Abstract Data curation is a fundamental yet often underappreciated aspect of cheminformatics and computational drug discovery. Large public and proprietary databases now provide vast amounts of chemical structure, physicochemical, absorption, distribution, metabolism, excretion, and toxicity (ADMET), and bioactivity data. However, these resources contain structural inconsistencies, annotation errors, and heterogeneous experimental conditions that can limit model performance and reproducibility. This narrative review summarizes why and how data should be curated before use in cheminformatics workflows. We frame chemical data curation around two complementary pillars: structural curation and curation of experimental conditions. On the structural side, we review existing standardization and quantitative structure–activity relationship (QSAR)-ready workflows, including handling of salts and mixtures, parent–child policies, aromatization, tautomer handling, stereochemistry validation, and duplicate detection with conflict resolution. On the experimental side, we synthesize evidence that assay protocols, measurement methods, and reporting practices introduce substantial uncertainty and bias in physicochemical and ADMET endpoints as well as bioactivity data, and we outline practical strategies for assembling condition-aware datasets from the literature and public databases. Across case studies, we highlight how curated structure–condition pairs yield more accurate, robust, and reproducible models than raw, unfiltered collections. Rather than introducing a new predictive method or performing a formal statistical meta-analysis, we provide a structured narrative synthesis of current best practices, tools, and decision points for data curation in cheminformatics. This review offers practical, evidence-based guidance on the structural and experimental-condition curation required to build reliable cheminformatics models. Scientific Contribution: This article does not introduce a new algorithm but provides a practice-oriented, structured synthesis of data curation in cheminformatics. We (i) formulate a two-pillar framework that treats structural curation and experimental-condition curation as equally important components of cheminformatics workflows; (ii) consolidate scattered best practices into concrete workflows, checklists, and decision maps for building “QSAR-ready” and condition-aware datasets; and (iii) integrate endpoint-specific case studies showing that rigorous curation materially improves predictive performance and reproducibility. We also identify open challenges and research directions for scaling and automating curation, including the use of workflow technologies and large language models, and for establishing community standards for condition metadata. Graphical Abstract
O desenvolvimento da Indústria 4.0 promoveu grandes mudanças na logística, impulsionando a automação, digitalização e integração dos processos operacionais. Com base neste contexto, este estudo tem como objetivo avaliar a aplicação das tecnologias da Indústria 4.0 em uma empresa de transporte e serviços localizada em Feira de Santana-BA, analisando os desafios e as possibilidades de sua implementação. Com isso, foi realizada uma pesquisa qualitativa, composta por pesquisa bibliográfica, entrevista com gestor e visita técnica à empresa. A análise foi conduzida por meio da matriz SWOT da empresa, permitindo a identificação de fatores internos e externos que influenciam a adoção dessas tecnologias. A partir deste diagnóstico, foram propostas estratégias para otimizar os processos em termos de logística, reduções diretas das limitações operacionais e novas oportunidades a serem exploradas dentro do contexto da Logística 4.0.
Production management. Operations management, Production capacity. Manufacturing capacity
Blockchain technology has attracted attention from public sector agencies, mainly for its perceived potential to improve transparency, data integrity, and administrative processes. However, its concrete value and applicability within government settings remain contested, and real-world adoption has been limited and uneven. This raises questions regarding the conditions that promote or impede adoption at the institutional level. Fuzzy-set qualitative comparative analysis is employed in this research to explore how the combined effects of national-level regulatory clarity, financial provision, digital readiness, and ecosystem engagement shape patterns of blockchain adoption in the European public sector. Rather than identifying any single factor as decisive, our findings reveal a plurality of institutional paths leading to high adoption intensity, with regulatory certainty and European Union funding appearing most frequently on high-consistency paths. In contrast, digital readiness indicators and national research and development budgets are substitutable, challenging resource-based perceptions of technology adoption and supporting a configurational understanding that accounts for institutional interdependence and contextuality. We argue that policy strategies cannot look for overall readiness but should place key institutional strengths relative to local conditions and public value objectives.
Information technology, Political institutions and public administration (General)
As digital infrastructure continues to expand, networks, web services, and Internet of Things (IoT) devices become increasingly vulnerable to distributed denial of service (DDoS) attacks. Remarkably, IoT devices have become attracted to DDoS attacks due to their common deployment and limited applied security measures. Therefore, attackers take advantage of the growing number of unsecured IoT devices to reflect massive traffic that overwhelms networks and disrupts necessary services, making protection of IoT devices against DDoS attacks a major concern for organizations and administrators. In this paper, the effectiveness of supervised machine learning (ML) classification and deep learning (DL) algorithms in detecting DDoS attacks on IoT networks was investigated by conducting an extensive analysis of network traffic dataset (legitimate and malicious). The performance of the models and data quality improved when emphasizing the impact of feature selection and data pre-processing approaches. Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.
