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This study of patent applications and scientific publications related to batteries is unique as it includes the volume of as well as qualitative indicators for both types of publications. Using carefully elaborated strategies to identify publications relating to batteries, this study provides data to discuss the critical balance to strike between investments in research and the more innovation-related aspects. The results show that China's dominance in publication volumes increases and that research with Chinese involvement is highly cited, whereas patent applications are slightly less valued than the world average. Quality-related indicators for Canada and the United States are very high for both scientific publications and patent applications. National differences in the proportions of patent applications and scientific publications are large, with Japan at one end with three patent applications per scientific paper and Canada at the other with almost seven scientific papers per patent application. On an actor level, data for Sweden indicate how the automotive industry started to file many patent applications in the decade starting in 2010. Finally, it is noted that this new approach to study a technological field appears promising as it gives new perspectives of relevance for policy actors and others. [ABSTRACT FROM AUTHOR]

Conventional detectors are mostly made up of complicated structures that are hard to use. A paper-based microfluidic chip, however, combines the advantages of being small, efficient, easy to process, and environmentally friendly. The paper-based microfluidic chips for biomedical applications focus on efficiency, accuracy, integration, and innovation. Therefore, continuous progress is observed in the transition from single-channel detection to multi-channel detection and in the shift from qualitative detection to quantitative detection. These developments improved the efficiency and accuracy of single-cell substance detection. Paper-based microfluidic chips can provide insight into a variety of fields, including biomedicine and other related fields. This review looks at how paper-based microfluidic chips are prepared, analyzed, and used to help with both biomedical development and functional integration, ideally at the same time. [ABSTRACT FROM AUTHOR]

Paper is one of the most significant inventions in human civilization, which considerably advanced global cultural development. Pulping is a key step in the conversion of fiber raw materials into paper. Since its inception, pulping has rapidly evolved, continually adapting to technological advancements. Researchers are constantly investigating various types of raw materials for pulping. In this review, some of the materials employed in pulping are outlined, and the fiber content, pulping method, as well as the strength of wood and non-wood crop straw as pulping raw materials are analyzed and discussed. In addition, this review explores the effects of different materials under various pulping conditions and assesses the future trends in raw material selection for pulping while considering the current global environmental pressures. [ABSTRACT FROM AUTHOR]

For this, various technologies have been studied, such as thin-film coating, which mixes nanocellulose with a coating color to create a gas barrier, or imparts water and moisture resistance by coating the paper in a biodegradable polymer [[4]]. To replace plastics in paper, the produced paper must not only have gas barrier properties, water and moisture resistance, and preservation properties like plastics [[3]], but also have the property to be decomposed quickly when thrown away. It is of great honor to present this Editorial for the Special Issue "Advances in Pulp and Paper Technologies". [Extracted from the article]

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research. [ABSTRACT FROM AUTHOR]

Carbon containing waste has a certain calorific value and utilization value due to the presence of carbon elements. However, the current treatment methods are mostly traditional landfill disposal, incineration, or expensive physical and chemical reaction methods, which clearly do not comply with the rules of the current clean and resource-saving society. In this paper, a new technology and system for carbon containing wastes is proposed, which comprehensively treats and recycles carbon containing wastes, including solid waste, wastewater, waste gas, etc., using pyrolysis–gasification two-stage-cycle technology. The calculation results indicate that the technical scheme proposed in this article is feasible and can achieve the recycling of intermediate and final products, the efficient and clean utilization of carbon containing waste, and the graded utilization of energy. The clean utilization rate of carbon containing waste can be effectively improved, the energy consumption and cost of separate treatments can be reduced, and zero waste emissions can be completely achieved by the comprehensive pyrolysis–gasification two-stage-cycle treatment and resource utilization technology, which is one of the preferred solutions for future resource-saving societies. [ABSTRACT FROM AUTHOR]

Under the premise of resource constraint, it is crucial to identify the product innovation opportunities contained in failed patents through external search in order to compensate for the shortcomings of enterprises' own technology. Due to the cost of patent research and development and the risk of infringement, this paper constructs a product innovation identification path that integrates the FOS and BERTopic model from the perspective of invalid patents. The path consists of three stages, including the identification of the problem to be solved by the product based on functional analysis, the extraction of the subject matter elements based on the core failed patent technology, and the generation and evaluation of innovative solutions based on TRIZ theory and the best- worst method (BWM). Finally, the feasibility of the path constructed in this paper is verified by taking a slurry pump as an example. The application results show that the product innovation identification path constructed in this paper can provide theoretical support for enterprises to carry out technological innovation activities efficiently. [ABSTRACT FROM AUTHOR]

