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In public libraries, especially those in rural settings, it is important that every dime of library funding is leveraged effectively into serving the community. As part of a year-long project beginning in January 2023, we are evaluating item-level cost-effectiveness for each circulating item housed at the public library in Lakeville, Indiana. Through the use of big(ish) data, some custom Python scripting, and machine learning algorithms we hope to answer: How much money is saved by library patrons through their use of the public library's physical collection? How much money is saved by the community through the operation of a public library based on the use of the circulating collection? And are there any non-obvious traits which make an item or title a more or less cost-effective circulating asset? In this column, I will describe the scripts, share initial findings, discuss challenges, and investigate next steps. [ABSTRACT FROM AUTHOR]

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Pile burning of thinned residues is a critical tool to dispose of fuels and to reduce wildfire risk in overstocked, fire-prone forests globally. However, cost estimates of pile burning are limited. In the Western United States, where fuel reduction and pile burning are key strategies to mitigate risk of severe wildfire, previous reports estimate that the average cost of pile burning after machine treatment is $543 ac−1 ($1,343 ha−1). There is, however, limited information on the costs of hand thinning and pile burning. In response, this study quantified the costs of cutting and yarding, piling, and burning residues via two pathways: the USDA Forest Service (USFS) Activity Tracking System (FACTS) database, and interviews with 11 USFS fire management professionals from California, Oregon, and Washington. Interviews highlighted cost drivers, implementation constraints, and opportunities for efficiency improvements. The average costs of piling and burning machine piles as determined from the interviews were $735 ± $464 ac−1 ($1,817 ± $1,146 ha−1; all mean ± SD), 80% higher than reported in the FACTS database and 35% higher than previous reports. The average costs of piling and burning hand piles as determined from the interviews were $1,291 ± $717 ac−1 ($3,190 ± $1,722 ha−1), 135% higher than reported in the FACTS database. Interview participants reported proximity to roads and terrain as key cost drivers, and described common practices, challenges, and constraints to pile burning. Geospatial analyses supported interviewee-identified cost drivers, district road density (a proxy for accessibility) and district maximum elevation (a proxy for terrain). Simulations of direct emissions from pile burning on National Forests included in this study indicated annual emissions of 11,322 metric tons (MT) of particulate matter (PM), 8,029 MT of PM10, and 6,993 MT of PM2.5 across the study area. In addition, pile burning on these National Forests annually emits >1.7 million MT CO2, 61,515 MT of carbon monoxide, 3,823 MT of methane, and 3,211 MT of non-methane hydrocarbons. Given the economic, human health, and climate implications of current pile burning practice, removing residues as feedstocks for carbon-negative utilizations is recommended as a near-term priority. Policy mechanisms, such as feedstock production, transport, or offtake subsidies of a similar magnitude to such avoided costs, could efficiently incentivize residue removal and support such climate-positive utilizations. [ABSTRACT FROM AUTHOR]

Building Information Modelling (BIM) has emerged as a transformative tool in the Architecture, Engineering, and Construction (AEC) industries, offering significant potential to improve project efficiency and outcomes. This study explores the impact of BIM implementation on project time and cost by analyzing critical factors such as design errors, unbudgeted changes, Requests for Information (RFIs), labour dynamics, and scheduling across multiple case studies. Quantitative results reveal that BIM adoption reduces project timelines by an average of 20% and costs by 15%, while also decreasing design errors by 30% and RFIs by 25%. Furthermore, BIM enhances collaboration among stakeholders, improves design visualization, and facilitates better risk assessment, leading to more informed decision-making throughout the project lifecycle. These findings are synthesized into a decision-making framework that estimates the probability of errors, evaluates their potential time and cost implications, and ensures alignment with project budgets. The framework serves as a strategic guide for project teams to assess the suitability of BIM for specific projects, thereby optimizing decision-making processes and improving overall project performance. Additionally, the study examines the role of BIM in sustainability by reducing material waste and improving resource allocation. This study addresses a critical gap in the field by systematically evaluating the interrelationships among BIM's impacts on key project parameters, which have often been treated in isolation in prior research. The importance of this work lies in its provision of a structured methodology to harness BIM's capabilities, demonstrating its value in delivering significant time and cost efficiencies while enhancing project quality. By integrating empirical analysis with practical applications, this research contributes to the growing body of knowledge on BIM adoption and provides actionable insights for AEC professionals seeking to optimize project outcomes. [ABSTRACT FROM AUTHOR]

This paper deals with the potential of the different modes of transport on the Eurasian connection, focusing on rail, and comparing with sea and air. Since 2008, a substantial uptake of the rail connection has been observed. The main question then is whether the competitiveness of this land bridge is actual and sustainable or not. First, the paper focuses on the latest developments of the actual physical capacity on the Eurasian connection. Mainly under Chinese impetus, substantial improvements of rail connections but also border crossings have been made. Second, the paper focuses on the actual competitiveness of the different modes on the Eurasian connection in the current-day transport volumes. It can be observed that also the transport on the Eurasian connection has been growing, especially by rail. However, by far the largest volumes of transport still happen by maritime transport. The third and main question of the paper is on whether the land bridge by rail would cost-wise be competitive enough to favour a large shift from sea to land. An adapted version of a chain cost model applied to seven city pairs shows that the cost of the maritime solution is notably higher the further away from the coast origin and/or destination are located. For rail, such significant cost differences are not found. Furthermore, the value of the goods plays a bigger role for rail than for maritime transport. Air transport, due to its higher charges, typically is only used by higher-value goods. When comparing the (combinations of) transport modes, it turns out that the rail costs are on average 1.5 times to twice as high as when using maritime transport, but the more westward the origin on the Chinese territory, the closer the ratio gets to 1. The sensitivity analysis shows that in particular using longer trains will allow strongly further reducing generalized rail chain costs, easily over a third of the base case costs. Substantially less generalized chain cost reductions are achieved when shortening transit times, even when the latter goes up to half the initial transit time. Fourth, looking at what would be needed to make a further shift to rail materialize on the Eurasian connection, three items pop up: rail border crossing capacity, traffic balance between both directions and backups to the TEN-T rail network. [ABSTRACT FROM AUTHOR]

