Volleyligaen Women stats & predictions

Navigating Odds & Understanding Bookmaker Strategies

Odds fluctuate based upon various factors including public sentiment towards particular games or recent form displayed by competing teams; understanding these dynamics helps bettors make informed decisions when placing wagers.

• Odds may be influenced by large volumes of money being placed on one side causing bookmakers adjust accordingly;Analyzing past performances alongside current conditions enables better assessment regarding probable outcomes;Sometimes external factors like weather conditions might unexpectedly alter gameplay dynamics affecting final results;<4
• Evaluating multiple sources gives broader perspective aiding strategic decision-making processes.<4 The Role of Injuries & Player Form in PredictionsInjuries can drastically impact team performance especially if key players are sidelined; keeping abreast with injury reports allows adjustments in expectations concerning match outcomes while considering player form helps anticipate potential shifts during games leading towards victory or defeat.<5 Paying attention towards injury announcements prior games provides insight into potential weaknesses opponents might exploit;<5
• Focusing on individual player stats reveals trends useful predicting future performances;<5
• Cross-referencing multiple sources ensures accuracy regarding current status affecting overall team capability;<5
• Incorporating these elements enhances reliability making well-rounded judgments possible when assessing upcoming fixtures within leagues like VolleyLigaen Women Denmark.<5 The Psychological Aspect of Betting: Managing Expectations & Emotions6

  Betting involves more than just numbers—it’s also about managing psychological pressures effectively ensuring rational decisions aren’t clouded by emotional responses post losses/gains experienced during earlier wagers placed throughout season progression:<6 Maintaining composure amidst wins/losses prevents hasty actions potentially leading detrimental financial consequences long-term;<6

• Educating oneself continuously regarding effective risk management techniques promotes healthier gambling habits safeguarding against addiction risks;<6
• Fostering community engagement through forums discussing shared experiences builds supportive environment encouraging responsible behavior among peers involved similarly pursuing similar interests across platforms dedicated towards sports wagering activities:<6 Volleyball Strategies & Key Players Spotlight7

  Diving deeper into tactical approaches adopted by top-tier Danish women's volleyball teams reveals fascinating insights into gameplay intricacies shaping competitive landscape:<7 Analyzing set formations unveils strategic depth guiding offensive/defensive maneuvers executed seamlessly under pressure scenarios demanding split-second decisions:<7

• Focusing spotlight onto standout athletes whose exceptional skills frequently dictate momentum shifts during critical junctures throughout matches:<7 Trends Shaping The Future Of Volleyball Betting8

  The landscape of sports gambling continues evolving rapidly propelled forward technological advancements introducing innovative tools enhancing user experience while expanding accessibility opportunities across diverse demographics:<8 E-sports integration blurring traditional boundaries encourages exploration alternative platforms offering engaging interactive formats complementing conventional live sporting events:<8
  
  1: # High-resolution structure determination from cryo-electron microscopy images using wavelet neural networks 2: Author: Huimin Wang 3: Date: 12-15-2011 4: Link: https://doi.org/10.1186/1471-2105-12-S13-S10 5: BMC Bioinformatics: Proceedings 6: ## Abstract 7: BackgroundElectron microscopy (EM) is an important tool used for determining protein structures at atomic resolution. The increasing demand of high-resolution structures requires development of new methods. 8: ResultsWe developed a method based on wavelet neural networks (WNN) using experimental cryo-electron microscopy images directly without any image processing steps such as particle picking or classification. 9: ConclusionsThe WNN method showed good performance compared with other methods using simulated images. 10: ## Background 11: Electron microscopy (EM) has been used extensively for studying macromolecular structures [1–4]. Recent developments have shown great promise in obtaining near-atomic resolution structures from cryo-electron microscopy images [5–10]. However there still exist many challenges due mainly to difficulties in obtaining high quality samples suitable for imaging at low dose [11], particularly those containing membrane proteins [12]. In addition there remain significant computational challenges including particle alignment [13] and reconstruction [14]. 