Abstract: Industry 5.0 presents itself as a strategy that puts the human factor at the centre of production, where the well-being of the worker is prioritized, as well as more sustainable and resilient production systems. For human centricity, it is necessary to empower human beings and, respectively, industrial operators, to improve their individual skills and competences in collaboration or cooperation with digital technologies. This research’s main purpose and distinguishing point are to determine whether Industry 5.0 is truly human-oriented and how human centricity can be created with Industry 5.0 technologies. For that, this systematic literature review article analyses and clarifies the concepts and ideologies of Industry 5.0 and its respective technologies (Artificial Intelligence, Robotics, Human-robot collaboration, Digitalization), as well as the strategies of human centricity, with the aim of achieving sustainable and resilient systems, especially for the worker.

Abstract: Zinc oxide nanoparticles (ZnO-NPs) have gained significant interest in the agricultural and food industry as a means of killing or reducing the activity of microorganisms. The antibacterial properties of ZnO-NPs may improve food quality, which has a direct impact on human health. ZnO-NPs are one of the most investigated inorganic nanoparticles and have been used in various related sectors, with the potential to rapidly gain attention and increase interest in the agriculture and food industries. In this review, we describe various methods for preparing ZnO-NPs, their characterizations, modifications, applications, antimicrobial activity, testing procedures, and effects, including bactericidal and bacteriostatic mechanisms. It is hoped that this review could provide a better understanding of the preparation and application of ZnO nanoparticles in the field of food and agriculture, and promote their development to advance the field of food and agriculture.

Abstract: Food packaging systems are continually impacted by the growing demand for minimally processed foods, changing eating habits, and food safety risks. Minimally processed foods are prone to the growth of harmful microbes, compromising quality and safety. As a result, the need for improved food shelf life and protection against foodborne diseases alongside consumer preference for minimally processed foods with no or lesser synthetic additives foster the development of innovative technologies such as antimicrobial packaging. It is a form of active packaging that can release antimicrobial substances to suppress the activities of specific microorganisms, thereby improving food quality and safety during long-term storage. However, antimicrobial packaging continues to be a very challenging technology. This study highlights antimicrobial packaging concepts, providing different antimicrobial substances used in food packaging. We review various types of antimicrobial systems. Emphasis is given to the effectiveness of antimicrobial packaging in various food applications, including fresh and minimally processed fruit and vegetables and meat and dairy products. For the development of antimicrobial packaging, several approaches have been used, including the use of antimicrobial sachets inside packaging, packaging films, and coatings incorporating active antimicrobial agents. Due to their antimicrobial activity and capacity to extend food shelf life, regulate or inhibit the growth of microorganisms and ultimately reduce the potential risk of health hazards, natural antimicrobial agents are gaining significant importance and attention in developing antimicrobial packaging systems. Selecting the best antimicrobial packaging system for a particular product depends on its nature, desired shelf life, storage requirements, and legal considerations. The current review is expected to contribute to research on the potential of antimicrobial packaging to extend the shelf life of food and also serves as a good reference for food innovation information.

Abstract: For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms.

Abstract: The use of dyes is widespread across almost all industries. Consequently, these dyes are found in various sources of water and food that humans, animals, and plants consume directly or indirectly. Most of these dyes are comprised of complex aromatic structures that have proven harmful. Congo red dye, a complex aromatic azo dye based on benzidine, is most commonly used in these dyes; its metabolites (benzidine and analogs) can be toxic, but Congo red dye itself is not always harmful. The present review summarizes the toxicity of Congo red dye towards different living forms. Herein, the primary emphasis has been given to the mutagenic, teratogenic, and carcinogenic consequences of Congo red and its metabolites. The mechanisms of azo dyes’ carcinogenicity have also been discussed. This review will undoubtedly be beneficial for researchers to understand the harmful effects of Congo red in genotoxic, teratogenic, mutagenic, and carcinogenic factors.

