Search Results (2,669)

Mulching is one of the most recommended practices in agriculture due to its positive effects on the plant/soil system. Very few experiments have been conducted to date to investigate the effect of mulching, with both organic and inorganic materials, on tobacco. The main aim of this study was to test the synergic effect of a soil-biodegradable (according to standard EN17033) mulching film (the commercial Mater-Bi^®, Novamont SpA, Novara, Italy) and drip irrigation (M-D) compared with that of bare soil and sprinkler/drip irrigation (first/second part of the growing season; BS-SD) on a tobacco crop (Nicotiana tabacum L., flue-cured Virginia) grown in the Tiber Valley (the tobacco cultivation district of Central Italy). BS-SD represents the standard practice applied by tobacco growers in the study area. The plants grown under the M-D management system grew more and developed faster than the plants grown under BS-SD conditions. Under the M-D system, yields increased in comparison with the BS-SD conditions (+29%, on average). The gross revenue obtained via the M-D-cured products also increased (+63%, on average) thanks to higher prices assigned by expert evaluators on the basis of the extrinsic quality traits (color, structure and texture, degree of ripeness, elasticity, lamina integrity, handling defects, and vein incidence). The economic value of the cured products increased with the leaf crowns; it was the lowest in the basal (B) leaves and the highest in the middle-upper (MU) leaves. The intrinsic quality traits of the cured leaves (total N and nitrate contents, alkaloids, and reducing sugars) also confirmed that the best quality was found in the M-D-cured products, as determined by expert evaluation. Interestingly, the reducing sugar (RS) contents of tobacco obtained using the M-D management system were 2.5-, 1.1-, and 0.9-fold greater than those under the BS-SD conditions (B, M, and MU products, respectively). An additional commercial value of the cured products was thus obtained with the M-D crop management system due to RS, an intrinsic quality trait considered by manufacturing industries. Full article

Heat stress (HS) is one of the key factors affecting an animal’s immune system and productivity, as a result of a physiological reaction combined with environmental factors. This study examined the short-term effects of heat stress on cow behavior, as recorded by innovative technologies, and its impact on blood gas parameters, using 56 of the 1070 cows clinically evaluated during the second and subsequent lactations within the first 30 days postpartum. Throughout the experiment (from 4 June 2024 until 1 July 2024), cow behavior parameters (rumination time min/d. (RT), body temperature (°C), reticulorumen pH, water consumption (L/day), cow activity (h/day)) were monitored using specialized SmaXtec boluses and employing a blood gas analyzer (Siemens Healthineers, 1200 Courtneypark Dr E Mississauga, L5T 1P2, Canada). During the study period, the temperature–humidity index (THI), based on ambient temperature and humidity, was recorded and used to calculate THI and to categorize the data into four THI classes as follows: 1—THI 60–63 (4 June 2024–12 June 2024); 2—THI 65–69 (13 June 2024–18 June 2024); 3—THI 73–75 (19 June 2024–25 June 2024); and 4—THI 73–78 (26 June 2024–1 July 2024). The results showed that heat stress significantly reduced rumination time by up to 70% in cows within the highest THI class (73 to 78) and increased body temperature by 2%. It also caused a 12.6% decrease in partial carbon dioxide pressure (pCO2) and a 32% increase in partial oxygen pressure (pO2), also decreasing plasma sodium by 1.36% and potassium by 6%, while increasing chloride by 3%. The findings underscore the critical need for continuous monitoring, early detection, and proactive management to mitigate the adverse impacts of heat stress on dairy cow health and productivity. Recommendations include the use of advanced monitoring technologies and specific blood gas parameter tracking to detect the early signs of heat stress and implement more timely interventions. Full article

