Search Results (6,644)

Weld lines in injection-molded plastics often act as structural weak points that reduce mechanical performance. Enhancing weld line strength is therefore essential to improve product reliability and service life. This study aims to develop and validate an injection mold system capable of localized cavity temperature control to strengthen weld line regions. A specialized mold with an integrated cooling layer was designed to enable rapid thermal response during molding. The Taguchi method was applied to optimize three key parameters—part thickness, melt temperature, and injection pressure—to maximize weld line flexural strength. Experiments based on an L25 orthogonal array revealed that weld line stress varied significantly across parameter combinations, with a maximum of 109.23 MPa. A subsequent validation test conducted under the optimal conditions (250 °C melt temperature, 1.5 mm part thickness, and 16 MPa injection pressure) achieved an enhanced weld line stress of 121.88 MPa, confirming the reliability of the Taguchi-based optimization. Among the factors studied, part thickness had the greatest influence, followed by injection pressure, while melt temperature had the smallest effect. These results demonstrate that combining cavity temperature control with systematic parameter optimization provides an effective strategy to enhance weld line strength in high-performance plastic components. Full article

This study focuses on the design and optimization of a cooling layer system integrated into a thin-thickness mold insert to enhance heat transfer efficiency, control mold temperature, and improve the quality of composite products during injection molding. The Taguchi method with an L25 (5⁴) orthogonal array was employed to investigate four key parameters: insert thickness, cooling layer thickness, water flow rate, and coolant temperature. Among 25 experimental combinations, five representative cases were selected for detailed analysis. The results indicate that the optimal configuration (0.5 mm insert, 10 mm cooling layer, 3.5 L/min flow rate, and 80 °C coolant temperature) successfully maintained a high and stable mold temperature, with a cavity temperature difference of only 3.6 °C at steady state and a simulation–experiment deviation ranging from 2.4% to 7.2%. This condition not only improved melt flowability and surface quality but also reduced defects such as weld lines, warpage, and shrinkage. In parallel, displacement measurements on PA6 and glass fiber-reinforced PA6 (PA6 + GF) composites revealed that increasing the fiber content from 0% to 30% reduced output displacement by more than 19% compared to neat PA6, highlighting the reinforcing effect of glass fibers and the relationship between temperature distribution and mechanical displacement behavior. The findings confirm that integrating a cooling layer into a thin-thickness mold, combined with Taguchi-based optimization, provides an effective approach to enhance through-thickness heat transfer, reduce deformation, and ensure the overall quality of composite injection-molded products in industrial applications. Full article

Background: Despite repeated re-emergence of Sudan ebolavirus (SUDV), its long-term human toll remains under-characterised. We assessed multisystem clinical, biochemical, and psychosocial outcomes ~25 years after the 2000 Gulu outbreak. Methods: We conducted a cross-sectional evaluation of 45 survivors of laboratory-confirmed SUDV and 30 age- and gender-matched community controls from the same region. Symptoms were assessed as current at the study visit using a structured checklist; for each symptom present, we recorded severity and duration from onset to the visit date. Standardised clinical examinations, haematological and biochemical assessments, anxiety and depression screening, and structured interviews on social support and stigma were performed. Group comparisons were assessed with Wilcoxon rank-sum and χ²/Fisher’s exact tests; correlations were assessed with Spearman’s ρ. Findings: Core physiological indices (vital signs, BMI, blood pressure, and body temperature) and mental health were comparable between survivors and controls. Nevertheless, survivors reported ongoing symptoms, including joint pain and visual impairment each in 36% (16/45), fatigue in 18% (8/45), and neurological symptoms in 13% (6/45). Subclinical laboratory deviations centred on hepatic and platelet biology: elevated total bilirubin occurred in 14% of survivors versus 6.7% of controls; thrombocytopenia or platelet morphological abnormalities in 12% versus 3.3%; haemoglobin abnormalities in 6% versus 0%. Among survivors, albumin and mean platelet volume declined with age (both p ≤ 0.03). Psychological morbidity was low (normal anxiety 82% (37/45; and normal depression 80% (36/45). Yet a social paradox emerged, despite universal post-outbreak support, 98% (44/45) described enduring stigma. To minimise differential recall bias, symptom inventories were not collected from controls; consequently, between-group comparisons for symptom prevalence were not performed, and symptom inferences are restricted to survivors and framed descriptively. Interpretation: A quarter-century after infection, SUDV survivors show preserved systemic physiology but carry chronic musculoskeletal, sensory, and neurological sequelae, alongside a discrete subclinical profile implicating hepatic function and platelet biology. Psychological resilience coexists with near-universal, persistent stigma, indicating that material support did not achieve full psychosocial reintegration. Given the lack of virological and deep immune profiling, proposed pathogenetic mechanisms, such as antigen persistence or immune-mediated injury, remain speculative and hypotheses-generating only. These findings argue for survivor-centred long-term care, embedded with epidemic preparedness frameworks that integrate musculoskeletal rehabilitation, ophthalmic and neurological services with comprehensive mental health care, and sustained anti-stigma community engagement. This dissociation, including short-lived support alongside enduring stigma, indicates that humanitarian relief alone does not secure durable psychosocial reintegration and should be complemented by long-horizon, survivor-centred services and community engagement. Funding: This study was supported by the Coalition for Epidemic Preparedness Innovations (CEPI) under the Universal Protocol for Standardising Assays and Advancing Vaccine Immunogenicity Assessments for Emerging and Re-emerging Viral Threats, implemented through the Uganda Virus Research Institute (UVRI) as part of CEPI’s Centralised Laboratory Network (CLN). Full article

