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.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

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.

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.

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.

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2=0.952, MAE=0.265, MSE=0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2=0.948, MAE=0.283, MSE=0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.

Research subjects of this study are four representative locations in the industrial complex, in the city of Banja Luka, Republic of Srpska, Bosnia and Herzegovina. 16 polycyclic aromatic hydrocarbons (PAHs) (∑16PAHs), humus and pH were determined. The main objective of the paper is to determine the concentration levels, to assess the probable sources of PAHs contamination in soil and groundwater and to determine the ecological risk. The ∑16PAHs in soil (at depths of 30 cm, 100 cm, 200 cm, 300 cm and 400 cm) ranged from 0.99 to 2.24 mg/kg, from 0.34 to 0.46, from 0.24 to 0.32, from 0.13 to 0.27 and from 0.13 to 0.47, with mean values of 1.70 mg/kg, 0.40 mg/kg, 0.28 mg/kg, 0.20 mg/kg and 0.26 mg/kg, respectively. The ∑16PAHs in groundwater ranged from 0.23 to 4.50 mg/m³, with a mean value of 1.42 mg/m³. Surface soil and groundwater are heavily contaminated. All values of ∑PAHs in soil layers were lower in the depths of the soil. Factor analysis indicates three sources of contamination, i.e. principal component (PC) PC1 (pyrogenic), PC2 (petrogenic) and PC3 (biomass), with 52.39%, 26.14% and 8.46% of the total variance, respectively. ∑PAH and PAHs indicate high ecological risk for most PAHs, which decreases with soil depth.

In this study, the prevalent methodology for design of the industrial policy in developing countries was critically assessed, and it was shown that the mechanism and content of classical method is fundamentally contradictory to the goals and components of the endogenous growth theories. This study, by proposing a new approach, along settling Schumpeter's economic growth theory as a policy framework, designed the process of entering, analyzing and processing data as the mechanism of the industrial policy in order to provide "theoretical consistency" and "technical and Statistical requirements" for targeting the growth stimulant factor effectively.

Cyber Physical Systems (CPS) is the integration of computation and physical process that makes a complete system such as the physical components, networked systems, embedded computers and software and linking together of devices and sensors for information sharing. Cyber Physical Systems are Smart Systems that comprises of the merging and integration of Industry Control Systems, Critical Infrastructures, Internet of Things (IoT) and Embedded Systems. Major industries such as the Chemical and Industrial Plants, Aviation Systems, National Grid, the Stock Exchange, Military Systems, and others depends heavily on these Cyber Physical Systems for financial and economic growth. The benefits of CPS nationally and globally are in the areas of Manufacturing, Energy, Transport, Healthcare and Communication. Cyber Physical Systems incorporates Physical systems, Digital systems and Human elements on network infrastructures to provide interactive systems. However, these three key components the Physical systems, Digital systems and Human elements may have inherent threats and vulnerabilities on them that may run the risk of being compromise, exploited, attacked or hacked. Cybercriminals in their quest to bring down these systems and may cause disruption of services either for fame, revenge, political motive, economic war, cyber terrorism and cyber war. The study seeks to review the risks that are associated with these three key components Physical systems, Digital systems and Human elements. The study considered four main risk mitigation goals for this purpose, and these are Business Value, Organizational Requirements, Threat Agent and Impact based on the review results. We used Analytical Hierarchical Process (AHP) to determine the relative importance of these goals that contributes to developing cybercrime and rich in CPS. For the results, the prioritized goals are then used to assess the risks using a semi-quantitative approach to determine the net threat level.

This paper highlights the problems associated with daylight use in industrial facilities. In a case study of a multi-story textile factory, we report how to evaluate daylight (as part of integral light) in the production halls marked F and G. This study follows the article in the Buildings journal, where Hall E was evaluated (unilateral daylight). These two additional halls have large areas that are 54 × 54 meters and are more than 5 meters high. The daylight is only on the side through the attached windows in envelope structures in the vertical position. In this paper, we want to present two case studies of these two production halls in a textile factory in the eastern part of Slovakia. These are halls that are illuminated by daylight from two sides through exterior peripheral walls that are against or next to each other. The results of the case studies can be applied in similar production halls illuminated by a ‘double-sided’ (bilateral) daylight system. This means that they are illuminated by natural illumination through windows on two sides in a vertical position. Such a situation is typical for multi-storied industrial buildings. The proposed approximate calculation method for the daylight factor can be used to predict the daylight in similar spaces in other similar buildings.

Dynamic neural networks (DNN) are types of artificial neural networks (ANN) that are designed to work with sequential data where context in time is important. In contrast to traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if dynamic of a certain process is emphasized. They are widely used in natural language processing, speech recognition and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial process of isomerization, it is crucial to measure the quality attributes affecting the octane number of gasoline. Process analyzers that are commonly used for this purpose are expensive and subject to failures, therefore, in order to achieve continuous production in case of malfunction, mathematical models for estimating the product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNN), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM) and dynamic polynomial models. The obtained results are satisfactory, which suggests a good possibility of application.