Abstract: There is a growing interest in green hydrogen, with researchers, institutions, and countries focusing on its development, efficiency improvement, and cost reduction. This paper explores the concept of green hydrogen and its production process using renewable energy sources in several leading countries, including Australia, the European Union, India, Canada, China, Russia, the United States, South Korea, South Africa, Japan, and other nations in North Africa. These regions possess significant potential for “green” hydrogen production, supporting the transition from fossil fuels to clean energy and promoting environmental sustainability through the electrolysis process, a common method of production. The paper also examines the benefits of green hydrogen as a future alternative to fossil fuels, highlighting its superior environmental properties with zero net greenhouse gas emissions. Moreover, it explores the potential advantages of green hydrogen utilization across various industrial, commercial, and transportation sectors. The research suggests that green hydrogen can be the fuel of the future when applied correctly in suitable applications, with improvements in production and storage techniques, as well as enhanced efficiency across multiple domains. Optimization strategies can be employed to maximize efficiency, minimize costs, and reduce environmental impact in the design and operation of green hydrogen production systems. International cooperation and collaborative efforts are crucial for the development of this technology and the realization of its full benefits.

Abstract: The rapid growth of the global population and changes in lifestyle have led to a significant increase in food waste from various industrial, agricultural, and household sources. Nearly one-third of the food produced annually is wasted, resulting in severe resource depletion. Food waste contains rich organic matter, which, if not managed properly, can pose a serious threat to the environment and human health, making the proper disposal of food waste an urgent global issue. However, various types of food waste, such as waste from fruit, vegetables, grains, and other food production and processing, contain important bioactive compounds, such as polyphenols, dietary fiber, proteins, lipids, vitamins, organic acids, and minerals, some of which are found in greater quantities in the discarded parts than in the parts accepted by the market. These bioactive compounds offer the potential to convert food waste into value-added products, and fields including nutritional foods, bioplastics, bioenergy, biosurfactants, biofertilizers, and single cell proteins have welcomed food waste as a novel source. This review reveals the latest insights into the various sources of food waste and the potential of utilizing bioactive compounds to convert it into value-added products, thus enhancing people’s confidence in better utilizing and managing food waste.

Abstract: In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from the Cleveland and IEEE Dataport. Optimizing model accuracy, GridsearchCV, and five-fold cross-validation are employed. In the Cleveland dataset, logistic regression surpassed others with 90.16% accuracy, while AdaBoost excelled in the IEEE Dataport dataset, achieving 90% accuracy. A soft voting ensemble classifier combining all six algorithms further enhanced accuracy, resulting in a 93.44% accuracy for the Cleveland dataset and 95% for the IEEE Dataport dataset. This surpassed the performance of the logistic regression and AdaBoost classifiers on both datasets. This study’s novelty lies in the use of GridSearchCV with five-fold cross-validation for hyperparameter optimization, determining the best parameters for the model, and assessing performance using accuracy and negative log loss metrics. This study also examined accuracy loss for each fold to evaluate the model’s performance on both benchmark datasets. The soft voting ensemble classifier approach improved accuracies on both datasets and, when compared to existing heart disease prediction studies, this method notably exceeded their results.

Abstract: Composting is the most adaptable and fruitful method for managing biodegradable solid wastes; it is a crucial agricultural practice that contributes to recycling farm and agricultural wastes. Composting is profitable for various plant, animal, and synthetic wastes, from residential bins to large corporations. Composting and agricultural waste management (AWM) practices flourish in developing countries, especially Pakistan. Composting has advantages over other AWM practices, such as landfilling agricultural waste, which increases the potential for pollution of groundwater by leachate, while composting reduces water contamination. Furthermore, waste is burned, open-dumped on land surfaces, and disposed of into bodies of water, leading to environmental and global warming concerns. Among AWM practices, composting is an environment-friendly and cost-effective practice for agricultural waste disposal. This review investigates improved AWM via various conventional and emerging composting processes and stages: composting, underlying mechanisms, and factors that influence composting of discrete crop residue, municipal solid waste (MSW), and biomedical waste (BMW). Additionally, this review describes and compares conventional and emerging composting. In the conclusion, current trends and future composting possibilities are summarized and reviewed. Recent developments in composting for AWM are highlighted in this critical review; various recommendations are developed to aid its technological growth, recognize its advantages, and increase research interest in composting processes.

