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The cascaded H‐bridge multi‐level converter is the main topology of high‐voltage and high‐capacity converter in renewable energy power generation, energy storage system and reactive power compensation equipment. The parameter design of cascaded H‐bridge multi‐level converters directly determines the material cost and performance. Taking the output current distortion rate and the ripple voltage of the capacitor of the cascaded H‐bridge multi‐level converter as the constraint conditions, this paper proposes an overall parameter optimal design method of the cascaded H‐bridge multi‐level converter based on the optimization of the total material cost. Finally, the effectiveness of the proposed parameter design method is verified by the simulation result. [ABSTRACT FROM AUTHOR]

In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO. [ABSTRACT FROM AUTHOR]

The complex compositional space of high entropy alloys (HEAs) has shown a great potential to reduce the cost and further increase the catalytic activity for hydrogen evolution reaction (HER) by compositional optimization. Without uncovering the specifics of the HER mechanism on a given HEA surface, it is unfeasible to apply compositional modifications to enhance the performance and save costs. In this work, a combination of density functional theory and Bayesian machine learning is used to demonstrate the unique catalytic mechanism of IrPdPtRhRu HEA catalysts for HER. At high coverage of underpotential‐deposited hydrogen, a d‐band investigation of the active sites of the HEA surface is conducted to elucidate the superior catalytic performance through electronic interactions between elements. At low coverage, a novel Bayesian learning with oversampling approach is then outlined to optimize the HEA composition for performance improvement and cost reduction. This approach proves more efficacious and efficient and yields higher‐quality structures with less training set bias compared with neural‐network optimization. The proposed HEA optimization theoretically outperforms benchmark Pt catalysts’ overpotential by ≈40% at a 15% reduced synthesis cost comparing to the equiatomic ratio HEA. [ABSTRACT FROM AUTHOR]

As vehicular technology advances, intelligent vehicles generate numerous computation‐intensive tasks, challenging the computational resources of both the vehicles and the Internet of Vehicles (IoV). Traditional IoV struggles with fixed network structures and limited scalability, unable to meet the growing computational demands and next‐generation mobile communication technologies. In congested areas, near‐end Mobile Edge Computing (MEC) resources are often overtaxed, while far‐end MEC servers are underused, resulting in poor service quality. A novel network framework utilizing sixth‐generation mobile communication (6G) and digital twin technologies, combined with task migration, promises to alleviate these inefficiencies. To address these challenges, a task migration and re‐offloading model based on task attribute classification is introduced, employing a hybrid deep reinforcement learning (DRL) algorithm—Dueling Double Q Network DDPG (QDPG). This algorithm merges the strengths of the Deep Deterministic Policy Gradient (DDPG) and the Dueling Double Deep Q‐Network (D3QN), effectively handling continuous and discrete action domains to optimize task migration and re‐offloading in IoV. The inclusion of the Mini Batch K‐Means algorithm enhances learning efficiency and optimization in the DRL algorithm. Experimental results show that QDPG significantly boosts task efficiency and computational performance, providing a robust solution for resource allocation in IoV. [ABSTRACT FROM AUTHOR]

How bridge refurbishment projects are performed requires a trade-off between the speed and cost of the project and the amount of traffic disturbances during the project. A possible way to help reach a better balance between these two extremes is the ASTRA Bridge developed in Switzerland. The ASTRA Bridge is a 236-meter long steel ramp system on wheels, which is placed on top of the bridge deck undergoing refurbishment to enable vehicles to continue to pass over the bridge while construction work progresses underneath. This study illustrates new refurbishment processes by using the ASTRA Bridge and presents the first quantitative analysis of the effects of using the ASTRA Bridge on the time, costs and traffic disturbances associated with bridge refurbishment. The bridge investigated is a short-span (50 m long) highway bridge requiring refurbishment of its superstructure. The analysis indicates that the use of the ASTRA Bridge resulted in reductions in duration and costs (14% and 3% for the example), and a substantial reduction in user costs (51% for the example). Although more analysis is required for different types of refurbishment projects, the initial results indicate that the ASTRA Bridge may become an integral part of future highway bridge refurbishment projects. [ABSTRACT FROM AUTHOR]

