Worse Results Research Articles

Study of short-term visual memory of athletes in cyclic sports, martial arts and esports

Background and Study Aim. To carry out a comparative analysis of indicators of short-term visual memory in athletes of cyclic sports (short track), martial arts (karate, taekwondo) and esports (CS:GO). Material and Methods. The study involved athletes of percussion martial arts (n=13, age 18.8±0.52, experienced athletes), elite players of semi-professional CS:GO teams (n=10, age 19.2±0.50), short track speed skaters (n=12, age 19.1±0.25, experienced athletes). Short-term visual memory was determined using a special program for tablet computers running iOS. Results. Certain trends have been established regarding the characteristics of the state of the body of elite athletes in martial arts, short track and esports in terms of averaged indicators. In terms of the short-term visual memory coefficient and the average percentage of errors, martial arts athletes showed the best result, and short trackers showed the worst result. The shortest average test duration was recorded for short trackers. At certain stages of the test, they dominated athletes of other specializations in terms of reaction time. However, the trend towards faster response was associated with a decrease in the accuracy of the selection, that is, a greater percentage of errors. Cyberathletes and martial arts athletes were characterized by better adaptive abilities than short track athletes. Conclusions. The specificity of sports activities of cyberathletes determined the best results of the most difficult stage of the test both in terms of speed and accuracy. This criterion can be recommended for esports selection and prediction. The control of the cognitive functions of athletes is expedient in the course of monitoring their physical condition and is a promising direction for predicting the reliability and success of competitive activity.

Relations between Development of E-Government and Government Effectiveness, Control of Corruption and Rule of Law in 2010–2020: a Cluster Analysis

Nowadays, an increasing number of citizens use information and communication technologies to interact with the government, and therefore it is necessary for the public sphere to constantly innovate and meet the expectations placed on it under the pressure of an ever-changing environment. The aim of the article is to compare the progress of e-government between 2010 and 2020 in the member states of the European Union based on selected global indices of e-government development. The article examines the relationship between the E-Government Development Index (EGDI) and selected indices within the Worldwide Governance Indicators. The goal of the article is achieved through correlation and cluster analysis. The results of these countries vary from state to state. It was found that according to the level of digitization of public administration and the quality of public services, EU countries can be divided into 5 groups. However, within the monitored period, the EU states achieved progress in the field of digitization and the quality of public administration. The average value of EGDI increased by about 34% between 2010 and 2022 and the share of countries at a very high level EGDI in 2010 was approx. 11% and in 2022 even 100%. The best results in this area are achieved by the states in the north of the EU (for example, Denmark, Finland and Sweden), on the contrary, the worst results are achieved by the states in the east of the EU (for example, Bulgaria and Romania). The results also showed that countries that were in the best cluster and the worst cluster in individual years are still in those clusters.

Results of Arthroscopic Treatment of Patients With Femoroacetabular Impingement Depending on the Type of Hip Deformity

Background. Femoroacetabular impingement (FAI) is one of the most common causes of pain syndrome and limited mobility in the hip joint among young and middle-aged individuals.
 The aims of this study: 1) To evaluate the impact of the type of hip joint deformity in patients with FAI on treatment outcomes; 2) To identify the type of hip joint deformity that is optimal for correction using arthroscopy; 3) To determine the impact of the acetabular structure in conditions of borderline dysplasia on the treatment outcome.
 Methods. A retrospective uncontrolled single-center study was conducted, including 121 patients (135 hip joints), among them 49 (40.5%) women and 72 (59.5%) men. Patients were divided into four groups based on the type of deformity. The first group included 33 (24.4%) joints with cam-type FAI, the second group — 72 (53.4%) joints with mixed-type, the third group — 17 (12.6%) joints with dysplasia (LCEA-O25°) and cam-type deformity of the femoral head-neck junction, and the fourth group — 13 (9.6%) joints with a combination of dysplasia, cam-type deformity, and retroversion of the acetabulum. All patients underwent physical examination and radiographic diagnostics. In patients with borderline dysplasia, the version of the acetabulum was additionally assessed. The iHOT-33 and HOS scales were used to evaluate the preoperative status and postoperative results.
 Results. The best treatment outcome was achieved in the first group of patients, which was statistically significantly different from the results in the third group. The treatment outcomes in the second group of patients did not show statistically significant differences from the first group according to the HOS questionnaire, but differed according to the iHOT-33 scale. The treatment outcomes in the fourth group of patients were almost indistinguishable on the iHOT-33 scale from the first group and on the HOS scale from the second group. In the third group, a statistically significant result on the HOS-Sport subscale was achieved in only 30% of patients, while in other groups it was not less than 58%. Other scales showed a slight superiority of treatment results in the first and fourth groups compared to the second and third groups. In the first years after surgery, all groups of patients showed a significant improvement in sports activity, but after 2 years, there was a tendency for a decrease in patients in the second and third groups.
 Conclusion. The highest results of arthroscopic treatment were shown by patients in the first group with isolated cam-type deformity, slightly worse were results by patients in the second group (with mixed-type). In patients with borderline dysplasia, the effectiveness of arthroscopy depended on the structure of the anterior wall of the acetabulum. The worst result was observed in patients with borderline dysplasia and insufficiently developed anterior wall of the acetabulum — in that group of patients, it is worth preferring isolated periacetabular osteotomy or in combination with arthroscopy.

