A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

Although being one of the most popular world fibres, wool industry is addressed with severe environmental allegations. While majority of scientific texts does not leave to much doubt about its production negative impact on the ecosystem, wool business does not stop to promote itself via green claims. The internet popular links present a divergent, positive picture of that material, where one might recognize parties being financially involved in such communication. As wool producers or organizations linked to wool business are top positioned in the web as providers of wool green image, the below article brings an interpretation of the industry greenwashing techniques. This comparative analysis of wool umbrella institutions’ webpages exposes myths endorsed about wool’s sustainability. The investigation follows case study of Woolmark brand, where associations to similar, highly ranked websites were conducted likewise. The results have been additionally matched with available scientific data from LCA relative statistics and The Higg Index, to provide contra argumentation from academic sources. The results of this paper could be used both by researchers or fashion lecturers, likewise by fashion marketers to recognize greenwashing techniques, to deliver adequate scientific numbers about unsustainable aspects of wool and to support them in creating a relevant content about wool’s environmental impacts.

This study aims to analyze the interconnection between the concepts of digital economy and sustainability. These concepts have become popular due to awareness of climate change and increased development and adoption of technologies. Researchers, business leaders and policymakers are exploring the many ways digital technologies can be used to address sustainability issues. Using a systematic literature review with a bibliometric analysis, we examined a sample of 92 studies indexed in SCOPUS to identify research activity on this topic through April 2023. We found that 2022 had the highest number of peer-reviewed papers on the subject, with 31 publications. During the research, we also identified multiple opportunities for this interconnection, such as developing renewable energy and sustainable technological solutions, smart cities and sustainable urbanization, and sustainable consumption. These opportunities enabled by digital technologies allow companies to adopt sustainable business strategies and develop sustainable products. Despite these opportunities, the digital economy presents several challenges that can hinder efforts to achieve sustainability goals, such as increased e-waste, high energy consumption and rising carbon emissions, digital divide, job insecurity, growing monopolies, and data protection. These issues must be addressed to enable optimal use of the opportunities presented in the digital economy to promote sustainability.

Background: Atopic dermatitis is a chronic condition that significantly affects the daily life of those who suffer from it, requiring complex and personalized management with active participation of the patient in the management of their disease. Objective: to describe the design and development process of a mobile application, using the Design Thinking method, for the follow-up of patients with atopic dermatitis. Methods: User-centered design and prototype creation method, differentiating the phases of the empathize, define, idea, prototype, and test. A purposive snowball sampling was used to recruit patients with atopic dermatitis, family members, and health professionals. Three two-hour workshops were held, attended by 25 people. Results: the parameters to be monitored will include objective data (physical, behavioral, treatments, environmental, integration of daily routines) and subjective data (user experience). On the other hand, it will figure out behavior patterns, set up rewards, foster a feeling of community, and use notifications and a chatbot. Conclusion: The active integration of all stakeholders at each stage of the design and development of the digital solution has improved its relevance and acceptance, resulting in a tool that is expected to significantly improve the management of atopic dermatitis.

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

The Circular Bioeconomy (CBE) combines the concepts of bioeconomy and circular economy. As an alternative concept to the current fossil-based, linear economy, it de-scribes an economy based on the efficient valorization of biomass. It is regional in nature and aims to improve sustainability. An analysis of the transition process, by identifying its success criteria and assessing its impacts through the modelling of technology-specific scenarios is necessary to ensure that CBE concepts are sustainable. However, a compre-hensive consideration of regional influences on both is lacking. Based on extensive literature research and an expert survey, we (i) present a compre-hensive catalog of CBE success criteria and discuss their region-specific character and (ii) develop a methodology based on evaluation matrices that enable to match CBE technolo-gies with regions. The matrices support the evaluation of technological and regional characteristics influencing the successful CBE implementation. The results show that the success criteria "biomass resources", "technological", and "social" are perceived as highly important, and that most of the success criteria are both region- and technology-specific, highlighting the relevance of developing matrices to match them. We describe such matrices indicatively for the two broadest and most im-portant success criteria clusters “social acceptance” and “biomass supply chain”. With this, we substantiate the regional nature of CBE and raise the awareness on the importance of considering regional conditions in CBE transition processes. Furthermore, we provide practical guidance on how regional conditions can be reflected in the selection of technologies, e.g. in regional CBE technology scenarios.

The digital economy, driven by information and communication technologies (ICT), has profoundly transformed in recent decades. The digitalization of society has given rise to an economic environment in which information, connectivity, and innovation play fundamental roles. In this context, a technology that has emerged as a fundamental pillar of the digital economy is the chain of blocks, commonly known as blockchain. Blockchain is a technology that has revolutionized the way online data and transactions are managed and shared. Through its ability to create secure, transparent, and decentralized ledgers, blockchain has paved the way for the digital economy, facilitating trust in digital transactions and enabling various applications ranging from cryptocurrencies to supply chain management and intellectual property. This study will delve into blockchain and its influence on the digital economy. It will explore how this technology has reshaped how companies interact, how consumers access services, and how new business models are developed in a constantly evolving digital environment. Additionally, the challenges and opportunities that blockchain presents in the context of the digital economy will be analyzed, and how it is helping to shape the future of business and society in general. As the exploration of blockchain and its impact on the digital economy progresses, it becomes evident how these two forces converge, generating a promising digital landscape full of significant opportunities and transformations. This phenomenon is consistently supported by a growing body of research and analysis, which underlines the growing influence of blockchain on the global economy (Smith, 2020; Johnson, 2019). The dynamic interplay between these two spheres, blockchain and the digital economy, constantly evolves and offers an exciting glimpse into the future regarding innovation and disruption across various sectors (Jones, 2021; Brown, 2018). As a result, significant opportunities are looming for those seeking to understand and capitalize on these emerging trends (García, 2022). Throughout this study, the current trends and most intriguing perspectives that shape this landscape will be broken down, offering a deeper insight into how blockchain and the digital economy are shaping an extraordinary digital future

The digital transformation of the global economy presents new challenges and opportunities for businesses and nations. With the growing role of digital technologies and the dynamic changes in the structure of the economy, there is a need for a better understanding of the processes shaping contemporary enterprises. This article analyzes the impact of digital transformation on businesses and the global economy, identifying key areas where these changes are most evident. As part of this discussion, a modification of the “9 Pillars of Industry 4.0” concept is proposed, incorporating solutions that have emerged since its inception. The resulting model, “11 Pillars of Industry 4.0,” enabled the analysis of the “born digital” and “born global” concepts, highlighting their numerous common features in the context of the digital transformation of economies. This became the basis for proposing a new concept: “born digibal” — enterprises that from the outset operate in multiple markets, with their main sphere of activity based on digital solutions.The conclusions of the article provide an assessment of the changes occurring in businesses and the global economy in the digital age. They suggest that the dynamics of globalization in the 21st century will be shaped by complex interactions between digital transformation and the actions of businesses and nations, requiring flexibility and innovation in adapting to new economic realities.

With the fintech industry growing at an unprecedented rate, it is critical to protect cybersecurity and ensure data privacy. The research presented here offers a thorough examination of the challenges faced by financial technology companies, highlighting the growing risks posed by malware, phishing, and network vulnerabilities. As essential components of a proactive defense plan, the report promotes strong cybersecurity measures like frequent security assessments, encryption, and stringent access controls. The findings highlight how important it is for fintech companies to give cybersecurity equal importance with open and honest data privacy policies in order to win over customers. In order to protect the industry from cybersecurity risks regulatory compliance, intrusion detection systems, and collaborative information sharing are considered essential components. The article advises businesses to include cybersecurity and data privacy into their core business operations and customer relations, highlighting the critical role these factors play in sustaining success in the rapidly evolving fintech industry.

To date, deep-tech entrepreneurship and sustainable entrepreneurship are both attracting the interest of researchers. Indeed, deep-tech is being employed to address future sustainable challenges in the present. Nevertheless, the process of deep-tech startups is filled with distinct obstacles. These types of startups may necessitate more targeted sustainable entrepreneurial process and specialised knowledge compared to conventional technological startups or general sustainable entrepreneurial process. Currently, there are only publications discussing either a sustainable en-trepreneurial process or conventional technical startups. Therefore, the aim of this article is to in-vestigate the process of sustainable entrepreneurs as they transition from startup ventures to scaleup enterprises within the context of deep-tech industry. Based on previous sustainable entre-preneurial processes, a multiple case study was conducted within six deep-tech startups in order to find out the sustainable process pattern. A conceptual model of sustainable entrepreneurial process in deep-tech industry is established including five phases with six activities: (i) Sustainable idea definition; (ii) Sustainable opportunity recognition and evaluation; (iii) Venture launch; (iv.i) Sustainable products/ services development; (iv.ii) New sustainable enterprise financing; and (v) Sustainable enterprise scaleup. This process enables sustainable deep-tech entrepreneurs to necessarily have an overview of key entrepreneurial stages to navigate from the startup to scaleup process.

Exopolysaccharide (EPS)-producing bacteria were isolated from the intestines of freshwater fish as prebiotics. Among the isolates, Bacillus sp. P1 was the potent EPS producer with a high EPS production and was then identified as Bacillus subtilis P1 based on 16S rRNA and biochemical characteristics. The produced microbial EPS was characterized its functional groups by FTIR, showing a 90.20% correlation to inulin while the EPS molecular weight was approximately 105 Da with a high PDI (>1.5). Moreover, the EPS from B. subtilis P1 was assessed for prebiotic properties by growing probiotic bacteria and the significant cell growth occurred in sugar-free TSB with 0.5% EPS (p<0.05). EPS exhibited approximately 55.37% DPPH radical scavenging at 20 mg/mL and inhibited certain fish pathogens at 400 μg/mL (10-13 mm inhibition zone). EPS cytotoxicity was confirmed (<1% hemolysis) prior to use as immunobiotics in Nile Tilapia (Oreochromis niloticus) diet supplementation. Nile tilapia growth and immune response were monitored after feeding with control (C): Basal Diet (BD) and treatment (T): BD + EPS 5 g/kg. In the T-group, WG, %SGR, and ADG significantly increased compared to control (p<0.05) by week 4th, with no negative effects on blood chemistry parameters. Lysozyme activity and respiratory burst activity in T-groups were significantly higher than control (p<0.05), with a 50% RLP survival rate of Nile Tilapia challenged with Aeromonas veronii in the T-group within two weeks. On the other hand, for the proximate analysis revealed higher protein content in the T-group. These findings suggest that EPS from B. subtilis P1 in fish diets efficiently supports fish growth and stimulates innate immune responses in Nile Tilapia, thus showing potential as the immunobiotics for aquaculture.

