China’s rural e-commerce has been developing rapidly. Taobao Villages are combination of e-commerce and rural industries. When rural e-commerce coverage evolves from Taobao village to Taobao town, the scale of industrial clusters has been expanding synchronously. This paper investigates flower and seedling industrial cluster in Xinhe Town, Yanji Town and Miaotou Town of Shuyang County, China, and conducts the econometric analysis of the expansive determinants of flower and seedling industrial cluster of Taobao Villages. An effective sample of 263 was obtained through a face-to-face survey of e-merchants of flower and seedling in the Shuyang County of Jiangsu Province. Bases on the structural equation modeling, series of test results show that the data can be used to calculate the path regression. The outcomes shows that creation of e-merchants of flower and seedling, integration of e-commerce platform, supply chain friendliness, involvement of e-commerce service providers, and governmental policy guidance are driving expansion of flower and seedling industrial cluster together, moreover, the five forces interact with each other. This implies that expansion of flower and seedling industrial cluster is a systematic process, each stakeholder needs to pay attention to the role of other forces, and five forces achieve a balanced situation in the cluster.

The year 2022 was characterized by several major events that impacted the business world in Indonesia and even globally after the COVID-19 pandemic devastated all business sectors in Indonesia and the world. The current business environment is experiencing accelerating turbulence characterized by increasing intensity of business competition, rapid changes in the market, and industrial connections climate. The industrial connection climate of an institution can be measured by the pattern of relationships that occur between workers and management. Industrial connections are fundamental to the sustainability of an organization and are understood as an important factor in influencing institutional performance. This research aims to look at the impact of the existence of industrial connections instruments, turbulence in the business environment on the industrial connections climate. Besides, how the industrial connections climate relates to firm performance. The research design is an inferential quantitative, using sampling and snowball sampling methods with total sample are 406. The data collected were analyzed using Structural Equation Model using LISREL version 8.70. This research used a survey to build an SEM model that tested the effect of organizational turbulence and industrial connections instruments on organizational climate directly, and tested mediation on organizational performance. All related indicators have outer loading more than 0.5 and T-value more than 1.96. Therefore, the conclusion is that all measurements are valid. Composite Reliability (CR) and VE for all indicators are >0.7 and >0.5 respectively. This research shows that industrial assosiation climate has a positive effect on business turbulence and industrial connections instruments. However, industrial connections instruments show a greater impact on the assosiation climate. This is because industrial connections instruments are the media used in the implementation of industrial connections in organizations. This research also confirms that industrial assosiation climate is positively related to organizational performance. The result of the indirect influence (IE) of 0.20 with t value is 8.47 means that the climate of Industrial connections is able to mediate the influence of Industrial connections Facilities on Organizational Performance.

Few studies have paid attention to the industrial lands along the Chinese Grand Canal from a cultural per-spective, but cities along the route like Hangzhou have relied on the Canal system to promote industrializa-tion over the past hundred years, and relocated, transformed, or demolished the factories to construct cul-tural space or products mostly after industrial recession at the beginning of 21st century. To deeply analyze this process is quite helpful to understand the typical cultural change and its gain and loss along the Canal especially after cultural and creative industries have been established as the pillar industries of China's na-tional economy. Therefore, along the Hangzhou section of the Canal, we dynamically monitored the land use of the selected 10 industrial lands, with the analysis of high-resolution satellite imageries and the in-dustrial past, the areas of different uses in 2022 were compared. Meanwhile, along with archival data, fieldwork, and experts’ consultation, we designed a mark sheet with factors and scores to comprehensively assess cultural changes over those lands, and its reasons and influence also have been argued, and the prob-lems and suggestions have been offered. This study can contribute to a better understanding of public poli-cy choices in China regarding cities along the Grand Canal after the industrial decline, and provide new perspectives for cultural research, heritage conservation, and urban planning of canals or waterfront spaces.

The environmental policy is compelling industries to treat their wastewater to a level that can be released into the domestic sewer system or even for reuse. However, many industrial branches are lacking space for traditional wastewater plant. Hence, electrokinetics wastewater treatment is a viable alternative as it is fast and does not require large space. However, the electrokinetics wastewater treatment is still mostly tested in the laboratory. In the current study, a prototype of electrokinetics wastewater treatment industrial-size device has been designed, constructed and tested under real conditions. In this paper the device is described and the results obtained from the operation of the device are presented and discussed.

In this paper, a land utilization efficiency evaluation model, which takes environmental loss into consideration, has been structured via taking advantage of the slack-based measure (SBM) model. Meanwhile, based on the panel data from 280 prefecture-level cities in China from 2003 to 2013, the paper thoroughly probed into, and discussed, the effect imposed by industry clustering and specialization on the utilization efficiency of urban land. Research results indicate several conclusions, as follows: (1) Taking environmental loss into account, the land utilization efficiency of prefecture-level cities in China is generally low. During the research period, the average value of the land utilization efficiency of prefecture-level cities in China is only 0.349, with, first, a declining trend, and then a rise. Geographically speaking, the land utilization efficiency presents a “depression in the center” phenomenon which means the land utilization efficiency of prefecture-level cities in the central China are relatively lower than in the east and west. Now, the difference among the urban land utilization efficiency in China significantly reflects the distinctions among Eastern, Western, and Central China. Moreover, the contribution degree of the difference of the land utilization efficiency among cities of central China to the aggregation difference shows an ascending momentum. Additionally, the relation between the population scale and land utilization efficiency in cities manifests as a U shape; (2) theoretically speaking, the relation between industry clustering and urban land utilization efficiency presents an inverted-U shape. However, this kind of relation is not significant in Western and Central China and medium-sized cities. Moreover, most of cities are still relatively far away from the inflection point or the critical value; and (3) the industry professional level has imposed a positive influence on urban land utilization efficiency. However, that influence is not significant in Eastern China and large cities. Consequently, strengthening the industry professional development of Western and Central China and small and medium-sized cities, facilitating diversified development of industries in Eastern China and large cities, and accelerating industrial clustering, all of these measures above will be conducive to improving urban land utilization efficiency in China.

This paper explores developing and deploying a fog computing platform tailored for industrial environments, focusing on predictive maintenance and advanced data management. The research investigates the integration of custom sensors and algorithms to meet specific industrial requirements, emphasizing the application of Long Short-Term Memory (LSTM) models for predicting equipment failures. The study enhances proactive maintenance strategies and real-time decision-making by applying machine learning techniques within a fog computing architecture. The findings underscore the platform’s potential to advance industrial data processing, edge intelligence, and maintenance practices, contributing to improved efficiency and reduced downtime in industrial operations.

The article discusses an alternative way of recycling radioactive waste (RW), presented in the form of radioactive building materials - concrete and reinforced concrete structures and metal fittings, with the further use of materials, obtained during recycling, in the space industry. That is, it is supposed to send radioactive waste into space not as a passive ballast, but as a payload that will operate in space under conditions of increased radiation.

Lignocellulosic fungi are highly versatile organisms with valuable applications in bioremediation processes, including the biodegradation of agro-industrial effluents. In this work, the use of a native strain of the white-rot fungus, Pycnoporus aff. sanguineus, was studied for its use in the bioremediation of the sugar industry waste called vinasse, originating from the San Martín del Tabacal Sugar Mill, located in the north of the Salta province, Argentina. We studied under controlled laboratory conditions the bioremediation process of 3 concentrations of vinasse (5, 10, and 25% in distilled water) with a native isolated strain. The results showed biomass growth at all three tested concentrations, with a maximum at the highest vinasse concentration (25%), while the percentages of color and Chemical Oxygen Demand (COD) removal indicated that the most efficient treatment was with 10% vinasse. The results obtained are promising for the treatment of effluents from the sugar industry using white-rot fungi considering the valuable subproducts of Pycnoporus spp. biomass.

Mass customization, small batch sizes, high variability of product types and a changing product portfolio during the life cycle of an industrial plant are current trends in the industry. Due to an increasing decoupling of the development of software and hardware components in an industrial context, compatibility problems within industrial control systems arise more and more frequently. In this publication, a strategy concept for compatibility testing is derived and discussed by means of literature review and applied research. This 4-phased strategy concept identifies incompatibilities between software and hardware components in the industrial control environment and enables test engineers to detect problems at an early stage. By automating the compatibility test on an external I-PC, the test can be run both when new software is installed on the industrial controller and when the controller is restarted. Thus, changes to the components are constantly detected and incompatibilities are avoided. Furthermore, early incompatibility detection can ensure that a system remains permanently operational. Based on a discussion, additionally strategies are identified to consolidate the robustness and applicability of the presented concept.

Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. In the presented work, an industrial exoskeleton for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is proposed, exploiting compliant actuation at the shoulder joint to increase safety and transparency in human-robot cooperation. A hierarchic model-based controller is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectory tracking, while an outer fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization.

The human-centric approach is a leading trend for future production processes, and collaborative roboticists are key to its realization. This article addresses the challenge of designing a new cus-tom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using “general-purpose standard" safety and motion control technologies. This is done on a non-conventional Cable-Driven Parallel Robot (CDPR). Safety is assured by safe commands to individual axes, known as Safe Motion Monitoring Functionalities, which limits the axes speed in the event of human intrusion. At the same time, the robot's Motion Controller applies an override to the toolpath speed to accommodate the robot's path speed to the axis’s limitations. The implementation of a new Pre-Warning Zone, prevent unnecessary stops due to the approach of the human operator. The article also details a real experiment that validates the effectiveness of the proposed strategy.

The adoption of an entrepreneurial mindset within the lenses of green entrepreneurship contributes to negotiating a transdisciplinary and multidisciplinary approach by intertwining industrial psychology and industrial engineering through [system dynamics] which forms part of the paper's focus. The methodology used in this study is a system dynamics methodology using data readily available online with a focus on quantitative methodology. This study's dynamic variable and the causal loop are formulated using the Euler mathematical equation with support from the Vensim modelling software (academic purposes license). Results and variables simulated in the current study are entrepreneurial mindsets [through subfield of education, values, intentions and gender representation] contribution and the conclusion is explained towards the end of the paper.

The growing demand in industrial and biotechnological settings for more efficient enzymes with enhanced biochemical features, particularly thermostability and thermotolerance, necessitates a timely response. Renowned for their versatility, thermostable enzymes, offer significant promise across a range of applications, including agricultural, medicinal, and biotechnological domains. This comprehensive review summarizes the structural attributes, catalytic mechanisms, and the relationships between structure and function of two major classes of thermostable enzymes: α-amylases and laccases. These enzymes serve as valuable models for understanding the structural basis of protein thermostability. Commercial significance of these enzymes and researchers' interest in further optimization and innovation, this article can greatly contribute to ongoing research on thermostable enzymes and aiding industries in optimizing production processes. It also give insights to the exploration of suitable strategies and factors for enhancing thermostability, resulting in heightened multipronged stability and notable enhancements in the enzymes’ industrial applicability.

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. We suggest a new health indicator based on vibration data measurements and compare its performance with state-of-the-art health indicators regarding different criteria. This evaluation is based on different data sets from robot test rigs. We find that the proposed health indicator can detect different faults, has low temperature sensitivity and works in instationary velocity regimes. A discussion of the validity of the results rounds up our contribution.

