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BACKGROUND: The American College of Surgeons requires continuous process improvement review to maintain trauma center verification. Paper-based systems to monitor and track performance improvement are commonly used but can be inadequate to monitor concurrent ongoing improvement effectively. OBJECTIVE: To describe the implementation of an electronic process to capture and monitor performance improvement in near real time. METHODS: In 2020, a Midwestern U.S. Level I adult trauma center and a Level II pediatric trauma center's trauma programs transitioned from a paper to an electronic file-sharing system for performance improvement. We converted our primary, secondary, and tertiary review documentation into a single electronic performance improvement file stored on the institution's virtual hard drive, accessible to designated staff, allowing continuous real-time updates. RESULTS: The electronic file-sharing and monitoring process reinvigorated the team and enhanced performance improvement efforts, leading to increased efficiency through documentation and effective loop closure. Real-time monitoring allowed the trauma program to identify opportunities for improvement and enact timely action plans, including targeted performance improvement projects, department education, and ongoing training. CONCLUSION: We found that implementing an electronic file-sharing system enhanced the trauma team's ability to monitor and trend performance improvement in real time. [ABSTRACT FROM AUTHOR]

This position paper aims to pave the way for a debate on a few under-explored, at least, perhaps even neglected, challenges we face when trying to improve building overall performance. Specifically, we suggest focusing on how the efforts to increase building energy efficiency, building safety, the home and workplace healthiness, and the comfort perceived by the users can be impaired by budget constraints, especially while operating in critical scenarios. On the one hand, restraints on capital expenditures by property owners and other investors affect the decision-making processes for the construction of new buildings or the renovation of existing ones. More beyond, rapidly developing demographic and other anthropological changes, as well as frequently occurred natural disasters, pose extra burdens on the players in the building industry and the real estate market. It has been a fact that the need to adapt to both budget constraints and challenging situations is seldom fully embedded in the studies focusing on improving building performance. Therefore, we call for attentions in research and publications to advocate for complementing the need. [ABSTRACT FROM AUTHOR]

Residency recruitment practices have undergone significant changes in the last several years. Interviews are nowconducted fully virtually leading to both positive and negative downstream effects, including decreased cost to applicants and programs, decreased time away from clinical activities, flexibility in scheduling, and increased applications for applicants and program directors. In response to these changes, the Association of Academic Physiatrists Residency and Fellowship Program Directors Council convened a workgroup consisting of program directors, program coordinators, residents, and medical students who reviewed the available literature to provide an evidence-based set of best practices for program leaders and applicants. Available data from the Association of American Medical Colleges and its relevance to future recruitment cycles are also discussed. [ABSTRACT FROM AUTHOR]

The construction industry is characterized by its complex and dynamic nature, requiring efficient resource management practices for successful project execution. This systematic literature review aims to comprehensively analyze and synthesize existing research on resource management practices within the construction sector. By examining a wide array of scholarly articles, conference papers, and industry reports, this review identifies and evaluates the various methodologies, strategies, and tools employed for effective resource management in construction projects. The review encompasses an extensive analysis of key themes, including but not limited to labor management, equipment utilization, material procurement, and technological advancements in resource optimization. It delves into the challenges faced by the construction industry concerning resource allocation, skill shortages, cost control, sustainability concerns, and the impact of unforeseen disruptions. Furthermore, this review critically assesses the implications of resource management practices on project performance, encompassing aspects such as project timelines, budget adherence, quality control, and stakeholder satisfaction. The findings from this systematic review provide a comprehensive understanding of the current state of resource management practices in the construction industry. Additionally, it identifies gaps in the literature and suggests areas for future research, emphasizing the need for innovative approaches and adaptive strategies to address the evolving demands and complexities within construction project management. [ABSTRACT FROM AUTHOR]

The August 2024 issue of the PM World Journal is the 12th anniversary edition, featuring 41 new works by 55 authors from 17 different countries. The edition includes a letter to the editor discussing project controls, two interviews on AI and project management in healthcare and a large-scale rail project, ten featured papers on various topics related to program and project management, seven series articles by authors from five countries, five advisory articles offering practical advice, three commentary articles on strategy, milestones, and emotional AI, and four reports from international correspondents. The issue also includes three second editions of previously published papers, five book reviews, and recent news articles related to project management. The managing editor, David L. Pells, has over 40 years of experience in project management and is an internationally recognized leader in the field. [Extracted from the article]

This article reviews the development of digital twin and summarizes the evolution of the concept of digital twin. The key words and abstracts of 1187 relevant papers in recent 5 years are quantitatively analyzed, and 13 key nodes are obtained. Then it analyzes the content of 9 systematic review articles and summarizes 15 core contents. Then select 115 application examples from the articles. Through the above three steps, nine descriptive general characteristics of digital twin system are summarized: Entity object; Virtual object; Twinning degree; Communication; Data; Functionality; Human-computer interaction; systematicness; and Cost control. This paper will explain these features in detail and summarize the application examples of classifying each feature. This paper aims to summarize the development of digital twin in recent years and propose a description method of system characteristics according to the application scenario of digital twin and the increasingly complex situation of the system. [ABSTRACT FROM AUTHOR]

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments. [ABSTRACT FROM AUTHOR]

