Variety Of Technologies Research Articles

Awareness and use of improved livestock feed technologies among agro-pastoral households in the West African Sahel.

Seasonal scarcity of quality feed continues to be a bottleneck for agro-pastoralists and is the most important limitation to livestock production in agro-pastoral systems in the West African Sahel. In this regard, diverse promising technologies to increase the production and availability of quality feed have been introduced. Despite the introduction of various feed technologies in the region, the adoption rate by smallholder farmers is very low. The objective of this study was to identify factors affecting the likelihood of using improved livestock feed technologies among agro-pastoral households in two regions of Niger. A logit model was employed using data collected from randomly selected 218 agro-pastoral households between February and April 2023. The study revealed low awareness and use of improved livestock feed technologies among agro-pastoral households. The findings suggested the positive impact of education level, membership of farmer groups and ownership of small ruminant on enhancing the probability of using improved livestock feed technologies. Households' geographical area was one of the determinant factors in using improved livestock feed technologies. The use of improved livestock feed technologies was also influenced by family size. The study provides guidance to inform strategies by the decision-makers to enhance the utilization of improved livestock feed technologies among agro-pastoral households in the West African Sahel. Suggested strategies may in turn alleviate the current problem of low productivity of livestock, contribute to improving household food security and nutrition among agro-pastoral communities.

Network Traffic Classification Against Adversarial Attacks Based Deep Learning

Background: Cybersecurity is a prominent concern in today's interconnected world, encompassing both local and remote wireless and wired access across diverse communication technology platforms. It is important to recognize the threat posed by hackers who are currently compromising organizational functionalities, bypassing security measures, and stealing hypersensitive information. Common hacking techniques such as Portscan, Distributed Denial of Service (DDoS) attacks, and the use of Botnets, are frequently utilized by hackers. Materials and Methods: The authors explore the advantages of using Machine Learning (ML) to classify attacks such as Port Scanning, DDoS, Botnet, and Botnet-Attempt in a mixture of both benign and attack traffic. They use various Artificial Neural Networks (ANN) structures, each having distinct properties, to train and test on a benchmark dataset (CICIDS2017). The aim is to identify the most effective ANN model and the optimal number of input features required to classify data that contains events of Portscan, DDoS, Botnet, and Botnet-Attempt attacks. Results: Various features are used as inputs for an ML model with single and multiple hidden layers, each with different neurons, to evaluate their impact on classification accuracy using a Python language. The best accuracy obtained is 99.71%, achieved by using all features of the dataset and 4 hidden layers, while the accuracy obtained using only 7 features is 97.6%. Conclusion: ANN models can perform well in classifying network traffic against adversarial attacks by using an optimal combination of features as input and hidden layers.

Interprofessional Education Collaboration between Chung Ang Medical School and Sungshin Nursing School

Interprofessional collaboration is crucial for patient-centered care and safety. Since healthcare students will be part of interprofessional teams in the future, they need to understand the unique contributions of various healthcare professions to patient care and develop skills in collaboration, communication, leadership, and mutual respect. In response to this need, healthcare faculties have adopted interprofessional education as an innovative teaching method. However, traditional health education has typically taken place within individual schools, resulting in a limited understanding of other professional roles and identities. In our study, we introduced an interprofessional education model involving two different colleges. A total of 152 undergraduate students, comprising 101 medical students from Chung Ang University and 51 nursing students from Sungshin Women’s University, participated in the program. A one-day interprofessional education program was conducted to promote collaboration between medical and nursing students. The program included team building and communication games, scenario-based simulations, such as a “room of errors,” and tabletop exercises. Key factors for successful interprofessional education include carefully planned scheduling, leadership, and commitment from participating colleges, faculty support and training, the use of diverse teaching methods and technology, and alignment regarding educational directions among the faculty. We believe that this model may provide valuable insights for healthcare institutions aiming to develop and implement interprofessional curricula.

