The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

The main objective of this study was to compare the removal efficiency of nutrients using Lactuca sativa, Medicago sativa and Phragmites australis in subsurface flow constructed wetlands with horizontal flow. In order to test water quality, fabricated reactors designed and the plants cultivated in the soil while their root were inside the wastewater. A long time study carried out from spring till end of autumn (9 months) in order to evaluate the difference in removal rate based on the seasonal changes. The highest removal rate was during summer which followed by spring and autumn. Thus, the effect of plants on the removal efficiency of organic matter (COD, BOD), TSS and nutrient (P and TN) appeared to be dependent on the seasonal growth. Phragmites australis the most sensitive species in order the removal of nutrient from wastewater.

In this study, Fluent software 2020R2 is used to simulate vertical axis turbine in two dimensions, aiming to analyze the influence of turbulence intensity on wake characteristics. The detailed flow field data are obtained by simulating the operation of turbine under different turbulence intensities. On this basis, the influence factor of turbulence intensity are creatively introduced into the wake mathematical formula of vertical axis turbine established by Lam, and some parameter calculations of the formula are supplemented and perfected. The results show that the theoretical model can well reflect the influence of turbulence intensity on the wake distribution of vertical axis turbines. The formula can provide a theoretical basis for the design and optimization of vertical axis turbines and the array arrangement, and can be used to predict the wake characteristics under different turbulence intensity operating conditions.

In recent years, Chinese manufacturing enterprises compete to chase the wave of "digital revolution", digital empowerment has become an important strategic path of technological reform for many manufacturing enterprises. Based on the micro data of listed companies, this paper investigates the impact of digital transformation of Chinese listed companies on the quality of their export products. It is found that digital transformation can significantly improve the quality of enterprises' export products. After a series of robustness tests, this conclusion is still valid, and there are two ways to improve innovation performance and total factor productivity. The export product quality enhancement effect of enterprise digital transformation also has the heterogeneity of ownership, region and industry. In the further study, this paper also examines the impact of digital transformation on the internal salary gap of enterprises, and finds that digital transformation may increase the internal salary gap of enterprises, and form a "masking effect" on the quality of export products. To a certain extent, this paper deepens the understanding of the study of enterprise digital transformation on the quality of export products and its differences, and provides certain guidance for enterprises to implement the strategy of digital transformation.

The undoped and tungsten (W)-doped vanadium dioxide (VO₂) thin films were prepared by electron beam evaporation associated with ion-beam assisted deposition (IAD). The influence of different W-doped contents (3%-5%) on the optical, electrical, structural, and thermos-mechanical properties of VO₂ thin films was investigated experimentally. Spectral transmittance results showed that with the increase of W-doped contents, the transmittance in the visible light range (400-750 nm) decreases from 60.2% to 53.9%, and the transmittance in the infrared wavelength range (2.5 μm to 5.5 μm) drops from 55.8% to 15.4%. As the W-doped content increases, the residual stress in the VO₂ thin film decreases from -0.276 GPa to -0.238 GPa, but the surface roughness increases. For temperature-dependent spectroscopic measurements, heating the VO2 thin films from 30°C to 100°C showed the most significant change in transmittance for the 5% W-doped VO₂ thin film. When the heating temperature exceeds 55°C, the optical transmittance drops significantly, and the visible light transmittance drops by about 11%. Finally, X-ray Diffraction (XRD) and scanning electron microscope (SEM) were used to evaluate the microstructure characteristics of VO₂ thin films.

The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

The search for growth and sustainability in any organization largely depends on its ability to carry out digital transformation and agility processes. These processes are essential to adapt to an increasingly changing and competitive business environment, allowing companies to be more efficient and effective in creating an unbreakable bond with their customers. This manuscript addresses the importance of detecting and eliminating anti-patterns in organizational culture for a successful digital and agile transformation. During the early stages of the transformation process, prejudices and paradigms rooted in the organization can become an obstacle to change. The goal of this writing is to offer a practical guide for leaders and teams in organizations seeking successful cultural transformation. Effective strategies are proposed to overcome the challenges faced by organizations during this early stage. In summary, the key steps to be followed in the initial stages of digital transformation with agility are described, including the importance of establishing strong leadership, defining a clear vision and scope of the transformational process, among others. A set of recommendations will be provided to overcome recurring anti-patterns, such as lack of stakeholder participation and commitment, lack of proper planning, and confusion of roles and responsibilities.

Growth hormone (GH) deficiency affects up to 1 in 4,000 children and is usually treated with daily injections of GH whilst the child is still growing. With children typically diagnosed around 5 years old, this can mean over 10 years of therapy, which can place a considerable burden on the child and parent. Over three-quarters of children are estimated to be not fully compliant with therapy, which can compromise their chances of attaining their target height. In recent years, interactive mobile health (smart phone or tablet) interventions using game-like concepts, so called ‘gamification’, have increased in popularity and have demonstrated success in promoting positive self-management behaviour in children with chronic conditions, such as diabetes. The application of gamified interventions has the potential to support adherence to therapy and positive behaviour in children with GH deficiency in a number of ways: 1) By providing education in a format that the child understands and accepts (e.g. using behavioural constructs to facilitate explaining why adherence is important); 2) By providing a mechanism to reduce the anxiety and stress associated with administering the injection (e.g. diversion with a virtual pet); and 3) By providing feedback to encourage on-going engagement (e.g. rewards, progression through levels).

This article investigates a proposed new mathematical model that considers the infected individuals using various rate coefficients such as transmission, progression, recovery and vaccination. The fact that the dynamic analysis is completely determined by the basic reproduction number is established. More specifically, local and global stabilities of the disease-free equilibrium and the endemic equilibrium are proved under certain parameter conditions when the basic reproduction number is below or above unity. A realistic computer simulation is performed for better understanding of the variations in trends of different compartments after the outbreak of the disease.

Artificial intelligence technologies based on deep convolutional neural networks and large lan-guage models have made significant breakthroughs in many tasks such as image recognition, target detection, semantic segmentation and natural language processing, but also face the problem of contradiction between the high computational capacity of the algorithms and the limited deployment resources. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is an important and essential technique for efficient deploy-ment and cost reduction in edge computing. This paper analyze various existing quantization methods, showcase the deployment accuracy of advanced techniques, and discuss the future challenges and trends in this domain.

Marine oil spills seriously threaten the coastal environment, challenge the safety of marine eco-system, and cause huge economic losses to marine fishery, aquaculture, and tourism industries. Timely and accurate oil spill monitoring and early warning can not only make the oil spill acci-dent be handled timely and effectively but also provide key information for investigating the re-sponsibility of the polluters. Polarimetric synthetic aperture radar (SAR) has shown its great po-tential in oil spill classification for its capacity to distinguish mineral oil and biogenic look-alikes. Traditional oil spill classification approaches rely on the scattering matrix elements or pre-designed scattering mechanism-based polarimetric SAR features, which limits its robustness and generalization in large-scale marine surveillance tasks. The recent development of deep learning frameworks, especially the convolutional neural network (CNN) provides a powerful tool for remote sensing image classification. However, conventional CNN only accepts re-al-valued input, which cannot efficiently analyze the polarimetric information carried by com-plex-valued SAR data. In this study, a new framework that employs the complex-valued neural network was proposed for marine oil spill detection. Sophisticated studies were designed to evaluate the contribution of the physical scattering mechanism-based features, complex-valued framework, and convolutional feature extraction structures respectively. Comparative studies were conducted on four study cases using Radarsat-2 and SIR-C fully polarimetric SAR data. The outcome of the proposed study may shed light on the development of an end-to-end marine spill classification system and enlarge its application in practical marine surveillance operations.

This paper exposes the initial part of a research consisting in a need analysis carried out by the Romanian partner in the Erasmus+ "Media Education for Aware Adults" project, regarding digital competences and media education of adult educators or trainers. After a short introduction on the subject, in the paper are presented the main findings of the desk research, and two focus group sessions, first one for identifying the necessary digital competences of adult educators, and second one for identifying the elements that educators need in order to adequately support adults in their learning process, especially in relation to the digital environment. The results of the initial research will lead to the development of the main intellectual outputs, namely the digital competences framework, handbook, online platform and training material on digital and media education. In their development will be taken into account, on the one hand, the needs identified in the initial research, and, on the other hand, the need for a sustainable approach to all activities, resources and outputs produced, as well as education itself, this goal being assumed through the project.

Plant Factory Transplanter (PFT) is a key component of the plant factory system. Its operation status directly affects the quality and survival rate of planted seedlings, which in turn affects the overall yield and economic efficiency. To monitor the operation status and transplanting quality of a transplanting machine in a timely manner, the primary task is to use a computerized and easy-to-use method to monitor the transplanting units. Inspired by the latest developments in augmented reality and robotics, a DT model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed. First, a data-driven and virtual model approach is combined to construct a multi-domain digital twin of the transplanting equipment. Then, taking the vibration frequency domain signal above the transplanting manipulator and the image features of the transplanting seedling tray as input variables, the evaluation method and configuration method of the PFT digital twin system are proposed. Finally, the effect of the transplanter is evaluated, and the cycle can be repeated to optimize the transplanter to achieve optimal operation parameters. The results show that the digital twin model can effectively use the sensor data to identify the mechanical vibration characteristics and avoid affecting transplanting quality due to mechanical resonance. At a transplanting rate of 3000 plants/h, the transplanting efficiency can be maintained at a high level and the vibration signal of X, Y, Z-axis above the transplanting manipulator is relatively calm. In this case, Combined the optimal threshold method with the traditional Wiener algorithm, the identification rate of healthy potted seedlings can reach 94.3%. Through comprehensively using the optimal threshold method and 3D block matching filtering algorithm for image threshold segmentation and denoising, the recognition rate of healthy seedlings has reached over 96.10%. In addition, the developed digital twin can predict the operational efficiency and optimal timing of the detected transplanter, even if the environmental and sensor data are not included in the training. The proposed digital twin model can be used for damage detection and operational effectiveness assessment of other plant factory equipment structures.

In the context of crowds innovation and the generative design driven by big language models, the personalized requirements has become the best engine for innovative design. The exploration of personalized requirements has become a key in significantly improving product innovation, concepts feasibility, and design interaction efficiency. To mine a large number of vague and unexpressed implicit requirements of personalized products, a domain knowledge graph-based method is proposed in this research. First, based on the classical theory of design science, the characteristics and categories of personalized implicit requirements are analyzed. Next, in order to improve the practicability and construction efficiency of the domain knowledge graph, a more informative ontology is constructed, and better performing NLP models are proposed. Then, a series of mining methods based on knowledge graph are proposed for the personalized implicit requirements of different categories. Finally, A platform was developed based on the technical solution proposed in this study, and an example verification was conducted in the field of electromechanical engineering. The efficiency improvement of the training model proposed in the research was analyzed, and the practicality of implicit requirement mining methods were discussed.

