Search

We examine the impact of activist hedge fund campaigns on corporate Intangible Capital (IC) investments, a key determinant of corporate long-term growth and performance. We document that post-activist campaigns, targeted firms reduce IC investments by more than 10% compared to carefully matched non-target firms. In cross-sectional analyses, we observe the largest effects in less competitive industries, for older target firms and for firms with high financial constraints suggesting that IC cuts are more common in low-discipline and low-barriers-to-invest contexts. Hedge funds are cutting down IC investments when they engage both in relatively short and longer interventions but especially so when they have less industry investment experience. Further, we do not find evidence of post-intervention increased investment efficiency in targeted firms, casting some doubt on the longer-term value-added of activist IC slashing campaigns. Finally, in an attempt to identify where the saved IC resources post intervention flow to, we observe that targeted firms increase corporate payouts and prefer share repurchase programs above cash dividends. [ABSTRACT FROM AUTHOR]

Exploring the evolution process of the industrial efficiency of the Chinese herbal medicine (CHM) is an important link to promote the development of characteristic ecological agriculture industry. The regional differences and spatiotemporal pattern of the CHM industry efficiency of China were calculated by using the data envelope analysis, the Malmquist index and the spatiotemporal and geographically weighted regression. The results showed that the spatial difference of the CHM industry efficiency was significant, and the average efficiency level was low. The provinces whose industry efficiency reached the effective level were mainly concentrated in northwest China. Technical efficiency has been the main reason that restricts the comprehensive industry efficiency. The impact of technology and labor factors on the CHM industry efficiency in the western China is higher than that in the eastern China, but the impact of economy and investment factors on the efficiency in the eastern China is higher than that in the western China. The education factor has little influence on the CHM industry efficiency, while the traffic factor has an increasing impact. It is suggested that strengthening investment in technology innovation, emphasizing and promoting the modernization and standardization of CHM processing are important means to improve the regional CHM industry efficiency. [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.)

Business orientation toward sustainable development goals and the circular economy are relevant research topics today in business theory and practice. The waste recycling sector is a key industry in the circular economy framework for promoting clean production and environmental sustainability. This study analyzes business performance in the recycling sector, focusing on efficiency indicators. The associations between firm efficiency and risk variables were also evaluated. The study goes through several methodological stages, including a Data Envelopment Analysis (DEA) multistage method and multivariate modeling: tobit regression, bootstrap models, and generalized method of moments (GMM). The main results indicate that the average efficiency of waste recycling firms is at a medium level. Large-sized firms achieve higher levels of efficiency than medium-sized and small firms, supporting the economies of scale hypothesis. The evidence also suggests that risk variables (rating score and Zscore) and capital structure are key determinants of firm efficiency in the waste recycling industry. [ABSTRACT FROM AUTHOR]

Slurry erosion testing is essential for evaluating the durability of materials under erosive conditions. This study examines the slurry erosion behaviours of chloroprene rubber (CR) under varying impact conditions to assess its durability. Traditional mass loss methods and qualitative techniques, including microscopy, SEM, and AFM, were employed to analyse eroded CR samples. Results indicate that cumulative material loss in CR increases linearly with sand impingement after approximately 60 kg of sand and correlates with an impact energy of about 30 kJ. The highest erosion rate was found at an impact angle of 15°. Erosion mechanisms vary with impact angle, affecting surface topography from cutting and ploughing at lower angles to deformation and crater formation at higher angles. Despite their efficacy, these methods are time-intensive and costly. This paper presents a novel approach utilising gloss measurement for continuous, non-destructive monitoring of eroded rubber surfaces. Gloss measurements are 24 times faster than traditional mass loss methods. Correlating gloss values with cumulative material loss, steady-state erosion, and impact energy offers significant time savings and an enhanced understanding of the erosion process. Experimental results demonstrate the effectiveness of gloss measurement as a reliable tool in slurry erosion testing of rubbers. The quantitative output from gloss measurements could support proactive maintenance strategies to extend service life and optimise operational efficiency in industrial applications, particularly in the mining industry. [ABSTRACT FROM AUTHOR]

Waste water treatment plants (WWTPs) are considered as energy-intensive industries. A comprehensive assessment of energy efficiency in sewage treatment reveals issues of energy waste, offers insights into the energy consumption structure, fosters optimization of energy management, and enhances overall energy utilization. However, the mathematical modeling for energy efficiency evaluation becomes challenging due to the ambiguity and uncertainty of the parameters regarding energy consumption and various indicators. In this paper, the newly constructed evaluation method was used to quantify the energy efficiency. This method employs dimensional reduction, projecting the numerical values of unit energy consumption for various pollutants onto a lowerdimensional space, and then computing the projected eigenvalues. Larger eigenvalues indicate higher energy efficiency. Based on the evaluation results, the back propagation (BP) neural network is used to simulate the sewage treatment process. The results demonstrated that when the inflow load is small and the concentration of pollutants is high, the energy efficiency of sewage treatment plants performs well. When the inflow load is high and the concentration of pollutants is low, the energy efficiency of sewage treatment plants is poor. Meanwhile, the energy consumption could be decreased by 11.61 %, 14 %, and 15.26 % respectively in different scenarios. [ABSTRACT FROM AUTHOR]

Understanding the financial dynamics of healthcare companies is essential for reducing costs while improving access to care and patient outcomes. This study aims to explore the financial efficiency of healthcare companies operating in an emerging stock market. We use conventional and fuzzy data envelopment analysis. We consider liquidity and leverage ratios as input criteria and profitability ratios as output criteria, each taken from Borsa Istanbul for the year 2022. The results indicate that financially inefficient healthcare companies can increase their efficiency by effectively managing debt and working capital, restructuring their asset base, and focusing on equity management. [ABSTRACT FROM AUTHOR]

