Search Results (41,862)

Objective: This study evaluates the efficacy of a novel approach to oral appliance therapy (“OAT”) for the treatment of obstructive sleep apnea (“OSA”). This novel approach utilizes a systemized, oximetry-informed, treatment protocol and a precision-custom oral appliance. Methods: Sixty consecutive patients diagnosed with OSA were treated at Sleep Better Austin (“SBA”) using a structured, multi-step protocol and a precision-custom oral appliance (ProSomnus EVO). Baseline and post-treatment apnea–hypopnea index (“AHI”) values were compared using a matched-pair design. The primary outcome was the percentage of patients achieving a residual AHI of <10 events/h. Secondary outcomes included severity classification improvement. Results: In total, 90% of patients achieved the primary endpoint, and 87% improved by at least one severity classification. The mean AHI improved by 63% from baseline with the precision-custom OAT in situ (p < 0.001). In the moderate-to-severe subgroup, AHI improved by 70%, with 100% of severe patients achieving a residual AHI of <20 and a ≥50% improvement, without patient preselection. No serious adverse events were reported, and all patients continued therapy at follow-up. Conclusions: Precision-custom OAT, when delivered through a standardized clinical protocol informed by oximetry, can be a highly effective and well-tolerated treatment for OSA. These findings support its broader adoption as a non-invasive alternative to continuous positive airway pressure (“CPAP”) and surgical interventions, particularly for patients seeking personalized, high-compliance solutions. Full article

This work aims towards the automatic detection of faults in industrial air-cooling equipment used in a production line for staple fibers and ultimately provides maintenance scheduling recommendations to ensure seamless operation. In this context, various deep learning models are tested to ultimately define the most effective one for the intended scope. In the examined system, four vibration and temperature sensors are used, each positioned radially on the motor body near the rolling bearing of the motor shaft—a typical setup in many industrial environments. Thus, by collecting and using data from the latter sources, this work exhaustively investigates the feasibility of accurately diagnosing faults in staple fiber cooling fans. The dataset is acquired and constructed under real production conditions, including variations in rotational speed, motor load, and three fault priorities, depending on the model detection accuracy, product specification, and maintenance requirements. Fault identification for training purposes involves analyzing and evaluating daily maintenance logs for this equipment. Experimental evaluation on real production data demonstrated that the proposed ResNet50-1D model achieved the highest overall classification accuracy of 97.77%, while effectively resolving the persistent misclassification of the faulty impeller observed in all the other models. Complementary evaluation confirmed its robustness, cross-machine generalization, and suitability for practical deployment, while the integration of predictions with maintenance logs enables a severity-based prioritization strategy that supports actionable maintenance planning.deep learning; fault classification; industrial air-cooling; industrial automation; maintenance scheduling; vibration analysis Full article

The paper presents some design principles of an inductive displacement transducer for measuring the displacement of rock specimens under high hydrostatic pressure. It consists of a single-layer, coreless solenoid mounted directly onto the specimen and connected to an LC oscillator located outside the pressure chamber, in which it serves as the inductive component. The specimen’s deformation changes the coil’s length and inductance, thereby altering the oscillator’s resonant frequency. Paired with a reference coil, the system achieves strain resolution of ~100 nm at pressures exceeding 400 MPa. Sensor design challenges include both electrical parameters (inductance and resistance of the sensor, capacitance of the resonant circuit) and mechanical parameters (number and diameter of coil turns, their positional stability, wire diameter). The basic requirement is to achieve stable oscillations (i.e., a high Q-factor of the resonant circuit) while maintaining maximum sensor sensitivity. Miniaturization of the sensor and minimizing the tensile force at its mounting points on the specimen are also essential. Improvement of certain sensor parameters often leads to the degradation of others; therefore, the design requires a compromise depending on the specific measurement conditions. This article presents the mathematical interdependencies among key sensor parameters, facilitating optimized sensor design. Full article

Generative artificial intelligence (GenAI) holds significant potential to enhance university students’ learning. However, over-reliance on it to complete academic tasks poses a risk to academic achievement by potentially encouraging cognitive outsourcing. Despite this growing concern and an expanding body of research on GenAI usage, the mechanisms through which GenAI dependency and perceived teacher caring affect their academic achievement and self-efficacy remain underexplored. Based on the theory of media system dependence, this study explores the mechanisms through which university students’ dependency on GenAI affects their academic outcomes, focusing on the mediating role of self-efficacy and moderating role of perceived teacher caring. A survey was conducted with 418 university students from Chinese public universities who had used GenAI for an extended period. The results revealed that GenAI dependency positively predicts false self-efficacy and negatively predicts academic achievement, exhibiting a significant Dunning–Kruger effect. Perceived teacher caring moderates the relationship between GenAI dependency and self-efficacy. High perceived teacher caring mitigates the Dunning–Kruger effect but has a weak moderating effect on academic achievement. These findings enhance the explanatory power of the media system dependency theory in educational contexts and reveal the pathways through which GenAI dependency and teacher caring affect learning processes and outcomes. This study expands the theoretical implications of teacher caring in the digital age and provides empirical evidence to aid higher education administrators in optimising AI governance and teachers in improving instructional interventions. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

