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Objectives. To evaluate the usefulness of a novel acoustic uroflowmetry- (UFM-) based mobile application (app) voiding diary (VD) focusing on the (1) compliance and (2) correlation with a conventional paper-based VD. Materials and Methods. A total of 78 patients were included between December 2019 and June 2020, and a subsequent review of all data was performed. The analyzed data were as follows: (1) survey of convenience/satisfaction/preference comparing the two methods, (2) compliance regarding the completeness of both methods, and (3) correlation of each metric (24-hour urine volume, nocturnal urine volume, nocturnal polyuria index, total number of voids, number of daytime voids, number of nocturnal voids, and maximal bladder capacity) between the two methods. Results. The survey results of convenience, satisfaction, and preference were as follows. With regard to convenience and satisfaction area, higher scores are reported in the mobile app VD (mean ± standard deviation (SD); convenience: 7.47 ± 2.19 [app] vs. 4.20 ± 2.49 [paper]; satisfaction: 7.36 ± 2.17 [app] vs. 5.07 ± 2.65 [paper]). The median score of the overall preference for using the mobile app instead of the paper-based VD was 9 out of 10 (mean ± SD 7.82 ± 2.68). We also found a good correlation between the two methods for nocturnal urine volume (r = 0.55 , p = 0.04), nocturnal polyuria index (r = 0.66 , p = 0.23), total number of voids (r = 0.9 , p = 0.02), number of nocturnal voids (r = 0.83 , p = 0.02), and maximal bladder capacity (r = 0.89 , p = 0.04). Conclusion. The acoustic UFM-based mobile app VD demonstrated favorable findings compared to the conventional paper-based VD. [ABSTRACT FROM AUTHOR]

Debt restructuring is an effective means to solve the financial difficulties of enterprises. At present, the operation methods and means of enterprises with different characteristics to participate in debt restructuring are different, and no universal debt restructuring scheme has been formed. In order to solve this problem, this paper uses the method of fuzzy set qualitative comparative analysis. This paper discusses the influence mechanism of multiple concurrent factors on enterprise debt restructuring and summarizes the selection rules of debt restructuring strategies for enterprises with different characteristics of debt. The research shows that there are five kinds of allocation for enterprises to realize debt restructuring. According to the different core conditions, enterprise debt restructuring can be divided into high-quality operation and management mode, technology leading mode, and debt-to-equity mode. The research results reveal the mechanism behind enterprise debt restructuring and provide a reference basis for the government and creditors to make corresponding decisions in the debt restructuring process. [ABSTRACT FROM AUTHOR]

Facing the dual background of risk society and the fourth scientific and technological revolution, digital emergency management (DEM) research has attracted wide attention worldwide. Based on the comparative analysis of data from Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, the research draws the knowledge map by CiteSpace, in order to analyse the spatiotemporal distribution, hotspots, and trends of DEM research. The following are the results: (1) DEM research shows a spiral growth, with a significant increase in the number of papers. Existing highly cited references are mainly carried out from the perspective of management or technology. (2) The complex countries' cooperation network has been formed from the USA, China, and other countries; the complexity of the institutions' cooperation network must be strengthened, and the density of the authors' cooperation network is low; DEM research has significant interdisciplinary attributes. (3) The hotspots of DEM research can be divided into three categories: means, processes, and objects, but the research emphases between international and China are different. (4) The evolution of DEM research can be divided into three stages between international and China: embryonic, development, and deepening stages, and research contents in each stage show some differences. (5) The trends of DEM research between international and China are different. The former are information technology, artificial intelligence, and machine learning, whereas the latter are emergency decision making, scenario analysis, and public health. [ABSTRACT FROM AUTHOR]

Introduction. The wide variety of methods for recording occlusal contacts and the contradictory data on the interpretation of the obtained markings provoked us to make a comparative laboratory study between different occlusal indicators. Purpose. Evaluation of a qualitative and quantitative method for registration of occlusal contacts in static occlusion in laboratory conditions. Materials and Methods. In completion of the objective, we designed an apparatus for registration of the occlusal contacts (AROC) in static occlusion which is used, corresponding to the MIP in clinical conditions. The occlusal indicators that were included in the study were articulating paper 100 μ, articulating foil 12 μ, and T-Scan Novus system with a sensor thickness of 100 μ. The collected primary statistical information was entered and processed with the statistical package SPSS Statistics 19.0, and the graphs were prepared using Microsoft Office 2019. We performed descriptive statistical analysis in this study. Comparisons were performed using one-way analysis of variance (ANOVA), Student's t -test, and Pearson coefficient method. For a significance level, p < 0.05 was chosen. Result and Discussion. With quality occlusal indicators, it is possible only to visually determine the size, number, and intensity of the marked contacts. After the statistical processing of the obtained data on the number of registered contacts, a significant difference is found in the number of contacts of certain teeth (18, 24, 25, 28, 38, 35, 34, 33, 44, 45, and 48) registered with articulating paper and articulating foil. The maximum force that is reported during the study with the T-Scan system is 93.72% and the forces in the right half of the dentition are 51.7% and in the left 48.9%. To visualize the location of the registered occlusal contacts with the system, it is intraoral to use a quality indicator and we recommend the use of articulating foil. Conclusion. Based on findings from the current in vitro simulation, we can conclude that the type of occlusal indicator influences the registration of contacts, and therefore, we propose as a method of choice to achieve a balanced occlusion in clinical practice to combine the use of one conventional and one quantitative method. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the relevance of early childhood education to occupational psychotherapy using a virtual-assisted affective interaction approach. Starting from the educational theory of interactive cognitive psychology, the theoretical basis for parent-child picture book education for interactive learning is explored, as well as the theoretical development after the introduction of AR technology. Firstly, the analysis of young children's emotions involves massive image processing, and the use of cloud computing architecture leads to high latency, while young children's safety is a latency-sensitive service. Secondly, face recognition accuracy based on static images is not high due to problems such as inconspicuous facial features of toddlers and low-quality kindergarten surveillance videos. In this paper, a face identity correction model based on location features is proposed and the superiority of the model is demonstrated through experiments. Finally, this paper analyzes and mines the emotional data of young children. The level of kindergarten teachers' awareness of early childhood mental health education generally showed an upward trend as their titles rose, and there were significant differences in the seven dimensions of early childhood mental health, the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, and effects; significant differences existed between teachers of different kindergarten natures, and there were significant differences in the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, effects, and mental health education for young children. Therefore, this paper proposes a face identity correction model based on position information, which considers both the correlation between pixel values in the spatial domain and the correlation between frames in the temporal domain. This paper has developed an emotion analysis system for kindergartens and put it into use in kindergartens to meet the needs of monitoring the safety of young children and evaluating early childhood education and has received good feedback from users, demonstrating the effectiveness of the system. [ABSTRACT FROM AUTHOR]

