Biochar is a carbon allotrope obtained by pyrolysis of biomass, usually agro-waste. Owing to the demand for sustainable development, biochar is continuously raising much hope in the scientific community. However, in order to impart it with new properties, its modification is required, either in situ during pyrolysis, or after the carbonization process. Herein, we propose a new direct approach to obtain bimetallic copper/nickel nanoparticle-loaded on olive stone biochar. The bimetallic-coated biochar and the reference materials bare biochar, copper-loaded and nickel-loaded biochar were prepared at 400 °C under a stream of dinitrogen from olive pit powder particles impregnated first with citric acid (CA), and then with copper and nickel nitrates. We have employed citric acid in the process in order to check its effect on the structural and textural properties of biochar supporting the metallic nanoparticles. Surprisingly, citric acid induced the formation of agglomerated or even raspberry-shaped, bimetallic copper/nickel nanoparticles. Large 450-500 nm-sized agglomerates of ~80 nm bimetallic CuNi NPs were noted for B-CA@CuNi. Interestingly, for biochar material prepared with initial Cu/Ni=10 molar ratio (B-CA@CuNi10/1), the bimetallic NPs formed unusual nano-raspberries (1748 nm in size) which are agglomerates of individual 10-20 nm-sized CuNi10/1 nanoparticles. The B-CA@CuNi and reference materials were characterized by Raman spectroscopy, scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD) and magnetometry. The B-CA@CuNi and B-CA@Ni materials could be attracted efficiently with a magnet, but not B-CA@CuNi10/1 due to a low nickel loading. B-CA@CuNi was tested as a catalyst for the degradation of methyl orange (MO). Discoloration was noted within 10 min, much faster than a similar material prepared in the absence of CA. B-CA@CuNi could be recycled at least 3 times with exhibit as fast discoloration catalysis performance. This paper stresses the important role of citric acid in shaping the bimetallic nanoparticles loaded in situ on biochar during the slow pyrolysis process and to enable faster catalysed discoloration of organic dye solution.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Amazon region, with its vast biodiversity and rich natural resources, presents a unique opportunity for the development of sustainable polymeric composites reinforced with residues from both the timber and agro-extractive industries. This article explores the potential of these Amazonian residues, such as sawdust, wood shavings, and agro-industrial by-products like açaí seeds and Brazil nut shells, to enhance the mechanical, thermal, and environmental properties of polymer composites. By integrating these natural materials into polymer matrices, significant improvements in composite performance have been achieved, including increased tensile strength, thermal stability, and biodegradability. The study also highlights the environmental and economic benefits of utilizing these residues, promoting waste reduction and supporting a circular economy in the region. Through case studies and detailed analyses, the article demonstrates the feasibility and advantages of incorporating Amazonian residues into composites for a wide range of applications, from construction materials to consumer goods. This approach not only adds value to the by-products of the Amazonian industries but also contributes to the global effort towards sustainable material development.

Industrial buildings play vital roles in a society, from shaping the economic, technological, cultural, and social fabric of society, to contributing to its growth, development, and resilience. Hence often at the end of their lifespans, they are “preserved” for their historical value through renovation. In consideration for renovation, often included are their historical significance, structural integrity, adaptive reuse, social sustainability, financial viability and environmental impacts. Among them, the carbon emissions associated with a project are becoming increasingly a factor when a historical building is to be sensitively renovated so that it can continue to contribute to local sustainability. However, embodied carbon is often shadowed by operational carbon and overlooked in the development of renovation options. This paper aims to argue for the need to include embodied carbon in the consideration and for guidelines for doing so in a renovation process. The argument is through a systematic review of current practices in the renovations of industrial heritage buildings between selected presentative countries, the developing ones and developed ones, on the belief that the former could learn valuable lessons from the latter who has gone through the process already. The argument also shows the difference in policy between different countries and articulates how the inclusion of this might support environmental targets in developing countries. Based on quantitative comparison, the review explains why embodied carbon (EC) is missing in developing countries. The study estimates the proportion and value of EC with the total life cycle in renovations of industrial buildings to support the argument. Above all, a calculation using a standard LCA tool (ISO14040 & 14044) applied to four successful examples and quantitative comparison highlights the benefits of including embodied carbon in renovations of industrial buildings and the carbon savings in developing countries and further supports the argument.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