As an energy carrier characterized by its high energy density and eco-friendliness, hydrogen holds a pivotal position in energy transition. This paper elaborates on the scientific foundations and recent progress of photo- and electro-catalytic water splitting, including the corresponding mechanism, material design and optimization, and the economy of hydrogen production. It systematically reviews the research progress in photo(electro)catalytic materials, including oxides, sulfides, nitrides, noble metals, non-noble metal, and some novel photocatalysts and provides an in-depth analysis of strategies for optimizing these materials through material design, component adjustment, and surface modification. In particular, it is pointed out that nanostructure regulation, dimensional engineering, defect introduction, doping, alloying, and surface functionalization can remarkably improve the catalyst performance. The importance of adjusting reaction conditions, such as pH and the addition of sacrificial agents, to boost catalytic efficiency is also discussed, along with a comparison of the cost-effectiveness of different hydrogen production technologies. Despite the significant scientific advancements made in photo(electro)catalytic water splitting technology, this paper also highlights the challenges faced by this field, including the development of more efficient and stable photo(electro)catalysts, the improvement of system energy conversion efficiency, cost reduction, the promotion of technology industrialization, and addressing environmental issues. [ABSTRACT FROM AUTHOR]

Fully harnessing the inherent flexible adjustment potential of steel enterprises and fostering their interaction with the power grid is a crucial pathway to advancing green transformation. However, traditional research usually takes reducing energy consumption as the optimization goal, which limits the adjustment response capability, or ignores the storage and conversion constraints of secondary energy sources such as gas, steam, and electricity, making it difficult to fully explore and reasonably utilize the potential of multi-energy coordination. This study considers the production constraints of the surplus energy recovery and utilization system, establishes a collaborative scheduling model for a gas–steam–power system (GSPS) in an iron and steel enterprise, and proposes a demand response strategy that considers internal production constraints. Considering the time-of-use (TOU) tariff, iron and steel enterprises achieve a dynamic optimization adjustment range of electricity demand response through the conversion and storage process of gas, steam, and power. The adjustment capability of the GSPS reaches 26.94% of the initial electricity load, while reducing the total system energy cost by 2.24%. There is vast development potential of iron and steel enterprises participating in electricity demand response for promoting cost reduction and efficiency improvement, as well as enhancing the power grid flexibility. [ABSTRACT FROM AUTHOR]

In a traction power supply system, the design of traction substations significantly influences both the system's operational stability and investment costs, while the energy management strategy of the flexible substations affects the overall operational expenses. This study proposes a novel two-stage system optimization design method that addresses both the configuration of the system and the control parameters of traction substations. The first stage of the optimization focuses on the system configuration, including the optimal location and capacity of traction substations. In the second stage, the control parameters of the traction substations, particularly the droop rate of reversible converters, are optimized to improve regenerative braking energy utilization by applying a fuzzy logic-based adjustment strategy. The optimization process aims to minimize the total annual system cost, incorporating traction network parameters, power supply equipment costs, and electricity expenses. The parallel cheetah algorithm is employed to solve this complex optimization problem. Simulation results for Metro Line 9 show that the proposed method reduces the total annual project costs by 5.8%, demonstrating its effectiveness in both energy efficiency and cost reduction. [ABSTRACT FROM AUTHOR]

Heat pipes filled with a thermodynamic medium are energy-saving and stable heat exchangers that have been used for years in various fields of science and technology, including building heating and cooling installations. This article presents the results of research on the energy efficiency of wall-mounted concrete heating and cooling modules with heat pipes, which can be a structural element of external and internal walls of buildings for various purposes. A series of measurement tests were performed, which allowed the determination of how the thermal power and control parameters of the module (amplification factor and time constants) change under operating conditions. A first- and second-order inertial model was used to describe the control properties of the module. The measurements were performed in heating and cooling mode for three different values of supply water flow, both when increasing the supply temperature and when decreasing it. Based on the results of the measurements, calculations and analysis, it was found that the thermal power and control parameters of the module change significantly; these changes result from both the design features of the module (the type of thermodynamic medium in the heat pipe and the technical aspects of the execution and assembly of the connections between the collector and the heat pipe) and the operating conditions (the value of the direction of temperature change and the flow of the supply water). It was shown that the supply temperature has a much greater impact on the values of the module's control parameters than the flow rate of the supply water. The tested module is characterized by slow changes in temperature on its surface (high values of time constants). The time of stabilization of the temperature on the module's surface, after step forcing, is 8–10 h. This can cause greater fluctuations in the indoor air temperature, lower thermal comfort in the room and lower energy efficiency of the process. These issues can be prevented by using complex algorithms for thermal comfort control, which in turn increase the cost of the control system. [ABSTRACT FROM AUTHOR]

Trochoidal machining could significantly improve cutting efficiency, enhance cutting stability, reduce cutting temperature, extend tool life, and reduce the cutting costs. However, in trochoidal machining, there are few studies focusing on modelling the instantaneous cutting power due to overlooking the importance of cutting temperature modelling. Also, instantaneous cutting power is an important basis for the optimization of trochoidal parameters and cutting parameters. In this work, we established a new and efficient method that could predict the instantaneous cutting power in trochoidal machining in high fidelity. First, the specific cutting energy of a given workpiece material, cutting tool, and cutting parameter in milling process was calibrated by cutting experiments. Second, the influence of the radial depth of cut on the specific cutting energy in milling process was quantitatively studied. Third, combining the obtained relationship between the specific cutting energy and radial depth of cut, the specific cutting energy curve in trochoidal machining was obtained. Then, a way to figure out the instantaneous material removal rate was proposed based on the acquired instantaneous 3D un-deform chip in trochoidal machining. Finally, based on the obtained specific cutting energy and instantaneous material removal rate, an accurate and efficient approach to predicting the instantaneous cutting power in trochoidal machining was proposed, and a practical application was demonstrated. The effectiveness of the proposed approach was validated by cutting experiments. The method proposed in this work could be adopted in cutting parameter optimization, tool-path optimization, and cutting temperature prediction in trochoidal machining. [ABSTRACT FROM AUTHOR]