A research sample comprising 285 prefecture‐level cities and listed enterprises in China between 2005 and 2020 was handpicked to examine the impact of regional industrial agglomeration on the labor investment efficiency of enterprises, along with its heterogeneity effect. The research discovered that industrial agglomeration hinders the labor investment efficiency of enterprises in general. However, manufacturing agglomeration showed an inverted U‐shaped relationship with the labor investment efficiency of enterprises. Producer services agglomeration significantly hinders the labor investment efficiency of enterprises. The mechanism test results indicate that industrial agglomeration affects the labor investment efficiency of enterprises via the micro transmission mechanism of financing constraint and labor cost. Industry‐specific tests revealed that the effect of manufacturing agglomeration on labor investment efficiency is significant in technology‐intensive and high‐technology industries, whereas the effect of producer services agglomeration on labor investment efficiency is more significant in resource‐intensive industries. The heterogeneity study showed that the effect of industrial agglomeration on the labor investment efficiency of firms differs significantly based on firm characteristics. These findings offer a better comprehension of the microeconomic consequences of industrial agglomeration and broaden the research outlook on the intrinsic mechanisms and extrinsic causal factors of enterprises' labor investment efficiency. [ABSTRACT FROM AUTHOR]

The activity program at PT. Cahaya Cipta Makmur was carried out to identify and overcome various problems faced by the company, such as the lack of formal work regulations, the absence of SOPs in the production unit, inadequate work facilities, and the unavailability of product catalogs. The objectives of this program are to improve the operational work structure, maintain product quality, and improve the product information system to consumers. The results of this activity include the preparation of work regulations, the creation of SOPs, the provision of work equipment, the creation of product catalogs, and the measurement of job satisfaction and employee performance, all of which have been successfully implemented in accordance with the predetermined plan. [ABSTRACT FROM AUTHOR]

As a platform for the trading of bulk commodities, securities, and futures, exchanges, characterized by platform‐like attributes, provide inexhaustible power for the further development of commodity economy through horizontal integration. This paper examines the international integration landscape of the modern exchanges through the lens of platform economy theory, specifically examining the ramifications of their horizontal integration on market efficiency. Employing the net utility hypothesis within the Hotelling model, we construct a nuanced trader utility model. Through a comparative analysis of platform profit, trader utility, and social welfare, the research discerns the impact of exchanges' horizontal integration. We highlight the intrinsic incentives for such integration within exchanges and put forth pertinent policy recommendations, adding a dimension of practical relevance to the academic discourse. The paper concludes by highlighting the role of government in promoting the efficiency of platform enterprises. [ABSTRACT FROM AUTHOR]

In metropolitan cities, it is very complicated to govern the optimum routes for garbage collection vehicles due to high waste production and very dense population. Furthermore, wrongly designed routes are the source of wasting time, fuel and other resources in the collection of municipal trash procedure. The Vehicle Routing Problem (VRP) published between 2011 and 2023 was systematically analysed. The majority of the surveyed research compute the waste collecting problems using metaheuristic approaches. This manuscript serves two purposes: first, categorising the VRP and its variants in the field of waste collection; second, examining the role played by most of the metaheuristics in the solution of the VRP problems for a waste collection. Three case study of Asia continent has been analysed and the results show that the metaheuristic algorithms have the capability in providing good results for large-scale data. Lastly, some promising paths ranging from highlighting research gap to future scope are drawn to encourage researchers to conduct their research work in the field of waste management route problems. [ABSTRACT FROM AUTHOR]

Objective Background Methods Results Conclusions To apply the 2019 joint American Academy of Neurology (AAN) and American Headache Society (AHS) quality measures for headache management to a cohort of neurology resident physicians and then assess outcomes related to guideline adherence.The optimization of headache management is essential to reduce both the individual and systemic impact of these disorders. In 2014, the AAN developed 10 quality measures for evidence‐based management of patients with headache. A workgroup updated and condensed its headache quality measures in 2019, narrowing the set to six measurements, four of which would primarily focus on the management of migraine and two of which would address the management of cluster headache.This quality improvement study was conducted using a pretest‐posttest study design. A pre‐intervention survey based on retrospective analysis of five clinic notes for adherence to the measures was designed and distributed to all neurology residents (n = 32) at a large, academic tertiary referral center. The intervention included the creation of an electronic medical record template to aid residents in following the measures during clinical encounters, as well as the provision of direct feedback based on pre‐intervention results. Finally, a post‐intervention survey was distributed for completion based on notes written during the intervention period. Analysis was limited to migraine, given the low percentage of cluster headache seen in clinic.An increase in adherence was seen in three of the four migraine‐related quality measures, with the Use of Abortive Medications for Migraine and Documentation of Counseling on Modifiable Lifestyle and Chronification Factors demonstrating statistically significant improvements (75.8% to 88.0% [p = 0.013] and 83.9% to 94.0% [p = 0.029] adherence, respectively). For secondary outcomes, the increase in the utilization of appropriate diagnostic criteria (82.6% to 93.2%, p = 0.018) was significant, and the self‐assessed confidence rating for adherence to guidelines was significant (p < 0.001).This study provides evidence that the quality improvement intervention led to increased adherence to the AAN and AHS migraine‐related measures. It is anticipated that increased adherence may lead to improved patient outcomes. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate the moderating effects of board characteristics such as board size, chief executive officer duality, number of board meetings, and diversity, on the relationship between intellectual capital efficiency and firm value in the Nigerian oil and gas downstream sector. Design/methodology/approach: We collected time-series cross-sectional data from eight (8) downstream-sector oil and gas companies quoted on the Nigerian Exchange Group for the period 2004–2020. We analysed the data using Prais–Winsten regression with panel-corrected standard errors. Findings: Overall, our results show no significant direct relationship between the modified value-added intellectual coefficient and our two measures of firm value (Tobin's Q and Price Earnings Ratio (PER)). However, the board size is found to moderate the intellectual capital efficiency–PER relationship significantly and negatively, whereas board diversity significantly positively moderates the association between the modified value-added intellectual coefficient and PER. Our multi-theory framework, which blends clean surplus, agency, stakeholder, and resource-based theories is found to be relevant in underpinning this study. Research limitations/implications: The research relies on 17-year panel data for eight downstream-sector oil and gas companies. Consequently, future research within intellectual capital efficiency in Nigeria could incorporate related sectors like midstream and upstream to enable comparability and expand generalization. Practical implication: Policymakers may adopt the study findings to serve as a robust empirical base to demand improved board diversity as a catalyst for boosting the potency of the intellectual capital efficiency-firm value relationship. Originality/value: Firstly, to the best of our knowledge, this study is the pioneer attempt to use board characteristics as moderators of the relationship between intellectual capital efficiency and firm value. Secondly, we develop and use a novel theoretical framework that combines clean surplus, agency, stakeholder, and resource-based theories to underpin the study. [ABSTRACT FROM AUTHOR]

