Search Results (918)

Estimation of rice crop yield at the village level is essential because village is the Insurance Unit (IU) for rice crop in many regions in India, and timely and accurate yield information at this scale supports timely and transparent claim settlements for farmers and supports local agricultural planning. To achieve this, a multi-source satellite data-based machine learning approach was used to estimate rice yield at the village level using optical and SAR data, climatic data and land surface model-derived parameters in Kakinada of Andhra Pradesh, India. The predictor dataset included seasonal cumulative rainfall, seasonal Normalized Difference Vegetation Index (NDVI)-Max, seasonal NDVI-Mean, seasonal Land Surface Water Index (LSWI)-Max, seasonal LSWI-Mean, season total Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and season total Root Zone Soil Moisture (RZSM), and season total backscatter of the Sentinel-1 VH polarization were used to represent crop greenness, moisture status, photosynthetic activity, soil water availability, canopy structure, and seasonal water supply. For model development and validation, village-level rice yield data from 2017 to 2023 was used, which was collected through Crop Cutting Experiment (CCE) at the maturity stage of Kharif season. In this study, four machine learning models such as Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) were evaluated. The multi-source satellite data and yield data for the period 2017–2021 were used to train the models, which were independently tested on 2022 data and then applied to predict the rice yield in 2023. Leave-One-Year-Out (LOYO) cross-validation was also conducted on the 2017–2022 data to assess temporal robustness and generalization capability across years. Among the evaluated models, Random Forest exhibited the best overall performance. For the independent test year 2022, RF achieved an R² of 0.465, RMSE of 415.34 kg ha⁻¹, MAE of 322.22 kg ha⁻¹, and MAPE of 10.36%. For the prediction year 2023, RF achieved improved accuracy with an R² of 0.838, RMSE of 325.75 kg ha⁻¹, MAE of 262.21 kg ha⁻¹, and MAPE of 7.68%. Further, LOYO cross-validation also showed the robustness of RF, achieving the highest mean R² of 0.702 and mean RMSE of 384.73 kg ha⁻¹. The results illustrate that multi-source satellite data combined with machine learning can be a reliable and operationally useful tool in predicting village-level rice yield, which can be used for crop insurance claim settlement. Full article

Background/Objectives: To quantify geographical distances to nearest general practitioner (GP) services for all household postcodes in the United Kingdom. Methods: We mapped household postcodes in the United Kingdom and computed distances to nearest GP practice, using centroid geographical coordinates (latitude and longitude). We also analysed the total number of GP practices throughout local area districts (LADs) in relation to population density. Results: As of December 2023, there were 7965 active GP practices across the UK, serving a total registered population of over 73 million patients. Analysis of 1.78 million household postcodes revealed that 98.8% were within 10 km of a GP practice (measured as a straight-line). The most distant postcode was in the Shetland. Throughout the UK, population density was weakly or strongly correlated with number of GP practices in the different LADs, with wide variations, and the strongest correlation observed in Northern Ireland. Conclusions: In the UK, geographical proximity to nearest GP practice was found to be within 10 km for the vast majority of residents. Weak to strong correlations between population density and number of GP practices were observed. Future work should quantify the impact of both staffing capacity and public transport availability on distance to GP surgeries across the UK, to better characterise structural determinants of primary care accessibility. Full article

The formation of direction-selective visual circuits is thought to involve the progressive refinement of synaptic connections during development. In biological visual systems, patterned spontaneous activity, such as retinal waves, has been proposed to provide structured spatiotemporal activity that contributes to the refinement of visual pathways before mature sensory experience is fully established. Motivated by this view of activity-dependent circuit organization, this study develops a Self-Organizing Map-Based Artificial Visual System, termed SOM-AVS, to examine how organized connectivity may emerge in a motion direction-detecting circuit. In the proposed model, local motion-detecting units extract elementary direction-related responses from visual input and project them to a global motion direction layer represented by a self-organizing map. Connections are progressively reshaped by winner selection and local cooperative updating, allowing initially unstructured connections to gradually acquire organized direction preference. After repeated exposure to generated retinal-wave-like activity data, the SOM layer develops topographically arranged regions corresponding to distinct motion directions. This organization suggests that direction-related response domains can emerge from activity-dependent self-organization without externally imposed labels. The proposed model should be regarded as a biologically motivated computational abstraction rather than a direct physiological reproduction of retinal-wave-driven circuit development. Within this scope, the model provides a computational framework for examining how retinal-wave-like activity and self-organizing plasticity may contribute to the formation of motion direction-related connectivity, offering a possible developmental interpretation for bio-inspired visual motion processing. Full article

