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Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article examines the impact of integration between Lean Accounting (LA) tools and Just-in-Time (JIT) techniques on Cost Reduction (CR) in a modern sustainable manufacturing environment in Saudi companies, Ethical Standards (ESs) mediate these two variables. A quantitative methodology was used for realizing the study goals and answering study questions. The main tool used for producing the primary data was a fully structured questionnaire with closed-ended questions which was designed and distributed online through a chosen sample of 109 managers and accountants in Saudi industrialization companies. Descriptive and deductive statistics were adopted to deduce the findings. The gathered responses analyzed by SPSS and AMOS and the results pointed out that there is a direct and indirect impact of LA tools and JIT techniques on CR in Saudi industrial companies especially when using ESs. Moreover, these findings could provide decision-makers in Saudi manufacturing companies with the importance of adopting both methods in an integrated way to maximize the profits of these firms. The practical implications of this study are that the implementation of these methods has a positive impact on the promotion of sustainable manufacturing and consequently the achievement of distinct sustainable performance. [ABSTRACT FROM AUTHOR]

The assessment of barriers to the application of cost-reduction techniques in delivering educational buildings in Nigeria is essential in addressing the infrastructural shortage, building performance, delay, cost, time overrun, and abandonment in the delivery of higher educational buildings (HEBs). This study examines barriers to applying cost-reduction techniques in educational buildings in southwestern Nigeria. Using a survey design, the questionnaire was distributed to stakeholders who participated in delivering the government intervention, private donors, and internally generated revenue educational buildings in public tertiary institutions in southwestern Nigeria from 2012 to 2022. A total of 150 copies of the questionnaire were administered, while 133 responses were obtained and analyzed. To begin with, data reliability and validity were examined using Bartlett's sphericity, Cronbach's alpha, and Kaiser–Meyer–Olkin (KMO) tests, accordingly, followed by descriptive, Kruskal–Wallis H test, and exploratory factor analysis. The six components obtained from exploratory factor analysis for explaining the barriers to applying cost-reduction techniques in educational buildings were as follows: ambiguity in HEB contracts awards and project executions, lack of control from the HEIs management over HEB project delivery, perceived political influence in HEB procurement, unrealistic contract requirements and change orders, non-prioritization of automation integration in HEB delivery, and deficiencies in contract documents and costing. This study recommends establishing a project-monitoring team involving independent consultants from project inception to reduce excessive errors, practices of assigning contracts to the lowest bidder, and excessive claims for variation orders that escalate the project's final sum. [ABSTRACT FROM AUTHOR]

This study examines the application of cost reduction managerial strategies in Chinese government agencies to understand their contributions to the literature of government reform. The study first provides a review of studies on the Chinese government reforms and in three cost reduction managerial strategies: laying-off employees, merging units, and targeting hidden costs. It then explains the research data and analytical techniques used to compare the importance and implementation of the three strategies and test the influence of personal and organizational factors to the strategies. The study concludes with discussions about lessons from the Chinese reform and implications for future studies. [ABSTRACT FROM AUTHOR]

The work discusses intermittent light scheduling for energy cost reduction in vertical farming, specifically for growing salad crops (Lactuca Sativa). The study explores how dynamically adjusting lighting schedules based on day-ahead energy prices can improve energy efficiency and reduce operational costs. The research uses a growth chamber equipped with LED lights, controlled environmental factors, and real-time data from the Nord Pool energy market to optimize energy use. The results show that intermittent lighting schedules, aligned with cheaper energy periods, lead to higher crop yields and significant energy cost savings compared to continuous lighting. The findings suggest that such adaptive lighting strategies can make vertical farming more economically viable and sustainable by optimizing resource use, aligning with global sustainability goals. The study also highlights the potential of vertical farms to act as flexible power consumers, adjusting their energy usage based on renewable energy availability. [ABSTRACT FROM AUTHOR]