Advanced air treatment systems have the potential to reduce airborne infection risk, improve indoor air quality (IAQ) and reduce energy consumption, but few studies reported practical implementation and performance. PlasmaShield^®, an advanced multi-modal HVAC-integrated system, was directly compared with a standard MERV-13 system in a post-surgical paediatric healthcare setting. The evaluation entailed monitoring of multi-size airborne particles, bioaerosols and key IAQ parameters. Measurements were taken for outside air, supply air and air in the occupied space for 3 days prior to, and after, the installation of the PlasmaShield system. Compared with the existing arrangement, very significant reductions in particle number concentrations were observed in the occupied space, especially with virus-like submicron particles. Significant reductions in airborne culturable bacteria and fungi were observed in the supply air, with more modest reductions in the occupied space. In the case of virus-like particles, there was an eight-fold improvement in equivalent clean air, suggesting a five-fold infection risk reduction for long-range exposure. The data suggest multiple benefits of airborne particle and bioaerosol reduction, with applications beyond healthcare. Long-term studies are recommended to confirm the combined IAQ, health and energy benefits. Full article

The production of Moringa oleifera capsules has emerged as a promising agricultural activity in Mexico, driven by the plant’s well-documented health and nutritional benefits. In response, farmers have begun producing Moringa oleifera as a dietary supplement, using artisanal methods with minimal production controls. This study aimed to estimate the processing times of the Moringa oleifera production system using simulation modeling with Arena^® software and to evaluate the economic feasibility of capsule production. Methodologically, it was approached as a case study in the state of Veracruz, and processing times were estimated through systematic sampling and modeled with Arena simulation software. Three simulation scenarios were considered to select a technically and economically viable alternative for agricultural producers, as follows: one with a 200-capsule encapsulator (1E200C), another with two 200-capsule encapsulators (2E200C), and a third with an 800-capsule encapsulator (1E800C). For the initial diagnosis, a research stay was conducted for 180 days, and the production capacity was set at 24,000 capsules due to raw material limitations. Results indicated that the 1E800C scenario reduced processing time by 60% compared to the 1E200C scenario and by 35% compared to the 2E200C scenario. Additionally, the 1E800C model required only Arena simulation software version 162 days (16 h) of work, while the 2E200C required 4 days (32 h) and the 1E200C required 7 days (56 h). In terms of production costs per unit of 90 capsules, the 1E200C was USD 3.93, the 2E200C was USD 3.64, and the 1E800C was USD 3.45. This study concluded that due to raw material limitations, the company can produce 12,800 units of 90 capsules per year. It is recommended to adopt the 1E800C encapsulator, which would lower the overall production costs by 12.23%, representing USD 0.48 per unit produced and resulting in an additional profit of USD 6150.50. Over a five-year evaluation period, the benefit–cost ratio was 5.03, the NPV was 922,370.11, and the IRR was 42.09%, indicating that this type of agribusiness in Mexico is both technically and economically feasible. Full article

This study validates a comprehensive cybersecurity audit model through empirical analysis in three higher education institutions in Canada. The research aims to enhance cybersecurity resilience by assessing the effectiveness of cybersecurity controls across diverse educational environments. Given the increasing frequency and sophistication of cyberattacks targeting educational institutions, this research is essential to ensure the protection of sensitive academic and personal data. Data were collected through detailed audits involving system vulnerabilities, compliance with security policies, and incident response management at each institution. The findings underscore the importance of tailored cybersecurity strategies and continuous auditing to mitigate cyber risks in the Canadian higher education sector. This study contributes to the field by validating a versatile audit tool that can be adapted to various institutional contexts, promoting enhanced cybersecurity practices and evaluating the effectiveness of cybersecurity safeguards across the higher education sector in Canada. The results of the audit model validations provide the cybersecurity maturity rating of each institution. Further research is recommended to refine the model and explore its application in other industries and sectors. Full article