The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN. [ABSTRACT FROM AUTHOR]

Isolated water and energy microgrids (IWEMGs) serve as vital solutions for enhancing the well-being of remote and rural communities, particularly in areas where water and energy resources are scarce. This has spurred research into the interdependence between the water and energy sectors (water–energy nexus), a field that has grown in response to technological advancements. Through a systematic optimization framework, this review critically evaluates the integration of various technologies within IWEMGs, encompassing infrastructure, management, and strategic planning, while considering economic and social impacts. IWEMGs incorporate diverse technologies for the infrastructure, management, and strategic planning of water and energy resources, integrating economic and social considerations to inform decisions that affect both immediate and long-term sustainability and reliability. This article presents an exhaustive review of the literature on IWEMG management, employing an approach that synthesizes existing studies to enhance the understanding of strategic IWEMG management and planning. It introduces a structured taxonomy for organizing research trends and tackling unresolved challenges within the field. Notably, the review identifies critical gaps, such as the lack of comprehensive data on water demand in isolated locations, and underscores the emerging role of game theory and machine learning in enriching IWEMG management frameworks. Ultimately, this review outlines essential indicators for forthcoming research, focusing on the optimization, management, and strategic planning of IWEMG resources and infrastructure, thereby setting a direction for future technological and methodological advancements in the field. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has emerged as a pivotal tool in viticulture, offering unique capabilities for various applications. This study provides a comprehensive overview of the current state-of-the-art applications of SAR in viticulture, highlighting its significance in addressing key challenges and enhancing viticultural practices. The historical evolution and motivations behind SAR technology are also provided, along with a demonstration of its applications within viticulture, showcasing its effectiveness in various aspects of vineyard management, including delineating vineyard boundaries, assessing grapevine health, and optimizing irrigation strategies. Furthermore, future perspectives and trends in SAR applications in viticulture are discussed, including advancements in SAR technology, integration with other remote sensing techniques, and the potential for enhanced data analytics and decision support systems. Through this article, a comprehensive understanding of the role of SAR in viticulture is provided, along with inspiration for future research endeavors in this rapidly evolving field, contributing to the sustainable development and optimization of vineyard management practices. [ABSTRACT FROM AUTHOR]

Although Ag‐coated Cu technology is considered to be an effective approach to mitigate the metallization expenses associated with silicon heterojunction (SHJ) solar cells, there is limited reporting on the photovoltaic performance and reliability of devices employing Ag/Cu electrodes. Herein, industrial SHJ solar cells were successfully fabricated, yielding an average efficiency of 25.18% with bifacial Ag/Cu fingers, which caused a 0.13% decline in efficiency but saved 46% in Ag consumption when compared to traditional Ag fingers. Performance degradation is demonstrated to originate essentially from the front rather than rear Ag/Cu fingers, as revealed by a comprehensive analysis of resistances enhancement and optical losses. Notably, the Ag/Cu fingers exhibited 1.4% lower printed qualification rate and similar high‐temperature stabilities in contrast to Ag fingers. In the results, valuable insights were provided for further optimization of low‐cost and high‐efficiency SHJ solar cells based on Ag/Cu electrodes. [ABSTRACT FROM AUTHOR]

Purpose: the aim of the article is to provide a broader context for the discussion on the significance and role of cybersecurity system in Poland. Cybersecurity is extremely significant for individual users, enterprises and entire countries. Cyberthreats might lead to serious consequences, for example data theft, loss of reputation, financial losses. They can be even dangerous for national security. Therefore, cybersecurity is an intrinsic element of the security system which needs to be constantly enhanced and adapted to the dynamically changing environment of threats. Design/methodology/approach: the article is cross-sectional. The implementation of the goal is based on a critical analysis of literature. Findings: the cross-sectional approach and literature review employed in this study have contributed to a comprehensive understanding of cybersecurity system in Poland. Practical implications: effective cybersecurity has a fundamental meaning for the stability and national security of Poland. Providing protection against cyberthreats is crucial for maintaining the continuity of essential services, securing data protection and citizens’ privacy, as well as for preventing potential disruptions in the functioning of economy and critical infrastructure. Social implications: an effective cybersecurity system builds public trust and strengthens the position of Poland on the international area as a country capable of protecting its interests in the digital world. In the face of dynamically changing threats, continuous improvement and adaptation of the cybersecurity system remains necessary to tackle contemporary challenges and ensure long-lasting stability and national security. Originality/value: this paper has provided an in-depth exploration of cybersecurity system in Poland the context of management. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the article is to present and assess the usefulness of the Kaizen method in the management and organization processes of enterprises from the construction sector. Design/methodology/approach: The research includes identifying tools in the area of Kaizen philosophy, assessing the possibilities of its implementation in the construction industry and identifying the most beneficial solutions. Elements of statistical and observational methods were used, as well as analyzes of literature reports. SWOT analysis was used to determine the level of suitability of the Kaizen method. Findings: The conducted research showed the need to implement Lean Management tools in the structures of construction companies, in particular the Kaizen method. However, certain barriers have also been noticed, limiting their use in the Polish construction industry. Research limitations/implications: The research presented is only a preliminary analysis, which is a starting point for further research on the use of the Kaizen method in construction, the method of selecting its tools and the benefits resulting from them. Due to knowledge, financial and technological deficiencies of construction companies, it is impossible to fully assess the analyzed aspect in the area of all types of enterprises. Practical implications: The obtained results show the positive impact of the Lean Management method on the functioning of the enterprise. Thanks to the use of Kaizen tools, it is possible to increase the efficiency of the company and solve most problems, including the generation of large amounts of waste, insufficient financial resources for planned projects, low level of competitiveness on the market, disorganization of work and lack of timeliness of initiated implementations. The presented SWOT analysis can be successfully used as a tool supporting the decision-making process aimed at adopting an appropriate company strategy. Thanks to its simplicity and speed of execution, it allows you to save time and initially reject unfavorable solutions. Moreover, an important factor in favor of its use is the ability to adjust the criteria and weights determined by the established preferences of a given company. Originality/value: The added value of the article is a practical presentation of the use of SWOT analysis to determine the most advantageous strategy necessary to be undertaken by construction companies in order to implement the principles of Lean Management. [ABSTRACT FROM AUTHOR]