In a modular multilevel converter (MMC) system, the sub‐module (SM) capacitors account for a large proportion of cost, volume and weight. Increasing modulation index (m) is an effective method to reducing the capacitance. Traditional MMC based on half‐bridge SM (HBSM) cannot boosting m while the hybrid MMC composed of HBSMs and full‐bridge sub‐modules (FBSM) can increase the m. The negative voltage is required when hybrid MMC works at the boosted m, which is only provided by the FBSMs because the HBSMs cannot output negative voltage. Therefore, appropriate control strategies are needed to achieve the normal operation of hybrid MMC under boosted m. This paper presents a control method suitable for hybrid MMC with high m based on multi‐harmonic voltage injection. The m is increased to 1.41 with the proposed multi‐harmonic voltage injection. Compared with the HBSM‐MMC, the proposed method reduces the total capacitance by more than 32%. Boosting m also reduces the RMS and peak value of the arm current by 18%. A comprehensive comparison was presented to demonstrate the cost and effect. Simulation and experiment verified the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

Every day, health centers receive a significant number of patients seeking care and hospitalization, leading to long waiting times. Simulation modeling techniques are considered an effective solution to this issue. However, some research efforts had limited success mainly due to the neglect of systematic and straightforward approaches to identify and eliminate time waste in the healthcare system. To address this limitation, this study developed an LSS approach by collecting arriving patients and staff in a health center and using it as a case study. Then, a simulation model of the current system was created using the SIMPROCESS software. Finally, alternative solutions to shorten the queue time were proposed and supported by statistical graphs and tables. This is expected to improve overall health outcomes and enhance the delivery of healthcare services. A scenario is presented to evaluate the impact of the proposed improvements, which includes adding a general practitioner, a pediatrician, a dentist, and another pharmacy counter. This scenario improves system performance by up to 21%, reduces patient waiting times by approximately 50%, and ensures a fair distribution of workload among staff. [ABSTRACT FROM AUTHOR]

This article delves into the implementation and benefits of collaborative logistics, a strategic approach wherein logistics stakeholders engage in close cooperation to optimize resources and processes. The significance of a robust technological infrastructure is underscored, enabling real-time information sharing and integrated planning, which are pivotal for enhancing supply chain efficiency, reducing costs, and fostering sustainability. The discussion includes various collaborative logistics models, such as shared transportation routes and warehouses, which are instrumental in minimizing waste and mitigating environmental impact. The article further emphasizes the critical role of trust, transparency, and comprehensive legal frameworks in ensuring the success of such collaborations. By incorporating sustainability aspects into inventory management practices, companies can not only achieve substantial economic and environmental benefits but also secure a long-term competitive advantage. This integrated approach aligns with contemporary demands for sustainable operations, ultimately contributing to reduced global environmental footprint and compliance with stringent regulatory standards. [ABSTRACT FROM AUTHOR]

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]

This article describes elements of the know-how of using carbon electrodes produced using the technology of molding around a rod when smelting silicon metal. Application of our know-how will dramatically increase the competitiveness of silicon metal production. Experts' concerns regarding the use of such electrodes were that such electrodes have a through axial hole. This significantly reduces the mechanical strength of such electrodes, which can presumably lead to problems associated with the breakage of the working side of the electrode, which is immersed in the smelting space of the furnace under the charge layer. Industrial testing of such electrodes was carried out in a 30 MVA furnace of "Tau-Ken Temir" LLP. During testing, we used an approach previously developed by our team for working with a furnace in the process of smelting silicon metal. In particular, we used an interval between top treatments of about 30 min and adhered to the principles of balanced smelting, i.e., provided a balance between the intensity of the uniform supply of the charge into the furnace and the current active electrical power. Industrial testing carried out over four weeks confirmed the stability of the operation of cheaper carbon electrodes with a through axial hole. The recovery of silicon into finished products was also improved to 88–89% and the specific energy consumption was reduced to 11.2–12.1 MWh/t of silicon metal from the initial value 14,752 MWh/t. Thus, we received additional evidence for the effectiveness of our approach in furnace operating compared to an approach based on the ultimate provision of gas and permeability of the furnace top due to excessively intense processing of the top and an uncontrolled, uneven supply of charge to the furnace. [ABSTRACT FROM AUTHOR]