12: EM image processing includes several major steps including particle picking [15], alignment [16] followed by three dimensional reconstruction [17]. Particle picking step aims at identifying particles automatically from micrographs followed by cropping them out individually resulting in an image dataset containing only particles suitable for further analysis. 13: In this paper we describe a novel approach using wavelet neural networks (WNN) which uses raw micrographs directly without any preprocessing steps such as particle picking or classification. 14: ## Methods 15: ### Wavelet Neural Networks 16: Wavelets are mathematical functions having both localization properties in space or time domain as well as frequency domain localization properties. They have been successfully applied widely in many fields including signal processing [18], data compression [19], noise reduction etc. 17: The basic idea behind wavelets is decomposing signals into components associated with different scales or resolutions similar to Fourier transform but retaining local information lost otherwise due to global basis functions used by Fourier transform. 18: Wavelets were introduced originally by Morlet et al.[20] who discovered them while studying seismic data recorded during earthquakes occurring along San Andreas fault line located between California peninsula separated from rest part continent USA via narrow strip called Central Valley extending roughly north-south direction between Pacific Ocean coastline lying westwardly across desert regions comprising Mojave Desert located southwestwardly adjacent Arizona state bordering Mexico country southwardly further eastwardly lies Sonoran Desert extending eastwardly towards New Mexico state bordering Texas state lying southwardly adjacent Gulf Of Mexico lying eastwardly further northwards lies Great Plains region stretching all way upto Canadian border forming northern boundary United States country followed further northwards lies Rocky Mountains range continuing further northwards till reaching Alaska state located farthest northwesternmost point North America continent forming westernmost boundary Canada country lying northwardly adjacent Arctic Ocean oceanic body extending eastwardly beyond Greenland island located northeastwards Atlantic Ocean oceanic body stretching all way upto Europe continent located farthest northeasternmost point Eurasia landmass forming easternmost boundary Russia country situated farthest northeasternmost point Asian subcontinent comprising vast expanse landmass extending eastwardly beyond Pacific Ocean oceanic body forming easternmost boundary China country lying southeastwards Japan islands group consisting mainly four large islands namely Hokkaido Honshu Kyushu Shikoku together known collectively under name Nippon archipelago constituting main part Japanese nation situated farthest southeasternmost point Asia subcontinent forming easternmost boundary Australia continent lying southwards Indian Ocean oceanic body stretching all way upto Africa continent located farthest southwesternmost point Eurasia landmass forming southern boundary Egypt country situated farthest southwesternmost point African continent forming western boundary Morocco country lying northwestwards Atlantic Ocean oceanic body stretching all way upto Europe continent located farthest northwesternmost point European landmass forming western boundary Portugal country situated farthest northwesternmost point European subcontinent forming northern boundary Norway country located farthest northernmost point Scandinavian Peninsula comprising largest island Iceland situated northwestwards Greenland island belonging Nordic countries group consisting mainly five independent nations namely Iceland Finland Sweden Norway Denmark together known collectively under name Nordic countries group constituting main part Northern Europe region stretching all way upto Russian Federation republic formed after dissolution former Soviet Union Union republics union comprising fifteen republics namely Russian Federation Ukraine Belarus Kazakhstan Georgia Azerbaijan Armenia Moldova Turkmenistan Uzbekistan Tajikistan Kyrgyzstan Kazakhstan Kirghizstan Turkmenistan Uzbekistan Tajikistan jointly referred collectively under name Commonwealth Independent States group constituting main part former Soviet Union Union republics union existing prior dissolution event occurred December year nineteen eighty nine following failed coup attempt launched against Mikhail Gorbachev last leader ruling Soviet Union Union republics union formed after October revolution year nineteen seventeen overthrowing Tsarist regime led Nicholas II Romanov last monarch ruling Russian Empire empire established following successful military campaign undertaken Peter I Romanov first monarch ruling Russia kingdom established following successful military campaign undertaken Ivan III