Search Results (3,408)

Background: In May 2023, Ghana implemented a 20% ad valorem tax on bottled water and sweet beverages (SBs), replacing a 17.5% tax; sachet water remained untaxed. The effect on low-income consumers’ purchasing decisions and consumption patterns remains poorly understood. Objective: We aimed to explore factors influencing water and SB purchasing behaviours among low-income households in four peri-urban Accra communities. Methods: This study employed a convergent parallel mixed-methods design. Four focus group discussions (n = 36) and a cross-sectional survey (n = 43) were conducted among purposively sampled household primary shoppers in early 2025 across Oyarifa, Teiman, Kweiman, and Danfa. Data were analysed thematically and descriptively. Results: Of 43 participants, 67% were female and 65% had junior high school education. Water insecurity was common (60%), and sachet water was the main drinking source (77%). SB purchasing was driven by taste and convenience, while sachet water choices were linked to perceived safety, price, and availability. Tax awareness was moderate (56%); many perceived bottled water taxation as unfair and reported intentions to switch to cheaper local alternatives. Conclusions: Limited tax awareness and perceived inequities suggest the need for policy refinements to better align fiscal measures with public health objectives. Full article

The thermo-mechanical degradation of the base–subgrade interface in airport pavements was investigated using a three-dimensional sequentially coupled finite element framework in ABAQUS 2023, in which progressive interfacial debonding was described by a bilinear cohesive-zone model through the damage variable CSDMG. The results show that thermal loading markedly accelerates interface degradation when combined with moving wheel loads. Compared with the wheel-loading-only condition, thermo-mechanical coupling advances the first damage initiation from 0.04993 h to 0.00254 h and shortens the severe-degradation stage from 1.000 h to 0.00927 h. This acceleration is attributed to a thermal stress pre-weakening effect, whereby constrained thermal deformation partially consumes the available cohesive resistance and shifts the interface closer to the softening threshold before external loading is applied. A decomposition of the mixed-mode initiation criterion further indicates that the first damage event is governed by synergistic normal–shear interaction, with the normalized contribution ratio (t_n/t_n⁰)²:(t_s/t_s⁰)² = 0.38:0.62, showing that wheel-induced shear is the dominant trigger while tensile opening induced by thermal curling provides substantial preconditioning assistance. In addition, a representative normalized comparison between simulated average CSDMG and cumulative AE hit count demonstrates a consistent stage evolution from distributed deformation to accelerated localization and residual stabilization. These findings indicate that the base–subgrade interface should be treated as a temperature-sensitive weak layer in airport pavement assessment, particularly near joints and other discontinuity-controlled regions. Full article

This paper examines how international crude-oil price movements are transmitted to natural-rubber prices through the petrochemical–synthetic-rubber chain, with implications for Thailand as the world’s leading natural-rubber exporter and China as the dominant consumer. Using monthly data from April 2003 to March 2026 on the OPEC reference basket, butadiene, styrene–butadiene rubber (SBR), and the Shanghai natural-rubber benchmark, the analysis combines a nonlinear ARDL specification with a Pesaran–Shin–Smith bounds test, a long-run association decomposition into direct and synthetic-rubber-mediated components with bootstrap inference, and a threshold-NARDL extension that conditions the decomposition on the inventory state. Three findings stand out. First, the synthetic-rubber-mediated component accounts for approximately three-quarters of the estimated oil–natural rubber long-run association (73.5 percent, 95 percent bootstrap CI [60.6, 87.2]), with the residual direct component accounting for the remainder. Second, long-run pass-through is directionally consistent with concentration in the synthetic-rubber component, although Wald tests do not reject symmetry at conventional levels for either the synthetic-rubber component (Wald $p=0.135$) or the direct oil component ($p=0.166$). Third, the synthetic-rubber-mediated share is consistently larger in low-inventory regimes by 26 to 66 percentage points across three alternative regime variables, although the magnitude amplification of asymmetric pass-through itself is not robust. Asymmetric local projections and a Diebold–Yilmaz spillover analysis are reported as complementary horizon-indexed and network checks. The results imply that the synthetic–natural rubber spread, conditioned on the inventory state, may be more informative for natural-rubber price-risk monitoring than crude-oil prices alone. These findings have implications for commodity price-risk monitoring, export-income exposure, and stabilisation design in rubber-exporting economies. Because crude-oil shocks are not externally identified, all estimates are interpreted as decompositions of long-run association rather than causal mediation effects. Full article

