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The increasing development of the competitive market has forced organisations to make great efforts in the processes of supply, production, and distribution to meet customer demand in the shortest time and at the lowest cost. A cross-docking (CD) system is one of the successful and practical strategies in this field considered by researchers in various fields. Also, business process management plays a key role in continuous improvement and increased productivity. In today's digital age, due to the ability to record all activities, process mining is an important method to identify the current situation and improve productivity. In this research, a newly established CD belonging to a chain store is studied to improve the current situation, in which different goods enter and then exit after different processes. The purpose of this study is to obtain the optimal number of doors and loaders as sources. First, helping an RFID system, all activities are recorded, and the current situation of the processes is monitored, and then, the real process model is identified using heuristic and inductive miner algorithms. After adapting to the event log by using the simulation process in Arena software, different scenarios are examined, and the best possible case is presented. [ABSTRACT FROM AUTHOR]

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Critics opine that full expensing of research and development (R&D) depresses near-term profits and incentivizes myopic managers to under-invest in R&D, compromising firm efficiency. Advocates of the expensing rule argue that little rigorous research evidence supports the claimed adverse consequences. We examine the impact of the R&D expensing rule on firm efficiency by exploiting an exogenous shock: a shift in the accounting regime in Germany from full expensing to partial capitalization of R&D when it mandated International Financial Reporting Standard (IFRS) adoption in 2005. We employ Stochastic Frontier Analysis and Data Envelopment Analysis to estimate efficiency for the same German firms before and after the IFRS adoption. We find robust evidence of efficiency improvement in the post-period relative to the pre-period for German R&D firms that report R&D expenditures, and for both early adopters and timely adopters. We also document that financially constrained firms and firms experiencing rapid R&D growth prior to the IFRS adoption show greater efficiency improvement. Moreover, we conduct three falsification tests to make sure our results are not attributable to other accounting changes associated with the IFRS adoption, and find no efficiency improvement for the three control groups (German "no-R&D" sample, U.K. firms, and Australian firms), respectively. We conclude that the change in the R&D reporting rule is the likely catalyst for improvements in efficiency of German R&D firms. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of efficiency within the medical laboratories industry, a sector of vital importance in healthcare due to its provision of essential diagnostic services. To this end, we employ three distinct stochastic frontier models to evaluate the technical efficiency of the medical laboratory industry. Our benchmark model stands out by considering the presence of heterogeneity, leading to robust findings compared to alternative models. The mean efficiency estimate is determined to be 70.6%, while the median efficiency estimate stands at 70.0%. Remarkably, our results highlight that the production technology employed in US medical laboratories demonstrates constant returns to scale. Moreover, our study delves into the intricate relationship between firm efficiency and market valuation. Our findings reveal a positive and statistically significant correlation between efficiency and market valuation. More specifically, a 1% increase in efficiency corresponds to a 1.08% increase in market valuation. [ABSTRACT FROM AUTHOR]

This study suggests a novel statistical distribution known as the neutrosophic loggamma distribution (NLGD) for analyzing the interval value data. The proposed distribution is derived from the transformation method, utilizing the approach of neutrosophic logic. The statistical characteristics of the proposed are studied within a neutrosophic framework. Basic statistical properties such as moments of origin, mode, mean deviation, and other related functions that are commonly employed in statistical applications are derived. In addition to the basic properties of the model, the estimation approach of maximum likelihood is also studied. The derived estimators of the proposed model are further assessed by the simulation method. Comparative research using real-world examples shows that NLGD does a better job of modelling complicated industrial growth and production measures than standard distribution. Application of the proposed model improves our understanding of the theoretical component and our ability to forecast outcomes in statistical applications. This, in turn, leads to improved decision-making and operational efficiency in many industrial sectors. [ABSTRACT FROM AUTHOR]

The advancement of the intelligent manufacturing industry (IMI) represents the future direction for the world's manufacturing sector, offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency. In this study, we took the industrial robot industry (IRI) as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective, and the modified diamond model (DM) was used to analyze the influencing factors. Results show that: 1) the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar, centered in the southeast. Key investment areas are in the east and south. The spatial distribution of China's IRI covers a multitude of provinces and obtains different scales of investment in different countries (regions). 2) The spatial correlation between foreign investors and China's provincial-level administrative regions (PARs) forms a network, and the network of foreign-invested enterprises is more stable. Different countries (regions) have distinct location preferences in China, with significant spatial differences in correlation degrees. 3) Overall, the interaction of these factors shapes the location decisions and correlation patterns of industrial robot enterprises. This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and suggestions for national IMI planning and relevant industry investors. [ABSTRACT FROM AUTHOR]

This study proposes a battery thermal management system based on L-shaped heat pipes coupled with liquid cooling. Experimental and computational fluid dynamics (CFD) numerical simulation studies have been conducted on the performance of the thermal management system. The thermal performance of three heat dissipation methods including forced air cooling, bottom liquid cooling and heat pipe coupled liquid cooling were compared. The results demonstrate that the coupling system can control the maximum temperature and temperature difference of the module at 30.12°C and 2.02°C at a 3C discharge rate. Compared with forced air cooling and bottom liquid cooling, the maximum temperature was decreased by 30.16% and 17.01% and the temperature difference was decreased by 72.14% and 77.20%, respectively. Studied the impact of factors such as coolant flow rate, the number of liquid-cooled plate channels, and the coolant inlet temperature under different ambient temperatures on the thermal management performance of the coupled system. By monitoring the maximum temperature of the module and the ambient temperature, a method for controlling the flow rate and the inlet temperature of the cooling water has been developed to implement an intermittent liquid cooling strategy for the battery module. Intermittent liquid cooling at various ambient temperatures can obtain similar thermal management performance to continuous liquid cooling, while significantly reducing liquid cooling energy consumption. Compared to continuous liquid cooling, intermittent liquid cooling can reduce energy consumption by a maximum of 97.05% and a minimum of 30.00%. [ABSTRACT FROM AUTHOR]