To achieve scientific cost forecasting, this study investigates structural changes in mining cost driven by the widespread adoption of mechanized mining, increased mining depths, and significant operational variations. Based on the backpropagation (BP) neural network, this study systematically analyzes the cost-composition characteristics of modern mining operations and applies activity-based costing to achieve refined cost accounting for each mining operation unit. Ten key influencing factors, including working space, stope temperature, stope depth, haulage distance, worker seniority and work efficiency, scraper efficiency, equipment service life, fuel and lubricant consumption rates, are identified by analyzing cost variation patterns. Principal component analysis (PCA) is used to reduce the dimensionality of the ten factors to simplify this model and enhance prediction accuracy. The PCA-BP neural network mining cost forecasting model is built with the principal components extracted as input variables. Actual cost data from an underground metal mine in Shandong Province is used for our model training and validation, with adopting linear regression, eXtreme Gradient Boosting (XGBoost), and a traditional BP neural network as the comparison models for performance evaluation. Our prediction results indicate that the PCA-BP model achieves an average relative error of 3.80% and a root mean square error of 1.43, both significantly outperforming the comparison models. The results demonstrate superior predictive accuracy and stability of our model. Validated with data from a typical deep mechanized gold mine in eastern China, the PCA-BP cost forecasting model requires parameter retraining based on local production conditions for applications in other regions. This study confirms that the model aligns well with the cost characteristics of modern underground metal mines and produces effective predictions, offering reliable quantitative support for the development of cost control strategies and optimization of cost planning in mining enterprises. Full article

WiFi channel state information (CSI) has become a compelling sensing modality for contactless human activity recognition. However, differences in datasets, preprocessing protocols and model configurations make consistent comparison and reproducibility challenging. This study presents a unified baseline evaluation of four widely adopted deep learning architectures: multilayer perceptron (MLP), convolutional neural network (CNN), gated recurrent unit (GRU) and a hybrid CNN–GRU model across multiple publicly available CSI datasets encompassing a range of sensing tasks. We harmonize the datasets, implement a standardized preprocessing and training pipeline to reduce experimental inconsistencies and support controlled within-dataset comparisons of model behavior. Evaluations include single-person activity recognition, fall-risk estimation, multiperson occupancy classification and localization-aware activity recognition, representing progressively higher temporal and spatial complexity. Our results show dataset-dependent trends: CNNs provide an efficient accuracy–complexity trade-off in several structured activity scenarios, whereas GRUs are advantageous when temporal dynamics are more prominent, although with greater training and inference costs. In contrast, MLPs generally underperform due to limited capacity to capture spatial and temporal dependencies. Confusion matrix analysis reveals that dynamic behaviors and low-motion states remain challenging to distinguish, underscoring the importance of temporal modeling. By releasing the complete experimental pipeline and benchmarking results, this work establishes a reproducible reference framework for the research community and highlights directions for future investigation, including cross-dataset generalization, hybrid model design and lightweight deployment strategies. Full article

To elucidate the molecular structural characteristics of weakly caking coal and the microscopic mechanism by which CO₂ inhibits coal–oxygen complexation, a weakly caking coal sample from the Dahaize coal mine in Shaanxi, China, was investigated using proximate and ultimate analyses, FTIR, XPS, and ¹³C NMR. On this basis, a representative coal macromolecular model was constructed and further analyzed using density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations. The molecular formula of the representative weakly caking coal from the Dahaize mine (RNM) unit was determined as C₁₇₆H₁₅₆N₂O₁₉S₂. The aromatic carbon fraction was 65.41%, and the bridge carbon/peripheral carbon ratio was 0.25, indicating a certain degree of aromatic condensation but a limited content of highly fused aromatic structures. DFT calculations revealed that the reactive sites were mainly located around edge oxygen-containing functional groups and bridging structures, with a maximum Fukui index of approximately 0.024. Adsorption simulations showed that O₂ and CO₂ adsorption on RNM followed Langmuir-type behavior over 303.15–363.15 K: adsorption capacity increased with pressure and decreased with temperature. At 8000 kPa, the CO₂ uptake was approximately 1.6 times that of O₂. In the binary O₂-CO₂ system, CO₂ preferentially occupied pore surfaces and high-energy adsorption sites, reducing the local enrichment of O₂. These results provide a molecular-level explanation for the inhibition of coal–oxygen complexation by CO₂ through competitive adsorption, site shielding, and decreased oxidation probability at active sites. Full article