This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML. The second section explains the methodology and data used in this study. The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams. The fourth section discusses these findings. Finally, the conclusion and suggestions for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection in order to achieve WHO’s ultimate goal of breast self-examination (BSE). A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, and non-invasive and adjunctive and complementary detection method. This research explores the potential of using machine learning (ML) techniques, specifically Bayesian Networks (BN) combined with Convolutional Neural Networks (CNN) to improve breast cancer diagnosis at early stages. Explainable artificial intelligence (XAI) aims to clarify the reasoning behind any output of artificial neural networks based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We have constructed two diagnostic expert models. In research model A, combining thermal images after XAI process together with medical records, an accuracy of 84.07% has been achieved, while model B, that includes also CNN prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of XAI to transform breast cancer diagnosis, increasing accuracy and reducing the risk of misdiagnosis.

Cybercrime Threat Intelligence (CTI) allows us to change our actions from reactive to proactive in the fight against threat actors, and to make more informed, data-driven security decisions. Thus this study proposes the practical implementation of CTI in the Arab world. This study focuses on strengthening CTI by integrating Indicators of Compromise (IoCs) and collecting realistic security alerts from honeypot systems and open source intelligence. The collected data is stored in the Malware Information Sharing Platform (MISP), an open source platform that allows users to create and share IoCs with other organizations while staying informed about new threats. It features an intuitive interface for data analysis and threat identification, facilitating sharing, storage, and correlation of IoCs. Therefore, we leveraged MISP to generate IoCs based on the collected data and analyzed the results to identify potential cyber threats. The implemented platform aims to provide organizations with actionable information to prevent, detect and respond to cybercrime. This study presents a practical approach to strengthening CTI in the Arab world and provides an opportunity to strengthen the region’s security posture.

This manuscript explores the significance of continuous improvement in software development and digital product management, highlighting its role in addressing challenges and achieving superior results in today's dynamic business landscape. Drawing on Lean Thinking and Agile principles, the paper emphasizes the importance of customer focus, multidisciplinary collaboration, respect for people, inspection and adaptation, experimentation, incremental delivery, and continuous feedback in driving continuous improvement. It identifies key problems associated with the lack of continuous improvement, including quality issues, stagnation, inefficiency, lack of learning, lack of participation and collaboration, and lack of adaptability. To overcome these challenges, the paper proposes a solution that leverages agile events and interactions, such as sprint retrospectives and daily scrum meetings, and introduces a "kaizen board" as a visual tool to manage and monitor the continuous improvement process. The manuscript highlights the benefits of continuous improvement in terms of quality enhancement, efficiency gains, improved customer satisfaction, and adaptability to change. It stresses the need for fostering a culture of learning and constant adaptation, and provides insights on applying continuous improvement across different dimensions: product, process, team, and culture. The paper concludes by emphasizing the significance of commitment and active participation from all team members for achieving sustainable progress and success in digital transformation efforts.

The digital economy is a fundamental feature of sustainability. The economic, social, and environmental dimensions of sustainability stand up for digitalization and digital transformation projects. In order to understand and practice the digital economy, the goal of our paper is to evaluate accurate variables regarding fintech scope for discussing the efficient implication of these projects. The principal component analysis is used to investigate and evaluate the sustainable digital economy indicators. An empirical case from health care services in Africa from World Bank projects discusses this challenge. Results help produce an effective understanding of how the digital economy enables facing fintech challenges in Africa.

Despite the demonstrated benefits of data monetization initiatives in achieving competitive advantages, many of these efforts struggle to realize their potential. Companies often find it challenging to sustain even initially successful data monetization initiatives due to significant data quality issues. This reflects a disconnect between advancements in data monetization research—spanning digitization, digitalization, and digital transformation—and practical implementation within companies. Consequently, misguided approaches to data monetization are relatively common. A critical prerequisite for successful data monetization is the establishment and maintenance of clean, high-quality data. This study underscores the importance of data quality by conducting an in-depth analysis of Medical Inc., a company engaged in preparing pristine customer master data for advanced customer analytics. The investigation aims to elucidate Medical Inc.’s approach to addressing data cleanliness challenges and to distill a general framework for the customer master data cleansing process. This framework illuminates a relatively unexplored aspect of data monetization supplementing existing literature on digitization, digitalization, and digital transformation.

This paper illustrates an experimental activity for the closed-loop position control of an actuator made using shape memory alloy (SMA) wire. A solution with the self-sensing effect was implemented to miniaturize the systems, i.e., without external sensors. A proportional control algorithm was initially used, demonstrating the idea's feasibility: the wire can behave simultaneously as an actuator and sensor. An experimental investigation was subsequently conducted for the optimization of the developed actuator. As for the material, a Flexinol wire, Ni-Ti alloy, with a diameter of 0.150 mm and a length of 200 mm, was used. Preliminarily, a characterization of the SMA wire at constant and variable loads was carried out: the characteristics detected are elongation vs. electric current and elongation vs. electrical resistance. The control system is PC-based with a data acquisition card (DAQ). A drive board has been designed and built to read the wire's electrical resistance and power it by pulse width modulation (PWM). A notable result is that the actuator works with good precision and in dynamic conditions, even when it is called to support a load up to 65% different from that for which the electrical resistance-length correlation has previously been experimentally obtained, on which the control is based. This opens up the possibility of using the actuator in a counteracting configuration with a spring, which makes hardware implementation and control management simple.

The authors argue that despite significant numbers of foreign workers present in the Russian labor market Russia remains unattractive for highly skilled foreign workers. The economic crisis the country has faced since 2014 has resulted in a further outflow of foreign nationals from OECD countries. So Russia has to look for employees among newcomers from the former Soviet states, the majority of whom come from Central Asia. Russian politics with regard to immigration is characterized by the collision between “geopolitical” and domestic policy rationales. On one hand, seeking to maximize its influence in the post-Soviet space Russia provides preferential conditions of employment for citizens of partner countries in the framework of the Eurasian Economic Union. On the other hand, the Kremlin is afraid to liberalize its approach to immigration and the integration of immigrants through fear of losing popularity among the electorate. The second substantial feature of Russian immigration politics is an extreme level of securitization of migration issues. The government considers these issues primarily through a policing lens. One cannot claim that the Russian state does not take any steps to liberalize “migratory regulation”; however, the effect of these measures is being vastly reduced by new restrictions. In addition, liberally designed laws do not bring the expected results due to the corrupt practices that pervade the whole of migration regulation.

Affective computing has emerged as a prominent technology in recent years, driven by the increasing demand for machines capable of understanding and interpreting emotions. The COVID-19 pandemic has intensified this need by reducing physical interactions and increasing reliance on digital communication, which often involves complex emotional exchanges. To enhance social interactions by identifying and addressing negative emotions, an interactive emotion-perception system based on multi-sensing interfaces and affective computing has been proposed. Integration of gesture, voice, and brainwave sensors is employed to provide users with a rich sensory experience and immersive environment. Design principles and system development processes are derived from the affinity diagram method and prototype development approach. The proposed system was constructed, and experiments were conducted with 60 participants. Interpersonal interaction scenarios were explored to understand emotional transmission mechanisms, aiming to enhance user satisfaction and positive interpersonal experiences. Data were collected through indirect observation and questionnaires, followed by evaluation and statistical analysis. Findings include: (1) The emotion-perception system enhances positive interpersonal interactions by identifying 36 distinct emotions, with overall user emotions being positive but exhibiting notable individual variations; (2) The system effectively stimulates positive emotions and strengthens interpersonal interactions, with affective computing technology fostering enjoyable emotional resonance and high-quality relationships; (3) A positive correlation was observed between semantic analysis and brainwave metrics of relaxation and concentration, showing increased concentration and enhanced positive emotional interactions during message transitions; and (4) The multi-sensing and affective computing technology provides a pleasant and relaxing emotional journey, with interactions shifting from enthusiasm to comfort, demonstrating the system’s capacity for a satisfying and enjoyable experience.

Based on the panel data from 31 provinces in China from 2012 to 2020, this paper uses the TOPSIS method to measure the development level of the digital economy and environmental quality. Then, a coupled coordination degree model is used to analyse the relationship between the digital economy and environmental quality. Kernel density estimation and the Dagum Gini coefficient method were used to reveal the dynamic evolution trend of the coupled coordination level between the digital economy and environmental quality and the regional trends of coupled indicators. Finally, the dynamics of the above relations were explored with an econometric model. The results show that the coupled coordination degree of the digital economy and environmental quality displays obvious regional heterogeneity, and the national coordination level exhibits dynamic convergence characteristics. The heterogeneity of the coupled coordination degree of the two systems in the eastern region has decreased year by year, except in the western region, the central region and the northeast region, where an upwards trend is observed. Overall, the Gini coefficient of the coupled coordination degree between the digital economy and environmental quality displays a certain downwards trend. The level of economic development, technological innovation level, industrial structure, degree of opening to the outside world, degree of environmental regulation, marketization index and financial development level have a positive influence on the coupled coordination of the digital economy and environmental quality. Environmental regulation is the core driver of the coupling of the two systems, and the economic development level, technological innovation level, industrial structure and degree of openness are the other major drivers. Therefore, in the processes of digital economy transformation and environmental governance, all regions should focus on balanced development between regions to optimize the overall efficiency of the two systems, consider external influence, enhance internal capabilities, and promote the coordination and co-development of the two systems.

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

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

The chemical stability of tetramethylammonium (TMA) head groups, both with and without the presence of a choline chloride and ethylene glycol-based deep eutectic solvent (DES), was studied using Density Functional Theory (DFT) calculations and ab initio Molecular Dynamics (MD) simulations. DFT calculations of transition state energetics (ΔEreaction, ΔGreaction, ΔEactivation, and ΔGactivation) for key degradation mechanisms, ylide formation (YF) and nucleophilic substitution (SN2), suggested that the presence of DES enhances the stability of the TMA head groups compared to systems without DES. Ab initio MD simulations across hydration levels (HLs) 1 to 5 indicated that without DES, YF dominates at lower HLs, while SN2 does not occur. In contrast, both mechanisms are suppressed in the presence of DES. Temperature also plays a role: without DES, YF dominates at 298 K, while SN2 becomes prominent at 320 K and 350 K. With DES, both degradation mechanisms are inhibited. These findings suggest DES could improve the chemical stability of TMA head groups in anion exchange membranes.

This paper proposes a heuristic approach for managing complex construction projects. The tool incorporates with Primavera P6 EPPM and Synchro 4D, enabling proactively clash detection and resolution of spatial conflicts during concurrent tasks. Additionally, it performs resource verification for sufficient allocation before task initiation. This integrated approach facilitates the generation of conflict-free and feasible construction schedules. By adhering to project constraints and seamlessly integrating with existing industry tools, the proposed solution offers a comprehensive and robust approach for construction project management. This constitutes to our knowledge the first dynamic digital twin for the delivery of a complex project.

