Search

The sulfuric acid-doping polyaniline (H-PANI-HSO4) are applied to conduct the electrochemical measurement and simulation calculation to investigate the capacitance, electronic structure and energy band properties. The H-PANI-HSO4 growing on graphite carbon paper (H-PANI-HSO4/GCP) is applied as an electroactive electrode to investigate electrochemical properties. The Faradaic capacitance of H-PANI-HSO4/GCP electrode is ascribed to the reversible redox reaction of bisulfate anion doping/dedoping protonated PANI (H-PANI). Cyclic voltammetry measurement at a scan rate of 5mVs−1 determines an equivalent mean response current of 0.64Ag−1 and a capacitance of 128.35Fg−1. Galvanostatic charge–discharge measurement determines specific capacitance from 129.06 to 116.88Fg−1 at current densities from 0.5 to 2.5Ag−1. Cyclic voltammetry-based capacitance at equivalent current density of 0.64Ag−1 is in accordance with galvanostatic charge–discharge-based capacitance at the current density of 0.57Ag−1. Electrochemical impedance spectrum measurements indicate that H-PANI-HSO4/GCP exhibits lower charge-transfer resistance, much lower Warburg resistance, higher quasicapacitance than H-PANI-HSO4 to approaching ideal capacitor. Density functional theory calculations indicate that H-PANI-HSO4 has a higher density of states (10.6 electron/eV) and lower bandgap energy (0.481eV) than H-PANI (5.24 electron/eV, 1.449eV), indicating its enhanced electronic conductivity. The electronic bandgap energy is accordingly decreased from 0.263eV for H-PANI-HSO4/GCP to 0 for H-PANI-HSO4/GCP. Electrochemical measurement and simulation calculation investigation proves that H-PANI-HSO4/GCP electrode with anion-doped and protonated state exhibits higher electronic conductivity and capacitance performance to act as superior electroactive material. [ABSTRACT FROM AUTHOR]

In recent times, among all the substrates used in microfluidic systems, cellulose paper is used as a handy, low-cost substrate that has gained attention for carrying fluid on its surface over capillary pressure. Cellulose paper substrate has exhibited great potential on microfluidic devices owing to prevalent obtainability, easy fluid (sample) flow system, flexibility, and low cost. Cellulose paper is fibrous, biocompatible, and hydrophilic in nature due to the hydroxyl group of the cellulose molecule. Based on the dominance of functional hydroxyl groups, cellulose is very reactive and every single cellulose fiber acts like a microchannel on the paper substrates. Aggregation of inter- and intra-cellulose fiber chains has a strong binding affinity to it and toward materials containing hydroxyls groups. In this paper, impact of inter- and intra-cellulose fiber on the paper substrate has been discussed through an experimental study. For the addition of work a "hydrophobic penetration-on-paper substrate (Hyp-POP)" method has been shown by using TiO2 ink as a hydrophobic material to design the microfluidic channel on the Whatman cellulose filter paper (grade 1) as a paper substrate. In this experimental study, the intra-cellulose fibers of paper substrate interact through hydrogen bonds with water molecules and form a hydrophilic surface on paper substrate while TiO2 binds with intra-cellulose fibers by electrostatic forces which change the crystallinity of intra-cellulose fiber and make the surface of paper substrate; hydrophobic. Field Emission Scanning Electron Microscope (FESEM) analysis is conceded for microfluidic channel analysis on the paper surface and EDS is carried out for TiO2 ink contents analysis. It has been experimentally observed that the printing material of TiO2 ink with 17.2% Ti content is suitable to integrate hydrophobic barrier on paper substrate for microfluidic channel fabrication. The wetting ability of Whatman cellulose filter paper (grade 1) was further evaluated by contact angle measurements (Data physics OCA 15EC). Using "Hyp-POP" method a hydrophobic pattern (width 3140 μ m) on paper substrate has been made for the flow of liquid (blue fountain ink) into a paper fluidic channel (width 1860 μ m) without any leakage. [ABSTRACT FROM AUTHOR]

A thermal stability analysis method of meta-aramid insulating paper based on computer molecular dynamics technology is designed in this paper. First, the raw materials and equipment for preparing meta-aramid insulating paper were determined to prepare meta-aramid insulating paper. Then, the internal structure and morphology of meta-aramid insulating paper are analyzed for the subsequent stability analysis. Finally, the basic principle of computer molecular dynamics technology is analyzed, which is used to analyze the thermal stability of meta-aramid insulating paper, mainly from the stability of its crystal region. The experimental results show that the proposed method is effective and feasible in analyzing the thermal stability of aramid insulating paper between samples. [ABSTRACT FROM AUTHOR]

The Laser Interferometer Space Antenna (LISA) mission, scheduled for launch in the mid-2030s, is a gravitational wave space observatory designed to detect sources emitting in the millihertz band. LISA is an European Space Agency flagship mission, currently entering the Phase-B development phase. It is expected to help us improve our understanding of our Universe by measuring gravitational wave sources of different types, with some of the sources being at very high redshifts (z ∼ 2 0). On February 23, 2022, we organized the first LISA in Greece Workshop. This Workshop aimed to inform the Greek scientific and tech industry communities about the possibilities of participating in LISA science and the LISA mission, with the support of the Hellenic Space Center. In this White Paper, we summarize the outcomes of the Workshop, the most important aspect of it being the inclusion of 1 5 Greek researchers to the LISA Consortium, raising our total number to 2 2. At the same time, we present a road map with the future steps and actions of the Greek Gravitational Wave community with respect to the LISA mission. [ABSTRACT FROM AUTHOR]

