Sectors Of Society Research Articles

러시아 정교와 반유대주의

When the Soviet system collapsed and the new “faith” that had dominated that society, Marxism-Leninism, was abandoned, it was not surprising that Orthodoxy was considered as the faith to replace it. Historically, in Russia, Orthodoxy and the state recognized each other's roles and always maintained a close relationship. Until just before the Russian Revolution, the Russian Orthodox Church was influential in all strata of society, including the intelligentsia. During the Soviet period, the Orthodox Church was forced to give way to the new communist ideology as a state ideology, and Orthodoxy, as a fundamental part of the system, was to be extinguished. However, during World War II, Stalin enlisted the Orthodox Church in the fight against Nazi Germany, and the Church and the state were restored to a cooperative relationship, and Russian Orthodoxy was able to survive in an atheistic Soviet society because of its political usefulness. In the post-Soviet period, Russian Orthodoxy was revived as an alternative to fill the ideological vacuum, gradually regaining its pre-revolutionary status in Russian society and becoming very close to the powers that be. Since Putin's rise to power, a number of phenomena have been observed in Russia, including the rise of nationalist tendencies in various sectors of Russian society, and the Russian Orthodox Church has joined the movement. The most prominent phenomenon is the fact that Russian nationalist ideology in the post-Soviet period has been linked to the Russian Orthodox Church, and the policies of the Putin administration, especially with regard to religion, can be interpreted as state-centered nationalist tendencies. The most characteristic of these phenomena is the role of Russian Orthodoxy in the spread of anti-Semitic ideology in modern Russia. This study examines the anti-Semitic tendencies of the Russian Orthodox Church, focusing on the publication of the Protocols of Zion.

Influencing Factors and Coping Strategies of Women's Fertility Intention under the Guidance of China's Development Policy

With the rapid development of China's social economy, people's living standards continue to improve. However, the willingness to have children has shown a downward trend, and the fertility rate has also declined. This phenomenon has attracted widespread attention from all sectors of society. This study aims to conduct an in-depth analysis of the influencing factors of women's fertility intentions under policy guidance. A questionnaire survey was conducted to investigate and analyze the current influencing factors of women's fertility intentions. Research has shown that policy orientation significantly affects women's reproductive decisions. Individual factors such as age, education level, and occupation, as well as family factors such as family income and place of residence, are closely related to fertility intentions. The dissemination channels of the fertility policy affect women's understanding of the policy, which in turn affects fertility decision-making. Obstacles in career development also affect women's consideration of childbirth. The social environment, especially family support, has a significant impact on women's reproductive decisions. Meanwhile, the balance between family and career is also an important factor affecting women's willingness to have children. Based on this, this article suggests further optimization and adjustment of the current fertility policy, such as improving maternity leave benefits, introducing flexible work arrangements, providing career development support, supporting fertility assistance services, and breaking workplace discrimination, to better meet the diverse fertility needs of women and promote sustainable social development.

Analysis of the Implementation of E-Litigation with Artificial Intelligence Approach in Procedural Justice and Access to Justice in Pretrial Proceedings

The development of Artificial Intelligence in the current digital era across every sector of society has become a topic of discussion among legal experts for its potential application within the realm of justice systems. Consistency and time efficiency have become key considerations for implementation within the judicial system. Pre-trial proceedings in Indonesia are often seen as a secondary trial process, leading to inconsistencies in time, understanding of legislation, and document management, resulting in disadvantageous situations for the suspects. The integration of Artificial Intelligence in legal proceedings can enhance efficiency, accuracy, and ensure access to justice for suspects within the legal process. In the context of pre-trial proceedings, AI can be utilized to aid in data processing and analysis, decision-making, and monitoring compliance with principles of procedural justice. The research problem in this study revolves around two main questions: First, how does the legitimacy and legal certainty of utilizing electronic media within the Indonesian judicial system stand? Second, how can procedural justice and access to justice be implemented through the utilization of electronic media, particularly artificial intelligence, in pre-trial proceedings? This research is a doctrinal or normative study, relying on secondary data sources consisting of primary legal materials and qualitative data.