The material most commonly used to filter and clean the intake air of vehicle internal combustion engines is pleated filter paper, which in most cases is shaped in the form of a cylinder or panel. The production technology has a low cost and is not complicated. In addition to high separation efficiency and filtration performance, pleated filter media are required to have low initial pressure drop, which depends on the geometry of the bed. Much research has been conducted in this area. Dust accumulated in the filter bed causes an increase in pressure drop, which is the cause of deformation and sticking of pleats. The lack of stability of the pleats, the need to strengthen them, and the need to obtain small sizes while achieving high efficiency and accuracy of filtration of engine intake air was the reason for the development of a different design and a new technology for making filter cartridges called PowerCore. The distinctive feature of these filters is axial flow in one direction of the air stream, which avoids turbulence and thus minimizes pressure drop. This paper presents a comparative analysis of a standard PowerCore and PowerCore G2 filter bed and two cylindrical filters with a pleated filter bed made of cellulose and polyester. The conditions and methodology of experimental testing of filters with test dust are presented. During the tests, the characteristics of separation efficiency and filtration performance, as well as pressure drop as a function of the mass of dust retained on the filter of two PowerCore filters and two cylindrical filters were performed. Three specimens of test filters with the same filtration area were made from each sample of filter bed. The results showed that in each test of the filter bed, there is an initial filtration period characterized by low (96–98%) initial separation efficiency and the presence of large (dpmax) dust grains. As the dust loading of the bed increases, the separation efficiency and filtration performance obtain higher and higher values. The initial period of filtration ends when the conventional value (99.9%) of separation efficiency is reached. The duration of this period depends on the type of filter bed and for the PowerCore G2 filter ends for a dust loading of km = 33.1 g/m2, and for the cellulose filter for km = 117.3 g/m2. During the initial period, the air behind the PowerCore G2 filter contains grains with sizes in the range of dpmax = 9–16 µm. Behind the cellulose filter, dust grains are much larger, dpmax = 17–35 µm. The total operating time of the PowerCore G2 filter, limited by the achievement of the permittivity resistance Δpwdop = 3 kPa, is twice that of the other filter compositions tested. [ABSTRACT FROM AUTHOR]

Through a systematic literature review, which is considered the most comprehensive way to analyze the field of memory forensics, this paper investigates its development through past and current methodologies, as well as future trends. This paper systematically starts with an introduction to the key issues and a notable agenda of the research questions. Appropriate inclusion and exclusion criteria were then developed, and a deliberate search strategy was adopted to identify primary research studies aligned with the research question. The paper goes into specific details of six different memory categories, notably volatile memory, interpreting their advantages and the tactics used to retrieve the data. A detailed comparison with existing reviews and other relevant papers is made, forming a broader and wider picture of the research. The discussion summarizes the main findings, particularly the rise of more complex and advanced cyber threats and the necessity of more effective forensic methods for their investigation. This review pinpoints the possibilities for future study with the purpose of staying ahead in the evolving technological landscape. This overview is undoubtedly an essential resource for professionals and researchers working in digital forensics. It allows them to stay competent and provides enough insight into the current trends while marking the future direction in digital forensics methodology. [ABSTRACT FROM AUTHOR]

Microbial-induced calcite precipitation (MICP) is an innovative technology in civil engineering. However, the high cost of components and the fragility of the treated soil limit its wide use. One of the possible solutions is organic waste incorporation at different stages of the technology. In the present study, we consider the use of spent brewer's yeast (BSY) to produce bacterial inoculates and wastepaper, flax shives and sawdust as reinforcing additives into the soil. We showed that the replacement of expensive components of LB medium by BSY extract increased biomass growth characteristics of Bacillus subtilis K51, B. cereus 4b and Micrococcus luteus 6 strains by 1.4, 1.5 and 1.8 times, respectively, while for B. subtilis 168, they were comparable to LB medium. The urease activities of all strains were not reduced compared to the control. Among the three kinds of cellulose-containing waste, wastepaper incorporation into MICP-treated soil samples led to an increase in compressive strength by 2.1 times and precipitated calcite percentage by almost 1.5 times compared to a sample without additives. Thus, we showed the potential for soil stabilization through MICP using organic waste. [ABSTRACT FROM AUTHOR]

Conventional outage management practices in distribution systems are tedious and complex due to the long time taken to locate the fault. Emerging smart technologies and various cloud services offered could be utilized and integrated into the power industry to enhance the overall process, especially in the fault monitoring and normalizing fields in distribution systems. This paper introduces smart fault monitoring and normalizing technologies in distribution systems by using one of the most popular cloud service platforms, the Microsoft Azure Internet of Things (IoT) Hub, together with some of the related services. A hardware prototype was constructed based on part of a real underground distribution system network, and the fault monitoring and normalizing techniques were integrated to form a system. Such a system with IoT integration effectively reduces the power outage experienced by customers in the healthy section of the faulted feeder from approximately 1 h to less than 5 min and is able to improve the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) in electric utility companies significantly. [ABSTRACT FROM AUTHOR]

Featured Application: Generation of a Dynamic Innovation Information System (DIIS) for optimized planning and decision-making thanks to the dynamic evaluation of innovations over their life cycle, applying a methodology for Digital Ecosystems in the Fourth Industrial Revolution and an innovation management model based on the Viable System Model. Innovations are essential for global development and market dynamics. Innovation management is central to organizations for gaining adaptability and dynamic capabilities to ensure their sustainability over time. Right decisions are essential for the implementation of innovations. However, on many occasions, especially in the product development process, decisions are taken based on static analysis, qualitative criteria, questionnaires, and/or quantitative evaluations that are outdated. Moreover, many innovation developments do not consider the existing databases in their information systems of similar innovation projects, especially in the early phases of new innovations when evaluations are mainly driven by area, group, or person. Furthermore, inventions are introduced in different regions, plants, and socio-economic situations, providing different results. In this context, considering that innovations shape our current and future world, including all products and services, as well as how humans, organizations, and machines interact, the significance of the paper is clear. Therefore, it is necessary to develop an innovation management model based on the Viable System Model to cope with any potential future environment based on internal organizational capabilities. For this purpose, the paper designs a Digital Ecosystem for the Fourth Industrial Revolution (DE4.0) based on the Plan-Do-Check-Act methodology applicable to any information system consisting of a digital twin, a simulation model, databases from existing information systems, and quality management techniques. This DE4.0 provides a huge advantage for the applicability and scalability of innovations as it allows one to plan, monitor, assess, and improve. Moreover, based on the conceptual model, a generic project evaluation scheme is developed, providing a platform for innovation project management and control during the whole innovation life cycle. As a result, the research provides a scientific and practical contribution for an integrated management of innovations based on the best information and set of techniques available. Based on this framework, a supply-chain case study is developed. The results show how, depending on the intended goals, the past experiences, the evolution of the innovation, and the innovation scope, indicators can be influenced towards reaching the initial goals and reducing the innovation risks. Finally, a discussion about the potential use and role of the DE4.0 for innovation projects and the related learning process is performed. [ABSTRACT FROM AUTHOR]