In cereal-based farming, there is significant influence of tillage and residue incorporation as sustainable practices, although their role in crop production is still unclear. Two field trials were executed during winter seasons to evaluate the best-performing crop residue and tillage system for the nutrition and yield of rice at the Soil Science Field Laboratory of Bangladesh Agricultural University. In the first trial, residues from soybean, black gram, and rice were incorporated into the soil with 75% recommended fertilizer doses (RFD). The grain yields were 5.97, 6.21, and 6.10 t ha⁻¹, respectively in rice, soybean, and black gram residue-treated plots, which were increased by 77.15, 84.27, and 81.01%, respectively, over the control. In the second trial, the residues from black gram and rice were incorporated with 100% RFD under conventional tillage (CT) and minimum tillage (MT) for two years, where CT with black gram residue plus 100% fertilizer (CT-I₂) exhibited the highest grain yield of 6.69 and 6.88 t ha⁻¹, increasing by 7.61% and 8% over 100% RFD. Both CT and MT performed similarly, and their combination with legume residue strongly influenced crop performance and nutrient uptake. Therefore, incorporating legume residue under minimum tillage can be a sustainable approach for better rice yield and nutritional uptake. Full article

Structured follow-up visits should be accessible for children at risk for developmental delay. Follow-up visits should include a serial neuromotor assessment in the first two years of life (e.g., 3–6, 12, 24 months corrected age), which are repeated during the transition to school. The diagnosis of neuromotor development may be prognostic for important skills later in life. The early diagnosis of a child’s general movements can be helpful in planning appropriately for proper treatment and intervention. These diagnostic assessments should be conducted by qualified healthcare professionals. The evaluation of neuromotor developmental health is specified in the national guidelines and funded by either a national government or public or private healthcare insurance and based on standardized assessment scales. The aim of this study is to show what elements of follow-up visits are recommended. Objectives: The group of patients for whom the structured follow-up systems are intended were children born very preterm (<32 weeks gestation) or full-term born children with severe neonatal complications. Material and methods: The methods for monitoring neurodevelopment include the following: The General Movements Assessment (GMA), the Ages and Stages Questionnaire (ASQ-3), the Bayley Scales of Infant and Toddler Development (BSID-4), and the Parent Report of Children’s Abilities-Revised (PARCA-R). Results: The results of follow-up visits should be registered. Conclusions: The benefits of follow-up neuromotor development assessments can be observed at school age and even in adulthood. Full article

Rural ecological revitalization (RER) is one of the five goals of China’s rural revitalization strategy. However, there is a lack of an effective index system to evaluate RER levels, which hinders the implementation of this national policy and reduces the effectiveness and efficiency of public resource input. Using the driver-pressure-state-impact-response (DPSIR) framework, this study developed an evaluation framework consisting of 5 subsystems, 12 secondary indicators, and 33 tertiary indicators. Using the entropy-weighted TOPSIS method, we analyzed a set of 30 provinces’ data and empirically determined the weights of each indicator. We found that the response subsystem had the largest weight (0.338), followed by the state (0.271), impact (0.148), pressure (0.130), and driver (0.113). We then evaluated the RER level in each province and found that five provinces had high RER levels, 16 provinces had moderate RER levels, and nine provinces had low RER levels. Using Moran’s I, we examined spatial autocorrelation of provincial RER levels at global and local dimensions. We found significant positive global autocorrelations across all subsystems, indicating that geological aggregation exists in all RER subsystems. The local autocorrelation results showed that low–low and high–high patterns were the dominant local autocorrelation patterns. According to the findings, we discussed the possible implications of this RER evaluation index system and provided policy recommendations for strengthening RER in different regions across the country. Full article

