bluuebunny/crossref_metadata_2025 huggingface dataset:
Bias in Data-Informed Decision Making
Authors: Dissanayake Harini , Bracewell Paul J.
Published: January 2023

With vast amounts of data becoming available in a machine-readable format, decision makers in almost every sector are focused on exploiting data and machine learning to drive the phenomena of automated decision making, whilst rapidly dissolving the human oversight in the process. The description of bias in decision making arising from machine learning outlined in this article sets to demonstrate the scale of the issue and the value of transparency in decision making that affects the daily life of ordinary humans. Fully automated solutions without appropriate governance can be problematic due to the replication of human biases which can be captured and imposed upon the training process of a machine learning models. Decision makers must be aware of limitations within the data and proceed with caution. As such, transparency in how data is used to make decisions is vital. Despite the ever-increasing reliance on algorithms in decision making, human oversight is critical. This article serves to raise awareness of this aspect of machine learning.
Bayesian Network-Based Decision Support for Pest Management
Authors: Gupta Neha
Published: January 2023

The Bayesian network can be used to estimate the effectiveness of alternate management decisions or policies because of being causal in nature. BNs have the ability to sustain clarity through explicit causal assumptions and so are generally used to model when the relationships to be described are not easily expressed using mathematical notation. Bayesian networks have many other attractive properties which make them particularly applicable in data analysis and decision making. BNs have simple causal and graphical structure and can be easily extended and altered. In this article, the authors discuss the concepts related to Bayesian networks and the role of Bayesian networks in development of decision support system for pest management.
Decision Support Systems and Data Science
Authors: Bihl Trevor , Young II William A. , Moyer Adam
Published: January 2023

Decision support systems (DSS) are information systems that facilitate human decision-making through the presentation and analysis of data. Primarily, these information systems allow people to improve decisions by using additional data and information that they have access to but do not always use to make a more informed decision. DSS efforts enable military and civilian leaders to improve strategic plans and make decisions. Increasingly, DSS are seen in use by both the public and decision makers to make sense of big data, as seen with COVID-19 presentations. Artificial intelligence (AI) can be used to provide rapid interpretation of the raw data and results for use within DSS systems. DSS is colloquially termed a “dashboard” and involves three main components: the database, model, and the user interface. This article explores archetypes of DSS and aims to discuss each component in equal measure since ignoring one aspect leads to various issues (e.g., a DSS employing a good model with appropriate data but poor implementation).
Decision-Making Approaches for Airport Surrounding Traffic Management
Authors: Kong Xiangfen , Zhao Peng
Published: January 2023

This article is designed to investigate the automatic decision support system in terms of analyzing airport surrounding road networks. Several decision-making approaches are illustrated and examined based on a broad range of data-driven methods, including data mining, machine learning, and deep learning. Each method has been investigated by providing a survey study that involves the most recent and comprehensive understanding of traffic engineering. As a specific problem in urban traffic congestions, airport surrounding traffic management can be referenced from similar studies in urban traffic congestion. The study can be used in improving the airport services in terms of operational efficiency and airport landside management, further supporting the construction of the smart airport.
The Diamond of Innovation
Authors: Ojeda Fernando
Published: January 2023

This first installment seeks to explain in its basic concepts the genesis and scientific support of the tool to be used. The Innovation Diamond, with each of the eight points, determines the importance of these as an assessment support against cases related to innovation, be it for a country, city, company, or business sector. In addition, it determines the different scopes that it may have to finally determine priority strategies in this matter to be applied. In the second part, the operative part of the tool will be shown. Once the component and basic actions of the Innovation Diamond have been explained, the genesis and creation of its “green version” is revealed. Finally, this article shows the real scope of the instrument, reviewing each of the cases for which it was used at the level of countries, companies, and academia between 2019 and part of 2021.
Converging International Cooperation Supported by Data Structures
Authors: Ahamer Gilbert
Published: January 2023

The target of any learning is to widen the present range of mental conceptions in a person (and thus in a society). Such an approach, based on “enlarging social spaces,” is helpful in international cooperation, especially during present days of confrontation. This article presents several positive examples for mutual cooperation. The cases presented show the practical contributions of IT, especially of e-learning platforms, in facilitating the exchange of fact-based concepts for the construction of social spaces and spaces of understanding. Societal learning can enlarge and approximate spaces of understanding. Learning changes interdisciplinary realities by providing a clear mental image of what the future can bring. Actually, building consensus is the key step in international development. IT tools have promoted the dialogue in these cases of cooperative learning both in developing countries and in administrations and within academia of industrialised countries: the project Schools on Ice, the UniGIS online curriculum, the UniNet network in Kyrgyzstan, Nepal, and Tajikistan.
Explainable Artificial Intelligence
Authors: Keppeler Vanessa , Lederer Matthias , Leucht Ulli Alexander
Published: January 2023

