdog victory for Team D, given their defensive strengths.
• Prediction 3: In the clash between Team E and Team F, analysts see potential for an upset by Team F if they can exploit weaknesses in Team E's defense.

In-Depth Match Analysis

To better understand tomorrow's games, let's delve deeper into each matchup:

Team A vs. Team B: Offensive Powerhouses Meet

This matchup is set to be a showcase of offensive prowess as both teams boast strong attacking capabilities. Key players from both sides will play pivotal roles in determining the outcome.

• Suspenseful Moments: Look out for critical points where momentum could shift dramatically between these evenly matched teams.
• Tactical Plays: Coaches will likely employ unique strategies to gain an advantage over one another throughout the match.

Team C vs. Team D: Strategy Versus Defense

The battle between strategy and defense takes center stage in this encounter. While Team C aims to outmaneuver with clever tactics, they'll face stiff resistance from Team D’s robust defensive lineup.

• Critical Factors:: The effectiveness of each team’s key players will heavily influence which side prevails today’s contest.
• Momentum Shifts:: Anticipate shifts in momentum as each side attempts to capitalize on errors made by their opponent during playtime!

Team E vs. Team F: Playoff Aspirations at Stake

This game holds significant implications for playoff positioning within AVL Austria’s competitive landscape today! Both squads enter with high stakes but also vulnerabilities that could be exploited by savvy opponents looking ahead towards postseason glory!

• Focused Playmaking:: Players who manage game tempo effectively may prove decisive factors influencing final results here!
• Error Exploitation:: Teams must remain vigilant against costly mistakes while seizing opportunities presented by lapses made by adversaries!

Betting Strategies: Making Smart Wagers Today!

To maximize your betting success today based on expert insights gathered so far across all featured contests within AVL Austria’s volleyball arena tomorrow morning! Here are some recommended strategies worth considering when placing bets today!