In recent years, green credit has significantly supported the development of the sustainable economy. However, the existing literature presents differing views on the impact of green credit on bank performance, which is crucial for the sustainability of green credit business. Meanwhile, FinTech is comprehensively empowering green credit business. This paper investigates whether FinTech influences the effect of green credit on bank performance. Based on an analysis of data from 127 Chinese commercial banks from 2007 to 2022, we find that green credit significantly enhances bank performance, and FinTech further amplifies this positive effect. This finding partially explains the conflicting views in the existing literature, as the impact of green credit on bank performance varies under different levels of FinTech. We believe that FinTech exerts its influence through three mechanisms: cost reduction, reputation enhancement, and risk mitigation. Heterogeneity analysis reveals that the impact of FinTech is more pronounced in city commercial banks, in samples with better green credit development, and during banking industry downturns. Finally, we recommend that banks actively develop FinTech and apply it to green credit businesses to maximize the positive effects of green credit. Simultaneously, regulators and governments should provide necessary support for banks. [ABSTRACT FROM AUTHOR]

Recently, a novel low‐cost coding digital receiving array based on machine learning (ML‐CDRA) has been proposed to reduce the required radio frequency channels in modern wireless systems. The spatial sensitivity of ML‐CDRA is studied which describes the spatial accumulation gain in different directions. It is demonstrated that the spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesis method is proposed based on the alternate projection by optimising the encoding network with the constraint of amplitude‐phase quantisation. Simulation results show that the proposed method can significantly improve the spatial sensitivity of ML‐CDRA. Furthermore, in the directions of interest, the spatial accumulation gain of ML‐CDRA can exceed the full‐channel digital receiving array. [ABSTRACT FROM AUTHOR]

Aiming at the power supply scheme (PSS) of the on‐board supercapacitor‐powered tram, considering the cost and margin of the PSS, a two‐stage method is designed to optimize the layout of the charging stations and the configuration of the supercapacitor (SC). First, the SC‐powered tram model and stable cycle operation model are established, and a two‐stage optimization problem model with the lowest PSS cost and the largest SC margin is established. Then, an improved dual‐population differential evolution algorithm is designed, and the layout of charging stations and the configuration of SC are co‐optimized in the first stage, and then the layout of charging stations is optimized again in the second stage. The simulation results show that co‐optimization can obtain a lower cost of PSS, and furthermore, the layout of charging stations can be optimized again to effectively improve the margin of SC, thereby improving the matching degree between the layout of charging stations and the connection scheme of SC. [ABSTRACT FROM AUTHOR]

The cost landscape in advanced technology systems is shifting dramatically. Traditionally, hardware costs took the spotlight, but now, programming and debugging complexities are gaining prominence. This paper explores this shift and its implications, focusing on reducing the cost of programming complex robot behaviors, using the latest innovations from the Generative AI field, such as large language models (LLMs). We leverage finite state machines (FSMs) and LLMs to streamline robot programming while ensuring functionality. The paper addresses LLM challenges related to content quality, emphasizing a two-fold approach using predefined software blocks and a Supervisory LLM. [ABSTRACT FROM AUTHOR]

Many schools face barriers that hinder access to services, including limited funding and staff trained in evidence-based interventions. In particular, rural schools must provide targeted intervention to promote academic growth and narrow achievement gaps despite these barriers. One seldom-considered solution to ease the resource costs in the provision of intervention services is to provide high school students with the training and supervision necessary to carry them out. Thirty elementary students participated in an explicit timing intervention administered by either a high school student or graduate student in school psychology. A 2 × 2 mixed factorial ANOVA found significant growth from pre- to post-test with no effects of interventionist education level. These findings imply that school-based interventionists may be able to expand the reach of their practice by recruiting and training older students to carry out interventions. [ABSTRACT FROM AUTHOR]

Abstract In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO.

