Search Results (6,978)

Delamination in laminated composites originates from premature matrix cracking within the interlaminar region, ultimately leading to ply separation under indirect loading. Among the techniques proposed to mitigate this failure mode, through-thickness stitching has emerged as a localized reinforcement strategy capable of enhancing interlaminar performance without modifying the in-plane laminate architecture. However, previous studies report that stitching can either improve or degrade the mechanical properties of the composite, with stitch density identified as a critical variable. This work aims to keep the tensile strength of a stitched composite at levels comparable to its unstitched counterpart. The reinforcement was applied using an eight-strand polyethylene thread (0.28 mm in diameter) embedded in a low-viscosity epoxy infusion system (MAX 1618 A/B) combined with a 90° biaxial fiberglass woven fabric. The tensile behavior of laminates was examined for three longitudinal stitching configurations consisting of 2, 3, and 5 continuous stitch lines. Results show that increasing stitch count produces a progressive reduction in tensile strength, attributed to stress concentration around stitch sites and microstructural effects such as resin-rich zones and fiber waviness. Full article

Engine oil degradation critically influences the performance, efficiency, and longevity of internal combustion engines. Conventional mileage or time-based replacement schedules often result in premature oil changes or delayed servicing, both of which compromise engine health and increase costs. This review examines recent advances in real-time oil condition monitoring and evaluates the feasibility of a low-cost microcontroller-based system that integrates physical sensors with machine learning models for continuous on-board oil health assessment. Drawing on established techniques from industrial lubrication monitoring, we propose an experimental framework that leverages electrical engineering principles, including sensor interface, analog front-end design, signal acquisition, and embedded AI deployment to enable accurate, affordable, and scalable oil health diagnostics. The review highlights opportunities for innovation in embedded systems and electrical engineering design, positioning AI-driven monitoring as a practical solution for predictive automotive maintenance. Full article

Sex determination in honey bees (Apis mellifera) is controlled by the complementary sex determiner (csd) gene, which directs female- or male-specific splicing of the downstream feminizer (fem) transcript. Previous studies have reported contradictory data on the expression of fem transcripts in both sexes, but no rigorous quantitative analysis across developmental stages had been performed. Here, we optimized Real-Time PCR conditions to reliably detect and quantify both female-specific (fem^F) and male-specific (fem^M) transcripts, addressing challenges posed by AT-rich sequences, repeated regions, and cDNA instability. Using these methods, we analyzed transcript levels in eggs, larvae, and pupae of both sexes. Our results show that fem^F is highly specific for females, with approximately 100-fold higher expression in females than in males, whereas fem^M is less sex-specific, with only ~10-fold higher expression in males even at early developmental stages. Notably, fem^F transcripts are detectable in males, and fem^M expression increases in females during later pupal stages. Quantitative comparison indicates that fem^M expression in males is similar to fem^F expression in females, indicating that despite the presence of the premature stop codon in the male transcript, this transcript is not degraded through the mRNA surveillance mechanism. Our study provides a framework for evaluating fem transcript dynamics and has important implications for interpreting sex-determination mechanisms in honey bees. Full article

Background/Objectives: Congenital anomalies of the kidney and urinary tract (CAKUTs) represent the leading cause of pediatric chronic kidney disease, yet the molecular mechanisms underlying these malformations remain incompletely understood. While genetic studies have identified numerous CAKUT-associated genes, conventional knockout approaches often result in embryonic lethality or fail to reveal tissue-specific gene functions. This review aims to synthesize findings from conditional knockout mouse studies that have elucidated the spatiotemporal requirements of key signaling pathways during kidney development. Methods: We conducted a narrative synthesis of studies employing Cre-loxP conditional gene targeting in mouse models, identified through systematic searches of PubMed and cross-referencing of key primary research. Studies were selected based on their use of lineage-specific Cre drivers (Six2-Cre, Hoxb7-Cre, Foxd1-Cre) to investigate nephron progenitor maintenance, ureteric bud branching morphogenesis, and stromal–epithelial interactions. Results: Conditional knockout studies have redefined CAKUT pathogenesis as a disorder of dose-dependent signaling, temporal regulation, and inter-compartmental communication. WNT/β-catenin signaling operates in a biphasic, dose-dependent manner in nephron progenitors, with Six2-Cre-mediated β-catenin deletion causing premature progenitor depletion. BMP and FGF pathways demonstrate dose-dependent and context-specific functions in progenitor maintenance, while GDNF/RET signaling is essential for ureteric bud outgrowth and branching. Importantly, stromal-specific deletions have uncovered non-cell-autonomous mechanisms regulating nephron formation. Haploinsufficiency studies demonstrate that partial pathway disruption can reduce nephron endowment without overt CAKUT, predisposing to adult-onset hypertension and chronic kidney disease. Conclusions: Conditional gene targeting has mechanistically redefined CAKUT from a collection of structural malformations to a spectrum of disorders arising from quantitative perturbations in lineage-specific signaling networks. These findings establish that phenotypic severity is determined by the degree of pathway disruption, the developmental timing of insult, and the compartment affected, providing a framework for interpreting oligogenic interactions and variable penetrance in human CAKUTs. Full article