An aviation accident report data-driven approach to scenario design for a centrifuge-based dynamic flight simulator

The use of flight simulators in investigating an aviation incident or accident related to human errors has been identified as an important part of a strategy to improve safety. This study aimed to replicate a real flight of the MiG-29 aircraft using a centrifuge-based dynamic flight simulator and to determine the simulator’s accuracy in recreating in-flight aircraft performance. A 60-second recording of the real flight of the MiG-29 aircraft, captured by the flight data recorder, was chosen for replication in the HTC-07 human training centrifuge simulator. To evaluate how accurately the simulator replicates the performance of the aircraft, the linear accelerations and angular velocities acting on a pilot during the real flight were compared with those during the replication of that flight in the simulator. The fit of these parameters was assessed using the root mean square percentage error (RMSPE) and the correlation coefficient (r). The highest replication accuracy was achieved for the vertical component of the linear acceleration (RMSPE=2068; r=0.98), while the worst result was obtained for the longitudinal component (RMSPE=14205; r=0.31). Inaccuracies were much more pronounced for the angular velocity. The roll angular velocity had the lowest replication error (RMSPE=12640). However, its correlation with the recorded velocity during the real flight was very weak (r=-0.02). Despite some inaccuracies in replicating other components of the acceleration and angular velocity vectors, the HTC-07 simulator seems valuable for investigating aviation incidents or accidents related to human factors.

Epigenome-Wide DNA Methylation Dynamics of Cannabis Addicts: Adolescents Performing a Socio-Educational Measure of Deprivation of Liberty - Case Study

Cannabis use is most likely to begin during adolescence or emerging adulthood; and such use during these developmental periods has been associated with some negative physical, mental, and cognitive health outcomes. Some of these long-term negative effects of this consumption during adolescence may be explained by epigenetic changes through a process called DNA methylation. However, little is known about the epigenetic impact of drug use during adolescence. The current study examined associations between DNA methylation and cannabis use during adolescence. Analyses focused on two genes, AHRR and NR2B because their methylation has been implicated in illicit drug use. Participants included twenty-seven adolescents separated into two groups, cannabis users and control. After identifying the subjects who would participate in the research, the general research framework was formed. The inclusion criteria were age between 13 and 18 years old. Those individuals diagnosed with cannabis dependence comprised the CUD (Cannabis Use Disorder) group, verified through a urine test for cannabis. Participants in the CONT group were healthy individuals, who did not have a history and/or criteria for diagnosing chemical dependency. DNA methylation values were measured with salivary DNA and averaged for each gene. Relationships between cannabis use and DNA methylation were tested using multivariate regressions, adjusting for sociodemographic data, and age. Adolescents with psychoactive substance use face a considerable risk of co-occurring mental health problems, something that may involve a more difficult life situation, social problems, as well as a worse result in complying with socio-educational measures.