The explainability of artificial intelligence (AI) is one of the central challenges for the wider use of the new technology in many industries and applications. The more powerful and efficient the algorithms of AI work, the less it is usually comprehensible to users. While there is widespread agreement on the basic requirement of explainability for AI applications, the design of an adequate AI explanation is rarely defined. This contribution presents basic concepts of explainability as well as current approaches to explanations for AI. It describes which methods are fundamentally suitable for considering an explanation to be complete and how it must be designed in order to be assessed as interpretable for AI.
Integration of Knowledge Management in Digital Healthcare Industries
Authors: Hedayati Mehrnaz Khalaj , Schniederjans Dara G.
Published: January 2023

Although digital technologies provide many advantages and opportunities for storing, processing, and sharing information in real-time, disseminating and managing knowledge are critical for value creation. A deeper understanding of the tools and processes as drivers of knowledge creation can also have a significant impact on service quality in the healthcare industry. Knowledge management theories presuppose an essential aspect of knowledge relates to human processing, challenging the technology basis of digital healthcare. Without the ability to leverage digital technologies through knowledge management, the creation of value is minimal. This study provides a conceptual knowledge management digital capability model (KMDCM) in order to enhance digital healthcare performance.
Learning Analytics for Smart Classrooms
Authors: Llurba Cèlia , Palau Ramon , Mogas Jordi
Published: January 2023

The main objective of this study is to use content analysis to explore the characteristics and trends on learning analytics (LA) in smart classrooms (SC). Articles related to the main topic introduce an overview of LA, understanding its meaning and the main characteristics, as well as highlighting the benefits of how LA can be used as a provided tool which assists teachers' and students' practice and how it can help teachers to think and make decisions related to teaching and the learning process. Considering LA can improve learning and provide assistance for at-risk or underachieving learners, the article also researches what experiences have been applied about LA in SC. In addition, most of the studies found referring to data collection do so either on different platforms such as the learning management system, content management system, or have relevant information on gesture-based learning studies and record body language and facial expressions such as capture of movements, gestures, and gazes, whose purpose is to evaluate behaviour in a SC to enhance students' performance.
Financial Analytics With Big Data
Authors: Wang Leon
Published: January 2023

Like in other industries, financial institutions are facing a new era of big data, which plays the leading role in transformation of financial and economic environment. This article dives into why big data is important in the financial field, and how the information it provides is useful and helpful to corporations and companies. Applying big data analytics provides financial firms with insight into how they can detect fraud, conduct real-time analytics, deal with risk, and direct customer portfolio. Specific personalized services can be offered for their customers via customer portfolio management, customer data management, and analytics, even with real-time analytics. Corporations can better forecast financial markets, trends, patters, and irregularities. Therefore, financial analytics has become a fundamental instrument for finance firms, professional, and individual investors, as well as financial researchers.
Portfolio Optimization for the Indian Stock Market
Authors: Sen Jaydip , Dutta Abhishek
Published: January 2023

Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This article presents a systematic approach to portfolio design using two risk optimization approaches, the hierarchical risk parity algorithm and the hierarchical equal risk contribution algorithm on seven critical sectors and the NIFTY 50 stocks listed in the National Stock Exchange of India of the Indian stock market. The portfolios are built following the two approaches on historical stock prices from Jan 1, 2016 to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021 to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of the HERC portfolio on both training and the test data for the majority of the sectors studied in this work.
Data Hierarchies for Generalization of Imprecise Data
Authors: Petry Frederick , Yager Ronald R.
Published: January 2023

Issues related to managing imprecise data in areas as diverse as spatial and environmental data, forensic evidence, and economics must be dealt with for effective decision making. To make use of such information, the authors settle on how the various pieces of data can be used to make a decision or take an action. This involves some sort of summarization and generalization of the pieces of data as to what conclusions they can support. To address these issues, use of fuzzy, interval valued, and intuitionistic concept hierarchies for generalization can extend previous approaches to deal with the uncertainty of data. A number of approaches to characterizing such decompositions for the resolution of the evidence using these hierarchies is also needed. The characterization of hierarchies indicates that set decompositions are needed to represent the uncertainty of the hierarchies. To characterize these decompositions, granularity measures and overlap measures must be developed and examples of each discussed. Additionally, information measures can be introduced to be used for these evaluations.
Fuzzy Logic-Based Classification and Authentication of Beverages
Authors: Pratihar Bitan , Kundu Madhusree
Published: January 2023

