Search Results (118)

This study aims to address the low cleaning efficiency (73.12%) and high cotton loss rate (12.50%) of existing stripper-and-stick cleaners when processing machine-harvested long-staple cotton. Based on the material characteristics of Xinjiang long-staple cotton, a novel impurity removal device was designed specifically for its cleaning. A mechanical model of cotton–impurity interaction was established to guide the design of U-shaped saw cylinder parameters. Static analyses conducted via Ansys Workbench revealed that both straight and arc-shaped sawteeth exhibited negligible deformations (≤4.73 × 10⁻⁴ mm) and von Mises stresses (≤9.52 MPa), meeting the practical operational requirements. Through orthogonal experiments (sawtooth type, linear velocity, and feed speed), the optimal operating parameters were determined as arc-shaped sawteeth, 7.5 m/s linear velocity, and 11 t/h feed speed. Prototype testing demonstrated that under these parameters, the impurity removal device achieved an average cleaning efficiency of 82.42% (a 9.30% improvement over conventional devices) and an average cotton loss rate of 8.34% (a 4.16% reduction compared to conventional devices). This study provides a valuable reference for the design and optimization of stripper-and-stick cleaners specifically tailored for machine-harvested long-staple cotton. Full article

Rural and remote communities often face significant challenges maintaining their health and well-being. In developing countries, these challenges are further compounded by (1) limited access to clean water, (2) ineffective sanitation, (3) low education and literacy levels and (4) low awareness of the role of personal hygiene practices in reducing communicable disease transmission. Existing health education materials distributed within such communities are often presented in a traditional written format, making them inaccessible to people with low literacy levels. Therefore, recent evidence supports an alternative health communication approach through the use of picture-based materials. This study has assessed the effectiveness of a cartoon-based pictorial educational tool (CBPET) in communicating key messages about hygiene and bacteria contained within the environment and bodily fluids. The CBPET that was developed was tested in a Tanzanian village, representative of a community with low literacy and a resource-poor setting. The CBPET was found to effectively deliver key health promotion messages to the local community. Developing healthcare education in a universal language format based on pictures or cartoons could be the way forward. Full article

Column semantic-type detection is a crucial task for data integration and schema matching, particularly when dealing with large volumes of unlabeled tabular data. Existing methods often rely on supervised learning models, which require extensive labeled data. In this paper, we propose SNMatch, an unsupervised approach based on a Siamese network for detecting column semantic types without labeled training data. The novelty of SNMatch lies in its ability to generate the semantic embeddings of columns by considering both format and semantic features and clustering them into semantic types. Unlike traditional methods, which typically rely on keyword matching or supervised classification, SNMatch leverages unsupervised learning to tackle the challenges of column semantic detection in massive datasets with limited labeled examples. We demonstrate that SNMatch significantly outperforms current state-of-the-art techniques in terms of clustering accuracy, especially in handling complex and nested semantic types. Extensive experiments on the MACST and VizNet-Manyeyes datasets validate its effectiveness, achieving superior performance in column semantic-type detection compared to methods such as TF-IDF, FastText, and BERT. The proposed method shows great promise for practical applications in data integration, data cleaning, and automated schema mapping, particularly in scenarios where labeled data are scarce or unavailable. Furthermore, our work builds upon recent advances in neural network-based embeddings and unsupervised learning, contributing to the growing body of research in automatic schema matching and tabular data understanding. Full article

Nuclear power technology has rapidly advanced with the growing global demand for clean energy. As one of the core components of nuclear power plants (NPPs), the design and lifespan evaluation of reactor pressure vessels (RPVs) are critically important. However, while fatigue analysis methods for RPVs in land-based NPPs are relatively well established, the application of these methods to floating nuclear power plants (FNPPs) faces great challenges. Existing analysis methods are difficult to directly apply, and no widely accepted fatigue analysis approach currently exists for this context due to the complex working conditions in marine environments. A time-domain design stress (TDDS) method is developed in this study for the fatigue analysis of RPVs in FNPPs. This method systematically analyzes the impacts of wave loads, internal pressure, and thermal effects on the fatigue life of RPVs by simplifying the wave environment into a time-domain model of roll and pitch motions and adopting the regular wave superposition techniques. This method further adjusts the initial phases of regular waves considering the uncertainty of various load combinations, and superimposes the stress components caused by regular waves with different initial phases, thermal loads, and pressure loads. Subsequently, stress history curves are analyzed using the rainflow counting method, and combined with the damage accumulation theory, the upper and lower limits of fatigue damage are obtained. The results demonstrate that compared to traditional methods in time-domain analysis, the proposed TDDS method provides greater accuracy in evaluating the fatigue life of RPVs in FNPPs, with the average error in fatigue damage values being only 0.033%. Furthermore, the TDDS method reduces analysis time by approximately 70%, which significantly improves computational performance. These findings underscore the reliability and effectiveness of this method in practical applications. Full article

