Loss Of Reputation Research Articles

Source-Based Plagiarism

Source-based plagiarism is an increasing problem in academia, since it can jeopardize the legitimacy and originality of scholarly work. Plagiarism is the unauthorized use of another person's intellectual property, such as ideas, data, or text, without due citation or attribution. This behaviour is deemed unethical and can have major implications, including the loss of academic reputation and employment opportunities. The issue of source- based plagiarism has been extensively researched in the literature, with numerous experts researching its prevalence, causes, and consequences. According to a study performed by (Devasena and Balaji, 2020), source-based plagiarism is a common practice that affects students, researchers, and even seasoned academics (Devasena & Balaji, 2020). The authors ascribe this to a lack of understanding about correct citation and citing procedures, as well as the pressure to generate original and new research. In the literature, many types of source-based plagiarism have been recognized, including verbatim copying, paraphrasing, and mosaic plagiarism (Shirazi et al., 2020). Verbatim copying entails repeating another person's work word- for-word, whereas paraphrase involves rephrasing someone else's thoughts in one's own words. Mosaic plagiarism, on the other hand, entails merging ideas and language from various sources without due acknowledgment. According to Davis and Carroll (2009), source-based plagiarism is defined as "the act of presenting the ideas or work of another as one's own, without giving appropriate credit" (Davis, M., & Carroll, J., 2009). To address the issue of source-based plagiarism, numerous tactics and technologies have been offered, such as plagiarism detection software, academic integrity training, and explicit citation and reference requirements (Karimzadeh and Karimzadeh, 2018). However, the effectiveness of these treatments is still being debated, with some academics challenging their impact on the underlying reasons of plagiarism (Rahimi and Yadollahi, 2021).

Corporate Social Accountability: Lessons from Coca-Cola’s Conflict at Plachimada

This paper explains the process by which a global corporate giant was made to account for its social responsibility by studying the conflict between Coca-Cola company and the local community in Plachimada (a small village in the south Indian state of Kerala) as a case study. The paper argues that the company suffered a long judicial battle and loss of reputation and finally lost ‘its license to operate’ for not discharging its responsibility to the local community (a major stakeholder). The analysis of the events in this conflict seems to suggest that the combination of new social movements and consumer activism in defence of human rights and environmental protection holds the promise of making the large global corporations more accountable for their social responsibilities. Fuelled by the speed of the internet and the resultant reach of media (including social media), these movements have become more effective and stronger during the last twenty years and the corporates therefore can ignore the responsibilities that society expects them to embrace only at their own peril.

Pro-active Performance Monitoring in Optical Networks using Frequency Aware Seq2Seq Model

Performance Monitoring (PM) and Fault Detection have always been a reactionary approach in Optical Networks for most service providers. Any kind of fault (power surge, ageing issues, equipment faults and failures, natural calamities, etc.) in an optical network is detected only after the fault has occurred and mitigation is performed afterward. The resultant service outages for end-users cause huge financial and reputation losses to the vendors. Therefore, there is a strong need for proactive detection of faults to limit disruption and provide uninterrupted services to clients. We achieve this objective by doing a multi-horizon time series prediction of Bit Error Rate at the receiver end of an optical circuit using our custom designed Frequency aware Sequence to Sequence (FaS2S) Neural Network. The predicted value of BER can be used to notify users of failure scenarios before they occur. Further corrective action, such as automatic re-routing or manual intervention can then be taken by the user. With this model, we can even configure the network properties dynamically during periods of low BER to push the network efficiency to its maximum capacity. See inference Video for BER inference capabilities of FaS2S.

Game study on safety supervision of imported aquatic products in China

With the expanding scale of aquatic product imports, safety issues have arisen. Among them, the lack of supervision by government agencies, illegal importation by unscrupulous enterprises and the lack of regulatory bodies such as consumers are major reasons for the occurrence of unsafe imported aquatic products. Therefore, this paper adopts a two-by-two static game model between government agencies and importers, importers and consumers, and a three-way sequential dynamic game model between government agencies, importers and consumers to analyze the causes of quality and safety problems of imported aquatic products in China. The results show that when the safety problems of imported fishery products occur, consumers choose to report importers when the compensation they receive due to reporting increases or when the cost of reporting is reduced by not reporting; government agencies choose to strictly regulate importers when the cost of regulation is reduced by loose regulation and reputation loss increases, or when the revenue from fines obtained by strict regulation increases. It is further verified through empirical evidence that both government agency regulation and consumer monitoring have positive effects on importers’ self-regulation. Finally, countermeasures to ensure the quality and safety of imported aquatic products in China are proposed.