Identifying ESG types of Chinese solid waste disposal companies based on machine learning methods

In the context of China's efforts to combat climate change and promote sustainable development, the solid waste treatment industry's environmental, social, and corporate governance (ESG) performance is receiving significant attention. To comprehensively assess the ESG performance of the solid waste treatment industry and identify company types, this study constructs a targeted ESG evaluation index system based on existing literature, SASB industry standards, and company reports and utilizes a random forest approach combined with K-means clustering to determine indicator weights. Based on this index system, the paper evaluates the ESG performance of 71 solid waste disposal companies (SWDCs) from 2013 to 2021 and identifies their ESG types from static and dynamic perspectives. In the static view, company types are determined based on annual ESG performance, while the dynamic view considers time-series changes to observe the evolution of company ESG types. The results show that the overall ESG performance of SWDCs falls within the 2-8-point range, indicating a noticeable high-low imbalance. Key initiatives to improve ESG performance in this industry include enhancing waste management measures, developing emergency plans, and reinforcing ESG disclosure. From a static perspective, this paper can identify companies into three categories: delayed development, single-wheel-driven, and coordinated development. Finally, from a dynamic perspective considering the time factor, companies are further subdivided into five types: continual leading, growth catch-up, slow progress, fluctuating change, and retrogressive inertia. This study not only provides targeted recommendations for different types of ESG companies but also helps various sectors of society better understand the ESG conditions of this high environmental risk industry, thereby enhancing the regulation and support for its sustainable development.

Barriers to help-seeking for postpartum depression mapped onto the socio-ecological model and recommendations to address barriers

Postpartum depression affects nearly a quarter of women up to a year after childbirth. Although it is treatable, significant barriers to help-seeking prevent women from being treated. This paper assesses key literature on the barriers for help-seeking among women with postpartum depression. The barriers identified have been mapped onto the socio-ecological model in addition to potential recommendations that professionals can use to address barriers on individual, interpersonal, organizational, community and societal levels. The recommendations provided are meant to serve as leverage points for professionals in efforts to create appropriate support and interventions. As such, this paper serves as a mapping tool for healthcare and public health professionals to assess obstacles to women's help-seeking and to guide multi-pronged interventions on various levels of the socio-ecological model that may increase help-seeking among women with postpartum depression. Holistically and comprehensively providing support to women will require significant effort throughout all sectors of society as opposed to isolated, siloed interventions.

New energy vehicle technology research - Charging technology

In recent years, Chinas economic development is becoming more and more rapid, leading to the increasingly serious pollution emission problem of traditional fuel vehicles. In order to solve the environmental problems, the transformation of automobiles has become an urgent problem. At the present stage, electric vehicles are also widely concerned by all sectors of society because of their low carbon emissions, energy saving and low use cost. With the rapid growth of electric vehicle sales, the demand for charging piles is rising, and the mainstream charging facilities for new energy vehicles are still in short supply. Therefore, we need to focus on efficient wireless charging to solve the major problems in the current electric vehicle industry. This paper analyzes the wireless dynamic energy transmission technology, extending the service life of the battery, how to reduce the cost, the optimization of coil parameters design, the offset of transmission coil and receiving coil, foreign body detection and overheating problems.

Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users

Reducing energy consumption is vital to save natural resources and contribute to the sustainable development in any sector of society. In the building sector, there are many well-known energy efficiency strategies currently being applied. However, considering the advances in technology and in comfort studies, it is possible to see that the current building sector scenario demands new energy efficiency strategies. Such strategies need to be capable of identifying and assuring comfortable environments according to users’ perceptions. Machine learning techniques can be a useful alternative to identify users’ preferences and control lighting and heating, ventilation and air-conditioning systems in buildings. This paper shows a systematic literature review on the use of machine learning algorithms on preference identification and environmental adequacy according to users’ demands. Its contribution is to explore beyond the performance and configurations of the algorithms, addressing users’ preference aspects as well. The strategies found in the literature provided promising results. The most used approach was supervised learning because data can be treated as categories. In general, the control systems have shown good performance, and so have the algorithms. Users were mostly satisfied with environmental conditions. Situations of dissatisfaction were associated with the occupant’s willingness to use the system more than with the control system’s performance. Furthermore, it is also possible to ally user-centred control and energy savings but this relies on occupants’ characteristics and the control strategies used. We underline the importance of identifying whether the users are willing to deal with an automatic control system before making any decision, even if the operation of the system is based on their preferred environmental conditions.

