Wrong Decisions Research Articles

Using Analytical Hierarchy Process to Select the Best Power Generation Technology in Libya

Selection of an appropriate power generation technology is a complex process. Because it is necessary to consider and take into account a wide range of parameters. The investment in the power generation field is too costly while resources are limited. This paper presents the determination of these parameters, which influence the selection of power generation technology in particular for developing countries. This paper answers the question of the best suitable technology for Libya because the investment in this field is so costly and making a wrong decision is not allowed, otherwise it will consume the big margin of the initial budget. Four main criteria are included, that is economic, environmental, technical and social. A sub-criteria are also included to support this decision. The performances of six technologies of a power generation are evaluated by Analytic Hierarchy Process (AHP) using expert choice software and the best technology is selected. The results showed that the solar photovoltaic power generation is the best technology. By means of the sensitivity analysis, it was found that the best alternative is not sensitive to a change in the weights estimated by the AHP.

Leadership Approaches from the Perspective of its Particularities in the Military Organization

Abstract The activity of a manager within an organization is influenced by their general and specialized knowledge as well as the skills necessary for organizing, leading, and guiding subordinates. The most important organizational resource is the human being. The consequences of wrong decisions made by military managers in peacetime cannot be as grave as those made during wartime. In war, we no longer speak of the quality of life of the personnel but particularly of their lives. Unlike civilian leaders, military leaders have the prerogatives to ask their subordinates to risk their lives if the situation demands it for the mission’s accomplishment. A manager must have the ability to work with people. They must also be aware of the problems that people face within the organization, get involved in solving these issues, empathize with them, and know what motivates them the most to encourage them to surpass themselves in the actions undertaken. When you take care of your soldiers, ensuring they are well dressed, equipped, fed, and cared for, you demonstrate good administrative skills. It is hard to imagine a good leader being a poorly prepared administrator or manager, even if this practice involves delegating competencies to subordinates. In this context, the concept of leader-manager is utilized.

Arrest Web Entanglement: Female Domestic Violence Survivors' Experiences with Police Intervention and Coercively Controlling Male Partners.

Although U.S.-based mandatory and preferred arrest laws and policies were created to promote domestic violence survivors' safety, at times they have contributed to the wrongful arrest of women defending themselves against their abusive partners. While these laws and policies are the subject of broad critique, less considered are domestic violence survivors' descriptions of the events that unfold after police officers respond to a domestic violence incident and before they make an arrest. This is an important area of inquiry as these events may highlight how the circumstances leading to wrongful arrest decisions are more complex than the laws and policies alone. Data from the present study came from the author's larger in-depth qualitative investigation of 33 cisgender women's descriptions of their legal and child protection systems involvement. The women were recruited from an antiviolence intervention agency receiving referrals from communities with mandatory and preferred laws and policies. The women had agency contact due to their use of force or alleged use of force. The respondents were diverse across race, age, class, ability, U.S. citizenship status, and sexual identity. The author analyzed the 33 women's 51 interview transcripts and extensive fieldnotes using rigorous iterative analysis and constructivist grounded theory. The analysis revealed that seven of the 33 women, all of whom identified domestic and sexual violence survivorship histories, described a patterned series of events that unfolded after the police arrived at the domestic violence incident and before the police made an arrest. In this study, the author details three of the seven women's stories to demonstrate how a series of events, including police prearrest questioning and their coercively controlling male partners' tactics, facilitated the women's entanglement in what the author refers to as an "arrest web." Their incremental disentanglement from the arrest's impact is also explored. Broad system-focused implications are discussed.

Responding to challenge call for machine learning model development in diagnosing respiratory disease sounds

The normal and abnormal sounds from the respiratory system shed great light on medical science by revealing the quality, diseases and changes in people’s lungs. In medicine, this easy and old method, realized by the stethoscope, facilitates the diagnosis of diseases by specialists. This manual method can sometimes lead to wrong decisions in terms of sound detection due to different audibility. Detailed sound analysis is crucial for accurately detecting lung diseases with high mortality rates. As technology advances, the development of automated approaches based on machine learning is of great interest as they provide modern and highly accurate analysis. In today’s most popular topic, i.e., the COVID-19 disaster, the conflict between early detection of respiratory disease and machine learning for sound signal processing is extremely important. In this study, a machine learning model was developed to detect respiratory system sounds such as sneezing/coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine learning model was developed in this study, which was a strong response to the challenge called the “Pfizer digital medicine challenge” on the “OSFHOME” open access platform. In the database provided in this challenge, which consists of 3 parts, features that effectively showed coughing/sneezing sound analysis were extracted from training, testing and validating samples. Based on the Mel frequency cepstral coefficients (MFCC) feature extraction method, mathematical and statistical features were prepared. The sequential forward selection (SFS) feature selection method was used to select the relevant and dominant variables among the obtained features to represent the model fully and accurately. Three different classification techniques were considered for successful respiratory sound classification in the dataset containing more than 3800 sounds. Support vector machine (SVM) with radial basis function (RBF) kernels, decision tree and ensemble aggregation classification methods were used as classification techniques. In an attempt to classify coughing/sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83% success.

