Search Results (114,097)

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and obtaining a golden reference chip; however, artificial intelligence can make everything possible. For the first time, this paper presents a systematic review of artificial-intelligence-based integrated circuit security approaches, focusing on the latest attack and defense strategies. First, the security threats of integrated circuits are analyzed. For one of several key threats to integrated circuits, hardware Trojans, existing attack models are divided into several categories and discussed in detail. Then, for summarizing and comparing the numerous existing artificial-intelligence-based defense strategies, traditional and advanced artificial-intelligence-based approaches are listed. Finally, open issues on artificial-intelligence-based integrated circuit security are discussed from three perspectives: in-depth exploration of hardware Trojans, combination of artificial intelligence, and security strategies involving the entire life cycle. Based on the rapid development of artificial intelligence and the initial successful combination with integrated circuit security, this paper offers a glimpse into their intriguing intersection, aiming to draw greater attention to these issues. Full article

This article presents a case of strategic and participatory institutional innovation in higher education, focused on developing teacher digital competence (TDC) as a key enabler of sustainable digital transformation. In response to the post-pandemic challenges faced by the National University of Cuyo (UNCuyo), a large and multi-campus public university in Argentina, the European CUTE methodology was adapted and implemented to align professional development with institutional planning. Grounded in the DigCompEdu framework, this action-oriented process moved beyond individual initiatives to create a coordinated, multi-level strategy involving educators, department leaders, and university authorities. Through a research-based design that included context analysis, participatory diagnosis, and co-designed interventions, the project built a shared understanding of digital teaching needs and institutional readiness. The implementation highlights how locally adapted frameworks, collaborative structures, and iterative decision-making can drive meaningful change across a complex university system. This case contributes to the international conversation on how higher education institutions can operationalize innovation at scale by investing in teacher competence, inclusive processes, and strategic alignment. Lessons learned from this experience are relevant for universities seeking to build institutional capacity for digital transformation in diverse educational contexts with potential downstream benefits for student learning and inclusion. Full article

Climate changes have increased interest in the circular economy, an alternative model that seeks to minimize environmental impact and maximize resource reuse. A key element of this model is individuals’ behaviors and attitudes, which determine the overall efficiency of recycling processes. The study fills the gap by investigating how selected socio-demographic factors affect attitudes and intentions toward recycling and material reuse in the Slovak Republic, by using the Perceived Characteristics of Innovating (PCI) framework. Through a two-way ANOVA, we tested the hypotheses that higher education correlates with stronger recycling attitudes and that women are more willing than men to engage in circular practices. The results show that gender differences in consumer attitudes towards the circular economy do occur, but their magnitude is often conditioned by education level. Education proved to be the strongest predictor of ecological behavior: respondents with higher education reported stronger beliefs in the importance of recycling and a greater willingness to act sustainably. The interaction between gender and education revealed that university-educated women hold the most pronounced pro-environmental attitudes, underscoring the importance of gender-sensitive educational strategies. It is recommended that environmental education and outreach focus on less-educated groups, particularly women, who have high potential to influence their communities. Full article

Background: In glaucoma screening programs, a large proportion of patients remain free of open-angle glaucoma (OAG) or have no need of intraocular eye pressure (IOP)-lowering therapy within 10 years of follow-up. Is it possible to identify a large proportion of patients already at the initial examination and, thus, to safely exclude them already at this point? Methods: A total of 6889 subjects received a complete ophthalmological examination, including objective optic nerve head and quantitative disc measurements at the initial examination, and after an average follow-up period of 11.1 years, complete data were available of 585 individuals. Two neural network models were trained and extensively tested. To allow the models to refuse to make a prediction in doubtful cases, a reject option was included. Results: A prediction for the first endpoint, ‘remaining OAG-free and no IOP-lowering therapy within 10 years’, was rejected in 57% of cases, and in the remaining cases (43%), 253/253 (=100%) received a correct prediction. The second prediction model for the second endpoint ‘remaining OAG-free within 10 years’ refused to make a prediction for 46.4% of all subjects. In the remaining cases (53.6%), 271/271 (=100%) were correctly predicted. Conclusions: Most importantly, no eye was predicted false-negatively or false-positively. Overall, 43% all eyes can safely be excluded from a glaucoma screening program for up to 10 years to be certain that the eye remains OAG-free and will not need IOP-lowering therapy. The corresponding model significantly reduces the screening performed by and work load of ophthalmologists. In the future, better predictors and models may increase the number of patients with a safe prediction, further economizing time and healthcare budgets in glaucoma screening. Full article

