Improve Cost Efficiency Research Articles

Evaluating Natural Feed for Crayfish Farming: Economic and Ecological Benefits

Crayfish is an aquaculture commodity that has high economic value, with growing international market demand. However, the growth of these lobsters tends to be slow, both in terms of body length and weight, which is often caused by limited quality feed and high production costs. The use of alai feed, such as silkworms and other organic materials, can be a solution to improve cost efficiency and support more optimal lobster growth. In addition, natural feed offers ecological benefits by reducing water pollution and organic waste, as well as supporting the sustainability of local ecosystems. This approach also has the potential to reduce the risk of disease in lobsters, as natural feed is more in line with their biological diet. By improving production efficiency and supporting sustainable livelihoods, it is an environmentally and economically sound solution for the aquaculture sector.

Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review

Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review

Feature Fusion Model Using Transfer Learning and Bidirectional Attention Mechanism for Plant Pipeline Leak Detection

The aging of power plant pipelines has led to significant leaks worldwide, causing environmental damage, human safety risks, and economic losses. Rapid leak detection is critical for mitigating these issues, but challenges such as varying leak characteristics, ambient noise, and limited real-world data complicate their accurate detection and model development. To address these issues, we propose a leak detection model that integrates stepwise transfer learning and an attention mechanism. The proposed model utilizes a two-stage deep learning process. In Stage 1, one-dimensional convolutional neural networks (1D CNNs) are pre-trained to extract root mean square (RMS) and frequency-domain features from acoustic signals. In Stage 2, the classifier layers of the pre-trained models are removed, and the extracted features are fused and processed using a bidirectional long short-term memory (LSTM) network. An attention mechanism is incorporated within the LSTM to prioritize critical features, enhancing the ability of the model to distinguish leak signals from noise. The model achieved an accuracy of 99.99%, significantly outperforming the traditional methods considered in this study. By effectively addressing noise interference and data scarcity, this robust approach demonstrates its potential to enhance safety, reduce risks, and improve cost efficiency in industrial pipelines and critical infrastructure.

Piecewise pulse‐width modulation for neutral‐point current reduction in NPC inverter

AbstractEffective neutral‐point balancing in a neutral‐point‐clamped inverter can yield substantial benefits, including cost reduction and efficiency improvement for the system. This article proposes a new piecewise pulse‐width modulation designed to reduce low‐frequency oscillations of the neutral‐point current without compromising the inverter power losses. The proposed piecewise modulation is simple to implement as it has one reference signal per phase. The performance of the proposed modulation is validated through comprehensive comparisons with alternative methods. The obtained simulation and experimental results demonstrate the effectiveness of the proposed piecewise modulation.

Use of Artificial Intelligence in Operational Efficiency and Business Management Strategic

Artificial Intelligence (AI) has emerged as a transformative technology, reshaping operational efficiencies and strategic business management across industries. This study employs a bibliometric analysis using VOSviewer to explore the intellectual structure, global collaboration, and thematic trends in AI research from 2000 to 2024. The findings reveal AI’s pivotal role in enhancing operational processes, particularly in cost reduction, efficiency improvement, and data-driven decision-making. Furthermore, AI’s integration into diverse fields such as healthcare, energy management, and cybersecurity underscores its multidisciplinary impact. The visualizations highlight the strong global collaboration among nations, with China, India, and the United States as major contributors to AI research. Despite these advancements, challenges such as ethical concerns, data privacy, and workforce displacement persist. This study emphasizes the need for ethical frameworks, workforce reskilling, and robust international cooperation to maximize AI's benefits while mitigating its challenges. By mapping current trends and identifying future directions, this research contributes to a deeper understanding of AI’s transformative potential in operational and strategic domains.