Creative industries: concept, features, and problems of legislative interpretation

The article explores the concept of creative industries, suggests their features, and analyses the list of creative industries of Ukraine in terms of correctness of legislative interpretations and substantive compliance with creative fields of activity. The definition of creative industries has been improved: these are areas of economic activity that create (produce) goods and/or provide services based on the use of intellectual (creative) capital and commercialize them by combining creative results with business solutions, which leads to the creation of added value and jobs. The use of the National Classifier of Ukraine DK 009:2010 "Classification of Types of Economic Activity" as the basis for the list of creative industries in 2019 was a wrong decision which, however, logically follows from the definition of creative industries introduced in 2018 in the Law of Ukraine "On Culture" which qualifies them as types, not spheres of economic activity. The literal application of this concept has led to the fact that the national list of creative industries has the following shortcomings: 1) artificial segmentation of one field of activity into several creative industries; 2) artificial unification of different areas of creative activity into one creative industry; 3) absence in the list of fields of activity which are essentially creative industries; 4) coverage by the list of fields of activity which do not produce creative products and do not provide creative services. It is concluded that a new, qualitatively different list of creative industries needs to be approved.

ОГЛЯД ТА АНАЛІЗ ІНФОРМАТИКИ ТА ІНФОРМАЦІЙНИХ ТЕХНОЛОГІЙ ЯК ОСНОВ СУЧАСНОГО ПРОГРАМУВАННЯ

The article focuses on the review and analysis of computer science and information technology as the foundations of modern programming, and the article presents theoretical aspects and concepts of their development that have a significant impact on technological progress and social development, which, in turn, contribute to innovations in various fields, from medicine to finance, making life easier and improving working conditions, creating new business sectors and labor markets that meet the requirements of the digital economy, changing the way of communication, education and interaction in society, including h The article analyzes software, which plays an important role in modern life, helping to solve various tasks and facilitating daily activities of users in various spheres of life. Accordingly, understanding its role and diversity is key to the effective use of technology in the modern world. In this article, we use the concept of logic in programming: decision-making (logic allows programs to make decisions based on conditions and circumstances), error avoidance (logical programming helps to avoid errors and wrong decisions in programs); code efficiency (the use of conditional statements and logical operations helps to make the code more efficient and readable); algorithms (developing effective algorithms requires logical thinking and the ability to make the right decisions at every step). In this article, we have also provided examples of programming languages. A programming language is a formal language construct designed to write computer programs. It defines the syntax and semantics that programmers use to create programs and interact with a computer. Today, there are many different programming languages, each of which has its own features and purpose. This article describes some of the most popular programming languages. Informatics and information technology have become not only the basis of modern programming, but also key determinants of technological progress and social development. Understanding their role and interconnection helps to unlock the potential of these industries for further development and achievement of new heights in technology and innovation.

The Determinants of Sustainable Innovation Expansion Strategy: the Case Study of Companies from a Declining Industry

Stimulation and improvement of innovative development is an extremely important component of economic growth in an economy, along with the companies’ competitiveness in stagnating industries, which is especially relevant for companies at the maturity stage of the life cycle, where the risk of transition to the decline stage is highest. Without new developments and a sustainable innovation strategy, a company loses its leading position in the industry and misses new opportunities, leading it to the stage of stability and decline. Thus, it is important to study the factors that contribute to R&D intensity and encourage innovations in detail. This study investigates the impact of high level and quality of companies’ patent activityon their financial potential in order to maintain stable innovation performance in the medium term. The sample comprises companies from the printer and camera sector between 2007 and 2020. The determinants of innovation expansion that characterize the technological readiness and market potential of firms to maintain their leading position in a highly competitive market are identified, using a case study method using the example of Canon and its competitor Xerox. The data are collected from Bloomberg and Orbis Patent Database. The results show that while high innovation activity is an important driver of growth, it does not always lead to better financial performance in the earlier stages of the life cycle. The study contributes to the literature by examining different characteristics of innovation activity and life cycle stages through the lens of external economic changes, which brings transparency and clarity in understanding the possible problems that may result from using already disclosed innovations of competitors as well as disclosing one’s own intellectual property rights. The study proves that the greatest effect of innovation activity is observed in companies whose R&D expenditures are close to the industry average values along with diversification of revenue. The results of the study can help policy makers, managers and shareholders to build effective corporate governance to achieve strategic goals and minimize the risks of making wrong management decisions in R&D investments.

