bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalence of depression and its correlation with anxiety, headache and sleep disorders among medical staff in the Hainan Province of China
Authors: Lu Guangshuang , Xiao Shaobo , He Jiaji , Xie Wei , Ge Wei , Meng Fanchao , Yang Ying , Yu Shengyuan , Liu Ruozhuo
Published: June 2023

Objective

This cross-sectional survey aimed to investigate the prevalence of depression among medical staff and its risk factors as well as the association between depression, anxiety, headache, and sleep disorders.

Methods

Stratified random cluster sampling was used to select medical staff from various departments of four hospitals in Sanya City. The Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Pittsburgh Sleep Quality Index (PSQI) were used to quantitatively assess depression, anxiety, and sleep disorders. Correlation and regression analyses were performed to determine factors affecting the depression occurrence and scores.

Results

Among 645 medical staff members, 548 (85%) responded. The 1-year prevalence of depression was 42.7% and the prevalence of depression combined with anxiety, headache, and sleep disorders was 23, 27, and 34.5%, respectively. The prevalence of depression in women, nurses, the unmarried or single group, and the rotating-shift population was significantly higher than that in men (48.3% vs. 27.1%, odds ratio OR = 2.512), doctors (55.2% vs. 26.7%, OR = 3.388), the married group (50.5% vs. 35.8%, OR = 1.900), and the day-shift population (35.2% vs. 7.5%, OR = 1.719). The occurrence of depression was correlated with anxiety, sleep disorders, headache, and migraines, with anxiety having the highest correlation (Spearman’s Rho = 0.531). The SDS was significantly correlated with the SAS and PSQI (Spearman’s Rho = 0.801, 0.503) and was also related to the presence of headache and migraine (Spearman Rho = 0.228, 0.159). Multiple logistic regression indicated that nurse occupation and anxiety were risk factors for depression, while grades of anxiety, sleep disorders and nurse occupation were risk factors for the degree of depression in multiple linear regression.

Conclusion

The prevalence of depression among medical staff was higher than that in the general population, especially among women, nurses, unmarried people, and rotating-shift workers. Depression is associated with anxiety, sleep disorders, headache, and migraines. Anxiety and nursing occupation are risk factors for depression. This study provides a reference for the promotion of occupational health among medical professionals.
How does social media use influence the mental health of pancreatic cancer patients: a chain mediating effect of online social support and psychological resilience
Authors: Wang Yi , Bao Sheng , Chen Yubing
Published: June 2023

Background

Pancreatic cancer is an extremely malignant disease that poses a serious threat to the mental health of patients. Many cancer patients now use social media for online social support. However, the impact of social media on mental health is currently inconsistent in the academic community. Therefore, this study aimed to examine the mediating effects of online social support and psychological resilience in the relationship between social media use and mental health of pancreatic cancer patients.

Methods

Four hundred and twenty-five valid questionnaires were collected through convenience sampling. All data were processed using SPSS 26.0 and AMOS 26.0. We examine the influence relationships among latent variables by constructing a structural equation model. Then SPSS Process Macro was used to test the chain mediating effect of the model.

Results

The results showed that (1) anxiety situations occurred in 22.2% of participants (N = 94), while the incidence of depression was 20.2% (N = 86). (2) Social media use positively influenced online social support (β = 0.990, p < 0.001), psychological resilience (β = 0.504, p < 0.001), and mental health (β = 0.330, p < 0.001); online social support positively influenced psychological resilience (β = 0.535, p < 0.001) and mental health (β = 0.354, p < 0.001); psychological resilience significantly and positively influenced mental health (β = 0.243, p < 0.001). (3) The chain mediating effect of online social support and psychological resilience was significant at 0.253 with a confidence interval of [0.178, 0.340].

Conclusion

Pancreatic cancer patients in China are exposed to a high burden of anxiety and depression, which requires urgent attention. Meanwhile, online social support and psychological resilience played a chain mediating role between social media use and mental health (anxiety and depression), and our results provide new insights and ways to support the mental health improvement of pancreatic cancer patients.
Localizing SDGs: the case of city planning in Malmö
Authors: Zinkernagel Roland , Neij Lena
Published: June 2023

