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Factors Influencing the Utilisation of Electronic Logistics Management Information Systems

Catherine Armah and Emmanuel G.K. Duncan — Thu, 18 Jan 2024 00:00:00 +0000

This article delves into the multifaceted landscape of Electronic Logistics Management Information Systems (eLMIS) utilization and the pivotal factors that shape their successful integration within organizations. As technology continues to redefine logistics operations, understanding the dynamics influencing the adoption and implementation of eLMIS becomes paramount for efficient supply chain management. The factors explored in this article include technological infrastructure, user training and competence, data security and privacy, cost considerations, integration with existing systems, organizational culture, change management, and regulatory compliance. A comprehensive analysis of these elements provides insights into the challenges and opportunities organizations face when embracing electronic logistics management systems.

In conclusion, the successful implementation of eLMIS relies on a robust technological infrastructure, necessitating organizations to invest in advanced IT frameworks for seamless integration. User training is identified as a critical factor, highlighting the importance of comprehensive training programs to ensure competence among stakeholders. Data security and privacy concerns underscore the need for building trust and implementing robust security measures. Cost considerations play a pivotal role, with organizations seeking cost-effective solutions and a clear return on investment. Integration with existing systems is crucial for smooth information flow, while organizational culture and change management are identified as influential factors, emphasizing the importance of fostering an innovative and adaptive work environment. Regulatory compliance is a key consideration, especially in industries with stringent regulations. In navigating these factors strategically, organizations can foster a holistic approach to eLMIS utilization, optimizing logistics operations and maintaining a competitive edge in the evolving global marketplace. As the logistics industry evolves, addressing and overcoming these influencing factors will be imperative for long-term success.

Emergency Services In Sub-Saharan Africa: Challenges And Opportunities

Hafez Adam Taher — Thu, 18 Jan 2024 00:00:00 +0000

Sub-Saharan Africa is faced with unique challenges in emergency services, necessitating a comprehensive understanding of public health and safety. This review systematically analyzes challenges, identifies opportunities, and provides policy insights. The literature review, spanning January 2000 to November 2023, synthesizes findings from 23 studies. Key themes include Infrastructure and Resource Limitation, with inadequate roads and communication addressed by innovations like mobile health and drone delivery. Human Resource Challenges emphasize shortages in trained personnel, addressed by initiatives like telemedicine. Organizational and Policy Challenges highlight fragmentation and suggest workforce strengthening and structured COVID-19 responses. Community Engagement stresses the role of trust and successful initiatives, empowering communities through interventions like tablet-based training. Financial Sustainability reveals funding challenges, prompting innovative mechanisms and sustainable solutions. The conclusion advocates a holistic approach integrating technology, community empowerment, and policy reforms to enhance emergency services in Sub-Saharan Africa.

An Evaluation Of High-Energy Batteries For Light-Duty Electric Vehicles From A Technical Perspective

P.Sathish, B.Chandra Mouli, S.Rajesh, V.Rajasubramanian, R Masilamani — Mon, 05 Feb 2024 00:00:00 +0000

Transportation accounts for a significant and rising share of world energy consumption and greenhouse gas emissions. CO2 emissions from the combustion of carbon-based fuels in vehicles account for 24% of global energy-related emissions, and transportation's share of global greenhouse gas emissions is expected to approach one-third in the coming decades unless significant changes in transportation energy requirements or carbon intensity are made [1,2]. Transportation energy usage and greenhouse gas emissions might be decoupled by substituting low-net-carbon energy carriers for fossil fuels. Electricity generated from renewable, nuclear, or fossil fuels with carbon capture is one alternative for supplying low-carbon energy for transportation. However, without a practical technique of transmitting power directly to moving cars, electric energy must be distributed through other means. The light-duty transportation energy service, battery performance criteria for usage in a successful light-duty electric car, and current research toward novel high specific energy battery designs are all discussed in this study. Fundamental study might pave the way for a large technological advancement in battery specific energy, which could help battery-electric cars reduce global greenhouse gas emissions dramatically.

Light-duty vehicles offer a unique energy service by giving direct access from point A to point B at a reasonable pace. Light-duty vehicles account for the bulk of global travel and more than 85% of travel in the United States [3,4]. As more of the world's population has access to contemporary transportation technologies, the percentage of global mobility done by light-duty vehicles is predicted to rise. Light-duty vehicles must be able to go defined distances without refuelling and must have enough power to complete manoeuvres needed by current transportation infrastructure. The energy service that light-duty cars offer may be assessed by looking at normal driving cycles and statistical data about their use.

Zig Bee- Protocol Based Dataset Acquisition Of Wireless Sensor Network Using Higher Security

Dr. Vijaya N. Aher, Prof. (Dr.) B.K Sarkar, Prof. (Dr.) Reena Singh — Tue, 13 Feb 2024 00:00:00 +0000

Wireless Sensor Networks (WSNs) play a crucial role in gathering data from diverse environments for applications ranging from environmental monitoring to industrial automation. However, ensuring the secure and reliable transmission of data in WSNs is paramount to safeguard sensitive information. This paper presents a novel approach for dataset acquisition in WSNs using the ZigBee protocol with enhanced security measures.