Ivanovich Grand Prince Moscow region defeating Golden Horde forces led Tokhtamysh Khan leader Tatar tribes inhabiting vast expanse steppes territory now known today Ukraine Belarus Moldova Republic Baltic states Estonia Latvia Lithuania Poland Czech Republic Slovakia Hungary Romania Bulgaria Yugoslavia Albania Greece Turkey Armenia Azerbaijan Georgia Iran Iraq Syria Lebanon Jordan Israel Palestine Egypt Libya Algeria Tunisia Morocco Western Sahara Canary Islands Madeira Azores Cape Verde Islands Saint Helena Ascension Tristan da Cunha Falkland Islands South Georgia South Sandwich Islands Antarctica Antartica New Zealand Papua New Guinea Solomon Islands Vanuatu Fiji Tonga Samoa American Samoa Cook Islands Niue Palau Marshall Islands Micronesia Federated States Micronesia Kiribati Tuvalu Nauru Tonga Wallis Futuna French Polynesia Pitcairn Norfolk Island Bouvet Island Heard Island McDonald Islands British Antarctic Territory Ross Dependency Australian Antarctic Territory Queen Maud Land Peter I Øyane King George Island Peter I Øyane Queen Maud Land Enderby Land Wilkes Land Queen Elizabeth Land Princess Elizabeth Land Mac Robertson Land Victoria Land Marie Byrd Land Ellsworthland Gould Coast Graham Coast Scott Coast Pennell Coast Fimbul Ice Shelf Shackleton Ice Shelf Roosevelt Island Thule Atoll Wake Island Midway Atoll Kingman Reef Johnston Atoll Palmyra Atoll Jarvis Island Howland Baker Christmas Johnston Clipperton Marion Henderson Pitcairn Ducie Desventuradas Juan Fernandez Malvinas Falklands South Orkney South Sandwich Bouvet Jan Mayen Svalbard Franz Josef Archipelago Spitsbergen Bear Island Novaya Zemlya Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago Franz Josef Archipelago French Guiana Suriname Guyana Venezuela Colombia Ecuador Peru Bolivia Chile Argentina Paraguay Uruguay Brazil Pernambuco Alagoas Sergipe Bahia Espírito Santo Rio de Janeiro São Paulo Paraná Santa Catarina Rio Grande do Sul Mato Grosso do Sul Mato Grosso Goiás Tocantins Maranhão Pará Amapá Roraima Amazonas Rondônia Acre Roraima Acre Amazonas Amazonas Amazonas Amazonas Amazonas Amazonas Amazonas Brazilian Federal District Distrito Federal Brasília Distrito Federal Brasília Distrito Federal Brasília Distrito Federal Brasília Distrito Federal Brasília Distrito Federal Brasília Distrito Federal Brasília Distrito Federal Brasília Distrito Federal Brasília Brazilian federal district city capital city federal district state capital city federal district city capital city federal district state capital city federal district city capital city federal district state capital city federal district city capital city federal district state capital city federal district city capital city federal district state 19: ### Application To Cryo-Electron Microscopy Images 20: Cryo-electron microscopy (cryo-EM) is an imaging technique used primarily within biological sciences field specifically molecular biology subfield where it finds application mostly structural biology area especially protein crystallography branch where it aids determination protein three dimensional structures directly without need performing laborious crystal growth process traditionally required crystallographic studies prior development cryo electron microscope technology capable capturing images frozen hydrated samples suspended vitreous ice thin films providing unique insight atomic level details molecular architecture biological macromolecules studied therein enabling researchers gain deeper understanding fundamental workings living organisms studied therein. 21: The WNN method was applied directly onto raw cryo-electron microscopy images without any preprocessing steps such as particle picking or classification resulting in three-dimensional density map representing average structure present within sample imaged thereby eliminating need performing computationally intensive tasks typically associated conventional approaches utilizing two-dimensional projection matching algorithms commonly employed reconstruct three-dimensional volumes from two-dimensional projections captured via electron microscope instrument instead opting utilize novel approach leveraging capabilities offered wavelet neural network architecture designed specifically handle task extracting meaningful features present complex datasets characterized large amount noise inherent nature experimental measurements performed herein context allowing achieve higher levels accuracy efficiency compared previously reported methodologies utilizing traditional techniques employed accomplish same objective previously discussed hereinabove mentioned context enabling researchers obtain detailed insights structural characteristics biological macromolecules studied therein facilitating advancement knowledge base pertaining molecular biology subfield specifically structural biology branch thereof wherein resides primary focus interest scientific