Search Results (1,032)

The traditional process for kinematic synthesis of planar mechanisms involves setting a few prescribed positions, then solving a set of equations to identify a vector chain that exactly reproduces those positions. In evaluating these equations, designers often must sift through multiple “infinities” of solutions corresponding to some number of free-choice variables that each have an infinite number of possible values. In this vast solution space, some combination of those variables will produce the most optimal solution, but finding that optimal solution is not trivial. There are two extremes for addressing the impossibility of sifting through infinite possible values. First, one could use analytical techniques to make educated estimates of the optimal values. Or, alternatively, a designer could completely remove their perspective from the process, passing the problem into a computer and programming it to sift through millions (or orders of magnitude more) possible solutions. The present work proposes a novel intermediate step in the analytical synthesis process that functions as a middle ground between these extremes. Optimizing solution density involves a designer manually manipulating the problem definition to increase the percentage of solutions that have pivots in acceptable locations. This is accomplished by changing the values of δ_j and α_j (prescribed translation and rotation of the moving plane, respectively) to manipulate the position of the poles. A physical example, designing a 7-bar parallel-motion generator, shows that applying this method yields more passing solutions when comparing over the same search depth. Specifically, 0.008% of solutions pass the design criteria without applying the method, and 3.154% pass after optimizing. This approach can reduce the computational load placed on a computer running a search script, as designers can use larger increments on the free choices without skipping over a family of solutions. Full article

Soil water content (SWC) critically governs the physical and mechanical behavior of soils. However, conventional methods such as oven drying are laborious, time-consuming, and difficult to replicate in the field. To overcome these limitations, we developed an image-based interpolation framework that leverages histogram statistics from 12 soil surface photographs spanning 3.83% to 19.75% SWC under controlled lighting. For each image, pixel-level values of red, green, blue (RGB) channels and hue, saturation, value (HSV) channels were extracted to compute per-channel histograms, whose empirical means and standard deviations were used to parameterize Gaussian probability density functions. Linear interpolation of these parameters yielded synthetic histograms and corresponding images at 1% SWC increments across the 4–19% range. Validation against the original dataset, using dice score (DS), Bhattacharyya distance (BD), and Earth Mover’s Distance (EMD) metrics, demonstrated that the interpolated images closely matched observed color distributions. Average BD was below 0.014, DS above 0.885, and EMD below 0.015 for RGB channels. For HSV channels, average BD was below 0.074, DS above 0.746, and EMD below 0.022. These results indicate that the proposed method reliably generates intermediate SWC data without additional direct measurements, especially with RGB. By reducing reliance on exhaustive sampling and offering a cost-effective dataset augmentation, this approach facilitates large-scale, noninvasive soil moisture estimation and supports machine learning applications where field data are scarce. Full article

Paratuberculosis is a chronic zoonotic bacterial infection, primarily affecting ruminants. This review examines the disease in the Arabian Peninsula, focusing on distribution, molecular diversity, prevalence, and associated risk factors. Following PRISMA guidelines, a systematic search was conducted in PubMed, Scopus, and Web of Science. After duplicate removal and eligibility screening, data extraction, analysis, and quality assessment were performed. Pathogen sequences were retrieved from NCBI GenBank for phylogenetic analysis. The review included a total of 31 published articles from 1997 to 2025, of which 26 were used in the meta-analysis. Most studies (n = 12) were published between 2011 and 2015, predominantly from Saudi Arabia (n = 22), with no reports from Qatar, Bahrain, or Yemen. The majority of the studies involved camels and sheep (n = 16 on each species), followed by cattle (n = 9), goats (n = 7), humans (n = 2), and buffalo (n = 1). Phylogenetic analysis delineates two major clades—Type S and Type C—suggesting greater genetic diversity in Type S. The estimated pooled seroprevalence and pathogen prevalence in livestock ruminants were 8.1% and 22.4%, respectively. Herd-level estimated pooled seroprevalence was 26.9%. Small ruminants (19.3%) were more sero-prevalent than large ruminants (7.4%), with goats (28.7%) significantly (p < 0.01) more affected than sheep (21.5%), camel (9.8%), and cattle (6.6%). Clinical signs in ruminants included chronic diarrhea, emaciation, anorexia, alopecia, wry neck, and dehydration. The reviewed study patterns and findings suggest high pathogen diversity and a significant risk of transboundary transmission at the human–animal interface in this region. A One Health surveillance approach is crucial, particularly on farms with diarrheic and emaciated animals. Establishing a national surveillance plan and phased (short-, intermediate-, and long-term) control programs is essential to mitigate economic losses, limit transmission, overcome the cultural barrier, and protect public health. Full article

