Choosing new energy vehicles for travel, especially electric vehicles, is an important component of building a low-carbon urban transportation system. However, the charging need of electric vehicle users is still constrained by the unreasonable layout and insufficient supply of public charging piles in the city. Private charging pile sharing as an alternative policy tool can play a very good role in solving this problem. But it needs decision-makers in urban transportation to take corresponding measures to promote. This paper constructs an evolutionary game model to study the decision behavior of participants in private piles sharing platform. Through numerical simulation analysis, it is found that under most parameter conditions, the government tends to establish a shared charging pile platform based on public interests. Private charging pile owners are influenced by the relationship between the cost of supply modification and revenue, and they tend to join the shared platform when they expect to recover the modification cost. The research conclusions of this paper will provide support for exploring how participants make decisions to maximize overall benefits in the development of low-carbon urban transportation.

The pace of implementation of renewable electricity storage in Europe is disappointingly slow. Several factors influence it, but there is a need to speed up the rate and increase the volumes in order to promote a 100% transition to renewable energy resources, expand the practice of using renewable energy, and contribute to the improvement of the user's quality of life. Also, an important factor is significantly reducing the global impact on the environment and climate change. Electricity from renewable energy sources such as solar and wind has a seasonal nature that cannot provide the necessary electricity consumption and cover peak loads. Also, the so-called "energy resource crisis" is a very topical problem at the moment, which reinforces the global need to increase the share of renewable energy resources in the overall balance of primary energy resources. It is precisely the wider integration of renewable electricity storage in practice that can help stimulate this. The availability of renewable electricity is constantly increasing and the level of technological innovation is rapidly developing, therefore it is valuable to analyze, look for connections and at the same time the main differences in the ways of accumulation in order to promote its availability from a private house to the national and European scale as a whole. Therefore, this article analyses and compares the different options for renewable electricity storage, from small batteries to large storage systems, arriving at the best solution according to needs, using analysis methods such as multicriteria decision analysis (MCDA) and TOPSIS. After comparing nine criteria, such as the amount of investment required, existing power density, efficiency, duration of operation and others, in both groups, it was concluded that lithium-ion batteries are currently the best solution among batteries, while in the group of large accumulation systems – pumped hydro storage.

Teaching and learning a new language is a complex process that requires more than just memorizing vocabulary and grammar rules. Immersing oneself in the target language's culture and history is essential for achieving fluency and accuracy. In recent years, researchers have explored the potential of music as a tool for language learning. This paper discusses the benefits of using music in language learning, including improved listening comprehension, pronunciation and fluency, and cultural understanding. The theoretical framework of sociocultural theory, cognitive load theory, and task-based language learning is presented to support the use of music in language learning. This paper also provides practical recommendations for learners to incorporate music into their language learning routine, as well as resources for finding music in the target language, such as online music platforms and language learning apps. The article concludes by encouraging language learners to incorporate music into their language learning routine and providing resources for finding music in the target language, such as online music platforms and language learning apps. Overall, this paper demonstrates that music can be a powerful and enjoyable tool for enhancing language learning outcomes.

More than fifty million people over the age of fifty have osteoporosis issues due to low bone mass, disease contribution, and not exercising continuously. Certain most important risk factors and causes of osteoporosis contain age, gender, hormones, visits to inexperienced doctors, and certain medical conditions. In this manuscript, we diagnosed certain frank operational laws under the availability of complex intuitionistic fuzzy (CIF) information. Furthermore, using the evaluated operational laws, we pioneered the theory of CIF frank weighted averaging (CIFFWA), CIF frank ordered weighted averaging (CIFFOWA), CIF frank hybrid averaging (CIFFHA), CIF frank weighted geometric (CIFFWG), CIF frank ordered weighted geometric (CIFFOWG), CIF frank hybrid geometric (CIFFHG) operators, and described their beneficial and valuable results with certain useful properties. Moreover, we aim to evaluate the most dangerous type of osteoporosis based on their symptoms, causes, and risk factors using diagnosed approaches. Finally, to enhance the worth of the evaluated operators, we compare the diagnosed result with certain prevailing results and illustrated their geometrical representation with the help of MATLAB.