Background: Type 2 diabetes (T2D) is associated with reduced cardiorespiratory fitness (CRF), a critical predictor of cardiovascular disease and all-cause mortality. CRF relies upon the coordinated action of multiple systems including the skeletal muscle where the mitochondria metabolize oxygen and substrates to sustain ATP production. Yet, previous studies have shown that impairments in muscle bioenergetics in T2D are not solely due to mitochondrial deficits. This finding indicates that factors outside the mitochondria, particularly within the local tissue microenvironment, may contribute to reduced CRF. One such factor is the extracellular matrix (ECM), which plays structural and regulatory roles in metabolic processes. Despite its potential regulatory role, the contribution of ECM remodeling to metabolic impairment in T2D remains poorly understood. We hypothesize that pathological remodeling of the skeletal muscle ECM in overweight individuals with and without T2D impairs bioenergetics and insulin sensitivity, and that exercise may help to ameliorate these effects. Methods: Participants with T2D (n = 21) and overweight controls (n = 24) completed a 10-day single-leg exercise training (SLET) intervention. Muscle samples obtained before and after the intervention were analyzed for ECM components, including collagen, elastin, hyaluronic acid, dystrophin, and proteoglycans, using second harmonic generation imaging and immunohistochemistry. Results: Positive correlations were observed with elastin content and both glucose infusion rate (p = 0.0010) and CRF (0.0363). The collagen area was elevated in participants with T2D at baseline (p = 0.0443) and showed a trend toward reduction following a 10-day SLET (p = 0.0867). Collagen mass remained unchanged, suggesting differences in density. Dystrophin levels were increased with SLET (p = 0.0256). Conclusions: These findings identify that structural proteins contribute to aerobic capacity and identify elastin as an ECM component linked to insulin sensitivity and CRF. Full article

The increasing adoption of electric vehicles (EVs) within intelligent transportation systems (ITSs) has elevated the importance of cybersecurity, especially with the rise in Vehicle-to-Everything (V2X) communications. Traditional intrusion detection systems (IDSs) struggle to address the evolving and complex nature of cyberattacks in such dynamic environments. To address these challenges, this study introduces a novel deep learning-based IDS designed specifically for EV communication networks. We present a hybrid model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) layers, and adaptive learning strategies. The model was trained and validated using the VeReMi dataset, which simulates a wide range of attack scenarios in V2X networks. Additionally, an ablation study was conducted to isolate the contribution of each of its modules. The model demonstrated strong performance with 98.73% accuracy, 97.88% precision, 98.91% sensitivity, and 98.55% specificity, as well as an F1-score of 98.39%, an MCC of 0.964, a false-positive rate of 1.45%, and a false-negative rate of 1.09%, with a detection latency of 28 ms and an AUC-ROC of 0.994. Specifically, this work fills a clear gap in the existing V2X intrusion detection literature—namely, the lack of scalable, adaptive, and low-latency IDS solutions for hardware-constrained EV platforms—by proposing a hybrid CNN–LSTM architecture coupled with an elastic weight consolidation (EWC)-based adaptive learning module that enables online updates without full retraining. The proposed model provides a real-time, adaptive, and high-precision IDS for EV networks, supporting safer and more resilient ITS infrastructures. Full article

This study evaluated the epidemiology and seasonal patterns of respiratory viruses in adults hospitalized with acute respiratory tract infections during two consecutive post-COVID-19 pandemic seasons. A retrospective study was conducted at the University Hospital “P. Giaccone”, Palermo, from September 2022 to September 2024. Multiplex molecular assays were used to detect the ten respiratory viruses most relevant from an epidemiological perspective in respiratory samples (n = 1110) of 1081 patients. A respiratory viral infection was identified in 29.6% of patients. The highest viral infection rate was observed in the 31–50 age group. Human rhinovirus/enterovirus (HRV/EV) was the most frequently detected (40%), followed by influenza A virus (IAV; 18.4%) and human coronaviruses (HuCoVs; 12.8%). Viral co-infections were identified in 10.9% of positive cases, with HRV/EV, adenovirus (ADV), and parainfluenza virus (PIV) being most frequently involved. Influenza and respiratory syncytial viruses (RSVs) showed a winter seasonality, while diverse circulation patterns were revealed for the other viruses. This study demonstrated a sustained circulation of respiratory viruses in adults hospitalized with severe respiratory symptoms, with HRV/EV accounting for most of them. Syndromic multiplex molecular testing, although limited to the detection of a small fraction of epidemiologically relevant known viruses, has proven to be a valuable tool, not only for diagnostic purposes but also for acquiring genotyping data and implementing epidemiological information from sentinel surveillance systems. Full article