With increasing research focus on industrial anomaly detection, numerous methods have emerged in this domain. Notably, memory bank-based approaches, coupled with kNN distance metrics, have demonstrated remarkable performance in anomaly detection (AD) and anomaly segmentation (AS). However, upon examination of the Back to the Feature (BTF) method applied to the Mvtec-3D AD dataset, it was observed that while it exhibited exceptional segmentation performance, its detection performance was lacking. To address this discrepancy, this study base improves the implementation of BTF, especially the improvement of the anomaly score metric. It posits that different "clusters" necessitate distinct k values in kNN distance metrics. For simplify, this assumption is distilled into the proposition that AD and AS tasks impose differing requirements on the k value in kNN distance metrics. Consequently, the paper introduces the BTM method, which utilizes distinct distance metrics for AD and AS tasks. This innovative approach yields superior AD and AS performance (I-AUROC 93.0%, AURPO 96.9%, P-AUROC 99.5%), representing a substantial enhancement over the BTF method (I-AUROC 5.7% ↑, AURPO 0.5% ↑, P-AUROC 0.2% ↑).

The philosophy of Industry 5.0 recognizes the transformative power of the industrial sector to add value to society and employees. Industry 5.0, distinct from its predecessors, relies on a tripod of sustainability, human-centric, and resilience. These three thematic principles aim to improve work safety, research and innovation, and strengthen industrial resilience and competitiveness. The potential for positive change is immense. However, a perfect storm is brewing for the future of work, brought about by the concerning trends of displacement and low skill levels of work-ers' competencies in handling the new technologies of Industry 5.0 in emerging economies. This paper will discuss Industry 5.0 and provide historical insights on the importance of acquiring the desired twenty-first-century competencies needed in the workforce for Industry 5.0. Two research questions guided the study in identifying these twenty-first-century competencies, how these com-petencies can be cultivated and taught in the education curriculum and policies of emerging economies, and the crucial im-portance of adapting Industry 5.0 to these emerging economies. The study concluded that emerging economies must play an active role in shaping the future, by creating policies that will advance social stability, resource preservation, and climate objec-tives, which will enable more productive production processes with lower waste and energy usage.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analysis based on artificial neural networks (ANN) were performed for robot manipulator systems used in the textile industry and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analysis of a 6 degree of freedom (DOF) industrial denim robot manipulator were carried out via 4 different learning algorithms of Delta-Bar-Delta (DBD), Online Back Propagation (OBP), Quick Back Propagation (QBP) and Random Back Propagation (RBP) for proposed neural network predictor. From the results obtained, it was observed that QBP based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modelling of trajectory control. In addition, 3-5-6 type ANN structure was also improved and its Root Mean Square Error (RMSE) and statistical R2 performances were compared to that of 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

Hemicellulose is one of the most important natural polysaccharides in nature. Hemicellulose from different sources varies in chemical composition and structure, which in turn affects the modification effects and industrial applications. Grain and oil by-products (GOBP) are important raw materials for hemicellulose. This article reviews the modification methods of hemicellulose in GOBP. The effects of chemical and physical modification methods on the properties of hemicellulose biomaterials were evaluated. The potential applications of GOBP hemicellulose in films, hydrogel, Three-dimensional (3D) printing and adsorbent are discussed. Future research directions and recommendations are also proposed to provide theoretical foundations and technical support for the efficient utilization of these by-products.

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 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.

In this study, a stepper driver, which is suitable for use in the automotive industry, designed for general use, capable of adaptive learning in the system in which it will be used, with CAN and Universal Asynchronous Receiver-Transmitter (UART) communication options, has been designed. This design is made with PIC18F25K22 Micro Controller Unit (MCU). Rotor speed, armature current, and terminal voltage can be measured on the proposed brushed DC motor drive system. It gives the desired response by evaluating the obstacle and high current situations. The speed measurement of the vehicle and the open and closed status of the automatic door can be monitored. Main contribution of the designed PCB is Adaptive Learning Algorithm (ALA) which uses pulses of the encoder and estimates position of the step and manage operation process to prevent mechanical damage at final point of the motor. On the other hand, unexpected hitting and pinching can be kept under control by watching current value. Beneficial side of this method is that life of the mechanical step increases due to management of the forward and backward operation of the step and prevents potential accidents.