Source code is essential for researchers to reproduce the methods and replicate the results of artificial intelligence (AI) papers. Some organizations and researchers manually collect AI papers with available source code to contribute to the AI community. However, manual collection is a labor-intensive and time-consuming task. To address this issue, we propose a method to automatically identify papers with available source code and extract their source code repository URLs. With this method, we find that 20.5% of regular papers of 10 top AI conferences published from 2010 to 2019 are identified as papers with available source code and that 8.1% of these source code repositories are no longer accessible. We also create the XMU NLP Lab README Dataset, the largest dataset of labeled README files for source code document research. Through this dataset, we have discovered that quite a few README files have no installation instructions or usage tutorials provided. Further, a large-scale comprehensive statistical analysis is made for a general picture of the source code of AI conference papers. The proposed solution can also go beyond AI conference papers to analyze other scientific papers from both journals and conferences to shed light on more domains. [ABSTRACT FROM AUTHOR]

Recent and forthcoming technological innovations highlight recycled paper products as a consumer market with promising potential. This study presents the examination of the recycled paper products purchasing behavior (RPPPB), as well as some of the factors that were assumed to affect these specific pro-environmental consumers' choices. Instead of the impact of the customary positive environmental attitudes, this study focused on those negative attitudes, namely Environmental Unconcern, that might be found able to inhibit RPPB. In addition, an effort was made to examine the role of an objective environmental knowledge scale, namely the Eco-literacy scale. A survey among 224 consumers residing in the Municipality of Thessaloniki, Greece was conducted. Multiple regression indicated that just Environmental Knowledge and not the Eco-literacy scale is a predictor of RPPPB. Further, SEM analysis though verified the theoretical assumption that knowledge moderates the relationship between attitudes and behavior. It was concluded that the negative direct relationship between environmental unconcern and the recycled paper products' purchasing behavior strengthens in the group of knowledgeable consumers. Implications are provided for all stakeholders that engage in the recycled paper products market. [ABSTRACT FROM AUTHOR]

In the recent years, the academic paper influence analysis has been widely studied due to its potential applications in the multiple areas of science information metric and retrieval. By identifying the academic influence of papers, authors, etc., we can directly support researchers to easily reach academic papers. These recommended candidate papers are not only highly relevant with their desired research topics but also highly-attended by the research community within these topics. For very recent years, the rapid developments of academic networks, like Google Scholar, Research Gate, CiteSeerX, etc., have significantly boosted the number of new published papers annually. It also helps to strengthen the borderless cooperation between researchers who are interested on the same research topics. However, these current academic networks still lack the capabilities of provisioning researchers deeper into most-influenced papers. They also largely ignore quite/irrelevant papers, which are not fully related with their current interest topics. Moreover, the distributions of topics within these academic papers are considered as varying and it is difficult to extract the main concentrated topics in these papers. Thus, it leads to challenges for researchers to find their appropriated/high-qualified reference resources while doing researches. To overcome this limitation, in this paper, we proposed a novel approach of paper influence analysis through their content-based and citation relationship-based analyses within the biographical network. In order to effectively extract the topic-based relevance from papers, we apply the integrated graph-based citation relationship analysis with topic modeling approach to automatically learn the distributions of keyword-based labeled topics in forms of unsupervised learning approach, named as TopCite. Then, we base on the constructed graph-based paper–topic structure to identify their relevancy levels. Upon the identified relevancy levels between papers, we can support for improving the accuracy performance of other bibliographic network mining tasks, such as paper similarity measurement, recommendation, etc. Extensive experiments in real-world AMiner bibliographic dataset demonstrate the effectiveness of our proposed ideas in this paper. [ABSTRACT FROM AUTHOR]

The feeding unit is an important functional part in the paper industry that requires the development of effective maintenance plan based on its reliability analysis. This study analyzes reliability of the considered unit employing trapezoidal fuzzy numbers in fuzzy λ – τ methodology with Petri net modeling. In this analysis, the collected crisp input data of each subsystem has been fuzzified using trapezoidal fuzzy numbers. Then fuzzy values of several reliability factors of feeding unit, namely, failure rate, repair time, reliability, availability, etc. at ±15%, ±25% and ±40% spread levels have been calculated. Further, the obtained fuzzy values of reliability factors have been defuzzified to analyze system behavior of feeding unit. The analysis is useful for plant maintenance and operation management to understand the behavior of feeding unit in a more realistic manner. [ABSTRACT FROM AUTHOR]

This paper provides a review of the literature on rigorous definitions and constructions in quantum field theory (QFT), spanning the period of seven decades. Compared with the ideas and constructions found in the modern physics literature, we conclude that none of the existing systems of QFT axioms can cover all the physical situations. Therefore, it is still an outstanding open problem to formulate a complete definition of QFT. We argue that the question is of relevance for both physicists and mathematicians. [ABSTRACT FROM AUTHOR]

The oil-immersed transformer is a crucial piece of equipment in the power system. Operating at the specified temperature is necessary to ensure the normal operation of the transformer. The insulation paper on the winding surface has a significant impact on the actual temperature of the transformers, which is often overlooked by researchers. The one-dimensional steady-state heat conduction model of the transformer is established by analyzing the heat diffusion process of winding to transformer oil. Atomic force microscope was used to observe the microsurface structure of insulation paper and copper. According to the experiment, the heat transfer resistance in the series process of heat transfer at 7 0 ∘ C is 0.0138 m2 K/W. Space thermal circuit model of transformer is established by thermoelectricity analogy method, and the simulation circuit is optimized according to the boundary conditions set up in the actual environment. The results show that the error of the hot spot temperature is closer to the measured temperature and decreases by 2.5% when considering the thermal resistance of insulation paper. [ABSTRACT FROM AUTHOR]