Thermal insulation applications on the exterior facades of buildings have been the subject of numerous studies from the past to the present. Some of these studies focus on the cost reduction effect of insulation, while others emphasize its ecological benefits. In this study, multi-objective optimization, the objectives of which are minimum cost and minimum CO2 emission, has been carried out with the NSGA-II method. In emission calculations, in addition to fuel-related emissions, the carbon footprint of all materials comprising the wall has also been included. The multi-objective optimization study examined four design variables: wall thickness, wall material (light concrete, reinforced concrete, and brick), insulation material (expanded polystyrene, extruded polystyrene, mineral wool, and polyurethane foam), and heating source (natural gas, electricity, fuel oil). Analyses have been carried out for four cities (Osmaniye, Bursa, Isparta, and Erzurum), which are located in different climatic regions, and considering solar radiation effects. An existing building has been taken as the base case scenario, and the study has determined the improvements in the total cost and the amount of CO2 released into the environment when the appropriate insulation material, insulation thickness, wall material, and heating source identified in the multi-objective optimization study have been used. At the cost-oriented optimum point in the study, the most suitable insulation material was found to be expanded polystyrene, the most suitable wall material was brick, and the most suitable heating source was natural gas. In the CO2-oriented optimum, in contrast to the cost-oriented approach, optimal results have been obtained when light concrete was selected as the wall material. [ABSTRACT FROM AUTHOR]

The presented article investigates the effects of technical innovations in administrative buildings on the financial side of the business, with a main focus on reducing energy costs and energy consumption. The administrative buildings in a business contribute significantly to the business's total energy consumption. The basic pillar of Industry 4.0 is the optimization of resources such as energy, which, in production enterprises, represents economic value—costs. The article is orientated to investigate the effect of the technical innovations of administrative buildings on the financial side of the enterprise, with a main focus on reducing energy costs and energy consumption. This research was conducted in Slovakia's manufacturing sector. In this research, we used economic and financial analysis and economic indicators. This research was conducted between 2019 and 2023. In this period, the results were positive. The results indicated a reduction in energy consumption of 143 GJ (39,722 kWh), reduced energy costs of EUR 6356, reduction in the energy cost structure of 1.3%, and the indicator, the payback period, was determined to be in the range of 6 to 12 years for individual technical innovations. The new design of administrative buildings is an advantage for manufacturing enterprises and can be used as a marketing tool to attract both customers and suppliers. The suggested energy, environmental, and economic sustainability model will allow production enterprises to improve their energy use in administrative buildings. [ABSTRACT FROM AUTHOR]

Tolerance allocation is a design task with a strong potential impact on manufacturing choices. In practice, however, it is often carried out with simple heuristics rather than with an optimization approach like those available in research literature. One reason could be the difficulty in predicting the economic benefits resulting from optimization. To allow for such considerations, the paper proposes a procedure to estimate the cost reduction that optimization allows compared to three traditional allocation methods (equal tolerances, precision factor, proportional to nominal). The chosen optimization method is based on the closed-form solution of a problem of cost minimization with a stackup constraint, using the extended reciprocal power cost-tolerance function. Compared to other methods, it provides analytical expressions of both the allocated tolerances and the associated costs. When applied to specific cases, these help recognize the conditions in which optimization allows a significant reduction in manufacturing costs. The results show that this occurs when the features of the same dimension chain have very different properties regarding a set of design variables with particular influence on the amount of machining required. [ABSTRACT FROM AUTHOR]

Management discretion in the decision to reduce payroll costs is an important but under-researched issue in management accounting. The authors leverage the experimental environment to test the role of organizational culture (close vs. distant) and managerial communion (concern for others) along with their interaction with sales decline persistence (one vs. two periods) on planned layoff decisions. The authors find that communal managers are hesitant to downsize employees and that a close organizational culture interacts with one period sales declines to reduce layoffs although the influence of culture is reduced with persistent sales declines. The authors also examine the influence of culture and communion on managers' preference for pay cuts as an alternative to layoffs. The authors find that a close culture and higher communion are associated with decisions to choose pay cuts over layoffs; however, these costs interact such that managers low in communion in a distant culture express a higher preference for layoffs. These findings illustrate the combined influence of economic, organizational, and dispositional factors on manager decisions about the extent and form of labor cost reductions due to sales declines. [ABSTRACT FROM AUTHOR]

Feature selection is a preprocessing step in machine learning that aims to reduce dimensionality and improve performance. The approaches for feature selection are often classified according to the evaluation of a subset of features as filter, wrapper, and embedded approaches. The high performance of wrapper approaches for feature selection is associated at the same time with the disadvantage of high computational cost. Cost-reduction mechanisms for feature selection have been proposed in the literature, where competitive performance is achieved more efficiently. This work applies the simple and effective resource-saving mechanisms of the fixed and incremental sampling fraction strategies with memory to avoid repeated evaluations in multi-objective permutational-based differential evolution for feature selection. The selected multi-objective approach is an extension of the DE-FSPM algorithm with the selection mechanism of the GDE3 algorithm. The results showed high resource savings, especially in computational time and the number of evaluations required for the search process. Nonetheless, it was also detected that the algorithm's performance was diminished. Therefore, the results reported in the literature on the effectiveness of the strategies for cost reduction in single-objective feature selection were only partially sustained in multi-objective feature selection. [ABSTRACT FROM AUTHOR]