COMPARISON OF LOGISTIC REGRESSION, MULTINOMIALNB, SVM, AND K-NN METHODS ON SENTIMENT ANALYSIS OF GOJEK APP REVIEWS ON THE GOOGLE PLAY STORE

Today's community activities cannot be separated from the name of transportation because it makes it very easy for people's daily activities. With the existence of transportation, people can more quickly reach their destination. With the Gojek application on the Google Play Store, it will help people travel or deliver goods. To determine service quality, sentiment analysis can be used to classify reviews. The purpose of this study is to compare which method has the best accuracy, so that it can classify reviews into positive or negative sentiments. The methods tested in this study are Logistic Regression, MultinomialNB, SVM, and K-NN. Performance assessment methods include score accuracy, recall, and precision, classification reports, and confusion matrix to determine the appropriate method for classifying reviews into positive or negative categories. Of the four methods tested, the one with the highest performance is the Logistic Regression method. Accuracy, recall and precision scores of the Logistic Regression method were 82.45%, 82.49%, 82.45% and 82.43%, respectively. Classification report also shows good results. In the confusion matrix, there are 111 and 124 True positives and True negatives. There are only 22 and 28 False positive and False negative results respectively. The method that has the lowest score is K-NN, with score accuracy, recall, and precision respectively were 52.28%, 59.43%, 93, 52%, and 65.65%. Classification report shows quite bad results. In the connection matrix, it produces True positives and True negatives 130 and 19. There are only 127 and 9 False positive and False negative results respectively. The results of this study state that using the Logistic Regression method is suitable for use in classifying positive and negative reviews in the review dataset on the Gojek application on the Google Play Store.

THE EFFICIENCY AND RELIABILITY OF BACKEND TECHNOLOGIES: EXPRESS, DJANGO, AND SPRING BOOT

Increasing popularity of web applications has led to the development of many technologies that enable their production, both on the client and server side. This article attempts to compare three most popular server-side frameworks – Django, Spring Boot and Express. Each of the selected technologies is based on a different programming language. These frameworks were compared in terms of request processing time and reliability. Within the conducted research three backend applications handling HTTP requests were created, all of them using the same database consisting of employees’ data. Afterwards, a series of load tests was performed to determine levels of efficiency and reliability of created applications for various numbers of virtual users sending requests to the server at the same time. Five test cases with the following number of requests: 1000, 2000, 4000, 8000, and 16000 were planned and performed for each type of HTTP requests handled by the server simultaneously. Based on the obtained results, it was concluded that the Spring Boot framework was the best in terms of request processing time and high reliability. However, it was noted that for many test cases under extreme load, it had a significantly higher percentage of incorrectly processed requests compared to the Express application, even though the application was noticeably slower. The worst results were observed for Django because the test application created for this framework revealed the longest requests processing time and the highest error rate during processing requests out of the three tested applications. The performed studies helped to determine the efficiency and reliability of the tested technologies at various levels of load. Furthermore, the studies were crucial in obtaining knowledge about the evaluated frameworks as well as their properties and formulating conclusions that will be able to help the developers choose technologies before the implementation of their programming projects.

Influence of the Results of Control of Intakes, Proteins and Anthropometry Nutritional Screening, Sarcopenia and Body Composition on the Clinical Evolution of Hospitalized Patients.

(1) Background: Hospital malnutrition and sarcopenia are common in inpatients and are associated with worse prognosis. Our objective is to determine the association of the positivity of CIPA (Control of Intakes, Proteins and Anthropometry) nutrition screening tool and sarcopenia and evaluate its prognostic implications (length of stay, readmissions and mortality) as well as different components of body composition. (2) Methodology: Cross-sectional single-center study and prospective six months follow-up for prognostic variables. On admission, CIPA and EWGSOP2 criteria were assessed. (3) Results: Four hundred inpatients, a median of 65.71 years old and 83.6% with high comorbidity, were evaluated. In total, 34.8% had positive CIPA and 19.3% sarcopenia. Positive CIPA and sarcopenia had worse results in body composition (fat mass (FM), fat-free mass (FFM) and appendicular skeletal muscle mass index (ASMI)) and dynamometry. Positive CIPA is significantly associated with worse prognosis (mortality (OR = 1.99), readmissions (OR = 1.86) and length of stay (B = 0.19)). Positive CIPA and sarcopenia combined are associated with a tendency to higher mortality (OR = 2.1, p = 0.088). Low hand grip strength (HGS) is significantly related to a higher length of stay (B = -0.12). (4) Conclusions: In hospitalized patients, malnutrition independently and combined with sarcopenia is associated with a worse prognosis but not body composition. Low HGS is related to a higher length of stay.