Abstract: Silver nanoparticles, also known as AgNPs, have been extensively researched due to their one-of-a-kind characteristics, including their optical, antibacterial, and electrical capabilities. In the era of the antibiotics crisis, with an increase in antimicrobial resistance (AMR) and a decrease in newly developed drugs, AgNPs are potential candidates because of their substantial antimicrobial activity, limited resistance development, and extensive synergistic effect when combined with other drugs. The effect of AgNPs depends on the delivery system, compound combination, and their own properties, such as shape and size, which are heavily influenced by the synthesis process. Reduction using chemicals or light, irradiation using gamma ray, laser, electron beams or microwave and biological synthesis or a combination of these techniques are notable examples of AgNP synthesis methods. In this work, updated AgNP synthesis methods together with their strength and shortcomings are reviewed. Further, factors affecting the synthesis process are discussed. Finally, recent advances and challenges are considered.

Abstract: Given the prevailing concerns about greenhouse gas emissions, global warming, and the growing demand for renewable resources, the pavement industry, among others, is actively engaged in researching and exploring low-carbon materials and technologies. Despite the growing interest in low-carbon asphalt pavement, there is still a significant knowledge gap regarding the use of various waste materials and technologies to achieve this goal. This study aims to close this gap by conducting a systematic review and scientometric assessment of the existing studies on the use of waste materials and technologies for low-carbon asphalt pavement. The study spans the years 2008 to 2023, and the scientometric analysis was conducted using the VOS viewer application. The study identifies materials and technologies in this area by examining co-authored country studies, publication sources, and keyword co-occurrence. It should be noted that a limited number of waste materials that allow CO2 emissions reduction was analyzed in this study. However, other waste categories, such as bio-oils and polymers, which can provide positive either environmental or economic impacts on the production of paving materials, were not considered in the scope of the study. Based on the current review, it was found that integrating recycled waste materials like recycled asphalt pavement, biochar, or crumb rubber with alternative mixing technologies such as warm mix asphalt and cleaner energy can significantly reduce CO2 emissions. China and the United States were identified as key research contributors to the low-carbon pavement. Furthermore, biomass-based fuel and electric construction equipment lower carbon and greenhouse gas emissions by 36–90% and 67–95%, respectively. However, before various recycled waste materials and technologies can be widely used in the asphalt industry, various challenges need to be addressed, including cost concerns, performance and durability concerns, standardization and regulations, availability, integration with existing facilities, and insufficient field and long-term data. The review identified critical research gaps, such as the absence of a homogeneous and reliable standard method for low-carbon asphalt pavement, limited field performance data, and a life cycle assessment approach in analyzing the emission reduction effects. The reviews will aid in the paradigm shift to a more carbon-friendly pavement industry that uses recycled waste materials and technologies.

Abstract: Organic waste management is an important concern for both industries and communities. Proper management is crucial for various reasons, such as reducing greenhouse gas emissions, promoting sustainability, and improving public health. Composted manure is a valuable source of nutrients and organic matter that can be used as a soil amendment in agriculture. Some important benefits of using composted manure in agriculture include: improves soil fertility, enhances soil structure, reduces soil erosion, suppresses plant diseases, and reduces reliance on synthetic fertilizers. Composted manure represents one of the most effective methods of organic waste valorization. Its macronutrients and micronutrients content can increase plant yield, without any reported negative or toxic effects on the soil and plants at various application rates. However, improper use of farmyard manure can have negative effects on the environment, such as air pollution from greenhouse gas emissions, soil acidification, and contamination of surface water and groundwater by nitrates and phosphates. The properties of the soil, including aeration, density, porosity, pH, water retention capacity, etc., can be improved by the structure and composition of manure. The slow-release source of nutrients provided by the nutrient content of compost can determine proper plants growth. However, it is crucial to use compost in moderation and regularly test soil to prevent excessive nutrient application, which can have adverse effects on plants and the environment.

Abstract: Lactic acid (LA) has broad applications in the food, chemical, pharmaceutical, and cosmetics industries. LA production demand rises due to the increasing demand for polylactic acid since LA is a precursor for polylactic acid production. Fermentative LA production using renewable resources, such as lignocellulosic materials, reduces greenhouse gas emissions and offers a cheaper alternative feedstock than refined sugars. Suitable pretreatment methods must be selected to minimize LA cost production, as the successful hydrolysis of lignocellulose results in sugar-rich feedstocks for fermentation. This review broadly focused on fermentative LA production from lignocellulose. Aspects discussed include (i). low-cost materials for fermentative LA production, (ii). pretreatment methods, (iii). enzymatic hydrolysis of cellulose and hemicellulose, (iv). lactic acid-producing microorganisms, including fungi, bacteria, genetically modified microorganisms, and their fermentative pathways, and (v). fermentation modes and methods. Industrial fermentative lactic acid production and purification, difficulties in using lignocellulose in fermentative LA production, and possible strategies to circumvent the challenges were discussed. A promising option for the industrial production and purification of LA that contains enzyme and cell recycling continuous simultaneous saccharification and fermentation coupled with membrane-based separation was proposed. This proposed system can eliminate substrate-, feedback-, and end-product inhibition, thereby increasing LA concentration, productivity, and yield.