Cities have an important role to play in implementing the UN Sustainable Development Goals (SDGs) and to mobilize sustainability at the urban level. Yet, municipalities encounter challenges in localizing the SDGs into their governance structures, and there is a need to advance our understanding of cities strengths and weaknesses in such processes. In this article we provide knowledge and reflections gained in analyzing the process of localizing the SDGs into the spatial planning of Smörkajen, a former industrial harbor site in Malmö, Sweden. By applying the analytical framework of Institutional Capacity Building, the study explores the process of localizing the SDGs in terms of building relational and knowledge capacities and to provide mobilization capacity by the formation of a sustainability strategy. The results illustrate an inclusive approach supporting relational capacity and numerous measures to enhance knowledge capacity, bringing about the formation of a draft sustainability strategy, strongly supported by the municipal participants. But rather than formally adopting the full strategy in the spatial planning of the Smörkajen area, the results of the process were only to be considered to the extent the traditional documents and processes allowed. In all, the results illustrate a strong support for the use of localized SDGs among municipal administrative units to mobilize sustainability, but also the challenges in actually implementing these in the formal planning and development process.
Constraining cosmological parameters from N-body simulations with variational Bayesian neural networks
Authors: Hortúa Héctor J. , García Luz Ángela , Castañeda C. Leonardo
Published: June 2023

Introduction: Methods based on deep learning have recently been applied to recover astrophysical parameters, thanks to the ability of these techniques to capture information from complex data. One of these schemes is the approximate Bayesian neural network (BNN), which has demonstrated to yield a posterior distribution into the parameter space that is extremely helpful for uncertainty quantification. However, modern neural networks tend to produce overly confident uncertainty estimates and introduce bias when applying BNNs to data.

Method: In this work, we implement multiplicative normalizing flows (MNFs), a family of approximate posteriors for the parameters of BNNs with the purpose of enhancing the flexibility of the variational posterior distribution, to extract Ωm, h, and σ8 from the QUIJOTE simulations. We compared the latter method with the standard BNNs and the Flipout estimator.

Results: We have found that the use of MNFs consistently outperforms the standard BNNs with a percent difference in the mean squared error of 21%, in addition to high-accuracy extraction of σ8 (r2 = 0.99), with precise and consistent uncertainty estimates.

Discussions: These findings imply that MNFs provide a more realistic predictive distribution closer to the true posterior, mitigating the bias introduced by the variational approximation and allowing us to work with well-calibrated networks.
Temporal response of post-activation performance enhancement induced by a plyometric conditioning activity
Authors: Barreto Marcos Vinicius Casais , Telles Juliana Ferreira da Silva , de Castro Marcela Rodrigues , Mendes Thiago Teixeira , Rodrigues Caio Portela , de Freitas Victor Hugo
Published: June 2023

Introduction

To better understand the post-activation performance enhancement (PAPE) effect promoted by a plyometric conditioning activity (CA), the aim of this study was to investigate the temporal response of PAPE after a plyometric CA.

Methods

Fourteen healthy and active adults visited the laboratory 3 times, with an interval of 7 days between each visit. On the first day they were familiarized with the countermovement jump (CMJ) test and plyometric CA. In the second and third visits, participants performed either plyometric CA or control (remaining seated) in a crossover design. The CMJ test was performed pre and 1-, 3-, 6-, and 9-min post the plyometric CA or control. The comparisons were performed using the repeated measure two-factor ANOVA and Bonferroni adjustment (significance level adopted P ≤ 0.05).

Results

Time (P < 0.01), condition (P < 0.01), and interaction (P < 0.01) effects were reported for CMJ comparisons. For the control condition, CMJ increased at 3 min compared to pre (P = 0.03) and at 3 min compared to 1 min (P = 0.03). For the plyometric CA, CMJ increased at 1- (P < 0.01), 3- (P < 0.01), and 6-min (P = 0.02) compared to pre. For condition comparisons, CMJ was different at 1- (P < 0.01), 3- (P < 0.01), 6- (P < 0.01), and 9-min (P = 0.02). The Effect size of the comparisons of all moments compared to pre was null (d < 0.20) for control and small (d < 0.50) for plyometric CA.

Discussion

It is possible to conclude that the plyometric CA promoted a PAPE effect for up to 9-min. Strength and conditioning coaches and practitioners may consider multiple sets of plyometric CA to produce immediate enhancement of power in the lower limbs.
Dropout and compliance to physical exercise in menopausal osteopenic women: the European “happy bones” project
Authors: Grazioli Elisa , Cerulli Claudia , Tranchita Eliana , Moretti Elisa , Mauri Caterina , Broccatelli Marianna , De Bellonia Simona , Abalașei Beatrice-Aurelia , Dumitru Iulian-Marius , Moraru Cristina-Elena , Iacob Radu-Mihai , Blancafort-Alias Sergi , González Aimar Intxaurrondo , Riau Àlex Domingo , Giménez i Sanz Albert , Ramos Graciela Inness , Pekel Haci Ahmet , Altunsoy Mustafa , Ozkan Secil , Dikmen Asiye Ugras , Sacchetti Massimo , Parisi Attilio
Published: June 2023

Introduction

Decline in muscle mass and bone density seem to be two of the most disabling side effects of menopause that negatively affect women's quality of life. Promoting physical activity protocols in the workplace can represent a focal point in the prevention and management of several diseases. The study aims to evaluate the compliance and drop-out of menopausal osteopenic women engaged in combined training performed inside and outside the workplace. Strength and balance were analyzed to evaluate the effect of this protocol on osteoporosis prevention and the risk of falling.