Though the share of the world population living in extreme poverty declined to 10 percent in 2015, from 16 percent in 2010 and 36 percent in 1990, data shows that the world is not on track in achieving the target of less than 3 percent of people living in extreme poverty by 2030. Hence the study sought to investigate the influence of AI on poverty reduction. Using content analysis one of the unobtrusive research techniques, the study found out that, the availability of relevant data is making AI be able to deliver value to humanity and AI has a strong influence on poverty in areas of relevant data collection through poverty maps, its ability to revolutionize agriculture, education, and the financial sector through digital financial inclusion. The study also discovered that many countries especially developing nations are not collecting as much data to identify the number of poor people and the regions where these people are located. However, the existence of AI is assisting to change this, or instance the study discovered that the research team at Stanford University is using satellite images to provide an alternative to map poverty, to identify the regions where poverty is more concentrated. Also, various robotics and AI programs such as Google and Stanford University’s Sustainability and Artificial Intelligence Lab, are coming forth with AI programs in agriculture which are doing a lot to improve farming, through the identification of diseases, prediction of crop yields, and location of areas prone to a scarcity among several other notable signs of progress in education. Therefore, the study recommends that governments, development institutions and other organizations that are striving to fight poverty to invest more in AI as well as adopting and scaling up its use as it presents benefits in the quest to ensure that poverty is reduced.

In recent decades, both in Italy and internationally, the phenomenon of abandoned industrial buildings has grown considerably, causing inevitable social, urban and economic impacts. At the same time, the cultural debate has increased awareness of the problem and the strategic role that abandoned industrial sites can play in responding to new needs. This contribution analyzes the dismantling process of the Akragas Montedison factory in Porto Empedocle, Sicily, examining the evolution of disused industrial areas from the 20th century to the present day and the possibilities of reconversion for appropriate reuse.

This study examines the practicality and limitations of using a FANUC CRX-10 iA/l collaborative robot to assemble a product component, highlighting the trade-offs between increased roboisation and reduced manual intervention. Through a detailed case study in the i-Labs laboratory, critical factors affecting precision assembly such as station layout, tooling design and robot programming are discussed. The findings highlight the benefits of robots for non-stop operation, freeing up human operators for higher value tasks despite longer cycle times. In addition, the paper advocates further research into reliable gripping of small components, a current challenge for robotics. The work contributes to open science by sharing partial results and methods that could inform future problem solving in robotic assembly.

Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.

Petrochemicals and oil refineries are industrial processes that use compounds and polymers derived directly or indirectly from natural gas or crude oil for chemical purposes. They have posed a number of short- and long-term risks to the environment and the people who live nearby. The study aims to determine how the presence of industrial areas, such as oil refineries, affects the local population's perception of their surroundings. The investigation centers on the Tehran Oil Refinery, a major refinery hub, and its perceived environmental impact. For this purpose, a Geographic Information System for Public Participation (PPGIS) covering three basic sections has been designed: socio-demographic information of the participants; self-perceived health; and landscape valuation before and after an intervention. The main observations show a complex interaction between industrial presence and the perception of landscape values. Participants' reactions to the manipulated Photographs reveal important details about the psychological impact of visual elements on emotional perception. There is also a direct relationship between the level of stress and emotional perception in the manipulated and original photos.This research adds to the larger debate about the effects of industrialization on the environment and society. It emphasizes the importance of considering public perception when planning and developing industrial projects. The goal is to strike a balance between industrial operational needs and environmental quality preservation, resulting in long-term urban growth.

The paradigm shift has revealed a need among industrial designers to have entrepreneurial capabilities. Being the most effective modality to exercise discipline today. The implementation of this approach requires reviewing the discursive scaffolding that the design has been using to set immovable high standards of complexity and quality, reducing the field of professional action and limiting the intervention alternatives. This article considers the dynamics and trends in people's lifestyles, explores possible strategies and specific content for the entrepreneurial training required by design.

Smart cities are a new approach to facilitating urban development and transformation, driving coordinated development between regions, and achieving sustainable urban development. In the era of smart cities, industrial cloud platforms, as a concrete representation and carrier of “industrial brains”, are gradually becoming one of the core competences of urban manufacturing industries. Case study and qualitative comparative analysis (QCA) of a knitting industrial platform in Yiwu City, China are carried out. The following conclusion can be drawn: the construction mechanism and path of the manufacturing “industrial brain” in the smart city are divided into three stages according to the construction of the industrial platform. These include: functional development (FD), trust-building (TB), and value co-creation (VCC). The research conclusion helps to more deeply understand the effects of industrial platforms on constructing “industrial brains”. It provides effective transformation strategies and guidance for the manufacturing industry in the smart city, and pushes the industry to develop towards an intelligent, efficient, and environmental-friendly one. The research provides useful reference for applying the industrial platform to other industries and fields. Exploring the construction mechanism and path of the manufacturing “industrial brain” in the smart city can also contribute to the establishment of a new industrial ecosystem in the era of smart cities.

Portugal’s EPAC silo network, initially planned in the 1930s but constructed and utilized primarily in the 1970s, consisted of 31 silos with a total capacity of 841,100 t. The network’s usage declined, however, due to market liberalization and Portugal’s accession to the European Economic Community in 1985. This study focuses on adapting a methodology to inventory and analyse the 31 silos in the EPAC network considering their general features, construction, technological facilities, and socioeconomic aspects. The silos are situated in 30 cities and towns, predominantly in the country’s key grain-growing regions, particularly the Alentejo region. While there are variations in design and construction, most EPAC silos contain two or three rows of circular reinforced-concrete cells and use the spaces between cells for storage. Their capacities range from 6,000 to 35,000 t. Some are inland grain reception and storage silos, while others are larger-capacity port silos designed to unload grain rapidly onto ships using mechanical or pneumatic unloaders. These structures are a significant part of Portugal’s agro-industrial heritage and have in some cases been repurposed as museums or event venues. Compared to other agro-industrial buildings, silos pose unique conversion challenges due to their height and design complexities. Examples of successful reuse in countries like Spain and Italy may provide insights for potential silo projects in Portugal. However, analysis suggests that such proposals and similar initiatives may be viable only in the more highly populated towns.

Strategic management sets the direction of a company for several years ahead. Managers and business owners who create strategy must anticipate and have the ability to see systemically—the paper deals with creating a logistics strategy for a company operating in the industrial sector. The first section summarises the theoretical background for strategy and logistics and current trends affecting logistics processes. The second chapter analyses the current state of the art in logistics strategy development and summarizes its problem areas. The central part of the paper is the proposal of a methodology for logistics strategy development in the industrial area by a progressive approach. The methodology is divided into 5 phases - preparatory, analytical, formulation, implementation, and evaluation and control phases. The methodology is partially validated. The created variants of the logistics strategy include the introduction of such elements that will lead to the gradual development of the Industry 4.0 trend in the given company. This is mainly the first variant called "automation," which includes the introduction of an automatic tractor for material import, the introduction of an automatic system for transport and storage of work in progress, and the introduction of a new logistics information system that will make greater use of already established barcodes (transparency, data in real-time).

The threat of fine particulate matter concentration (PM2.5) is increasing globally, Tackling this issue requires an accurate understanding of its trends and drivers. The article investigates the PM2.5 characteristics of 285 prefecture-level cities in China from 2000-2018 based on multiscale geographically weighted regression(MGWR), and the results show that（1）previous studies based on classical MGWR models may be somewhat unstable, while MGWR can reflect the scale of influence of different variables on the dependent variable, and its regression results are more reliable.（2）PM2.5 is very sensitive to carbon emission(CE) factors, and there is a high degree of spatial heterogeneity, and the influence scale of location is the smallest among all variables, close to the municipal scale.（3）In 2000, the constant term all, IS, OFT, CE, and LT positively affect PM2.5, while GDP (jurisdiction) and UR negatively affect PM2.5; in 2010, the constant term all, GDP (jurisdiction), IS, OFT and LT positively affect PM2.5, while UR and CE negatively affect PM2.5; in 2018 the constant term all, IS, OFT and CE factors positively affect PM2.5, and GDP (jurisdiction), UR and LT negatively affect PM2.5.

Standard Ethernet (IEEE 802.3 and the TCP/IP protocol suite) is gradually applied in industrial control system (ICS) with the development of information technology. It breaks the natural isolation of ICS, but contains no security mechanism. A modified intrusion detection system (IDS), which is strongly correlated to specific industrial scenario, is necessary for modern ICS. On the one hand, this paper outlines attack models, including infiltration attacks and our creative forging attack. On the other hand, we proposes a hierarchical IDS, which contains a traffic prediction model and an anomaly detection model. The traffic prediction model, which is based on autoregressive integrated moving average (ARIMA), can forecast the traffic of ICS network in the short term and precisely detect the infiltration attacks according to abnormal changes in traffic pattern. The anomaly detection model using one-class support vector machine (OCSVM) is able to detect malicious control instructions by analyzing the key field in EtherNet/IP packets. The experimental results show that the hierarchical IDS has an outstanding performance in detecting infiltration attacks and forging attack compared with other two innovative IDSs.

Anomaly detection is crucial in large-scale industrial manufacturing as it helps detect and localise defective parts. Pre-training feature extractors on large-scale datasets is a popular approach for this task. Stringent data security, privacy regulations, high costs and acquisition time hinder the development of large-scale datasets for training and benchmarking. Despite recent work focusing primarily on the development of new anomaly detection methods based on such extractors, not much attention has been paid to the importance of the data used for pre-training. This study compares representative models pre-trained with fractal images against those pre-trained with ImageNet, without subsequent task-specific fine-tuning. We evaluated the performance of eleven state-of-the-art methods on MVTecAD, MVTec LOCO AD, and VisA, well-known benchmark datasets inspired by real-world industrial inspection scenarios. Further, we propose a novel method to create a dataset by combining the dynamically generated fractal images creating a ``Multi-Formula'' dataset. Even though pre-training with ImageNet leads to better results, fractals can achieve close performance to ImageNet under proper parametrisation. This opens up the possibility for a new research direction where feature extractors could be trained on synthetically generated abstract datasets mitigating the ever-increasing demand for data in machine learning while circumventing privacy and security concerns.

This article examines the pressing challenges facing Industrial Organization (IO) in the contemporary economic landscape, with a particular focus on merger analysis. The rapid pace of technological advancement and emergence of novel business models have strained conventional analytical frameworks. We explore how the rise of digital platforms, data as a competitive asset, and multi-sided markets have exposed gaps in our understanding of market power and consumer welfare. Through an examination of empirical methodologies, including structural models and difference-in-differences approaches, we highlight the limitations of current techniques in accurately predicting merger outcomes. The paper argues for a disciplinary reinvention of IO, drawing upon insights from behavioural economics, network theory, and data science. We posit that an interdisciplinary approach is essential for developing more robust analytical tools capable of navigating the complexities of modern markets and informing effective antitrust policy in an increasingly interconnected global economy.

