Search Results (6,722)

The objective of this study was to identify the factors that most strongly influence the postharvest quality of ‘Gala Schniga^® SchniCo Red(s)’ apples under conditions of simulated transport and simulated trading at elevated temperature following long-term storage. The study was conducted over two storage seasons (2022/2023 and 2023/2024) on fruit originating from the experimental orchard of the Warsaw University of Life Sciences (SGGW-WULS) in Warsaw. The effects of harvest date (optimal—OHD and delayed by 14 days—DH), four variants of 1-MCP (1-methylcyclopropene) application: (control, Harvista™—preharvest, SmartFresh™—postharvest, and Harvista™ + SmartFresh™), controlled-atmosphere storage technology (ULO 1: 1.2% CO₂ and 1.2% O₂; ULO 2: 0.6% CO₂ and 0.6% O₂), storage period (5, 7, and 9 months), duration of simulated transport (4 or 6 weeks at 1 °C in normal atmosphere), and shelf life (0, 7, and 14 days at 25 °C) were analyzed. Five quality parameters were evaluated: firmness (F), soluble solids content (SSC), titratable acidity (TA), SSC/TA ratio, and 1-aminocyclopropane-1-carboxylic acid (ACC) content. Stepwise regression with backward elimination was applied to identify significant predictors, and partial eta squared (η²) was calculated to compare the relative strength of effects. Postharvest 1-MCP application had the greatest impact on maintaining firmness and TA (F: η² = 75.8%; TA: η² = 56.3%), whereas shelf life was the key factor in the deterioration of quality parameters after removal from storage (F: η² = 55.5%; TA: η² = 30.1) and in increasing the SSC/TA ratio (η² = 29.6%). Harvest date strongly differentiated firmness (η² = 51.3) and significantly affected TA (η² = 14.4), while storage period had the greatest effect on ACC content (η² = 14.2) and TA decline (η² = 15.6). Preharvest 1-MCP application had a smaller effect on F and TA but significantly reduced SSC (η² = 24.9), highlighting the importance of the timing of ethylene inhibitor application. The effects of simulated transport and preharvest weather indicators were statistically significant but relatively small compared with the effects of postharvest technological decisions and exposure time under retail conditions. The results indicate that maintaining target quality parameters throughout an extended supply chain requires precise determination of the harvest date, prioritizing postharvest 1-MCP application, and limiting shelf life under elevated-temperature conditions. Full article

Background/Objectives: Clostridioides difficile infection (CDI) represents an important healthcare-associated complication in hospitalized patients, particularly in those exposed to antibiotics, prolonged hospitalization, and intensive treatment during COVID-19. This study aimed to design, evaluate, and interpret machine learning models for predicting CDI occurrence in hospitalized COVID-19 patients across individual pandemic waves, with respect to administered treatment and clinical characteristics. Methods: Anonymized clinical data from 3848 COVID-19-positive patients treated at the University Hospital of L. Pasteur in Košice, Slovakia, were analyzed following the CRISP-DM methodology. Four classification models were compared: logistic regression, Random Forest, XGBoost, and a multilayer perceptron. Missing values were addressed using MICE and KNN imputation, and class imbalance was handled through oversampling techniques. Given the low CDI prevalence of 2.68%, model performance was primarily assessed using the precision–recall area under the curve (PR-AUC), with AUROC reported for comparability. Interpretability was supported using SHAP, LIME, and odds ratio analysis. Results: The best-performing models achieved PR-AUC values up to 0.160, representing more than a fivefold improvement over the random baseline of 0.027. XGBoost reached the highest AUROC of 0.823, followed by Random Forest with 0.798. Inflammatory markers were identified as important predictors of CDI risk. A Flask-based decision-support web application was developed to provide CDI risk estimation with patient-specific explanations. A preliminary pilot usability evaluation involving two physicians yielded a mean System Usability Scale score of 73.75; however, the very small evaluator sample limits the generalizability of this finding. Conclusions: Interpretable machine learning models can support clinically meaningful CDI risk stratification in highly imbalanced COVID-19 hospital datasets. The proposed decision-support tool shows potential for future integration into clinical workflows, although external and prospective validation is required. Full article