The MINimal emission FLUXes (MINFLUX) technique in optical microscopy, widely recognized as the next innovative fluorescence microscopy method, claims a spatial resolution of 1–3 nm in both dead and living cells. To make use of the full resolution of the MINFLUX microscope, it is important to select appropriate fluorescence probes and labeling strategies, especially in living-cell imaging. This paper mainly focuses on recent applications and developments of fluorescence probes and the relevant labeling strategy for MINFLUX microscopy. Moreover, we discuss the deficiencies that need to be addressed in the future and a plan for the possible progression of MINFLUX to help investigators who have been involved in or are just starting in the field of super-resolution imaging microscopy with theoretical support. [ABSTRACT FROM AUTHOR]

Nonlinear optical imaging is a versatile tool that has been proven to be exceptionally useful in various research fields. However, due to the use of photomultiplier tubes (PMTs), the wide application of nonlinear optical imaging is limited by the incapability of imaging under ambient light. In this paper, we propose and demonstrate a new optical imaging detection method based on optical parametric amplification (OPA). As a nonlinear optical process, OPA intrinsically rejects ambient light photons by coherence gating. Periodical poled lithium niobate (PPLN) crystals are used in this study as the media for OPA. Compared to bulk nonlinear optical crystals, PPLN crystals support the generation of OPA signal with lower pump power. Therefore, this characteristic of PPLN crystals is particularly beneficial when using high-repetition-rate lasers, which facilitate high-speed optical signal detection, such as in spectroscopy and imaging. A PPLN-based OPA system was built to amplify the emitted imaging signal from second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy imaging, and the amplified optical signal was strong enough to be detected by a biased photodiode under ordinary room light conditions. With OPA detection, ambient-light-on SHG and CARS imaging becomes possible, and achieves a similar result as PMT detection under strictly dark environments. These results demonstrate that OPA can be used as a substitute for PMTs in nonlinear optical imaging to adapt it to various applications with complex lighting conditions. [ABSTRACT FROM AUTHOR]

We report on the fabrication of zinc oxide (ZnO) nanorod-based flexible, cost-effective single electrode triboelectric nanogenerator (STENG) on cellulose paper platform. In particular, the influence of surface functional groups of ZnO nanorods on the STENG output has been investigated. The STENG has been designed with chemically grown ZnO nanorod-coated paper as active triboelectric layer and Teflon/ Aluminum as counter triboelectric layer/ electrode. It has been found that post-annealing of nanorods at 125∘C improves the output voltage by twice compared to the pristine nanorods and delivers an output power of ∼ 0. 2 μ W/cm2. With annealing up to 125∘C, the optimal removal of the chemisorbed water from the defect sites of ZnO surface increases the intrinsic carrier concentration leading to maximum charge transfer and hence higher triboelectric output. On the other hand, a higher extent of oxygen accumulation from air at 150∘C lowers the intrinsic carrier concentration resulting in the reduced output. It has been proposed that the competition between the events like water removal and oxygen accumulation under different annealing temperatures can influence the STENG output largely. Finally, the optimized STENG has been demonstrated to work as wearable bio-mechanical sensors operating through bio-mechanical stimuli. [ABSTRACT FROM AUTHOR]

The editorial in Volume 10 Issue 3 of Water Economics & Policy discusses five research papers that address various aspects of water scarcity, water quality, and water use efficiency. The papers cover topics such as groundwater scarcity, drought impacts, water pricing for conservation, ecological footprints of water, and coastal water quality regulations. Each paper utilizes different methodologies and provides theoretical, empirical, and policy components. The findings from these papers can inform policy decisions at regional and national scales, offering valuable tools for policy evaluation and implementation. [Extracted from the article]

With the prevalence of artificial intelligence technologies, big data has been utilized to higher extent in many cross-domain fields. This paper concentrates on the digital assessment of vocational education level in some specific areas, and proposes a graph neural network-based assessment model for this purpose. Assume that all vocational colleges inside a specific region are with a social graph, in which each college is a node and the relations among them are the edges. The graph neural network (GNN) model is formulated to capture global structured features of all the nodes together. The GNN is then employed for the sequential modeling pattern, and the evolving characteristics of all the colleges can be captured. Some experiments are also conducted to evaluate the performance of the proposed GNN-VEL. It is compared with two typical forecasting methods under evaluation of two metrics. The results show that it performs better than other two methods. [ABSTRACT FROM AUTHOR]