The present study examined music-induced dopamine release, as measured by a proxy measure of spontaneous eye blinks. Specifically, we explored the effects of uplifting and sombre tones in different sections of Vivaldi's Four Seasons to investigate the affective content of musical pieces within one composition. Seventeen participants listened to four concertos (Major modes: Spring, Autumn, Minor modes: Summer and Winter) and a silence condition while completing the 3-Stimulus Odd-ball attention task. Electrooculograms were recorded from electrodes placed above and under the left eye. Self-reported arousal and music preference measures were also gathered during the testing session. In addition, the P3a Event-Related Potential (ERP) component was analysed as another potential index of dopamine function. Results revealed significant differences in the blink rates during music listening and silence, with the largest effect observed for the sad, melancholic Winter concerto. However, no significant correlation was found between blink rate and music preference or arousal. Furthermore, no reliable association was found between blink rate and the P3a ERP component, suggesting that these measures tap into different aspects of dopamine function. These findings contribute to understanding the link between dopamine and blink rate, particularly in response to classical music. Crucially, the study's discovery that the Winter concerto, with its sorrowful tone, significantly increased the blink rate highlights the significance of sad music and perhaps the programmatic qualities of this concerto to induce a strong emotional response.

Safety is very important in aviation since a loss of safety frequently results in both fatalities and financially damaging situations that are typically unrecoverable. Thus, achieving safety as much as possible is the primary goal of practically all aviation technology work. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based classifier is created to estimate the fault risk factor of airplanes. Five categories of real fleet data belonging to structure, electrical, avionic, motor systems, and incident statistics of the planes have been used for classifier development. A risk factor determination for each plane is the output of the developed intelligent classifier, and it can be used to identify general overhaul candidate planes and stop defects and crashes before they happen. The obtained results show that using ANFIS provides a great capability in processing many inputs and outputs depending on different types and classes in the aviation industry and thus predicting the failure risk of the airplane efficiently.

In this article, we estimate the value of Government Expenditure on Education-GEE in the context of Environmental, Social and Governance-ESG dataset of the World Bank. We use data from 193 countries in the period 2011-2020. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. Furthermore, we apply the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of the future value of GEE. We found that the Polynomial Regression is the best predictive algorithm. The Polynomial Regression predicts an increase in GEE of 7.09% on average for the analysed countries.

We analyze the question of GDP Growth-GDPG rate in the context of Environmental, Social and Governance-ESG framework. We use World Bank data for 193 countries in the period 2011-2020 using different econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS. We found that GDPG rate is positively associated, among others, to “Government Effectiveness” and “Prevalence of Undernourishment” and negatively associated among others to “Unemployment” and “Research and Development Expenditure”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow method and we found the presence of four clusters in the sense of GDPG rate. Finally, we confront eight machine learning algorithms to predict the value of GDPG rate and we found that the Polynomial Regression is the best predictor. The Polynomial Regression predicts an increase of GDPG rate equal to 2.88% on average for the analysed countries.

This research aimed to determine salient factors affecting the decision to become a beginning organic farmer. New and beginning organic farmers have unique characteristics, showcasing their dedication to environmental justice and social justice at the expense of their own businesses. This research employed a multiple-case case study design. We conducted semi-structured interviews with 40 first-generation farmers who operate organic farms in Arkansas, Florida, or Georgia. We analyzed interview transcripts using the qualitative analysis approach of coding. Our results revealed the reasons that people with little practical knowledge start farms. They are inspired by those around them who succeed and encouraged by influential characters in the field who assure them they can do something they love and be profitable. This research showed that first-generation farmers find inspiration and develop values rooted in food justice. Our findings have implications for developing and implementing current and future programmatic activities that aim to enhance beginning farmer training and workforce development. Identifying sources of inspiration will help researchers and service providers target newer and beginning farmers to support a vibrant food system, including a burgeoning market opportunity, developing strong communities around food, and building grassroots solutions.

In the paper we suggest an implementation of the non-commutative logic and apply its operators for decision-making in the group of autonomous agents. The suggested operators extend the uninorm and absorbing norm aggregators and use an additional asymmetry parameter that defines the “level of non-commutativity”. The value of this parameter is specified using the perception bias of humans measured in the experiments. The suggested operators and decision-making method are illustrated by simulated behavior of mobile robots in the group.

We find at least 4 quantum properties that are indeed universal in some number systems; and we can use them for quantum consciousness. Using the sets B={0,1}, N, Z, or Q has the +4 quantum properties that one can trust, as archetypes of easy communication to friends and foes, to ignoranti or cognoscenti, and for quantum computing. This paper proposes the use of Bayesian geometry to reduce the space of possible results, reducing or increasing initial beliefs by successive experimentation. This is exemplified in biology and mathematics, and can be applied to physics and other sciences. The conclusion is that an experimental fact can be absolutely verified. Any mathematical choice (as verified by computers) militating against R=Q, is under an absolutely low chance to succeed. Infinitesimals are denied based on 3^16 independent factors. This is not a probabilistic result, but based on reduction of the Bayes geometry for updating beliefs. This work applies to non-probabilistic and unknown phenomena, such as those found in oscillations (neutrino, health), evolution, and quantum computing, denies Gödel's uncertainty, and solves the liar's paradox by reflection. This connects different scientific fields with one another, in an internet, i.e., in a network of connections of multiple open networks, using the set Q, resembling a hologram, and includes entanglement.