This article aims at designing a fuzzy logic-based classification and authentication system for commercially available tea and water brands. Electronic tongue instrumentation-based experimentally collected data had been used to design the classification and authentication system. Tea and water samples of six different types were evaluated using pulse voltammetric technique. However, this data generation and reporting are not under the scope of the current study. The classifier/authenticator design is a three-step procedure. At first, identification of the data in subspace was done using Sammon's non-linear mapping technique followed by entropy-based fuzzy clustering of water/tea data set formed and, finally, designing the expert system optimized using a particle swarm optimization to authenticate unknown water and tea samples with 100% efficiency. Thus, it deals with dimensionality reduction of the data set for visualization, clustering based on similarity, and development of a fuzzy logic-based expert system, as it is a powerful tool for dealing with imprecision and uncertainty.
AI Is Transforming Insurance With Five Emerging Business Models
Authors: Zarifis Alex , Cheng Xusen
Published: January 2023

Artificial intelligence and related technologies are creating new opportunities and challenges for organizations across the insurance value chain. Incumbents are adopting AI-driven automation at different speeds, and new entrants are attempting to use AI to gain an advantage over the incumbents. This research explored four case studies of insurers' digital transformation. The findings suggest that a technology focused perspective on insurance business models is necessary and that the transformation is at a stage where we can identify the prevailing approaches. The findings identify the prevailing five insurance business models that utilize AI: (1) focus and disaggregation, (2) absorbing AI into existing model, (3) incumbent expanding beyond model, (4) dedicated insurance disruptor, and (5) tech company disruptor.
Emerging New Technologies and Industrial Revolution
Authors: Rusko Rauno , Kosonen Mika
Published: January 2023

The economic history is full of technical disruptive innovations, which have changed the markets and the ways of living and business. The innovations, starting from steam power, electricity, telegraphs, internal combustion engine, and continuing with mainframes, PCs, the internet, and mobile/social networking, have changed the business world. Still, new disruptive technologies are developing, and new innovations are emerging. This study maps the drivers of industrial revolution and considers the promising contemporary potentially disruptive innovations, such as blockchain technology and cybernetics. This article suggests the existence of two (or even more) industrial revolutions at the same time.
The Application of the Internet of Things in Managing Supply Chains
Authors: Drake Matthew J.
Published: January 2023

Internet of things (IoT) devices collect data about current environmental conditions faced by people, products, equipment, or machinery in the supply chain. Leading organizations are using this real-time data to proactively manage their supply chains more efficiently and effectively. This article discusses the application of IoT technology in the supply chain and the common benefits it can generate such as supply chain visibility, supply chain traceability, supply chain transparency, supply chain risk management, supply chain collaboration, and flexibility and agility. It also presents the challenges and risks that firms face when implementing IoT technology in their supply chains.
Malware Detection in Network Flows With Self-Supervised Deep Learning
Authors: Woolman Thomas Alan , Lunsford Philip
Published: January 2023

This article explores the application of anomaly detection models from network flow data using multi-layer perceptron autoencoding neural networks, for the purpose of self-supervised detection of novel network intrusion events and malware classes over unrestrained internet connections. The authors utilized network flows rather than more detailed (and larger) packet capture logs in order to create a more cost-effective and potentially faster anomaly detection tool that could more easily scale enterprise class network traffic analysis. Unsupervised/self-supervised deep learning anomaly detection was used against this less-granular dataset to maximize the likelihood of detecting novel network activities within the less-detailed dataset without relying on pre-defined rules and training data. The authors conclude with a test of statistical significance against known threat classes (unknown to the anomaly detection model) that the proposed methodology results were statistically significant for detecting threat classes in unrestrained internet networks using network flow data.
Social Networks and Analytics
Authors: Zhao Yuehua , Zhang Jin
Published: January 2023

Social network analysis is becoming a prominent data analytics method. In the big data era, high volumes of a wide variety of valuable data can be easily generated or collected at a high velocity. By using advanced social network analysis, researchers from a range of disciplines can analyze large-scale datasets to learn about relationships underlying social networks and community structures by analyzing the linkage patterns among individuals. In this article, the authors address the basic concepts of social network and analytics as well as the applications of social network analysis methods in big data analysis.
Convex Nonparametric Least Squares for Predictive Maintenance
Authors: Chung William , Yeung Iris M. H.
Published: January 2023

A nonparametric regression method, convex nonparametric least square (CNLS), as a machine learning approach is introduced and discussed to the application of predictive maintenance. With the industrial internet of things sensors, predictive maintenance (PdM) becomes viable to identify maintenance issues in real time by predicting the next error of the system. Machine learning (ML) methods, such as support vector machine (SVM), are popular to develop the prediction model for PdM. On the other hand, regression-based methods are considered inappropriate due to their pre-defined function forms and the non-linear nature of the PdM models. The convex nonparametric least squares (CNLS) method can overcome the above shortcomings of the regression-based methods. One of the attractive properties of the CNLS method is its regression-based analysis without pre-defined non-linear function forms. In addition, the CNLS method does not need to find the appropriate Kernel function like the SVM. Hence, the CNLS method is introduced for developing a predicting model for PdM.
Predicting Estimated Arrival Times in Logistics Using Machine Learning
Authors: Poschmann Peter , Weinke Manuel , Straube Frank
Published: January 2023