The paper focuses on the cutting behavior of Glass Fiber Reinforced Polymer (GFRP) composites and GNP–GFRP composites that contain varying amounts of Graphene Nano Platelets (GNP). GFRP composites are increasingly being used in a variety of industrial applications due to their excellent mechanical properties, such as high strength, stiffness, and low weight. However, their machining and cutting behavior can be challenging due to the presence of the reinforcing fibers. Therefore, the study aims to investigate the machining behavior of GFRP composites and the effect of adding GNP on their cutting behavior. The effect of different parameters such as cutting speed, feed rate and reinforcement rate on cutting forces and delamination factor is investigated. In addition, the tensile strength and fatigue behavior of the composite materials with the best and worst delamination factors were also determined. Addition of up to 0.2 wt.% of GNP to GFRP composites resulted in an increase in cutting forces and delamination factor when drilling GFRP composites. While the cutting force and delamination factor decreased with the increase in cutting speed, the cutting force and delamination factor increased with the increase in the feed rate. Analysis of variance (ANOVA) was performed to determine the effects of drilling parameters and reinforcement ratio on cutting force and delamination factor according to full factorial experimental design. The most efficient factor on the cutting forces is found to be feed rate (84.97%), followed by the reinforced rate (6.48%) and cutting speed (6.13%). The most efficient factor on the delamination factor is determined to be feed rate for (44.49%), followed by the reinforced rate (29.20%) and cutting speed (21.73%). [ABSTRACT FROM AUTHOR]

The novel coronavirus disease — COVID-19 is a historic catastrophe that has caused many devastating impacts on human life and wellness. Researchers in academia and industry strive to understand the causes of this pandemic disease and find new therapeutics combating it. Consequently, the number of COVID-19 related publications increases rapidly, and it is too difficult for medical researchers and practitioners to keep up with the latest research and development. Literature filtering and categorization, and knowledge discovery can use text mining as a powerful tool. In this paper, we propose a text mining method to explore the categories of COVID-19 related themes and identify the standard methodologies that have been used. We discuss the potential limitations of this preliminary study and present future perspectives related to COVID-19 research. This paper provides an quantitative and qualitative mixed analysis example of using some research papers by data mining method to dig out several hidden information and set up a foundation for data scientists to develop more effective algorithms to deal with COVID-19 related problems. [ABSTRACT FROM AUTHOR]

The European Water Framework Directive (WFD) is one of the most analyzed environmental legislations in the scientific literature, influencing the water management in some non-European countries. The WFD has the strong ambition of achieving a good ecological status of water bodies across all river basins in Europe. However, the advances towards sustainable management are falling far behind the planned schedule. The emphasis of the Directive is focused on water quality rather than on water quantity. The advances during the last quarter of the century since its inception have been strong on urban and industrial point pollution, but not on agricultural non-point pollution that remains high and even increases in major basins. Water quantity aspects have been mostly left aside in the Directive, despite the fact that water scarcity is a serious problem in Southern European countries, and will become more critical with climate change in most basins across Europe. Some policy measures of the WFD need to be reformed, in particular measures for abating agricultural pollution, and new measures for addressing water scarcity. The narrow focus of the WFD on water pricing to solve at the same time issues of financing, water allocation and efficiency, environment, opportunity costs and pollution abatement, should be broadened. The challenge is giving more emphasis to command & control and collective action instruments, and designing combinations of instruments adapted to sectoral and spatial locations in basins. This overhaul of the water policy instruments by the European Commission will be needed to advance in the sustainable management of river basins in Europe. [ABSTRACT FROM AUTHOR]

To address the issues in clothing pattern style migration, this paper proposes a digital simulation and re-editing method for clothing patterns based on deep learning and somatosensory interaction. First, the proposed method encodes the black-and-white line drawing image, generating random noise images through a diffusion process, introducing color information for synthesis, and using a decoder to reconstruct a colored image. Afterwards, an improved VGG19 model is used to reconstruct content features and perform linear color transformation on style images, enabling pattern style migration through the construction of a Gram matrix and resulting in colored clothing texture patterns. Finally, a KinectV2 is utilized for fabric simulation, overlaying colorful clothing texture patterns to achieve 3D virtual dressing. The experimental results show that the proposed method improves the structural similarity index measure (SSIM) by 9–11% and the peak signal-to-noise ratio (PSNR) by 3–8% when compared to existing algorithms. The experiments provide evidence that the proposed method effectively mitigates color overflow, delivers precise image coloring, and accomplishes realistic restoration of clothing texture. Furthermore, the method offers an improved garment fit to fulfill the user's interaction requirements. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the International Journal of Semantic Computing, discussing a special issue on Transdisciplinary Artificial Intelligence. The special issue includes seven papers, four from the 5th IEEE International Conference on Transdisciplinary AI and three from the 6th International Conference on Artificial Intelligence for Industries. The papers cover a range of topics, including future visioning in AI research, utilizing satellite imagery to predict socioeconomic indicators, smart city wildfire risk analysis, leveraging community health workers for predicting emergency department readmissions, optimization of 2D irregular packing using deep reinforcement learning, real-time sampling strategies for regression, and efficient dynamic IC design analysis using graph-based semi-supervised learning. The papers underwent a rigorous review process and have been extended with new and unpublished materials. The authors present their findings and highlight the potential applications and implications of their research. [Extracted from the article]

In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision. [ABSTRACT FROM AUTHOR]

A recent highly publicized study [Park, M., Leahey, E. and Funk, R. J., Papers and patents are becoming less disruptive over time, Nature613 (2023) 138–144] claiming that science has become less disruptive over recent decades represents an extraordinary achievement but with deceptive results. The measure of disruption, CD5, in this study does not account for differences in citation amid decades of exponential growth in publication rate. In order to account for both the exponential growth as well as the differential impact of research works over time, here we apply a weighted disruption index to the same dataset. We find that, among research papers in the dataset, this weighted disruption index has been close to its expected neutral value over the last fifty years and has even increased modestly since 2000. We also show how the proportional decrease in unique words is expected in an exponentially growing corpus. Finding little evidence for recent decrease in disruption, we suggest that it is actually increasing. Future research should investigate improved definitions of disruption. [ABSTRACT FROM AUTHOR]