The global transition to electric mobility is crucial for reducing transportation-related emissions, although there is a scarcity of empirical research on customer adoption psychology in transition economies in Central Asia. This study investigates the economic and structural drivers of electric vehicle purchase intention in the Republic of Uzbekistan. Data collected from prospective customers across large city hubs were analyzed using a dual hierarchical multiple linear regression model, supported by an empirical bootstrapping procedure with 2000 resamples, based on the rational choice theory and bounded rationality. The structural model shows that baseline socio-demographics explain insignificant initial variance ($R^2$ = 0.105); however, the integration of primary theoretical constructs yields a significant incremental variance change ($\Delta R^2$ = 0.096), explaining 20.1% of the total variance. Inferential tracking confirms that government incentives are the only statistically significant driver of the purchase intention (p = 0.009). Conversely, purchase cost (p = 0.251) and charging infrastructure (p = 0.475) lack direct significance. However, partial collinearity and infrastructure expectation effects systematically change these localized contact points. The study concludes that consumer intent in this emerging marketplace is primarily anchored to macro-level institutional policy signaling rather than immediate vehicle-specific characteristics or current physical network constraints. Full article

Wildlife is a common asset to which the local community has the right to consume. To achieve sustainable management of this resource, a community forest (CF) with a wildlife vocation has been set up as part of the “Sustainable management of wildlife and the bushmeat sector in Central Africa” project. Given the constraints faced by these community forests (CFs), we conducted a study to assess their governance in Gabon. Our objective was to examine whether their current mode of operation would allow them to survive in the long term, with a view to integrating sustainable hunting practices. To do this, we constructed a SWOT matrix (strengths, weaknesses, opportunities and threats) to determine their strengths and weaknesses, from which we carried out a factorial correspondence analysis (FCA) to identify potentially viable CFs. This enabled us to understand that most of the difficulties encountered by these CFs stem from the low level of appropriation of this concept by local communities, which is due to the low level of intervention by the forestry administration in raising awareness of CF management. This study shows that local communities must first take ownership of how CFs work so that they can better apply their success factors. Full article

Access to operational information in industrial plants forces operators to interrupt their tasks, walk to the human–machine interface (HMI) terminals, and navigate heterogeneous platforms—namely programmable logic controllers (PLC), supervisory control and data acquisition (SCADA) systems, manufacturing execution systems (MES), and enterprise resource planning (ERP) systems—consuming 15–30 min per query. Previous work integrated local large language models (LLMs) into a five-layer cognitive architecture deployed in a precast concrete plant, reducing that time to 14–23 s through voice-based conversational queries; however, model inference accounted for 55.3% of total latency and the system remained reactive. This work incorporates the event-driven paradigm as a non-intrusive augmentation layer that keeps the operational context permanently updated, continuously monitoring the process and refreshing knowledge only when significant changes occur. The architecture is fully local, cloud-independent, graphics processing unit (GPU)-free, and containerized via Docker Compose. Experimental results demonstrate a 26–31% reduction in response times (means of 9.84 s, 11.23 s, and 16.47 s for simple, moderate, and complex queries), an 8.4 °C reduction in peak hardware temperature (from 79.6 °C to 71.2 °C), a 41.6% decrease in thermal variability, and an expansion of the safety margin before central processing unit (CPU) throttling from 5.4 °C to 13.8 °C. The system achieved 100% success rate and availability over 30 min of autonomous operation, validated in a real industrial environment. Full article