Abstract The cascaded H‐bridge multi‐level converter is the main topology of high‐voltage and high‐capacity converter in renewable energy power generation, energy storage system and reactive power compensation equipment. The parameter design of cascaded H‐bridge multi‐level converters directly determines the material cost and performance. Taking the output current distortion rate and the ripple voltage of the capacitor of the cascaded H‐bridge multi‐level converter as the constraint conditions, this paper proposes an overall parameter optimal design method of the cascaded H‐bridge multi‐level converter based on the optimization of the total material cost. Finally, the effectiveness of the proposed parameter design method is verified by the simulation result.

Abstract As vehicular technology advances, intelligent vehicles generate numerous computation‐intensive tasks, challenging the computational resources of both the vehicles and the Internet of Vehicles (IoV). Traditional IoV struggles with fixed network structures and limited scalability, unable to meet the growing computational demands and next‐generation mobile communication technologies. In congested areas, near‐end Mobile Edge Computing (MEC) resources are often overtaxed, while far‐end MEC servers are underused, resulting in poor service quality. A novel network framework utilizing sixth‐generation mobile communication (6G) and digital twin technologies, combined with task migration, promises to alleviate these inefficiencies. To address these challenges, a task migration and re‐offloading model based on task attribute classification is introduced, employing a hybrid deep reinforcement learning (DRL) algorithm—Dueling Double Q Network DDPG (QDPG). This algorithm merges the strengths of the Deep Deterministic Policy Gradient (DDPG) and the Dueling Double Deep Q‐Network (D3QN), effectively handling continuous and discrete action domains to optimize task migration and re‐offloading in IoV. The inclusion of the Mini Batch K‐Means algorithm enhances learning efficiency and optimization in the DRL algorithm. Experimental results show that QDPG significantly boosts task efficiency and computational performance, providing a robust solution for resource allocation in IoV.

Abstract In a modular multilevel converter (MMC) system, the sub‐module (SM) capacitors account for a large proportion of cost, volume and weight. Increasing modulation index (m) is an effective method to reducing the capacitance. Traditional MMC based on half‐bridge SM (HBSM) cannot boosting m while the hybrid MMC composed of HBSMs and full‐bridge sub‐modules (FBSM) can increase the m. The negative voltage is required when hybrid MMC works at the boosted m, which is only provided by the FBSMs because the HBSMs cannot output negative voltage. Therefore, appropriate control strategies are needed to achieve the normal operation of hybrid MMC under boosted m. This paper presents a control method suitable for hybrid MMC with high m based on multi‐harmonic voltage injection. The m is increased to 1.41 with the proposed multi‐harmonic voltage injection. Compared with the HBSM‐MMC, the proposed method reduces the total capacitance by more than 32%. Boosting m also reduces the RMS and peak value of the arm current by 18%. A comprehensive comparison was presented to demonstrate the cost and effect. Simulation and experiment verified the proposed method.

Abstract Aiming at the power supply scheme (PSS) of the on‐board supercapacitor‐powered tram, considering the cost and margin of the PSS, a two‐stage method is designed to optimize the layout of the charging stations and the configuration of the supercapacitor (SC). First, the SC‐powered tram model and stable cycle operation model are established, and a two‐stage optimization problem model with the lowest PSS cost and the largest SC margin is established. Then, an improved dual‐population differential evolution algorithm is designed, and the layout of charging stations and the configuration of SC are co‐optimized in the first stage, and then the layout of charging stations is optimized again in the second stage. The simulation results show that co‐optimization can obtain a lower cost of PSS, and furthermore, the layout of charging stations can be optimized again to effectively improve the margin of SC, thereby improving the matching degree between the layout of charging stations and the connection scheme of SC.