This paper examines the impact of non-deceptive copycats considering the authentic firm's effort in fighting copycats. We study how non-deceptive copycatting affects the authentic firm's quality investment, demand, profitability, and consumer welfare. We reveal that, in contrast to the benchmarking scenario of no copycatting, non-deceptive copycats do not necessarily reduce the quality and market share of the genuine product, depending on the extent of copycat imitation and the quality investment efficiency of the genuine firm. Closer copycat imitation can either enhance or reduce consumer and social welfare, contingent upon the extent of copycat imitation and the quality investment efficiency of the genuine product. Furthermore, the authentic firm benefits from the presence of such copycats with sufficiently efficient anti-copycat effort and copycat penalty. In addition, we demonstrate that the genuine firm can drive copycat products out of the market with sufficiently efficient brand quality investment and low imitability, which further differentiates the quality between the genuine product and the copycat, and thus shrinks the demand for the copycat product. Finally, the main model is also generalised to study multiple scenarios: Stackelberg competition, two-feature copycat imitation, and with different production costs. [ABSTRACT FROM AUTHOR]

Improving the Yangtze River Economic Belt's industrial energy efficiency is not only an important measure to alleviate China's energy shortages but also a drive to promote green economic development. The Super-EBM model, Malmquist productivity index, exploratory spatial data analysis, and Spatial Dubin model are used in this article to investigate the spatial-temporal dynamic development characteristics and influencing factors of industrial energy efficiency in 108 cities of the Yangtze River Economic Belt from 2011 to 2020. The findings demonstrate that the industrial energy efficiency of the Yangtze River Economic Belt and its three urban agglomerations went through three stages, including the "oscillation period," "stability period," and "enhancement period," and decreases from east to west in the spatial dimension. The Yangtze River Delta urban agglomeration has the highest industrial energy efficiency, followed by the Middle-reach Yangtze River urban agglomeration, and the Chengdu-Chongqing urban agglomeration is the lowest. Further, this article identifies seven influencing factors including government intervention, industrial structure, degree of openness, R&D investment, urbanization, economic development, and environmental regulation. This article provides suggestions for industrial energy efficiency improvement. [ABSTRACT FROM AUTHOR]

The ecological accountability reform is an emerging environmental regulatory tool used by transition economies to correct failures and deviations in the traditional central-local environmental governance mode. This paper applies the propensity score matching with difference-in-differences method to test the impact of the central environmental inspection, an ecological accountability mechanism, on the investment efficiency of enterprises. The results reveal that the central environmental inspection can improve the corporate investment efficiency by resisting the urge to over-investment. Furthermore, this policy can improve corporate investment efficiency by dual functions of the incentive effect of increasing R&D investment and the pushback effect of financial risk. After a series of robustness checks, the results remained reliable. Heterogeneity tests show that the effect gradually appears in regions that implemented policy lately or with re-inspection, indicating the necessity of establishing a long-term mechanism. What's more, the positive effect of the central environmental inspection on corporate investment efficiency will be more significant in SOEs, large companies, and companies in provinces with weak environmental regulations. To achieve carbon peaking and carbon neutrality goals, this paper provides implications for improving collaborative governance system of local environmental protection and strengthening the linkage of environmental regulation and enterprise investment. [ABSTRACT FROM AUTHOR]

Bicarbonate electrolysis, as a carbon utilization technology with high efficiency and potential for industrial applications, provides a promising pathway for CO2 emission reduction. However, how to inhibit serious hydrogen evolution reaction (HER) and increase the relatively low CO2 concentration at the electrode‐electrolyte interfacial is challenging. Here, we introduce three typical aminopolycarboxylic acids (APCAs) with different amounts of carboxylic acid roots into 3 M KHCO3 to enhance the Faradaic efficiency of CO (FECO) from 51.2% to 68.0% at 100 mA cm−2. Spectroscopic characterization confirms that the role of APCAs in confining the activity of water dissociation and improving the availability of CO2. The strategy adopted in this work that introducing APCAs into the electrolyte to balance the content of CO2 and H2O for improving the electrocatalytic performance, can serve as a reference for other electrocatalytic systems. [ABSTRACT FROM AUTHOR]

This paper investigates the potential use of the International Roughness Index (IRI) in generating optimal rehabilitation plans at the network-level. The IRI data used in the study was obtained using the portable vehicle-mounted IRIMETER-2 profilometer, which is a product of Englo LLC, Tallinn, Estonia. An optimum rehabilitation model is proposed to minimize the average IRI value at the network-level subject to variable and budget constraints. The model mainly focuses on using major rehabilitation strategies that can produce a major improvement in pavement condition. An alternate maximization model that can use other pavement condition indicators, such as the present serviceability index (PSI) and pavement condition index (PCI), is also presented. The PSI and PCI can be estimated from the IRI using correlation models. The proposed optimum models are linear in form and can easily be solved using the proposed cost-effectiveness ratio. The sample results presented for a 27.1-km suburban highway indicate the reliability of using the IRI data to generate optimal rehabilitation plans. A statistical uncertainty analysis of IRI measurements produced a mild impact on optimal solutions derived using ten independent IRI tests and 99% confidence level. The uncertainty analysis has also indicated that the use of a single IRI test provides results that are statistically indifferent from those obtained using ten IRI tests. [ABSTRACT FROM AUTHOR]

With the deepening of industrial division of labor, supply chain efficiency has become a critical driver of companies' core competitiveness. Using a sample of Chinese A‐share listed companies from 2007 to 2023, this paper empirically investigates the impact of digital transformation on supply chain efficiency. We find that firms with a higher degree of digital transformation are associated with higher supply chain efficiency. Mechanism tests show that digital transformation facilitates supply chain efficiency via lower customer demand uncertainty as well as raw material disruption risk. The positive effect of digital transformation is more pronounced for non‐state‐owned enterprises and for firms with less stable customers and more geographically dispersed suppliers. Moreover, there is much degree prompt in supply chain efficiency for firms located in a better digital environment. This paper sheds light on how digital transformation influences supply chain efficiency and provides significant insights for firms to improve supply chain efficiency. [ABSTRACT FROM AUTHOR]

The economic consequences of water efficiency that results from water policies remains controversial. Studies on how water rights trading (WRT), which allocates water resources in and across industries, influences corporate efficiency can reveal the behavioral response of market participants to increase water efficiency. Considering the WRT pilot implemented in 2014 as a quasi‐natural experiment, we adopt the difference‐in‐difference‐in‐differences method to evaluate its impact on corporate total factor productivity (TFP). Our findings show that WRT significantly stimulates the TFP of firms with high water costs. This effect is achieved by promoting water‐related green innovation and optimizing resource allocation instead of alleviating corporate risks. Furthermore, the impact of WRT is more pronounced in areas with lower levels of water endowment and market development. The research findings have strikingly important policy implications regarding water management in China through WRT. [ABSTRACT FROM AUTHOR]