The development requirements for reusable liquid rocket engines have set higher standards for the reliability design of turbopump structures. To address the issue of bearing failure in turbopumps under harsh conditions such as low temperature, high speed, heavy load, and repeated start-stop cycles, this paper proposes replacing ball bearings with thrust foil bearings. We used liquid nitrogen instead of liquid hydrogen and liquid oxygen to simulate the ultralow temperature and low viscosity operating environment of turbopumps, evaluating the potential feasibility of using thrust foil bearings in this context. The numerical calculations primarily include the formulation of the foil deformation equations based on the link-spring model for the wavy foil and the thin plate finite element model for the top foil. These equations are solved by coupling the Reynolds lubrication equation, the fluid film thickness equation, and the foil deformation equations using the finite difference method, Newton-Raphson method, and finite element method. A detailed analysis is conducted on the effects of rotational speed, fluid film thickness, thrust disk inclination angle, and wavy foil interface friction coefficient on the static and dynamic characteristics of thrust foil bearings. The research findings provide guidance for the application of thrust foil bearings in liquid rocket turbopumps.

Improving the level of farmers' participation in collective action is an important way to promote rural revitalization and improve the sustainable livelihood level of farmers in underdeveloped areas. Based on 312 questionnaires of farmers in Shanxi Province, this paper investigates the status of farmers' participation in collective action, their sustainable livelihood level and the sustainable livelihood effect of collective action participation. The results show that: farmers' willingness to participate in collective action is strong, and the participation effect is obvious, but the participation frequency is low, and the participation time is less; The farmers' sustainable livelihood level is relatively low, Empirical research shows that the level of collective action participation will have a significant positive impact on Farmers' livelihood risk prevention, livelihood capital accumulation, livelihood mode selection and livelihood income improvement, and the frequency, time and value perception of participation in collective action will have varying degrees of impact on Farmers' sustainable livelihoods. In order to promote farmers' active participation in collective action, it is proposed to strengthen the construction of collective organizations Provide incentives and improve the quality and skills of farmers to enhance the collective concept and other policy recommendations.

Evaluation ecosystem service value (ESV) is critical for “lucid waters and lush mountains are invaluable assets”. However, the ESV estimation and spatial-temporal analysis for mountainous soil erosion area is rare, e.g., Changting county, China. In this study, we developed a remote sensing driven mountainous equivalent factor (RS-MEF) method to estimate the ESV of soil erosion area in subtropical mountain. It is a hybrid of a conventional equivalent factor framework and the remote sensing techniques for mountains, and achieves several highlights of spatial adjustment using carbon sink with vegetation canopy, improvement spatial resolution, and removal topographic effect. Using the RS-MEF method, we estimated that the ESV of Changting county was about 15.80 billion CNY in 2010 and 34.83 billion CNY in 2022. Specifically, the ESV per unit area of major soil erosion area (MSEA) in the county was less than that of non-major soil erosion area (n-MSEA), however, the ESV growth rate of MSEA from 2010 to 2022 was faster than that of n-MSEA. Therefore, the ESV gap between two areas was shortened from 28.99% in 2010 to 15.83% in 2022. Topographic gradient effect analysis illustrates that the area with high elevation and small slope is one control focus in the next step. Our study indicates that the ecological restoration in the county has made great achievements in the ESV perspective, with low ESV area shrank evidently in MSEA; and the achieved knowledge of the ESV growth and inherent factors is valuable and instructive for the next soil and water conservation.

The opportunities and advantages of implementing Artificial Intelligence (AI) and Machine Learning (ML) to optimize the process control of wastewater treatment in the Oil and Gas industry are discussed in this work. Discussion is centered around five major points: 1) Various types of Oil and Gas wastewater (Sour, Oily, Produced) and industry standards/methods for treatment, including specific case studies and challenges when treating certain wastewater. This section aims at the common types of wastewater in the Oil and Gas industry and how they are generally treated. 2) How AI/ML has been implemented in wastewater treatment in various industries; summary of which AI/ML languages were used along with a brief overview of how they work. This article focused on an understanding of the specific case studies and AI explanations to understand the effectiveness of wastewater treatment using AI/ML. 3) AI that is currently implemented in Oil and Gas such as personal protective equipment (PPE), smartwatches radiofrequency identification etc. This showed that AI/ML already plays a successful role in the Oil and Gas industry and has proven to help optimize many processes and components. Many of these AI/ML implementations are now industry standards and have greatly benefitted the industry. This section demonstrates that the implementation and the use of AI/ML have been proven to be a successful endeavor and that expanding the role of AI/ML would be beneficial. 5) Using the established points from the previous sections the paper outlines why AI/ML is ideal for Oil and Gas wastewater treatment; this argument centers around fit-for-purpose treatment, and the possible uses of the treated water. A determination of the best-suited AI languages, possible implementation scenarios, and how this would shift the way Oil and Gas industries treat and approach wastewater is presented to the reader.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores into cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

CO2 has characteristic properties and reactions at converter smelting temperature, and the chemical reaction between CO2 and elements such as C and Si in the molten pool has bubble proliferation and cooling effects, which can effectively improve the kinetic and thermodynamic conditions of converter smelting. Here, an experimental study and industrial test on the application of CO2 in converter smelting were carried out. The smelting effects of Mode-1 and Mode-2 with total CO₂ injection amounts of 229 Nm³ and 196 Nm³, respectively, were compared, and the changes in molten steel and slag compositions, dust removal, and gas are analyzed. The test results show that converter top and bottom blowing CO₂ technology (COMI-B)technology had significant metallurgical advantages over the N-Mode; the dephosphorization rate increased by 4.2%, slag (FeO) content was reduced by 2.04%, end point nitrogen content of molten steel was reduced by 20%, gas recovery increased by 8.29 Nm³/t, and soot production reduced by 14.7%. The results of the study provide reference for the application of COMI-B technology in converters in the iron and steel industry and develop a new path for resource utilization of CO₂.

Blockchain innovation stands out enough to be noticed and adopted in various countries and organizations around the world. Many businesses, including finance, medical services, inventory networks, security, libraries, and the internet of things, are currently under attack. For the benefit of the blockchain, many businesses incorporate blockchain technology into their frameworks. Despite its solidarity, blockchain faces a few challenges in security, protection, adaptability, and other areas. This paper examines the forward leap in blockchain innovation, as well as its applications and challenges. While many blockchain papers focus on digital currencies, IoT, and security, this paper focuses on the overall best in a class of blockchain innovation, its new twists and turns, and choices, particularly in areas other than cryptographic forms of money. The investigators' goal is to provide a thorough audit of the cryptography underlying blockchain to better understand the innovation. The examiners also conduct general research on people and venture blockchains, as well as future exploration opportunities and their implications for blockchain innovation.

Economic expedience of waste heat recovery systems (WHRS), especially for low temperature difference applications, is often questionable due to high capital investments and long pay-back periods. By its simple design isobaric expansion (IE) machines could provide a viable pathway to utilize otherwise unprofitable waste heat streams for power generation and particularly for pumping liquids and compression of gases. Different engine configurations are presented and discussed. A new method of modelling and calculation of the IE process and efficiency is used on IE cycles with various pure and mixtures as a working fluid. Some interesting cases are presented. It is shown in this paper, that the simplest non-regenerative IE engines are efficient at low temperature differences between a heat source and heat sink. Efficiency of non-regenerative IE process with pure working fluid can be very high approaching Carnot efficiency at low pressure and heat source/heat sink temperature differences. Regeneration permits to increase efficiency of the IE-cycle to some extent. Application of mixed working fluids in combination with regeneration permits to significantly increase the range of high efficiencies to much larger temperature and pressure differences.

Quantum cell automata (QCA) is a nanotechnology that uses a transistor-free structure to design digital circuits and overcome the limitations listed for CMOS circuits. The computing circuits that make up the bulk of computers can be designed and using this technology. Subtractors are a big part of computational circuits. This research deals with the design of a subtractor that due to the innovation in the design of this circuit and the novel arrangement of quantum cells of its kind has caused the number of cells used in the design of the circuit to reach its lowest value compared to previously designed circuits. The size of the circuit as well as the loss power are minimized compared to the circuits designed by the subtractors. Another trait clears of this circuit compared to many previous designs is its single-layer design, which reduces latency and noise in the output waveform. The circuit designed by the QCADesigner simulator is tested and simulated inadition the results are displayed in the simulated waveform. Also, the results of calculations for power dissipation in each QCA cell were evaluated by QCAPro software, which is a powerful tool for estimating power loss simulation. It has less implementation and level (single layer) than Previous designs.

The rapid growth of the Information and Communications Technology (ICT) sector requires additional infrastructure, such as more micro-datacenters and telecom stations, to support the higher internet speeds and low latency requirements of 5G net-works. The increased power requirements of the new ICT technologies necessitate the proposal of new power supplies in an attempt to retain the increase in energy demand and running costs. This work provides an in-depth theoretical analysis on the losses of the individual stages of commercially available PSU and proposes a new multicell PSU, Buck-PFC converter, which offers a higher overall efficiency at varying load levels. The theoretical results are verified using simulation results, via PSIM Thermal Module, and using experimental data. The results indicate that multi-cell structures can improve the overall PSU ef-ficiency by 1.2% at 50% rated power and more than 2.1% at full power. Finally, taking into consideration the economic implica-tions of this study, it is shown that the proposed multicell structure may increase the PSU costs by 10.78% but the payback pe-riod is in the order of just 3.3 years.

Recently, significant events took place that added immensely to the sociotechnical pressure for developing sustainable composite recycling solutions, namely (1) a ban on composite landfilling in Germany in 2009, (2) the first major wave of composite wind turbines reaching their End-of-Life (EoL) and being decommissioned in 2019-2020, (3) the acceleration of aircraft decommissioning due to the COVID-19 pandemic, and (4) the increase of composites in mass production cars, thanks to the development of high volume technologies based on thermoplastic composites. Such sociotechnical pressure will only grow in the upcoming decade of 2020s as other countries are to follow Germany by limiting and banning landfill options, and by the ever-growing number of expired composites EoL waste. The recycling of composite materials will therefore play an important role in the future, in particular for the wind energy, but also for aerospace, automotive, construction and marine sectors to reduce environmental impacts and to meet the demand. The scope of this manuscript is a clear and condensed yet full state-of-the-art overview of the available composite recycling technologies of both low and high Technology Readiness Levels (TRL). TRL is a framework that has been used in many variations across industries to provide a measurement of technology maturity from idea generation (basic principles) to commercialization. In other words, this work should be treated as a technology review providing guidelines for the sustainable development of the industry that will benefit the society. The authors propose that one of the key aspects for the development of sustainable recycling technology is to identify the optimal recycling methods for different types of composites. Why is that the case can be answered with a simple price comparison of E-glass fibers (~2 $/kg) versus a typical carbon fiber on the market (~20 $/kg) – which of the two is more valuable to recover? However, the answer is more complicated than that – the glass fiber constitutes about 90% of the modern reinforcement market, and it is clear that different technologies are needed. Therefore, this work aims to provide clear guidelines for economically and environmentally sustainable End-of-Life (EoL) solutions and development of the composite material recycling.