E-LIBRARY SYSTEM

Abstract- This research paper explores the transformative role of electronic libraries (e-libraries) in modernizing access to information and enhancing user experiences. With the proliferation of digital technologies, traditional libraries are increasingly embracing digital platforms to expand their reach and services. Through a comprehensive literature review and empirical analysis, this study investigates the key factors influencing the adoption and utilization of e-libraries by both patrons and library professionals The research delves into the technological infrastructure, usability features, and content diversity offered by e-libraries, examining how these factors contribute to improved accessibility and user satisfaction. Additionally, the paper explores the challenges and opportunities associated with the transition from physical to digital library environments, including issues related to digital literacy, privacy concerns, and digital rights management .furthermore, the study investigates the impact of e-libraries on information-seeking behaviors, learning outcomes, and community engagement. By analyzing user feedback, usage statistics, and qualitative data, the research aims to identify best practices and recommendations for optimizing e-library services to better meet the diverse needs of patrons. Index Terms – Frontend development HTML ,CSS,JavaScript ,Backend development Java , Database management MySQL

Comprehensive analysis of carbon emission reduction technologies (CRTs) in China's coal-fired power sector: A bottom-up approach

Carbon dioxide (CO2) reduction technologies (CRTs) in the coal-fired power sector play an imperative role in the mitigation of environmental challenges and reducing CO2 emissions to help achieve the 2 °C target. However, a compelling necessity persists for a unified framework that can effectively and accurately estimate the costs and potentials associated with CRTs, arising from the diversity of technologies and unit types. Therefore, this study employs a bottom-up approach to analyze the costs and potential of 25 advanced CRTs in the coal-fired power sector, excluding CO2 capture, utilization and storage (CCUS), across 19 types of coal-fired power units. This analysis amalgamates the CO2 reduction supply curve (CRSC) approach with levelized cost of CO2 reduction (LCOC) and a broken-even analysis which could reflect the sector's average carbon reduction cost. The outcomes reveal the following key insights: (1) These technologies collectively harbor a substantial CO2 conservation potential amounting to 925.61 Mt CO2, with a broken-even price of CNY 410.1/tCO2. This reduction could potentially lower 20%–25 % of CO2 emissions in China's power system in 2022, highlighting the crucial role of CRTs in achieving a low-carbon transition and national climate targets; (2)Steam turbine flow path retrofit (T8) not only offers a relatively high CO2 reduction potential (>60 Mt CO2), but also features a low cost (<CNY 150/tCO2) and high profit (>CNY 0.85/MWh). Prioritizing the development of this technology can significantly accelerate the low-carbon transition of coal power; (3) Carbon price have a paramount influence on the cost-effectiveness of CRTs and driving their adoption.

Divergent paths for green transition: regional disparities in technology diversification

ABSTRACT This study explores the regional diversification processes into green technologies (2000–17) for different types of regions. Utilising patent and Eurostat data, we analyse these processes along the economic strength of regions and the nature of their knowledge base. Our findings reveal that both structurally strong and weak regions can successfully diversify into green technologies if they possess related technological capabilities. However, brown regions cannot do so, but regions specialised in both technologies can. While green relatedness density is important for green diversification on average, it becomes less effective in regions with strong specialisation in brown or both technologies.

Harnessing Soil Diversity: Innovative Strategies for Potato Blight Management in Central-Eastern Poland

Faced with increasing climate challenges, this pioneering study introduces groundbreaking approaches to ensure the resilience of potato crops in east-central Poland. Our research was aimed at developing an innovative, cost-effective system tailored to the diverse local conditions of this region. Conducted between 2018 and 2020, the study analyzed integrated and organic production systems across different soil types and potato varieties. The experiment was conducted using a randomized block design with three replications in two locations. Integrated production systems involve the strategic use of mineral fertilization and chemical protection to optimize crop growth and health, whereas organic production systems rely solely on natural inputs and biological pest control methods. Integrated practices, enhanced by mineral fertilization and chemical protection, notably delayed the onset of late blight by 16 days and extended the critical infection period by 17% compared to their organic counterparts. Remarkably, the integrated systems resulted in a 49% increase in total production and a 52% increase in commercial yields, demonstrating their effectiveness in enhancing crop resilience. Different potato varieties exhibit varied responses to cultivation systems, influencing both yield and disease resistance. Further investigation into these varietal responses can help optimize cultivation practices, leading to improved efficiency and sustainability in potato farming. Location and soil conditions have a significant impact on potato yield and the spread of potato blight. Specific soil properties such as pH levels, organic matter content, soil texture (clay, silt, and sand composition), moisture retention capacity, and nutrient availability are crucial in determining potato productivity and disease dynamics. Further research into these soil properties and the adaptation of varieties to local conditions can contribute to increased productivity and stability in potato production. This study not only paves the way toward sustainable agriculture but also highlights the crucial role of soil diversity in shaping resilient farming practices. Potato producers in the eastern-central region of Poland should implement integrated production systems using mineral fertilization and chemical plant protection, adapted to local soil conditions and potato varieties. These practices can delay the appearance of late blight and increase the total and marketable potato yield. Further research on soil properties and variety adaptation may increase production stability and efficiency. Promoting soil diversity and modern technologies will ensure resilient and sustainable agricultural production in the face of climate change.