From a legal standpoint, this paper critically examines the potential and challenges in deploying Artificial (AI) Intelligence in corporate governance in Nigeria. The examination revealed that leveraging AI in corporate governance could enhance corporate efficiency in Nigeria through improved decision-making, risk management, financial reporting, and stakeholder protection and engagement. However, possible bias and data privacy breaches are significant risks that pose ethical challenges when AI is deployed in corporate governance. Particularly, Nigeria is bisected by several socio-economic challenges, such as a lack of a robust AI framework and insufficient technological expertise to develop and optimize AI systems. Furthermore, Nigeria currently lacks comprehensive national AI legislation, thereby resulting in the absence of a legal basis for the effective deployment of AI in corporate governance and board management. Against this backdrop, this paper proposes an AI-based corporate governance framework, which can be adapted into future legislative reforms to streamline decision-making processes and improve board accountability and stakeholders' protection in companies. Overall, it argues that AI legislation and policies are vital to the success of efforts to implement AI-based corporate governance in Nigeria. [ABSTRACT FROM AUTHOR]

While the efficiencies of lab‐sized perovskite solar cells are continuously rising, a variety of challenges have to be overcome to realize remotely similar efficiencies in an industrial context. Any changes in the preparation process, device size, device architecture, and material type are likely to result in efficiency loss. To date, there have been no solutions that can produce large‐area modules with performance comparable to that of laboratory devices. However, depending on the type of deposition process, the dominant loss mechanisms differ significantly, which can guide the further optimization of processes and devices. In this study, a meta‐analysis of state‐of‐the‐art perovskite solar cells and modules with different preparation methods, area sizes, and material compositions, is presented. Moreover, the efficiency losses are divided into five figures of merit and they are visualized to discuss the efficiency‐limiting loss mechanisms that must be overcome for commercialization. [ABSTRACT FROM AUTHOR]

In the context of promoting the orderly expansion of capital investment and rational allocation of resources to achieve green and circular economic development. Green finance, as a new engine to promote the sustainability of enterprises, holds significant importance in exploring the positive effect of green finance policies on optimizing the investment decisions of enterprises and guiding them to efficiently utilize their resources to maximize value creation. Using A-share listed companies in Shanghai and Shenzhen from 2012 to 2022 as the research sample, we apply the Difference-in-Differences (DID) method to test the impact of the green finance reform and innovation pilot zones (2017) on the investment efficiency of enterprises in the pilot regions. We also adopt a two-step method to test the mechanisms of financial resource misallocation and agency costs. The study reveals that the green finance policy significantly enhances the investment efficiency of enterprises in the pilot areas. Financial resource misallocation and agency costs are important influence mechanisms. Drawing on resource allocation theory and agency theory, the study concludes that the green finance policy alleviates financial resource misallocation by directing financial resources toward high-efficiency enterprises. Moreover, the policy effectively reduces agency conflicts caused by power separation and information asymmetry, ensuring that enterprises can maximize the benefits of their investments. Heterogeneity analysis shows that non-state-owned enterprises and low-tech innovative enterprises in the pilot areas have disadvantages in terms of capital stock and loan credit, so the policy can improve their investment efficiency effectively. Based on these findings, we recommend that to leverage the positive effects of the green finance policy, it is essential to enhance the guiding role of the government, strengthen market mechanisms, and bolster corporate initiatives. This study complements the research on the economic effects of location-orientated comprehensive green finance policies on enterprises, considering the dual aspects of resource allocation efficiency and corporate governance, and makes up for the shortcomings of the existing literature. The study's conclusions offer valuable insights for enhancing green finance to support enterprises in achieving efficient production. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this paper is to investigate the effect of working capital efficiency (WCE) and its components on the composite financial performance of a sample of Indian firms. Design/methodology/approach: Our sample includes 796 non-financial listed firms from 2015–16 to 2021–22. Sample firms' profitability, liquidity, solvency, cash flow management, and financial and operational leverage have been used to classify them into companies with high composite financial performance (HCFP) and with low composite financial performance (LCFP) by using K-Means Clustering technique. A composite financial performance score (CFPS) of 1 has been assigned to HCFP and 0 to LCFP. We have used logistic regression models with fixed effect to estimate the effect of cash conversion cycle (CCC) and its components, i.e. inventory days, accounts receivable days and accounts payable days on CFPS in the presence of control variables such as growth, leverage, firm size, and age. Findings: The study finds that CCC and inventory days are inversely associated with CFPS. This finding shows that the firms' WCE leads to superior financial performance on a composite basis. Research limitations/implications: The research findings are based on samples drawn from the population of the listed Indian non-financial companies. Since the operation, financial practices, working capital policies, and management styles of firms vary greatly among nations, the results of this study should be extended to firms in other countries after taking into account the degree of resemblance to the sample firms. Practical implications: The findings of this study hold significant value for industry practitioners, as they provide guidance in determining the optimal allocation of funds for working capital and devising strategies for effectively managing inventory levels, credit sales, and vendor payments in order to increase the overall value of the company. This study aims to help investors in building their investment portfolios by identifying companies with superior composite financial performance. Investors can enhance the construction of their investment portfolios by strategically selecting companies that demonstrate superior overall performance. Social implications: The results of our study will help companies improve their WCM strategies to enhance their overall value, and their significance increases manifold during economic downturns. Business firms that perform well by efficiently managing their working capital have a multiplier effect on the economy and society at large in the form of GDP contribution, labor income, taxes to the government, investment in capital assets, and payments to suppliers. Originality/value: To understand the impact of WCE on firms' performance, the extant working capital literature focuses on some specific characteristics such as profitability, valuation, solvency, and liquidity. The limitation of employing a single parameter is its inability to present the comprehensive performance evaluation of firms. This study is among the earliest studies that focus on the holistic evaluation of WCE's impact on the composite performance of a company. [ABSTRACT FROM AUTHOR]

In the context of digital transformation, financial centralization has emerged as a crucial strategy for enhancing the efficiency and effectiveness of corporate financial management. This paper explores the role of Artificial Intelligence (AI) in financial centralization within Chinese enterprises, particularly under the framework of digital corporate financial management. By analyzing multiple case studies, the research demonstrates how AI applications streamline financial processes, mitigate risks, and improve decision-making accuracy. The study focuses on three key AI-driven applications: automated reconciliation, intelligent report generation, and big data-based risk management. Additionally, the research examines the unique challenges and opportunities that private and state-owned enterprises face during AI-driven financial transformation. Through in-depth analysis, the paper offers valuable insights into how AI is reshaping the financial landscape, providing both practical and theoretical contributions. These findings offer valuable implications for financial managers, policymakers, and researchers seeking to leverage AI in the evolving field of intelligent financial management. [ABSTRACT FROM AUTHOR]