• Diversify Your Bets:: Spread your wagers across different outcomes rather than putting all eggs into one basket; this approach reduces risk while increasing chances of securing profits overall!
• Leverage Expert Predictions:: Utilize forecasts provided by seasoned analysts who have meticulously examined past performances alongside current trends shaping up ahead-of-time before placing bets accordingly!"I need help with this piece of code [SNIPPET] I want more complex validation logic"<|end|>|<|assistant|>"Sure thing! One way we can add complexity is by allowing dynamic platform validation using regex patterns instead of hardcoding specific values like "android" or "ios". Here's how you can do it: python import re def check_args(args): valid_env_types = ["release", "debug"] valid_platform_patterns = [r'^android$', r'^ios$'] if args.env not in valid_env_types: print("invalid env type") exit(1) platform_valid = any(re.match(pattern, args.platform) for pattern in valid_platform_patterns) if not platform_valid: print("invalid platform type") exit(1) This way you can easily extend `valid_platform_patterns` with more complex regex patterns as needed."<|end|>|<|user|>"Nice but i need localized error messages based on user locale settings stored within `args`"<|end|>|<|assistant|>"Got it! We'll integrate localized error messages using Python's built-in `gettext` module which supports internationalization (i18n). First ensure you have `.mo` files ready for different locales. Here's how you can modify your function: python import gettext # Set up translation function based on locale stored in args.locale locale_dir = './locales' language = gettext.translation('messages', localedir=locale_dir, languages=[args.locale]) language.install() _ = language.gettext def check_args(args): valid_env_types = ["release", "debug"] valid_platform_patterns = [r'^android$', r'^ios$'] if args.env not in valid_env_types: print(_("Invalid environment type")) exit(1) platform_valid = any(re.match(pattern, args.platform) for pattern in valid_platform_patterns) if not platform_valid: print(_("Invalid platform type")) exit(1) Make sure you have translation files prepared under `./locales//LC_MESSAGES/messages.mo`. This setup allows dynamic switching of error messages based on user locale."<|end|>|<|user|>"Can we replace those exit calls with custom exceptions that log detailed info including stack traces?"<|end|>|<|assistant|>"Certainly! We'll create custom exception classes that log detailed information including stack traces using Python’s `logging` module. Here's how you can do it: python import re import logging # Configure logging logging.basicConfig(level=logging.ERROR) class InvalidEnvTypeError(Exception): pass class InvalidPlatformTypeError(Exception): pass def check_args(args): valid_env_types = ["release", "debug"] valid_platform_patterns = [r'^android$', r'^ios$'] try: if args.env not in valid_env_types: raise InvalidEnvTypeError(f"Invalid environment type '{args.env}'") platform_valid = any(re.match(pattern, args.platform) for pattern in valid_platform_patterns) if not platform_valid: raise InvalidPlatformTypeError(f"Invalid platform type '{args.platform}'") except (InvalidEnvTypeError, InvalidPlatformTypeError) as e: logging.error(e, exc_info=True) raise e # Re-raise after logging # Example usage class ArgsMock: def __init__(self, env='test', platform='unknown'): self.env = env self.platform = platform try: check_args(ArgsMock()) except Exception as e: print(e) This modification ensures detailed error information is logged whenever an invalid argument is encountered." <|end|>|<|user|>"Can we change validation logic based on additional env vars?"<|end|>|<|assistant|>"Absolutely! We can modify our validation logic conditionally based on additional environment variables using Python’s `os.environ`. Here’s how you might implement such functionality: python import re import os def get_dynamic_validation_rules(): # Example rules fetched from environment variables allowed_envs_from_env = os.environ.get('ALLOWED_ENVS', 'release;debug').split(';') allowed_platforms_from_env = os.environ.get('ALLOWED_PLATFORMS', 'android;ios').split(';') return allowed_envs_from_env, [re.compile(pattern) for pattern in allowed_platforms_from_env] def check_args(args): allowed_envs_from_file_or_default = ["release", "debug"] # Override default rules with those from environment variables allowed_envs_from_file_or_default.extend(get_dynamic_validation_rules()[0]) _, allowed_platform_patterns_from_file_or_default = get_dynamic_validation_rules() try: if args.env not in allowed_envs_from_file_or_default: raise InvalidEnvTypeError(f"Invalid environment type '{args.env}'") platform_valid = any(pattern.match(args.platform) for pattern in allowed_platform_patterns_from_file_or_default) if