Demand response (DR) can potentially provide a cost-efficient alternative for balancing the electricity grid by replacing fossil-fuelled power plants for the provision of flexible capacity. This paper aims to quantify the cost reduction potential of DR from a system perspective. Historical data of balancing markets are studied using regression and average bid price analysis to quantify the effect of the participation of DR resources on the price of flexible capacity for the provision of balancing reserves by focusing on two case studies in Great Britain and the Netherlands. It is estimated that DR bids are, on average, 35% lower than the market average. The regression analysis concluded that 1% higher participation of DR in balancing markets leads, on average, to a 2.7% lower prices for flexible capacity. The results verify the hypothesis that flexible DR capacity is offered at a lower price on balancing markets compared to conventional generation resources, resulting in lower costs for grid operators to balance the grid, thus reducing societal costs for electricity provision and overall emissions through the integration of low-carbon balancing resources. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in cost reduction. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The application of business analytics in cost reduction is paramount in today's fiercely competitive business landscape. This article highlights the pivotal role of business analytics in optimizing financial performance by identifying inefficiencies and areas for cost reduction. By harnessing the power of data, business analytics enables data-driven decision-making, differentiates between cost drivers, streamlines processes, and optimizes inventory and pricing strategies. Various methodologies and techniques, along with a diverse range of software solutions, support organizations in their cost reduction endeavors. Leveraging analytics not only provides a competitive edge but also enhances customer satisfaction, mitigates financial risks, and optimizes supplier relationships. This multifaceted approach empowers organizations to achieve cost reduction goals and secure long-term financial success. [ABSTRACT FROM AUTHOR]

In this paper, two novel classes of implicit exponential Runge–Kutta (ERK) methods are studied for solving highly oscillatory systems. First of all, symplectic conditions for two kinds of exponential integrators are derived, and we present a first-order symplectic method. High accurate implicit ERK methods (up to order four) are formulated by comparing the Taylor expansion of the exact solution, it is shown that the order conditions of two new kinds of exponential methods are identical to the order conditions of classical Runge–Kutta (RK) methods. Moreover, we investigate the linear stability properties of these exponential methods. Numerical examples not only present the long time energy preservation of the first-order symplectic method, but also illustrate the accuracy and efficiency of these formulated methods in comparison with standard ERK methods. [ABSTRACT FROM AUTHOR]

Research on innovative content within academic articles plays a vital role in exploring the frontiers of scientific and technological innovation while facilitating the integration of scientific and technological evaluation into academic discourse. To efficiently gather the latest innovative concepts, it is essential to accurately recognize innovative sentences within academic articles. Although several supervised methods for classifying article sentences exist, such as citation function sentences, future work sentences, and formal citation sentences, most of these methods rely on manual annotations or rule-based matching to construct datasets, often neglecting an in-depth exploration of model performance enhancement. To address the limitations of existing research in this domain, this study introduces a semi-automatic annotation method for innovative sentences (IS) with the assistance of expert comments information and proposes a data augmentation method by SAO reconstruction to augment the training dataset. Within this paper, we compared and analyzed the effectiveness of multiple algorithms for recognizing IS within academic articles. This study utilized the full text of academic articles as the research subject and employed the semi-automatic method to annotate IS for creating the training dataset. Then, this study validated the effectiveness of the semi-automatic annotation method through manual inspection and compared it with rule-based annotation methods. Additionally, the impacts of different augmentation ratios on model performance were also explored. The empirical results reveal the following: (1) The semi-automatic annotation method proposed in this study achieves an accuracy rate of 0.87239, ensuring the validity of annotated data while reducing the manual annotation cost. (2) The SAO reconstruction for data augmentation method significantly improved the accuracy of machine learning and deep learning algorithms in the recognition of IS. (3) When the augmentation ratio in the training set was set to 50%, the trained GPT-2 model was superior to other algorithms, achieving an ACC of 0.97883 in the test set and an F1 score of 0.95505 in practical application. [ABSTRACT FROM AUTHOR]