This population-based study aimed to analyze the annual incidence and case fatality trends, and the clinical-epidemiological profile of gastroschisis in the state of Paraná, Brazil, between 2013 and 2024. Specifically, temporal trends in annual incidence and mortality rates related to gastroschisis were examined. Maternal, gestational, and neonatal characteristics were analyzed. Data from the Live Birth Information System and the Mortality Information System were analyzed using polynomial regression modeling. During the study period, 1,798,727 live births were recorded, including 491 cases of gastroschisis and 179 related deaths. The mean incidence was 2.73 per 10,000 live births. A significant 39.5% decrease over the study period was observed (p < 0.001). The case fatality rate was 36.5%. The mothers of children with gastroschisis were: young mothers (<25 years old; 77%), with low education (87.7%) and no partner (59.1%). High frequencies of cesarean deliveries (84.3%), prematurity (57.3%), low birth weight (63.7%), and low Apgar scores were also observed. The profiles of the mothers and children at birth were unfavorable when compared to the population of live births. Gastroschisis incidence in Paraná declined significantly from 2013 to 2024. While the annual incidence showed a decreasing trend, mortality fluctuated. The persistently high case fatality rate underscores the need for public policies focused on prenatal care and specialized neonatal management. Full article

Background/Objectives: Severe oral mucositis is widely viewed as a transient toxicity of antineoplastic therapy. Whether its long-term consequences differ between cancers that directly damage the upper aerodigestive tract (cancers of the lip, oral cavity, pharynx [CLOP]) and systemic hematologic malignancies is unknown. The aim of this study was to compare lifetime risks of mortality, dysphagia, malnutrition, respiratory disease, and cardiovascular disease in propensity-score-matched survivors of CLOP cancer versus leukemia with and without a history of ulcerative oral mucositis. Methods: Population-based retrospective cohort study using the TriNetX US Collaborative Network (90 healthcare organizations, >110 million patients). We identified 80,526 adults with a personal history of CLOP cancer (ICD-10-CM Z85.81) and 43,684 with leukemia (Z85.6) from 2005 to 2024. Cohorts were stratified by presence/absence of severe oral mucositis (K12.31 or K12.33 at any time). Separate 1:1 propensity-score matching was performed within each cancer type on age, sex, race/ethnicity, hypertension, diabetes, BMI, ECOG status, and external causes of morbidity. Exposures included documented severe (ulcerative) oral mucositis. Main outcomes and measures were all-cause mortality and incident dysphagia, malnutrition, respiratory disease (J00–J99), influenza/pneumonia (J09–J18), and circulatory disease (I00–I99) after the index date. Results: After 1:1 matching, 4181 CLOP patients with mucositis were compared with 4181 without, and 2508 leukemia patients with mucositis were compared with 2508 without. In CLOP survivors, mucositis was associated with markedly higher lifetime mortality (adjusted HR 1.94, 95% CI 1.87–2.01), dysphagia (HR 3.42, 95% CI 3.28–3.57), malnutrition (HR 2.81, 95% CI 2.66–2.97), any respiratory disease (HR 1.68, 95% CI 1.63–1.73), and influenza/pneumonia (HR 1.79, 95% CI 1.72–1.86). In leukemia survivors, mucositis conferred only modest or null excess risk (mortality HR 1.12, 95% CI 1.05–1.19; dysphagia HR 1.18, 95% CI 1.07–1.30; malnutrition HR 1.24, 95% CI 1.12–1.37; any respiratory disease HR 1.09, 95% CI 1.03–1.15). Conclusions and Relevance: Severe oral mucositis is a powerful, durable prognostic determinant in cancers of the upper aerodigestive tract, where it identifies patients associated with elevated lifelong risk of swallowing dysfunction, aspiration-related lung disease, malnutrition, and premature death. The markedly attenuated effect in leukemia survivors suggests that direct high-dose radiation-induced structural damage to the pharynx and oral cavity—rather than systemic immunosuppression or chemotherapy intensity alone—is the dominant mechanism. Full article