Abstract Recently, a novel low‐cost coding digital receiving array based on machine learning (ML‐CDRA) has been proposed to reduce the required radio frequency channels in modern wireless systems. The spatial sensitivity of ML‐CDRA is studied which describes the spatial accumulation gain in different directions. It is demonstrated that the spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesis method is proposed based on the alternate projection by optimising the encoding network with the constraint of amplitude‐phase quantisation. Simulation results show that the proposed method can significantly improve the spatial sensitivity of ML‐CDRA. Furthermore, in the directions of interest, the spatial accumulation gain of ML‐CDRA can exceed the full‐channel digital receiving array.

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association 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.)

Mixed integer linear programming (MILP) has been gaining traction as a method for solving optimal pump scheduling problems in water distribution networks (WDNs). However, inclusion of variable speed pumps (VSPs) in MILP pump scheduling frameworks has not been given adequate treatment. This article addresses this gap by describing a methodology for formulating and solving optimal pump scheduling problems with VSPs using MILP and piece-linear approximations of network components. The methodology proceeds in four steps: (a) WDN simulation with initial pump schedule(s), (b) approximation of network components, including VSP, using linear and piece-linear functions around the chosen operating points, (c) formulation of a fully parameterised mixed integer linear programme, and (d) solution of the optimisation problem and WDN simulation with optimal pump schedule(s). The methodology is coded in MATLAB/OCTAVE and Python and is publicly available on GitHub. It was applied to solve a pump scheduling problem on a two variable speed pump single-tank network that allows the reader to easily understand how the methodology works and how it is applied in practice. The results show that the formulation is robust and the optimiser is able to return a globally optimal solution for a range of operating points. HIGHLIGHTS Mathematical description of variable speed pumps (VSPs) for MILP formulations is introduced.; Formulation of pump schedule optimisations using MILP is described from first principles.; A pump scheduling framework for VSPs is designed and released as open-source software.; Problem size calculation formulae are provided for a generic network.;

The analysis of the performance of any activity and especially of companies has always been a topic of great interest for a multitude of categories of information users. The article focuses on the financial performance of companies, on the relevant indicators for its analysis as well as on the presentation of ways to reduce costs that can contribute to increasing financial performance. Regarding the indicators which reflects financial performance, the result obtained (Income-Expenses) or the net accounting assets (equity) are the elements that enter into the calculation of most of them or that influence the factors that are taken into account in the calculation of the indicators. At the same time, the result obtained is an item of equity. Therefore, I can say that all these indicators are directly or indirectly influenced by the result obtained and consequently by the expenses/costs of the company. The centralization/decentralization of the company's activity, the monitoring of the indicators with which the performance is valuated, the delimitation of expenses into fixed and variable, the implications of applying the cost calculation methods, the relevance of costs are aspects covered in the article that can support the managers/owners of the company in the search for solutions to increase the financial performance of companies.

FEATURES / [Profitability] Make a Stronger Business Case for Sustainability When greener products and processes add costs, managers can shift other levers to maintain profitability. By Karel Cool, Atalay Atasu, [...]