The construction industry is a resource and energy intensive industry and thus, has been criti-cized due to rising environmental concerns. As a result of that it has gained heightened interest in the concept of Circular Economy (CE) over the last decade to its ability to promote the slowing, reducing, and closing production and consumption cycles of materials and products used in the construction practices. Digital technologies hold potential to enhance the construction industry's ability to integrate the concept of CE in its practices. However, a clear understanding of digital technology (DT) related barriers that hinder practical implementation of CE, appears to be lacking within the sector. In light of this, this study aims to identify the barriers to adopting DTs to im-plement CE practices in the construction industry. To achieve this aim, a systematic literature review was conducted by reviewing twenty-three (23) relevant papers published until January 2024 in Scopus database. The VOS viewer software was used to perform a co-occurrence analysis of keywords to identify new and popular study areas in the field and content analysis was used to analyse the major barriers to adapt DTs to implement CE in the construction industry. The study identified thirty-four (34) barriers to use DTs to implement CE and these were categorized into nine areas such as: infrastructure, organizational, regulatory, standardization, investment, nature of the construction industry, technological, stakeholder and data related barriers. These findings will be beneficial for construction practitioners and policy makers to adopt DTs to integrate CE practices in the construction industry.

Pore structure characterization and fractal analysis have great significance for understanding and evaluating tight limestone reservoirs. In this work, the pore structure of tight limestone is characterized combined with low-temperature nitrogen adsorption (LTNA) and low-field nuclear magnetic resonance (NMR), and the fractal dimension of tight limestone pore structure is discussed based on LTNA and NMR data. The results indicate that the pores of tight limestone have H3 and H4 types, the pore size distribution (PSD) of H3 type is a wave distribution ranging from 2 to 10 nm, and the PSD of H4 type is a unimodal distribution ranging from 2 to 10 nm. The transverse relaxation time (T2) spectrum of tight limestone shows single peak (DF), double peak (SF) and three peak (TF), and the T2 spectrums for micropores, mesopores and macropores range from 0.1 to 10 ms, from 10 to 100 ms, and greater than 100 ms, respectively. The LTNA fractal dimension of tight limestone (DL) ranges between 2.4446 and 2.7688, with an average of 2.5729, and the NMR fractal dimensions of micropores (DNMR1), mesopores (DNMR2) and macropores (DNMR3) are distributed between 0.3744 to 1.1293, 2.4263 to 2.9395 and 2.6582 to 2.9989, respectively. Moreover, there is no correlation between fractal dimension and permeability and porosity, a negative correlation between DL and average pore radius, a positive correlation between DL and specific surface area, and a positive correlation between DNMR2 and DNMR3 and micropore content, while DNMR2 and DNMR3 are negatively correlated with the content of mesopores and macropores.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

The concept of Digital Twins has been in the field for a long time, constantly challenging the specification, modeling, design, implementation, and exploitation of complex cyber-physical systems. Despite the various foundations, standards, and platforms in Systems Engineering, there are ongoing challenges with Verification and Validation methodology. This study aims to establish a generic framework that addresses the various aspects of Digital Twinning. The multifaceted nature of the problem requires raising the abstraction level in both the Real (Actual) and Virtual domains, effective dissemination of information resources, and a design inspired by verification and validation. The proposed framework combines the Quintuple Helix model with the Problem and Operational Domains of a Real (Actual) Twin, the Solution and Implementation Domains of a Virtual Twin, and the Execution Domain as the bridge that links them. Verification and Validation dimensions follow the Meta Object Facility abstraction layers (Instance, Model, Meta-model, and Meta-meta-model) mapping over five Helices. Embedding the complexity reduction mechanisms in the proposed framework builds a suite for extendible and verifiable digital twinning in simulation and real-time scenarios.

The LTTD process for highly concentrated crude oil-contaminated soil was studied to evaluate the remediation efficiency. In addition, the environmental impact difference between HTTD and LTTD was studied. OpenLCA software obtained from PE International was used to perform the analyses. The total ADP environmental impact of the HTTD process was 1.63E-04 MJ, whereas the total GWP impact was 4.14E+02 kg CO2-eq. In contrast, the LTTD process exhibits total ADP and GWP environmental impacts of 1.29E-04 MJ and 2.78E+02 kg CO2 eq, respectively. Analysis of the overall environmental impact reduction revealed that replacing the HTTD process with the LTTD process resulted in a total ADP reduction of 3.40E-05 MJ (20.5%) and a GWP reduction of 1.36E+02 kg CO2 -eq (32.9%). It is inferred that the LTTD treatment of contaminated soil can significantly reduce the environmental impact compared with conventional incineration and HTTD processes. Additionally, the LTTD-treated soil with coke or carbonized residue could act like biochar as a soil amendment. Approximately 50% of organic hydrocarbon contaminants are sequestered into coke or carbonized residues on the soil surface. With additional analyses of various oil-contaminated soils, the potential utility of LTTD-treated soils is expected to be maximized.

This study investigates the factors influencing consumers' intentions to purchase overseas travel packages via social media in Thailand, with a focus on consumer trust and perceived risks. A mixed-methods approach was employed, combining qualitative and quantitative research techniques. In the qualitative phase, the Fuzzy Set Delphi Method was applied to gather expert consensus from 19 participants. For the quantitative phase, a survey was conducted among 600 individuals who had experience purchasing overseas travel packages on social media in Thailand. First-order and second-order confirmatory factor analyses were used to examine the relationships between eight identified factors: social media influencer, e-WOM, trust, perceived risk, brand image, rating review, personal attitude, and destination image. The results indicate that trust and perceived risk are the most significant factors affecting purchase intention. Trust plays a crucial role in customers' decision-making processes, while perceived risk influences their tendency to avoid purchasing packages if the risk is perceived as high. The insights gained from this study contribute to the understanding of factors influencing purchase intentions for overseas travel packages through social media in Thailand, providing valuable information for travel agencies to develop effective marketing strategies.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioural as-sessments, enabling a comprehensive evaluation of the hens' health, behaviour, and population counts. To address the issue of insufficient data samples in the health detection process for indi-vidual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environ-ments, various data enhancement methods such as rotation, shearing, colour enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for ap-plying visible light images and corresponding thermal infrared images in the health detection, behavioural analysis, and counting of caged laying hens under large-scale farming.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

The information about the BCoV receptors, co/receptors that might be involved in the BCoV entry to the host cells is inconceivable. In addition to that, the roles of other host cell proteases, such as Furin and TMPRSS2, have not yet been well studied during BCoV replication. The current study’s main objectives are to identify some novel BCoV receptors and host cell proteases that might fine-tune the BCoV replication, tissue tropism, and molecular pathogenesis. We used a combination of some in silico prediction, molecular docking, and gene expression profile tools to predict some new BCoV receptors out of some known other coronavirus receptors and some host cell enzymes that are potentially involved in BCoV replication. To achieve these goals, we applied the in-silico prediction tools to check the potential interactions between eh BCoV/S glycoprotein and some known coronaviruses receptors (ACE2, APN1, NPR1, CECAM-1, AXL, and DPP4). We also established some potential models for the interaction between the BCoV/S and some common host cell proteases proved to play important roles in other coronavirus replication cycle (Furin, TMPRRS2, and Cathepsin-L). To confirm our prediction models, we assessed the gene expression profiles of these potential receptors and proteases in some bovine cell lines infected with BCoV using the next generation sequencing and the qRT-PCR assay. Our results show that the NPR1, AXL1 and the ACE2 have the strongest and firm binding affinities to the BCoV-S protein. Our prediction models and gene expression profiles show the potential bindings of the Furin and TMPRSS2 to the BCoV-S polybasic cleavage site (RRSRR|A). Notably, high expression of the host cell Furin was observed in response to both BCoV/Ent and BCoV/Resp infection, whereas TMPRSS2 expression was upregulated in the case of the BCoV/Ent infection, positioning Furin as a common receptor for different BCoV isolates. Further research is needed to study the roles of these top-ranked receptors and host cell proteases in molecular pathogenesis, and tissue tropism.

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

The central aim of this paper is to present the Green Web Meter software which is designed to support the measurement of digital sustainability performance of websites and web apps, as well as providing sound based analytic tools for improving their overall quality. We analyzed scientific literature and specifically selected three quality assessment models (E-S Qual, Sitequal, Webqual 4.0) which we used as a starting point for identifying the most crucial aspects for the monitoring activities. A consistent set of Key Performance Indicators (KPIs) suitable for automated tracking has been provided. We finally developed model-based software able to deliver solutions for real-time website performance analysis and reports. We structured the model to offer accurate references for digital sustainability reporting by aggregating the KPIs into an Environmental, Social, and Governance (ESG) framework and computing a comprehensive score for its pillars.

A study analyzed financial statements in the beer sectors of Italy and Spain during 2019-2021 using compositional data analysis (CoDa) to gauge pre- and post-pandemic financial health and aid sector-level decision-making. Geometric averages were employed to mitigate statistical issues, offering more accurate insights than arithmetic averages. The exploratory, quantitative methodology calculated key financial ratios and profitability metrics. Results indicated satisfactory short-term solvency. Returns and margins remained subpar despite pandemic policies. Geometric averages proved essential in portraying sector health accurately, underscoring the need for their use in classic brewing sector ratios. The study also showcased CoDa's efficacy in diagnosing sectoral financial health for informed decision-making. Furthermore, a web analysis revealed varying transparency levels in communicating non-financial indicators, with Spain excelling in waste, water, and energy disclosures, while Italy prioritized energy and waste with less transparency.

We present an updated design process for adapting and integrating existing cyber risk assessment approaches for impact assessment for the risk from IoT to the digital economy. The new design process includes a set of changes to the original standards (e.g. NIST) that are adapted for the IoT cyber risk in this paper. This paper also presents a new framework for impact assessment of IoT cyber risk, specific for the digital economy.

