Search Results (2,022)

In the field of large-scale equipment manufacturing and precision assembly, the measurement reference field serves as the core foundation for achieving high-precision global coordinate unification. To enhance the accuracy and reliability of the joint adjustment solution during the construction of the reference field, a combined adjustment method integrating dual constraints of distance–scale factor with adaptive weighting is proposed. This method utilizes high-precision scale bars to provide absolute distance constraints, thereby determining the global spatial scale and mitigating scale divergence during coordinate estimation. Simultaneously, approximate coordinates of target points are employed as pseudo-observations for regularization, ensuring the full-rank solvability of the adjustment system. Upon this foundation, Helmert variance component estimation is adopted to dynamically adjust the weights of distance observations, pseudo-observations, and scale bar constraints according to iterative residuals. Comparative verification experiments were conducted utilizing a nose-cone model datum field and SA professional metrology software. The results demonstrate that the proposed algorithm achieves an average point precision of 0.021 mm, comparable to the 0.019 mm precision of the professional software. In regions with locally weak geometric strength, the maximum point error of the proposed algorithm is 0.029 mm, superior to the 0.173 mm of the control group, with the maximum deviation of datum artifact spatial length reconstruction being 0.015 mm. Enhancing geometric constraints and assigning adaptive weights to observations are effective ways to improve the accuracy of absolute coordinate solutions for the reference field. Full article

Urban regeneration processes are increasingly intertwined with participatory practices aimed at integrating local knowledge and civic engagement into design and planning decisions. However, public participation often fails to influence decision-making meaningfully or to anticipate the unintended consequences of proposed interventions. This paper presents a methodological framework developed during a participatory process for the restoration of Piazza Umberto I, a historic urban square in Bari, Southern Italy. The process was structured around seven online workshops held between March and May 2021, involving 45 registered participants and an average attendance of about 30 participants per session, including residents, civic associations, students, professionals, economic actors, and municipal representatives. Through a sequential funnel—problems, opportunities, visions, solutions, methodological principles, validation, and proposal—the process elicited and organized participants’ knowledge across five analytical domains and eight long-term vision categories: History, Nature, Education, Culture, Economy, Society, Experience, and Democracy. The validated workshop outputs were then translated into a fuzzy cognitive map and explored through cross-impact analysis to identify intended impacts, unintended effects, leverage points, and trade-offs among proposed solutions. Link weights were assigned through a semi-quantitative scale representing the direction and relative strength of influence, and a ±20% sensitivity analysis was conducted to test the robustness of the main ranking patterns. The results show that some proposals, such as ecological restoration, public art programming, and cultural or educational activation, operate as broad-spectrum leverage points, while others generate more selective effects or latent tensions, particularly between ecological preservation, economic activation, accessibility, and civic use. This paper does not propose a predictive or statistically inferential model; rather, it demonstrates how participatory knowledge can be operationalized into a transparent, exploratory, and semi-quantitative decision-support framework. By linking deliberation with systems-oriented reasoning, the study contributes to urban planning debates on participatory governance, anticipatory decision-making, and the management of unintended consequences in public-space regeneration. Full article

Background: Bladder cancer disproportionately affects non-high-income countries, yet clinical trials underrepresent global diversity. We assessed global availability of bladder cancer trials, their alignment with disease burden, and barriers to equitable care. Methods: We queried ClinicalTrials.gov for adult bladder cancer trials from June 2019 to June 2024, excluding observational and non-oncologic trials. Trial characteristics were summarized descriptively, and country data came from the Global Cancer Observatory. Countries were classified per World Bank Ranking (WBR) into high-income (HICs), upper middle-income (UMICs), lower middle-income (LMICs), and low-income countries (LICs). Trials were categorized as HIC-only, non-HIC, or mixed-income trials. Fisher’s exact and Kruskal–Wallis tests compared groups. Multivariable logistic regression assessed associations between trial availability and WBR, national health expenditure, and gross national income (GNI). Univariable linear regression and ANOVA assessed the association between the mortality-to-incident ratio and WBR. Results: Of 611 trials, 75.1% were HIC-only, 16.9% non-HIC, and 8.0% mixed-income trials. Non-HIC trials were mainly academic-sponsored (80.6%), while all mixed-income trials had pharmaceutical sponsorship (p < 0.001). Non-HIC trials had lower enrollment, less pharmaceutical funding, fewer multinational collaborations, and fewer basket, multi-arm, early-phase designs (all p < 0.001). Mixed-income trials were larger, led by HICs, had broader eligibility criteria, more novel therapies, and more frequent use of overall survival endpoints. Trial availability was lower in UMICs (p = 0.011), LMICs (p = 0.024), and absent in LICs, and positively associated with higher national health expenditure (p = 0.007) and GNI (p = 0.001). Conclusions: Bladder cancer trials remain concentrated in HICs. Mixed-income trials expand access in non-high-income countries, but are exclusively led by HICs and require balanced sponsorship, early-phase research, and lasting local benefits. Full article