The demand-side management (DSM) research field has expanded due to rising energy consumption. In the traditional electrical grid, unknown energy usage results in high costs. This paper introduces a reinforcement learning-based self-adaptive learning-black widow optimization (RL-SAL-BWO) approach for dynamic load scheduling and power allocation, aimed at improving energy efficiency and reducing costs and energy consumption. The proposed strategy utilizes pricing signals and real-time load profiles to estimate the changing energy consumption within residential buildings. To optimize energy allocation across different appliances, this algorithm considers both energy efficiency and load characteristics. The RL agent, comprising action space, reward function, and Q-value function, is utilized for decision-making on power allocation and load scheduling. The SAL algorithm automatically adjusts the exploration rate and learning rate which leads to enhanced efficiency. By exploring the solution space, the BWO improves the learning process. Through the integration of RL, SAL, and BWO techniques, energy efficiency is increased, energy consumption is reduced, and electricity costs are lowered. The smart grid is utilized for estimating changes in energy consumption. The purpose of this is to estimate changes in energy consumption, aiding in informed decisions about energy management and infrastructure planning. The proposed approach is implemented using MATLAB R2021b software, followed by the evaluation and calculation of performance metrics. The findings demonstrate that the proposed strategy significantly enhances energy efficiency by 18.5%, reduces energy consumption by 31.91%, and decreases electricity costs by 40.66%. Furthermore, the computation time reduction of the proposed approach is 13.7 s. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Water Treatment is the property of CNOOC Tianjin Chemical Research & Design Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The biggest challenge in launching Micro‐LED displays is cost reduction. In our previous report (1), we proposed a super cost‐effective test method embedding EL and electrical testing (PEMPTM). In this paper, we report the validity of PEMPTM by comparing the external quantum efficiency (EQE) of LED emission and photodetection characteristics. [ABSTRACT FROM AUTHOR]

Despite many advantages of distributed generation (DG), it can have adverse effects on the network protection coordination by raising the short-circuit level of buses. This issue can often be resolved by incorporating fault current limiters (FCLs) that are properly located and sized in the network. We propose an efficient method to simultaneously attain the optimal location and size of DG and FCL units for protection coordination of overcurrent relays and cost reduction in a distribution network. The proposed method involves three stages. First, two separate objective functions are derived, representing the operation times of relays and the network costs associated with the installation of DGs and FCLs and system losses. The Strength Pareto Evolutionary Algorithm 2 (SPEA-2) is then used for finding a Pareto-optimal solution set for the interrelated multiobjective problem at hand. Finally, the k-means clustering method is utilized to group the best solutions. In contrast to the conventional weighted sum (WS) method, the proposed method is more computationally efficient while being capable of treating complex networks with non-convex Pareto front solutions. These features are demonstrated by implementing the proposed approach in the 14-bus IEEE test grid and comparing the results with those obtained using the conventional WS method. [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]

Biological denitrification is a generally economically viable and reliable technology for nitrate (NO3−-N) and nitrite (NO2−-N) removal from wastewater. Nitrate/nitrite reduction requires an electron donor, which can be supplied from influent wastewater organic compounds, added as an external carbon source, or by endogenous respiration. Decisions regarding the selection of an external carbon source should take into consideration its cost, sludge generation quantity, the rate and efficiency of denitrification, and chemical storage safety. The expense of waste management and external carbon sources accounts for up to 50% of the overall cost of wastewater treatment. The carbon source characteristics required for biological denitrification affect the microbial community structure, denitrification rate, and intermediate products. This review is based on a bibliometric analysis and a systematic literature review providing information and insight into the topic of the denitrification process using different carbon sources. In this review, the main focus was on discussing alternative carbon sources vs. traditional carbon sources in terms of the carbon source price, C/N ratio, denitrification efficiency and rate, operational parameters, and advantages and disadvantages, as well as the limitations in the denitrification process. Future perspectives may include the operating parameters influencing the stability of the removal performance; the maintenance and improvement of nitrate removal; a study of the diversity of the microbial community; research on the application of new alternative carbon sources in denitrification; and N2O emission detection and minimisation. [ABSTRACT FROM AUTHOR]