Effective inventory management is crucial for optimizing resources and ensuring smooth maintenance operations. This paper explores two primary replenishment systems-Economic Order Quantity (EOQ) and Order Level Control-designed to address the challenges posed by demand variability and lead-time uncertainty. The EOQ model determines the optimal order size to minimize total inventory costs, while replenishment systems guide the timing of orders. We analyze the Fixed Order Quantity System (Q System), which maintains a constant order size with variable ordering frequency, and the Fixed Order Interval System (P System), which fixes the ordering intervals but varies the order size. Each system's advantages and challenges are discussed, highlighting their applicability based on demand stability, lead-time consistency, and inventory value. Through a comparative analysis, this paper provides insights into selecting the appropriate replenishment strategy to achieve efficient inventory control, cost minimization, and resource optimization. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to demonstrate how critical realism can be mobilised as a meta-theory, or philosophical under-labourer, for research on space accounting and how this may further inquiries into the known as well as the unknown implications of space exploration and commercialisation. Design/methodology/approach: This is a conceptual paper that applies critical realism to the field of space accounting using cost management in space contracts as an illustrative example. Findings: Adopting a naturalised version of critical realism that recognises the complex interplay between natural and social realities, the author nuances the distinction between intransitive and transitive objects of knowledge and advances a framework that may be used as a starting point for a transfactual mode of reasoning. The author then applies this mode of reasoning to the topic of cost management in the space sector and illustrates how it may enhance our insights into what causes cost overruns in space contracts. Research limitations/implications: By adopting a naturalised version of critical realism, the author establishes a philosophical framework that can support the broadly based, inter-disciplinary research agenda that has been envisaged for research on space accounting and possibly inform policy development. Originality/value: This paper is the first to apply a critical realist perspective to space accounting and lays a philosophical foundation for future research on the topic. [ABSTRACT FROM AUTHOR]

This paper highlights the tight relationship between the picking and packing processes in warehouse management and the need to consider them as an integrated problem. The study describes and models this integrated problem as a mixed-integer programming model, to optimise overall labour costs by determining the assignment of the subsets of orders, i.e. batches, for picking and packing. To address the issue of model complexity, the paper presents a statistical-based framework for generating approximate models and selecting the optimal one through examination. Based on the examination results, a pair-swapping heuristic is additionally proposed to be combined as a hybrid algorithm. Numerical experiments based on a real-world case demonstrate the effectiveness of the framework-proposed and selected hybrid algorithm by comparison with other framework-proposed approximate models, a solver, and existing heuristics. Our findings indicate that the combined usage of integrated picking and packing processes planning and the hybrid algorithm proposed and selected within the statistical-based framework can effectively reduce the cost of warehouse management. [ABSTRACT FROM AUTHOR]

In this paper, a new delayed fractional-order model including susceptible migratory birds, infected migratory birds and predators is proposed to discuss the spread of diseases among migratory birds. Fear of predators is considered in the model, as fear can reduce the reproduction rate and disease transmission rate among prey. First, some basic mathematical results of the proposed model are discussed. Then, time delay is regarded as a bifurcation parameter, and the delay-induced bifurcation conditions for such an uncontrolled system are established. A novel periodic pulse feedback controller is proposed to suppress the bifurcation phenomenon. It is found that the control scheme can successfully suppress the bifurcation behavior of the system, and the pulse width can be arbitrarily selected on the premise of ensuring the control effect. Compared with the traditional time-delay feedback controller, the control scheme proposed in this paper has more advantages in practical application, which not only embodies the advantages of low control cost and easy operation but also caters to the periodic changes of the environment. The proposed control scheme, in particular, remains effective even after the system has been disrupted by a constant. Numerical simulation verifies the correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

Effective cost management is necessary to implement projects successfully. However, in practice organizations may face unique challenges that need to be addressed. To achieve this goal, the paper explores project cost management challenges, using survey responses from 51 managerial/senior-level staff on the open-ended questions connected to the project cost management processes. Data analysis, supported by process approach, was conducted with the use of systematic coding. The findings reveal that managing projects creates challenges, connected to the project cost management processes such as (1) cost management planning, (2) cost estimation, (3) cost budgeting, and (4) cost control. The research allowed for the discovery of a set of project cost management challenges, definition of their causes and effects, and development of possible countermeasures. Implications and recommendations for future research are given. [ABSTRACT FROM AUTHOR]

Upscaling Voluntary Sustainability Standards (VSS) can generate ecological and social benefits at a scale that is meaningful to address pressing environmental issues such as climate change and biodiversity losses. Lack of resources and risks of implementation gaps may, however, hinder the success of upscaling VSS. This paper aims to fill the gap in the literature regarding options to reduce certification costs to upscale VSS amid the limited financial resources available for certification. The paper presents the result of action research involving 3,507 independent oil palm farmers who are members of six farmer groups in two districts in Central Kalimantan to achieve Roundtable on Sustainable Palm Oil (RSPO) certification. The research found that achieving economies of scale by increasing the total number of certified farmers can reduce the certification costs per farmer. By establishing a district-level entity, the costs related to audits, RSPO membership fees, and the establishment of farmer groups, such as developing standard operating procedures (SOPs), can be lowered. Implementation gaps were not found with the increase of farmers joining the project based on the training frequency and the external audit findings reports on farmers' compliance with RSPO principles and criteria. The findings of this study provide a basis for promoting the upscaling of VSS, including through the RSPO jurisdictional approach. [ABSTRACT FROM AUTHOR]