Automated inspection is vital in modern industrial manufacturing, optimizing production processes and ensuring product quality. Welding, a widely used joining technique, is susceptible to defects like porosity and cracks, compromising product reliability. Traditional nondestructive testing (NDT) methods suffer from inefficiency and limited accuracy. Many researchers have tried to apply deep learning for defect detection to address these limitations. This study compares traditional algorithms with deep learning methods, specifically evaluating the SwinUNet model for weld segmentation. The model achieves an impressive F1 score of 96.31, surpassing traditional algorithms. Feature analysis utilizing class activation maps confirms the model's robust recognition and generalization capabilities. Additionally, segmentation results for different welding defects were compared among various models, further substantiating the recognition capabilities of SwinUNet. The findings contribute to the automation of weld identification and segmentation, driving industrial production efficiency and enhancing defect detection. [ABSTRACT FROM AUTHOR]

Pavement management systems (PMS) are considered to be crucial tools for agencies in managing their pavement networks and using accessible budgets in an appropriate technique. In this paper, a newly developed condition rating index [modified pavement condition rating (MPCR)] was used to assess the current and future pavement performance. The pavement future performance in terms of MPCR was predicted using two models: (1) a stochastic Markov chain model, and (2) an artificial neural network (ANN) model. In Markov chain modeling, the Long-Term Pavement Performance (LTPP) database was used to develop the transition probability matrix (TPM). An ANN model for future pavement performance prediction was developed based on the same LTPP data. For both models, 245 data points from the LTPP data were used for deterioration modeling. Both models were verified using LTPP data and data obtained from the General Authority of Roads, Bridges and Land Transport (GARBLT), Egypt. The Markov chain and ANN results were compared. The Markov chain model performed better than the ANN model, with a coefficient of determination, R2 , of 0.9 and RMS Error (RMSE) of 0.1268. the ANN model yielded an R2 of only 0.53 and a RMSE of 0.2205. Increasing the number data points did not lead to a significant improvement in the ANN model accuracy. A multiobjective particle swarm optimization (PSO) model is used for fund allocation to maximize the average pavement condition and minimize the maintenance cost. The developed optimization model was tested on two benchmark optimization problems to ensure its validity for real-life applications. The developed optimization model was used on a network of roads and was found to be capable of generating optimal or near-optimal solutions for the maintenance decisions to keep the pavement in good workable condition. As such, the current study presents a comprehensive development tackling different modules of a pavement management system. [ABSTRACT FROM AUTHOR]

Industrial thermal processes are pivotal in modern industrial production, significantly impacting energy utilization and production costs. With the growing scarcity of global energy resources and heightened environmental protection standards, enhancing the thermal efficiency of industrial processes has become a research focus. Intelligent algorithms offer a novel approach to this challenge by integrating thermodynamic principles with optimization techniques, enabling precise modeling and efficient optimization of complex thermal processes. However, traditional mathematical models and optimization algorithms still lack in complexity, efficiency, and accuracy. This paper proposes a systematic research framework grounded in thermodynamic fundamentals. The study comprises two main parts: firstly, establishing a comprehensive mathematical model of industrial thermal process efficiency that incorporates various thermodynamic factors and process parameters; secondly, employing an optimized Gaussian process regression method for predicting and optimizing thermal efficiency. This research not only enriches the theoretical foundation of industrial thermal process optimization but also provides scientific guidance and technical support for practical applications, enhancing energy utilization and reducing production costs. [ABSTRACT FROM AUTHOR]

The modeling of blind people's behavior is an important topic in pedestrian evacuation dynamics. In order to study movement characteristics of blind people in evacuation process and the effect of indoor obstacles on the evacuation efficiency of blind people, an extended cellular automata model is proposed to simulate blind people evacuation by considering the influence of obstacles. The control variable method is adopted to study the effect of indoor obstacles on the blind evacuation efficiency rule. The numerical simulation is applied to analyze the evacuation process of blind people under different setting (e.g. the location, size and other factors) of obstacles and different evacuation scenarios. The shunt effect of obstacles is discussed and the layout strategy of obstacles is optimized. The results show that the longitudinal obstacles in front of the exit can effectively play the diversion role of obstacles when the obstacle is at a certain distance from the exit, which will improve the evacuation efficiency of the blind. Furthermore, a horizontal obstacle of appropriate length can constrain the trajectory of the blind people and improve the efficiency of evacuation, but when the horizontal obstacle is too long, it will induce extra jam on both sides of the obstacle and hinder the evacuation of the blind. The finding can provide the theoretical basis for the blind people's organization, management and the optimization of evacuation strategies during an emergency. [ABSTRACT FROM AUTHOR]

Normal and stable operations of hydraulic systems are of great importance to the safety and efficiency of industrial production processes. Accurate and prompt diagnosis of fault types and degrees can ensure hydraulic systems return to normal in the early stage of faults and thus can prevent serious accidents. However, the structure of hydraulic systems is complex, and some faults may occur simultaneously. In addition, many hydraulic systems have multi-rate data collected from different sensors. Such problems cause great challenges to fault diagnosis in hydraulic systems. Motivated by the above issues, this paper proposes a deep learning method to diagnose faults and identify fault degrees in hydraulic systems using fully convolutional networks (FCNs) and deep feature fusion. The main contributions are twofold: (1) A new fault diagnosis framework is designed to identify both the fault types and degrees in the presence of multiple faults; and (2) deep feature extractors composed of multiple superimposed convolutional blocks are designed to extract deep features from multi-rate time series, and such features are then fused via flattening and concatenating and fed into the fault diagnosis model. Case studies based on a hydraulic system test bed are provided to demonstrate the effectiveness and superiority of the proposed fault diagnosis method. [ABSTRACT FROM AUTHOR]