GPS spoofing remains a critical threat in the use of autonomous vehicles. Machine-learning-based detection systems, particularly support vector machines (SVMs), demonstrate high accuracy in their defense against conventional spoofing attacks. However, their robustness against intelligent adversaries remains largely unexplored. In this work, we reveal a critical vulnerability in an SVM-based GPS spoofing detection model by analyzing its decision boundary. Exploiting this weakness, we introduce novel evasion strategies that craft adversarial GPS signals to evade the SVM detector: a data location shift attack and a similarity-based noise attack, along with their combination. Extensive simulations in the CARLA environment demonstrate that a modest positional shift reduces detection accuracy from $99.9\%$ to $20.4\%$, whereas similarity to genuine GPS noise-driven perturbations remain largely undetected, while gradually degrading performance. A critical threshold reveals a nonlinear cancellation effect between similarity and shift, underscoring a fundamental detectability–impact trade-off. To our knowledge, these findings represent the first demonstration of such an evasion attack against SVM-based GPS spoofing defenses, suggesting a need to improve the adversarial robustness of machine-learning-based spoofing detection in vehicular systems. Full article

The accelerated global urbanization process has positioned land use/land cover change modeling as a critical component of contemporary geographic science and urban planning research. Traditional approaches face substantial challenges when addressing urban system complexity, multiscale spatial interactions, and high-dimensional data associations, creating urgent demand for sophisticated analytical frameworks. This review comprehensively evaluates machine learning applications in land use prediction through systematic analysis of 74 publications spanning 2020–2024, establishing a taxonomic framework distinguishing traditional machine learning, deep learning, and hybrid methodologies. The review contributes a comprehensive methodological assessment identifying algorithmic evolution patterns and performance benchmarks across diverse geographic contexts. Traditional methods demonstrate sustained reliability, while deep learning architectures excel in complex pattern recognition. Most significantly, hybrid methodologies have emerged as the dominant paradigm through algorithmic complementarity, consistently outperforming single-algorithm implementations. However, contemporary applications face critical constraints including computational complexity, scalability limitations, and interpretability issues impeding practical adoption. This review advances the field by synthesizing fragmented knowledge into a coherent framework and identifying research trajectories toward integrated intelligent systems with explainable artificial intelligence. Full article

The objective of this study is to examine the impact of Chief Executive Officer (CEO) attributes on corporate performance in Jordan, a representative frontier market. The analysis focuses on four key CEO attributes, comprising two socio-demographic variables—age and educational—and two corporate governance-related ones—tenure and origin. Return on assets (ROA) and return on equity (ROE) are used as proxies for firm performance. Using a sample of 416 firm-year observations from companies listed on the Amman Stock Exchange (ASE) during 2015–2023, the study employs the system GMM methodology to estimate dynamic panel data models, addressing potential endogeneity and capturing the dynamic nature of firm performance. The results show that CEO age has a positive but insignificant effect, whereas CEO education and tenure significantly enhance firm performance. Conversely, CEO origin has a statistically negative impact on firm performance, reflecting the value of insider CEOs. The significant effects of CEO education, tenure, and origin—observed within the models that also incorporated firm- and country-level controls—reflect their incremental contribution to firm performance in frontier markets. Robustness checks, including controls for the COVID-19 pandemic and industry effects, confirm these findings. The study contributes to the literature by demonstrating the applicability of established theories—namely Upper Echelons, Stewardship, Resource Dependence, and Human Capital Theories—while identifying the CEO traits that drive success in frontier markets. It also offers practical guidance for shareholders, board directors, and policymakers in designing effective leadership and governance strategies. Full article

African swine fever (ASF) remains a major threat to swine production in Central Luzon, Philippines. This study assessed ASF detection and farm-level risk factors in Central Luzon using a risk-based surveillance framework. Pooled blood samples from five pigs per farm were collected in 277 farms across seven provinces and tested by real-time PCR. The analysis yielded an apparent farm-level prevalence of 26.7% (95% CI: 21.6–32.3), defined by one pooled 5-pig blood sample per farm. However, these values reflect risk-based surveillance outcomes rather than population-representative prevalence. Detection varied by province, with high rates in Bataan (80.5%) and Nueva Ecija (55.0%), moderate detection in Zambales (24.3%), lower detection in Pampanga (5.0%) and Tarlac (20.0%), and no positives in Aurora or Bulacan. Survey data were available for 201 farms. Firth-penalized logistic regression identified the absence of perimeter fencing as the only statistically significant predictor of ASFV detection. Veterinary oversight and consultancy showed protective but non-significant trends. These results highlight structural and professional biosecurity gaps, emphasizing the need for expanded veterinary outreach, fencing support, and training to mitigate ASF risk in smallholder-dominated production systems. Full article