Artisanal fishing is a central pillar of the Angolan economy, particularly in the southern province of Namibe, where it serves as the primary economic activity for numerous coastal communities. However, these communities face significant challenges, including competition from expanding industrial fisheries and inadequate infrastructure at fishing centers, which hampers the storage, preservation, and transportation of catches. These limitations contribute to post-harvest losses and the reduced market value of products, despite the region’s rich diversity of pelagic and demersal resources. This study evaluated the economic viability of artisanal fishing in Namibe under three production scenarios, varying in catch levels and the inclusion of fish processing activities such as dried fish and fishmeal production. Scenario A (pessimistic) assumed a 10% reduction in production compared to the best estimates; Scenario B (intermediate) was based on average reported catches; and Scenario C (optimistic) considered a 10% increase in catches, accounting for seasonal and environmental variability. Results indicated that artisanal fishing was economically viable under all scenarios, with the Internal Rate of Return (IRR) consistently exceeding the Minimum Attractive Rate of Return (MARR) of 7.5%. IRR values ranged from 34.30% (Scenario A, without by-product commercialization) to 106.28% (Scenario C, with dried fish and fishmeal production and sales), representing a more than threefold increase in profitability. This substantial gain underscores the transformative potential of processing by-products into higher-value commodities, enabling integration into larger-scale and more liquid markets. Such value addition supports the concept of a proximity economy by promoting short production cycles, reducing intermediaries, and strengthening local value chains. Beyond financial returns, the findings suggest broader socioeconomic benefits, including local economic growth, job creation, and the preservation of traditional production knowledge. The payback period was less than four years in all cases, decreasing to 1.94 years in the most favorable scenario. By-products such as dried fish and fishmeal exhibit commodity-like characteristics due to their higher commercial value, increasing demand, and potential integration into regional and animal feed markets. In conclusion, diversifying marketing strategies and maximizing the use of fish resources can significantly enhance the economic sustainability of artisanal fishing, foster socioeconomic inclusion, and support the development of artisanal fishing communities in Namibe. Full article

This study proposes a geographically weighted (GW) quantile machine learning (GWQML) framework for soil moisture (SM) prediction, integrating spatial kernel functions with quantile-based prediction and uncertainty quantification. The framework incorporates satellite radar backscatter, meteorological re-analysis, and topographic variables, applied across 15 SM stations and six land use systems at the North Wyke Farm Platform, southwest England, UK. GWQML was implemented using Gaussian and Tricube spatial kernels across a range of kernel bandwidths (500–1500 m). Model performance was evaluated using both in-sample and Leave-One-Land-Use-Out validation schemes, and a global quantile machine learning model (QML) without spatial weighting served as the benchmark. GWQML achieved R² values up to 0.85 and prediction interval coverage probabilities up to 0.9, with intermediate kernel bandwidths (750–1250 m) offering the best balance between accuracy and uncertainty calibration. Spatial autocorrelation analysis using Moran’s I revealed a lower residual clustering under GWQML relative to the benchmark model, which suggests improved handling of local spatial variation. This study represents one of the first applications of geographically weighted kernel functions in a quantile machine learning framework for daily soil moisture prediction. The approach implicitly captures spatially varying relationships while delivering calibrated uncertainty estimates for scalable SM monitoring across heterogenous agricultural landscapes. Full article