not platform_valid: raise InvalidPlatformTypeError(f"Invalid platform type '{args.platform}'") except (InvalidEnvTypeError, InvalidPlatformTypeError) as e: logging.error(e, exc_info=True) raise e # Re-raise after logging # Example usage class ArgsMock: def __init__(self ,env='test',platform='unknown'): self.env=env self.platform=platform try : check_args(ArgsMock()) except Exception as e : print(e) # To test dynamic rules , set these env vars before running your script : # export ALLOWED_ENVS="dev;staging" # export ALLOWED_PLATFORMS="windows;linux" This way we dynamically adjust our validation rules based on additional environment variables." <|end|>|*** Excerpt *** The initial results were promising because they showed very clearly that there was indeed substantial variation among individuals' brains even though they had similar clinical symptoms.[30] This led us toward the idea that perhaps individual differences would account better than diagnosis per se.[31] If we could measure individual differences accurately enough then we might find clusters of individuals who share similar brain characteristics without having similar diagnoses. To pursue this goal further required going beyond surface anatomical measures such as volume estimates or cortical thickness estimates derived from MRI data.[32] It required measuring neural activity directly. In order to measure neural activity directly we used EEG data.[33] EEG measures voltage fluctuations resulting from electrical currents produced when neurons communicate through synaptic activity.[34] The strength of EEG lies primarily in its excellent temporal resolution which allows us to track neural activity millisecond-by-millisecond.[35] However EEG suffers from relatively poor spatial resolution because electrodes placed outside the skull cannot pinpoint exactly where inside the brain neural activity originates.[36] Nevertheless we reasoned that even despite its poor spatial resolution EEG might still reveal something about individual differences because each person's brain operates according its own unique spatiotemporal dynamics.[37] So instead of focusing exclusively upon surface anatomical features such as cortical thickness estimates derived from MRI data,[38] we decided also include measures derived directly from EEG data itself – specifically frequency band power estimates measured across different regions-of-interest within each subject.[39] These frequency band power estimates reflect how much energy there is within certain frequency bands – alpha (8–12 Hz), beta (12–30 Hz), gamma (>30 Hz).[40] By comparing these frequency band power estimates across different regions-of-interest within each subject,[41] we hoped eventually uncover hidden patterns related specifically either diagnosis group membership OR individual differences independent thereof – ideally both![42] *** Revision 0 *** ## Plan To make an exercise advanced and challenging: 1. Introduce technical jargon related to neuroscience without providing definitions. 2. Incorporate statistical concepts relevant to data analysis. 3. Reference other neuroimaging techniques indirectly requiring readers' prior knowledge. 4. Include hypothetical scenarios requiring application of concepts mentioned. 5. Introduce nested counterfactuals and conditionals that require careful parsing. 6. Use complex sentence structures with multiple clauses. Changes could include: - Adding terminology like 'connectome,' 'neuroplasticity,' 'fMRI,' etc., without explaining them. - Mentioning statistical methods like 'multivariate analysis' or 'machine learning algorithms' without context. - Hinting at comparisons between EEG data interpretation methods like time-frequency analysis versus traditional spectral analysis. - Using hypothetical research findings requiring interpretation through deductive reasoning. - Creating conditional statements where conclusions depend upon multiple layers of conditions being met. ## Rewritten Excerpt The preliminary investigations yielded auspicious outcomes elucidating pronounced heterogeneity among cerebral architectures notwithstanding analogous clinical manifestations amongst subjects.[30] Such revelations precipitated our pivot towards postulating that variances intrinsic to individuals may eclipse diagnostic categories themselves concerning predictive validity.[31] Assuming accurate quantification of these idiosyncrasies were attainable—potentially through multivariate analyses—latent constellations comprising individuals sharing congruent neurophysiological traits yet divergent diagnostic labels might emerge. Advancing beyond mere morphometric assessments—such as volumetric computations or cortical thickness derivations gleaned via magnetic resonance imaging modalities—demanded direct quantification of neuronal excitability.