Background/Objectives: Chronic alcohol use accelerates biological and cognitive aging, yet it remains unclear how cognitive aging progresses during abstinence in alcohol use disorder (AUD). It is also unknown to what extent this follows models such as accelerated aging or the age-related decline as proposed by the vulnerability hypothesis. This study examined age-related changes and cognitive recovery during abstinence in patients with AUD. Methods: A total of 197 clinically admitted patients, referred for detoxification and extensive neuropsychological examination, were included. Neuropsychological testing was administered in the second and sixth week of admission using well-normed instruments. Using both multi-assessment and cross-sectional data, relationships between age and normed cognitive outcome scores were examined. Results: After six weeks of abstinence, age-related deviations were observed for perceptual reasoning (PRI), verbal comprehension (VCI), and short-term memory (SMI) but not for ten other cognitive indices. During admission, age significantly influenced the change in belonging to a specific recovery category. Each additional year of age reduced the odds of showing no cognitive impairment by 5% and reduced the odds of cognitive recovery by approximately 4%, compared to non-improvers. Conclusions: Age-related influences appear limited to specific cognitive functions and do not follow a uniform or easily interpretable pattern. Perceptual reasoning seems negatively affected after age 60 for participants with six weeks of abstinence. Older participants showed a reduced likelihood of cognitive recovery and a reduced likelihood of having no cognitive problems at all. The findings do not support accelerated aging and are still too weak to be considered evidence for the vulnerability hypothesis. Implications for future research are discussed. Full article

Urban rail wheels endure prolonged exposure to frequent starts and stops, heavy cyclic loads, and complex track conditions, which often lead to premature failure modes such as wear, fatigue cracking, and corrosion in conventional wheel materials. These limitations restrict their ability to meet the evolving demands of modern rail systems for enhanced durability and performance. To address this, the present study uses laser cladding to deposit high-entropy alloy coatings with systematically varied aluminium content onto wheel substrates. The study compares phase composition, microstructure, and mechanical properties across the different coatings. Results show that increasing Al content transforms the coating microstructure from a single face-centred cubic (FCC) phase to a dual-phase structure of FCC and body-centred cubic (BCC) phases, accompanied by notable grain refinement. Among the variants, the CrMnFeCoNi(Al)₈ coating has the densest microstructure and the most favourable mechanical performance. It achieves a microhardness of 399.62 HV_0.5 in the as-clad state and 450 ± 5 HV_0.5 after heat treatment, representing an increase of approximately 12.6%. This coating also demonstrates improved corrosion resistance, with an open-circuit potential 0.07 V higher than the CL60 substrate. Multi-body dynamics simulations confirm that the clad wheels maintain excellent operational stability and safety under service conditions. Full article

Dendroctonus armandi is a major primary pest of Chinese white pine in the Qinling–Bashan forest region. By feeding on the phloem and vectoring symbiotic fungi that cause blue stain in the sapwood, it drives rapid decline and mortality of host trees. As a key wood-boring forest insect, its outbreaks are closely linked to adaptive strategies in energy metabolism. Adipokinetic hormone (AKH) is a highly conserved insect neuropeptide and plays a major role in regulating energy metabolism. This study aimed to determine how the AKH gene regulates energy use in D. armandi under different stress conditions. We cloned the DaAKH gene and its receptor gene, DaAKHR, from D. armandi. DaAKH and DaAKHR showed the highest expression in emerged adults and the lowest levels in pupae. In larvae and in adult males and females, DaAKH transcripts were predominantly expressed in the head, whereas DaAKHR was enriched in the fat body. Under starvation and cold stress, DaAKH and DaAKHR expression were significantly upregulated; under heat stress, expression first increased and then decreased. Across stress treatments, RNAi significantly downregulated DaAKH and DaAKHR expression in D. armandi. Under starvation, RNAi reduced mortality, lowered lipid metabolism, and led to lipid accumulation, thereby mitigating premature energy depletion and starvation-induced death. By contrast, under heat and cold stress, RNAi significantly increased mortality, significantly reduced triglyceride and glycogen consumption, and suppressed metabolism. These results indicate that DaAKH and DaAKHR regulate energy allocation under starvation stress and help maintain adaptive capacity under temperature stress in D. armandi. By tuning energy metabolism, DaAKH and DaAKHR help resist environmental stress and maintain reproduction and population size. This study advances understanding of the physiological responses and molecular mechanisms of D. armandi under stress conditions and provides a new avenue for metabolism-targeted control. Full article

