Efficiency Savings Research Articles

Evaluation of Public Transportation System through Social Network Analysis Approach

In response to the phenomenon of global warming, the transportation sector aims to mitigate carbon emissions by promoting the use of public transportation. This study employs social network analysis to propose effective improvements to the public transportation system, focusing on bus stop locations and route networks in Hwaseong City, South Korea. Two networks were constructed based on existing public transportation routes and usage data at each bus stop. The findings and implications are as follows: Analyzing the public transportation network from a network perspective can effectively contribute to improving the public transportation network route system. By evaluating centrality and brokerage for the existing routes, it is possible to identify inefficient routes and develop efficient route modification plans. Based on actual usage patterns, excessive bus supply and unnecessary bus stop locations can be identified, allowing for the establishment of appropriate operational plans. This can lead to improved operational efficiency and cost savings. Rational route design and operational planning can enhance public transportation services and promote increased use of public transportation. Ultimately, this contributes to sustainable development through carbon reduction in the transportation sector.

Electrospun nanofibrous membranes with functionalized 2D nanofillers for efficient micropollutant removal from water

In response to emerging challenges in water pollution, such as the contamination caused by dyes and antibiotics, scientists have been driven to develop state-of-the-art remediation techniques. The electrospun nanofiber membrane effectively eliminates antibiotics and dyes from industrial wastewater. This research work primarily centres on creating electrospun membranes based on polyacrylonitrile (PAN) nanofiber membranes and mixed matrix membranes (MMM), PAN@MMM-1 and PAN@MMM-2, crafted through the electrospinning process. Enhanced membrane performance has been achieved by incorporating functionalized clay materials, alginate-bentonite (Alg-Bnt) and lanthanum-bentonite (La-Bnt) nano clay, as nanofillers. These membranes offer a combination of ease of use, high efficiency, energy and cost savings, and an environmentally friendly and ecologically beneficial nature. The study conducted a comprehensive analysis of the materials and membranes using a range of advanced technologies such as Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HR-TEM), energy dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectrometer (XPS), X-ray diffraction spectroscopy (XRD), and the plate colony counting method for assessing antibacterial activity. UV–visible spectroscopy and HPLC were employed to determine the concentrations of antibiotics and dye compounds in the feed and permeate. The results revealed that the PAN@MMM-1 membrane exhibited a water permeability value up to 18.8 L m−2 h−1 and achieved rejection rates of up to 99.08 % for BSA, 97.82 % for cephalexin, 98.23 % for diclofenac, and 97.24 % for sulfamethoxazole. Moreover, it demonstrated impressive rejection rates of 99.39 % for methyl orange and 96.77 % for methyl violet, highlighting its remarkable efficacy in purifying wastewater.

Comparative Study of Machine Learning Techniques for Insurance Fraud Detection

Insurance fraud has been a constant presence in the realm of insurance. However, as strategies and methods for committing insurance fraud have evolved, the frequency and volume of such fraudulent activities have also increased. An example of this is vehicle insurance fraud, which involves collaborating to fabricate false or exaggerated claims related to property damage or personal injuries resulting from an accident. Machine learning techniques seems to be more beneficial and great way to address the fraud in the insurance industry. This paper comprehensively examines existing research through a systematic literature review. This review aims to identify previously attempted approaches and evaluate which machine learning algorithm is best suited for this specific problem. This paper proposes a methodology for identifying fraudulent insurance claims. This approach can significantly improve efficiency and cost savings for insurance companies in handling such cases. The most popular traditional machine learning algorithms used to identify insurance fraud in the auto industry were found to be support vector machine, logistic regression, and random forest.

A qualitative exploration of barriers, facilitators and best practices for implementing environmental sustainability standards and reducing food waste in veterans affairs hospitals.