We study Kauffman's model of folded ribbon knots: knots made of a thin strip of paper folded flat in the plane. The folded ribbonlength is the length to width ratio of such a folded ribbon knot. The folded ribbon knot is also a framed knot, and the ribbon linking number is the linking number of the knot and one boundary component of the ribbon. We find the minimum folded ribbonlength for 3 -stick unknots with ribbon linking numbers ± 1 and ± 3 , and we prove that the minimum folded ribbonlength for n -gons with obtuse interior angles is achieved when the n -gon is regular. Among other results, we prove that the minimum folded ribbonlength of any folded ribbon unknot which is a topological annulus with ribbon linking number ± n is bounded from above by 2 n. [ABSTRACT FROM AUTHOR]

A Central Bank Digital Currency (CBDC) is a country's fiat currency that exists in a digital form. This digital currency becomes a liability on the balance sheet of a nation's central bank when it ceases to be a liability on the balance sheet of one of the nation's commercial banks and gets transferred to the central bank. Instead of printing paper money, the central bank can create CBDCs by opening electronic bank accounts at commercial banks that are backed by the full faith and credit of the nation's central bank. The goal of a CBDC is to provide the nation's government, businesses and consumers with a liquid and accessible currency that supplies privacy, transferability, convenience, and financial security. If CBDCs become highly popular, someday the world might operate with only one global CBDC. [ABSTRACT FROM AUTHOR]

In this paper, a voltage differencing buffered amplifier (VDBA)-based second-order sinusoidal oscillator (SO) configuration, which provides voltage output at low impedance, is proposed. The circuit employs single VDBA, three resistors and two grounded capacitors, which presume its easy monolithic integration with preferred circuit simplicity. The nonidealities of VDBA due to parasitic and tracking errors are taken into consideration to study the nonideal behavior of proposed SO. Mathematical modeling of phase noise is computed in the paper. Active and passive sensitivities are calculated for the proposed configuration and are found to be less than unity rendering circuit insensitive to component variations followed by phase noise study. The functionality of the proposed topology is validated through cadence virtuoso ADE spectre tool at 180-nm technology node. Experimental verification is executed through commercially available ICs AD844 by good results. The total harmonic distortion (THD) is found to be below 3.5%. The observed frequency of oscillation is found to be in close agreement with the theoretical values. The PVT and Monte Carlo statistical analyses are also carried out to confirm the sensitivity of the proposed SO. [ABSTRACT FROM AUTHOR]

This paper aims to comprehensively explore the pivotal role of simulation and modeling in the field of Artificial Intelligence (AI). It focuses on elucidating the diverse applications of simulation and modeling in training AI systems, optimizing algorithms, and enhancing decision-making processes. To achieve this objective, we conducted an extensive review of the literature from the Scopus database, employing a well-defined selection process. We utilized keywords such as "simulation," "modeling," "Artificial Intelligence," and related terms to identify relevant papers published within the last 10 years. The selection criteria included assessing the relevance, quality, contribution, and recent citations of the papers. After a rigorous screening process, we selected 40 papers with the highest overall scores for inclusion in our review. The selected papers encompass a wide range of domains where simulation and modeling play a vital role in advancing AI applications. These domains include manufacturing, healthcare, energy consumption prediction, public sector decision-making, education, environmental modeling, and more. Our review highlights how AI leverages simulation and modeling to improve predictive accuracy, optimize resource allocation, and enhance decision-making processes across diverse sectors. We also discuss the potential future directions in the integration of simulation and modeling with AI, emphasizing its significance in various fields. [ABSTRACT FROM AUTHOR]

Concerns about sustainability challenges are growing. Companies are under pressure to rethink existing innovation paradigms. In this context, research interest in the resource-constrained innovation concept, frugal innovation, has increased. In this paper, we examine the impact of frugal innovation on the economic, social, and ecological sustainability dimensions by conducting a systematic literature review of 44 papers. We find that frugal innovation is a promising approach to promote sustainable development from an economic perspective and a social perspective. Nevertheless, positive and negative effects exist in all three dimensions. Thus, a positive relationship is not inherent but must be proactively formed. External factors can influence the impact of frugal innovation on sustainability. For future research we recommend (1) establishing a unified understanding of frugal innovation through a clear definition, (2) broadening the research focus to a global perspective, (3) assessing the impact of frugal innovation along the entire product life cycle, and (4) investigating characteristics that make frugal innovation sustainable. [ABSTRACT FROM AUTHOR]

In recent years, the healthcare industry has faced numerous challenges in ensuring efficient and reliable supply chain management. The emergence of novel supply chain-based frameworks has presented promising solutions to these challenges. This survey paper aims to explore and analyze the latest advancements in supply chain frameworks specifically tailored for the healthcare industry. We review the existing literature, identify key trends, and highlight the potential impact of these frameworks on improving patient care, optimizing inventory management, enhancing operational efficiency, and reducing costs. Additionally, we discuss the challenges and opportunities associated with the implementation of these frameworks and suggest future research directions. [ABSTRACT FROM AUTHOR]