The aim of the article is to demonstrate the use and benefit of machine learning (ML) in logistics by means of a significant, practice-relevant application: the prediction of estimated times of arrival (ETA) in intermodal transport chains. Based on a real use case, the article first provides an approach for the methodical procedure for the implementation of ETA predictions and a description of essential development phases. Subsequently, a cross-actor prediction approach for the combined road-rail transport of containers in the port hinterland is designed, and ML-based prediction models for specific logistics processes are prototypically implemented and evaluated. Finally, an outlook on future research directions is given.
ERP and Time Management
Authors: Bronner Anthony , Zaraté Pascale
Published: January 2023

Most of today's companies use management software to support their staff organizing their activity. In this context, this software makes it possible to centralize company data, and to extract useful information from it for users in order to make decisions, such as the organization of their working time. However, the amount of available data is too large for a user to be analyzed on their own. Thus, it is likely that the user will not take the most satisfactory decision according to the criteria imposed by the managers, and the real-time evolution of the activity of the company. In this article, the authors present a recommender system that takes into account the activity of the company, the managers' criteria, as well as the end-user's own criteria in order to guide the user's activities. It can be used for any type of activity sector as soon as data are available. The recommender system is an additional module for an existing ERP. The human-computer interface (HCI) is presented, as well as a database. The system is operable and fully customizable.
Ontological Metamodel of Sustainable Development
Authors: Deliyska Boryana , Ivanova Adelina
Published: January 2023

The article is addressed to a wide range of specialists involved in data science including ontological data modeling and knowledge extracting in the various fields related to sustainable development (SD). A hierarchically structured ontological metamodel of SD is developed consisting of a conceptual layer of interconnected common, domain and application ontologies, and a physical layer of instance databases and documents. The SD common ontology is the top level of the conceptual layer and is associated with the next level of the SD domain ontologies in the economy, society, and nature. The third level is of the SD application ontologies and is illustrated by an example application ontology of firm sustainability. All ontologies of the SD metamodel have links to the relevant external ontologies and to specific instance databases. The proposed metamodel is a useful tool for SD knowledge structuring and classification, as well as a base for building machine learning models as a part of artificial intelligence.
Artificial Intelligence for Sustainable Humanitarian Logistics
Authors: Oguntola Ibrahim Opeyemi , Ülkü M. Ali
Published: January 2023

Artificial intelligence (AI) can improve operational processes by utilizing faster computational capabilities, data, and innovative algorithms. This article reviews the latest research on the applications of AI technology to sustainable humanitarian logistics (SHL) through the sustainability lens. In a broad sense, the cultural, economic, environmental, and societal pillars of the quadruple bottom line (QBL) are covered. Examples of AI-based logistics and supply chain tools already in use in non-profit, humanitarian organizations are emphasized. The authors then conclude that AI can assist SHL in its goal of saving as many lives as possible during disasters while embracing the QBL pillars. As for all emerging technologies, smoothening the collaboration between humans and AI during operations requires a fundamental change in mindset and culture. Moreover, all stakeholders involved in SHL (e.g., public, government, help organizations, the environment, cultures) are affected. There is therefore room for future research on why, when, where, and how to better utilize AI within SHL contexts.
AUTOMATON
Authors: Palmquist Adam , Barbopoulos Isak , Helmefalk Miralem
Published: January 2023

This article displays a design ethnographic case study on an ongoing machine learning project at a Scandinavian gamification start-up company. From late 2020 until early 2021, the project produced a machine learning proof of concept, later implemented in the gamification start-up´s application programming interface to offer smart gamification. The initial results show promise in using prediction models to automate the cluster model selection affording more functional, autonomous, and scalable user segments that are faster to implement. The finding provides opportunities for gamification (e.g., in learning analytics and health informatics). An identified challenge was performance; the neural networks required hyperparameter fine-tuning, which is time-consuming and limits scalability. Interesting further investigations should consider the neural network fine-tuning process, but also attempt to verify the effectiveness of the cluster models selection compared with a control group.
Nonlinear System Identification Based on an Online SCFNN With Applications in IoTs
Authors: Lin Ye , Huang Yea-Shuan , Lin Rui-Chang
Published: January 2023