Currently, there is a trend in the energy sector towards the application of edge computing techniques to facilitate active monitoring of distribution networks. The adoption of this technique is crucial for applications involving distributed monitoring systems that require real-time data processing with low latency. An edge computing environment ensures an adequate response to two time-level response requirements. One for events that could trigger a serious problem in the distribution network, and a less demanding one for the management of energy. This article justifies and analyzes an architecture specifically designed to provide an adequate response to the two levels of time demand that set the procedure followed for the monitoring, storage and local diagnosis of several photovoltaic plants located on the same distribution network, with the aim of studying their joint production. One of the main contributions is related to the expansion of the capabilities of Phasor Measurement Units (PMUs) to monitor solar radiation or energy production perimeters by sector. The second major contribution is to guarantee the quality of the measurements and low latency in communications, using as a reference the IEEE C37.118-2011 synchrophasor standard in cooperation with the Time Sensitive Networking (TSN) synchronization protocol that guarantees simultaneity in distributed measurements. In short, a procedure is sought that allows a real-time response with the use of computing techniques very close to the origin of the measurements, guaranteeing exhaustive control from the moment the capture begins until the parameters are stored in a time series database. Full article

This study reframes street-level sustainable urban renewal as a coordination problem between street vitality and relative low-carbon performance, rather than treating vibrant activity and carbon-pressure reduction as separate planning objectives. Its main contribution is an integrated street-level diagnostic framework that combines multidimensional vitality measurement, township-constrained carbon-emission reference estimation, vitality–carbon mismatch identification, and interpretable nonlinear mechanism analysis within unified street analytical units. Although previous studies have substantially advanced the measurement of street vitality and urban carbon emissions, these two strands of research have often developed separately. As a result, limited evidence is available on whether high-vitality streets also perform well in low-carbon terms, where vitality–carbon mismatches emerge, and which built-environment conditions are associated with more coordinated outcomes. Taking the five central districts of Chengdu, China, as a case, this study integrates multi-source activity, mobility, built-environment, and emission-related data. Street vitality is measured through activity agglomeration, temporal continuity, functional support, and external connectivity, while relative low-carbon performance is derived from the reverse normalization of length-normalized carbon-emission intensity based on a township-constrained street-level emission reference estimate. The results show that street vitality and low-carbon performance are spatially uneven and frequently mismatched, as high activity does not automatically translate into stronger low-carbon performance, and lower-carbon pressure does not necessarily indicate a vibrant urban environment. More coordinated streets are associated with context-specific combinations of functional organization, transport operation, built form, street-interface quality, and ecological background. Nonlinear diagnostic results further suggest that coordination is favored by moderate, balanced, and locally adapted built-environment conditions rather than by the simple maximization of individual indicators. These findings shift the discussion from whether vitality and low-carbon performance are desirable in isolation to how they can be jointly diagnosed and improved in street-level urban renewal. Full article

Let $\mathcal{A}=\{p+Jq:p,q\in \mathbb{C},J^2=-1\}$ be the commutative bicomplex-type algebra in which J commutes with the scalar imaginary unit. A Cauchy–Riemann-type operator $\overline{D}$ is studied on domains in ${\mathbb{C}}^2$. In the active coordinates $\xi =z_1-i{z}_2$ and $\eta =z_1+i{z}_2$, the equation $\overline{D}f=0$ is diagonal in the idempotent basis: the $e_{+}$-component is holomorphic in $\xi$ with $\eta$ as the parameter, while the $e_{-}$-component is holomorphic in $\eta$ with $\xi$ as the parameter. The expression $e_{+}F\left(\xi \right)+e_{-}G\left(\eta \right)$ is the parameter-independent subcase. From this decomposition, one obtains a slice characterization, a criterion for separatedness, a comparison with ordinary holomorphic functions of two complex variables, active-variable Cauchy formulas and estimates, local series with parameter-dependent coefficients, reflection symmetry, and Hardy and Bergman kernel lifts on the separated Hilbert spaces. Full article

