Search Results (48,984)

Improving the cultivated land use eco-efficiency (CLUE) is crucial to achieving sustainable land use and the green transformation of agriculture. This study is based on the data from 353 prefecture-level cities in China from 2013 to 2021. The slacks-based measurement (SBM)-undesirable model, the social network analysis (SNA), and the fuzzy set qualitative comparative analysis (fsQCA) are adopted to measure and analyze the spatial patterns, network characteristics, and multiple driving pathways of inefficiency in the cultivated land use eco-efficiency in prefecture-level administrative units. Results show the following: (1) From 2013 to 2021, CLUE in the study areas shows spatial heterogeneity, with most efficiency values at a moderate level and showing a fluctuating downward trend over time. (2) The nine major agricultural regions have formed a complex association network, with the overall network connectivity being weak but efficiency relatively high. The hierarchical structure is gradually flattening, and inter-regional cooperation is increasing. (3) There are significant differences in influence, control, and accessibility within individual networks, and the collaborative network is developing into a “multi-core-hierarchical” structure. (4) The formation of inefficiency involves multiple concurrent mechanisms. Four typical inefficiency paths were identified, with significant heterogeneity across different agricultural regions. In the future, differentiated land use and ecological protection policies should be implemented based on the spatial network characteristics and inefficiency driving pathways of each agricultural region to promote the coordinated improvement of CLUE. Full article

Introduction: As global cancer incidence and survival rates continue to rise, understanding the experiences and needs of individuals in the survivorship phase is critical to inform policies that promote equitable care and adequate support for cancer survivors. Objective: The objective of this study was to examine the lived social and workplace experiences of cancer survivors in Newfoundland and Labrador (Canada) using a qualitative research design. Methods: The study was open to cancer survivors of majority age who resided in Newfoundland and Labrador after their diagnosis. Between June 2023 and August 2024, twenty-five individuals participated in the study. Data were collected virtually through focus groups, individual interviews, or written responses. Demographic and socioeconomic characteristics of participants were collected via a survey. Thematic analysis was performed on all qualitative data. Two patient partner investigators informed the research throughout the entire project. Results: Data were rich and diverse, revealing a range of positive and negative experiences in social and workplace settings. Major themes included stigma in social and workplace environments, financial toxicity, workplace accommodations, social support and information needs. Young participants had unique challenges. Participants offered recommendations aimed at enhancing available supports and improving the quality of life of cancer survivors. Overall, findings highlight shared experiences across different regions and cultures while also painting the local context. Discussion: The results of this study reveal diverse experiences among cancer survivors within social and workplace settings. The findings and resulting recommendations can inform meaningful improvement to policies and programs, thus promoting equity and enhancing the lived experiences of cancer survivors. Full article

Background: Robot-assisted radical prostatectomy (RARP) is the predominant surgical approach for localized prostate cancer in high-volume centers worldwide. However, comprehensive real-world data describing complete institutional transition from open to robotic surgery remain limited. This study evaluated perioperative and early oncological outcomes of a contemporary RARP cohort and characterized the transition from open radical prostatectomy (ORP) to RARP in a European center. Methods: We analyzed 520 consecutive patients who underwent RARP between January 2023 and December 2025. Perioperative, pathological, and biochemical outcomes were assessed. Biochemical recurrence was defined as prostate-specific antigen ≥0.2 ng/mL. Institutional data from 2011 to 2025 were reviewed to evaluate procedural trends and the transition from ORP to RARP. Surgeon-specific and institutional learning curves were analyzed using operative time and linear regression models. Results: Following the introduction of robotic surgery in 2018, annual RARP volume increased from 37 procedures to 205 in 2025. Since 2023, RARP accounted for more than 99% of all radical prostatectomies. Median operative time decreased from 185 min in 2023 to 165 min in 2025, with consistent downward trends observed across all surgeons. Linear regression confirmed progressive improvement in operative efficiency, with learning rates ranging from −0.22 to −0.92 min per case. Estimated blood loss was minimal, no patients required transfusion, and major complications occurred in four patients (0.8%). Hospital stay decreased from 2 days to predominantly 1 day. During follow-up, 36 patients developed biochemical recurrence or PSA persistence. Biochemical recurrence-free survival differed significantly according to pathological stage (log-rank p < 0.001), with 24-month estimates of 93.7%, 91.5%, and 82.1% for pT2, pT3a, and pT3b disease, respectively. Conclusions: RARP provides favorable perioperative safety, minimal morbidity, and favorable early oncological outcomes in a high-volume setting. The complete institutional transition from ORP to RARP, together with demonstrated surgeon-specific and institutional learning effects, supports the feasibility and safety of implementing RARP as the institutional standard within a structured robotic program. Full article

