Nuclear magnetic resonance (NMR) and Mass spectrometry (MS) are two mainly used techniques in metabolomics that can measure abundance of hundreds of metabolites. The data generated by either techniques have already allowed a huge progress in various research fields from personalized medicine to pharmaceutical sciences. Combining measurements from both platforms through a simple concatenation also led valuable discoveries. In this work, however, we propose an alternative data fusion approach based on deep learning that aims to make use of each type of measurement and combine the derived information in a different layer for disease prediction. Rather than feeding in all data as a single vector, we create dual "pipelines", operating on NMR and MS datasets separately, and then combine the outputs into a single prediction. We measure the performance of our dual-pipeline approach by using Alzheimer’s and Parkinson’s data for diagnostic prediction. We not only build custom deep learning (DL) architectures for both data sets, but also differentiate pipelines as to how to handle NMR and MS, i.e., separately or concatenated. Our results show that the dual-pipeline approach performs better than a single traditional DL pipeline which relies on traditional data fusion approach when both NMR and MS data are available.

Mental health disorders such as suicidal ideation, bipolar disorder, depression, and anxiety pose public health challenges in Turkey. Approximately 20% of the population is estimated to be affected by some form of mental illness. Social media is used for sharing personal experiences and opinions in Turkey. This presents an opportunity to use textual data for detecting mental health disorders. This study aims to examine the predictive performance of various machine learning classifiers for identifying mental health conditions in Turkish language texts from social media. Given the lack of structured and unstructured datasets focused on Turkish mental health, we have mined and processed textual data from the social media platform Reddit. This led to the creation of the Turkish Mental Health Disorder (TMHD) Corpus. The TMHD dataset and the machine learning experiments conducted aim to improve the understanding of predicting mental health issues in Turkey using Artificial Intelligence.

This data article describes a large-scale Android application dataset built by enriching a previously released Google Play removal corpus with external malware intelligence from VirusTotal Premium v3 Enterprise and Quark-Engine 25.3.1. The source corpus contains 870,514 applications with 48 metadata features. Of these, 803,320 applications (92.3%) were matched to VirusTotal reports, 772,706 (88.7%) were successfully parsed from Quark-Engine scans, and 772,506 (88.7%) had paired analyses from both sources (with an additional 27 applications having VirusTotal data only, included in the summary dataset). The released paired dataset combines the original metadata with ten selected derived features—five from VirusTotal and four from Quark-Engine—yielding 58 analytical features per application, or 59 columns when the MD5 linkage key is included. The release contains processed extensive tables, a compact summary dataset, and five consensus-based validated subsets whose sizes range from 38,302 to 80,368 applications depending on the VirusTotal threshold.

Accurate prediction of protein-protein interaction interfaces is critical for understanding molecular recognition and guiding therapeutic design. This study presents a comprehensive machine learning pipeline for predicting interface residues in permanent homodimeric protein complexes. Using a curated dataset of 1311 homodimers, we benchmarked six widely used machine learning algorithms and identified multilayer perceptron and XGBoost as top performers, achieving Matthews correlation coefficients (MCC) exceeding 0.93. To enhance interpretability and efficiency, we employed recursive feature elimination to derive a minimal set of six biologically meaningful features, including solvent accessibility, surface roughness, planarity, and average protrusion index, that retained high predictive power (MCC > 0.90). Structurally stratified models tailored to α-helical, β-strand, and membrane proteins demonstrated comparable or improved accuracy relative to generalized models, particularly when utilizing the reduced feature subset. As a preliminary demonstration of generalizability, we applied our approach to an external heterodimer complex (PDB ID: 9ETL). While limited to a single case study, the structurally specialized models maintained high accuracy, suggesting potential applicability beyond the training domain. Furthermore, our residue-level feature-driven models demonstrated highly competitive performance when compared against the baseline established by the general-purpose ColabFold pipeline. The results highlight the importance of structural context in interface prediction and demonstrate that compact, structure-aware models can achieve high accuracy while reducing computational complexity. This work provides a scalable, interpretable, and biologically informed approach to protein interface prediction, with implications for large-scale structural descriptor, drug target characterization, and protein engineering applications.