Outcomes of Treatment of Pectoralis Major Muscle Injury

Background. The incidence of injuries to the pectoralis major muscle (PMM) is steadily increasing, necessitating an assessment of outcomes of surgical treatment.
 Objective: to analyze the outcomes of surgical treatment for PMM injuries.
 Material and Methods. The study is based on the analysis of surgical treatment outcomes of 84 patients with PMM injuries who were hospitalized in the Department of Microsurgery and Reconstructive Surgery of the Upper Extremity at the State Institution "Institute of Traumatology and Orthopedics of the National Academy of Medical Sciences of Ukraine" from 2003 to 2023. The patients underwent a rehabilitation program and staged observation for up to 12 months after surgical intervention. The age of the patients ranged from 16 to 62 years; they were divided by age into three groups: up to 25 years, 25-40 years, and 41 years and older. Functional assessment of the PMM was performed using the Quick Disabilities of the Arm, Shoulder and Hand (Quick DASH), The Score and Subjective Evaluation by Patients with a Pectoral Major Tear (SEPPMT), and the American Shoulder and Elbow Surgeons (ASES) scales at the time of admission, at 6 months, and at 12 months after the surgery.
 Results. When comparing baseline functional parameters for acute and chronic PMM injuries using the Quick DASH, SEPPMT and ASES scales preoperatively, patients with acute injuries had worse results than those with chronic injuries (p < 0.001). At 6 months after surgical treatment, the group of patients with acute injuries showed significantly lower scores compared to the group with chronic injuries. At 12 months after surgical treatment, the group with acute injuries had significantly higher scores compared to the group with chronic injuries (p < 0.001), though still corresponding to excellent and good results.
 Conclusions. When comparing baseline functional parameters for acute and chronic PMM injuries using the Quick DASH, SEPPMT, and ASES scales preoperatively and at 6 and 12 months after surgical treatment, patients who underwent surgery within two weeks after the trauma demonstrated better results. In cases of chronic injuries, the results were slightly worse, but still were close to good results.

Knowledge, Attitude, And Factors Affecting Human Papilloma Virus Vaccine Uptake Among Female Undergraduate Students In Public Tertiary Institutions In Lagos State Nigeria.

BACKGROUND: Human Papillomavirus is responsible for about 99% of all cervical cancers. Cervical cancer remains a leading cause of morbidity and mortality in women from Low and middle-income countries. Vaccination against human papillomavirus (HPV) is an effective way to prevent the disease, however, uptake remains low in Nigeria. This study aimed to assess the knowledge, attitudes, and factors affecting Human papillomavirus vaccine uptake among female undergraduate students in public tertiary institutions in Lagos State, Nigeria. METHODS: This was a descriptive cross-sectional study. A multistage sampling method was used to select 355 female undergraduate students from Lagos State University and the University of Lagos. Data was gathered using a self-administered structured questionnaire. Data analysis was done using Epi info version 7 statistical software and Chi square was used to test for statistical significance. RESULTS: There were 355 respondents giving a response rate of 100%. The responders' average age was 23 years old. 63.4% and 61.3% had heard about cervical cancer and cervical cancer screening respectively. Only 27.6% had heard of Human Papilloma virus vaccine. While most of the respondents (53.8%) had poor overall knowledge of cervical cancer, majority had a positive attitude towards cervical cancer prevention and screening (80.8%). Only 1.1% of the respondents had received the HPV vaccine, and a lack of awareness about the vaccine was the most commonly reported barrier to uptake (64.7%). Also, only 3.7% had ever done a cervical cancer screening and the most commonly reported reason was “Fear of bad result”(31%). There was no association between overall knowledge of cervical cancer and uptake of cervical cancer screening (p=0.08). CONCLUSION: In conclusion, despite the positive attitude towards cervical cancer prevention and screening, HPV vaccine uptake remains low among young women in this population, and knowledge about the vaccine is lacking. Increasing awareness of the link between HPV and cervical cancer and promoting the vaccine as a safe and effective method of prevention, may help to increase uptake.