Cyclic triaxial tests are often used to evaluate the behavior of soils under seismic loads. The stress conditions imposed on a soil specimen during a cyclic triaxial test, however, are very different than those acting on an element of soil during an earthquake. One major difference is that the element in the field is subjected to a change in total confining stress, whereas in a conventional cyclic triaxial test the total confining stress (as applied through the cell pressure) is held constant. This use of constant cell pressure is usually justified by the assumption that in a saturated specimen the change in total stress is offset by a change in pore pressure, thus resulting in no change in the effective confining stress or liquefaction susceptibility. A laboratory study using cyclic triaxial tests was conducted on several soils to assess the validity of this assumption. For each soil, two series of stress-controlled cyclic triaxial tests were run: one set with a constant cell pressure, and thus a constant total confining stress, and a second set with a variable total stress/cell pressure. These tests were then compared in terms of both the resulting cyclic resistance curves and the amount of energy dissipated to trigger liquefaction. It was found that the two conditions of confining stress yielded results that were not statistically different. Therefore, the assumption that the change in pore pressure caused by the variation in total stress is offset by the change in pore pressure and thus results in no change in effective stress or liquefaction susceptibility appears valid. Based on these findings, cyclic triaxial tests performed with constant cell pressure, and thus a constant total confining stress, provide valid results for liquefaction analyses. Full article

Objectives: Volleyball playing positions are associated with different functional demands. This study compared postural control, jump performance, and upper-limb mobility across playing roles in competitive male volleyball players. Methods: Fifty male volleyball players competing in the Italian Serie C championship were equally distributed across five roles: middle blockers (MB), liberos (LIB), opposite hitters (OH), setters (SET), and outside hitters (HIT). Using a wearable inertial sensor, athletes performed bipodalic balance tasks with eyes open and closed, dominant- and non-dominant-leg single-leg balance, squat jump (SJ), countermovement jump (CMJ), and bilateral upper-limb flexion and extension tests. Results: Significant role-related differences emerged in balance and jump performance. In bipodalic balance, the eyes-open condition showed a mixed pattern, with HIT displaying the largest ellipse area and SET showing the highest path-related values, whereas in the eyes-closed condition, HIT showed the highest values across all stabilometric parameters. In the single-leg stance, OH showed the largest postural excursions on the dominant side, while LIB stood out on the non-dominant side. In jump tests, MB showed the best vertical performance in both SJ and CMJ, whereas LIB and SET generally showed the lowest outputs. Temporal differences also emerged across roles. Upper-limb mobility was similar across roles in flexion, while extension showed a role-specific pattern, with SET displaying greater ROM than LIB, HIT, and OH. Conclusions: Volleyball roles are associated with distinct functional profiles in balance, jump mechanics, and upper-limb mobility. This integrated assessment may support more specific training, monitoring, and injury-prevention strategies. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

Teacher–AI collaboration is increasingly present in educational settings, yet little is known about how it is conceptualized in empirical research and what this implies for teacher preparation. This review synthesizes 39 empirical studies on teacher professionalization published between 2015 and 2025 to examine how responsibilities for detecting, diagnosing, and acting on educational data are distributed between teachers and AI systems. Results indicate a predominant focus on learning analytics and natural language processing tools, largely operating at intermediate to high levels of automation. In these configurations, teachers are primarily positioned as interpreters or monitors of AI outputs. In addition, our analysis identifies a consistent pattern: as AI systems assume greater pedagogical autonomy, teacher training described in the literature remains brief, procedural, and largely limited to technical familiarization. These findings suggest that different automation configurations entail distinct competence demands, and that teacher preparation must move beyond technical training to conceptualize teacher–AI collaboration as ongoing professional sensemaking within hybrid intelligent systems grounded in educational values. Full article

In recent years, synergistic effects between inorganic clay minerals (e.g., montmorillonite, sepiolite, kaolinite) and bio-based flame retardants (e.g., chitosan-based, lignin-based, phytate-based) have achieved certain progress in the area of polymer flame retardancy. The effects of bio-based flame retardants are exerted through mechanisms such as catalytic char generation and vapour-phase hindrance. However, they have limitations when used alone, including insufficient thermal stability and the need for a high dosage. Inorganic clays form physical barriers through their layered or tubular structures. The high thermal stability of these structures suppresses heat and mass transfer, thereby offsetting the shortcomings of bio-based flame retardants. This synergistic combination greatly improves the flame retardancy of polymer composites, often strengthening their mechanical performance in the process. It therefore offers great potential for the design of multifunctional, eco-friendly flame-retardant polymer composites. Nevertheless, a systematic review of the synergistic mechanisms, fabrication approaches and application progress of different inorganic clay minerals when combined with various bio-based flame retardants is still lacking. Therefore, this article offers a comprehensive review of the current developments of synergistic systems that incorporate various primary clays, such as sepiolite and montmorillonite, with bio-based flame retardants for usage in polymers. Before this, the synergistic flame-retardant mechanism and the key preparation techniques of the composite system were explained in detail. Finally, this article puts forward solutions to the current challenges and sets out prospects for innovation in the designing of flame-retardant materials and the optimisation of processes. The aim is to promote the sustainable growth of efficient, eco-friendly flame-retardant materials. Full article

