Key Problem Research Articles

‘라이즈’ 도입과 광주광역시 고등교육의 현황, 문제, 대안

If higher education jurisdiction is partially delegated/transferred to local governments according to the Regional Innovation System and Education (RISE), which is scheduled to be introduced in 2025, Korea's higher education system will experience structural changes. The core content of structural change is the dualization of guidance/supervision authority for higher education institutions. The central government and local governments jointly govern higher education. The purpose of this paper is to discuss win-win cooperation measures between Gwangju City and universities in response to the ‘RISE’. To achieve the purpose of this paper, three topics were intensively discussed based on literature analysis. First, in order to understand the relationship between local governments and universities, the relationship between the two was identified and the meaning of win-win cooperation was critically discussed. Second, in order to understand the current status of higher education in Gwangju city, the reality of higher education in Gwangju was analyzed at the national/regional level, focusing on key indicators. Third, in the problems and alternatives of Gwangju higher education, key problems were pointed out and alternatives were presented based on the four factors that make up the higher education system (purpose structure, governance structure, financial structure, and performance management). In the conclusion, it was emphasized that public value should be emphasized in the direction of innovation in the higher education system at the national/regional level and a special change in perception in local governments' understanding of universities was emphasized.

Blockchain for finance: A survey

AbstractAs an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point‐to‐point transactions, and decentralized autonomous organizations. Here, the authors focus on blockchain‐based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third‐party verification by using consensus‐based verification. The 12 most popular blockchain platforms are investigated and 6 platforms that are related to finance are elaborated on, seeking to provide a panorama of securities trading practices. Meanwhile, this survey provides a comprehensive summary of blockchain‐based securities trading applications. Numerous practical applications of blockchain‐based securities trading are gathered and they are categorized into four distinct categories. For each category, a typical example is introduced and how blockchain contributes to solving the key problems faced by FinTech companies and researchers explained. Finally, interesting observations are provided ranging from mainstream blockchain‐based financial institutions to security issues of decentralized finance applications, aiming to picture the current blockchain ecosystem in finance.

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting.

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human-machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.

USING BIM MODELING FOR THE CALCULATION OF HEAT LOSSES AND GENERAL ENERGY EFFICIENCY AT THE DESIGN STAGE

Problem statement. The issue of energy efficiency is one of the key problems in the construction industry. Increasing attention to this aspect has led to the development of new technologies and tools to improve the energy efficiency of buildings. One of the powerful software complexes for BIM modeling is Revit. The program has built-in tools for analysis. One such tool is Energy Analysis and the My Insight application in Revit software. This tool allows you to calculate the energy consumption of the building and its energy efficiency. With its help, you can perform an analysis of the building's energy consumption in different seasons and under different operating conditions. To achieve energy efficiency of the building in Revit, you can choose materials and change their thermal characteristics or choose materials that have high energy efficiency and durability, develop new material connection nodes and calculate their heat loss. The use of energy-saving technologies, such as energy-efficient windows, solar panels and others, allows you to take into account the contribution of these technologies to the overall energy consumption of the building using the same Insight application that is relevant today. The purpose of this work is to increase the energy efficiency of the house at the design stage using the ВIM model with the help of the Insight cloud service. Conclusions. Energy efficiency improvements in Revit software and its Insight application help reduce building energy consumption and reduce heating and air conditioning costs. This not only helps save the planet's resources, but also reduces costs for building owners. Using these tools allows you to create energy-efficient and climate-resistant buildings, which is an important task in today's world.

Identification and evaluation of the evolution stage of the agricultural machinery industry cluster in Shandong Province

AbstractAgricultural machinery industry clusters have great potential to solve key technological problems in China, and it is crucial to accurately identify the stage of cluster evolution. Based on the location entropy method, this paper finds that the location quotient coefficient is greater than 1.2 and the average annual growth rate is 1.11%, which indicates that the agricultural machinery industry in Shandong Province has a high degree of agglomeration, but the agglomeration speed is slow. Using the Groundings agglomeration—Economic network—Social network—Service system model, it is found that the agricultural machinery industry cluster in Shandong province is in the growth stage, in which the service system has the most significant influence on its development level. The weights of service system, social network, economic network, and basic resource aggregation derived from the Analytic Hierarchy Process model are 0.410, 0.321, 0.151, and 0.118, respectively, where agglomeration degree of the agricultural machinery industry, raw material production of agricultural machinery enterprises, exchange of tacit knowledge and intermediary service level are the four indicators with the greatest weights in the influences on sustainable development of the agricultural machinery industry. Because of the strong fuzzy nature between the indicators, this paper applies the Fuzzy Comprehensive Evaluation method to quantify the stage of evolution of Shandong Province's agricultural machinery industry cluster.

Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road

How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance.

Investigations on magnesium ion conducting gellan gum-based biopolymer blend electrolytes for energy storage applications

The economical and ecological polymer electrolytes are essential for energy storage devices. The key problem we are trying to resolve is how to improve ionic conductivity and safety in polymer electrolytes, which are essential for energy storage devices to operate effectively. We have prepared biopolymer blend electrolytes with different concentrations of magnesium chloride (MgCl2) by solution casting technique and the prepared electrolytes are investigated. Increase of amorphous nature of the polymer membranes is confirmed by X-ray diffraction analysis. Fourier transform infrared (FTIR) spectroscopy used to define complex formation between salt and polymers. Thermal properties of the electrolytes are confirmed by differential scanning calorimetry technique. The higher ionic conductivity of 1.04 × 10−5 S/cm has been achieved for 8 wt.% MgCl2 mixed gellan gum: polyvinyl alcohol (PVA) polymer blend at the room temperature. The present work highlights the preparation and characterization of gellan gum: PVA: MgCl2 polymer blend electrolytes with enhanced ionic conductivity and safety. The optimized polymer electrolyte is applied for energy storage devices.

Coupling coordination relationship of water resources, eco-environment and socio-economy in the water-receiving area of the Lower Yellow River

Fully understanding the interrelationship and the coordinated development level of water resources (WR), eco-environment (EE) and socio-economy (SE) is an essential precondition for regional sustainable development. We constructed a complex analytical framework consisting of WR, EE and SE subsystems in the water-receiving area of the Lower Yellow River, and quantitatively investigated the coupling coordination relationship among them. An improved obstacle degree model was developed to analyze the level and the major obstacles of coordinated development from 2001 to 2020, together with the coupling coordination degree model. A prediction model combined by the autoregressive integrated moving average model, the gray model and the artificial neural network model was built to explore the development trend from 2021 to 2025. It was found that (1) the coordinated development level showed a continuous growth from reluctant coordination in 2001–2002 to high coordination in 2015–2020. Spatially, Shandong province was slightly more coordinated than that of Henan province on the whole. (2) With sustained rapid SE development and growing pressure on water supply and environmental capacity, the major obstacles for coordinated development changed from SE factors to WR and EE factors before and after 2010. (3) The coordinated development level will continue to increase in 2021–2025, especially in the “promote WR and EE protection” scenario. In the future, water scarcity will become the key problems for coordinated development. Taking account of the available water diversion from the Yellow River, local government should put more effort into water use efficiency improvement. These results offer an important reference for decision-making and management of regional sustainable development.

Health Management of Bearings Using Adaptive Parametric VMD and Flying Squirrel Search Algorithms to Optimize SVM

Bearing, as one of the core parts of rotating machinery, has a running state which is related to the overall operation of the system. Due to the bearing structure and its complex operating environment, running condition monitoring and fault diagnosis is always a key problem in the field of bearing health management, which is of great significance for bearing maintenance and equipment reliability and safety. In view of the difficulty in parameter selection and poor feature extraction ability of variational mode decomposition (VMD) in existing feature extraction, this paper uses the flying squirrel search algorithm (SSA) to optimize the parametric of decomposition layer k and penalty factor α in VMD, and forms an adaptive VMD signal decomposition method. To solve the problem of high dimensionality and long extraction time of multi-domain fault feature set, kernel principal component analysis (KPCA) is used to reduce feature dimensionality. Then, the processed features are input into the support vector machine (SVM) for fault diagnosis and classification, and the parameter optimization ability of SSA is used again to build the SSA-SVM fault diagnosis model. To evaluate the running state of bearings, an alarm threshold method based on the root mean square value calculated by cosine similarity and 3σ is proposed to divide samples of different health states. Finally, the method constructed in this paper is compared with other methods by using simulation and experimental data sets, and the running condition monitoring and fault diagnosis of rolling bearings are successfully realized, which shows the superiority and effectiveness of the method proposed in this paper.