Methods

73 menopausal women were enrolled in 5 European countries. They performed 72 lessons of a combined training proposed in the working place (IW) or sport center (SC).

Results

Out of the total 39 women enrolled in the IW, 12.8% had to leave the program, while out of the 34 women enrolled in SC, 41.2% did not complete the training. According to the compliance results, 47% of women that completed the trained IW and 85% in the SC recorded high compliance (p = 0.019). Moreover, the strength of the lower limbs (p < 0.001) and static balance (p = 0.001) significantly improved in the whole group.

Discussion

In conclusion, proposing well-structured training in the workplace for menopausal women seems to reduce drop-out. Strength and balance results suggest its positive impact on bone health and risk of falls, despite where it is performed.
The Extraction of Maximal-Sum Principal Submatrix and Its Applications
Authors: Zhang Yizheng , Luo Liuhong , Li Hongjun
Published: June 2023

Extracting k-order maximal-sum principal submatrix from an n-order real matrix is a typical combinatorial optimization problem and an NP-hard problem. To improve the computational efficiency of solving this problem, we, in this paper, propose an accelerated algorithm with row-by-row updates, called the fusion row update accelerated algorithm, which works by reducing the number of addition operations for submatrix elements. The new algorithm is applied to accelerate color combination selection and maximize color difference, which improves the readability of data visualization results; it is also applied to accelerate stock investment portfolio selection and minimize correlation degree, which decreases the investment risk in the view of daily return volatility.
Activity of α-d-Galactosidase in Long-Stored Seeds of Vicia hirsuta
Authors: Gojło Ewa
Published: June 2023

Mature seeds of many crop species contain substantial amounts of soluble carbohydrates, including raffinose family oligosaccharides (RFOs) and cyclitol galactosides (Gal-C). These substances are hydrolysed by α-D-galactosidase (EC 3.2.1.22) during the early stages of germination, providing metabolic energy for the germination process. A decrease or inhibition of α-D-galactosidase activity can significantly reduce the germination rate. This study aimed to investigate changes in α-D-galactosidase activity during the long-term storage of Vicia hirsuta seeds and evaluate the association between the loss of enzyme activity and the decline in seed vigour and viability. V. hirsuta seeds were stored at 22 °C and 35% relative humidity for up to 25 years, and α-D-galactosidase activity and seed vigour changes were monitored at different time points. Storage of V. hirsuta seeds resulted in a loss of viability and vigour, accompanied by a decrease in α-D-galactosidase activity. The enzyme activity showed a significant correlation with seed germinability and vigour. Monitoring α-D-galactosidase activity in seeds subjected to long-term storage could be a simple and rapid method for determining the decline in vigour in seeds that utilize soluble galactosides as their storage materials.
Coupled Dynamics of Soil Water and Nitrate in the Conversion of Wild Grassland to Farmland and Apple Orchard in the Loess Drylands
Authors: Lu Yanwei , Li Peiyue , Li Min , Wen Mingyi , Wei Haoyan , Zhang Zhiqiang
Published: June 2023

Understanding the dynamics of soil water and nitrate in response to typical agricultural crops in dryland ecosystems are crucial for assessing ecological consequences and informing land use planning. This study was conducted in the Changwu tableland, a representative area for agricultural crop cultivation in the Loess Plateau of China. Fifteen soil profiles, including grassland, farmland, and young, mature, and old apple orchards, were sampled to investigate the effects of different land uses on soil water and nitrate dynamics using a “space-for-time” substitution approach. The results showed that the soil water content and nitrate content in farmlands were comparable to those in wild grassland. However, significant differences in soil water were observed below a depth of 2 m in apple orchards, with mature and old orchards experiencing water deficits compared to grassland of 624.9 mm and 690.0 mm, respectively. Moreover, a dried soil layer formed below a depth of 5 m in these orchards. In terms of soil nitrate, the concentration in the 0–5 m depth of apple orchards was significantly higher than that in agricultural land and grassland, and it increased with the age of the orchards. However, below 5 m, the residual nitrate stock per unit depth in apple orchards decreased to levels comparable to grassland and farmland, primarily due to the inhibitory effect of the dried soil layer on downward migration and leaching processes. Furthermore, the relationship between nitrate and soil water at 0–5 m soil depths differed during the conversion from grassland to farmland and apple orchard, with positive and negative correlations observed, respectively. This indicates that water plays a key role in influencing nitrate movement, and distinct hydrological processes occur for soil water and nitrate nitrogen under different land use change conditions. In conclusion, converting grassland and farmland to apple orchards can lead to soil water decline and nitrate accumulation in the vadose zone, posing potential threats to ecosystem sustainability and security in dryland regions. Therefore, implementing appropriate water-fertilizer management practices is crucial for promoting sustainable land use in loess drylands, with potential implications for similar areas worldwide.
Performance Evaluation of BD Phoenix and MicroScan WalkAway plus for Determination of Fosfomycin Susceptibility in Enterobacterales
Authors: Bondi Alessandro , Curtoni Antonio , Peradotto Marco , Zanotto Elisa , Boattini Matteo , Bianco Gabriele , Iannaccone Marco , Barbui Anna Maria , Cavallo Rossana , Costa Cristina
Published: June 2023