The increasing focus on sustainability and the adoption of green practices across industries underscore the necessity of equipping future professionals with competencies in implementing sustainable solutions. This study explores the perceptions of former students from two departments within the technological sector towards sustainability practices. A cross-sectional online survey was carried out among alumni from two programs at the University of Thessaly, a university recognized for its commitment to sustainability and green initiatives as a member of the INVEST European University alliance. The survey questionnaire included background inquiries and 16 statements designed to gauge alumni perspectives towards sustainability practices. Specifically, it aims to assess their views through self-assessment and compare differences between the two alumni groups. Responses were collected on a 5-point Likert scale ranging from strongly disagree to strongly agree. A total of 300 graduates from the Computer Science and Telecommunications as well as Electrical Engineering undergraduate programs were encouraged to take part in the survey, achieving a response rate of 69%. Of those who responded, 61% (n = 126) represented the Computer Science and Telecommunications group, while 39% (n = 81) were from the Electrical Engineering program. Our findings indicate a generally positive attitude towards sustainability, with statistically significant differences in perspectives on the implementation of sustainable technologies and stakeholder engagement. These results underscore the necessity of integrating sustainability education deeply within the curricula of technological programs to cultivate a workforce capable of addressing future environmental challenges. This study illuminates the critical role of educational frameworks in shaping the sustainability competencies of technology professionals.

Globally, over 1.4 billion adult people remain unbanked. This worrisome phenomenon was exacerbated by the outbreak of the COVID-19 pandemic, which further created a new dimension of inequality in accessing financial services. Digital financial inclusion promises to be an effective tool for addressing this socioeconomic ill and propelling economic development. Given the limited studies on the subject in the context of the developing economies, it is imperative to understand the existing policies, practices, and barriers to digital financial inclusion in the developing economies so as to provide cutting-edge interventions for redress. It is against this background that this study seeks to address the following research questions: (1) What is the state of digital financial inclusion in the developing economy? (2) What are the policies and practices regarding digital financial inclusion in the developing economy? (3) What are the barriers to digital financial inclusion and innovative interventions for redress? Findings reveal that about 44% of the adult population in developing countries does not have access to financial services, with only a few countries that have made significant progress and gains through policy and practice such as mobile financial services, mobile money interoperability, native connectivity, human capital development, and digitalization of public services for digital financial inclusion. The findings also identify challenges and implications with recommendations, which are discussed in detail in the paper.

The textile sector is responsible for a number of environmental impacts. Due to the in-creasing quantities of textiles, their share is rising and a trend reversal from a linear to a circular textile chain is needed. This article presents the background, methodological approach and results of a participatory textile development model. In the commercial B2B sector, three textile prototypes were developed together with users and trialled over several months in three application areas. Textile development took into account the requirements of fibre regeneration in the product design and focused on innova-tive chemical recycling solutions. The three sustainably aligned textiles were subject-ed to spectroscopic and textile-technological tests. Screening life cycle assessments an-alysed their environmental profile and compared it with reference textiles that are used as standard. Overall, it is clear that the three textiles can match conventional ref-erence textiles in terms of quality and have considerable environmental benefits com-pared to the reference textiles. The user survey did identify concerns about a high arti-ficial fibre content, although a general rejection of recycled fibres was not observed. The results show that a transformation is possible, but must start with the fibre com-position; recycling, on the other hand, is of minor importance.

The present study drops the line for the flow characteristics of fly ash-based (FA) geopolymers reinforced with polypropylene (PP) fibers undergoing the extrusion process during three-dimensional printing. Applying to the Herschel-Bulkley rheological model the research is ready with the sound theoretical basis which is used to understand the flow behavior of these materials under various conditions. A combination of experimental and numerical techniques that are based on the Finite Element Method (FEM) in COMSOL has been used in this study. The results of both the experimental and simulation approaches are then compared and the behavior of the material during extrusion is then examined. The experimental results proved PP fiber content has significant effects on rheological properties. The experiments on mixes which are either devoid of fibers or possess a high content of fiber, encountered problems like a high static yield stress and material segregation. Mixes with fibers of moderate content showed smoother extrusion processes, which means that a suitable range of fiber addition exists for the fibers that optimally balance the mechanical properties and extrudability. Numerical simulations typically agreed with experimental data for fiber contents up until it reached a certain level but not above, suggesting the existence of more complex interactions inside the fibers and the necessity for the subsequent model refinements. The investigation outlines a 0.25% and 0.5% fiber content range that leads to enhanced performance without bringing the extrusion process to greater complications and further making the production of proper, architecturally printed structures possible.

In China, "new quality productive forces" have become an important focus for high-quality development; accelerating this development is an important task for the Chinese government in 2024. Such development relies on the traction of the digital economy. In view of this, developing “new quality productive forces” driven by the Chinese digital economy requires focusing on blocking points, such as the insufficient vitality of data, inadequate algorithmic regulation, the uneven distribution of computing power resources and the immature market environment. In order to dredge these blocking points, it is necessary to establish basic rules on data, strengthening algorithm supervision, building a national integrated computing power network, and optimizing the market environment.

The digital economy promotes economic development, industrial upgrading, and environmental protection. This article calculates the green total factor productivity (GTFP) based on the SBM-DDF model and uses the entropy method and principal component analysis to calculate the digital economy index. We use panel data from 282 Chinese cities to test the driving effect of the digital economy on green total factor productivity. The study results show that the digital economy significantly increases GTFP, and the heterogeneity of this impact is further explored. We also explored the mechanism of the digital economy in promoting green. The digital economy can indirectly increase industrial production efficiency by promoting innovation in green technologies.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model that is often referenced in both academics and practitioner circles will be discussed and shown to be faulty on several levels, including. a lack of definitional agreement. A new DIKW framework will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move to Intelligent Digital Twins (IDT) with Artificial Intelligence (AI).

Our article AI, cultural heritage and bias examines the challenges and potential solutions for using machine learning to interpret and classify human memory and cultural heritage artifacts. We argue that bias is inherent in cultural heritage collections (CHCs) and their digital versions, and that AI pipelines may amplify this bias. We hypothesise that effective AI methods require vast, well-annotated datasets with structured metadata, which CHCs often lack due to diverse digitisation practices and limited interconnectivity. The paper discusses the definition of bias in CHCs and other datasets, exploring how it stems from training data and insufficient humanities expertise in generative platforms. We conclude that scholarship, guidelines, and policies on AI and CHCs should address bias as both inherent and augmented by AI technologies. We recommend implementing bias mitigation techniques throughout the process, from collection to curation, to support meaningful curation, embrace diversity, and cater to future heritage audiences.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance and overcoming anisotropic mechanical properties. This paper analyzes and compare the machinability of 3D printed PLA, PETG and carbon fiber reinforced PETG focusing on surface roughness and burr formation. A design of experiments (DoE) with a full factorial design was used, considering three factors: rotation speed, feed rate and depth of cut. Each factor had different levels: rotational speed at 3000, 5500 and 8000 rpm; feed rate at 400, 600 and 800 mm/min; and depth of cut at 0.2, 0.4, 0.6, and 0.8 mm. Machinability was evaluated by roughness and burr height using a profilometer for all the materials under the same milling conditions. To evaluate the statistical significance of the influence of various processing parameters on surface roughness and burr formation in 3D printed components made of three different materials PLA, PETG and carbon fiber reinforced PETG, an analysis of variance (ANOVA) test was conducted. This analysis investigated whether variations in rotational speed, feed rate and depth of cut resulted in measurable and significant differences in machinability results. Results showed that milling parameters significantly affect roughness and burr formation, with optimal conditions for minimizing any misalignment highlighting the trade-offs in parameter selection. These results provide insights into the post-processing of FDM-printed materials with milling, indicating the need for a balanced approach to parameter selection based on application-specific requirements.

Our study reviews the significance of archaeological parks in the tourism sector, with a specific focus on how digital presence and visitor feedback impact their cultural and touristic appeal. Utilizing statistical analyses such as regression and cluster analysis on data sourced from both Hungarian and Western European parks, the research scrutinizes TripAdvisor and Google ratings, alongside the volume of photographs and reviews. The findings highlight a modest correlation between digital visibility and overall visitor ratings, underscoring the complex relationship between online engagement and the quality of visitor experience. Notably, the cluster analysis identified two distinct groups, signifying diverse levels of visitor engagement and digital visibility across the parks. The conclusions drawn advocate for a comprehensive strategy to enhance visitor experiences, emphasizing the integration of digital engagement with the promotion of the parks cultural and historical values. By exploring the influence of digital presence on the appeal of archaeological parks, our study sheds light on the evolving interface between urban studies and digital heritage, highlighting how digital tools and online platforms are becoming increasingly integral in shaping the perceptions and experiences of urban cultural sites.

The effective macroscopic model describing interactions of a viscous compressible heat-conducting fluid and a two-level fine bristly thermoelastic structure is derived from microstructure by means of the Allaire–Briane multi-scale convergence method. This new model naturally generalizes the isothermal formulation earlier constructed by the authors in 2020, see in Siberian Electronic Mathematical Reports, vol. 17 (https://doi.org/10.33048/semi.2020.17.100). In applications, the established model can be used, for example, in description, with account of heat transfer phenomemon, of airflow near surface of plant’s leaf, in simulation of epithelium surfaces of blood vessels, and in design of biotechnological devices operating in liquids.

The flourishing landscape of agritourism in Albania has witnessed a steady increase in destinations over the years. Recognizing their pivotal role in driving economic development in rural areas, considerable attention is directed towards the promotion and expansion of these agricultural tourism spots. To enhance interactivity with visitors, these agritourism destinations actively leverage social networks and various digital platforms. However, the wealth of information pertaining to these destinations is currently scattered across different platforms, posing a challenge to accessibility. To address this issue, these points are strategically placed on Google Maps, along with essential details collected from users, such as websites, contact numbers, visitor ratings, and comments. Nevertheless, other valuable data remains dispersed on various social networks. In response to this fragmentation, this study proposes the development of algorithms and scripts to systematically collect and analyze this dispersed information. By doing so, the goal is to centralize and streamline the data, enabling a comprehensive understanding of the agritourism landscape in Albania. This approach not only facilitates efficient access to information for potential visitors but also provides valuable insights for stakeholders involved in the development and management of these agritourism destinations. The study aims to contribute to the sustainable growth of agritourism in Albania by harnessing the power of digital platforms to create a unified and organized database. Through this integration, the research seeks to unveil patterns, trends, and opportunities that can further guide policy-making, marketing strategies, and overall decision-making processes in the realm of agritourism development.