This Special Issue on "Selected Papers from IEEE ICASI 2018" includes excellent papers presented at the IEEE ICASI 2018 regarding the "applied system innovation" topic. Mechanical engineering and design innovations are both academic and practical engineering fields, which involve systematic technological materialization through scientific principles and engineering designs. Technological innovations in mechanical engineering include IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanosciences and nanotechnology. The aim is to encourage the attendees at the IEEE ICASI 2018 to publish their experimental and theoretical research relating to applied system innovation. [ABSTRACT FROM AUTHOR]

The technology for transcranial magnetic stimulation (TMS) has significantly changed over the years, with important improvements in the signal generators, the coils, the positioning systems, and the software for modeling, optimization, and therapy planning. In this systematic literature review (SLR), the evolution of each component of TMS technology is presented and analyzed to assess the limitations to overcome. This SLR was carried out following the PRISMA 2020 statement. Published articles of TMS were searched for in four databases (Web of Science, PubMed, Scopus, IEEE). Conference papers and other reviews were excluded. Records were filtered using terms about TMS technology with a semi-automatic software; articles that did not present new technology developments were excluded manually. After this screening, 101 records were included, with 19 articles proposing new stimulator designs (18.8%), 46 presenting or adapting coils (45.5%), 18 proposing systems for coil placement (17.8%), and 43 implementing algorithms for coil optimization (42.6%). The articles were blindly classified by the authors to reduce the risk of bias. However, our results could have been influenced by our research interests, which would affect conclusions for applications in psychiatric and neurological diseases. Our analysis indicates that more emphasis should be placed on optimizing the current technology with a special focus on the experimental validation of models. With this review, we expect to establish the base for future TMS technological developments. [ABSTRACT FROM AUTHOR]

Simple Summary: This review seeks to understand how scientific research on trace elements in beef cattle has been developed and how the interaction of this theme with topics related to animal, environmental and human health has been established. Given the duality of many of the trace elements, being known as nutrients in small amounts or toxic when they exceed small concentrations, we brought the One Health perspective to analyse how researchers approach this research theme. In this work, we propose a path through scientific production to promote innovation, sustainability of animal production, food safety and human health. The objective was to investigate the context, approach and research topics present in the papers that analysed trace elements in beef cattle to identify gaps and scientific perspectives for the sustainable management of trace elements in livestock. The main research groups came from the United States, Spain, Japan, Brazil, India and Slovakia, which represented 31% of the papers produced. Only 37% of studies addressed aspects that integrated animal, environmental and human health. The reviewed papers concerned 56 elements and 15 bovine tissues (Cu, Zn, Pb, liver, muscle and kidney highlighted). The main gaps were (1) lack of research in developing countries, (2) the need to understand the impact of different environmental issues and their relationship to the conditions in which animals are raised, and (3) the need to understand the role of many trace elements in animal nutrition and their relationship to environmental and human health. Finally, we highlight possible ways to expand knowledge and provide innovations for broad emerging issues, primarily through expanding collaborative research networks. In this context, we suggest the adoption of the One Health approach for planning further research on trace elements in livestock. Moreover, the One Health approach should also be considered for managers and politicians for a sustainable environmental care and food safety. [ABSTRACT FROM AUTHOR]

This Special Issue of Applied Sciences provides a collection of original papers on smart manufacturing technology with the aim of: examining emerging aspects of digitalization in the industrial and biomedical fields, as well as in business management and sustainability; proposing and developing a new approach useful for companies, factories, and organizations to achieve greater innovation and productivity—as well as sustainability—by applying smart manufacturing technologies; and exploring new ideas and encouraging research directions so as to obtain autonomous and semiautonomous processes, high-quality products, and services with a greater integration and interconnection of resources while reducing costs. The advantages of new methods and experimental results obtained in the collected contributions are discussed promoting further design, implementation, and application in the various fields. [ABSTRACT FROM AUTHOR]

The paper by Feng et al. [[11]] proposes an image encryption algorithm, IEA-IF-DLT, that utilizes plane-level image filtering and discrete logarithmic transformation to achieve a balance between security and efficiency. In the last few decades, the relationship between mathematics and algorithms has become increasingly important and influential in computer science. The scheme utilizes versatile bit-depth management, encrypting the most significant bits of each pixel with the AES-CTR cipher algorithm and embedding additional data in the least significant bit planes of the encrypted pixels using code division multiplexing. [Extracted from the article]

Accounting has been evolving to follow the latest economic, political, social, and technological developments. Therefore, there is a need for researchers to also include in their research agenda the emerging topics in the accounting area. This exploratory paper selects technological matters in accounting as its research object, proposing a literature review that uses archival research as a method and content analysis as a technique. Using different tools for the assessment of qualitative data, this content analysis provides a summary of those papers, such as their main topics, most frequent words, and cluster analysis. A top journal was used as the source of information, namely The International Journal of Accounting Information Systems, given its scope, which links accounting and technological matters. Data from 2000 to 2022 was selected to provide an evolutive analysis since the beginning of this century, with a particular focus on the latest period. The findings indicate that the recent discussions and trending topics in accounting, including matters such as international regulation, the sustainable perspective in accounting, as well as new methods, channels, and processes for improving the entities' auditing and reporting, have increased their relevance and influence, enriching the debate and future perspectives in combination with the use of new technologies. Therefore, this seems to be a path to follow as an avenue for future research. Notwithstanding, emerging technologies as a research topic seem to be slower or less evident than their apparent development in the accounting area. The findings from this paper are limited to a single journal and, therefore, this limitation must be considered in the context of those conclusions. Notwithstanding, its proposed analysis may contribute to the profession, academia, and the scientific community overall, enabling the identification of the state of the art of literature in the technological area of accounting. [ABSTRACT FROM AUTHOR]