The current Internet of Things (IoT) technology has entered a relatively mature development stage, and more and more IoT devices can readily access the Internet. However, along with this, the IoT system still faces fragile security of device nodes, easy data tampering, and low system stability. To this end, this paper proposes a smart contract-based security model for IoT systems. The proposal is based on the super ledger Fabric blockchain platform having decentralised, tamper-proof, and programmable features. These features achieve credible authentication of IoT device nodes on the one hand and tamper-proof data storage on the other hand. Further, with these features, we gain a trustworthy environment for enhancing the security of the whole IoT system. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in a significant shift towards electronic commerce (e-commerce), as people have turned to various methods of e-commerce to deal with imposed mobility restrictions. This paper provides a bibliometric analysis of the research conducted in the field of e-commerce, focusing on studies related to the COVID-19 pandemic. The study aims to identify the key features, most influential papers, prevalent themes and methodologies, and the relationship between the terms used in the relevant publications. The data for the analysis was obtained from the Scopus database and consisted of 629 English-language research papers, book chapters, and review papers published between August 2020 and December 2022. BibExcel and VOSviewer software tools were utilized to assist with the selection of relevant information, perform the bibliometric analysis, and generate graphical representations of the results. The analysis indicates that China was the most productive geographical region, with the United States and India following closely behind. Sustainability journal from Switzerland was found to be the most productive in the field, followed by Frontiers in Psychology and the Journal of Retailing and Consumer Services. Bina Nusantara University in Indonesia was identified as the most productive institution, with Bucharest University of Economic Studies in Romania and Zhejiang University in China following closely behind. The most influential paper was found to be by Kim (2020), followed by Dannenberg et al. (2020), and Tran (2021). To address the limitations in the current literature, future research can undertake a comparative study of the impact of the COVID-19 pandemic on the field of e-commerce over time, as well as analyze the dominant themes both prior to and post-pandemic. Investigating the correlation between disruptive events like the COVID-19 pandemic and the factors that drive e-commerce adoption constitutes a worthy and captivating field for future research. [ABSTRACT FROM AUTHOR]

Evolutionary studies of cultural complexity often assume that group members select the best information available in the group, effectively diffusing the best innovations, whose advantages are subsequently passed on to the next generation. This would seem to describe the ideal of the scientific process — each cohort of papers in a field surfacing the best innovations, refining them and passing on to the next "layer" or cohort of scientific works. Here, we use academic journal databases to explore this "forking" (branching) process in the evolution of a scientific paradigm. We apply citation network visualization and Latent Dirichlet allocation topic analysis to three different paradigms defined pragmatically as the set of papers citing a highly influential paper in each respective case. Our three case studies indicate a founder effect in how the seminal paper is highly-embedded in the citation network, and yet peripheral to the evolution of topics in subsequent "layers" of publications within the paradigm. This and additional evidence suggest certain topics are selected and followed, while others are left behind. From these case studies, we discuss how hitherto undeveloped ideas of the past might be located in the topic space of seminal works of the same fruitful time period. [ABSTRACT FROM AUTHOR]

Nowadays, environmental problems have attracted the attention of many craftsmen and researchers. They have developed various policies and practices to protect the environment throughout the supply chain. Since suppliers are one of the supply chain partners, paying attention to these policies by them will be very effective in keeping the supply chain green. In today's dynamic environment, there is uncertainty in all decisions of the organizations. The wood and paper industry also faces uncertainty in choosing green and environmentally friendly suppliers. These uncertainties must be taken into account when selecting suppliers. Decision-making techniques will cover uncertainty if developed in a gray environment. Many techniques have been developed in a gray environment. But using a combination of DANP and MABAC techniques in a gray environment will be very useful because of the benefits of each. Therefore, using the combination of DANP and MABAC in a gray environment in order to select a green supplier in the wood and paper industry is a gap that is felt and in this paper we try to overcome it. The research findings show that the identified criteria will be very effective in selecting the green supplier and Gray DANP–MABAC can be used for managers' decisions in the wood and paper industry. [ABSTRACT FROM AUTHOR]

Ecotourism potential is a fundamental research effort needed to promote sustainable regional tourism development, and scholars have paid greater attention to it in recent years, which has led to an increase in the publication of literature on this important and widely recognized topic. However, there is still no clear picture of the development direction of research on ecotourism potential. To address this issue, this study attempts to provide a comprehensive analysis of the existing research state in this field. This paper presents a more detailed review of ecotourism potential, a bibliometric analysis of 949 articles selected from 1,027 publications retrieved from the Web of Science Core Collection database, and a systematic descriptive and network analysis of the bibliometric data using the Biblioshiny tool. Here are some interesting findings: (1) research on the ecotourism potential in the academy started in 1992, with a rapid growth in the number of publications after 2013, with an overall interdisciplinary character and the emergence of a research community represented by Milcu, Balmford, Gallagher, and others; (2) ecotourism, conservation, tourism, management, and biodiversity are high-frequency keywords in this field, revealing the main research directions; (3) the development and evolution of research focus are mainly on three aspects: wildlife and biodiversity conservation, nature-based tourism and environmental values, and the modes and contributions of community participation. Future research should focus on the scientific references of ecotourism potential for promoting regional sustainable development. This paper sheds some light on the comprehensive bibliometric studies on ecotourism potential and improves our understanding of the evolution of this academic discourse theme over the last three decades. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDGs) serve as a common aim that brings many nations together, led by the United Nations with the purpose to achieve a set of 17 targets by 2030. One very important factor in achieving these aims is finance. The advancement in financial development all around the world will be able to speed up achieving SDGs where governments, policymakers, and people will be able to grow the economic development and infrastructure of the country, provide adequate job opportunities for many people, reduce poverty, as well as improve sustainability around the world. In opposition to that, some researchers see the advancement in financial development as an activity that will jeopardize achieving SDGs as financial development might lead to the production of carbon footprints at a faster rate. Using bibliometric analysis, this paper was able to discover that there is an increase in attention and interest in the field with the rise in publications in recent years, especially in 2022. Furthermore, this paper also identifies research hotspots within the field while also finding and analyzing the most impactful authors and publications on the topic. Ultimately, this paper hopes to provide a better definition for the relationship between financial development and SDGs with the use of bibliometric analysis, where data is collected from the Scopus database and analyzed using VOSviewer. [ABSTRACT FROM AUTHOR]