In the pursuit of China’s dual carbon goals, identifying spatio-temporal changes in industrial carbon emission efficiency and their influencing factors in cities at different stages of development is the key to effective formulation of countermeasures to promote the low-carbon transformation of Chinese national industry and achieve high-quality economic development. In this study, we used balanced panel data of 270 Chinese cities from 2005 to 2020 as a research object: (1) to show spatio-temporal evolution patterns in urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using Global Moran's I statistics; and (3) to use the hierarchical regression model for panel data to assess the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The results show the following: (1) the industrial carbon emission efficiency of Chinese cities exhibited an upward trend from 2005 to 2020, with a spatial distribution pattern of high in the south and low in the north; (2) China's urban industrial carbon emission efficiency is characterized by significant spatial autocorrelation, with increasing and stabilizing correlation, and a relatively fixed pattern of spatial agglomeration; (3) there is a significant inverted-U-shaped relationship between the digital economy and the industrial carbon emission efficiency of cities. The digital economy increases carbon emissions and inhibits industrial carbon-emission efficiency in the early stages of development, but inhibits carbon emissions and promotes industrial carbon emission efficiency in mature developmental stages. Therefore, cities at all levels should reduce pollution and carbon emissions from high-energy-consuming and high-polluting enterprises, gradually reduce carbon-intensive industries, and accelerate the digital transformation and upgrading of enterprises. Western, central and eastern regions especially should seek to promote the sharing of innovation resources, strengthen exchanges and interactions relating to scientific and technological innovation, and jointly explore coordinated development routes for the digital economy.

In hydraulic systems, energy dissipation can be significant. The pressure drops that can occur in the hydraulic circuit, influenced by the adopted drive architecture, result in an absorbed power often significantly greater than that required by the mechanical system. In this paper, a comparative study of energy efficiency among five drive common architectures in industrial hydraulic axes is carried out. The analysis is applied to a hydraulic blanking press with variable speed and force, a fairly frequent industrial system, e.g. in the production of semi-finished brass products. Standard, regenerative, high-low, variable displacement pump and variable speed drive for a fixed displacement pump configurations have been analysed and compared. An adequate and optimized sizing of the various components of the system has been carried out in each case and subsequently the energy consumption has been estimated for a load cycle common to all the considered cases. The results show that the choice of power generation architecture of the hydraulic system has a very significant impact on energy efficiency and consequently operating costs and carbon footprint. The performed quantification of the potential energy efficiency of the considered drive architectures can be very useful in guiding energy-conscious choices.

Keywords: glycerin; Ni-based catalyst; lanthanum; semi-industrial scale; hydrogen production; biorefinery; monitoring.

Uzbekistan, as an emerging national economy, aims to attract foreign direct investment (FDI) to foster the development of its free economic zones (FEZs). While recent policy reforms have enhanced the country's appeal to investors and facilitated the inflow of international capital, challenges persist in attracting investment. This study employs an economic geographical approach to analyze and propose solutions to these obstacles. By examining the spatial patterns of FEZs and FDI through a comprehensive geographical lens, this research utilizes document analysis as an alternative to empirical analysis, considering the broader context rather than focusing on a specific FEZ. The findings reveal that the insufficiency of an innovation-driven environment, logistical systems, energy resource-related issues, among others, negatively impact the inflow of FDI into FEZs. Moreover, the study underscores the significance of economic geography in understanding these factors. Finally, relevant insights and recommendations are provided from an economic geographical perspective.

The industry is in a continuous evolution in the context of Industrial Internet of Things (IIoT) and Industry 4.0 requirements and expected benefits. Some sectors allow a higher reconfiguration dynamics considering the interference capabilities and process/equipment renewals, but others have considerable inertia that is many times justified. In most encountered situations, the reality confirms that the industry is struggling with new demands as interoperation and efficiency improvements. The water industry makes no difference, being a sector with critical infrastructures and highly varied subsystems, where invasive interference in legacy solutions tends to be avoided. Following previous successful footsteps in researching a proactive decentralized historian, the current work focuses on a case-study that refers to a water treatment and distribution facility that is operated for several years and the current operating regime was established by local operators following accumulated observations, restrictions and response strategies. The proactive historian was tailored for the current case-study and it was applied and tested in the suboptimal functioning scenario where the water sources configuration was manually selected and used for water availability and energy efficiency, but without assuming current/future failures or different water demands. The proposed low-cost historian targeted to improve the functioning and operation of the water facility considering energy efficiency and other impacting outcomes of the current strategy, and to establish an automatic functioning regime in a completely non-invasive manner towards the local legacy solution. The results were satisfactory, proving that the historian is able to adapt to a particular and suboptimal functioning real industrial scenario, to establish recipes in a process-aware manner, and to interoperate with the local legacy solution in order to apply improving actions.

Leasing and recycling are important issues to achieve low-carbon sustainable development of construction machinery industry, however there are some dilemmas bothering which is caused by excessive restriction of product service life. Focusing on the problem of pricing and coordination, a two-stage model of leasing closed-loop supply chain based on the Industrial Internet platform practical application was proposed. Under the centralized and decentralized decision-making scenarios, the price of leasing and selling, maintenance effort and recovery rate are calculated respectively. By using the Shapley value method to optimize the selling price, and the two-part pricing contract to calculate the compensation fee, the global profit is reasonably distributed, while the supply chain achieving Pareto optimality. The performance of different contracts on the improvement of supply chain profit was investigated. And the correlation among parameters was also explored through sensitivity analysis and numerical simulation. The findings showed that maintenance and recycling of construction machinery can be improved with the advantage of the Industrial Internet platform, while achieving the supply chain coordination by contracts. The enhancement of maintenance effort can decrease the remanufacturing cost which is positively correlated to the selling price and leasing price of product, furthermore promoting the recovery rate of used products. In the future, reasonable control of platform cost can help improve the recycling of construction machinery.