Energy efficiency strategies in industrial breweries examine the inefficiency of thermal systems from a thermodynamic perspective. However, understanding the costs of inefficiencies in systems, including non-thermodynamic costs, requires exergoeconomics. This study examined wort production in a standard Tier-1 brewery from the tripod of energy, exergy, and exergoeconomics analyses to assess the performance of brewing sections and to pinpoint components that contributed the most to exergy destruction and product cost rate. The energy analyses for the production system showed that the total specific energy for processing 10.05 tons of brew grains to 346.98 hL high-gravity wort was (86 ± 1) MJ/hL at an operational energy efficiency of 30.35%. The exergetic analyses showed that the cumulative exergetic destruction was 3.2737 MW, with the brewhouse section contributing 89.25% of the system's inefficiencies. Also, the analyses showed that the wort kettle (42.7911%), mash tun (10.8086%), preheater (10.0683%), whirlpool (8.3522%), and adjunct kettle (6.2705%) are the top five components with the highest rates of cumulative exergy destruction. The exergoeconomic analyses revealed that the cost rate of processing chilled wort was estimated to be 0.0681 USD/s per overall exergetic efficiency of 6.61%. The five most significant components are the wort kettle (53.70%), whirlpool (16.42%), mash filter (10.44%), mash tun (6.875%), and adjunct kettle (3.31%) based on the relative total cost increases for the production processes. Additionally, wet steam throttling resulted in a 2.51% increase in exergetic efficiency, a 1.60% drop in exergetic destruction rate, and a decrease in cost rates to 0.0675 USD/s. [ABSTRACT FROM AUTHOR]

This research studied the performance of a spindle‐controlled steam ejector using models such as ideal gas and wet steam under various operating conditions based on computational fluid dynamics (CFD). A wet steam model incorporating nonequilibrium condensation was employed to simulate the complex flow phenomena within the ejector. The structure of the flow, entrainment ratio (Er), and shock wave characteristics of the steam ejector were examined in two different models. Results indicate that the spindle position has a substantial impact on steam ejector performance. For the ideal gas and wet steam models, the optimal spindle position (SP‐5) at a .1 MPa motive pressure achieves the highest entrainment ratios (Er) of 1.01 and 1.042, respectively. However, an ejector with a fixed geometry achieves Er values of only .517 and .549 for the ideal gas and wet steam models, under identical working conditions. This represents a substantial improvement of 89.8% over the fixed‐geometry ejector. The wet steam model consistently predicts 2%–4% higher Er values compared with the ideal gas model across all spindle positions. The study also reveals that increasing the motive pressure from .1 to .3 MPa reduces Er by up to 45.8% at the optimal spindle position, with the shock train length extending to 35% of the mixing chamber at .3 MPa. These findings offer insights for improving the design and optimization of variable‐geometry steam ejectors, potentially increasing efficiency in industrial applications. [ABSTRACT FROM AUTHOR]

In the industrial ecosystem, both the manufacturing and edge clouds collaborate via virtualisation technology, playing a crucial role in the efficient aggregation and processing of vast data from endpoints, offering computational and analytical support for enhanced decision-making in all industrial operations. Consequently, ensuring the reliability of virtualised manufacturing environments is paramount. Remote attestation offers a viable solution, however, current schemes are plagued by inefficiencies and privacy leakage issues. To address these concerns, we propose an efficient remote attestation approach. The approach actively conceals sensitive industrial information in measurement logs by using a hash algorithm, thereby preventing privacy breaches. Furthermore, it streamlines log entries with a log compression algorithm, enhancing remote verification efficiency in the industrial information architecture. Extensive experiments were conducted in an authentic cloud environment by applying this approach across both mesh and hierarchical remote verification structures, reducing time overhead by approximately 81.32%. Additionally, it marginally impacts server performance with only a 1.49% reduction. The results affirm the proposed approach's exceptional efficacy in decreasing verification durations and mitigating remote verification attacks, demonstrating its promising utility and feasibility in the intelligent industrial platform. [ABSTRACT FROM AUTHOR]

The research paper investigates the nexus between energy transition reporting practices and corporate efficiency of listed European companies from the agri-food sector. The study relies on key energy-related and financial indicators and logistic regression analysis conducted on 219 EU business entities publishing their financial and non-financial reports between 2004 and 2023. Based on the distribution of financial metrics in the sample, we assumed that entities reporting data on energy transition, either partially or fully, could achieve higher profitability and simultaneously have lower liquidity than non-reporting companies. The panel data model indicated a strong relationship between the reporting status of a business entity, its revenue, and the time variable, demonstrating that the likelihood of providing information on energy transition was associated with the size of a company and increased systemically over time. However, further regression analysis revealed a lack of a persistent, significant relationship between the key financial indicators and the entities' energy transition reporting status. Considering the substantial changes in the EU regulatory environment associated with introducing CSRD, we conclude that with increasing legal pressure over time, companies will gradually shift towards standardized industry-related reporting practices, resulting in more consistent and transparent evidence of their energy transition strategies. [ABSTRACT FROM AUTHOR]

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution's accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG. [ABSTRACT FROM AUTHOR]

AbstractThis paper investigates the impact of foreign direct investment (FDI) on zombie firms at both the macro and micro levels, revealing a significant negative effect on their prevalence at the city level. Our results show that FDI accelerates zombie firm exits as local governments cease subsidies, recognizing that foreign firms can sustain local economic growth. This result is robust to alternative methods of identifying zombies, alternative measures for FDI, and diverse regression methodologies. This shift is predicated on the recognition that foreign firms can sustain local economic growth. Consequently, financial constraints and profitability among zombie firms deteriorate significantly, while healthy firms remain unaffected. These results support the crowding-out effect but find no evidence for technology spillover, challenging some conventional expectations about the benefits of FDI. Our findings contribute to a nuanced understanding of FDI spillover effects and its role in the targeted disposal of non-viable firms. [ABSTRACT FROM AUTHOR]