Simcryocluster: a semantic similarity clustering method of cryo-EM images by adopting contrastive learning

BackgroundCryo-electron microscopy (Cryo-EM) plays an increasingly important role in the determination of the three-dimensional (3D) structure of macromolecules. In order to achieve 3D reconstruction results close to atomic resolution, 2D single-particle image classification is not only conducive to single-particle selection, but also a key step that affects 3D reconstruction. The main task is to cluster and align 2D single-grain images into non-heterogeneous groups to obtain sharper single-grain images by averaging calculations. The main difficulties are that the cryo-EM single-particle image has a low signal-to-noise ratio (SNR), cannot manually label the data, and the projection direction is random and the distribution is unknown. Therefore, in the low SNR scenario, how to obtain the characteristic information of the effective particles, improve the clustering accuracy, and thus improve the reconstruction accuracy, is a key problem in the 2D image analysis of single particles of cryo-EM.ResultsAiming at the above problems, we propose a learnable deep clustering method and a fast alignment weighted averaging method based on frequency domain space to effectively improve the class averaging results and improve the reconstruction accuracy. In particular, it is very prominent in the feature extraction and dimensionality reduction module. Compared with the classification method based on Bayesian and great likelihood, a large amount of single particle data is required to estimate the relative angle orientation of macromolecular single particles in the 3D structure, and we propose that the clustering method shows good results.ConclusionsSimcryoCluster can use the contrastive learning method to perform well in the unlabeled high-noise cryo-EM single particle image classification task, making it an important tool for cryo-EM protein structure determination

The framework of multi-target tracking based on neural network and motion model prediction

Multi-target tracking technology is a key problem in many application areas, including robotics, video surveillance, and autonomous driving, and its purpose is to find tracking targets that match the characteristics in a continuous image or sensing sequence information and to form a reasonable trajectory for each target. This paper proposed a method that combines the two main existing approaches for multi-target tracking by applying the Kalman filter for motion model prediction to support the neural network target tracking under poor visibility and target shield.

Green Printing for Scalable Organic Photovoltaic Modules by Controlling the Gradient Marangoni Flow.

Despite the rapid development in the performances of organic solar cells (OSCs), high-performance OSC modules based on green printing are still limited. The severe Coffee-ring effect (CRE) is considered to be the primary reason for the nonuniform distribution of active layer films. To solve this key printing problem, the cosolvent strategy is presented to deposit the active layer films. The guest solvent Mesitylene with a higher boiling point and a lower surface tension is incorporated into the host solvent o-XY to optimize the rheological properties, such as surface tension and viscosity of the active layer solutions. And the synergistic effect of inward Marangoni flow generation and solution thickening caused by the cosolvent strategy can effectively restrain CRE, resulting in highly homogeneous large-area active layer films. In addition, the optimized crystallization and phase separation of active layer films effectively accelerate the charge transport and exciton dissociation of devices. Consequently, based on PM6:BTP-eC9 system, the device prepared with the co-solvent strategy shows the a power conversion efficiency of 17.80%. Moreover, as the effective area scales to 1 and 16.94 cm2, the recorded performances are altered to 16.71% and 14.58%. This study provides a universal pathway for the development of green-printed high-efficiency organic photovoltaics.

Modeling the Temporal Response of Gated ZnO Nanowire Field Emitter by Considering the Charging and Self-Heating Effect for Improving the Response Speed

ZnO nanowire is a promising candidate for large-area gated field emitter arrays. How to improve its temporal response is one of the key problems to be solved for applications. In this work, a device model for a gated ZnO nanowire field emitter with consideration of charging and self-heating effect has been established to investigate its temporal response. It is found that while the charging effect is responsible for the delay at the beginning of the pulse, the self-heating effect which induces delay due to the thermal conduction process can shorten the charging time because of its lowering of nanowire resistance. The response time can be minimized when these two effects are balanced at an optimal field which is below the critical field for thermal runaway. We further investigate the optimal response time of a nanowire with the same resistance but a different length, radius, and electrical properties. The results imply that a lower heat capacity and higher critical temperature for thermal runaway are in favor of a shorter response time, which must be taken into account in the reduction in nanowire resistance for improving response speed. All the above should be useful for the device design of a fast-response gated ZnO nanowire field emitter array.