The exponential growth of machine learning (ML) applications in smart grid (SG) research over the past decade has generated a vast and fragmented body of literature that lacks systematic synthesis. This study addresses that gap by presenting a comprehensive bibliometric and science mapping analysis of the ML–smart grid (MLSG) research landscape to date, drawing on 4156 peer-reviewed publications indexed in the Elsevier Scopus database from 2009 to 2025. The principal contributions of this study are fourfold. First, it provides a rigorous quantitative mapping of MLSG publication growth from one document in 2009 to 1163 publications in 2025, representing a growth rate of 116,200%, thereby establishing a definitive baseline for tracking future scholarly development in the field. Second, it identifies the key actors driving MLSG research, including the most prolific authors (Nadeem Javaid, Alsabaan M.), leading institutions (King Saud University, Tennessee Technological University), and dominant nations (India, China, United States), which offers researchers and funding bodies actionable intelligence on collaboration opportunities and research leadership. Third, through keyword co-occurrence and cluster analysis, the study maps the three dominant thematic hotspots structuring current MLSG research—Smart Grid Security, Power Load Forecasting, and Advanced Energy Management—providing a structured intellectual framework that can guide future research prioritization. Fourth, the study delivers a critical thematic literature review of these three hotspots, synthesizing the most impactful ML methodologies and applications reported across 4156 publications, including deep learning-based intrusion detection, ensemble forecasting models, and reinforcement learning-driven energy management. Collectively, these contributions offer a robust evidence base for researchers, policymakers, and industry practitioners seeking to navigate, benchmark, and advance the field of ML-enabled smart grid systems. Full article

Circadian clocks are endogenous timing systems that coordinate plant physiology, metabolism, development, and stress responses with daily and seasonal environmental cycles. In crops, circadian and photoperiodic pathways influence agronomically important traits including photosynthesis, carbon allocation, flowering time, growth, stress resilience, and nutritional quality. Although flowering time and photoperiod response pathways have long been indirectly exploited during domestication and breeding, the broader potential of circadian regulation for crop improvement and time-sensitive management remains only partially developed. This review examines the role of plant circadian clocks in crop productivity, with emphasis on molecular mechanisms, crop-specific clock-associated loci, breeding strategies, and chrono-agricultural applications. We summarize conserved and divergent features of the plant clock, including transcriptional repression and activation modules, environmental entrainment, and post-transcriptional regulatory layers. We then discuss how circadian regulation shapes productivity traits and highlight examples from rice, wheat, barley, maize, soybean, sorghum, tomato, and other crops. These examples show that agricultural adaptation often involves fine-tuning or rewiring circadian and photoperiodic outputs rather than maintaining a universal optimal clock state. Finally, we evaluate chrono-agriculture as an emerging framework for aligning management practices with biological timing. While controlled-environment agriculture and high-value horticultural systems are currently the most practical settings for testing chrono-agricultural strategies, open-field applications require careful consideration of environmental variability, sensor limitations, labour, machinery logistics, economic feasibility, and multi-environment validation. Integrating circadian biology with crop genetics, phenotyping, modelling, and agronomy may provide new opportunities to improve productivity, resilience, resource-use efficiency, and quality traits in sustainable agricultural systems. Full article

Background/Objectives: Proteolysis Targeting Chimeras (PROTACs) are heterobifunctional small molecules that induce ubiquitin–proteasome–mediated degradation of target proteins and have matured from proof-of-concept chemistry to a clinically validated therapeutic modality, with the first Phase 3 readout reported in 2025. A systematic bibliometric analysis covering this pivotal-trial era, however, has been lacking. This study aimed to map the historical trajectory, current research front, and emerging frontiers of PROTAC research. Methods: We analyzed 2630 PROTAC-related publications indexed in the Web of Science Core Collection (WoSCC) from 2001 to 2025 using a combined toolkit of CiteSpace, HistCite, the Alluvial Generator, and R (ggplot2), covering co-occurrence networks, burst detection, keyword clustering, citation historiography, alluvial flow analysis, and reference co-citation timeline visualization. Results: China and the USA led global output, and the Chinese Academy of Sciences, China Pharmaceutical University, and Harvard University were the most productive institutions; the Journal of Medicinal Chemistry was the leading publishing venue, and Alessio Ciulli, Jian Jin, and Craig M. Crews anchored the author network. Keyword burst analysis showed that early research centred on E3 ubiquitin ligase recruitment and small-molecule PROTAC design, whereas the current hotspots, resolved through keyword clustering and co-citation timelines, included structural basis and ternary complex design, EGFR-directed degradation, oral bioavailability optimization, applications in multiple myeloma and Alzheimer’s disease, tumour-targeted delivery, and computational/AI-driven design. Conclusions: This study extends the bibliometric record of PROTACs across 2001–2025 and identifies oral bioavailability, E3 ligase repertoire expansion, and CNS-penetrant degrader design as the emerging frontiers likely to shape the next phase of the field. Full article