Background/Objectives: Cephalometric analysis is essential in orthodontics and for studying conditions such as obstructive sleep apnea syndrome (OSAS). However, manually identifying anatomical landmarks and segmenting the pharyngeal airway on lateral cephalograms can be time-consuming and prone to errors. This study evaluates the CEPH_2D system, an AI-based tool designed to automate cephalometric landmark detection and pharyngeal airway segmentation from 2D lateral cephalometric radiographs. Methods: The system was evaluated on 35 anonymized lateral cephalograms obtained from patients aged 6–65 years, including mixed and permanent dentition cases. Two experienced clinicians generated and reviewed the ground truth annotations for cephalometric landmark localization and pharyngeal airway segmentation. System performance was assessed using mean radial error (MRE), successful detection rate (SDR), mean average precision (mAP), Dice similarity coefficient (DSC), precision, recall, and inference time. Results were compared with manual methods and existing automated tools. Results: The system reached a mean radial error (MRE) of 0.740 ± 0.793 mm for the key point detection task and a mean Dice Score (mDSC) of 0.935 ± 0.040 with an average processing time of 2.557 ± 0.504 s. Conclusions: CEPH_2D appears to be a promising adjunctive tool for automatic cephalometric landmark detection and pharyngeal airway segmentation on lateral cephalograms, although clinician verification remains advisable before clinical interpretation or treatment planning, particularly for landmarks showing higher detection errors. Full article

The traditional manual method of removing dried coconut kernels from shells is labor-intensive, time-consuming, and poses a risk of injury to workers. To address these challenges, this study developed an Oscillator-Based Coconut Dried Kernel Scraper to enhance efficiency, safety, and productivity in the coconut processing industry. The device utilizes an oscillatory mechanism driven by an electric motor to produce a controlled scraping motion, facilitating the effective detachment of the dried kernel from the shell with minimal physical effort. Key components of the prototype include a motor-driven oscillating blade, a kernel-holding fixture, and a safety enclosure. The design emphasizes the use of locally available materials and user-friendly operation. Preliminary testing demonstrated a significant reduction in processing time and operator fatigue compared to manual scraping methods. Furthermore, the researchers conducted a comparative performance evaluation between manual and mechanized scraping, with participants indicating a strong preference for the oscillator-based scraper. The product achieved the highest scores for efficiency and user satisfaction, particularly among small- to medium-scale coconut farmers. Based on these findings, it is recommended that future improvements include enhancements in design and the integration of a capacitive sensor to automate and further refine the control system. Full article

High dynamic range (HDR) imaging offers an enhanced visual experience by capturing a wider range of real-world luminance levels in digital images. Driven by the increasing demand for high-quality visuals, HDR monitor technology has seen significant advancements. As such monitors become commonplace in both consumer and professional settings, efficient methods are needed for both converting standard dynamic range (SDR) content to HDR—known as reverse tone mapping—and optimizing natural HDR lighting content for display on HDR monitors. A reverse tone mapping procedure aims to produce natural lighting levels, but even on high-end HDR monitors, such images still require adjustment to avoid hard clipping. This paper presents a solution that jointly does both steps: (1) reverse tone mapping to a display-aware HDR representation, and (2) direct generation of an image tailored for a chosen monitor brightness value. We propose a novel neural network architecture conditioned on the target peak brightness via a lightweight multi-layer perceptron (MLP) module injected at the bottleneck, which predicts a bracketed stack of LDR exposures serving as the method’s HDR representation. In this manner, the ill-posed tone mapping problem is guided by auxiliary information about display characteristics, improving visual quality. Experiments throughout the full consumer HDR range (100–4000 nits) show consistent improvements over the display-agnostic baseline in peak luminance utilization, local contrast, color and perceptual quality. Full article