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools. [ABSTRACT FROM AUTHOR]

The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data Envelopment Analysis (DEA) and a random forest model. The non-parametric DEA model is combined with the parametric SFA model to analyze the real green innovation efficiency on the basis of removing environmental factors and random factors. Then, the random forest model based on a nonlinear relationship is used to evaluate factors impacting green innovation efficiency. This paper proposes a comprehensive evaluation method designed to assess the green innovation efficiency of new-energy enterprises. By applying this method, companies can gain a comprehensive understanding of the current performance in green innovation, facilitating informed decision-making and accelerating sustainable development. [ABSTRACT FROM AUTHOR]

The development of smart cities has led to transforming modern city operations by applying emerging technologies from the fourth industrial revolution such as the Internet of Things, BIM and machine learning. To prepare for the future development of cities, it is necessary to investigate the current status of city development, research topics and the direction toward future cities. In this study, a systematic literature review was conducted following the combination of literature review guides by Kitchenham and the PRISMA statement. The review identified 141 peer-reviewed academic papers from web-based archives, such as Web of Science and SCOPUS, that were categorized into five topics related to smart cities and urban areas. The technologies applied in the research were analyzed to determine the direction future cities should take based on current smart cities and emerging technologies that will shape individuals' daily lives. However, it was discovered that research papers on critical areas, such as mobility and management, were lacking compared to those on building design and urban planning. This paper concludes with an example of a future urban area that has been transformed due to adoption of future mobility technology to address the shortcomings of the reviewed literature on building and urban environments. [ABSTRACT FROM AUTHOR]

Immersive technologies are revolutionary technological advancements that offer users unparalleled experiences of immersion in a virtual or mixed world of virtual and real elements. In such technology, user privacy, security, and anonymity are paramount, as users often share private and sensitive information. Therefore, user authentication is a critical requirement in these environments. This paper presents a systematic literature review of recently published research papers on immersive technology-based user authentication mechanisms. After conducting the literature search in September 2023 using Scopus, the selection process identified 36 research publications that were further analyzed. The analysis revealed three major types of authentications related to immersive technologies, consistent with previous works: knowledge-based, biometric, and multi-factor methods. The reviewed papers are categorized according to these groups, and the methods used are scrutinized. To the best of our knowledge, this systematic literature review is the first that provides a comprehensive consolidation of immersive technologies for user authentication in virtual, augmented, and mixed reality. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of drone technology, securing unmanned aerial vehicles (UAVs) presents critical challenges and demands unique solutions. This paper offers a thorough examination of the security requirements, threat models, and solutions pertinent to UAVs, emphasizing the importance of cybersecurity and drone forensics. This research addresses the unique requirements of UAV security, outlines various threat models, and explores diverse solutions to ensure data integrity. Drone forensics, a field dedicated to the investigation of security incidents involving UAVs, has been extensively examined and demonstrates its relevance in identifying attack origins or establishing accident causes. This paper further surveys artifacts, tools, and benchmark datasets that are critical in the domain of drone forensics, providing a comprehensive view of current capabilities. Acknowledging the ongoing challenges in UAV security, particularly given the pace of technological advancement and complex operational environments, this study underscores the need for increased collaboration, updated security protocols, and comprehensive regulatory frameworks. Ultimately, this research contributes to a deeper understanding of UAV cybersecurity and aids in fostering future research into the secure and reliable operation of drones. [ABSTRACT FROM AUTHOR]

Technological advancements for diverse aspects of life have been made possible by the swift development and application of Internet of Things (IoT) based technologies. IoT technologies are primarily intended to streamline various processes, guarantee system (technology or process) efficiency, and ultimately enhance the quality of life. An effective method for pandemic detection is the combination of deep learning (DL) techniques with the IoT. IoT proved beneficial in many healthcare domains, especially during the last worldwide health crisis: the COVID-19 pandemic. Using studies published between 2019 and 2024, this review seeks to examine the various ways that IoT-DL models contribute to pandemic detection. We obtained the titles, keywords, and abstracts of the chosen papers by using the Scopus and Web of Science (WoS) databases. This study offers a comprehensive review of the literature and unresolved problems in applying IoT and DL to pandemic detection in 19 papers that were eligible to be read from start to finish out of 2878 papers that were initially accessed. To provide practitioners, policymakers, and researchers with useful information, we examine a range of previous study goals, approaches used, and the contributions made in those studies. Furthermore, by considering the numerous contributions of IoT technologies and DL as they help in pandemic preparedness and control, we provide a structured overview of the current scientific trends and open issues in this field. This review provides a thorough overview of the state-of-the-art routing approaches currently in use, as well as their limits and potential future developments, making it an invaluable resource for DL researchers and practitioners and it is a useful tool for multidisciplinary research. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