The article presents research on concrete creep in bridge structures, focusing on the influence of concrete mix composition and the use of advanced rheological models with fractional-order derivatives. Laboratory tests were performed on nine mixes varying in blast furnace slag content (0%, 25%, and 75% of cement mass) and air-entrainment. The results were used to calibrate fractal rheological models—Kelvin–Voigt and Huet–Sayegh—where the viscous element was replaced with a fractal element. These models showed high agreement with experimental data and improved the accuracy of creep prediction. Comparison with Eurocode 2 revealed discrepancies up to 64%, especially for slag-free concretes used in prestressed bridge structures. The findings highlight the important role of mineral additives in reducing creep strains and the need to consider individual mix characteristics in design calculations. In the context of modern bridge construction technologies, such as balanced cantilever or incremental launching, reliable modeling of early-age creep is particularly important. The proposed modeling approach may enhance the precision of long-term structural behavior analyses and contribute to improved safety and durability of concrete infrastructure. Full article

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article

Humidity and oxygen have significant impacts on the accuracy of hydrogen detection, especially for metal oxide semiconductor sensors at room temperature. Addressing this challenge, this study employs a screen-printed 1 × 2 resistive sensor array made from an identical 1 wt.% platinum-modified tin oxide nanoparticle material. Fabrication variability between the two sensing elements was intentionally leveraged to enhance array output differentiation and information content. Systematic hydrogen-sensing tests were conducted on the array under diverse oxygen and moisture conditions. Three distinct feature types—the steady-state value, resistance change, and area under the curve—were extracted from the output of each array element. These features, integrated with their quotient, formed a nine-feature vector matrix. A multiple linear regression model based on this array output was developed and validated for hydrogen prediction, achieving a coefficient of determination of 0.95, a mean absolute error of 125 ppm, and a mean relative standard deviation of 7.07%. The combined information of the array provided significantly more stable and precise hydrogen concentration predictions than linear or nonlinear models based on individual sensor features. This approach offers a promising path for mass-producing highly interference-resistant, precise, and stable room-temperature hydrogen sensor arrays. Full article

Background: Super morbid obesity (SMO), defined as a body mass index (BMI) ≥ 50 kg/m², represents a distinct and increasingly prevalent subgroup of patients undergoing bariatric surgery. Compared to individuals with lower BMI, patients with BMI ≥ 50 kg/m² often exhibit unique clinical, psychological, and social characteristics that may influence treatment outcomes. Objective: This study aimed to compare demographic, metabolic, and psychiatric profiles of patients with BMI ≥ 50 kg/m² and non-super morbid obesity (NSMO; BMI < 50 kg/m²) who were evaluated prior to bariatric surgery. Methods: A total of 319 patients were recruited between December 2022 and December 2023 at a bariatric center in Gdansk, Poland. All participants underwent a comprehensive preoperative assessment, including laboratory testing, psychometric screening (BDI, PHQ-9), and psychiatric interviews. Patients were stratified into class IV obesity and NSMO groups for comparative analysis. Results: Patients with BMI ≥ 50 kg/m² were significantly older and more likely to report a history of lifelong obesity, family history of obesity, and childhood trauma. They had higher rates of obesity-related health problems such as hypertension, obstructive sleep apnea, and chronic venous insufficiency, as well as worse liver function and lipid profiles. Although the overall psychiatric burden was high in both groups, patients with BMI ≥ 50 kg/m² reported fewer prior diagnoses of depression and eating disorders, despite similar scores on screening tools. Conclusions: Patients with BMI ≥ 50 kg/m² represent a clinically distinct population with elevated metabolic risk, complex psychosocial backgrounds, and possibly underrecognized psychiatric burden. These findings underscore the need for multidisciplinary preoperative assessment and individualized treatment strategies in this group of patients. Full article