Application of microfluidic technologies in veterinary science with a view toward development of animal‐on‐a‐chip models

AbstractThe advancement of veterinary science relies on the adoption of modern technologies, more recently including molecular diagnostics, genomic research, precision medicine approaches, and advanced diagnostic imaging. Recent advancements in microfluidics have brought tremendous attention to human disease modeling, diagnosis, and drug development. Specifically, organ‐on‐a‐chip, a subset of microfluidic technology, is characterized by its ability to mimic the human in vivo microenvironment and improve cost efficiency in drug development. Recent studies have demonstrated huge potential in translating human‐centered microfluidic technologies to veterinary science, which can help to deepen our understanding of animal diseases and disorders and develop targeted treatments for diverse animal species, including companion animals, livestock, and wildlife. Further, the ongoing impact of climate change has heightened the threat of diseases among animal populations as well as the potential impact of zoonotic pathogens. New tools for in‐depth exploration of animal physiologies and diseases are essential to mitigate the risk of species extinction and safeguard animal well‐being. Building upon the achievements in human‐based microfluidic studies, we propose the comprehensive integration of this technology into veterinary research. This review provides an overview of microfluidic technology, its current applications in veterinary science, and discusses future directions and challenges toward the development of animal‐on‐a‐chip systems.

Real-World Retrospective Audit on the Use of the TechnoClone TECHNOSCREEN ADAMTS-13 Assay in the Diagnostic Process for Thrombotic Thrombocytopenic Purpura at LabPlus, New Zealand.

The TECHNOSCREEN ADAMTS-13 assay (ADSC) is a new lateral flow test which is simple and quick to perform, with a high negative predictive value (NPV); it may improve the diagnostic workflow for TTP. LabPlus in Auckland, New Zealand, performs all ADAMTS13 tests in the Auckland and Northland regions. The ADSC was used at LabPlus between 2022 and 2023 as part of a protocol where results of 0 IU/mL and 0.1 IU/mL were confirmed with the TECHNOZYM ADAMTS-13 Activity chromogenic ELISA assay (ADATS). The aim was to improve cost efficiency by reducing the need for the labour-intensive ADATS, particularly in low probability requests. This retrospective audit analyses the efficacy and efficiency of the testing protocol. All ADAMTS-13 tests performed in the year before the ADSC (March 2021-March 2022) and after the ADSC protocol was introduced (March 2022-March 2023) were collected from the LabPlus laboratory information system (LIS), and correlated with clinical information from the electronic health record. Statistical analysis was performed. Ninety-four test requests were audited. ADSC had an NPV of 100% on diagnostic samples. The protocol increased the median turn-around time (TAT) by 18 h (p = 0.01). If an ADSC of 0.1 IU/mL was taken as a negative, the median TAT was decreased by 17 h (p = 0.0003) in diagnostic samples from LabPlus adjacent hospitals. ADSC has a high NPV but did not improve TAT when used as part of a testing protocol for our central laboratory receiving samples from a large area.

Enhancing environmental sustainability through code-driven process integration in the petrochemical industry

Balancing various objectives and navigating uncertainties, reducing CO2 emissions and enhancing energy efficiency in industry presents a complex challenge. While previous studies primarily focused on conventional optimization methods, this research introduces an innovative approach: a multi-criteria optimization framework tailored to address uncertainties. The primary objective is to optimize energy consumption, minimize emissions, and improve cost efficiency simultaneously within the petrochemical industry. To effectively manage uncertain variables, this study integrates decision-making simulations and expert insights through a hybrid methodology to yield optimal outcomes. Employing three distinct preference categories, the model formulates comprehensive decision-making strategies. Empirical findings underscore the model’s efficacy in reducing CO2 emissions, bridging crucial gaps in existing research, and advocating sustainable practices in the sector. Departing from conventional methodologies, this research leverages advanced decision-making techniques adept at handling uncertainty. The framework identifies pivotal emission sources and advocates economically viable reduction strategies. Its adaptability enriches our comprehension of emission challenges by considering diverse factors and expert perspectives. Professional assessments affirm the model’s success and propose a Coding-Based Prototype as a strategic tool for addressing uncertainties. These results underscore the imperative for policy reforms, such as embracing carbon capture technologies, to bolster global sustainability and foster enduring growth in the industrial domain.