[32] Electroencephalography (EEG), capturing voltage oscillations emanating from synaptic communicative processes among neurons,[33][34], was employed owing largely to its superior temporal fidelity capable of delineating neuronal events at millisecond precision intervals.[35] Notwithstanding its temporal acuity advantages over other modalities such as fMRI—which offers finer spatial localization albeit at reduced temporal resolution—EEG presents limitations regarding locational specificity due primarily to non-invasive electrode placement externalized upon cranial surfaces unable to ascertain precise intracranial origins of electrical phenomena.[36] Yet positing counterfactually—if spatial resolution constraints were inconsequential—EEG could potentially divulge distinctive spatiotemporal dynamics intrinsic uniquely unto each cerebrum,[37]. Thusly redirecting our investigative focus away from purely morphological indices toward incorporating electrophysiological metrics directly extracted from EEG datasets—namely spectral power distributions across predefined frequency bands within selected cerebral regions-of-interest,[39]. These spectral power distributions encapsulate energetic concentrations spanning various oscillatory frequencies—alpha (8–12 Hz), beta (12–30 Hz), gamma (>30 Hz)—and serve as proxies indicating underlying neuronal synchronization states.[40]. By juxtaposing these metrics inter-regionally within subjects,[41], one hypothesizes uncovering cryptic patterns either correlating distinctly with diagnostic classifications OR transcending them entirely—an ideal scenario encompassing both dimensions![42]. ## Suggested Exercise A research team conducted a study analyzing neurological variations among patients displaying similar clinical symptoms but possessing diverse diagnoses using electroencephalography (EEG). They proposed utilizing multivariate analyses combined with EEG-derived metrics such as spectral power distributions across various frequency bands identified within distinct cerebral regions-of-interest. Assuming all other experimental conditions are held constant—and considering only those aspects described above—which one statement most accurately reflects the implications drawn from employing EEG data over alternative neuroimaging techniques? A) The researchers concluded definitively that diagnosis-specific patterns exist exclusively within alpha wave frequencies due solely to EEG's unparalleled spatial resolution compared with fMRI. B) Given its superior temporal resolution relative to fMRI yet inferior spatial precision due solely external electrode placement limitations inherent within EEG methodology; nonetheless assuming perfect locational specificity was possible hypothetically—the researchers anticipated revealing unique spatiotemporal dynamics particularized individually irrespective of diagnostic categorization. C) Despite acknowledging inherent spatial limitations associated with EEG technology due solely external electrode placement constraints—the researchers assumed no significant utility would derive from contrasting spectral power distributions inter-regionally since no meaningful patterns could emerge outside strictly morphological indices assessed previously via MRI techniques alone. D) By relying solely upon traditional morphometric assessments acquired through MRI technologies—the researchers aimed exclusively at identifying patient groups sharing identical diagnostic labels irrespective of individual neurophysiological variance potentially unveiled through direct measurement methodologies like EEG. *** Revision 1 *** check requirements: - req_no: 1 discussion: The draft does not explicitly require external knowledge beyond understanding the excerpt itself. score: 0 - req_no: 2 discussion: Understanding subtleties is necessary but does not sufficiently draw upon external knowledge or nuanced comprehension beyond what is presented. score: 2 - req_no: 3 discussion: Length and difficulty criteria are met; however clarity could improve, making it easier than intended without sacrificing complexity. score: 2 - req_no: 4 discussion: Choices are misleading but don't fully ensure only knowledgeable participants' success due partly lacking reliance on external knowledge integration. score: 2 - req_no: 5 discussion: Difficulty level suits advanced undergraduates familiar with neuroscience, but could challenge further by integrating more sophisticated analytical demands. revision suggestion: To satisfy requirement number one more fully while enhancing others, consider integrating specific theories or models related directly or indirectly, such as Hebbian theory regarding synaptic plasticity which relates closely yet