Brucellosis is one of the most common etiologies of laboratory-acquired infections worldwide, and handling of living brucellae should be performed in a Class II biological safety cabinet. The low infecting dose, multiple portals of entry to the body, the great variety of potentially contaminated specimens, and the unspecific clinical manifestations of human infections facilitate the unintentional transmission of brucellae to laboratory personnel. Work accidents such as spillage of culture media cause only a small minority of exposures, whereas >80% of events result from unfamiliarity with the phenotypic features of the genus, misidentification of isolates, and unsafe laboratory practices such as aerosolization of bacteria and working on an open bench without protective goggles or gloves. Although the bacteriological diagnosis of brucellae by traditional methods is simple, the Gram stain and the biochemical profile of the organism, as determined by commercial kits, can be misleading, resulting in inadvertent exposure and contagion. The use of novel identification technologies is not hazard-free. The MALDI-TOF technology requires an initial bacterial inactivation step, while the instruments’ reference database may misidentify Brucella as belonging to other Gram-negative species. The rapid identification by the FISH method mistakes brucellar isolates for members of the closely related Ochrobactrum genus.

The practice of collecting, treating and management of solid waste prior to disposal has become a necessity in developing and modern societies. Over the years, it is known that most wastes that are disposed have a second hand value. However, the construction cost for conventional Material Recovery Facility(s) (MRFs) has been a major barrier for implementation. These technologies require considerable technical expertise, which is often not available in developing nations to successfully operate the MRFs. Covenant University; a private mission institution through her waste to wealth scheme is focused on managing and processing used materials to reusable products. These include Pet bottles, Paper wastes, Food wastes from cafeteria, plastic food packs, nylon, tin cans and others. Specific areas chosen for the Survey include the residential areas for staff and students and the two cafeterias. The waste generated was characterized based on the waste stream so as to quantify the amount of recyclable waste generated and most occurring. The survey involved the use of structured questionnaires, on-site observations and measurements. The study reveals an average amount of recyclable waste generated per day in the institution as 13.46% pet bottles, 4.03% paper, 55.56% food waste, 12.64% plastic, 9.63% nylon and 4.68% tin cans. The study established that adequate waste characterization is a requirement for effective integrated solid waste management which would boost resource recovery, reuse and recycling.

The effect of the strain rate on damage in carbon black filled EPDM stretched during single and multiple uniaxial loading is investigated. This has been performed by analysing the stress-strain response, the evolution of damage by Digital Image Correlation (DIC), the associated dissipative heat source by InfraRed thermography (IR), and the chains network damage by swelling. The strain rates were selected to cover the transition from quasi-static to medium strain rate conditions. In single loading conditions, the increase of the strain rate yields in a preferential damage of the filler network while rubber network is preserved. Such damage is accompanied by a stress softening and an adiabatic heat source rise. Conversely, increasing the strain rate in cyclic loading conditions yields in a filler network accommodation and a high self-heating whose combined effect is proposed as a possible cause of the ability of filled EPDM to limit damage, by reducing cavities opening during loading and favoring cavities closing upon unloading.

The study sought to gauge the impact of COVID-19 pandemic in unleashing digital transformation in the education sector in South Africa. In order to gauge the impact, the study tracked the rate at which the 4IR tools were used by various institutions during the COVID-19 lockdown. Data were obtained from secondary sources, mainly newspaper articles, magazines and peer-reviewed journals. The findings are that, in South Africa, during the lockdown, a variety of 4IR tools were unleashed from primary education to higher and tertiary education where educational activities switched to remote learning (online learning). These observations point to the fact that South Africa generally has, some pockets of excellence to drive the education sector into the 4IR, which has the potential to increase access. Access to education, particularly at a higher education level, has always been a challenge due to a limited number of spaces available. Much as this pandemic has brought with it massive human suffering across the globe, there is an opportunity to assess successes and failures of deployed technologies, costs associated with them, and scaling these technologies to improve access.

(1) Background: Grapes and their associated by-products (as grape pomace, GP) stand out for their polyphenol content, which makes them a source of bioactive compounds with antioxidant capacity. The aim of this research was to determine if the inclusion of 50 g/kg of GP in the diet of hens could enrich egg with antioxidants and to study its effect on internal and external egg quality parameters. (2) Methods: A trial was conducted with two genetic lines of hens, which were fed either a control diet or a diet containing 50 g/kg of GP. Performance, internal and external egg quality, and egg yolk content of vitamins E and A, and gallic acid were determined; (3) Results: In eggs laid by hens fed GP diet, Haugh units and yolk colour score were enhanced, and eggshell became thinner, but without affecting the breaking strength. No dietary effect was observed on vitamin contents in yolk. Higher gallic acid content was observed in yolk of eggs laid by hens fed the GP diet, suggesting that some dietary phenolic compounds could be transferred to the eggs. Hen genetics influenced egg weight, albumen Haugh units, shell thickness and α- and γ-tocopherol concentration in yolks; (4) Conclusions: Dietary inclusion of GP improved the internal quality of eggs, enriching yolks with a phenolic compound, but reduced shell thickness.

Addressing the issue of low bonding strength in Ti/Mg laminated composites due to interfacial oxidation, this study employs a differential temperature rolling method using longitudinal induction heating to fabricate Ti/Mg composite plates. The entire process is conducted under an argon gas protective atmosphere, which prevents interfacial oxidation while achieving uniform deformation. The effects of reduction on the mechanical properties and microstructure of the composite plates are thoroughly investigated. Results indicate that as the reduction increases, the bonding strength gradually increases, mainly attributed to the increased mechanical interlocking area and a broader element diffusion layer. This corresponds to a transition from brittle to ductile fracture at the microscopic tensile-shear fracture surface. When the reduction reaches 47.5%, the Ti/Mg interfacial strength reaches 63 MPa, which is approximately a 20% improvement compared to the bonded strength with previous oxidation at the interface. Notably, at a low reduction of 17.5%, the bonding strength is significantly enhanced by about 1 time. Additionally, it is found that a strong bonded interface at high reduction is beneficial in hindering the propagation of interfacial cracks during tensile testing, enhancing the ability of the Ti/Mg composite plates to resist interfacial delamination.

Pyrops candelaria is one of the common pests of fruit trees, but the research on the pathogenic microorganisms it may carry is very limited. Therefore, it is essential to reveal the pathogenic microbes it carries and their potential hazards. This study found a new virus from the transcriptome of P. candelaria, which was first reported in P. candelaria and named as PyCaV (Pyrops candelaria associated virus). RACE and bioinformatics assay revealed that the full-length of PyCaV is 10855 bp with the polyA tail, containing a single open reading frame (ORF) encoding a polyprotein consisting of 3171 amino acid (aa). The virus has a typical Iflavirus structure, including two rhv domains, a RNA helicase domain (HEL), a 3C cysteine protease domain (Pro), and a RNA-dependent RNA polymerase domain (RdRp). Further phylogenetic analysis revealed that this virus belongs to family Iflaviridae and sequence alignements analysis suggedted PyCaV is new member in unassigned genus of family Iflaviridae. Further in-depth analysis of virus-infection-part showed that PyCaV is distributing in the whole longan lanternfly, including its head, chest, and abdomen, but more PyCaV was identified in the chest. The distribution of PyCaV in different parts of P. candelaria was further explored, which showed that more PyCaV were detected in its piercing-sucking mouthparts and chest viscera. Statistical analysis showed that the PyCaV infection was affected by time and locations.

Since the discovery of the phenomenon of surface-enhanced Raman scattering (SERS), it has gradually become an important tool for the analysis of material compositions and structures. The applications of SERS have been expanded from the fields of environmental and materials science to biomedicine, due to the extremely high sensitivity and non-destructiveness of SERS-based analytical technology that even allows single molecule detection. This article provides a comprehensive overview of Surface-Enhanced Raman Scattering (SERS) phenomenon. The content is divided into several main sections: basic principles and the significance of Raman spectroscopy; historical advancements and technological progress in SERS, as well as various practical applications across different fields. We also discussed how electromagnetic fields contribute to the SERS effect, the role of chemical interactions in enhancing Raman signals, modeling and computational approaches to understand and predict SERS effects.

The rebound behaviors of rubber balls based on ethylene-propylene-diene monomer (EPDM) / chlorinated butyl rubber (CIIR) blends were investigated systematically. A novel method was proposed to characterize the damping capacity of the EPDM/CIIR blends. The damping capacity can be represented by the rebound height of rubber balls, and lower rebound height corresponds to better damping capacity. A mathematical model expressed by an equation obtained through theoretical derivation has been proposed to predict the rebound height of the rubber balls. The energy dissipation rate (EDR) defined by the ratio of the height loss to the rebound time was proposed to further characterize the damping capacity. The EDR value shows the highest for the pure CIIR and lowest for the pure EPDM, showing a decrease trend with the increase of EPDM content in the rubber blends. By comparison one can obtain that the damping capacity of the EPDM/CIIR blends decreases with the decrease of external excitation, the conclusion of which plays a key role in the formulation design of viscoelastic damping rubber materials.

Background: Shared value creation in Base of the Pyramid (BoP) communities is a crucial process towards building sustainable societies. BoP communities in developing countries represent more than four billion people who live on low incomes with limited access to basic products and services. Current or emerging technologies offer promising solutions for organisations pursuing manufacturing opportunities in BoP communities. This study seeks to explore the literature on how BoP communities may become active participants in sustainably manufacturing products using micro-manufacturing factories. The research question posed is: What are the core concepts that need to be taken into consideration for creating shared value through micro-manufacturing factories in BoP communities? Method: A Systematised Literature review (SLR) was completed following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) method for data selection criteria and analysis. The SLR is used to explore the state of literature with regards to creating manufacturing shared value in BoP communities with the objective to identify study gaps and to explore shared value creation concepts. Results: Literature indicates BoP initiatives that have pursued to engage BoP communities through various innovation strategies. The findings of the review is organised under three strategic pillars: Capability building strategy, Implementation process, and Growth strategy. The capability building strategy defines the users’ intention to create shared value in BoP communities with Micro-manufacturing factories (MMF). It is followed by the implementation process which guides the users to create manufacturing shared value in BoP communities. This is followed by a growth strategy to scale for impact.

Osteoarthritis of the hand (HOA) is one of the most common musculoskeletal diseases affecting health. Osteophytes, documented by imaging procedures are often used as diagnostic criteria. The gold standard is the radiographic image, however, more sensitive and saver methods such as ultrasound are gaining in importance. We investigated the prevalence, grade and pattern of osteophytes by high resolution ultrasound investigation (US) in a population-based cross-sectional study. Factory workers were recruited at work. 26 finger joints of each participant were examined with US with grading the occurrence of osteophytes in a semiquantitative score ranging 0-3, increasing scores indicated larger osteophytes. 427 participants (mean age 53.5 years, range. 20-79 years) were investigated, resulting in 11,000 scored joints. At least one osteophyte was found in 4,546/11,000 (41.3%) joints or 426/427 (99.8%) participants but grade 2 or 3 osteophytes were seen in only 553 (5.0%) joints. The number of os-teophyte sum score increased with age (0.18/year of age increase, p<0.001). Distal interphalange-al joints (DIP) were predominantly affected with 61%, followed by proximal interphalangeal joints (PIP) with 48%, carpometacarpal joint 1 (CMC1) with 39% and metacarpophalangeal joints (MCP) with 16%. In conclusion, US is a feasible screening tool for osteophytes and HOA. Grade 1 osteophytes are a common finding in the working population when assessed by ultrasound and their prevalence is increasing with age. Prevalence of grade 2 or 3 osteophytes is less common and reflects clini-cal presence of HOA. Follow up investigations are necessary to recognize the predictive rele-vance of early osteophytes.