The proposed system leverages the ZigBee protocol, a widely adopted standard for low-power, short-range wireless communication in IoT applications. By employing ZigBee, the system achieves efficient and energy-conscious communication among sensor nodes, optimizing data transfer within the network. Furthermore, the implementation integrates advanced security mechanisms to fortify data integrity, confidentiality, and authenticity in the wireless data acquisition process.

Digital Methods Of Assessing Students’ Learning In Education Using Machine Learning

Deshmukh Seema Namdeo, Dr. Vandana Singh, Dr. Shikha Mishra — Sat, 09 Mar 2024 00:00:00 +0000

In contemporary education, the integration of digital methods and machine learning algorithms for assessing students' learning has gained significant traction. This research paper investigates the application of machine learning techniques and digital assessment tools to enhance the evaluation process in educational contexts. By examining various methodologies and approaches, the potential benefits, challenges, and future directions of employing digital methods for assessing student learning outcomes. It discusses the advantages of machine learning-based assessment systems, including scalability, personalized feedback, and adaptive learning pathways, while also addressing concerns related to data privacy, algorithmic bias, and result interpretation. Additionally, the paper explores the integration of digital assessment tools into existing educational frameworks and assesses their impact on teaching practices and student engagement. Through a review of relevant literature and case studies, this paper highlights emerging trends and best practices in the field of digital assessment in education.

Reliability analysis of stochastic model of a seed processing plant With preventive maintenance

Deepal, Pooja Bhatia — Fri, 15 Mar 2024 00:00:00 +0000

The paper deals with a model of a seed processing plant which has centrifuge system situated at Yunick Agro, Hisar, Haryana (India) on the basis of real data collected. The system goes for inspection whenever there is any fault. Depending on time of repair and cost of repairs, faults are classified as minor, major or neglected faults.The occurrence of a minor fault leads to degradation whereas occurrence of a major fault leads to failure of the system and neglected faults and some minor faults are repairable on-line by preventive maintenance. After inspection, repair being carry out according to fault is repairable or non repairable. Replacement or labour redundancy is used in case of non-repairable faults. Considering all these aspects and using the real data collected from the plant, various measures of system effectiveness such as MTSF, Reliability, Availability and Busy period etc. are derived by using Semi-Markov process and Regenerative Point technique. The functioning of the plant’s machine is examined using numerical results and graphs derived thereof. From the plots so obtained, we get cut-off points of profit for different values of rates of major faults/ revenue of per unit Uptime.

Impact of Explainable Artificial Intelligence (XAI) on Data-Driven Decision-Making (D3M)

Zeba, M. Afshar Alam, Harleen Kaur, Ihtiram Raza Khan, Bhavya Alankar — Wed, 03 Apr 2024 00:00:00 +0000

Data-driven decision-making is important in driving research through explained and augmented data results. To drive these complex dynamics explainability and monitoring of result are crucial for cognitive understanding. Therefore, decoding applications which supply blind results under the black box landscape is necessary for data-centric extensive decision-making.

To identify data predictions generated through software mining or machine learning agents, there is a need to systematically analyze the interpretations. In this case, the software agents lacking interpretive skills are unable to explain the predicted outcome scenarios. Consequently, the focus of this paper is on interpreting data by using Explainable Artificial Intelligence (XAI) for making high-stakes decisions, or data-driven decision-making, to enable policy scenarios. In this paper, we have examined XAI to support decision-making systems that oversee high-stakes risk data. The conversion of observational data into knowledge, therefore involves the use of cognitive support to both XAI and decision-makers.

Social Media and Health Outcomes: Challenges and Prospects (A Narrative Review)

Habiba Sinare — Wed, 03 Apr 2024 00:00:00 +0000

The impact of social media on health of users has been prominent in the academic discourse in recent times. Because of the significant time people spent on social media, investigations into the health benefits and risks about these platforms have become vital. The present study aimed to conduct a narrative review of social media and its health outcomes. Keywords and phrases such as Social media, mental health, psychological health, Web 2.0, physical health were employed to search literature via google scholar, SCOPUS, NCBI, Science direct, Research gate and PubMed. In addition to the health outcomes of social media, the inclusion criteria also comprised specific social media platforms such as Facebook, Instagram and Pinterest to increase scope of search.Findings revealed social media present positive outcomes such as promotes physical activities, healthy dietary patterns, sense of belonging, strengthened connections, social and emotional support for mentally ill people, good moods, alleviated stress levels, improved cognitive skills and general wellbeing. However, negative health outcomes such as severe depression, promotion of unhealthy lifestyles, cyberbullying, blackmail and sextortion were associated with social media usage. The study recommends promotion of self-regulation as an efficient tool for deriving maximum health benefits from social media platforms.