This study explores the industrial property market in Hong Kong, a sector characterized by its unique features and atypical market behaviour. Particularly, this research employs machine learning techniques to predict property prices based on a set of features, including location, square footage, floor level, accessibility to mass transit railway station and the like. To ensure transparency and understanding, the Shapley value is employed to quantify the relative importance of each feature in predicting property prices. Our analysis reveals the existence of non–linear relationships among these features, as demonstrated by the wide distribution of SHAP values for most features, which are illustrated in a beeswarm plot that span both sides of the baseline. This finding indicates a complex interaction among such features as square footage, age, floor level, carpark, proximity to mass transit railway stations, location, and property prices. The results contribute valuable insights into the relationships between industrial property characteristics and their corresponding values, thereby equipping stakeholders with enhanced understanding of the market to support informed decision–making.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

This paper deals with the formulation of a Cobot kinematic model by developing both the direct and inverse kinematics and choosing the noa transformation matrix for defining the end-effector position and orientation with respect to the base frame. These algorithms have been validated via software in Matlab environment and by means of suitable experimental tests, which have been carried out by using a UR5e Cobot. Moreover, a specific industrial application regarding the robotized assembling of a multi-cylinder IC engine, has been designed in terms of layout and corresponding automatic cycle that is intended as a sequence of the robot elementary actions. In particular, the proposed robotized cell is composed by the UR5e Cobot along with its controller and a suitable electropneumatic circuit that includes a PLC with its user interface, which are both governed by means of a specific electronic board and a switch control box. The IC engine in scale of 1:5 and specific parts of the end-effector, which takes the role of vacuum gripper and torque limiting screwdriver, have been designed and manufactured by using a 3D printer.

Within the tourism industry, the creation, popularization, and dissemination of visual assets and cultural symbols attract significant attention. However, sports tourism delves deeper into this realm, emphasizing content innovation, creativity, and the application of knowledge. Amid the rapid development of a national unified market, the sports tourism industry chain, with intellectual property at its core, has become a crucial factor. Currently, the industry chain faces issues such as "short chains," "broken chains," and "narrow chains." To address these challenges, decentralization is necessary to ensure the stability of industry chain connections and promote value-driven chain development. Furthermore, it is crucial to address the diminishing marginal returns and diversification of the industry chain.

This paper discusses the design and implementation of a portable IoT station. Communication and data synchronization issues in several installations are addressed here, making possible a detailed analysis of the entire system during its operation. The system proposed is interpreted as an additional tool which can be utilized in several applications, where the system operator requires a synchronized data stream, combining multiple communication protocols into one single time stamp. The hardware selected for the portable IoT station complies with the International Electrotechnical Commission (IEC) industrial standards. A short discussion regarding interface customization shows how easily the hardware can be modified so that it is integrated with almost any system. A Programmable Logic Controller enables the Node-Red to be utilized. This open-source programming tool employs flows to define operations for each global variable nominated in the Modbus register. Two applications are presented and discussed in this paper, each application has a distinct methodology utilized to publish and visualize the acquired data. The portable IoT Station is highly customizable, consisting of a modular structure and providing the best platform for future research and development of dedicated algorithms.

Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity in smallholdings have led to a dramatic conversion of rubber plantations to more profitable crops. The literature analysis performed in this paper aimed at better understanding the ins and outs that could make rubber-based agroforestry more attractive for farmers. A library was set-up consisting of 415 scientific references. Of the 232 journal articles, 141 studies were carried out on rubber agroforestry. Since 2011, the number of studies per year has increased. Studies on rubber-based agroforestry systems are performed in most rubber producing countries, in particular in Indonesia, Thailand, China and Brazil. These studies focus more or less equally on perennials (forest species and fruit trees), annual intercrops and mixed plantations. Of the 47 annual crops associated with rubber in the literature, 20 studies dealt with rice, maize, banana and cassava. Agronomy is the main discipline in the literature followed by socio-economy and then ecology. Only 4 papers are devoted to plant physiology and breeding. The discussion section has attempted to analyze the evolution of rubber agroforestry research, progress in the selection of food crop varieties adapted to agroforestry systems, and to draw some recommendations for rubber-based agroforestry systems associated with food crops.

The industrial metaverse paradigm is generally a virtual environment that integrates various technologies such as augmented reality (AR) and mixed reality (MR) to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the industrial Metaverse to drive sustainability and resource efficiency is compelling. Utilizing advanced technologies like the Industrial Metaverse is essential for staying competitive in today’s rapidly evolving business environment. However, the environmental impact of the technologies underpinning the industrial metaverse, like data centres and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that by 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains. As the metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. However, the specific extent to which this substitution can alleviate environmental concerns remains understudied, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the industrial metaverse as well as explore the environmental impact of the industrial metaverse. The methodological approach is that of integrative literature review design wherein multiple sources from web science and databases such as ACM library, IEEE Library, and Google Scholar, were analysed to provide a comprehensive understanding of the developments in the industrial metaverse. Specifically, firstly by considering the Industrial metaverse architecture we elucidate the concept of the "industrial metaverse" and the associated enabling technologies. Secondly, an exploration through discussion, of the prevalent use cases and deployment of the emerging industrial metaverse. Thirdly, exploration of the impact of the industrial metaverse on the environment. In the fourth instance, we address novel security and privacy risks as well as upcoming research challenges, keeping in mind that the industrial metaverse is based on a strong data fabric. Results point to the Industrial Metaverse as having both positive and negative environmental effects by energy consumption, e-waste, and pollution. Overall research, however, indicates that most industrial metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Ultimately for sustainability in the industrial metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, examining the effects of products on the environment as well as creating a circular economy.

The study presents the evaluation of integrating Extended Reality applications in situ to foster visitors' experience at the Tomato Industrial Museum "D. Nomikos". This paper firstly provides an outline of the methodological, technological, and museological approaches undertaken to integrate XR in a quest to enhance visitors’ meaningful engagement with the industrial heritage-related exhibition. Storytelling and narration are key elements of the museums’ museological design [1] through audio tours and interactive AR-based visualization of the machinery’s role as well as internal function. This paper presents and discusses the evaluation findings. We adopted the User Experience Questionnaire method as the main vehicle for gathering quantitative data and analyzing the results with the use of corresponding metrics and benchmarks embedded in this evaluation tool. Moreover, we conducted semi-structured interviews and combined the qualitative data accrued in the form of accounts, suggestions, and remarks to reach more rich and dependable conclusions. This paper aims to offer both an instance of practice to the research community and a broader discussion of the challenges and prospects connected to the quest of employing emerging technologies and assessing their impact on visitors’ museum experience.

The high-quality development of the industrial software industry is of strategic significance to enhancing the core competitiveness of the manufacturing industry and promoting the high-quality development of China's industrial economy. By integrating the "capital- technology-environment-human" production factor theory and configuration perspective, this paper constructs a comprehensive analysis framework that drives the total factor productivity (TFP) of the industrial software industry. It uses 40 typical industrial software firms as case samples and uses fuzzy set Qualitative Comparative Analysis (fsQCA) was used to empirically explore the influencing factors and complex mechanisms that achieve high-quality development of the industrial software industry. It is found that: (1) a single industrial factor is hardly a necessary condition to drive industrial software industry; (2) there are four paths to achieve high TFP, which are summarized as "technology-human-environment" balanced driving type, "capital-human-environment" balanced driving type, "technology-capital" dual driving type, and "capital" single driving type. There are four driving mechanisms. There are also four not-high TFP configurations with asymmetric characteristics; (3) under certain conditions, the combination of capital factors, capital factors, environmental factors and human factors can drive TFP in a "all roads lead to Rome". In this process, the government's attention plays a more universal role. The study not only expands the application scenarios of fsQCA, but also provides decision guidelines for the practice of strategic emerging industrialization represented by industrial software industry.

In the era of Industry 4.0, the focus on optimizing manufacturing processes is crucial. This study provides a comprehensive analysis of the EnPAS system, a proprietary automated control and monitoring tool developed by KAD3, implemented in the production process of Corten steel furnishings - the "Lila" chair and "Line" planter. Designed to enhance operational efficiency, EnPAS facilitates improved workflow by streamlining production stages and reducing downtime. The research design involves a comparative analysis of the workshop's production process, both before and after the implementation of the EnPAS system. Elements such as de-greasing, oxidation, and finishing stages, among others, were examined. The implementation of the EnPAS system led to a significant reduction in the total production time for both products. Further, there was a substantial decrease in waiting time and setup time. These findings unequivocally indicate the transformative impact of the EnPAS system on the manufacturing process, showcasing its potential in driving efficiency improvements, reducing production times, and increasing output. The study underscores the importance of leveraging such advanced control and monitoring tools to optimize modern manufacturing processes and does not exaggerate conclusions not backed by the research.

Digital transformation of agriculture can support economic growth and food and nutri-tion security in Africa. This study provides an overview of the status of digital agriculture in five west African countries, analyzing their efforts in developing the enabling environment and inno-vations while formulating recommendations based on the identified gaps for the effective trans-formation of the sector. Information was retrieved through a literature search from various sources, including web pages and databases of national agricultural and digital transformation institutions and start-ups of the five target countries (Benin, Burkina Faso, Côte d'Ivoire, Ghana, Nigeria) and regional/international institutions. There have been increasing agri-digital initia-tives in the five countries, which were grouped into seven categories based on their objectives. Steady progress was also observed in mobile internet adoption, despite the differences in deploy-ing crucial infrastructure to promote digital agriculture. The mobile connectivity index (MCI) in all five countries is below 60. Nonetheless, Ghana and Cote d'Ivoire demonstrated more efforts in internet and electricity access, especially in rural areas. Benin and Nigeria have developed sep-arate documents depicting the roadmap for Digital Agriculture, while the other countries are working to create one or have it embedded in their national development plans. Similarities and specificities exist among countries for laws and processes protecting Agri-digital innovators. To be competitive and self-reliant in the global e-economy, these countries must reposition them-selves to accelerate changes in digital agriculture through effective governance and synergy of actions in different sectors and across nations.

Introduction: The imbalance between technological development and ideological strengthening has resulted in the fading of the Pancasila ideological values of millennials. Technology that accompanies everyday life seems to be the main character in human life. Yet if humans themselves do not use technology wisely, technology can be a threat to both individuals and the life of the nation and state. If technology is increasingly out of control, threats to the Pancasila ideology are very likely to occur, such as in the G 30S PKI incident. Therefore, it is necessary to take action both from the government and the community as individuals to safeguard the development of technology to be used wisely. That way, we can prevent the threat to the Pancasila ideology together. Writing this article aims to analyze how the role and influence of the Pancasila ideology on the millennial generation in the 4.0 industrial revolution and to find solutions so that the values of Pancasila remain attached to the next generation of the Indonesian nation. Method: Writing This article uses a qualitative method by using literature reviews from the results of related research journals that have been published online through websites and other online media. Results: Thirteen journals and one book have met the criteria for the inclusion of a predetermined review. Research is based on the stigma of society regarding technological developments in the Industrial revolution 4.0. Therefore, the government is expected to be more severe in implementing human capital management. Conclusion: By procuring human capital management, it will be possible that Human Resources (HR) in Indonesia will be able to carry out the ideals of a golden Indonesian generation with Pancasila values inherent in the hearts of the people and the nation's future generations.