The heat exchanger plays a key role in the S-CO2 power cycle of power generation systems based on waste heat and has a large impact on their cost control and compactness. In this paper, we take the channel shape of a microchannel heat exchanger as the research object and combine orthogonal tests and numerical simulation, taking the microchannel cross-section length/short-axis ratio, volume ratio and filling rate as independent variables, to numerically study multi-channel thermal–fluid–solid coupling and explore the effects of different microchannel cross-section length/short-axis ratios, volume ratios and filling rates on the thermal hydraulic and mechanical properties of the heat exchanger. The results show that a change in the channel volume ratio has a greater impact on the thermal hydraulic performance of the heat exchanger and that its heat transfer performance is only marginally affected by a change in the channel filling rate. Additionally, when other geometric parameters are kept to a certain level, the closer the shape of the channel is to a circle, the better its mechanical properties are. Within the range of permissible designs, a change in the channel volume ratio does not have an obvious impact on the mechanical properties of the microchannels, while the channel filling rate has the most significant impact. The most significant effect of the channel filling rate on the mechanical properties occurs through the channel volume ratio. [ABSTRACT FROM AUTHOR]

Population Health Management – often abbreviated to PHM – is a relatively new approach for healthcare planning, requiring the application of analytical techniques to linked patient level data. Despite expectations for greater uptake of PHM, there is a deficit of available solutions to help health services embed it into routine use. This paper concerns the development, application and use of an interactive tool which can be linked to a healthcare system's data warehouse and employed to readily perform key PHM tasks such as population segmentation, risk stratification, and deriving various performance metrics and descriptive summaries. Developed through open-source code in a large healthcare system in South West England, and used by others around the country, this paper demonstrates the importance of a scalable, purpose-built solution for improving the uptake of PHM in health services. [ABSTRACT FROM AUTHOR]

This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of clearance. The model is based on direct input and validation from field decision-makers for their practical applicability and effectiveness, and accurate historical demining data extracted from military databases. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes military-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algorithmic precision for maximal safety and effectiveness in demining. [ABSTRACT FROM AUTHOR]

The relationship network between enterprises will change their adoption behavior of AI technology and this micro-decision-making mechanism will eventually decide whether AI technology can diffuse and the extent of diffusion on the macro level. However, the existing AI technology diffusion research mostly focuses on the integration of AI technology with other industries from the industrial level, ignoring the complexity of the micro-complex game process and interactions within the enterprise network on the macro technology diffusion. In this regard, this paper builds a game model of AI technology diffusion in core and non-core enterprises from the levels of market competition and policy incentives based on complex network evolutionary game theory. It does this through simulation analysis that examines the mechanism of key factors affecting the diffusion of AI technology, as well as the influence and combination effects of pertinent policies. The study shows that (1) AI technology diffuses more effectively in non-core enterprises than it does in core enterprises; (2) changes in parameters like technology cost and policy regimes have a more evident impact on core enterprises than non-core ones; (3) in market competition, increasing the network average degree, the proportion of AI technology products in the mainstream market, the opportunity cost, the cost reduction factor, or decreasing the cost of AI technology can all promote the diffusion of AI technology; (4) under policy incentives, increasing the proportion of AI technology subsidies and the influence of high-tech identification of enterprises can both promote the diffusion of AI technology. [ABSTRACT FROM AUTHOR]

Featured Application: The research in this paper can provide a feasible technical means for the assembly sequence optimization of aviation reducers with complex structures, effectively promote the progress of the complex assembly sequence optimization technology of aviation reducers, and realize the comprehensive evaluation of the overall assembly quality and performance index of aviation reducer products. It can be applied to the assembly control of tanks, armored vehicles, ships, and other complex equipment to improve their assembly quality. An assembly sequence evaluation is one of the most important research directions of assembly sequence planning (ASP) for complex mechanical transmission products. Currently, aviation reducers lack a multi-perspective and multi-level evaluation of their assembly sequence. The existing evaluation indicators vary. The evaluation methods have low effectiveness and poor practicability. Therefore, a comprehensive multidimensional evaluation method for complex assembly sequences is proposed in this paper. A multidimensional comprehensive evaluation of the overall assembly quality and performance indices of aviation reducer products is realized. Firstly, the main factors affecting assembly sequence planning are considered: the attributes of the basic unit parts and the cost control of the assembly process. An evaluation index system of assembly sequence planning based on the two dimensions of assembly cost and ontology is constructed. Then, according to the multidimensional evaluation index, fuzzy evaluation theory is used to establish a fuzzy set and a matrix for each dimensional evaluation index. The index weight is divided. A comprehensive evaluation model and the function of each dimension are established. After a comprehensive evaluation, the multidimensional assembly sequence evaluation method for aviation reducers is formed. Finally, the method is applied to the assembly process of the primary reducer of a helicopter's main reducer, and a comprehensive evaluation of its assembly sequence scheme is completed to verify the feasibility of the proposed method. This article constructs a complex assembly sequence evaluation method that includes 12 evaluation indicators, improves the assembly sequence planning evaluation index system of aviation reducers, and can effectively promote the progress of optimization technology for complex assembly sequences of aviation reducers. [ABSTRACT FROM AUTHOR]