The application of cost escalation rates to commercial construction projects has historically been a straightforward task for estimators. However, the onset of the post-COVID-19 recovery period has introduced unprecedented challenges. The surge in inflation, followed by sharp disinflation triggered by one of the most aggressive interest rate hike cycles in Federal Reserve history, has created significant obstacles in forecasting future costs, a situation unfamiliar to many contemporary construction cost estimators. Unlike previous research that predominantly focused on cost indices tracking labor rates and building materials, our study integrates the Turner Building Cost Index, which also accounts for the competitive condition of the marketplace. Although traditional academic forecasting tools may perform well during periods of gradual economic expansion, they often falter amidst recessions or sudden economic shocks. Recognizing the crucial role of the overall economy in future cost projections, our paper rigorously examines current economic conditions and emphasizes concerns stemming from recent monetary policy actions by the Federal Reserve. We introduce an integrated forecasting approach, combing quantitative analysis with qualitative insights from industry experts—a process referred to as decision science analysis. This method allows estimators to incorporate a comprehensive view of the current economic landscape, transcending conventional academic models. Our methodology projects costs across three scenarios: best-case, average, and worst-case. In the best-case scenario, assuming the US economy avoids a recession or sudden economic shock, the annual escalation rate is forecasted at 2.8% over the next 7 years. In contrast, a worst-case scenario characterized by a severe recession could cause a decrease in cost by 13% within 2 years of the index peak. This study underscores the importance of considering macroeconomic conditions during periods of heightened economic uncertainty. Furthermore, it showcases how effective collaboration between industry and academia can yield a robust and comprehensive forecasting approach, adaptable to any economic climate. Practical Applications: The leading US authority and educator in cost estimating has emphasized the necessity for practitioners to incorporate unexpected downturn in forecasts, as outlined in its guidelines. This study directly aligns with such industry calls for the inclusion of economic impacts on cost forecasting, contributing valuable perspectives to the existing body of knowledge. By introducing economic indicators, the study provides a deeper understanding of the current economy, employing a data-driven approach of forecasting commercial construction indices amid economic uncertainty. Diverging from conventional academic approaches, the research integrates a qualitative dimension through decision science and scenario analysis, fostering a closer connection between industry practitioners and academia. The deliberate use of simplified language and a straightforward technical structure enhances accessibility and adaptability, ensuring that the findings are readily embraced by industry professionals. This research is particularly relevant for construction cost estimators and project stakeholders aiming to enhance their understanding of imminent risks and opportunities in cost management. Regardless of the economic trajectory, readers will gain the capability to forecast future costs with heightened confidence and precision, offering invaluable insights for strategic decision-making in construction projects. We encourage continual dialogue between academia and industry, fostering a dynamic exchange of insights and knowledge. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high production cost and complex control of the inverted arch of an unsupported prestressed concrete composite slab, a flange truss high-ductility concrete composite slab floor is proposed to change the structure and pouring material to meet the requirements of no support during construction. The crack distribution and bending performance of the flange truss high-ductile concrete composite slab floor (CRHDCS) under different structures are clarified through the test and numerical analysis of four different rib plate structure floors. According to the analysis results, the calculation formulas of the cracking moment and short-term stiffness before cracking are modified, and the equivalent short-term stiffness formula of a single web member of the "V" truss to this kind of bottom plate is established. The results show that, unlike the short-term stiffness-change law of typical concrete composite slabs after cracking, the short-term stiffness of the designed bottom plate in this paper includes a short-term increase stage. The numerical simulation results are the same as the experimental ones; the maximum error is 10%. The maximum errors between the modified cracking moment and the short-term stiffness calculation formula are 6% and 8%, respectively. The influence rates of removing flange plate, truss-inverted binding, and adding rib plate on the cracking bending moment of foundation structure are −81.5%, 11.0%, and 22.2% respectively, and the influence rates on short-term stiffness are −87.6%, −1.5%, and 37.5% respectively. [ABSTRACT FROM AUTHOR]

In this paper, the full state dependent event-triggered aperiodic intermittent control (FE-AIC) strategy based on input constraints is introduced to minimize energy consumption and enhance speed tracking accuracy in the high-speed train (HST) operation. Firstly, a dynamic model based on multi-mass-point (MMP) for HST has been established, transforming the cruise control problem into an error asymptotic convergence problem. Secondly, restricted FE-AIC (RFE-AIC) controller is designed separately in the presence and absence of external disturbances to realize tracking objects. The proposed control scheme is not only based on control input constraints, but also intermittent control with full state event dependence. The RFE-AIC scheme and the conditions for determining parameters are given, which ensures the stability of the ideal tracking speed and coupler deviation at the equilibrium point. Eventually, the availability of the proposed method in cruise control is confirmed through numerical simulations. It is proved that the RFE-AIC has better performance compared with the self-triggered and guaranteed optimal cruise control methods. • The RFE-AIC is applied in HST systems with disturbances and parameter uncertainties to reduce control costs due to intermittent event-triggered control. • The RFE-AIC method ensures the asymptotic speed tracking reachability of the HST system with fewer control iterations. • The robust RFE-AIC can effectively improve the velocity accuracy of the HST system with disturbances, and an optimization algorithm is provided to obtain the minimum control gain under input constraints. • Numerical simulations verify the effectiveness of the proposed RFE-AIC method. [ABSTRACT FROM AUTHOR]

Different project management processes have been used in software engineering to support managers in keeping project costs manageable. One of the essential processes in software engineering is to accurately and reliably estimate the required effort and cost to complete the projects. The domain of software cost estimation has witnessed a prominent surge in research activities in recent years and being an evolving process, it keeps opening new avenues, each with advantages and disadvantages, making it important to work out better options. This research aims to identify the factors that influence the software effort estimation using the constructive cost model (COCOMO), and artificial neural networks (ANN) model by introducing a novel cost estimation approach, COCOMO-ANN (CANN), utilizing a partially connected neural network (PCNN) with inputs derived from calibrated values of the COCOMO model. A publicly available dataset (COCOMONASA 2), various combinations of activation functions, and layer densities have been systematically explored, employing multiple evaluation metrics such as MAE, MRE, and MMRE. In the PCNN model, the ReLU activation function and a 1000-dense layer have demonstrated better performance. While layer density generally correlates with better outcomes, this correlation is not universally applicable for all activation functions and outcomes vary across different combinations. The use of the relationships between 26 key parameters of COCOMO in PCNN produced better results than FCNN by 0.59%, achieving an MRE of 6.55 and an MMRE of 7.04. The results indicated that the CANN model (COCOMO & ANN) presented better results than existing models. [ABSTRACT FROM AUTHOR]

The growing adoption of off-site construction methods has increased the critical role of mobile cranes within the construction sector. This study develops a Construction Digital Twin (CDT) framework to optimize crane lifting positions and capacities for the installation of prefabricated bridge decks. By integrating 3D site modeling, Building Information Modeling (BIM), and crane simulations within the Unity game engine, the CDT overcomes the limitations of conventional 2D-based planning by providing a three-dimensional representation of site conditions. An exhaustive search method identifies optimal crane configurations, enhancing precision and efficiency. Simulation calibration using video analysis of real bridge deck installations aligns crane speed and cycle times with actual operations, improving reliability. Case studies demonstrate the CDT's ability to reduce crane operation costs by 27% when employing a smaller capacity crane while maintaining operational efficiency. Additional DFA-focused simulations with varying deck dimensions revealed a potential 10% cost reduction by optimizing crane operations and deck design strategies. The CDT framework supports early-stage planning, reduces operational risks, and contributes to cost-effective and safer construction practices, offering a scalable solution adaptable to various construction scenarios. [ABSTRACT FROM AUTHOR]

