Search Results (37)

Background: Action video games—particularly first-person-shooter (FPS), real-time-strategy (RTS), multiplayer-online-battle-arena (MOBA), and battle-royale (BR) titles—have been linked to enhanced visuospatial skills, yet their impact on brain structure remains unclear. Purpose: To examine, using a cross-sectional design, whether long-term exposure to high-speed genres is associated with variations in cortical thickness and white matter microstructure. Methods: Structural and diffusion MRI were acquired from 27 video-game players (VGPs) and 19 non-video-game players (NVGPs). FreeSurfer-derived cortical thickness and DSI-Studio quantitative anisotropy (QA) were compared between groups, co-varying for intracranial volume. All p-values were Holm–Bonferroni- and FDR-corrected; bootstrap 95% CIs are reported. Results: VGPs showed greater cortical thickness in right inferior and superior parietal, supramarginal, and precuneus cortices (η_p² = 0.12–0.21) and higher QA along right SOG–SPL and left SOG–IPL tracts. Conclusions: Frequent action gaming is associated with greater cortical thickness in the dorsal stream and enhanced occipito-parietal connectivity. However, causal inference is precluded; longitudinal work is warranted. Full article

Increasingly, renewable energy resources, energy storage systems (ESSs), and demand response programs (DRPs) are being discussed due to environmental concerns and smart grid developments. An innovative home appliance scheduling scheme is presented in this paper, which incorporates a local energy grid with wind turbines (WTs), photovoltaic (PV), and ESS, which is connected to an upstream grid, to schedule household appliances while considering various constraints and DRP. Firstly, the household appliances are specified as non-shiftable and shiftable (interruptible, and uninterruptible) loads, respectively. Secondly, an enhanced mathematical formulation is presented for smart home energy management which considers the real-time price of upstream grids, the price of WT, and PV, and also the sold energy from the smart home to the microgrid. Three objective functions are considered in the proposed energy management: electricity bill, peak-to-average ratio (PAR), and pollution emissions. To solve the optimization problem, a novel modification-based grey wolf optimizer (GWO) is proposed. When the wolves hunt prey, other wild animals try to steal the prey or some part of the prey, hence they should protect the prey; therefore, this modification mimics the battle between the grey wolves and other wild animals for the hunted prey. This modification improves the performance of the GWO in finding the best solution. Simulations are examined and compared under different conditions to explore the effectiveness and efficiency of the suggested scheme for simultaneously optimizing all three objective functions. Also, both GWO and improved GWO (IGWO) are compared under different scenarios, which shows that IGWO improvement has better performance and is more robust. It has been seen in the results that the suggested framework can significantly diminish the energy costs, PAR, and emissions simultaneously. Full article

Relationships between water demand, pressure, and leakage highlight the need for accurate supply to match demand. This study addresses the challenges of forecasting short-term water demand and was part of the Battle for Water Demand Forecasting competition involving 10 real-world District Metered Areas in Italy. A nine-layer convolutional neural network model was proposed that considers demand from previous time steps, time of the day, weather conditions, day type, and other deterministic temporal factors to predict water demand. Bayesian optimization was used for hyperparameter tuning. The model can predict and forecast short-term water demand with reasonable accuracy. Full article

Antimicrobial resistance poses a global health threat, with Staphylococcus aureus emerging as a notorious pathogen capable of forming stubborn biofilms and regulating virulence through quorum sensing (QS). In the quest for novel therapeutic strategies, this groundbreaking study unveils the therapeutic potential of Paederia foetida Linn., an Asian medicinal plant containing various bioactive compounds, contributing to its antimicrobial activities, in the battle against S. aureus. Through a comprehensive approach, we investigated the effect of ethanolic P. foetida leaf extract on S. aureus biofilms, QS, and antimicrobial activity. The extract exhibited promising inhibitory effects against S. aureus including the biofilm-forming strain and MRSA. Real-time PCR analysis revealed significant downregulation of key virulence and biofilm genes, suggesting interference with QS. Biofilm assays quantified the extract’s ability to disrupt and prevent biofilm formation. LC-MS/MS analysis identified quercetin and kaempferol glycosides as potential bioactive constituents, while molecular docking studies explored their binding to the QS transcriptional regulator SarA. Computational ADMET predictions highlighted favorable intestinal absorption but potential P-glycoprotein interactions limiting oral bioavailability. While promising anti-virulence effects were demonstrated, the high molecular weights and excessive hydrogen bond donors/acceptors of the flavonoid glycosides raise concerns regarding drug-likeness and permeability. This integrated study offers valuable insights for developing novel anti-virulence strategies to combat antimicrobial resistance. Full article

