Search Results (19,184)

A reconfigurable, flexible, and modular fixture system is proposed for the milling of irregular thin-walled parts, which are commonly encountered in aerospace applications and require high machining precision. Conventional universal fixtures often fail to provide adequate support for such components, particularly in terms of clamping stress, operational efficiency, and vibration control, while dedicated fixtures tend to be costly. The developed design, guided by the Theory of Inventive Problem Solving (TRIZ), incorporates six independently adjustable jaws. These jaws can be reconfigured according to the part geometry, enabling flexible adaptation to different workpiece shapes. Through a pre-processing step applied to the jaw surfaces, the fixture achieves area-based contact with the part, replacing the line-contact mode typical of conventional setups. This improves clamping stability, reduces localized stress, and mitigates vibration during machining, thereby contributing to improved accuracy. In addition, once the jaw positions are determined based on a trial cut of the initial workpiece, they remain fixed for subsequent parts, streamlining the setup process. Machining trials showed that surface-contact clamping reduced roundness tolerance from 0.0523 mm to 0.0287 mm, flatness from 0.0106 mm to 0.0036 mm, and parallelism from 0.0190 mm to 0.0105 mm. The resulting fixture offers a combination of structural simplicity, adaptability, cost-effectiveness, and machining reliability, making it suitable for thin-walled components in small-batch or varied production contexts. Full article

In multi-mission air operations, area-patrol missions and long-range missions typically share a limited tanker fleet while imposing different constraint structures: patrol refueling is bounded by hard time windows, whereas long-range missions are governed by restricted-zone avoidance, detour tolerances, and multi-segment fuel verification. Conventional scenario-wise independent planning splits resources and wastes cross-region ferry mileage. This paper adapts the established paradigms of the location–routing problem (LRP) and the vehicle routing problem with time windows (VRPTW) to this joint refueling scenario: a joint planning model prioritizes the number of tanker sorties over total system flight distance, and a decoder-coupled adaptive large neighborhood search (ALNS) integrates airspace selection, task assignment, tanker routing, and dual-timeline rendezvous decoding, with all mission hard constraints embedded in a deterministic, reproducible evaluator that adjudicates feasibility at every search iteration. Experiments at three scales (17, 42, and 100 tasks) show 100% mission coverage and 100% patrol time-window satisfaction: relative to scenario-wise independent planning, tanker sorties decrease by 16.2–19.7% and tanker flight distance by 14.6–15.5% (significant after Bonferroni correction on 90 paired replicates per scale); against genetic algorithm (GA) and ant colony optimization (ACO) baselines—and against a route-encoding GA under an equal solution-space representation—the method is superior in solution quality and runtime ($p<0.001$), and the separation persists when the baselines receive a 25-fold evaluation budget. Monte Carlo simulations characterize how plan feasibility degrades under execution-time disturbances. Within the studied instance families, the framework yields executable joint refueling plans within operational runtimes. Full article

Spray disinfection is a necessary measure to reduce virus infection in modern large-scale pig farms. At present, spray disinfection in pig barns in China mainly relies on manual backpack-type, semi-automated and fixed equipment, which have associated problems such as high labor intensity, short spraying range, disinfectant waste and unstable distribution of droplet deposition. To solve these problems, an embedded conical air-assisted spray device for disinfection was designed in this paper. The nozzle is centrally embedded in the conical air duct outlet to form a coaxial gas−liquid-coupled atomization structure. High-speed airflow from the axial fan extends the spraying range. Using the method of computational fluid mechanics (CFD), simulation models of the conical duct and the cylindrical duct are established. The results show that when the fan’s rotation rate is the same, the outlet airflow speed of the former is 39.57% higher than that of the latter, and the area-weighted average speed uniformity index rises by 8.33%. Tests on device obstacle avoidance and spray performance were carried out. The results show that the overall success rate for obstacle avoidance was 93.3%. Under enclosed pig-barn conditions, the average spraying range of the device was 5.45 m, which was 64.16% higher than that of the spraying operation without airflow assistance. When the device sprayed under the three-zone comprehensive optimal parameters, the average coefficient of variation of the lateral droplet deposition mass was 10.23%, demonstrating that the overall spray deposition coverage is relatively uniform. The research carried out can provide a theoretical and engineering reference for the design and parameter optimization of similar sprayers for disinfection in large-scale pig farms. Full article