In China, a majority of the proven crude oil reserves are found in clastic rock reservoirs, which typically exhibit low natural energy levels. Water injection has become the most widely adopted technique for maintaining reservoir pressure and enhancing oil recovery in such formations. However, conventional water injection strategies heavily rely on empirical knowledge, often failing to accurately characterize the dynamic inter-well connectivity between injection and production wells. This limitation hinders the effective management of fluid injection and production processes. To address this challenge, we propose an intelligent optimization method for water allocation in high-water cut, low-permeability reservoirs. Our approach employs a Bidirectional Long Short-Term Memory (BiLSTM) neural network to learn the complex patterns from historical injection data in a data-driven manner. Furthermore, we design a well distance and time joint attention mechanism, which is integrated after the dual BiLSTM layers to enhance the model’s ability to capture the critical dynamic relationships among wells. This mechanism decouples temporal pattern recognition and the spatial physical constraints, laying the foundation for interpretable injection strategy optimization. We name this architecture “AttBiLSTM”, which is designed for optimizing injection strategies for individual layers in separate-layer water injection wells (The layer refers to the basic geological unit or flow unit within a vertically heterogeneous reservoir that is delineated and requires independent water injection regulation). Using field data from the Xinjiang Oilfield, we validate the proposed method and compare its performance against traditional water injection schemes and mainstream data-driven models. The experimental results demonstrate that the AttBiLSTM model effectively establishes a nonlinear mapping between the injection volumes and oil production rates, showing strong performance in both production prediction and injection optimization. An independent numerical reservoir simulation verification confirms that the optimized scheme increases well group oil production by over 3.6%, with no premature water breakthrough risk in a 5-year development cycle. This study provides a novel and practical technical framework for efficiently developing low-porosity, low-permeability, and highly heterogeneous reservoirs. Full article

Background/Objectives: People experiencing homelessness (PEH) face greater morbidity, multimorbidity, and premature mortality than the general population. Medical data on this population in Gdańsk remain scarce. The aim of this study was to assess the prevalence, age distribution, comorbidity burden, and healthcare utilization of selected neurological and vascular diseases among homeless men in Gdańsk, Poland. Methods: A retrospective secondary analysis was performed using data from 551 men residing in shelters operated by the largest PEH support organization in Gdańsk. A random sample of 226 individuals (95% confidence level) was analyzed, selected by randomization in Microsoft Excel. Data were extracted from interviews, verified medical documentation, and staff records. Results: Mean age was 57.0 (SD 12.9) years (median 60). Among the studied sample, essential (primary) hypertension (20.4%), heart failure (10.2%), atrial fibrillation (8.9%), and chronic obstructive pulmonary disease (8.4%) were the most common conditions. Sequelae of cerebrovascular disease (ICD-10: I69) affected 8.9% of participants; this subgroup was older and had higher rates of disability certification and hospitalization than the overall sample. Epilepsy (12.0%) and polyneuropathy (4.0%) differed in age distribution, disability rates, and comorbidity burden, with the epilepsy subgroup displaying high substance-use prevalence. Overall, 44.0% of the sample had been hospitalized since 2019. Conclusions: Homeless men in Gdańsk present a high burden of neurological and vascular disease at comparatively young ages, along with substantial multimorbidity. These findings highlight structural inequalities in healthcare access and the need for integrated, equity-oriented health and social care interventions. Full article