To improve the healthfulness of foods offered while accelerating the use of environmental sustainability practices, it is important to engage hospital food service operators in the adoption of such practices. The purpose of this study was to explore barriers, facilitators and best practices for implementing environmental sustainability standards in food service among veterans affairs (VA) hospitals in the United States. We conducted an online survey with 14 VA hospital food service directors and then 11 qualitative interviews. The survey assessed motivations for initiating sustainability standards and included a self-rating of implementation for each of five standards: increasing plant-forward dishes, procuring and serving sustainable foods that meet organic/fair trade and other certifications, procuring and serving locally produced foods, reducing food waste and reducing energy consumption. Interviews were transcribed verbatim. Qualitative analysis, including coding of themes and subthemes, was conducted by two coders to determine barriers, facilitators and best practices for each of these five standards. Quantitative methods (counts and frequencies) were used to analyse the survey data. Participants had an average of 5 years of experience implementing sustainability standards. The top three motivators cited were reducing food waste, serving healthier foods and increasing efficiency or cost savings. Barriers revolved around patient preferences, contractual difficulties and costs related to reducing waste. Facilitators included taste testing new recipes that include more sustainable food options and easy access to sustainable products from the prime vendor. Best practices included making familiar dishes plant-forward and plate waste studies to prevent overproduction. Although there were many barriers to implementation, food service directors had solutions for overcoming challenges and implementing food service sustainability standards, which can be tested in future sustainability initiatives.

Multi-Objective Particle Swarm Optimization for Enhancing Chiller Plant Efficiency and Energy Savings

This study aims to enhance operational efficiency in chiller plants by implementing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The primary objectives are to simultaneously reduce energy consumption and increase cooling efficiency, addressing the challenges posed by variable environmental and operational conditions. Employing the MOPSO algorithm, this research conducts a detailed analysis using real-time environmental data and operational parameters. This approach facilitates a dynamic adaptation to changes in ambient temperature and electricity pricing, ensuring that the algorithm's application remains effective under fluctuating conditions. The application of MOPSO has resulted in significant reductions in energy use and improvements in cooling efficiency. These results demonstrate the algorithm's capacity to optimize chiller plant operations dynamically, adapting to changes in environmental conditions and operational demands. The study finds that MOPSO's adaptability to dynamic operational conditions enables robust energy management in chiller plants. This adaptability is crucial for maintaining efficiency and cost-effectiveness in industrial applications, especially under varying environmental impacts. The paper contributes to the field by enhancing the understanding of how advanced optimization algorithms like MOPSO can be effectively integrated into energy management systems for chiller plants. A novel aspect of this research is the integration of real-time data analytics into the optimization process, which significantly improves the sustainability and operational efficiency of HVAC systems. Furthermore, the study outlines the potential for similar research applications in large-scale HVAC systems, where such algorithmic improvements can extend practical benefits. The findings underscore the importance of considering a broad range of environmental and operational factors in the optimization process and suggest that MOPSO's flexibility and robustness make it a valuable tool for achieving sustainable and cost-effective energy management in industrial settings.

Utilizing predictive analytics to enhance supply chain efficiency and reduce operational costs

This study investigates the application of predictive analytics to enhance supply chain efficiency and reduce operational costs. The primary objective is to understand how predictive analytics can be leveraged to optimize various aspects of supply chain management, including demand forecasting, inventory management, and logistics. The research methodology involved a comprehensive literature review, coupled with a case study analysis of several organizations that have successfully implemented predictive analytics in their supply chain operations. Key findings reveal that predictive analytics significantly improves demand forecasting accuracy, which in turn optimizes inventory levels, reduces stockouts and overstock situations, and enhances overall supply chain responsiveness. Additionally, predictive analytics helps in identifying potential disruptions in the supply chain, allowing for proactive measures to mitigate risks and maintain continuity. The study also highlights the cost benefits, where organizations reported a notable reduction in operational costs due to improved efficiency and better resource allocation. The conclusions drawn emphasize the transformative potential of predictive analytics in supply chain management, suggesting that its strategic implementation can lead to substantial improvements in efficiency and cost savings. This research underscores the need for organizations to invest in advanced analytics tools and skills to fully harness the benefits of predictive analytics in their supply chain operations.