In the context of China's"dual carbon"goal, the development of green finance has emerged as a hot research topic. This study, based on the 2017 Green Finance Reform and Innovation Pilot Zone policy and the panel data of Chinese non-financial A-share listed companies from 2003 to 2019 and a difference-in-differences (DID) model, investigates the impact of green finance on firm investment efficiency and its underlying mechanisms. The findings reveal that in the Green Finance Reform and Innovation Pilot Zones the firm investment efficiency has been significantly improved, and mechanistically, the policy has achieved the efficiency by alleviating financing constraints, mitigating under-investment and over-investment, and increasing R&D investment, which provides valuable insights for advancing the construction of China's green financial system and achieving high-quality economic development. Therefore, China should continuously innovate and improve its green finance regulatory framework, fully leverage the transformative power of green finance, and contribute to the realization of the"dual carbon"goals. [ABSTRACT FROM AUTHOR]

Vanadium is an extremely valuable industrial metal typically obtained from processing metallurgical slags. This article provides a comprehensive review and critical evaluation of the technologies of vanadium extraction from slags aiming to identify the most prospective technology. The review covers traditional approaches for treating V‐bearing slags, such as salt roasting followed by acid leaching and ammonium precipitation, as well as promising alternative methods in this field, such as bioleaching, ultrasound‐assisted leaching, and vaporization. The current industrial practice remains energy‐intensive and is associated with chemical waste generation, thus leaving room for further improvements. Overall, this work provides valuable insights into the recycling of vanadium from steelmaking slags including extraction efficiencies of industrial and lab‐scale methods, and discusses the advantages and disadvantages of each method setting the stage for future advancements in this field. The vaporization method is one of the most promising among those reviewed. [ABSTRACT FROM AUTHOR]

This study examines the characteristics of patients with giant cell arteritis (GCA), the utilization of imaging in GCA diagnosis, and variations in GCA management among specialties. Subjects were identified from the Dallas VAMC database spanning 2010 to 2021 using ICD-9/10 codes for GCA and polymyalgia rheumatica, and a list of temporal artery biopsies (TAB). Patients lacking sufficient data to meet the ACR 1990 classification criteria for GCA were excluded. Categorical variables were compared using Fisher's exact test. Continuous variables were analyzed with the Kruskal–Wallis test. Among 209 identified patients, 41 were excluded due to insufficient data for ACR classification. The cohort comprised 91.9% males with a median age of 69. Of the remaining 168 patients, 42 received a final diagnosis of GCA, and 15 of these were confirmed with a positive TAB. The most reported initial symptoms were visual disturbances (75.5%) and headaches (67.7%). Ophthalmology was the initial physician for 46% of patients. GCA correlated with co-existing autoimmune diseases, glucocorticoid-sparing treatments, and consultation with a rheumatologist (p < 0.05). There were no significant differences in clinical features or management of the positive and negative TAB GCA groups. GCA presents with heterogeneous symptoms making diagnosis challenging. Scalp tenderness and headaches were significantly higher in GCA patients, but sub-group analysis revealed no significant differences among GCA patients. Vascular assessments and adjunct imaging modalities are underutilized. The establishment of multidisciplinary or fast-track clinics may enhance the optimization of GCA management. Key Points • The most common presenting symptoms were blurry vision/visual loss (75.5%), headache (67.7%), and scalp tenderness (35.9%) in descending order. • In sub-group analysis, no significant differences were found between GCA sub-groups, but when compared to the non-GCA group, were found to have significantly higher rates of headache and scalp tenderness. • Compared to other specialties, rheumatologists were more likely to use advanced imaging, and to prescribe glucocorticoid-sparing treatments. • Systematic and comprehensive assessment and multidisciplinary approach could improve diagnosis and management. [ABSTRACT FROM AUTHOR]

Healthcare infrastructure (HI) is essential for ensuring access to quality medical services, controlling diseases, and enhancing human health and the overall livability of cities. As public health emergencies such as COVID-19 pose considerable challenges, effectively assessing and enhancing the resilience of HI operations has become imperative. Notably, public health emergencies exhibit significant spatial and temporal variability, and HI is a complex system comprising multiple units with various interactions such as geographic relationships and social cooperation, which need to be carefully considered in resilience assessment. This study proposes an integrated framework for dynamic resilience assessment of HI operations, considering spatiotemporal characteristics of emergencies and multiple interrelations among healthcare facilities. First, an improved susceptible-exposed-infectious-removed-hospitalized-Fangcang-dead model was developed to simulate various scenarios across different epidemic phases, given the spatial heterogeneity in outbreak locations and temporal variability in transmission capacity. Furthermore, considering the multiple interrelations among hospitals, the complex HI network was constructed by integrating geographic locations and social cooperation. Finally, under various outbreak scenarios, the cascading failure process of HI was accurately simulated to dynamically assess resilience during different time periods of the epidemic. The proposed method was applied to assess the resilience of HI operations during the COVID-19 pandemic in Wuhan, China, demonstrating its effectiveness and applicability. It was found that 38% of the hospitals in the entire HI system experienced failure during the initiation phase of the epidemic, and this percentage rose to 78% during the acceleration phase. Moreover, HI's operational resilience presented obvious variations among different outbreak scenarios. Dynamic resilience assessment under various epidemic scenarios can inform the development of targeted optimization strategies for improving HI's operational resilience, encompassing pre-emergency prevention, during-emergency response, and postemergency restoration. Our study provides valuable insights into enhancing HI's operational resilience, which has obvious strengths in supporting the optimization of management strategies during public health emergencies. Practical Applications: Healthcare infrastructure (HI) plays a vital role in crisis response for the prevention and control of epidemics. Accurately assessing and improving the operational resilience to public health emergencies is crucial for maintaining the reliable services they provide. This study proposes an integrated framework for dynamic resilience assessment of HI operations, carefully considering both the spatiotemporal characteristics of emergencies and the multiple interrelations among healthcare facilities. Based on dynamic assessment of HI's operational resilience under various epidemic scenarios, targeted optimization and management strategies can be developed for resilience improvement throughout the entire process, regarding pre-emergency prevention, during-emergency response, and postemergency restoration. The proposed framework offers a fine-grained assessment of the dynamic evolution of HI's operational resilience and emphasizes the critical role of infrastructure network construction and emergency scenario setting in resilience planning and risk management. This study provides valuable guidance for creating optimal system configuration and emergency response strategies that can be implemented to foster the resilience of HI. [ABSTRACT FROM AUTHOR]