Bi-objective dynamic tugboat scheduling with speed optimization under stochastic and time-varying service demands

With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage service punctuality across a heterogeneous fleet. For the first time, we develop an extended Markov decision process framework that integrates both reactive task assignments and proactive waiting decisions, considering the dual objectives. Subsequently, an initial schedule for known requests is established using a mixed-integer linear programming model, and an anticipatory approximate dynamic programming method dynamically incorporates emerging demands through task assignments and waiting plans. This approach is further enhanced by an improved rollout algorithm to anticipate future scenarios and make decisions efficiently. Applied to the Singapore port, our methodology achieves a 12.8% reduction in the total sail cost compared to the tugboat company’s scheduling practices, resulting in significant daily savings. The results with benchmarking against three methods demonstrate improvements in cost efficiency and service punctuality, meanwhile, extensive sensitivity analysis provides managerial insights for operational practice.

Mechanical Calculation of Collar Eye Grinding Shoes for the Entire Series of Casing

In response to the problem of low efficiency in milling/grinding, the new collar eye grinding shoes can greatly improve operational efficiency, shorten the adjustment well project period, and achieve the goal of cost reduction and efficiency improvement. The movement trajectory of the tool blade wing of the lead eye shoe grinding tool affects the tool design results. Therefore, research has been conducted on the movement trajectory of the tool blade wing. The study shows that when the size of the lead eye shoe grinding tool's supporting wing is constant, the maximum size of the lead eye shoe grinding tool blade wing is related to the scale of the casing to be processed, and is not related to the scale of the casing on the previous level.

Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates

To address the technical challenges, production quality issues, and inefficiencies caused by the heavy reliance on traditional manual processing of small assembly plates in the shipbuilding industry, this paper presents the design and analysis of a track-based automatic welding device. This equipment provides a solution for achieving batch and continuous welding in the field of automatic welding technology. The design section includes the mechanical design of the equipment’s core mechanisms, the design of the operating systems, the development of visual scanning strategies under working conditions, and the formulation of multi-layer and multi-pass welding processes. The analysis section comprises the static analysis of the equipment’s mechanical structure, kinematic analysis of the robotic arm, and inspection analysis of the device. Compared with manual welding, multi-layer and multi-pass welding experiments conducted using the equipment demonstrated stabilized welding quality for small assembly plates. Under the conditions of single plates with different groove positions and gaps, when the gap was 4 mm, processing efficiency increased by 7.35%, and processing time was reduced by 10.2%; when the gap was 5 mm, processing efficiency increased by 10.7%, and processing time decreased by 7.39%. The welding formation rate for the overall processing of single plate panels and web grooves increased by 11.48%, total material consumption decreased by 13.4%, and unit material consumption decreased by 13.5%. For mass production of small assembly plates of the same specifications, processing time was reduced by 16.7%, and there was a 41.4% reduction in costs. The equipment effectively addresses the low level of automation and heavy dependence on traditional manual processing in the shipbuilding industry, contributing to cost reduction and efficiency improvement.

‫‬‬‬‬‬The Role Of Modern Techniques Of Management Accounting and Cost Accounting in Improving Cost Efficiency and Quality Of Care in Urology: a Theoretical Study

Economic and demographic developments in the past few decades have increased the pressure on efficiency in the healthcare industry. Modern management accounting and cost accounting techniques are a critical component in improving the efficiency and quality of care in urology. With the ever-increasing costs of healthcare, the need for a sophisticated accounting system that can provide accurate data on costs and revenues is more important than ever. The aim of this research was to investigate the role of modern techniques in management accounting and cost accounting as a means of enhancing cost efficiency and quality of care in urology. The research highlights the importance of training medical staff in these techniques, as this can improve overall performance and reduce costs without compromising the quality of care provided to patients. These techniques have been shown to help physicians and administrators make informed decisions, which helps improve the efficiency and quality of care. They also contribute to improving communication between medical teams, enabling better patient care. By focusing on improving resource and cost management, these techniques can play a pivotal role in achieving superior and effective health outcomes.

Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review.

The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology.

Strategic Migration of Adobe Experience Manager : A Comprehensive Framework for On-Premise to Cloud Transition

This article examines the strategic migration of Adobe Experience Manager (AEM) from on-premise environments to cloud platforms, addressing the growing demand for scalable and efficient content management solutions in the digital landscape. Through a comprehensive analysis of migration processes, this research presents a systematic framework for organizations undertaking AEM cloud transitions. The article investigates key components of migration, including code refactoring, configuration adjustments, content transfer strategies, and environment adaptation. Utilizing a mixed-method approach, incorporating both quantitative performance metrics and qualitative case study analysis, we evaluate the efficacy of migration tools such as Adobe's Cloud Acceleration Manager and custom scripts for business logic. Our findings reveal significant improvements in scalability, cost-efficiency, and system performance post-migration, while also highlighting potential challenges and mitigation strategies. This article contributes to the body of knowledge on enterprise content management systems and provides practical insights for organizations considering AEM cloud migration, offering a roadmap for successful transition in an increasingly cloud-centric technological ecosystem.

Clarify the process of overshoot voltage generation of proton exchange membrane electrolyzer under intermittent operation

Overshoot voltage phenomenon often occurs when the proton exchange membrane (PEM) electrolyzer is in the intermittent operation, which makes H2 production energy consumption increase significantly and its lifetime reduce rapidly, especially to the high-power electrolyzers in the industrial applications. Up to now, the phenomenon has been rarely understood and its process is elusive. Therefore, in this work, firstly, we find that the polarization curve of PEM electrolyzers happening S distortion synchronize with the appearance of overshoot voltage phenomenon. Secondly, through in-situ Raman characterization, we find that involvement and disappearance of H2O in the hydrogen evolution reaction (HER) is the key factor causing and resolving this phenomenon. Finally, we used EPMA to exclude metal cations affection. Also, by micro-CT, EIS test, visualization PEM electrolyzer inside model and COMSOL electromagnetic simulation, we find that anode catalyst layer porosity reduction directly affects water absorption of membrane as well as current variation leads to eddy currents generation which is deepen unequal current distribution. These are two important reasons for the involvement of H2O in HER. In summary, these findings elucidate the mechanism of overshoot voltage phenomenon for the first time, which contributes to design novel electrical protection schemes and advanced membrane electrodes for PEM electrolyzers, thus they can help to achieve cost reduction and efficiency improvement in the green hydrogen industry.

Digital tools in primary total knee arthroplasty-Prevalence in the German-speaking region

Digital tools are being increasingly used worldwide in primary knee arthroplasty. This study aimed to analyze the utilization density of digital tools, the preferred alignment strategies, and the obstacles and benefits of implementing these technologies in German-speaking countries. An online survey with 57questions about digital tools in primary knee arthroplasty and their usage was conducted among members of the Arthroplasty Working Group (AE). The survey included questions on navigation, robotics, patient-specific instruments, individualized implants, and augmented reality. The survey revealed that 18% of hospitals use navigation and 17% use robotic systems in primary total knee arthroplasty surgery. The main reasons for not implementing supportive technologies were high acquisition and ongoing costs, as well as longer surgical duration. Patient-specific instruments and individualized implants currently play aminor role. Patient-specific alignment strategies, such as kinematic (navigation: 35%; robotics: 44%) and functional alignment (navigation: 15%; robotics: 35%), are preferred in this context. With conventional instrumentation predominantly mechanical alignment was applied (79%). The results indicate arelatively high utilization density of digital tools, which are mainly used to perform personalized alignment strategies in primary knee arthroplasty in German-speaking countries. This was particularly evident in high-volume hospitals. Economic aspects were the main reasons for not using these technologies. Future developments should aim to simplify the systems and thus achieve improved cost efficiency.