Sustainable bio-economy is considered to be one of the most promising routes towards the transition to a circular and climate-neutral economy. The valorization industry of bio-waste and agri-food by-products represents a key player in bioeconomy. In this article the design and the development of a web platform aiming at promoting synergies enabling the agricultural waste valorization is presented. The platform consists of: (i) the AgriPLaCE Waste Management Database which provides the users with an extended list of potential utilization measures for a variety of agricultural waste streams (ii) the AgriPLaCE Synergies Tool which aims to create synergies between different actors involved in the value chain from agricultural waste production to waste treatment and new valuable products’ exploitation. At the initial stage, the conceptual design of both tools took place by conducting an in-depth analysis for the user needs and services alongside the system architecture. Following, the AgriPLaCE platform development stage took place, with the implementation of all the necessary subsystems. The results showed that multiple potential collaborations can arise by the use of the AgriPLaCE platform, while users can also deepen on alternative and emerging treatment technologies and valuable products that can derive from a wide range of agricultural waste streams.

The stress imposed by ethanol to Saccharomyces cerevisiae cells are one of the most challenging limiting factors in industrial fuel-ethanol production. Consequently, the toxicity and tolerance to high ethanol concentrations has been the subject of extensive research, allowing the identification of several genes important for increasing the tolerance to this stress factor. However, most studies were performed with well characterized laboratory strains, and how the results obtained with these strains work in industrial strains remains unknown. In the present work we have tested three different strategies known to increase ethanol tolerance by laboratory strains in an industrial fuel-ethanol producing strain: overexpression of the TRP1 or MSN2 genes, or overexpression of a truncated version of the MSN2 gene. Our results show that the industrial CAT-1 strain tolerates up to 14% ethanol, and indeed the three strategies increased its tolerance to ethanol. When these strains were subjected to fermentations with high sugar content and cell-recycle, simulating the industrial conditions used in Brazilian distilleries, only the strain with overexpression of the truncated MSN2 gene showed improved fermentation performance, allowing the production of 16% ethanol from 33% of total reducing sugars present in sugarcane molasses. Our results highlight the importance of testing genetic modifications in industrial yeast strains under industrial conditions in order to improve the production of industrial fuel ethanol by S. cerevisiae.

Independent Learning Independent Campus (MBKM) is a program to develop an educational ecosystem that is in harmony with learning in higher education and the needs of industry. The limited link and match between universities and the business and industrial world in Indonesia are obstacles in realizing Indonesia as a developed country in the Industrial Revolution 4.0 era. The purpose of this study is to analyze the impact of the Independent Learning Independent Campus (MBKM) program on improving the performance of Ibn Khaldun University Bogor. The method used in this study is descriptive statistics with quantitative analysis. The sample that participated in this study consisted of all active students, lecturers, and administration staff. The research data was obtained through distribution to 6100 students, 233 lecturers, and 150 education staff. The research stages include socializing the understanding of MBKM, filling out the survey, calculating the distribution of respondents' filling, and concluding the assessment category using the weighted mean score (WMS) method. Based on the result, the implementation of MBKM in University of Ibn Khaldun Bogor based on all indicators can conclude that MBKM improve the performance of Ibn Khaldun Bogor University in the good category with an average percentage assessment criteria above 75%

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super-efficient SBM model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing of the industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, the industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: " stationary period", " recession period " and "growth period ". However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted 'U' shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

Copper is a heavy metal used in many industries and known for its negative impacts on the environment and human’s health. A novel structured metal organic framework (MOF) was used for copper adsorption for this work. SEM, EDAX, XRD and FTIR were done to confirm the structure of MOF prepared. MOF of 0.05 gm was used to examine its ability in Cu+ 2 removals with different initial concentrations of Cu+2 and pH values (5, 7 and 9). The prepared MOF was able to achieve Cu removal with 94.6%, 93%, 91.5%, and 92.5 % for the initial concentrations of 5, 10, 15, and 20 ppm respectively. It also performed very well for pH 5 and 7 with average removal ranging from 93.9%-95% for pH 5 and 7 for the initial concentrations of 5, 10, and 15 respectively which indicate that the prepared MOF is of high ability in Cu+ 2 removal.

Based on panel data of 31 provincial-level administrative units (excluding Hong Kong, Macao and Taiwan) in China from 2015 to 2019, using fixed effect model and mediation effect model, this paper makes an empirical analysis on the interaction among digital economy, industrial structure upgrade and high-quality economic development. The results show that, from the overall effect, the digital economy can significantly promote the high-quality development of the economy, and there are regular regional differences, and the relationship between the promotion intensity and the development level of the digital economy is proportional. From the mediation effect, in the relationship between digital economy and high-quality economic development, the upgrade of industrial structure has played a significant part of the mediation effect, the proportion of mediation effect is 25.17% . In addition, the mediation effect of industrial structure upgrade has obvious regional difference, the western area is the strongest, the eastern area is the second, the central area is the weakest. Based on the above research,the concrete policy suggestions are given to promote the development of digital economy and upgrade the level of industrial structure to promote the high-quality development of economy.

The Equator Principle is the goal of promoting the harmonious development of my country's economy and society. It promotes the green transformation and upgrading of the industrial structure by strictly controlling the flow of commercial bank funds, so as to achieve the goal of environmental protection and the coordinated development of the national economy. This paper uses factor analysis to reduce the dimensions of 10 financial indicators and non-financial indicators that measure the operating performance and development potential of joint-stock commercial banks, and then compares the comprehensive operating performance, liquidity, and safety of Industrial Bank and comparable banks. And the level of profitability, analyze the difference in comprehensive operating performance of joint-stock commercial banks joining the Equator Principles compared with commercial banks that have not joined the Equator Principles. The results of the study found that joining the Equator Principles of joint-stock commercial banks can improve comprehensive operating performance in the short term. In the long run, the development trend of Industrial Bank will be similar to that of comparable banks in the same category. Joining the Equator Principles of Industrial Bank can improve its liquidity and profitability, but it will not in the long run. Conducive to the improvement of the asset quality of joint-stock commercial banks. In this regard, the government, enterprises and financial institutions should work together to help commercial banks achieve an effective balance between operating performance and social responsibility, so as to achieve the goals of "carbon peak" and "carbon neutral".