We estimate the determinants of Emissions-COE in the context of Environmental, Social and Governance-ESG model at world level. We use data of the World Bank for 193 countries in the period 2011-2020. We found that the level of COE is positively associated, among others to “Methane Emissions”, “Research and Development Expenditures”, and negatively associated among others to “Renewable Energy Consumption” and “Mean Drought Index”. Furthermore, we have applied a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four cluster. Finally, we apply eight machine-learning algorithms for the prediction of the future value of COE and we find that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predicts a reduction in the level of COE equal to 5.69% on average for the analysed countries.

This thesis presents findings from an online experiment aimed at evaluating individuals' attitudes towards their own cognitive biases that lead to objective mistakes. In a number of incentivized decision problems, a participant in the experiment might make a mistake and choose a dominated lottery (FOSD lottery), which is likely to result from a particular heuristic or cognitive bias. In this case, she is then confronted with her error under one of two treatments: a confrontation with an explanation only regarding the mistake or one with the addition of the supposed mechanism (bias). Following this explanation, her comfort level with her choice is measured. A bias that acted as a mechanism for the observed error could be considered advantageous (i.e., useful given alternative costs) or disadvantageous (i.e., mostly harmful) in other decision-making scenarios encountered during one's lifetime. Consequently, the perception of cognitive biases is subjective and may vary among decision makers, who may emphasize either the positive or negative aspects of such biases. The proposed methodology allows to determine whether these biases are perceived as useful rules of thumb, despite leading to a dominated lottery choice in that particular context, or unfavorable.Results revealed significant differences between the two treatments and across five different decision problems. Interestingly, the results showed that in only 60% of the cases, the participants felt uneasy about their mistakes. In the remaining instances, the participants demonstrated either indifference or a sense of comfort with their choice of a dominated lottery. The net effect of providing information on the cognitive biases that presumably lead to the mistakes, measured as the difference between treatments, revealed a negative perception of the mechanism behind the error in one problem, contentment with the mechanism for another, and no significant effect for the remaining biases.

This study explores the implications of merging two fundamentally different types of banks: Islamic and conventional banks. The research aims to provide insight into such a merger's unique opportunities and challenges and offer strategic guidance for future mergers. A balanced scorecard-based strategic analysis using Quantum Spherical Fuzzy Decision-Making Approach was used to develop the merged bank's short- and long-term strategic plans. The balanced scorecard included 12 key performance indicators (KPIs) in 4 groups, and the methodology also incorporated several questions to guide the analysis. The study results offer valuable insights into the potential opportunities and challenges of merging these two types of banks and strategic recommendations for stakeholders at all levels. The study is a valuable guideline for future mergers between similar or different types of banks. Overall, the findings suggest that a well-planned merger strategy is essential for avoiding challenges and maximizing the benefits of merging Islamic and conventional banks. By integrating the strengths of both types of banks, a merged entity could create a competitive advantage and potentially improve financial performance. However, this requires careful consideration of cultural differences, regulatory challenges, and other factors that could impact the merger's success.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

: Polymer single screw extrusion is a major industrial processing technique used to obtain plastics products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficiently extruder screws and decide whether a conventional screw will do the job efficiently, or a barrier-screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. Processing of two polymers, Low Density Polyethylene and Polypropylene, is analyzed. A methodology based on the use of Artificial Intelligence (AI) techniques, namely data mining, decision making and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP the use of both types of screws would be feasible.

In this article, we estimate the role of Political Stability and Absence of Violence and Terrorism-PS in the context of Environmental, Social and Governance-ESG data at world level. We analyse data from 193 countries in the period 2011-2020. We apply Panel Data with Fixed Effects, Panel Data with Random Effects and Pooled Ordinary Least Square-OLS. We found that PS is positively associated, among others, to Population Density and Government Effectiveness, and negatively associated, among others, to Research and Development Expenditure and Maximum 5-day Rainfall. Furthermore, we apply the k-Means algorithm optimized with the application of the Elbow Method and we find the presence of four clusters. Finally, we propose a confrontation among eight different machine-learning algorithms for the prediction of PS and we find that the Polynomial Regression shows the higher performance. The Polynomial Regression predicts an increase in the level of PS of 0.25% on average for the analysed countries.

Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

In this article, we analyse the determinants of Regulatory Quality-RQ for 193 countries in the period 2011-2020. We use a database from ESG-Environment Social Governance of the World Bank. We apply OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index”. We also found that the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method and we find the presence of five clusters. In adjunct, we confront eight machine learning algorithms to predict the value of RQ and we found that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of -1,29%. In the end, we present a network analysis with the Euclidean distance and we found the presence of a structure of seven networks using augmented data.

Background: Chikungunya virus (CHIKV) diagnosis have become a challenge for primary care physicians in areas where zika virus and/or dengue virus are present. Case definitions for the three arboviral infections are overlapping. Methods: A cross-sectional analysis was carried out. A bivariate analysis was made using confirmed CHIKV infection as the outcome. Variables with significant statistical association were included in an agreement consensus. Agreed variables were analyzed in multiple regression model. The area under the receiver operating characteristic (ROC) curve was calculated to determine a cut-off value and performance. Results: 295 patients with confirmed CHIKV infection were included. A screening tool was made using symmetric arthritis (4 points), fatigue (3 points), rash (2 points) and ankle joint pain (1 point). The ROC curve identified a cut-off value and a score ≥ 5.5 was considered positive to identify CHIKV patients with a sensibility of 64.4% and a specificity of 87.4%, positive predictive value of 85.5%, negative predictive value of 67.7%, area under the curve of 0.72, and an accuracy of 75%. Conclusion: We developed a screening tool for CHIKV diagnosis using only clinical symptoms as well as proposed an algorithm to aid the primary care physician.

Finding a suitable support is a key process in the life history of climbing plants. Climbers that find a suitable support have greater performance and fitness than those that remain prostrate. Numerous studies on climbing plant behavior have elucidated the mechanistic details of support searching and attachment. Far fewer studies have addressed the ecological significance of support-searching behavior and the factors that affect it. Among these, the diameter of supports influences their suitability for twining plants. When support diameter increases beyond some point climbing plants are unable to maintain tensional forces and therefore lose attachment to the trellis. Here we further investigate this issue by putting pea plants in the situation to choose between supports of different diameters while their movement was recorded by means of a three-dimensional motion analysis system. The results indicate that the way climbing plants move can vary depending on whether they are presented with one or two potential supports. Furthermore, when presented with a choice between a thin and a thick support, the plants showed a distinct preference for the former than the latter. The present findings shed further light on how climbers make decisions as far as support search is concerned, and provide evidence that plants adopt one of several alternative plastic responses in a way that optimally corresponds to environmental scenarios.

Efficient natural resource management prevents and reduces negative impacts, such as environmental damage, misappropriation of resources, and conflicts; several strategies can be leveraged to conserve, protect, and enhance natural resources. Multi-criteria decision analysis (MCDA) is useful in providing solutions to addressing decision-choice problems. In this study, the natural resource under evaluation is the chestnut forest, with the objective of valorising its supply chains. The methodology applied is A'WOT, which allows previously identified factors through a qualitative SWOT matrix, to be ordered through an objective quantification using AHP (Analytic Hierarchy Process), which is a multicriteria decision-support method. The survey was conducted with a group of chestnut resource (n=20) experts. The SWOT matrix identified a total of 20 factors: 6 strengths and 6 weaknesses and 4 factors each for opportunities and threats. The results express a clear stakeholder interest, which identifies the significant role of civil society in directing management choices for the provision and enhancement of ecosystem and vocational services. This study evaluated the adaptability of decision-support tools applied to a real case of forest resource management to identify and order factors useful to enhance the resource and stimulate the supply chains to achieve greater added value. In a general sense, the methodological potential emerged to replicate or improve the research in other geographical regions, whether regional or extra-regional, or even on a larger scale, such as on a national level.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.

Autonomous systems and intelligent machines are involved in almost all areas of human activity and they are now more and more present in our everyday life. The reason for this extensive use certainly resides in all the benefits these machines offer to the users. In experimental settings, numerous studies have demonstrated the positive effects that the introduction of autonomous systems have on human decision-making and performance. However, studies have shown in addition that the introduction of these systems can have important negative effects as well. Considering that autonomous systems are now introduced in sensitive domains like the military or medicine, we need more than ever a comprehensive understanding of the effects they cause on human performance and decision-making, and particularly in tasks and contexts with a social or moral dimension. The aim of this narrative review is threefold. First, we will provide an overview of the main effects on a human agent’s decision-making and performance produced by the introduction of autonomous systems. Second, we will review the conditions identified as underlying factors of these effects, and see how current models of human – autonomous systems interaction integrate those conditions. Third, we will conclude this review by highlighting new directions for future investigations.