Genome-scale engineering and custom synthetic genomes are reshaping the next generation of industrial yeast strains. The Cre-recombinase mediated chromosomal rearrangement mechanism of designer synthetic Saccharomyces cerevisiae chromosomes, known as SCRaMbLE, is a powerful tool which allows rapid genome evolution upon command. This system is able to generate millions of novel genomes with potential valuable phenotypes, but the excessive loss of essential genes often results in poor growth or even the death of cells with useful phenotypes. In this study we expanded the versatility of SCRaMbLE to industrial strains, and evaluated different control measures to optimise genomic rearrangement, whilst limiting cell death. To achieve this, we have developed RED (Rapid Evolution Detection), a simple colorimetric plate-assay procedure to rapidly quantify the degree of genomic rearrangements within a post-SCRaMbLE yeast population. RED-enabled semi-synthetic strains were mated with haploid progeny of industrial yeast strains to produce stress tolerant heterozygous diploid strains. Analysis of these heterozygous strains with the RED-assay, genome sequencing and custom bioinformatics scripts demonstrated a correlation between RED-assay frequencies and physical genomic rearrangements. Here we show that RED is a fast and effective method to evaluate optimal SCRaMbLE induction times of different Cre-recombinse expression systems for the development of industrial strains.

It is since long known, that SAW devices, resonators as well as delay lines, can be used as passive wireless sensors for physical quantities like temperature and pressure as well as gas sensors or ID-Tags. The sensors are robust, work passively without battery, can be applied at high temperatures and provide a high resolution. Nevertheless, if the devices should be readout wirelessly in an industrial environment, several constraints have to be taken into account, especially when more than one quantity or device needs to be measured at the same time. The paper addresses the challenges that have to be tackled when establishing multi-sensor-wireless-readout for industrial applications. Major issues here are the legal ISM-band regulations, as well as sampling time and costs, which impose severe restrictions to any system design. We describe several design approaches and their constraints. We have successfully designed sensors based on reflective delay lines that allow the parallel readout of four independent temperature sensors in the 2.45 GHz ISM-band. These devices have been fabricated, positively tested and demonstrate the applicability of SAW sensors for industrial applications.

China combines green energy and industrial policy in its power market reform with various policy initiatives, including price support scheme for electricity from renewable sources and subsidies in the push for broader use of greener energy. This study focuses on the impacts of power market reform on the stock price volatility of listed power companies: 1) we use the Iterative Cumulative Sums of Squares (ICSS) algorithm to identify structural break points in stock prices; 2) we analyze the characteristics of stock price volatility based on the GARCH model; 3) we report the impact of power regulation on stock price fluctuations based on the Autoregressive Distributed Lag (ARDL) model. The result suggests three structural breaks in China’s power stock price volatility were related to the promulgation of power market reform policies. We find that industrial policies promote the reduction of power stock price fluctuations and its impact on power stock price volatility is consistent in the long run. However, our study also indicates the recent policies related to renewable energy do not have a very significant impact on the power stock market.

Solvents are widely-used in all aspects of chemical sciences. One of the disadvantages of conventional solvents is attributed to the adverse impacts they pose on human health and ecological systems. Emerging class of solvents such as ionic liquids have been proposed to alleviate this problem. In this study, aquatic toxicity and biodegradability of two common industrial solvents are compared to those of two ionic liquids. Results from this study highlight the importance of solvent selection considering the information on the toxicity, biodegradability and fate and transport properties of selected solvents altogether.

BAT-based regulations necessitate defining Key Environmental Issues (KEIs) to focus data collection and discussions on the main topics when establishing Best Available Techniques (BATs) at the sector level. However, Article 14 of the Industrial Emissions Directive (IED) suggests that some significant environmental issues may not be covered by BAT conclusions but still require the implementation of BATs at the local level, even in the absence of defined sectoral BAT references. The IED, along with the associated Commission Implementing Decisions and guides, does not offer guidelines for selecting KEIs at the local level, whether by the competent authority or the operator. To ensure full compliance with the IED by installations under its scope, this paper proposes a methodology for determining KEIs locally. Based on the environmental aspects of the installation, the sensitivity of the environment, and the levels of emissions or consumption, this methodology has been tested on a case study at the plant level to demonstrate its effectiveness. The paper then discusses the contributions and limitations of the methodology and suggests areas for future research.

This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. For this purpose, an experimental database comprising 1592 samples of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated by an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of the mortar with good prediction accuracy and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF =0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, bulk density and the open porosity of the substrate are significant factors. Furthermore, the study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.

Non-woven jute (NWJ) produced from the carpet industry waste was oxidized by H2O2 or alkali treated by NaOH and compared with water-washed samples. Changes in the structure of the NWJ, tracked by XRD, showing that both chemical treatments disrupt hydrogen bond networks between cellulose Iβ chains of the NWЈ fibers. Thereafter, nano carbon nitride (nCN) was impregnated, using layer-by-layer technique, onto water washed jute sample (nCN-Jw), NaOH treated sample (nCN-Ja) and H2O2 treated sample (nCN-Jo). Analysis of the FTIR spectra of the impregnated samples, revealed that nCN anchors to the water washed NWЈ surface through hemicellulose and secondary hydroxyl groups of the cellulose. In the case of chemically treated samples nCN is preferentially bonded to the hydroxymethyl groups of cellulose. The stability and reusability of nCN-J samples were assessed by tracking the photocatalytic degradation of Acid Orange 7 (AO7) dye under simulated solar light irradiation. Results from up to ten consecutive photocatalytic cycles demonstrated varying degrees of effectiveness across different samples. nCN-Jo and nCN-Ja samples exhibited declining effectiveness over cycles, attributed to bond instability between CN and jute. In contrast, the nCN-Jw sample consistently maintained high degradation rates over ten cycles, with a dye removal percentage constantly above 90%.

The main purpose of this study was to design a workplace in the conditions of a digital factory. The goal of this study is to compile a proposal for improving the layout solution in the selected enterprise to reduce material flows between individual workplaces. In addition, the workplace research study uses methods of material flow analysis with the application of visTABLE software support, which serves as a tool for designing production processes. The main result of the study was the reduction of the production time of the beam by 10 minutes and the creation of proposals for solutions for the company. The study helped with the visualization of processes within the digital factory to simplify and clarify processes at the workplace.

This paper describes design of experimental methodology developed to measure the working properties of accuracy of the path traversed by a collaborative robot. The methodology proposed here uses a collaborative robot and a laser measuring system Gepard from Raytec. The main parts of the measuring chain and the ISO 9283 standard are described. The proposed experimental methodology should examine working properties of industrial robots, such as the properties of position and path. The focus of this paper lies on the path accuracy of robots. Currently, the interest in this topic is on the rise and the measuring systems capable of recording this parameter are too costly. This paper focuses on experimental measuring of the path properties, describing them in more detail. The measuring and results were processed in the software tool developed for Gepard.

We explore the use of industrial sources of silicon and surfactant for obtaining low-cost MCM-41 materials and evaluate their performance as CO₂ adsorbents. All of them presented a high specific surface area with different structural characteristics and textural properties. The template was removed by using thermal treatments in an air atmosphere or a washing process. Preservation of silanol groups proved to be more effective under washing or mild thermal treatment conditions with the advantage of their lower cost and environmental benefit. Surface reactivity against CO₂ was enhanced by anchoring APTS to silanol groups through wet grafting. All amino-functionalized materials showed a performance as CO₂ adsorbents comparable to those reported in the literature. Samples with a higher concentration of silanol groups showed better performance. Our studies indicate that adsorbed CO₂ is retained at least up to 50°C, and the presence of aminopropyl groups is a key issue in CO₂ chemisorption on the MCM-41 surface. The chemisorbed gas at very low pressures points to the potential use of these materials for CO₂ storage.

Tourism gentrification presents issues of privatization of public space, the transformation of public services to cater to tourists, the erosion of community social bonds, and the commodification of regional consumption, which has far-reaching impacts on the well-being of local residents and a desire to relocate. Thus, tourism gentrification in traditional industrial areas is defined as a process driven by the reconfiguration of spatial functions and the transformation of service provisions, resulting in displacement. This study employs Q methodology aims to understand the residents’ perceptions of tourism gentrification with a specific focus on the 798 Industrial Art Zone in Beijing, China. The results reveal four dimensions that form residents’ stress: neighboring environment, community attachment, economic interest, and cultural identity. Based on the stress threshold theory, this study further proposes a framework to explain the decision-making processes of residents in relation to their intent to relocate due to stress from interactions with migrants and visitors and changes in the place functions of industrial areas and communities. The findings offer valuable theoretical knowledge surrounding tourism gentrification from residents’ perspectives and suggest practical guidance for policymakers and destination managers in reducing residents’ stress and promoting sustainable development of industrial tourism.

The physicochemical treatment/recovery techniques, including precipitation, membrane technology, solvent extraction, foam fractionation, adsorption, and aqueous two-phase systems, will be reviewed. The bio-treatment processes, based on microorganisms and/or enzymes consuming nutrients available in food-processing wastewater as low-cost substrates to produce valuable products, will be discussed in detail, including the production of microalga biomass in wastewater treatment systems. Finally, future research direction will be proposed to integrate the circular economy and develop integrated food processing wastewater management systems.

Product assembly is usually one of the last steps in the entire production process. This activity is typically entrusted to assembly workers because it is generally not possible to automate every type of product. For complex products, assembly can take a long time until the fitter learns the procedure and is able to assemble the product on his own. This contribution presents a cus-tom-developed system that enables controlled assembly of the extruder and can be used for complex and diverse products. The system serves to guide the fitter precisely and shows him which part to use at which time. The proposed system will show and describe on the display all necessary assembly steps and parts. Two-step verification is used to ensure that the correct part is picked from the stack. The contribution is supported by the implementation of a case study in a small company with a sample of 30 employees, which demonstrates that the proposed system shortens the extruder assembly time and significantly reduces the error rate. The presented solution is scalable and flexible, as it can be easily adapted to display the assembly steps of another product.

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study's objectives. This study focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover. this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of Precision, Accuracy, Recall values, F1 score, R.O.C. curves, and Confusion matrix. The results demonstrated that the X.G.B. model outperformed in terms of all the matrices with an Accuracy of 97.50 %, 97.60 % Precision, 97.60 % Recall values, and 97.50 % F1 score. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.

The mining sector generates a substantial quantity of stone waste and tailings, which constitutes an environmental risk. The most prevalent method for disposing of this industrial waste is dumping, which contributes to soil deterioration and water contamination while acquiring precious land. It can be recycled using a number of processes, such as the promising geopolymerization technique, which transforms waste into value. This study reviews current developments in the manufacturing of mine tailings-based geopolymer composites from industrial waste as a possible sustainable building material. This paper also gives in-depth studies on the characteristics and behaviors of mine tailings composites used in geopolymer manufacturing, including durability, microstructure, thermal and leaching properties. This review also identifies knowledge gaps that must be filled in order to advance mine tailings composites for geopolymers.