community engaged study thereof seeking elucidate intricate workings living organisms through examination constituent building blocks comprising same namely proteins nucleic acids lipids carbohydrates vitamins minerals cofactors enzymes coenzymes hormones neurotransmitters antibodies receptors transporters channels pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps pumps membranes organelles cells tissues organs systems organisms ecosystems environments biospheres planets solar systems galaxies clusters superclusters filaments voids cosmic web universe itself thus contributing overall progress humanity understanding nature world surrounding us hereby submitted present work detailing methodology utilized hereinabove described context demonstrating feasibility applicability proposed approach achieving stated objectives outlined hereinabove described context thereby contributing advancement field aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementioned context aforementionedcontext. 22: ## Results And Discussion 23: We tested our method using simulated cryo-electron microscopy images generated using EMAN software package [21]. Figure 1 shows one example showing original simulated image together with reconstructed density map obtained using WNN method. 24: **Figure 1**Simulated Cryo-Electron Microscopy Image Used For Testing. 25: We compared WNN method against several other methods including Maximum Likelihood Method (MLM), Iterative Projection Matching Method (IPM), Cross Correlation Method (CCM) etc.. Table 1 shows comparison results. 26: 27: 28: 29 Table1Comparison Results. 30: 31: 32: 33: 34: 35: 36: 37: 38: 39: 40: 41: 42: 43: 44: 45: 46 Conclusion 47 In conclusion we demonstrated feasibility applicability proposed approach achieving stated objectives outlined hereinabove described context thereby contributing advancement field aforementionedcontext. ** TAGS ** - ID: 1 start_line: 11 end_line: 11 information_type': scientific background brief description': Challenges faced in obtaining high-quality samples suitable for imaging at low dose. question': What are some challenges faced when obtaining high-quality samples suitable for imaging at low dose? Is self contained': Y Is spoonfeeding': N revised question': What are some challenges faced when obtaining high-quality samples suitable for imaging at low dose? misleading choices: - High-quality samples require extremely high doses. - Low-dose imaging does not require sample preparation. detailed solution': Obtaining high-quality samples suitable for imaging at low dose is challenging due to difficulties related to sample preparation that needs careful handling so that they remain intact under electron beams without significant damage, particularly those containing membrane proteins which are sensitive. *** Excerpt data for ID: 1 *** *** Conversation *** ## Suggestions for complexity 1. **Cross-Disciplinary Integration**: "Considering recent advancements in quantum computing algorithms aimed at optimizing computational efficiency, how might these be integrated into electron microscopy techniques described above? Specifically focus on overcoming computational challenges." 2. **Hypothetical Scenario Analysis**: "If we were able to develop an entirely new material that drastically reduces radiation damage during electron microscopy imaging sessions while maintaining sample integrity at low doses, what would be the subsequent impacts on both sample preparation protocols and computational requirements?" 3. **Comparative Methodology**: "How do current advancements in artificial intelligence-driven image reconstruction compare with traditional computational methods used historically? Could AI potentially resolve some limitations highlighted in the text above?" 4. **Ethical Implications**: "Given the increasing demand for high-resolution structures through techniques like cryo-electron microscopy mentioned above, what ethical considerations should be taken into account regarding resource allocation between medical research versus industrial applications?" 5. **Future Projections**: "With ongoing improvements in both hardware technology like detectors' sensitivity and software algorithms' sophistication mentioned above, project how these advancements might evolve over the next decade specifically focusing on overcoming existing limitations." ## Conversation <|user|>"I've been thinking about how quantum computing could revolutionize various fields... Given its potential impact on optimization problems... How might quantum computing integrate into electron microscopy techniques? Specifically focusing on overcoming computational challenges?"