In this article we estimate the level of Control of Corruption for 193 countries in the period 2011-2020 using data from the ESG World Bank Database. Various econometric techniques are applied i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS. Results show that “Control of Corruption” is positively associated, among others, to “Government Effectiveness” and “Political Stability and Absence of Violence/Terrorism”, while it is negatively associated among others to “Agriculture, Forestry, and Fishing Value Added as Percentage of GDP” and “GHG Net Emissions/Removals by LUCF”. A cluster analysis implemented with the k-Means algorithm optimized with the Elbow Method shows four clusters. A confrontation among eight Machine Learning algorithms is proposed for the prediction of Control of Corruption. Polynomial Regression is the best predictor for the training data. The level of Control of Corruption is expected to growth by 10.36% on average.

Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber-attacks, threats, risks, damages, or unauthorized access. Artificial Intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real-world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI-based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this paper, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness", emphasizing multi-aspects AI-based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero-day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI-based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

In music, the role of the interpreter is to play her/his part manipulating the performance parameters in order to offer a sonic rendition of the piece capable of conveying specific expressive intentions. Since the 1980s there has been a growing interest in computational expressive music performance (EMP). This research field has two fundamental objectives: the understanding of the phenomenon of human musical interpretation and the automatic generation of expressive performances. Rule based, statistical, machine and deep learning approaches have been proposed, most of them devoted to the classical repertoire, in particular to piano pieces. On the contrary, we present an introduction to the role of expressive performance within popular music and to the contemporary ecology of pop music production, based on the use of Digital Audio Workstations (DAWs) and virtual instruments. After an analysis of the tools related to expressiveness commonly available to modern producers we propose a detailed survey of research into the computational EMP field, highlighting the potential and limits of what is present in literature with respect to the context of popular music, which by its nature cannot be completely superimposed on the classical one. In the concluding discussion we suggest possible lines of future research in the field of computational expressiveness applied to pop music.

Aaron Copland’s only full-length opera, The Tender Land has had a difficult production history. Critics of the 1954 New York City Opera Premiere bemoaned the atypical storyline, the music declared too dry, and the singing too sparse. The opera’s failure to dazzle initial audiences is likely because it was originally written for television. Copland was commissioned to write this opera for television in 1952, but was rejected by NBC in 1953, with no official explanation given as to why. The New York City Opera premiere, therefore, was not an accurate representation of Aaron Copland's vision. The opera eventually did find receptive audiences, each success marked by a smaller, more intimate venue, not unlike the intimacy afforded on a sound stage. Perhaps most notable of these was the touring 1993 University of Minnesota production that performed in barns and farmhouses throughout the Midwest. Through this tour, the opera reached a rural community mirrored in the story, providing much more opportunity for the audience to connect to the characters. However, the question remains what caused NBC to initially reject the opera. This paper exposes the likely political obstacles that prevented Copland’s opera from premiering in its intended medium, and the subsequent consequences of displacing The Tender Land from its television premiere.

Opera and oratorio overtures exist in an indeterminate state between purely symphonic and dramatic music. While they can be and often are extracted for symphonic programs, they are ultimately tied to the story to which they grant the audience a preview of the story and music yet to come. Those that are easily extracted are so independent due to their structure, one well known to the symphony: the sonata form. In studying overtures from the 19^th century, however, one can trace a decline of the sonata form overture for one that is more subservient to the dramatic story soon to follow it. In this paper, three representatives of the early, mid and late period of the highlight this steady shift away from sonata form. Using Hepokoski and Darcy’s Elements of Sonata Theory: Norms, Types, and Deformations in the Late-Eighteenth-Century Sonata, I will categorize these overtures into one of the five sonata types listed therein. The increasing number of deformations as one moves through the overtures chronologically will illustrate the deliberate decline of use of the sonata form for the overture in favor of a clearer reflection of the upcoming story.