Gender-affirming hormone therapy in Portugal: knowledge, safety and adherence

Background: More research is needed to understand the knowledge, experiences, safety, and adherence of individuals on gender-affirming hormone therapy (GAHT) for gender dysphoria treatment. Aim: To assess the knowledge of the Portuguese transgender population on GAHT about the therapy, examine the experiences of those who initiated it through the Portuguese National Health System, and evaluate the safety, monitoring and adherence to hormonal therapy. Methods: Cross-sectional study with Portuguese transgender adults who had hormonal therapy. Participants completed an original questionnaire that utilized an ordinal Likert-style scale that ranged from 0 (worst result) to 6 (best result) for most items. Descriptive statistics and non-parametric tests were used to analyze both categorical and continuous variables. The level of statistical significance was set at 0.05. Results: Participants’ knowledge on GAHT was rated with a median of 5 points, relying mostly on the internet for information. 14.1% felt insufficiently informed. 71.2% faced obstacles obtaining GAHT, with long waiting times being the most common. Feminizing therapy was significantly more expensive. Initial consultation was often with psychologists/sexologists, with endocrinologists commonly being the first providers of GAHT. 43.3% didn’t undergo informed consent and 11.3% had no pretreatment blood tests. Only 53.2% discussed fertility with medical professionals, and of those, only 8.0% opted for it. 79.6% regularly underwent routine analyses and 87.3% were followed up in medical appointments, mainly with an endocrinologist. 6.3% of participants discontinued therapy, mostly due to adverse effects and difficulty obtaining prescriptions. Discussion: Internet sources dominated as the main information channel for GAHT, emphasizing the importance of healthcare professionals guiding patients to reliable sources. Limited healthcare provider knowledge and access barriers were identified. Primary care physicians had limited involvement, raising safety concerns. Inadequate fertility counseling and follow-up in family medicine were found. Discontinuation of therapy and nonadherence highlighted the need for comprehensive care and monitoring.

Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods

Drug abuse are on the rise, with many users enter the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, and they can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consist of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Networks (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1 score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% precision, 87% precision, 82% recall, 84% f1 score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and nonusers.

Социально-экономическое развитие регионов Российской Федерации: проблемы и тенденции

The article examines the process of dynamics of differentiation in the socio-economic regional development of the economy, which intensified during the period of economic reforms in the post-Soviet period. Regional economies in the period under review from 2011 to 2020 demonstrated high sensitivity and a sharp decline after the introduction of the first wave of economic sanctions and the slowdown in economic processes against the backdrop of the new coronavirus infection pandemic. Conducted analysis of statistical indicators of socio-economic development of regions of the Russian Federation 2011–2020 allows us to talk about the presence of a high level of asymmetry in the development of regions. As a result, the differentiation of GRP per capita values in the regions of the Russian Federation for 2011–2020 was revealed, as was the emerging trend of a slight reduction in regional disparities. A study of the main socio-economic indicators of 2020 was carried out for federal districts (in % compared to 2011), and groups of regions were identified that in 2020 showed the best and worst results by type of economic activity. The results indicate that, despite the decline in economic indicators of the Russian Federation in 2014–2015, the economies of many regions have demonstrated rapid recovery from external shocks. Despite this, there remains a significant imbalance of development between different regions, which is based on the economy’s orientation toward the export of resources. The level of GRP per capita in regions rich in natural resources is 17 times higher than the level of other subjects, and the number of those lagging behind is 3 times higher than prosperous regions in Russia. Throughout the entire period under review, fluctuations in these ratios remain virtually unchanged. All this is a source of tension in society, increasing social discontent and, as a consequence, narrowing the range of means of state regulation.

Selection of an algorithm for classifying data quoted on the Day Ahead Market of TGE S.A. in MATLAB and Simulink using Deep Learning Toolbox