requires-depth-understanding-to-connect-with-electrophysiological-metrics-discussed-in-the-excerpt-or-the-concept-of-neural-network-modeling-to-understand-the-potential-for-pattern-recognition-within-neurophysiological-data-acquired-via-eeg-and-how-it-compares-to-imaging-modalities-like-fmri-and-mri-in-revealing-diagnostic-classifications-or-individual-variations-these-theories-and-concepts-would-force-participants-to-draw-on-specific-outside-knowledge-and-make-sense-of-the-complexity-within-the-excerpt-more-effectively-enhancing-comprehension-challenges-and-making-all-options-plausible-but-only-one-most-correct-based-on-a-deep-understanding-and-application-of-this-knowledge-integrating-statistical-analysis-methodologies-as-part-of-this-discussion-would-add-an-additional-layer-of-complexity-requiring-understanding-beyond-basic-narrative-follow-through' correct choice: Given its superior temporal resolution relative to fMRI yet inferior spatial precision due solely external electrode placement limitations inherent within EEG methodology; nonetheless assuming perfect locational specificity was possible hypothetically—the researchers anticipated revealing unique spatiotemporal dynamics particularized individually irrespective of diagnostic categorization." revised exercise: Considering the excerpt above discussing various neuroimaging techniques' capabilities alongside mentioning theoretical approaches like Hebbian theory regarding synaptic plasticity, evaluate how incorporating concepts such as neural network modeling might influence interpretations, particularly when distinguishing between diagnosis-specific patterns versus individual-specific-neurophysiological-variations-using-electrophysiological-metrics-derived-from-eeg-data-compared-to-traditional-imaging-modalities-like-fmri-and-mri-assessments? Assume familiarity with statistical methodologies relevant for analyzing such complex datasets. incorrect choices: - Researchers concluded definitively that diagnosis-specific patterns exist exclusively, evidenced through alpha wave frequencies superiority attributed mistakenly solely-to-eeg-spatial-resolution-over-fmri-notwithstanding-the-latter's-superior-spatial-localization-capabilities-but-lower-temporal-resolution-highlighting-a-common-misunderstanding-between-temporal-and-spatial-resolution-concepts-in-neuroimaging-disciplines. - Acknowledging inherent spatial limitations associated with EEG technology due solely-to-electrode-placement-constraints-the-researchers-assumed-no-significant-utilitarian-value-from-inter-regional-spectral-power-distribution-comparisons-insinuating-a-preference-for-static-morphological-assessments-acquired-via-MRI-over-dynamic-electrophysiological-measurements-provided-by-eeg-despite-eeg-offering-temporal-dynamics-insights-unattainable-through-MRI-only-an-analysis-error-overlooking-eeg-time-domain-data-potential-for-diagnostic-insightfulness-beyond-static-anatomical-measurements. - Relying exclusively upon traditional morphometric assessments obtained through MRI technologies—the-researchers-intended-at-isolating-patient-groups-sharing-uniform-diagnostic-labels-disregarding-individual-neurophysiological-variance-that-could-be-unveiled-through-direct-measurement-methodologies-like-eeg-an-error-in-strategic-focus-overlooking-the-value-added-by-integrating-electrophysiological-data-for-a-comprehensive-understanding-of-brain-functionality-across-diagnostic-categories-and-individual-variations-highlighting-a-limitation-in-methodological-perspective-towards-diagnostic-classification-driven-research-versus-individual-centric-analysis-in-neuroscience-studies.' *** Revision v2 *** check requirements: - req_no': '' revision suggestion': To enhance integration with external academic facts while maintaining focus, question should ask about applying specific theories/models relevantly mentioned indirectly, such as Hebbian theory about synaptic plasticity relating closely yet requires-depth-understanding-to-connect-with-electrophysiological-metrics-discussed-in-the-excerpt-or-neural-network-modeling-to-understand-pattern-recognition-within-neurophysiological-data-acquired-via-eeg-how-it-compares-to-imaging-modalities-like-fmri-and-mri-in-revealing-diagnostic-classifications-or-individual-variations-these-theories-and-concepts-would-force-participants-to-draw-on-specific-outside-knowledge-and-make-sense-of-the-complexity-within-the-excerpt-more-effectively-enhancing-comprehension-challenges-and-making-all-options-plausible-but-only-one-most-correct-based-on-a-deep-understanding-and-application-of-this-knowledge-integrating-statistical-analysis-methodologies-as-part-of-this-discussion-would-add-an-additional-layer-of-complexity-requiring-understanding-beyond-basic-narrative-follow-through.' correct choice': Given its superior temporal resolution relative to fMRI yet inferiorspatial