Long-term water flooding development has exacerbated reservoir heterogeneity, and traditional polymer gels are unable to simultaneously meet the requirements of high injectability and strong plugging strength. If the viscosity of the polymer is high, its injectability will be poor; on the contrary the viscosity is low, the plugging strength will be poor, which severely restricts the oil recovery effect. This study synthesized an NBAP through free radical polymerization followed by a substitution reaction, and a plugging system (NBAP-B1) was subsequently formed by combining the polymer with a Cr³⁺ crosslinking agent. Rheological experiments demonstrated that the system exhibited significant shear thinning behavior, as well as excellent temperature and salt resistance. Gelation experiments indicated that the NBAP-B1 system featured controllable gelation time (20~150 h) and high gelation strength (J grade), along with excellent resistance to both high temperature and high salinity. Microscopic analysis revealed that the gel formed by NBAP-B1 possessed a dense and uniform three-dimensional network structure. Injection and plugging experiments demonstrated that NBAP-B1 exhibited optimal injectability and outstanding plugging performance. Additionally, profile control and displacement tests revealed a 18.37% enhancement in oil recovery efficiency by water flooding after the application of NBAP-B1 for conformance control. Collectively, these results demonstrate that the NBAP exhibits significantly superior performance compared to single component systems. It combines excellent injectability with high strength plugging capability, offering an effective approach for enhancing oil recovery in low permeability reservoirs. Full article

This study presents a molecular dynamics (MD) investigation of water evaporation in copper nanochannels, with a focus on accurately modeling copper–water interactions through forcefield calibration. The TIP4P/2005 water model was coupled with the Modified Embedded Atom Method (MEAM) for copper, and the oxygen–copper Lennard–Jones (LJ) parameters were systematically tuned to match experimentally reported water contact angles (WCAs) on Cu (111) surfaces. Contact angles were extracted from simulation trajectories using a robust five-step protocol involving 2D kernel density estimation, adaptive thresholding, circle fitting, and mean squared error (MSE) validation. The optimized forcefield demonstrated strong agreement with experimental WCA values (50.2°–82.3°), enabling predictive control of wetting behavior by varying ε in the range 0.20–0.28 kcal/mol. Using this validated parameterization, we explored nanoscale evaporation in copper channels under varying thermal loads (300–600 K). The results reveal a clear temperature-dependent transition from interfacial-layer evaporation to bulk-phase vaporization, with evaporation onset and rate governed by the interplay between copper–water adhesion and thermal disruption of hydrogen bonding. These findings provide atomistically resolved insights into wetting and evaporation in metallic nanochannels, offering a calibrated framework for simulating phase-change heat transfer in advanced thermal management systems. Full article