Financial balance is a requisite for the sustained operation of urban regeneration projects in the stock-based planning period. From the perspective of financial balance, this paper analyzes the operation strategy and mechanism of the LM Apartments Project in Shuiwei Village of Shenzhen from four aspects: the financial input in the initial stage, the revenue and expenditure in the operational stage, the financial relationship between multiple stakeholders, and the control of non-financial costs. The findings reveal that under the tripartite cooperation between the government, the state-owned enterprise, and the village collective, the LM Apartments Project in Shuiwei Village is a model of urban village regeneration that effectively supplements Shenzhen's government-subsidized housing system. Of the three parties, the government provides policy support and asset-light investment; Shum Yip Group, a state-owned enterprise, has carried out innovative practices of urban village regeneration while making a small profit; the Shuiwei village collective has achieved sustained economic benefits and a high-quality living environment. The paper further discusses the external effects of this practice and probes into the conditions for the wide application of this mode in the hope of providing a useful reference for promoting the application of the financial balance mechanism in urban village regeneration projects. [ABSTRACT FROM AUTHOR]

The Location-Routing Problem (LRP) becomes a more intricate subject when the limits of capacities of vehicles and warehouses are considered, which is an NP-hard problem. Moreover, as the number of vehicles increases, the solution to LRP is exacerbated because of the complexity of transportation and the combination of routes. To solve the problem, this paper proposed a Discrete Assembly Combination-Delivery (DACA) strategy based on, the Binary Equilibrium Optimizer (BiEO) algorithm, in addition, this paper also proposes a mixed-integer linear programming model for the problem of this paper. Our primary objective is to address both the route optimization problem and the assembly group sum problem concurrently. Our BiEO algorithm was designed as discrete in decision space to meet the requirements of the LRP represented by the DACA strategy catering to the multi-vehicle LRP scenario. The efficacy of the BiEO algorithm with the DACA strategy is demonstrated. through empirical analysis utilizing authentic data from Changchun City, China, Remarkably, the experiments reveal that the BiEO algorithm outperforms conventional methods, specifically GA, PSO, and DE algorithms, resulting in reduced costs. Notably, the results show the DACA strategy enables the simultaneous optimization of the LRP and the vehicle routing problem (VRP), ultimately leading to cost reduction. This innovative algorithm proficiently tackles both the assembly group sum and route optimization problems intrinsic to multi-level LRP instances. [ABSTRACT FROM AUTHOR]

Healthcare is a critical area where blockchain technology (BT) is being heralded as a potential game-changer for facilitating secure and efficient data sharing. The purpose of this review is to examine BT applications, performance challenges, and solutions in healthcare. To begin, This review paper explores popular blockchain networks for data exchange, encompassing both public and permissioned platforms, such as Ethereum and Hyperledger Fabric. This paper analyzes the potential applications of BT's decentralized, immutable, and smart contract capabilities in healthcare settings, including secure and interoperable health data exchange, patient consent management, drug supply chain oversight, and clinical trial management. The healthcare industry might greatly benefit from the increased privacy, transparency, and accessibility that these technologies provide. Despite BT's promising medical uses, the technology is not without its drawbacks. High energy consumption, throughput, and scalability are all concerns. We wrapped up by discussing the solutions that have been implemented, including consensus processes, scalability measures like sharding, and off-chain transactions that are designed to mitigate the drawbacks. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to introduce the concept of using the well-established Shared Lives approach to support survivors of domestic abuse with complex needs including those with learning and physical disabilities, older people and carers. Design/methodology/approach: Survivors with complex needs are often excluded from traditional domestic abuse support services thus increasing their risk and making recovery impossible. Using the Shared Lives approach in suitable cases could address gaps in provision and improve the outcomes for survivors with disabilities, older survivors and carers. Findings: The paper draws on evidence from reports and research about the experiences of these cohorts of survivors to explain how the Shared Lives approach could increase support options for specific categories of survivors. Practical implications: Using the Shared Lives approach to support cohorts of victims/survivors who experience barriers to accessing support could improve outcomes for these people, reduce risk of serious harm and improve quality of life. Originality/value: With the recent recommendation from Association of Directors of Adult Social Services around increasing use of Shared Lives, this paper provides one potential way to meet this recommendation. [ABSTRACT FROM AUTHOR]

The integration of the advanced manufacturing industry and the modern service industry is an important direction for the transformation and upgrading of China's manufacturing industry. However, the existing research focuses more on the level, motivation, and economic effects of the integration of manufacturing and producer services, and less on its environmental effects. In this paper, based on the panel data from 30 provincial regions in China from 2005 to 2019, an evaluation system for the integration of manufacturing and producer services (IMP) is constructed, and the carbon emission efficiency (CEE) is calculated through the super-efficiency SBM model. Based on the analysis of the influence mechanism of IMP on CEE, the mediation effect model and the spatial Durbin model are used to test the influence of the IMP on CEE. The results show that the IMP can significantly improve CEE, and indirectly promote CEE through four intermediary factors: value addition, cost reduction, industrial agglomeration and structure upgrading. Finally, we found the level of economic development, property rights structure and regional innovation can promote CEE to varying degrees. The intensity of environmental regulation, opening level and the energy consumption structure inhibit the efficiency of carbon emissions. The total impact of the IMP on CEE is greater in the western region than in the eastern and central regions, and the western region shows a more significant spatial spillover effect. The IMP has a greater effect on improving CEE, and shows a strong spatial spillover effect. [ABSTRACT FROM AUTHOR]