Community-based tourism (CBT) destinations are increasingly expected to align visitor experiences with climate responsibility, yet local managers often lack product-level carbon evidence that can guide practical route redesign and service decisions. This study addresses this aggregation-to-action gap by developing a route-level carbon footprint baseline for a CBT itinerary in Ban Boonjaem, Phrae Province, Thailand. Using an exploratory and applied case study design, the study treats one completed six-hour, non-overnight itinerary as the functional unit and applies a life-cycle-informed operational boundary covering transportation, food and beverage consumption, and solid waste generated during the route test. Primary activity data were collected from one organized route test involving 20 Thai domestic volunteer tourists and were matched with relevant emission factors to estimate total and per-tourist emissions. The tested itinerary generated 0.2234 tCO2e, equivalent to 223.4 kgCO2e in total and approximately 11.2 kgCO2e per tourist per trip. Transportation was the largest emission domain, accounting for 55.89% of total route emissions, followed by food and beverage consumption at 38.55%, while waste contributed 5.56%. Together, transportation and food and beverage represented 94.44% of measured emissions, indicating that the route’s carbon profile was shaped mainly by mobility arrangements and service provisioning rather than waste generation alone. The study contributes a transparent, route-specific operational baseline for low-carbon CBT management. The findings should be interpreted as case-specific decision-support evidence rather than as a destination-wide carbon inventory or statistically generalizable estimate. Full article

The beef production chain plays a strategic role in Brazilian and global agri-food systems and faces growing demands for sustainability, transparency, and traceability. Building on official Animal Transit Guide (GTA) records from Mato Grosso do Sul, Brazil, this study examines whether a parsimonious agent-based model (ABM) can generate the main structural signatures of an observed cattle-trade network. The empirical benchmark is a directed and weighted network with 20,827 nodes and 258,120 weighted edges. The ABM represents producers and slaughterhouses as spatial agents connected by trade decisions based on three mechanisms: destination attractiveness, defined as the accumulated pull of a slaughterhouse based on previous simulated throughput; geographic distance, representing spatial friction; and relational memory, representing the tendency to repeat previous commercial ties. Producer choice is formalized through a local utility function that combines attractiveness, distance penalty, and relational memory under capacity, sourcing-radius, and saturation constraints. In the simulated scenarios, the top-five slaughterhouses accounted for 38.49 ± 2.56% of throughput at reduced scale and 14.40 ± 0.65% at intermediate scale, while weighted mean distances were 11.94 ± 0.56 and 9.07 ± 0.39 model units, respectively. The model reproduced, in structural and mechanistic terms, the emergence of dominant hubs, the concentration of flows, and the bounded increase in transaction distance with connectivity around the empirical threshold of kw ≈ 256. Sensitivity analyses indicated that attractiveness increases concentration, distance localizes transactions, and relational memory can stabilize repeated ties when recurrent activation is represented. Rather than reconstructing individual transactions, estimating policy impacts, or identifying a unique parameter vector, the model provides a generative explanation of how local trade rules can produce macro-level network patterns consistent with the observed cattle-trade regime. These findings support future prospective analyses of cattle governance, traceability, and sustainability within the broader context of Livestock 4.0. Full article

Acne vulgaris is a prevalent inflammatory disorder of the pilosebaceous unit involving follicular hyperkeratinization, altered sebum production, Cutibacterium acnes proliferation, microbiome imbalance, and immune activation. Although antibiotics, retinoids, benzoyl peroxide, and keratolytic agents remain central to clinical management, their long-term use may be limited by irritation, recurrence, adherence issues, and increasing antimicrobial resistance. This narrative review critically evaluates the dermatological relevance of Melaleuca alternifolia tea tree essential oil (TTEO), Mentha piperita peppermint essential oil (PPEO), and polyhydroxy acids (PHAs), as well as their incorporation into biopolymeric delivery systems for acne-oriented topical applications. Following SANRA principles, evidence from clinical, preclinical, ex vivo, and in vitro studies was synthesized, with emphasis on antimicrobial activity, inflammatory modulation, keratolytic and barrier-supportive effects, formulation stability, and release behavior. TTEO shows the strongest clinical support among the reviewed natural bioactives, including reductions in lesion counts and acne severity when applied as conventional or nanoemulsion-based formulations. PPEO is mainly supported by experimental evidence, particularly antimicrobial activity against acne-associated microorganisms, anti-inflammatory potential, and menthol-related neurocutaneous effects, whereas acne-specific clinical validation remains limited. PHAs, particularly gluconolactone, are better supported for barrier improvement, hydration, tolerability, and seboregulation than for direct acne lesion reduction. Hydrogels, electrospun nanofibers, polymeric films, nanoencapsulation systems, and controlled-release platforms may improve local retention, protect volatile or irritation-prone compounds, and modulate active release at the skin surface. However, most biopolymeric platforms still rely on early-stage or indirect dermatological evidence. Overall, biopolymeric delivery systems offer a rational formulation strategy to improve the stability, tolerability, and localized action of selected acne-relevant bioactives, but their clinical translation requires standardized composition, reproducible fabrication, skin-relevant release assays, safety assessment, and controlled human studies. Full article

