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Sales among the top 100 foodservice equipment and supplies dealers totaled $15.65 billion in 2023, which is up from $14.54 billion in 2022, according to the FE&S 2024 Distribution Giants study. In 2023, sales increased for 80% of dealers within the top 100, who shared their data. Read Article Distribution Giants Joseph M. Carbonara April 03, 2023 Sales among the top 100 FE&S dealers totaled $14.51 billion in 2022, which is up from $11.63 billion in 2021, according to the FE&S 2023 Distribution Giants study. In 2022, sales increased for 95% of dealers within the top 100, compared with 85% last year. Read Article Distribution Giants The Editors April 01, 2022 Sales among the top 100 foodservice equipment and supplies dealers totaled $11.72 billion in 2021, up from $9.4 billion in 2020, according to the FE&S 2022 Distribution Giants study. In 2021 sales increased for 85% of the dealers within the top 100, compared with 16% last year. That means 15% saw a decline in revenues in 2021, which is an indicator of how much the industry has fluctuated from one year to the next. Read Article Distribution Giants Joseph M. Carbonara April 01, 2021 Sales among the top 100 foodservice equipment and supplies dealers totaled $9.4 billion in 2020, down 14.37% from 2019’s total of $10.92 billion, according to the FE&S 2021 Distribution Giants study. In 2020 sales increased for only 16% of the dealers within the top 100, compared with 70% last year. That means 84% saw a decline in revenues in 2020, which should come as a surprise to nobody. This represents the first time since 2008 that total sales among the top 100 foodservice equipment and supplies dealers declined. Read Article Distribution Giants The Editors April 01, 2020 Sales among the top 100 foodservice equipment and supplies dealers totaled $11.06 billion in 2019, up 7.9 percent from 2018’s total of $10.25 billion, according to FE&S’ 2020 Distribution Giants study. This year, sales increased for 70 percent of the dealers within the top 100, while 26 percent reported a decline in sales. Four percent of dealers reported no change in sales. Read Article Distribution Giants The Editors April 01, 2019 Sales among the top 100 foodservice equipment and supplies dealers totaled $10.296 billion in 2018, up 7.82 percent from $9.549 billion in 2017, according to FE&S’ 2019 Distribution Giants study. Sixty-nine percent of the top 100 dealers

Let's evaluate both hands strength, and then bin them into classes, one for each hand type (High Card, Pair, etc)>> p1_score = evaluator.evaluate(board, player1_hand)>>> p2_score = evaluator.evaluate(board, player2_hand)>>> p1_class = evaluator.get_rank_class(p1_score)>>> p2_class = evaluator.get_rank_class(p2_score)">>>> p1_score = evaluator.evaluate(board, player1_hand)>>> p2_score = evaluator.evaluate(board, player2_hand)>>> p1_class = evaluator.get_rank_class(p1_score)>>> p2_class = evaluator.get_rank_class(p2_score)or get a human-friendly string to describe the score,>> print "Player 1 hand rank = %d (%s)\n" % (p1_score, evaluator.class_to_string(p1_class))Player 1 hand rank = 6330 (High Card)>>> print "Player 2 hand rank = %d (%s)\n" % (p2_score, evaluator.class_to_string(p2_class))Player 2 hand rank = 1609 (Straight)">>>> print "Player 1 hand rank = %d (%s)\n" % (p1_score, evaluator.class_to_string(p1_class))Player 1 hand rank = 6330 (High Card)>>> print "Player 2 hand rank = %d (%s)\n" % (p2_score, evaluator.class_to_string(p2_class))Player 2 hand rank = 1609 (Straight)or, coolest of all, get a blow-by-blow analysis of the stages of the game with relation to hand strength:>> hands = [player1_hand, player2_hand]>>> evaluator.hand_summary(board, hands)========== FLOP ==========Player 1 hand = High Card, percentage rank among all hands = 0.893192Player 2 hand = Pair, percentage rank among all hands = 0.474672Player 2 hand is currently winning.========== TURN ==========Player 1 hand = High Card, percentage rank among all hands = 0.848298Player 2 hand = Pair, percentage rank among all hands = 0.452292Player 2 hand is currently winning.