The article contains an analysis leading to the selection of an algorithm for classifying data listed on the Day-Ahead Market of TGE S.A. in MATLAB and Simulink using Deep Learning Toolbox. In this regard, an introduction to deep learning methods, classification methods, and classification algorithms is provided first. Particular attention was paid to the essence of three important deep learningmethods in the classification, i.e. the methods called: Stochastic Gradient Descent Momentum, Root Mean Square Prop and Adaptive Moment Estimation. Then, three architectures of artificial neural networks used in deep learning were characterized, i.e.: Deep Belief Network, Convolutional Neural Network and Recurrent Neural Network. Attention was paid to the selection parameters of algorithms for learning deep artificial neural networks that can be used in classification, such as: accuracy, information losses and learning time. Practical aspects of research experiments were also shown, including selected results of research conducted on volume and fixing 1 data quoted on the TGE S.A. Day-Ahead Market. After analyzing the obtained test results for the hourly system, it was noted that the least suitable algorithm for classification purposes was the Stochastic Gradient Descent Momentum algorithm, which in each case had worse results than the other two algorithms, i.e. the Adaptive Moment Estimation algorithm and the Root Mean algorithm Square Prop. However, the best algorithm turned out to be the Adaptive Moment Estimation algorithm, which obtained the highest accuracy, which was at a level comparable to the Root Mean Square Prop algorithm, with the latter algorithm having larger losses.

Study on activated sludge composition and concentration setting for increasing COD efficiency in dairy industry wastewater

The dairy industry’s wastewater is the pollution problems that must be concerned. Since dairy wastewater tends to be easily biodegradable, the commonly wastewater treatment technology is biological treatment. The efficiency of reducing COD in the aeration tank of PT. AN was about 39.54% compared to the theoretical that can be achieved until 90%. Therefore, this research objective was to improve the efficiency of COD reduction in the aerobic-activated sludge process of PT. AN. The research was done by laboratory experimental method with variations of the concentration and type of the activated sludge applied, also variations in Bacillus cereus bacteria addition. The performance was measured by the COD reduction efficiency after 9-hour aeration and 3-hour sedimentation. The result showed that by increasing 100% of the activated sludge concentration, the process could increase the COD reduction from 20.3% to 49.8%. The 50:50 combination of existing activated sludge and other activate sludge taken from non-dairy industry’s WWTP got worse result, with the COD reduction from 20.3% to 15.9 %. The better result was shown by Bacillus cereus bacteria addition, the COD reduction can achieve 67.0% with 4.7 % bacteria addition and 97.0% COD reduction can be achieved with 9% bacteria addition.

Delirium risk screening and assessment among older patients in general wards and the emergency department: a best practice implementation project.

The aim of this project was to improve compliance with evidence-based criteria regarding risk of delirium and the assessment of delirium among older patients in the general hospitalization wards and the emergency department. More than 50% of older hospitalized patients experience delirium. Some studies have highlighted the need to implement an orientation protocol in the emergency department and to continue this in the general wards, with the aim of decreasing the delirium rate among older patients admitted to hospital. The project followed the JBI evidence implementation framework. We conducted a baseline audit, a half-way audit, and final audit of 50 patients at risk of delirium admitted to the emergency department and the general wards, respectively. The audits measured compliance with eight criteria informed by the available evidence. In the final audit, three of the eight criteria achieved more than 50% compliance in the general wards: pressure injury screening (96%); monitoring changes (74%); and performing interventions (76%). In the emergency department, worse results were reported because of the service conditions. The exception was the criterion on the training of nurses on the topic, with 98%. The integration of a tool to screen for delirium in older patients in the hospital's electronic clinical history records increased the percentage of compliance with audit criteria regarding the use of the scale and delirium detection (rising from 0% to 32% in the final audit in the general wards). Through the implementation of this project, validated and evidence-based evaluation will ensure that nurses are supported through appropriate measures to reduce patient confusion and aggression resulting from delirium.

The Influence of Physical Activity and Chess Sports on Children’s Physical Condition and Cognitive Functions

Background. Children who play chess improve their academic results, cognitive and social skills, however, both cognitive and physical abilities are important for healthy development. Recent evidence suggests that physical activity has a positive impact not only on children’s physical abilities but cognitive functions as well. Yet it is not clear the effect of physical activity and chess on children’s physical condition and cognitive functions.
 The aim. To identify the influence of physical activity and chess sports on children’s physical condition and cognitive functions.
 Methods. The study involved 30 children (8-9 years). They were equally divided into three groups: chess-playing (CHESS), physical activity and chess-playing (PA+CHESS), and physical activity (PA). The fnger reaching test was used to assess flexibility, 10x5 m shuttlecock test was used to assess dexterity, handgrip strength was measured using the dynamometer, cognitive functions were evaluated using “MoCA-LT” and Bourdon-Anfmov tests.
 Results. Flexibility and handgrip strength did not differ signifcantly between groups (p>0.05). The dexterity in CHESS group was worse than in CHESS+PA and PA groups (p<0.05). The dexterity results of the CHESS+PA and PA groups did not differ (p>0.05). The cognitive functions of the CHESS+PA group, as assessed by the MoCA test, were better than those of the PA group (p<0.05). No difference was found between other groups (p>0.05).
 Conclusions. Children who play chess and participate in sport activity have better cognitive functions than children who only do sport. The worst results of dexterity test were in children who play chess only.
 Keywords: physical condition, cognitive functions, chess sports, children.