Abstract: Unexpected instances have posed challenges to production lines over the last few years. The latest COVID-19 global epidemic is one notable example. In addition to its social impact, the virus has destroyed the traditional industrial production system. Industry 4.0 requires adapting to changing prerequisites with adaptability. However, the next movement, Industry 5.0, has emerged in recent years. Industry 5.0 takes a more coordinated approach than Industry 4.0, with increased collaboration among humans and machines. With a human-centered strategy, Industry 5.0 improves Industry 4.0 for greater sustainability and resilience. The concept of Industry 4.0 is the interconnection via cyber-physical systems. Industry 5.0, also associated with systems enabled by Industry 4.0, discusses the relationship between “man and machine,” called robots or cobots. This paper discusses the industry 5.0 possibilities, the restrictions, and future analysis potentials. Industry 5.0 is a new paradigm change that tends to bring negotiated settlement because it places less prominence on technology and assumes that the possibilities for advancement are predicated on collaboration between humans and machines. This paper aims to examine the potential implementations of Industry 5.0. Once the current progress and problem were discovered, the previous research on the investigated topic was reviewed, research limitations were found, and the systematic analysis procedure was developed. The classifications of industry 5.0 and the sophisticated technology required for this industry revolution are the first subjects of discussion. There is additional discussion of the application domains enabled by Industry 5.0, such as healthcare, supply chain, production growth, cloud industrial production, and so on. The research also included challenges and problems investigated in this paper to understand better the issues caused by organizations among some robotic systems and individuals on the production lines.

Abstract: Attempting to address optimization problems in various scientific disciplines is a fundamental and significant difficulty requiring optimization. This study presents the waterwheel plant technique (WWPA), a novel stochastic optimization technique motivated by natural systems. The proposed WWPA’s basic concept is based on modeling the waterwheel plant’s natural behavior while on a hunting expedition. To find prey, WWPA uses plants as search agents. We present WWPA’s mathematical model for use in addressing optimization problems. Twenty-three objective functions of varying unimodal and multimodal types were used to assess WWPA’s performance. The results of optimizing unimodal functions demonstrate WWPA’s strong exploitation ability to get close to the optimal solution, while the results of optimizing multimodal functions show WWPA’s strong exploration ability to zero in on the major optimal region of the search space. Three engineering design problems were also used to gauge WWPA’s potential for improving practical programs. The effectiveness of WWPA in optimization was evaluated by comparing its results with those of seven widely used metaheuristic algorithms. When compared with eight competing algorithms, the simulation results and analyses demonstrate that WWPA outperformed them by finding a more proportionate balance between exploration and exploitation.

Abstract: The growing complexity of data derived from Industrial Internet of Things (IIoT) systems presents substantial challenges for traditional machine-learning techniques, which struggle to effectively manage the needs of predictive maintenance applications. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process, reducing the necessity for manual hyperparameter tuning and computational resources, thereby positioning themselves as a potentially transformative innovation in the Industry 4.0 era. This research introduces two distinct models: AutoML, employing PyCaret, and Auto Deep Neural Network (AutoDNN), utilizing AutoKeras, both aimed at accurately identifying various types of faults in ball bearings. The proposed models were evaluated using the Case Western Reserve University (CWRU) bearing faults dataset, and the results showed a notable performance in terms of achieving high accuracy, recall, precision, and F1 score on the testing and validation sets. Compared to recent studies, the proposed AutoML models demonstrated superior performance, surpassing alternative approaches even when they utilized a larger number of features, thus highlighting the effectiveness of the proposed methodology. This research offers valuable insights for those interested in harnessing the potential of AutoML techniques in IIoT applications, with implications for industries such as manufacturing and energy. By automating the machine-learning process, AutoML models can help decrease the time and cost related to predictive maintenance, which is crucial for industries where unplanned downtime can lead to substantial financial losses.