Supply chain quality level decision considering block-chain technology

Aiming at the problem of unqualified product quality, a two-level supply chain game model under two modes is established. The backward induction method is used to solve the decision variables to be analyzed and explore the application conditions of blockchain technology. We found that the supply chain with the smaller application cost, the smaller qualified rate of product quality and service quality, and the larger loss of reputation is the most suitable to use blockchain technology. Product quality, service quality and market price are improved. In addition, we also discuss the influence of sensitivity coefficient on product quality and service quality. The smaller the cost coefficient of product quality and the larger the importance of product quality are, the better the effect of product quality improvement is. The smaller the cost coefficient of service quality and the larger the importance of service quality are, the better the effect of service quality improvement is. It provides a reliable basis for better quality decision.

Compilation of reputation loss scales in the oil and gas pipeline industry

AbstractOil and gas pipeline accidents not only caused personnel casualties, environmental pollution, and economic losses but also brought great panic to the public. In order to prevent accidents, consequence assessment (CA) has been widely used in the oil and gas pipeline industry. It aims to quantify the potential damage or loss due to unexpected eventualities. However, the current CA methods mainly pay attention to tangible loss, that is, human loss, production loss, asset loss, and environmental loss; the intangible loss such as reputation loss is often neglected. This leads to the deterioration of the quality of CA for the oil and gas industry. To settle existing research limitations, this article proposes to compile the reputation loss scale for the oil and gas pipeline industry to measure the intangible loss of oil and gas pipeline accidents. The compilation methodology mainly involves three stages, namely, the selection of reputation loss indicators, a compilation of reputation loss scale based on the Likert scale, and validation of reputation loss scale. The most important innovation is to quantify reputation loss based on the existing research. Therefore, this study can improve the limitations of current CA methodologies for the oil and gas pipeline Industry.

Weathering the Storm: Discordant Learning about Reputations for Reliability

Abstract It is well established that state reputations impact international politics, but less is known about how these reputations change. We investigate one form of change by examining how individuals process new information. Using a logic of discordant learning, we expect good reputations to survive new and incongruent information that counters expectations. Good reputations can help states “weather the storm” in times of crisis. Such buffers have their limits, however, as strong incongruent signals can trigger large corrections in a state’s reputation. To analyze these expectations, we focus on alliance reliability. Using a pair of survey experiments, we find that individuals alter their perceptions of a state’s reputation when observing signals that deviate from the state’s prior reputation, and that good reputations are able to “weather the storm”. We also find that strongly incongruent signals affect good reputations more than others, suggesting “the bigger they are, the harder they fall” may also apply. Even in these large corrections, however, a reputation for reliability has lasting benefits. The analysis helps us understand when to expect changes in reputations for alliance reliability, which in turn may inform when reputation loss can influence alliance politics.

Firms, activist attacks, and the forward-looking management of reputational risks.

A growing literature investigates how activists launch attacks against firms to improve environmental practices, a situation typically referred to as "private politics." Whether firms self-regulate in response has been shown to depend on reputational risks. However, reputation management in this literature is mostly reactive, whereas firms could be expected to anticipate and prevent reputation loss when faced with the threat of activism. How they would do so is not obvious, nonetheless, because firms have to consider two opposite effects: (1) a "reputational damage mitigation" effect, through which firms can pre-emptively align to what is expected from them, and (2) a "target enhancing" effect, in which self-regulation makes firms more visible and likely to be criticized. We show, theoretically and empirically, that these two effects actually co-exist and create heterogeneity in firms' responses when they witness activist attacks in their industry. The real impact of activism on the development of more sustainable practices is thus not only greater than if we solely considered the responses of firms that suffer direct attacks, as many firms start self-regulating before being targeted, but also varies within industries.

Implementation of an integrated approach of the compliance system for the socio-economic development of the company

To ensure compliance with the law and the obligations assumed, the company needs a management system that will ensure this compliance. Compliance is an integral part of the management system and ensures compliance with the regulatory environment, the norms of corporate culture and business ethics; identifies risks of non-compliance of organizations and employees with legal norms, established regulatory rules and standards; carries out measures to eliminate the risks of applying legal sanctions or regulatory sanctions, significant financial loss or loss of reputation as a result of non-compliance with business regulations. In an organization, compliance is applied through the implementation of a compliance management system, which is a complex of interrelated and interacting elements of the organization to ensure its compliance with the requirements of the regulatory environment and achieve its goals. In the article, the author presented how to implement a compliance system in an enterprise.