The current research investigates cost overrun factors in structural projects, focusing on the Middle East and North Africa (MENA) region using Egypt as a model. A systematic literature review was conducted, analyzing 405 research papers published between 2000 and 2024, from which 69 relevant papers were selected to identify 48 key factors contributing to cost overrun. Using K-means clustering, these factors were grouped into three clusters based on their probability and impact, which were classified for their risk levels. To ensure robust analysis, a survey was conducted to gather expert opinions, resulting in 369 valid responses from owners, contractors/subcontractors, and management firms/consultants. The fuzzy group decision-making approach (FGDMA) was conducted to rank all 48 factors, offering a detailed assessment of their relative importance. Based on these rankings, the top 20 factors were identified for analysis to examine variations in stakeholder priorities, capturing differences in perspectives among multi-stakeholders. Sensitivity analysis and Tornado charts explored the critical variations among stakeholders, with management firms/consultants and owners prioritizing design-related risks, such as inconsistencies and delays in approvals, while contractors/subcontractors focused more on material waste. This novel integration presents a structured approach for analyzing, prioritizing, and mitigating cost overrun risks, offering a comprehensive framework that provides practical insights for stakeholders to improve cost and risk management strategies. [ABSTRACT FROM AUTHOR]

This study analyzes the distribution of operating costs along the value chain of an open-pit copper mine with a focus on key operational units or operations such as drilling, blasting, loading, hauling, stockpiling, blending, crushing, milling, and flotation. Using process costing analysis, key cost drivers were identified, and their individual contributions to total expenses were quantified. Results revealed that comminution processes dominate the operational cost structure, with milling accounting for 6.18 USD/ton, representing 59.1% of total operating costs, and crushing costing 1.15 USD/ton, that is, 11% of total operating expenditure. The study also highlighted several opportunities for cost reduction and enhanced mining sustainability through strategies such as energy consumption optimization, the use of alternative energy sources, and optimized blast design. Finally, valuable insights aimed at promoting sustainable resource utilization, improved cost efficiency, and data-driven decision-making in mining operations are offered to mine planners and operators. This is eventually expected to lay the foundation for benchmarking work on the establishment of a baseline and standards for similar mining operations. [ABSTRACT FROM AUTHOR]

In the digital era, the demand for real-time streaming services highlights the scalability, data sovereignty, and privacy limitations of traditional centralized systems. VidBlock introduces a novel decentralized blockchain architecture that leverages the blockchain's immutable and transparent characteristics along with direct communication capabilities. This ecosystem revolutionizes content delivery and storage, ensuring high data integrity and user trust. VidBlock's architecture emphasizes serverless operation, aligning with the principles of decentralization to enhance efficiency and reduce costs. Our contributions include decentralized data management, user-controlled privacy, cost reduction through a serverless architecture, and improved global accessibility. Experiments show that VidBlock is superior in reducing latency and utilizing bandwidth, demonstrating its potential to redefine live video streaming in the Web3.0 era. [ABSTRACT FROM AUTHOR]

The coal cost of coal-fired units accounts for more than 70% of the total power generation cost. In addition to determining coal costs, coal blending strategies (CBS) significantly impact various types of costs, such as pollutant removal and emissions. To address these issues, we propose a framework for generating cost-effective CBS. The framework includes a unit output condition recognition module (UOCR) that enables the adaptive classification of output conditions based on historical operation datasets, performing intelligent condition recognition with Imitator and pre-trained image classification models using blending strategies and unit parameters as inputs. The cost-effective strategy generation module (CESG) employs a surrogate model to evaluate the economic viability of strategies in terms of coal and environmental costs, among other factors. It also employs UOCR as another surrogate model to validate strategy feasibility. Cost-effective strategies are generated via a population-based metaheuristic algorithm. In the case study, the UOCR achieved an average training accuracy of 96.64%, and the generated cost-effective strategies reduced costs by an average of 3.37% compared to currently implemented strategies. [ABSTRACT FROM AUTHOR]

We propose and study a new multilevel method for the numerical approximation of a Gibbs distribution π on Rd , based on (overdamped) Langevin diffusions. This method relies on a multilevel occupation measure, that is, on an appropriate combination of R occupation measures of (constant-step) Euler schemes with respective steps γr=γ02−r, r=0,...,R. We first state a quantitative result under general assumptions that guarantees an ε-approximation (in an L2-sense) with a cost of the order ε−2 or ε−2|log ε|3 under less contractive assumptions. We then apply it to overdamped Langevin diffusions with strongly convex potential U:Rd→R and obtain an ε-complexity of the order O(dε−2log3(dε−2)) or O(dε−2) under additional assumptions on U. More precisely, up to universal constants, an appropriate choice of the parameters leads to a cost controlled by (λ¯U∨1)2λ¯U−3dε−2 (where λ¯U and λ¯U respectively denote the supremum and the infimum of the largest and lowest eigenvalue of D2U). We finally complete these theoretical results with some numerical illustrations, including comparisons to other algorithms in Bayesian learning and opening to the non–strongly convex setting. Funding: The authors are grateful to the SIRIC ILIAD Nantes-Angers program, supported by the French National Cancer Institute [INCA-DGOS-Inserm Grant 12558]. [ABSTRACT FROM AUTHOR]

In recent years Quantum Cellular Automata (QCA) technology has emerged as an ideal option to substitute the current CMOS technology. QCA offers operation in the terahertz range, small area, and low power in nano-scale circuit design. This paper explores the application of quantum dot cellular automata(QCA) technology in efficient floorplanning of digital code converters using the tile based architecture of QCA XOR gate. The proposed code converter circuits exhibits the benefits of low cell count, area, cost and low energy dissipation. The suggested layouts have achieved the 11.42% reduction in cell count, 29.53% reduction in total occupational area,30.93% reduction in cost and 11.52% increase in area utilization factor in comparison to similar counterparts. The functional validity of the suggested designs were validated using QCADesigner 2.0.3 tool. In addition, the energy dissipation analysis were calculated using the QCAPro tool at standard tunelling energy levels o 0.5EK, 1.0EK, 1.5EK. [ABSTRACT FROM AUTHOR]

This paper investigates how product market competition affects the performance of closely held small and medium enterprises (SMEs) in developing economies. In contrast to prior findings that focus on large publicly traded companies in developed economies, we find that market competition has a negative effect on firm performance. Our findings are robust to different measures of competition and firm performance and survive after addressing endogeneity issues. We provide evidence that the adverse effect of competition is channeled through increased corruption. Our findings further suggest that firms respond to competition by attempting to acquire more financial resources and government support, adopt quality improvement and cost reduction policies. The adverse effect of competition is especially strong for smaller firms. [ABSTRACT FROM AUTHOR]