The Intelligent Libraries: Innovation for a Sustainable Knowledge System in the Fifth (5th) Industrial Revolution

Abstract The Fifth Industrial Revolution is ushering in a profound era of technological advancement and societal transformation, prompting a critical examination of the evolving role of libraries in this digital landscape. Traditionally regarded as the custodians of knowledge, libraries must adapt to these changing dynamics of the digital world while preserving their core principles. This study looks into the strategic integration of advanced technologies, aiming to redefine the mission of libraries in the face of complex challenges. The Fifth Industrial Revolution encompasses a convergence of diverse technologies, including the Internet of Things (IoT), cloud computing, biotechnology, nanotechnology, and quantum computing, all underpinned by the currency of data. Artificial intelligence (AI) is a focal point, promising enhancements in user experiences and operational efficiency, however, it also raises critical ethical and privacy concerns that challenge fundamental library principles. This research employs an interpretive content/document analysis approach to examine the evolving roles of libraries, considering their pivotal function in bridging the digital divide, providing equitable access to technology and digital resources, advancing research and innovation, and fostering community engagement. The study underscores the imperative to address challenges related to data privacy, digital inclusion, resource allocation, intellectual property, and sustainability. However, despite these challenges, libraries are poised to remain indispensable pillars of education, research, and community interaction. To do so, they must embrace technology strategically to redefine their roles and expand their influence within evolving knowledge systems.

Comprehensive Evaluation of Regional Road Transport Safety Service Level

Ensuring road transport safety is a critical imperative for regional development and public welfare. This abstract outline a comprehensive evaluation framework designed to assess the service level of regional road transport safety. The proposed methodology integrates diverse parameters, encompassing infrastructure, technology, policy, and human factors, to provide a holistic understanding of the safety landscape. Using real-time data integration and powerful analytics, the assessment system combines quantitative and qualitative indicators. Technology aspects of infrastructure evaluations centre on the effectiveness of automated transportation systems and their influence on accident reduction; architecture evaluations also cover the layout of roads, advertising, and maintenance requirements. Policy evaluation is examining current laws and the ways in which they are enforced while considering how they affect the behaviour of drivers and public safety. The proposed method uses DCNN method for intelligent road transport safety. Using DCNN algorithms to monitor and regulate traffic congestion in smart cities represents a significant leap in the use of deep learning in traffic management.

A Review on Alzheimer's Disease Classification Using Deep Learning

In recent years, there has been a substantial amount of research dedicated to using Deep Learning (DL) methods for the classification of Alzheimer's disease (AD) and other related tasks, specifically focusing on magnetic resonance imaging (MRI) data. According to a comprehensive analysis of recent studies, it seems that deep learning models, especially those that include the creation of different structures, have significant potential to improve the precision of identifying and classifying Alzheimer's disease at an early stage. This work aims to emphasize the importance of effective data preparation tactics and feature learning approaches, as well as the investigation of hybrid models using diverse deep learning technologies. This study primarily focuses on doing performance analysis of deep learning algorithms using the latest approaches. Finally, provide a concise overview and analysis of several methods that might enhance the effectiveness of identification and classification using deep learning.

The role of strategic orientations for digital innovation: When entrepreneurship meets sustainability

Today, firms increasingly need to strategically manage their digital innovation while aligning it with their strategic behaviors (i.e., entrepreneurial and sustainability orientations). Given the lack of previous research on alignment and digital innovation, our study explores and tests how a firm's sustainability and entrepreneurial orientations influence digital innovation. Given that firms can implement diverse digital technologies, we study how digital technologies applied in the firm moderate the orientations' effects on digital innovation. Following strategic alignment theory, we build a model that assumes positive and negative alignment effects. We test our model using primary and secondary data including 350 cases from manufacturing firms. Our findings reveal that a sustainability orientation improves digital innovation. Yet, its positive effect declines when the firm has a greater entrepreneurial orientation. Surprisingly, the positive effect of a sustainability orientation declines as well when the firm implements a greater set of digital technologies. Consequently, our study contributes counterintuitive insights towards understanding that ‘the more is not necessarily the better’.

A Universal Coulombic Efficiency Compensation Strategy for Zinc-Based Flow Batteries.