Following the transfer of hospital services to provincial government oversight, it is essential to understand how such changes impact service quality and efficiency. This study, conducted from 1 March 2023 to 31 March 2024 at sub-district health-promoting hospitals in Thailand, aims to identify the factors affecting hospital service quality post-transfer and evaluate the role of professional nurses in maintaining service standards under new governance. Utilizing a mixed-methods design based on the Global Fund-HSS framework (2012), which categorizes factors into accessibility, processes, productivity, and outcomes, we collected data from 340 nurses and 400 patients through structured questionnaires. These questionnaires were divided into seven sections, capturing metrics such as the personal data of nurses, hospital service activities, and leadership qualities. We employed a Likert scale to gauge the perceptions and expectations of service quality and conducted paired t-tests to compare performance metrics before and after the administrative transfer. One-way ANOVA was used to assess variability among different service units, and binary logistic regression helped identify the key determinants of service quality. The analysis revealed a significant correlation between the attitudes and competencies of healthcare teams and the levels of service quality. Notably, service units transferred less than 50% exhibited minimal changes, indicating that the degree of transfer significantly impacts service outcomes. Despite these variations, the fundamental mission of promoting health remained consistent. The study emphasizes the critical role of nurses and recommends further research to identify additional factors that could improve service quality in transferred healthcare facilities, potentially contributing to enhanced healthcare delivery across restructured health systems. Full article

In recent years, graph-based learning methods have gained significant traction in point-of-interest (POI) recommendation systems due to their strong generalization capabilities. These approaches commonly transform user check-in records into graph-structured data and leverage graph neural networks (GNNs) to model the representations of both POIs and users. Despite their effectiveness, GNNs face inherent limitations in message passing, which can impede the deep extraction of meaningful representations from the graph structure. To mitigate this challenge, we introduce a novel framework, Multi-view Contextual Graphs via Convolutional Neural Networks for Point-of-Interest Recommendation (MCGRec). The MCGRec framework consists of three primary components. Firstly, it employs a personalized PageRank (PPR) sampling technique based on super nodes to transform the graph-structured data into a grid-like feature matrix. This step is crucial as it prepares the data for subsequent processing by convolutional neural networks (CNNs), which are adept at extracting spatial features from grid-like structures. Secondly, a CNN is utilized to extract the representations of POIs from the constructed feature matrix. The usage of CNNs enables the capture of local patterns and hierarchical features within the data, which are essential for accurate POI representation. Lastly, MCGRec incorporates a novel approach for estimating user preferences that integrates both geographical and temporal factors, thereby providing a more comprehensive model of users’ behaviors. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on real-world datasets. Our results demonstrate that MCGRec outperforms state-of-the-art POI recommendation methods, showcasing its superiority in terms of recommendation accuracy. Full article

Assessing the vibration signature produced by a rotating component of the wind turbine enables the identification of operational conditions and the detection of potential faults at an early stage. The main purpose is to enhance the sustainability of wind turbines while increasing the lifespan and uptime of their operational systems. This vibration analysis is based on the processing of the signal provided by sensors, which often incorporates piezoelectric transducers. This paper evaluates the consequences of employing piezoelectric sensors used for vibration measurement on electrical machines integrated into wind turbines by conducting a life cycle assessment (LCA). The widespread use of piezoelectric materials is due to their high sensitivity to vibrations, although their selection is also influenced by regulatory restrictions. This research focuses on the environmental impact of piezoelectric accelerometers used commonly in condition monitoring systems. The collected literature data on the manufacturing processes are inputted into the LCA model which is powered by the Ecoinvent 3 database. The impact assessment is carried out using the European ILCD 2011 Midpoint+ method by calculating the unique scores of the selected impact categories. The results are presented and discussed in terms of environmental indicators, as well as ecological recommendations on the design. Full article

The World Health Organization (WHO) articulated a vision in 1986, hoping that countries around the world would actively promote the construction and development of healthy cities. In the context of China, a paradigm shift in urban development has been triggered by the deep implementation of the Healthy China Strategy and the gradual completion of the comprehensive national spatial planning. This shift emphasizes a micro-level focus, advocating a human-centered approach to urban space exploration. In this paper, the Dingwangtai Area of Changsha City is selected as a case study. A street space health evaluation index system called “5D+” is constructed from six dimensions, namely, human perception, degree of mixing, density, distance to transit, destination accessibility, and devise. This research adopts the community as the fundamental unit of analysis, employing the Entropy-weighting TOPSIS method for the computation of indicators. The results of this study show that the Dingwangtai Area exhibits relative deficiencies in the dimensions of density, devise, and destination accessibility. Specifically, at the community level, the Ma Wang Street Community and the Fanhou Street Community have scored particularly low. In response, this paper proposes targeted measures and detailed recommendations aimed at optimizing the design of the street, enhancing the human experience, enriching functional attributes, and refining the street network. The ultimate goal is to propose a Healthy Streets Evaluation Index System based on the concept of a healthy city and to explore its relationship with healthy streets so as to provide valuable insights for the development of healthy city streets. Full article