Background: Fosfomycin is an old bactericidal drug that has gained increasing interest in the last decade for its potential use in multi-drug resistant gram-negative infections. However, evidence on fosfomycin susceptibility testing reports a poor correlation between commercial methods vs. reference agar dilution (AD) for Enterobacterales (EB). The study aimed at assessing the performance of two automated systems for the determination of fosfomycin susceptibility in EB clinical isolates. Methods: Fosfomycin susceptibility testing results of two collections of 100 non-duplicate clinical EB strains obtained using two different platforms (BD Phoenix and MicroScan WalkAway Plus) were compared with those obtained by AD. Categorical agreement (CA), major error (ME) and very major error (VME) rates were calculated. Results: BD Phoenix exhibited a 6.9% rate of false-resistant results and achieved a CA of 69%, whereas MicroScan WalkAway Plus achieved 3.7% of false-resistant results and 72% of CA. Both automated systems showed poor detection of resistant isolates, with 49.1% and 56.2% of false-susceptible results for BD Phoenix and Microscan WalkAway Plus, respectively. Conclusions: Overall, agar dilution remains the most suitable method for routine laboratory antimicrobial susceptibility testing of fosfomycin on Enterobacterales strains, given the poor performance of automated systems. The application of both automated systems, in the clinical laboratories reporting of fosfomycin, should be reviewed in light of the accuracy results falling below the acceptable threshold.
Mask Guidance Pyramid Network for Overlapping Cervical Cell Edge Detection
Authors: Zhang Wei , Fan Huijie , Xie Xuanhua , Wang Qiang , Tang Yandong
Published: June 2023

An important indicator of cervical cancer diagnosis is to calculate the proportion of diseased cells and cancer cells, so it is necessary to segment cells and judge the cell status. The existing methods are difficult to deal with the segmentation of overlapping cells. In order to solve this problem, we put forward such a hypothesis by reading a large number of literature, that is, image segmentation and edge measurement tasks have unity in high-level features. To prove this hypothesis, in this paper, we focus on the complementary between overlapping cervical cell edge information and cell object information to get higher accuracy cell edge detection results. Specifically, we present a joint multi-task learning framework for overlapping cell edge detection by the mask guidance pyramid network. The main component of the framework is the Mask Guidance Module (MGM), which integrates two tasks and stores the shared latent semantics to interact in the two tasks. For semantic edge detection, we propose the novel Refinement Aggregated Module (RAM) fusion to promote semantic edges. Finally, to improve the edge pixel accuracy, the edge consistency constraint loss function is introduced to our model training. Our extensive experiments have proved that our method outperforms other edge detection efforts.
Sanitation of Apple Cultivars from AP Phytoplasma and ApMV and ACLSV Viruses Using In Vitro Culture and Cryo-Knife Therapy in Liquid Nitrogen
Authors: Sedlák Jiří , Semerák Matěj , Rejlová Martina
Published: June 2023

Systemic infections with phytoplasmas and viruses threaten the production of healthy plant material under the fruit species certification system. We tested the possibility of sanitation using in vitro culture and cryotherapy. The starting material of the cultivars Golden Delicious (clones A and B), Virginia Crab, and Panenské zlepšené was taken from in vivo plants that tested positive for apple proliferation phytoplasma. The Táborita cultivar was obtained from already established in vitro cultures that had tested positive for apple proliferation phytoplasma, apple mosaic virus, and apple chlorotic leaf spot virus. Cultivars Golden Delicious A, Virginia Crab, and Panenské zlepšené were sanitated from the phytoplasma in the first step, i.e., by sterilization and a subsequent transfer to in vitro conditions. Golden Delicious B remained infected with the phytoplasma, and both viruses, after the in vitro culture phase and together with Táborita, were subjected to cryotherapy by vitrification. In Golden Delicious B, three out of thirteen initial shoot tips regenerated after a liquid nitrogen treatment. Four mericlones were regenerated from 10 initial cryopreserved shoot tips of Táborita. None of the three pathogens were detected by PCR in the regenerated Golden Delicious B mericlones. On the contrary, in the case of Táborita, infection with all the pathogens was detected after regeneration. The results obtained indicate the potential applicability of in vitro cultivation techniques or, if necessary, subsequent cryopreservation as a method for sanitizing against systemic microbial contamination. However, further research on the relationship between pathogens and specific genotypes is needed.
3D-GPR-RM: A Method for Underground Pipeline Recognition Using 3-Dimensional GPR Images
Authors: Bai Xu , Yang Yu , Wen Zhitao , Wei Shouming , Zhang Jiayan , Liu Jinlong , Li Hongrui , Tian Haoxiang , Liu Guanting
Published: June 2023