Alkaline zinc-iron flow batteries (AZIFBs) are well suited for energy storage because of their good safety, high cell voltage, and low cost. However, the occurrence of irreversible anodic parasitic reactions results in a diminished coulombic efficiency (CE), unbalanced charge state of catholyte/anolyte and subsequently, a poor cycling performance. Here, a universal CE compensation strategy centered around the oxygen evolution reaction (OER) on the cathodic side, is reported. This strategy aims to equalize the charge state of the [Fe(CN)6]3-/4--based catholyte and counteract pH fluctuations. The OER process can be implemented either directly on the electrode through electrochemical reaction or in an external catalytic reactor column via a redox-mediated process. This innovative approach effectively mitigates the gradual accumulation of [Fe(CN)6]3- in discharged catholyte and [Zn(OH)4]2- in charged anolyte by consuming the extra OH- during a continuous cycling process. As a result, AZIFBs demonstrate exceptional cycling performance with an extremely low capacity fading rate of 0.0128%/day (or 0.0005%/cycle) over 600 cycles at 80% state of charge (SOC). The proposed CE compensation strategy not only provides an effective way to address the CE loss issue for AZIFBs, but also can be applied to diverse battery technologies encountering CE loss caused by water/oxygen-induced parasitic reactions.

Vietnam, Thailand, and Indonesian Government Policies regarding Rice Cultivation in the Face of Climate Change

The discussion about climate change adaptation usually focuses on technology or policies. It also predominantly addresses the transportation and industrial sectors. When discussing the agricultural sector, the emphasis is often on livestock farming, which indeed generates significant greenhouse gas emissions. Apart from livestock, another significant emitter in agricultural activities is rice cultivation. In the case of Vietnam, rice cultivation produces a greater amount of greenhouse gases compared to the transportation sector. However, rice cultivation is highly vulnerable to climate change. While rice cultivation is prevalent in developing countries such as Vietnam, Thailand, and Indonesia, these countries may not necessarily have sufficient resources to cope with climate change. Therefore, this study will compare how these three countries respond to the issue of climate change based on case studies of policies related to rice cultivation. The research employs the theory of ecological modernization, which seeks solutions to climate change issues through environmentally friendly technological innovations and government policies. Policies that encourage public and private sector participation in climate change prevention are also considered. The study finds that each country has a different orientation, leading to the implementation of diverse policies and technologies according to the context and history of each nation. The mentioned history refers to the history of rice cultivation in a specific country. Meanwhile, the context of each country includes factors such as the challenges faced by a country, the orientation of farmers in rice cultivation, and the role of a country in the international market.

Sopa: a technology-invariant pipeline for analyses of image-based spatial omics

Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa (https://github.com/gustaveroussy/sopa), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.

A Systematic Review of Isolated Water and Energy Microgrids: Infrastructure, Optimization of Management Strategies, and Future Trends

Isolated water and energy microgrids (IWEMGs) serve as vital solutions for enhancing the well-being of remote and rural communities, particularly in areas where water and energy resources are scarce. This has spurred research into the interdependence between the water and energy sectors (water–energy nexus), a field that has grown in response to technological advancements. Through a systematic optimization framework, this review critically evaluates the integration of various technologies within IWEMGs, encompassing infrastructure, management, and strategic planning, while considering economic and social impacts. IWEMGs incorporate diverse technologies for the infrastructure, management, and strategic planning of water and energy resources, integrating economic and social considerations to inform decisions that affect both immediate and long-term sustainability and reliability. This article presents an exhaustive review of the literature on IWEMG management, employing an approach that synthesizes existing studies to enhance the understanding of strategic IWEMG management and planning. It introduces a structured taxonomy for organizing research trends and tackling unresolved challenges within the field. Notably, the review identifies critical gaps, such as the lack of comprehensive data on water demand in isolated locations, and underscores the emerging role of game theory and machine learning in enriching IWEMG management frameworks. Ultimately, this review outlines essential indicators for forthcoming research, focusing on the optimization, management, and strategic planning of IWEMG resources and infrastructure, thereby setting a direction for future technological and methodological advancements in the field.

ModelXGlue: a benchmarking framework for ML tools in MDE

AbstractThe integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate the quality of modeling tools. However, a consensus is yet to be reached on which MDE tasks can derive substantial benefits from ML and how progress in these tasks should be measured. This paper introduces ModelXGlue , a dedicated benchmarking framework to empower researchers when constructing benchmarks for evaluating the application of ML to address MDE tasks. A benchmark is built by referencing datasets and ML models provided by other researchers, and by selecting an evaluation strategy and a set of metrics. ModelXGlue is designed with automation in mind and each component operates in an isolated execution environment (via Docker containers or Python environments), which allows the execution of approaches implemented with diverse technologies like Java, Python, R, etc. We used ModelXGlue to build reference benchmarks for three distinct MDE tasks: model classification, clustering, and feature name recommendation. To build the benchmarks we integrated existing third-party approaches in ModelXGlue . This shows that ModelXGlue is able to accommodate heterogeneous ML models, MDE tasks and different technological requirements. Moreover, we have obtained, for the first time, comparable results for these tasks. Altogether, it emerges that ModelXGlue is a valuable tool for advancing the understanding and evaluation of ML tools within the context of MDE.