The increasing adoption of virtual reality for industrial tasks such as virtual assembly, inspection, and operator training necessitates a standardized approach for evaluating and selecting appropriate hardware. This paper addresses this need by introducing a comprehensive methodology to assess the usability of commercially widespread virtual reality headsets specifically for industrial applications with hand-held controllers. We conducted a large-scale comparative study involving five leading headsets (HTC VIVE Pro 1 and 2, HTC VIVE XR Elite, Meta Quest Pro, and Meta Quest 3) and 60 demographically balanced participants. The evaluation was based on a protocol of 15 distinct tasks designed to measure performance in near and far-field object manipulation, interaction fidelity, visual clarity, ergonomics, and long-term comfort. By combining quantitative Key Performance Indicators with subjective user feedback and rigorous inferential statistical analysis, our findings reveal significant performance disparities among the devices. The results demonstrate that, while certain headsets excel in high-precision tracking for assembly tasks, others offer superior comfort, visual quality, and ease of use for inspection and prolonged sessions. Ultimately, this study concludes that no single headset is universally superior; the optimal choice is highly task-dependent. The proposed methodology provides a robust, evidence-based framework to guide industries in making informed virtual reality hardware selections tailored to their specific needs. Full article

The choice of a symmetric encryption algorithm in practice is rarely as straightforward as it may appear from theoretical comparisons alone. In addition to security considerations, real-world selection often depends on execution time, reliability, entropy-related behavior, resource efficiency, and suitability for different types of data. This paper presents an experimental software platform for benchmarking and multi-criteria recommendation of symmetric encryption algorithms. The platform combines automated encryption and decryption tests, metric collection, comparative analysis, and result visualization within a unified evaluation workflow. It also incorporates a multi-criteria model that transforms raw experimental measurements into an overall ranking and supports context-aware recommendation according to the requirements of a given usage scenario. The experimental study includes repeated tests on different input categories in order to examine algorithm behavior under varied operating conditions. The obtained results show that algorithm performance and overall suitability are strongly dependent on the evaluation perspective and the application context, which suggests that no single symmetric method should be regarded as universally optimal. The proposed platform offers a practical basis for comparative cryptographic analysis and may be useful both for research purposes and for informed decision-making in security-oriented software environments. Full article

Static analysis of student programming submissions has proven a useful supplement to manual evaluation in university courses, but existing approaches focus on local code-quality issues and rarely check higher-level design decisions such as architectural conformance. We propose an architecture-aware static-analysis methodology for student submissions written in C# and structured according to the Model-View (MV) or Model-View-ViewModel (MVVM) architectures. A deterministic clustering algorithm assigns user-defined types to architectural layers by combining heuristic rules derived from SDK conventions with course-specific information, and our 10 proposed violation checks—covering layer-dependency rules, encapsulation, event handling, and dependence on concretions—are evaluated on the recovered layer structure. We implemented the methodology as an open-source analyzer integrated with an automated submission-evaluation system used in a university course focused on event-driven applications, and evaluated it on 947 submissions containing 13,126 user-defined types from past semesters. The analyzer assigned more than 98% of types to their correct layer and surfaced more than 6000 architectural and design issues. The results show that architecture-aware static analysis is a viable complement to manual grading and produces actionable feedback for both students and lecturers. Full article