The increasing demand for machined components of superior quality at competitive costs has driven machine and cutting tools manufacturers to innovate in machine tool technologies and cutting tools materials. Consequently, optimizing the machining process becomes an important subject of research. Particularly, optimizing machining parameters like cutting speed, depth of cut and feed rate stands out. Literature review highlights that optimization considers various "objective functions" such as minimum production cost, maximum production rate, and minimum energy consumption. However, one aspect not objectively considered is the customer's demand within a specific timeframe, known as takt time. The relevance, originality and contribution of this work is the development of a method that, considering the current machining parameters and the takt time for a given machining stage, defines the optimized cutting speed for each cutting tool to meet the takt time at the lowest production cost. An algorithm based on classical equations for calculating machining times and costs for machining processes with multiple cutting tools was developed. A software in Excel receives process data, "x and K" coefficients from Taylor's life equation and calculates the optimized cutting speeds. The present method was applied in a heavy machining industrial environment using actual production data and machining parameters. Taylor coefficients x and K were obtained through cutting tool life tests carried out on the part under analysis. Results showed potential machining cost savings of up to 6.2% compared to traditional methods for obtaining takt time on shop floor. [ABSTRACT FROM AUTHOR]

A reduced-cost explicitly correlated second-order Møller–Plesset (MP2-F12) approach is presented. Based on our recent reduced-cost explicitly correlated coupled-cluster method [J. Chem. Theory Comput., 19, 174 (2023)], the frozen natural orbital, the natural auxiliary basis, and the natural auxiliary function approximations are utilised to decrease the size of the molecular orbital basis and the auxiliary basis sets required for the density fitting and for the expansion of the explicitly correlated geminals. The necessary modifications of the above approximations for MP2-F12 are discussed. The performance of the new method is tested for atomisation and reaction energies. Our results demonstrate that speedups of 1.5–4 can be achieved in the calculation of MP2-F12 correlation energies with a moderate loss of accuracy. [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]

To describe a simple variation of burr hole craniostomy for the management of chronic subdural hematoma (CSDH) that uses a frontal drainage system to facilitate timely decompression in the event of tension pneumocephalus and spares the need for additional surgery. We conducted a retrospective analysis of 20 patients with CSDH who underwent burr hole craniostomy and 20 patients who underwent the same procedure alongside the placement of a 5 Fr neonatal feeding tube as a backup drainage for the anterior craniostomy. Depending on the situation, the secondary drain stayed for a maximum of 72 hours to be opened and used in emergency settings for drainage, aspiration, or as a 1-way valve with a water seal. The outcomes of 20 patients who underwent this procedure and 20 controls are described. One patient from each group presented tension pneumocephalus. One was promptly resolved by opening the backup drain under a water seal to evacuate pneumocephalus and the other patient had to undergo a reopening of the craniostomy. The described variation of burr hole craniostomy represents a low-cost and easy-to-implement technique that can be used for emergency decompression of tension pneumocephalus. It also has the potential to reduce reoperation rates and CSDH recurrence. Prospective controlled research is needed to validate this approach further. [ABSTRACT FROM AUTHOR]

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.)

The newly developed computational fluid dynamics, transport, and chemical kinetics-based monolith catalyst dimensioning methodology consists of the following steps: (i) initial calculations, which generate some of the data, e.g., average inlet fluid velocity used in the (ii) computational fluid dynamics (CFD) modelling, which uses the laminar flow interface and the transport of diluted species interface while the user has to provide the kinetics of the reactions; (iii) the model order reduction uses a modified version of the plug flow reactor model and the linear pressure variation model; and (iv) the dimensioning optimization algorithm extracts the optimal monolith catalyst's channel geometry, which satisfies the user's performance constraints and reduces material consumption. Therefore, the methodology enables chemical engineers to quickly and efficiently design and dimension monolith catalysts for many different applications in an environmentally friendly way, which enables them to reduce both the material and operating costs while maintaining sufficient catalyst performance and, therefore, achieve its cost-effective performance. [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]

Taking the large-size shipbuilding block as the research object, this article describes the general phenomenon of the long blasting operation time and long painting operation time, and analyzes its impact and reasons. Through a series of refined management practices such as fine production preparation, strengthened process control and improved equipment efficiency, a new process of "block blasting completion in the morning" is put forward to achieve good results such as production efficiency improvement and cost reduction as well as social benefits. [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]