In this paper, an online self-constructing fuzzy neural network (SCFNN) is proposed to solve four kinds of nonlinear dynamic system identification (NDSI) problems in the internet of things (IoTs). The SCFNN is capable of constructing a simple network without the need for knowledge of the NDSI. Thus, carefully setting conditions for the increased demands for fuzzy rules will make the architecture of the constructed SCFNN fairly simple. The applications of neural networks in IoTs are discussed. The authors also propose a new identification model for NDSI. Through an experimental example, it is proved that online learning can arrange membership functions in a more appropriate vector space. The performance of the online SCFNN is compared with both MLP and RBF through four extensive simulations. The comparison terms are convergence rate, training root mean square error (RMSE), test RMSE, and prediction accuracy (PA). The simulation results show that SCFNN is superior to MLP and RBF in NDSI problems.
Forecasting Short-Term Solar PV Using Hierarchical Clustering and Cascade Model
Authors: Wang Ben-Yi , Yu Kun-Ming V. , Sodsong Nattawat , Chuang Ken H.
Published: January 2023

With the large-scale deployment of solar PV installations, managing the efficiency of the generation system became essential. Generally, the power output is heavily influenced by solar irradiance and sky conditions which are consistently changing. Thus, the ability to accurately forecast the solar PV power is critical for optimizing the generation system, estimating revenue, sustaining profits, and ensuring the quality of service. In this paper, the authors propose a solar PV forecasting model using multiple blocks of GRUs and RNN in a cascade model combined with hierarchical clustering to improve the overall prediction accuracy of solar PV forecast. This proposed model is a combination of hierarchical clustering, the Pearson correlation coefficient for feature selection, and the cascade model with GRU layer from k-means clustering and hierarchical clustering. These results, which are evaluated using NRMSE, show that hierarchical clustering is more suitable for solar PV forecast than k-means clustering.
Association between Emotional Labour and Psychological Well-Being: With Special Reference to Doctors Working at COVID - 19 Treatment Units in Gampaha District, Sri Lanka
Authors: Vindya I. W. S. , Rathnakara K. A. K. S.
Published: January 2023

In the context of Sri Lanka, the significance of the health industry to the country's GDP is highlighted. Because the country's social health protection and quality health care have an impact on economic growth and development. As a result, the health sector is one of the most important industries in Sri Lanka. This study aimed to examine the effects of emotional labour on the psychological health of outstanding reference doctors employed by Covid 19 treatment facilities in the Gampaha District Sri Lanka . The independent variable was emotional labour, and the dependent variable was psychological well-being. Doctors employed by Covid 19 treatment facilities in the Gampaha district were chosen as the population and simple random sampling method was utilized to select the sample. Questionnaires were used to gather information from the 97 doctors that made up the sample. The doctors' response rate was 100 percent. Statistical Package for Social Sciences (SPSS) version 21.0 was used to analyze the data. For univariate analysis, the mean score and standard deviation for each variable were analysed, and the correlation coefficient was employed for bivariate analysis. The impact of the independent varible on the dependent variable was examined using multiple regression. Doctors' emotional labour and psychological health have changed depending on their gender, level of education, experience, employment history, and marital status. The study’s findings indicates that emotional labour has an impact on psychological well-being reference to the surface acting and psychological well-being , deep acting and, genuine acting. According to the Pearson correlation analysis values and regression analysis values surface, deep ,genuine acting with psychological well-being hypotheses were tested. It can be concluded that there is an impact emotional labour on psychological well-being.This paper also makes suggestions for future research, including the need for a more quantitative approach to producing better results and the need to add more statistical analytical tools to the study's currently chosen base with different cultured societies on psychological well-being.
Increased Inflammasome Activation Is Associated with Aging and Chronic Myelomonocytic Leukemia Disease Severity
Authors: Andina Nicola , de Meuron Louise , Schnegg-Kaufmann Annatina Sarah , Sarangdhar Mayuresh Anant , Ansermet Camille , Bombaci Giuseppe , Batta Kiran , Keller Nino , Porret Naomi A , Angelillo-Scherrer Anne , Bonadies Nicolas , Allam Ramanjaneyulu
Published: January 2023