The spatial public goods game on hypergraphs with heterogeneous investment

The emergence of cooperation in the multi-person social dilemma has been extensive investigated under the pairwise interactions-based spatial public goods game (PGG). However, higher-order interactions are more suitable to describe the group relationships in real society and have attracted intensive interests. In this work, we propose an extension of the spatial PGG on hypergraphs, where cooperators are allowed to allocate their investments unevenly. A tunable parameter, α, is introduced to characterize the investment preference. α>0 means that cooperators will allocate more investment to the PGG group from which they obtain higher payoff, while for α<0, cooperators will allocate more investment to the group from which they gain lower payoff. Results from numerical simulations indicate that compared with the results of homogeneous case, allocating more resources to profitable groups can effectively promote cooperation. Additionally, for a relative high synergy factor r, there exists a moderate negative value of α that will induce the lowest cooperation level, larger or smaller values of α can enable a higher cooperation level relative to the worst result. Moreover, the nontrivial results are robust against hypergraphs with different orders.

SynDroid: An adaptive enhanced Android malware classification method based on CTGAN-SVM

Android mobile phones have the highest market share nowadays, bringing a boom of Android application programming as well as malicious software (malware) issues. Traditional machine learning and deep learning methods are widely used in Android malware detection and classification, both require plenty of application samples to train classifiers. However, the collected samples are always imbalanced, because the distribution of malware categories differs hugely in the real environment. For Android malware samples with high dimension features, and Class Imbalance Ratio coming to more than 100:1, traditional methods become weak. To fill the gap, we propose an Android malware classification model named SynDroid. The core step of SynDroid uses CTGAN-SVM to generate qualified high-dimension samples, and adaptively discards bad results. Besides, we propose KS-CIR test to help the model to determine which classes of data needed to be enhanced most. This new proposed test can measure the data in respect of both samples' quality and quantity. Lastly, Random Forest is taken as a classifier to finish the classification task. The performance of SynDroid is evaluated on CCCS-CIC-AndMal2020 on the accuracy, precision, recall and F1-score. Both longitudinal and horizontal comparison experiments have been done with traditional oversampling methods, cost-sensitive learning, and other complicated methods. The result shows that the proposed method gets 12% more accuracy than the method on the same dataset and alleviates imbalanced data problems.

Research on apple identity recognition method based on hyperspectral technology and chemometrics method

This study focuses on using hyperspectral technology and chemometric methods to identify apples from four production areas and ten varieties to improve the accuracy of apple identification. Physical and chemical data and hyperspectral images of apple samples were collected for preprocessing and feature selection of spectral data. In the next step, K-nearest neighbour algorithm (KNN) and the support vector machine-radial basis kernel function (SVM-RBF) models were established. Regarding the identification of apple varieties based on origin information, research was conducted for both the same and different origins. For the same origin modeling, the best results were obtained from the KNN model, with accuracy, F1-score, and recall all reaching 100% for both the modeling and prediction sets. The PLS-DA prediction results were slightly inferior to KNN, and SVM modeling produced the worst results. The PLS-DA model produced the best results for predicting different apple origins, with accuracy, F1-score, and recall all achieving over 98% for both the modeling and prediction sets. Regarding the identification of apple origin based on variety information, research was conducted for both the same and different varieties. The best model for identifying the origin of apples with the same variety was SD-PLS-DA, with accuracy, F1-score, and recall of 97.38%, 99.59%, and 99.60% for the modeling set, and 99.61%, 97.35%, and 97.84% for the prediction set. For different varieties, both the KNN and PLS-DA models were effective, with the KNN model outperforming the PLS-DA model, and all indicators reaching 100% for both the modeling and prediction sets. The results indicated that the use of variety and origin identification models in this study was effective for apple identification, and can accurately identify apples.