Using Giddens (1984) structuration theory (ST), in this paper we illustrate how the efficiency‐driven approaches adopted by a large stated‐owned public company in Latin America (Latin American Multiutility Conglomerate [LAMC]) implicitly resulted in triggering a dam disaster with far‐reaching socio‐economic, environmental, and human consequences. Data for the study were derived through document analysis and conducting unstructured, semi‐structured, and email interviews. Our findings show that the internalization of efficiency as a corporate value at LAMC was further rationalized through the adoption of new public management (NPM)‐based management accounting practices (MAPs) embedded within the market‐led development approach. These MAPs connected agencies and structures in a dialectic way and continued reproducing efficiency through day‐to‐day operations and by enabling the company to champion itself as a successful NPM adopter. However, throughout this process, the socio‐environmental and human costs relating to "the dam project" were overlooked, making the disaster inevitable. The paper questions the market‐led development approach, and NPM‐based MAPs, and calls for further empirical work delineating how MAPs can be implicated in public value creation and promoting publicness in emerging economies. Such work is of paramount importance not only to prevent the "unexpected and unwanted effects of public sector accounting" under NPM and market‐led development but also to save the lives and livelihoods of poor and vulnerable community members in emerging economies. [ABSTRACT FROM AUTHOR]

Three-dimensional solar steam evaporators with efficient water purification performance have received increasing attention recently. Herein, elastic polymer covalent organic frameworks (PP-PEG) containing PEG chains with intriguing adaptability to guests are prepared by forming porphyrin rings. PP-PEG foams demonstrate full spectrum absorbance and excellent photothermal conversion properties. Through well-designed thermal management and optimization of the hydrophilicity and PEG chain length, we obtain a highly efficient solar evaporator with an evaporation rate of 4.89 kg m−2 h−1 under 1 sun in self-contained mode. The optimized solar evaporation rate is increased to 18.88 kg m−2 h−1 under 1 sun with a facile truncated cone reflector, exceeding all known solar steam evaporators. This innovative design holds immense promise for desalination and water purification owing to its simple preparation, high efficiency and durability. 3-d solar steam evaporators with efficient water purification performance have received increasing attention recently. Herein, porphyrin-PEG (PP-PEG) containing foams with good photothermal conversion properties are prepared. The PP-PEG foams achieve an evaporation rate of 4.89 kg m−2 h−1 under 1 sun. [ABSTRACT FROM AUTHOR]

Facing the challenge of improving efficiency and stability in industrial processes, this study examines the impact of implementing advanced human-machine interface (HMI) systems, complemented by the internet of things (IoT) and artificial intelligence (AI). The integration of a PLC and HMI-controlled system has resulted in a 22.85% increase in efficiency, stabilizing production and reducing process variability. Tools such as PLCSIM and TIA PORTAL were crucial for validating control logic and programming. Additionally, the study explores the potential of AI and IoT to amplify these benefits, suggesting a significant advancement in automation that could transform operational efficiency and quality in related industries. These findings provide a relevant framework for companies looking to integrate emerging technologies into their operations, promoting continuous improvement and more informed management. [ABSTRACT FROM AUTHOR]

Despite growing interest in workplace democracy, the question whether nonworker stakeholders, like suppliers and local communities, warrant inclusion in the governance of democratic companies, as workers do, has been largely neglected. We inspect this question by leaning on the boundary problem in democratic theory. We first argue that the question of who warrants inclusion in democratic workplaces is best addressed by examining why workplace democracy is warranted in the first place, and offer a twofold normative benchmark—addressing objectionable corporate power and upholding efficiency—to assess the principles of democratic inclusion on offer. Against this benchmark, we next argue that the All-Affected Principle is unfit due to its over- and underinclusive extensional results, and that the All-Subjected Principle, whose variants we examine alongside their extensional results, holds more promise when coupled with independent considerations of justice or a constrained variant of the All-Affected Principle. These combinations need not entirely preclude, but speak against, nonworker stakeholder inclusion. [ABSTRACT FROM AUTHOR]

The article uses the spatial Durbin model to examine the spatial spillover effect and mechanism of outward foreign direct investment (OFDI) on China's green total factor energy efficiency. The study found that, from an overall perspective, outward direct investment negatively impacts local green total factor energy efficiency and has a negative spatial spillover effect on green total factor energy efficiency in geographically related regions. From a regional perspective, the impact of outward direct investment on regional green total factor energy efficiency shows significant regional heterogeneity. In contrast, outward direct investment in the eastern region only promotes improving local green total factor energy efficiency. Mechanism analysis found that outward direct investment negatively impacts the region's green total factor energy efficiency by inhibiting green technology innovation and industrial structure upgrading. These research conclusions can provide important references for other countries to optimize their foreign investment policies and achieve sustainable development. [ABSTRACT FROM AUTHOR]

This paper investigates how environmental, social, and governance (ESG) efficiency impacts corporate innovation, highlighting its role as a crucial indicator of resource utilization within firms. Analyzing data from A‐share listed companies in China between 2009 and 2021, we find that ESG efficiency levels are positively correlated with corporate innovation outputs. This indicates that higher ESG efficiency contributes to greater innovation. Our result also reveals that the relationship between ESG efficiency and corporate innovation is moderated by the firm's ownership structure. Specifically, the negative moderating effects of ownership are more pronounced in regions with lower economic development or stringent environmental regulations. Technology‐based firms are particularly affected, exhibiting greater vulnerability to these negative effects. These findings confirm that ESG efficiency is a significant mechanism linking ESG practices to enhanced innovation capabilities. By exploring both the efficiency aspects of ESG performance and the institutional factors influencing ESG‐innovation dynamics, our study makes a notable contribution to the literature, offering new insights into how effective ESG practices can strategically drive innovation within firms. [ABSTRACT FROM AUTHOR]

This research seeks to combine a comprehensive analysis of the literature regarding energy efficiency and inverters with an analysis of a configuration of a hybrid energy system including a generator, an inverter and a battery. The inverter's power output is analyzed under the influence of different distribution functions during a robust modulation procedure for achieving optimum energy saving. The modulation is tested on two processing machines independently: (a) an electric discharge machine, and (b) an injection molding machine. Based on the research methodology, problem formulations and the conducted analysis, a novel research software called "Inverter Pro V1" was programmed to analyze the estimations and performance of inverters in an energy system. An optimal levelized cost of electricity was determined given certain systems design and operating hours, and a sensitivity analysis was undertaken that identified a range of energy savings. In the sensitivity and optimization analysis, interactions among the primary decision parameters including the system's capacity, operating time, energy saving, cost of energy and payback period are investigated. It is also found that for each system, the inverter modulation categories with the optimum payback period belong to a modulation of inverter designed according to the Sawtooth function with a 45% duty cycle and switching intervals of 0.35 s. By considering the operating hours of 12.5 h per day, and the calculated system's investment cost of €31,782.158, the optimum payback period is estimated to be 1.7729 year. The study results are to inform policy with regard to assisting sustainable electricity production. [ABSTRACT FROM AUTHOR]

