Search Results (5,769)

Background: this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined framework of material flow analysis (MFA) and sustainable supply chain planning to improve demand forecasting and inflow management across the plastic bag lifecycle. Method: the research adopts a quantitative method using the XGBoost algorithm for forecasting and is supported by a polymer-based MFA framework that maps material flows from production to end-of-life stages. Result: the findings indicate that while production processes achieve high efficiency with a yield of 89%, more than 60% of plastic bag waste remains unmanaged after use. Moreover, scenario analysis demonstrates that single interventions are insufficient to achieve circularity targets, whereas integrated strategies (e.g., reducing export volumes, enhancing waste collection, and improving recycling performance) are more effective in increasing recycling rates beyond 35%. Additionally, the study reveals that increasing domestic recycling capacity and minimizing dependency on exports can significantly reduce environmental leakage and strengthen local waste management systems. Conclusions: the study’s novelty lies in demonstrating how machine learning and material flow data can be synergized to inform circular supply chain decisions and regulatory planning. Full article

Intensive vegetable farming emits high nitrous oxide (N₂O) due to traffic-induced compaction, highlighting the need for preventing nitrogen (N) losses through better traffic management. This study examined the effects of Controlled Traffic Farming (CTF) and Random Traffic Farming (RTF) on N₂O emissions using intact soil cores (diameter: 18.7 cm; depth: 25 cm) collected from a vegetable production system in Pukekohe, New Zealand. Soil cores from CTF beds, CTF tramlines, and RTF plots were analysed under fertilised (140 kg N/ha) and unfertilised conditions. N₂O fluxes were monitored over 58 days using gas chambers. The fertilised RTF system significantly (p < 0.05) increased N₂O emissions (5.4 kg N₂O–N/ha) compared to the unfertilised RTF system (1.53 kg N₂O–N/ha). The emission from fertilised RTF was 46% higher than the maximum N₂O emissions (3.7 kg N₂O–N/ha) reported under New Zealand pasture conditions. The fertilised CTF system showed a 31.6% reduction in N₂O emissions compared to fertilised RTF and did not differ significantly from unfertilised CTF. In general, CTF has demonstrated some resilience against fertiliser-induced N₂O emissions, indicating the need for further investigation into its role as a greenhouse gas mitigation strategy. Full article

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, and facility location and allocation. Real-world case studies from cities like Braga, Lisbon, Uppsala, and Cyprus demonstrate how MOEAs can enhance operational efficiency, boost energy recovery, and reduce environmental impacts. While these algorithms offer significant advantages, challenges remain in computational complexity, adapting to dynamic environments, and integrating with emerging technologies. Future research directions highlight the potential of combining MOEAs with machine learning and real-time data to create more flexible and responsive waste management strategies. By leveraging these advancements, MOEAs can play a pivotal role in developing sustainable, efficient, and adaptive waste disposal systems capable of meeting the growing demands of urbanization and stricter environmental regulations. Full article

Direct seeding has gained prominence as a labor-efficient and environmentally sustainable alternative to conventional transplanting in rice cultivation. In direct seeding systems, early-stage management is crucial for stable seedling establishment, with sowing uniformity measured by seed counts being a critical indicator of success. However, conventional manual seed counting methods are time-consuming, prone to human error, and impractical for large-scale or repetitive tasks, necessitating advanced automated solutions. Recent advances in computer vision technologies and precision agriculture tools, offer the potential to automate seed counting tasks. Nevertheless, challenges such as domain discrepancies and limited labeled data restrict robust real-world deployment. To address these issues, we propose a density estimation-based seed counting framework integrating semi-supervised learning and background augmentation. This framework includes a cost-effective data acquisition system enabling diverse domain data collection through indoor background augmentation, combined with semi-supervised learning to utilize augmented data effectively while minimizing labeling costs. The experimental results on field data from unknown domains show that our approach reduces seed counting errors by up to 58.5% compared to conventional methods, highlighting its potential as a scalable and effective solution for agricultural applications in real-world environments. Full article