This paper presents a dashboard-oriented visual analytics workflow for classification-related exploratory analysis based on Information Gain (IG), histogram segmentation, and complementary localized interpretation through the Precise Piecewise Correlation (PPC) method. The workflow is designed to support the construction of a primary dashboard view by prioritizing attributes with stronger relevance to the decision variable and inspecting their class-related behavior within segmented histogram intervals. Rather than introducing a new standalone feature-selection metric, this study formalizes how established analytical components can be integrated into a coherent dashboard framework for structured visual inspection. The proposed workflow was examined on three datasets from different application domains: the Iris dataset, an educational performance dataset, and an Oil and Gas dataset. Across these cases, IG-based prioritization identified attributes that provided clearer class-related structure in the primary dashboard view, while histogram segmentation supported interval-level interpretation of class concentration and overlap. A compact quantitative evaluation further showed that top-ranked IG subsets retained strong discriminative information under standard classification models, whereas lower-ranked subsets generally performed less favorably. Entropy-based segment analysis additionally indicated lower local class uncertainty for higher-ranked attributes. A small user study provided preliminary user-centered support for the interpretability and practical usefulness of the proposed dashboard structure. The results suggest that the proposed workflow can support dashboard-based inspection of class-related patterns across different contexts. Full article

Accurate identification of weak nodes is a prerequisite for online security assessment, preventive control, and resilience enhancement in modern power systems. However, conventional single-layer graph-learning models mainly emphasize local neighborhood aggregation and are insufficient for characterizing vulnerability propagation from equipment-level disturbance to regional congestion and system-level transfer constraints. This paper proposes a mechanism-aware hierarchical graph-learning framework for weak-node identification in complex interconnected power grids. We emphasize that attention, fusion, and gating operations are standard neural-network mechanisms and are not claimed as new generic deep-learning blocks. The contribution of this paper is the power-system-specific formulation: constructing an electrically meaningful local-supernode hierarchy, defining reproducible mechanism-based node and branch-vulnerability proxies, and interpreting weak-node rankings through node–line–corridor coupling evidence. In the validated implementation, a local graph convolutional encoder and a supernode/global graph convolutional encoder generate 32-dimensional local embeddings and 16-dimensional global embeddings, which are concatenated and decoded by a 48 → 24 → 1 multilayer perceptron to obtain node vulnerability scores. Experiments are conducted on reproducible IEEE benchmark data generated from pandapower standard systems, with representative comparisons on the IEEE 57-bus, 145-bus, and 300-bus systems and a detailed structural interpretation on the IEEE 145-bus case. The present results validate the ability of the implemented local–global hierarchical model to reproduce the proposed mechanism-based vulnerability proxy on representative small- and medium-scale benchmarks. Full article

A graph G is locally irregular if every two adjacent vertices have distinct degrees and is locally irregular-connected if for every two vertices u and v of G, there is a locally irregular $u-v$ path in G. For a finite set S of two or more positive integers with maximum element k, it is known that there exists a graph of order $k+1$ with degree set S. This result is extended by showing that there is a locally irregular-connected graph of order $k+1$ with degree set S. Characterizations are established for all locally irregular-connected graphs of cycle rank at most 2. All sets S of positive integers are determined for which there is a locally irregular-connected graph of cycle rank at most 2 with degree set S. The minimum order of a locally irregular-connected graph with a prescribed degree set is determined as well. Other results and open questions are also presented. Full article

The increasing energy demand and environmental impact of transportation systems have intensified the need for more efficient railway energy management strategies. Although electric railway systems provide a sustainable alternative, the dynamic nature of traction power systems and the inadequate use of regenerative braking energy still result in significant energy losses. In order to improve energy efficiency and state-of-charge (SOC) stability, this study proposes an optimized fractional-order proportional-integral (FOPI) controller for the control of a wayside energy storage system (ESS) in a DC railway network. The parameters of the FOPI controller are tuned via recent metaheuristic tool of barrel theory-based optimizer (BTO) such that the error between the desired and actual charging/discharging voltages of the ESS is minimized under nonlinear and time-varying operating conditions. The BTO is characterized by strong exploration/exploitation balance that prevents the approach from falling in local optima. Also, the approach has low sensitivity to user-defined parameters. The proposed approach was evaluated using a MATLAB/Simulink (version 2021b) model of a double-track DC railway system incorporating realistic train operations and three distinct traffic scenarios including ideal, perturbed, and stochastic conditions. The BTO was compared to other approaches of particle swarm optimization (PSO) and gray wolf optimizer (GWO). Also, statistical tests using the Friedman, Kruskal–Wallis, ANOVA, and Wilcoxon rank tests were conducted to assess the suggested approach. The obtained results confirm the robustness and competence of the proposed controller compared to either the conventional static control approach or optimized controller via the comparable approaches. As a result, the suggested controller achieved higher total energy savings, improved utilization of regenerative braking energy, and enhanced power demand distribution across substations. While minor increases in SOC deviation were observed in certain scenarios, the overall system performance showed improved robustness and adaptability. These findings highlight the effectiveness of integrating fractional-order PI control designed via the suggested BTO for advanced energy management in railway applications. Full article