Autonomous navigation in dynamic environments presents a significant challenge for mobile robotic systems. In this paper, we propose a novel approach utilizing Convolutional Neural Networks (CNNs) for multi-object detection in 3D space and 2D segmentation using Bird’s Eye View (BEV) maps derived from 3D LiDAR data. Our method aims to enable mobile robots to localize movable objects and their occupancy, crucial for safe and efficient navigation. To address the scarcity of labeled real-world datasets, a synthetic dataset based on simulation environment is generated to train and evaluate our model. Additionally, we employ a subset of the NVIDIA r2b dataset for evaluation in real-world. Furthermore, we integrate our CNN-based detection and segmentation model into a ROS2-based framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. Our experimental results demonstrate promising performance, showcasing the potential applicability of our approach in future assembly systems. While further validation with real-world data is warranted, our work contributes to the advancement of perception systems by proposing a solution for multi-source multi-object tracking and mapping.

Industrial products e-commerce refers to the specific application of the e-commerce concept in the field of industrial products transactions. It enables industrial enterprises to conduct transactions via Internet platforms and reduce circulation and operating costs. Industry literature such as policies, reports and standards related to industrial products e-commerce contains abundant and crucial information. Through systematical analysis of the literature information, it is beneficial to explore and grasp the development characteristics and trends of the industrial products e-commerce. To study the characteristics and development status of industrial products e-commerce in China, literature comprising 18 policy documents, 10 industrial reports and 5 standards is deeply analyzed by employing text mining methods. Firstly, natural language processing (NLP) technology is utilized to pre-process the text data related to industrial products commerce. Then, word frequency statistics and TF-IDF keyword extraction are performed, and the results of word frequency statistics are visually displayed. Subsequently, the feature set is obtained by combining the manual screening method. The original text corpus is used as the training set by employing the skip-gram model in word2vec, and the feature words are transformed into word vectors in the multi-dimensional space. The k-means algorithm is used to cluster the feature words into groups. Moreover, the latent dirichlet allocation (LDA) method is also utilized to further group and discover the features. The research results based on text mining provide supportive decisions for uncovering the development characteristics and trends of industrial products e-commerce in China.

Enzyme catalysis has traditionally been used by various human cultures for creating value, long before its basic concepts have been uncovered, by preparing useful products through the trans-formation of raw materials available from natural resources. Tremendous scientific and techno-logical progress has been accumulated globally in understanding what constitutes an enzyme, what reactions enzymes can catalyze, and how to search, develop, apply and improve enzymes to make desired products. The exquisite properties of enzymes as nature's preferred catalysts, such as high selectivity, diversity and adaptability, enable their optimal work, whether in single reactions or in multiple reactions. Excellent opportunities for resource efficient manufacturing of compounds needed are provided by the actions of enzymes working in reaction cascades and pathways within the same reaction space, like molecular robots along a production line. Enzyme catalysis plays an increasing role for industrial innovation and responsible production in various areas, such as green and sustainable chemistry, industrial or white biotechnology. Sources of inspiration can be current manufacturing or supply chain challenges, the treasure of natural enzymes or the opportunities of engineering tailor-made enzymes. Making best use of the power of enzyme catalysis is essential for changing the way how current products are manufactured, how renewable biobased resources can replace fossil-based resources, and how the safety, health environment aspects of manufacturing processes can be improved towards cleaner and more sustainable production.

The production of recombinant enzymes, mainly used for obtaining pure and functional target molecules, holds significant importance in modern biotechnology. This study aimed to produce recombinant α-amylases (Amy3500 and Amy1974), derived from B. amyloliquefaciens MDC1974 and B. subtilis MDC3500 respectively, using the pBE-S shuttle vector, and to characterize them. Both α-amylase genes were molecularly cloned into the E. coli/B. subtilis pBE-S shuttle vector, both with (Amy1974sig and Amy3500sig) and without their own signal peptides (Amy1974 and Amy3500), along with a signal peptide originating from the plasmid, and tested in flask fermentations. For recombinant Amy3500, the amylase variants resulted in similar levels of volumetric activity (700-750 U/mL). In contrast, the expression of Amy1974 nearly doubled compared to Amy1974sig with double signal peptides, reaching 2000 U/mL. The SDS-PAGE estimated the molecular weight of Amy1974 α-amylase to be 54.6 kDa, which was in accordance with the theoretical molecular mass calculations. However, the estimated molecular weight of Amy3500 amylase was significantly reduced when exiting the producer cells. Ca2+ had no significant activating effect on the activities of Amy1974 amylase and Amy3500 amylase, likely due to its tight binding to the protein scaffold. Both enzymes exhibited broad activity peaks between 45-70°C, with a maximum at 65°C. The Amy1974 and Amy3500 α-amylases demonstrated broad pH optima and pH-dependent thermostability, with optimum pH values at 6.5 and 5.8, and thermal stability peaks at pH 7.6 and 5.9, respectively. Both α-amylases displayed high relative activity against various starches, including corn amylopectin and potato amylose, while showing comparatively lower activity towards cyclodextrins. The Amy1974 amylase converts starch to dextrins of varying lengths, whereas Amy3500 almost entirely converts starch into glucose.