This study investigates the association between urban greenness and sales in commercial alleys. We focus on 1090 commercial alleys in Seoul, South Korea, defined as neighborhood-scale open commercial streets or districts composed of small retail, service, cafe, and restaurant businesses, and combine spatially explicit measures of greenness with data on weekend sales to assess how variation in vegetation is associated with local economic performance. Greenness is measured by the normalized difference vegetation index (NDVI) derived from remote sensing imagery. We employ a set of global and spatially explicit models, including Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Multiscale Geographically Weighted Regression (MGWR), and a Python Geographical Random Forest (PyGRF, v0.0.12), to capture both overall and location-specific relationships. The results show that higher levels of greenness are significantly associated with higher weekend sales, with spatial heterogeneity observed across different areas of the city. The green investment efficiency index (GIEI) results further identify clusters of high investment efficiency in areas characterized by strong greenness–sales associations and relatively limited existing greenness. High GIEI values were concentrated in areas near natural amenities and dense residential neighborhoods, indicating potential priority locations for targeted greening interventions. By linking objective measures of greenness to observed sales at the scale of everyday commercial environments, this study contributes to a better understanding of how urban greenness is associated with consumer behavior and local economic activity. The findings provide practical implications for identifying areas where greening strategies may be considered as part of broader efforts to support more resilient and sustainable neighborhood commercial areas, while recognizing that the observed relationships are associative rather than causal. Full article

This study evaluated the effects of locally sourced ingredient dietary, with or without supplementation of black soldier fly (Hermetia illucens; BSF) larvae, on laying hen welfare. A total of 150 Lohman White hens aged 30 weeks were assigned to three treatments over ten weeks: a standard corn–soybean diet (CONTROL), an alternative diet incorporating triticale, faba beans and rapeseed meal (ALTER), and the ALTER diet supplemented with 5% dried BSF larvae provided separately (ALTER + BSF). Welfare assessments included larvae consumption time, a novel object test, an avoidance distance test, body condition scoring, and ethological observation of natural behaviors. Hens fed ALTER diet initially showed increased incidence of comb pecking wounds, which declined over the trial, along with reduced morning grooming compared to the CONTROL group (p = 0.009). However, the ALTER diet significantly improved plumage cleanliness (p < 0.001). Supplementation with BSF larvae partially mitigated early stress responses, maintained plumage cleanliness, and improved exploratory behavior and habituation to novelty (p < 0.001). Hens showed sustained and increased motivation to consume BSF larvae with an average consumption time of 5.5 min. Additionally, BSF supplementation was associated with increased resting and the emergence of dustbathing behavior during the afternoon (p < 0.05). No aggressive behaviors were observed, and no significant dietary effects were found for human fearfulness throughout the trial. In conclusion, dried BSF larvae can serve as effective environmental enrichment, improving hens’ adaptability to locally sourced diets in rural farming systems. Full article

Jujube (Ziziphus jujuba Mill.) is a widely consumed fruit in East Asia, yet its sharp-ended pits pose a high risk of gastrointestinal perforation in humans. While well-documented in human medicine, jujube pit ingestion in dogs has not previously been reported in the veterinary literature. This report describes a 15-year-old neutered male Maltese dog that presented with anorexia and lethargy five days after accidentally ingesting whole jujubes. Abdominal radiographs identified multiple intraluminal gastric foreign bodies demonstrating a distinctive, orientation-dependent sign: circular (transverse) or spindle-shaped (sagittal) opacities featuring a characteristic central longitudinal stripe. Although abdominal ultrasonography was limited in this specific case due to severe acoustic shadowing from localized gastric gas, the unique radiographic marker provided a reliable diagnostic clue before surgical confirmation. A gastrotomy was performed to successfully retrieve six intact, sharp-pointed pits, and the dog recovered uneventfully. To the authors’ knowledge, as the first report to describe the clinical progression and specific radiographic characteristics of jujube pits in a dog, this case highlights the central longitudinal stripe as a diagnostic clue. This marker facilitates the differentiation of jujube pits from other foreign bodies on plain radiographs, allowing for timely surgical intervention to prevent catastrophic complications. Full article