Carassius auratus exhibits significant physiological and behavioral alterations under the combined stress of temperature and dissolved oxygen (DO) fluctuations, which are common challenges in aquaculture. In this investigation, we employed controlled thermal and DO gradients to characterize the multidimensional response profile of this species. The key findings revealed that thermal elevation profoundly influenced blood glucose and cortisol concentrations. Notably, exposure to hyperoxic conditions markedly attenuated stress responses relative to hypoxia at equivalent temperatures: cortisol levels were significantly suppressed (reductions of 60.11%, 118.06%, and 34.72%), while blood glucose levels exhibited concurrent increases (16.42%, 26.43%, and 26.34%). Distinctive behavioral patterns, including floating head behavior, surface swimming behavior, and rollover behavior, were identified as indicative behaviors of thermal–oxygen stress. Molecular analysis demonstrated the upregulated expression of stress-associated genes (HSP70, HSP90, HIF-1α, and Prdx3), which correlated temporally with elevated cortisol and glucose concentrations and the manifestation of stress behaviors. Furthermore, a muscle texture assessment indicated that increased DO availability mitigated the textural deterioration induced by heat stress. Collectively, this work establishes an authentic biomarker framework, providing crucial threshold parameters essential for the development of intelligent, real-time environmental monitoring and dynamic regulation systems to enhance climate-resilient aquaculture management. Full article

Wheelchair-mounted assistive robotic manipulators can provide reach and grasp functions for power wheelchair users. This in-lab study evaluated a vision-guided shared control (VGS) system with twelve users completing two multi-step kitchen tasks: a drinking task and a popcorn making task. Using a mixed methods approach participants compared VGS and manual joystick control, providing performance metrics, qualitative insights, and lessons learned. Data collection included demographic questionnaires, the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and exit interviews. No significant SUS differences were found between control modes, but NASA-TLX scores revealed VGS control significantly reduced workload during the drinking task and the popcorn task. VGS control reduced operation time and improved task success but was not universally preferred. Six participants preferred VGS, five preferred manual, and one had no preference. In addition, participants expressed interest in robotic arms for daily tasks and described two main operation challenges: distinguishing wrist orientation from rotation modes and managing depth perception. They also shared perspectives on how a personal robotic arm could complement caregiver support in their home. Full article

Background and Objectives: Extramedullary plasmacytoma (EMP) is a rare monoclonal B-cell neoplasm that typically affects the head and neck region, with a predilection for the sinonasal tract. Clinical presentation is often nonspecific, leading to delayed diagnosis. This study aims to improve our understanding of sinonasal EMP by reviewing the recent literature and presenting a case series from our clinical experience. Materials and Methods: A systematic review of published cases of sinonasal EMP from 2000 to 2023 was conducted using the PubMed database, yielding 28 eligible cases. Additionally, we retrospectively analyzed three patients diagnosed and treated at our institutions. Inclusion criteria included histologically and immunohistochemically confirmed EMP without evidence of systemic multiple myeloma. Data on demographics, tumor location, symptoms, treatment, and outcomes were collected and analyzed descriptively. Results: Sinonasal EMP most commonly presented with unilateral nasal obstruction and epistaxis. Tumors were primarily located in the nasal cavity and paranasal sinuses, often extending beyond a single anatomical site. In the literature cohort, the most frequent treatment was combined surgery and radiotherapy (35.71%), followed by radiotherapy alone (17.86%). Recurrence was reported in 10.71% of cases, and 7.14% of patients died due to disease progression. All three patients in our case series underwent surgical excision; two received postoperative radiotherapy. No recurrences or progression to multiple myeloma were observed during follow-up (12–24 months). Conclusions: Sinonasal EMP is a rare but radiosensitive tumor with a favorable prognosis when treated with surgery and/or radiotherapy. Early diagnosis, histopathological confirmation, and exclusion of systemic disease are essential. Multidisciplinary management and long-term follow-up are critical due to the risk of recurrence and transformation into multiple myeloma. Full article

Effective water infrastructure planning and management is key to sustainable water supply globally. This research assesses water infrastructure planning and management in Gauteng, South Africa, amid growing challenges from rapid urbanisation, high water demand, climate change, and resource scarcity. These challenges threaten the achievement of Sustainable Development Goals 6 and 11; hence, an integrated approach is required for water sustainability. The study responds to a gap in the literature, which often treats planning and management separately, by adopting an integrated, multi-institutional approach across the water value chain. A mixed-methods triangulation strategy was employed for data collection whereby surveys provided quantitative data, while two sets of structured interviews were conducted: the first round to determine causal relationships among the critical success factors and the second round to validate the proposed framework. The findings reveal a misalignment between infrastructure planning and implementation, contributing to infrastructure backlogs and a short- to medium-term focus. Infrastructure management is further constrained by inadequate system redundancy, leading to ineffective maintenance. External factors such as delayed adoption of 4IR technologies, lack of climate resilient strategies, and fragmented institutional coordination exacerbate these issues. Using Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis, the study identified Strategic Alignment and a Value-Driven Approach as the most influential critical success factors in water asset management. The research concludes by proposing an integrated water infrastructure and planning framework that supports sustainable water supply. Full article