ChatGPT/GPT-4 (large language models): Opportunities and challenges of perspective in bariatric healthcare professionals.

ChatGPT/GPT-4 is a conversational large language model (LLM) based on artificial intelligence (AI). The potential application of LLM as a virtual assistant for bariatric healthcare professionals in education and practice may be promising if relevant and valid issues are actively examined and addressed. In general medical terms, it is possible that AI models like ChatGPT/GPT-4 will be deeply integrated into medical scenarios, improving medical efficiency and quality, and allowing doctors more time to communicate with patients and implement personalized health management. Chatbots based on AI have great potential in bariatric healthcare and may play an important role in predicting and intervening in weight loss and obesity-related complications. However, given its potential limitations, we should carefully consider the medical, legal, ethical, data security, privacy, and liability issues arising from medical errors caused by ChatGPT/GPT-4. This concern also extends to ChatGPT/GPT -4's ability to justify wrong decisions, and there is an urgent need for appropriate guidelines and regulations to ensure the safe and responsible use of ChatGPT/GPT-4.

Evaluation of Telemedicine Support for Medics from the Perspective of Course Instructors.

Health services in the theater/district areas often contain difficulties. Although telemedicine has a huge potential to support medics in the area, there are challenges as well. Our aim is to evaluate the telemedicine support that can be provided to the medics from the perspective of course instructors. Our study was carried out in The University of Health Sciences, Türkiye. All of the instructors of a medic course were asked to fill a 13-question questionnaire and Likert-type scale, sent via e-mail. Among 79 instructors, 71 of them (55 physicians, 16 nonphysicians) responded and were included in the study. The distributions of the categorical variables in the groups were analyzed with the Pearson Chi-square. Of the 71 participants, 37 (52.11%) stated that there is a need for legal and ethical regulations for telemedicine (medical liability, malpractice, obtaining consent from the casualties, civilians, cross border missions, and rank-related problems) and patients' rights, additionally, to clarify the responsibilities of the doctors giving telemedicine support and the medics in the area. It was observed that physicians' and nonphysician group's opinions about the telepsychotherapy were statistically significantly different (χ2 = 8.675, P = .013). Most of the instructors believed that telemedicine could carry the knowledge and skills of specialist physicians to the field of operation. Access to high-quality health services in a short time through telemedicine support can increase the courage and commitment of the personnel. It is thought that with telemedicine, medics will not feel alone in the field, their worries about making wrong decisions will decrease, and their knowledge and skills will increase. Participants were particularly concerned about who will be held responsible for problems arising from interventions carried out using telemedicine, and strict legal and medical regulations are needed.

The importance of schistocytes in a patient in VA-ECMO

The presence of schistocytes can be responsible for spurious thrombocytosis and spuriously low red blood count (RBC). The hemoglobin concentration will be correct (as the method usually used, destroys the red cells and converts a substantial proportion of the hemoglobin to a stable pigment) but mean corpuscular hemoglobin (MCH) is falsely high. The platelets and RBC histograms of the full blood count analyzers play an important role in the identification of schistocytes and must be carefully analyzed before reporting the previously full blood count parameters. In patients in ECMO, where can be expected the presence of a small number of schistocytes, this evaluation is particularly important to avoid wrong clinical decisions.

Research on the Application of Entropy Method and Efficiency Coefficient Method in Financial Risk Early Warning of Enterprises

In the process of development, enterprises need to focus on two major issues, namely the increasingly fierce competition and the problems in the financial management of the enterprise. If you don't want the financial situation of the enterprise to deteriorate, you need to pay attention to the internal operation and management of the enterprise, because if there are problems in the internal financial management of the enterprise,it will lead to the change of the market environment and the lack of timely and accurate information, so as to make wrong decisions. Therefore,it is necessary to build a financial risk early warning model to reduce losses. Taking Shandong Longda Meishi Co., Ltd. as an example, this paper uses the entropy method and the efficacy coefficient method to construct a financial risk early warning model, and gives a risk early warning to the financial data of Shandong Longda Meishi Co., Ltd. from 2018 to 2022. The empirical results show that the early warning results are consistent with the actual situation of the enterprise, which proves that the early warning model built by this method is scientific and effective.