This paper takes the frame as the research object and explores the vibration characteristics of the frame to address the vibration problem of a 1-MSD straw-crushing and residual film recycling machine in the field operation process, and an accurate identification of the modal parameters of the frame is carried out to solve the resonance problem of the machine, which can achieve cost reduction and increase income to a certain extent. The first six natural frequencies of the frame are extracted by finite element modal identification and modal tests, respectively. The rationality of the modal test results is verified using the comprehensive modal and frequency response confidences. The maximum frequency error of modal frequency results of the two methods is only 6.61%, which provides a theoretical basis for the optimal design of the frame. In order to further analyze the resonance problem of the machine, the external excitation frequency of the machine during normal operation in the field is solved and compared with the first six natural frequencies of the frame. The results show that the first natural frequency of the frame (18.89 Hz) is close to the external excitation generated by the stripping roller (16.67 Hz). The first natural frequency and the volume of the frame are set as the optimization objectives, and the optimal optimization scheme is obtained by using the Optistruct solver, sensitivity method, and grey correlation method. The results indicate the first-order natural frequency of the optimized frame is 21.89 Hz, an increase of 15.882%, which is much higher than the excitation frequency of 16.67 Hz, and resonance can be avoided. The corresponding frame volume is 9.975 × 107 mm3, and the volume reduction is 3.46%; the optimized frame has good dynamic performance, which avoids the resonance of the machine and conforms to the lightweight design criteria of agricultural machinery structures. The research results can provide some theoretical reference for this kind of machine in solving the resonance problem and carrying out related vibration characteristics research. [ABSTRACT FROM AUTHOR]

Pectin lyase (PMGL) is an industrially important enzyme with widespread applications in the food, paper, and textile industries, owing to its capacity for direct degradation of highly esterified pectin. In this study, PMGL-Ba derived from Bacillus licheniformis underwent mining and heterologous expression in P. pastoris. Furthermore, diverse strategies, encompassing the optimization of expression cassette components, elevation of gene dosage, and co-expression of chaperone factors, were employed to augment PMGL-Ba production in P. pastoris. The signaling peptide OST1-pre-α-MF-pro and promoter AOX1 were finally selected as expression elements. By overexpressing the transcription factor Hac1p in conjunction with a two-copy PMGL-Ba setup, a strain yielding high PMGL-Ba production was achieved. In shake flask fermentation lasting 144 h, the total protein concentration reached 1.81 g/L, and the enzyme activity reached 1821.36 U/mL. For further scale up production, high-density fermentation transpired in a 5 L fermenter for 72 h. Remarkably, the total protein concentration increased to 12.49 g/L, and the enzyme activity reached an impressive 12668.12 U/mL. The successful heterologous and efficient expression of PMGL-Ba not only furnishes a valuable biological enzyme for industrial applications but also contributes to cost reduction in the utilization of biological enzymes in industrial applications. [ABSTRACT FROM AUTHOR]

The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explore how hierarchical accountability can be enacted and accounting control systems mobilized in a way that promotes a sense of felt responsibility. Design/methodology/approach: The paper draws on interviews, shadowing and observations to explore the implementation of a strategy for "increasing accountability" in a Norwegian Oil Company. The case provided an opportunity to explore the dynamics of hierarchical accountability and felt responsibility, and in particular Roberts (2009) concept of "intelligent accountability", in an empirical context. Findings: The case study explores how the strategy of increasing accountability at OilCo was enacted around three operational issues; the control of costs, roles and relationships in the complex matrix structure, and the operation of the management system. It traces how the long history of Beyond Budgeting practices and philosophy in OilCo resulted both in an explicit recognition of the incompleteness of accounting numbers, and trust-based practices which avoided many of the dysfunctional individual and organizational effects typically associated with the exercise of hierarchical control. Originality/value: The paper explores empirically how OilCo's embrace of Beyond Budgeting practices and philosophy had created the conditions under which a more intelligent form of accountability could emerge. As a European case study, it calls into question the Anglo-American tradition of accounting research which suggests that externally imposed accountability within a hierarchy mitigates against employees' felt responsibility. [ABSTRACT FROM AUTHOR]

Currently, countries worldwide are embracing digital strategies, enabling enterprises to utilize digital technology, digital supply chains, blockchain, and additional digital measures to increase their competitiveness. This paper analyzed the correlation between the digital transformation of manufacturing export enterprises and their business and export performance, focusing on China's manufacturing export enterprises through empirical analysis. The study investigated the influence of digital transformation on enterprise performance. Using the Resource Based View theory and Trade theory, hypotheses were proposed and regression models were developed to analyze a sample of 1007 enterprises listed on the Shanghai and Shenzhen Stock markets from 2012 to 2019. The study conducted regression analysis, intermediate effect test, robustness test, stage lag, and heterogeneity analysis. The study found that (1) Manufacturing export enterprises listed in the stock market implemented digital transformation, leading to a significant positive impact on their overall performance. (2) Digital transformation led to cost reduction, improved R&D intensity, and enhanced human resources, among other benefits for enterprise performance. (3) According to the fractal analysis, non-state-owned enterprises exhibited more favorable effects on enterprise performance, and the digital transformation of manufacturing export companies in developed regions had a more significant impact on their performance. Finally, the study's empirical results yielded pertinent proposals for digital transformation. [ABSTRACT FROM AUTHOR]