Beech forests in the Italian peninsula are actively managed and they also support a high level of biodiversity. Hence, biodiversity conservation can be synergistic with timber production and carbon sequestration, enhancing the overall economic benefits of forest management. This study aimed to evaluate the effect of forest management regimes on bird communities in the Italian Peninsula during 2022 through audio recordings. We studied the structure, composition, and specialization of the breeding bird community in four managed beech stands (three even-aged beech stands aged 20, 60, and 100 years old, managed by a uniform shelterwood system; one uneven-aged stand, managed by a single-tree selection system) and one uneven-aged, unmanaged beech stand in the northern Apennines (Tuscany region, Italy). Between April and June 2022, data were collected through four 1-hour audio recording sessions per site, analyzing 5 min sequences. The unmanaged stand hosted a richer (a higher number of species, p < 0.001) and more specialized (a higher number of cavity-nesting species, p < 0.001; higher Woodland Bird Community Index (WBCI) values, p < 0.001; and eight characteristic species, including at least four highly specialized ones) bird community, compared to all the managed forests; moreover, the latter were homogeneous (similar to each other). Our study suggests that the unmanaged beech forests should be a priority option for conservation, while in terms of the managed beech forests, greater attention should be paid to defining the thresholds for snags, deadwood, and large trees to be retained to enhance their biodiversity value. Studies in additional sites, conducted over more years and including multi-taxon communities, are recommended for a deeper understanding and generalizable results. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Anti-Ku antibodies are rare autoantibodies associated with connective tissue diseases (CTDs), but their clinical significance remains poorly understood due to limited studies. Semi-quantitative immunodot assays yield positive, negative, or borderline results, with the clinical relevance of borderline findings remaining unclear. The purpose of this study is to characterize the clinical spectrum of anti-Ku-positive patients and evaluate the clinical significance of anti-Ku-borderline results in CTD management. A retrospective cohort study was conducted at Hôpital Erasme, including all patients with anti-Ku-positive or borderline results, over a 10-year period. Clinical and biological data were collected from medical records and analyzed for disease associations, organ involvement, and outcomes. Among 47 anti-Ku-positive patients, systemic lupus erythematosus (SLE) and Sjögren’s syndrome (SS) were the most common diagnoses. Interstitial lung disease (ILD) occurred in 23.4% and renal involvement in 12.8% of patients. Cytopenia was significantly associated with glomerulonephritis. Organ damage, particularly pulmonary and renal involvement, correlated with increased mortality. In the borderline group (n = 33), SLE and SS remained the predominant diagnoses. During follow-up, three patients died (all with isolated ILD without associated CTD), one required chronic dialysis, and one underwent lung transplantation. ILD was present in 7/22 (31.8%) borderline patients, and renal involvement in 7/32 (21.9%). This study demonstrates significant associations between anti-Ku antibodies and organ damage, with increased mortality risk. The high prevalence of pulmonary and renal involvement in anti-Ku-borderline patients suggests that these results carry substantial clinical significance and should prompt comprehensive CTD evaluation. These findings support treating borderline anti-Ku results with the same clinical vigilance as positive results, given their similar association with severe organ involvement and adverse outcomes. Full article

This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R²_CV = 0.980; FSS: R²_CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article

Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. Results: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. Conclusions: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies. Full article

Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving purposes. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This paper introduces a novel multi-modal deep learning-based methodology for recognizing excavator activities, utilizing multi-stream input data. It processes point clouds and RGB images using the two-stream long short-term memory convolutional neural network (CNN-LSTM) method to extract spatiotemporal features, enabling the recognition of excavator activities. A comprehensive dataset comprising 495,000 video frames of synchronized RGB and point cloud data was collected across multiple construction sites under varying conditions. The dataset encompasses five key excavator activities: Approach, Digging, Dumping, Idle, and Leveling. To assess the effectiveness of the proposed method, the performance of the two-stream CNN-LSTM architecture is compared with that of single-stream CNN-LSTM models on the same RGB and point cloud datasets, separately. The results demonstrate that the proposed multi-stream approach achieved an accuracy of 94.67%, outperforming existing state-of-the-art single-stream models, which achieved 90.67% accuracy for the RGB-based model and 92.00% for the point cloud-based model. These findings underscore the potential of the proposed activity recognition method, making it highly effective for automatic real-time monitoring of excavator activities, thereby laying the groundwork for future integration into digital twin systems for proactive maintenance and intelligent equipment management. Full article

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article