A new branch of fake review detection research -- A review of fake review detection in the Chinese film industry in the post-epidemic era

In the post-pandemic era, Chinese moviegoers increasingly rely on online movie reviews, but fake reviews by spreaders can mislead moviegoers to make wrong decisions. Fake review detection has been developed to a certain extent in China. However, there is a lack of application research in the film industry. This paper summarizes some of the more advanced fake review detection methods in China in the post-epidemic era from the perspectives of review text detection and reviewer detection, introduces their indicators, feature selection methods, and training methods, and further discusses the specific steps of these methods in the detection of fake movie reviews combined with the characteristics of fake movie reviews. The research of this paper can bring guidance for the future detection of fake movie reviews, and provide a decision-making basis for consumers and investors.

The role of trust and perceived risk on Muslim behavior in buying halal-certified food

PurposeThe purpose of this study to examine the factors that encourage/inhibit Muslim behavior in buying halal-certified food (HCF), based on two theories, the knowledge-attitude-behavior model and the attitude-behavior-context model; and study the impact of trust and perceived risk on Muslim behavior in buying HCF, and their role in moderating the relationship between halal awareness and religious commitment with Muslim behavior in buying HCF.Design/methodology/approachThe research population target is Muslims aged 18 years or older who reside in Greater Jakarta and have purchased certified halal food at least once in the past month. The survey method is a self-administered survey using a purposive sampling technique. The online survey has been successful in getting 283 Muslim respondents. In analyzing the causal relationship and hypothesis testing, this research uses the partial least square – structural equation model.FindingsThis study reveals several results: attitude, halal awareness, religious commitment, trust and perceived risk have a significant influence on the frequency of Muslims buying HCF. Attitude mediates the impact of halal awareness, religious commitment and trust on the frequency of Muslims buying HCF; perceived risk and trust moderate the relationship between religious commitment and the frequency of Muslims buying HCF.Originality/valueResearch on halal food is still limited, including in Indonesia. Meanwhile, the study explores the actual behavior of consumers, particularly in a certified halal food context, which is still rare in the existing literature. At the same time, the intention-behavior gap can lead to wrong decisions. Furthermore, this study also studies how Muslims feel when they consume foods that are not certified as halal. Research like this has an immense opportunity to be developed because not many have been developed.

Decision Support System for Selection of Outstanding Students Using the AHP and SAW Methods

It is very important for outstanding students to be directed and guided to get coaching related to the development of each student's personal potential so that superior and quality students are created. The process of selecting outstanding students can get wrong decisions because the process of selecting outstanding students is based on subjectivity, this allows many selected outstanding students not to reach the desired standard and do not get the best candidates. Therefore, a decision support system was created that can carry out the calculation process for all selections for the selection of outstanding students. This final project will implement the AHP and SAW methods in forming a system. The stages are carried out by comparing feature weights with the AHP method. Then the next stage is to rank using the SAW method to get selected outstanding students. Of the 72 students who were selected from the school, they were then selected to become 20 outstanding students based on the highest-ranking order. Software testing is done by comparing the results of school calculations with system calculations. Based on the results of the tests carried out, an accuracy value of 80% was obtained.

4A_486/2022, Arrêt du 26 avril 2023 [Double jeopardy and res judicata – Misstated party position in award was not relevant to the outcome – Wrong decision on burden and standard of proof no ground for annulment]

4A_486/2022, Arrêt du 26 avril 2023 [Double jeopardy and res judicata – Misstated party position in award was not relevant to the outcome – Wrong decision on burden and standard of proof no ground for annulment]

On incomplete matrix information completion methods and opinion evolution: Matrix factorization towards adjacency preferences

Completeness and consistency are widely acknowledged as indispensable prerequisites for applying fuzzy preference relations in resolving real-world problems. Interactions between decision-makers in the field of group decision-making can have a profound effect on preference changes, especially in situations of incomplete information. However, preference matrices generated using existing models often suffer from low consistency during opinion evolution, which further increases the risk of wrong decisions. To cope with these problems, this paper proposes an opinion evolution method under incomplete information. The method consists of two main components: (1) A preference prediction model based on matrix factorization algorithm and social trust networks under incomplete information, and (2) A model for decision-makers’ preference evolution under dynamic trust scenarios. In Part (1), to simplify the preference collection method and reduce the occurrence of inconsistent information, this paper presents the concept of a consistent adjacency fuzzy preference matrix. To advance the utilization of machine learning algorithms for consistent preference relations, this paper employs the matrix factorization method with stochastic gradient descent to predict missing preferences in adjacency fuzzy preference matrix. In Part (2), to address the shortcomings in past opinion evolution models, this paper simulates the change of decision-makers’ opinions in different stages of interactions under variable trust weights by using alternative ranking similarity and trust thresholds. To illustrate the practical application of the proposed model, this article uses the enterprise supplier selection problem as a case study. The model’s effectiveness and feasibility are then validated through a comparative analysis with existing methods.