Heat stress typically suppresses systemic immunity in fish; however, its effects on the brain—an organ traditionally regarded as immune-privileged—remain unclear. In this study, we performed histopathological examination and RNA-seq analysis on the brains of juvenile largemouth bass (Micropterus salmoides) exposed to control (28 °C) and elevated (36.5 °C) water temperatures for 8 weeks. Histological analysis revealed distinct cytoarchitectural and pathological changes in specific brain regions. RNA-seq analysis identified a total of 1240 differentially expressed genes, with 22 heat shock protein genes notably showing significant up-regulation. The immune system-associated genes emerged as the most prominently affected category. Gene set enrichment analysis (GSEA) based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations revealed that up-regulated genes were enriched in immunity-related pathways, including the NOD-like receptor (NLR) signaling pathway, Toll-like receptor (TLR) signaling pathway, and cytosolic DNA-sensing pathway. Additionally, the levels of apoptosis and necroptosis were moderately increased. GSEA based on Gene Ontology (GO) terms indicated that down-regulated genes were primarily associated with cell division. Protein–protein interaction (PPI) and clustering analysis identified 41 core genes in the top three clusters, encompassing those related to nuclear chromosome segregation, ribosome biogenesis, and stress response. The inhibition of genes involved in nuclear chromosome segregation may disrupt cellular homeostasis by significantly impairing microtubule dynamics. In contrast, genes associated with ribosome biogenesis and stress response were up-regulated, which could counteract the adverse effects caused by long-term heat stress. We propose that brain-specific immune activation, particularly via the NLR and TLR signaling pathways, acts as a compensatory strategy to counterbalance heat-induced cell death, thereby revealing a novel neuro-immune adaptation axis. Full article

This paper presents the experimental implementation and validation of the two-chamber method presented in Part I for the high-precision determination of the temperature coefficient of resistance (TCR) of current shunts. The two-chamber approach enables improved thermal isolation and independent temperature control of the reference and test shunts, which significantly reduces the measurement uncertainty. In this part, the complete experimental setup is described, including the thermoelectric temperature control, the current generation and the data acquisition system with synchronized high-resolution digital multimeters (DMMs). The experimental measurements were carried out for different resistance ratios ranging from 0.1 to 10. The results confirm the theoretical predictions and the uncertainty analysis from Part I. The influences of the stability of the current source, the temperature uniformity and the synchronization accuracy on the measurement results are evaluated. The two-chamber method shows high repeatability, ease of use and suitability for laboratory and interlaboratory tests, and thus represents a robust alternative to classical TCR determination methods. Full article

This work presents a hybrid model that integrates a mechanistic multicomponent transport scheme in hollow-fiber membranes with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The physical model incorporates pressure drops on the feed and permeate sides (Hagen–Poiseuille), non-ideal gas behavior (Peng–Robinson equation of state), and temperature-dependent viscosity; species permeances are treated as constant for model validation. After validation, a post-validation parametric exploration of permeance variability is carried out by perturbing the methane (CH₄) permeance by one decade up and down. From an initial set of 18 variables, 4 key parameters were selected through rigorous statistical analysis (Pearson correlation, variance inflation factor (VIF), and mean absolute error (MAE)); likewise, other physical criteria have been considered: permeance, retentate volume, retentate pressure, and retentate viscosity. Trained with 70% of the simulated data and validated with the remaining 30%, the model achieves a coefficient of determination (R²) close to 0.999 and a root mean square error (RMSE) below 8 × 10⁻⁸ m³/h in predicting the methane volume in the retentate, effectively responding to both steady and dynamic fluctuations. The combination of first-principles modeling and adaptive learning captures both steady-state and dynamic behavior, positioning the approach as a viable tool for real-time analysis and supervisory control in petrochemical membrane operations. Full article

For the fault issues in the workshop environmental control system sensor network, a fault diagnosis and recovery method based on medium-to-long-term predictions is proposed. Firstly, a temperature observer based on the Informer model is established. Then, the predicted data temporarily replaces the missing real data, and the model predicts the state of the sensor system within the step size. Secondly, the predicted data is combined with the measured temperature series, and residuals are utilized for real-time detection of sensor faults. Finally, the predicted data at the time of the fault replaces the real data, enabling the recovery of fault data; experiments are conducted to verify the effectiveness of the proposed method. The results indicate that when the prediction horizon is 1, 5, 10, 20, and 50, the average fault diagnosis rates under four fault levels are 94.40%, 95.28%, 94.79%, 92.52%, and 93.35%, respectively. The average coefficients of determination for data recovery are 0.999, 0.997, 0.995, 0.985, and 0.915, respectively. This achieves medium-to-long-term predictions in the field of sensor fault diagnosis. Full article