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

Background/Objectives: Conventional surgery gland using the naked eye remains the standard for submandibular gland resection. Owing to its excellent automatic focus and high magnification, the ORBEYE 3D system introduces precise submandibular resection with less stress. Therefore, we examined the usefulness of the exoscope in submandibular gland resection for the first time. Methods: This retrospective study enrolled 12 patients who underwent submandibular gland resection using an ORBEYE exoscope at the Department of Head and Neck Surgery and Otorhinolaryngology at Kansai Medical University from April 2021 to March 2024. Surgical outcomes were retrospectively reviewed. Results: The mean age of the 12 patients (six females and six males) was 58.5 years (23–83 years). The final histopathology showed pleomorphic adenoma in eight cases, non-sebaceous-type lymphadenoma and carcinoma ex pleomorphic adenoma in one case each, and sialolithiasis with chronic inflammation in two cases. We performed facial artery and vein sparing in all cases except one in which level I neck dissection, including the submandibular gland, was performed. In all cases, we identified the marginal mandibular branches of the facial, hypoglossal, and lingual nerves, as well as the submandibular duct. No postoperative complications occurred in any patient. The mean operative time was 104 min (80–129 min). The mean blood volume lost was 20 mL (5–43 mL). Conclusions: Using ORBEYE in submandibular gland resection will contribute to preserving the mandibular marginal branch of the facial and lingual nerves and the facial artery and vein, and visualizing the submandibular duct, which are important structures surrounding the submandibular gland.

In resent years, China’s digital economy has achieved significant global recognition for its instrumental role in supporting sustainable economic and social development. Based on the panel data from China's provinces between 2013-2021, this paper investigates the impact and transmission mechanism of financial deepening on digital economy development. The study finds that financial deepening significantly improves digital economy development. Heterogeneity analysis indicates that financial deepening offers greater support to digital economy development in central and western regions than in the east. Further analysis shows that financial deepening improves technological innovation, which in turn promotes the digital economy development. After applying a test of robustness, the results proved significant.

The general synthesis of boron-containing cyclic compounds (boracycles) necessitates toxic organotin precursors or highly reactive boron halides. Here we report the synthesis of seven- and five-membered boracycles utilizing arylboronic acid pinacol esters (ArBpins) as stable boron sources. Grignard reagents generated from 2,2′-dibromodibenzyl or 2,2′-dibromobiphenyl reacted with ArBpins, where Ar = 9-anthryl (Anth), 2,4,6-trimethylphenyl (Mes), 2,4,6-triisopropylphenyl (Tip), to give 10,11-dihydro-5H-dibenzo[b,f]borepins or dibenzoborole derivatives. This Bpin-based method was successfully applied to a one-shot double boracycle formation, providing a dihydrodibenzoborepin–anthracene–dihydrodibenzoborepin triad molecule in a good yield. The dihydrodibenzoborepin bearing the Anth group was directly converted to the unsaturated borepin by NBS/AIBN. All products were characterized by NMR, HRMS, and in some cases, single-crystal X-ray diffraction analysis. Additionally, the photophysical properties of the products are also reported.

Exergy analysis evaluates the efficiency of system components by quantifying the rate of entropy generation. In general, the exergy destruction rate or irreversibility rate was directly obtained through the exergy-balance equation. However, this method cannot determine the origin of the component's entropy generation rate, which is a very important factor in system design and improvement. In this study, a thorough energy, exergy thermoeconomic analysis of a proton exchange membrane fuel cell (PEMFC) was performed, providing the heat transfer rate, entropy generation rate and cost loss rate of each component. The irreversibility rate of each component is obtained by the Gouy-Stodolar theorem. Detailed and extensive exergy and thermoeconomic analyses of PEMFC system have determined that water cooling units experience the greatest heat transfer among the components in the studied PEMFC system, resulting in the greatest irreversibility and thus the greatest monetary flow loss.

This paper experimentally reveals some of the resources offered by the evolution of the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor at no-load and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. The paper proposes a procedure for extracting this periodic component description (as a pattern, by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g. a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments which show that a slight change in the no-load running conditions of the motor (e.g. by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern.

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

The Circular Economy has been of growing significance within academic, policymaking and industry groups. Latest developments in the field of Circular Economy has led to an expansion of CE studies focused on interrogating CE as a paradigm, its relationship with sustainability and concepts and definitions of the Circular Economy. Research has also identified the significant potential of applying circular approaches to areas of the economy, including manufacturing and Industry 4.0, which, with data, is enabling latest the advances in digital technologies. This is the first review paper to integrate the fields of CE and digital technologies resulting in a framework which provides directions for policymakers and guidance for future research. To achieve this, we conduct a systematic literature review of the empirical literature related to digital technologies, industry 4.0 and circular approaches, from the point of the 9 Rs. The systematic literature review (SLR) is based on peer-reviewed articles published between 2000-2018. The findings reveal that while research on the circular economy has been on an annual rise, research on digital technologies enabled circular economy is still relatively an untouched area of research across all nine (9) circular approaches. As such this is an area rife for further research. This paper also presents illustrative charts and graphs to summarize the current trends in circular economy research in manufacturing. From this, a framework for future circular economy research for manufacturing for digital technologies is proposed.

The goal of farmers operating in a market economy is to maximise profit. In the face of the changing political situation, the main social interest, besides food security, should be energy security. This article aims to indicate the direction of agricultural policy development so that it guarantees both the economic security of producers—farmers—and Europe’s energy security. The work uses data obtained from the agri benchmark Cash Crop database. DEA method and stepwise multiple regression were used. The research showed that plants are already grown in Europe which will ensure high energy efficiency of production without compromising farm financial results (including oats, peas and winter rye) and that improving the involvement of certain inputs results in improved production efficiency (among others, by expenditure on agricultural advisory services). The significance of the presented research results from the fact that the recent COVID pandemic and escalation of the military conflict in Ukraine have led to a renewed interest in the concept of EU energy security, i.e. generation of generating the greatest possible amount of energy within the EU, to be used both for food and non-food purposes.

Disease, trauma, and aging account for a significant number of clinical disorders. Regenerative medicine is emerging as a very promising therapeutic option. The design and development of new cell-customized biomaterials able to mimic ECM functionalities represent one of the major strategy to control the cell fate and stimulate tissue regeneration. Recently, hydrogels have received a considerable interest for their use in the modulation and control of cell fate during regeneration processes. Several synthetic bioresponsive hydrogels are being developed in order to facilitate cell-matrix and cell-cell interactions. In this review new strategies and future perspectives of such synthetic cell microenvironment will be highlighted.

Autosomal dominant polycystic kidney disease (ADPKD) is an inherited disorder characterized by renal tubular cystic dilatations. The cysts can develop anywhere along the nephron and over time the cystic dilatation lead to kidney enlargement. On the other hand, the cysts begin to reduce the number of functional nephrons as a consequence of cystic expansion that further contributes to the decline in renal function over the years. The pressure exerted by the dilated cysts lead to compensatory mechanisms that further contribute to the decline in renal function. These structural changes are responsible of glomerular hyperfiltration states, albuminuria, proteinuria, and haematuria. However, the presentation of ADPKD varies in children from a completely asymptomatic child with incidental ultrasound detection of cysts to a rapidly progressive disease. There have been reports of early onset of ADPKD in children younger than 2 years that showed a more rapid decline in renal function. ADPKD is caused by mutation in PKD1 and PKD2 genes. Today, the PKD1 gene mutation seems to account for up to 85% of the cases worldwide, and it is associated with worse renal outcomes. Individuals with PKD2 gene mutation seem to present a milder form of the disease, with a more delayed onset of end stage kidney disease. The cardinal sign of ADPKD is the presence of renal cysts during renal ultrasound. The current guidelines provide the clinicians the recommendations for genetic testing in children with a positive family history. Given that the vast majority of children with ADPKD present with normal or supra normal kidney function we explored the glomerular filtration rates dynamics and the renal ultrasound adjusted percentiles. 14 out of 16 patients had kidney percentile over 90%. The gene mutations were equally distributed among our cohort. In addition, we compared the modified Schwartz formula to the quadratic equation after adjusting the serum creatinine measurements. It seems that even though children with ADPKD have enlarged kidneys, the renal function is more likely normal or near normal when the quadratic estimation of glomerular filtration rate is used (qGFR tended to be lower, 111.95 ± 12.43 ml/min/1.73m2 compared to Schwartz eGFR 126.28 ± 33.07ml/min/1.73m2, p=0.14). Also, when the quadratic equation was employed, not even a patient reached the glomerular hyperfiltration thresholds. The quadratic formula showed that glomerular filtration rates are linear or slightly decreasing after 1-year of follow-up (Quadratic ΔeGFR=0.32±5.78ml/min/1.73m2) as opposed to Schwartz formula that can falsely classify children in a hyperfiltration state ΔeGFR=7.51±19.46ml/min/1.73m2), p=0.019.

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with Social Network Analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.

Glucose is a simple sugar molecule. The sugar molecule is chemically symbolised as C6H12O6. This means that the glucose molecule contains 6 carbon atoms (C), 12 hydrogen atoms (H) and 6 oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally after eating or drinking, our body breaks down the sugars in food and uses them to obtain energy for our cells. To do this, our pancreas produces insulin. Insulin "pulls" sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas can't produce enough insulin. As a result, the level of glucose in the blood rises. This can lead one to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack and damage to blood vessels etc. In this study, a non-invasive method for monitoring blood glucose has been developed and is therefore easily usable. The device developed can measure the glucose level continuously and eliminates the disadvantages of the invasive system. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in the blood; these sensors have a wavelength of 940 nm. The sensor is placed on the tip of the finger and the output of the optical sensor is then connected to the microcontroller at the analogue input. Another sensor used, but only to provide more medical information was the heartbeat sensor. After processing and regression analysis, the glucose level is predicted, and data is sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods. The study consists of a hardware platform inserted into an armband and then a small black parallelepiped-shaped box in which the optical sensors are inserted. The hardware consists of a microcontroller, a Near-IR optical sensor, a heartbeat sensor and a Bluetooth module.

Currently, there has been a tendency for companies to direct the development of their businesses following circular economy trends associated with sustainability. European Community guidelines have established deadlines for the implementation of management strategies that allow creating and maintaining an effective and resource-efficient economic system, reducing the amount of en-ergy and materials used in production. In this scenario, this study makes sense, which aims to an-alyze academic studies on these topics with the aim of understanding future trends. In this sense, the objective of this study is to understand the potential impacts on business development of the inte-gration of sustainability and the circular economy. This article uses a systematic literature review, which has the objective of gather and analyze materials from different authors, carrying out an analysis to answer a specific question. The findings indicate that CE and sustainability are con-nected and businesses should implement sustainability and CE appropriate strategies to increase their competitiveness and improve resource efficiency. These strategies may integrate innovative technologies, the use of sharing platforms, product life extension, resource recovery to minimize waste and the integration of sustainability concepts in business as well as their communication with their stakeholders to change trends and behaviors.