Different from traditional images, light field images record not only spatial information but also angle information. Due to the large volume of light field data brings great difficulties to storage and compression, light field compression technology has attracted much attention. The epipolar plane image (EPI) contains a lot of low rank information, which is suitable for recovering the complete EPI from a part of EPI. In this paper, a light field image coding framework based on EPI restoration neural network has been proposed. Compared with previous algorithms, the proposed algorithm further takes advantage of the inherent similarity in light field images, and the proposed framework has higher performance and robustness. Experimental results show that the proposed method has superior performance compared to the state-of-the-art both in quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Based on the actor network theory, this paper collects 20 representative corporate public opinion data through microblogs, uses the qualitative comparative analysis method to analyze these typical cases from the configuration perspective, identifies the elements and condition combination paths of corporate online public opinion hotness generation from four dimensions: enterprises, netizens, media, and government, and explores the generation mechanism of corporate network public opinion hotness. The results show three modes with high hotness of corporate network public opinion generation, which are internal and external linkage, internal leading, and external restriction. The public opinion hotness generation modes of consumers' rights and interests and managers' problems are different. Therefore, different measures should be taken to reduce the hotness of negative public opinion for different causes of corporate public opinion. Based on this, this paper puts forward some guidance suggestions, including strengthening the identification and contact with opinion leaders, strengthening the cooperation with the government and authoritative media, and strengthening the feedback response level of corporate network public opinion. This study helps to prevent and resolve public opinion crisis and provides reference for corporate public opinion governance. [ABSTRACT FROM AUTHOR]

This paper presents forecast and investigation of the variation in ionospheric Total Electron Content (TEC) during the solar eclipses (SEs) of December 2019 and June 2020 using three different methods: Deep Autoregressive model (DeepAR), Feed-Forward Neural Network (FFNN), and Ordinary Kriging-based Surrogate Model (OKSM), and the TEC data predicted by DeepAR, FFNN, and OKSM were compared with the actual TEC during the observation days. The study was conducted based on GPS data taken from the IISC receiver located in Bangalore, India, during the SEs which happened on 26.12.2019 and 21.06.2020. The TEC data were examined to assess the effect of solar eclipses on TEC values. Eighty-day prior TEC data for the IISC station are gathered from IONOLAB servers along with the other parameter data like Dst, Ap, F10.7, and Kp taken from OMNIWEB servers which were used to predict TEC. The reliability of the forecasted results is evaluated using numerical factors like Normalized Root Mean Square Error (NRMSE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The study demonstrates the usefulness of combining multiple methods for analyzing TEC variations during SEs and highlights the potential of OKSM, FFNN, and DeepAR models for studying TEC variation in the same context. The findings may be useful for satellite broadcasting and navigational services and for further research into the influence of solar eclipses on the TEC changes. [ABSTRACT FROM AUTHOR]

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter <2.5 cm). Different from normal liver tissue (P < 0.01), there was a significant difference (P < 0.01) compared with adjacent tissue (P < 0.01). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients. [ABSTRACT FROM AUTHOR]

Tissue adhesives as a physical barrier to microorganism penetration provide an alternative method with many advantages for wound closure in surgical settings compared to the clinical standard. This raises the need of developing and conducting in vitro methods that are sensitive and reproducible to assess their microbial barrier properties. In this study, three different polyurethane-based tissue adhesives with different physicochemical properties were evaluated in comparison to Dermabond® as a clinical gold standard for topical wound closure. Here, physicochemical properties varied in lactide concentration, viscosity, processing, and the full polymerization time. To evaluate the microbial barrier function, a 5 μl aliquot of E. coli Lux inoculum containing at least 1 × 10 9 CFU / ml was applied to the surface of each test adhesive and sterile filter paper as the control that was placed on an agar plate and incubated at 37°C. Plates were observed for bacterial growth (morphology), the adhesion of the adhesive/filter paper, and bioluminescence after 24, 48, and 72 hours. The data presented in this in vitro model indicated that polyurethane-based tissue adhesives with lactide concentration ≥ 5 % provided a suitable barrier against microbial penetration with 95% confidence of 99% efficacy for 72 h along with Dermabond®. Interestingly, the here described method was able to discriminate between the different physicochemical properties showing a better microbial barrier function with increasing lactide concentration of the adhesive. Overall, the results of this study showed the noninferiority between Dermabond® and the two abovementioned polyurethane-based tissue adhesives. [ABSTRACT FROM AUTHOR]

The heart and soul of the Chinese people and the greatest cultural soft power of contemporary China are excellent Chinese healthy cultural environments. Short video platforms and short videos have a chance to develop as the Internet revolution continues to intensify and mobile clients gain popularity. This essay examines the challenges involved in producing brief videos that promote a healthy cultural environment in the new context of internet plus, as well as the means of transmitting such videos and creative methods for doing so. Excellent communication content is created in this paper from two perspectives of visual presentation and in-depth meaning, allowing users to fully understand the connotation of a healthy cultural environment. Through the theme of a healthy cultural environment, topic discussion and characteristic activities will be carried out to encourage users' spontaneous sharing of the interactive behavior of communication and the fission communication of brief videos of a healthy environment. This paper develops a model for intelligent recommendation and short video content analysis based on this. In order to further encourage the creative dissemination and development of short videos about healthy cultural environments, the model is used to choose high-quality videos and accurately recommend them to the appropriate users and to serve as an example of how a quick video can be used to communicate innovation and a healthy cultural environment. [ABSTRACT FROM AUTHOR]

The development of manufacturing industry affects the operation of Chinese economy, and it is necessary to study the possible configuration paths of servitization transformation to support sustainable development. Based on previous studies of manufacturing servitization transformation, it is extended to explore multifactor combinations which affects servitization transformation performance. This paper constructs technology-organization-environment (TOE) framework of servitization transformation performance evaluation and uses fuzzy-set Qualitative Comparative Analysis (fsQCA) method to analyze the key factors and mechanism of servitization transformation with the data of 28 Chinese manufacturing enterprises. The results reveal that the six major factors are not the necessary conditions, and single-factor cannot effectively promote servitization transformation of Chinese manufacturing enterprises. There are four configurations which can be divided into three paths to improve servitization transformation performance. Based on the results of analysis, it proposes strategies to promote the development of Chinese manufacturing enterprises, such as developing targeted servitization strategies, enhancing technology development, and improving technology applications. The findings reveal the mechanism of multiple factors behind the evolution of manufacturing servitization transformation and take useful suggestions for the high-level development of Chinese manufacturing enterprises. [ABSTRACT FROM AUTHOR]

When decision-makers' judgments are uncertain, they often express their opinions using grey linguistic variables. Once used, the data often retains its grey nature throughout all subsequent decision-making iterations. Multicriteria decision-making (MCDM) is a tool used when making complicated decisions and in circumstances where several criteria require evaluation to choose the most desirable option. Grey data serves as the basis for several MCDM methods. This paper compares two MCDM methods, Grey-Linear-Programming (GLP) and Grey-Best-Worst-Method (GBWM), in terms of the weights of decision criteria and their rankings. Moreover, Grey-The Technique for Order of Preference by Similarity to Ideal Solution (GTOPSIS) was used to rank the weights of the two methods. Study findings demonstrated that GBWM requires more mathematical calculations than GLP, based on linear programming's classic simplex method. On the other hand, when GTOPSIS follows GLP, the alternative rank does not change compared to when GTOPSIS followed GBWM. For the applications used in this comparison, GLP procedure is considered simpler than GBWM procedure. [ABSTRACT FROM AUTHOR]