Aging causes chronic low-grade inflammation known as inflamm-aging. It is a risk factor for several chronic disorders, including chronic myelomonocytic leukemia (CMML), a hematological malignancy that is most prevalent in older people. Recent studies suggest a critical role for the NLRP3 (NOD-, LRR- and pyrin domain-containing protein 3) inflammasome in inflamm-aging. However, the mechanisms involved in NLRP3 activation in aging and its involvement in CMML progression are not fully understood. In this study, we report that aging increases IL-1β production upon NLRP3 activation in human CD14+ monocytes. Interestingly, we found that the TLR1/2 agonist Pam3CSK4 directly activates the NLRP3 inflammasome in monocytes from older but not from younger healthy donors. Furthermore, we observed a dichotomous response to NLRP3 inflammasome activation in monocytes from a small cohort of CMML patients, and some patients produced high levels of IL-1β and some patients produced low levels of IL-1β compared with older healthy donors. Intriguingly, CMML patients with heightened NLRP3 activation showed increased treatment dependency and disease severity. Collectively, our results suggest that aging causes increased sensitivity to NLRP3 inflammasome activation at a cellular level, which may explain increased inflammation and immune dysregulation in older individuals. Furthermore, NLRP3 inflammasome activation was dysregulated in a small cohort of CMML patients and was positively correlated with disease severity.
Transcutaneous Hitching Sutures in Paediatric Minimally Invasive Surgery
Authors: Khanna Sanat Kumar , Kishore Kamal , Panchal Gaurav
Published: January 2023

Objectives:

Transcutaneous hitching sutures in paediatric minimally invasive surgery (MIS) is a unique and rare technique. This technique has been used previously in adult patients undergoing gastric resections and laparoscopic cholecystectomy; however, its use in paediatric population has never been reported in the world literature. The primary objective of this study was to bring out the advantages and feasibility of this technique in minimally invasive gastrointestinal, hepatobiliary, urological and thoracoscopic surgeries on paediatric patients.

Materials and Methods:

This retrospective observational study was conducted on 167 paediatric patients who underwent MIS surgery for different indications between April 2016 and March 2020 at two paediatric surgery tertiary care centres.

Results:

A total of 167 patients, including 91 boys and 76 girl patients between the age group of new-born period to 12 years were operated. The mean hospital stay was 4 days. Five out of 167 cases (3%) had post-operative surgical emphysema, which resolved spontaneously. At 6-month follow-up, parental satisfaction was 100%, and in 99% of patients, scars were imperceptible.

Conclusion:

This versatile technique is of exemplary utility, especially in paediatric patients where there is a paucity of working space at low intra-abdominal pressure, and eases the dissection even in narrow and closed spaces with a better functional and cosmetic outcome.
Paediatric Mandibular Fractures
Authors: Agbara Rowland , Fomete Benjamin , Omeje Kelvin Uchenna
Published: January 2023

Background:

Paediatric mandibular fractures unlike adult mandibular fractures are said to be rare. When not recognised early, they may be associated with significant problems such abnormal mandibular growth.

Materials and Methods:

This was a retrospective observational study of paediatric patients with mandibular fractures presenting to a university teaching hospital from August 1999 to December 2019. Information sourced from patient’s case notes and operating records included age, sex, aetiology, investigation, concomitant injuries, treatment and complications. The retrieved data were recorded in a personal computer, analysed with appropriate statistical tool and presented in tables and figures.

Results:

A total of 550 patients who presented with mandibular fractures within the period of the study constituted the study population. Of these, 84 (15.3%) occurred in the paediatric age group. The ages of the patients ranged from 1 to 17 years with a mean age of 10.3 ± 5.0 years and there was a gender ratio (male: female) of 1.9:1. Road traffic accident accounted for most mandibular fractures. The imaging modality used in assessing mandibular fractures was mainly plain radiography and treatment was administered in 60 (71.4%) patients, with closed reduction method (n = 45; 75.0%) being the most frequent technique used. The period of hospital stay ranged from 3 to 90 days with a mean of 16.8 days.

Conclusion:

Mandibular fractures in paediatric population were noted to be more common in males and the main aetiology was pedestrian-related road traffic accidents. There is a need for continual review of injury prevention strategies in the paediatric group.
MiR-141-3p Promotes Malignant Progression in Prostate Cancer through AlkB Homolog 5-Mediated m6A Modification of Protein Arginine Methyltransferase 6
Authors: Li Xun , Liu Bide , Wang Shuheng , Li Jiuzhi , Ge Xiaohu
Published: January 2023

Prostate cancer (PCa) is one of the leading causes of cancer-related death in males worldwide and exploring more reliable biomarkers for PCa is essential for the diagnosis and therapeutics for the disease. Although the functions of miR-141-3p and AlkB homolog 5 (ALKBH5) were identified in some cancers, whether they were involved in the development of PCa remains unclear. In this study, reverse transcription-quantitative polymerase chain reaction unveiled that the expression of ALKBH5 was reduced in PCa tissues and was negatively correlated with miR-141-3p. ALKBH5 attenuated the malignant development of PCa through suppressing the growth, migration, invasion, and sphere formation abilities of PCa cells. In addition, the luciferase activity assay identified that ALKBH5 was corroborated as a downstream target of miR-141-3p. Moreover, miR-141-3p expression was boosted in PCa tissues and cells and inhibition of miR-141-3p suppressed the tumor growth of PCa in vivo. Moreover, ALKBH5 was confirmed to suppress protein arginine methyltransferase 6 (PRMT6) expression through N6-methyladenosine (m6A) modification. We further identified that miR-141-3p-modulated PRMT6 level through mediating ALKBH5. Furthermore, PRMT6 level was positively correlated with miR-141-3p level and negatively associated with ALKBH5 level. Finally, rescue assays also uncovered that miR-141-3p aggravated PCa development by regulating PRMT6. In conclusion, miR-141-3p accelerated the malignant progression of PCa through ALKBH5-mediated m6A modification of PRMT6, which might offer a novel insight into the role of miR-141-3p and ALKBH5 in the treatments of PCa patients.
“He is Human, Too”
Authors: Movrin David
Published: January 2023