Background/Objectives. Country-level evidence on the economic footprint of home care nursing is still scarce, particularly in systems where tariffs for community-based nursing are lacking. In Italy, recent laws have expanded home care; yet planning and funding remain constrained by the absence of robust micro-costing evidence. Objectives. To estimate the accounting cost of home care nursing visits in Italy using a bottom-up micro-costing approach and to identify the main cost drivers influencing expenditure. Methods. A multicentre, cross-sectional study was conducted. Data were collected in two phases: (1) a national survey of 3949 home care nurses from 70 Local Health Authorities (April–October 2023), describing workload, travel time, and the most frequently performed activities; and (2) a time-and-motion study of 527 consecutive home visits performed by 83 nurses in three Local Health Authorities (March 2024). Direct costs were estimated from the Italian National Health Service perspective and included nursing time, travel time and transportation, back-office activities, and materials. Personnel costs were derived from national collective labour agreements and inflation-adjusted. A base-case scenario estimated accounting costs directly measured in the study. An extended, illustrative scenario explored the economic value of nursing activities by applying existing outpatient tariffs. Deterministic and probabilistic sensitivity analyses (10,000-iteration Monte Carlo simulation) were performed. Results. The mean accounting cost of home care nursing was €27.78 per patient per day. At the provider level, the corresponding daily cost per nurse was €190.00, assuming a mean caseload of 6.84 patients per nurse per shift. In the extended scenario, the imputed economic value of nursing activities increased the estimated daily cost to €120.81 per patient and €826.32 per nurse. Sensitivity analyses identified organizational factors (particularly the number of patients per shift and the number of activities per visit) as the dominant cost drivers, while material and transportation costs had a comparatively limited impact. Conclusions. Home care nursing in Italy appears to be delivered at a relatively low accounting cost, with organizational factors playing a greater role than unit prices in determining expenditure. The absence of a dedicated reimbursement framework for nursing activities may result in a substantial under-recognition of the economic value of home-based nursing care. These findings provide preliminary evidence to support workforce planning, reimbursement policies, and the sustainable development of territorial care services. 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

Background/Objectives: Metabolic dormancy in biofilms leads to reduced drug efficacy in these communities. Different pharmacokinetics and adverse side effects complicate the simultaneous delivery of multiple drugs at appropriate concentrations to the infection site. This study aimed to develop chitosan-coated mesoporous silica nanoparticles loaded with curcumin and amphotericin B (CS@MSNs-Cur-AmB) and to evaluate their antibiofilm activity combined with antimicrobial photodynamic therapy (PDT) against Streptococcus mutans and Candida albicans dual-species biofilms. Methods: CS@MSNs-Cur-AmB were developed. The structure and morphology of the nanoparticles were evaluated using Fourier transform-infrared spectroscopy (FTIR), zeta potential, field emission scanning electron microscopy (FESEM), and thermogravimetric analysis (TGA). Cytotoxicity toward human gingival fibroblasts was assessed. Colony-forming units per milliliter (CFU/mL) were determined. The metabolic activity of biofilm-forming cells was measured using the tetrazolium (MTT) assay. Results: Physicochemical analyses confirmed the synthesis of CS@MSNs-Cur-AmB, revealing a particle size of 228 nm and thermal stability up to 600 °C. Cytotoxicity assays showed that CS@MSNs-Cur-AmB exhibited good biocompatibility (>90%). CS@MSNs-Cur-AmB improved antimicrobial activity, which was further enhanced by blue light-emitting diode (LED) irradiation. CS@MSNs-Cur-AmB under LED irradiation showed the strongest effect, reducing metabolic activity to 27.74 ± 4.08% (1 W/cm², 1 min), p < 0.001). Conclusions: Formulating two drugs in nanocarrier systems may improve therapeutic efficacy by increasing local concentration and reducing systemic exposure. This offers an effective strategy for combating oral biofilms. Full article