This study investigated the efficacy of advanced oxidation process (AOP) for the reduction of pollution loads in mixed agro-food industrial wastewaters (dairy and slaughterhouse) in Nablus city, Palestine. Bench-scale Jar tests using an advanced oxidation process (AOP) were performed as a pretreatment stage. Initial results on direct applications of Fenton’s process on mixed agro-food wastewater (COD: 15400-18200 mg/l) were unsatisfactory. Hence, the performance of the Fenton process was applied on three mixed wastewater samples with different pre-treatment trials: (A) coagulant (FeCl3.6H2O) addition, (B) settling (2h), and use of flocculent (lime Ca(OH)2) in sample (C). Preceded with lime, Fenton`s process (Sample C) was most effective in the removal of organic carbon and nitrogen (89% COD; 80% TKN). The removal efficiency in inorganic loads (91% TSS; 62% TS) were achieved under H2O2/COD (w/w ratio 2:1), H2O2/Fe+2 (w/w ratio 10:1) and acidic conditions (pH = 3). The adoption of AOP technology by agro-food industries could ensure compliance with municipal by-laws and acquire connection permits to sewerage networks.

This paper investigated the effectiveness of natural wetlands (Phragmites australis) along Wadi Zomer in reducing the organic and inorganic pollution loads from diverse industrial discharges including occasional emergency discharges from Nablus West Sewage Treatment Plant (NWSTP), Palestine. We monitored physical and chemical parameters at four selective sampling stations (S1-S4) along Wadi Zomer with a length of 5 km downstream of NWSTP to assess the purification capacity of Wadi Zomer treatment wetlands (water, sediment, and vegetation) with Phragmites australis in pollution loads reduction. The results showed that S2 (0+0.5 km) and S3 (0+3.0 km) reflected an increase in pollution loads due to illicit industrial discharge and sewer overflow discharge from NWSTP during emergency conditions. BOD values varied significantly along the sampling sites from 6.64 mg/l (S1) to 437.10 mg/l (S3). The BOD at S1 and S2 in water samples were below the Palestinian Water Standard (PWS) compared to S3 and S4 with 437.1 and 333.9 mg/l, respectively. Water samples from all sites (S1-S4) showed a decreasing tendency in heavy metals concentrations (Fe>Cu>Zn>Cr >Ni) and were below the PWS limits, sediment samples followed the same decrease pattern for Zn, Cr, and Ni content with Wadi Zomer flow course. The concentration of Fe (6687 mg/kg) and Cu (1384.7 mg/kg) were highest in the sediment samples (S1-S4); this might be due to non-point sources of pollution. The research demonstrated that phytoremediation is a sustainable nature-based technology for the restoration of heavily polluted surface water bodies in Palestine.

The paper presents the possibility of using KINECT v2 module to control an industrial robot by means of gestures and voice commands. It describes elements of creating software for off-line and on-line robot control. The application for KINECT module was developed in C# language in Visual Studio environment, while the industrial robot control program was developed in RAPID language in RobotStudio environment. The development of a two-threaded application in RAPID language allowed to separate two independent tasks for the IRB120 robot. The main task of the robot is performed in thread no. 1 (responsible for movement). Simultaneously working thread no. 2 ensures continuous communication with the KINECT system and provides information about the gesture and voice commands in real time without any interference in thread no. 1. The applied solution allows the robot to work in industrial conditions without negative impact of communication task on the time of robot’s work cycles. Thanks to the development of a digital twin of the real robot station, tests of proper application functioning in off-line mode (without using a real robot) were conducted. Obtained results were verified online (on the real test station). Tests of correctness of gesture recognition were carried out, the robot recognized all programmed gestures. Another test carried out was the recognition and execution of voice commands. A difference in the time of task completion between the actual and virtual station was noticed - the average difference was 0.67 s. The last test carried out was to examine the impact of interference on the recognition of voice commands. With a 10dB difference between the command and noise, the recognition of voice commands was equal to 91.43%. The developed computer programs have a modular structure, which enables easy adaptation to process requirements.

The paper analyses how between 1956 and 2009 the agrarian metabolism of the Barcelona Metropolitan Region (BMR) has become less functional, losing circularity in biomass flows and in relationship to its landscape. We do so by adopting a Multi-EROI and flow-fund (MuSIASEM) analyses and its nexus with landscape functional structure. The study of agricultural flows of Final Produce, Biomass Reused and External Inputs is integrated with that of land use, livestock, power capacity and population changes between 1956 (at the beginning of the Green Revolution) and 2009 (fully industrialized agriculture). A multi-scale analysis is conducted at the landscape scale (seven districts within the Barcelona metropolitan region) as well as for the functions deployed, within an agroecosystem, by the mutual interactions between its funds (land-uses, livestock and farming population). A complex nexus between land, livestock, dietary patterns and energy needs is shown; we conclude that from the perspective of the circular bioeconomy the agrarian sector has gone worse hand in hand with the landscape functional structure. Therefore, a novel perspective in landscape agroecology is opened

The article presents a cyber-physical system for acquiring, processing and reconstructing images from measurement data. The technology was based on process tomography, intelligent measurement sensors, machine learning, Big Data, Cloud Computing, Internet of Things as a solution for Industry 4.0. Industrial tomography enables observation of physical and chemical phenomena without the need of internal penetration and allows real-time monitoring of production processes. The application includes specialized intelligent devices for tomographic measurements and dedicated algorithms for solving the inverse problem. The work focuses mainly on electrical tomography and image reconstruction using deterministic methods and machine learning, the reconstruction results were compared, different measurement models were used. The researches were carried out for synthetic data and laboratory measurements. The main advantage of the proposed system is the possibility of spatial data analysis and their high processing speed. The presented research results show that the process tomography gives the possibility to analyse the processes taking place inside the facility without disturbing the production, analysis and detection of obstacles, defects and various anomalies. Knowing the characteristics of a given solution, the application allows you to choose the appropriate method to reconstruct the image.