Ground penetrating radar (GPR), as a non-destructive and rapid detection instrument, has been widely used for underground pipeline detection. However, as the interpretation of 3-dimensional GPR images is still manually performed, the process is inefficient. Aiming at solving the challenges of automatic recognition for underground pipelines, we propose a recognition method based on a deep learning algorithm, which uses 3-dimensional GPR images and the improved 3D depth-wise separable convolution block. In order to expand the number of samples in the dataset, we propose a data augmentation method based on three-dimensional matrix rotation and use a wavelet-based denoising method to filter out the direct wave interference. To prove the effectiveness and efficiency of our method, we compared the classification performance of the improved 3D depth-wise separable convolutional block with the traditional 3D convolutional block and the ordinary 3D depth-wise separable convolutional block under the same conditions. According to the experiment’s results, the number of parameters of the method we proposed is 66.9% less than that of the traditional 3D convolution method, while the classification performance is similar. Furthermore, compared with ordinary 3D depth-wise separable convolution, our method can significantly improve the classification and recognition ability of the neural network, while the number of calculations and the number of parameters remain almost the same. This study demonstrates the effectiveness of 3D-CNN in the field of GPR image interpretation. An improved 3D depth-wise separable convolutional block is also proposed. It greatly reduces the amount of calculation and parameters while ensuring classification performance. It is better than the existing algorithms in performance. At the same time, to obtain the position and direction of the pipeline, in this study, a conic fitting method using the Canny operator is proposed to locate the vertices of B-Scan images and record their horizontal and vertical coordinates. This method can estimate the direction of the pipeline and it lays the foundation for future work such as measuring the pipeline depth.
Optimizing Efficiency of Machine Learning Based Hard Disk Failure Prediction by Two-Layer Classification-Based Feature Selection
Authors: Wang Han , Zhuge Qingfeng , Sha Edwin Hsing-Mean , Xu Rui , Song Yuhong
Published: June 2023

Predicting hard disk failure effectively and efficiently can prevent the high costs of data loss for data storage systems. Disk failure prediction based on machine learning and artificial intelligence has gained notable attention, because of its good capabilities. Improving the accuracy and performance of disk failure prediction, however, is still a challenging problem. When disk failure is about to occur, the time is limited for the prediction process, including building models and predicting. Faster training would promote the efficiency of model updates, and late predictions not only have no value but also waste resources. To improve both the prediction quality and modeling timeliness, a two-layer classification-based feature selection scheme is proposed in this paper. An attribute filter calculating the importance of attributes was designed, to remove attributes insensitive to failure identification, where importance is gained based on the idea of classification tree models. Furthermore, by determining the correlation between features based on the correlation coefficient, an attribute classification method is proposed. In experiments, the models of machine learning and artificial intelligence were applied, and they included naïve Bayesian, random forest, support vector machine, gradient boosted decision tree, convolutional neural networks, and long short-term memory. The results showed that the proposed technique could improve the prediction accuracy of ML/AI-based hard disk failure prediction models. Specifically, utilizing random forest and long short-term memory with the proposed technique showed the best accuracy. Meanwhile, the proposed scheme could reduce training and prediction latency by 75% and 83%, respectively, in the best case compared with the baseline methods.
Research Practice and Progress of Models and Algorithms Applied in Topic Identification and Prediction Based on the Analysis of CNKI
Authors: Guo Sicheng , Si Li , Liu Xianrui
Published: June 2023

As a hot topic in the field of library and information, the research on topic recognition and trend prediction has been paid close attention by academic circles. This paper uses a systematic literature review, bibliometric analyses and classification methods. Through a systematic literature review, 96 studies about topic identification and evolution prediction models are selected from the CNKI database. By using VOSviewer to conduct bibliometric analyses, the key research content and themes are revealed. Through the classification method, EXCEL is used to summarize models and algorithms used in the literature comprehensively. It is found that topic identification models and algorithms can be divided into four categories: ① Topic model based on LDA and related derivative models. ② Machine learning and deep learning methods. ③ Methods based on reference relation. ④ Text mining methods. Trend prediction models and algorithms mainly cover two categories: ① deep learning or machine learning models and algorithms based on time sequence; ② link prediction algorithms based on complex network. At the same time, we have also summarized the common index system involved in each study and the way to evaluate the effectiveness of the method, thus this paper comprehensively reveals the application progress in academic circles of topic identification and prediction models and algorithms from the last 10 years and beyond, based on the CNKI database. The purpose is to determine the most popular models and algorithms applied in research, generalize the corresponding indicator systems and validation methods, and finally provide references for model choice or evaluation when identifying and predicting topics in the future. Thus, this paper can help us to understand the overall progress made in text analysis research, and provides a useful reference for selecting and applying the appropriate models, algorithms and indicators.
Energy Sources of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency
Authors: Mikołajczyk Tadeusz , Mikołajewski Dariusz , Kłodowski Adam , Łukaszewicz Andrzej , Mikołajewska Emilia , Paczkowski Tomasz , Macko Marek , Skornia Marika
Published: June 2023