The paper analyses the passage from Athanasius’ Vita Antonii, where Antony is corresponding with Constantine, Constantius and Constans, contrasting it with a very different portrayal of Constantine in Eusebius’ Vita Constantini. While Eusebius, in a famous statement, presented the emperor as a sort of a bishop, the Greek Vita Antonii tried to present a very different model, one that seems to be sceptical of the imperial power and perhaps of earthly society as such. The point was not lost on those who later developed the genre, as can be seen in the writings of Sulpicius Severus, the author of Vita Martini, written at the end of the fourth century, where the three emperors from Vita Antonii are paralleled with three different emperors, in a much sharper relief, subverting secular power and underlining the superiority of its spiritual alternative.
Crisis, state of emergency and state terrorism: particularities of the dictatorship in Argentina (1976−1983)
Authors: Yañez César Manuel Román
Published: January 2023

In Argentina, the 1976 coup d’état was a change of political regime and not just a change of government, from one of democratic origin turned authoritarian to another with fascistic tendencies, since the constant amplification of the State of exception will constitute a structure of state power of fascist projection, whose central characteristics were the expansion of the repressive apparatus and the institutionalization of new branches of the State: the Detention and Extermination Centers and the Task Forces. The character of the regime, however, had a dense sedimentation that combined a complex axiology. The notion of exceptionality allows us to elaborate the problem from a stage prior to State terrorism and to observe how legal and political statutes, for example the exception, secrecy, and military intelligence in a framework of severe capitalist crisis, derived in authoritarian forms of democratic regime and were generating the conditions for the coup d’état. Once the last dictatorship occurred, the concept of exception gave us access to correlate the repressive model with the characteristics of the political regime.
Operation Toba in Northeast Argentina: the spread of the State of Terror (1975–1977)
Authors: Silva Jussaramar da
Published: January 2023

The purpose of this article is to demonstrate how Argentine experience of internal repression was passed on to Paraguay as a result of the meetings between its intelligence services. The meetings were carried out by the military in the 1970s, aiming at joint operations of political persecution and extermination of the opponents of the dictatorships that prevailed in these two countries. The author analyzes documentation transmitted during the meeting called IIda Regional Bilateral Meeting of Intelligence between Paraguay and Argentina in 1978. Documentation reports to the Paraguayan military the strategies and tactics of war developed by the Argentine armed forces in the so-called Operation Toba. The importance of discussing Operation Toba is to demonstrate the similarity of the repressions that took place in other countries of the Southern Cone. The techniques and tactics used illustrate that such massacres have repressive synchrony and are consistent with a single internationalized process of repression. Although the repression called Operation Toba was developed only by the Argentine State, it demonstrates an international chain of repression, as well as the similarity in the modus operandi of the precepts of the widespread Counter-Revolutionary War Doctrine, developed by the French during the fighting in Indochina and Algeria in the 1950s.
CHALLENGES AND MANAGERIAL STRATEGIES OF COFFEE COOPERATIVES FROM THE HUATUSCO REGION IN MEXICO: A LEADERS' PERSPECTIVE
Authors: Velandia Margarita , Trejo-Pech Carlos , Rodríguez-Padrón Benigno , Servín-Juárez Roselia , Stripling Christopher
Published: January 2023

Coffee cooperatives from the Huatusco region in Mexico have played an important role in helping coffee farmers in this region maintain economic viability in the midst of Mexico’s economic crisis and the international crisis of the coffee sector over time. While these types of organizations have played an important role, not all of these organizations have survived over time. Using information from interviews with leaders of five longeval coffee producer cooperatives in the Huatusco coffee region, this study tries to identify factors that could explain the longevity and long-term sustainability of these organizations. Additionally, using information from these interviews, we evaluate challenges that could put at risk the long-term sustainability of these associations and strategies to overcome these challenges from the cooperative’s leadership perspective. Findings from this study suggest the motivations behind the creation of these producer associations that goes beyond accessing government resources, an institutional structure where there is a democratic process behind the election of leaders, leadership positions have a definite term, and association strategies to increase member engagement could explain the longevity of these organization. Regarding challenges the cooperatives would be facing in the future, the aging population of growers is perceived by cooperative leaders as one of the major challenges they will be facing in the future. Increasing youth involvement in the cooperatives through training and educational activities was perceived by cooperative leaders as a strategy to overcome this challenge.
Proposed Design of Service Quality Performance Management System for Indonesian HSR
Authors:   School of Business and Management, Institut Teknologi Bandung, Annice Felicia , Lestari Yuliani Dwi ,   School of Business and Management, Institut Teknologi Bandung
Published: January 2023