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.

Telecoupling, interactions between social-ecological systems across large, often global, distances, drives negative impacts from the forest-based carbon market. Yet, these negative impacts have been underreported; and therefore, likely vastly underestimated. We identify how these unintended negative impacts may occur and provide recommendations for the forest-based carbon market to better account for externalities by prioritising positive social impact, expanding comprehensive ecological outcomes and improving transparency of investments. We call for a carbon market that is designed to account for its global interconnectedness.

Product attribute extraction is an growing field in e- commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Under- standing the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

The volume of fluid (VOF) method is a popular technique for the direct numerical simulations of flows involving immiscible fluids. A discrete volume fraction field evolving in time represents the interface, in particular, to compute its geometric properties. The height function method (HF) is based on the volume fraction field, and its estimate of the interface curvature converges with second-order accuracy with grid refinement. Data-driven methods have been recently proposed as an alternative to computing the curvature, with particular consideration for a well-balanced input data set generation and symmetry preservation. In the present work, a two-layer feed-forward neural network is trained on an input data set generated from the height function data instead of the volume fraction field. The symmetries for rotations, reflections, and the anti-symmetry for the phase swapping have been considered to reduce the number of input parameters. The neural network, establishing a correlation between curvature and height function values, can efficiently predict the local interface curvature. We compare the trained neural network to the standard Height Function method to assess its performance and robustness.

This study investigates the possible mechanisms related to the wide range of relative humidity in cirrus clouds (RHi). Under the closed adiabatic assumption, the impacts of vertical motion and ice crystal deposition/sublimation on RHi are investigated through in situ observations and parcel model simulations. Vertical motion is an active external force that changes the RHi, and ice crystal deposition/sublimation plays a role in mitigating the change in RHi. They are the two most important mechanisms involved in controlling the RHi fluctuation during cirrus evolution and could well explain the wide range of RHi in wave-related cirrus clouds. Furthermore, a comparison of cloud statistical characteristics from both observations and simulations shows that a very low value (e.g., 0.001) for the water vapor ice deposition coefficient is highly unlikely.

In this study, the multi-relaxation-time lattice Boltzmann method is applied to investigate the oscillatory instability of lid-driven flows in two-dimensional semi-elliptical cavities with different vertical to horizontal aspect ratios K in the range of 1.0--3.0. The program implemented in this study is parallelized using CUDA (compute unified device architecture), a parallel computing platform, and computations are carried out on NVIDIA Tesla K40c GPU. To carry out precise calculations, the CUDA algorithm is extensively investigated, and its parallel efficiency indicates that the maximum speedup is 47.6 times faster. Furthermore, the steady--oscillatory Reynolds numbers are predicted by implementing the CUDA-based programs. The amplitude coefficient is defined to quantify the time-dependent oscillation of the velocity magnitude at the monitoring point. The simulation results indicate that the transition Reynolds numbers correlate negatively with the aspect ratio of the semi-elliptical cavity, and are smaller than those of the rectangular cavity at the same aspect ratio. In addition, the detailed vortex structures of the semi-elliptical cavity within a single period are also investigated when the Reynolds number is larger than the steady--oscillatory value to determine the effects of periodic oscillation of the velocity magnitude.

The Particle-In-Cell (PIC) method has been developed in order to investigate microscopic phenomena, and with the advances of computing power, newly developed codes have been used for several fields such as astrophysical, magnetospheric, and solar plasmas. PIC applications have grown extensively with large computing powers available on supercomputers such as Pleiades and Blue Waters in the US. For astrophysical plasma research PIC methods have been utilized for several topics such as reconnection, pulsar dynamics, non-relativistic shocks, relativistic shocks, relativistic jets, etc. PIC simulations of relativistic jets have been reviewed with the emphasis on the physics involved in the simulations. This review summarizes PIC simulations, starting with the Weibel instability in slab models of jets, and then focuses on global jet evolution in helical magnetic field geometry. In particular we address kinetic Kelvin-Helmholtz instabilities and mushroom instabilities.

Chlorotoxin (CTX) is a 36–amino acid peptide with 8 Cys residues that forms four Cys-Cys bonds. It has high affinity for the glioma-specific chloride channel and matrix metalloprotease-2. Structural and binding properties of CTX analogs with various Cys residue substitutions with L-a-aminobutyric acid (Abu) have been previously reported. Using 4.2 ìs molecular dynamics, we compared the conformational and essential space sampling of CTX and analogs [Abu/Ser^2,19]CTX, [Abu/Ser^5,28]CTX, [Abu/Ser^16,33]CTX, [Abu/Ser^20,35]CTX, and [Abu/Ser^{2,5,16,19,20,28,33,35}]CTX. The native and substituted peptides maintained a high degree of a-helix propensity from residues 8 through 21, with the exception of [Ser^5,28]CTX and [Abu^16,33]CTX. In agreement with previous circular dichroism spectropolarimetry results, the C-terminal b-sheet content varied less from residues 25 through 29 and 32 through 36 and was well conserved in all analogs, except: [Abu^16,33]CTX, [Ser^20,35]CTX, [Abu^{2,5,16,19,20,28,33,35}]CTX, and [Ser^{2,5,16,19,20,28,33,35}]CTX. The Cys¹⁶-Cys³³ and Cys²⁰-Cys³⁵ Cys-Cys bonds appear to be required to maintain the ab-motif of CTX. Their selective substitution with Ser^16,33 however, may mitigate the destabilizing effect of Cys¹⁶-Cys³³ substitution by the formation of an inter residue H-bond from OgH of Ser¹⁶ to OgH of Ser³³ bridged by a water molecule. All peptides shared considerable sampled conformational space, which explains the retained receptor binding of the nonnative analogs.

In the present paper, laminar forced convection nanofluid flows in a uniformly heated horizontal tube were revisited by direct numerical simulations. Single and two-phase models were employed with constant and temperature-dependent properties. Comparisons with experimental data showed that the mixture model performs better than the single-phase model in the all cases studied. Temperature-dependent fluid properties also resulted in a better prediction of the thermal field. A particular attention was paid to the grid arrangement. The two-phase model was used then confidently to investigate the influence of the nanoparticle size on the heat and fluid flow with a particular emphasis on the sedimentation process. Four nanoparticle diameters were considered: 10, 42, 100 and 200 nm for both copper-water and alumina/water nanofluids. For the largest diameter dnp = 200 nm, the Cu nanoparticles were more sedimented by around 80 %, while the Al₂O₃ nanoparticles sedimented only by 2.5 %. Besides, it was found that increasing the Reynolds number improved the heat transfer rate, while it decreased the friction factor allowing the nanoparticles to stay more dispersed in the base fluid. The effect of nanoparticle type on the heat transfer coefficient was also investigated for six different water-based nanofluids. Results showed that the Cu-water nanofluid achieved the highest heat transfer coefficient, followed by C, Al₂O₃, CuO, TiO₂, and SiO₂, respectively. All results were presented and discussed for four different values of the concentration in nanoparticles, namely φ = 0, 0.6, 1 and 1.6%. Empirical correlations for the friction coefficient and the average Nusselt number were also provided summarizing all the presented results.

We conducted a study exploring the potential of the medicinal plant Ziziphus spina-christi as a source of antimicrobial compounds. With the growing threat of antibiotic resistance, we were particularly interested in evaluating plant-derived natural products for their ability to fight infections. We collected Z. spina-christi leaves from four different regions in Sudan and extracted them with ethanol. Our phytochemical screening revealed the presence of several secondary metabolites, including flavonoids, triterpenes, sterols, tannins, saponins, cardiac glycosides, and reducing sugars. Alkaloids and anthraquinones were not detected. We then tested the ethanolic extracts for antimicrobial activity against a range of bacterial and fungal pathogens using the agar well diffusion method. Interestingly, we observed significant differences between the extracts from different locations. The extract from Khartoum city exhibited the highest antimicrobial potency, followed by the extracts from Al-Rahad, Al-Hidiba, and finally Al Damazin. Based on these findings, we concluded that Z. spina-christi leaves contain a diverse array of phytochemicals with potential medicinal applications. The variation in antimicrobial effects between the extracts may be attributed to environmental factors influencing the plant's chemical composition. Further research is warranted to identify the specific bioactive compounds responsible for the observed antimicrobial activities. This could pave the way for the development of plant-based antimicrobial agents to combat drug-resistant pathogens and alleviate the global public health crisis posed by the rise of antibiotic resistance.

The vibrant landscape of Malaysia serves as a dynamic backdrop for the convergence of artificial intelligence (AI) and social science, reshaping knowledge and inquiry amidst tradition and modernity. With Malaysia leading a technological renaissance driven by digital transformation and innovation, the ethical implications of AI-driven decision-making in public policy and governance emerge as pressing research problems. Addressing concerns such as algorithmic bias and discrimination requires interdisciplinary collaboration to ensure transparency, accountability, and fairness. Exploring questions regarding the distribution of welfare benefits and strategies for enhancing transparency and accountability in urban planning underscores the need for contextually relevant frameworks and stakeholder engagement. Through comprehensive research objectives, including investigating algorithmic biases and analyzing their social implications, scholars aim to contribute to a deeper understanding of the complex interplay between AI technology, social dynamics, and public policy. As Malaysia navigates the challenges and opportunities presented by AI deployment, prioritizing interdisciplinary collaborations, longitudinal studies, and capacity-building efforts will be essential for shaping a future that upholds principles of social justice and collective well-being.

In an era of rapid technological advancement, Egypt stands at the crossroads of digital transformation. Our research delves into the intersection of economy and 5G technology, exploring how these forces can reshape the nation’s economic landscape. By examining the impact on GDP, we aim to guide policymakers, telecom operators, and citizens toward a more connected and prosperous future. Join us on this journey as we unravel the potential of 5G and pave the way for inclusive growth.

Healthcare sector is a truly dynamic and multidisciplinary system. Progressive practice of computational technologies in healthcare has enhanced electronic therapeutic measures in the care units. The healthcare decision-makers are accenting on the operational usage of digital technology in the system, to improve the health service by reducing human error and excessive load also by increasing possibility to attend more patients simultaneously. Sophisticated medical instruments are using advanced robotics, artificial intelligence to enhance their efficacy. Virtual treatments, self-diagnostics is increasing over time with the advancement of technical support in health. Electronic Health Records (EHR) of patients has created huge medical data (clinical information), which is undoubtedly a good resource for health professionals to take critical decisions and implement them accordingly. The aim of this article is to elucidate how technological developments are associated with clinical improvements in terms of constructing an upgraded health ecosystem. It is imperative to analyze how recent digital paradigms in health are helping to tackle regular and rare clinical challenges.

Core-shell particles are heterogenous micro- or nanoparticles with solid, liquid or gas core encapsulated by a protective solid shell. The unique composition of core and shell materials imparts smart properties to the particles. Core-shell particles are gaining increasing attention as tuneable and versatile carriers for pharmaceutical and biomedical applications including targeted drug delivery, controlled drug release, and biosensing. This review first provides an overview of fabrication methods for core-shell particles, followed by a brief discussion on their application and a detailed analysis on manipulation including assembly, sorting, and triggered release. We compile current methodologies employed for manipulation of core-shell particles and demonstrate how existing methods of assembly and sorting micro/nanospheres can be adopted or modified for core-shell particles. Various triggered release approaches for diagnostics and drug delivery are also discussed in detail.