In factory automation systems, hybrid wired/wireless networks are often deployed to connect devices of difficult reachability such as those mounted on mobile equipment. A widespread version of these networks makes use of Access Points (APs) to implement wireless extensions of Real--Time Ethernet (RTE) networks via the IEEE 802.11 Wireless LAN (WLAN). Unfortunately, APs may introduce random delays in packet forwarding, mainly related to the their internal behaviors (e.g. code processing times) that negatively impact on the whole performance of the automation systems. Consequently, the knowledge of these delays represent a crucial design information. This paper presents an original and effective method to measure the delays introduced by APs, exploiting a hybrid loop-back link and a simple set-up with moderate instrumentation requirements. The method, which requires an initial calibration by means of a reference AP, has been successfully tested on some commercial APs. As it will be shown, the proposed measurement procedure is general and, as such, can be profitably adopted in even different scenarios.

In China, environmental pollution generated via industrialization as well as the profound changes in the social structure and gradual maturation of the social hierarchy have jointly contributed to the Chinese people's increased environmental consciousness and appeals for environmental justice (EJ). Because of the absence of an EJ theory and a lack of empirical research focused on China, this paper proposes a ‘Pyramid Model’ for China’s EJ research that involves the following three factors: basic demographic and socioeconomic factors, U.S.-based EJ principles, and Chinese characteristics. A nationwide pilot analysis of environmental inequity at the prefecture level is conducted by empirically studying the association of demographics and socioeconomic status with sources of industrial pollution in China. The prefecture-based results are shown to be robust and indicate that areas inhabited by ethnic minorities and western regions in China carry disproportionate environmental burdens. However, a different picture for migrants is presented, revealing that Chinese migrants are not currently exposed to greater levels of industrial pollution, and relevant interpretations of these findings are provided. The results also show that environmental inequality associated with income level, which is observed in the U.S., does not occur in China.

In this work, we examined the effect of tropospheric emissions on tropospheric ozone (O₃) by conducting three-dimensional (3D) chemistry transport model (CTM) simulations. For the control run, the CTM model simulates tropospheric O₃ levels with a complete set of anthropogenic, biomass burning, and vegetation emissions [8]. For the no-emission simulation, all anthropogenic, biomass burning, and vegetation emissions were turned off. Comparisons of results from these two simulations exhibit the emission impacts on the tropospheric O₃. In the no-emission simulation, distinctive low surface O₃ with concentrations less than 5 ppbv prevail over the Amazon basin, tropical South America, tropical South Africa, Southeast Asia. Transport of air from these land areas downwind contributes to the low O₃ over the remote marine boundary layer. In contrast, elevated O₃ levels over the extra-tropical remote marine boundary layer are less supported by the anthropogenic and biomass burning emissions but more sustained by the downward transport of O₃ from the stratosphere. These results demonstrate that the northern hemisphere continental areas (north of 30^◦N ), polar regions, and tropical continental regions are more sensitive to the tropospheric emissions. The northern hemisphere winter is mostly dominated by the stratospheric processes, while the tropospheric emissions dominate over the southern hemisphere tropical continental areas from tropics to 30^◦S latitudinal bands. The northern hemisphere continental regions are increasingly dominated by tropospheric emissions from spring, to reach maxima in summer, and started to reduce in autumn months.

.Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to Internet opens the cloud manufacturing to the massive attack surface of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this goal by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with the predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks such that the blockchain peers can timely detect high deviations and take actions. The research defined the constraints related to physical boundaries and weight lifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This research followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, booster risk management, strengthen cybersecurity, and advance sustainability efforts.

Curdlan is a water-insoluble polymer that has structure and gelling properties useful in a wide variety of applications such as in medicine, cosmetics, packaging and the food and building industries. The capacity to produce curdlan has been detected in certain soil-dwelling bacteria of various phyla, although the role of curdlan in their survival remains unclear. One of the major limitations of the extensive use of curdlan in industry is the high cost of production during fermentation, partly because production involves specific nutritional requirements such as nitrogen limitation. Engineering of the industrially relevant curdlan-producing strain Agrobacterium sp. ATTC31749 is a promising approach that could decrease the cost of production. Here, during investigations on curdlan production, I found that curdlan was deposited as a capsule. Curiously, only a part of the bacterial population produced a curdlan capsule. This heterogeneous distribution appeared to be due to the activity of Pcrd, the native promoter responsible for the expression of the crdASC biosynthetic gene cluster. To improve curdlan production, Pcrd was replaced by a promoter (PphaP) from another Alphaproteobacterium, Rhodobacter sphaeroides. Compared to Pcrd, PphaP was stronger and only mildly affected by nitrogen levels. Consequently, PphaP dramatically boosted crdASC gene expression and curdlan production. Importantly, the genetic modification overrode the strict nitrogen depletion regulation that presents a hindrance for maximal curdlan production and from nitrogen rich, complex media, demonstrating excellent commercial potential for achieving high yields using cheap substrates under relaxed fermentation conditions.