This systematic review evaluates China’s Rural Revitalization Policy, focusing on sustainable agriculture, food security, and poverty alleviation initiatives from 2010 to 2024. The study addresses critical gaps in understanding how these combined efforts impact long-term food security and ecological sustainability in impoverished areas, moving beyond the short-term outcomes often emphasized in existing literature. Following the PRISMA 2020 guidelines, we reviewed 33 peer-reviewed publications from the Web of Science and Scopus databases, employing bibliometric analyses in RStudio to assess citation patterns, collaboration networks, and thematic evolution. Our analysis reveals significant progress across three interconnected domains. First, poverty alleviation initiatives achieved a 12.3% reduction in rural poverty through integrated agricultural modernization and targeted support programs. Second, agricultural productivity increased by 9.8% through technological integration and sustainable farming practices, strengthening food security outcomes. Third, environmental sustainability improved notably, with a 15.7% increase in clean water access, demonstrating a successful balance between economic growth and ecological protection. China emerged as the largest contributor (15.2%) to research in this field, with substantial international collaboration (42.4% of publications involving cross-border co-authorship). Despite these achievements, significant regional disparities persist, particularly between eastern and western regions, where targeted interventions are needed. The findings highlight the need for regionally tailored approaches: eastern regions require focus on sustainable intensification, western regions need fundamental infrastructure development, and central regions would benefit from strengthened urban–rural linkages. This study provides valuable insights for policymakers and researchers working on rural development initiatives while identifying areas requiring further research, particularly in long-term sustainability assessments and climate resilience strategies. Full article

The alarming increase in single-use plastic (SUP) consumption, exacerbated by the COVID-19 pandemic and its detrimental effects on environmental and public health, underscores the urgent need for the public to adopt more environmentally responsible behaviours. This study presents citizen science (CS) initiatives undertaken two years after the onset of the pandemic as an effective means to engage and mobilise citizens in reducing plastic consumption. It combines an inventory of CS actions targeting SUP with a self-designed and implemented CS project. Unlike the predominantly technical focus of existing initiatives, our ‘Plastic Detective’ project was designed to collect behavioural data related to SUP use. The majority of completed and ongoing CS projects addressing SUPs focused on beach clean-ups, which primarily involved reporting the distribution and composition of plastic litter. However, no initiatives were found to address consumer behaviour. CS activities were predominantly initiated in a bottom-up manner, largely coordinated by non-governmental organisations (NGOs), and were mostly confined to the Northern Hemisphere. In our project, volunteers were asked to observe, sample and categorise behaviours in their surroundings using a model that distinguished between SUP use and the adoption of alternative materials or practices. Additionally, participants’ perceptions of SUP were assessed through pre- and post-surveys. Our findings reveal that young people, in particular, are eager to participate in CS initiatives. Regardless of age, gender or location, all respondents—despite acknowledging the need to reduce SUP consumption—identified producers as the key group responsible for addressing plastic pollution. We conclude that CS can act as an effective knowledge broker between research and non-research communities, fostering behavioural change towards more sustainable practices. Moreover, CS initiatives can play a vital role in popularising scientific findings and influencing policy-making processes to promote environmentally friendly alternatives to SUPs. Full article

Despite the demonstrable benefits of data monetization initiatives for achieving competitive advantages, many of these efforts struggle to realize their potential. Companies often find it challenging to sustain even initially successful data monetization initiatives due to formidable data quality issues. This reflects a disconnect between advancements in data monetization research—which range from digitization to digitalization and digital transformation—and their practical implementation within companies. Consequently, misguided approaches to data monetization are relatively common. A critical prerequisite for successful data monetization is the establishment and maintenance of clean, high-quality data. This study underscores the importance of data quality by conducting an in-depth analysis of Medical Inc., a company that prepares pristine customer master data for advanced customer analytics. The investigation aims to elucidate Medical Inc.’s approach for addressing data cleanliness challenges and developing a general framework for the process of cleansing customer master data. This framework illuminates a relatively unexplored aspect of data monetization, thereby supplementing existing research on digitization, digitalization, and digital transformation. Full article