Mobile robots can perform tasks on the move, including exploring terrain, discovering landmark features, or moving a load from one place to another. This group of robots is characterized by a certain level of intelligence, allowing the making of decisions and responding to stimuli received from the environment. As part of Industry 5.0, such mobile robots and humans are expected to co-exist and work together in a shared environment to make human work less tiring, quicker, and safer. This can only be realized when clean, dense, and economical energy sources are available. The aim of the study is to analyze the state of the art and to identify the most important directions for future developments in energy sources of robotic power systems based mainly on batteries. The efficiency and performance of the battery depends on the design using different materials. Work environments and performance requirements are considered in this systematic review to classify solutions that help developers choose the best-suited power system for specific application. Indirectly, the aim of the work is to generate discussion within the scientific and engineering community. A narrative review of publications from six major bibliographic databases according to preset inclusion criteria is combined with a critical analysis of current and future technologies. The main findings of the review allow answering the question of what is the role of modern power source technologies, artificial intelligence, and ground-breaking research work in global policies related to energy saving, green policies, and sustainable development. The main opportunities and threats are discussed, and a brief feasibility analysis is carried out. The novelty of the article relates not only to the analysis of technologies, but also to approaches and their use under conditions of limited resource availability, when resource usage must be minimized. The article provides an overview of batteries, their specifications, classifications, and their advantages and disadvantages. In addition, we propose (1) an algorithm for selecting the main energy source for robot application, and (2) an algorithm for selecting an electrical system power supply. Current mobile robot batteries are, in most cases, the robot’s biggest limitation. Progress in battery development is currently too slow to catch up with the demand for robot autonomy and range requirements, limiting the development of mobile robots. Further intensive research and implementation work is needed to avoid years of delay in this area.
Debris Flow Classification and Risk Assessment Based on Combination Weighting Method and Cluster Analysis: A Case Study of Debris Flow Clusters in Longmenshan Town, Pengzhou, China
Authors: Li Yuanzheng , Shen Junhui , Huang Meng , Peng Zhanghai
Published: June 2023

Debris flows can damage infrastructure and threaten human life and property safety, especially in tourist attractions. Therefore, it is crucial to classify and evaluate the risk of debris flows. This article takes 14 debris flows in Longmenshan Town, Pengzhou, Sichuan, China, as the research object. Based on on-site geological surveys, combined with drone images and multiple remote sensing images, the essential characteristics of each debris flow are comprehensively determined. A total of nine factors are used as the primary indicators affecting the risk of debris flow: drainage density, roundness, the average gradient of the main channel, maximum elevation difference, bending coefficient of the main channel, the loose-material supply length ratio, vegetation area ratio, population density, and loose-material volume of unit area. The subjective weights of each indicator are obtained using the Analytic Hierarchy Process, while the objective weights are obtained using the CRITIC method. Based on this, the distance function is introduced to couple the subjective and objective weights, determine each indicator’s combined weights, and obtain the integrated evaluation score values of different debris flow hazards. Considering the integrated evaluation score of debris flow, cluster analysis was used to classify 14 debris flows and cluster effectiveness indicators were introduced to determine the effectiveness of debris flow classification. A quantitative standard for the risk of debris flow within the study area was proposed, and finally, a risk assessment of debris flow in the study area was made. Comparing the results of this paper with the gray correlation method, the coupled synergistic method, and the geological field survey results, proves that the proposed method is feasible and provides a reasonable scientific basis for the study of the hazard assessment of regional debris flow clusters and other related issues within the scope of the Jianjiang River basin and other areas.
Power System Analysis during Fast Desynchronization from Synchronous Area and Operation in Islanded Mode
Authors: Deltuva Ramūnas , Lukočius Robertas , Balsevičius Renatas , Kriuglaitė-Jarašiūnienė Miglė
Published: June 2023