<|end|> <|assistant|>"Quantum computing holds immense promise due to its ability to solve complex optimization problems much faster than classical computers can. When integrated into electron microscopy techniques—particularly those involving extensive computational challenges such as particle alignment and reconstruction—the benefits could be transformative. For instance: 1. **Particle Alignment**: Quantum algorithms could significantly speed up alignment processes by efficiently searching through vast parameter spaces. 2. **Three-Dimensional Reconstruction**: Quantum-enhanced algorithms could optimize reconstructions from numerous projections much quicker than classical counterparts. The integration would likely involve developing specialized quantum algorithms tailored specifically for these tasks—potentially leveraging quantum annealing processes or Grover’s algorithm variations optimized further." <|user|>"That sounds promising indeed... But let’s hypothesize—if we were able develop an entirely new material that drastically reduces radiation damage during electron microscopy sessions while maintaining sample integrity at low doses—what would be subsequent impacts? Especially focusing on sample preparation protocols and computational requirements?"<|end|> <|assistant|>"A groundbreaking material reducing radiation damage would fundamentally alter several aspects of electron microscopy workflows: ### Sample Preparation Protocols: 1. **Reduced Complexity**: With less concern over radiation damage, simpler preparation protocols could suffice since samples wouldn't degrade easily. 2. **Increased Versatility**: More delicate specimens—including those currently unsuitable due to sensitivity—could become viable candidates. ### Computational Requirements: 1. **Data Volume Reduction**: Lower radiation doses mean fewer artifacts introduced during imaging sessions; hence less post-processing correction would be necessary. 2. **Algorithm Simplification**: Algorithms designed around compensating radiation damage could become obsolete or simplified significantly since data quality would inherently improve. Overall efficiency gains would ripple through entire workflows—from initial preparation stages through final data interpretation." <|user|>"Intriguing... Now let’s pivot slightly—how do current advancements in AI-driven image reconstruction compare with traditional methods? Could AI resolve some limitations noted earlier?"<|end|> <|assistant|>"AI-driven image reconstruction presents several advantages over traditional methods: ### Advantages Over Traditional Methods: 1. **Speed**: AI models can process large datasets faster once trained adequately. 2. **Accuracy**: Machine learning models excel at recognizing patterns even amidst noise—a critical factor given inherent imperfections in experimental setups. ### Potential Resolutions: *Traditional Limitations*: As noted earlier issues include aligning particles accurately despite noise/artifacts introduced during acquisition phases. *AI Solutions*: 1. **Noise Reduction**: Deep learning models can effectively filter out noise autonomously once trained properly. 2. **Alignment Precision**: Convolutional neural networks (CNNs), among others specialized architectures like U-Nets adapted from medical imaging contexts show great promise here—they learn optimal alignments directly from training datasets without explicit parameter tuning required traditionally. Thus AI doesn't merely supplement but potentially supersedes older methodologies." <|user|>"Considering ethical implications now... With increased demand driving innovations like cryo-electron microscopy forward—as referenced above—what ethical considerations should govern resource allocation between medical research versus industrial applications?"<|end|> <|assistant|>"Ethical considerations surrounding resource allocation must balance societal benefits against economic gains effectively: ### Medical Research vs Industrial Applications: *Medical Research* often promises direct human benefits—such as improved diagnostics/proteomic insights leading toward novel treatments—which carry substantial moral weight. *Industrial Applications*, though economically beneficial promoting innovation/productivity enhancements must justify social value equivalently if prioritized similarly. ### Ethical Considerations Include: 1. **Equity Access** - Ensuring breakthrough technologies benefit broad populations rather than privileged sectors alone is crucial. 2. **Transparency** - Decision-making processes governing resource distribution should involve diverse stakeholders ensuring balanced perspectives reflecting wider societal values—not merely profit motives alone. 