What Is the Problem with Misinformation? Fact-checking as a Sociotechnical and Problem-Solving Practice

ABSTRACT Misinformation is a complex and global problem of social and technical dimensions. It is a problem that is exacerbated and sought to be solved by using diverse technologies. It is also a problem that flourishes on platforms and can lead to partnerships with platform companies. These sociotechnical dimensions of misinformation as a problem involve different actors. Some actors create or contribute to the problem, while others perceive it as their problem to solve and work to address it. Identifying the problem of misinformation is at the heart of the issue of problem-solving in fact-checking, as different actors have interests in how problems are discursively presented. This article draws on an international interview study conducted throughout 2020–2022 with 46 fact-checking actors (21 fact-checkers, 14 journalists, and 11 newsroom managers). This article analyzes how these actors reflect on “misinformation problems,” and how these problems become “fact-checking problems” for the actors to work with and solve. Ultimately, the article argues that fact-checking must be approached as a sociotechnical and problem-solving-oriented practice. Doing so highlights specific obstacles in information distribution and platform affordances.

Effect of Partial Substitution of Chemical Fertilizers with Organic Fertilizers on N2O and NO Emissions from a Peach Orchard

Organic fertilizer substitution has been promoted as a weight loss, efficient, and diversified fertilizer substitution technology in agricultural production. However, there is a lack of comprehensive assessment of the impact of organic fertilizers on N2O and NO emissions from orchards. In this study, N2O and NO emissions from peach orchards were observed annually using static dark box-gas chromatography to compare the effects of chemical fertilizer application alone and partial replacement of chemical fertilizer treatment on NO emissions from peach orchards. The results showed that the partial replacement of chemical fertilizers with organic fertilizers reduced the total N2O and NO emissions from peach orchards by 15.0 % and 9.4 %, respectively. The N2O and NO emission factors were reduced by 21.3 % and 21.1 %. The mineral N content of the soil in the organic fertilizer treatment was lower than that in the chemical fertilizer treatment alone. The organic fertilizer treatment increased the contribution of AOA to nitrification and decreased the contribution of AOB, thus reducing N2O and NO from nitrification. In addition, the results of the dual isotope mixing model[δ18O(N2O/H2O) vs. δ15NSP] indicated that the bacterial denitrification/nitrifying bacterial denitrification (bD/nD) process served as the primary pathway for N2O emissions in peach orchards. Partial substitution with organic fertilizers enhanced soil denitrification, resulting in larger reductions in the amounts of N2O and NO. Therefore, partial substitution of organic fertilizer is a viable measure to mitigate nitrogen oxide emissions from orchards and to achieve green and low-carbon development in agriculture.

Strain-engineered particle diffusion in uniaxially deformed polymer networks

Precise control of particle diffusion is highly valuable in diverse modern technologies. Traditional methods for regulating particle diffusion are often limited by inherent properties of liquid medium. This study explores mechanical deformation as a new design space to engineer particle diffusion in stretchable soft materials. Combining experimental and theoretical analyses, we discover that particle diffusivity in a uniaxially deformed polymer network is governed by two dimensionless parameters: the ratio of particle size to the polymer network's mesh size and the stretch ratio applied to the polymer network. We further demonstrate a transition in the relationship between particle diffusivity and stretch ratio, characterized by a monotonic decrease for small particles and a non-monotonic trend for large particles. This transition is attributed to the synergistic effects involving geometric transformation of the deformed polymer network and energy modulation of the stretched polymer chains. This work introduces a new mechanism for controlling particle diffusivity in polymeric materials and establishes a theoretical foundation for developing previously inaccessible transport-based technologies.

Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging.

Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data. Our findings indicate that the ultrasound sensor can track motion in deep-seated organs (bladder) as well as respiratory-related motion. The Time-of-Flight camera offers ease of interpretation and performs well in detecting surface motion (respiration). The Pilot-Tone demonstrates efficacy in tracking bulk respiratory motion and motion of major organs (liver). Simultaneous use of all three sensors could provide complementary motion information outside the MRI bore, providing potential value for motion tracking during position-sensitive treatments such as radiation therapy.