In a constantly and rapidly changing global environment, one of the main priority tasks for every country is preserving, maintaining, and operating an independent and individually robust and stable energy system. This paper aims at researching electrical power systems’ (EPSs) behavior during desynchronization from a synchronous area, its stability in islanded mode, and its synchronization. The analysis of EPS behavior was accomplished utilizing numerical simulations in a widely used programming/simulation package. The sudden tripping of the EPS into an isolated island mode with known generation and load values was simulated, analyzed, and discussed. We investigated the behavior of an isolated EPS in the case of the loss of a certain amount of active power, and determined the maximum power that must be available to ensure the reliable operation of the isolated EPS and the power reserve that must be maintained to prevent the EPS from triggering UFLS. The simulation of the synchronization of an isolated EPS with a synchronous area was accomplished and analyzed. The obtained results were applied to reveal the sequence of actions that will help an EPS to ensure and maintain the stable and reliable operation of electrical installations during desynchronization.
High-Resolution Lacustrine Records of the Late Holocene Hydroclimate of the Sikhote-Alin Mountains, Russian Far East
Authors: Razjigaeva Nadezhda , Ganzey Larisa , Grebennikova Tatiana , Kopoteva Tatiana , Klimin Mikhail , Arslanov Khikmatulla , Lyashchevskaya Marina , Panichev Alexander , Lupakov Sergey
Published: June 2023

There is little information about moisture changes in different altitudinal belts in mountainous regions of the southern Russian Far East. We present ecological and taxonomic compositions of the diatom flora and identify the botanical composition of peat in small mountain lake/mire complexes located in the Central Sikhote-Alin Mountains, within large landslides on the paleovolcanic slopes. Frequent changes in diatom assemblages and peat-forming plants indicate unstable hydroclimatic conditions with varying degrees of wet and dry conditions up to the overgrowth of the lakes. Frequent change in sphagnum mosses with different trophic preferences was identified. The chronology is based on 11 radiocarbon dates. Accumulation rates reached 1.7–1.9 mm/year, and the temporal resolution for the reconstructions was up to 30–40 yr. The tendencies of lake evolution depended on different scale hydroclimatic changes over the last 4400 yr. The most detailed data for the last 2600 yr were obtained from the Nizhnee Lake sequence, which is more sensitive to climatic changes. The main reason for the change in the hydrological regime of the lakes was variations in precipitation during short-term climatic changes. The sediment record of moisture fluctuations is relatively well correlated with regional patterns reflecting summer monsoon intensity and cyclogenesis activity.
Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review
Authors: Zedan Mohammad , Zulkifley Mohd , Ibrahim Ahmad , Moubark Asraf , Kamari Nor , Abdani Siti
Published: June 2023

Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.
The Gender Gap in STEM Careers: An Inter-Regional and Transgenerational Experimental Study to Identify the Low Presence of Women
Authors: Martínez Macarena , Segura Francisca , Andújar José Manuel , Ceada Yolanda
Published: June 2023

Currently, the number of job offers in STEM careers (Science, Technology, Engineering and Mathematics) is growing up, but by contrast, the number of graduates in these fields is decreasing, particularly women graduates. Consequently, if we do not promote the training of women in STEM careers, the gender gap, far from narrowing, will continue to widen. This paper presents the research carried out in the ALAS project (Accompanying girLs towArds STEM careers), which consists of an experimental analysis based on a multi-model study to discover the possible causes of this low participation of women in STEM fields. The models used are the (1) expectancy–value theory of motivation, (2) social role theory, and (3) gender stereotypes theory. Additionally, participatory workshops have been carried out, with the aim of capturing the students’ reactions when they are introduced to STEM practices. The surveyed target groups range from primary education groups up to university graduates and enterprise employees, including both students and teachers. The obtained results show that there are still social patterns that make young people differentiate certain types of activities based on gender, especially at secondary school age. Nevertheless, the findings reveal that beyond the three studied models, a key factor in young people’s decision to be enrolled in STEM careers is their educational environment.
Simple Chargers for a Small DC Micro-Grid for a Home Emergency Power System
Authors: Himmelstoss Felix A. , Votzi Helmut L.
Published: June 2023

Recently, the danger of a long blackout is discussed in Europe. Blackouts can be caused by failures in the energy distribution, errors in large power plants or even cyber-attacks. This can lead to a chain reaction and a disintegration of the mains. Longer blackouts have an extreme impact on the economy as a whole and on local households. Therefore, a small local grid at home which can supply the most important loads over some time has garnered increasing interest. With a small direct current (DC) grid, critical loads such as for deep freezers and refrigerators can be supplied, and some LED lights can be used in the evening or at night. Solar generators (panels) can be used to charge energy storage devices, e.g., batteries. A DC grid can not only be used in the case of an emergency, but can also be used to reduce energy consumption out of the public mains and reduce energy bills. The architecture of the household emergency DC grid is discussed; suggestions for batteries are given; two simple chargers, based on DC-DC-converters like the Buck (step-down) and on the Boost (step-up) converters, are shown; dimensioning suggestions are given; and simple, robust controllers, a P-controller with disturbance feedforward and a hysteresis controller, are treated and tested via simulations. The goal of the paper is to show a simple autonomous home energy system without an external fieldbus, LAN or internet connection with special focus on simple charger topologies.
SGooTY: A Scheme Combining the GoogLeNet-Tiny and YOLOv5-CBAM Models for Nüshu Recognition
Authors: Zhang Yan , Zhang Liumei
Published: June 2023