Abstract: Lithium is a vital raw material used for a wide range of applications, such as the fabrication of glass, ceramics, pharmaceuticals, and batteries for electric cars. The accelerating electrification transition and the global commitment to decarbonization have caused an increasing demand for lithium. The current supply derived from brines and hard rock ores is not enough to meet the global demand unless alternate resources and efficient techniques to recover this valuable metal are implemented. In the past few decades, several approaches have been studied to extract lithium from aqueous resources. Among those studied, chemical precipitation is considered the most efficient technology for the extraction of metals from wastewater. This paper outlines the current technology, its challenges, and its environmental impacts. Moreover, it reviews alternative approaches to recover lithium via chemical precipitation, and systematically studies the effects of different operating conditions on the lithium precipitation rate. In addition, the biggest challenges of the most recent studies are discussed, along with implications for future innovation.

Abstract: Thermochemical techniques have emerged as promising and sustainable approaches for converting diverse waste materials into valuable products, including chemicals and fuels. This study critically assesses the strengths and limitations of various thermochemical processes, focusing on their potential for large-scale implementation and commercial viability. The investigation encompasses a comprehensive examination of processes such as pyrolysis, gasification, and liquefaction, aiming to compare them based on crucial parameters including energy efficiency, product yield, product quality, and environmental impact. Through this comparative analysis, the study aims to identify the most suitable thermochemical treatment for specific waste materials, thereby facilitating the development of sustainable and economically feasible waste management strategies. By providing valuable insights into the selection and optimization of thermochemical processes, this research contributes to the advancement of waste-to-value technologies and supports the transition towards a circular economy.

Abstract: Green extraction techniques are more and more relevant due to major sustainable goals set by the United Nations. Greener extraction processes are being designed through the use of unconventional extraction techniques and green solvents, resulting in less hazardous processes which, consequently, reduces environmental impacts. This is also in line with the main principles of green chemistry. Additionally, greener extraction techniques intend to solve different drawbacks that are often related to conventional extraction techniques such as the high environmental impact. Biorefineries are a major player in developing greener extraction processes. These facilities take full advantage of several biomass sources, such as food waste, microalgae, and lignocellulosic biomass, in order to create high-value products, energy, alternative fuels, and bioactive compounds. Herein, a state-of-the-art review is presented, focused on presenting the greenest and least hazardous extraction processes that have been reported on the main biomass sources of a biorefinery—food waste, microalgae, and lignocellulosic biomass. Bioactive compounds such as phenolic compounds, bioactive pigments, and fatty acids are important in several sectors, mainly, the health, pharmaceutical, and agro-food sectors. Moreover, the bioactive compounds obtained through the aforementioned biomass sources and the different extraction procedures used will be presented and the authors will attempt to discuss, compare, and provide information about the most effective extraction techniques for each compound. Therewith, this review article should serve as a guide for industries, academics, and biorefineries in the future development of optimized and greener extraction procedures. Such analysis is lacking and could be very helpful for future research biorefinery projects since it tackles all of the major biomass sources of a biorefinery in a review article. To the best of our knowledge, this brings a novelty to the scientific community.

Abstract: Accurate prediction of the compressive strength of concrete is of great significance to construction quality and progress. In order to understand the current research status in the concrete compressive strength prediction field, a bibliometric analysis of the relevant literature published in this field in the last decade was conducted first. The 3135 journal articles published from 2012 to 2021 in the Web of Science core database were used as the database, and the knowledge map was drawn with the help of the visualisation software CiteSpace 6.1R2 to analyse the field at the macro level in terms of spatial and temporal distribution, hotspot distribution and evolutionary trends, respectively. Afterwards, we go into the detail and divide concrete compressive strength prediction methods into two categories: traditional and machine-learning methods, and introduce the typical methods of each. In addition, a boosting-based ensemble machine-learning algorithm, namely the gradient boosting regression tree (GBRT) algorithm, is proposed for predicting the compressive strength of concrete. 1030 sets of concrete compressive strength test data were collected as the dataset, of which 60% were used to train the model, 20% to validate the model and 20% to test the trained model. The coefficient of determination (R2) of the GBRT model was 0.92, the mean square error (MSE) was 22.09 MPa, and the root mean square error (RMSE) was 4.7 MPa, which is an excellent prediction accuracy compared to prediction models constructed by other machine-learning algorithms. In addition, a five-fold cross-validation analysis was carried out, and the eight input variables were analyzed for their characteristic importance.