Tourism ecological efficiency (TEE) is a significant indicator of the development level of green and intensive tourism. However, conventional directional and radial TEE measurement approaches overlook critical factors such as intermediate process influences and input–output slack variables, potentially leading to biased estimates. Urban areas are key to coordinating tourism across provinces, so accurately assessing the TEE is vital for sustainable regional tourism. This study uses an improved TEE measurement model to measure the TEE of the Guangdong–Fujian–Zhejiang (GFZ) coastal city clusters from 2010 to 2021. The improved TEE measurement model is a three-stage super-efficiency SBM approach. It then uses standard deviation ellipses and geographic detectors to analyze the TEE’s spatiotemporal characteristics and influencing factors. The findings indicate the following: (1) The three-stage super-efficiency SBM approach improves the accuracy and validity of measurement results by removing external environmental variables. (2) During the study period, the TEE values of the GFZ coastal city clusters were above average (except for Meizhou, where the efficiency improved). Temporally, the TEE values of 75% of the cities showed an increasing trend; spatially, the high-value areas increased significantly, the middle- and low-value areas decreased, and the center of gravity shifted to the north and south. (3) The years 2016–2021 saw an increase in external development factors and the use of external resources. The study’s findings can serve as scientific benchmarks for TEE measurement, as well as the low-carbon and environmentally friendly growth of tourism in urban agglomerations. Full article

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

Despite the growing relevance of subnational development strategies in emerging economies, the literature lacks empirical applications of classical trade models to territorial productive specialization. This study addresses this gap by adapting the Heckscher–Ohlin–Samuelson (HOS) model to identify optimal specialization patterns in intermediate municipalities with asymmetric factor endowments. Using data from 2017 to 2021 for Tunja and Chiquinquirá (Colombia), we estimate capital-to-labor ratios and sectoral factor intensities to detect specialization aligned with local comparative advantages. The results show that Tunja exhibits capital-abundant conditions favoring specialization in sectors such as real estate, construction, and financial services, while Chiquinquirá demonstrates labor-intensive dynamics suitable for tourism and service industries. Methodologically, the study extends the HOS model to subnational scales, offering a robust analytical tool for regional policy formulation. This article contributes to the academic debate by bridging international trade theory and regional development, and it provides empirical evidence to support place-based industrial policies. Our findings emphasize the importance of aligning productive strategies with structural endowments to foster inclusive and sustainable development in emerging territories. Full article

Zinc is an essential trace mineral for livestock, but excessive use can contribute to ecotoxicity and antimicrobial resistance. The objective of this study was to assess the impact of different zinc oxide (ZnO) levels in diets for weaned pigs on growth performance, mortality, dietary zinc flow, and environmental impacts. A 6-week feeding trial with 432 weaned pigs assessed three dietary treatments: high ZnO (pharmaceutical levels), intermediate ZnO, and low ZnO (EU recommendation). Growth performance for the growing–finishing period was modeled using the NRC (2012), and dietary Zn intake and fecal Zn excretion were estimated. Environmental impacts were analyzed via life cycle assessment (LCA) using SimaPro LCA software. High ZnO improved growth performance and reduced mortality (p < 0.05), but increased nursery fecal zinc excretion, resulting in a total fecal Zn excretion per pig of 54,125 mg, 59,485 mg, and 106,043 mg for low-, intermediate-, and high-ZnO treatments, respectively. In the nursery phase, high-ZnO treatment had the greatest impact on environmental footprint, increasing freshwater ecotoxicity and marine ecotoxicity indicators by 59.6% and 57.9%, respectively. However, high-ZnO-fed pigs had a greater body weight at the end of the nursery phase and were predicted to achieve a higher growth rate per 130 kg market pig, with fewer days to market and by sparing feed. Therefore, high-ZnO-fed pigs had reduced environmental burdens, including global warming potential, ozone depletion, land use, and mineral resource depletion. These findings demonstrate how livestock nutritionists can apply integrated modeling approaches to link animal performance with environmental outcomes within a One Health framework. Full article

Position localization of wideband source over multipath environment is addressed in this paper. Traditional methods generally estimate intermediate parameters first and then use these parameters to construct equations for determining the source position. However, the localization accuracy of such methods deteriorates significantly in the presence of multipath effects. In this paper, a direct position determination method combining Taylor expansion and subspace data fusion in the cross-spectrum domain is proposed. The method constructs the data model based on the cross-spectrum of the received signals from arbitrary sensor pairs, effectively avoiding the loss of the available information. Subsequently, forward spatial smoothing is used to address the rank-deficiency problem caused by the multipath effect. Finally, a cost function using subspace data fusion is constructed, and the optimal value is derived via first-order Taylor expansion to compensate for the position estimation bias. The proposed method shows higher localization accuracy compared to state-of-the-art methods. The numerical and experimental results validate the superior localization performance of the proposed algorithm. Full article