Agrotourism is increasingly promoted in Albania as a pathway for sustainable rural development, linking agricultural traditions with tourism growth. However, waste management remains one of the sector’s most pressing challenges, with rural areas often lacking reliable collection infrastructure and institutional support. This study examines how agrotourism businesses in Albania manage waste, what challenges they face, what factors shape these challenges, and how national legal, institutional and policy frameworks influence outcomes. A qualitative research design was applied, combining semi-structured interviews with seven agrotourism operators and four experts, field observations and documentary analysis of national strategies and EU directives. The findings reveal that businesses adopt diverse minimization, reuse and composting practices, yet these emerge largely out of necessity due to infrastructural gaps. Persistent challenges include irregular municipal services, limited staff capacity, visitor behaviour and seasonal fluctuations. To the best of the authors’ knowledge, this is among the first studies to examine waste management practices specifically within Albanian agrotourism. It shows that operators already act as active problem-solvers, but stronger institutional engagement is needed to consolidate local practices and advance Albania’s transition toward sustainable tourism. Full article

Mining generates various alterations to the environment, affecting flora, fauna, soil, and air quality. To contribute to solving this problem, this study proposes a methodology to identify the best algorithm and data combination for measuring land cover (LC) changes induced by open-pit mining in Mexico. The methodology uses remote sensing (RS) techniques with multi-temporal Landsat 5 and 8 satellite imagery and supervised LC classification with remote sensing and machine learning (ML) algorithms. The results showed that the spectral angle mapping (SAM) algorithm and the combination of bands 6, 5, and 4 yielded the best results, with an accuracy of 85.16% and a Kappa coefficient of 0.79. Land cover (LC) change measurements revealed an increase in water body surface area of 556.83 ha, mining cover of 1729.35 ha, infrastructure of 2.61 ha, and bare soil of 1488.15 ha, while also showing a loss of soil of 2372.49 ha, scrubland of 1444.59 ha, and vegetation of 9.45 ha. The use of supervised classification of multi-temporal satellite imagery allowed for the measurement of land cover alterations. These alterations highlight the need for sustainable management strategies, environmental restoration, and the importance of continued monitoring for informed decision-making. It is recommended to explore variations in classification categories, band combinations, spectral indices, and techniques such as deep learning to improve the accuracy of LC classification. Full article

Constructing and optimizing fishways is essential for ecosystem conservation. In conventional weir–orifice combined fishways, strong jets form local high-velocity zones and hinder continuous fish migration. To solve this problem, cylinder regulating structures are arranged inside fishway pool chambers. Five schemes with various cylinder types and layouts are proposed for hydraulic comparison. Physical experiments and RNG k–ε simulations are adopted to systematically explore how these structures adjust weir–orifice flow distribution and reconstruct pool flow fields, using indicators including weir overflow ratio, velocity distribution, turbulent kinetic energy, dissipation rate and vortex characteristics. The results reveal that cylinder structures block bottom-orifice jets, strengthen lateral flow diffusion and accelerate momentum dissipation, converting jet-dominated flow into a dispersed field with moderate velocities. When the layout changes from a single cylinder to an array, the rising weir overflow ratio mitigates bottom jets and cuts the maximum velocity by around 24.1%. The three-semi-cylinder array achieves lower peak turbulent kinetic energy and smaller vortex areas, effectively restraining turbulence and vortex evolution. This scheme performs well under the maximum discharge of 1.4Q. These findings provide a potential hydraulic regulation strategy for fishway retrofitting and optimization. Full article

Reliable wireless connectivity in underground public transportation can vary with train position because rolling stock modifies propagation paths and local interference conditions. This study proposes a transport-state-aware radio-planning framework for a three-node distributed antenna system (DAS) in a reference underground metro station. A three-dimensional 3.5 GHz reference station model, three operating states (empty station, train at platform, and train entering), a discrete set of 69 candidate antenna positions, and multi-objective placement/power optimization are combined to evaluate received power, SNIR, joint service coverage, and serving-area balance. The optimized configuration increased worst-state joint service coverage from 66.12% to 68.26%, improved worst-state P5 received power from −71.92 to −71.49 dBm, and reduced aggregate transmit power from 753.57 to 682.49 mW. The mean serving-area coefficient of variation decreased from 0.202 to 0.114. Threshold analysis showed that the optimized layout was advantageous at moderate and high SNIR requirements but not at a relaxed 5 dB criterion. The results support treating underground DAS deployment as a transport-state-aware infrastructure-planning problem rather than a static geometric-spacing problem. Full article