Under the double pressures of economic growth and ecological environment protection, sea green transformation and the sustainable development of mariculture are critical. This paper constructs an evolutionary game model with the government as the main body and mariculture farmers (enterprises) as the main body and puts forward the research hypothesis. Based on 2006–2019 longitudinal data of nine provinces along China's coast, using multi-period Difference-in-Difference (DID) and dual robust estimation, we empirically investigate the national oceanic ranch demonstration zones for the influence of the green sea aquaculture and their mechanism of action. The results showed that (1) the efficiency of green level of mariculture industry in China is not high, and the establishment of national marine pasture demonstration zone has not effectively promoted the improvement of green level of mariculture industry; (2) the institutional environment, unreasonable industrial structure, and lack of scientific and technological innovation have an effect on the national oceanic ranch demonstration area as the main causes of failure to effectively promote marine green farming; (3) the establishment of the national multi-period demonstration area in the north significantly hindered the growth of the green level of mariculture and fell into the "policy trap", while the establishment of the national multi-period demonstration area in the south significantly promoted the growth of the green level of mariculture. The conclusions of this paper provide an empirical basis and reference for the improvement of the national marine pasture demonstration zone policy and the green transformation of mariculture to a certain extent. [ABSTRACT FROM AUTHOR]

This paper examines the impact and implications of ChatGPT and other generative AI technologies within the African context while looking at the ethical benefits and concerns that are particularly pertinent to the continent. Through a robust analysis of ChatGPT and other generative AI systems using established approaches for analysing the ethics of emerging technologies, this paper provides unique ethical benefits and concerns for these systems in the African context. This analysis combined approaches such as anticipatory technology ethics (ATE), ethical impact assessment (EIA), and ethical issues of emerging ICT applications with AI (ETICA) with specific issues from the literature. The findings show that ChatGPT and other generative AI systems raise unique ethical concerns such as bias, intergenerational justice, exploitation of labour and cultural diversity in Africa but also have significant ethical benefits. These ethical concerns and benefits are considered crucial in shaping the design and deployment of ChatGPT and similar technologies responsibly. It further explores the potential applications of ChatGPT in critical domain areas such as education, agriculture, and healthcare, thereby demonstrating the transformative possibilities that these technologies can have on Africa. This paper underscores the critical role of AI governance as Africa increasingly adopts ChatGPT and similar AI systems. It argues that a comprehensive understanding of AI governance is essential not only for maximising the benefits of generative AI systems but also for facilitating a global dialogue. This dialogue aims to foster shared knowledge and insights between the Global North and the Global South, which is important for the development and creation of inclusive and equitable AI policies and practices that can be beneficial for all regions. [ABSTRACT FROM AUTHOR]

Under the new wave of scientific and technological revolution, the construction industry finds itself with a critical need to alter the traditional and outdated production mode through technological innovation in order to realize industry transformation and move towards a new era characterized by digitalization, informatization, and intelligence. As intelligent construction is the indispensable pathway for the transformation and upgrading of the construction industry, it is of great significance to conduct in-depth research on its evaluation indicators and causality. This paper adopts the system dynamics method, based on the overall structure of intelligent construction, extracts the causality chain and causal feedback loop of intelligent construction, and presents a causality diagram and system dynamics diagram to build a robust system dynamics model for intelligent construction. On this basis, an evaluation index system for intelligent construction is constructed from the five dimensions—investment, design, construction, operation, and environment—for a holistic assessment of the current state of intelligent construction. The research aims to provide a valuable reference for professionals focusing on intelligent construction and the broader development of the industry. [ABSTRACT FROM AUTHOR]

This paper presents a thorough examination of methane capture from Polish coal mines, contextualized within the framework of the European Union's (EU) climate policy objectives. Through a strategic analysis encompassing the interior of coal mines, the surrounding environment, and the macro environment, this study elucidates the complex dynamics involved in methane emissions and capture initiatives. The key findings include a declining trend in absolute methane emissions since 2008, despite fluctuations in coal extraction volumes, and a relatively stable level of methane capture exceeding 300 million m3/year since 2014. The analysis underscores the critical role of government support, both in terms of financial incentives and streamlined regulatory processes, to facilitate the integration of methane capture technologies into coal mining operations. Collaboration through partnerships and stakeholder engagement emerges as essential for overcoming resource competition and ensuring the long-term success of methane capture projects. This paper also highlights the economic and environmental opportunities presented by methane reserves, emphasizing the importance of investment in efficient extraction technologies. Despite these advancements, challenges persist, particularly regarding the low efficiency of current de-methanation technologies. Recommendations for modernization and technological innovation are proposed to enhance methane capture efficiency and utilization. [ABSTRACT FROM AUTHOR]

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers. [ABSTRACT FROM AUTHOR]

The circular bioeconomy offers solutions to curb the effects of climate change by focusing on the use of renewable, biological resources to produce food, energy, materials, and services. The substitution of fossil products by wood-based products can help avoid or reduce greenhouse gas emissions over the life cycle of products. However, it is important to understand the potential impacts of large-scale material substitution at the market level. This study aimed to assess the role of selected wood-based products in the circular bioeconomy, the possible changes in their markets, and investigate which elements could ensure the environmental sustainability of these products. The demand for graphic paper has declined over the last 15 years, while the demand for packaging has increased. Cross-laminated timber and man-made cellulosic fibres have seen their global consumption increase over the last decade. While there are benefits associated with the substitution of non-renewable materials by wood-based products, there is still limited understanding of the substitution effects at market-, country- and global level. Some factors enabling the further uptake of wood-based products include initiatives that stimulate technological change, incentives to produce or consume less fossil-based and more bio-based alternatives, and the promotion and marketing of wood-based products as viable alternatives to non-renewable materials. [ABSTRACT FROM AUTHOR]