Enhancing customer experience through AI-driven language processing in service interactions

In today’s competitive service industries, delivering exceptional customer experiences is paramount. This paper explores how AI-driven language processing technologies enhance customer service interactions. Key technologies such as Natural Language Processing (NLP), chatbots, virtual assistants, speech recognition, and machine learning are crucial in transforming customer service. These technologies enable personalized, efficient, and consistent customer interactions, significantly improving overall customer satisfaction. However, implementing these technologies has challenges, including data quality, system integration, privacy concerns, user acceptance, and financial investment. Addressing these challenges is essential for successful AI adoption. Future trends indicate further advancements in AI capabilities, integration with IoT and blockchain technologies, and evolving ethical and regulatory considerations. The long-term impact of AI on service industries promises more efficient operations, cost savings, and superior customer experiences, giving companies a competitive advantage in the market. This paper provides insights into leveraging AI to enhance customer experience, outlining the benefits and challenges while speculating on future directions and implications.

The HHL algorithm: Implementation and research directions

Linear systems of equations lie at the heart of numerous scientific and engineering challenges. In cutting-edge arena like artificial intelligence, machine learning and neuro-computation, these systems serve as a fundamental tool for mathematical modeling. Classical algorithms for solving linear systems have been extensively developed and forms the backbone of diverse applications across various scientific disciplines. While classical algorithms exist for solving linear systems, they often encounter limitations termed “NP-completeness” as data complexity increases. The emerging field of quantum computing offers a revolutionary approach to deal with these kinds of problems. The Harrow–Hassidim–Lloyd (HHL) algorithm tackles these challenges and opens new avenues for research. This study delves into the contemporary effectiveness of the HHL algorithm to address systems of linear equations. By examining recent research in quantum machine learning, we aim to assess the HHL algorithm’s potential to revolutionize the process of optimizing hyperparameters for machine learning models, resulting in increased efficiency and cost savings. This paper meticulously analyzes the HHL algorithm and explores its evolution from conception to the latest advancements. A comprehensive examination of the HHL algorithm, including its evolution over time, is thoroughly explored. The investigation delves into the potential challenges and limitations that might hinder the practical deployment of the HHL algorithm. Identifying these roadblocks will pave the way for future research and development efforts.

Tubo pneumático hospitalar: Um aliado da enfermagem para a qualidade do atendimento prestado aos pacientes

The pneumatic conveying system, known as pneumatic mail, is very useful in terms of agility in internal hospital processes, moving or moving materials and samples of biological material between different sectors. Although essential, there is a gap in the literature on its effectiveness, highlighting the need for research. Invented in the nineteenth century, it evolved into complex networks after George Medhurst's first functional model. These systems are elementary in hospital logistics, transporting samples, medications, and documents quickly and safely. The study looks at its advances, including the use of artificial intelligence for pressure and speed control. The objectives are clear: to understand, highlight benefits and identify challenges to promote advances in hospital logistics. Despite the costs and challenges of implementation, the system offers efficiency, security, and resource savings. For nursing, it represents a valuable tool, freeing up time and promoting safety. In summary, pneumatic systems are essential allies in patient care, addressing time, safety, and operational efficiency.

Assessing the environmental performance of green mortgage‐backed securities

AbstractThe green bond market is growing substantially, bringing with it a focus on economic and environmental performance. Yet while extensive work exists examining the former, there is little concrete evidence regarding the efficacy of green bond use‐of‐proceeds. Concurrently, the demand for ESG‐compliant investments provides an opportunity to direct capital toward the rehabilitation of one of the most energy‐intensive asset classes: real estate. One program in this space, the Fannie Mae Green Rewards green bond program, offers incentives to borrowers to increase multifamily building energy and water efficiency. Although all program participants must complete a set of preapproved projects targeting energy and water efficiency within 12 months of loan origination, there exists substantial variation in the realization of postorigination efficiency outcomes, and in the variation between projected and actual efficiency improvements. We find that fixed interest rates and supplemental financing loan structures are associated with postorigination energy efficiency improvements, as are newer, larger, and high‐quality assets. However, the ex ante estimates of efficiency savings provided to prospective investors prove unrelated to the efficiency outcomes. These findings highlight opportunities to improve program transparency and calibration across the green bond universe.