Background: Chemical eye burns are a serious ophthalmic emergency that can lead to permanent vision loss in severe cases. This study aims to evaluate structural changes in the posterior segment of the eye in individuals who have experienced chemical burns. Methods: The study included 64 eyes from 54 patients with chemical burns (chemical burn group) and 87 healthy eyes from 87 subjects (control group), matched by age and sex. Patients had confirmed burns with limbal ischemia, no glaucoma, normal intraocular pressure, and no major ocular or systemic diseases. Burned eyes were examined during the acute phase and again at 3 months, with some followed up at 6 months if significant retinal asymmetry was detected. Retinal nerve fiber layer (RNFL) thickness was assessed in four quadrants, and ganglion cell complex (GCL++) thickness was analyzed using automated segmentation of optical coherence tomography (OCT) maps. Results: This study compared measurements between the burn group, the control group, and timepoints. OCT analysis revealed no significant difference in total RNFL thickness between burn patients and controls (mean difference: −1.14 µm, 95% CI: −3.92 to 1.64). Similarly, GCL++ thickness did not differ significantly between groups (mean difference: −0.97 µm, 95% CI: −3.31 to 1.37). At 6-month follow-up, a non-significant decline in both RNFL and GCL++ thicknesses was observed. Logistic regression identified higher Dua grade as an independent predictor of RNFL thinning (OR: 4.816, 95% CI: 1.103–21.030; p = 0.037). Patients with severe ocular chemical burns (Dua grade ≥ 3) demonstrated reduced RNFL thickness in all quadrants compared to healthy controls. The most pronounced reductions were observed in the nasal and superior quadrants (p = 0.007 and p = 0.069, respectively); however, after applying Bonferroni correction for multiple comparisons, only the difference in the nasal quadrant remained statistically significant (adjusted p = 0.035). Conclusions: Although overall RNFL and GCL++ thicknesses did not differ significantly between burn patients and healthy controls, patients with severe ocular chemical burns (Dua grade ≥ 3) showed a significant reduction in RNFL thickness, in the nasal quadrant. Higher Dua grade was identified as an independent predictor of RNFL thinning. These findings suggest a potential association between burn severity and posterior segment changes, highlighting the need for further longitudinal studies with larger cohorts. Full article

Environmental protection is one of the key challenges facing mankind today. Finding out what young people, referred to as Generation Z, think about this issue is extremely important, as they will be the first to experience the negative effects of environmental degradation. Research has shown that Generation Z has the greatest hope for solutions from the technological sphere. Thus, the economic and political spheres should support the development of technology in this area. The social sphere is rated lowest, which may reflect young people’s personal withdrawal and the delegation of responsibility for the environment’s future to engineers, entrepreneurs, and politicians. It is equally important to learn what constitutes an environmental imperative for Generation Z. It is based on new energy sources, energy producers, and the state’s pursuit of a policy of international cooperation in this area, supported by national legislative activity toward entrepreneurs and citizens. Research has demonstrated the need to raise awareness among young people, with a focus on individuals treated as subjects in their interaction with modern technology. Full article

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

Compression pants, as functional sportswear providing external pressure, are widely used to enhance athletic performance and accelerate recovery. However, systematic investigations into their effectiveness during anaerobic exercise and the impact of different pressure levels on performance and post-exercise recovery remain limited. This randomized crossover controlled trial recruited 20 healthy male university students to compare the effects of four garment conditions: non-compressive pants (NCP), moderate-pressure compression pants (MCP), high-pressure compression pants (HCP), and ultra-high-pressure compression pants (UHCP). Anaerobic performance was assessed through vertical jump, agility tests, and the Wingate anaerobic test, with indicators including time at peak power (TPP), peak power (PP), average power (AP), minimum power (MP), power drop (PD), and total energy produced (TEP). Post-exercise blood lactate concentrations and heart rate responses were also monitored. The results showed that both HCP and UHCP significantly improved vertical jump height (p < 0.01), while MCP outperformed all other conditions in agility performance (p < 0.05). In the Wingate test, MCP achieved a shorter TPP compared to NCP (p < 0.05), with significantly higher AP, lower PD, and greater TEP than all other groups (p < 0.05), whereas HCP showed an advantage only in PP over NCP (p < 0.05). Post-exercise, all compression pant groups recorded significantly higher peak blood lactate (Lamax) levels than NCP (p < 0.05), with MCP showing the fastest lactate clearance rate. Heart rate analysis revealed that HCP and UHCP induced higher maximum heart rates (HR_max) (p < 0.05), while MCP exhibited superior heart rate recovery at 3, 5, and 10 min post-exercise (p< 0.05). These findings suggest that compression pants with different pressure levels yield distinct effects on anaerobic performance and physiological recovery. Moderate-pressure compression pants demonstrated the most balanced and beneficial outcomes across multiple performance and recovery metrics, providing practical implications for the individualized design and application of compression garments in athletic training and rehabilitation. Full article