A collection of interrelated social variables determines crime deprivation at the neighborhood level in England. In penalized regression, strong intercorrelations pose a fundamental problem for variable selection. Previous research has used Elastic-Net (ENET) or the Least Absolute Shrinkage and Selection Operator (LASSO) to analyze individual waves of the English Indices of Deprivation (IoD) at the Lower Super Output Area (LSOA) level. This leaves questions about what the recently released IoD 2025 reveals about the crime–deprivation relationship, whether variable selection is stable across IoD waves, and whether the Correlation-Adjusted Elastic-Net (CAEN), which embeds actual pairwise Pearson correlations into the penalty matrix, achieves greater sparsity than LASSO and ENET. Using a single consistent Python (3.14.2) implementation that removes cross-software confounds observed in previous CAEN1 assessments, this work applies LASSO, ENET, and CAEN1 to all four publicly available releases of the IoD: 2010 ($n$ = 32,482), 2015 ($n = \mathrm{32,844}$), 2019 ($n = \mathrm{32,844}$), and 2025 ($n = \mathrm{33,755}$). In the CAEN penalty, the diagonal elements guarantee positive semi-definiteness and global convergence of coordinate descent. To ensure an equitable comparison with the classic ENET, the CAEN penalty rescales the penalty factor to correct for double shrinkage. In three out of four waves, CAEN1 outperforms LASSO in terms of sparsity over 50 stratified 70/30 splits using 10-fold cross-validation (CV). Building a six-predictor model that neither LASSO nor ENET could, the CAEN1 set both Income and Employment to zero in 2015. For the mean square error, the differences in prediction accuracy are negligible in magnitude (ΔMSE < 0.002). IDACI, Health and Disability, and Living Environment are the most consistently prominent determinants of neighborhood crime deprivation, according to a 15-year longitudinal study. A corrected resampled $t$-test confirms that the methods differ little inout-of-sample MSE in three of the four waves, with only a negligible CAEN1 excess reaching significance in 2010. A simulation further shows that in higher-dimensional, strongly collinear, and low signal-to-noise settings, CAEN2 coupling recovers correlated predictor groups that LASSO fragments. Full article

The Long-Range Wide-Area Network (LoRaWAN) is one of the leading low-power wide-area network specifications owing to its capabilities for long-range communication and energy savings. For large-scale sensing applications by a large number of LoRa nodes, it is important to improve communication performance and energy saving. However, redundant sensing and transmissions consume node energy, while simultaneous transmissions, particularly from hidden nodes, cause packet collisions. Centralized optimization of these problems requires the collection of network-wide information and may impose substantial communication overhead due to its narrow communication bandwidth. In this paper, we propose a distributed method for jointly controlling sensing coverage, node energy consumption, and transmission timing using locally exchanged information. Our main idea is to learn from the swarm intelligence of organisms that perform efficient reproductive behavior. The proposed method extends a previously developed mathematical model that reproduced the chorus and satellite behavior observed in three Japanese tree frogs. Whereas the original model describes the satellite behavior of a frog relative to a nearby caller, the proposed method generalizes this interaction to multiple wireless nodes associated with the same sensing target. By embedding target-point and node-state information in transmitted packets, each node identifies the kth-ranked node associated with the target and autonomously determines whether to remain active or enter a low-power satellite state. This mechanism regulates the time- and target-averaged number of active sensing nodes toward k without collecting global node-distribution information. We further introduce an in-phase-flag mechanism that modifies node-specific phase interactions to suppress persistent packet collisions between hidden nodes located two hops apart. Simulation results show that the proposed method reduces transmission energy consumption by 65% for average 1-coverage and by 46% for average 2-coverage compared with the method without satellite-state control. In the collision evaluation, the two-hop packet collision rate was 9.36% without phase control and 4.49% with the basic phase-control mechanism. By additionally applying the in-phase-flag-based hidden-node collision-control mechanism, the two-hop collision rate was further reduced to 3.51%, while maintaining a low one-hop collision rate. These results demonstrate that the proposed extension of the frog-behavior model can jointly regulate sensing redundancy and suppress data collisions through distributed local interactions. Full article