How to improve the high resolution and remove the noise of composite infrared thermal wave detection image have become important research topics in machine vision. Therefore, this paper proposes a composite image enhancement algorithm based on edge adaptive total variation of direction. Using the improved adaptive directional total variation model to construct the guidance image of the guidance filter can provide better structural information for the guidance filter and conduct the guidance filter. This process is iterated to eliminate noise and ladder effect. Experimental results show that this algorithm is superior to other advanced methods in objective evaluation index and subjective vision. The algorithm proposed in this paper can not only effectively eliminate noise and ladder effect, but also highlight image details and structure information, which proves the effectiveness of this algorithm. Discover the cutting-edge algorithm for high-resolution improvement and noise elimination in infrared thermal wave detection images. Our innovative composite image enhancement technique, based on edge adaptive total variation, outperforms traditional methods. The improved directional model provides better structural information, optimizing the guidance filter process. Experimental results reveal its superiority in objective evaluation and subjective vision. This algorithm effectively eliminates noise and ladder effect while highlighting image details, validating its remarkable effectiveness. [ABSTRACT FROM AUTHOR]

Aiming at the poor transient convergence and real-time performance of the horizontal vibration state variables of the high-speed elevator car system, and the low control accuracy and stability, this paper proposes an adaptive control strategy to ensure the transient response of the elevator car system. First, a prescribed preset transient performance function is introduced into the controller design to control the variation range of the state variables and ensure the steady transient performance of the elevator car; Second, representing the information of observation error/control error through algebraic operations, designing an adaptive law based on e-correction, estimating unknown parameters in the elevator car system, and achieving online parameter updates; Then, using neural networks to learn and compensate for unknown dynamics in the elevator car system, and solving the online estimation problem of neural network weights through adaptive laws, so that the tracking error and weight estimation error converge to a tight set near zero; Finally, using MATLAB/SIMULINK to compare and analyze the four control algorithms of passive control, PID control, adaptive control based on gradient descent method and transient response adaptive control proposed in this paper under two different rail excitations: Random excitation and pulse excitation. The simulation results show that the adaptive control strategy proposed in this paper effectively suppresses the horizontal vibration of the elevator car, makes the state variables have faster convergence speed and smaller convergence error, and ensures the stable and transient performance of the elevator car system. [ABSTRACT FROM AUTHOR]

The author shares his theory on market creation and evolution from his perspective as an academic and a practitioner who has been involved in financial innovation for five decades. Rather than being spontaneously created, markets appear to follow a clear set of steps (the "seven-stage" process) from inception to maturity. It is critical that the benefits from transacting are greater than the costs of establishing the market — otherwise the evolution does not occur. Historical examples of the process outlined by the author can be found in commodity, equity, fixed income and environmental markets. The author provides observations and lessons that his evolutionary framework shares with financial inventive activity in new and existing products in our rapidly evolving digital world. Recent digital innovations such as the blockchain have evolved in a framework that did not require a legislative or regulatory framework (or what the author calls a "permissionless" environment). A properly designed regulatory environment can be permissionless regarding inventive activity (withness the Commodity Futures Trading Commission in the United States). A permissionless environment has historical precedent in other "analog" innovations such as wheat trading in the 19th century or financial futures in the mid-1970s. Following the seven-stage process, those innovations arose from structural changes or latent economic demands. Their standards and infrastructure were only much later understood and adopted by the regulators. Given that, the author suggests that since changes now occur at hyper speed, the implications for the future shape of derivatives markets are likely to be profound. [ABSTRACT FROM AUTHOR]

Despite half a century's extensive scholarly research in the decision-making process of individuals around the adoption of new innovations, innovation practitioners are still not able to capitalise sufficiently on the existing knowledge base to successfully commercialise new products. Evidence of this stems from consumers' continued rejection of new offerings at alarming rates. This makes the marketing of innovation, with a focus on influencing consumers' decision-making process, one of the most challenging corporate challenges for new product managers. To address this gap, this paper sequentially applies a systematic literature review (SLR), followed by extensive descriptive and bibliometric analyses, to reveal and assess the current state of knowledge in this field. Furthermore, the analysis identifies six prominent future research paths that could fill identified gaps in knowledge. Using the results of the bibliometric section, practitioners can pinpoint specific research areas of interest, enabling them to access and utilise findings from past studies more effectively. [ABSTRACT FROM AUTHOR]

The International Journal of Semantic Computing has published a special issue on Semantic Computing, which includes five selected papers from the 17th IEEE International Conference on Semantic Computing. Semantic Computing focuses on the derivation, description, integration, and use of semantics for various resources. The papers in the special issue cover topics such as intent detection, constructing probabilistic models from knowledge graphs, unsupervised estimation of subjective content descriptions, the influence of noisy labels on question answering systems, and automatic domain-adaptive sentiment analysis. Each paper presents innovative approaches and findings in their respective areas of research. [Extracted from the article]

An introduction is presented in which the author discusses articles within the issue on topics including the role of energy security in poverty reduction in least-developed Asian economies, effect of COVID-19 pandemic on poverty in different Asian sub-regions, and impact of natural disasters on energy poverty.