Students at academic risk are a major concern for higher education institutions, as their struggles can lead to various challenges, including mental health issues, withdrawals, dropouts, and financial strains. Therefore, detecting the early signals of academic risk at any level, as well as studying the underlying factors toward failure and success, have become a crucial task. Although existing studies have successfully elucidated certain factors within specific contexts, there remain unidentified contributors that warrant further research. In this paper, our aim is to investigate this challenging problem specifically for the Computer Science and Engineering Students at a regional campus by leveraging institutional sources such as the Canvas Learning Management System (LMS) and the Banner Enterprise Resource Planning (ERP) data. We hypothesize that these information-rich platforms can not only offer us the most informative attributes, but also predict student success when coupled with a machine learning algorithm. Hence, we evaluate multiple machine learning models-Decision Trees, Random Forest Classifier (RFC), Naive Bayes, K-Nearest Neighbors, and Support Vector Machines-to predict student success using select features suggested by RF. Our findings show that while overall GPA is a stronger indicator for success, RFC emerges as the best-performing model, achieving 95 % accuracy and 0.98 % AUC. When the feature set is extended to 20 attributes again from the databases, we observe that RFC performs the best with the sensitivity 98.80 % and the specificity $\mathbf{9 4. 7 0 \%}$. Model performances and attribute rankings based on their impact on risk or success suggest this current framework can be further utilized for an early alert system. While faculty is encouraged to step in and can send these early warnings to the advising office, an automated approach, which can trigger the alert system as soon as it gets updated by seeing all changes in LMS and ERP, could be a more promising and effective way of saving students from failures.

The goal for classification is to correctly assign labels to unseen samples. However, most methods misclassify samples with unseen labels and assign them to one of the known classes. Open-Set Classification (OSC) algorithms aim to maximize both closed and open-set recognition capabilities. Recent studies showed the utility of such algorithms on small-scale data sets, but limited experimentation makes it difficult to assess their performances in real-world problems. Here, we provide a comprehensive comparison of various OSC algorithms, including training-based (SoftMax, Garbage, EOS) and post-processing methods (Maximum SoftMax Scores, Maximum Logit Scores, OpenMax, EVM, PROSER), the latter are applied on features from the former. We perform our evaluation on three large-scale protocols that mimic real-world challenges, where we train on known and negative open-set samples, and test on known and unknown instances. Our results show that EOS helps to improve performance of almost all post-processing algorithms. Particularly, OpenMax and PROSER are able to exploit better-trained networks, demonstrating the utility of hybrid models. However, while most algorithms work well on negative test samples -- samples of open-set classes seen during training -- they tend to perform poorly when tested on samples of previously unseen unknown classes, especially in challenging conditions.

Computing has become an essential field of study, with programming as its core component. However, pedagogical aspects of teaching programming remain under-explored and poorly understood. The selection of a student's first programming language has sparked considerable debate within the community, often referred to as a “culture war”, and presents curricular challenges that extend to subsequent language choices. This research full paper investigates the transitions between different programming languages and measures students' learning experiences as they progress through a single language. We utilize Mindshift Learning Theory (MLT) to examine the transfer of knowledge across programming languages as students go through courses in a pre-requisite chain in our curriculum. Our study focuses on three aspects: novelty (encountering new, unique concepts), change (differently applying previously exposed topics), and carryover (directly transferring concepts to new contexts). Our methodology includes an online survey of current and past students transitioning through these courses. We aim to understand how perceived transitions impact mastery and confidence in programming languages. We designed the survey questions, identified target classes, and implemented incentive mechanisms to ensure a statistically significant response rate. Our results are partially aligned with earlier findings in the literature, which correlated carryover, changed, and novel concepts with knowledge scores of object oriented (OO) developers, despite a totally different demographic. Namely, our student participants transitioning to C/C++ (procedural programming) and OO C++ mostly have high, moderate, and low knowledge scores on the programming concepts they perceive as carryover, novel, and changed respectively. On the other hand, those transitioning to OO Java have higher scores for the changed concepts.

Japan intended to utilise the Tokyo 2020 games as a means to regain the confidence and rectify the damage to the tourism industry caused by the earthquake, tsunami, and Fukushima nuclear reactor incident. Nevertheless, in response to the pandemic crisis, novel strategies and initiatives were devised with the intention of organising the games with restricted participation, thereby mitigating the achievement of the intended tourism objectives. The projects utilised in the games received complete endorsement from the IOC and were included in the roster of recommendations for future game implementations. Our research in this context focuses on analysing the newly developed methodical tourism heritages for the games. We used the document analysis technique, which is a qualitative research method. Our research focused on evaluating projects related to agora houses, digitalization, artistic works, and the exhibition of national identity. It is significant as it allows for the examination of Olympic history's pioneering achievements.