Background/Objectives: The transition from the intensive care unit (ICU) to the hospital ward is a critical high-risk period for patients. Early ICU discharge reduces costs and frees up ICU resources but can lead to readmission or unexpected death if patients are discharged prematurely. Despite the availability of risk stratification tools such as the Stability and Workload Index for Transfer (SWIFT) score, predicting ICU readmission remains challenging and inconsistent. However, artificial intelligence (AI) and machine learning (ML) techniques have recently shown promise in improving clinical decision support systems, particularly in the ICU. This study aimed to identify the risk factors and assess the performance of AI models in predicting readmission or death within seven days of ICU discharge using the MIMIC-IV (between 2008 and 2019) and Kangwon National University Hospital (KNUH, between 1 January 2016 and 28 February 2023) databases. Methods: This retrospective cohort study utilized the MIMIC-IV database for model training and internal validation and the KNUH database for external validation. Various machine learning and deep learning models have been developed to predict ICU readmission or death within seven days of discharge. The performance of the primary model, GRU-D++, was compared to the SWIFT score. Statistical analysis focused on the area under the receiver operating characteristic curve (AUROC) data to evaluate model accuracy. Results: The GRU-D++ model outperformed the SWIFT score, achieving AUROC of 0.802 and 0.756 for internal and external validations, respectively. Both datasets demonstrated that the GRU-D++ model provided better predictive performance for ICU readmission or death within seven days than the traditional SWIFT score. Conclusions: Our findings suggest that the GRU-D++ deep learning model is a valuable tool for the early detection of patient deterioration after ICU discharge, potentially aiding the prevention of ICU readmission. This study highlights the potential of AI to improve clinical decision-making in intensive care settings. Full article

Surgical necrotising enterocolitis (NEC) continues to carry significant morbidity and mortality in preterm and very-low-birth-weight infants. This review presents up-to-date evidence to guide the shift from medical to surgical treatment and to improve management during and after surgery. Need for surgery is best anticipated through dynamic clinical assessment, supported by laboratory markers of systemic inflammation or ischemia and targeted imaging, while pneumoperitoneum remains the sole absolute indication for immediate intervention. In infants without perforation, the timing of surgery remains challenging: delayed surgery after clinical deterioration worsens long-term outcomes, whereas very early surgery often reflects severe disease leading to greater bowel loss, highlighting the need for carefully timed intervention after brief stabilisation. Laparotomy remains the cornerstone of surgical management, with peritoneal drainage serving as a temporising option for the most unstable infants and laparoscopy emerging as a feasible adjunct. Long-term complications, including strictures, short bowel syndrome, neurodevelopmental impairment, bronchopulmonary dysplasia and severe retinopathy of prematurity highlight the need for better predictive tools, enhanced imaging of bowel viability, and rigorous nutritional support, while long-term quality-of-life outcomes remain insufficiently studied. Full article

A hybrid metaheuristic methodology that combines the Red-billed Blue Magpie Optimization (RBMO) algorithm with the Aquila Optimizer (AO) is introduced in this work as the RBMOAO method. The novel algorithm addresses a critical shortcoming of the standard AO: its exploration-to-exploitation ratio across different optimization stages is inefficient, yielding premature convergence and low diversity within the population. This is achieved by using RBMO’s Group-Based Directional Perturbation (GDP) and its dynamic convergence factor (CF) as part of the methodology. The early stages of the optimization process are characterized by a grouping methodology to maintain population diversity through coordinated exploration across subgroups of varying sizes using GDP. Later iterations are characterized by a CF-guided updating process that increases the resolution of the search for the best areas, thereby improving convergence precision without sacrificing solution quality. Empirical testing of the proposed methodology using the CEC 2015 and CEC 2020 test sets demonstrated RBMOAO’s superior performance compared to other metaheuristics, outperforming other optimizers in 73.33% of CEC 2015 functions and 80% of CEC 2020 functions, with statistical significance in the increased precision and robustness of solutions across all problem types. Additionally, the RBMOAO methodology demonstrated outstanding performance in constrained engineering design problems. In addition to optimization, an RBMOAO-optimized ensemble architecture was implemented to predict cybersecurity intrusion threats, achieving an accuracy of 89.6%. Through the dynamic calibration of the base learner weights via metaheuristic search, the RBMOAO ensemble achieved the top ranking. These results illustrate the wide range of applications of the RBMOAO methodology and provide support for its deployment in the context of high-stakes predictive analytics. Full article