Background/Objectives: Chronic home oxygen therapy—long-term oxygen therapy (LTOT)—improves survival and quality of life in chronic respiratory failure when used ≥15 h/day, but adherence is frequently suboptimal and specific patient-reported outcome measures (PROMs) are scarce. To develop, validate and apply a specific PROM for patients on LTOT. Methods: A prospective observational cohort study was conducted at San Juan de Alicante University Hospital (April 2024–December 2025) following a four-stage process: conceptual framework definition and expert workshop, content validation and item reduction, cognitive interviews with pilot reliability testing (n = 25), and field application to 120 consecutive chronic LTOT users. The LTOT-PROM was designed to capture the patient-perceived impact attributable to LTOT during the previous 4 weeks. Internal consistency was assessed with Cronbach’s α and test–retest reproducibility with the intraclass correlation coefficient (ICC). Results: The final instrument comprises 15 scored items in two dimensions—Daily Activity (9 items) and Adverse Effects (6 items)—plus one ambulatory-only mobility item excluded from the total score. Cronbach’s α was 0.814 (95% CI 0.681–0.906) for Daily Activity, 0.743 (95% CI 0.548–0.872) for Adverse Effects and 0.808 (95% CI 0.677–0.902) for the total scale; total ICC(A,1) was 0.890 (95% CI 0.767–0.950). Among the 120 patients (62 men, 58 women; mean age 78 ± 13 years; mean therapy duration 40 ± 32 months), 68% reported reduced effort for daily activities, 66% reported a reduction in dyspnoea and 67% reported improved self-confidence; 49% reported morning airway dryness and 7% abandoned the equipment due to nasal dryness or rhinitis. Conclusions: The LTOT-PROM is a brief, reliable and reproducible oxygen-specific instrument for assessing the recent patient-perceived impact of LTOT in routine clinical practice. Further studies should evaluate structural validity, external validity and the relationship between LTOT-PROM scores and objective adherence measures. The construct was predefined as the patient-perceived impact attributable to LTOT during a standardised 4-week recall window, and cognitive interviews confirmed that respondents interpreted the items as experienced benefit/burden during that period rather than as week-to-week symptom change. Full article

Background/Objectives: Nonmetric dental traits assessed using the Arizona State University Dental Anthropology System (ASUDAS) are valuable in forensic identification due to their population-specific variation. However, intranational variability within Indian populations remains underexplored. To evaluate variation in ASUDAS dental morphological traits among individuals with different early-life geographic backgrounds and assess their forensic applicability. Methods: A cross-sectional observational pilot study was conducted on 55 dental casts of individuals aged 18–22 years. Subjects were grouped into Maharashtra (n = 37) and non-Maharashtra (n = 18) based on residence from birth to 10 years. A total of 42 crown traits were assessed using ASUDAS criteria. Statistical analysis included chi-square or Fisher’s exact test (p < 0.05), and intraobserver reliability was evaluated using Cohen’s kappa. Results: Significant differences were observed in maxillary traits such as shoveling (p = 0.004), interruption grooves (p = 0.01), canine accessory distal ridge (p = 0.022), hypocone (p = 0.029), premolar accessory ridge (p = 0.007), tuberculum dentale (p = 0.021), and double shoveling (p = 0.001), and mandibular traits including premolar accessory cusp/protoconule (p < 0.001), anterior fovea (p = 0.005), and deflecting wrinkle (p < 0.001). Conclusions: The observed variations reflected population heterogeneity, supporting the forensic relevance of ASUDAS traits and the need for region-specific databases. Full article

The lipoate-protein ligase A (LplA) identified in Escherichia coli K-12 exhibits structural homomeric oligomerization and reversible lower critical solution temperature (LCST)-type phase separation in vitro. In this study, based on the ability of LplA to form condensates, it was utilized as a temperature-sensitive purification tag in the field of protein purification for the first time, and a novel and convenient one-step purification method was established. A universal vector was developed for the fusion expression of LplA and the target protein. The fusion protein forms condensates upon heating, separating from the solution, and redissolves in buffer at lower temperatures, enabling the purification of the target protein from cell lysates. Through exploration of phase separation temperatures, 30 °C was determined to be the optimal purification temperature. Subsequently, three enzymes of different molecular sizes (lipase EstA, endoglucanase BcsZ, and endoglucanase EglS) demonstrated the versatility of this condensate-based purification method. Furthermore, the specific activity and purification efficiency of the purified enzymes were comparable to those of enzymes purified by conventional affinity chromatography. This research contributes to the introduction of condensates into protein purification applications, offering potential support for the large-scale production and purification of functional proteins. Full article

University orientation programmes are a primary mechanism through which new students become familiar with campus facilities, academic spaces, and institutional procedures. However, many orientation activities are delivered as single in-person sessions, limiting opportunities for students to revisit spatial and procedural information after the event. To help address this constraint, a digital twin-inspired metaverse orientation application, the Digital Twin Metaverse Orientation (DTMO), was designed in Unity and hosted on Spatial.io as a spatially faithful virtual replica of a faculty environment. An exploratory pilot evaluation was conducted with 30 university students from multiple faculties after a facilitator-guided orientation session. The System Usability Scale (SUS), Net Promoter Score (NPS), and two open-ended questions were used to examine perceived usability, recommendation intention, and the reasons underpinning recommendation decisions. The application obtained a mean SUS score of 86.83, corresponding to an excellent perceived-usability rating, and an NPS of 53.33, indicating positive immediate recommendation intention. Qualitative responses suggested that participants valued the DTMO for engagement, accessibility, ease of navigation, and support for spatial familiarisation, while some participants emphasised that it should complement rather than replace physical orientation. These pilot findings indicate promising user reception in a small, guided-session sample, but they do not establish orientation effectiveness, learning transfer, wayfinding performance, retention, belonging, institutional integration, or sustained use. Further research with broader samples and outcome-based measures is therefore needed. Full article