This study explores how natural language processing (NLP) can supplement content analyses of political documents, particularly the manifestos of political parties. NLP is particularly useful for tasks such as: estimating the similarity between documents, identifying the topics discussed in documents (topic modeling), and sentiment analysis. This study applies each of these techniques to the study of political party manifestos. Document similarity may be used to gain some insight into the way parties change over time and which political parties are successful at bringing attention to their policy agenda. Categorizing text into topics may help objectively categorize and visualize the ideas political parties are discussing. Finally, sentiment analysis has the potential to show each political party’s attitude towards a policy area/topic. This study specifically applies these techniques to the manifestos produced by the political parties of New Zealand, from 1987 to 2017 (a period of significant party system change in New Zealand). It finds that NLP techniques provide valuable insights, although there is a need for significant fine-tuning.

The present research aimed to investigate the effect of healthy life skills on life satisfaction and the correlation between the variables in university students and varsity licensed athletes. 249 university students selected according to the random sampling method participated in the study. Participants were students at Hatay Mustafa Kemal University, School of Physical Education and Sports. The "Healthy Life Skills" scale developed by Genç and Karaman (2019) and “Satisfaction with Life” scale adapted to Turkish by Dağlı and Baysal (2016) were used as the data collection tools. Pearson Correlation and Simple Linear Regression analyses were used in the analysis of the data. According to analysed results, there were positive correlations between healthy life skills and life satisfaction. Besides, healthy nutrition and access to health-related resources were predictors of life satisfaction in both groups, but the importance given to health and health priority predicted life satisfaction only in varsity licensed athletes.

This study was conducted to examine the leisure satisfaction levels of individuals participating in the Ultra Trail (trail running) organization as recreation. The sample group of the study consisted of 313 individuals selected from the Salamon Cappadocia Ultra Trail organization by random sampling method. The personal information form used in the study was created by the researchers. Besides, "Leisure Satisfaction Scale" was developed by Beard and Ragheb (1980) and adapted to Turkish language by Karlı et al. (2008). The data obtained in the study were analyzed using the SPSS 20.0 (Statistical Package for Social Sciences) program. The Kolmogorov-Smirnov test was used to analyze whether the data showed a normal distribution or not, and Mann Whitney U test and Kruskall Walliss tests were used in the data analyze. When the findings of the study were examined it was found that male and single participants had higher leisure satisfaction scores. In addition, the total score rankings of the leisure satisfaction scale psychological sub-dimension of the participants in the 29-38 age group were significantly higher than the other groups.

Background and Aims: Preliminary evidence suggests that the social constraints needed to reduce the prevalence of COVID-19 have increased participation in sedentary behavior and reduced children's opportunities to participate in physical activity. Therefore, the aim of the present study was the effect of virtual physical education on physical activity and reduction of sedentary of elementary students in the Covid-19 era. Materials and Methods: A Semi-experimental design was conducted (pre-test and post-test) with two experimental and control groups. For this purpose, thirty 8 to 11 year old male students in Tabriz who had low scores in the Physical Activity Questionnaire (PAQ-C) (1 to 2.33) and high scores in the Inactive Researcher's Questionnaire, were selected by convenience sampling and randomly assigned to experimental and control groups (equal to 15 people in each group). Then, the experimental group underwent virtual physical education training for 10 sessions and two 45-minute sessions each week. No intervention was performed on the control group during this period. All subjects completed the Physical Activity Questionnaire (PAQ-C) and the sedentary researcher-made questionnaire before and after the training. Univariate analysis of covariance was used to analyze the data. Results: The results showed that there was a significant difference between the level of physical activity and sedentary of elementary students in the experimental and control groups. The intensity of the effect is equal to 0.58 and 0.66, respectively. The amount of physical activity in the experimental group (m = 2.57) was significantly higher than the control group (m = 1.95). The rate of sedentary in the experimental group (m = 3.25) was significantly lower than the control group (m = 3.64). Conclusion: The results showed that virtual training of physical training (watching video models and teaching the basic concepts of physical activity) is effective in increasing physical activity and reducing sedentariness of 8 to 11 year old male students during COVID-19. Keywords: Virtual Physical Education, physical activity, sedentary, Students, COVID-19.

Similar to any spoof detection systems, power grid monitoring systems and devices are subject to various cyberattacks by determined and well-funded adversaries. Many well-publicized real-world cyberattacks on power grid systems have been publicly reported. Phasor Measurement Units (PMUs) networks with Phasor Data Concentrators (PDCs) are the main building blocks of the overall wide area monitoring and situational awareness systems in the power grid. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. In this paper, we consider a stealthier data spoofing attack against PMU networks, called a mirroring attack, where an adversary basically injects a copy of a set of packets in reverse order immediately following their original positions, wiping out the correct values. To the best of our knowledge, for the first time in the literature, we consider a more challenging attack both in terms of the strategy and the lower percentage of spoofed attacks. As part of our countermeasure detection scheme, we make use of novel framing approach to make application of a 2D Convolutional Neural Network (CNN)-based approach which avoids the computational overhead of the classical sample-based classification algorithms. Our experimental evaluation results show promising results in terms of both high accuracy and true positive rates even under the aforementioned stealthy adversarial attack scenarios.