In this paper, we address the problem of compatibility between digital signature schemes and in-network aggregation approaches. In the IoT world, the gateways alter the signed network flows when performing in-network aggregation. Therefore, existing conventional approaches are not suitable for verifying the authenticity of the original flows. This raises the need for energy-effective and secure schemes that enable the destination to validate aggregated network flows. In this regard, a lightweight homomorphic signature scheme is proposed which supports the implementation of aggregation procedures without affecting the verification process. We demonstrate the unforgeability and the privacy of our scheme. We also perform an analytical study of its energy-efficiency. The results suggest that the proposed scheme considerably decreases the processing overhead of the existing set-homomorphic signature schemes. Moreover, it does not add any communication overhead to traditional (non-homomorphic) signature schemes. This, in turn, improves the energy consumption by 30% compared to existing homomorphic signature techniques.

(1) Background: Spain launched an official campaign, #EsteVirusLoParamosUnidos, to try and unite the efforts of the entire country through citizen cooperation to combat coronavirus. The research goal is to analyze the Twitter campaign’s repercussion on general citizen feeling. (2) Methods: The research is based on a composite design that triangulates from a theoretical model, a quantitative analysis and a qualitative analysis. (3) Results: Of the 7357 tweets in the sample, 72.32% were found to be retweets. Four content families were extracted: politics, education, messages to society and defense of occupational groups. The feelings expressed ranged along a continuum, from unity, admiration and support at one end to discontent and criticism regarding the health situation at the other. (4) Conclusions: The development of networked sociopolitical and technical measures that enable citizen participation facilitates the development of new patterns of interaction between governments and digital citizens, increasing citizens’ possibilities of influencing the public agenda and therefore strengthening citizen engagement vis-à-vis such situations.

Digital Evidence is considered as an important type of evidence in many legal cases. Many legislations have dedicated laws to the collection, handling and admissibility of digital evidence. New technologies and new devices are rapidly being developed, which creates new sources of digital evidence. This presents a challenge to law enforcement agencies and digital investigators to stay up to date with the rapid development in the digital field. This paper discusses a relatively new source of digital evidence which is the evidence extracted from Wearable devices. A Fitbit fitness tracker is one of the most common wearable devices used by many people today. This paper presents a case study whereby data extracted from a Fitbit was used as a digital evidence. The admissibility and the challenges of using Wearables as digital evidence is also discussed.

The clustering methods of Software Defined Networking (SDN) gained popularity due to their ability to offer improved scalability, consistency, dependability, and load balancing within overlay networks and SDN partitions. This paper delved into the effects of increasing the number of OpenFlow-enabled southbound devices on the establishment and coordination of SDN-Controller clusters. Specifically, we examined the volume of east-west cluster packet communication concerning the number of southbound devices within the topology. Many research studies focused on bandwidth and the number of bytes in East-West communication. While bandwidth referred to the maximum rate at which data could be transferred, and the number of bytes reflected the volume of data being transmitted, the number of packet communications directly influenced the efficiency and responsiveness of network operations. Our investigation encompassed the impact of SDN controllerto- controller communication within the cluster concerning the rising number of OpenFlow switches connected to various topologies, including tree (star-bus network), linear, and torus configurations. This study presented data on communication patterns within Open Network Operating Systems (ONOS) and OpenDaylight (ODL) clusters, revealing differing levels of controller communication with southbound network expansions. We evaluated the scalability of ODL and ONOS controllers by scrutinising the effect of increasing the number of southbound devices on control communication volume. Our analysis revealed varied communication patterns within ONOS and ODL clusters, resulting in different volumes of control communication with southbound expansions. The findings indicated that in small to medium-sized SDNs, ODL outperformed ONOS, notably with faster cluster discovery. Conversely, ONOS demonstrated greater efficiency in larger networks owing to its centralised communication architecture. Finally, we provided recommendations for selecting the most suitable controllers based on the size of southbound networks, aiming to provide practical guidelines for optimal network performance.

In this study, 3D printing of poly-l-lactic acid (PLLA) scaffolds reinforced with graphene oxide (GO) nanoparticles via Digital Light Processing (DLP) was investigated to mimic bone tissue. Stereolithography is one of the most accurate additive manufacturing method, but the dominant available materials used in this method are toxic. In this research, a biocompatible resin (PLLA) was synthetized and functionalized to serve the purpose. Due to the low mechanical properties of the printed product with the neat resin, graphene oxide nanoparticles in three levels (0.5, 1, and 1.5 Wt.%) were added with the aim of enhancing the mechanical properties. At first, the optimum post cure time of the neat resin was investigated. Consequently, all the parts were post-cured for three hours after printing. Due to the temperature-dependent structure of GO, all samples were placed in an oven at 85 ° C for different time periods of 0, 6, 12, and 18 hours to increase mechanical properties. The compression test of heat treated samples reveals that the compressive strength of the printed parts containing 0.5,1, and 1.5 % of GO increased by 151,162 ad 235%, respectively. Scaffolds with the designed pore sizes of 750 microns and a porosity of 40% were printed. Surface hydrophilicity test was performed for all samples showing that the hydrophilicity of the samples increased with increasing GO percentage. The degradation behavior of the samples was evaluated in a PBS environment, and it revealed that by increasing GO, the rate of component degradation increased, but the heat treatment had the opposite effect and decreased the degradation rate. Finally, besides improving biological properties, a significant increase in mechanical properties under compression can introduce the printed scaffolds as a suitable option for bone implants.

Graphene foam prepared by chemical vapor deposition method is a promising thermal interfacial material. However, the thermal properties of graphene foam highly depend on the experimental fabrication conditions during the chemical vapor deposition process. Aiming to reveal how to prepare the appropriate graphene foam for the various thermal management scenarios, the influence of experimental conditions on thermal properties of graphene foam is investigated. Furthermore, the contribution of thermal conductivity and thermal radiation to the effective thermal coefficient of graphene foam is carried out for comparison. Research results show that, the porosity and the cross-section shape of struts of growth template are two critical factors to affect the thermal transport of graphene foam, especially with the increase of temperature. In addition, the deposition time of graphene determines the wall thickness and affects the thermal conductivity directly. The thermal radiation contributes more than thermal conductivity when the temperature climbs continuously. Comparatively, the effective thermal coefficient of graphene foam composite with high porosity and circular shape struts is much superior to others at high temperature. The research findings can provide important guidance for the graphene foam fabrication and its applications in the field of thermal management.

Special machinery engineering is of great importance to the manufacturing industry and makes a comparatively large contribution to many economies. Digital transformation is already well advanced in many producing industries, and modern ICT technologies such as agents, service orientation, digital twins and artificial intelligence are being used with increasing success. In addition to improving specific product characteristics such as reliability or flexibility, the adoption of modern ICT technologies to ensure sustainability is being intensively discussed. So far, however, there has been little uptake of these technologies in the special machinery industry; sustainability receives little attention. This article examines in detail the reasons for and impediments to the adoption of modern ICT technologies based on a study among special machinery manufacturers. Observations of existing challenges were gathered during daily work, described in detail, and used to derive conclusions about causal barriers. From this, detailed requirements are derived to promote the adoption of modern ICT technologies in the special machinery engineering sector and, ultimately, to bring sustainability more into focus.

Grain shape and chalkiness are important appearance quality traits of rice, which also affect the yield and milling quality. In this study, 190 F2 families derived from the cross between Hua 5178S and Hua 611 were sequenced to construct a genetic linkage map. Combined with F2 and F2:3 generations grain shape and chalkiness phenotype, a total of 15 QTL were detected. There are 8 grain shape QTL distributed on chromosomes 2, 5, 6, 8, and 10; 7 chalkiness QTL distributed on chromosomes 2, 3, and 6. A QTL cluster affecting multiple chalkiness traits was found on chromosome 6, qWBR6.1, qWCR6 and qCR6 were selected for genetic effect verification. It was found that qWBR6.1, qWCR6 and qCR6 explained 13% and 24%, and 29% of the phenotypic variations respectively, and qWCR6 and qCR6 were co-mapped. This study laid a foundation for further cloning of rice appearance quality genes.

The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scale offering potential models of technology progression and disruption. This paper focuses on how the potential theories of technology progression can be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects (particularly considering these geographically) might be derived, based on the use of big data analytic techniques. Given the magnitude of the economic, social and political implications of many disruptive technologies, the ability to quantify disruptive change at the earliest possible stage could deliver major returns by reducing uncertainty, assisting public policy intervention and managing the technology transition through disruption into deployment. However, determining when this stage has been reached is problematic because small random effects in the timing, direction of development, the availability of essential supportive technologies or “platform” technologies, market response or government policy can all result in failure of a technology, its form of adoption or optimality of implementation. This paper reviews some of the key models of technology evolution and their disruptive effect including, in particular, the geographical spread of disruption. It suggests a methodology for utilising the recent explosion of open and web-discoverable data to determine a methodology to achieve this earlier determination and considers the potential exploitation of big data modelling and predictive analytical techniques to achieve this goal.

Cooperation is essential in social life, involving collaborative efforts for mutual benefit. Individual differences in the cooperativeness trait are pivotal in these interactions. Previous research suggested that Duchenne smiles signal cooperative intent, and human-like eyes are theorized to enhance cooperation evolutionarily. However, the relationships between cooperative behavior, cooperativeness and possible behavioral cues signaling cooperative behavior are yet to be studied. In the current study, we explored the relationships among cooperativeness, Duchenne smiling with gaze, IBS during conversation, and their impacts on cooperative behavior. We hypothesized Duchenne smiling with gaze would mediate cooperativeness’ impact on cooperative behavior. Additionally, using functional near-infrared spectroscopy (fNIRS), we evaluated inter-brain synchrony (IBS) in the left prefrontal region, expecting it to predict cooperative behavior. The results demonstrated that cooperativeness significantly predicted Duchenne smiling with gaze and cooperative behavior, however, Duchenne smiling with gaze did not mediate the relationship between them. We further conducted a path analysis to examine the role of IBS during conversation in successive cooperative behavior. The path analysis result showed that cooperativeness directly affected cooperative behavior. Cooperativeness significantly predicted Duchenne smiling with gaze, however, neither Duchenne smiling with gaze nor IBS during conversation predicted successive cooperative behavior. These results suggest dispositional factors like cooperativeness may play a more decisive role than momentary expressional cues or neural synchrony in naturalistic unstructured communication in shaping cooperative behavioral outcomes after the communication. The study highlights how personality traits like cooperativeness shape nonverbal communication and social interactions.

The use of pesticides such as rotenone can lead to toxicity, dysfunction and neuronal death in humans. The Copaifera Reticulata Ducke Oil-Resin (CPOR) is widely used by the natives of the Amazon, however, studies on its potential neuroprotective effect are lacking. This study investigated the cytoprotective effect of CPOR on total cell cultures of the ventral midbrain (VMC), hippocampus (HC) and cerebral cortex (CCC) exposed to rotenone at different concentrations. The total cell cultures of VMC, HC and CCC were exposed for 72 or 24 hours to rotenone, CPOR and rotenone + CPOR. Cell viability was measured using the MTT test and compared using one-way ANOVA. Exposure to rotenone reduced the cell viability of CMV, HC and CCC. Exposure to CPOR reduced the cell viability of CMV. The cell viability of CMV exposed simultaneously to rotenone and CPOR was higher compared to rotenone alone. The cell viability of HC exposed simultaneously to rotenone and CPOR was higher than that exposed to rotenone. CMVs are more vulnerable to rotenone toxicity and more sensitive to CPOR. Considering simultaneous exposure, CPOR revealed a possible neuroprotective effect against rotenone damage only for VMC and HC.