Background. Cancer diagnosis is a complex and multifaceted process that can be stressful and anxiety-provoking for patients. Evidence-based tools and information aids that can be used for guiding and supporting patients during cancer investigations and after diagnosis are limited. This paper presents a user-centred codesign project that aims to develop a cancer pathway support tool for patients, carers, and healthcare professionals. Method. A mixed-method codesign approach was used including prototype development (January–March 2022), three online codesign workshops (April–June 2022), one-to-one feedback, and beta testing informed by the standardised Theoretical Framework of Acceptability (TFA) questionnaire (July–October 2022). Nine individuals with lived experience of cancer contributed to the project and are referred to as codesigners. Results. The codesigners valued the potential importance of a tool that can be used by the patients and carers if they want specific information about cancer investigations, diagnosis, and treatment. The ability to select what they need as opposed to long leaflets and generating their own questions for the healthcare providers were highlighted as important aspects of improving patient care. The tool was collectively designed to provide clear definitions of the cancer care pathway and easily accessible links from trusted resources and includes practical information to minimise the burden that can be experienced from preparation for appointments and tests. Beta testing results with a small sample of potential users including patients, carers, and healthcare providers (n = 23) showed high acceptability of the guide (range = 7–35, mean = 28.52, and standard deviation = 3.88) based on the TFA questionnaire. Conclusion. A cancer pathway support tool called "Your Cancer Pathway Support Guide (YCPSG)" was developed as a result of an iterative codesign process aiming to improve patient experience and outcomes for people referred on a suspected cancer pathway or who have been diagnosed with cancer. The tool provides information and support in both digital and PDF formats. Further studies are needed to evaluate the potential impact of "Your Cancer Pathway Support Guide" on patient outcomes and experience and the wider system. [ABSTRACT FROM AUTHOR]

With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named "NWDAF-NFPP" for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive analysis of construction and demolition waste (C&D waste) management in European Union (EU) Member States. The study utilizes official data and reports to assess the current C&D waste management status, revealing substantial disparities in recycling rates across countries, ranging from 100% to 24%. These variations emphasize the importance of knowledge sharing and collaborative efforts to improve C&D waste practices. The paper explores the complex factors influencing C&D waste management performance, offering a nuanced discussion of these dynamics. It provides a comprehensive set of practical strategies to address existing challenges, with a focus on reducing C&D waste sent to landfills or incineration. These recommendations align with the EU's commitment to sustainable resource management and the transition to a circular economy. The research's informed measures, driven by environmental responsibility and economic prudence, can significantly diminish C&D waste's environmental impact. Policymakers, environmentalists, and industry stakeholders can utilize these findings to develop and implement improved C&D waste management practices, promoting sustainability and resource efficiency across the EU. This research contributes valuable insights to the field of C&D waste management, offering a road map for improvement and highlighting the need for collaboration among EU Member States to create a more sustainable and resilient future. [ABSTRACT FROM AUTHOR]

Background. Coronavirus disease (COVID-19) is an infectious illness that spreads widely over a short period of time and finally causes a pandemic. Unfortunately, the lack of radiologists, improper COVID-19 diagnosing procedures, and insufficient medical supplies have all played roles in these devastating losses of life. Deep learning (DL) could be used to detect and classify COVID-19 for potential image-based diagnosis. Materials and Methods. This paper proposes an improved deep convolutional neural network (IDConv-Net) to detect and classify COVID-19 using X-ray and computed tomography (CT) images. Before the training phase, preprocessing methods such as filtering, data normalization, classification variable encoding, and data augmentation were used in conjunction with the proposed IDConv-Net to increase the effectiveness of the detection and classification processes. To extract essential features, deep CNN is then employed. As a result, the suggested model can identify patterns and relationships crucial to the image classification task, resulting in more precise and useful diagnoses. Python and Keras (with TensorFlow as a backend) were used to carry out the experiment. Results. The proposed IDConv-Net was tested using chest X-rays and CT images collected from hospitals in Sao Paulo, Brazil, and online databases. After evaluating the model, the proposed IDConv-Net achieved an accuracy of 99.53% and 98.41% in training and testing for CT images and 97.49% and 96.99% in training and testing for X-ray images, respectively. Further, the area under the curve (AUC) value is 0.954 and 0.996 for X-ray and CT images, respectively, indicating the excellent performance of the proposed model. Conclusion. The findings of our proposed IDConv-Net model confirm that the model outperformed compared to existing COVID-19 detection and classification models. The IDConv-Net outperforms current state-of-the-art models by 2.25% for X-rays and 2.81% for CT images. Additionally, the IDConv-Net training approach is significantly quicker than the current transfer learning models. [ABSTRACT FROM AUTHOR]

Botnets are conglomerations of traded PCs (bots) that are remotely controlled by its originator (botmaster) under a command-and-control (C&C) foundation. Botnets are the making dangers against cutting edge security. They are the key vehicles for several Internet assaults, for example, spam, distributed denial-of-service (DDoS) attack, rebate distortion, malware spreading, and phishing. This review paper depicts the botnet examined in three domains: preview of botnets, observation, and analysis of botnets, apart from keeping track of them and protecting against them too. We have also attempted to the various ways to indicate differing countermeasures to the botnet dangers and propose future heading for botnet affirmation look into a consolidated report on the energy investigation and future headings for botnet break down are also been presented in this paper. [ABSTRACT FROM AUTHOR]

In recent days, additive manufacturing (AM) plays a vital role in manufacturing a component compared to subtractive manufacturing. AM has a wide advantage in producing complex parts and revolutionizing logistics panorama worldwide. Many researchers compared this emerging manufacturing methodology with the conventional methodology and found that it helps in meeting the demand, designing highly complex components, and reducing wastage of materials, and there are a wide variety of AM processes. The process of making the components in full use of technology with several manufacturing applications to meet the above is studied along with the properties of AM, and subsequently, the advantages of AM over the subtractive methods are described. In this paper, the achievements in this manner with considerable gains are studied and are concluded as a paradigm shift to fulfil the AM potential. [ABSTRACT FROM AUTHOR]

This paper presents a performance comparison between switching and reduced-order models of grid-following inverters. Different models have been developed to study the inverter's operation using MATLAB and PSIM simulation tools. These models offer a tradeoff between accuracy and computational burden. The switching model fully represents the discontinuities in the inverter due to switching, but such a model demands high computational resources. The inverter representation is simplified using reduced-order models, which is common in the literature. However, the fidelity of these simplified models might not be adequate for some applications. Therefore, it is essential to investigate the performance of such models closely to ensure their suitability depending on the purpose of the analysis. Hence, a comparative study between switching and reduced-order models is presented here by simulating different scenarios and operation conditions. The results have validated the efficacy of the reduced-order model in representing the grid-following inverter's operation in both steady state and response to a step change resulting from active or reactive power variation. Unlike the switching model, the findings illustrate that the reduced-order modeling fails to represent harmonics and losses. After simulating different scenarios, this work highlights the main differences between the switching and reduced-order models to facilitate the choice of the proper modeling method for researchers. [ABSTRACT FROM AUTHOR]