This study proposes a modification of the Weighted Sum Model (WSM) that formalizes the evaluation generation for alternatives. By integrating objective data with decision-makers’ (DMs’) subjective preferences, the modification addresses a gap in the classical WSM, where evaluations are traditionally assigned subjectively, despite the availability of measurable data describing the alternatives. The modification introduces a structured mechanism for handling heterogeneous data by distinguishing between numerically represented and matrix-represented criteria. The quantitative criteria are processed through normalization procedures aligned with individual DMs’ preferences. Meanwhile, the qualitative characteristics are decomposed into sets of options and structured as binary matrices. The applicability of the modified model is demonstrated through a case study on replacing photovoltaic modules in a public building. Results indicate that changes in DMs’ preferences lead to observable differences in the generated evaluations and in the ranking of the alternatives, even when identical objective data is used. Ultimately, these results demonstrate that the modified WSM improves the flexibility and transparency of the decision-making process, providing a more realistic representation of experts’ preferences. From a sustainability perspective, it facilitates more informed and balanced decisions in the management of energy systems and public infrastructure. Full article

Sustainable land-use regulation in historical and cultural cities requires balancing heritage conservation, development demand, cropland retention, and urban–rural spatial restructuring. However, the stage-wise reorganization of urban–rural construction land under these coupled pressures remains insufficiently understood. Taking Jingzhou District, China, as a case study, this study uses land-use data from 2000, 2005, 2010, 2015, and 2020 and integrates stage-wise random-forest analysis, consistency-based interaction-network mining, and multi-scenario simulation within the intPLUS framework. Population, GDP, and areal-water distance layers were matched to the corresponding stage-terminal snapshots where applicable, whereas 2020 POI data were used as contemporary spatial-context proxies. From 2000 to 2020, urban industrial land (UIL) expanded from 16.63 to 46.42 km², increasing by approximately 179.1%, whereas rural settlements (RS) increased more moderately from 56.59 to 60.27 km², increasing by approximately 6.5%. The stage-wise RF and interaction-network results show that UIL and RS followed different spatial association structures, with stronger UIL self-reinforcement and stronger RS self-continuity in the later stage. Historical validation showed overall accuracy values of approximately 91% and Kappa values around 0.80, but FoM values remained relatively low, ranging from 0.098 to 0.176. Class-specific mapping accuracy was higher for RS (81.90–82.37%) than for UIL (55.20–66.93%), indicating a weaker performance in locating UIL change. Therefore, the 2035 simulations should be interpreted as parameter-conditioned regulatory comparisons rather than deterministic pixel-level forecasts. The scenario results indicate that the conservation-oriented limited growth was associated with the restricted UIL expansion and better cropland retention under the prescribed demand and constraint settings, while the RS reduction occurred only under explicit village-consolidation and construction-land quota reallocation assumptions. By distinguishing UIL and RS, this study provides differentiated regulation-oriented evidence for sustainable land-use governance in historical and cultural cities. Full article

Magnetic anomaly detection is vulnerable to environmental noise and insufficient prior target information, making non-periodic anomaly signals difficult to detect at low-signal-to-noise-ratio (SNR) conditions. This paper proposes a detection method based on a multi-parameter-constrained mirror dual-branch biased monostable stochastic resonance (SR) system. Nonlinear odd-order bias terms are introduced into the conventional biased monostable potential function to build a multi-parameter-controllable SR model. This improves regulation of potential-well width, depth, and wall morphology, enhancing noise-energy utilization and responses to non-periodic features. Considering peak-type, valley-type, and bipolar anomaly morphologies, a mirror dual-branch SR structure is developed to cooperatively detect features with different polarities. To preserve temporal waveforms and time–frequency structures during parameter optimization, a composite metric combining the correlation coefficient and wavelet-domain image structural similarity index is constructed. Multi-fidelity robust Bayesian optimization is used to obtain a unified robust parameter set for the magnetic anomaly signal family. Experiments with simulated colored noise and measured geomagnetic noise show that the proposed method effectively recovers magnetic anomaly features under strong noise. At −19 dB SNR, its detection probability remains above 80%. Compared with orthogonal basis function decomposition, empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise, the method achieves better noise suppression, feature preservation, and detection performance under low-SNR conditions. Full article