Basics of Orthopedic Commercial and Government Bundling

The health care system is transitioning from fee-for-service models to value-based care frameworks, with bundled payments emerging as a promising approach to improve cost efficiency and patient outcomes. Bundled payments consolidate costs for multiple services into a single payment for specific treatments or conditions. This study reviews the development and implementation of bundling models in orthopaedics, comparing government and commercial approaches and discussing their benefits and challenges. A review of the literature was conducted to examine existing bundling programs in orthopaedics, including Medicare initiatives like the Bundled Payments for Care Improvement and Comprehensive Care for Joint Replacement model, as well as commercial programs from insurers such as Cigna and UnitedHealthcare. The review focused on published studies, reports, and case examples to identify trends, outcomes, and barriers associated with these models. Government programs such as Comprehensive Care for Joint Replacement have demonstrated improved care coordination and cost control but face challenges in engaging physicians and aligning financial incentives. Commercial models, with greater flexibility in their prospective payment structures, allow for customization and better financial predictability but require careful negotiation and compliance with quality metrics. Both approaches highlight the importance of robust data management, multidisciplinary collaboration, and innovative partnerships to improve care delivery. Condition-based bundling, though conceptually aligned with value-based care, remains in its early stages within orthopaedics. Bundled payment models represent a key strategy for advancing sustainable and efficient health care delivery in orthopaedics. Despite challenges like stakeholder alignment and resource demands, these models offer potential to enhance care quality and reduce costs. Future directions should prioritize collaboration, technology integration, and the resolution of implementation barriers to fully realize the benefits of bundling in orthopaedic practices.

Micro Gas Turbines in the Global Energy Landscape: Bridging the Techno-Economic Gap with Comparative and Adaptive Insights from Internal Combustion Engines and Renewable Energy Sources

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition.

An efficient and noticeable wet etching method for PZT and its application in scanning mirrors

This study introduces an innovative wet etching method for magnetron sputtering lead zirconate titanate (PZT) thin films, leveraging a solution of HBF4 as the etchant and HCl for intermediate product removal. Compared to traditional dry etching techniques, the focus is on improving cost efficiency and etching performance. Wet etching offers numerous advantages, including lower costs, faster etching rates, minimal lateral etching, and smoother etch profiles, making it a viable alternative for fabricating MEMS piezoelectric devices. The effectiveness of this method was substantiated through the production and performance assessment of a 1D scanning mirror, demonstrating that the wet etched PZT retained its piezoelectric properties and was efficiently integrated into MEMS applications without adverse effects on its piezoelectric functionality. Results confirm that this wet etching approach not only maintains the structural integrity of PZT films but also supports the manufacturing of MEMS devices with precise dimensional control and reliable operational performance.

An Exploratory Study on Digital Transformation in Business Operations of China’s Food Industry

In recent years, the changes that have taken place in the food industry in China are unprecedented. The integration of digital technologies reshapes traditional practices with higher efficiency and gives chances for sustainable development. This paper investigates the impact of digital technologies on business operations within this sector in areas like production efficiency, supply chain optimization, financial performance, marketing, and regulatory compliance. Among others, Internet of Things (IOT), Artificial Intelligence (AI), big data, cloud computing, and blockchain have entirely revolutionized traditional practices toward more precise operations, enhanced decision-making, and improved product safety and transparency. Empirical evidence highlights key benefits such as higher production precision, lower waste, smoother operations of the supply chain, and better financial performances. On the other hand, high costs of implementation, a lack of digital infrastructure, and a shortage of human resources are some of the major obstacles. These potential obstacles notwithstanding, the potential for innovation and growth is very immense. It delivers long-term cost reduction, efficiency improvement, and competitiveness in the global marketplace only for companies that make strategic investments in digital technologies and upskill their workforce. Evidence of support from the side of the government is given by the “Overall Layout Plan for the Construction of Digital China,” which involves enhanced digital infrastructure combined with digital talent cultivation. The following essay underlines the potential of digital technologies to transform China’s food industry, trying to explain how these developments can become important drivers for both sustainable growth and competitiveness.