This study presents a robust framework designed to address the limitations of passive infrared (PIR) sensors in home-based elderly monitoring, particularly focusing on false detections and sensor blind times, which compromise data accuracy. While PIR sensors are low-cost and privacy-preserving, their inherent inaccuracies hinder their use in reliable monitoring systems. To overcome these challenges, we propose a novel spatiotemporal data cleaning framework that integrates non-deterministic tracking (NDT) and late-binding adjustment (LBA). This framework enhances the quality and accuracy of sensor data by filtering out false positives and omissions through analysis of walking speed and sensor connectivity. Simulations demonstrated significant improvements in movement tracking accuracy, and real-world experiments involving three elderly participants further validated the framework’s practicality. The experiments confirmed that the proposed method can remove errors such as false positives and false negatives from PIR sensors. It can achieve 90% accuracy in tracking the movements of elderly people, highlighting the potential for this framework to be applied in the real world. The key scientific contribution of this research lies in the development of a scalable, non-wearable indoor tracking solution that reduces the need for dense sensor arrays, making it cost-effective and adaptable to existing infrastructure with minimal modifications. This framework contributes to advancing the field of indoor localization and offers a reliable solution for sensor-based monitoring systems, especially in elderly care, addressing the urgent needs of an aging global population. Full article

Hydrogen energy is a promising alternative to traditional fossil fuels, offering a clean and sustainable solution to address the challenges of climate change and environmental degradation. Fuel cells provide direct and environmentally friendly conversion of chemical energy from a fuel source into electrical energy, emitting only water vapor when utilizing hydrogen from renewable sources. This study delves into the design of a portable proton-exchange membrane fuel cell (PEMFC) device tailored for household use in rural areas. The research focuses on achieving a minimum peak power of 1000 W and a voltage of 220 VAC at 50 Hz for the fuel cell. Employing theoretical calculations derived from existing formulas and literature reviews, various fuel cell components are meticulously assessed, including real power, voltage drop, performance under current load, and pressure drop on the bipolar plate. Additionally, the study encompasses the selection of auxiliary components like converters, inverters, fans, and others. The resultant fuel cell design showcases a device capable of generating a peak power of 1132.32 W with an efficiency rating of 48.66%. Identifying suitable auxiliary components further contributes to developing a practical and efficient portable power solution for rural households. Full article

Currently, a large amount of pharmaceutical waste (PW) and its derivatives are being produced and, in some cases, inadequate management or treatment practices are applied. In this regard, this research explores the adoption of several alternatives to deal with these problems, including biocarbon within the framework of the circular economy. Photocatalytic nanomaterials have been also extensively discussed as a feasible way to remove pharmaceutical compounds in wastewater. Although there are existing reports in this area, this document provides a detailed study of the synthesis process, experimental conditions, the integration of photocatalysts, and their impact on enhancing photocatalytic efficiency. Additionally, the low cost and ease of fabrication of lab-scale microbial fuel cells (MFCs) are thoroughly examined. This innovative technology not only facilitates the degradation of hazardous compounds in wastewater but also harnesses their energy to generate electricity simultaneously. The aforementioned approaches are covered and discussed in detail by documenting interesting recently published research and case studies worldwide. Furthermore, this research is of significant importance because it addresses the valorization of PW by generating valuable by-products, such as H₂ and O₂, which can occur simultaneously during the photodegradation process, contributing to more sustainable industrial practices and clean energy technologies. Full article

Nanofluids have gained significant attention as a promising solution to several challenges in drilling operations. Nanoparticles, due to their exclusive properties such as high specific surface area, strong adsorption potential, and excellent thermal conductivity, offer significant potential to improve the efficiency and performance of drilling processes. Regardless of the advancements in drilling fluids and techniques that have improved borehole stability, hole cleaning, and extreme operational condition (HTHP) management, limitations still persist. This review discusses a detailed summary of existing research on the application of nanofluids in drilling, exploring their types, properties, and specific uses in areas such as fluid loss control, wellbore stability, and thermal management. It also reports the challenges and future potential of nanotechnology in drilling, including nanoparticle stability, environmental considerations, and cost concerns. By synthesizing current research and highlighting gaps for further study, this review intends to guide researchers and industry professionals in effectively integrating nanofluid usage to optimize drilling practices and support a more sustainable energy future. Full article

