Reviewerly/exHarmony huggingface dataset:
Uprooted minds. A social psychoanalysis for precarious times <b>Uprooted minds. A social psychoanalysis for precarious times</b> . 2nd Edition: by Nancy Caro Hollander. Routledge, New York, 2023, pp. 516, 130.00 £ (hb), 32.99 £ (pb) ISBN 9781032387109

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsMariana VelykodnaMariana Velykodna has a PhD in Psychological Sciences, is a Clinical Psychologist, Head of the Psychoanalytic Psychology and Psychotherapy Division of the National Psychological Association of Ukraine, certified Psychoanalyst of the Ukrainian Association of Psychoanalysis, certified Psychoanalytic Psychotherapist of the European Confederation of Psychoanalytic Psychotherapies, Psychoanalyst in private practice (Kyiv, Ukraine), and an Associate Professor of Practical Psychology Department, at Kryvyi Rih State Pedagogical University (Kryvyi Rih, Ukraine).
Obstacle avoidance USV in multi-static obstacle environments based on a deep reinforcement learning approach

Unmanned surface vehicles (USVs) are intelligent platforms for unmanned surface navigation based on artificial intelligence, motion control, environmental awareness, and other professional technologies. Obstacle avoidance is an important part of its autonomous navigation. Although the USV works in the water environment (e.g. monitoring and tracking, search and rescue scenarios), the dynamic and complex operating environment makes the traditional methods not suitable for solving the obstacle avoidance problem of the USV. In this paper, to address the issue of poor convergence of the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm of Deep Reinforcement Learning (DRL) in an unstructured environment and wave current interference, random walk policy is proposed to deposit the pre-exploration policy of the algorithm into the experience pool to accelerate the convergence of the algorithm and thus achieve USV obstacle avoidance, which can achieve collision-free navigation from any start point to a given end point in a dynamic and complex environment without offline trajectory and track point generation. We design a pre-exploration policy for the environment and a virtual simulation environment for training and testing the algorithm and give the reward function and training method. The simulation results show that our proposed algorithm is more manageable to converge than the original algorithm and can perform better in complex environments in terms of obstacle avoidance behavior, reflecting the algorithm’s feasibility and effectiveness.
NORTH EASTERN YAKUTIA GOLD MINERAL RAW MATERIAL BASE: ANALYSIS, EVALUATION AND PROSPECTS

The paper presents a detailed review of ore and alluvial gold mineral raw material base of North East of Yakutia and estimates the economic efficiency of development of gold deposits not involved in the exploitation. Research methods: analysis and comparison, qualitative and quantitative features of the mineral raw material base of Yakutia, formed and replenished databases on a number of deposits of the region; methods of analogy, economic-statistical, methods of system analysis and decision support, evaluation of efficiency of development of deposits. The article provides a detailed analysis of the distributed and undistributed fund of ore and placer gold deposits and shows that the level of the distributed fund is quite high. It is noted that there are certain reserves of a promising and developed gold raw material base in the North-East of Yakutia. The authors identify factors that reduce the efficiency of development of the mineral resource base of gold in the North-East of Yakutia: territorial separation, location in areas with weak or absent infrastructure, availability, for the most part, of small and medium-sized stocks at high levels. The article shows that objects of ore and placer gold due to the complexity of geological and industrial types of objects have increased operating and capital costs and infrastructure limitations. This article presents the authors' developed set of geotechnological and economic measures that can increase the efficiency of the exploited deposits, involve in the development of small and medium-sized gold ore objects. The article provides the calculations which show that the mineral raw material base of ore and placer gold of the North-Aast of the Republic of Sakha (Yakutia) is sufficient to maintain the annual metal production at more than 20 tons per year, and the raw material potential of this region enhances to increase this level for a long time with appropriate investments in geological exploration. The assessment of economic efficiency confirms that the introduction of gold ore and placer deposits in the North-East of Yakutia to the economic circulation generates substantial income to investors and create new jobs.
RENEWABLE ENERGY - FLOATING SOLAR FARMS IN ROMANIA