ES-SECS/GEM: An Efficient Security Mechanism for SECS/GEM Communications

Industry 4.0, as a driving force, is making massive achievements, notably in the manufacturing sector, where all key components engaged in the production processes are being digitally interconnected. However, when combined with enhanced automation and robotics, machine learning, artificial intelligence, big data, cloud computing, and the Internet of Things (IoT), this open network interconnectivity renders industrial systems more vulnerable to cyberattacks. Cyberattacks may have a variety of different impacts and goals, but they always have negative repercussions for manufacturers. These repercussions include financial losses, disruption of supply chains, loss of reputation and competitiveness, and theft of corporate secrets. Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) is a legacy Machine-to-Machine (M2M) communication protocol used profoundly in the semiconductor and other manufacturing industries. SECS/GEM is mainly designed to be utilized in a trusted, controlled, and regulated factory environment separated from external networks. Industry 4.0 has revolutionized the manufacturing industry and has brought SECS/GEM back to the limelight, as SECS/GEM is completely devoid of security features. This research proposes ES-SECS/GEM, an Efficient Security mechanism that provides authentication, integrity, and protection against cyberattacks. The proposed mechanism is compared to other security mechanisms in terms of processing time, control overhead, and resilience against cyber-attacks. The ES-SECS/GEM demonstrated promising results, suggesting that it allowed SECS/GEM devices to only connect with authorized industrial equipment, maintained message integrity, discarded forged messages, and prevented cyberattacks on SECS/GEM communications. In terms of processing time and control, ES-SECS/GEM likewise outperformed other mechanisms and incurred the lowest values for these metrics.

Attitude to the traditional wedding of students from Central Asia studying in Moscow

A traditional wedding is an important part of the cultural code of the inhabitants of Central Asia. Can a wedding become a point of conflict between generations? The article is devoted to the attitude to wedding traditions of young people from Central Asian countries (Uzbekistan, Kazakhstan and Kyrgyzstan) who study in Moscow. The research method is asemi-structured interview (N=23). In the interview, the distance between "what parents think is right" and "what kind of wedding I want" is clearly visible. Young people want to hold their own wedding more privately and less burdensome in terms of effort and money spent. There are several reasons for the reproducibility of wedding traditions: - respect for the ancestors, the desire to avoid conflict with relatives, - disenfranchisement of girls as a gender norm, - the need of families to demonstrate their success, - fear of shame, loss of reputation. The attitude of young people to a traditional wedding varies. Someone is willing to voluntarily follow traditional wedding rituals. Someone moderately criticizes wedding traditions, but is ready to obey them so as not to upset relatives. Someone sharply criticizes traditional wedding ceremonies and intends to hold a wedding contrary to the traditional canon.

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL-CTD model with increased accuracy of 99.47%.

Резонансный Катар: феномен внешнеполитической идентичности малой монархии

Foreign policy of Qatar, a dwarf emirate with considerable geographic and demographic limitations, demands special attention. Not only has this small Gulf monarchy succeeded in overcoming its smallness, but also managed to establish itself as an influential actor, both regionally and internationally, in the shortest possible time, breaking and revolutionizing conventional perceptions of capabilities and opportunities of small states in world politics. The paper aims to explore various characteristics of Qatar’s foreign policy, its main principals and goals, instruments and paths of their implementation, as well as its achievements. The author also intends to determine the essence of Qatari foreign political identity and its components, arguing that it was the key to the small Gulf emirate’s global success. Through formulation and assertion of its exceptionalism, skilled promotion of its image as a modern and progressive state, useful military and political partner, reliable supplier of fuel, sought-after international mediator and donor of humanitarian assistance, powerful investor, a transport, cultural, educational, financial hub and a tourist destination in combination with efficient balancing strategy, Doha was able to carve a distinguished niche for itself and cement its position as an important global actor. Despite of a number of setbacks in foreign policy tactics and reputational losses that followed, regardless of speculations about the unrepairable damage to all the previous achievements, the young emir has proven his commitment to the resonant foreign political identity his predecessor had shaped. Its core elements include demonstrative independence and self-sufficiency, exceptionalism distinguishing the emirate form other Gulf monarchies, refusal to accept the bounds of conventional perceptions of “friends” and “foes”, combination of traditions and modernity, as well as vision and proactivity.