The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare systems. Maintaining patient privacy is paramount in healthcare while providing smart insights and recommendations. This study proposed the adoption of federated learning to develop a scalable AI model for post-stroke assessment while protecting patients’ privacy. This research compares the centralized (PSA-MNMF) model performance with the proposed scalable federated PSA-FL-CDM model for sensor- and camera-based datasets. The computational time indicates that the federated PSA-FL-CDM model significantly reduces the execution time and attains comparable performance while preserving the patient’s privacy. Impact Statement—This research introduces groundbreaking contributions to stroke assessment by successfully implementing federated learning for the first time in this domain and applying consensus models in each node. It enables collaborative model training among multiple nodes or clients while ensuring the privacy of raw data. The study explores eight different clustering methods independently on each node, revolutionizing data organization based on similarities in stroke assessment. Additionally, the research applies the centralized PSA-MNMF consensus clustering technique to each client, resulting in more accurate and robust clustering solutions. By utilizing the FedAvg federated learning algorithm strategy, locally trained models are combined to create a global model that captures the collective knowledge of all participants. Comparative performance measurements and computational time analyses are conducted, facilitating a fair evaluation between centralized and federated learning models in stroke assessment. Moreover, the research extends beyond a single type of database by conducting experiments on two distinct datasets, wearable and camera-based, broadening the understanding of the proposed methods across different data modalities. These contributions develop stroke assessment methodologies, enabling efficient collaboration and accurate consensus clustering models and maintaining data privacy. Full article

The global shift towards electric vehicles (EVs) has necessitated a paradigm shift in engineering education, emphasizing hands-on experiences and innovative learning approaches. This review article presents a comprehensive analysis of the design and fabrication process of an educational EV prototype, highlighting its significance in preparing future engineers for the rapidly evolving EV industry. The article delves into the historical development and recent trends in EVs, providing context for the growing importance of practical skills in this field. A detailed examination of the key components and systems in modern EVs, such as battery packs, electric motors, transmission systems, and chassis design, lays the foundation for understanding the complexities involved in EV prototype development. The methodology section explores the research approach, conceptual design, simulations, material selection, and construction techniques employed in the creation of an educational EV prototype. The evaluation and testing phase assesses the prototype’s performance, safety, and reliability, offering valuable insights into the lessons learned and areas for improvement. The impact of such projects on engineering education is discussed, emphasizing the importance of hands-on learning experiences and interdisciplinary collaboration in preparing students for future careers in the EV industry. The article concludes by addressing common challenges faced during EV prototype projects and providing recommendations for future educational initiatives in this field. Full article

The paper presents a computing procedure with the goal of suggesting applicable solutions to improve complex cases of major recurrent depression. The focus is on identifying the patients’ illness patterns and on finding solutions for alleviating problematic symptoms. The illness patterns synthesize the outcomes of the relationship between the patients’ life themes and symptoms. The testing of the effectiveness of illness improvement solutions was conducted by evaluating and comparing the Beck scores of patients after each psychotherapy session. In addition to latent semantic analysis used to identify semantic relationships between life themes and symptoms, the research also employed the correlation method to find life themes/symptoms that are experienced undistortedly and associations between life themes that amplify latent symptoms. The computing system was applied to eleven patients with severe forms of depression and their progress was monitored for six months. The results obtained following the application of the computing system demonstrated its ability to describe personalized illness patterns and to significantly improve, through its suggestions, the illness of all patients. These findings recommend the use of the computing system in severe cases of major recurrent depression. Full article