Transductive meta-learning with enhanced feature ensemble for few-shot semantic segmentation

This paper addresses few-shot semantic segmentation and proposes a novel transductive end-to-end method that overcomes three key problems affecting performance. First, we present a novel ensemble of visual features learned from pretrained classification and semantic segmentation networks with the same architecture. Our approach leverages the varying discriminative power of these networks, resulting in rich and diverse visual features that are more informative than a pretrained classification backbone that is not optimized for dense pixel-wise classification tasks used in most state-of-the-art methods. Secondly, the pretrained semantic segmentation network serves as a base class extractor, which effectively mitigates false positives that occur during inference time and are caused by base objects other than the object of interest. Thirdly, a two-step segmentation approach using transductive meta-learning is presented to address the episodes with poor similarity between the support and query images. The proposed transductive meta-learning method addresses the prediction by first learning the relationship between labeled and unlabeled data points with matching support foreground to query features (intra-class similarity) and then applying this knowledge to predict on the unlabeled query image (intra-object similarity), which simultaneously learns propagation and false positive suppression. To evaluate our method, we performed experiments on benchmark datasets, and the results demonstrate significant improvement with minimal trainable parameters of 2.98M. Specifically, using Resnet-101, we achieve state-of-the-art performance for both 1-shot and 5-shot Pascal-5i\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5^{i}$$\\end{document}, as well as for 1-shot and 5-shot COCO-20i\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20^{i}$$\\end{document}.

Varieties of just transitions in the European car industry

ABSTRACT This article examines the responses and strategies developed by business, unions, and governments to the electric turn in the industry in Germany and France, Europe’s main car-producing countries. We concentrate on the role of history and institutions in the determination of adjustment paths. Since institutions reflect specific histories, the electric transition in the industry can take on different forms in different countries. In both countries, governments play a supportive role, leading in France, and following in Germany. The strong works councils in German car companies are reluctant to engage in a rapid transition that would devalue the assets of the workforce and endanger past investments in internal combustion-related technology. Trade unions, in contrast, who organise the workforce in the wider industry, are in favour of a faster transition as it will secure future employment. The French EV industry, in contrast, is now a booming sector, after several decades of deep restructuring with massive employment losses. Its key short-term problem is to train enough workers to staff the rapidly expanding car battery industry. Lacking a deeply rooted training system like the German one, the industry has a relatively free hand in selecting and preparing its future workforce.

A Poloxamer 407/chitosan-based thermosensitive hydrogel dressing for diabetic wound healing via oxygen production and dihydromyricetin release

The current clinical treatment of diabetic wounds is still based on oxygen therapy, and the slow healing of skin wounds due to hypoxia has always been a key problem in the repair of chronic skin injuries. To overcome this problem, the oxygen-producing matrix CaO2NPS based on the temperature-sensitive dihydromyricetin-loaded hydrogel was prepared. In vitro activity showed that the dihydromyricetin (DHM) oxygen-releasing temperature-sensitive hydrogel composite (DHM-OTH) not only provided a suitable oxygen environment for cells around the wound to survive but also had good biocompatibility and various biological activities. By constructing a T2D wound model, we further investigated the repairing effect of DHM-OTH on chronic diabetic skin wounds and the mechanisms involved. DHM-OTH was able to reduce inflammatory cells and collagen deposition and promote angiogenesis and cell proliferation for diabetic wound healing. These in vitro and in vivo data suggest that DHM-OTH accelerates diabetic wound repair as a novel method to efficiently deliver oxygen to wound tissue, providing a promising strategy to improve diabetic wound healing.

An adaptive protection coordination for microgrids utilizing an improved optimization technique for user-defined DOCRs characteristics with different groups of settings considering N-1 contingency

Due to the increased integration of distributed generation (DG) in electric power systems, optimal coordination of overcurrent relays has become a key problem in power distribution systems. However, there is a lack of expertise in the creation of optimum microgrid coordination that takes into account all N-1 scenarios through the nonstandard relay characteristics. This work presents a novel technique for optimal coordination of directional overcurrent relays (DOCRs) in terms of relay curve settings (A and B), time dial setting (TDS) and plug setting (PS) to achieve the shortest running time and attain optimal settings. The optimization is carried out using a modified version of the Hanger Games Search algorithm (MHGS). The performance of the proposed method is assessed using the 14 bus distribution system while considering all the N-1 contingencies. DIgSILENT software was utilized to perform the required power system analysis, such as power flow and short circuit analysis. The MHGS method is used to determine the best settings for the DOCRs problem. The results are compared to the traditional HGS as well as those obtained by other current optimization approaches provided in the literature in order to demonstrate the effectiveness and superiority of the proposed MHGS in lowering relay operation time for optimum DOCRs coordination.