Abstract: Third-generation solar cells are designed to achieve high power-conversion efficiency while being low-cost to produce. These solar cells have the ability to surpass the Shockley–Queisser limit. This review focuses on different types of third-generation solar cells such as dye-sensitized solar cells, Perovskite-based cells, organic photovoltaics, quantum dot solar cells, and tandem solar cells, a stacked form of different materials utilizing a maximum solar spectrum to achieve high power conversion efficiency. Apart from these solar cells, other third-generation technologies are also discussed, including up-conversion, down-conversion, hot-carrier, and multiple exciton. This review provides an overview of the previous work in the field, alongside an introduction to the technologies, including their working principles and components. Advancements made in the different components and improvements in performance parameters such as the fill factor, open circuit voltage, conversion efficiency, and short-circuit current density are discussed. We also highlight the hurdles preventing these technologies from reaching commercialization.

Abstract: A solid-state cold-spray technique was employed for depositing the copper-coated graphite reinforced copper-based composite coatings on aluminum alloy 6061 T6 substrate under different process parameters. The optimum process parameters of the cold-sprayed coatings were predicted in terms of surface roughness, thickness and adhesion. The surface roughness was measured using a 3D profilometer, the thickness and element constitution were detected by an optical microscope and scanning electron microscope furnished with an energy-dispersive spectral analyzer and the adhesion was detected by the scratch test method. The microstructures of the deposited coatings were also observed by a scanning electron microscope. The results show that when the coating is not oxidized and dense, the copper-coated graphite reinforced copper-based composite coating at 800 °C, 5.5 MPa, possesses the lowest surface roughness, the maximum thickness and the highest adhesion among the cold-sprayed coatings. In addition, the surface roughness, thickness and adhesion of the deposited coatings are all linear with particle velocity.

Abstract: In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

Abstract: Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement.

Abstract: Green synthesized silver nanoparticles (AgNPs) have been used against antibiotic-resistant bacteria and chemo-resistant cancer cells. We synthesized AgNPs from Acacia nilotica pods, evaluating their antibacterial activity against eight bacterial strains and anticancer efficiency against two colon cancer cell lines, SW620 and SW480. Expression levels of eight genes (β-catenin, APC, TP53, Beclin1, DKK3, Axin, Cyclin D1, and C-myc) were checked by a reverse transcription-polymerase chain reaction in cancer cells before and after treatment with A. nilotica extract and A. nilotica-AgNPs. Prepared nanoparticles were characterized through ultraviolet-visible (UV-vis), Zetasizer, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Fourier transform infrared spectroscopy (FTIR) was used to identify the functional group in extracts. At first, AgNPs were confirmed by a sharp peak of surface plasmon resonance at 375 nm. The Z-average size was 105.4 nm with a polydispersity index of 0.297. TEM showed particle size of 11–30 nm. The prepared AgNPs showed promising antibacterial activity against bacterial strains and cytotoxic activity against the cancer cell lines. Expression levels of all the genes were affected by extract and AgNPs treatment. Overall, this study recommended both A. nilotica pods and A. nilotica-AgNPs as attractive candidates for antibacterial and anticancer applications.

Abstract: ABTS (2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic) acid) is a widely used compound for determining the total antioxidant capacity (TAC) of plant extracts, food, clinical fluids, etc. This photometric assay is based on the reduction by the presence of antioxidant compounds of a well-known metastable radical (ABTS•+) which can be formed via several different approaches and be used in many different determination methodologies such as automated photometric measures in microplates, clinical robots, valuable titrations, and previous liquid chromatographic separation. Another interesting aspect is that, in some cases, the ABTS/TAC method permits sequential hydrophilic and lipophilic antioxidant activity determinations, obtaining total antioxidant activity values through the summatory data of both types of antioxidants. In this work, we present a review of several aspects of the ABTS/TAC, highlighting the major achievements that have made this method so widely used, e.g., ABTS radical formation in hydrophilic or lipophilic reaction media, measurement strategies, automatization, and adaptation to high-throughput systems, as well as the pros and cons. Moreover, some recent examples of ABTS/TAC method applications in plant, human, and animal samples are discussed.