Object tracking and detection are fundamental and challenging tasks in various computer vision applications, spanning surveillance, vehicle navigation, and autonomous robot control. These tasks are particularly critical in the context of video monitoring within dynamic environments, where the detection and tracking of objects, such as people and automobiles, play a pivotal role. In today’s world, as we combat crime and terrorism, ensure public safety, and manage traffic effectively, advanced computer vision technology has become indispensable. Video monitoring in dynamic environments is at the forefront of this battle, providing crucial insights and real-time information for decision making. Object-tracking-based techniques emerge as a strong choice, especially for detecting stationary foreground objects. These methods exhibit robust performances when the camera remains stationary, even in scenarios in which the ambient lighting conditions gradually change. This stability makes them well suited for applications requiring consistent and reliable object detection. In the contemporary landscape, one of the most pressing concerns revolves around the recognition of objects and the real-time tracking of their locations. Achieving these objectives is paramount for enhancing security, safety, and efficiency across various domains. However, it is essential to acknowledge that, in some scenarios, such as remote or isolated locations with limited Internet connectivity, access to advanced object-tracking and detection technologies may be constrained. Therefore, addressing these challenges and developing robust, offline-capable solutions remains a critical area of research and development in computer vision. In conclusion, object tracking and detection are pivotal technologies in computer vision, with applications spanning from surveillance to traffic management. In dynamic environments, they play a crucial role in enhancing security and safety. However, addressing the challenges related to real-time tracking and detection in resource-constrained settings is an ongoing research endeavor. Full article

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve. Full article

This cross-sectional study aimed to explore the association between esports participation and physical health and examine the difference in physical health problems between esports participants and non-esports participants. A total of 1549 young adults participated in this investigation. A total of 633 participants were categorized as esports participants, and they were involved in six types of esports games: shooting games, multiplayer online battle arena games, strategy card games, sports games, real-time strategy games, and fighting games. An online survey was conducted to gather data on their demographic information, gaming behavior, traditional sports behavior, and physical health problems. The results demonstrated that esports players reported significantly higher participation in traditional sports compared to non-esports players. No significant differences were found in sleep duration or the selected physical health issues between the two groups. Overall, the findings suggest that esports participation was not associated with negative physical health in this sample of young adults. Furthermore, this study found that players who conscientiously took intermittent breaks and maintained a standardized sitting posture during gameplay had a lower likelihood of reporting neck and back pain. These findings have important implications for challenging conventional negative perceptions of esports and promoting a more objective understanding and appreciation of esports and the sustainable development of esports players. Future research is necessary to explore potential causal relationships between esports participation and health outcomes and to develop a healthier esports practice modality from a sports science perspective. Full article

Water resource pollution, particularly in river channels, presents a grave environmental challenge that necessitates a comprehensive and systematic approach encompassing assessment, forecasting, and effective management. This article provides a comprehensive exploration of the methodology and modeling tools employed to scrutinize the process of river channel pollution due to silting, rooted in the fundamental principles of hydrodynamics and pollutant transport dynamics. The study’s methodology seamlessly integrates numerical simulations with state-of-the-art neural network techniques, with a specific focus on the physics-informed neural network (PINN) method. This innovative approach represents a groundbreaking fusion of artificial neural networks (ANNs) and physical equations, offering a more efficient and precise means of modeling a wide array of complex processes and phenomena. The proposed mathematical model, grounded in the Euler equation, has been meticulously implemented using the Ansys Fluent software package, ensuring accuracy and reliability in the computations. In a pivotal phase of the research, a thorough comparative analysis was conducted between the results derived using the PINN method and those obtained using conventional numerical approaches with the Ansys Fluent software package. The outcomes of this analysis revealed the superior performance of the PINN method, characterized by the generation of smoother pressure fluctuation profiles and a significantly reduced computation time, underscoring its potential as a transformative modeling tool. The calculated data originating from this study assume paramount significance in the ongoing battle against river sedimentation. Beyond this immediate application, these findings also serve as a valuable resource for creating predictive materials pertaining to river channel silting, thereby empowering decision-makers and environmental stakeholders with essential information. The utilization of modeling techniques to address pollution concerns in river channels holds the potential to revolutionize risk management and safeguard the integrity of our vital water resources. However, it is imperative to underscore that the effectiveness of such models hinges on ongoing monitoring and frequent data updates, ensuring that they remain aligned with real-world conditions. This research not only contributes to the enhanced understanding and proactive management of river channel pollution due to silting but also underscores the pivotal role of advanced modeling methodologies in the preservation of our invaluable water resources for present and future generations. Full article