Despite intensive HIV education and prevention efforts in the past years, stigmatizing attitudes toward people living with HIV (PLWH) remain a major barrier to HIV prevention and treatment efforts in Vietnam. The purpose of this study was to examine the prevalence of stigmatizing attitudes regarding HIV and identifying correlative factors that impact perceptions of PLWH among women migrant workers working in the industrial zones (IZ) in Hanoi, Vietnam. A cross-sectional study was conducted among 1061 women migrant workers aged 18 to 29 from January to November 2020 in Hanoi, Vietnam. Stigmatizing attitudes toward PLWH were measured using a four-item scale. Multiple logistic regression was conducted to examine factors associated with stigmatizing attitudes. Over seventy-six (76.2 %) of the participants reported having at least one of the four stigmatizing attitudes. Greater levels of stigmatizing attitudes toward PLH were significantly associated with lower HIV knowledge, lower education and being Kinh (the ethnic majority in Vietnam). A high level of stigmatizing attitudes toward PWH among the study participants suggests that there is an urgent need for the development of appropriate culturally interventions and outreach education activities to reduce stigmatizing attitudes toward PWH among women migrant workers working in the IZs in Vietnam.Despite intensive HIV education and prevention efforts in the past years, stigmatizing attitudes toward people living with HIV (PLWH) remain a major barrier to HIV prevention and treatment efforts in Vietnam. The purpose of this study was to examine the prevalence of stigmatizing attitudes regarding HIV and identifying correlative factors that impact perceptions of PLWH among women migrant workers working in the industrial zones (IZ) in Hanoi, Vietnam. A cross-sectional study was conducted among 1061 women migrant workers aged 18 to 29 from January to November 2020 in Hanoi, Vietnam. Stigmatizing attitudes toward PLWH were measured using a four-item scale. Multiple logistic regression was conducted to examine factors associated with stigmatizing attitudes. Over seventy-six (76.2 %) of the participants reported having at least one of the four stigmatizing attitudes. Greater levels of stigmatizing attitudes toward PLH were significantly associated with lower HIV knowledge, lower education and being Kinh (the ethnic majority in Vietnam). A high level of stigmatizing attitudes toward PWH among the study participants suggests that there is an urgent need for the development of appropriate culturally interventions and outreach education activities to reduce stigmatizing attitudes toward PWH among women migrant workers working in the IZs in Vietnam.

New materials are obtained by transforming fly ash wastes into a valuable composite, with tandem adsorption and photodegradation properties. Mild hydrothermal synthesis, from titanium dioxide, Platinum nanoparticles and zeolite materials obtained from a waste, fly ash, as support, was involved in the composite preparation. The Platinum nanoparticles extended the photocatalytic activity of the composite in Visible range (Eg = 2.1 eV). The efficiency of tandem adsorption and photocatalytic activity of the new composite were evaluated to 80.70% for Bemacid Blau and 93.89% for Bemacid Rot, after 360 min, the irradiation time, with H2O2 addition.

Eco-friendly and low-cost water-based nanolubricants containing rutile TiO2 nanoparticles (NPs) were developed for accelerating their applications in industrial-scale hot steel rolling. The lubrication performance of developed nanolubricants was evaluated in a 2-high Hille 100 experimental rolling mill at a rolling temperature of 850 ℃ in comparison to that of pure water. The results indicate that the use of nanolubricant enables to decrease the rolling force, reduce the surface roughness and the oxide scale thickness, and enhance the surface hardness. In particular, the nanolubricant consisting of 4 wt% TiO2, 10 wt% glycerol, 0.2 wt% sodium dodecyl benzene sulfonate (SDBS) and 1 wt% Snailcool exhibits the best lubrication performance by lowering the rolling force, surface roughness and oxide scale thickness up to 8.1%, 53.7% and 50%, respectively. The surface hardness is increased by 4.4%. The corresponding lubrication mechanisms are attributed to its superior wettability and thermal conductivity associated with the synergistic effect of rolling, mending and laminae forming that are contributed by TiO2 NPs.

This paper studies the influence mechanism of industrial agglomeration and foreign direct investment (FDI) on green total factor productivity (GTFP). We use the SBM Directional Distance Function to measure the GTFP of Chongqing's manufacturing industry from 1999 to 2015. The results show that the level of GTFP in Chongqing's manufacturing industry is relatively low, which is contrary to the current green development mode. By clarifying the conduction path of industrial agglomeration and FDI on GTFP, we use the panel Tobit model to study the effect of industrial agglomeration and FDI on GTFP. The main findings are: the higher the level of industrial agglomeration, the more beneficial it is to increase GTFP. FDI has an inhibitory effect on GTFP. The spillover effect of FDI on GTFP is not significant. At the same time, FDI counteracts the role of industrial agglomeration in promoting GTFP. The findings in a present study indicate that, according to Chongqing's experience, the "pollution haven" is established. Therefore, relying solely on foreign technology to promote the development of the manufacturing industry has many drawbacks clearly. Only by improving the ability of independent innovation is the reliable way to enhance GTFP effectively.

Industrial hemp (Cannabis sativa L.) is primarily a dioecious plant, and monoecious varieties developed for high yield. Production practices vary by varieties prompting the need for the present study aimed to describe the floral characteristics and evaluate pollen quantity and viability of a monoecious (Henola) and three dioecious (CFX-2, Canda, and Joey) varieties. All varieties have five lanceolate anthers, basifixed to a threadlike filament. Anther length was significantly different but not anther width among varieties. The longest length (0.38 ± 0.046 cm) was recorded in Henola, and shortest (0.34 ± 0.043 cm) in CFX-2. Anther width ranged from 0.088 ± 0.0024 to 0.095 ± 0.0021cm. Pollen grains were triporate and spheroidal in shape and size differed significantly with the largest in Joey (27.83 ± 0.78 μm) and Henola (27.489 ± 0.99 μm), and smallest in Canda (22.04 ± 0.56 μm). The number of pollen grains differed significantly among varieties ranging from 29,183 in Henola to 104,548 in Joey. Even though Henola recorded the lowest pollen number it had the highest percentage (69.3%) of viable pollen and Canda recorded the lowest. There was a moderate, positive and significant relationship (r = 0.496) between anther length and the number of pollen grains in Joey. The relationship in Henola was moderate and non-significant (r = 0.356), weak and non-significant in Canda (r = 0.188), and in CFX-2 (r = 0.037). The findings from our study provide information for growers and researchers on hemp breeding and cultivation practices that may contribute to prevent cross pollination.

Mexican yuca (Yucca decipiens Trel.) is native to the semidesert region across north-central Mexico. Based on its empirical medicinal uses, the flour produced from leaves and stems of yuca was evaluated to determine its agro-industrial and medicinal uses (food, beer, and mezcal). The flour was characterized based on the granulometry, rheology, texture, and functional properties, which were analyzed with the RStudio software. The results indicate that the water absorption capacity of yuca flour (data here) is similar to wheat flour; however, it absorbs half the volume of oil than wheat flour. Therefore, it has a hydrophilic behavior similar to that of wheat and a lower oil requirement for formulas. The expansion capacity of yuca flour is like that of wheat flour, showing an ideal gluten content for agro-industrial purposes (food) that require this structural component. The flour had a remarkable foam formation, high stability, and low-fat content. These results are particularly important for foods that require foaming properties. Fermentation matched the parameters of the 56-60 Physicochemical Test Methods of the Cereal & Grains Association; consequently, their flours are classified as equal to flours produced from soft and weak wheat, supporting their use for fermentation processes. The internal friction values (0.85-0.92) suggest a limited flow; however, its high density shows a fine granulometry that facilitates the sacking, handling, and storing of the flour, complying with the Mexican standards, and allowing it to be added to combinations aimed to enrich food.

Tianjin is currently in the phase of urban stock renewal, yet its industrial heritage encounters significant challenges, including constrained spatial development, a misalignment between industrial progress and the reuse of industrial heritage, and an understated cultural representation. This study applies geospatial analysis to delineate the distribution characteristics of Tianjin's industrial heritage across time, space, and typology. Moreover, the geographic detector is employed to assess the impact of various factors on the development of Tianjin's industrial heritage, ultimately elucidating the correlations between existing challenges and objective patterns. The conclusion of this paper presents three strategic approaches for the renewal of industrial heritage, emphasizing historical culture, environmental space, and sustainable development. These strategies are designed to provide a solid research foundation for protecting and revitalizing Tianjin's industrial heritage resources.

This article underscores the immense potential for substantial economic growth and development that can be harnessed through effective participation in global value chains (GVCs). It emphasises the role of policymakers in adeptly navigating GVCs, prioritising tasks, exploring different forms of GVC governance, and fostering a conducive environment for foreign investments. By effectively managing power dynamics and supply chain risks, countries can attract valuable foreign investors, enhance market connectivity, and improve infrastructure and services, leading to significant economic growth. The potential benefits of GVC participation are vast, and policymakers can shape the situation by understanding and addressing strategic inquiries, laying the foundation for a prosperous future. Furthermore, the article explores the potential for a country to enhance its involvement in GVCs and progress to more lucrative activities by strengthening existing connections between GVCs and the local economy. By enhancing the capacity of local stakeholders to acquire knowledge, policymakers can play a crucial role in maximising the benefits from GVC spillovers, positively impacting a country's economic development.

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

The transition to cleaner energy sources, particularly in hard-to-abate industrial sectors, often requires gradual integration of new technologies. Hydrogen, crucial for decarbonization, is explored as a fuel in blended combustions. Blending or replacing fuels impacts combustion stability and heat transfer rates due to differing densities. An extensive literature review examines blended combustion, focusing on hydrogen-methane mixtures. While industrial burners claim to accommodate up to 20% hydrogen, theoretical support is lacking. A novel thermodynamic analysis methodology is introduced, evaluating methane-hydrogen combustion using the Wobbe index. Findings highlight practical limitations beyond 25% hydrogen volume, necessitating a shift to "totally hydrogen" combustion. Blended combustion can be proposed as a medium-term strategy, acknowledging hydrogen's limited penetration. Higher percentages require burner and infrastructure redesign.

Industrial robots are slowly but surely entering manufacturing companies. This paper explores the effects that robots have on productivity, exports, quality, sustainability and labor in European manufacturing companies. There is lack of research on industrial robot usage and its effects in developed countries. Most research is done on Chinese companies and often the data is outdated. The data in this paper is from the European Manufacturing Survey project conducted in 2022 and includes 476 manufacturing companies. Results of the effects of Industrial robots on Quality, Labor Productivity, Exports and Environmental saving technologies are done by use of T-test between industrial robots’ adopters and non-adopters. However, the effect of higher investment into environmental technology by industrial robots’ adopters was researched by 2-step OLS regression analysis with control variables representing the contextual characteristics of companies. The results show positive effects on all variables. Results show that larger usage of robots are in low to medium-technology-intensity industries, that robots contribute to labor productivity, exports and that companies employing robots usually also employ environmentally saving technologies.

Oil spills upset the delicate balance of the environment. Remediation methods are employed to recover areas affected by contaminants. Green surfactants have considerable potential with regards to enhancing environmental remediation methods. The aim of the present study was to formulate a green natural detergent containing the biosurfactant produced by Starmerella bombicola ATCC 22214 grown in mineral medium containing 1.2% canola oil, 10% sucrose, and 0.5% corn steep liquor. The biosurfactant reduced surface tension to 31.84 mN/m and was produced at a yield of 22 g/L. Twelve formulations were initially proposed using cottonseed oil as the natural solvent and three different stabilisers (vegetable wax, hydroxyethyl cellulose, and sodium alginate). The natural detergent was assessed with regards to emulsifying capacity and stability over a 10-day period. Ecotoxicity of the formulated detergent was investigated using the marine recruitment test on metal plates covered with paint into which the biosurfactant was incorporated as well as tests with the microcrustacean Artemia salina and vegetable seeds. Dispersing capacity and the potential for removing oil from rock surfaces were also investigated. The formulation designated H, which was composed of 1% biosurfactant, 40% cottonseed oil and 2% hydroxyethyl cellulose, achieved the best results. The formulation exhibited both stability and emulsifying capacity (100% of petroleum). The ecotoxicity tests revealed no significant toxicity, demonstrating the safety of the natural detergent. The detergent achieved satisfactory oil dispersion and solubilised 98% of oil impregnated on rock. The results indicate the potential application of the natural detergent formulated in this study for the efficient remediation of environmental compartments contaminated with oil and petroleum derivatives.