The introduction of exoskeletons in the industry has focused on improving worker safety. Exoskeletons have the objective of decreasing the risk of injury or fatigue when performing physically demanding tasks. The exoskeleton’s effects on the muscles is one of the most common focus in the assessments. The present study aims to analyse the muscle interactions generated by using a passive lumbar exoskeleton during load-handling tasks in laboratory conditions with and without an exoskeleton. Electromyographic data of the muscles involved in the task were recorded from twelve participants performing load-handling tasks. Correlation Coefficient, Coherence Coefficient, Mutual Information, and Multivariate Sample Entropy were calculated to determine if there were significant differences in muscle interactions between the two test conditions. Results showed statistically significant differences for all pairs of muscles and indicated that the use of the exoskeleton implied more constant values throughout the exercise. The Directed Conditional Granger Causality was obtained to study the directionality of the interactions, with significant differences in two muscle pairs Gluteus-Quadriceps and Gluteus-Lumbar in the gravity-positive direction in both cases. In conclusion, EMG parameters chosen appear to be appropriate measurements for studying the exoskeleton effects over muscle couplings.

This study aims to determine the present and future scopes of the digital economy in Bangladesh to build a developed and prosperous country by 2041. Simultaneously, it analyses the components of the Digital Economy from numerous perspectives from contributing to Bangladeshi economy with an adaptation of the 4th Industrial Revolution and emerging technologies. In the methodology section, qualitative approach is conducted to determine the research objectives where secondary data is used. Around 100 papers are collected and 51 articles are used for conducting this study. The findings of the study are to present the current state of the digital economy which is developing trends that contribute to national GDP for the adaptation of the 4IR for the transformation of smart Bangladesh. From the conceptual implication, companies and individuals are aware of the digital economy, which would lessen digital divide and create a strong link between technology and the economy. Furthermore, establishing customer confidence and trust would be a big issue for Bangladesh's digital platform economy in the future days. From a practical standpoint, IT industries and commercial organizations would have to deal with emerging 4IR and its technologies. The job market would also be expanded in a variety of areas, which would ultimately benefit the national economy. According to the study's conclusions, the digital economy is predicted to expand its commercial and corporate opportunities. Future research might be extended based on qualitative approach towards a digital Bangladesh and create a better developing economy.

This paper explores the dynamic interplay of K-12 education, the Fourth Industrial Revolution (IR 4.0), and internationalization in the Philippines. Through qualitative content analysis, key themes emerged, shedding light on the impact of K-12 education on global competitiveness, challenges in embracing IR 4.0 technologies, and opportunities arising from internationalization efforts. The findings underscore the significance of curriculum alignment with international standards, comprehensive digital literacy training, and fostering a global mindset to prepare students for the digitally-driven, interconnected world. Understanding the implications of these themes is crucial for the Philippines' education system, as it navigates towards a transformative and globally competitive future. Further, this research offers evidence-based recommendations and addresses research gaps, contributing to a broader understanding of how the convergence of these forces shapes the future of Philippine education. Future research avenues may explore long-term impacts on workforce preparedness, innovative IR 4.0 integration, and the effectiveness of internationalization efforts on educational outcomes. Eventually, this study inspires an education system that nurtures learners equipped to thrive in an ever-evolving global landscape.

The subject of study in the article is the method of industrial equipment vibration diagnostics using Allan variance. The goal is to increase the precision and accuracy of industrial equipment's vibration diagnostics processes by developing and implementing IoT-oriented solutions based on intelligent sensors and actuators per the IEEE 1451.0-2007 standard. Tasks: justify the feasibility of using platform-oriented technologies for vibration diagnostics of industrial equipment and choose a cloud service for the implementation of the platform; develop software and hardware solutions of the IoT platform for vibration diagnostics of industrial equipment; calibrate the vibration diagnostics system and check the measurement precision and accuracy. The methods used are the microservice approach, multilevel architecture, and assessing equipment state-based Allan variance. We obtained the following results. The architecture of the IoT system for vibration diagnostics of industrial equipment developed and presented in the article is three-level. The level of autonomous sensors provides readings of vibration acceleration indicators and transmits data to the Hub level, which is implemented based on a BeagleBone single-board microcomputer through the BLE digital wireless data transmission channel. BeagleBone computing power provides work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the condition of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is applied. Verifying the accelerometer calibration method for vibration diagnostics of industrial equipment was performed using a unique stand. Correct operation of the entire system is confirmed by the coincidence of expected and measured results. In the next step, we plan the development of additional microservices that will provide the possibility of using time series analysis methods and modern artificial intelligence technologies for complex diagnostics and forecasting of the equipment state.

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a probabilistic Hough transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive probability Hough transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.

Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

Abstract: This review presents current literature on different nanocomposites coatings and surface finishing for different substrates (such as textiles, concrete, plastics, stones, metals and so on). In particular, this study is focused on smart materials, drug delivery systems, and industrial, antifouling and nano/ultrafiltration membrane coatings. Each of these nanostructured coatings shows interesting properties for different fields of application. In this review, particular attention is paid to the synthesis and the consequent physico-chemical characteristics of each coating and, therefore, to the different parameters that influence the substrate deposition process. Several techniques used in the characterization of these surface finishing coatings are also described. In this review, the sol-gel and polymerization method for preparing stimuli-responsive coatings as smart sensor materials is described; functional polymers and nanoparticles sensitive to pH, temperature, phase, light and biomolecules are also treated. Finally, nanomaterials based on phosphorus, borates, hydroxy carbonates and silicones are used and described as flame retardant coatings; organic/inorganic hybrid sol-gel coatings for industrial applications are illustrated, together with functional nanofiller (carbon nanotubes, metallic oxides, etc.) and polymers employed for nano/ultrafiltration membrane and antifouling coatings. In the last decades, several research institutes and industries have collaborated for the advancement of nanotechnology by optimizing conversion processes of conventional materials into coatings with new functionalities for intelligent, innovative, eco-sustainable and advanced applications.