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.

Digital transformation of agriculture can support economic growth and food and nutri-tion security in Africa. This study provides an overview of the status of digital agriculture in five west African countries, analyzing their efforts in developing the enabling environment and inno-vations while formulating recommendations based on the identified gaps for the effective trans-formation of the sector. Information was retrieved through a literature search from various sources, including web pages and databases of national agricultural and digital transformation institutions and start-ups of the five target countries (Benin, Burkina Faso, Côte d'Ivoire, Ghana, Nigeria) and regional/international institutions. There have been increasing agri-digital initia-tives in the five countries, which were grouped into seven categories based on their objectives. Steady progress was also observed in mobile internet adoption, despite the differences in deploy-ing crucial infrastructure to promote digital agriculture. The mobile connectivity index (MCI) in all five countries is below 60. Nonetheless, Ghana and Cote d'Ivoire demonstrated more efforts in internet and electricity access, especially in rural areas. Benin and Nigeria have developed sep-arate documents depicting the roadmap for Digital Agriculture, while the other countries are working to create one or have it embedded in their national development plans. Similarities and specificities exist among countries for laws and processes protecting Agri-digital innovators. To be competitive and self-reliant in the global e-economy, these countries must reposition them-selves to accelerate changes in digital agriculture through effective governance and synergy of actions in different sectors and across nations.

Abstract Incretin mimetics, also known as glucagon-like peptide-1 (GLP-1) receptor agonists, are a class of medications used to treat type 2 diabetes by mimicking the actions of incretin hormones in the body. These hormones are responsible for promoting insulin release from the pancreas in response to nutrient intake, as well as decreasing glucagon secretion, slowing gastric emptying, and promoting satiety. By imitating these actions, incretin mimetics help to regulate blood sugar levels in individuals with diabetes and glucose intolerance. One of the key benefits of incretin mimetics is its ability to lower blood sugar levels without causing hypoglycemia. This is especially important for individuals with diabetes who may experience dangerous drops in blood sugar levels when using other medications. Additionally, incretin mimetics have been shown to promote weight loss in some individuals, making them a valuable option for those struggling with obesity in addition to diabetes. Incretin mimetics are administered by injection, once or twice daily, depending on the specific medication. They are often combined with other diabetes medications, such as metformin or insulin, for optimal blood sugar control. These medications are generally well-tolerated, with common side effects including nausea, vomiting, and diarrhea, which usually diminish over time as the body adjusts to the medication. Studies have shown that incretin mimetics can help improve cardiovascular outcomes in individuals with diabetes, including reducing the risk of heart attacks and strokes. This is particularly significant given the increased risk of cardiovascular disease in individuals with diabetes. Incretin mimetics also have been shown to preserve pancreatic beta-cell function, which is responsible for producing insulin. This can help slow the progression of diabetes and reduce the need for higher insulin doses over time. Despite their many benefits, incretin mimetics are not without limitations. They can be expensive compared to other diabetes medications, which may make them less accessible for some individuals. Additionally, there have been concerns about the potential risk of pancreatitis and pancreatic cancer associated with the use of these medications. However, more research is needed to fully understand these risks. It is important for healthcare providers to carefully assess each individual's unique needs and medical history when considering the use of incretin mimetics. They may not be appropriate for everyone, particularly those with a history of pancreatitis or pancreatic cancer. Patients should also be educated about these medications' proper administration and potential side effects to ensure their safety and efficacy. Overall, incretin mimetics have emerged as an important therapeutic option for individuals with type 2 diabetes, offering a unique mechanism of action that can help improve blood sugar control, promote weight loss, and reduce cardiovascular risk. With ongoing research and development in this field, incretin mimetics continue to show promise as valuable tools in managing diabetes and its associated complications and comorbidities.

Businesses use technological and social media marketing to respond to the Covid-19 pandemic. This study aims to understand better the factors that impact the spread of new technologies, the effectiveness of social media advertising, and the longevity of businesses. The study used a quantitative strategy based on partial least squares structural equation modeling to learn about the phenomenon of interest as much as possible. Dhaka, Bangladesh, small and medium-sized businesses participated in the survey. According to the findings, the popularity of internet and e-business technologies may be attributed to their apparent usefulness and ease of use. It is connecting internet/e-business technology, social media marketing, and the long-term success of small and medium-sized enterprises. Still, the expense factor was insufficient to prove that SMEs engage in social media advertising. Mediating effects between components may be better understood via internet/e-business technology and social media marketing, as shown by this study. This study is the first of its kind in Bangladesh and sheds light on the resilience of SMEs in the face of the Covivirus-19 pandemic, notwithstanding its focus on the capital city of Dhaka.

This study introduces a novel computational approach for testing conditional independence (BB CI test) within causal Directed Acyclic Graphs (DAGs), leveraging Bayesian non-parametric bootstrap and machine learning techniques. Our method offers an alternative for validating the assumptions underpinning causal DAGs. Through simulation studies and an industrial case analysis, we demonstrate the test procedure in accurately assessing conditional independence, comparing it with the Generalized Covariance Measure (GCM) test. Our findings suggest that the BB CI test is advantageous in scenarios where existing methods may falter due to violations of model assumptions. This research contributes to the causal inference literature by providing a computational tool for researchers and practitioners to validate causal models.

Globalization is a basic feature of contemporary digital society related to increasing level of informatization, electroniztion and communications through the global network and share different information. Regardless of the many advantages of the digital age, related to the intellectualization of various activities and objects (e-governance, e-health, social computing, smart home, smart city, etc.), the need for secure protection of users' privacy should not be overlooked. In this reason, the goal of the article is to determine the specific features of the main components of the globalization and the impact on the privacy and data protection. Based on the analysis of possible impacts the main recommendation for adequate data security and privacy protection are determined.

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

In an era characterized by the ever-shifting landscapes of the digital realm, this paper intricately examines the interplay between digital culture, cultural dynamics, and beliefs in academia. Utilizing insights from cutting-edge language models, the study uncovers nuanced cultural expressions among Arts and Sciences students navigating the digital domain. Employing qualitative methods including interviews, observations, in-depth literature reviews, and thematic analysis, findings highlight digital interactions' transformative impact on cultural narratives and the potential of digital ethnography as a lens for understanding intricate cultural phenomena. This research enriches digital ethnography and cultural dynamics, offering fresh perspectives on the symbiotic relationship between digital culture and traditional beliefs, underscored by data from a maximum purposeful sampling of thirty (30) participants. Overall, this exploration emphasizes digital culture's dynamic role in shaping and reflecting cultural convictions, contributing to the understanding of cultural dynamics and evolving digital ethnography in the academic landscape.

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

Digital technologies are becoming increasingly relevant in the processes of documenting, surveying, enhancing and preserving Cultural Heritage, also through applications that are increasingly optimized and oriented towards new data management and accessibility processes. This great potential can find significant levels of application in the tourism sector, seeking new strategies to access, discover, understand cultural assets. In this direction, many digital applications have already found interesting outcomes in the tourism sector, but there is large room for improvement in the applications for “minor”, small or neglected cultural sites, not included in conventional tourism routes, which play a key-role in social inclusion and territorial cohesion, and for the development of social, economic and environmental sustainability. The paper presents a conceptual framework or possible outline to foster the use of digital technologies through a set of integrated bottom-up and top-down actions, to facilitate connections of minor sites into larger networks, contributing to search for new forms of active fruition and social participation.

As the digital era advances, many industries continue to expand their use of digital technologies to support company operations, notably at the customer interface, bringing new commercial opportunities and increased efficiencies. However, there are new sets of responsibilities associated with the deployment of these technologies, encompassed within the emerging concept of corporate digital responsibility (CDR), which to date has received little attention in the academic literature. This exploratory paper thus looks to make a small contribution to addressing this gap in the literature. The paper adopts a qualitative, inductive research method, employing an initial scoping literature review followed by two case studies. Based on the research findings, a simple model of CDR parameters is put forward, and the article concludes with a discussion of a number of emergent issues - fair and equitable access, personal and social well-being, environmental implications, and cross-supply chain complexities - that suggest possible directions for future research.

The inverse Finite Element Method (iFEM) is a model-based technique to compute the displacement (and then the strain) field of a structure from strain measurements and a geometrical discretization of the same. Different literature works exploit the error between the numerically reconstructed strains and the experimental measurements to perform damage identification in a Structural Health Monitoring framework. However, only damage detection and localization are performed, without attempting a proper damage size estimation. The latter could be based on machine learning techniques, however, an a priori definition of the damage conditions would be required. To overcome these limitations, the present work proposes a new approach in which the damage is systematically introduced in the iFEM model to minimize its discrepancy with respect to the physical structure. This is performed with a maximum likelihood estimation framework, where the most accurate damage scenario is selected among a series of different models. The proposed approach is experimentally verified on an aluminum plate subjected to fatigue crack propagation, which enables the creation of a Digital-Twin of the structure itself. The strain field fed to the iFEM routine is experimentally measured with an Optical Backscatter Reflectometry fiber and the methodology is validated with independent observations of lasers and the Digital Image Correlation.

Background and Aims: Nurses are increasingly engaging with digital technologies to enhance safe, evidence-based patient care. Digital literacy is now considered a foundational skill and an integral requirement for lifelong learning, and includes the ability to search efficiently, critique information and recognise the inherent risk of bias in information sources. However, at many universities, digital literacy is assumed. In part, this can be linked to the concept of the Digital Native, a term first coined in 2001 by the US author Marc Prensky, to describe young people born after 1980 who have been surrounded by mobile phones, computers, and other digital devices their entire lives. The objective of this paper is to explore the concept of the Digital Native and how this influences undergraduate nursing education. Materials and Methods: A pragmatic approach has been used for this narrative review, working forward from Prensky’s definition of the Digital Native and backward from contemporary sources of information extracted from published health, education, and nursing literature. Results: The findings from this narrative review will inform further understanding of digital literacy beliefs and how these influence undergraduate nursing education. Recommendations for enhancing the digital literacy of undergraduate nursing students are also discussed. Conclusions: Digital literacy is an essential requirement for undergraduate nursing students and nurses, and is linked with safe, evidence-based patient care. The myth of the Digital Native negates the reality that exposure to digital technologies does not equate with digital literacy and has resulted in deficits in nursing education programs. Digital literacy skills should be a part of undergraduate nursing curricula, and National Nursing Digital Literacy competencies for entry into practice as a Registered Nurse should be developed and contextualised to individual jurisdictions.