Digital twin technology is revolutionizing traditional manufacturing paradigms. In modern manufacturing systems, digital twin technology is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.

Background: Asthma is a widespread chronic respiratory disease that poses a significant public health challenge. The current study investigated the associations between air pollution and asthma severity among individuals residing near the Sohar industrial port (SIP) in Oman. Despite the presence of multiple major industrial complexes in Oman, limited knowledge regarding their impact on respiratory health is accredited. Hence, the primary objective of this study is to offer valuable insights into the respiratory health consequences of industrial air pollution in Al Batinah North. Methods: The state health clinics' records for patient visits related to asthma were collected for the timeframe spanning 2014 to 2022. Exposure was defined as the distance from SIP, Majan Industerial Area (MIA) and Sohar Industerial Zone (SIZ) to determine high, intermediate, and low exposure zones (12 km, respectively). Exposure effect modifications by age, gender, and smoking status were also examined. Results: The conducted cross-sectional study of 410 patients (46.1% males and 53.9% females) living in over 17 areas around SIP revealed that 73.2% of asthmatics were under 50 years old, with severity significantly associated with closeness to the port. Risk ratios were estimated to be (RR:2.42; CI95%: 1.01–5.78), (RR:1.91; CI95%: 1.01–3.6), and (RR:1.68; CI95%: 0.92–3.09) for SIP, MIP and SIZ areas, respectively, compared to the control area. Falaj Al Qabail (6.4 km) and Majees (6 km) had the highest number of asthma patients (N. 69 and N. 72), and highest percentages of severe asthma cases among these patients (28% and 24%) with significant risk ratios (RR:2.97; CI95%: 1.19–7.45 and RR:2.55; CI95%: 1.00–6.48), correspondingly. Moreover, severe asthma prevalence peaked in the 25-50 age group (RR:2.05; CI95%: 1.26–3.33), and this linkage between asthma and age was much more pronounced in males than females. Smoking and exposure to certain contaminant (dust and smoke) increased also the risk of severe asthma symptoms, but their effects were less important in the high risk zone, suggesting much more important risk factor. A neural network model accurately predicted asthma risk (94.8% accuracy), with proximity to Sohar Industrial Port as the most influential predictor. Conclusion: The study highlights the High asthma burden near Sohar Industrial Port, linked to port proximity and smoking and wind direction as major risk factors. These findings inform vital public health policies to reduce air pollution and improve respiratory health in the region, prompting national policy review.

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the industrial revolutions which have continued expansion and progress into the fourth industrial revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. The significance of standardized data and its mode of collection cannot be overstated as it has a major impact on the process outcomes. In order to achieve a better understanding of the impact of this, this study methodically reviewed the literature on the subject of the data collection and processing mechanism of 4IR technologies in the context of the transport industry with an emphasis on the areas of accomplishment of several studies and drawing attention to their limitations. Although the industry has advanced significantly as a result of the adoption and application of various research studies in this field, considerable focus needs to be placed in the areas of standardized data collection and computational approaches, which will ultimately assist in achieving various benefits in the transportation industry.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

This paper presents the design and implementation of a remote supervision and control system for a gunpowder drum filling plant. The use of technologies such as SCADA (Ignition), IoT (Node-RED), ERP (Odoo) and digital twins (Factory I/O) to achieve remote monitoring and operation of the plant is described. The solution allows full control of the production process from any location through customized graphical interfaces. The challenges encountered in integrating multiple systems and platforms are discussed, along with the security measures implemented within the Azure cloud environment. The results demonstrate the effectiveness of 4.0 technologies in automating and digitizing critical industrial processes. This research will lay the groundwork for the adoption of these approaches in companies seeking greater efficiency, traceability and safety in their operations. The experience gained allows us to successfully face the digital transformation in modern manufacturing.

The digital economy (DE) has become a major force driving the optimization and upgrading of industrial structures (ISOU), and research on the driving mechanism has yet to be verified. To this end, the driving influence of DE on the ISOU is targeted, and the role played by economic resilience (ER) and innovation is explored. Based on the panel data of 31 provinces in China from 2011 to 2020, the driving influence of the DE on the ISOU is analyzed using a two-way fixed-effects model. The empirical study finds that the DE presents a positive driving effect on both industrial structure advancement and rationalization, and the driving effect is still significant after the addition of control variables and the robust-type test with one period of lag; the DE presents regional differences in ISOU, with the driving effect of the DE on industrial structure advancement in the eastern region having a more significant performance than that of the central and western regions, and the driving influence on the rationalization of industrial structure in the eastern and western regions exceeds that in the central region; the impact of DE on the ISOU has a mediating role through innovation, ER has a moderating role in the process, and innovation as a partial mediator also has a threshold effect, and finally countermeasure suggestions are put forward.

Gastrodia elata Bl., one of the well-known edible plants in China, belongs to the family of Orchidaceae. In recent years, the consumption rate of this plant as food supplements or health products is gradually rising in China. However, the traditional industrialization processing procedures (TP) for this plant was found to be too complicated and not friendly to the environment with time and energy consuming. Hence, we investigated whether the processing procedures could be simplified without affecting its overall chemical profile and anti-oxidation activity. In this experiment, a new processing procedure (NP) was designed for processing this plant, and the chemical compositions of the extract for Gastrodia elata produced by two different processing procedures were identified by UPLC-Q-TOF/MS. It was found that phenols and parisins were the most abundant chemical components for this extract. Among all the identified components, the contents of gastrodin and its citric acid derivatives were obviously higher than that of other components. Therefore, the contents of six gastrodin derivatives and ethanol soluble extract rate for Gastrodia elata produced by two different processing procedures, traditional and newly designed processing procedures, were further comparatively investigated for the validation of the applicability. The DPPH radical scavenging activities of the extract from Gastrodia elata produced by two different processing procedures were also investigated. The results demonstrated that the contents of gastrodin and p-hydroxybenzyl alcohol in all samples were slightly decreased in NP samples compared with that of TP samples (except batches of S3 and S6 samples), but no significant difference was observed between these two groups (p = 0.8273 for gastrodin and p= 0.2320 for p-hydroxybenzyl alcohol). Additionally, the total contents of parisins were also in a decreased trend in NP samples but with no significant difference when comparing with that in TP samples (p = 0.1721). The results for ethanol soluble extract rate also had the same situation (p = 0.1094). Taken together, it was suggested that the chemical profiles and contents of gastrodin derivatives and ethanol soluble extract rate for NP samples was not significant different to that of TP samples. The DPPH radical scavenging activity of the extract of Gastrodia elata produced with NP method was not significant different from that of TP samples. As a result, the industrial processing procedures for the preservation of this material plant could be simplified to a more time and energy saving procedure without changing the quality of the products, which is also consistent with the concept of green and sustainable industrial production.

Air pollution is an important factor affecting human health and daily life. Chinese government is making vigorous efforts to control air pollution. The upgrading of industrial structure is a problem-solving tool in environment and economic growth cases. This paper aims to explore the relationships among environmental regulation, the upgrading of industrial structure and air pollution. The PVAR (Panel Vector Auto Regression) model and moderation effect model has been used to conduct empirical analysis based on panel data of 30 provinces in China from 2004 to 2020. The results of analysis indicate (1) the environmental regulation can significantly reduce the air pollution, but the deterioration of air quality could not effectively influence environmental regulations. (2) The upgrading of industrial structure can weaken the air pollution and air pollution hinders the upgrading of industrial structure. (3) With the improvement of environmental regulation, the industrial structure is constantly upgraded, but the upgrading of the industrial structure has a negative effect on the improvement of environmental regulation. (4) The upgrading of industrial structure can positively moderate the influence of environmental regulation on air pollution.

The paper deals with the design of a systematic procedure for implementing strategy changes into internal business processes for a project-oriented production type of organization. In the first part of the contribution, a summary of the theoretical starting points for the field of strategic management and change management is presented. In the second part, the contribution deals with the analysis of the current situation in the area of the impact of the change of strategy on the management of business processes. In the last part, the proposal of the procedure for implementing strategy changes into internal business processes and the verification results are presented. The proposed procedure includes steps where the selection and verification of key performance indicators at individual levels of management plus the quantification of the impact of the change in strategy on the processes takes place. The management can thus monitor and evaluate the chosen processes in accordance with the fulfilment of the chosen strategy of the company. The last chapter presents the verification of the proposal for the systematic introduction of changes into the processes.

The aim of this review is to present the Quality by Design (QbD) model as a suitable methodology to perform research in the academic Costa Rican institutions that teach Pharmacy. Pubmed, Science Direct, and Google Scholar databases were screened for original and review papers, as well as short communications published not more than 10 years ago. Publications were screened by title and abstract. Relevant references were used to develop three important themes: The University’s Research Model in Costa Rica, QbD Model, and QbD as a Research Methodology for Industrial Pharmacy in the Academy. In this sense, the QbD model is a great methodology for carrying out research projects regarding Pharmaceutical Sciences but especially for Drug Development. Academic research based on this model enables training and developing practical, scientific, and leadership skills in pharmacy students. The generated knowledge can be shared in the classrooms, which represents an ideal environment to communicate their research results and to foster collaborative work between researchers, professors, and students. The participation of all these actors allows a high level of commitment to research work, which benefits the scientific advancement of the university and society. It is important to visualize the student body as potential key actors in the research process, encouraging in them the desire to become trained scientific researchers who want to pursue a career in the academy, giving continuity to it.

The growing concern about climate change and the recognition of the planet’s limits led society to look for alternatives that promote the balance between the natural and the built environment. The circular economy emerges as an alternative to the linear economic model, inspired by natural metabolisms, by circulating resources in continuous loops, where their intrinsic value is maintained and improved. This research proposes a closed-loop strategy in the built environment by studying innovative constructive solutions that aim to find use, value, and inspiration in what is considered waste. A literature review is conducted on the circular design strategies, re-use and recycle typologies, and waste transformation processes. Then, the development of a methodology for qualitative evaluation and selection of re-used and upcycled construction materials from post-consumer waste and by-products is presented and then applied to thirty-five cases of constructive solutions from plastic, wood, paper, steel, aluminium, and agricultural waste. The research reports that the developed framework is adequate. The analysed alternative materials have good environmental performance and can be used as building materials despite their functional limitations, reflecting the enormous potential of waste as a resource for the construction industry.

This purpose of the study presented in this article is to comparing different service quality measurements between Kano and TRIZ that plays the critical roles in the catering industrial. Data collected from a DINESERV questionnaire comprises service-quality standards to increase customer satisfaction of mobile dining car. Finally, the TRIZ is standardized measure designed to improve the idealization of strategy for selecting the most appropriate service quality model. In addition, the preferences of more than one decision maker are internally aggregated into the TOPSIS procedure. All these things provide several important theoretical and practical implications for developing a successful mobile catering app.