Abstract: Plasma-activated water (PAW) is water that has been treated with atmospheric pressure plasma. Due to the presence of reactive oxygen and nitrogen species (RONS), PAW can be used in various applications such as (1) surface disinfection and food decontamination, (2) enhancement in seed germination, and (3) enhancement in surface cooling in the nucleate boiling regime. Briefly, for surface disinfection, the reactive species in PAW can induce oxidative stress on microbes; for enhancement of seed germination, the reactive species in PAW can trigger seed germination and provide nutrients; for enhancement in surface cooling, the reactive species cause a reduction in the surface tension of PAW, facilitating the phase-change heat transfer and, quite unexpectedly, minimizing the surface oxidation. Here, we review the physicochemical properties of PAW, the three commonly used techniques (plasma jet, dielectric barrier discharge, and corona discharge) for generating atmospheric pressure plasma, and the use of PAW for the above three applications. In particular, we review the recent development of the miniaturization of the plasma generator integrated with an acoustic neutralizer to produce plasma-activated aerosols, elimination of the need for storage, and the interesting physicochemical properties of PAW that lead to cooling enhancement.

Abstract: Printed circuit boards (PCBs) are primarily used to connect electronic components to each other. It is one of the most important stages in the manufacturing of electronic products. A small defect in the PCB can make the final product inoperable. Therefore, careful and meticulous defect detection steps are necessary and indispensable in the PCB manufacturing process. The detection methods can generally be divided into manual inspection and automatic optical inspection (AOI). The main disadvantage of manual detection is that the detection speed is too slow, resulting in a waste of human resources and costs. Thus, in order to speed up the production speed, AOI techniques have been adopted by many PCB manufacturers. Most current AOI mechanisms use traditional optical algorithms. These algorithms can easily lead to misjudgments due to different light and shadow changes caused by slight differences in PCB placement or solder amount so that qualified PCBs are judged as defective products, which is also the main reason for the high misjudgment rate of AOI detection. In order to effectively solve the problem of AOI misjudgment, manual re-judgment is currently the reinspection method adopted by most PCB manufacturers for defective products judged by AOI. Undoubtedly, the need for inspectors is another kind of labor cost. To reduce the labor cost. of manual re-judgement, an accurate and efficient PCB defect reinspection mechanism based on deep learning algorithm is proposed. This mechanism mainly establishes two detection models, which can classify the defects of the product. When both models have basic recognition capabilities, the two models are then combined into a main model to improve the accuracy of defect detection. In the study, the data provided by Lite-On Technology Co., Ltd. were implemented. To achieve the practical application value in the industry, this research not only considers the problem of detection accuracy, but also considers the problem of detection execution speed. Therefore, fewer parameters are used in the construction of the model. The research results show that the accuracy rate of defect detection is about 95%, and the recall rate is 94%. Compared with other detection modules, the execution speed is greatly improved. The detection time of each image is only 0.027 s, which fully meets the purpose of industrial practical application.

Abstract: Electric vehicles (EVs) represent a promising green technology for mitigating environmental impacts. However, their widespread adoption has significant implications for management, monitoring, and control of power systems. The integration of renewable energy sources (RESs), commonly referred to as green energy sources or alternative energy sources, into the network infrastructure is a sustainable and effective approach to addressing these matters. This paper provides a comprehensive review of the integration of RESs and EVs into power systems. The bibliographic analysis revealed that IEEE Access had the highest impact among journals. In order to enhance the classification of the reviewed literature, we have provided an analytical summary of the contributions made by each paper. The categorization facilitated the recognition of the primary objectives explored in the reviewed works, including the classification of EVs and RESs, the incorporation of RESs and EVs into power systems with an emphasis on emissions, the establishment of EV charging stations and parking facilities, EV batteries and battery energy storage systems, strategies for managing the integration of RESs with EVs, EV aggregators, and the financial implications. In order to provide researchers with a valuable synopsis of the implementation particulars, the papers were bifurcated into two primary classifications, namely mathematical algorithms and heuristic algorithms. The mixed integer linear programming algorithm and particle swarm optimization algorithm were commonly utilized formulations in optimization. MATLAB/Simulink was the primary platform used for executing a considerable portion of these algorithms, with CPLEX being the dominant optimization tool. Finally, this study offers avenues for further discourse and investigation regarding areas of research that remain unexplored.