TRPOMI instrument aboard Sentinel-5P is a relatively new, high-resolution source of information about atmosphere composition. One of the primary atmospheric trace gases that we can observe through it is nitrogen dioxide. By now, we were using the chemical weather model (GEM-AQ) as a mean for estimating nitrogen dioxide concentration on a regional scale. Although well established in atmospheric science, the GEM-AQ simulations were always based on emission data, which in the case of the energy sector were reported by stack owners. In this paper, we attempted to compare the TROPOMI and GEM-AQ derived VCDs over Poland with a particular focus on large point emitters. We also checked how cloudy conditions influence TROPOMI results. Finally, we tried to link the NO2 column number densities with surface concentration using boundary layer height as an additional explanatory variable

The SI unit of length, the Metre, is presently defined by taking the fixed numerical value of the fundamental constant `c', the invariant speed of light in vacuum. This definition has the same physical basis as the previous definition, as the length of the path travelled by light in vacuum during a time interval of 1/299 792 458 of a second. With the atomic standard second defined in terms of the ground state hyperfine transition in Caesium-133, this definition is supposed to provide a universally reproducible standard of length. However, this relies on Einstein's singular postulate that the relative velocity of light in vacuum is an invariant constant that is independent of any inertial motion of the reference laboratory. I argue that the basis of the definition of the standard metre should be changed to the specific form, "the length of the path equal to the two-way propagation of light in vacuum during a time interval of 1/299 792 458 of a second", to be compatible and consistent with the fact that it is only the two-way relative velocity that is consistent with being an invariant. The null result in the Michelson-Morley two-way experiment, and in all such experiments to date, is consistent with a Galilean one-way propagation of light (relative velocity $c'=c\pm v$) as well as an invariant relative velocity of light. All practical methods and protocols related to the implementation of length standard involve also a two-way propagation, not conforming to the present definition. Besides, this redefinition is absolutely necessary because the relative velocity of light in one-way propagation is indeed Galilean, and not an invariant, as proved here in multiple ways including a direct experiment.

This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The data, collected from various patent databases, was quantitatively analyzed to identify technology trends and direct future development. The analysis reveals distinct emphases on technologies such as lithium-ion and waste battery recycling, highlighting notable differences in patent activities among key companies and countries. China's large number of patents in battery manufacturing processes contrasts with the USA's focus on electrochemical cell construction and storage systems, while Korea shows significant activity in waste battery technology. The study underscores the competitive landscape and innovation efforts in the battery recycling sector, providing insights into the technological focuses and priorities of each country. It also identifies key challenges, such as the need for consistent innovation and broader geographic coverage in Korea, enhancing patent influence and international presence in China, and ensuring high patent quality and fostering innovation in lagging sectors in the United States. Addressing these challenges through enhanced collaboration, increased R&D investments, and supportive policies is crucial for strengthening the global position and driving further innovation in the battery recycling sector.

The sustainable development of resource-based cities has long been a focal point for both governmental policies and academic research. A wealth of academic literature identifies the 'resource curse' as a critical barrier to sustainable development in these urban areas. Despite extensive studies, a consensus on effective strategies to circumvent or resolve this challenge remains elusive. Recent empirical data reveals a noticeable pattern of economic growth differentiation in China's resource-based cities. Why do some resource-based cities experience economic decline while others achieve sustainable economic growth? Therefore, this paper analyzes the reasons for the divergence of economic growth in resource cities from the perspective of heterogeneity. The results of the study show that the phenomenon of the resource curse is conditional, and the heterogeneity of demo-graphic changes, geographic location and institutional environment has a significant impact on the economic growth of resource cities. These factors are investigated to elucidate the observed diversification and heterogeneity in the economic growth of resource-based cities. Building upon this analysis, the paper proposes a framework for differentiated sustainable development pathways tailored to the unique contexts of these cities. This approach not only contributes to the current understanding of the 'resource curse' but also offers novel insights for future research aiming to adopt a more nuanced and context-specific perspective in examining sustainable urban development.

The present study focuses on the yield, chemical composition, and antioxidant activity of essential oils from the leaves and fruit of wild-grown bay laurel (Laurus nobilis L.) in Montenegro. The chemical composition of isolated essential oils was determined by GC/MS and GC/FID. Antioxidant activity was determined using the DPPH assay. The laurel bay essential oil (LEO) yield was 0.88% in fruits and 2.65% in the leaves. The results obtained from this study indicate that the LEOs obtained from the different plant parts (leaves, fruit) display different compositions. Fifty components were identified in LEO leaves, with 1,8-cineole (39.4%), linalool (13.9%), α-terpinyl acetate (11.2%), sabinene (6.7%), methyl eugenol (5.7%), β-pinene (3.2%), and α-pinene (3.1%) being the most abundant ones. Fifty-five components were isolated in LEO fruits with 1,8-cineole (34.2%), α-pinene (6.6%), sabinene (6.1%), β-bisabolene (5.8%), β-pinene (4.8), α-terpinyl acetate (4.5%), and α-terpineol (4.3%) as the main components. However, this study is the first to present the chemical composition of the LEO fruits from Montenegro. The results indicate that the composition of LEO is similar to recent studies from Balkan and Mediterranean areas. The degree of DPPH radical neutralization increased with the incubation time. The LEO isolated from leaves showed better antioxidant activity (EC50 value of 1.43 mg/mL) in comparison to the LEO isolated from fruits (EC50 value of 3.74 mg/mL) during the incubation time of 120 min. Taken together, this study indicates that the LEO from leaves and fruits possesses significant antioxidant activities (possibly due to the high level of 1,8-cineole) and is an important component in the food and pharmaceutical industries. All plant parts of L. nobilis can be successfully used in LEO extraction and isolation.

Nepal, primarily an agricultural country, heavily relies on agricultural residue and fuelwood for daily energy requirements. In 2022, total energy consumption was 640 PJ, with traditional sources accounting for 64.17%, and fuelwood comprising 58.53% of total fuel consumption. The estimated potential supply of agricultural residue is 26 million tonnes, yielding about 442 million GJ of energy in 2021. Biomass trading often emphasizes volume or weight, necessitating a rapid and non-destructive assessment of energy properties for mutual benefit, aiding in the identification, management, and utilization of biomass sources. In this study, 200 biomass samples were collected in two batches (126 and 74 samples) from various locations in Nepal. Using Partial Least Squares Regression (PLSR), a model was developed correlating higher heating value (HHV) from a bomb calorimeter and spectral data from Fourier Transform near-infrared spectroscopy (FT-NIRS) (3595 – 12,489 cm-1) sensor. PLSR models incorporated raw spectra, eight preprocessing techniques, the multi-preprocessing five-range method, and a genetic algorithm. Outliers in the first batch were identified, and the first batch divided into an 80% calibration set and a 20% validation set, while the second batch was designated as an unknown sample set. The optimum PLSR model, utilizing first derivative preprocessing, improved accuracy by 6.77%, with coefficients of determination in the calibration set, validation set, and unknown set as 0.9694, 0.9578, and 0.8089, respectively. Root mean square errors were 132.4790 J/g, 189.4800 J/g, and 360.8845 J/g for the calibration set (RMSEC), validation set (RMSEP), and unknown set (RMSEUN), respectively. The prediction to deviation ratio (RPD) for the validation set and unknown set was 4.9 and 2.4, respectively. The cross validation model of combined sample data of every sets showed the R2CV of 0.95 and RPDCV of 4.6 indicating the model could serve as a reliable and swift non-destructive alternative for evaluating biomass HHV using NIRS and ready for updating for industrial use. However, incorporating a larger number of representative samples is crucial to enhance accuracy and develop a more comprehensive global model for predicting biomass HHV.

This study aims at highlighting and illustrating innovative actions for achieving and/or improving accurate medical coding within a DRG system. Already grouped DRG cases recorded in the first DRG implementation year in the region of Crete were examined. A sample of 130,000, from a total of 200,000, cases were analyzed and audited, through a process consisting of three stages: i) Digitalization, ii) Auditor training, and iii) Control &amp; consultation. The results indicated that a significant proportion of DRG coding, with a length of stay exceeding one day, were reclassified into different DRG categories. This was not primarily due to errors in the selection of the principal diagnosis, but resulted from coding errors, including omission of secondary diagnoses coding, medical procedures that should have been included and utilization of less specific codes. The findings indicate that medical coding control, consulting services and the adoption of targeted actions can be instrumental for proper implementation of the DRG coding system, and for ensuring transparent allocation of resources within hospitals. Ultimately, this shortens the coding learning curve, from four years typically reported in the international literature, to a more efficient duration of two years, leading to rational and documented clinical and financial management of hospitals.

This paper presents a thorough investigation into the shear strength capacity of reinforced concrete deep beams, with a focus on improving predictive accuracy beyond existing code provisions. Analyzing 198 deep beams from 15 investigations, the study considers parameters such as concrete compressive strength (fc’), shear span to effective depth ratio (av/d), and reinforcement ratios (ρs, ρv, ρh ). Introducing a novel predictive model (Equation 7), the study rigorously evaluates it using nonlinear regression analysis and statistical metrics (MAE, RMSE, R2). The proposed model demonstrates a significant reduction in the coefficient of variation (COV) to 27.08%, surpassing existing codes' limitations. Comparative analyses highlight the model's robustness, revealing improved convergence of data points and minimal sensitivity to variations in key parameters. The findings suggest that the proposed model offers enhanced predictive accuracy across diverse scenarios, making it a valuable tool for structural engineers. This research contributes to advancing the understanding of shear strength in reinforced concrete deep beams, offering a reliable and versatile predictive model with implications for refining design methodologies and enhancing the safety and efficiency of structural systems.

Objectives: To develop a multivariate model for risk factors specific to early onset preeclampsia (EOP) and late onset preeclampsia (LOP) in our entire population (singleton and twin pregnancies). Material and methods: 20 year-observational population-based historical cohort study (2001-2020). All consecutive births delivered at the Centre Hospitalier Universitaire Hospitalier Sud Reunion’s maternity. A standardized validated epidemiological perinatal data base. Results: During the 20-year period, there were 81,834 pregnancies and 83,555 infants born, 1232 dichorionic and 350 monochorionic twin pregnancies. There were 2120 cases of preeclampsia, of which 2001preeclamptic singleton pregnancies and 119 twin pregnancies (incidence 7.5% in twin pregnancies vs 2.5% singletons, OR 3.0, p<0.001). Independent risk factors for EOP and LOP in a multivariate model (controlling for the two major confounders: namely maternal ages -both risks for EOP and LOP- and maternal pre-pregnancy BMI – specific risk factor for LOP) were: history of preeclampsia (aOR 11.7 for EOP, 7.8 for LOP, p < 0.0001), chronic hypertension (aOR 7.3 for EOP, 3.9 for LOP, p < 0.0001), history of perinatal death (aOR 2.2 for EOP, p < 0.0001 and 1.48 for LOP, p= 0.007), primipaternity (aOR 3.0 for EOP and 3.6 for LOP, p = 0.001), dizygotic twin pregnancies (aOR 3.7 for EOP, p < 0.0001 and 2.1 for LOP, p = 0.003), monozygotic twin pregnancies (aOR 3.98 for EOP, p = 0.003 and NS for LOP), ovulation induction (aOR 5.6 for EOP, p = 0.004 and NS for LOP), and in vitro fertilization (aOR 2.8 for EOP, p = 0.05 and NS for LOP). Specific to LOP and NS for EOP: renal diseases (aOR for LOP 2.9, p= 0.007) and gestational diabetes mellitus (aOR 1.2, p= 0.04). Conclusions: Maternal ages over 35 years, chronic hypertension , history of preeclampsia, ovulation induction, in vitro fertilizations, history of perinatal deaths and twin pregnancy (in our experience, especially mono zygotic twin pregnancies) are significant risk factors for EOP. New paternity is an independent factor for both EOP and LOP.