One of the main issues in power systems relates to scheduling of energy resources. With the ever-increasing penetration of renewable energies with intermittent power output, this issue has turned into an even more significant problem. Renewable energy sources (RESs) have captured attention due to their low environmental emission and also low running cost. One drawback that may be brought into power systems is the surplus power generation by such generation technologies that should be carefully addressed in power system-related problems. This paper proposes the unscented transform modeling to consider the stochastic behavior of charge and discharge of EVs, random performance of photovoltaic, load demand and wind turbine systems. Due to the unpredictable nature of solar and wind power outputs, as well as plug-in electric vehicle owners' behavior when supplying or receiving power from the grid, a stochastic programming-based approach is proposed to operate microgrids in grid-connected configuration mode. The integration of vehicle to grid (V2G) has a good ability to minimize the operating cost of the MG. An integrated optimization model is presented in this study for optimal operation of the MG with high penetration of PEVs and RESs. Modified sunflower optimization algorithm (MSFO) algorithm is applied in this paper to address the optimization problem. The single-objective stochastic optimization is used for minimizing the total operating cost over the day taking into consideration the uncertainties due to the RESs' power output intermittency, including wind speed and solar irradiance and load demand forecast error. Several case studies are taken into account to show the efficiency of the optimal operation with PEVs. [ABSTRACT FROM AUTHOR]

The production of coconut tree seedlings is an important step in the production process, as it substantially affects the productive performance of the adult plant, and the way of obtaining seedlings directly reflects the added costs. To minimize costs, the introduction of biostimulants can be considered a viable and sustainable technology. This study aimed to evaluate the effects of applying Bacillus cereus in promoting growth and reducing the costs of producing Brazilgreen dwarf coconut seedlings. The study has two stages, the first was an experiment carried out in a commercial nursery in the state of Pará-Brazil. The design was completely randomized, with two treatments: control with water (100% mineral fertilization) and B. cereus inoculation (50% mineral fertilization), with 10 replicates each. Biometric parameters and the quality of seedlings were evaluated. In the second stage, the production of stimulated seedlings was compared to that of commercial seedlings, and the effective operating cost (COE) and the total operating cost (TOC) were determined. Biostimulation with B. cereus promotes the growth of coconut tree seedlings, increases seedling quality, and reduces nursery time. In addition, the cost of production is reduced by 10%. Thus, microbial technology is a positive strategy for the production of Brazilian green dwarf coconut seedlings. Using B. cereus can guarantee obtaining seedlings with high performance and at a lower cost. These results may favor obtaining adult plants with high productivity. [ABSTRACT FROM AUTHOR]

Given the ever-growing electricity consumption and environmental anxiety with the predominant usage of conventional fuels in power plants, it is crucial to explore suitable alternatives to address these issues. Renewable energy sources (RESs) have emerged as the preferred choice for meeting energy requirements due to their minimal pollution. This study proposes a new idea to minimize operational costs and achieve the most cost-effective grid with minimum cost. Meanwhile, the transportation sector is gradually replacing conventional fossil-cars with electric ones, specifically plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs), which have gained significant consideration. These vehicles can join to the main grid and engage in energy exchange through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. Additionally, the concept of microgrid (MG) is proposed to optimize the potential of PEVs through smart infrastructure. Using the V2G capability, the operating costs are reduced, providing opportunities to incorporate PEVs into the network. Therefore, effective operation of MGs becomes highly significant. This paper suggests management of a MG consisting of PEVs and RESs. The approach utilizes a stochastic programming technique called unscented transformation (UT). The problem is addressed as a single-objective stochastic optimization problem with the aim of minimizing the operation cost. The proposed approach employs the hybrid whale optimization algorithm and pattern search (HWOA–PS) to solve the stochastic problem. The obtained outcomes are compared with those of other approaches to validate its effectiveness. [ABSTRACT FROM AUTHOR]

This article explores the challenges faced by cystic fibrosis (CF) patients in low- to middle-income countries in accessing the triple combination therapy of elexacaftor, tezacaftor, and ivacaftor (ETI). The therapy is currently expensive and not widely available due to regulatory approval and cost issues. The authors propose a strategy using pharmacokinetic enhancement to reduce the cost of treatment, which involves the use of boosting agents like ritonavir. They also suggest the potential use of generic ETI formulations. Preliminary data from South Africa shows promising results, but further clinical trials are needed to evaluate the effectiveness, safety, and cost-effectiveness of this approach. [Extracted from the article]