A mathematical model was established to describe the sublimation and diffusion of water molecules and their adsorption onto cold traps. This model was used to analyze the combined influence mechanisms of sublimation temperature and ambient pressure on the vapor deposition process of water ice. Tunable Diode Laser Absorption Spectroscopy (TDLAS) was employed to provide real-time feedback on water vapor concentration within the experimental apparatus. Based on this feedback, the sublimation temperature was dynamically adjusted to maintain the concentration dynamically stabilized around the target value. A dedicated apparatus for generating controlled water vapor flow fields and detecting concentration was constructed. The accuracy of both the mathematical model and Finite Element Analysis (FEA) simulations was verified through comparative experiments. Laser triangulation was utilized as a method to detect the thickness of the adsorbed ice film on the sample surface. Leveraging this technique, a water vapor deposition and adsorption verification system was developed. This system was used to test the differences in water adsorption performance across various materials and to measure the correlation between the thickness of the adsorbed/deposited ice film on the samples and both deposition time and sublimation temperature. Full article

Phytoindication represents a long-established ecological approach; however, its conceptual basis remains contested, particularly concerning whether it is merely a surrogate for measuring environmental factors or a distinct method for assessing biotic system responses. In this study, we analysed vegetation communities of the sandy terrace in the Dnipro-Oril Nature Reserve (Ukraine) using ecological indicator values, naturalness, and hemeroby indices. The Dnipro-Oril Nature Reserve provides an ideal setting for this study, as it integrates strong natural gradients of soil moisture, nutrient availability, and topography with pronounced anthropogenic influences from the surrounding industrial landscape. This allows the assessment of both natural and human-driven components of ecological variability within a single system. A dataset of 1079 relevés was collected and classified into 24 associations. Multivariate analyses were applied to reveal different aspects of vegetation–environment relationships: MANOVA was used to assess whether plant associations differed significantly in their ecological indicator profiles, CCA to identify the main gradients of species composition constrained by environmental factors, and partial CCA to isolate the specific patterns of vegetation response attributable to individual predictors while controlling for covariates. We found that the indicator values were not independent but strongly intercorrelated, reflecting integrated biotic responses rather than methodological artefacts. This was confirmed by consistent ecological interpretation of the principal component structure and the concordance between ordination patterns and vegetation classification results. Two primary gradients were identified: a natural gradient, which combines soil moisture and nutrient availability with decreasing light, temperature, continentality, and soil pH; and an anthropogenic gradient, represented by the hemeroby–naturalness axis. The interplay of these gradients offers a comprehensive explanation for vegetation structure across various spatial scales, with natural factors shaping community types and anthropogenic influences exerting broader, less specific effects due to their diffuse impact across multiple plant associations. Our findings reveal a novel conceptual perspective, supporting the view that phytoindication is a unique ecological tool for assessing the integrated response of plant communities to environmental drivers, including both natural and anthropogenic gradients, rather than a simplified or less precise substitute for instrumental measurements. Nevertheless, the use of phytoindication does not eliminate the need for instrumental measurements in situations requiring precise quantification of specific physical or chemical environmental parameters. The correlated structure of indicator values revealed in this study demonstrates that phytoindication patterns are specific to each landscape. Therefore, comparative assessments across regions or time periods should be based on the correlation patterns of indicator values rather than their absolute scores. Full article