This study investigated the asymmetric impact of natural resource exploitation on climate change in resource-rich African countries, based on panel data from 1980 to 2022. The dynamic common correlated effect (DCCE) and dynamic seemingly unrelated regression (DSUR) econometric techniques were employed to evaluate the long-term effects of positive shocks and negative shocks to natural resource exploitation. The findings revealed a positive relationship between both positive and negative shocks to natural resource exploitation and temperature, with increases in natural resource exploitation exerting a more intensified impact on temperature than decreases. In contrast, both positive and negative changes in natural resource exploitation are negatively related to precipitation, with an increased exploitation intensity having a more pronounced effect on rainfall patterns. The study also highlights the critical role of control variables such as GDP per capita, urban population, and total energy consumption in altering temperature and precipitation patterns. The findings underscore the importance of adopting sustainable natural resource extraction practices, integrating green technologies, and promoting collaboration across natural resource exploitation and renewable energy value chains to mitigate the negative impacts of natural resource exploitation.
In response to the challenges posed by the complex field of food safety, the FOODSAFETY4EU project established four social labs conducting multi-actor co-creation processes. These labs served as platforms for developing and piloting innovative ideas aimed at addressing these challenges. Due to COVID-19 pandemic, the lab process, typically held in-person, had to be converted to the virtual space. This means that all workshops, meetings, and collaboration processes and pilot activities solely took place online. This resulted in the novel situation of teams collaborating virtually throughout the entire social lab processes. Virtual collaborations were already on the rise before the pandemic, evidenced by an increase in virtual meetings and workshops. This study examines the requirements and challenges for effective team collaboration in virtual social lab processes. It investigates virtual collaboration, team dynamics, and the use of online tools. Findings reveal advantages such as increased participation, but also drawbacks including technical issues and role accountability. Despite challenges, all four virtual social labs finally succeeded in engaging diverse stakeholders and achieving significant outcomes addressing food safety challenges.
Під час робочого процесу навантажувач перемішується на майже горизонтальних майданчиках, допустимий ухил яких. Розрахунок поздовжньої стійкості навантажувачів ведеться з умови перекидання вперед з урахуванням того, що деформуються пневматичні шини, якщо пневмоколісний хід. Кут додаткового нахилу навантажувача вперед внаслідок деформації опор визначається співвідношенням сили тяжкості навантажувача з вантажем жорсткість ґрунту під переднім та заднім котками гусеничного ходу або радіальна жорсткість передніх та задніх пневматичних шин навантажувача на пневмоколісному ході; відстань між центром ваги навантажувача та вертикальною віссю, що проходить через точку перекидання. Тому при розрахунку поздовжньої стійкості гусеничного та пневмоколісного навантажувачів. Найменший запас поздовжньої стійкості має навантажувач у разі руху під ухил з одночасним гальмуванням машини та робочого обладнання при його опусканні. Положення робочого обладнання відповідає максимальному вильоту.
Gilson D. Honvoh, Roger S. Zoh, Anand Gupta
et al.
BackgroundObesity has become an important threat to children’s health, with physical and psychological impacts that extend into adulthood. Limited physical activity and sedentary behavior are associated with increased obesity risk. Because children spend approximately 6 h each day in school, researchers increasingly study how obesity is influenced by school-day physical activity and energy expenditure (EE) patterns among school-aged children by using wearable devices that collect data at frequent intervals and generate complex, high-dimensional data. Although clinicians typically define obesity in children as having an age-and sex-adjusted body mass index (BMI) value in the high percentiles, the relationships between school-based physical activity interventions and BMI are analyzed using traditional linear regression models, which are designed to assess the effects of interventions among children with average BMI, limiting insight regarding the effects of interventions among children categorized as overweight or obese.MethodsWe investigate the association between wearable device–based EE measures and age-and sex-adjusted BMI values in data from a cluster-randomized, school-based study. We express and analyze EE levels as both a scalar-valued variable and as a continuous, high-dimensional, functional predictor variable. We investigate the relationship between school-day EE (SDEE) and BMI using four models: a linear mixed-effects model (LMEM), a quantile mixed-effects model (QMEM), a functional mixed-effects model (FMEM), and a functional quantile mixed-effects model (FQMEM). The LMEM and QMEM include SDEE as a summary measure, whereas the FMEM and FQMEM allow for the modeling of SDEE as a high-dimensional covariate. The FMEM and FQMEM allow the influence of the time of day at which physical activity is performed to be assessed, which is not possible using the LMEM or the QMEM. The FMEM assesses how frequently collected SDEE data influences mean BMI, whereas the FQMEM assesses the effects on quantile levels of BMI.ResultsThe LMEM and QMEM detected a statistically significant effect of overall mean SDEE on log (BMI) (the natural logarithm of BMI) after adjusting for intervention, age, race, and sex. The FMEM and FQMEM provided evidence for statistically significant associations between SDEE and log (BMI) for only a short time interval. Being a boy or being assigned a stand-biased desk is associated with a lower log (BMI) than being a girl or being assigned a traditional desk. Across our models, age was not a statistically significant covariate, and white students had significantly lower log (BMI) than non-white students in quantile models, but this significant effect was observed for only the 10th and 50th quantile levels of BMI. The functional regression models allow for additional interpretations of the influence of EE patterns on age-and sex-adjusted BMI, whereas the quantile regression models enable the influence of EE patterns to be assessed across the entire BMI distribution.ConclusionThe FQMEM is recommended when interest lies in assessing how device-monitored SDEE patterns affect children of all body types, as this model is robust and able to assess intervention effects across the full BMI distribution. However, the sample size must be sufficiently large to adequately power determinations of covariate effects across the entire BMI distribution, including the tails.