L-Theanine, a unique non-protein amino acid, is an important bioactive component of green tea. Previous studies have shown that L-theanine has many potent health benefits, such as anti-anxiety effect, regulation of immune response, relaxing neural tension, reducing oxidative damage. However, little is known concerning whether L-theanine can improve the clearance of mitochondrial DNA (mtDNA) damage in organisms. Here, we reported that L-theanine treatment increased ATP production and improved mitochondrial morphology to extend the lifespan of UVC-exposed nematodes. Mechanistic investigations showed that L-theanine treatment enhanced the removal of mtDNA damage and extended lifespan by activating autophagy, mitophagy, mitochondrial dynamics, and mitochondrial unfolded protein response (UPRmt) in UVC-exposed nematodes. In addition, L-theanine treatment also up-regulated the expression of genes related to mitochondrial energy metabolism in UVC-exposed nematodes. Our study provided a theoretical basis for the possibility that tea drinking may prevent mitochondrial related diseases.

Introduction: Hypertension is a serious public health problem in Morocco. The objective of this pilot study was to estimate the salt intake of Moroccan adults by measuring 24-hour urinary sodium excretion. Methods: 132 participants were recruited from the central region of Morocco. Sodium, potassium and creatinine levels were measured using the 24-hour urine samples. Additional data included anthropometric measurements and socio-demographic questionnaire. Results: The daily sodium, potassium and creatinine excretions were 2779.1±1334.9 mg, 1350.0±642.8 mg and 820.3 ± 381.2 mg, respectively. Data analysis revealed that 71.2% of the participants had a daily sodium intake more than 2000 mg (5g of salt) recommended by the WHO. The mean of potassium excretion was 1350.0±642.8 mg /day, this average was lower than 3.51g per day recommended by the WHO. Conclusion: Public health measures to reduce sodium and increase potassium consumption in order to decrease the population’s risk of high blood pressure and heart disease are recommended.

The present study comprises the second part of our previous work that dealt mainly with the phytochemical and physicochemical characterization of green coffee, clove, cinnamon/clove and nutmeg ethanolic extracts of grape origin. In the present study, we focused on producing a discriminating model concerning green coffee, clove, cinnamon, cinnamon and clove, and nutmeg fine powders based on multivariate analysis of variance and supervised learning on data of volatile compounds analysis, carried out with solid phase dynamic extraction in combination with gas chromatography/mass spectrometry. Results showed that 7 volatile compounds namely ethylene, methanol, 3-methylpentane, ethyl acetate, 9-hexadecen-1-ol, toluene, and methyl acetate could differentiate the investigated samples resulting in a 100% classification rate using the cross-validation method of linear discriminant analysis. Results were further confirmed using partial least squares regression analysis. The study contributes to the typification of green coffee, cinnamon, clove, cinnamon and clove and nutmeg, based on selected volatile compounds, and provides further support to the literature by means of possible adulteration given the statistical models developed.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

In order to meet the needs of an increasingly complex research landscape, researchers engage in “collaborative prosumption” through open data sharing and reuse. Although significant gains have been achieved in this regards because of growing requirements from funding agencies, governments and journals, the question of how reuse of openly available data for new research contribute to sustainability is yet to be appropriately addressed in the literature. Therefore, relying on a three stage stratified clustered random sampling of the Journal of Applied Econometrics data archive (JAEDA), the present research provides a case study of the value of research data recycling for sustainable research and economic development. More specifically our analysis show that reformatting from wide to long format, openly shared equity price index data on eleven European countries’ extracted from JAEDA, and augmented with country level geospatial Meta data, provides a new basis for interesting descriptive analytics and spatio-temporal econometric modeling and inference. Given the ever-increasing volume of openly available research data, our study provides a first-hand insight on open data reuse, which should benefit all stakeholders in the research community, as they seek sustainable solutions for scientific productivity and progress.

The mineralogical indices, e.g., Quartz Index (QI), Carbonate Index (CI), Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data have been applied to various geological materials. The indices are proved to be robust in extracting geological information at the local scale. Considering the relatively narrow spatial coverage of each ASTER scene compared to LANDSAT, many scenes are needed when mosaicking the images to be mapped at regional scales. We develop a system to search ASTER data for a target area given the vast and expanding ASTER data archive. The data is then conditioned, prioritized, and the indices are calculated before finally mosaicking the imagery. The maps of the indices covering the very wide region of the central Tibetan Plateau are produced with this system. The characteristic features of the indices relating to their geology in the study area are analyzed and discussed. Many interesting lithological and structural information that are not currently well understood in the central Tibetan Plateau, the highest and most extensive plateau in the world, with an average elevation of over 4,500 meters above sea level, for example, distributions of the mafic-ultramafic rocks along the suture zones, the quarzitic and marine sedimentology accreted to the Eurasian continent and sulfate layers related to the Tethys and neo-Tethys geological setting can be retrieved from the processed ASTER images.

Despite energy-related financial concerns and the growing demand for sustainability, many energy efficiency measures are not being implemented in industrial practice. There are a number of reasons for this, including a lack of knowledge about energy efficiency potentials and the assessment of energy savings as well as the high workloads of employees. This article describes the systematic development of an expert system, which offers a chance to overcome these obstacles and contribute significantly to increasing the energy efficiency of production machines. The system employs data-driven regression models to identify inefficient parameter settings, calculate achievable energy savings, and prioritize actions based on a fuzzy rule base. Proposed measures are first applied to an analytical real-time simulation model of a production machine to verify that constraints required for the specified product quality are met. This provides the machine operator with the expert means to apply proposed energy efficiency measures to the physical entity. We demonstrate the development and application of the system for a throughput parts cleaning machine in the metalworking industry.

The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different product. In addition to the timestamps and the twenty recorded features derived from Numerical Control (NC) control variables, the data set also contains labels for the different production phases. For this purpose, up to 23 phases were assigned, which are based on a generalized milling process. The data set consists of thirteen .csv files, each representing a series production. The data set was recorded in a production company in the contract manufacturing sector for components with real series orders in ongoing industrial production.

Porcine parvovirus 8 (PPV8), a novel virus in the Parvoviridae family, was first identified in 2022 in lung samples of domestic pigs from China. Retrospective analyses showed that it had been circulating in China since 1998, but no other countries had reported its presence so far. A recent study conducted in South America did not detect any PPV8-positive samples in that region. Here we report the detection of PPV8 in Hungarian and Slovakian pig farms and the estimated prevalence of the virus in Hungary. Altogether 2230 serum, 233 oral fluid, and 115 processing fluid samples were systematically collected from 23 Hungarian and 2 Slovakian pig farms between 2020 and 2023. A real-time quantitative PCR method was developed to detect the viral genome. Our results revealed the presence of PPV8 on 65% of the Hungarian farms and on both Slovakian farms included in our study, marking its first detection in Europe. Oral fluid samples showed the highest positivity rates, reaching up to 100% in some herds. The viral genome was successfully detected in serum and processing fluid samples too, but with significantly lower prevalence rates of 4% and 5%, respectively. Genetic analysis of 11 partial VP2 sequences demonstrated high similarity to the original Chinese strain but with unique amino acid mutations, suggesting possible local evolution of the virus. Our study presents the first scientific evidence of PPV8 infection outside of China and offers a comprehensive assessment of its prevalence in the Hungarian pig population. Further research is required to understand its potential impact on swine health.

Objective Hypertension is one of the most common chronic diseases, affecting more than 20 % of the population. The side effects experienced due to antihypertensive medications such as tired-ness, muscle pain, and insomnia are often a significant predictor of poor adherence to therapy. The goal of the current study is to present the frequency, type, seriousness, and severity of adverse drug reactions reported to the BDA via Individual Case Safety Reports (ICSR) and following differentiation of messages found in more than one patient. Methods We conducted a retrospective analysis of the reported adverse drug reactions (ADR) reported in the Bulgarian Drug Agency database after treatment with antihyperlipidemic medicines, angiotensin-converting enzyme (ACE) - inhibitors, and sartans for the period 2017-2021. Each ICSR form was observed and data for suspected medicine and type of adverse reaction was analyzed. Results The total number of processed notifications for adverse drug reactions (ADR) included in the database is 142. The highest number of ADRs was reported for ARB (58), followed by antihyperlipidemic medicines (55) and ACE inhibitors (29.) Most of the assessed adverse events experienced by more than one patient fall into the probable and related categories based on Global Introspection method classification. Therefore, they have been investigated and are consistent with exposure in the population. Conclusion Cardiovascular medicines from the groups of ACE inhibitors, sartans and statins are with high share of reported ADRs in the BDA system. Some of them are severe and need further investigation.

Electricity consumption is often the hotspot of life cycle assessment (LCA) of products, industrial activities, or services. The objective of this paper is to provide a consistent, scientific, region-specific electricity-supply-based inventory of electricity generation technology for national and regional power grids. Marginal electricity generation technology is pivotal in assessing impacts related to additional consumption of electricity. China covers a large geographical area with regional supply grids; these are arguably equally or less integrated. Meanwhile, it is also a country with internal imbalances in regional energy supply and demand. Therefore, we suggest an approach to achieve a geographical subdivision of the Chinese electricity grid, corresponding to the interprovincial regional power grids, namely the North, the Northeast, the East, the Central, the Northwest, and the Southwest China Grids, and the China Southern Power Grid. The approach combines information from the Chinese national plans on for capacity changes in both production and distribution grids, and knowledge of resource availability. The results show that nationally, marginal technology is coal-fired electricity generation, which is the same scenario in the North and Northwest China Grid. In the Northeast, East, and Central China Grid, nuclear power gradually replaces coal-fired electricity and becomes the marginal technology. In the Southwest China Grid and the China Southern Power Grid, the marginal electricity is hydropower towards 2030.

The aim of the study was to identify limiting factors for reusing wood through the recycling of window frames by conducting research under fully controlled conditions. The research involved manufacturing new window frames, seasoning them, and then shredding them into wood particles to prepare a three-layer particleboard. The proportion of wood particles in recycling was 0, 5, 10, 25, 50, and 100 parts by weight of the manufactured particleboard. Mechanical property tests were conducted: modulus of elasticity (MOE) and modulus of rupture (MOR), internal bond (IB), screw withdrawal resistance (SWR), and physical properties: density profile (DP), thickness swelling (TS) after water immersion, water absorption (WA), as well as formaldehyde emission and total volatile organic compounds (TVOC) tests. The conducted research indicates a significant potential for utilising wood from this sector of the wood industry, particularly considering variants with a higher proportion of recycled wood. MOR and MOE results are most promising for variants above 50 parts by weight of recycled wood. Based on the results obtained, it is clear that the production process should be improved or the raw material modified to enhance the internal bonding of particleboard, as these results were the weakest. Thus, recycled wood from window joinery has the potential to be reincarnated as particleboard, which continues to be widely used in their production.