Different aspects of modern society can be transformed by the deployment of Artificial Intelligence (AI). AI-powered tools have promoted changes in the financial industry by applying inventive methods for data analysis and automating processes, efficiency enhancement, cost reduction and more personalised services to customers. However, AI algorithms may activate significant ethical and regulatory concerns that should be addressed by the industry and society as a whole. In line with the Erasmus+ project Transversal Skills in Applied Artificial Intelligence - TSAAI (KA220-HED - Cooperation Partnerships in higher education), which aims to establish a training platform, this paper focuses on an analysis of study programmes in formal tertiary education across consortium countries (Spain, Estonia, North Macedonia, Croatia, Germany, and Slovenia) with a special focus on applied artificial intelligence and development of curriculum that will integrate teaching guidelines covering the areas of application of AI technology in the financial and insurance sectors. To this end, a Systematic Review of Literacy (SRL) on the web methodology identifying the existing employability requirements in AI and the Learning-Centred Syllabus (LCS) methodology for curriculum development was applied, with the presented curriculum expected to serve as a framework to develop teaching materials to help students, academics and employees enhance their professional skills, thus satisfying labour market needs. [ABSTRACT FROM AUTHOR]

Crypto-assets are rapidly becoming a mainstream phenomenon in the global financial system. This is owing to developments in technology, an increase in token issuances and the existence of tens of thousands of old and emerging crypto-asset projects around the world. There is consensus that the financial sector stands to gain from crypto-assets, for example in terms of cost reductions, increased effectiveness and openness. The rapid expansion of cryptocurrencies' ecosystem has intensified the attention of regulatory communities. Regulators are increasingly being challenged to respond fast and appropriately to protect customers, investors and populations from crypto-related risks, address the risks themselves and still promote technological advancement in this area. Currently, there are notable crypto regulatory loopholes in many jurisdictions of the world, not the least of which are those in the European Union. These gaps have helped to add to legislative ambiguity and weak protection of investor and property rights as regards to cryptocurrencies. Several European countries have made proactive attempts to develop domestic regulatory frameworks for crypto-assets. Many national, regional and international supervisory and regulatory institutions have participated in the crypto discourse which is demonstrated by issuance of various reports, analyses and policy statements highlighting risks and providing regulatory proposals. Following thorough consultation and review of the cryptocurrency ecosystem, the European Commission has recently asserted its authority over these very dynamic virtual assets and launched a framework for a regulation on Markets in Crypto-assets to address the risks germane to the crypto-asset markets within the context of the European Union. This research paper evaluates the European Union's present regulatory approach to cryptocurrencies. Particularly, the paper focuses on the issues, challenges and consideration of these approaches. The paper synthesizes extant research findings, expert opinions and institutional reports of the major regulatory, supervisory, oversight and advisory bodies, as well as those of relevant national and international agencies within the European Union. [ABSTRACT FROM AUTHOR]

The large majority of electric cars have a single-speed gearbox, because electrified powertrains provide maximal power across a wide operating range, and single-speed simplifies construction and reduces capital costs. Nevertheless, multi-speed transmissions have also been developed for electric cars, and some of them have recently appeared as commercial products. This paper aims to compare, through some practical examples, solutions with single-speed and dual-speed transmissions. In particular, given the very smooth driving of electric cars, for dual-speed solutions, a dual-clutch gearbox was considered. Finally, a continuously variable transmission (CVT) was also used. Different solutions were analyzed from a technical–economic point of view, based on a simulation of the vehicle under standardized driving cycles, thus evaluating the capital and running electricity costs. The obtained results show that the comparison between the two solutions is very open, and in the majority of cases, the advantages in terms of efficiency overcome the disadvantages due to the additional capital costs. For a rather low battery cost of 150 €/kWh, the total cost reduction moves from about 100–150 € up to 1500–2000 €, depending on the electricity cost, along the whole vehicle lifespan. [ABSTRACT FROM AUTHOR]

This article aims to summarize performance indicators used in telephone triage services research, and make recommendations for the selection of valid indicators to measure the performance of telephone triage. We describe what kind of frameworks, performance indicators, or variables have been used for evaluating telephone triage performance by systematically mapping the telephone triage performance measurement. The objective was to find measures for each Triple Aim dimension. A scoping review method was used following Joanna Briggs Institute guidelines. Using this method, we defined indicators to measure the performance of telephone triage. We used the Triple Aim framework to identify indicators to measure the overall performance of telephone triage. The Triple Aim framework consists of improving the patient experience of care, improving the health of populations, and reducing cost per capita. The scoping review was performed using CINAHL, Medline, EBSCOhost, and PubMed electronic databases. The eligibility criterion was research published in English between 2015 and 2023. The inclusion focused on the use and performance of telephone triage services and system-focused studies. A total of 1098 papers were screened for inclusion, with 57 papers included in our review. We identified 13 performance indicators covering all Triple Aim dimensions: waiting times, access, patient satisfaction, the accuracy of triage decision, severity and urgency of the symptoms, triage response, patient compliance with the advice given, follow-up healthcare service use, and running costs of service. We didn't find any earlier framework covering all Triple Aim dimensions properly. Measuring the performance of telephone triage requires an extensive and comprehensive approach. We presented performance indicators that may be included in the framework for measuring the performance of telephone triage to support overall performance measurements of telephone triage. [ABSTRACT FROM AUTHOR]