Influence of drying condition on the drying constants and activation energy for robusta coffee using heat pump drying

Abstract Heat pump drying (HPD) is a method of drying agricultural products which has great potential due to its high efficiency and energy savings. Coffee is a heat sensitive product, HPD was chosen as an alternative to existing drying methods for coffee processing. Understanding of the HPD process is important to know for optimizing the coffee drying process with HPD especially in drying characteristics. The purpose of this research is to determine the number for the drying rate constant and the energy of activation in coffee drying. The material used in this study was wet parchment Robusta coffee beans (Coffea canephora). The research was carried out experimentally for 5 h at 700 lpm air flow with varying drying air conditions by adjusting the drying temperature and specific humidity. The drying temperatures used were 60, 65, 70, 75, and 80 °C. The drying temperature is limited to 80 °C based on SNI 7467-2008. Variations in specific humidity are determined based on temperature settings in the refrigeration system of 10, 15, 20 °C and without a refrigeration system. The maximum drying rate constant was 10.69×10-3 s-1, achieved at a temperature of drying variation of 80 °C while a specific humidity of 6.16 g H2O/kg dry air, while the minimum activation energy value was 23.43 kJ/mol at the same specific humidity. The lowest drying rate constant value was 5.79×10-3 s-1, achieved at a temperature of drying variation of 60 °C and a specific humidity of 17.24 g H2O/kg dry air, while the greatest activation energy level was 26.18 kJ/mol at the same specific humidity.

The application of seed priming agents in enhancing drought resilience of rice: a comprehensive review

Abstract Rice (Oryza sativa L.) serves as a staple food for over half of the world’s population, and its production is vital for global food security. However, the increasing demand for rice is threatened by the rising occurrence of drought stress, driven by erratic rainfall patterns attributed to unpredictable global climate. Drought stress can significantly affect rice at various growth stages, with seed germination being particularly vulnerable due to reduced water intake. Direct Seeded Rice (DSR), an alternative planting technology, offers water efficiency and cost savings but faces challenges like poor stand establishment. Seed priming, a technique that enhances seedling emergence and early growth, has proven to be a practical approach for improving rice tolerance under suboptimal conditions, including drought. The present article is intended to discuss (i) the mechanism and benefits of seed priming, (ii) seed priming techniques, potential agents, and effectiveness factors, (iii) the mechanism of seed priming to improve drought tolerance, (iv) challenges and opportunities, to promote the seed priming strategy as a future, cost-effective research tool to increase rice yield and productivity under drought stress.

When Does Saving Power Save the Planet?

The computing industry accounts for 2% of the world's emissions. Power-efficient computing is a frequent topic of research, but saving power does not always save the environment. Jevons' paradox states that resource savings from increases in efficiency will be more than compensated for by increased demand by a process called rebound --- making these ineffective ways to decrease emissions. This is not the case for all applications within computing: applications whose demand is inelastic with respect to power consumption can have reduced power consumption. We analyze several large fields within computer science, including ML, the Internet and IoT, and provide directions on where power efficiency savings will help reduce carbon emissions. We present the economic tools needed to decide whether power-efficiency improvements are likely to result in reduced or increased emissions. We conclude that many problems in computer science do have characteristics of rebound, meaning that green energy is the only solution for many fields.

Research on deep learning models and data preprocessing techniques for driver fatigue detection

Detecting signs of fatigue while driving is one of the most important and urgent issues in today's modern transportation system. Early detection and warning will significantly reduce the risk of traffic accidents. To accomplish this, several methods have been proposed. In recent years, methods based on deep learning techniques have attracted great attention due to their high efficiency and cost savings in detecting and warning about signs of fatigue while driving. In this paper, we study a method to improve the accuracy of deep learning models based on the data preprocessing technique, applied to detect signs of fatigue while driving. Data preprocessing helps the training images become more accurate, noise-reduced, diverse and rich instead of just using the main image. To ensure high reliability and accuracy, the methods are trained and tested on large datasets including thousands of images of all types. Experimental results show that the combination of the VGG-16 model and the data augmentation technique gives better results than the traditional deep learning model, with an accuracy of over 96%.