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment but has also posed significant challenges to network security, especially the physical layer security challenges,which is crucial for the reliable transmission of messages interconnected devices and sensors. Focus on the enhancing the physical layer security in the IIoT communication, this paper proposed a mechanism of Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate device is known, while eavesdropper on the random distribution of device locations, forming the SSOP model related to device locations from the perspective of insecure regions (ISR) and secure regions (SR), and deduce the closed-form expression for its upper bound. Subsequently under the constraint of SSOP conditions, establish an optimization model with the objective of system throughput maximization based on Spatial Secrecy Outage Probability(SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in the MATLAB, we verified the accuracy of the definition and derived upper bound of SSOP. The experimental results also show that SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. These performance are crucial for understanding and optimizing the security performance of IIoT communication systems.

In this work, we explore the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behaviour of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behaviour. We centre this study on a small WF of five WTs. We use the fact that the same signals from different turbines in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, we use averaged signals from the SCADA system and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations and overlapings are explored. The capability to automatically identify WTs whose signals differ from the group’s behaviour can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.

Industry 5.0 philosophy recognizes the effectiveness and productivity of the industry sector to add value to society and place workers' comfort as it focal aim . The difference between Industry 5.0 and its predecessors, and unlike Industry 4.0, industry 5.0 thematic principles rely on a tripod of sustainable, human-centric and resilient, these thematic principles underlined the industry 5.0 vision of social value, improving work-safety , application of technology to produce wealth beyond jobs, research and innovation , and strengthen of industrial resilience and competitiveness. But recently, there is a general belief that a perfect storm is brewing for the future of work, brought about by ever-more concerning trends of displacement and low skill levels of workers competencies to handle the new technologies of industry 5.0 in emerging economies. This paper therefore, will discuss the desired twenty-first-century competencies that are needed in the workforce for Industry 5.0, two research questions guided the study to identify these twenty-first-century competencies, and how these competencies can be cultivated and inculcated in the education curriculum and policies of emerging economies and the potential of the adaptation of Industry 5.0 on these emerging economies. the study concluded that emerging economies must possess the ability to create policies that will advance social stability, resource preservation, and climate objectives, which will enable more productive production processes with lower waste and energy usage.

Industrial Control Systems (ICS) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing its ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data.

.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.

For water-stressed regions like Iran improving the effectiveness and productivity of agricultural water-use is of utmost importance due to climate change and unsustainable demands. Therefore, a hydro-economic model has been developed here for the Zarrine River Basin with the central concept of that demands are value-sensitive functions, where quantities of water-uses at different locations and times have a changeable economic benefits. To do this, the potential crop yields and the surface and groundwater resources, especially Boukan Dam inflow are simulated using the hydrologic model, SWAT, based on predicted climatic scenarios i.e. quantile mapping-downscaled projections. Then, to allocate the agricultural water based on the agro- economic crop water productivity (AEWP) of crops, a basin-wide water management tool, MODSIM, is customized. Next, a simulation- optimization model has been developed using a coupled CSPSO-MODSIM, to optimize the total AEWP, considering climatic impact and crop pattern scenarios, for 2020-2038, 2050-2068 and 2080-2098 periods. Finally, the optimum crop pattern and crop water irrigation depths are presented for different RCPs and periods. The results indicated that this approach will improve considerably the AEWPs and decrease the agricultural water-use up to 40%. Thus, this integrated model is able to support water authorities and other stakeholder in a water-scarce basin, as is the study area.

In today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system; an industrial system that deploys Internet of Things (IoT), robotics, and Neural Network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys trendy technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNN’s.