Tool wear monitoring is crucial for quality control and cost reduction in manufacturing processes, of which drilling applications are one example. Identification of the wear area in images of cutting inserts is important to building a reliable ground truth for the development of indirect monitoring approaches. Therefore, we present a semantic image segmentation pipeline for wear detection on microscopy images of cutting inserts. A broadly used convolutional neural net, namely a U-Net, is trained with different preprocessing and optimisation task configurations: On the one hand the problem is considered as binary problem, and on the other hand as multiclass problem by differentiating the wear into two different types. By comparing these two problem formulations we investigate whether the separation of the two wear structures improves the performance of the recognition of the wear types. For both problem formulations three loss functions, i. e., Cross Entropy, Focal Cross Entropy, and a loss based on the Intersection over Union (IoU), are investigated.The use of different augmentation intensities during training suggests adequate but not too excessive augmentation, and that with optimal augmentation the choice of loss function gets less important. Furthermore, models are trained on image tiles of different sizes, which has an impact on producing artefacts on the whole image predictions performed by the overlap-tile strategy. In summary, the best performing models are binary models, trained on data with moderate augmentation and an IoU-based loss function. [ABSTRACT FROM AUTHOR]

Background: Childhood cancer is not a high priority in health care financing for many countries, including in Ghana. Delayed care seeking and treatment abandonment, often due to the financial burden of care seeking to families, are common reasons for a relatively low overall survival (OS) in low-and middle-income countries. In this study, we analyzed the cost-effectiveness of extending health insurance coverage to children with Burkitt lymphoma (BL) in Ghana. Methods: We developed a Markov model in Microsoft Excel to estimate the costs and effects of BL treatment when National Health Insurance Scheme (NHIS) was provided compared to the status quo where NHIS does not cover care for childhood cancer. The analysis was undertaken from the societal and health system (payer) perspective. Both costs (measured in $) and effects, measured using disability adjusted life years (DALYs), were discounted at a rate of 3%. The time horizon was a lifetime. Probabilistic sensitivity analysis was done to assess uncertainty in the measurement of the incremental cost-effectiveness ratio (ICER). A budget impact analysis was undertaken from the perspective of the NHIS. Results: In the base-case analysis, the intervention (NHIS reimbursed treatment) was less costly than current practice ($8,302 vs $9,558). The intervention was also more effective with less DALYs per patient than the standard of care (17.6 vs 23.33). The ICER was -$219 per DALY averted from societal perspective and $113 per DALY averted from health system perspective. The probabilistic sensitivity analysis showed that the intervention is likely to be both less costly and more effective than current practice in 100% of the 1,000 simulations undertaken. Conclusion: Providing health insurance coverage to children with BL is potentially cost-effective. The effectiveness and cost-savings relating to this strategy is driven by its positive impact on treatment initiation and retention. Based on this evidence, there has been a policy change where Ghana's NHIS has prioritized financing for cancer treatment in children. [ABSTRACT FROM AUTHOR]

Introduction: The financial stability of hospitals directly impacts their ability to fulfill their primary mission of enhancing healthcare. This study identifies and prioritizes cost reduction and revenue enhancement strategies for Iranian hospitals. Method: This investigation employed a mixed-methods design, incorporating both qualitative and quantitative approaches. A systematic review of scholarly articles was initially conducted to identify key strategies for cost reduction and revenue enhancement in hospitals. Insights from hospital administrators regarding successful practices and recommended financial improvement measures were subsequently collected through surveys. The combined strategies from these phases were then assessed and ranked using the TOPSIS technique. Findings: This study identified 12 primary strategies and 71 sub-strategies across four dimensions. Notably, strategies aimed at enhancing the quality of care (0.9030), refining process quality (0.7926), and bolstering care provision infrastructure (0.7910) were deemed the most critical. Among the sub-strategies, priority was given to implementing a comprehensive health information system (HIS) (0.7926), identifying and reducing the causes of cancelled surgeries and visit appointments (0.7919), and developing strategies to decrease hospital infection rates (0.7854). Conclusion: Enhancing the quality of care and upgrading service delivery processes are crucial for improving hospitals' economic performance. Elevating service quality not only improves the economic performance of hospitals but also enhances their financial metrics. [ABSTRACT FROM AUTHOR]

Objective: This study analysed the characteristics of new drugs listed under the pharmaco-economic evaluation exemption (PEE) system from 2015 to 2022 in South Korea and examined the factors influencing the pricing decisions under this system. Methods: A mixed-methods statistical approach was used to comprehensively evaluate the factors influencing drug pricing under PEE system. Descriptive statistics provide an overview of the dataset, while inferential statistics, including t-tests and Pearson's correlation analyses, are used to explore variable associations. Multiple and hierarchical regression models identify and quantify the key determinants of drug prices, controlling for multicollinearity among the variables. Results: From 2015 to 2022, 30 new drugs were listed under the PEE system. The average annual number of new drugs was four, but this figure significantly increased to eight in 2022. The "KOR/A7 lowest" variable exhibited a strong negative correlation with the budget impact variable (coefficient: 0.838, P < 0.001), indicating that drugs with higher budget impact tend to have lower prices compared to the A7 countrie's lowest price. Conclusion: Since the introduction of the PEE system in South Korea, patient access to new drugs has significantly improved. However, the rising expenditure on pharmaceuticals has made budget impact a significant consideration in pricing decisions, highlighting the need for ongoing monitoring of drug expenditure by payers. As the system evolves, enhanced oversight and policy adjustments will be crucial for balancing cost containment with equitable patient access. Developing tiered RSA models based on drug classification or therapeutic impact could be a viable approach to achieving this balance. [ABSTRACT FROM AUTHOR]

Pressures on honey bee health have substantially increased both colony mortality and beekeepers' costs for hive management across Europe. Although technological advances could offer cost-effective solutions to these challenges, there is little research into the incentives and barriers to technological adoption by beekeepers in Europe. Our study is the first to investigate beekeepers' willingness to adopt the Bee Health Card, a molecular diagnostic tool developed within the PoshBee EU project which can rapidly assess bee health by monitoring molecular changes in bees. The Bee Health Card, based on MALDI BeeTyping®, is currently on level six of the Technology Readiness Level scale, meaning that the technology has been demonstrated in relevant environments. Using an on-line survey from seven European countries, we show that beekeepers recognise the potential for the tool to improve colony health, and that targeted economic incentives, such as subsidises, may help reduce cost being a barrier to the adoption and frequent use of the tool. Based on the description of the tool, 43% of beekeepers appear to be moderately confident in the effectiveness of the Bee Health Card. This confidence could increase if the tool was easy to use and not time consuming, and a higher confidence could also contribute to raising the probability of accepting extra costs linked to it. We estimate that, in the worst-case scenario, the cost per single use of the Bee Health Card should be between €47–90 across a range of European countries, depending on the labour and postage costs. However, the monetary benefits in terms of honey production could exceed this. In order to successfully tackle colony health issues, it is recommended using the BHC five times per year, from the end to the beginning of winter. Finally, we discuss the knowledge needs for assessing beekeeper health tools in future research. [ABSTRACT FROM AUTHOR]

Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. The intensification of societal aging and the increase in life stress have led to a growing number of patients suffering from both heart disease and depression. These patients often need to use cardiovascular drugs and antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness and patient safety. To predict interactions between drugs used to treat these two diseases, we propose a method named Multi-Drug Features Learning with Drug Relation Regularization (MDFLDRR). First, we map feature vectors representing drugs in different feature spaces to the same. Second, we propose drug relation regularization to determine drug pair relationships in the interaction space. Experimental results demonstrate that MDFLDRR can be effectively applied to two DDI prediction goals: predicting unobserved interactions among drugs within the drug network and predicting interactions between drugs inside and outside the network. Publicly available evidence confirms that MDFLDRR can accurately identify DDIs between cardiovascular drugs and antidepressants. Lastly, by utilizing drug structure calculations, we ascertained the severity of newly discovered DDIs to mine the potential co-medication risks and aid in the smart management of pharmaceuticals. [ABSTRACT FROM AUTHOR]

Background: Existing cardiovascular risk prediction models still have room for improvement in patients with type 2 diabetes who represent a high-risk population. This study evaluated whether adding metabolomic biomarkers could enhance the 10-year prediction of major adverse cardiovascular events (MACE) in these patients. Methods: Data from 10,257 to 1,039 patients with type 2 diabetes from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation, internal and external validation. A total of 249 metabolites were measured with nuclear magnetic resonance (NMR) spectroscopy. Sex-specific LASSO regression with bootstrapping identified significant metabolites. The enhanced model's predictive performance was evaluated using Harrell's C-index. Results: Seven metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, three male- and one female-specific metabolite(s)). Especially albumin and the omega-3-fatty-acids-to-total-fatty-acids-percentage among males and lactate among females improved the C-index. In internal validation with 30% of the UKB, adding the selected metabolites to the SCORE2-Diabetes model increased the C-index statistically significantly (P = 0.037) from 0.660 to 0.678 in the total sample. In external validation with ESTHER, the C-index increase was higher (+ 0.043) and remained statistically significant (P = 0.011). Conclusions: Incorporating seven metabolomic biomarkers in the SCORE2-Diabetes model enhanced its ability to predict MACE in patients with type 2 diabetes. Given the latest cost reduction and standardization efforts, NMR metabolomics has the potential for translation into the clinical routine. [ABSTRACT FROM AUTHOR]

The microgrid (µG) is an integration of distributed generation and local loads with energy storage system. Cost minimization is one of the main objectives in modern power systems.Economic dispatch(ED) is a fundamental problem related to µG and the conventional grid. Economic dispatch(ED) provides the optimal output of generators in order to reduce the total operating cost. Emission dispatch (EMD) is one of the other major problems associated with CG. The emission dispatch (EMD) solution provides the optimal generator operation to reduce harmful pollutants for a specific load demand. Multi-objective economic emission dispatch (MEED) provides a compromise between ED and EMD. In this paper, two test systems have been proposed. Test system one consists of Six CG. Static ED, EMD, and MOEED analysis has been provided for test system one. Test system two consists of four CG, One wind turbine generator (WTG), and one photovoltaic module (PVM).This paper intends to provide sensitivity analysis and uncertainty regarding the curtailment cost of RES. CPLEX solver in GAMS has been proposed to optimize the three fundamental problems. Comparative study and sensitivity analysis show optimal results, and the GAMS solver provides a more comprehensive framework. Reduction in cost due to uncertainty in ED is 9.58% as compared to 9.7% for test system two. The cost has been reduced in MEED by 9.33% as compared to 9.46%. MEED comparison shows the increment in cost of 2.66 %, but the emission is reduced by 18.98 % for test system two. [ABSTRACT FROM AUTHOR]

This research develops and applies a tool that allows the breakdown of time objectives to the same level of detail traditionally applied to cost, while also providing a favorable production scheme to ensure the project quality. This tool introduces an innovative approach to planning and execution monitoring through cascading dashboards, representing production packages and activities across designated project zones. This approach reinterprets the Last Planner System for jobs on-site in conjunction with the Location-Based Management System. The primary dashboard facilitates the management of complex work structures—typically involving hundreds of rows in Gantt chart representations or numerous lines in Line of Balance diagrams—while enabling the easy identification of activity cycles and gaps between activities in each zone. The tool offers a four-dimensional planning visualization—what, where, when, and who—enhancing the understanding of activity sequences and workflows across project zones, while also contributing to product quality improvement. Furthermore, it has been demonstrated through its application that the tool provides reliable, real-time information that supports decision-making, optimizes resource allocation, and improves overall project competitiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model's adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations. [ABSTRACT FROM AUTHOR]

In order to provide a reliable basis for the cost management of photovoltaic power generation, it is necessary to accurately predict the depreciation expense of photovoltaic power generation. Therefore, a hierarchical quantitative prediction method of photovoltaic power generation depreciation expense based on matrix task prioritization considering uncertain risks is proposed. Based on the conditional value-at-risk theory, a more comprehensive risk measure than VaR is provided, and the uncertainty risk value of photovoltaic power generation is calculated by considering the average loss exceeding this loss value. According to the calculated risk value, a double-layer photovoltaic power generation cost planning model is constructed, the upper and lower objective functions of the model are determined, and the constraint conditions are designed; Obtain a cost planning objective function solution base on a matrix task prioritization method, and generating a prioritization table; Prediction of photovoltaic power generation depreciation expense based on long-short memory neural network for each solution in the sorting table. In practical application, the test results show that this method can complete the risk quantitative analysis of uncertain factors, and the tracking ability and fitting degree of prediction are good; An ordered list of solutions of each objective function can be generated; The method in this paper is used to predict the depreciation expense of photovoltaic power generation in the first 10 solutions of priority ranking, and the maximum deviation of the prediction result is -0.65 million yuan. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques combined with Internet of Things (IoT) sensors for tool condition monitoring (TCM) have emerged as a great potential for the online monitoring of the milling process. Monitoring tool degradation is necessary in modern manufacturing industries to ensure production safety, workpiece quality, and cost reduction. An automatic TCM system can be realized, based on the combination of ML and IoT sensors. It provides a live report of tool conditions, identifies the best time for tool replacement, reduces machine downtime, ruins the surface quality of machined parts due to elevated tool degradation, and, consequently, increases the sustainability of the process. A major challenge in developing such a system is utilizing a low-cost and non-invasive monitoring tool that can make timely and accurate decisions about cutting tool replacement, without interfering with the machining process. Audio signal analysis can meet this need, by using a contactless, low-cost microphone for the acquisition. A convolutional neural network was developed and compared with various ML techniques to solve the TCM problem. The issue was approached as a regression and a classification problem, and a thorough analysis was performed between the two approaches. Results demonstrated that addressing the problem as a regression enables industries to adapt the desired results to their production policies such as preventing surface quality or using the cutting tool as much as possible. Conversely, the classification results are more straightforward for operators and maintenance personnel to interpret, thereby simplifying decision-making on tool replacements. [ABSTRACT FROM AUTHOR]