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture’s future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.

Abstract: This study aimed to evaluate the application of chemical defensives and long-life inoculant, in mixtures or not, under the nodulation process of plants and the quality of soy seeds during storage. The first was carried out in a greenhouse and completely randomized design was used, in an 8 x 2 factorial scheme, with three replications, and the treatments consisted of applications of seed treatments via industrial: (T1 - control; T2 - MaximAdvanced; T3 - Fortenza; T4 - long-life inoculant; T5 - MaximAdvanced + Fortenza); T6 - MaximAdvanced + long-life inoculant; T7 - Fortenza + long-life inoculant; T8 - MaximAdvanced + Fortenza + long-life inoculant), which were sown in pots containing soils from two cultivation areas; the second in the laboratory, using a completely randomized design, in a factorial scheme 8 x 7, with four replications, with treatments consisting of eight combinations of industrial seed treatment in the greenhouse, stored for two months. That seed treatment industrial with fungicide/insecticide/inoculant, applied in isolation or not, does not influence the soy nodulation process. Soy seeds, regardless of the type of treatment received with fungicide/insecticide/long-life inoculant products, in mixtures or in isolation, may be stored for a period of 40 days for commercial purposes.

Understanding how industrial agglomeration affects agricultural green production efficiency is essential for green agricultural development. This study uses the super-efficient EBM-GML to measure and analyze the spatial and temporal evolution characteristics and core sources of dynamics of agricultural green production efficiency in China by using panel data from 30 Chinese provinces from 2006 to 2020. It also empirically investigates the relationships between industrial agglomeration, land transfer, and agricultural production efficiency. By using fixed, intermediary, and threshold effects models, the internal links between the industrial agglomeration, land transfer, and agricultural green production efficiency are examined. The findings indicate that the green production efficiency of Chinese agriculture exhibits the regional characteristics of being “high in the west and low in the east, high in the south and low in the north” in terms of space; in terms of time series, the overall trend is that of growing, with an average annual growth rate of 11.45%, and the growth primarily depends on the “single-track drive” of green technological progress. By promoting land transfer, industrial agglomeration can increase the agricultural green production efficiency and decomposition index. Land transfer has a double-threshold effect on the influence of industrial agglomeration on agricultural green production efficiency. As a result, this study suggests adhering to the idea of synergistic development, promoting agricultural green development, strengthening the development of industrial agglomerations, promoting the quality and efficiency of industry, improving land-transfer mechanisms, and placing a focus on resource efficiency improvements, as well as other policy recommendations.

We are calling for a paradigm shift in engineering education. In times of the Fourth Industrial Revolution (“4IR”), a myriad of potential changes is affecting all industrial sectors leading to increased ambiguity that makes it impossible to predict what lies ahead of us. Thus, incremental culture change in education is not an option any more. The vast majority of engineering education and training systems, having remained mostly static and underinvested in for decades, are largely inadequate for the new 4IR labor markets. Some positive developments in changing the direction of the engineering education sector can be observed. Novel approaches of engineering education already deliver distinctive, student centered curricular experiences within an integrated and unified educational approach. We must educate engineering students for a future whose main characteristics are volatility, uncertainty, complexity and ambiguity. Talent and skills gaps across all industries are poised to grow in the years to come. The authors promote an engineering curriculum that combine timeless didactic tradition, such as Socratic inquiry, project-based learning and first-principles thinking with novel elements (e.g. student centered active and e-learning by focusing on the case study and apprenticeship pedagogical methods) as well as a refocused engineering skillset and knowledge. These capabilities reinforce engineering students’ perceptions of the world and the subsequent decisions they make. This 4IR engineering curriculum will prepare engineering students to become curious engineers and excellent communicators better navigating increasingly complex multistakeholder ecosystems.

This study investigated the reduction of organic loads from mixed agro-food industrial wastewaters (dairy and slaughterhouse) of Nablus city using advanced oxidation process (AOP), a high- rate chemical oxidation reaction. Bench-scale Jar tests using an advanced oxidation process (AOP) were performed as a pretreatment stage. Direct applications of classical Fenton’s process on mixed raw agro-food wastewater samples (COD: 15400-18200 mg/l) revealed unsatisfactory results. The performance of the Fenton process was evaluated using three mixed samples with different pre-treatment trials: (A) coagulant (FeCl3.6H2O) addition, (B) settling (2h) allowed, and use of flocculent (lime Ca(OH)2) in sample (C). Compared with other partial treatments, sample (C), Fenton`s process lime preceded, was the most effective in the removal of organic (89% COD; 80% TKN) and inorganic loads (91% TSS; 62% TS) under H2O2/COD (w/w ratio 2:1), H2O2/Fe+2 (w/w ratio 10:1) and acidic conditions (pH =3). Obtained results comply with Nablus municipal by-law (COD below 2000 mg/l), which help decision-makers within the agro-food industries install pollution reduction systems. Investment in the Fenton-based peroxidation process, allow agro-food industries to obtain connection permits to sewage networks.