With advances in science and technology and changes in industry, research on promising future technologies has emerged as important. Furthermore, with the advent of a ubiquitous and smart environment, governments and enterprises are required to predict future promising technologies on which new important core technologies will be developed. Therefore, this study aimed to establish science and technology development strategies and support business activities by predicting future promising technologies using big data and deep learning models. The names of the "TOP 10 Emerging Technologies" from 2018 to 2021 selected by the World Economic Forum were used as keywords. Next, patents collected from the United States Patent and Trademark Office and the Science Citation Index (SCI) papers collected from the Web of Science database were analyzed using a time-series forecast. For each technology, the number of patents and SCI papers in 2022, 2023 and 2024 were predicted using the long short-term memory model with the number of patents and SCI papers from 1980 to 2021 as input data. Promising technologies are determined based on the predicted number of patents and SCI papers for the next three years. Keywords characterizing future promising technologies are extracted by analyzing abstracts of patent data collected for each technology and the term frequency-inverse document frequency is measured for each patent abstract. The research results can help business managers make optimal decisions in the present situation and provide researchers with an understanding of the direction of technology development. [ABSTRACT FROM AUTHOR]

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts. [ABSTRACT FROM AUTHOR]

The global interest in environmental issues and sustainable energy has propelled extensive research in photovoltaic (PV) technologies. Brazil has emerged as one of the top ten solar energy producers and flexible PV suppliers in the world. In this context, organic photovoltaic cells (OPVs) have garnered attention due to their flexibility and ability to integrate into various surfaces, albeit facing challenges regarding lifespan and efficiency compared to silicon cells. This review examines the current state of research on OPVs and thin-film solar technologies, employing the systematic literature review methodology Proknow-C. The review includes an analysis of raw materials such as conductive polymers, fabrication processes including film deposition and encapsulation, and technological advancements that enhance the efficiency and stability of OPVs. Utilizing the Proknow-C methodology, a bibliographic portfolio was constructed to identify the current state of research in this field. Of 268 papers from major scientific databases, only 30 were deemed relevant to the theme, highlighting a significant research gap. This paper is a valuable resource for researchers, providing an updated overview and a foundation for future investigations in organic photovoltaics. The results emphasize the importance of materials such as conductive polymers and donor–acceptor molecules and the role of nanotechnology in advancing OPVs. Innovations in manufacturing techniques, such as inkjet and blade coating-based printing techniques, are shown to increase efficiency by providing precise control over film thickness and uniformity and reducing costs through lower material waste. Overall, this review highlights the necessity of further exploration and collaboration within the scientific community to address the challenges and propel advancements in organic photovoltaic cells. These advancements are crucial for transitioning to cleaner energy sources, reinforcing the ongoing significance of thin-film technologies in energy innovation. [ABSTRACT FROM AUTHOR]

The increasing concern over environmental sustainability has prompted various industries to reassess their practices and explore greener alternatives. Dentistry, as a significant contributor to waste generation, is actively seeking methods to minimize its environmental footprint. This paper examines the environmental implications of clear aligner therapy (CAT) in orthodontics and explores strategies to prioritize sustainability in aligner manufacturing and usage. CAT has gained popularity as a viable alternative to traditional fixed appliances due to advancements in biomaterials and computer-aided design (CAD) and manufacturing (CAM) technologies. The global market for clear aligners is expanding rapidly, with significant growth projected in the coming years. To address these challenges, this paper proposes adopting the principles of reduce, reuse, recycle, and rethink (4Rs) in orthodontic practices. Strategies such as minimizing resource consumption, incorporating recycled materials, and promoting proper aligner disposal and recycling can significantly reduce environmental harm. This paper explores emerging technologies and materials to mitigate the environmental impacts of CAT. Additionally, initiatives promoting aligner recycling and repurposing offer promising avenues for reducing plastic waste and fostering a circular economy. In conclusion, while CAT offers numerous benefits in orthodontic treatment, its environmental impact cannot be overlooked. By implementing sustainable practices and embracing innovative solutions, the orthodontic community can contribute to a more environmentally conscious future while continuing to provide quality care to patients. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AD) of food waste (FW) is considered an environmentally sustainable process that can divert the disposal of FW to landfill and prevent greenhouse gas (GHG) emissions in managing the FW. Although several studies have attempted to demonstrate the AD of FW, low methane yields and a high incidence of process instability have been reported due to the rapid generation and accumulation of volatile fatty acids (VFAs). This paper reviews the recent research and development with high variation in FW composition, such as the carbon-to-nitrogen (C/N) ratio and, consequently, the effect of its physicochemical composition on process performance and methane yields. The paper highlights the significance of optimizing the anaerobic co-digestion (AcoD) of FW with carbon-rich substrates such as garden waste (GW) and/or the addition of trace elements as strategies that can improve the process performance and methane yields from FW. This review focuses on the factors effecting the feasibility of food organics and garden organics (FOGO) as a substrate for methane production. The review also critically analyses the prospects of enhancement of biomethane yield by optimizations of the impactful parameters. The progress in research related to these methods and identifying existing limitations to efficient AD of FOGO are the key findings of this review. This review also assesses the impact of nanotechnology on the process performance of the digester. The integration of FO and GO in AD processes has demonstrated enhanced biogas yields, improved process stability, and better waste management outcomes compared to the digestion of either substrate alone. Despite these advantages, challenges such as feedstock variability, process optimization, and the need for advanced pretreatment methods remain. Addressing these issues through continued research and technological innovations will be crucial for maximizing the efficiency and scalability of AD systems. Moreover, the economic feasibility and policy frameworks supporting AD need further development to promote broader adoption. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of water injection management practices for complex and impactful onshore and offshore carbonate reservoirs. It delves into the fundamental aspects of waterflooding design, surveillance techniques, and monitoring methods tailored for the unique challenges posed by carbonate formations. Two case studies from the Permian Basin in Texas and two from Lula Field offshore Brazil and Agbami Field offshore Nigeria are examined considering scientific principles into practice to provide insights into best practices, lessons learned, and strategies to maximize the benefits derived from real noteworthy waterflood operations. The paper underscores the significance of rigorous reservoir characterization, including understanding reservoir architecture, heterogeneities, fracture networks, fluid communication pathways, and rock–fluid interactions. It emphasizes the crucial role of integrated multidisciplinary teams involving geologists, reservoir engineers, production engineers, and field operators in ensuring successful waterflood design, implementation, and optimization. Through the case studies, the paper highlights the importance of designing pattern configurations, well placements, and injection/production strategies to the specific reservoir characteristics, continually optimizing these elements based on surveillance data. It also stresses the necessity of comprehensive data acquisition, advanced analytics, numerical simulations, and frequent model updates for effective reservoir management and decision-making. The paper is impactful in terms of the lessons learned from the actual case studies, and how can these be implemented in actual field projects. Different case studies documented in the paper provide the challenges facing them and how different authors have addressed their problems in unique ways. The paper distills the information and important findings from a variety of case studies and provides succinct information that is of immense value as a reference. Important findings of these case studies are connected using creativity and are innovative as they introduce unique techniques and establish successful ideas to create new value in terms of maximizing oil recovery. Most importantly, this paper explores the application of innovative technologies, such as intelligent completions, 4D seismic monitoring, and water–alternating gas (WAG) injection, which can significantly improve waterflood performance in complex carbonate reservoirs. In summary, the paper provides a thorough understanding of the factors contributing to the success and failure of waterfloods in carbonate reservoirs through case studies based on factually and technically sound operations. It documents guidelines for optimizing waterflood performance and reducing or eliminating the potential for failures, reinforcing positive results in these challenging yet invaluable hydrocarbon resources. [ABSTRACT FROM AUTHOR]