Limited research has been conducted on the impact of financial digitalization in the context of MSMEs, particularly in the city of Medan. Therefore, this research aims to investigate the impact of financial digitalization on savings and cost reduction in MSME entrepreneurial companies in Medan City. This research uses a research design with quantitative data collected from 89 MSMEs. Quantitative analysis shows a significant positive relationship between financial digitalization and savings and cost reduction in MSMEs. The moderating effects of firm size and technological readiness were also identified, indicating that smaller MSMEs and those with higher technological readiness may gain more benefits from financial digitalization. The findings further support the positive impact of financial digitalization, as MSMEs report increased accuracy and reliability of financial data, faster processing of financial transactions, and reduced manual documentation and recordkeeping. However, challenges related to limited financial resources, lack of technical skills, and resistance to change were identified as barriers to implementing financial digitalization in MSMEs. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of this paper is to investigate the extent to which real-time monitoring of vehicles and the collection of operational data about them through telematic systems can contribute to the reduction of operating costs in a transport company. The study focuses on specific factors such as the driver's actions, which can affect the vehicle's technical condition and diesel consumption levels. Methodology: The research is based on an analysis of operational data obtained from the vehicles of a transport company engaged in international freight transportation. The data were primarily analyzed for an indicator related to total fuel (diesel) consumption while driving. Linear models were created using the least squares method, and the best-fitting models were selected to draw conclusions about the impact of telematic systems on the company's costs. Results: The study found that the introduction of a telematic system can have a positive impact on a transport company's operations and help reduce costs associated with the execution of transport orders. The analysis of the data showed that individual factors, such as driving time, average axle load, and average travel speed, can affect the total driving fuel consumption of all vehicles and contribute to the overall cost reduction. Theoretical contribution: This paper contributes to the understanding of the role of telematic systems in the optimization of fleet planning and management, which is a crucial aspect of the logistics of transporting goods. The research provides insights into the potential of real-time monitoring and data collection for improving the efficiency and cost-effectiveness of transport companies. Practical implications: The findings of this study can be useful for transport companies looking to improve their operations and reduce costs. The research highlights the importance of using telematic systems in the right way to carry out the transportation of goods and suggests that real-time monitoring and data analysis can be key to minimizing costs and maximizing profits. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and Aim: Gastric intestinal metaplasia (GIM) is a high‐risk factor for the development of gastric cancer. Narrow‐band imaging (NBI) enables endoscopic grading of GIM (EGGIM). In the era of climate change, gastrointestinal endoscopists are expected to reduce greenhouse gas emissions and medical waste. Based on the diagnostic performance of NBI endoscopy, this study measured the environmental impact and reduced cost of implementing EGGIM during gastroscopy. Methods: Using NBI endoscopy in 242 patients, EGGIM classification and operative link on GIM (OLGIM) staging were prospectively performed in five different areas (lesser and greater curvatures of the corpus and antrum, and the incisura angularis). We estimated the environmental impact and cost reduction of the biopsy procedures and pathological processing if EGGIM were used instead of OLGIM. Results: The diagnostic accuracy of NBI endoscopy for GIM was 93.0–97.1% depending on the gastric area. When a high EGGIM score ≥ 5 was the cut‐off value for predicting OLGIM stages III–IV, the area under the curve was 0.862, sensitivity was 81.9%, and specificity was 90.4%. The reduction in the carbon footprint by EGGIM was −0.4059 kg carbon dioxide equivalents per patient, equivalent to 1 mile driven by a gasoline‐powered car. The cost savings were calculated to be $47.36 per patient. Conclusions: EGGIM is a reliable method for identifying high‐risk gastric cancer patients, thereby reducing the carbon footprint and medical costs in endoscopy practice. [ABSTRACT FROM AUTHOR]

In recent years, the logistics industry has undergone tremendous development because of the growth of e‐commerce, providing supply chain members with various options for logistics service strategies. Furthermore, cost reduction effort (CRE) and fairness concern have become even more significant on the logistics operation for e‐commerce supply chain because of changing market conditions and consumer demands. Considering CRE and fairness concern, we propose a Stackelberg game model where the manufacturer acts as a leader to analyze how a manufacturer or a retailer strategically decides between two logistics service strategies, i.e., the manufacturer‐provided logistics service and the retailer‐provided logistics service. When the retailer provides logistics service in the absence of fairness concern, a "win‐win" outcome can be achieved. However, profit does not always increase in proportion to the logistics service cost factor when fairness concern is considered. Additionally, there is a situation where the optimal logistics service strategy for the manufacturer and the retailer is contrary to each other in different ranges of fairness concern intensity when the logistics service provider both has CRE and fairness concern. As a consequence, it becomes important for both the manufacturer and the retailer to balance their fairness concern and consider the effectiveness of CRE when making decisions about adopting logistics service strategy. [ABSTRACT FROM AUTHOR]