In the era of extensive data acquisition from manufacturing and transportation processes, the utilization of machine learning and deep learning techniques has emerged as a potent force for informed decision-making and optimized deliveries in contemporary urban landscapes. This study presents a novel approach grounded in deep learning, where product data are systematically gathered to construct a multilayer perceptron neural network model. This model proves instrumental in efficiently classifying product flows within the urban milieu. To validate its efficacy, machine learning classifiers are deployed, and their performance is juxtaposed with the neural network model. Addressing the critical question of the paper's significance, our experimental evaluation unequivocally demonstrates the superior classification accuracy of the proposed multilayer perceptron model when compared to traditional machine learning models operating on the same product dataset. This advancement is not merely a theoretical achievement but translates into tangible improvements in last-mile delivery processes, marked by significant cost reduction and the mitigation of delays. The transformative potential of our approach is further underscored by the strategic application of a deep learning algorithm for optimization and illustrative purposes. This holistic methodology not only positions our work as a noteworthy contribution to the realm of product classification but also establishes a concrete pathway for enhancing the sustainability and efficiency of urban logistics. This paper, thus, goes beyond the conventional application of machine learning models, offering a paradigm shift in the intersection of deep learning, urban logistics, and sustainable development. [ABSTRACT FROM AUTHOR]

In this paper, the low energy cost synchronization control strategy of Markovian switching complex systems/networks is mainly studied and analyzed through multiple perspectives. Firstly, in order to achieve synchronization of Markovian switching complex networks with low energy cost, a control scheme based on the optimal node selection strategy that does not depend on the network coupling strength is improved, and a finite-time controller with a simpler structure is constructed. Secondly, based on the event-triggered control strategy an effective trigger event is designed to achieve the low energy cost synchronization of Markovian switching complex networks on the basis of reducing the information transmission and interaction between networks. Finally, the two control strategies mentioned in this paper are compared and analyzed from multiple perspectives through numerical simulations to better guide practical engineering. [ABSTRACT FROM AUTHOR]

In order to improve the practical effect of the power environmental protection platform, this paper combines the big data technology to develop and design the power environmental protection platform. In this paper, a power factor correction (PFC) and resonant half-bridge joint controller is used to combine the traditional two-level topology into one-level topology. Moreover, this paper adopts a fixed frequency half-bridge LLC resonant converter with a simple control circuit to obtain a stable resonant half-bridge controller with a fixed output voltage. The choice of this overall power topology architecture can not only meet the design requirements, save costs and simplify the control of the circuit, but also improve the efficiency of the whole machine and the performance of the power environmental protection platform. The experimental research shows that the power environmental protection platform based on big data proposed in this paper can effectively improve the effectiveness of power environmental protection strategies. [ABSTRACT FROM AUTHOR]

Prior to designing detailed subsidy contract terms for public–private partnerships (PPPs), local governments often face the challenge of selecting the basic modes of a subsidy (in this case, passenger demand subsidy or vehicle-kilometer subsidy). However, most studies do not discuss the reasons for choosing a particular mode or its applicability. This paper addresses this gap by constructing Stackelberg game models to analyze the characteristics of two subsidy modes: vehicle-kilometer subsidy and passenger demand subsidy. Four situations derived from these two subsidy modes are then modeled and analyzed, exploring the relationships among parameters and clarifying the strategies of local governments. Additionally, a comparison is made between the two subsidy modes to demonstrate the optimal utilization of subsidies. Based on this analysis, an efficiency-oriented subsidy framework is proposed to overcome the shortcomings of existing subsidies and provide new insights into subsidy design for PPPs. The results indicate that the vehicle-kilometer subsidy loses its incentive or regulatory effects on the private sector when the vehicle-kilometer is predetermined. Furthermore, the subsidy mode, the local government's goals, and financial stress will influence key parameters (price and vehicle-kilometer); further, the preference of both parties for subsidy modes is closely related to price. The proposed efficiency-oriented subsidy framework offers significant advantages in reducing prices, increasing consumer surplus and efficiency, conserving social resources, and strengthening cost control. This paper contributes to the literature by emphasizing the importance of selecting appropriate subsidy modes, exploring the differences between two common modes, and further providing a novel and effective subsidy mode. Further, this paper can also guide mutual parties to optimize their strategies and enhance the overall efficiency and performance in PPP urban rail transit projects. [ABSTRACT FROM AUTHOR]

It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized $3\times 3$ window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X–2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained. [ABSTRACT FROM AUTHOR]

The increasing number of electric vehicles (EVs) necessitates the installation of more charging stations. The challenge of managing these grid-connected charging stations leads to a multi-objective optimal control problem where station profitability, user preferences, grid requirements and stability should be optimized. However, it is challenging to determine the optimal charging/discharging EV schedule, since the controller should exploit fluctuations in the electricity prices, available renewable resources and available stored energy of other vehicles and cope with the uncertainty of EV arrival/departure scheduling. In addition, the growing number of connected vehicles results in a complex state and action vectors, making it difficult for centralized and single-agent controllers to handle the problem. In this paper, we propose a novel Multi-Agent and distributed Reinforcement Learning (MARL) framework that tackles the challenges mentioned above, producing controllers that achieve high performance levels under diverse conditions. In the proposed distributed framework, each charging spot makes its own charging/discharging decisions toward a cumulative cost reduction without sharing any type of private information, such as the arrival/departure time of a vehicle and its state of charge, addressing the problem of cost minimization and user satisfaction. The framework significantly improves the scalability and sample efficiency of the underlying Deep Deterministic Policy Gradient (DDPG) algorithm. Extensive numerical studies and simulations demonstrate the efficacy of the proposed approach compared with Rule-Based Controllers (RBCs) and well-established, state-of-the-art centralized RL (Reinforcement Learning) algorithms, offering performance improvements of up to 25% and 20% in reducing the energy cost and increasing user satisfaction, respectively. [ABSTRACT FROM AUTHOR]