Brood protection is essential but not sufficient for population survival of lapwings Vanellus vanellus in central Switzerland

Lapwing are among those ground nesting bird species that suffered strong population declines following agricultural intensification in many parts of Europe. The key problem appears to be a low breeding success which, depending on the situation, may be due to predation of eggs and chicks, starvation of chicks especially during dry conditions, agricultural activities leading to direct killings, and suboptimal breeding habitat. Here, we report on a population of 40–60 lapwing breeding pairs in an intensively cultivated arable landscape in central Switzerland, where protection from farming activities, implementation of special lapwing habitat and nest fencing to exclude terrestrial predators (mainly red fox) started in 2005 and is still ongoing. Chicks were ringed and families subsequently observed every 2–3 days. Hatching success for fenced nests built before May was high. Low hatching success was observed in unfenced nests due to high predation rates and in late nests due to abandonment by the female when the vegetation was growing too tall (e.g. maize). Regularly, chicks disappeared during the night shortly after hatching. Most likely many of them first left the fence and were predated outside. Our observations from a fenced field with wet soils and puddles suggest that lapwing may produce sufficient offspring if predation can be reduced and if large enough areas with suitable habitat are available.

Applied Capabilities of the Theory of Fictive Capital: the Concept of a New Economic Reality

After the global crisis of 2008–2009 a new economic reality was established in the socio-economic system, one of the signs of which was the overaccumulation of complex problems generated by transformations, for the solution of which the possibilities of fictitious capital are in demand. The disclosure of such possibilities presupposes further development of the theory of fictitious capital. The article explores the applied possibilities of the theory of fictitious capital, arising in the specific conditions of the new economic reality, associated with meeting the needs of two processes: an accelerated transition to a “green economy”; expanded generation of intellectual capital. The focus of the study is on additional opportunities for solving the key problems of the new economic reality associated with the intensive development of the theory of fictitious capital and the disclosure of its additional applied potential, adequate to the conditions of this reality. The study reveals the growing needs of the socio-economic system, in which a new economic reality has been established, in a comprehensive solution of many interrelated problems caused by technological shifts and socio-economic transformations and hindering the further sustainable and effective development of this system. For a comprehensive solution of such problems, it is necessary to solve the following tasks: to find qualitatively new resources for an accelerated transition to a “green economy”; ensure the expanded generation of intellectual capital, which has become a key factor in modern development. The theory of fictitious capital, adapted to the conditions of the new economic reality, reveals the application possibilities of fictitious capital in relation to the specified tasks, which additionally confirms the validity of the conclusion that a good theory is the most practical. Implementation of the applied possibilities of fictitious capital makes it possible to identify and practically apply qualitatively new resources for an accelerated transition to a “green economy”, as well as to ensure the expanded generation of intellectual capital, which plays a key role in resolving the complex problems of the new economic reality. At the same time, in both cases, the starting point for such a sale is debt securities designed and issued for a period corresponding to the duration of the processes whose needs they provide.

After truth, after shame … after information politics? Rethinking the epistemologies of human rights in the digital-authoritarian conjuncture

ABSTRACT Since the contemporary, mainstream human rights movement rose to prominence in the late-1970s, both knowing about and doing human rights has been marked by a very particular mode of activism, information politics, which mobilises ‘brute' facts with the aim of shaming governments into reform. However, this article suggests that the global rise of the far-right has intertwined with reactionary forms of digital culture, engendering both the retreat of shame as an operative concept in global affairs and nourished a growing ‘post-truth' suspicion of facts. The article theorises this problem as a conjunctural shift that represents both a political and epistemological crisis for human rights. Following the lead of debates within Science and Technology Studies (STS) and media studies, I contend that the present conjuncture requires us to consider what comes after information politics by re-imagining its epistemological basis. The article brings together Donna Haraway's theoretical work on ‘Situated Knowledges', with Maurizio Lazzarato’s political epistemology to develop a more perspectival, combative approach to human rights knowledge-making. I argue this epistemic framework is able not only to navigate key problems posed by the digital-authoritarian present but also to address many issues with information politics identified by earlier critics.