Adolescence is a critical period characterized by heightened emotional and behavioral challenges, profoundly impacting young people’s social adaptation and future development. Previous research has predominantly employed traditional latent variable analysis to examine factors associated with emotional and behavioral problems, with less attention to the underlying symptom-level structure and prospective associations of these problems. This study utilized longitudinal data from the China Family Panel Studies (CFPS) collected in 2018 (T1) and 2022 (T2). It combined contemporaneous network analysis with cross-lagged panel network analysis (CLPN), employing the 14-item adolescent problem behavior measure included in the CFPS to examine contemporaneous and prospective symptom-level associations among emotional and behavioral problems in adolescents and to compare gender-stratified networks. The study included 1082 adolescents (mean age at T1 = 12.09 ± 1.54), with boys accounting for 54.5%. In the contemporaneous networks, worry about finishing homework (I10) had the highest expected influence at both waves, although other highly connected nodes differed across networks. In the CLPN, worry about having someone to play with at school (I11) and sadness (I7) had the highest out-expected influence (O-EI) values, whereas difficulty paying attention (E4) and being easily distracted (E6) had the highest in-expected influence (I-EI) values. Permutation tests did not provide evidence of significant between-gender differences. The findings provide a symptom-level perspective on contemporaneous and prospective associations among adolescent emotional and behavioral problems and identify symptom areas for further longitudinal and intervention research. Full article

To address the problems of preferential acid channeling, limited treatment coverage, and nonuniform stimulation during acidizing of fractured carbonate reservoirs, a viscoelastic surfactant (VES) self-diverting acid system was investigated with emphasis on the relationship between its microstructure and rheological properties, as well as its effectiveness in core acidizing. The rheological properties and microstructure of the VES self-diverting acid were characterized by rheological measurements and cryogenic scanning electron microscopy. Core-flooding experiments, nuclear magnetic resonance (NMR) T₂ spectroscopy, and computed tomography (CT) scanning were conducted to investigate the evolution of pore structure before and after acidizing. The results show that the acid breakthrough volume exhibits a V-shaped trend with increasing injection rate, initially decreasing and then increasing. The optimal injection-rate range under the investigated reservoir conditions was determined to be 1.5–2.0 mL/min. After acid flooding at the optimal injection rates, the higher-T₂ peak area increased by 11.97–23.64%, while the lower-T₂ peak area increased by 25.72–28.97%. Quantitative CT analysis showed that the fracture volume of all tested cores increased by more than 120%, and dissolution-enlarged zones of different sizes as well as small branching channels were observed in the CT slices. These results demonstrate that VES self-diverting acid can effectively enhance acid coverage and improve stimulation uniformity in fractured carbonate rocks, providing experimental evidence and theoretical support for acidizing-based reservoir stimulation. Full article

The field of neuroplacentology is a growing area that focuses on the influence of placental function on brain development. This field has expanded our understanding of the placenta’s crucial role in proper neurodevelopment as well as placental anomalies that increase the risk of adverse neurodevelopmental outcomes. Neuroplacentology studies have revealed distinct roles of placentally provided hormones and nutrients in neurodevelopment whose disruption by perinatal problems, including inflammation, can increase the risk of neurodevelopmental disorders such as autism spectrum disorder and ADHD. Placental insulin-like growth factor signaling significantly influences brain growth in animal model studies and is tightly linked with human neurodevelopmental outcomes. Insulin-like growth factor signaling is well-established as a regulator of placental function and is fundamental in fetal brain cell developmental processes and plays a role in inflammation. Placental insulin-like growth factor signaling underlies the placental production and delivery of hormones and nutrients that are necessary for fetal neural cell proliferation, differentiation, and growth. It is especially important to study this pathway in the placenta due to aberrant insulin-like growth factor signaling found in conditions such as preterm birth, fetal growth restriction, gestational diabetes, and obesity. All these conditions are linked to a greater risk for neurodevelopmental disorders, neuroinflammation, and other anomalies. This research subfield has also revealed sex differences in the insulin-like growth factor signaling pathway that will contribute to understanding the etiologies of these conditions. This area of study has and will continue to identify specific mechanisms, many currently in model systems, which could eventually be leveraged in preventative and interventive care to improve neurodevelopmental outcomes. Full article