========== RIVER ==========Player 1 hand = High Card, percentage rank among all hands = 0.848298Player 2 hand = Straight, percentage rank among all hands = 0.215626========== HAND OVER ==========Player 2 is the winner with a Straight">>>> hands = [player1_hand, player2_hand]>>> evaluator.hand_summary(board, hands)========== FLOP ==========Player 1 hand = High Card, percentage rank among all hands = 0.893192Player 2 hand = Pair, percentage rank among all hands = 0.474672Player 2 hand is currently winning.========== TURN ==========Player 1 hand = High Card, percentage rank among all hands = 0.848298Player 2 hand = Pair, percentage rank among all hands = 0.452292Player 2 hand is currently winning.========== RIVER ==========Player 1 hand = High Card, percentage rank among all hands = 0.848298Player 2 hand = Straight, percentage rank among all hands = 0.215626========== HAND OVER ==========Player 2 is the winner with a StraightAnd that's Deuces, yo.PerformanceJust how fast is Deuces? Check out performance folder for a couple of tests comparing Deuces to other pure Python hand evaluators.Here are the results evaluating 10,000 random 5, 6, and 7 card boards:5 card evaluation:[*] Pokerhand-eval: Evaluations per second = 83.577580[*] Deuces: Evaluations per second = 235722.458889[*] SpecialK: Evaluations per second = 376833.1776046 card evaluation:[*] Pokerhand-eval: Evaluations per second = 55.519042[*] Deuces: Evaluations per second = 45677.395466[*] SpecialK: N/A7 card evaluation:[*] Pokerhand-eval: Evaluations per second = 51.529784[*] Deuces: Evaluations per second = 15220.969303[*] SpecialK: Evaluations per second = 142698.833384Compared to pokerhand-eval, Deuces is 2400x faster on 5 card evaluation, and drops to 300x faster on 7 card evaluation.However, SpecialKEval reigns supreme, with an impressive nearly 400k evals / sec (a factor of ~1.7 improvement over Deuces) for 5 cards, and an impressive 140k /sec on 7 cards (factor of 10).For poker hand evaluation in Python, if you desire a cleaner user

Release for: PC PlayStation 4 PlayStation 5 Xbox One Xbox Series X|S This wide range of supported systems ensures both current and last-gen console owners can experience Bigby Wolf’s latest adventure. The game’s availability across different platforms allows more players to dive into the noir-inspired world of Fabletown. PC gamers can look forward to experiencing the upgraded visuals powered by Unreal Engine 5. Frequently Asked Questions The Wolf Among Us 2 has generated significant interest and curiosity among fans. Here are answers to some common questions about the game’s development, release, and availability. What is the confirmed release date for The Wolf Among Us 2? Telltale Games has not announced a specific release date for The Wolf Among Us 2. The game was initially slated for a 2023 release but has been delayed to 2025 now. On which platforms will The Wolf Among Us 2 be available? The Wolf Among Us 2 is planned for release on multiple platforms. These include Xbox Series X/S, Xbox One, PlayStation 4, PlayStation 5, and PC. Has The Wolf Among Us 2 been announced to be episodic like its predecessor? Telltale Games has not officially stated whether The Wolf Among Us 2 will follow an episodic format. The company has not released details about the game’s structure or release format. Where can I find the latest trailer for The Wolf Among Us 2? The most recent official trailer for The Wolf Among Us 2 was released on February 9, 2022. Fans can find this trailer on Telltale Games’ official YouTube channel or website. What are the reasons for the delay in the release of The Wolf Among Us 2? Telltale Games cited several factors for delaying The Wolf Among Us 2 to 2024. These include prioritizing staff well-being, avoiding rushed development, and switching to

Or Outside Selected Features) Select Height FieldSelect Contour IntervalSelect Base ContourSelect Output Contour Shape file nameSelect Build Surface by Delaunay, if elevation points have unequal distances among them.Select Build Surface by Matrix, if elevation points have equal distances among them.Select Do not build Surface, if Height layer is a Tin SurfacePress Build G) Converting 2D to 3D Surface AnalysisConverting 2D to 3D Surface AnalysisMenu → Surface → Converting 2D to 3D Surface AnalysisCheck Use Height Layer as Tin Surface or NotSelect Height Layer Name Select Height Criteria (All Feature or Inside Selected Features or Outside Selected Features) Select Height FieldSelect Destination 2D GIS Shape FileSelect Criteria of the