With the development of society, the intangible cultural heritage of Chinese Nüshu is in danger of extinction. To promote the research and popularization of traditional Chinese culture, we use deep learning to automatically detect and recognize handwritten Nüshu characters. To address difficulties such as the creation of a Nüshu character dataset, uneven samples, and difficulties in character recognition, we first build a large-scale handwritten Nüshu character dataset, HWNS2023, by using various data augmentation methods. This dataset contains 5500 Nüshu images and 1364 labeled character samples. Second, in this paper, we propose a two-stage scheme model combining GoogLeNet-tiny and YOLOv5-CBAM (SGooTY) for Nüshu recognition. In the first stage, five basic deep learning models including AlexNet, VGGNet16, GoogLeNet, MobileNetV3, and ResNet are trained and tested on the dataset, and the model structure is improved to enhance the accuracy of recognising handwritten Nüshu characters. In the second stage, we combine an object detection model to re-recognize misidentified handwritten Nüshu characters to ensure the accuracy of the overall system. Experimental results show that in the first stage, the improved model achieves the highest accuracy of 99.3% in recognising Nüshu characters, which significantly improves the recognition rate of handwritten Nüshu characters. After integrating the object recognition model, the overall recognition accuracy of the model reached 99.9%.
A Novel Pull-Up Active-Clamping Wireless Charging Circuit with Low Cost and Voltage Stress
Authors: Pan Bo , Li Houji , Wang Yong , Zhang Deshu
Published: June 2023

The traditional single-switch circuit has the advantages of fewer components, no shoot-through problems, and suitability for high-frequency wireless charging applications. However, because of the high voltage stress of the switch, the bulky inductor, and the narrow soft-switching range, its power and applications have some limitations. To relieve this problem, this paper proposes a pull-up active-clamping circuit, which not only offers a low component count with no bulky inductors, but also greatly reduces the switch voltage stress. In addition, a wide range of soft switching can be achieved by designing a primary-side compensation capacitor. A detailed parametric design method is given and compared with existing circuits from the aspects of switch voltage stress, component count, efficiency, cost, and so on. Finally, a 1 MHz, 180 W active-clamping wireless charging system is built to verify the proposed circuit and design method.
Mechanism and Control of Asymmetric Floor Heave in the Gob-Side Coal Roadway under Mining Pressure in Extra-Thick Coal Seams
Authors: Wang Deqiu , Zheng Yun , He Fulian , Song Jiayu , Zhang Jianlong , Wu Yanhao , Jia Pengpeng , Wang Xiaohui , Liu Baoping , Wang Feifei , Zhang Yajiang , Tao Kai
Published: June 2023

Due to their tense mining succession relationship, gob-side roadways may undergo significant deformation under multi-mining pressure. In this article, many methods, such as on-site research, a theoretical analysis, a numerical simulation and an industrial experiment, are used to research the mechanism of asymmetric floor heave in a gob-side coal roadway affected by mining pressure during the mining of extra-thick coal seams. Our main research is as follows: (1) By monitoring the floor deformation in the roadway on site, it is concluded that the roadway floor shows asymmetry, indicating that the floor displacement near the coal pillar side is relatively large. (2) Based on a lateral overburden structure model of the roadway, the calculation formulas of the horizontal vertical stress caused by the roadway excavation and the excavation of the upper working face are derived separately, and the vertical stress coupling curves on both sides of the roadway during the mining of the upper working face are obtained through a numerical simulation. It is concluded that the cause of the asymmetric floor heave in the roadway is an uneven distribution of vertical stress. (3) The numerical simulation shows a symmetrical distribution of the floor displacement curve during the roadway excavation with a max. displacement of 49.5 mm. The floor displacement curve during the mining of the upper working face is asymmetric with a max. displacement of 873 mm at a distance of 1 m from the central axis near the coal pillar side. The range of the plastic zone in the roadway gradually expands with the mining of the upper working face, and the maximum depth of floor failure is 5.5 m. (4) According to the cooperative control principle of “roof + two sides + floor”, an asymmetric floor heave joint control scheme of “floor leveling + anchor cable support + concrete hardening” is proposed. The floor deformation monitoring results indicate that the max. floor heave at the measurement point near the coal pillar in the roadway is 167 mm, and the floor heave is effectively controlled.