As a result of global efforts to combat the rise in global climate change and carbon dioxide emissions, there has been a substantial increase in renewable energy investment for both residential and utility power generation. Solar power facilities are estimated to be among the major contributors to global decarbonization in terms of capacity by 2050. Consequently, the majority of economically significant countries are progressively implementing utility-scale photovoltaic (U-PV) systems. Nevertheless, a major obstacle to the expansion of U-PV generation is the identification and assessment of direct current (DC) faults in the extensive array of PV panels. In order to address this obstacle, it is imperative to provide an evaluation method that can accurately and cost-effectively identify and locate potential DC faults in PV arrays. Therefore, many studies attempted to utilize thermal cameras, voltage and current sensors, power databases, and other detecting elements; however, some of these technologies provide extra hurdles in terms of the quantity and expense of the utilized hardware equipment. This work presents a sophisticated system that aims to diagnose and locate various types of PV faults, such as line-to-ground, line-to-line, inter-string, open-circuit, and partial shading events, within a PV array strings down to a module level. This study primarily depends on three crucial indicators: precise calculation of the PV array output power and current, optimal placement of a limited number of voltage sensors, and execution of specifically specified tests. The estimation of PV array power, along with selectively placed voltage sensors, minimizes the time and equipment required for fault detection and diagnosis. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate that the proposed approach can accurately diagnose and localize faults with only NS/2 voltage sensors, where NS is the number of PV array parallel strings. [ABSTRACT FROM AUTHOR]

Effective vendor-buyer collaboration is a successful supply chain management (SCM) cornerstone. By working together, both parties can achieve significant benefits. This collaboration approach fosters open communication, which allows for better communication, improved efficiency, service levels, and reduced costs, i.e., a win-win situation. Ultimately, this partnership strengthens the entire supply chain, increasing efficiency and profitability for vendors and buyers. This study aims to investigate the dynamics of collaboration between supply chain partners. It focuses on a scenario where a single vendor supplies a single product to multiple buyers through a vendor-managed inventory (VMI) and consignment stock (CS) agreement. Managing inventory and product quality is a key challenge, as the components may be imperfect. Buyers are responsible for inspecting these items, but the process is susceptible to misclassification. To address these issues, the research aims to develop an optimization model that incorporates costs associated with collaboration, quality, inspection, and potential misclassification. In addition, developing a methodology to identify the optimal solution minimizes overall costs and maximizes benefits for both vendors and buyers. Furthermore, a numerical example is presented for tangible illustration, offering practical insights into the benefits of partner collaboration in SCM. The findings underscore the efficacy of implementing a collaborative approach between vendors and buyers to minimize supply chain costs. This collaborative policy necessitates a willingness from both parties to forgo their individual cost-minimization strategies in favor of optimizing the overall system cost through cooperation. Through this sacrifice, synergistic benefits emerge, resulting in enhanced efficiency, reduced expenses, and improved overall performance within the supply chain ecosystem. [ABSTRACT FROM AUTHOR]

The paper investigates the impact of AI on the support for logistics in a practical case study for Aramex, a leading Jordanian logistic company. Applying AI-driven technologies of automation, predictive analytics, and route optimization significantly improved the operations of Aramex while reducing costs and enhancing customer satisfaction. It points to the key performance indications: operational cost reduction by 25%, increase in shipping volume by 30%, and a cut in carbon emission by 29.17%. On the other hand, some of the challenges observed included high initial investment costs and also skill gaps regarding how to manage the AI system. The paper demonstrated the ways AI can enable logistics to be scalable, flexible, and sustainable; usage needs to overcome both financial and technical barriers. [ABSTRACT FROM AUTHOR]

Disposable surgical attire is an established practice in every developed and standardized healthcare system. Research has indicated its significant advantages in the work process and patient safety. The application of disposable surgical attire is highly justified to prevent hospital infections. The effectiveness of its use depends on understanding the differences in levels of patient and staff protection, as well as its proper utilization. Optimal selection of disposable surgical attire involves maximizing protection with minimal costs, considering numerous risk factors: type of procedure, patient status, type of surgery or intervention, degree of infection risk, and similar. Therefore, we must increase our knowledge of disposable surgical attire's characteristics and potential applications. [ABSTRACT FROM AUTHOR]