The paper presents the possibility of floating photovoltaic farms on the reservoirs of the Olt river in Romania. For the analysis, a database with the solar irradiance evolution for one year in the four possible locations was created, using the PVGIS-SARAH2 database. The locations were chosen according to the surface area of the reservoir, geographical position, and possibility of connection to the national energy system. After the analysis, it was observed that during the cold season, the solar irradiance is higher in locations 1 and 2, while locations 3 and 4 show higher irradiance during the warm season. Simulations of electricity production were carried out for the location of solar farms, with an installed capacity of 1 MWh, with a southern positioning of the panels having different tilt angles of: 0, 15, 30 and 45 degrees. The paper continues with a statistical analysis of the monthly and annual electricity production when installing solar farms in the analysed locations.
APPROACH TO THE VISUALIZATION OF TIMELINES OF IMAGES GENERATED BY FORECASTING MODELS

In this paper, we address some challenges of protracted loading times and reduced performance when managing large image sets delivered as output by popular meso-scale weather and air-quality models when used in graphic engines of associated software applications. Following an examination of several distinguished models' output, we introduce an innovative approach centered on a three-tier software system architecture, supplemented by strategies including lazy-loading, image compression, and load stream prioritization. This design is fully compatible with contemporary software technologies, enabling efficient implementation. We validate our proposed architecture through the development of a prototype web application using React and Next.JS, offering a comparative analysis of its performance. Additionally, we delve into the some noteworthy implementation issues encountered and contemplate the potential application of the concept suggesting alternative suites of app development technologies.
Drivers of the next-minute Bitcoin price using sparse regressions

Purpose This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading? Design/methodology/approach Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted. Findings Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information. Originality/value To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.
The Main Research Directions in the Study of Regional Book Culture: on the Example of the Republic of Buryatia

In recent years, the number of research on regional book culture has increased. This trend can be traced in Buryatia, where, on the contrary to the consistency of surveys carried on during the Soviet period, they are scattered today. The lack of the holistic idea of the main directions to study Buryatia book culture in different historical periods dictates the need to summarize information on the research carried out, to determine the degree of development of the scientific problem. The purpose of the article is to analyze published sources reflecting various aspects of the development of Buryatia book culture. As a result, a historiographic analysis of scientific works devoted to the history and modern state of book publishing, book distribution, printing industry, republican periodicals was carried out, the research activities of the Department of Library and Information Resources of VSGIK in this direction were described. Summing up the results of many years of work on the study of the topic made it possible to identify gaps and determine the prospects for its further development.
Forensic discrimination between traditional and compressive imaging by blurring kernel investigation

Abstract Image forensics encompasses a set of scientific tests to investigation of a suspected event via intrinsic clues of imaging pipeline. Traditional image sensing at the Nyquist-Shannon rate as well as the new modality of compressive imaging below the rate are two main types of sensing in photography and imaging applications. Hence, for forensic investigators, it would importantly necessitate the ability to discriminate among images captured by them. However, due to the complex nonlinear nature of imaging processes, investigating imagers’ traces is a difficult task. To this intent, we first systematically model the imaging pipelines as an encoder-decoder pair. For exploring distinguishable traces, we mathematically simplify and linearize the pair for compressive imaging and two main forms of traditional image sensing with or without compression. Our theoretical analyses on the approximate linear models reveal blurring kernels of different imagers have discriminability. To validate it in real-world scenarios, we considered the whole imaging process as an inverse problem and estimated the blurring kernel based on a deconvolution approach, where the discriminability is also justified by information visualization. Then, we designed a pipeline classification system, where a deep convolutional neural network is trained by the estimated blurring kernels to be able to classify the three imaging systems. Our results in compressive imaging identification show an accuracy improvement about 3.7 % in comparison to the best result among compared methods. Implementation codes are available for research and development.
PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