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have transformed the management of type 2 diabetes mellitus and obesity. However, their sustained effect on delaying gastric emptying raises new challenges in gastrointestinal endoscopy performed under sedation. This narrative review aims to summarize current evidence on the impact of GLP-1 RAs on gastric motility and to propose clinical strategies to mitigate associated procedural risks. Methods: A narrative review was conducted integrating findings from scintigraphy, capsule endoscopy, gastric ultrasound, and existing clinical guidelines. Emphasis was placed on studies reporting residual gastric content (RGC), anesthetic safety outcomes, and procedural feasibility in patients undergoing endoscopy while treated with GLP-1 RAs. Results: GLP-1 RAs significantly increase the prevalence of clinically relevant RGC, despite prolonged fasting, with potential implications for airway protection and sedation safety. Although the risk of pulmonary aspiration remains low (≤0.15%), procedural delays, modifications, or cancellations can occur in up to 30% of cases without adapted protocols. Several professional societies (AGA, ASGE, AASLD) advocate for individualized management based on procedure type, symptomatology, treatment phase, and point-of-care gastric ultrasound (POCUS), in contrast to the systematic discontinuation recommended by the ASA. Conclusions: Effective management requires personalized fasting protocols, risk-based stratification, tailored anesthetic approaches, and interprofessional coordination. We propose a clinical decision algorithm and highlight the need for training in gastrointestinal pharmacology, POCUS, and airway management for endoscopists. Future priorities include prospective validation of clinical algorithms, safety outcome studies, and the development of intersocietal consensus guidelines. Full article

Background/Objectives: Men have historically contributed significantly to mental health nursing, particularly in inpatient settings, where their presence has supported patient recovery and safety. Despite this legacy, men remain under-represented in the nursing workforce, and addressing this imbalance is critical to workforce sustainability. This study offers a novel contribution by exploring the lived experiences, motivations, and professional identities of men in mental health nursing, an area that has received limited empirical attention. The aim of the study is to examine the characteristics, qualities, and attributes of mental health nurses who are male, which contributes to their attraction to and retention within the profession. Methods: A qualitative interpretive inquiry was conducted among nurses who were male and either currently or previously employed in mental health settings. Two focus groups were conducted using semi-structured questions to explore their career pathways, motivations, professional identities, and perceived contributions. Thematic analysis was used to identify key themes and patterns in their narratives. Results: Seven participants, with 10–30 years of experience, participated. They had entered the profession through diverse pathways, expressing strong alignment between personal values and professional roles. Five themes emerged and centred on mental health being the heart of health, personal and professional fulfillment, camaraderie and teamwork, a profound respect for individuals and compassion, and overcoming and enjoying the challenge. Conclusions: Mental health nurses who are male bring unique contributions to the profession, embodying compassion, resilience, and ethical advocacy. Their experiences challenge traditional gender norms and redefine masculinity in health care. Fostering inclusive environments, mentorship, and leadership opportunities is essential to support their growth. These insights inform strategies to strengthen recruitment, retention, and the future of mental health nursing. Full article

Waste activated sludge (WAS), a byproduct of livestock wastewater treatment, poses significant disposal challenges due to its low biodegradability and potential environmental impact. Anaerobic digestion (AD) offers a sustainable approach for methane recovery and sludge stabilization. This study evaluates the biomethane potential (BMP) of WAS and its co-digestion with swine slurry (SS), water lily (Nymphaea spp.), and lotus (Nelumbo nucifera) shoot biomass to enhance methane yield. Batch BMP assays were conducted at substrate-to-inoculum (S/I) ratios of 1.0 and 0.5, with methane production kinetics analyzed using the modified Gompertz model. Mono-digestion of WAS yielded 259.35–460.88 NmL CH₄/g VS_added, while co-digestion with SS, water lily, and lotus increased yields by 14.89%, 10.97%, and 16.89%, respectively, surpassing 500 NmL CH₄/g VS_added. All co-digestion combinations exhibited synergistic effects (α > 1), enhancing methane production beyond individual substrate contributions. Lower S/I ratios improved methane yields and biodegradability, highlighting the role of inoculum availability. Co-digestion reduced the lag phase limitations of WAS and plant biomass, improving process efficiency. These findings demonstrate that co-digesting WAS with nutrient-rich co-substrates optimizes biogas production, supporting sustainable sludge management and renewable energy recovery in livestock wastewater treatment systems. Full article