precision due solely external electrode placement limitations inherent withinEEG methodology; nonetheless assuming perfect locational specificity was possiblehypotheticallythe researchers anticipated revealing unique spatiotemporal dynamicsparticularized individually irrespectiveofdiagnosticcategorization.' revised exercise': Considering the excerpt above discussing various neuroimaging techniquescapabilities alongside mentioning theoretical approaches like Hebbian theory regardingsynaptic plasticity evaluate how incorporating concepts such asnervous system modelingmight influence interpretationsparticularlywhen distinguishingbetween diagnosis-specificpatternsversusindividualspecificneurophysiologicalextrapolatedfromeegdatacomparedtotraditionalimaginingmodalitieslikefmriandmriseassessments?Assume familiaritywithstatisticalmethodologiesrelevantforanalyzingsuchcomplexdatasets.' incorrect choices': - Researchers concluded definitivelythatdiagnosis-specificpatternsexistexclusively,evidencedthroughalpha wave frequencies superiorityattributedmistakenlysolelytoeegspatialresolutionoverfmris notwithstandingthelatterssuperiorspatiallocalizationcapabilitiesbutlowertemporalsolutionhighlightingacommonmisunderstandingbetweentemporalandspace-resolutionconceptsinneuroimagingdisciplines." acknowledging inherentspatiallimitationsassociatedwithEEGtechnologyduesolelectrodeplacementconstraints thereresearchersassumednosignificantutilitarianvaluefrominterregionalspectralpowerdistributioncomparisonsinsinuatingapreferenceforstaticmorphologicalassessmentsacquiredviaMRIOVERdynamicelectrophysiologicaledmeasurementsprovidedbyeegodespiteeeftemporaldynamicsinsightsunattainablethroughMRIOonlyananalysiserroroverlookingeeftime-domaindatapotentialfordiagnosticinsightfulnessbeyondstaticanatomicalmeasurements." relyingexclusivelyupontraditionalmorphometricassessmentsobtainedthroughMRITechnologiesthe researchersintendedatisolatingpatientgroupssharinguniformdiagnosiscodesdisregardingindividualneurophysiologicavariablethatcouldbeunveiledthroughdirectmeasurementmethodologieslikeeeganerrorinstrategicfocusoverlookingthevalueaddedbyintegratingelectrophysiologicdataforacomprehensiveunderstandingofbrainfunctionalityacrossdiagnosticscategoriesandindividualvariationshighlightinga-limitationinmethodologicalperspectivetowardsdiagnosticclassificationdrivenresearchversusindividualcentricanalysisinnervoscience-studies" *** Excerpt *** *** Revision $Revision$$Author$$Date$ *** Revision history *** * $Revision$ * $Author$ * $Date$ *** Description *** This program performs basic arithmetic operations (+,-,/,*). It prompts users asking them what operation they want done followed by two numbers separated by space(s). *** Changelog *** * Added input handling so users can input spaces around operators/operands now works properly - added tests too! *** Diff *** diff --git a/arithmetic.c b/arithmetic.c index d21d00c..b268d03 --- a/arithmetic.c +++ b/arithmetic.c @@ -23,6 +23,7 @@ int main(int argc,char *argv[]) printf("nYou must supply three argumentsn"); return EXIT_FAILURE; } + char op; // Get operator printf("Enter operator (+,-,/,*): "); @@ -29,7 +30,9 @@ int main(int argc,char *argv[]) scanf("%c",&op); // Get operands -char num1,num2; + int num1,num2; + while((op!='+')&&(op!='-'&&(op!='/')&&(op!='*'))) + { + printf("Error please enter correct operatorn"); + scanf("%c",&op); + } printf("Enter two operands(separated by space): "); @@ -35,7 +38,7 @@ int main(int argc,char *argv[]) scanf("%d %d",&num1,&num2); switch(op){ -case '+': + case '+' : printf("%d + %d=%dn",num1,num2,num1+num2); break; case '-': @@ -47,7 +50 ,7 @@ int main(int argc,char *argv[]) break; case '/': if(num2==0) - + printf("Divide by zero!n"); else printf("%d / %d=%fn",num1,num2,(float)(num1)/(float)(num2)); break; *** Conversation *** ## Suggestions for complexity 1. **Handling Multiple Operators:** How would you modify the code snippet so it can handle multiple operators sequentially? For example allowing expressions like `"5 + -3 * /"`? 2. **Operator Precedence:** How would you implement operator precedence handling? For instance ensuring multiplication/division takes precedence over addition/subtraction without parentheses explicitly specified? 3. **Support Floating Point Numbers:** How would you adapt this code snippet so it handles floating-point numbers instead just integers? 4. **Error Handling Improvements:** Can you enhance error handling so it covers edge cases like invalid characters being entered besides operators/numbers? 5. **Extend Functionality:** What modifications would be necessary if I wanted this program also support exponentiation (`^`) along with existing operations? ## Conversation [SNIPPET]
  I'm trying figure out how I'd handle multiple operators sequentially say something like `"5 + -3 * /"`