In today's dynamic business world, organizations are increasingly relying on innovative technologies to improve the efficiency and effectiveness of their human resource (HR) management. Our study uses historical time and attendance data collected with the MojeUre time and attendance system to predict employee absenteeism, including sick and vacation leave, using machine learning methods. We integrate employee demographic data and the absence profiles on timesheets showing daily attendance patterns as fundamental elements for our analysis. We also convert the absence data into a feature-based format suitable for the machine learning methods used. Our primary goal in this paper is to evaluate how well we can predict sick leave and vacation leave over short- and long-term intervals using tree-based machine learning methods based on the predictive clustering paradigm. This paper compares the effectiveness of these methods in different learning settings and discusses their impact on improving HR decision-making processes. [ABSTRACT FROM AUTHOR]

Molecularly imprinted membranes (MIMs) have been a focal research interest since 1990, representing a breakthrough in the integration of target molecules into membrane structures for cutting-edge sensing applications. This paper traces the developmental history of MIMs, elucidating the diverse methodologies employed in their preparation and characterization on two-dimensional solid-supported substrates. We then explore the principles and diverse applications of MIMs, particularly in the context of emerging technologies encompassing electrochemistry, surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), and the quartz crystal microbalance (QCM). Furthermore, we shed light on the unique features of ion-sensitive field-effect transistor (ISFET) biosensors that rely on MIMs, with the notable advancements and challenges of point-of-care biochemical sensors highlighted. By providing a comprehensive overview of the latest innovations and future trajectories, this paper aims to inspire further exploration and progress in the field of MIM-driven sensing technologies. [ABSTRACT FROM AUTHOR]

In response to the international appeal of developing low-carbon economy and realizing carbon peaking and neutrality goals, China has been exploring the construction of the carbon emissions trading market for years. Without the guidance of advanced technology, there would not be a low-carbon economy in the first place. Based on panel data of 30 provinces and cities in China from 2005 to 2020, this paper takes Beijing—which is the first pilot city in China—as the only treated group and uses the Synthetic Control Method to study the impact of the pilot policy on low-carbon technology innovation. The results show that, firstly, the number of low-carbon technology patents in Beijing increases significantly after the implementation of the pilot policy, proving a positive influence on technology innovation. Secondly, the policy effect has a certain time lag and is sensitive to the shock from both domestic and foreign carbon market, but this effect is gradually stable over time. This paper confirms that technological innovation is the key means of promoting the development of the low-carbon economy and calls on various carbon trading markets to pay attention to the internal mechanism of promoting low-carbon technology innovation to stimulate the vitality of market entities. [ABSTRACT FROM AUTHOR]

Population growth exacerbates the pressure on land carrying capacity, affecting the sustainability of agricultural production, and also impacts non-agricultural industries. This paper utilizes grain price data from southern China during the Qing Dynasty (1776–1910) to examine the impact of population and land pressure on the development of the commodity economy under the "involution" of smallholder agriculture. This study finds that under conditions of stagnant technological advancement and limited natural resources, population growth during the Qing Dynasty created significant "Malthusian" population pressure. This pressure on land first resulted in the over-concentration of agricultural labor and saturation of the farming population. Surplus labor, unable to be absorbed by agriculture, shifted to non-agricultural sectors, engaging in the transportation and trade of grain. The pressure on land carrying capacity facilitated the cultivation and processing of cash crops, and product trade was supported by efficient waterway transportation. These activities generated commercial profits that alleviated survival pressures and promoted the prosperity of the commodity economy. However, this prosperity did not accompany significant productivity improvements; instead, it was a product of "involution" agriculture under high population density pressures. [ABSTRACT FROM AUTHOR]