Copyright of Acta Sting is the property of Akademie STING, o.p.s. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model's consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model. [ABSTRACT FROM AUTHOR]

The novelty of the paper is the analysis of the possibilities of reducing the operating costs of a mine water pumping station in an abandoned coal mine. To meet the energy needs of the pumping station and reduce the carbon footprint, "green" energy from a photovoltaic farm was used. Surplus green energy generated during peak production is stored in the form of green hydrogen from the water electrolysis process. Rainwater and process water are still underutilized sources for increasing water resources and reducing water stress in the European Union. The article presents the possibilities of using these waters, after purification, in the production of green hydrogen by electrolysis. The article also presents three variants that ensure the energy self-sufficiency of the proposed concepts of operation of the pumping station. [ABSTRACT FROM AUTHOR]

With a significant share of renewable power generation integrated into the power supply, it is crucial to timely adjust the regulating peak load for coal-fired power plants equipped with CO2 capture to ensure the stable operation of the multi-energy supply system. In this paper, the effects of varying boiler loads on the techno-economic performance of the retrofitted power plant were studied. Furthermore, the potential for reducing the cost of CO2 capture was investigated, and early opportunities for demonstration were discussed. Results showed that when the boiler load decreased from 100% turbine heat acceptance condition to 50% turbine heat acceptance condition, the cost of CO2 capture increased from 37.0 $/t CO2 to 57.0 $/t CO2, cost contribution of energy penalty and extra capital investment also increased from 20.6 $/t-CO2 to 25.7 $/t-CO2, and from 16.4 $/t-CO2 to 31.3 $/t-CO2, respectively. Furthermore, by improving separation efficiency from 0.15 to 0.5, a 25% to 30% reduction in CO2 capture cost can be achieved. The cost of CO2 capture could decrease by 42.2–50.5% when the cumulative capacity reaches 250 GW under the high investment learning rate scenario. According to the distribution of coal prices and renewable energy sources in China, the early demonstration projects of multi-energy supply systems should prioritize the northern region. The results of this work can provide informative references for making roadmaps and policies for CO2 emission reduction toward carbon neutrality. [ABSTRACT FROM AUTHOR]

The popularization of renewable energy is limited by wasteful problems such as curtailed wind power and high economic costs. To tackle these problems, we propose a bi-layer optimal planning model with the integration of power to gas and a ground source heat pump for the existing integrated energy system. Firstly, the inner layer optimizes the daily dispatch of the system, with the minimum daily operation cost including the penalty cost of curtailed wind power. Then, the enumeration method of outer-layer optimization determines the device capacity of various schemes. After that, optimal planning can be achieved with the minimum daily comprehensive cost. The result of this example shows that the improved system can reduce curtailed wind power and system costs, thus improving the overall economy. Finally, the influences of algorithms and gas prices on planning optimization are studied. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Carbon capture, utilization, and storage (CCUS) have garnered extensive attention as a target of carbon neutrality in China. The development trend of international CCUS projects indicates that the cluster construction of CCUS projects is the main direction of future development. The cost reduction potential of CCUS cluster projects has become a significant issue for CCUS stakeholders. To assess the cost reduction potential of CCUS cluster projects, we selected three coal-fired power plants in the coastal area of Guangdong as research targets. We initially assessed the costs of building individual CCUS projects for each plant and subsequently designed a CCUS cluster project for these plants. By comparing individual costs and CCUS cluster project costs, we assessed the cost reduction potential of CCUS cluster projects. The results show that the unit emission reduction cost for each plant with a capacity of 300 million tonnes per year is 392.34, 336.09, and 334.92 CNY/tCO2. By building CCUS cluster project, it could save 56.43 CNY/tCO2 over the average cost of individual projects (354.45 CNY/tCO2) when the total capture capacity is 9 million tonnes per year (by 15.92%). Furthermore, we conducted a simulation for the scenario of a smaller designed capture capacity for each plant. We found that as the capture scale increases, the cost reduction potential is higher in the future. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)