Destination 2D GIS Shape FileSelect Output 3D Shape file nameSelect Build Surface by Delaunay, if elevation points have unequal distances among them.Select Build Surface by Matrix, if elevation points have equal distances among them.Select Do not build Surface, if Height layer is a Tin SurfacePress Convert H) Calculating Area and Volume of Surface(s)Calculate Area and Volume of Surface(s)Menu → Surface → Calculate Area and Volume of Surface(s)Check Use Height Layer as Tin Surface or NotSelect Height Layer Name Select Height Criteria (All Feature or Inside Selected Features or Outside Selected Features) Select Height FieldSelect Height of the Calculating PlaneSelect above Plane or Below PlaneSelect Build Surface by Delaunay, if elevation points have unequal distances among them.Select Build Surface by Matrix, if elevation points have equal distances among them.Select Do not build Surface, if Height layer is a Tin SurfacePress Calculate 8) GIS Misalliance ToolsA) Converting AutoCAD files to GIS Shape FilesConverting AutoCAD file to Shape FilesMenu → Tools → Converting AutoCAD file to Shape FileB) Converting GIS Shape Files to AutoCAD filesConverting Shape File to AutoCAD fileMenu → Tools → Converting Shape File to AutoCAD file C) Converting GIS Shape Files to

PLoS ONE 2014, 9, e91144. [Google Scholar] [CrossRef] [PubMed] Figure 1. The model of FGS data analysis determines the prevalence and burden in different geographic locations over multiple years. Figure 1. The model of FGS data analysis determines the prevalence and burden in different geographic locations over multiple years. Table 1. Prevalence of Female Genital Schistosomiasis (FGS) among the total samples collected in Ghana, Zambia, and Tanzania. Table 1. Prevalence of Female Genital Schistosomiasis (FGS) among the total samples collected in Ghana, Zambia, and Tanzania. Data SourceTotal SamplesTotal FemalesPositive Females (S. haematobium Prevalence *)Negative Females (S. haematobium Prevalence *)Ghana 20139039 (43%)31 (79.5%)8 (20.5%)Zambia 201613380 (60%)46 (57.5%)34 (42.5%)Zambia 201711060 (54.5%)45 (75%)15 (25%)Tanzania 201810470 (67.3%)43 (61.4%)27 (38.6%) Table 2. Prevalence of FGS based on the DNA concentrations of PCR-positive individuals in Ghana, Zambia, and Tanzania. Table 2. Prevalence of FGS based on the DNA concentrations of PCR-positive individuals in Ghana, Zambia, and Tanzania. DNA Concentration (ng/μL)DNA Concentration Total Females in Ghana 2013Total Females in Zambia 2016Total Females in Zambia 2017Total Females in Tanzania 2018Gr. A (0.5–3)Low9 (29.04%)42 (91.30%)40 (88.89%)41 (95.35%)Gr. B (4–10)Medium 16 (51.61%)4 (8.70%)3 (6.67%)0 (0%)Gr. C (10–above)High6 (19.35%)0 (0%)2 (4.44%)2 (4.65%) Table 3. Infection prevalence of FGS based on hematuria, urine filtration, and the PCR diagnostic test. -- = absence of positive or negative samples. Table 3. Infection prevalence of FGS based on hematuria, urine filtration, and the PCR diagnostic test. -- = absence of positive or negative samples. Data SourceHematuriaUrine FiltrationPCR PositiveNegativePositiveNegativePositiveNegativeGhana 20138 (18.6%)35 (81.4%)----31 (79.5%)8 (20.5%)Zambia 20163 (3.7%)79 (96.3%)082 (100%)46 (57.5%)34 (42.5%)Zambia 20177 (11.7%)53 (88.3%)3 (5%)57 (95%)45 (75%)15 (25%)Tanzania 2018--------43 (61.4%)27 (38.6%) Table 4. Comparison among females and males detected as positive for S. haematobium, S. mansoni, and dual infections. Male = M, Female = F. Table 4. Comparison among females and males detected as positive for S. haematobium, S. mansoni, and dual infections. Male = M, Female = F. LocationTotal Female Total MaleFemaleMaleS. haematobiumS. mansoniCo-OccurrenceS. haematobiumS. mansoniCo-OccurrenceGhana 2013394731 (36%)33 (38%)27 (69%)39 (45%)41 (47.7%)35 (74.5%)Zambia 2016805146 (35%)61 (46.6%)33 (41.3%)32 (24.4%)34 (26%)20(39%)Zambia 2017605045 (41%)47 (42.8%)47 (78%)37 (33.6%)38 (34.6%)31 (62%)Tanzania 2018703443 (41.4%)54 (52%)37 (52.9%)24 (23.1%)28 (27%)23 (67.7%) Table 5. Prevalence of Female Genital Schistosomiasis (FGS) among different female age groups. -- = absence of positive or negative samples. Table 5. Prevalence of Female Genital Schistosomiasis (FGS) among different female age groups. -- = absence of positive or negative samples. Location Total FemalesS. haematobium PositiveGr. A (0–10 years) Gr. B (11–20