BACKGROUND Hidradenocarcinoma (HAC) is a rare adnexal carcinoma. To the best of the authors' knowledge, there are no published systematic reviews on HAC. OBJECTIVE To incorporate a case series from the authors' institution and systematically integrate reported information to provide a reference tool for optimization of diagnosis and management. METHODS A comprehensive MEDLINE search was conducted from database inception to 2021 using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This yielded 225 studies with 165 cases of HAC. References of included articles were also searched. In addition, 9 patients with HAC were identified from the authors' institution over the past 10 years. RESULTS The mean age of HAC presentation is 60 years with a slight male predilection (60%). The head and neck is the most commonly affected region. Over 36% of cases either presented with metastatic disease or went on to metastasize. The most common treatment type was wide local excision, followed by Mohs micrographic surgery. CONCLUSION Early detection with accurate histologic interpretation is prudent in all cases of HAC. Wide local excision is the current first-line treatment. However, Mohs micrographic surgery offers complete marginal analysis with evidence of reduced risk of metastasis and better outcomes compared with wide local excision. Currently, there are no National Comprehensive Cancer Network guidelines for the treatment of HAC, and consensus guidelines are limited to tumor and nodal metastasis staging provided by the American Joint Committee on Cancer, eighth edition. Thus, this case series and systematic review integrates important aspects of diagnosis, workup, and management of HAC. [ABSTRACT FROM AUTHOR]

The presented article deals with the issue of inventory management optimization through modelling and simulation in the software Tecnomatix Plant Simulation. Based on an in-depth analysis of the supply chain with an emphasis on inventory management, a simulation model was developed. It was optimized using the inventory theory methodology in combination with a discrete-event simulation approach. Dynamic simulation made it possible to perform experiments and optimize the existing state. The solution to the problem is elaborated on a case study to achieve the efficiency of the supply process of a specific operation of a clothing retail chain, which is struggling with the problems of excessive and inaccurate supply. [ABSTRACT FROM AUTHOR]

Efficient and effective handling of loading and unloading processes at ports is key to achieving set volume targets. In this context, intelligence strategy plays a crucial role in strengthening loading and unloading activities. This study aims to analyze how the implementation of intelligence strategy can enhance loading and unloading activities at Teluk Lamong Port. Through a case study approach, we explore the implementation of intelligence strategy by port authorities and its impact on achieving volume targets. The research findings indicate that well-integrated intelligence strategies can provide timely and relevant information to stakeholders, enabling more efficient resource management and enhancing coordination among various parties involved. The practical implications of these findings underscore the importance of employing intelligence strategies to enhance productivity and operational efficiency at ports, thereby contributing to the attainment of set volume targets. [ABSTRACT FROM AUTHOR]

Haining, as the top ten industrial counties in Zhejiang, a strong manufacturing city, to keep pace with the pace of the digital era, to deepen the digital application of all aspects of the manufacturing production process, to provide a strong momentum for the transformation and upgrading of the manufacturing industry in Haining. Based on the background of digital economy, this paper analyzes the necessity of improving the innovation ability of China's manufacturing enterprises, as well as the problems faced by Haining's manufacturing enterprises in improving their innovation ability, such as the construction of innovation platforms that need to be perfected, the degree of enterprise innovation investment is not high, and the cultivation of digital innovation talents is facing serious challenges, etc., and from the coordination and optimization of the construction of innovation platforms, accelerating the promotion of the construction of digital infrastructure, deepening the reform of the system and mechanism for the development of talents, building The new pattern of digital transformation of higher education and multiple inputs, focusing on stimulating the vitality of innovation and other five aspects of the digital economy era in the context of Haining's manufacturing enterprises to enhance the innovation capacity of countermeasures, aimed at accelerating the creation of Haining City with Haining's recognizable "142" advanced manufacturing clusters, the construction of "manufacturing strong city digital strong city". The purpose is to provide useful reference for Haining City to accelerate the creation of "142" advanced manufacturing clusters with Haining City's recognizability and to build a "strong manufacturing city and digital city." [ABSTRACT FROM AUTHOR]

We study the impact of bank credit on firm productivity. We exploit a matched firm‐bank database, covering all the credit relationships of Italian corporations, to measure idiosyncratic supply‐side shocks to credit availability and estimate a production model augmented with financial frictions. We find the effect of credit supply to be asymmetric: contractions harm TFP growth, halting productivity‐enhancing activities; positive credit supply shocks have limited effects. This points toward a role of financial stability in preserving productivity growth. [ABSTRACT FROM AUTHOR]

This study analyzes arthropod biomass and abundance to track the changes in arthropod occurrence in relation to pesticide use in three winter wheat cropping systems managed at different intensities (organic, conventional, and hybrid). Arthropod occurrence was surveyed using three collection tools: sweeping nets, eclector traps, and yellow traps. Sampling was conducted over three years from 2020 to 2022 with 588 samples collected. The wet weight of the captured organisms was determined and arthropod abundance calculated. The application of a NOcsPS (no chemical-synthetic pesticides) strategy, a new hybrid cultivation method realized with optimized use of nitrogen fertilizers but without chemical-synthetic pesticides, showed a higher arthropod occurrence and performed more convincingly regarding produced arthropod biomass and abundance than the other cropping variants. The results also demonstrate a dependence of the obtained insect indices on the collection method. Although arthropod biomass and abundance correlated for all collection methods, the combination of various methods as well as multiple procedures of sample analysis gives a more realistic and comprehensive view of the impact of the wheat cultivation systems on the arthropod fauna than one-factor analyses. [ABSTRACT FROM AUTHOR]