Exposure to chemical pollutants and biological aerosol in indoor facilities for recreational and sport horses

BackgroundDue to the increasing prevalence of equine non-infectious respiratory disease, the air contamination in equine housing (Stables A-C) and training facilities (indoor riding arenas A - C) was investigated. The aim of the study was to monitor gaseous pollutants, bioaerosols, and dust concentrations at three different sites (stables and riding halls), where different floor materials were used in the riding halls.Materials and methodsAir quality was monitored in housing for horses and in riding halls in terms of dust concentration, the presence of gaseous chemical pollutants, and concentrations of biological aerosol. Statistical analysis was performed using analysis of variance (ANOVA). The levels obtained were compared with acceptable limits.ResultsAmong the gaseous pollutants identified, the highest concentration was obtained for ammonia in stables B and C (16.37 and 22.39 mg/m3, respectively). Standards for total dust were exceeded in stables B and C and in riding halls B and C. The highest numbers of bacteria and fungi were recorded in stables A and C and in riding hall B. Ulocladium sp. had the highest percentage share among the moulds identified.ConclusionsThe results confirm that the wrong choice of bedding in the stable and indoor riding arenas may contribute, even in short training periods, to equine non-infectious respiratory disease (equine asthma). Bioaerosol suspended in the air together with released gaseous pollutants can exacerbate this phenomenon, which even in the case of short training periods can lead to equine asthma of varying degrees of severity. For this reason, the choice of floor material in riding halls should be treated as a priority, as the wrong decision can shorten the period during which the horse can be used for recreational purposes.

Diversity in Education: The U.S. Supreme Court Got It Wrong

ABSTRACT Diversity has repeatedly been shown to be a vital part of education. This paper explores the benefits and importance of diversity in education and presents studies and supporting research. In the case Students for Fair Admissions, Inc. v. President and Fellows of Harvard College, the United States Supreme Court found that race-based affirmative action in college admissions is unconstitutional because it violates the Equal Protection Clause of the Fourteenth Amendment, which prohibits discrimination based on race. As colleges and universities are trying to be more inclusive and varied, the Supreme Court’s finding just made it more difficult for colleges to secure a diverse population which will hurt education and likely lead to an adverse outcome for society. Diversity has many important benefits and is especially advantageous in education. Diversity increases creativity, critical thinking, helps reduce achievement gaps, and improves academic performance, student motivation, and intellectual engagement. Increasing diversity can increase empathy, appreciation of other cultures, and can help create a sense of community on campus. It can also help create inclusive attitudes and respect for others, can aid in removing biases and stereotypes, and can help prepare students for careers in an increasingly interconnected world. When weighing the benefits of diversity against what is actually written in the law, it seems the 2023 U.S. Supreme Court made the wrong decision.

Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder

Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided.

Developing and testing a custom algorithmic trading strategy using exponential moving average, relative strength index, and sentiment analysis

<p>Stock trading is a popular and important profession that requires near-to-perfect data analytical skills, mathematical and statistical knowledge, and a broad understanding of buying and selling stocks. Often, due to the number of factors to consider and the intervention of human bias, traders and investors make wrong decisions that cost them millions of dollars. Therefore, automated algorithmic trading has gained traction in the marketplace due to its ability to process huge amounts of data, perform mathematical calculations and make quick and effective decisions. Most algorithmic trading strategies rely on a single technical indicator; however, it has been found that combining two or more indicators makes a trading strategy profitable. Therefore, this paper proposes a custom algorithmic trading strategy that combines important technical indicators such as the Exponential Moving Average and Relative Strength Index and utilizes sentiment analysis of financial news as well. This combination of technical indicators and sentiment analysis is not prevalent in existing research. The performance of the strategy was tested on fifteen stocks from different sectors of the US market using Python’s VectorBt library. The results showed that most of the stocks produced a higher win rate with the custom strategy as compared to other strategies, with the highest win rate of 88% for the S&P 500 index. To carry out sentiment analysis, a NLP model using BERT was developed which achieved an accuracy of 84%. Finally, to test the strategy on real-time data, paper trading was carried out on the Alpaca API and after six months the portfolio’s ROI is 6.26%.</p>