Image restoration is an interesting ill-posed problem. It plays a critical role in the concept of image processing. We are looking for an image that is as near to the original as possible among images that have been skewed by Gaussian and additive noise. Image deconstruction is a technique for restoring a noisy image after it has been captured. The numerical results achieved by the prox-penalty method and the split Bregman algorithm for anisotropic and isotropic TV denoising problems in terms of image quality, convergence, and signal noise rate (SNR) are compared in this paper. It should be mentioned that isotropic TV denoising is faster than anisotropic. Experimental results indicate that the prox algorithm produces the best high-quality output (clean, not smooth, and textures are preserved). In particular, we obtained (21.4, 21) the SNR of the denoising image by the prox for sigma 0.08 and 0.501, such as we obtained (10.0884, 10.1155) the SNR of the denoising image by the anisotropic TV and the isotropic TV for sigma 0.08 and (-1.4635, -1.4733) for sigma 0.501. [ABSTRACT FROM AUTHOR]

This paper aims to establish strategies of coping with COVID-19 pandemic adopted by the clients of social assistance from the perspective of social workers. The qualitative research among Polish social workers and family assistants was conducted from September to November 2021. Based on 30 in-depth interviews with social work professionals, groups of social assistance clients' behaviours were identified and classified into the theoretical models of reactions to social change by Merton and Giddens. The following types of behaviour have been selected and categorized into four groups of reactions when dealing with social workers in a pandemic situation: coolheaded calculation, defiance, confusion, and adaptation. At the same time, reactions to the situation of social change were categorized into one of the strategies of active or passive behaviour: innovation, contestation, retreat, and acceptance. In addition, we discussed the impact of clients' coping strategies on social workers and established three attitudes of the surveyed social workers: adapting to the pandemic situation and customer behaviour, assuming the role of an emotional buffer and informant, and expecting support from superiors and coworkers. Knowing the behaviour of social assistance clients in a challenging situation makes it possible to adopt tailor-made measures. [ABSTRACT FROM AUTHOR]

The power transformer is an essential component of the electrical network that can be used to step up and step down voltage. Dissolved gas analysis (DGA) is the most reliable method for the identification of incipient faults in power transformers. Various DGA methods are used to observe the generated key gases after oil decomposition. The main gases included are hydrogen (H2), ethylene (C2H4), acetylene (C2H2), methane (CH4), and ethane (C2H6). There is a lack of research that can compare the performance of various DGA methods in identification of faults in power transformer. In addition, it is also not clear which DGA method is optimal for identification of faults in power transformer. In this paper, the comparative performance study of seven DGA methods such as Roger's ratio, key gas, IEC ratio, the Doernenburg ratio, the Duval triangle, three-ratio method, and the relative percentage of four gases is carried out in order to identify the optimal technique for fault identification in transformer. The data of various power transformers installed in "RAWAT" NTDC grid station, Islamabad, and "UCH-II" power station, Balochistan, are considered for the comparative analysis. This analysis shows that the three-ratio method provides better performance than other DGA methods in accurately identifying the faults in power transformers. The three-ratio method has 90% accuracy in identifying the faults in power transformer. [ABSTRACT FROM AUTHOR]

Vulnerable communities (including people from refugee, Indigenous, culturally and linguistically diverse, and low socioeconomic backgrounds) represent the most at-risk populations facing inequities and negative health, economic, and social outcomes. The recent COVID-19 pandemic both highlighted and fuelled these disparities. Community gardening has emerged as a community-based solution to address these inequities, yet the research literature has largely considered outcomes for the general population rather than those with the most need. This paper represents the first systematic review to summarise the evidence on the broad impact of community gardening on outcomes for vulnerable populations. A systematic search of 13 databases (PubMed, Medline, Scopus, ScienceDirect, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Web of Science, Academic Search Complete, Education Source, Education Resources Information Center, Psychology and Behavioral Science Collection, SocINDEX, and Allied Health and Complementary Medicine Database) for English language articles from 1985 to 2022 was conducted. There were 33 studies identified where females were substantially overrepresented in the studies compared to males, and the main criteria for vulnerability included low socioeconomic-status and culturally diverse populations. Findings revealed that community gardening provides a wide range of benefits for vulnerable populations, with social connection, health, education, and nutrition being the more commonly cited. A relative emphasis on benefits of social connections, education, and nutrition is apparent for vulnerable populations in comparison to reviews considering the general population. The quality of studies was evaluated as moderate with little information provided about program characteristics. These shortcomings reduce the understanding of what characteristics are most likely to result in improvements and limit the capacity of practitioners to translate research into policy and practice for vulnerable communities. [ABSTRACT FROM AUTHOR]

Unnecessarily prolonged stays in hospitals can have negative impacts on patients and present avoidable costs to health and social care systems. This paper presents the qualitative findings of a multi‐methods study of the social care causes of delayed transfers of care (DTOC) for older people in England. The quantitative strand of this study found that DTOC are significantly affected by homecare supply. In this paper, we explore in depth how and why social care capacity factors lead to delays, from the perspectives of those working within the system. We examined the local transfer arrangements in six English local authority (LA) sites that were purposively sampled to include a range of DTOC performance and LA characteristics. Between March and December 2018, 52 professionals involved in arranging or facilitating discharge from hospitals in these sites provided qualitative data, primarily through semi‐structured interviews. Topics included discharge teams and processes, strategic issues and perceived causes of delays. The thematic analysis uncovered the nuances behind the causes of DTOC previously categorised broadly as 'provider capacity' and 'patient choice'. In particular, our analysis highlights the lack of fit between available provision and the needs of people leaving hospital (theme 1); workforce inconsistencies (theme 2) and a myth of patient choice (theme 3). We are now at a turning point in the development of policy to reduce DTOC in the English system, with the full implications of a new national discharge to assess programme yet to be seen. Our research shows the significance of the alignment of service capacity, including the type and location of provision, with the needs and preferences of those leaving hospital. As the new system becomes established, attendance to such nuances behind blockages in the system will be more important than ever. [ABSTRACT FROM AUTHOR]