The self-assembly is an intriguing process of great importance in general soft matter and thus re-ceives an ever-growing scientific interest. Its most trivial example is the entropy-driven crystalli-zation of hard spheres. Past works have established the similarities and differences in the phase behavior of monomers and chains made of hard spheres. In the present work, inspired by the dif-ference in the melting points of the pure components, we study, through Monte Carlo simulations, the phase behavior of athermal mixtures composed of fully flexible polymers and individual monomers of uniform size. We analyze how the relative number fraction and the packing density affect crystallization and the established ordered morphologies. A synergetic effect is observed in the crystallization leading to synchronous crystallization of the two species. Structural analysis of the resulting ordered morphologies shows perfect mixing and thus no phase separation, which could split the system into polymer-rich and monomer-rich domains. Due to the constraints imposed by chain connectivity, the local environment of the individual spheres is systematically more spherical and more symmetric compared to the one of spheres belonging to chains. In general, increasing polymer content leads to reduction of the degree of crystallinity. According to the present findings, relative concentration is another determining factor to control the phase behavior of hard colloidal systems based on polymers.

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

Global attention to climate change has surged since the advent of the Paris Agreement, intensifying the importance of measuring and managing carbon productivity indicators on a national level. Nevertheless, concerns persist regarding the reliability of such measurements because of inherent discrepancies in implementing and operating national-level carbon productivity indicators, coupled with their inherent uncertainty. This study proposes a multiple regression model to address these issues aimed at refining national-level carbon productivity indicators metrics, accounting for factors such as the gross domestic product and total greenhouse gas emissions by sectors. The objective was to offer insights into enhancing and effectively utilizing current indicators, enabling a more nuanced interpretation of the variation in the carbon productivity indicators across diverse industrial landscapes. This study showed that adjustments of the carbon productivity metrics reflect disparities in emissions across industrial structures, with countries characterized by high emissions from non-service industries showing improving trends. In addition, this paper proposes an auxiliary indicator estimating method for carbon productivity, emphasizing its utility in interpreting productivity indicators within the context of varying industrial compositions across OECD countries. This study underscores the inadequacies of the current national productivity estimating method, pinpointing areas requiring refinement. Specifically, the method for estimating the auxiliary indicator for carbon productivity guarantees enhanced rationality when integrated with current methodologies. Moreover, by elucidating the nuances of industrial structures, this study advocates for more sophisticated approaches to interpreting and managing the productivity indicators tailored to each unique economic landscape of each country. Nevertheless, the limitations stemming from data availability underscore the need for further research, particularly in refining the national-level carbon resource productivity indicators analyses and exploring the thematic productivity variations in greater depth. By addressing these gaps, future studies will contribute to a more comprehensive understanding of national-level carbon resource productivity indicators dynamics and reveal targeted strategies for sustainable development.

Chromium (Cr) metal has garnered significant attention in alloy systems owing to its exceptional properties, such as high melting point, low density, and superior oxidation and corrosion resistance. However, its processing capabilities are hindered by the high Ductile-Brittle Transition Temperature (DBTT). Recently, powder bed fusion-laser beam for metals (PBF-LB/M) has emerged as a promising technique, offering the fabrication of net shapes and precise control over crystallographic texture. Nevertheless, research investigating the mechanism underlying crystallographic texture development in pure Cr via PBF-LB/M still needs to be conducted. This study explores the impact of scan speed on relative density and crystallographic texture. At the optimal scan speed, an increase in grain size attributed to epitaxial growth was observed, resulting in the formation of cubic texture. Consequently, a reduction in High Angle Grain Boundaries (HAGB) was achieved, suppressing defects such as cracks and enhancing relative density up to 98.1%. Furthermore, with increasing densification, Vickers hardness also exhibited a corresponding increase. These findings underscore the efficacy of PBF-LB/M for processing metals with high DBTT properties.

Businesses now formally enter the phase of "think power differentiation and gap" in response to the new economic climate. While AI, big data, empowerment via the internet, and other aspects of the digital economy help to improve technical capabilities and save operating costs, they also push businesses to innovate their strategies at a faster rate. Enterprises are strategically choosing to de-enterprise, moving from focusing on a single business functioning benefit to building an ecosystem of platform services that will give them a competitive edge. Additionally, the organization's structure is evolving continually from a vertical model to a curved working style, in order to achieve this, businesses must recognize the importance of data analysis in day-to-day management and operations, encourage the development of an organizational atmosphere that is driven by data, fully utilize digital technology, modularize areas or production links that perform related tasks, and encourage the reform of organizational structures. By streamlining the output of the product or service to the external community while also enhancing the company's financial performance model, switching from process-to result-oriented evaluation, and putting in place a customized authority and accountability framework. Replace outcome-focused evaluation with process-focused evaluation, and create a customized incentive scheme that balances power, accountability, and rewards.

Simple Summary: The distribution of typical bacterial redox enzymes such as pyruvate-ferredoxin oxidoreductase (PFOR) in protozoa remains interestingly puzzling. Previous studies have demonstrated diverse cellular localizations of PFOR in some amitochondriate anaerobic protozoa. PFOR is of particular pharmacological importance because it catalyzes the reductive bio-activation of nitro-based prodrugs to cytotoxic radical metabolites. Metronidazole was developed primarily as an antiprotozoal agent against infections caused by Trichomonas vaginalis. However, its antimicrobial spectrum was subsequently expanded to cover anaerobic bacterial infections. It has been shown that mutations in the genes encoding PFOR result in inherent resistance of PFOR-possessing anaerobic protozoa and bacteria to nitro-based prodrugs. Deciphering the evolutionary history of PFOR is crucial in deepening our understanding of the evolution of anaerobic pathogens and unfold new approaches for drug discovery and targeting in pathogen chemotherapy. Abstract: The present frontrunners in the chemotherapy of infections caused by protozoa are nitro-based prodrugs that are selectively activated by PFOR-mediated redox reactions. This study seeks to analyze the distribution of PFOR in selected protozoa and bacteria by applying comparative bioinformatics approaches to test the hypothesis that PFOR was most likely acquired through horizontal gene transfer (HGT) from bacteria. Furthermore, to identify other genes that were putatively acquired using proteome-wide and gene expression analyses. A plausible explanation for the patchy occurrence of PFOR in protozoa is based on the hypothesis that bacteria are potential sources of genes that enhance optimal adaptation of protozoa in hostile environments. The exclusively expressed proteins obtained from Entamoeba histolytica and the putative gene donor, Desulfovibrio vulgaris, showed an over-expression of eleven genes involved in intermediary metabolism. If these results can be reproduced in other PFOR-possessing protozoa, it would provide a more validated evidence to support the horizontal transfer of pfor from bacteria.

K-band radar waves have high penetration and low attenuation coefficients. However, the wavelength of this radar wave is relatively short; thus, designing and preparing both broadband and wide-angle radar wave absorbers in this band presents considerable challenges. In this study, a resin-based K-band radar wave absorber with a biomimetic lotus leaf structure was designed and formed by UV curing. Here, microscale lotus leaf papillae and antireflection structures were prepared using a DLP 3D printer, and the contact angle between the material and water droplets was increased from 56° to 130°. In addition, the influence of the geometric parameters of the lotus leaf antireflection structure on the electromagnetic absorption performance and mechanical strength was investigated. After simulation optimization, the maximum electromagnetic loss of the lotus leaf structure 3D printed sample was −32.3 dB, and the electromagnetic loss was below −10 dB in the 20.8–26.5 GHz frequency range. When the radar incidence angle was 60°, the maximum electromagnetic loss was still less than −10 dB. The designed lotus leaf structure has a higher mechanical energy absorption per unit volume (337.22 KJ/m3) and per unit mass (0.55 KJ/Kg) than commonly used honeycomb lightweight structures during the elastic deformation stage, and we expect that the designed structure can be used as an effective lightweight material for K-band radar stealth.

The Fuzzy Transportation Problem (FTP) represents a significant extension of the Classical Transportation Problem (TP) by introducing uncertainly and imprecision into the parameters involved. Various algorithms have been proposed to solve the FTP, including fuzzy linear programming, metaheuristic algorithms and fuzzy mathematical programming techniques combined with Artificial Neural Networks. This paper presents the application of Trigonometric Acceleration Coefficients-PSO (TrigAc-PSO), a variation of the Classical Particle Swarm optimization algorithm, which is an innovative algorithm originally developed for solving the TP. TrigAC-PSO, has demonstrated remarkable success in optimizing various problem domains in crisp environments.

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including "artificial intelligence," "applications," "mental health," "anxiety," "LLMs" and "depression". Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for over-reliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.

Cybersecurity is a crucial concern for local governments, as they serve as the primary interface between public and government services, managing sensitive data and critical infrastructure. While technical safeguards are integral to cybersecurity, the role of well-structured policy is equally important, as it provides structured guidance to translate technical requirements into actionable protocols. This study reviews local governments’ cybersecurity policies to provide a comprehensive assessment of how these policies align with NIST CSF, which is a widely adopted and commonly used cybersecurity assessment framework. The review offers local governments a mirror to reflect on their cybersecurity stance, identifying potential vulnerabilities and areas needing urgent attention. The study further extends to developing a cybersecurity policy framework, which local governments can use as a strategic tool. It provides valuable information on crucial cybersecurity elements that local governments must incorporate into their policies to protect confidential data and critical infrastructure.

Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

Aural rehabilitation with hearing aids can decrease the attentional requirements of cognitive resources by amplifying the deteriorated-frequency sound in hearing loss patients and improve the auditory discrimination ability like speech-in-noise perception. As aural rehabilitation with an intelligible-hearing sound also can be hopeful, the aim of this study was to evaluate the effectiveness of the aural rehabilitation with intelligible-hearing sound for hearing loss patients. Adult native Japanese speakers (17 males and 23 females, 68.43±9.23 years) showed over 30 dB at 125, 250, 500, 1000, 2000, 3000, 4000, 8000, 10000, and 12000Hz on any side, were recruited and underwent Mini-Mental State Examination Japanese. we conducted self-evaluation questionnaire for hearing problems of voice, gap detection test, fast speech test, and speech-in-noise test, pure tone audiogram, speech perception test using Japanese 67-S, cortical auditory evoked fields, and magnetic mismatch negativity before and after the non-intelligible-hearing (N=20) and intelligible-hearing (N=20) sound therapy with listening to music for one hour a day for 35 days. Better hearing ear was defined using a four-frequency pure-tone average at the thresholds of 500, 1000, 2000 and 4000 Hz. After the sound therapy, speech-in-noise test with signal to noise ratio +10 in better hearing ear showed significant improvement (p<0.05), and N1m-P2m amplitudes showed the significant increase in Lt superior temporal gyrus at the stimulus from better hearing ear (p<0.05). The significant enhancement of the magnetic mismatch negativity amplitude at Lt superior temporal gyrus was exhibited after the sound therapy (p<0.01). Intelligible-hearing sound therapy can improve the ability of speech-in-noise perception in better hearing ear and the enhancement of central cortex response, which reflect the ability of working memory, was proved by cortical auditory evoked fields and magnetic mismatch negativity. Intelligible-hearing sound therapy can be a valuable aural rehabilitation method for sensory neural hearing loss as same as hearing aids.