Mevcut araştırma, lisanslı olarak spor yapan ve yapmayan üniversite öğrencilerinde sağlıklı yaşam becerileri ve yaşam doyumu arasındaki ilişkinin incelenerek sağlıklı yaşam becerilerinin yaşam doyumu üzerindeki etkisini araştırmak amacıyla yapılmıştır. Araştırma nicel araştırma tekniklerinden ilişkisel tarama modeline göre tasarlanmıştır. Araştırmaya tesadüfi örnekleme yöntemine göre seçilen 249 üniversite öğrencisi katılmıştır. Katılımcılar Hatay Mustafa Kemal Üniversitesi Beden Eğitimi ve Spor Yüksekokulunda öğrenim görmektedir. Araştırmada veri toplama aracı olarak Genç ve Karaman (2019) tarafından geliştirilen “Sağlıklı Yaşam Becerileri” ölçeği ve Dağlı ve Baysal (2016) tarafından uyarlanan “Yaşam Doyum Ölçeği” kullanılmıştır. Verilerin analizinde Pearson Korelasyon ve basit doğrusal regresyon analizlerinden faydalanılmıştır. Analiz sonuçlarına göre sağlıklı yaşam becerileri ile yaşam doyumu arasında pozitif ilişkiler olduğu anlaşılmaktadır. Ayrıca sağlıklı beslenme ve sağlıkla ilgili kaynaklara ulaşma her iki grupta da yaşam doyumunun yordayıcısı durumunda iken, sağlığa verilen önem ve sağlık önceliği sadece lisanslı olarak spor yapan katılımcılarda yaşam doyumunu yordamaktadır.

The Flint Water Crisis (FWC) was an avoidable public health disaster that profoundly affected the city's residents, a majority of whom are Black. Although many scholars and journalists have called attention to the role of racism in the water crisis, little is known about the extent to which the public attributed the FWC to racism as it was unfolding. In this study, we used natural language processing to analyze nearly six million Flint-related tweets posted between April 1, 2014, and June 1, 2016. We found that key developments in the FWC corresponded to increases in the number and percentage of tweets that mentioned terms related to race and racism. Similar patterns were found for other topics hypothesized to be related to the water crisis, including water and politics. Using sentiment analysis, we found that tweets with a negative polarity score were more common in the subset of tweets that mentioned terms related to race and racism when compared to the full set of tweets. Next, we found that word pairs that included terms related to race and racism first appeared after the January 2016 state and federal emergency declarations and a corresponding increase in media coverage of the FWC. We conclude that many Twitter users connected the events of the water crisis to race and racism in real-time. Given growing evidence of negative health effects of second-hand exposure to racism, this may have implications for understanding minority health and health disparities in the US.

Food samples are routinely screened for food-contaminating beetles (i.e., pantry beetles) due to their adverse impact on the economy, environment, public health and safety. If found, their remains are subsequently analyzed to identify the species responsible for the contamination; each species poses different levels of risk, requiring different regulatory and management steps. At present, this identification is done through manual microscopic examination since each species of beetle has a unique pattern on its elytra (hardened forewing). Our study sought to automate the pattern recognition process through machine learning. Such automation will enable more efficient identification of pantry beetle species and could potentially be scaled up and implemented across various analysis centers in a consistent manner. In our earlier studies, we demonstrated that automated species identification of pantry beetles is feasible through elytral pattern recognition. Due to poor image quality, however, we failed to achieve prediction accuracies of more than 80%. Subsequently, we modified the traditional imaging technique, allowing us to acquire high-quality elytral images. In this study, we explored whether high-quality elytral images can truly achieve near-perfect prediction accuracies for 27 different species of pantry beetles. To test this hypothesis, we developed a convolutional neural network (CNN) model and compared performance between two different image sets for various pantry beetles. Our study indicates improved image quality indeed leads to better prediction accuracy; however, it was not the only requirement for achieving good accuracy. Also required are many high-quality images, especially for species with a high number of variations in their elytral patterns. The current study provided a direction toward achieving our ultimate goal of automated species identification through elytral pattern recognition.