Exploring Cybersecurity Challenges and Emerging Trends in Cutting-Edge Technologies

In today's technologically driven landscape, a profound comprehension and adept application of cybersecurity have become non-negotiable. The stakes are high; without fortified defenses, systems, crucial files, data repositories, and virtual assets hang precariously in the balance, vulnerable to a plethora of potential threats. This imperative extends universally; whether it's a cuttingedge tech conglomerate or a modest momandpop shop, the need for robust cybersecurity measures remains unwavering. As the cybersecurity realm continues its relentless evolution, so too does the ingenuity of cyber adversaries, who continually refine and enhance their arsenal of hacking techniques, relentlessly probing for weak points within organizational defenses. This perpetual catandmouse game underscores the indispensable nature of cybersecurity in protecting the interests of military institutions, government agencies, financial enterprises, healthcare providers, and corporate entities alike. These entities serve as custodians of vast reservoirs of data, spanning an array of sensitivities—from confidential financial records and proprietary intellectual property to deeply personal information. Any breach or unauthorized access to such data could unleash a cascade of adverse consequences. Thus, the deployment of comprehensive cybersecurity protocols emerges as a strategic imperative, serving as a bulwark against potential threats and preserving the sanctity of invaluable assets across multifarious sectors of society. Keywords: cyber security, cyber crime, cyber ethics, social media, cloud computing, android apps.

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.

What does sustainability have to say about recent research in media management? Systematic review of recent media management research

ABSTRACT Sustainability related to the SDGs Agenda 2030 is a pressing issue that concerns all sectors of society. Media companies play a significant role in promoting sustainability, as they have a major influence on society’s behaviour. This article examines recent literature in the media management field to identify the aspects of sustainability that researchers focus on and the proposals they make to integrate the three aspects of sustainability – economic, social and environmental – into current business models. A systematic literature review was conducted using the triple-layered business model framework. This study reveals that media companies prioritise economic sustainability due to recent industry turbulence. However, social sustainability, particularly improving employees’ working conditions, remains an area that requires attention. Although companies recognise the importance of their audiences’ needs, they have much work to do to become socially and environmentally sustainable.

A Machine Learning Pipeline for Emotion Recognition based on Brain Topographic Maps Derived from Electroencephalogram Signals

Emotion recognition is an increasingly relevant field due to its direct implications for various sectors of society. The area aims to enhance the understanding of how emotions influence human behavior. Exploring brain activity analysis through electroencephalogram signals becomes possible when considering that emotions can manifest non-verbally. In this scenario, machine learning applications prove promising due to the complexity of recognizing emotions from electrical signal data from the brain. The case study focuses on DEAP, a recognized dataset constructed through experiments in electroencephalography, exposing subjects to musical and visual stimuli. The main objective of this work is to present a pipeline for the classification of emotions based on images of topographic maps generated from the EEGLAB tool and electroencephalogram signals. Additionally, the contributions of this work include the presentation of a structured dataset created through the mapping of temporal, spatial, and frequency data derived from topographic images and models for predicting dimensional emotions of arousal and valence based on the new dataset. Results demonstrate accuracies of 85.46% and 85.05% for the classification of low/high arousal and valence emotions, respectively.

UPAYA PREVENTIF CEGAH KEJAHATAN SIBER DAN PENYALAHGUNAAN TRANSAKSI ELEKTRONIK MELALUI SOSIALISASI UU ITE

Cyber security is a crucial aspect in today's digital era, especially with the increasing use of electronic transactions in various sectors of society. Through a community service approach, this programme implements a series of counseling activities, and a cybersecurity awareness campaign in Sukun Sub-district. This activity aims to increase cyber security awareness and electronic transactions in Sukun Sub-district, Malang City. The methods used include seminars and distribution of educational materials. Evaluation was conducted to measure the effectiveness of the programme in improving the knowledge and behavior of the community regarding cyber security and electronic transactions. The results of the activity evaluation questionnaire showed a significant increase in community understanding and awareness of cybersecurity threats and steps that can be taken to protect themselves when conducting electronic transactions. In conclusion, the community service approach is an effective strategy in increasing cybersecurity awareness and electronic transactions in Sukun Sub-district, Malang City. It is hoped that this activity can make a positive contribution in creating a safer and more trusted digital environment for the community.