Chlorella vulgaris is of great importance in numerous exploratory or industrial applications (e.g., medicals, food, and feed additives). Rapid quantification of algal biomass is crucial in photobioreactors for the optimization of nutrient management and the estimation of production. The main goal of this study is to provide a simple, rapid, and not-resource-intensive estimation method for determining the algal density of C. vulgaris according to the measured parameters using UV–Vis spectrophotometry. Comparative assessment measurements were conducted with seven different methods (e.g., filtration, evaporation, chlorophyll a extraction, and detection of optical density and fluorescence) to determine algal biomass. By analyzing the entire spectra of diluted algae samples, optimal wavelengths were determined through a stepwise series of linear regression analyses by a novel correlation scanning method, facilitating accurate parameter estimation. Nonlinear formulas for spectrometry-based estimation processes were derived for each parameter. As a result, a general formula for biomass concentration estimation was developed, with recommendations for suitable measuring devices based on algae concentration levels. New values for magnesium content and the average single-cell weight of C. vulgaris were established, in addition to the development of a rapid, semiautomated cell counting method, improving efficiency and accuracy in algae quantification for cultivation and biotechnology applications. [ABSTRACT FROM AUTHOR]

Pinus occidentalis (Swartz) is the primary timber species in the Dominican Republic (DR). Despite its economic importance, studies conducted on this species are scarce, making it difficult to estimate current inventory levels. This study aims to enhance the accuracy of estimating the total bole volume of P. occidentalis in different ecological zones (EZs) within La Sierra, evaluating and comparing two established volume equations—combined variable (CV) and Schumacher and Hall (S&H) across nine modeling variants. An indicator variables analysis determined the necessity of distinct equations for two EZs. Fitting included both linear and nonlinear models. Our comprehensive statistical analysis included goodness-of-fit metrics to evaluate each model variant's performance rigorously. The second modeling variant (SH02) for the SH equation was most effective in the Dry Ecological Zone, showing superior performance in both the fitting and validation phases. Similarly, the third modeling variant (SH03) for the SH equation emerged as the best fit for the Combined Intermediate and Humid Ecological Zones, achieving the lowest overall ranking sum among tested variants. SH02 and SH03 provide reliable and precise volume estimations, allowing for the optimization of forestry management practices for P. occidentalis trees. The SH models outperformed the CV model variants' consistency in parameter estimation. This tailored approach ensures more accurate volume predictions, which is crucial for sustainable management and conservation efforts. [ABSTRACT FROM AUTHOR]

Korean pine is one of the most important tree species in northeastern China, where Korean pine plantations produce timber and edible seeds. Often, seeds create more income than timber. Predicting the timber and cone yields of alternative management schedules of the plantations involves uncertainty because the future climatic conditions for tree growth and cone production are unknown. This study developed a simulation model that generates stochastic variation around the predictions of tree growth and cone yield models, allowing the forest manager to seek cutting schedules that maximize the expected amounts of timber or cones, or the expected economic profit, under uncertain future states of nature. Stochastic analysis also facilitates management optimizations for different risk attitudes. The differential evolution algorithm and the developed stochastic simulation model were used to optimize the management of planted Korean pine. Timber and cone yields of a management schedule were calculated under 100 different scenarios for tree growth and cone production. When the growth and cone yield scenarios were stationary (no temporal trends), the optimal management schedules were similar to those of deterministic optimization. The benefits of stochastic optimization increased when it was assumed that the tree growth scenarios may contain climate-change-induced trends. Non-stationary growth variation led to shorter optimal rotation lengths than stationary growth variation. Increasing risk tolerance shortened optimal rotations. [ABSTRACT FROM AUTHOR]

The global shift towards sustainable transportation, exemplified by the increasing adoption of Electric Vehicles (EVs), represents a vital response to contemporary environmental and energy challenges. This study investigates the determinants of consumers' Desire to Purchase EVs within the unique context of the Medan market, where consumer preferences and market dynamics differ from more globally examined settings. Additionally, it explores the mediating roles of Consumer Awareness Campaigns and Local Industry Partnerships in shaping purchase intent. Through a comprehensive survey of 832 respondents in Medan, Indonesia, the study reveals crucial insights. Government incentives, including tax breaks and rebates, emerge as influential factors, significantly increasing consumers' inclination to adopt EVs. Equally vital is the accessibility and availability of charging infrastructure, which is pivotal in bolstering consumer confidence in EVs. Consumer perceptions and attitudes have a significant bearing on purchase intent, with positive perceptions regarding environmental benefits, cost savings, and driving experiences contributing to the heightened desire for EV ownership. Effective financial management strategies also play a positive role, emphasizing the significance of sound financial planning and resource allocation. Further-more, this research emphasizes the instrumental role of Consumer Awareness Campaigns in shaping purchase intent. These campaigns are a powerful tool for elucidating the multifaceted advantages of EV ownership, encompassing environmental consciousness, economic feasibility, and enhanced driving experiences. Local Industry Partnerships within the Medan EV market equally contribute to heightened purchase intent, reflecting the synergistic effects of collaborative initiatives. [ABSTRACT FROM AUTHOR]

We examine the impact of covenant violation on corporate operational efficiency. Using an aggregate measure of operational efficiency developed by Demerjian et al. (Management Science, 58, 2012, 1229–1248), we provide strong empirical evidence that covenant violations hinder firms from achieving operational efficiency. Our finding is robust to alternative definitions of operational efficiency and various model specifications to address potential endogeneity issues. Further analyses show that lower operational efficiency is attributable to covenant‐violating firms' under‐investments in capital and labour. In addition, the negative effect of covenant violation on operational efficiency is not universally the same, and is less evident in violating firms with greater agency problems. [ABSTRACT FROM AUTHOR]

The processes of the management system transformation in various spheres of socio-economic relations towards the growth of the role of publicity, information accessibility, and digitalization of management algorithms are among the current global trends. The study aims to provide a comprehensive analysis of the aspects of public administration mechanism implementation in the context of digital transformation. The research identifies the main issues of the management paradigm and options for its solution. The study was carried out based on general scientific methods of cognition. They include logical and structural analysis, comparison, theoretical modeling, abstraction, induction, and deduction, as well as statistical methods, specification, generalization, and formalization. The article explores the innovative aspects of public administration in the context of global digital transformation. The authors have considered the public administration digitalization process's main issues, challenges, and achievements. It has been determined that the effective use of digital transformation opportunities is an essential prerequisite for intensifying the effectiveness of economic processes. Moreover, the authors developed a universal model of effective digitalization of the public administration system. Furthermore, the practical significance of the research findings is seen in the possibility of their application during the development of relevant programs. The latter aims to improve the efficiency of the public administration system, increase the productivity of various spheres of socioeconomic activity, and form a versatile, effective model of digital optimization of management processes in the context of sustainable development. [ABSTRACT FROM AUTHOR]