Information strategies for preventing destructive behavior of civic activism in criminal youth subculture

This article aims to explore the motivational reasons driving youth engagement in destructive and criminal communities, offering insights into the dynamics and implications of their involvement. To achieve this goal, a comprehensive study was conducted, employing a mix of quantitative and qualitative research methods, including surveys, expert interviews, and focus group discussions. These methods facilitated an in-depth examination of the motivations and deterrents influencing young individuals' participation in these communities. The obtained results allowed us to draw conclusions that the most effective information and prevention strategies are based on education, demonstration of the regulated nature of phenomena, revealing their systematic nature, authorship, hierarchical structure (controllability). Preventive strategies can also be based on the nature of the motivational mechanisms of the youth audience, the basis of positive motivation (desire to join the group) is the desire for active, active self-actualization, the desire to get unusual (extreme) experiences, as well as the desire for security and joining a new ideology. Among the restraining motivational factors: fear of becoming dependent (rigid, humiliating hierarchy), threat of bullying (possibility of personal injuries), loss of positive reputation, high level of conflictogenicity and blackmail.

Methodological principles of forming a mechanism for assessing environmental damage from the functioning and development of industrial real estate within the boundaries of the urban environment

It is not possible to imagine the implementation of investment and construction projects for industrial purposes in isolation from the environment. During the construction and further operation of enterprises, it is necessary to remember the close relationship between nature and man and plan the processes of waste disposal from harmful substances. In pursuit of profit, management often forgets about air and water purification measures, which causes deterioration of the natural environment around the enterprise, complaints from residents, loss of business reputation, and often financial losses due to the need to eliminate the consequences of emissions. Thus, during the construction of industrial facilities, an important task is not only the calculation of profits from operation, but also environmental protection measures, which must necessarily be carried out by enterprises. The growth of the country's economy largely depends on the growth of gross domestic product (GDP). In 1934, when this indicator was first introduced, it was most often used to understand how much the country is ready for hostilities and how many products enterprises can produce to ensure stable functioning. Later, in the 40s, the indicator was associated with the volume of industrial production, since the service sector at that time was poorly developed. And accordingly, the calculation of the GDP indicator was a very necessary mechanism: the growth of GDP per capita from one thousand to 10 thousand. dollars meant a rapid leap in the material well-being of the population.

Methods of Ensuring Information Security in BIM

Information security in BIM (Building Information Model) platforms is crucial, especially for mission-critical projects. Improper distribution and loss of confidential information can potentially lead to threats to physical security, financial losses, and loss of trust and reputation. The article discusses common methods of ensuring cybersecurity, potential threats, and measures to prevent them for the preparation and development of conceptual foundations for the Information Security Strategy of the process of building and structure information modeling. Tasks: 1) Identification of the most common information protection methods and identification of BIM security elements; 2) Analysis of potential cybersecurity threats for the information modeling process; 3) Consideration of common collaboration models in BIM. This work will allow developing an Information Security Strategy in BIM processes, improving the approach to ensuring an appropriate level of cybersecurity in BIM platforms, and developing universal steps for planning and implementing an effective information security strategy.

InfoSec Practices - a Survey Conducted in Greek Hospitals.

Langerhans The information technology is present in every aspect of private, social, and professional sphere and is constantly evolving whilst remaining vulnerable to security alerts and attacks. The healthcare sector contains sensitive information and can be compromised causing even fatal delays to healthcare delivery, loss of reputation of the organizations, and traumatic experiences for patients who might be stigmatized by the disclosure of their medical files. Infosec practices that are applied by nurses can make the difference in defending or compromising healthcare data. The aim of this research is to investigate the Infosec practices which are applied by nurses who work in Greek hospitals. Correlation between the Infosec practices, the knowledge on Infosec policies, and attitude towards Infosec policies will be further examined. The KAP model is applied and the HAIS-Q tool consisted of 45 items in five areas of interest was used. A sample of 277 nurses was collected. Confidentiality issues and consent were respected. IBM SPSS 25.0 was used for the statistical analysis. Descriptive analysis (mean, st. dev.) and inferential statistics, ANOVA, and Pearson Corerelations were conducted. The significance level was set up to 0.05. A strong correlation between knowledge on Infosec policies and attitude towards Infosec policies, attitude towards Infosec policies and Infosec practices, and knowledge on Infosec policies and Infosec practices was established. Nurses apply good Infosec practices in three out of five areas of interest, while their practices concerning the report of security violations and their practices related to the Internet usage are average. Overall, nurses' Infosec awareness is good. The findings showed that nurses demonstrate average to good knowledge on Infosec policies, good attitude towards Infosec policies, good to average Infosec practices and good total Infosec awareness.The most vulnerable area is the Internet usage.