Interlayer anions modulated ZnAl-layered double hydroxides for enhanced photocatalytic CO2 reduction

Photocatalytic CO2 reduction has drawn widespread attention across all sectors of society. Layered double hydroxides (LDHs) have emerged as promising photocatalysts, renowned for their adjustable bandgaps and reaction sites. As a typical anionic layered material, LDHs exhibit intricate interactions between interlayer anions and brucite-like metal layers, greatly influencing their inherent physicochemical properties. In this work, we synthesized ZnAl-LDH samples intercalated with four distinct anions: carbonate (CO32–), chloridion (Cl–), nitrate (NO3–), and dodecyl sulfate (DS–), denoted as LDH-CO3, LDH-Cl, LDH-NO3, and LDH-DS, respectively. These LDHs samples have comparable crystallinity and morphology but differ in their interlayer spacing. XRD results reveal interlayer spacings of 0.77 nm for LDH-CO3, 0.78 nm for LDH-Cl, 0.88 nm for LDH-NO3, and 1.24 nm for LDH-DS, between adjacent brucite-like ZnAl hydroxide layers. Remarkably, LDH-NO3 demonstrates the highest photocatalytic CO2 reduction performance, with CO as the primary reduction product. Specifically, the average CO production rate of LDH-NO3 are 3.80 μmol g–1 h–1, surpassing that of LDH-CO3, LDH-Cl, and LDH-DS by 1.9, 2.1, and 2.5 times, respectively. This superior performance can be attributed to the intermediate interlayer spacing of LDH-NO3, which facilitates strong light absorption and a robust local built-in electric field. Theses properties enhance charge generation and separation abilities, leading to efficient photocatalytic reactions. The obtained results highlight the critical role of controlling small molecules within the interlayer, alongside defects, in the design and fabrication of layered-structure photocatalysts. This study provides valuable insights into optimizing the performance of LDH-based photocatalysts for CO2 reduction and potentially other photocatalytic applications.

The Influence of Parasocial Relations on Contemporary Chinese Young Women's Idol Worship: A Study on Fan Culture

With the booming development of socio-economic and Internet technology, social culture has also continued to improve and enrich the times. Fan culture is given a new cultural connotation, and the fans cover the coverage of all sectors of society, which has attracted widespread attention. This study explores the correlation between parasocial relationships and celebrity worship among young Chinese women, utilizing questionnaires to examine how social connections influence celebrity worship behavior and its impact on well-being. The findings reveal a significant association between parasocial connections with celebrities and the level of celebrity worship. Despite potential adverse effects, celebrity worship provides emotional support and fosters a positive outlook on the future through parasocial relationships. The research also delves into the psychological mechanisms underlying the formation of these relationships, emphasizing the role of social media and celebrity culture. Understanding celebrity worship among young Chinese women contributes to our understanding of fan behavior, personal identity development, and the broader socio-cultural context of media and celebrity in contemporary society.

How sociology can help us save ourselves

How sociology can help us save ourselves

Sentiment analysis of online reviews of energy-saving products based on transfer learning and LBBA model

With the exacerbation of global climate change and the growing environmental awareness among the general public, the concept of green consumption has gained significant attention across various sectors of society. As a representative example of green consumer products, energy-saving products play a crucial role in the timely realization of dual carbon goals. However, an analysis of online comments regarding energy-saving products reveals that the majority of these products still exhibit shortcomings in terms of efficacy, noise level, cost-effectiveness, and particularly, energy-saving appliances. This study focuses on the user-generated online comments data from the Taobao e-commerce platform for Grade 1 energy-saving refrigerators. By employing text mining techniques, the study aims to extract the essential information and sentiments expressed in the comments, in order to explore the consumption characteristics of Grade 1 energy-saving refrigerators. Moreover, the LBBA (LDA-Bert-BiLSTM-Attention) model is utilized to investigate the consumer topics of interest and emotional features. Initially, the LDA model is adopted to identify the attributes and weights of consumer concerns. Subsequently, the Bert model is pre-trained with the online comment data, and combined with the BiLSTM algorithm and Attention mechanism to predict sentiment categories. Finally, a transfer learning approach is utilized to determine the sentiment inclination of user-generated online comments and to identify the primary driving factors behind each sentiment category. This research employs sentiment analysis on online comments data regarding energy-saving products to uncover consumer sentiment attributes and emotional characteristics. It provides decision-makers with a comprehensive and systematic understanding of public consumption intentions, offering decision support for the efficient operation and management of the energy-saving product market.