The origin of a product, consumer familiarity, and purchasing identity are factors that affect perception of cheese consumption. The present study aims at identifying consumers’ conceptualizations and attitudes towards local Greek cheeses of the North-Aegean Sea islands, such as Ladotyri, Graviera, Kasseri, Kaskavali, Melichloro, and Kalathaki, some of which have a Protected Designation of Origin (PDO) status, as opposed to cheeses of non-Greek origin, such as Cheddar, Regatto, and Gouda. Sensory and emotional attributes of local, local PDO, and imported cheeses, and drivers associated with consumers’ choice and acceptance above and beyond their sensory attributes were studied, using three methods: a) Flash Profile to gain insight into the sensory positioning of products and description of samples, b) qualitative analysis of focus groups to pinpoint consumer knowledge, preference, and consumption criteria, and c) a new methodology for natural language processing and sentiment analysis of social media posts to determine consumer conceptualizations. Social media posts have proven to be a valuable source of linguistic and cultural data for cheeses. Local cheeses, including PDO products, were found to be linked to village life and family gatherings, home, tradition, and childhood memories, with saltiness and hardness being their main sensory attributes. Imported cheeses were linked to fast food, pizza, and snacking, with elasticity and gumminess as prominent sensory qualities. The main criterion of purchase was intended usage and versatility, taste and texture, price, and familiarity. The findings provide key sensory attributes, information about consumer purchasing criteria, and relevant vocabulary for the promotion of cheeses as agri-food and gastronomic identity key products.

Flexible wearable pressure sensors offer numerous benefits, including superior sensing capabilities, a lightweight and compact design, and exceptional conformal properties, making them highly sought after in various applications including medical monitoring, human-computer interaction, and electronic skin. Because of their excellent characteristics, such as simple fabrication, low power consumption, and short response time, capacitive pressure sensors have received widespread attention. As a flexible polymer material, polydimethylsiloxane (PDMS) is widely used in the preparation of dielectric layers for capacitive pressure sensors. The Young's modulus of the flexible polymer can be effectively decreased through the synergistic application of sacrificial template and laser ablation techniques, thereby improving the functionality of capacitive pressure sensors. In this study, a novel sensor was developed through a series of processes including sacrificial template method using NaCl microparticles, CO₂ laser ablation, and sandwiching a porous PDMS dielectric layer between two flexible electrodes to create a capacitive pressure sensor. The sensor demonstrates a sensitivity of 0.694 kPa-1 within the pressure range of 0-1 kPa, and can effectively detect pressures ranging from 3 Pa to 200 kPa. The sensor demonstrates stability for up to 500 cycles, with a rapid response time of 96ms and a recovery time of 118ms, coupled with a low hysteresis of 6.8%. Furthermore, our testing indicates that the sensor possesses limitless potential for use in detecting human physiological activities and signaling emergencies.

Based on the panel data of 114 Chinese resource-based cities from 2013 to 2022, this paper uses principal component analysis, Super-SBM and Malmquist index to measure the development level of digital economy and green total factor productivity of resource-based cities, and empirically studies the impact of digital economy on The impact effect and mechanism of green total factor productivity in resource-based cities. The research found that: (1)From 2013 to 2022, the overall green total factor productivity of resource-based cities in my country showed a trend of first decline and then an upward trend; (2) Digital economy can promote the improvement of green total factor productivity of resource-based cities, and digital economy has more long-term effects than short-term effects. (3)Mechanism analysis shows that energy efficiency and industrial struc-ture optimization are important paths for the digital economy to promote green total factor productivity in resource-based cities. (4)Heterogeneity research shows that digital economy has the largest effect on green total factor productivity in mature cities and the least effect on growth cities; From a regional perspective, the digital economy has the greatest effect on promoting re-source-based cities in the eastern region, followed by the central region, and the western region is weaker.

AbstractIntroduction: Consistent utilization of institutional delivery service reduces maternal morbidity and mortality. However, service utilization could in turn influenced by the women’s satisfaction with the service provided by the health care providers. In addition, their satisfactions with the institutional delivery service remains understudied. Assessment of the maternal satisfaction with maternal health service is a major input to improve maternal health services. Objective: To assess the maternal satisfaction with maternal health service and its predictors among the women who gave birth in last 12 months in Darara district, Southern Ethiopia.Methods: A community-based cross-sectional study was done using a mixed method among the sample of 520 women in Darara district, South Ethiopia from June 1-30, 2023. A systematic and purposive sampling procedure was utilized to choice the study subjects. A structured, validated and pretested interview-administrated questionnaire and in-depth interview were utilized to collect the data. The data were entered into Epi data version 3.1 and analyzed using SPSS version 25. The chi-square (X²) test was utilized to determine the overall association between independent and dependent variables. Both bi-variable and multivariable binary logistic regression analyses were conducted. Important assumptions of the binary logistic regression were checked to be satisfied. The coding and analysis of the qualitative data were done using thematic content analysis technique by Atlas-Ti software and presented in narratives. Results: The total maternal satisfaction with maternal health service was 55.2% (95% CI = 50.8-59.4). The identified predictors of maternal satisfaction were distance from household to health post (AOR = 3.59; 95% CI= 1.71 - 7.55), absence of preparation of HEWs during procedure (AOR= 2.87; 95% CI = 1.74 - 4.74), acceptability of the HES by HHs (AOR= 2.18; 95% CI = 1.14 - 4.18), having a reliable communication method with HEWs (AOR = 3.47; 95% CI = 1.77 - 6.79) and received training on the model family (AOR= 2.48, 95% CI= 1.86 - 2.57). These quantitative results were supported by the individual, organizational, socio-demographic and cultural qualitative findings. Conclusions: This study indicated that 55.2% of women were satisfied with maternal service.Distance from household to health post, absence of preparation of HEWs during procedure, acceptability of the Health Extension Service (HES) by household, having a reliable communication method with HEWs and received training on the model family were significant predictors of maternal satisfaction. The interventions to increase maternal satisfaction with HES need to focus on household-based acceptability of them and their reliable communication method in planning and implementing the services. Similarly, expansion of training of model family in the community would increase maternal satisfaction levels.

Observe the synergistic effect and dose-effect relationship of Trimethoprim (TMP) on bactericidal activity with Flos Lonicerae in vitro. Microamount chessboard dilution method was conducted to determine the minimal inhibitory concentration (MIC) of Trimethoprim, Flos Lonicerae, as well as the combination of Trimethoprim and Flos Lonicerae separately against Staphylococcus aureus, Escherichia coli in vitro and Salmonella. The pour plate count method was used to determine the combined bactericidal activity of Flos Lonicerae combined with Different concentrations TMP. The results showed that the MIC values of the combination of Flos Lonicerae with TMP was much less than the MIC values of the independent use of Flos Lonicerae or TMP, The FIC values of the combination of Flos Lonicerae with TMP were between 0.5 and 1, there was additive effect between them. The bactericidal rates were fitted with least square method, the 95% confidence intervals of the optimal blending quantity about the combination of Flos Lonicerae with TMP on the test organisms were 231μg·mL^-1-249μg·mL^-1, 237μg·mL^-1-259μg·mL^-1, and 235 -259μg·mL^-1

Lynch syndrome (LS) is one of the most common hereditary cancer syndrome in human populations, associated with germline variants in MLH1, MSH2/EPCAM, MSH6 and PMS2 genes. The advent of next generation sequencing has proven a significant impact in germline variant detection in the causative genes; however, a large proportion of patients with clinical criteria still receive uncertain or negative results. PMS2 is the least frequent reported gene, associated with up to 15% of LS cases with late-onset disease and low penetrance phenotype; however, the proportion of PMS2-LS cases is considered to be highly underestimated. In this context, we consulted public available databases (ClinVar, LOVD) for missense and intronic PMS2 variants and performed an in silico analysis of the genomic sequence. Splicing bioinformatics tools proven effective in other genes were used to assess both the wild-type DNA sequence and reported genetic variants. Splicing alterations were predicted in silico and using GTEx short-read RNA expression data. Out of the 2384 missense variants discovered, 90% were classified with uncertain significance (VUS). 4.9% of missense variants were shown to have a potential splicing consequence (DS > 0.2) using SpliceAI. As described in the original publication, SpliceAI-visual was proven effective in annotation of short intronic variants (< 50 bp). Four short intronic variants were identified using SpliceAI-visual as potentially splicing disturbing, in spite of using a lower threshold (DS > 0.1). Moreover, we observed that Splicing Regulatory Elements (SREs) may play a fundamental role in alternative splicing in PMS2 gene, ESE motifs being overrepresented in highly expressed exons 5, 11 and 14. Computational analysis performed in our study serves as a valuable filtering step for guiding further RNA sequencing experiments. Additional functional data is necessary to evaluate the significance of our findings.

This study presents an advanced Simulated Shearer Machine Cutting Experiment System enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to mirror the operational dynamics of actual mining cutters accurately. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time-domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal-rock identification research, offering significant insights into the nuances of shearer operation.

The efficacy and safety of organic UV-filters could benefit from their incorporation into lipid nanoparticles because of the ability of such carriers to reflect solar radiations, thus acting synergically as physical sunscreens. In this work, bemotrizinol (BMTZ), a broad-spectrum UV-filter, was loaded into nanostructured lipid carriers (NLC) whose lipid matrix contained different oils to assess the effects of the lipid core composition on the properties of the resulting NLC. Subsequently, the effects of incorporating different concentrations of optimized BMTZ-loaded NLC on the technological properties of O/W emulsions (pH, viscosity, spreadability, occlusion factor, in vitro BMTZ release and skin permeation, in vitro sun protection factor) were assessed. Optimized BMTZ-loaded NLC showed mean size=190.6±9.8 nm, PDI=0.153±0.013, ζ-potential=-10.6±1.7 mV, loading capacity=8% w/w. The incorporation of increasing concentrations of optimized BMTZ-loaded into emulsions provided slight increase of spreadability, lower viscosity and no change of pH, occlusion factor and BMTZ release compared to the control. No BMTZ skin permeation was observed from all formulations. About 20% increase of sun protection factor values was obtained for vehicles containing BMTZ-loaded NLC. Therefore, BMTZ-loaded NLC incorporation into emulsions could be a promising strategy to develop safer and more effective sunscreen formulations.