This study seeks to assess the health, social and economic outcomes associated with rough sleeping among women and compare those outcomes with those of (1) men sleeping rough, and (2) women experiencing other forms of homelessness (such as being housed in temporary supported accommodation due to family and domestic violence). The paper analyses survey data using the Vulnerability Index‐Service Prioritization Decision Analysis Tool (VI‐SPDAT) collected from 2735 women experiencing homelessness and 3124 men sleeping rough in Australian cities from 2010 to 2017. We find that women sleeping rough report poorer physical and mental health outcomes and greater problematic drug and or alcohol use relative to both men sleeping rough and women experiencing other types of homelessness (all p < 0.5). Women sleeping rough report significantly higher levels of crisis service utilisation (Β = 17.9, SE = 3.9, p < 0.001) and interactions with police in the previous 6 months (Β = 1.9, SE = 0.3, p < 0.001) than women experiencing homelessness not sleeping rough. Women sleeping rough also report greater healthcare utilisation, and, therefore, healthcare costs, than women experiencing homelessness not sleeping rough and men sleeping rough (all p < 0.05). From a policy perspective, the evidence presented in this paper supports a social determinants approach that moves from addressing symptoms of poor health outcomes associated with homelessness to preventing and ending homelessness with a particular focus on the life trajectories of women. Integrated services and homelessness strategies need to be developed through a gender lens, providing women sleeping rough with tailored permanent housing with wrap‐around supportive housing to address poor health outcomes. [ABSTRACT FROM AUTHOR]

In the context of the new era, the distinctive function of BD (big data) analysis and prediction also introduces a new way of thinking to university IPE (ideological and political education), broadens the domain of university IPE, and enhances the curricular offerings of IPE universities. In order to enhance the intelligence and personalization of the intelligent teaching system, this paper describes in detail the design and implementation processes for each component of the system. It also uses the association mining rule algorithm of data mining. To maintain population diversity, a population initialization method and a neighborhood-based search operator are used, both of which are based on a thorough consideration of the characteristics of complex networks. The neighborhood search strategy enhances the local search capability of the TLBO (Teaching-Learning Based Optimization) algorithm. The optimized TLBO algorithm presented in this paper achieves the highest average modularity value of 0.5238 through testing on real-world data sets. The outcomes demonstrate that the algorithm performs well and is successful in identifying problems in the community. [ABSTRACT FROM AUTHOR]

Yan'an is one of the "two holy places" of the Chinese nation and the Chinese revolution and is one of the first cities of historical and cultural significance and an outstanding tourist city in China, as announced by the state council. The evaluation of the effectiveness of environmental conservation is one of the very important elements of the conservation of Yan'an's architectural heritage. However, the existing evaluation methods cannot provide new solutions for decision-making, the meaning of the comprehensive evaluation function is unclear, the naming clarity is low, there is less quantitative data and more qualitative components, and the results are not easily convincing. This paper proposes a method for evaluating the practical effects of environmental class measures in the conservation of Yan'an's architectural heritage based on recurrent neural networks. The recurrent neural network makes full use of the memory function in the network, considers the causal relationship of the actual effect, and efficiently evaluates the existing measures. In comparison with factor analysis and hierarchical analysis, this paper has greater applicability in evaluating the practical effects of environmental measures in the conservation of Yan'an's architectural heritage and is basically consistent with the results of the theoretical analysis. It provides a scientific basis for the construction and implementation of environmental measures for the architectural heritage of Yan'an. [ABSTRACT FROM AUTHOR]

The water environment is one of the basic elements that constitute the environment. It is an important place for the survival and development of human society, and it is also the most seriously disturbed and damaged area by humans. The pollution and destruction of water environment has become one of the major environmental problems in the world today. The essence of urban water space landscape design under the concept of integrating people's ecological design is the ecological landscape design of urban water spaces, while the development of ecological landscape design in the field of urban water space landscape design is still in its infancy, and the interpretation of its concept is also different. The ecological design of the landscape reflects a new dream of human beings, a new aesthetics and value: the true cooperative and fraternal relationship between man and nature. At present, the ecological design of urban water space landscape has not put forward a more accurate concept, clear principles and standards, and a complete and systematic theoretical basis, which requires further research, discussion, and continuous practice by this generation of designers to improve it. To this end, this paper proposed a research method on the integration of water environment governance in urban and rural spaces with natural ecological landscape design. This paper mainly talked about the status quo of water environment and its network sensor algorithm research and analyzed its coverage area one by one. Then, the water quality extraction is introduced in detail. And finally, the data analysis of the Beijing river waters, the analyzer rainfall, water quality, and so on are carried out in the experimental part. It could be seen from the experimental results that there were currently 22 reclaimed water plants in six urban areas of Beijing, with a daily water treatment capacity of 4.08 million cubic meters and a sewage treatment rate of 98%. As of 2016, 440 million cubic meters of reclaimed water has been reused. With the commissioning of the new reclaimed water system, the proportion of reclaimed water in the river and lake environment will continue to increase. [ABSTRACT FROM AUTHOR]

It is necessary to pay attention to and study the physical and mental health development of college students. On the basis of statistical analysis of the questionnaire, this paper studies the impact of pretest mental health intervention on college students' English test scores, summarizes the problems and characteristics of college students' mental health, and explores the reasons for psychological problems. Based on the research results and related survey data of the mental health status of college students by scholars, this paper believes that these reasons mainly include students' personal reasons, school and social environment pressure, and insufficient family support factors. The data analysis shows that in the relationship between English education and mental health in colleges and universities, the amount of mental health intervention of students' extracurricular exercise has a stable and low negative correlation with the scores of each factor of SCL-90 (Symptom Checklist 90) and is negatively correlated with the scores of each subscale. The experimental results show that there are extremely significant differences in academic performance (F = 5.084 , P < 0.01), and there is a certain linear relationship between students' mental health level and students' English exercise feeling. The lower the fatigue score, the higher the mental health level of the students, which effectively promotes the mental health development of college students. [ABSTRACT FROM AUTHOR]

This qualitative study aimed to explore Iranian women's life stories living with compulsive sexual behaviour. Data were collected between 2014 and 2016 in two cities. Forty‐four semi‐structured interviews were conducted by using theoretical sampling and constant comparative analysis. Four thematic categories were constructed from the data, including "scars left on the spirit and mind,"; "bizarre beliefs regarding sexuality and gender,"; "compensation for emptiness and the loss,"; and "difficulty in emotional regulation." This paper shows how abusive patterns of relationships in childhood could lead to sexual compulsivity. The paper results can direct our attention to the importance of health and social care and training to recognise abusive situations and support these children. [ABSTRACT FROM AUTHOR]

The increased use of social media among digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data preprocessing, and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension leads us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored. [ABSTRACT FROM AUTHOR]

Active citizen participation in research is important to generate societal impact of outcomes. Even so, many times, the community input in these processes is limited or non-existent, and co-creation processes are often not documented. This paper describes a structured and systematised protocol to co-create a nature-based social prescribing intervention addressed to face loneliness in urban deprived neighbourhoods. As a result, a nature-based social prescription menu was co-developed with stakeholders from health, social prescription, nature, associative, and research sectors. The menu consists of open spaces and nature activities and will be offered to the participants of a randomised controlled trial to test actions for social prescribing in natural spaces. [ABSTRACT FROM AUTHOR]