Anomaly detection becomes considerably harder when labels are scarce and the data are described by several heterogeneous views. A handful of labelled samples rarely delineates the normal region well, and the unlabelled pool is itself often contaminated by anomalies, so treating unlabelled data as normal and feeding them into a one-class boundary constraint injects incorrect supervision. We address this with SMDL-SVDD, a semi-supervised multi-view support vector data description (SVDD) framework built on dictionary representation learning. The guiding idea is to let unlabelled samples act at the level of representation rather than the SVDD boundary, so no class assumptions are imposed on them. SMDL-SVDD jointly learns a synthesis and an analysis dictionary in each view to obtain sparse representations, and constrains the SVDD hypersphere using only the small sets of labelled normal and labelled anomalous samples. A graph Laplacian regulariser over all training samples preserves the local manifold structure, while a cross-view consistency term on the unlabelled samples exploits their geometric distribution and shares information across views. The view-specific decision functions are then fused into a single anomaly score. We solve the joint problem by alternating convex search and analyse its convergence. Across 23 anomaly detection tasks derived from six public datasets, SMDL-SVDD gives the highest area under the receiver operating characteristic curve (AUC) on 21, improving the mean AUC by 8.87–10.62 percentage points over single-view one-class methods, by 5.10–7.64 points over representative semi-supervised detectors and by 4.11–5.24 points over multi-view one-class methods. Significance, noise, label-ratio, parameter, convergence, ablation and runtime studies confirm that the gains are stable; the ablation shows that manifold regularisation contributes substantially to the improvement and acts complementarily to the cross-view consistency constraint. Full article

Background: Memory problems are amongst the most troublesome and least understood of the symptoms in functional cognitive disorder (FCD) and in persistent post-concussion symptoms (PPCS). Standard neuropsychological testing typically shows only minor objective deficits, which results in a clear mismatch between the level of subjective distress and the actual degree of impairment. Current explanations of this discrepancy either attribute it to undetected hippocampal storage failure or regard it as a non-specific functional overlay; we believe that both of these explanations are incomplete. Methods: This is a narrative review incorporating an integrative hypothesis, not a systematic review. The literature was found using searches of databases and by tracing citations, and the sources were chosen on the basis of their conceptual relevance rather than according to a comprehensive protocolMethods of Literature Selection. The following three types of claims are distinguished throughout: those findings which have been established empirically in humans, those which are based on animal studies, and the hypotheses put forward in this paper. Results: Current evidence shows that episodic memory is spread out over a hippocampal–entorhinal–prefrontal–anteromedial thalamic–cortical control system, with the anteromedial thalamus being responsible for deciding which traces are stabilised and the prefrontal areas providing support for retrieval and for metacognitive evaluation. We suggest that the memory symptoms in both cases are due to a malfunction of this control system rather than to a failure of storage. In the case of FCD, entry is thought to occur in a top-down manner via abnormally precise priors of cognitive failure, impaired global metacognition, hypervigilant self-monitoring and overfitting of the self-model. With regard to PPCS, entry is believed to take place in a bottom-up way as a result of a real neurometabolic and network injury, after which fatigue, sleep disruption and hypermonitoring keep the symptoms going; in susceptible individuals, a concussion may serve as a gateway to FCD. Maladaptive, use-dependent plasticity is proposed as the common underlying mechanism. Conclusion: A unified framework incorporating metacognitive and systems consolidation processes is able to explain the discrepancy and makes eight predictions that can be tested together with provisional criteria for the transition from a primary-FCD to an organic-PPCS. The framework is put forward for future testing rather than being presented as a fully established explanation. Full article

Opisthorchis viverrini (OV) infection is still a major problem in Northeast Thailand. In this study, we looked at locations where no OV was detected (0% prevalence) by analyzing 519 georeferenced points alongside 10 environmental indices from satellite data. We found that 457 of these points, or about 88.1%, showed zero prevalence. When we looked at individual factors, the Enhanced Vegetation Index (EVI) tended to be higher and the Standardized Precipitation Index (SPI6) tended to be lower in these zero-prevalence areas. However, once we applied the Bonferroni correction, these differences were not statistically significant. Using Principal Component Analysis, we identified four components that explained 88.04% of the variance. Among the machine learning models we tested, Logistic Regression performed best with a Balanced Accuracy of 0.6053. When we used SHAP analysis based on a Random Forest model, EVI and SPI6 stood out as having the biggest impact. We also found significant spatial clustering of these zero-prevalence locations through spatial autocorrelation. Overall, these results point toward certain environmental factors linked to zero OV prevalence, specifically EVI and SPI6. That said, because the results did not hold up after multiple testing corrections, they should be treated as exploratory rather than definitive. This study offers a way to combine satellite data with spatial analysis and machine learning, but it also shows that we really need larger, more balanced datasets to get clearer answers in the future. Full article