Healthcare knowledge management systems can mitigate hospitals' operational inefficiency. As a healthcare information technology, the electronic health record (EHR) receives much attention from medical institutions due to its considerable impact on operational cost performance. This paper focuses on EHR systems to address operational inefficiency by which patients pay more for health care services, and many U.S. hospitals are filing for bankruptcy. From the theoretical perspective of the practice-based view, this paper introduces a path to implement EHR systems for improving cost performance. The empirical investigation is archival data of 200 hospitals collected from the U.S. healthcare agencies. Findings contribute to prior work by hypothesizing moderating and mediating roles in EHR systems implementation. This paper introduces absorptive capacity and monitoring mechanisms as enablers of implementing EHR systems. The results showed that hospital monitoring strengthens the relationship between absorptive capacity and electronic health record systems implementation, which results in better operational cost performance. Theoretically, this study supports the long-term potential benefits of EHR adoption, and its findings are consistent with optimizing efficiency through data standardization and interoperability. From a practical perspective, this study supports hospitals' investments in evolving healthcare information technology systems through the development of a knowledge-based system employing EHR, particularly when hospitals are merging or need a financial strategic plan to control expenses. [ABSTRACT FROM AUTHOR]

Politicians and policymakers call for public sector innovation to address societal challenges and to streamline and cut costs. This results in rationales to handle both smaller and more complex types of innovation. To explore how these rationales materialize at a workplace level, this paper aims to examine perceptions of why innovation in the public sector is needed, what innovation is, and how innovation work can be conducted. The findings demonstrate difficulties in establishing an organizational purpose for innovation and translating this understanding into practice. The result is a gap between the rhetorical why and the practical what and how of innovation. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of distributed energy resources (DERs), the effectiveness of voltage control in distribution networks faces significant challenges. Aiming at this problem, a novel distributed stochastic model predictive control (DSMPC) scheme was proposed in this paper to achieve voltage regulation considering the uncertainty of DERs. In the proposed control scheme, based on the photovoltaic (PV) prediction error model, multiple operation scenarios are selected to characterize the uncertainty of PV generation. Then, based on these selected scenarios, a DSMPC approach is developed to minimize the cost of control actions by coordinating the PV inverters and battery energy storage systems. Simulation involving the modified IEEE 34-bus verified that the proposed method can work effectively under the uncertainty of DERs and achieves a comparable control performance with a well-designed centralized controller. [ABSTRACT FROM AUTHOR]

Many advanced technologies have been employed in high-performance active matrix displays, including liquid crystal displays, organic light-emitting diode displays, and micro-light-emitting diode displays. On the other side, there exists a strong demand for cost reduction, and it is one of the low-cost schemes for integrating the driver circuit in a panel based on thin-film transistor technologies. This paper reviews the overall concept, operation principles, and various circuit approaches in shift registers for scanning pulse generation. In addition, it deals with the implementation of additional functionalities in gate drivers to support pixel compensation, multi-line driving, in-cell capacitive touch screen, pixel sensing, and adaptive scanning region control. [ABSTRACT FROM AUTHOR]

Healthcare is considered one of the necessities for sustaining life. However, frequent emergencies raise the risk of supply chain disruption, seriously threatening people's lives and health security. Therefore, building a resilient healthcare supply chain is an important initiative to manage the healthcare crisis effectively. Based on the secondary supply chain formed by medical supply manufacturers and retailers, this paper constructs an evolution game model of resilience investment decisions under the non-disruption and disruption symmetry scenarios of the supply chain and analyzes the stabilization strategies employed by both parties based on their asymmetry strategy choices. Subsequently, the numerical simulation is used to analyze the impact of various parameters on the evolutionary results and their evolutionary trends. The results of the study show that additional benefits from resilience investment, potential costs, disruption losses, market encroachment revenue, "free-rider" benefits, additional benefit increase coefficient, resilience investment reduction coefficient, disruption loss reduction coefficient, additional unit cost reduction coefficient, and market encroachment revenue coefficient all influence the resilience investment decisions. Finally, based on the simulation results, specific recommendations are formulated to improve the resilience of the healthcare supply chain. [ABSTRACT FROM AUTHOR]

This paper presents the design of a sliding mode controller to compensate hysteresis nonlinearity and achieve precision motion tracking for a novel piezoelectric tip/tilt platform driven by a PZT actuator. The sliding mode control scheme is based on the unique physical characteristics of the piezoelectric tip/tilt platform. The proposed scheme effectively guides the platform state onto a predefined sliding surface and ensures its sustained movement along this manifold. This approach reduces tracking errors compared to conventional methodologies. The stability of the sliding mode control scheme is demonstrated by the Lyapunov theory framework. It achieves precise motion control with minimal tracking error on a piezoelectric tip/tilt platform. The properties of the controller have been confirmed through experimental tests. The proposed control scheme enhances the robust tracking and stability performance on the piezoelectric tip/tilt platform, outperforming traditional control schemes. Compared with the P562.6CD produced by PI Germany, the proposed innovative approach not only boosts the platform's resolution by 32% but also implements a 33% reduction in cost. [ABSTRACT FROM AUTHOR]