In Brazil, the use of Eucalyptus is focused on the production of wood or pulp for the paper industry but without any general recovery of waste, with leaves and branches being left on the ground. One possibility is to use these residues as raw materials in the production of industrially relevant and value-added compounds such as essential oil. The aim of the present study was to investigate the chemical composition, yield, anti-inflammatory/antinociceptive activities, and acute toxicity in mice, as well as the antimicrobial effects of essential oils from the leaves of 7 varieties of Eucalyptus and hybrids against Escherichia coli, Staphylococcus aureus, and Candida albicans. The extraction of oils was carried out using hydrodistillation, and they were analyzed by gas chromatography coupled to mass spectrometry. Urocam and Grancam were the plants that obtained the highest oil yield, with yields of 3.32 and 2.30%, respectively. The main chemical components identified in these plants were 1.8 cineole and α-pinene. The antinociceptive effect of the 7 oils (50 mg/kg, p.o.) was initially assessed in the acetic acid-induced writhing test. In this assay, a significant (p < 0.05) antinociceptive/anti-inflammatory effect was observed from 4 tested essential oils (E. benthamii, E. saligna, and the hybrids Urocam and Grancam) when compared to the vehicle-treated group. This effect was then confirmed in the formalin-induced paw licking test. No toxicological effects or alterations were observed in motor coordination after the administration of the studied oils to the animals. In the antimicrobial evaluation, the seven essential oils inhibited the growth of S. aureus, E. coli, and C. albicans at different concentrations. Collectively, these results demonstrate that the essential oil from the leaves and branches of Eucalyptus species and varieties present potential biomedical applications and represent a source of antimicrobial and/or anti-inflammatory compounds. [ABSTRACT FROM AUTHOR]

Aim. This paper aimed to examine the current state of nurse safety behavior, and the effect of work engagement on it, and to explore the mediating effect of self-efficacy between work engagement and nurse safety behavior in specialized cancer hospitals. Background. Nurse safety behavior affects the quality of care and outcomes for patients with cancer. The research proves that self-efficacy is associated with work engagement, but the relationship between self-efficacy, work engagement, and nurse safety behavior needs to be clarified. Methods. This study used a convenience sampling method to survey 957 nurses from January to February 2023. The survey included tertiary specialized cancer hospitals in five provinces in China. Sociodemography information, the Work Engagement Scale, the Self-Efficacy Scale, and the Nurse Safety Behavior Scale were used for the survey. A Questionnaire Star program collected all the data automatically. The Statistical Package for the Social Sciences version 24 was employed for Pearson correlation and multiple regression analysis to examine the association between work engagement, self-efficacy, and nurse safety behavior. Structural equation models were built using Analysis of Moment Structures version 24.0 software. Results. The results showed that the nurse safety behavior was at a high level in specialized cancer hospitals. There was a significant positive relationship between the variables work engagement, self-efficacy, and nurse safety behavior with coefficients ranging from 0.244 to 0.564. The results of structural equation modeling revealed that self-efficacy partially mediated the effect between work engagement and nurse safety behavior, accounting for 25.3% of the total effect. Conclusion. Self-efficacy mediates the relationship between work engagement and nurse safety behavior, and by increasing self-efficacy, nurse safety behavior can be improved, thus ensuring patient safety. Implications for Nursing Management. Managers should promote high work engagement and nurse safety behaviors by fostering nurses' self-efficacy. [ABSTRACT FROM AUTHOR]

With the promotion of China's international status, the study of Chinese characters has become the trend of scholars all over the world. Nowadays, the international promotion of Chinese has become an important part of the national diplomatic strategy. With the rapid development of big data visualization technology, Chinese culture can be spread quickly, and it is easy to achieve the above requirements to achieve visual accessibility. In view of the problems existing in the current dissemination of Chinese culture, such as too scattered dissemination content, unsystematic dissemination channels, and poor dissemination effect, this paper puts forward a method of Chinese character culture dissemination based on big data visualization. Firstly, the visual features of Chinese characters are recognized, and the rules of Chinese characters are detected; secondly, the large data visualization analysis of Chinese character imaging is carried out, and the performance comparison under different iterations is compared. Chinese visual technology has obvious advantages in the effect and performance of Chinese character transmission. Finally, the development trend of Chinese communication is analyzed, and the acceptance, speed, coverage, understanding, learning degree, and practical degree of Chinese communication abroad are compared. The big data visualization model has certain advantages in functional comparison. [ABSTRACT FROM AUTHOR]

Sports officials, players, and fans are concerned about overseas player rankings for the IPL auction. These rankings are becoming progressively essential to investors when premium leagues are commercialized. The decision-makers of the Indian Premier League choose cricketers based on their own experience in sports and based on performance statistics on several criteria. This paper presents a scientific way to rank the players. Our research examines and contrasts different multicriteria decision-making algorithms for ranking foreign players under various criteria to assess their performance and efficiency. The paper uses three MCDM algorithms, TOPSIS, TODIM, and NR-TOPSIS, for foreign players ranking in the Indian Premier League. Our analysis is limited to the batsmen and bowlers only. We perform the analysis using Python language, a popular high-level programming language. Finally, we perform a sensitivity analysis to determine the stability of each method when the weights of the criterion or the value of a parameter was changed. Our analysis exhibits the superiority of TODIM over TOPSIS and NR-TOPSIS. [ABSTRACT FROM AUTHOR]

Introduction: Systemic barriers impacting smoking cessation in pregnant women may include nicotine replacement therapy (NRT) access and training. Aim: Assess general practitioner (GP) and obstetrician's agreement with system-based changes to improve the management of smoking in pregnancy; compare group responses. Methods: National cross-sectional survey with two samples: (1) online survey emailed to a random sample of 500 GPs from Royal Australian College of General Practitioners (RACGP); (2) paper survey posted to 5571 GPs and obstetricians from Royal Australia and New Zealand College of Obstetricians and Gynaecologists (RANZCOG). Percentages agreeing that specified changes would improve the management of smoking in pregnancy were calculated. Pairwise comparisons used Kruskal–Wallis tests (RACGP/RANZCOG GPs/obstetricians). Results: N = 378 participated. Response rates 8.4% (N = 42; online survey) and 6% (N = 335; paper survey), respectively. Total percentages agreeing with system-based changes: 79% training, 64% oral NRT subsidy, 62% Medicare item for smoking cessation, 54% improved access to NRT patches. Within RANZCOG, more GPs (73.1%) agreed that oral NRT should be subsidised (P = 0.001) than obstetricians (53.7%). Conclusion: GPs and obstetricians agreed that system changes would improve their management of smoking in pregnancy. Oral NRT subsidy was the only pairwise group difference. Subsequently, oral NRT has been subsidised; in time, this may influence prescribing and quit rates. [ABSTRACT FROM AUTHOR]

In traditional manufacturing enterprises, there are common problems of low added value of products, low profit, and poor business performance. As a result, they endeavor to transform themselves into intelligent manufacturing. To help with their transformation, this paper proposes a decision support model for managers to improve the business performance under different configurations of supply chain concentration and staff structure. Through the fuzzy set qualitative comparative analysis, the membership degree is given to the variables, and then the configuration analysis is carried out. We find that, to facilitate intelligent manufacturing, the concentration degree of supply chain or the structure of employee education should be adjusted according to the results from the qualitative comparative analysis of fuzzy sets. Two configuration paths to improve business performance are found. When the supply chain concentration degree is relatively decentralized, manufacturing enterprises should expand the proportion of sales personnel and production personnel. In other words, when the sales personnel and production personnel reach the saturation state, low concentration of suppliers and customers is more conducive to the improvement of business performance. The configuration of high proportion of production personnel and low customer concentration tends to lock enterprises in the lower end of the value chain. Therefore, it is critical for enterprises to improve the education level of employees to transform into intelligent manufacturing and improve their business performance. [ABSTRACT FROM AUTHOR]