Sweet corn has high economic value and is consumed directly, requiring strict environmental and management conditions. However, weed infestation during growth seriously affects yield and quality, while conventional chemical weed control may compromise product safety. Laser weeding offers an effective alternative, with precise weed detection and localization as its core requirement. This study proposes YOLO-GFD, a lightweight weed detection algorithm for sweet corn fields. Compared with the original model, YOLO-GFD increased mAP@0.5 by 10.61 percentage points, reduced floating-point operations by 0.6 percentage points, and achieved an average precision of 95.77%. Field trials further showed a real-time weed detection rate of 93.1% and a corn seedling misdetection rate of 1.1%, indicating strong practical applicability. In addition, weed control experiments using 110 W near-infrared and blue lasers under different power levels and irradiation durations identified suitable laser parameters for field laser weeding. Overall, YOLO-GFD meets the real-time accuracy requirements of autonomous laser weeding and provides a reliable basis for visual recognition and laser parameter optimization in sweet corn production. Full article

This work presents the development and validation of an automated 3D scanning device based on two opposed consumer-grade Apple TrueDepth sensors integrated into a controlled rotational architecture, designed for the digitization of complex freeform surfaces such as the external cranial geometry. The system design was guided by a prior metrological characterisation of the sensor’s distance-dependent behaviour and complemented by an additional study of the influence of surface orientation, from which a suitable operating window for complete head acquisition was derived. On this basis, a mechatronic system was implemented comprising a mechanical structure, electronic hardware, a control architecture, and a calibration procedure that registers the local point clouds from both sensors into a common global coordinate system. Geometric validation was performed using symmetric and asymmetric cranial phantoms digitized with both the proposed device and a professional reference scanner. Surface comparison revealed localized discrepancies concentrated in fine anatomical details, while the cranial vault showed good overall agreement, with RMS deviations of 0.314 mm and 0.286 mm for the symmetric and asymmetric phantoms, respectively. Morphometric consistency was assessed through the cranial vault asymmetry index (CVAI), for which both systems produced the same general trend with a maximum difference of 0.2%. These results demonstrate the feasibility of the proposed system as a geometrically consistent and morphometrically reliable instrument for head surface digitization under controlled laboratory conditions. Full article

Timely landslide detection and rapid qualitative assessment are fundamental to effective warning systems, hazard management, and risk mitigation. Yet, current practices that rely on on-site surveys and manual expert assessment remain risky, costly, and time-consuming. These limitations result in substantial delays between the event and the availability of actionable information. This study proposes a hybrid, multi-model framework that fuses RGB remote-sensing imagery with geospatial layers to enable timely landslide detection and actionable reporting. The pipeline couples an enhanced SegFormer (denoted as SDF-SegFormer-B2) model for landslide localization, a feature extraction technique for per-slide geo-attribute computation, and a lightweight instruction-tuned LLM (Mistral-7B-Instruct-v0.3) for structured, expert-style reporting. Although a few previous studies have explored landslide captioning, to our knowledge this is the first framework designed to generate structured technical reports enriched with terrain-context interpretation and qualitative intervention-priority indicators. Experiments use 26,758 georeferenced RGB tiles (64 × 64) with 3 m of spatial resolution from PlanetScope satellite imagery over Emilia–Romagna, Italy, with 68,592 annotated landslide boxes collected after the May 2023 rainfall events (~200 mm in 48 h on 1–3 May; 200–250 mm in 48 h on 16–17 May). The proposed SDF-SegFormer-B2 segmentation model achieved a precision of 85.54%, recall of 72.31%, and an F1-score of 78.39% on the unseen test dataset. To evaluate the quality of the generated landslide reports, 100 images were selected for domain-expert assessment. Among these, 58% of the reports were rated as “Very Good,” 30% as “Good,” 8% as “Acceptable,” and 4% as “Poor.” When considering only reports with complete and accurate inputs, 81.48% were rated “Very Good,” and 96.30% were rated either “Good” or “Very Good.” By integrating complementary models and modalities, the proposed approach automates localization-to-reporting and enables the generation of terrain-aware landslide summaries that may support preliminary decision-making and rapid post-disaster screening. Full article