Summary: We propose a flexible stochastic production frontier model with fixed effects for the panel data in which the semiparametric frontier is additive with bivariate interactions. To avoid potential misspecification and/or "wrong skew problem" due to distributional assumptions, we model the conditional mean of the inefficiency to depend on environmental variables and to be known up to a vector of parameters. We propose a difference‐based estimator for parameters characterizing the conditional mean of the inefficiency term, a profile series estimator, and a kernel‐based one‐step backfitting estimator for the frontier to facilitate inference. We establish their asymptotic properties and show that each component in the frontier estimated by the kernel‐based backfitting has the same asymptotic distribution as the one estimated with the true knowledge on the other components in the frontier (i.e., the oracle property). Through a Monte Carlo study, we demonstrate that the proposed estimators perform well in finite samples. Utilizing a panel of Chinese firm‐level data in 2000–2006, we apply our method to estimate the frontier and efficiency scores and conclude that export plays a significant role in reducing the efficiency of firms. [ABSTRACT FROM AUTHOR]

The logistic and transportation plays an integral part in maintaining a well-functioning organization. One of the most extensively used, original, famous, and popular non-parametric methods for evaluating the efficiency of organizations is the Data Envelopment Analysis, DEA technique. Suppose we can formulate the concept of effectiveness in the DEA technique. In that case, we will be able to measure the productivity of organizations since productivity is a blend of efficiency and effectiveness. Several studies have been developed, e.g., the "Malmquist Productivity Index" (MPI) and the "Lunberger Productivity Index" (LPI), which assess the productivity of corporations through the DEA technique, but these models do not display all factors in a system. Also, they need at least two periods to appraise productivity. Furthermore, their two components of efficiency and effectiveness are not considerably evident. Moreover, sensitivity analysis is not possible in these models. Therefore, a model was presented that can measure the relative productivity of decision-making units through the technique of DEA, simultaneously in a period through the two elements of efficiency and effectiveness with the feature of sensitivity analysis and its solution method is more reliable due to the use of multi-objective planning method. In addition, a case study was used to indicate the application of the proposed model, which demonstrated that a branch could be efficient but unproductive. [ABSTRACT FROM AUTHOR]

This study intends to investigate the effect of a company's corporate governance aspects, measured by the size of the board of directors, on sustainable growth and the role of innovation efficiency as a moderating variable. The samples were taken from manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the 2017-2021 period using purposive sampling. There were 44 companies that met the criteria. The technical analysis used is Partial Least Square (PLS-SEM) with WarpPLS 8.0 software, which is reliable for processing small samples. Stochastic frontier analysis (SFA) analysis through Frontier 4.1 is used for the measurement of innovation efficiency. The results of this study show that corporate governance has positive and significant effects on sustainable growth. Furthermore, innovation efficiency is a moderating factor that can strengthen the influence of corporate governance on sustainable growth. The novel of this study provides evidence relating to the importance of management in human resources and management in innovation to increase the company's productivity through innovation efficiency, which has an important role in increasing the company's sustainable growth. [ABSTRACT FROM AUTHOR]

Raising efficiency encourages logistics companies to use modern technologies. Using Internet of Things (IoT) devices improves logistical efficiency. The aim of the research is to determine the role of IoT in increasing the efficiency of logistics processes. The study was based on information about the logistics activities of the Supply Chain of one of the largest logistics companies in the world -- DHL Group. The study covers the period 2009-2022. The research was conducted using linear regression models tested by the least squares method. The impact of the use of IoT devices was introduced into the model through the use of a dummy variable. It was established that the implementation and use of IoT devices has a positive, statistically significant effect on such indicators of logistical efficiency as Profit Margin and Operating Efficiency Ratio. It was proved that IoT positively affects logistical efficiency and does not entail reducing jobs, which is a socially important factor. The obtained results can be applied in developing and justifying the policy of implementing IoT in the activities of logistics companies. The conducted research opens up prospects for further studies, in particular regarding the impact of IoT on other performance indicators of logistics companies, particularly on their market capitalization. [ABSTRACT FROM AUTHOR]

Copyright of Optimum: Journal of Economics & Management Sciences / Ekonomi ve Yönetim Bilimleri Dergisi is the property of Optimum: Journal of Economics & Management Sciences 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 multiplicity of factors involved in the innovation process makes its measurement and evaluation a complex endeavor. In this study we propose a new approach to measure and decompose the efficiency of national innovation systems in the wine industry considering the relationship between the innovation environment and economic performance. The analysis applies the data envelopment analysis approach to quantify the relative efficiency of each national system using a set of four indicators to describe the innovative environment in the wine industry as model inputs, and an index of international market performance as output. The results demonstrate a clear perspective of the innovation process within the wine industry, identifying the systems that efficiently use the available resources and those that encounter difficulties in translating innovation into economic performance. The proposed approach also captures the dynamics of the international innovation landscape in the wine industry, providing potential country-level strategies and opportunities to increase innovation systems' efficiency. [ABSTRACT FROM AUTHOR]

Consumers are increasingly seeking products with a higher degree of customization, and this has intensified the search for more flexible production systems. Unexpected phenomena, including those impacting the economy and public health, have shown the need for high reactivity by manufacturers. Reconfigurable Manufacturing Systems are one of the means to respond to new market needs. They are designed to be able to quickly change their structure and thus adapt their functionalities and capabilities. A central question with regard to these types of systems is when and how they should be reconfigured. It is, however, a question that needs to be addressed simultaneously alongside the classic resource allocation and scheduling problems, since the problems are interdependent. This paper proposes four models that address the problem sequentially, partially sequentially, and integrally. Our numerical analysis demonstrates the advantage and importance of treating these problems in an integrated manner. In addition, we provide and compare three exact methods for the integrated process planning, layout, and scheduling optimization problems. The exact methods are integer linear programming, constraint programming, and constraint programming with search strategies, which our numerical examples show to be greatly superior to the two others. Finally, we present a problem at an industrial scale. [ABSTRACT FROM AUTHOR]