The importance of data in current societal activities cannot be overstated, yet we know little about data governance and application. Using the Chinese Government Data Empowerment Initiative, this paper examines the process of data-driven business innovation. Using the staggered DiD model, we found that government data points effectively facilitate firms' product innovation, with higher-quality data correlating with better innovation performance. Government-authorized data points aid firms in accumulating experience in applying and managing data, thereby enhancing their production performance. Furthermore, these data help firms improve the quality of their product innovations and achieve iterative product upgrades. We also found that government-authorized data points not only generate product innovations for government use but also stimulate the output of commercial product innovations. This research provides important insights into data governance and enterprise data management decisions. [ABSTRACT FROM AUTHOR]

With the rapid development of digital technology and the increasing focus on the global supply chain network, it has become a new challenge for international companies to select digital technology innovation projects in an efficient way, so as to improve their supply chain position and competitiveness. Prior works have identified the effects of digital technology adoption on companies' supply chain positions; however, there has been limited research on the impact of digital technology innovation heterogeneity on companies' supply chain position and the pathways through which this effect plays out. Hence, based on the global supply chain panel data from Chinese new energy vehicle companies, this study used a two-way fixed-effects model and causal stepwise regression analysis to study the impact of digital technological innovation on companies' supply chain position and the dynamic mechanisms between them. The empirical results show that all three types of digital technology innovations, in the design and development process, the production and manufacturing process, and the sales and after-sales process, significantly enhance the company's supply chain position. Further mechanism analysis shows that digital technology innovations enhance the company's managerial efficiency and profitability mainly by reducing costs and increasing revenues, which ultimately improves the company's supply chain position. This paper can provide a reference for policy makers to promote the application and development of a company's digital technology and enhancing the supply chain position. [ABSTRACT FROM AUTHOR]

Hydrogenation of CO2 represents a promising pathway for converting it into valuable hydrocarbons and clean fuels like dimethyl ether (DME). Despite significant research, several challenges persist, including a limited understanding of reaction mechanisms, thermodynamics, the necessity for catalyst design to enhance DME selectivity, and issues related to catalyst deactivation. The paper provides a comprehensive overview of recent studies from 2012 to 2023, covering various aspects of CO2 hydrogenation to methanol and DME. This review primarily focuses on advancing the development of efficient, selective, and stable innovative catalysts for this purpose. Recent investigations that have extensively explored heterogeneous catalysts for CO2 hydrogenation were summarized. A notable focus is on Cu-based catalysts modified with promoters such as Zn, Zr, Fe, etc. Additionally, this context delves into thermodynamic considerations, the impact of reaction variables, reaction mechanisms, reactor configurations, and recent technological advancements, such as 3D-printed catalysts. Furthermore, the paper examines the influence of different parameters on catalyst deactivation. The review offers insights into direct CO2 hydrogenation to DME and proposes paths for future investigation, aiming to address current challenges and advance the field. [ABSTRACT FROM AUTHOR]

This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communications, the occupation of time and frequency resources becomes more crowded. The exploitation of space resources is necessary and the diversity combining techniques have substantial applications. Diversity combining achieves great diversity gains and improves the ability to combat multipath fading, among which the maximum ratio combining (MRC) performs as the optimal linear combining approach. However, MRC suffers from detrimental factors such as channel fading and no Gaussian noise in practical scenarios. In this paper, we focus on a comprehensive investigation of MRC. Starting from the MRC principle and system model, we summarize typical scenarios and analyze the channel fading statistics. For the influential factors, we further review related literature on channel correlation, cochannel interference (CCI) and impulsive noise. Major performance criteria and performance bounds are derived and compared. MRC confronts new developing challenges and the major development directions are reviewed. The paper finally discusses recent works and open problems for MRC applications and development. Emerging techniques such as artificial intelligence provide novel solutions for MRC performance improvements. The paper aims to present a summarized insight to assist readers in clarifying the analyzed methodology of MRC, so as to motivate new technology integration and extensive applications of advanced communication systems. [ABSTRACT FROM AUTHOR]

(1) Background: Numerous oral drugs exhibit limited bioavailability due to their poor solubility and poor intestinal permeability. The smartFilm technology is an innovative approach that improves the drug aqueous solubility via incorporating the drug in an amorphous state into a cellulose-based matrix, i.e., paper. smartFilms can be transformed into a free-flowing physical form (i.e., paper granules) that can be compressed into tablets with optimum physico-chemical and pharmaceutical properties. The aim of this study was to investigate if smartFilm tablets are suitable for improved oral delivery of poorly water-soluble drugs. (2) Methods: Curcumin is a poorly soluble drug with low intestinal permeability and was used for the production of curcumin-loaded smartFilms. The curcumin-loaded smartFilms were transferred into smartFilm granules which were then compressed into curcumin-loaded smartFilm tablets. The tablets were characterized regarding their physico-chemical and pharmaceutical properties, and the intestinal permeability of curcumin was determined with the ex vivo porcine intestinal model. The ex vivo intestinal permeability of curcumin from the smartFilm tablets was compared to a physical mixture of curcumin and paper and to a classical and to an innovative commercial product, respectively. (3) Results: The produced curcumin-loaded smartFilm tablets fulfilled the European Pharmacopoeia requirements, incorporated curcumin in amorphous state within the cellulose matrix and exhibited an enhanced dissolution rate. The ex vivo intestinal permeation data were shown to correlate to the in vitro dissolution data. The ex vivo intestinal permeation of curcumin from the smartFilm tablets was about two-fold higher when compared to the physical mixture and the classical commercial product. No differences in the ex vivo bioavailability were found between the smartFilm tablets and the innovative commercial product. (4) Conclusions: smartFilm tablets are a cost-effective and industrially feasible formulation approach for the formulation of poorly water-soluble drugs, i.e., BCS class II and IV drugs. [ABSTRACT FROM AUTHOR]

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider's operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate. [ABSTRACT FROM AUTHOR]

With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%. [ABSTRACT FROM AUTHOR]