In some countries, the development of High-Speed Railway (HSR) represents a significant technological advancement which helps modern society’s value of time and various activities. Despite of having complex operational support and significant financial outlays, studies have shown that HSR sector has several growth barriers due to poor punctuality, reliability, pricing, and inconvenient passenger journeys. These brought specific impact on deciding factors whether or not passengers choose the transport option as it affected passenger satisfaction level. Recognizing the global challenge, this paper anticipates the impending Indonesian HSR operation by observing passengers’ relevant service quality attributes. The findings served as the performance management design’s foundation. In order to assist HSR operators to improve passenger experience and satisfaction, this study aims to identify the relevant service quality of Indonesian HSR potential passengers as one of many solutions to eliminate lack of demand problem. Qualitative and quantitative data analysis from in-depth interviews with several Indonesian railway expert and information from prior publication of Asian HSR which was used to carry out external benchmarking, validate results, support design process, and create contextual performance indicators for attributes using Knowledge-Based Performance Management System (KBPMS). This paper produced numbers of service quality performance indicators whose prioritization was arranged in specific order. Five variables are used to measure tangibility attributes’ performance, whereas six variables are used to measure reliability attributes’ performance. Tangibility and reliability were chosen based on Indonesian railway market preference and common success factor of HSR best practices. This performance indicators were specifically developed based on these attributes’ sub-attributes which has been contextualized. In contrast to other countries, Indonesian HSR place a distinctive value on physical facilities as it affects travelling motivation. These new insights would direct Indonesian HSR operators to develop targeted solutions to increase passenger satisfaction and economic benefits.
The Role of Time Budget Pressure in Moderating the Influence of Independence and Competence on Audit Quality in Bali Regional Public Accounting Firms
Authors:   Faculty of Economics, University of Warmadewa, Denpasar, Bali-Indonesia, Ekayani Ni Nengah Seri , Sariani Ni Ketut , Diviariesty Kadek
Published: January 2023

Study this aim for analyze the influence of auditor independence and competence on audit quality with Time Budget Pressure as a moderating variable in the Bali region. This research is a quantitative research using survey data which obtained from questionnaire. Population in study this is an auditor working at a Public Accounting Firm in the Bali Region in 2022. Withdrawal sample in study this is use method saturated sample of 65 auditors. The analysis tool used is moderated regression analysis (MRA). The research findings show that auditor independence and competence are proven as predictors of audit quality. Time Budget Pressure is able to moderate the influence of Independence on Audit Quality, but is unable to moderate the effect of Competence on Audit Quality in Public Accounting Firms in the Bali Region.
The Effect of Counseling Group towards Self-efficacy at Victim Bullying Students
Authors:   Universitas Jenderal Achmad Yani, Indonesia, Yani Roy Andry , Mustikasari Mustikasari , Imelisa Rahmi Imelisa ,   Sekolah Tinggi Ilmu Kesehatan Cirebon, Indonesia,   Universitas Indonesia, Indonesia
Published: January 2023

Bullying is still being a phenomenon in Indonesia. Victims of bullying is not just passive actors in bullying situations. In 2021 the data of Junior High School in Cirebon City, bullying cases reached to 83 cases. One of the efforts to increase self-efficacy for victims of bullying is by providing counseling services using counseling group strategies. The purpose of this research is to determine the effect of counseling group towards self-efficacy at victim bullying students. This research used analytical method, quasi experimental design with pretest and posttest group design. The sample of this research was 42 victim bullying students who have been recorded in the counseling section and meet the inclusion criteria. The data collecting of this research was using questionnaires and analyzed with independent sample t tests and paired tests. The results of this research showed that the characteristics of students who were victims of bullying were mostly women (57.1%) with an average age of 13.86 years. The average self-efficacy before the intervention was 27.9 points and after the intervention was 38.3 points. There is a correlation between gender characteristics and self-efficacy for victim bullying students, but there is no difference in age with self-efficacy for victim bullying students. There is an effect of counseling group towards self-efficacy at victim bullying students of Junior High School in Cirebon City with p value of 0.000. The school is expected to be able to take an action to prevent bullying case with health education providers and increase self-efficiency at victim bullying students using counseling group.