The rapid development of electric vehicles provides users with convenience of life. When users enjoy the V2G charging service, privacy leakage of their charging location is a crucial security issue. Existing privacy-preserving algorithms for EV access to charging locations suffer from the problem of nondefendable background knowledge attacks and privacy attacks by untrustworthy third parties. We propose a personalized location privacy protection scheme (PPVC) based on differential privacy to meet users’ personalized EV charging requirements while protecting their privacy. First, by constructing a decision matrix, PPVC describes recommended routes’ utility and privacy effects. Then, a utility model is constructed based on the multiattribute theory. The user’s privacy preferences are integrated into the model to provide the route with the best utility. Finally, considering the privacy preference needs of users, the Euclidean distance share is used to assign appropriate privacy budgets to users and determine the generation range of false locations to generate the service request location with the highest utility. The experimental results show that the proposed personalized location privacy protection scheme can meet the service demands of users while reasonably protecting their privacy to provide higher service quality. Compared with existing solutions, PPVC improves the charging efficiency by up to 25%, and 8% at the same privacy protection level.
Path Optimization Using Metaheuristic Techniques for a Surveillance Robot

This paper presents an innovative approach to optimize the trajectories of a robotic surveillance system, employing three different optimization methods: genetic algorithm (GA), particle swarm optimization (PSO), and pattern search (PS). The research addresses the challenge of efficiently planning routes for a LiDAR-equipped mobile robot to effectively cover target areas taking into account the capabilities and limitations of sensors and robots. The findings demonstrate the effectiveness of these trajectory optimization approaches, significantly improving detection efficiency and coverage of critical areas. Furthermore, it is observed that, among the three techniques, pattern search quickly obtains feasible solutions in environments with good initial trajectories. On the contrary, in cases where the initial trajectory is suboptimal or the environment is complex, PSO works better. For example, in the high complexity map evaluated, PSO achieves 86.7% spatial coverage, compared to 85% and 84% for PS and GA, respectively. On low- and medium-complexity maps, PS is 15.7 and 18 s faster in trajectory optimization than the second fastest algorithm, which is PSO in both cases. Furthermore, the fitness function of this proposal has been compared with that of previous works, obtaining better results.
Reconstructed Prototype Network Combined with CDC-TAGCN for Few-Shot Action Recognition

Research on few-shot action recognition has received widespread attention recently. However, there are some blind spots in the current research: (1) The prevailing practice in many models is to assign uniform weights to all samples; nevertheless, such an approach may yield detrimental consequences for the model in the presence of high-noise samples. (2) Samples with similar features but different classes make it difficult for the model to be distinguished. (3) Skeleton data harbors rich temporal features, but most encoders face challenges in effectively extracting them. In response to these challenges, this study introduces a reconstructed prototype network (RC-PN) based on a prototype network framework and a novel spatiotemporal encoder. The RC-PN comprises two enhanced modules: Sample coefficient reconstruction (SCR) and a reconstruction loss function (LRC). SCR leverages cosine similarity between samples to reassign sample weights, thereby generating prototypes robust to noise interference and more adept at conveying conceptual essence. Simultaneously, the introduction of LRC enhances the feature similarity among samples of the same class while increasing feature distinctiveness between different classes. In the encoder aspect, this study introduces a novel spatiotemporal convolutional encoder called CDC-TAGCN. The temporal convolution operator is redefined in CDC-TAGCN. The vanilla temporal convolution operator can only capture the surface-level characteristics of action samples. Drawing inspiration from differential convolution (CDC), this research enhances TCN to CDC-TGCN. CDC-TGCN allows for the fusion of discrepant features from action samples into the features extracted by the vanilla convolutional operator. Extensive feasibility and ablation experiments are performed on the skeleton action dataset NTU-RGB + D 120 and Kinetics and compared with recent research.
Students’ internal driving force or environment external driving force? Configuring digital learning power heterogeneity in a smart education environment

Smart education, with its intelligent, individualized, and technologized content, represents people’s lofty expectations for future education. It provides a good learning platform for teaching and an important environment in which students’ digital learning power can be developed in the context of the information technology era. Digital learning power is the ability to use and study information technology. Based on the Ecosystem Theory, this study applied the fsQCA method for analyzing the effectiveness of the development paths of digital learning power in a smart education environment. The study verified that different configurations could achieve a same goal, thus refreshing the understanding of teachers and students on how multiple concurrent factors can influence digital learning power. The key to teaching in a smart education environment relies on how teachers understand their students and how they boost their students’ internal learning driving forces instead of depending on intelligent technologies. This study shows that in order to develop students’ digital learning power in a smart education environment, the coordination between students and technologies, rather than the application of a single intelligent device, should be emphasized.
Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors

The COVID-19 pandemic has sped up the acceptance of online education as a substitute for conventional classroom instruction. E-Learning emerged as an instant solution to avoid academic loss for students. As a result, educators and academics are becoming more and more interested in comprehending how students behave in e-learning settings. Behavior analysis of students in an e-learning environment can provide vision and influential factors that can improve learning outcomes and guide the creation of efficient interventions. The main objective of this work is to provide a system that analyzes the behavior and actions of students during e-learning which can help instructors to identify and track student attention levels so that they can design their content accordingly. This study has presented a fresh method for examining student behavior. Viola–Jones was used to recognize the student using the object’s movement factor, and a region-shrinking technique was used to isolate occluded items. Each object has been checked by a human using a template-matching approach, and for each object that has been confirmed, features are computed at the skeleton and silhouette levels. A genetic algorithm was used to categorize the behavior. Using this system, instructors can spot kids who might be failing or uninterested in learning and offer them specific interventions to enhance their learning environment. The average attained accuracy for the MED and Edu-Net datasets are 90.5% and 85.7%, respectively. These results are more accurate when compared to other methods currently in use.
The impact of VR and AR technologies on the academic achievements of medical students: the age aspect

ABSTRACTThe use of the innovative technologies, in particular virtual and augmented reality technologies, can have a significant impact on the training of future doctors. The sample set of the experiment consisted of 211 students from I.M. Sechenov First Moscow State Medical University. The students were divided into 4 age groups: full-time students of the Faculty of Medicine (Institute of Clinical Medicine) (group 1) and students of the Faculty of Postgraduate Education (groups 2, 3 and 4). To diagnose age-related parameters of readiness for learning using virtual technologies, the McVeigh’s questionnaire with 13 items was used. The results of the study revealed that students who studied using virtual reality technologies showed significantly better results than students of the control group. At the same time, differences were noticed in terms of the factors affecting the quality of digital learning among students of different ages. In the future, scientists may pay attention to the possibility of using AI technologies or deep learning to improve the knowledge of medical students. Additionally, researchers may consider gender, demographic and cultural aspects in the analysis of their differences in adaptation to the digital environment of education.KEYWORDS: AgeARdigital educationinfluencemedicineVR Data availabilityData will be available on request.Disclosure statementNo potential conflict of interest was reported by the author(s).
Controllable Unsupervised Snow Synthesis by Latent Style Space Manipulation

In the field of intelligent vehicle technology, there is a high dependence on images captured under challenging conditions to develop robust perception algorithms. However, acquiring these images can be both time-consuming and dangerous. To address this issue, unpaired image-to-image translation models offer a solution by synthesizing samples of the desired domain, thus eliminating the reliance on ground truth supervision. However, the current methods predominantly focus on single projections rather than multiple solutions, not to mention controlling the direction of generation, which creates a scope for enhancement. In this study, we propose a generative adversarial network (GAN)-based model, which incorporates both a style encoder and a content encoder, specifically designed to extract relevant information from an image. Further, we employ a decoder to reconstruct an image using these encoded features, while ensuring that the generated output remains within a permissible range by applying a self-regression module to constrain the style latent space. By modifying the hyperparameters, we can generate controllable outputs with specific style codes. We evaluate the performance of our model by generating snow scenes on the Cityscapes and the EuroCity Persons datasets. The results reveal the effectiveness of our proposed methodology, thereby reinforcing the benefits of our approach in the ongoing evolution of intelligent vehicle technology.
Fixed‐time adaptive tracking control for MIMO nonlinear system with input delay saturation based on echo state network

Abstract The paper expounds the question of echo state network‐based fixed‐time adaptive tracking control for a type of multi‐input multi‐output nonlinear strict‐feedback system with input delayed saturation. Based on the feature that echo state network can obtain better estimation performance at lower computational cost, it is utilized to approximate the unknown nonlinear function in the controller design course. By constructing an auxiliary system, a time‐delay system with input saturation is eliminated. Moreover, command filtering techniques can be applied to avoid the “complexity explosion” problem in the backstepping method. Then, the closed‐loop system is attested to be semi‐global practical fixed‐time stable. In the end, a simulation shows the valid scheme.