Abstract In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed model was the Stacked Conditional Variation AutoEncoder (Stack-CVAE), which acts as an agent in reinforcement learning so that the resulting chemical formulas have the desired chemical properties and show high binding affinity with specific target proteins. We generated 1000 chemical formulas using the chemical properties of sorafenib and the three target kinases of sorafenib. Then, we confirmed that Stack-CVAE generates more of the valid and unique chemical compounds that have the desired chemical properties and predicted binding affinity better than other generative models. More detailed analysis for 100 of the top scoring molecules show that they are novel ones not found in existing chemical databases. Moreover, they reveal significantly higher predicted binding affinity score for Raf kinases than for other kinases. Furthermore, they are highly druggable and synthesizable.
With over 30,000 confirmed cases, Brazil is currently the country most affected by COVID-19 in Latin America, and ranked 12th worldwide (John Hopkins University & Medicine, 2020). Despite all evidence, a strong rhetoric undermining risks associated to COVID-19 has been endorsed at the highest levels of the Brazilian government, making President Jair Bolsonaro the leader of the “coronavirus-denial movement” (Friedman, 2020. To support this strategy, different forms of misinformation and disinformation1 have been leveraged to lead a dangerous crusade against scientific and evidence-based recommendations (Ireton & Posetti, 2018).
Peter Trebuňa Technical University of Košice, Faculty of Mechanic al Engineering, Institute of Management, Industrial and Digital engineering, Park Komenského 9, 042 00 Košice, pete r.trebuna@tuke.sk (corresponding author) Marek Mizerák Technical University of Košice, Faculty of Mechanic al Engineering, Institute of Management, Industrial and Digital engineering, Park Komenského 9, 042 00 Košice, e-ma il: rekmizerak@gmail.com Ladislav Rosocha Technical University of Košice, Faculty of Mechanic al Engineering, Institute of Management, Industrial and Digital engineering, Park Komenského 9, 042 00 Košice, e-ma il: ladislav.rosocha@gmail.com
Perguruan tinggi sebagai pusat pendidikan harus menghasilkan lulusan yang mampu bersaing agar memberikan dampak yang besar bagi bangsa dan negara. Universitas Islam Negeri Raden Fatah Palembang merupakan perguruan tinggi yang telah menerapkan teknologi knowledge management. Melalui paradigma baru saling berbagi pengetahuan, menangkap pengetahuan, mencipta pengetahuan baru, dan memori pengetahuan organisasi yang dimiliki perguruan tinggi setiap waktu seharusnya menjadi trigger kreativitas dan inovasi dari mahasiswa untuk berprestasi. Responden penelitian berjumlah 205 orang dari Fakultas Sains dan Teknologi, Ilmu Tarbiyah dan Keguruan, Ekonomi dan Bisnis Islam, dan Fakultas Syariah dan Hukum. Variabel eksogen yaitu Personal Learning, Job Procedure, Learning Organization, dan Technology, sedangkan variabel endogen yaitu Learning Creativity, dan Learning Achievement. Tools yang digunakan aplikasi AMOS (Analysis of Moment Structure) diolah menggunakan analisis SEM (Structural Equation Model) dengan penghapusan mahalanobis (Outlier) pada model struktural dievaluasi dan diperoleh goodness of fit setelah modifikasi model. Hasil penelitian memperlihatkan bahwa Kreativitas Belajar dipengaruhi sangat kuat oleh Job Procedure, Learning Organization, dan Technology. Personal Learning sama sekali tidak mempengaruhi Learning Creativity. Tetapi Learning Creativity sangat kuat mempengaruhi Learning Achievement.
Emma L. Schymanski, Christoph Ruttkies, Martin Krauss
et al.
Abstract Background The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. Results The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in “Category 2: Best Automatic Structural Identification—In Silico Fragmentation Only”, won by Team Brouard with 41% challenge wins. The winner of “Category 3: Best Automatic Structural Identification—Full Information” was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. Conclusions The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for “known unknowns”. As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for “real life” annotations. The true “unknown unknowns” remain to be evaluated in future CASMI contests. Graphical abstract .