To support Unmanned Aerial Vehicle (UAV) joint electromagnetic countermeasure decisions in real time, coordinating multiple UAVs for efficiently jamming distributed hostile radar stations requires complex and highly flexible strategies. However, with the nature of the high complexity dimension and partial observation of the electromagnetic battleground, no such strategy can be generated by pre-coded software or decided by a human commander. In this paper, an initial effort is made to integrate multiagent reinforcement learning, which has been proven to be effective in game strategy generation, into the distributed airborne electromagnetic countermeasures domain. The key idea is to design a training simulator which close to a real electromagnetic countermeasure strategy game, so that we can easily collect huge valuable training data other than in the real battle ground which is sparse and far less than sufficient. In addition, this simulator is able to simulate all the necessary decision factors for multiple UAV coordination, so that multiagents can freely search for their optimal joint strategies with our improved Independent Proximal Policy Optimization (IPPO) learning algorithm which suits the game well. In the last part, a typical domain scenario is built to test, and the use case and experiment results manifest that the design is efficient in coordinating a group of UAVs equipped with lightweight jamming devices. Their coordination strategies are not only capable of handling given jamming tasks for the dynamic jamming of hostile radar stations but also beat expectations. The reinforcement learning algorithm can do some heuristic searches to help the group find the tactical vulnerabilities of the enemies and improve the multiple UAVs’ jamming performance. Full article

Considering how difficult it is for a pin in the ocean to be found, painstaking searches among historical documents and eyewitness accounts often end up with more unknowns and questions. We developed a three-tier geo-spatial tech-based approach to discover and unfold the lost WWII heritage features in the countryside of Hong Kong that can be applied in other contexts. It started with an analysis of historical texts, old maps, aerial photos, and military plans in the historical geographic information system (HGIS) Project ‘The Battle of Hong Kong 1941: a Spatial History Project’ by Hong Kong Baptist University to define regions/points of interest. Then, 3D point clouds extracted from the government’s airborne LiDAR were migrated to form a digital terrain model (DTM) for geo-registration in GIS. All point clouds were geo-referenced in HK1980 Grid via accurate positioning using the global navigation satellite system—real-time kinematics (GNSS-RTK). A red relief image map (RRIM) was then used to image the tunnels, trenches, and pillboxes in great detail by calculating the topographical openness. The last tier of the tech work was field work involving ground validation of the findings from the previous two tiers and on-site imaging using terrestrial LiDAR. The ground 3D LiDAR model of the heritage feature was then built and integrated into the DTM. The three-tier tech-based approach developed in this paper is standardised and adopted to streamline the workflow of historical and archaeological studies not only in Hong Kong but also elsewhere. Full article

A viral transport medium (VTM) was developed following the Centers for Disease Control and Prevention, USA (US-CDC) standard operating procedure (SOP) DSR-052-05 with necessary improvisation and was used for storing coronavirus disease 2019 (COVID-19) swab specimens. Considering Bangladesh’s supply chain and storage conditions, improvisation was essential for extending sample storage time while retaining efficiency. In-house VTM was produced using Hank’s balanced salt solution (HBSS) supplemented with 1% bovine serum albumin V (BSA), 0.5 µg /mL of gentamicin sulfate, and 100 µg/mL of fluconazole. The produced VTM composition, quality, sterility, specificity, and efficiency were verified in-house and through an independent contract research organization (CRO). An accelerated stability study projected that under the recommended temperature (4 °C), it would remain stable for four months and preserve samples for over a month. The real-time reverse transcriptase–polymerase chain reaction (rRT-PCR) test detected the targeted N gene and ORF1ab gene from the VTM stored samples. Our VTM is equally as effective as the Sansure Biotech VTM in keeping SARS-CoV-2 RNA specimens detectable in rRT-PCR (100% sensitivity and specificity in random and blinded samples). In conclusion, the BRiCM VTM will make the battle against pandemics easier by effectively collecting and storing nasopharyngeal and oropharyngeal swabs for COVID-19 detection. Full article

Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events. Full article

Background: Drug checking is a proven harm reduction strategy and provides real-time information on the market of new psychoactive substances (NPS). It combines chemical analysis of samples with direct engagement with people who use drugs (PWUD), giving the ability to increase preparedness and responsiveness towards NPS. Next to that, it supports rapid identification of potential unwitting consumption. However, NPS cause a toxicological battle for the researchers, as factors such as the unpredictability and quick shift of the market complicate the detection. Methods: To evaluate challenges posed towards drug checking services, proficiency testing was set up to evaluate existing analytical techniques and investigate the capability to correctly identify circulating NPS. Twenty blind substances, covering the most common categories of substances, were analyzed according to the existing protocols of the existing drug checking services, including several analytical methods such as gas chromatography–mass spectrometry (GC-MS) and liquid chromatography with diode array detector (LC-DAD). Results: The proficiency test scores range from 80 to 97.5% accuracy. The most common issues and errors are mainly unidentified compounds, presumably due to no up-to-date libraries, and/ or confusion between structural isomers, such as 3- and 4-chloroethcathinone, or structural analogs, such as MIPLA (N-methyl-N-isopropyl lysergamide) and LSD (D-lysergic acid diethylamide). Conclusions: The participating drug checking services have access to adequate analytical tools to provide feedback to drug users and provide up-to-date information on NPS. Full article

A haptic sensor coupled to a gamepad or headset is frequently used to enhance the sense of immersion for game players. However, providing haptic feedback for appropriate sound effects involves specialized audio engineering techniques to identify target sounds that vary according to the game. We propose a deep learning-based method for sound event detection (SED) to determine the optimal timing of haptic feedback in extremely noisy environments. To accomplish this, we introduce the BattleSound dataset, which contains a large volume of game sound recordings of game effects and other distracting sounds, including voice chats from a PlayerUnknown’s Battlegrounds (PUBG) game. Given the highly noisy and distracting nature of war-game environments, we set the annotation interval to 0.5 s, which is significantly shorter than the existing benchmarks for SED, to increase the likelihood that the annotated label contains sound from a single source. As a baseline, we adopt mobile-sized deep learning models to perform two tasks: weapon sound event detection (WSED) and voice chat activity detection (VCAD). The accuracy of the models trained on BattleSound was greater than 90% for both tasks; thus, BattleSound enables real-time game sound recognition in noisy environments via deep learning. In addition, we demonstrated that performance degraded significantly when the annotation interval was greater than 0.5 s, indicating that the BattleSound with short annotation intervals is advantageous for SED applications that demand real-time inferences. Full article

(1) Backgrounds and Objectives: The global battle to contain the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is still ongoing. This cross-sectional study aimed to detect the seroprevalence of anti-SARS-CoV-2 IgM/IgG among previously symptomatic/asymptomatic and vaccinated/unvaccinated inhabitants of Sakaka City, Aljouf, Saudi Arabia. (2) Methods: Blood samples of 400 participants were tested for the presence of anti-SARS-CoV-2 IgM/IgG using colloidal gold immuno-chromatography lateral flow immunoassay cards. (3) Results: The prevalence of anti-SARS-CoV-2 IgM and IgG positivity was 45.8% and 42.3%, respectively. Statistically significant correlations (p < 0.05) were found between the previous RT-PCR testing for SARS-CoV-2-RNA and positivity for IgM and/or IgG. The highest seroprevalence of IgM and IgG were detected among smokers, participants aged ≥40 years, and patients with chronic diseases. Although most of the participants (58.5%) did not previously experience COVID-19 like symptoms, the anti-SARS-CoV-2 IgM and IgG seropositivity amongst them was 49.1% and 25.6%, respectively, with higher seroprevalence among males than females. At the time of the study, the SARS-CoV-2 vaccination rate at our locality in Saudi Arabia was 43.8% with statistically significant correlation (p < 0.001) between being vaccinated and anti-SARS-CoV-2 IgM and/or IgG positivity, with more positivity after receiving the second vaccine dose. (4) Conclusions: Public assessment reflects the real scale of the disease exposure among the community and helps in identifying the asymptomatic carriers that constitute a major problem for controlling the SARS-CoV-2. To limit the spread of the virus, rigorous implementation of large-scale SARS-CoV-2 vaccination and anti-SARS-CoV-2 serological testing strategies should be empowered. Full article