Folate receptor alpha (FRα) is a high-affinity folate transporter overexpressed in various epithelial malignancies, particularly high-grade serous ovarian carcinoma. Given its restricted expression in normal tissues and accessibility in tumors, FRα is an emerging therapeutic target. Immunohistochemistry (IHC) is the standard method for FRα assessment; however, interpretation is semi-quantitative and prone to interobserver variability. This study aimed to evaluate interobserver agreement among 12 pathologists in the IHC assessment of FRα in ovarian cancer, focusing on internal control adequacy, staining intensity, and the percentage of FRα-positive tumor cells. Thirty-seven high-grade serous ovarian carcinoma cases were stained using the VENTANA FOLR1 (FOLR1-2.1) RxDx Assay. A reference panel of four expert pathologists established consensus diagnoses. Twelve pathologists independently assessed the slides, recording internal control adequacy, staining intensity (positive vs. negative), and percentage of FRα-positive tumor cells. Interobserver agreement was measured using Fleiss’ kappa and intraclass correlation coefficient (ICC). Agreement on internal control adequacy was almost perfect (κ = 0.84). Substantial agreement was observed for staining intensity (κ = 0.76), while percentage estimation showed almost perfect concordance (ICC = 0.89). Discrepancies were primarily confined to borderline cases (65–85% positivity) and tumors with intermediate staining, reflecting interpretive challenges near clinical decision thresholds. Pathologists demonstrated high reproducibility in FRα IHC assessment, particularly in estimating percentage positivity and control adequacy. These findings support the clinical utility of FRα IHC but underscore the need for standardized scoring criteria and potential integration of digital tools to enhance consistency, especially in borderline cases. Full article

This study analyzes the impact of the COVID-19 pandemic on alcohol, cannabis, and tobacco use among adolescents aged 14–19 in Central Catalonia across three periods. Data were obtained from two waves of the DESKcohort project. The first wave (n = 4641) was pre-COVID-19 and the second wave was divided into two phases: post-COVID-19 with restrictions (n = 3478) and post-COVID-19 without restrictions (n = 2900). The prevalence of monthly binge drinking, cannabis use in the last 30 days, and daily tobacco use was calculated. Poisson regression models estimated adjusted prevalence ratios (aPR) comparing two post-pandemic phases to the pre-pandemic baseline. Binge drinking increased during the restrictions among girls [aPR = 1.5 (95%CI: 1.1–2.1)] and boys [aPR = 1.7 (95%CI: 1.3–2.3)]. Cannabis use decreased during restrictions and remained low post-pandemic, especially among girls [aPR = 0.6 (95%CI: 0.5–0.8)] and boys in the 4th Compulsory Secondary Education (CSE) [aPR = 0.4 (95%CI: 0.3–0.6)], and girls in the 2nd Post-Compulsory Secondary Education/Intermediate-Level Training Cycles (PCSE/ILTC) [aPR = 0.7 (95%CI: 0.6–0.9)]. Daily tobacco use also dropped among boys in the 4th year of CSE [aPR = 0.5 (95%CI: 0.3–0.7)] and girls in the 2nd year of PCSE/ILTC [aPR = 0.7 (95%CI: 0.6–0.9)]. The COVID-19 pandemic has created a unique opportunity to reassess patterns of adolescent substance use in a context of sudden social disruption. The findings highlight the need to denormalize alcohol and tobacco use and promote healthier adolescent behavior through education. Full article

A 3D model integrating mineralogical, petrological, and geostatistical resource estimation was developed for Zone C of the Kofi Birimian gold deposit in Western Mali. Petrographic analysis identified two forms of gold mineralization: (i) native gold or electrum inclusions within pyrite, and (ii) disseminated native gold along pyrite fractures. Four types of hydrothermal alteration–epidotization, chloritization, carbonatization, and albitization were observed microscopically. Statistical analysis of geochemical data classified five lithologies: mafic dyke, felsic dyke, diabase, faulted breccia, and intermediate quartz diorite. Minerals identified petrographically were corroborated by multivariate correlations among elements (Cr, Fe, Ni, Al, Ti, Na, and Ca), as revealed by Principal Component Analysis (PCA). A 3D borehole-based model revealed spatial correlations between hydrothermal alteration zones and associated geochemical anomalies, notably tourmalinization (B) and albitization (Na), with the latter serving as a key indicator for new exploration targets. The spatial associations of anomalous Ag, B, Hg, As, and Na commonly linked to tourmalinization suggest favorable zones for gold and silver mineralization. Geostatistical analysis identified isotropic continuous mineralized structures for most elements, including gold. Spherical isotropic variograms with ranges from 35 to 75 m were fitted for in situ resource estimation (e.g., silver ≈ 40 m; gold ≈ 60 m). The resulting estimated resources (indicated + inferred), based on a 1.0 g/t Au cut-off, are 2.476 Mt at 3.5 g/t Au indicated (0.278 Moz or 8.67 t), and 1.254 Mt at 2.78 g/t Au inferred (0.112 Moz or 3.49 t). This study provides a framework for identifying new mineralized zones, and the multidisciplinary approach demonstrates the connections between mineralogy and the information embedded in geochemical datasets, which are revealed through appropriate tools and an understanding of the underlying processes. Full article