A number of recent or novel metaheuristic algorithms have been explored in the subject of optimization; although now not all of these algorithms are as efficient as their creators claim, a few have been confirmed to be a pretty efficient and useful tool for addressing complex optimization problems. On the other aspect, LINGO is a software tool used for linear, nonlinear, optimization problems. This paper's objective is to study how well the LINGO optimizer performs as contrasted to metaheuristic optimization strategies for over current relay coordination. The operation of directional over current relay is demonstrated with the applicability of LINGO optimizer and metaheuristic strategies. In this study, two distinct case studies, which include a mixed overhead line‐underground cable and a DER primarily based IEC microgrid benchmark, are tested for comparative evaluation among the LINGO optimizer and metaheuristic strategies. During the first stage of case study I, the crow search algorithm (CSA), a novel metaheuristic technique, was proposed, and its results are contrasted with the most popular conventional techniques: Particle swarm optimization (PSO) and water cycle algorithm (WCA). It reveals that implementing the proposed CSA algorithms improved over all time setting concerning the most common traditional techniques such as PSO and WCA. And, after implementing the LINGO optimizer, it is proven that compared with other metaheuristic optimization techniques, the second one proposed LINGO optimizer provides advanced consequences. In a similar manner, both proposed methods were applied and determined their performances in case study II and once more LINGO proved its strength over the metaheuristic approach. [ABSTRACT FROM AUTHOR]

High-lift systems are designed to expand the flight envelope and have a most important effect on the size of the wing, economy, and safety of many airliner configurations. Even a small increment of lift using a high-lift system can significantly impact an aircraft's profitability. The effective design of the airfoil shape with the required aerodynamic performance is still difficult. In the early days, the designs of airfoils were randomly set up and tested in the flow section, and then, the Wright brothers emerged with a cambered section. NACA has provided an appropriate airfoil definition that supports us in making airfoil designs using formulas, not randomly. This paper describes the influence of aerodynamic analysis of wing with flaps at various deflection angles. Aerodynamic variables for the aircraft wing, which is made up of the NACA airfoil 6412 model with and without flaps, have been studied at various angle of attack (AOA) (i.e., -4, -2, 0, 2, 4, 6, 8, 10, 12, and 16) and different Mach number at 0.2, 0.3, and 0.4. Also, the analysis was done for the 15000 ft altitude to check the density effects for the real-time applications. The coefficients of lift and drag are gained by examining the pressure distribution over the surface of the wing. Lift increases as the approach ascends from a low to a high angle of attack, and the most extreme lift is produced at a specific point. After that, when the angle of attack increases further, the drag component increases, so the stall occurs at that point in time. The results showed that the NACA 6412 airfoil obtained the maximum lift at 14°, and the lift value started to decrease. The CFD computations are performed in Ansys Fluent by performing hybrid mesh using ICEM-CFD. The analysis is performed for various configurations of the wing section, and the effects of flow parameters like angle of attack, altitude, and the gap distance between the main wing and slotted flap were compared to identify the better configurations. [ABSTRACT FROM AUTHOR]

This paper selects 93 high-tech enterprises listed in 2019 in China's A-share market and comprehensively integrates six variables on three levels, namely, internal guarantee, internal incentives, and external institutions, through fuzzy-set qualitative comparative analysis (fsQCA). On this basis, a discussion was conducted about the multiple concurrent factors and various paths stimulating entrepreneurship. The results show that (1) a single factor does not necessarily lead to high entrepreneurship; (2) high entrepreneurship could be stimulated by four paths: driving mechanism with salary incentive as the core and market mechanism as the support, driving mechanism with government intervention and market mechanism as synergistic drivers, driving mechanism with equity and control power as dual drivers under external institutions, and driving mechanism with external institutions and internal incentives as joint drivers; (3) market competition plays an indispensable role in stimulating entrepreneurship, while management capability suppresses entrepreneurship; (4) nonhigh entrepreneurship could be generated by five paths, which are asymmetric relative to the configurations of high entrepreneurship. [ABSTRACT FROM AUTHOR]

A full-thickness skin graft (FTSG) is useful for covering small skin and soft tissue defects. In this paper, we suggest FTSG in consideration of the relaxed skin tension line (RSTL) concept for scar quality improvement since FTSG has disadvantages, including contour irregularities and mismatches of color and texture. We conducted a retrospective chart review of twenty-one patients with skin cancer on the face who underwent wide excision and FTSG by a single surgeon from October 2013 to July 2019. Twenty-one patients with skin cancer on the face were divided into RSTL-matched and RSTL-unmatched groups, and FTSG was performed. Each group was subjected to scar assessment three months after surgery. Observer assessment was performed by five independent observers using the observer component of the patient and observer scar assessment scale (POSAS) and Vancouver scar scale (VSS). Our results indicate that there were significant differences between the RSTL-matched and RSTL-unmatched groups in the VSS and POSAS components. In addition, the RSTL-matched group showed a natural appearance with surrounding tissue in the dynamic animation phase compared to the unmatched group. RSTL-matched FTSG can be an attractive option for face skin and soft tissue defect coverage. (An earlier version of this paper has been presented at the International Conference on PRS Korea 2020.) [ABSTRACT FROM AUTHOR]

Summary: A multilevel inverter produces an output to approximate a desired sine wave. The closeness of the output waveform to the sine wave depends on the logic applied for switching angle calculation, resulting in different switching angle calculation methods. In this paper, various switching angle calculation methods are compared based on of the parameters quantifying the quality of the output waveform. The main focus of this work is on the study of convergence of the output waveform to a smooth waveform as number of levels increases theoretically. The convergence is analytically proved and validated numerically by computing up to 201 levels. Also, a parameter quantifying the deviation of the output waveform from the sine wave is defined, and its closed form expression is derived. This parameter is strongly correlated to and computationally less expensive than the total harmonic distortion. Hence, the deviation is proposed as a better measure of the quality of the output wave form. The convergence and the deviation of the output waveform are also used as the basis of the comparison of different switching angle calculation methods. These two give a better insight into the switching angle calculation methods. The analysis presented in this paper provides a good understanding of various switching angle computation methods and helps in choosing an appropriate method for a given application. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an inflammatory disease damaging the myelin sheath in the central and peripheral nervous system in the brain and spinal cord. Optic Neuritis (ON) is one of the most prevalent ocular demonstrations of MS. The current diagnosis protocol for MS is MRI, but newer modalities like Optical Coherence Tomography (OCT) are already of interest in early detection and progression analysis. OCT reveals the symptoms of MS in the Central Nervous System (CNS) through cross-sectional images from neural retinal layers. Previous works on OCT were mostly focused on the thickness of retinal layers; however, texture features seem also to have information in this regard. In this research, we introduce a new pipeline that constructs layer-stacked (LS) images containing data from each specific layer. A variety of texture features are then extracted from LS images to differentiate between healthy controls and ON/None-ON MS cases. Furthermore, the definition of texture extraction methods is tailored for this application. After performing a vast survey on available texture analysis methods, a treasury of powerful features is collected in this paper. As a primary work, this paper shows the ability of such features in the diagnosis of HC and MS (ON and None-ON) cases. Our findings show that the texture features are powerful to diagnose MS cases. Furthermore, adding information of conventional thickness values to texture features improves considerably the discrimination between most of the target groups including HC vs. MS, HC vs. MS-None-ON, and HC vs. MS-ON. [ABSTRACT FROM AUTHOR]