Electrochemical Wastewater Treatment Technologies Through Life Cycle Assessment: A Review

AbstractElectrochemical wastewater treatment technologies are gaining attraction as sustainable alternatives for industrial and municipal wastewater management. This study conducts a comprehensive life cycle assessment to assess the environmental and economic sustainability of electrochemical methods such as electrocoagulation, electrooxidation, and electroreduction. By analyzing key stages, from raw material extraction to end‐of‐life disposal, the review aims to provide insight into their overall sustainability performance. The study also delves into environmental impact categories and utilization of methods used in quantifying the environmental implications. Moreover, a cost structure analysis and cost‐effectiveness evaluation offer insights into the economic viability of these technologies. Despite facing challenges like high initial costs and regulatory constraints, electrochemical technologies demonstrate competitive advantages in treatment efficiency and energy savings. Collaborative efforts and supportive policy frameworks are deemed crucial for overcoming barriers and fostering the widespread adoption of electrochemical technologies, thereby advancing sustainable wastewater management practices.

Batch Operating Limestone Treatment Systems (BOLTS): Greater Efficiency and Cost Savings

Reclamation Highlights: A novel batch operating limestone treatment system was used to treat mine drainage. Batch limestone treatment can decrease necessary limestone volume and overall cost. Treating mine drainage in batches could foster mine reclamation innovation.

Effects of multi-spring wires on hydrothermal performance of double tube heat exchanger

Double-tube heat exchangers play a vital role in industries by facilitating efficient heat transfer between two fluids while keeping them physically separated. Their design offers versatility, reliability, and ease of maintenance, contributing to improved process efficiency, energy conservation, and cost savings across a wide range of applications. This study investigates the influence of multi-spring wires on the performance of double-tube heat exchangers using A CFD turbulence model. The analysis covers friction factor, Nusselt number, and exergy efficiency across Reynolds numbers spanning from 4500 to 7500. Employing a steel spring wire of 2 mm diameter and 1000 mm length in three configurations (1, 2, and 3 springs), the results reveal a consistent increase in Nusselt number with rising Reynolds number. Notably, a 3-wire configuration demonstrates a 112 % enhancement in Nusselt number compared to plain tubes, with a maximum friction factor increase of 134 %. Exergy efficiency experiences significant improvement, rising by 61 % with the 3-wire setup compared to plain tubes. Additionally, the study predicts temperature, pressure, and velocity distributions along the tube through numerical analysis of the results. This study improves the energy efficiency and sustainability of double-tube heat exchangers using multi-spring inserts, lowering emissions and costs while advancing thermal management systems.

Benefits of Using Data-Driven Lean Manufacturing in Textile and Apparel Manufacturing Units in Pakistan

The objective of the present study is to investigate the advantages of lean manufacturing in textile and apparel manufacturing facilities in Pakistan. This study examines the influence of lean manufacturing on the time-saving, process efficiency, and industry improvement. Modern organizations are employing lean manufacturing as a critical concept in their operations. There are numerous advantages to employing lean manufacturing; consequently, the textile industry is also transitioning to this methodology. The quantitative methodology employed in this investigation is employed to investigate the current subject matter. The study's data is gathered from the customers and employees of textile and apparel manufacturing units in Pakistan. In order to accumulate data from the respondents of the investigation, questionnaires were implemented. The study's findings suggest that lean manufacturing is significantly positively correlated with industry improvement, process efficiency, and time savings. These three factors are also essential for the success of an organization. This investigation also encompasses practical and managerial implications, future direction, and limitations.

Charting the course: Essential elements of a digital asset management governance framework

While we all agree digital asset management (DAM) systems are valuable, effective governance often falls by the wayside. However, governance is the key to ensuring a successful DAM journey. This paper argues that strong governance serves as the foundational key to maximising the return on DAM investment. More than just guardrails, it fosters collaboration, streamlines workflows and empowers data-driven decision making. Beyond user adoption and seamless integrations, well-defined governance impacts brand consistency, security and compliance, efficiency and cost savings, and data-driven insights. Governance provides guardrails for asset creators through consumers of content, and can impact user adoption, integrations and the overall success of DAM implementation. This is why it is so important to set aside the time to establish, document and enforce governance across the organisation. This paper explores core focus areas for establishing a robust DAM governance framework, empowering organisations to navigate the vast ocean of digital assets with clarity, efficiency and ultimately, success.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization.

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.