Wild mushroom harvesting is an activity practiced throughout the United States (U.S.) and holds a place of both cultural and economic importance. Mushroom harvesting on public lands in the U.S. takes two primary forms: (1) commercial harvest (for sale) or (2) personal harvest (for one’s own consumption or for sharing to others). As mushroom harvesting has grown in popularity, particularly in urban and suburban areas, ready access to information surrounding harvests on public lands has become increasingly important to the mushroom harvesting community, and ultimately to fungal conservation and sustainable exploitation. In this study, documents pertaining to harvesting on state and federal public lands in the U.S. were analyzed for their accessibility for personal and commercial harvesters. Scores were assigned based on access (ranked 0–5), with higher scores indicating greater access to the public. Overall, personal harvest (Min = 1, Max = 5, Average = 2.96) was permitted to some extent in every state, with the greatest access provided in Oregon, Nebraska, Wisconsin, and Michigan. Permits were often not required (Min = 0, Max = 3, Average = 0.7), with Montana and South Dakota having the most permitting requirements. Commercial harvest was associated with more limited access, and had greater associated regulation (Min = 0, Max = 4, Average = 1.02). Seventeen states that allowed for personal harvest did not allow for commercial harvest. Permitting was almost always required for commercial harvest (Min = 0, Max = 4, Average = 1.06), with Oregon having the most developed commercial permitting requirements. Access to public lands was found to be highly variable in the U.S. and is governed by a variety of local, state, and federal regulations. Information, depending on its source, was at times easy to access through a website, pamphlet, or phone call. However, in many cases information was out of date or difficult to find, and studies on the impacts of commercial and personal mushroom harvesting are limited. As a result, it is important that land managers develop communication mechanisms with the public for information sharing, to provide open and frequent communication, and for building long-term trust and relationships with harvesters. We offer some example mechanisms to land/resource managers and university/public educational partners as a starting point. Full article

Particle swarm optimization (PSO) has been widely used in engineering optimization because of its simple structure and easy implementation. However, standard PSO and most of its variants mainly learn from the personal best position and the global best position. Thus, they often fail to preserve and reuse population-level knowledge generated during the search process. This problem becomes more evident when the search state changes or the swarm falls into stagnation, at which point useful search information may be ignored or forgotten. To address this issue, this paper proposes a state-adaptive knowledge recall PSO algorithm, termed SKRPSO. It includes three cooperative components. First, a state-aware adaptive aggregation mechanism adjusts the elite knowledge-pool size according to population dispersion and builds a rank-weighted knowledge vector for stable population-level guidance. Second, a stagnation-driven knowledge recall mechanism stores historical knowledge associated with global improvements in a bounded memory buffer and recalls recently successful knowledge with a time-decay preference when stagnation is detected. Third, a knowledge-fusion position update strategy uses current aggregated knowledge during normal search and recalled knowledge under stagnation, balancing local exploitation and stagnation escape. Experiments on the CEC2017 benchmark suite show that, based on 30 independent runs, SKRPSO achieves the best mean error on 22 of 29 functions and the best overall Friedman average rank of 1.431 among all compared algorithms. Engineering design results further indicate stable performance. Full article

Condensation heat transfer and frictional pressure drop of R410A, R455A, R454C, and a newly proposed ternary low-GWP (Low-Global Warming Potential) refrigerant (R1234yf/R13I1/R32) were experimentally investigated in a horizontal smooth tube. The heat transfer results established R410A and R1234yf/R13I1/R32 as the upper and lower performance bounds, respectively, while the two low-GWP blends occupied an intermediate range. The hydraulic behavior showed a different ranking from the heat transfer performance, indicating that thermal advantage and pressure-drop reduction are controlled by different refrigerant properties. Among the three established correlations, Shah (2022) provided the best overall agreement and was selected as the basis for further development. The Bell–Ghaly mixture correction was modified by replacing the vapor quality weighting term with a Zivi-based void fraction and the uniform phase equilibrium slope with a local pointwise slope. The proposed correlation reduced the overall Mean Absolute Deviation (MAD) from 16.9% to 9.3% and Mean Relative Deviation (MRD) from +16.7% to +4.3% across all 150 data points compared to Shah (2022). Full article