Crop cultivation is one of the oldest activities of civilization. For a long time, crop production was carried out based on knowledge passed from generation to generation. However, due to the rapid growth in the human population of the world, human knowledge-based cultivation is not enough to meet the demanding need. To address this issue, the usage of machine learning-based tools has been studied in this paper. An experiment has been carried out over 0.3 million data. This dataset identifies 46 prominent parameters for cultivation, which is collected from the Department of Agriculture Extension, Bangladesh. Comparison between neural networks and numbers of machine learning algorithms has been carried out in this research. It is observed that the neural network outperforms the other methods by maintaining an average prediction accuracy of 96.06% for six different crops. Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a comparative analysis has been conducted on GaN high electron mobility transistor (HEMT) technology on Si and SiC substrates. Small‐signal characteristics of 2‐mm GaN‐on‐Si and GaN‐on‐SiC devices have been investigated. Both devices have the same gate length of 0.5 μm. Special emphasis has been put on the temperature dependence of the buffer/substrate loading effects arising from the Si substrate. As a matter of fact, the "cold" pinch‐off admittance (Y‐) parameter measurement showed significant loading effect for the Si‐based device with respect to the SiC‐based one. This has been clearly supported by the analysis of the extracted parameters of the small‐signal equivalent‐circuit model. The model was also validated by simulating active scattering (S‐) parameters, which showed a very good agreement with the corresponding measurements. The results of this paper highlight the impact of buffer/substrate leakage currents on small‐signal characteristics and the importance of taking this into account during the modeling phase of the GaN‐on‐Si HEMT technology. The lower thermal conductivity of this substrate increases the internal temperature, thus stimulating more leakage and reduction of the device power efficiency. [ABSTRACT FROM AUTHOR]

Protocols for treating acne with isotretinoin have varied widely because some factors associated with relapse and treatment duration have not yet been fully determined. This paper evaluates the effectiveness of conventional, low, and intermittent isotretinoin dosage protocols in the treatment of moderate acne and investigates the relationships between GAGS score, treatment duration and relapse rate. The 107 patients with moderate acne were randomly divided into three groups who received isotretinoin for 24 weeks (Group A: 0.5–1 mg/kg/day; Group B: 0.25–0.4 mg/kg/day; Group C: 0.5–0.7 mg/kg/day for 1 week out of every 4 weeks). The results show that both conventional and continuous low doses achieved full clinical effectiveness with minimum relapse rates. However, fewer side effects and better patient satisfaction were reported with the low dose. Significant differences in GAGS scores after 24 weeks were found between groups B and C (p = 0.037) and between groups A and C (p < 0.001), while no significant differences were found between groups A and B (p = 0.153). It was observed that relapse rate increased with initial GAGS score. The average relapse rate was 58.0% for those with initial GAGS scores of 25–30 compared with 5.5% for those with scores of 19–24. It was also noticed that, among the patients who relapsed, the highest percentage (56.3%) were in the age group <20 years. However, GAGS (β = 0.646, t = 8.323, p < 0.001) was found to be a better predictor of relapse than age (β = 0.083, t = 1.073, p = 0.286). These results suggest that initial GAGS score is an important factor in determining the treatment protocol for moderate acne. Furthermore, this study recommends that a low‐dose treatment is most suitable when GAGS <25, as it can achieve complete clearance of acne with minimal relapses and side effects. [ABSTRACT FROM AUTHOR]

Under the background of 18F-FDG-PET/CT multimodal whole-body imaging for lung tumor diagnosis, for the problems of network degradation and high dimension features during convolutional neural network (CNN) training, beginning with the perspective of dividing sample space, an E-ResNet-NRC (ensemble ResNet nonnegative representation classifier) model is proposed in this paper. The model includes the following steps: (1) Parameters of a pretrained ResNet model are initialized using transfer learning. (2) Samples are divided into three different sample spaces (CT, PET, and PET/CT) based on the differences in multimodal medical images PET/CT, and ROI of the lesion was extracted. (3) The ResNet neural network was used to extract ROI features and obtain feature vectors. (4) Individual classifier ResNet-NRC was constructed with nonnegative representation NRC at a fully connected layer. (5) Ensemble classifier E-ResNet-NRC was constructed using the "relative majority voting method." Finally, two network models, AlexNet and ResNet-50, and three classification algorithms, nearest neighbor classification algorithm (NNC), softmax, and nonnegative representation classification algorithm (NRC), were combined to compare with the E-ResNet-NRC model in this paper. The experimental results show that the overall classification performance of the Ensemble E-ResNet-NRC model is better than the individual ResNet-NRC, and specificity and sensitivity are more higher; the E-ResNet-NRC has better robustness and generalization ability. [ABSTRACT FROM AUTHOR]

The extensive use of the pump as a turbine (PAT) for micro-hydropower applications has a significant value from economic and technical viewpoints. However, the unavailability of the characteristics curve and relatively lower efficiency are the two basic limitations when considering pumps for power-generating applications. In this paper, the performance of the PAT is analyzed using the computational fluid dynamics (CFD) software called Ansys CFX in conjunction with standard k - ε. Then, experiments were done to verify the results of the simulation. Measurement inaccuracy effects are also taken into account. The initial performance of the PAT is refined by controlling basic design parameters (i.e., increasing the number of impeller blades, decreasing blade thickness, blade tip rounding, and adjusting blade inlet angle). Additionally, a new modification method known as blade grooving is also introduced in this research. Finally, all listed modification techniques are applied simultaneously to achieve maximum performance. The output of the study confirms that the adopted modification techniques have a positive effect on performance improvement. When the number of impellers is increased, the power output is enhanced by 5.72%, and blade grooving provides the most efficiency improvement, i.e., 7.00%. But decreasing blade thickness has no remarkable impact on the performance; the power output and efficiency are improved by 1.24% and 2.60%, respectively. The maximum performance improvement was achieved when the modification techniques are applied simultaneously with 10.56 and 10.20 percent of power and efficiency increments, respectively. From the entire study, it can be concluded that the chosen design parameters have an important effect on stabilizing the internal flow, decreasing the required head, decreasing the hydraulic loss in the impeller, and increasing the overall performance. The study also helps to figure out which modification technique is the most practical. [ABSTRACT FROM AUTHOR]