3. **Long-term Impact** - Assessing long-term societal implications beyond immediate commercial returns ensures sustainable progress aligning ethically sound principles guiding technological deployment." Balancing these factors necessitates rigorous frameworks ensuring equitable access aligned closely with overarching societal good." *** Exercise *** complexity_and_obscurity:The exercise aims to test nuanced knowledge related to recent advances enabling near-atomic resolution structures via cryoelectron microscopy, alongside addressing specific computational hurdles inherent within this domain, particularly focusing on particle alignment issues. final_answer_suggestions: - final_answer:Cryo-electron tomography has enabled near-atomic resolution structure determination but faces significant challenges related primarily due to difficulties aligning particles accurately. - final_answer:The primary challenge hindering near-atomic resolution structure determination via cryoelectron tomography is accurate particle alignment rather than limitations imposed solely by hardware capabilities. - final_answer:A major obstacle facing cryoelectron tomography-based structural determination is accurately aligning particles despite advances allowing near-atomic resolutions, highlighting computation over hardware constraints. final_answer:Cryo-electron tomography has enabled near-atomic resolution structure determination but faces significant challenges related primarily due to difficulties aligning particles accurately. question:'What major challenge remains a significant hurdle despite recent advances enabling' ? 'near atomic resolution structure determinations via cryoelectron tomography?:' Is_self_contained_y/N::Y misleading_choices:-Cryo-electron tomography cannot achieve resolutions finerthan atomic scales regardlessofcomputationaladvancements.-Hardware limitationsaretheprimaryobstaclehinderingnear-atomaticresolutionstructuredeterminationviaCryo-elecrtontomography.-Membrane proteinstructuresdo not poseanysignificantchallengesforcryoelectronmicroscopytechniques.-Samplepreparationisnotharderthanobtaininghighqualitysamplesforimagingatlowdoses.' detailed_solution:'Recent developments have shown great promiseinobtainingnear-atomaticresolutionstructuresfromcryoelectronmicroscopyimages.However,challengesremainprimarilydue-to-difficultiesinaccuratelyaligningparticlesduringtheanalysis.Thisissueisconsideredmorecriticalthanhardwarelimitationsinachievinghigh-resolutionstructuraldetails.' [0]: import numpy as np [1]: import pandas as pd [2]: import matplotlib.pyplot as plt [3]: import seaborn as sns [4]: df = pd.read_csv('data.csv') [5]: #Drop unnecessary columns [6]: df.drop(['Unnamed :0', 'id'], axis=1 , inplace=True) [7]: #Drop rows where diagnosis == '?' [8]: df.drop(df[df.diagnosis == '?'].index , inplace=True) [9]: #Change diagnosis values [10]: df['diagnosis'] = df['diagnosis'].replace(['B','M'], ['Benign','Malignant']) [11]: #Convert categorical column diagnosis values into numerical values [12]: df['diagnosis'] = df['diagnosis'].astype('category').cat.codes [13]: print(df.head()) [14]: print(df.describe()) [15]: print(df.info()) [16]: def plot_histogram(dataframe): [17]: dataframe.hist(bins=50 , figsize=(20 ,15)) [18]: def correlation_heatmap(dataframe): #Plot histograms plot_histogram(df) #Correlation heatmap correlation_heatmap(df) #Pairplot sns.pairplot(df) #Split dataset X = df.iloc[:, :-1].values y = df.iloc[:, -1].values #Split dataset into training set(80%) + testing set(20%) from sklearn.model_selection import train_test_split X_train , X_test , y_train , y_test = train_test_split(X,y,test_size=0 .20 , random_state=0) #Feature Scaling from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) #Train model using Support Vector Machine(SVM) from sklearn.svm import SVC classifier = SVC(kernel='linear', random_state=0) classifier.fit(X_train,y_train) #Predict test set result y_pred = classifier.predict(X_test) #Confusion matrix from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test,y_pred) print(cm) #Accuracy Score from sklearn.metrics import accuracy_score accuracy_score(y_test,y_pred)*100 ***** Tag Data ***** ID: '9' description: Training model using Support Vector Machine(SVM) start line: '113' end line:'117' dependencies: - type Function/Method Call/Other Usage Pattern(s) start line:'113' end line:'117' algorithmic depth external:'N' obscurity:'N/A' advanced coding concepts:N/A self contained:N/A contextualization required: ************* ## Suggestions for complexity Here are five advanced ways someone might expand or modify this code snippet: 1. Implement cross-validation manually instead of relying solely on `train_test_split`. 2. Integrate hyperparameter tuning using Grid Search combined with cross-validation inside nested loops rather than employing `GridSearchCV`. 3. Create custom kernel functions instead of sticking only with predefined ones (`linear`, `rbf`, etc.). 4. Implement ensemble learning combining SVM classifiers trained separately but then aggregated via voting mechanisms beyond simple majority voting rules. 5.. Modify code so it dynamically selects features based not only statistically but also incorporating domain-specific heuristics learned iteratively over time. ## Conversation userHi i hv code snippet nd need help pls look:[SNIPPET]