Over the last 25 years, Asia's economic rise has been extraordinary. Its share of global gross domestic product (GDP) has risen from 5.8% to 22.9%. The first phase of high economic growth — up to 1995 — saw Asia enter the global supply chain primarily with labor-intensive/low-cost manufacturing. Domestic consumption was a fairly low share of GDP; Asia was manufacturing mainly for consumption in the US and Europe. As such, it was primarily a rule-taker. In the second phase — from 1995 to 2020 — it gradually turned into an economic force joining the US and Europe in shaping the global economy, exercising significant influence upon the value chain, the cycles of the global economy, transport and logistics, the global capital markets and consumption patterns (consumer preferences and tastes). While not yet among the leading rule-makers, it had become difficult for policymakers (public and private) to make decisions without Asia's consent. To form an opinion of today's emerging third phase — post 2020 — the intriguing question is whether the Asian countries have adopted what may be termed Anglo-American economic thinking (basically, the primacy of the market). Or whether behind the curtain, the Asian economy works in its own way diverging from the American and British economic schools. Since demographics and sheer economic scale mean that Asia will dominate the global economy in the years to come, the nature of the Asian economy will be of crucial importance for the future global economy. The conclusion of this paper is that "Asia" in many respects differs — and fundamentally so — from market economy principles. How this prospect should be interpreted is also evolving, as circumstances change. Certainly, the repercussions of COVID-19 have not been the same in the US, Europe, East Asia and South Asia — and this may suggest that socio-political structures have a stronger impact on economic outcomes than economic theory teaches, thus calling into question the global validity of market economy principles. [ABSTRACT FROM AUTHOR]

Due to increasing environmental regulation and customers' demand for environmentally friendly products, organizations have been required to adopt sustainable manufacturing practices by implementing clean technology (Cleantec) to manufacture green products. By adopting environmental practices, organizations can also achieve qualitative and quantitative benefits that help them remain competitive in the market while meeting governmental environmental policies, such as lowering energy and the cost of materials. The significant number of articles addressing sustainability in manufacturing published in the past few years attests to the importance of the topic. However, not many studies have been developed to understand the decision-making process in sustainable manufacturing. Therefore, the objective of this paper is to conduct a systematic literature review on the application of multi-attribute decision-making (MADM) methods in sustainable manufacturing. A total of 158 papers, published between 2009 and 2018, met the criteria set in the research methodology. The 158 papers were then analyzed and classified into seven categories: (i) SM domain, (ii) activity within the organization, (iii) decision goals, (iv) decision-makers involved (group or individual), (v) uncertain data, (vi) SM aspects (social, environmental, and economic), and (vii) MADM methods. Among the results, we identified that AHP is the most applied MADM method and, regarding the activities of the organization, MADM methods have been the most frequently applied to strategy management and supply chain. We also identified a tendency to consider uncertain and imprecise data in the decision-making process. Another result is that all the three domains — economic, environmental and social — were considered in most of the papers, followed by the combination of the economic and environmental perspectives. In the conclusion, some recent trends and future research directions are highlighted. [ABSTRACT FROM AUTHOR]

With 3D products being widely applied, more attention has focused on studying stereo image super-resolution (SR). Current stereo image SR studies mainly aim to improve the performance by the additional information from a pair of low-resolution stereo images. However, it is challenging for stereo image SR to fully exploit self-similarity information from its own image and parallax information between stereo image pairs. In line with these challenges, this paper presents a Two-Branch Network (TBNet) to integrate self-similarity information and parallax information for SR. In the TBnet, a stereo parallax transfer module with an encoder–decoder structure was first proposed to sufficiently transfer multi-scale parallax information and preserve the stereo consistency between stereo images. This paper further presented a residual pyramid self-attention module to employ self-similarity information to take advantage of self-predictive power. Finally, extensive experiments demonstrate the superiority of our model over the state-of-the-art performance in terms of objective and perceptual quality and the accuracy of disparity estimation. [ABSTRACT FROM AUTHOR]

Traditional requirement verification methods often fail to fully consider the modeling of time-dependent behaviors during system execution, which may lead to inconsistent system requirements, thereby compromise software reliability and correctness. To address this limitation, this paper proposes the Scenario-Specific Verification Method for System Requirements Consistency via Time Modeling. The Scenario-Specific Verification Method converts use case descriptions and problem graphs into Clock Constraint Specification Language (CCSL) constraints, transformed into clock graphs for verifying requirement consistency. The resulting constraints are solved with the Z3 solver to validate correctness, thereby enhancing system reliability. The novel time modeling approach provides techniques for verifying temporal dependencies in requirements. A case study demonstrates the efficiency and accuracy of the Scenario-Specific Verification Method in detecting inconsistencies. The main contributions include that a technique is demonstrated to formalize use case descriptions as CCSL constraints, improving on natural language and a method is presented utilizing CCSL time modeling and clock graphs to analyze requirements for consistency. Overall, an innovative solution for requirement verification using time modeling is offered by this paper, contributing to the enhancement of system development process reliability and correctness. [ABSTRACT FROM AUTHOR]