In the heating, ventilation, and air conditioning (HVAC) systems of mushroom growing control rooms, traditional rule-based control methods are commonly adopted. However, these methods are characterized by response delays, leading to underutilization of energy-saving potential and energy costs that constitute a disproportionately high share of overall production costs. Therefore, minimizing the running time of the air conditioning system is crucial while maintaining the optimal growing environment for mushrooms. To address the aforementioned issues, this paper proposed a sensor optimization method based on the combination of principal component analysis (PCA) and information entropy. Furthermore, model predictive control (MPC) was implemented using a gated recurrent unit (GRU) neural network with an attention mechanism (GRU-Attention) as the prediction model to optimize the air conditioning system. First, a method combining PCA and information entropy was proposed to select the three most representative sensors from the 16 sensors in the mushroom room, thus eliminating redundant information and correlations. Then, a temperature prediction model based on GRU-Attention was adopted, with its hyperparameters optimized using the Optuna framework. Finally, an improved crayfish optimization algorithm (ICOA) was proposed as an optimizer for MPC. Its objective was to solve the control sequence with high accuracy and low energy consumption. The average energy consumption was reduced by approximately 11.2%, achieving a more stable temperature control effect. Full article

A broad-temperature-range wavelength tracking system employing a thermistor monitoring circuit and a tunable optical filter is proposed and experimentally demonstrated. In this scheme, a thermistor monitoring circuit is utilized to acquire the real-time resistance values of a distributed feedback laser diode (DFB-LD). When the mapping relationship curve among thermistor resistance, temperature, and center wavelength of the DFB-LD is established, the drive voltage of the narrowband tunable optical filter is dynamically adjusted to regulate its filter window. Therefore, wavelength tracking is achieved by matching the filter window and the center wavelength of the DFB-LD. The experimental results show that the proposed system can achieve adaptive wavelength tracking within the operation band of 1539.4 nm to 1548.6 nm across a temperature range from −40 °C to 60 °C. The wavelength detection resolution and the minimum step of wavelength control are better than 0.79 pm and 0.1 nm, respectively. By exploiting the conversion characteristics between the thermistor and the center wavelength of the DFB-LD, this approach transforms laser wavelength detection into a low-cost, real-time electrical measurement, significantly enhancing transmission stability and reliability of laser sources in complex thermal environments. Full article

Biodegradable zinc-based alloys have recently emerged as promising candidates for temporary biomedical implants due to their favorable biocompatibility, appropriate degradation rate, and relatively simple processing. In this study, the Zn-0.1Mg alloy was investigated after being processed by means of a two-step equal-channel angular pressing (ECAP) route, consisting of the first pass at 150 °C followed by a second pass at room temperature. The mechanical properties were evaluated using uniaxial tensile tests at different strain rates, while the microstructure and phase composition were analyzed using synchrotron hard X-ray diffraction and transmission electron microscopy (TEM). The processed alloy exhibited a remarkable enhancement in both strength and ductility compared to the annealed state. At the lowest applied strain rate, a fracture elongation of up to 240% was achieved at room temperature, representing a unique manifestation of superplasticity under ambient conditions. Diffraction analysis confirmed the stability of the supersaturated Zn matrix with minor Mg₂Zn₁₁ intermetallic phase. TEM observations revealed an ultrafine-grained microstructure and activation of non-basal slip systems, which enabled efficient plastic flow. These findings demonstrate that controlled severe plastic deformation provides an effective pathway for tailoring Zn-Mg alloys, opening opportunities for their use in the next generation of bioresorbable low-to-moderate load orthopedic fixation devices, e.g., plates, screws, suture anchors and craniofacial miniplates. Full article

The generation of high-quality test cases remains challenging due to combinatorial explosion and difficulty balancing exploration-exploitation in complex parameter spaces. This paper presents a novel Hybrid Artificial Bee Colony (ABC) algorithm that uniquely combines ABC optimization with Simulated Annealing temperature control and adaptive scout mechanisms for automated test case generation. The approach employs a four-tier categorical fitness function discriminating between boundary-valid, valid, boundary-invalid, and invalid values, with first-occurrence bonuses ensuring systematic exploration. Through comprehensive empirical validation involving 970 test suite generations across 97 parameter configurations, the hybrid algorithm demonstrates 68.3% improvement in fitness scores over pairwise testing (975.9 ± 10.6 vs. 580.0 ± 0.0, p < 0.001, d = 42.61). Statistical analysis identified three critical parameters with large effect sizes: MutationRate (d = 106.61), FinalPopulationSelectionRatio (d = 42.61), and TotalGenerations (d = 19.81). The value discrimination system proved essential, uniform weight configurations degraded performance by 7.25% (p < 0.001), while all discriminating configurations achieved statistically equivalent results, validating the architectural design over specific weight calibration. Full article