SummarySmartphone users are constantly facing the risks of losing their private information to third‐party mobile applications. Studies have revealed that the vast majority of users either do not pay attention to privacy or unable to comprehend privacy messages. Developers though have exploited this fact by asking users to grant their apps an enormous number of permissions. In this article, we propose and evaluate a new security‐centric ranking algorithm built on top of the Elasticsearch engine to help users evade such apps. The algorithm calculates an intrusiveness score for an app based on its requested permissions, received system actions, and users' privacy preferences. As such, we further propose a new approach to capture these preferences. We evaluate the ranking algorithm using a million Android applications, contextual data and APK files, that we collect from the Google Play store. The results show that the scoring and reranking steps add minor overhead. Moreover, participants of the user studies gave positive feedback for the ranking algorithm and the privacy preferences solicitation approach. These results suggest that our proposed system would definitely protect the privacy of mobile users and pushes developers into requesting least amount of privileges. Still, there are many risks that endanger the users' privacy.

Bu çalışma, rekreasyonel bir etkinlik olarak Ultra Trail (patika koşusu) organizasyonlarına katılan bireylerin serbest zaman tatmin düzeylerini incelemek amacıyla gerçekleştirilmiştir. Çalışma grubunu basit seçkisiz örnekleme yöntemi ile Salamon Cappadocia Ultra Trail organizasyonundan seçilen 313 kişi oluşturmaktadır. Çalışmada kullanılan kişisel bilgiler formu araştırmacı tarafından oluşturulmuştur. Kullanılan veri toplama aracı ise Beard ve Ragheb (1980) tarafından geliştirilen ve Karlı ve ark. (2008) tarafından Türkçe’ye uyarlanan “Serbest Zaman Tatmin Ölçeği” dir. Araştırmada elde edilen veriler SPSS 20.0 programı kullanılarak analiz edilmiştir. Verilerin normal dağılım gösterip göstermediği Kolmogrov-Smirnov testi ile analiz edilmiş ve normal dağılım göstermediği anlaşılmıştır. (p<0,05). Araştırmanın bulguları incelendiğinde, cinsiyet değişkenine göre erkek katılımcıların; medeni durum değişkenine göre ise bekâr katılımcıların serbest zaman tatmin puanlarının daha yüksek olduğu belirlenmiştir. Ayrıca 29-38 yaş grubunda olan katılımcıların serbest zaman tutum ölçeği psikolojik alt boyutu toplam puan sıralamaları diğer gruplara göre anlamlı şekilde yüksektir.

Purpose: Evidence revealed that physical activity has positive effects on quality of life and mental health. Nevertheless, this topic has received little attention among special groups such as autism. Thus, this study was designed to examine the associations between physical activity with health-related quality of life and wellbeing among children with autism. Method: 65 children with autism (aged 9 to 13 years old) from special school and 70 typically developing children from regular primary schools (aged 9 to 12 years old) participated in this study. Physical activity, health-related quality of life, and wellbeing were measured by using standard questionnaires. Pearson correlation, regression analysis, and independent t test were used for data analysis. Results: Both children with and without autism had low physical activity. Children without autism had significantly higher levels of physical activity, health-related quality of life, and wellbeing compared with children with autism. Physical activity was positively associated with health-related quality of life and wellbeing among both children with and without autism. Conclusion: These findings, together, indicate that physical activity is a critical concern for children with autism. Accordingly, it is necessary to adopt appropriate strategies to increase the level of physical activity among children with autism. Keywords: Autism, Physical Activity, Quality of Life, Wellbeing, Children

Various devices and monitoring systems have been developed and deployed in order to monitor the power grid. Indeed, several real-world cyberattacks on power grid systems have been publicly reported. For the transmission and distribution, Phasor Measurement Units (PMUs) constitute the main sensing equipment of the overall wide area monitoring and situational awareness systems by collecting high-resolution data and sending them to Phasor Data Concentrators (PDCs). In this paper, we consider data spoofing attacks against PMU networks. The data between PMUs and PDC(s) are sent through the legacy networks, which are subject to many attack scenarios under with no, or inadequate, countermeasures in protocols, such as IEEE 37.118-2. We consider one potential attack, where an adversary may simply keep injecting a repeated measurement through a compromised PMU to disrupt the monitoring system. This attack is referred to as a Repeated Last Value (RLV) attack. We develop and evaluate countermeasures against RLV attacks using a 2D Convolutional Neural Network (CNN)-based approach, which operates in frames for each second mimicking images, in order to avoid the computational overhead of the classical sample-based classification algorithms, such as SVM. Further, we take this frame-based approach and use it with Support Vector Machine (SVM) for performance evaluation. Our preliminary results show that frame-based CNN as well as SVM provide promising results for RLV attacks while the efficacy of CNN over SVM frame becomes more pronounced as the attack intensity increases.