We examine whether and how research and development (R&D) mediates the association between environmental, social, and governance (ESG) pillars and corporate efficiency among companies within the supply chain of Microsoft Corporation from 2012 to 2020. An estimation of corporate efficiency using data envelopment analysis (DEA) indicates that companies should first improve their innovational efficiency (mean DEA score = 0.371), then operational efficiency (mean DEA score = 0.659), and finally profitability efficiency (mean DEA score = 0.695). Moreover, a mediation analysis confirms the mediating role of R&D intensity. Overall, R&D should be one of the spotlights in the supply chain amidst the highlights on ESG in this dynamic and challenging business world for companies to achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

With financial technology (FinTech) emerging as a pivotal force driving business model innovation and reshaping market competitiveness, its potential contribution to sustainability has garnered widespread attention. Drawing on carbon emissions data at the county level from 2011 to 2017 in China, alongside information on the FinTech companies, this study reveals that FinTech significantly reduces regional carbon emissions intensity. This effect is particularly pronounced in developed regions and metropolitan cities. These findings withstand rigorous scrutiny, including the application of instrumental variable strategies, controlling for financial attributes, and robustness checks altering model specifications. Mechanism analysis indicates that FinTech fosters optimization and upgrading of industrial structure and promotes the development of the ICT industry, while simultaneously driving down the proportion of coal in electricity generation and per unit GDP energy consumption, and increasing the proportion of new energy generation, thereby enhancing overall energy efficiency. The evidence presented herein supports the role of FinTech in enhancing Nationally Determined Contributions and achieving the objectives of the Paris Agreement. [ABSTRACT FROM AUTHOR]

ABSTRACT The rapid advancements of intelligent technologies have brought about the potential vulnerability of confidential gradient information linked to cost functions when solving distributed optimization and its related problems. Within the context of distributed energy management, safeguarding such private information has risen to paramount importance. This article investigates a distributed energy management problem (DEMP) to minimize cost while simultaneously satisfying multiple local constraints and protecting the private gradient information of the cost function. To this end, a new privacy‐preserving distributed optimization algorithm under the framework of gradient tracking over time‐varying graphs is proposed for solving the DEMP. Specifically, the auxiliary variables are designed for each node in the algorithm to update the gradient while the original state variables are responsible for the interaction with original neighbors and auxiliary variables. Consequently, the devised algorithm can protect the confidentiality of private cost gradient information, even in the presence of eavesdroppers within the network. In contrast to the homomorphic encryption, signal masking method, and some other algorithms without utilizing the state decomposition, the designed algorithm does not require extra information or computing resources. Moreover, it is proven that the designed algorithm could theoretically converge to the exact optimum of the DEMP at a rate of O((lnk)/k)$$ O\left(\left(\ln k\right)/\sqrt{k}\right) $$ under some mild assumptions. [ABSTRACT FROM AUTHOR]

The management of a three‐dimensional warehouse is a key part of the upper computer monitoring and management system for three‐dimensional warehouses. Currently, there are problems such as unreasonable planning and inability to respond to real‐time demands. Therefore, further improvement is needed to optimize the management of storage locations. The main purpose of the research is to achieve better storage stability and improve storage and retrieval efficiency. First, the study constructed a multiobjective mathematical model based on the weight, frequency of use, and category of the goods. Three objective functions were constructed. Therefore, the operational efficiency of the stacker crane and the turnover rate of items were improved. Meanwhile, the overall stability of the shelves was ensured, and the management efficiency of the warehouse was improved. At the same time, the study introduced the GA‐PSO algorithm to solve the mathematical model and optimize the goods location planning. These results confirmed that the proposed algorithm had significantly lower iteration times than traditional particle swarm optimization in different warehouse sizes and types of goods. The iteration required to reach the optimal value in Situation 1 was only 80, which was 90 fewer than PSO. Meanwhile, in Situation 2, the optimization results of the proposed algorithm in four objective functions were as high as 42.94%, 26.03%, 30.72%, and 46.15%, respectively, which increased by 1.20%, 8.04%, 5.61%, and 7.38% compared to PSO. The proposed algorithm can achieve more efficient and intelligent warehouse management, improving the efficiency and accuracy of logistics operations. It is significant for logistics industry development and enterprise competitiveness enhancement. [ABSTRACT FROM AUTHOR]

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model's adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model's potential as a reliable tool for real-time RUL prediction in industrial applications. [ABSTRACT FROM AUTHOR]

With the development of sustainable economy, new energy materials are widely used in various industries, and many cars also adopt new energy power batteries as power sources. However, it is currently not possible to accurately diagnose faults in power batteries, which results in the safety of power batteries not being guaranteed. To address this issue, this study utilizes the Whale Optimization Algorithm to improve the Long Short-Term Memory algorithm and constructs a fault diagnosis model based on the improved algorithm. The purpose of using this model for fault diagnosis of power batteries is to strengthen the safety management of batteries. This study first conducted experiments on the improved algorithm and obtained an accuracy of 95.3%. The simulation results of the fault diagnosis model showed that the diagnosis time was only 1.2s. The analysis of the power battery showed that after using this model, the safety performance has been improved by 90.1%, while the maintenance cost has been reduced to 20.3% of the original. The above results verify that the fault diagnosis model based on the improved algorithm can accurately diagnose faults in power batteries, thereby improving the safety of power batteries. [ABSTRACT FROM AUTHOR]