The study examines the evolution, knowledge structure, and trends in snowmelt runoff prediction models. It identifies research hotspots, future directions, and offers a theoretical basis for accurate simulation and prediction. Utilizing CiteSpace software, 556 core Chinese and English publications from 2010 to 2025 were visually analyzed. Research on snowmelt runoff simulation shows: (1) Chinese publications are prominent in core journals like “Journal of Glaciology and Geocryology,” while English publications appear in high-impact journals like “Water Resources Research.” (2) Institutions like the University of Chinese Academy of Sciences, the Northwest Institute of Eco-Environment and Resources, and the University of California have formed a cross-regional research network. (3) International collaboration involves 42 countries, with a focus on China, the United States, and India. However, domestic institutional cooperation needs improvement. (4) Research trends in snowmelt runoff simulation have progressed from empirical statistics to remote sensing and model-driven physical mechanisms, and now to the integration of artificial intelligence with physical models. (5) The Chinese literature focuses on cold regions, while the English literature emphasizes intelligent modeling. This shift indicates a move towards “physical–intelligent” hybrid modeling. Future research should address challenges like model applicability in data-scarce areas, improving interpretability of complex models, quantifying uncertainties, and developing physically constrained deep learning models. Collaboration among institutions is crucial for enhancing water resource management and disaster warning systems in cold regions. Full article

This study comparatively examines the effects of different active and passive energy strategies on energy performance, carbon emission reduction, economic feasibility, and thermal comfort potential in a university building in Ankara. This study uses a university building with 8760 h of recorded operational electricity consumption data as a real-world reference case and evaluates different retrofit strategies through dynamic building energy simulations. Simulation results were evaluated not only in terms of total energy consumption but also in terms of operational carbon emissions, levelized cost of energy (LCOE/LCOSE), and the potential for improving indoor temperature stability through passive design strategies. The results show that PV system integration provides the highest energy and carbon reduction performance by reducing the net grid electricity consumption by 89.76%. Among passive systems, the Trombe wall scenario provided the highest energy savings and the lowest LCOSE value. PCM application stood out in terms of indoor temperature stability potential, while the green roof system contributed to temperature control, especially during the summer. In addition, an economic sensitivity analysis based on the discount rate was carried out to reveal the strengths and weaknesses of the proposed strategies in terms of sustainable building design. The study contributes to the comparative analysis of active and passive retrofit strategies in university buildings by offering an integrated and multi-dimensional evaluation approach supported by real operational data. Full article

Real-time assessment of reading comprehension remains a challenge in educational research. Traditional evaluation methods, such as questionnaires, provide delayed and retrospective measures and therefore do not capture the dynamic nature of comprehension during reading. This exploratory study investigates whether beta-band electroencephalography (EEG) activity can be used to estimate EEG-derived indicators related to reading comprehension during academic reading. The study included 40 university students who read a conceptually demanding scientific text while EEG signals were continuously recorded. Beta-band activity (13–30 Hz) was extracted from six cognition-related channels and segmented into non-overlapping 2 s windows. Principal component analysis (PCA) was applied for dimensionality reduction, followed by kernel ridge regression (KRR) for prediction. At the window level, the proposed KRR–PCA framework achieved a mean absolute error (MAE) of 5.797, a root mean square error (RMSE) of 7.783, an MAE-based accuracy of 94.2%, and an explained variance of R² = 0.275 on a held-out test set. At the participant level, aggregated predictions showed a significant correlation with questionnaire-based comprehension scores (r = 0.59), indicating that EEG-derived features captured meaningful inter-individual differences. The framework also generated time-resolved prediction profiles that reflected fluctuations in EEG-derived comprehension estimates during reading. These findings suggest that beta-band EEG contains information related to reading comprehension and may support the development of future EEG-based educational monitoring systems. Further validation using larger cohorts and time-resolved comprehension measures is needed to confirm the practical applicability of the approach. Full article