Digital equity

ELearning has been touted as the way in which universities can enable participation by large numbers of students from non-traditional cohorts. There is no doubt that the flexibility of access that eLearning allows makes study accessible for a number of cohorts, including those engaged in full-time work or caring duties. However, cohorts such as incarcerated students and other students without Internet access, are sitting on the wrong side of the digital divide and are increasingly marginalised by the very technology anticipated to overcome their exclusion from study. This paper examines the fundamental issues of equity involved with eLearning, and particularly for incarcerated students. The very issue of access to the Internet is fraught with rates of access varying widely between different sectors of society. This discussion prompts higher education providers to think beyond business-as-usual when speaking of increasing participation in higher education.

Analysis and control measures of vibration and noise sources in urban rail transit under the perspective of standard governance

With the rapid and large-scale development of rail transit engineering in China, the vibration and noise problems caused by rail transit operations are increasingly receiving widespread attention from all sectors of society. This paper is based on literature research and comprehensively analyzes the vibration and noise sources of urban rail transit. It systematically analyzes the composition, characteristics, and causes of vibration and noise in rail transit, and provides systematic governance measures for vibration and noise reduction in rail transit from two aspects: noise sources and propagation paths. The research results of this article have significant reference value for the engineering practice of vibration and noise reduction in rail transit.

Reckoning with epistemological and ontological dissonance: a narrative inquiry into settler Canadian professors’ experiences decolonizing and Indigenizing the academy

ABSTRACT The Truth and Reconciliation Commission's (TRC) Calls to Action urge Canadian higher education (among other sectors of society) to learn the shameful truth about Indian Residential Schools and work towards reconciliation with Indigenous peoples. Through narrative inquiry, non-Indigenous academics shared their experiences of a faculty learning community on Indigenization of their teaching and curricular practices. They each, subsequently, co-composed with the researcher a narrative account based on those experiences. This paper presents one of the resonant threads that arose from the analysis of those narrative accounts, namely: Epistemological and Ontological Dissonance.

Malware Detection in Files and URL’s Using Machine Learning

The rampant extrusion of personal or sensitive data by malicious software has inflicted significant damage worldwide, posing a critical threat to individuals and various sectors of society. Cyber attackers engage in relentless warfare against computer systems, deploying malware to breach crucial data. Consequently, safeguarding this data has become a paramount concern for researchers. This paper aims to address this challenge by proposing the development of an application leveraging machine learning algorithms to discern between malicious and legitimate files effectively. In the contemporary era, marked by pervasive technological advancements, the widespread adoption of the internet has facilitated unprecedented connectivity but also heightened the risk of cyber threats perpetrated by malicious actors. These threats, often executed through the deployment of malware, have inflicted substantial financial losses, amounting to billions of dollars globally. Consequently, safeguarding against malicious attacks has become a paramount endeavor in the ongoing battle against cybercrime. To address this imperative, this paper endeavors to leverage machine learning algorithms to prognosticate the likelihood of malware infections in computer systems, utilizing a supervised learning approach alongside gradient boosting algorithms. Drawing upon a publicly available dataset, meticulously partitioned into training and testing sets, the study embarks on four distinct experiments employing the aforementioned algorithms. Notably, the findings underscore Light GBM as the most efficacious model, boasting an impressive AUC Score of 0.73926, thus signifying a pivotal advancement in fortifying cyber defenses through predictive analytics. Malware poses a significant threat in today's digital landscape, with various forms proliferating over the last decade, leading to substantial financial losses for organizations. Defined as malicious software, malware wreaks havoc on users' computers through various means. To address this challenge, a solution leveraging machine learning techniques has been proposed to detect malware in downloaded files. By analyzing features such as MD5 hash, Optional Header size, and Load Configuration Size, different machine learning algorithms are trained to distinguish between malicious and benign files effectively. Through rigorous training and comparison using Validation and Test datasets, the SVM Classifier emerged as the most accurate, achieving up to 99.99% accuracy in detecting malware types like Adware, Trojan, Backdoors, Unknown, Multidrop, Rbot, Spam, and Ransomware. This approach not only identifies malware but also mitigates potential risks to users' systems, safeguarding against detrimental impacts from infections.