The rapid advancement of unmanned aerial vehicles (UAVs) has greatly increased the application of various swarm intelligence algorithms in UAV path planning. To address the potential issues with the dung beetle optimizer (DBO) in UAV trajectory planning, such as low convergence accuracy, tendency to get trapped in local optima, and imbalance between global search and local exploration, a hybrid algorithm termed DBO-PSO is proposed by integrating DBO with particle swarm optimization (PSO) to solve the UAV path planning model. The Kent chaotic map is introduced to enhance population diversity and distribution uniformity, and the velocity–position update mechanism of PSO is incorporated into DBO to strengthen its global search capability. Comparative experiments are conducted on CEC2022 benchmark functions, and multiple classical swarm intelligence algorithms are selected for comparison using six evaluation metrics, along with Wilcoxon rank-sum and Friedman statistical tests. An ablation study is also performed to evaluate the contribution of each improvement component. The path planning experimental results demonstrate that compared to DBO, PSO, IDBO, and ECFDBO under the population size of 50, DBO-PSO reduces the total path cost by 44.2%, 17.3%, 8.9%, and 45.1%, respectively. The ablation study verifies that both improvement components contribute positively, which demonstrates its competitive performance and practical applicability in UAV three-dimensional path planning. The source codes to support the presented results are publicly available on GitHub. Full article

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article

Background/Objectives: Complex chronic patients represent a heterogeneous and high-risk population, for whom accurate prognostic tools are essential to guide clinical decision-making, optimize resource allocation, and support tailored interventions. The PROFUND index was developed for mortality prediction in polypathological patients, but its performance has not yet been evaluated in an ambulatory integrated care model. Methods: A retrospective observational study was conducted using two cohorts. Cohort H included complex chronic patients admitted to the Internal Medicine Department between March 2023 and February 2024. Cohort A comprised complex chronic patients followed by a multidisciplinary chronic care program between November 2016 and December 2023. PROFUND scores were derived from electronic health records. Discrimination for 12-month mortality was assessed using Kaplan–Meier curves, log-rank tests, and receiver operating characteristic curve analysis. Calibration was evaluated by comparing observed mortality with expected mortality based on the original PROFUND index and improved through intercept and slope recalibration. Results: A total of 660 patients were included in cohort H and 540 in cohort A. One-year mortality was 38.0% and 30.2%, respectively. Discriminatory performance was good in hospitalized patients (AUC 0.760; 95% CI 0.724–0.797) and moderate to good in ambulatory patients (AUC 0.705; 95% CI 0.656–0.754). Calibration analyses demonstrated systematic overestimation of mortality, particularly in the ambulatory cohort and intermediate–high risk strata, while recalibration improved agreement between predicted and observed risks. Conclusions: The PROFUND index provides useful risk stratification for 12-month mortality in CCP across care settings but overestimates absolute risk, particularly in ambulatory case management populations. Local recalibration may improve prognostic accuracy, support individualized care planning, and advance care planning discussions and allocation of multidisciplinary follow-up intensity. Full article

Soil microbes play pivotal roles in nutrient cycling and ecosystem functioning across diverse farmland systems. Orchard grass coverage has been demonstrated to effectively alter microbial community structure and promote nutrient cycling. However, the effects of soybean intercropping on soil bacterial community characteristics and nutrient contents in citrus orchards remain poorly understood. In this study, a field experiment was conducted in a citrus orchard involving three planting patterns: clean tillage (CT), natural grass (NG), and soybean intercropping (SI). The physicochemical properties and bacterial community structure of the topsoil (0–40 cm depth) were determined. Results showed that compared with CT, NG and SI significantly increased cation exchange capacity (CEC), soil organic matter (SOM), alkali-hydrolyzable nitrogen (AN), and available potassium (AK). SI further elevated soil pH and available phosphorus (AP) relative to CT and NG. Bacterial diversity ranked SI > NG > CT, with PCoA showing lower community variation under SI. A total of 31 bacterial phyla were detected in the citrus orchard soil, with Cyanobacteria (17.20~40.81%), Proteobacteria (15.04~24.19%), Acidobacteriota (8.95~14.66%), and Chloroflexi (3.93~21.13%) identified as the dominant phyla. SI enriched Cyanobacteria and Proteobacteria but reduced Acidobacteriota, Chloroflexi, and Actinobacteriota. Mantel tests confirmed CEC and SOM as key drivers of bacterial community structure. Overall, soybean intercropping improves soil microecology and exhibits great potential for soil quality improvement in citrus orchards under local conditions. Full article

Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty. Full article