Uncertain parameters are pervasive across various domains, necessitating sophisticated approaches for their effective management within optimization problems. This review extensively examines state-of-the-art models and algorithms to tackle uncertain optimization challenges. We delve into a broad spectrum of contemporary research hotspots, including stochastic programming, fuzzy optimization, interval optimization, and polymorphic uncertain optimization. The paper meticulously analyzes essential solution methods such as the expectation method, chance-constrained optimization, sample average approximation, robust optimization, distributionally robust optimization, data-driven method, α-cut set method, and fuzzy expectation method. Each approach is scrutinized for its applicability, strengths, and limitations, offering a nuanced perspective on its practical value. By presenting these insights, we establish a robust theoretical foundation and provide valuable guidance for future research in uncertain optimization. This research highlights significant advances and identifies promising future research directions, making a substantial contribution to the evolving optimization field under uncertainty. [ABSTRACT FROM AUTHOR]

Developing a green economy is key to achieving the 2030 Sustainable Development Goals. This paper uses the SBM-GML index, which includes non-desired outputs, to measure the trend of regional green economic efficiency changes and analyze the impact mechanism and realization path of industrial transformation on green economic efficiency. The research results show that advanced industrial structure has a positive influence on green economic efficiency nationwide, while energy utilization structure and energy utilization efficiency have positive partial intermediary effects in the influence path; industrial structure rationalization is also significantly positively related to green economic efficiency nationwide, and the mediating effect of energy utilization is positive. The impact of industrial transformation on green economic efficiency has regional heterogeneity, and the mediating effect of energy use also differs. Among them, the impact effect in the eastern region is basically consistent with the national sample, but is negative in the central and western regions. This paper proposes countermeasures in terms of adjusting the industrial structure, improving energy efficiency, and perfecting industrial and energy policies, which can provide theoretical and practical references for promoting the transformation and upgrading of regional industrial structure, optimizing energy utilization, and advancing the efficiency of the national and regional green economy. [ABSTRACT FROM AUTHOR]

This study analyzes the significance of the leverage ratio on the efficiency of banking companies with the aim of assessing the readiness and suitability of banking companies in facing the implementation of Basel 4 or Basel 3.1. Data from 47 banking companies in Indonesia from the period 2008 to 2023 were used as material for this study. The data used in the research were analyzed using the Dynamic Stochastic Frontier (DSF) method. This method allows for more accurate efficiency analysis by avoiding assumptions of technology at different times. The researchers hypothesize that banking companies with a leverage ratio level higher than the minimum limit applied in BASEL have better efficiency, and the results of this study show corresponding results within the leverage ratio limit of 20%. [ABSTRACT FROM AUTHOR]

Motivated by biological systems, vortex rings can enhance the propulsive efficiency in industrial systems. To study the vortex properties during the formation and post-formation stages, impulsively starting jets are investigated by simulations. The effects of the stroke ratio and nozzle geometry are studied at a fixed jet Reynolds number of 2500. The stroke ratio at the formation number is found to be not enough to produce a vortex ring with maximum circulation. The stroke ratio is suggested to be about twice as large as the formation number. An alternative criterion based on the circulation ratio is proposed to describe the onset of pinch-off. This criterion states that the pinch-off would start when the vortex ring attains about 80% of the total jet circulation. During the formation stage, the scaling laws for vortex trajectories and circulation are proposed for continuous formation. By combining the suggested scaling relations and Saffman's velocity formula, the evolution of non-dimensional energy can be predicted. During the post-formation stage, the scaling laws for vortex properties (e.g., the vortex ring diameter, translational velocity, and circulation) are found to be independent of both the nozzle configuration and the vortex Reynolds number. On the grounds of the invariance of impulse in vortex decay, the scaling laws of vortex motion are derived for the non-dimensional energy, circulation, and diffusivity scale of the vortex core. In consequence, the normalized energy and circulation found in experiments can be successfully derived from the similarity model for both nozzle configurations. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is pivotal in advancing industrial automation, offering significant improvements in connectivity and efficiency. However, the integration of heterogeneous devices within IIoT systems presents substantial challenges, primarily due to the diversity in device hardware, protocols, and functionalities. In this paper, we propose a new pattern language specifically designed to enhance interoperability and operational efficiency across industrial settings. Drawing from a case study of the State Company for Automotive Industry (S.C.A.I.) in Iraq, this study details the development and integration of eleven interrelated patterns. These patterns were carefully combined based on identified relationships, forming a comprehensive pattern language that addresses key aspects of system heterogeneity, including device communication, data security, and system scalability. The pattern language was validated using the Delphi process theory, engaging industry experts to refine and optimize the framework for practical application. The implementation of this pattern language led to significant improvements in system integration, enabling seamless communication between diverse devices and enhancing operational workflows. The case study demonstrates the practical viability of the proposed pattern language in enhancing interoperability within real-world Industrial Internet of Things (IIoT) applications. Furthermore, the replicable nature of this framework makes it a valuable resource for other industrial environments seeking to harness the power of IIoT technologies. [ABSTRACT FROM AUTHOR]

China is the largest producer of jujubes in the world, with the Xinjiang region accounting for about 50 % of the total production. However, local jujube processing still relies on traditional methods, leading to reduced industrial efficiency. Research shows that jujube polysaccharides possess various biological properties, such as antioxidant, anti-tumor, and anti-inflammatory activities. Enhancing the extraction of jujube polysaccharides to boost their food, medicinal, and economic value has become an important development direction of the jujube industry. This paper reviews the current research on the active functions of grey jujube polysaccharides, extraction technologies, and recent advancements in their production and application. It systematically introduces both traditional and emerging extraction technologies and summarizes the current state of production and application of grey jujube polysaccharides. The aim is to provide references for other researchers interested in jujube polysaccharides and to extend the value chain of the grey jujube industry. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) sensor networks are critical enablers of Industry 4.0, facilitating real-time monitoring and control of industrial processes. However, significant challenges to their deployment in industrial settings remain, such as a lack of support for interoperability and manageability with existing industrial applications and the specialized technical expertise required for the management of private 5G sensor networks. This research proposes a solution to achieve interoperability between private 5G sensor networks and industrial applications by mapping Yet Another Next Generation (YANG) models to Open Platform Communication Unified Architecture (OPC UA) models. An OPC UA pyang plugin, developed to convert YANG models into OPC UA design model files, has been made available on GitHub for open access. The key finding of this research is that the proposed solution enables seamless interoperability without requiring modifications to the private 5G sensor network components, thus enhancing the efficiency and reliability of industrial automation systems. By leveraging existing industrial applications, the management and monitoring of private 5G networks are streamlined. Unlike prior studies that explored OPC UA's integration with other protocols, this work is the first to focus on the YANG–OPC UA integration, filling a critical gap in Industry 4.0 enablement research. [ABSTRACT FROM AUTHOR]