Transitioning to low-carbon energy systems is crucial for sustainable development, particularly in oil-rich developing countries (ORDCs) that face intertwined economic and environmental challenges. This review uses the PRISMA methodology to systematically assess the current state and prospects of low-carbon thermal electricity generation and utilization technologies in ORDCs. The study emphasizes clean thermal technologies such as biogas, biofuels, biomass, hydrogen, and geothermal energy, focusing on solutions that are technically feasible, economically viable, and efficient in combustion processes. These nations face significant challenges, including heavy reliance on fossil fuels, transmission losses, and financial constraints, making energy diversification urgent. The global shift towards renewable energy and the need to mitigate climate change presents an opportunity to adopt low-carbon solutions that align with Sustainable Development Goals related to energy access, economic growth, and climate action. This review aims to (1) evaluate the current state of low-carbon thermal electricity technologies, (2) analyze the technical and economic challenges related to combustion processes and energy efficiency, and (3) provide recommendations for research and policy initiatives to advance the transition toward sustainable thermal energy systems in ORDCs. The review highlights practical approaches for diversifying energy sources in these nations, focusing on overcoming existing barriers and supporting the implementation of clean thermal technologies. Full article

Background/Objectives: Managing caries lesions that affect the inner third of the dentin is crucial to ensuring pulp vitality; the clinician must make decisions that will affect the vitality of the tooth. Our purpose is to understand the behavior of Spanish dentists in treating deep cavities and to examine whether variations exist based on their academic training and/or years of professional experience. Methods: This study was approved by the ethics committee of the Balearic Islands CEI-IB. A survey was conducted using the SurveyMonkey platform with 11 questions, the first 4 of which focused on defining the characteristics of the respondents. The following six concerned a clinical case of deep caries in tooth number 4.7, and the last regarded the opinion of the actual treatment of the case. The survey was sent by email in April 2022. The results were analyzed with the SPSS 29.0 program using the chi-square test. Results: A total of 347 responses were obtained (93.95%), and those surveyed stated that they apply minimal intervention concepts in their treatments, with 90.49% performing conservative dentistry treatments daily. A total of 56.48% of the respondents had bachelor’s degrees, 12.39% had graduated, 33.14% had a postgraduate degree, 38.90% had a master’s degree, and 17% had a doctorate. Most (40.63%) had been in professional practice for 16–30 years. Conclusions: Significant differences were identified regarding years of professional experience in terms of decision-making in methods of treatment and the choice of materials used for pulp protection. Likewise, significant differences were found regarding the academic training of the respondents, the cavity cleaning method selected, and the use of chemical substances for removing carious dentin. We can conclude that academic training and years of professional practice influence decision-making at some points in treating deep caries lesions. Full article

With the booming of renewable clean energies towards reducing carbon emission, demands for lithium-ion batteries (LIBs) in applications to transportation vehicles and power stations are increasing exponentially. As a consequence, great pressures have been posed on the technological development and production of valuable elements key to LIBs, in addition to concerns about depletion of natural resources, environmental impacts, and management of waste batteries. In this paper, we compile recent information on lithium, nickel, and cobalt, the three most crucial elements utilized in LIBs, in terms of demands, current identified terrestrial resources, extraction technologies from primary natural resources and waste. Most nickel and cobalt are currently produced from high-grade sulfide ores via a pyrometallurgical approach. Increased demands have stimulated production of Ni and Co from low-grade laterites, which is commonly performed through the hydrometallurgical process. Most lithium exists in brines and is extracted via evaporation–precipitation in common industrial practice. It is noteworthy that at present, the pyrometallurgical process is energy-intensive and polluting in terms of gas emissions. Hydrometallurgical processes utilize large amounts of alkaline or acidic media in combination with reducing agents, generating hazardous waste streams. Traditional evaporation–precipitation consumes time, water, and land. Extraction of these elements from deep seas and recycling from waste are emerging as technologies. Advanced energy-saving and environmentally friendly processes are under extensive research and development and are crucial in the process of renewable clean energy implementation. Full article

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research. Full article