Blockchain technology is expected to have a radical impact on most industries by boosting security, transparency, and efficiency. This work considers the potential benefits of blockchain-focused applications in industrial process monitoring. The research design facilitates a detailed bibliometric analysis and delivers insights into the intellectual structure of blockchain technology's application in industry via scientometric approaches. The work also approaches numerous sources in various industrial sectors to identify the transformative role of blockchain in industrial processes. Aspects such as blockchain technology's impact on industrial processes' transparency are discussed, while the paper does not ignore that success stories in applying blockchain to industrial sectors are often exaggerated due to a highly competitive environment that the cryptocurrency domain has become. Finally, the work presents major research avenues and decision-making areas that should be tackled to maximize the disruptive potential of blockchain and create a secure, transparent, and inclusive future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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 study investigates the impact of media sentiment on firms' investment efficiency in the Chinese market during 2007–2017. We find that increased media sentiment can lead to overinvestment and thus distort investment efficiency, but it has no significant effect on underinvestment. Further, mediation analysis shows that financing constraints mediates the media sentiment effects on overinvestment. To mitigate potential endogenous problems, we employ instrumental variable approach and propensity score matching method. The main findings hold after a battery of robustness tests. Further tests show that corporate governance factors can ameliorate the adverse effect of news sentiment on corporate investment efficiency. [ABSTRACT FROM AUTHOR]

With people's increasing attention to public health issues, biomedical enterprises have ushered in a good development opportunity. However, the rapid expansion of industrial scale makes the contradiction between enterprise development and environmental governance gradually emerge. This requires us to form a set of perfect environmental efficiency evaluation framework to make the evaluation of environmental development efficiency of pharmaceutical companies more objective and accurate. At present, many literatures on environmental efficiency assessment in the fields of green finance ignore the bias in comparison between different companies due to factors such as competition or resource allocation. At the same time, the existing studies lack the influence of disturbance factors on the output of efficiency evaluation data. Based on the sustainable development report of Shanghai Pharmaceutical as the research background, this study establishes an efficiency evaluation framework, which mainly includes three parts: (1) data preprocessing. Establish efficiency evaluation standard framework according to research background, collect relevant data, and process the data. (2) Calculate the game cross‐efficiency DEA model. We calculate the efficiency considering the game situation and compared with the efficiency calculated by the traditional DEA model. (3) Three different scenarios in robust optimization are introduced to reduce the uncertainty in efficiency evaluation. This paper uses the efficiency evaluation framework to evaluate the environmental efficiency of 13 pharmaceutical companies and finally gives policy recommendations. [ABSTRACT FROM AUTHOR]

Modern corporate success is primarily reliant on a perfect balance of institutional effectiveness, innovation, and sustainability. Therefore, the primary goal of this research is to evaluate the impact of institutional frameworks on corporate innovation in developing countries. To achieve the said objective, we use firm and country data from multiple sources, and the final dataset consists of 24,166 observations from 41 developing countries during 2014–2018. For the analysis, we employ ordered probit and instrumental variable ordered probit regression models. According to the results, the success of corporate innovation in developing countries hinges on the strength of legal, financial, administrative, political, and religious institutions. Moreover, such associations stand out more clearly in nations with strong and stable institutional frameworks. The regression results show that innovative enterprises are more prevalent in strong institutional environments compared to those in poor institutional environments. These results also indicate that a strong rule of law, protection of property rights, easy access to financing, developed financial markets, an ethical environment, control of corruption, a stable political system, a democratic government, religious diversity, and freedom all contribute to corporate innovation. Moreover, institutional efficiency and corporate viability fuel the engine of corporate innovation, encouraging enterprises to create game‐changing products and services. These results contribute to the field of innovation management by considering institutional settings for sustainable development. The results have practical applications for building robust institutions and a creative culture. [ABSTRACT FROM AUTHOR]

Waste management industry is key to achieve environmental commitment, clean production, and Circular business models; as well as to promote sustainable development. The aim of this study is to contribute to the Circular Economy measuring business performance and firm efficiency of waste collection activities and their determinants, with emphasis on sustainable development and environmental challenges. The main research goals of this study are as follows: first, to examine firm efficiency scores in waste collection activity (dangerous and non‐dangerous waste); second, to analyze the main fluctuation patterns of business efficiency; third, to evaluate the differences in the average efficiency according to region; finally, to examine the relationship between efficiency score and environmental challenges (investment in environmental protection and COVID‐19 pandemic impact). The efficiency of firms is estimated using non‐parametric production frontiers in a sample of 5441 observations for the period 2010–2020. We used several non‐parametric tests, such as the Mann–Whitney U test and the Kruskal–Wallis test; as well as several regression models (Tobit and bootstrapping regressions) to test the relationship between efficiency scores and environmental variables. The main findings of this study provide new empirical evidence and go a step further, not only at the firm level, but also at the regional level. [ABSTRACT FROM AUTHOR]

This study explores the integration of activity-based costing (ABC) and the theory of constraints (TOC) with carbon tax policies to drive decarbonization in the Taiwanese glass industry. Employing a mathematical programming approach, four distinct models are developed to assess the impact of different carbon tax structures, carbon trading mechanisms, and recycled material utilization on corporate profitability and carbon emissions. The findings reveal that strategically applying ABC and the TOC with well-designed carbon tax policies can effectively incentivize emission reduction while maintaining industrial competitiveness. The models incorporating carbon trading and tax allowances demonstrate the potential for creating win–win situations, where companies can increase profitability by investing in cleaner technologies and processes. This study contributes to the literature on sustainable manufacturing and provides actionable insights for policymakers and industry leaders seeking to implement effective carbon pricing mechanisms that drive economic growth and environmental sustainability in tandem. [ABSTRACT FROM AUTHOR]