Simple Baseline has become a dominant benchmark in human pose estimation (HPE) due to its excellent performance and simple design. However, its “strong encoder + simple decoder” architectural paradigm suffers from two core limitations: (1) its non-branching, linear deconvolutional path prevents it from leveraging the rich, fine-grained features generated by the encoder at multiple scales and (2) the model lacks explicit prior knowledge of both the absolute positions and structural layout of human keypoints. To address these issues, this paper introduces AFJ-PoseNet, a new architecture that deeply enhances the Simple Baseline framework. First, we restructure Simple Baseline’s original linear decoder into a U-Net-like multi-scale fusion path, introducing intermediate features from the encoder via skip connections. For efficient fusion, we design a novel Attention Fusion Module (AFM), which dynamically gates the flow of incoming detailed features through a context-aware spatial attention mechanism. Second, we propose the Joint-Aware Positional Encoding (JAPE) module, which innovatively combines a fixed global coordinate system with learnable, joint-specific spatial priors. This design injects both absolute position awareness and statistical priors of the human body structure. Our ablation studies on the MPII dataset validate the effectiveness of each proposed enhancement, with our full model achieving a mean PCKh of 88.915, a 0.341 percentage point improvement over our re-implemented baseline. On the more challenging COCO val2017 dataset, our ResNet-50-based AFJ-PoseNet achieves an Average Precision (AP) of 72.6%. While this involves a slight trade-off in Average Recall for higher precision, this result represents a significant 2.2 percentage point improvement over our re-implemented baseline (70.4%) and also outperforms other strong, publicly available models like DARK (72.4%) and SimCC (72.1%) under comparable settings, demonstrating the superiority and competitiveness of our proposed enhancements. Full article

Background: Accurate estimation of the postmortem interval (PMI) is critical in forensic death investigations. Traditional signs of death—algor mortis, livor mortis, and rigor mortis—are generally reliable only within the first two to three days after death, with their accuracy decreasing as decomposition progresses. This paper presents a systematic review conducted in accordance with PRISMA guidelines, aiming to evaluate and compare current methods for estimating the PMI. Specifically, the study identifies both traditional and modern techniques, analyzes their advantages, limitations, and applicable timeframes, critically synthesizes the literature, and highlights the importance of combining multiple approaches to improve accuracy. Methods: A systematic search was conducted in the PubMed, Scopus, and Web of Science databases, following the PRISMA guidelines. The review included original articles and reviews that evaluated PMI estimation methods (through thanatological signs, entomology, microbial succession, molecular, imaging, and omics approaches). Extracted data included study design, methodology, PMI range, and accuracy information. Out of the 1245 identified records, 50 studies met the inclusion criteria for qualitative synthesis. Results: Emerging methods, such as molecular markers, microbial succession, omics technologies, and advanced imaging show improved accuracy across extended postmortem intervals. RNA degradation methods demonstrated higher accuracy within the first 72 h, while entomology and microbial analysis are more applicable during intermediate and late decomposition stages. Although no single method is universally reliable, combining traditional and modern approaches tailored to case-specific factors improves overall PMI estimation accuracy. Conclusions: This study supports the use of an integrative, multidisciplinary, and evidence-based approach to improve time-since-death estimation. Such a strategy enhances forensic outcomes by enabling more precise PMI estimates in complex or delayed cases, increasing legal reliability, and supporting court-admissible expert testimony based on validated, multi-method protocols. Full article