(1) Background: Pigs can be another host of Shiga toxin-producing E. coli (STEC), suggesting that pork products could be a potential risk to public health. A USDA National Animal Health Monitoring System (NAHMS) study revealed that Shiga toxin genes were detected in more than half of samples nationwide but only about a quarter of samples from the state of Illinois. To characterize the presence of STEC in Illinois pigs better and to explore the discrepancy between Illinois and other swine-producing states, we increased the sampling size and collected samples in different regions of the state and in different months to detect Shiga toxin genes in Illinois finisher pigs and subtyped the Shiga toxin genes further to assess any potential risk of STEC originating from Illinois pigs to human health. (2) Methods: Fecal samples were collected from 471 Illinois finisher pigs at different locations from October 2021 to September 2022. DNA samples were extracted from individual fecal samples and PCR-tested for Shiga toxin genes (stx1, stx2) and then toxin subtypes (stx2a, stx2c, stx2d, and stx2e). (3) Results: The data showed that the stx2 gene was detected in 61% of the fecal samples (285/471), whereas stx1 was detected only in 0.4% of the samples (2/471). The data also indicated a lower prevalence of stx genes in the samples collected in certain cold months (36% in October and 19% in March) compared to that in those from warm months (56% to 100% from April to September). Stx2d, a subtype associated with severe human illness, was detected in 2% of the samples (10/471); in contrast, stx2e, which is expressed by E. coli strains causing diarrhea and edema disease in pigs, was the most detected (49%; 229/471). (4) Conclusions: The high prevalence of Shiga toxin genes in the fecal samples from Illinois finisher pigs suggests that Stx-positive E. coli strains circulate in Illinois pig farms. However, the highly detected stx2e-positive STEC (or enterotoxigenic E. coli, ETEC) strains are associated with diarrhea and edema disease in pigs, indicating the need for disease prevention or control for pigs but unlikely a safety concern for Illinois pork products or a major risk of human illnesses. Full article

Conducting a safety simulation assessment of gas release from the vent mast during the design stage holds significant importance for ship design and system operation safety on LNG-powered vessels. Based on a large-scale practical LNG-powered vessel, this paper employs the CFD method to carry out a safety assessment of the natural gas dispersion, and proposes an optimization design method to address the issue where the vent mast height of large-scale LNG-powered vessels fails to meet specifications. The influencing factors of gas dispersion are discussed. The simulation results indicate that the vent mast height, wind direction, and wind velocity significantly affect the gas dispersion behavior. A lower vent mast height results in a greater risk of flammable gas clouds accumulating on the deck surface. Hazards analysis of the 6 m vent mast condition with windless suggests that a cryogenic explosion hazard zone is formed on the deck centered around the mast position, with the maximum gas concentration reaching 30% and the minimum temperature below −55 °C. The gas cloud spreads along the wind direction, and the extension distance is positively correlated with wind speed. With the increase in wind velocity, the height and volume of flammable gas clouds decrease. When the wind speed is 15 m/s, the volume of the flammable gas cloud is less than half of that at 5 m/s and less than one-tenth of that at 0 m/s. Higher wind velocity can notably promote gas diffusion. Full article

Triploid breeding is a promising approach for developing seedless varieties, but the long juvenile phase of perennial fruit trees necessitates efficient early selection. In loquat (Eriobotrya japonica), a fruit crop with high demand for seedlessness, the relative contributions of hybridity, ploidy level, and parent-of-origin effects (POEs) to triploid seedling vigor remain elusive. To dissect these factors, we established a comprehensive experimental system comprising reciprocal diploid (2x), triploid (3x), and tetraploid (4x) hybrids from two genetically distinct cultivars. The ploidy, hybridity and genetic architecture of hybrid and parental groups were verified using flow cytometry, chromosome counting, newly developed InDel markers and genome-wide SNP analysis. Phenotypic evaluation of eight vigor-related traits revealed that plant height and soluble starch content were the most robust indicators of triploid heterosis in loquat. Notably, paternal-excess triploids [3x(p)] consistently outperformed all other groups. Quantitative analysis revealed POE as the main positive driver of triploid heterosis (+10.37% for plant height), far exceeding the negative impacts of hybridity (−12.75%) and ploidy level (−20.87%). These findings demonstrate that POE predominantly drives seedling vigor heterosis in triploid loquat. We propose a practical breeding strategy that combines prioritizing paternal-excess crosses with novel InDel markers for rapid verification of superior seedless progeny. Full article