: Fluoride toxicity has become a global public health concern, and its significant source of exposure is drinking water. This study aimed to determine groundwater fluoride level and assess its related human health risks. Its level was measured from groundwater samples using an ion-selective electrode. The total hazard index was assessed based on the chronic daily oral intake and dermal absorbed dose of groundwater fluoride. Analyses were conducted using ArcGIS and SPSS. This study reported groundwater fluoride ranged from 0.3 mg/L to 38 mg/L, with a mean annual level of 15 mg/L. Groundwater fluoride level above 5 mg/L was reported in 60 % of the water samples. Total hazard index values among sampled locations varied from 0.18 to 22.56. Half (51.5 %) of infants, 85.5 % of children, 82.5 % of adolescents, and 67.5 % of adults had a risk of developing a non-carcinogenic health effect. This study demonstrated that the fluoride level of the groundwater sources was high, and the water was not suitable for drinking, a result that is of special importance for children and adults since they are a hypersensitive subgroup of the population.

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article presents a comprehensive review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that heart rate variability (HRV) and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

The structural, elastic, and electronic properties of orthorhombic Cd2SiO4 and Hg2GeO4 were examined under varying pressure conditions using first-principles calculations based on densi-ty-functional theory employing the projector augmented wave method. The obtained cell pa-rameters at 0 GPa were found to align well with existing experimental data. We delved into the pressure-dependence of normalized lattice parameters and elastic constants. In Cd2SiO4, all lat-tice constants decreased as pressure increased, whereas in Hg2GeO4, parameters a and b de-creased while parameter c increased under pressure. Employing the Hill average method, we calculated the elastic moduli and Poisson’s ratio up to 10 GPa, noting an increase with pressure. Evaluation of ductility/brittleness under pressure indicated both compounds remained ductile throughout. We also estimated elastic anisotropy and Debye temperature under varying pres-sures. Cd2SiO4 and Hg2GeO4 were identified as indirect band gap insulators, with estimated band gaps of 3.34 eV and 2.09 eV, respectively. Interestingly, Cd2SiO4 exhibited a significant increase in band gap with increasing pressure, whereas the band gap of Hg2GeO4 decreased under pres-sure, revealing distinct structural and electronic responses despite their similar structures.

The advent of artificial intelligence (AI) has revolutionized digital marketing, offering unprecedented levels of personalization. AI-driven personalization leverages machine learning algorithms and data analytics to tailor content, advertisements, and recommendations to individual consumers based on their behavior, preferences, and demographics. This paper explores the effectiveness of AI-driven personalization in enhancing customer engagement, increasing conversion rates, and improving overall marketing efficiency. It also examines the ethical considerations associated with these technologies, including data privacy, consent, and the potential for manipulation or bias. While AI-driven personalization can lead to more relevant and engaging consumer experiences, it raises critical ethical questions that must be addressed to ensure responsible and fair use. The study concludes with recommendations for marketers and policymakers to balance the benefits of AI personalization with the need for ethical standards and consumer protection.

We seek to examine effective collaboration when engaging in value cocreation with the customer for mobile financial services in EMs. Our study is a motivation for multi-stakeholder engagement in value cocreation with financial inclusion of all consumers including marginalized populations. Furthermore, our work discovers that social sustainability is a product of social creativity and results in financial inclusion. We apply a qualitative descriptive methodology utilizing exemplars from extant research and phenomenology to analyze the data from the contexts of Ghana and Kenya. The results show that value is co-created for mobile financial services in networks in emerging markets when actors integrate resources for social and environmental sustainability as essential elements for mobile financial inclusion.

The pop-in effect in nanoindentation of metals represents a major collective dislocation phenome-non that displays sensitivity in the local surface microstructure and residual stresses. To under-stand the deformation mechanisms behind pop-ins in metals, large scale molecular dynamics simulations are carried out to investigate the pop-in behavior and indentation size effect in unde-formed and deformed Cu single crystals. Tensile loading, unloading and reloading simulations are performed to create a series of samples subjected to a broad range of tensile strains with/without pre-existing dislocations. The subsequent nanoindentation simulations are con-ducted to study the coupled effects of pre-strain, the presence of resulting dislocations and surface morphology, as well as indenter size effects on the mechanical response in indentation processes. Our work provides detailed insights into the deformation mechanisms and microstruc-ture-property relationships of nanoindentation in the presence of residual stresses and strains.

Simulating the non-perturbative and non-Markovian dynamics of open quantum systems is a very challenging many body problem, due to the need to evolve both the system and its environments on an equal footing. Tensor network and matrix product states (MPS) have emerged as powerful tools for open system models, but the numerical resources required to treat finite temperature environments grow extremely rapidly and limit their applications. In this study we use time-dependent variational evolution of MPS to expore the striking theory of Tamescelli et al. that shows how finite temperture open dyanmics can be obtained from zero temperature, i.e. pure wave function, simulations. Using this approach, we produce a benchmark data set for the dynamics of the Ohmic spin-boson model across a wide range of coupling and temperatures, and also present detailed analysis of the numerical costs of simulating non-equilibrium steady states, such as those emerging from the non-perturbative coupling of a qubit to baths at different temperatures. Despite ever growing resource requirements, we find that converged non-perturbative results can be obtained, and we discuss a number of recent ideas and numerical techniques that should allow wide application of MPS to complex open quantum systems.

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ODE-based optimization approach based on Marcelin’s 1910 mass action equation is used to obtain the maximum entropy distribution, (2) the predicted metabolite concentrations are compared to those generally expected from experiment using a loss function from which post-translational regulation of enzymes is inferred, (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrations and reaction fluxes, and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1, 6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime’s understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an open- accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data–driven grey–box identification approach provided the physically meaningful nonlinear continuous–time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte–Carlo analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model–based and data–driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

Machine safety is not only a prerequisite for successful production, but also the foundation for sustainability and growth of any manufacturing organisation. The latest approaches in this rapidly developing field are the integration of effective risk management tools and strategies into occupational health and safety (OHS) systems. In this article, using a specific example from practice, we will show how using multi-criteria decision making (AHP) support Machinery Safety Decision Making (MSDM) from the point of reducing losses. Using Classification and Regression Tree Analysis (CART), we estimated the efficiency, cost-effectiveness and thus the sustainability level of the relevant safety measures. These were proposed risk reduction measures that typically raised uncertainty among managers regarding the estimation of cost-effectiveness. The advantage of application decision trees approach is possibility to identify and establish relatively homogeneous groups of undesirable events and their impact on the organisation's objectives. A comprehensive model has been developed to support management decision making in manufacturing organizations in implementing and improving safety measures in line with manufacturing sustainability goals.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

This comprehensive review has systematically examined the use of heterogeneous solid acid catalysts in the production of biodiesel from wastewater-derived sludge by using the PRISMA methodology. The manuscript highlighted the composition and characteristics of wastewater-derived sludge, presenting both the opportunities and challenges associated with its use as a feedstock for biodiesel production. Various types of catalysts were dis-cussed, with a detailed exploration of heterogeneous solid acid catalysts, including zeolites, hetero-polyacid (HPA), mixed metal oxides, and sulphonic acid group catalysts. The advantages and limitations of these catalysts were critically analyzed, providing a balanced view of their potential in industrial applications. The application section delved into the catalytic transesterification reaction, mechanisms of biodiesel production, and the effects of catalyst loading on yield. Performance metrics such as catalytic activity, stability, recyclability, cost-effectiveness, and environ-mental impact were thoroughly evaluated, offering a clear understanding of the efficacy of these catalysts. Synthesis and characterization techniques were also reviewed, shedding light on the latest preparation methods and characterization techniques. Recent advances in catalyst development were presented, showcasing the innovative strides made in enhancing catalyst performance. The environmental and economic implications of using solid acid catalysts for bio-diesel production were assessed, emphasizing the importance of sustainability and economic viability.

The interfacial polymerization (IP) of piperazine (PIP) and trimesoyl chloride (TMC) has been extensively utilized to synthesize nanofiltration (NF) membranes. However, it is still a huge challenge to monitor the IP reaction, because of the fast reaction rate and the formed ultra-thin film. Herein, two effective strategies were applied to reduce the IP reaction rate: (1) the introduction of hydrophilic interlayers between the porous substrate and the formed polyamide layer, and (2) the addition of macromolecular additives in the aqueous solution of PIP. As a result, in situ Fourier transform infrared (FT-IR) spectroscopy was firstly used to monitor the IP reaction of PIP/TMC with hydrophilic interlayers or macromolecular additives in the aqueous solution of PIP. Moreover, the formed polyamide layer growth on the substrate was studied in a real-time manner. The in situ FTIR experimental results confirmed that the IP reaction rates were effectively suppressed and that the formed polyamide thickness was reduced from 138 ± 24 nm to 46 ± 2 nm according to TEM observation. Furthermore, an optimized NF membrane with excellent performance was consequently obtained, which included boosted water permeation of about 141–238 (L/m²·h·MPa) and superior salt rejection of Na₂SO₄ > 98.4%.

A pump station is a very important flood control facility for mitigating inundation of urban lowland areas. It is not easy to secure a site to increase the capacity of a pump station in an urban area because of various limitations or to maximize the discharge capacity of the pump sump. Adding a facility to improve the pump capacity of a pump station may affect the flow characteristics, such as unexpected increases in the flow velocity and vorticity, and cause severe problems with operating the pump station. To solve those problems, anti-vortex devices (AVDs) have been developed and adopted for appropriate design standards. The Korean design criteria for AVDs are based on experience and foreign standards because not enough data on the AVD are available. In this study, the flow in the sump was numerically simulated at various AVD heights to collect information on improving the design and efficiency of a pump station. Consequently, an appropriate height for the AVD and changes in the flow pattern and vortex in the pump sump were determined and compared with 12 cases of inflow conditions with respect to the vertical location.

Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual-semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual-semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual-semantic features using self-distillation and disentanglement network (SDDN). The aim is to utilize self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual-semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual-semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual-semantic features. Finally, the consistent visual-semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN).

The intracellular calcium dynamics in vascular endothelial cells (VECs) in response to wall shear stress (WSS) and/or adenosine triphosphate (ATP) have been commonly regarded as an important factor in regulating VEC function and behavior including proliferation, migration and apoptosis. However, the effects of time-varying ATP signals have been usually neglected in the past investigations in the field of VEC mechanobiology. In order to investigate the combined effects of WSS and dynamic ATP signals on the intracellular calcium dynamic in VECs, a Y-shaped microfluidic device, which can provide the cultured cells on the bottom of its mixing micro-channel with stimuli of WSS signal alone and different combinations of WSS and ATP signals in one single micro-channel, is proposed. Both numerical simulation and experimental studies verify the feasibility of its application. Cellular experimental results also suggest that a combination of WSS and ATP signals rather than a WSS signal alone might play a more significant role in VEC Ca²⁺ signal transduction induced by blood flow.