Acute infectious diarrhea (AID) represents an important clinical entity both regarding morbidity and mortality rates, even in industrialized countries, and it leads to one of the major public health burdens, among gastroenterological diseases, with significant healthcare costs. Oral rehydration solution is the cornerstone of the therapy, but despite its proven efficacy in avoiding dehydration, it is still underused as it does not reduce the duration of diarrhea; hence, it is perceived as ineffective by caregivers. In this narrative review, we collected literature regarding the use of racecadotril, deeply discussing its role in the treatment of AID in both adults and children. Racecadotril has been studied in wide populations of patients, in many countries, and in different clinical settings. Its effectiveness in reducing the stool output and the duration of diarrhea has been proven, not only in the early phase of the disease. Racecadotril has been shown to increase the likelihood of home management of AID, to reduce hospitalizations and parenteral rehydration needs resulting in healthcare costs reduction. The current new formulations require only two-daily doses for adults and the pediatric syrup should simplify its use. Plain language summary: Racecadotril an effective anti-diarrheal drug Acute diarrhea is one of more frequent infectious diseases, with risk of dehydration if not adequately treated especially in children and the elderly, and this impacts on the healthcare costs beyond on the caregivers. Racecadotril, the first and only intestinal antisecretory drug, acting on the abnormal intestinal hypersecretion, decreases the loss of water and electrolytes from the gut, so reducing the dehydration risk, it increases the likelihood of home management of diarrhea. The efficacy of racecadotril has been demonstrated in all settings (inpatients, outpatients, and community-based), in patients of all ages (children, adults, and the elderly), in many countries both of high-income and low-middle-income. In addition, the safety of racecadotril has always been comparable to placebo and better than loperamide. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to compare different treatment modalities to correct ill-fitted maxillary complete denture either by the conventional relining method or by scanning the relining impression and digitally construct a new denture regarding patient satisfaction, denture retention, and adaptation. Materials and methods: Twelve edentulous patients suffering from loose maxillary complete dentures were selected, dentures' borders and fitting surfaces were prepared, and relining impressions were taken, the impressions were scanned and the STL files were used for CAD/CAM milling (computer aided designing/ computer aided manufacturing) of new maxillary dentures (Group A), then the relining impression went through the conventional laboratory steps to fabricate (Group B) maxillary dentures. Both groups were evaluated regarding patient satisfaction by a specially designed questionnaire, retention values were measured by a digital force gauge at denture insertion appointment and two weeks later, geomagic software was used to evaluate dentures adaptation to oral tissues. Results: Both groups (A and B) were completely satisfied with their dentures except regarding esthetics, all group A and 50% of group B were satisfied. Both groups showed a statistically significant increase in retention values at the two-week follow-up period compared to those at denture insertion time, with higher values were for group B. Finally, the relined dentures showed better oral tissue adaptation than digitally constructed dentures. Conclusion: Relined maxillary dentures showed better retention, esthetics, and denture adaptation with lower cost than digitally constructed maxillary dentures which showed acceptable retention and adaptation, with better time and data saving. Trial registration: Clinical trials number: NCT06366321. With registration date on ClinicalTrials.gov public website: 13/ 4/ 2024. [ABSTRACT FROM AUTHOR]

Predictive maintenance has gained increasing attention recently with the availability of sensors and connectivity of equipment. Yet, it would be difficult to obtain a wide range of data, especially with legacy devices. This paper describes an intelligent method for predicting a near future condition using the past information for an environment in which data are limited to the alarm logs from industrial machinery. Since machine learning methods are proven to be efficient in classification tasks using time series data, three of them are selected to predict an alarm two hours in advance using the past occurrences. These methods are neural networks, random forests, and extreme gradient boosting. The performances of these three methods are compared, and it is aimed to find the optimal configuration among hyperparameter values. According to the obtained results, extreme gradient boosting gives the highest F1-score of 0.767 with number of trees equal to 500, maximum depth of 128, and an input window of alarm occurrences from the last day. This work consists of a comparative study aiming to identify the best machine learning method for alarm predictions, which potentially provides important insights into the operation and maintenance of machinery, bringing the possibility of considerable cost reductions. [ABSTRACT FROM AUTHOR]

Sepsis is a complex and potentially life-threatening syndrome characterized by an abnormal immune response to an infection, which can lead to organ dysfunction, septic shock, and death. Early diagnosis is crucial to improving prognosis and reducing hospital management costs. This narrative review aims to summarize and evaluate the current literature on the role of monocyte distribution width (MDW) as a diagnostic biomarker for sepsis, highlighting its advantages, limitations, and potential clinical applications. MDW measures the volumetric distribution width of monocytes, reflecting monocytic anisocytosis, and is detected using advanced hematological analyzers. In 2019, it was approved by the FDA as a biomarker for sepsis due to its ability to identify systemic inflammatory response at an early stage. Thirty-one studies analyzed by us have shown that an increased MDW value is associated with a higher risk of sepsis and that its combination with clinical parameters (such as qSOFA) and other biomarkers (CRP, PCT) can enhance diagnostic sensitivity and risk stratification capacity. Despite its high sensitivity, MDW has lower specificity compared to more established biomarkers such as procalcitonin, thus requiring a multimodal integration for an accurate diagnosis. The use of MDW in emergency and intensive care settings represents an opportunity to improve early sepsis diagnosis and critical patient management, particularly when combined with other markers and clinical tools. However, further studies are needed to define a universal cut-off and confirm its validity in different clinical contexts and pathological scenarios. [ABSTRACT FROM AUTHOR]