Marketing is one of the important stages, the decisive factor affecting the success of production and business activities of every business. Therefore, marketing is considered to be the first and most important stage in the process of introducing, bringing products to market and branding of businesses, especially in the period when the disease situation is taking place very complicated worldwide. This study provides both qualitative and quantitative results for the following research objectives. Firstly, the study will assess the situation and analyze the appropriateness of marketing models in the current market situation trend. Secondly, the study proposes an approach in building a new type of marketing model, which is 3D web-based marketing applied in e-commerce to support the modeling of enterprise products in the form of Interactive 3D products are similar to real products. Thirdly, testing and evaluating 3D e-commerce web marketing model at Truong Hai Auto Showroom, Thai Nguyen City branch. This study also proposes a number of solutions for the research and deployment of 3D web marketing model application for businesses in the current market situation.

Automation is at the core of modern industry. It aims to increase production rates, decrease production costs, and reduce human intervention in order to avoid human mistakes and time delays during manufacturing. On the other hand, human assistance is usually required to customize products and reconfigure control systems through a special process interface called Human Machine Interface (HMI). Machine Learning (ML) algorithms can effectively be used to resolve this tradeoff between full automation and human assistance. This paper provides an example of the industrial application of ML algorithms to help human operators save their mental effort and avoid time delays and unintended mistakes for the sake of high production rates. Based on real-time sensor measurements, several ML algorithms have been tried to classify paper rolls according to paper grammage in a white paper mill. The performance evaluation shows that the AdaBoost algorithm is the best ML algorithm for this application with classification accuracy (CA), precision, and recall of 97.1%. The generalization of the proposed approach for achieving a cost-effective mill construction by reducing the total number of the required physical sensors will be the subject of our future research.

This paper utilizes dynamic panel threshold technology to conduct a nonlinear test on the direct effect between environmental regulation and economic fluctuations and the effect of industrial structure upgrading by taking 35 industrial sectors in China from 2003 to 2016 . The research has found that there is an inverted "U" relationship between environmental regulation and economic fluctuations, with the enhancement of environmental regulations, the economic fluctuation increases first and then decreases. The cross-terms of environmental regulation and industrial structure rationalization or industrial structure upgrading are significantly negative, which indicates that the enhancement of environmental regulation is conducive to promoting industrial structure upgrading and reducing the economic fluctuations. While the rationalization factors of industrial structure and advanced industrial structure are significantly negative, indicating that both forms of industrial structure upgrading are conducive to reducing the economic fluctuations. Environmental regulation has technical innovation thresholds for industrial structure upgrading and economic fluctuations, but there are no human capital or FDI thresholds. In the rationalization model of industrial structure, there is a nonlinear "U" relationship between environmental regulation and economic fluctuations when the proportion of scientific research expenditure is more than 1.35%. With the enhancement of environmental regulation, the economic fluctuation reduces first and then increases, and the corresponding inflection point value is 2.398% of the environmental regulation level. At the same time, the environmental regulation can indirectly reduce economic fluctuations by pushing down the industrial structure upgrades. In the advanced model of industrial structure, there is a “U” relationship between environmental regulation and economic fluctuation when the proportion of scientific research expenditure is greater than 1.26%. With continuous enhancement of environmental regulation, the economic fluctuation reduces first and then increases. The corresponding inflection point value is 1.78% of the environmental regulation level, and environmental regulation can indirectly reduce economic fluctuations by promoting the industrial structure at the same time.

Industries, which are mainly responsible for high energy consumptions, need to invest in research projects in order to develop new managing systems for rational energy use and to tackle the devastating effects of climate change caused by human behavior. The study reported in this paper concerns the forging industry, where the production processes generally start with the heating of the steel in furnaces and continue with other processes, such as heat treatments and mechanical machining. One of the most critical operations, in terms of energy loss, is the opening of the furnace doors for the insertion and extraction operations. During this time, the temperature of the furnaces decreases by hundreds of degrees in a few minutes. Because the dispersed heat needs to be supplied again through the combustion of fuel, increasing the consumption of energy and the pollutant emissions, the evaluation of the amount of the lost energy is crucial for the development of operating or mechanical systems able to contain this dispersion. To perform this study, CFD simulation software was used. Results show that at the door opening, because of temperature and pressure differences between the furnace and the ambient, turbulences are generated. Results also show that the amount of energy lost for an opening of 10 minutes for radiation, convection and conduction is equal to 5606 MJ where convection is the main contributor with 5020 MJ. The model created, after being validated, has been applied to perform other simulations in order to improve the energy performance of the furnace. Results show that a reduction of the opening time of the door allows energy savings and limits pollutant emissions.

Urbanization is considered as a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, are troubled by its negative effect — the aggravating environmental pollution. Many researchers have indicated that rapid urbanization stimulated the expansion of industrial production scale and increased industrial pollutant emissions. However, this judgement contains a grave deficiency in that urbanization not only expands industrial production scales but can also increase industrial labour productivity and change the industrial structure. To modify this deficiency, we first decompose the influence which urbanization impacts on industrial pollutant emissions into the scale effect, the intensive effect and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects’ impacts by applying the spatial panel model with data from 282 Chinese cities between 2003 and 2013. Our results indicate that (1) there are significant reverse U-shapes between Chinese urbanization rate and its industrial pollutant emissions; (2) the scale effect and the structure effect have aggravated Chinese industrial waste water discharge, sulphur dioxide emissions and soot (dust) emissions, while the intensive effect has generated a decreasing and ameliorative impact on that aggravated trend. The definite relationship between urbanization and industrial pollutant emissions depends on the combined influence of the scale effect, the intensive effect and the structure effect; (3) there are significant spatial autocorrelations of industrial pollutant emissions between Chinese cities, but the spatial spillover effect from other cities does not aggravate local urban industrial pollutant emissions, we offer an explanation to this contradiction that the vast rural areas surrounding Chinese cities have served as sponge belts and have absorbed the spatial spillover of cities’ industrial pollutant emissions. According to the results, we argue that this type of decomposition of the influence into three effects is necessary and meaningful, it establishes a solid foundation for understanding the relationship between urbanization and industrial pollutant emissions, and effectively helps to meet relative policy making.

This paper focuses on CEOs, who are considered the strategic leaders of corporations because of their crucial role in making important decisions. This research paper examines how female CEOs influence green innovation (GI) in Asian industrial sector. Its main objective is to answer the following research questions: Are there differences in GI endeavors between Asian industrial firms with female CEOs and those with male CEOs? Does the size of the industrial firms have a positive moderating effect on the relationship between CEO gender and GI? Based on our research questions, the size of firms is likely a determining factor in the GI of female CEOs. This research employs rigorous econometric modeling to analyze a substantial dataset of Asian industrial listed companies spanning from 2013 to 2022. It has been proven that industrial firms led by females CEOs are more inclined to have environmentally friendly practices. Furthermore, the size of the industrial firms amplifies the beneficial influence of female CEOs on the firm's chances of engaging in GI initiatives. Our outcomes stay robust to endogeneity issues using two-step GMM estimators. This study proposes that stakeholders, particularly in Asian countries, should promote the increased representation of females in CEO roles, particularly within large corporations. This is because women-led companies demonstrate superior performance in GI endeavors. Thus, regulators need to implement policies that promote the inclusion of women in CEO positions within large-scale enterprises. These policies may potentially strengthen the private sector’s capacity to foster sustainable innovation.

More than 400 million tons of livestock waste are generated annually by approximately 25,000 concentrated animal feeding operations (CAFOs) located in the United States (US). These operations give rise to externalities, including adverse local and global health impacts from CAFO waste emissions, which can potentially outweigh their economic viability. However, a shortage of evidence synthesis research on US-based CAFO emissions may hinder effective policy development. This scoping review (ScR) study, conducted following guidance from the Joanna Briggs Institute, searched databases including Scopus, Web of Science, PubMed and Embase in May 2020, yielding ten publications meeting inclusion criteria. Results suggest possible CAFO worker exposures to multidrug-resistant Staphylococcus aureus (MDRSA), campylobacteriosis and cryptosporidiosis. Communities near CAFOs experienced higher rates of adverse health impacts compared to those in non-CAFO areas, with patterns suggesting proximity may correlate with increased odds of detrimental health effects. Implicit global health threats include Methicillin-resistant Staphylococcus aureus  (MRSA), MDRSA, campylobacteriosis, tuberculosis, and cryptosporidiosis. These studies provide foundational insights into CAFO proximity, density patterns, and adverse public health effects, indicating a need for evidence-informed environmental health policies to minimize local and global risks.

The introduction of the Industrial Internet of Things (IIoT) has led to major changes in the industry. Thanks to machine data, business process management methods and techniques could also be applied to them. However, one data source has so far remained untouched: The network data of the machines. In the business environment, process mining, for example, has already been carried out based on network data, but the IIoT, with its particular protocols such as OPC UA, has yet to be investigated. With the help of design science research and on the shoulders of CRISP-DM, we first develop a framework for process mining in the IIoT in this paper. We then apply the framework to real-world IIoT network traffic data and evaluate the outcome and performance of our approach in detail. We find tremendous potential in network traffic data but also limitations. Among other things, due to the dependence on process experts and the existence of case IDs.

The silos built in the 1930s for Italy’s ammasso (obligatory grain stockpile) stood as powerful symbols of the country’s fascist era. Many of them were destroyed in World War II; only a few remain today, and recent demolitions underscore the urgency of their preservation. This study adapts a methodology developed for inventorying Spanish silos based on their general features, construction, technological facilities, and socioeconomic aspects and applies it to the 30 remaining ammasso silos. These silos are located in grain-producing areas near communication routes and are mostly owned by agricultural consortia. Over half of them are disused and in disrepair. All of them have machinery for receiving, storing, and dispatching grain, and most have equipment for cleaning, sorting, weighing, and packaging if needed. These structures are part of Italy’s agro-industrial heritage and should be protected. Some have been made over into commercial spaces, leisure areas, museums, and even homes. Vertical-cell silos are challenging to convert, unlike horizontally-oriented silos and other open-plan agro-industrial buildings. Examples of silo reuse in other countries, such as Spain and Portugal, can offer useful insights for the ammasso silos. However, socio-economic indicators suggest that such projects are most viable in large population centres.

The state of Veracruz has ample water resources. However, due to population and industrial growth, there has been an increase in the discharge of wastewater into surface water bodies. To analyze the water quality in the Bobos River, Veracruz, and its sustainability, researchers collected nine samples from nine different points in the reservoir. They measured various parameters such as temperature, pH, dissolved oxygen, conductivity, total nitrogen, nitrate, chemical oxygen demand, total phosphorus, phosphate, dissolved solids, and fecal coliforms. The results showed that the river's water quality is at risk. The concentration of pollutants such as chemical oxygen demand and fecal coliforms is high. Conductivity values ranged from 0.26 to 3.81 mS. If preventive and corrective measures are not taken, the river's ability to purify pollutants will be severely compromised. This study evaluates the physical, chemical, and microbiological characteristics of the water in Bobos River. It specifically investigates the influence of the nearby citrus industry on the river's water quality. The study also aims to promote environmental sustainability.

This analysis examines the wide range of occupational and environmental health risks that affect people in diverse settings. This article emphasizes the importance of conducting exposure and risk assessments to safeguard public health by analyzing various hazards, including arsenic, oil accidents, lead, welding fumes, and mycotoxins. Various methodologies are utilized in these research articles to conduct the study, emphasizing hazard identification, exposure assessment, and risk characterization. The results uncover harmful substances, vulnerable demographics, and occupational duties linked to increased hazards. The report suggests various mitigation strategies, such as targeted interventions, enhanced community efforts, and improved exposure estimates. Notwithstanding the constraints identified in certain studies, the review emphasizes the necessity for continuous investigation to improve assessments of exposure, comprehend the cumulative effects, and examine the wider societal consequences. The significance of sector-specific risk assessments, protective measures, and ongoing monitoring is underscored to ensure mental and physical health protection.