We study maintenance service operations in large facilities portfolios with a focus on modeling logistics of associated spare parts and their storage locations serviced by personnel from several trades who move around the buildings in their vehicles. The key challenge is to reduce the cost of spare parts inventory, storage, and vehicles while providing responsive customer service. To address these challenges, we propose (i) inventory models to determine stock levels and (ii) storage space in personnel vehicles to allocate the mix and location of spare parts by trade. The two models can be readily integrated, and conceptually, technician vehicles serve as “forward-reserve storage” that improves service responsiveness. The proposed model is applied to a large facility of a non-profit organization with 330 buildings, 17,000 spare parts, and 254 vehicles. Compared to prevailing practice, the proposed model can potentially reduce $1.27 million worth of inventory costs, a reduction of ~77%. [ABSTRACT FROM AUTHOR]

Renewable energy is a promising solution to combat the scarcity of electricity, particularly in isolated and rural areas. Microgrids (MG) can be employed for installing systems with different energy sources, such as renewable energy components and conventional energy sources like utility grids or grid-connected inverter systems. The amount of energy produced by renewable sources depends on their location, which has implications for energy production. This research aims to explore MG and their challenges for efficient operation. The study discusses various AI models used by researchers to mitigate problems associated with MG planning. Additionally, the paper presents a case study based on the most beneficial ML tool like clustering to gain insights into an existing MG system. The paper also delves into the issues related to PV, a connected distributed energy resource (DER), such as forecasting, and predictive management to reduce maintenance costs, and how AI tools can address them. Furthermore, forecasting methods such as LSTM and GRU models are discussed because of the stochastic nature of PV production. [ABSTRACT FROM AUTHOR]

Downy mildew is a production limiting factor for Brazil's main onion producing region. In the present study we tested two management strategies, namely the utilization of a resistant cultivar and the implementation of a disease alert system. Our objective was to mitigate the frequency of systemic fungicide applications while concurrently sustaining optimal yield levels. By carrying out fungicide sprays only when the alert system indicated a severe risk and using the cultivar "SCS379 Robusta", it was possible to reduce the number of sprays from 11 (calendar) to 5 (alert system), maintaining statistically similar yields (32.58 tonnes/ha – calendar; and 30.76 tonnes/ha – alert system). The reduction in spray applications using an integrated management strategy reduced the cost of spraying by more than half from BRL 0.069/Kg to BRL 0.033/Kg. This study demonstrates that implementation of an integrated management strategy can reduce chemical use and cost without compromising the protection of onion using alert system and more susceptible varieties. [ABSTRACT FROM AUTHOR]

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This research focuses on the dynamic landscape of today’s competitive market, where production management must meet clients’ expectations for high-quality products and shorter lead times while managing internal disruptions due to inevitable defects and unpredictable equipment failures. Achieving these operational goals without compromising product quality, missing deadlines, or experiencing production disruptions is essential for minimizing operational expenses. The study examines the dynamics of the system’s operating costs through the development of models, mathematical formulations, optimization techniques, and algorithm proposals. It demonstrates the system’s convexity and establishes the optimal batch time for implementing the proposed methodologies. The research results show relevant failure costs of 3.51 %, overtime added costs of 4.57 %, outsourcing setup costs of 0.73 %, outsourcing variable costs of 41.82 %, quality-related costs of 2.98 %, in-house variable costs of 40.42 %, and in-house holding costs of 3.55 %. The study develops strategies for optimal overtime use to meet production targets without excessive labor costs and provides a structured framework for making informed outsourcing decisions that balance cost savings with quality and reliability considerations. Overall, the research provides a robust framework for reducing operational costs while maintaining or improving the quality and reliability of manufacturing processes [ABSTRACT FROM AUTHOR]