ПРОГНОЗНЫЙ МОНИТОРИНГ ТЕХНИЧЕСКОГО СОСТОЯНИЯ ПУТЕМ АНАЛИЗА СОСТАВЛЯЮЩИХ СИГНАЛА ВИБРАЦИИ ПОДШИПНИКОВ ВРАЩАЮЩИХСЯ ЭЛЕКТРИЧЕСКИХ МАШИН

Relevance In industry, any failures and unplanned downtime of machines or systems can degrade or interrupt a company's core business, potentially leading to significant penalties and irreparable loss of reputation. Existing traditional maintenance approaches (failure or routine maintenance) suffer from some assumptions and limitations, such as high prevention or repair costs and inadequate or inaccurate mathematical degradation processes. Due to the trend of intelligent manufacturing, data mining, and artificial intelligence, predictive maintenance control is proposed as a new type of maintenance paradigm only after analytical models predict certain failures or degradations. Equipment condition monitoring can reduce the likelihood of failures occurring. Successful technical diagnostics and monitoring of complex electromechanical subsystems and assemblies provides an increase in service life and reliability of the systems as a whole. Aim of research A general overview of maintenance aims and objectives, which mainly include detection and prevention of failures and malfunctions, maintaining performance within established limits, condition prediction in order to improve machine reliability and full use of its resource, cost minimization, maximization of availability/reliability and multi-criteria optimization. Object of research The object of the study is the technical condition of rolling bearings of rotating electric machines. The main defects that are characteristic of this object are analyzed. In addition, a practical and effective sequential preventive maintenance method for rotating electrical machines is proposed, consisting of four steps: feature generation and fault diagnosis selection, data dimensionality reduction and feature fusion, fault detection and diagnosis, and estimation of the remaining useful life of rotating electrical machine bearings. Vibration signal analysis (from the time domain waveform and spectrum of the original or derivative signal, characteristics can be calculated and their effectiveness in diagnosing a fault can be assessed). Research methods To verify the performance of the proposed method, bearing failure is simulated in the Simulink MATLAB module. The bearing failure is created by feeding the vibration perturbation into the shaft system. Results Based on the results of the simulation, a conclusion on the operability of the proposed method for investigating the technical condition of rotating electric machines is drawn.

МЕТОДЫ ПРОГНОЗНОГО МОНИТОРИНГА ТЕХНИЧЕСКОГО СОСТОЯНИЯ ЭЛЕКТРИЧЕСКИХ МАШИН

Relevance During operation of electromechanical machines, their specifications may change, which can lead to machine failures. Like all electromechanical equipment, rotating machinery is subject to many different adverse effects, such as thermal and environmental stresses and mechanical damage, which require the utmost attention. In industry, any machine or system failure or unplanned downtime can degrade or interrupt a company's core business, potentially resulting in significant fines and immeasurable loss of reputation. Existing traditional approaches to maintenance (maintenance by failure or regulation) suffer from some assumptions and limitations, such as high prevention or repair costs, inadequate or inaccurate mathematical degradation processes. Due to the trend toward smart manufacturing, data mining, and artificial intelligence, predictive maintenance is proposed as a new type of maintenance only after analytical models predict certain failures or degradations. Modern systems for assessing the technical condition of electromechanical equipment are decision support systems based on machine learning. Aim of research The aim of this research is to provide a general overview of maintenance goals and objectives, which mainly include cost minimization, availability/ reliability maximization, and multicriteria optimization. In addition, an overview of existing approaches for fault diagnosis and prediction in predictive maintenance systems is proposed, which include two main subcategories: knowledge-based approaches and traditional machine learning methods. Research methods Currently, many methods based on processing information from measurement transducers, which use acoustic and vibration sensors, current and voltage sensors, temperature sensors, and electromagnetic transducers, using machine learning techniques, have been developed for fault diagnosis and failure time prediction of electromechanical units. Results As a result, the paper presents a brief overview of the aims and approaches of a predictive maintenance system for electromechanical systems based on machine learning techniques used in various electromechanical equipment. An overview of various predictive maintenance methods is presented. An overview of existing approaches based on machine learning is given.

Ungarn, der EU-Rechtstaatlichkeitsmechanismus und Russlands Krieg gegen die Ukraine: die politische Geiselnahme außenpolitischer Entscheidungen

The European Union’s (EU) as well as NATO’s internal cohesion and their capacity to act is crucial to meet the security challenges due to Russia’s invasion of Ukraine. The article discusses Hungary’s threats to veto vital EU sanctions against Russia as well as NATO enlargement. In particular, we focus on how Hungary has resorted to hostage-taking strategies in the field of the EU’s Foreign and Security Policy as well as NATO to gain EU concessions in unrelated policy areas like the Article 7 procedure or the COVID recovery funds. In doing so, Hungary aims to articulate its veto threat in a credible way, but at the same time it is trying to minimise the associated loss of reputation. To date, Hungary has succeeded in obtaining selective concessions from the other EU member states without derailing the sanctions policies against Russia or the NATO enlargement process.