Since China’s economic reform launched in the late 1970s, rural industrial parks (RIPs) have played a crucial role in the rise of the manufacturing industry in the Pearl River Delta (PRD), transforming the former agricultural land into a location of world-famous factories. However, as China has put forward higher requirements for economic development and stricter control measures for environmental pollution, RIPs with low-end industries and low efficiency present an urgent need for renovation and upgrade. By analyzing the spatial and socioeconomic cross-sectional dataset of rural industrial parks in Nanhai District of Foshan City, this paper attempts to classify the existing industrial parks into groups and propose corresponding redevelopment strategies. The results of the spatial analysis show that the spatial distribution of rural industrial parks is closely related to their location and transportation conditions. Failure of the market has resulted in large tracts of advantageous land being taken up by inefficient industrial parks. Cluster analysis and overlay analysis are used evaluate the difficulty of redevelopment and divide the industrial parks into three clusters: retained RIPs, medium-term-removed RIPs, and near-term-removed RIPs. It is shown that some of the industrial parks are still in good form while the majority need substantial reconstruction. Taking economic and institutional factors into consideration, the authors put forward that different strategies should be adopted for the future renovation of medium-term-removed and near-term-removed RIPs.

The global predicament of solid waste generation looms as a mounting crisis, profoundly impacting the world, particularly in developing nations. Each day, the world's population contributes millions of tonnes of municipal solid waste, with projections pointing toward further escalation in the coming years. This surge in waste, predominantly composed of cellulose-rich materials, correlates directly with heightened pollution, a threat that transcends borders and imperils humanity as a whole.To confront this ever-growing issue, cellulase, a potent hydrolytic enzyme capable of digesting cellulose, a chief component of solid waste, is explored as a potential solution. In this study, we harness cellulase enzymes derived from the digestive tracts of both Archachatina marginata and Oryctes rhinoceros larvae, employing them as biodegradation agents for cellulose-based waste materials.The cellulase enzymes are methodically isolated and partially purified via ammonium sulfate precipitation at 65% saturation. Their efficacy in breaking down a variety of waste categories, encompassing kitchen waste, leaves, paper, wood, and industrial waste, is meticulously assessed and benchmarked against carboxymethyl cellulose (CMC), a well-established laboratory substrate. The study also ascertains the specific activity of these enzymes.Spectrophotometric analyses of the cellulase enzymes' actions on the diverse waste materials unveil intriguing results. Archachatina marginata and Oryctes rhinoceros larvae exhibit distinct performance metrics, registering percentage ranges of (11.04% - 28.31%) and (33.69% - 189.11%) on wood waste, (76.52% - 196%) and (108.56% - 567.01%) on paper waste, (78.57% - 148.14%) and (163.33% - 436.76%) on leaf waste, (57.16% - 179.95%) and (63.38% - 457.05%) on kitchen waste, and (59.95% - 77.51%) and (117.26% - 155.23%) on industrial waste, respectively. This study underscores the substantial biodegradation potential inherent in cellulase enzymes from both Oryctes rhinoceros larvae and Archachatina marginata when applied to diverse waste materials. While the rates and impacts of waste degradation vary between these sources, their promising contributions to waste management come into focus, offering sustainable and innovative approaches to tackle the pressing global challenge of solid waste proliferation.

The study aims to analyze the characteristic parameters of rooftop photovoltaic (PV) power generation on industrial plant buildings in the Ningxia region of China, in order to evaluate the impact of passive design characteristic parameters on the benefits of PV power generation and determine the degree of impact of different passive design characteristic parameters. The methodology involves analyzing the characteristics of existing industrial plant buildings in Ningxia, China, and conducting a series of regional studies on the parameters of plant PV power generation influencing factors. The simulation results revealed that five features, including roof form, PV panel laying pattern, PV panel laying area, azimuth angle, and PV module material, have a significant impact on PV power generation benefits of industrial plant buildings.The study further uses MATLAB 2022b to build a backpropagation neural network model with 5 neurons in the implicit layer to predict the PV power generation benefits. The model calculates the whole-life cycle power generation benefits and the whole-life cycle reduction of carbon emissions as output data. Finally, the Mean Impact Value (MIV) method is employed to select the feature parameter values one by one within their range, and the passive design feature parameters that have the greatest influence on the prediction results are identified as the PV panel laying method, the PV panel tilt angle, and the PV material parameters, respectively.

This article postulates that, in principle, it is possible (1) to develop linkages between the extractive sector and other economic sectors; (2) for such linkages to contribute to economic diversification; and (3) for economic diversification to potentially drive positive economic transformation. We argue that achieving this three-stepped pathway is impossible without also achieving some level of political and social transformation. Empirically, many resource-rich countries have failed to develop linkages. Or, if they have, the linkages built remain limited to the development of supply chains serving the extractive sector, contributing little to economic diversification. In this article, we refer to limited linkages and dependence on exporting unprocessed minerals as the centripetal force of the extractive sector. This implies that capital investments and economic activities are concentrated around the extractive sector. We propose an alternative theoretical model that strives to foster the centrifugal force of the extractive sector – a term we use to capture the three-stepped pathway. This model is underpinned by multiple factors and their interactions, which are within the purview of the complex state and market relationship (posing as a challenge) and the role of minerals in the global transformation towards a clean energy system (posing as an opportunity).

Taking Shanxi Province, a typical energy region in China, as the research object, data on economic development quality and environmental pollution intensity from 2001 to 2021 were selected. Linear regression, numerical simulation, and Pearson correlation coefficient were used to analyze the evolution characteristics of economic development quality and environmental pollution intensity in Shanxi Province in the past 20 years, and to explore the relationship between economic development and environmental pollution. The results indicate that since 2001, Shanxi Province has made long-term progress in economic development, with GDP increasing by nearly 10 times and an average annual growth rate of around 7%. In the past 20 years, the main pollutants have shown a trend of increasing first and then decreasing steadily, with a turning point occurring during the "12th Five Year Plan" period. This shows that the environmental policies and investments of the Chinese and Shanxi provincial governments in the past 10 years have been effective in the new era. The numerical simulation curve results show that the per capita GDP exhibits a classic inverted "U" curve relationship with wastewater and SO2 emissions, with a turning point occurring at around 20000 yuan per capita GDP; However, there is a monotonic decreasing trend with Chemical oxygen demand and ammonia nitrogen emissions, and a monotonic increasing trend with solid waste generation. There is no turning point yet. The correlation analysis results further support the fitting curve conclusion.

The contemporary discourse focuses on the fourth industrial revolution and its impact on compa-nies. An explicative, quantitative, questionnaire survey was conducted with managers of 101 companies in Hungary and 54 in Austria. Our study focused on assessing their preparedness for Industry 4.0 and the key factors they take into account when implementing Industry 4.0 initiatives. We also examined whether there is a correlation between the areas in which companies have made improvements to support Industry 4.0. To complement our quantitative analysis, we conducted a series of four semi-structured interviews with two Hungarian and two Austrian companies. The aim of the interviews was to further investigate the digital readiness of companies and to formulate recommendations for companies venturing into the field of digitalisation. A particular focus was put on comparing the digital maturity of these two neighbouring Central European countries, given the significant differences in their digital maturity as shown by the macro indicators of the Digital Economy and Society Index (DESI). Therefore, we conducted a micro-level analysis to investigate the extent to which DESI scores correspond to the digital performance of companies at the orga-nisational level.

MultiCal is an affordable, high-precision measuring device designed for the on-site calibration of industrial robots. Its design features a long measuring rod with a spherical tip that is attached to the robot. By restricting the rod’s tip to multiple fixed points under different rod orientations, the relative positions of these points are accurately measured beforehand. A common issue with MultiCal is the gravity deformation of the long measuring rod, which introduces measurement errors into the system. This problem becomes especially serious when calibrating large robots, as the length of the measuring rod needs to be increased to enable the robot to move in a sufficient space. To address this issue, we propose two improvements in this paper. Firstly, we suggest the use of a new design of the measuring rod that is lightweight yet has high rigidity. Secondly, we propose a deformation compensation algorithm. Experimental results have shown that the new measuring rod improves calibration accuracy by 20% to 39%, while by using the deformation compensation algorithm, the accuracy increases by 6% to 16%. In the best configuration, the calibration accuracy is similar to that of a measuring arm with a laser scanner, producing an average positioning error of 0.274 mm and a maximum positioning error of 0.838 mm. The improved design is cost-affordable, robust, and has sufficient accuracy, making MultiCal a more reliable tool for industrial robot calibration.

This study utilized panel data from 31 Chinese provinces over a period of nine years to investigate the impact of green finance on the upgrading of the agricultural industrial structure. A fixed-effect model was employed, and the findings indicate that green financing has a positive effect on the growth of China's agricultural industry. However, regional disparities exist, particularly in the uneven distribution of green financing across the eastern, central, and western regions. Moreover, it emphasizes the need to consider regional differences and tailor development strategies accordingly. To promote further development and transformation of China's agricultural industrial structure, the study recommends innovative green financial products, improved regulations and policies, and the integration of digital technologies.

The digital twin (DT) research field is experiencing rapid expansion, yet the research in industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. The study findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the break-even point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. The study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development.

This paper attempts to compare two different approaches to solve the problem of accident rates prediction based on human factors for industrial workers. One of the methods has already been done using Fuzzy c-Means Clustering and proved to be working with decent results. The second method which will be covered in this paper is using Artificial Neural Networks. The primary goal of this work is to insure that ANN will work efficiently in such prediction problem. The second goal is to reveal the fact that which one of the two selected methodologies is better at defining the estimation of accident rates among people who work in different industrial fields. The purpose has been achieved when the outcome of the ANN was obtained and compared accordingly with the output of the research previously carried out with Fuzzy c-means clustering method. Comparing the outcomes of these two different methods gave an immense insight on which features are more important than others when it comes to laborers properties with completely different background such as varying levels of health, knowledge, experience, training and physical properties. At the end of the research, it becomes clear that accident rates estimation for laborers with properly trained Artificial Neural Network gives better results when it is compared with Fuzzy c-Means Clustering method. Standard deviation method was used to calculate the validity of ANN technique. The result was compared with Fuzzy c-mean clustering technique. Impressive improvement of 8.8% in the accident rate prediction was achieved using Tailored-Made-ANN.

Concrete, as one of the essential construction materials is responsible for a vast amount of emissions. Using recycled materials and gray water can considerably contribute to the sustainability aspect of concrete production. Thus, finding a proper replacement for fresh water, in the production of concrete, is significant. The usage of industrial wastewater, instead of water in the concrete can is considered in this paper. In this study, 450 concrete samples are produced with different amounts of wastewater. The mechanical parameters such as slump, compressive strength, water absorption, tensile strength, electrical resistivity, rapid freezing, half-cell potential, and appearance are investigated. The results showed that the usage of industrial wastewater does not significantly change the main characteristics of concrete. Although, increasing the concentration of the wastewater can decrease durability and strength features nonlinearly.