The breakthrough of articulated tractors has led to a significant increase in productivity and a cost reduction of agricultural operations. The performance of the tractor implement system depends on the understanding of the tractors’ energy parameters. Fertilizer seeders operate between 6 and 9 km h-1, while seed drills allow higher speeds. Increasing sowing speed improves operational efficiency. However, it is important to adequately set the machinery, to optimize the energy parameters, considering the balance between productivity and sustainability. This study evaluated the energy and operating parameters of a 398kW articulated agricultural tractor in a sowing operation at different speeds. The experiment was conducted in a complete randomized block design. Five theoretical speeds were chosen for the sowing operation (6, 7, 8, 9 and 10 km h-1), with seven replications (35 units). The tractor operating parameters were measured: operational speed, slippage, engine speed, hourly fuel consumption, engine thermal efficiency, specific fuel consumption, drawbar force, drawbar efficiency, fuel consumption per area and field operating capacity. The data were subjected to normality test, when significant, variance analysis. Results showed high field capacity in the highest speed and low fuel consumption per area at the highest speed. [ABSTRACT FROM AUTHOR]

In order to design satellite with low cost and high effectiveness, an optimization method of life cycle cost-effectiveness for satellites is proposed in this paper. Since most satellites are unrepairable and their performances experience degradation in orbit, a life cycle effectiveness model based on ADC model is constructed. Then, a multidimensional parameter cost model is proposed to estimate the satellite cost. Finally, an optimization model and the solution method are developed by synthesizing the life cycle effectiveness model and the multidimensional parameter cost model based on CAIV. A remote sensing satellite is adopted to demonstrate the proposed method, the result suggests that this method could optimize the performance, reliability and weight indicators of satellite system, which might be applied for project design demonstration, task management, and life cycle cost control of satellites. [ABSTRACT FROM AUTHOR]

One of the most significant solution concepts in cooperative grey game theory is the grey Shapley value. This value is a fascinating one among the models and methods of operations research, and has been the subject of extensive study by other researchers. The objective of this study is to characterize and redefine this value in cooperative games where coalition values are grey numbers. In this study, the grey Shapley value is characterized by the following axioms: G -gain loss, G -null player, and G -differential marginality. Finally, this study concludes with an investigation of some applications involving production costs. This study is based on an investigation of the costs incurred when milk producers collaborate. [ABSTRACT FROM AUTHOR]

State highway agencies (SHAs) frequently need to cluster projects based on their scope similarity to support various construction planning tasks such as cost estimation. Few recent studies have presented systematic methods that employ cost composition and pay item descriptions for automated project clustering. However, they suffer from two main drawbacks, including the reliance on unit bid prices, which are unavailable at the time cost estimation is conducted, and the lack of thorough validation of their effectiveness in supporting cost estimation. To address these limitations, this study presents a novel quantity-weighted term frequency–inverse document frequency (QW-TFIDF) project vectorization method using both the text description and quantity information of pay items. QW-TFIDF was validated in terms of its effectiveness in supporting project clustering and cost estimating. Its performance was compared with state-of-the-art approaches, including cost-weighted term frequency–inverse document frequency (CW-TF-IDF) and pay item cost-based similarity determination methods. The results showcased the superiority of the new method over existing ones, thus providing a new means for SHAs to enhance their project clustering practices, particularly in early-stage cost estimation, which in turn, will facilitate better budget forecasting, cost management, and resource allocation. [ABSTRACT FROM AUTHOR]

This study aims to assess the feasibility of expanding the powder size distribution (PSD) of Ti-6Al-4V grade 5 powder for LPBF to achieve cost reduction. Parameter optimization to minimize the degradation of mechanical properties due to the expanded particle size distribution was conducted. Mechanical tests for specimens built using optimized parameters revealed minor reductions in strength: 3.9% in tensile yield strength, 1.1% in compressive strength, 5.5% in shear strength, and 4.5% in bearing yield strength—all of which complied with MMPDS standards. Statistical analysis, using the Anderson–Darling test, demonstrated stable mechanical performance and minimal variation between the original and expanded PSDs. These results highlight the potential of an expanded PSD to achieve cost reductions while maintaining compliance with industry standards, offering a practical solution for LPBF applications in cost-sensitive and high-performance industries. [ABSTRACT FROM AUTHOR]

Driven by the trend of integrating monitored data into reliability management to explore innovative and practical approaches for managing reliability, researchers in the industry–university–research community have proposed random warranties. Existing random warranties use the limited mission cycle as a warranty-expiry limit instead of a measurement tool for controlling costs. This either shortens the warranty period for consumers or increases costs for manufacturers. To tackle these issues, this paper integrates mission cycles into the reliability management during the warranty stage and defines and models a renewal repair–replacement warranty with random charge (RRRW-RC) to manage the warranty-stage reliability of products. In the RRRW-RC, the limited mission cycles, acting as a usage limit, are used as a measurement tool to recover the fractional replacement cost within the warranty stage. This is designed to compensate manufacturers for replacement losses without shorting the warranty period, thus achieving the goal of reducing the warranty costs and not shortening the warranty period. The RRRW-RC can classify the usage habits of consumers into the heavy usage type and light usage type. Therefore, based on the usage classification results generated by the RRRW-RC, this paper also customizes two random maintenance policies to manage the post-warranty reliability of products. The first policy includes preventive/corrective replacement and "whichever occurs first," and is thus referred to as customized bivariate random maintenance first (CBRMF). By revising "whichever occurs first" to "whichever occurs last," the second policy is similarly represented and is called customized bivariate random maintenance last (CBRML). The policies defined above are modeled in terms of cost and time measures or cost rates, and their derivative policies are presented and modeled by setting parameter values. Numerical investigations are carried out to explore the management insights hidden in the proposed policies. Numerical investigations reveal that, by setting the failure number at an appropriate value, the warranty cost of the RRRW-RC can be minimized and its warranty period can be extended. [ABSTRACT FROM AUTHOR]

3P-2L-VSC converter system suffers from complex control structure, cumbersome parameter design and slow system dynamic response under conventional double-loop scheme. In this paper, model predictive direct voltage control strategy with hybrid cost function and loss observer is designed to improve system performance and simplify system control structure. First, a model predictive direct voltage control strategy is proposed based on Euler discrete differential and integral method to enhance system dynamic response, which eliminates the cascaded intermediate link and achieves direct targets control. Second, a system power loss observer is designed based on Lyapunov function to improve prediction accuracy for FCS-MPC and system steady performance. Third, an improved hybrid cost function with targets and intermediate state is designed to track targets and reduce intermediate state fluctuation, which overcomes the lack of inner current loop. Finally, the advantages of the proposed strategy are verified in simulation and experiment, where the maximum RMSEs of steady state V dc , i q and i d are 0.045, 0.81 and 0.58 at rated power respectively, and the recovery time of DC voltage is reduced by about 30 ms and 35 ms compared with PI-MPC when DC load and DC voltage change. • Model predictive direct voltage control is designed to improve system transient performance. • The hybrid cost function with control targets and intermediate state is designed to reduce intermediate state fluctuation. • The system loss observer is designed to improve prediction accuracy of targets for FCS-MPC. [ABSTRACT FROM AUTHOR]