High-dimensional feature selection aims to identify compact and discriminative feature subsets from large feature spaces. In multi-objective feature selection (MOFS), this task remains challenging because the search space grows exponentially with dimensionality, and conventional binary evolutionary operators may generate ineffective perturbations in sparse high-dimensional spaces. To address these issues, this paper proposes CORAL, a rank-memory search framework for MOFS. CORAL uses a joint continuous score–cardinality representation to model feature priorities and subset sizes and applies Top-K decoding to obtain binary feature subsets. A rank-memory mechanism is introduced to extract feature occurrence information from elite solutions and guide score-space variation. In addition, elite local refinement and feature-number-stratified environmental selection are used to refine candidate subsets and maintain solutions across different sparsity regions. Experiments on 18 benchmark classification datasets show that CORAL achieves balanced performance in terms of solution-set quality, test classification performance, feature compactness, and computational efficiency. Ablation results further demonstrate the complementary roles of rank memory, elite local refinement, and stratified environmental selection. Full article

Due to climate change and rapid urbanization, cities worldwide face the dual challenge of improving flood resilience and providing accessible green space within limited land resources. Sponge City parks offer a landscape-based approach for integrating stormwater management with park services. However, how park morphology structures this combined performance remains insufficiently understood. This study examines 26 Sponge City parks in Shanghai and evaluates how node-, line-, and patch-type morphologies are linked to stormwater storage and service provision. Using geospatial analysis, DEM-derived catchment delineation, land-cover interpretation, and statistical analysis, this study compares estimated stormwater storage, storage efficiency, local park availability, and land-cover composition across different park morphologies. The results show that estimated performance of stormwater management and park service provision vary across morphological types, but these differences do not follow a simple node–line–patch hierarchy. Rather, the observed patterns are jointly shaped by park morphology, catchment setting, land-cover allocation, and surrounding urban context. These findings suggest that Sponge City parks should not only be evaluated by total stormwater storage. Their contribution depends on morphology, scale, catchment setting, land-cover allocation, and urban context. The study provides a morphology–performance perspective to support more differentiated planning of multifunctional green infrastructure. Full article

Numerous factors influence the formation of blasting craters in engineering blasting. Based on the actual parameters of the Daye Iron Mine, this study established six sets of single-hole blasting crater numerical models with different borehole diameters using ANSYS(19.0)/LS-DYNA(R13) software. The variation in blasting crater volume with the scaled depth was analyzed to determine the optimum scaled depth for each borehole diameter, and a functional relationship between the optimum scaled depth and borehole diameter was derived through curve fitting. Furthermore, using a borehole diameter of 0.076 m as a case study, a double-hole blasting crater was developed to investigate the effect of varying hole spacing on blasting crater volume and to determine the optimal hole spacing. The blasting parameters were optimized based on the numerical simulation results. The results show that within the range of borehole diameters considered, the blasting crater volume initially increases and then decreases with increasing scaled depth of the explosive charge. The fitted relationship between the optimum scaled depth and borehole diameter is y = −180.7197x³ + 86.3754x² − 9.5504x + 1.0782. For a borehole diameter of 0.076 m, the optimum scaled depth is 0.7278 m/kg^1/3, and the optimal hole spacing is 0.52 m. Based on blasting similarity theory, the calculated optimum burial depth of the explosive charge is 0.59 m, the critical burial depth is 1.1 m, and the recommended row spacing ranges from 0.95 m to 1.18 m. The findings of this study provide a theoretical basis for optimizing blasting parameters at the Daye Iron Mine and similar mining operations. Full article

The advancements made in the mass spectrometry imaging (MSI) field have allowed for the generation of very large-scale data sets. These data are often interrogated by machine learning (ML), although storing and handling data sets of this size can be difficult. To aid impacted researchers, we seek to evaluate feature reduction strategies that will minimize the amount of data stored while still maintaining the ability to correctly classify the data. Two different feature selection strategies are tested on six different data sets, leveraging XGBoost as the machine learning algorithm. The study provides evidence that selecting features based on the greatest average abundance across all samples is best suited to scale down the feature set at a more modest trimming level, while selecting features based on statistical analysis via a Student’s t-test is better suited for a more aggressive trimming level. These trends were present regardless of training set size or cross-validation strategy. The results from this work provide insight into when these feature filtering steps can be used effectively and when another data reduction strategy, including not restricting the data set, should be considered. Full article