Digital enabled manufacturing systems require high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; However, this presence of workers in the shared workspace with robots decreases the productivity as the robot is not aware about the human position and intention which leads to concerns about the human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (Cobots). The main idea here is, to significantly enhance safety by a combination of recognition of human actions using visual perception and at the same time interpreting physical human-robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if a physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection which in combination, are expected to lead to the enhancement of human safety and an increase of the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

Reducing emissions from the iron and steel industry is essential to achieve the Paris climate goals. A new system to reduce the carbon footprint of steel production is proposed in this article by coupling hydrogen direct reduction of iron ore (H-DRI) and natural gas pyrolysis on liquid metal surface inside a bubble column reactor. If grid electricity from EU is used, the emissions would be 435 kg CO₂/tls without considering methane leakage from the extraction, storage and transport of natural gas. Solid carbon, produced as a by-product of natural gas decomposition, finds applications in many industrial sectors, including as a replacement for coal in coke ovens. Specific energy consumption (SEC) of the proposed system is approximately 6.3 MWh per ton of liquid steel(tls). It is higher than other competing technologies, 3.48 MWh/tls for water electrolysis based DRI, and, 4.3-4.5 MWh/tls for natural gas based DRI and blast furnace-basic oxygen furnace (BF-BOF) respectively. Utilization of large quantities of natural gas, where the carbon remains unused, is the major reason for high SEC. Preliminary analysis of the system revealed that it has the potential to compete with existing technologies to produce CO₂ free steel, if renewable electricity is used. Further studies on the kinetics of the bubble column reactor, H-DRI shaft furnace, design and sizing of components, along with building of industrial prototypes are required to improve the understanding of the system performance.

The paper proposes a game theory model of price oligopoly with a heterogeneous product, where total demand depends linearly on the minimum market price. This model develops the Bertrand oligopoly for the case of imperfect price elasticity of demand. The most interesting result is an asymmetric Nash equilibrium with different prices and sales in the situation of symmetric oligopolists. The asymmetry is explained by the fact that a decrease in the minimum market price leads not only to a reallocation of consumers among firms, but also to an increase in total demand. Modifications of a two-stage game with a leader and followers and the price discrimination model are also considered. Microeconomic substantiation of the model is given on the basis of the spatial differentiation approach.

Abstract: The traditional model of closed circuit ball milling systems has been used for several decades, however, if the classifier of the closed circuit ball mill system performs the duties of both pre-classification and check-classification, the characterization error of the traditional model is large. To address this problem, a new model is obtained by modifying and supplementing the traditional one. The most important feature of the new model compared to the traditional model is that the circulating load is considered to be the fresh feed for the entire system rather than the ball mill. The error between the slurry level height calculated using the traditional model and the actual is 52.31%, which is 13.77 times higher than that of the new model. When the relative capacity varies greatly, that of the ball mill calculated by the traditional model fluctuates between 0.95 and 1.15, while the new model is 0.6 to 1.4. The new model has a higher accuracy than the traditional model in characterizing the production status of the grinding system, which is of some significance for industrial production.

In the field of robotics education, introductory courses would ideally utilize heavy industrial arms for hands-on learning. This would provide students with valuable experience in joint programming, which involves direct control of each joint motor in the arm to accomplish desired path planning and differential movements. This practice requires consideration of the physical dynamics of the arm. However, the use of heavy industrial arms has several drawbacks. They are large and expensive, require specialized maintenance, can pose safety risks, and they typically do not allow for direct control of the joint motors. One solution is to use a small, lightweight, toy-like arm. While these are cost-effective and provide direct access to the joint motors without posing safety risks, their lightweight construction means they exhibit minimal dynamic behavior, which limits their educational value. Another solution involves a robotic arm simulator that uses a virtual arm. However, a virtual arm doesn't offer the same level of hands-on engagement and excitement as a physical arm. We propose a hybrid solution that combines a small physical arm with a virtual arm. This approach provides students with the experience of working with an industrial arm, but without the associated difficulties. We demonstrate how this approach successfully improved the completion rate of joint programming assignments, offering a promising solution for practical robotics education.

The persistence of diseases that affect construction workforce as a result of activities on construction sites poses a danger to the sustainable development of the industry. This resulted to a huge loss of skilled labour and economic development of the industry and the entire country. The arrival of the fourth industrial revolution (4IR) technologies urges an urgent need to assess the effect of the technology’s biological driver on the construction occupation related diseases. Therefore, this study is aimed at assessing the effect of 4IR on the construction occupation related diseases in Gauteng, South Africa. The study is quantitative in design and questionnaire survey were administered to project and Health and Safety (H&S) managers in Gauteng, South African construction sector using a proportionate simple random sampling technique. For data analysis, the Warp PLS-SEM 8.0 software algorithm was used for the analysis of the collated data. The study found that the effects of the 4IR’s biological driver variables ranges between moderate to high effects for genome sequencing (GENSE) and Neurotechnology (NEURO) respectively. The combined predictive relevance of the two (2) variables predicts 64% of the construction occupation related diseases. This implies that the adoption of the driver would help reduce the causes of construction-related diseases. Hence, implies that continuous deployment of 4IR technologies would ensure that construction occupation related diseases are easily identified and put on alert.

The upgrade of energy infrastructure to those of smart grids, necessarily goes through the provision of integrated technological solutions that ensure the interoperability of their business capabilities and reduce the risk of devaluation of the systems used. The heterogeneity of the infrastructures and the dynamics of their operating environment, requires the continuous reduction of the complexity, the faster execution of the processes and the easy addition of innovative counterparts. Also, the integrated management of the overall ecosystem demands the provision of end-to-end interconnection, quality assurance, the definition of strict security policies, collaborative integration and correlation of events. In this respect, every design detail can be critical to the success or failure of a costly and ambitious project, such as that of smart energy networks. This work presents communication operating standards specific to the smart electricity networks applications, which should be taken into account in the process of planning and implementation of new infrastructures.