The rapid advancements in the Fourth Industrial Revolution and artificial intelligence (AI) necessitate a transformative approach to leadership in higher education. This paper investigates the current state of leadership in Iraqi universities and proposes strategies to enhance leadership capabilities aligned with technological advancements. Through a comprehensive review of existing literature, qualitative analysis, and quantitative data collection, the study identifies key challenges and opportunities for developing effective university leadership. The findings underscore the importance of strategic human resource management, high-performance work systems, and technological integration in fostering leadership excellence. Practical recommendations for policymakers and educational leaders in Iraq are provided, highlighting the need for strategic investments in leadership development to meet the demands of the evolving educational landscape.

Today, with the increase in population, technological developments, industrialization and urbanization, the problems related to waste management (WM) have become increasingly important to sustainably global clean environment. The gradual change in the quality of the elements that make up the environment, increasing environmental problems, and gaining global quality have caused societies to focus more on environmental problems. Waste management is a form of management that includes the prevention, non-prevention, reuse, recovery, and disposal of domestic, medical, hazardous, and non-hazardous wastes. In this study, it is aimed to prioritize critical success factors with the Analytical Hierarchy Process (AHP) in industrial waste management and to determine the most important critical success factor. The four main criteria and 23 sub-criteria were scored by the AHP method by five expert environmental engineers. Accordingly, after critical success factors were determined, survey questions were prepared to make employees rank these factors. While "national/local waste management strategies and policies" factor was the most important critical success factor according to experts, the most important critical success factor for employees was "enterprise waste management strategies and policies". In addition, differences in the priorities of CSFs were found in the opinions of employees in different sectors.

China’s leading development of a complete battery value chain for Electric Vehicles (EV) is restructuring the global automotive sector. The competitive advantage of China's EV battery industry lies on its firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork in an EV battery cluster in South China, this paper identifies what the Chinese EV battery industry’s core competency is and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability to supply lithium-ion batteries vehicle manufacturers (OEMs) with stable and consistent quality as well as competitive prices. This competency is acquired by firms through technological learning at workshop level while taking use of their experiences accumulated in mass production of batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge on battery manufacturing at large scale is also facilitated by the industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings illuminate the likely strategic strengths, weaknesses, and futures of the Chinese EV battery industry in global competition.

Industrial hemp (Cannabis sativa L.) is an attractive candidate for sustainable pest management due to its abundance of bioactive compounds with potential pesticidal properties. Solvent choice has a significant impact on the extraction efficiency of bioactive compounds. Deep Eutectic Solvents (DESs) are gaining popularity in extraction because they are safe and environmentally friendly, making them viable alternatives to organic solvents (OSs). This research first compared the extraction efficiency of OSs on the extraction of phytochemicals from the infloresences of two hemp varieties, Citrus and Cherry Dwarf. Inflorescences were extracted using three OSs, ethanol, ethyl acetate, and hexane. The highest level of cannabidiol (CBD; 0.69%) was extracted from Cherry Dwarf using ethanol while the level of delta-9 tetrahydrocannabinol THC (0.19%) was essentially the same in both. Therefore, Cherry Dwarf was selected to compare the extraction efficiency of DESs to the OSs. The DESs were choline chloride/ethylene glycol, citric acid/ethylene glycol, menthol/lauric acid, choline chloride/urea and choline chloride/glycerol. In the targeted analysis, choline chloride/ethylene glycol extracted the highest amount of CBD (0.87%) followed by choline chloride/urea (0.78%). As some DESs outperformed ethanol, the popular solvent for extracting cannabinoids, DESs are viable candidates to replace organic solvents.

This study explores the pivotal biofilm life cycle, essential for comprehending intricate processes in biofilm formation and dispersal. With profound implications for medicine, environmental science, and industrial applications, the life cycle involves initial attachment, irreversible attachment, maturation, and dispersion. Employing bibliographic analysis, the paper unravels critical aspects, unveiling key keywords, prominent research countries/regions, and significant research organizations. This comprehensive approach provides insights into the current biofilm research landscape, identifying gaps for future exploration. Biofilm's influence extends to medical conditions, environmental ecosystems, and industrial challenges, accentuating the urgency of understanding its life cycle. The study also delves into global research distribution and influential organizations, guiding collaborative efforts. As a forward-looking initiative, it proposes future research opportunities, aiming to steer advancements and address challenges in biofilm science. Ultimately, this analysis contributes to the ongoing progress of biofilm research, fostering interdisciplinary collaboration and inspiring future initiatives.