Already more than 140 countries consider or have pledged to reach net-zero emission targets by 2050 or earlier and the share of global emissions falling into an emission pricing scheme has steeply increased over the past three years. Even where there are no direct implications for industry (yet), there is a series of subtle pressure points driving an increasing number of companies across the globe to work towards climate neutrality and pledging ambitious emission reduction goals. This article sheds light on the pressure points, the subtle triggers, the underlying considerations as well as the hoped-for benefits for industrial companies from achieving net-zero emissions. The observations and ideas presented in this paper are derived from quantitative data obtained via the Energy Efficiency Index of German Industry (EEI) and qualitative data. Not only societal, work force, supply chain and investor expectations play a large role, but also many strategic considerations which have the potential to make the company more resilient and profitable, particularly in time of crisis. Those companies that do not move towards decarbonisation, on the other hand, may face a costly late-mover disadvantage. This piece uncovers subtle interconnections, helping stakeholders from industry and beyond to grasp opportunities and challenges ahead.

This study aimed to identify the influencing factors that drive the adoption of smart construction technologies by highway construction companies. Using expert interviews and expert scoring, interview data were collected from 25 experts in the field, and the TOSE framework was proposed based on the TOE framework, identifying four dimensions and fourteen influencing factors. The results were analyzed using the Fuzzy-DEMATEL-ISM method, and the findings were then summarized according to the evaluation criteria to determine the validity of the fourteen hypotheses and the extent to which they drive the adoption of intelligent construction technologies by motorway construction companies. The findings of this paper will be of great value to decision-makers and participants in highway construction companies, as well as to other companies in the construction industry, in their decision to adopt smart construction technologies.

Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. Therefore, the restoration of degraded landscapes is essential to maintain the functionality of ecosystems, especially those with greater social, economic and environmental vulnerability. Nevertheless, policy-makers are frequently challenged by deciding on where to prioritize restoration actions, which usually includes to deal with multiple and complex needs under an always short budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. To help decision-makers taking informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called "RePlant alpha" which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis, with at least six indicators and for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical.

Directed movement towards a target requires spatial working memory, including processing of sensory inputs and motivational drive. In a stimulus-driven, operant conditioning paradigm designed to train zebrafish, we present a pulse of light via LED’s and/or sounds via an underwater transducer. A webcam placed below a glass tank records fish swimming behavior. During operant conditioning, a fish must interrupt an infrared beam at one location to obtain a small food reward at the same or different location. A timing-gated interrupt activates robotic-arm and feeder stepper motors via custom software controlling a microprocessor (Arduino). “Ardulink”, a JAVA facility, implements Arduino-computer communication protocols. In this way, full automation of stimulus-conditioned directional swimming is achieved. Precise multi­day scheduling of training, including timing, location and intensity of stimulus parameters, and feeder control is accomplished via a user-friendly interface. Our training paradigm permits tracking of learning by monitoring, turning, location, response times and directional swimming of individual fish. This facilitates comparison of performance within and across a cohort of animals. We demonstrate the ability to train and test zebrafish using visual and auditory stimuli. Current methods used for associative conditioning often involve human intervention, which is labor intensive, stressful to animals, and introduces noise in the data. Our relatively simple yet flexible paradigm requires a simple apparatus and minimal human intervention. Our scheduling and control software and apparatus (NemoTrainer) can be used to screen neurologic drugs and test the effects of CRISPR­-based and optogenetic modification of neural circuits on sensation, locomotion, learning and memory.

Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence arousal space. Four additional songs were selected from DEAM to act as a benchmark in this study and filter out low quality ratings. A total of 277 participants participated in annotating the dataset, and their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.

Transformation studies have been leaning towards the more practical aspects of change processes and have not yet dealt sufficiently with their personal and political dimensions. They are arguably constrained in doing so if they are either overly focussed on systems and how to control them or on individualistic values and behaviours. In this study we show how the actually-occurring societal transformations that people face can be usefully approached through the lenses of dialogical sense-making and critical phenomenology. While distinct, these approaches share a concern with aspects missed when transformation is abstracted and alienated from people’s lives; namely people’s lived experiences during times of change, and the conditions of possibility for these experiences. Dialogical sense-making explores how people create meaning around transformations, through interactions with other people, with different lines of arguments, and as part of broader public discourse. Critical phenomenology engages with subjectivity and lived experience, and with the role of foundational as well as socio-culturally dominant yet contingent structures in shaping our ways of perceiving, experiencing and knowing the world. Through a discussion of insights from these approaches, we show how they offer tools that enable new questions about transformative change as it is experienced and made sense of. Situating analyses of tranformation from within a focus on experience brings us closer to understanding the significance of change processes in people’s lives, and allows for an inquiry into the conditions of experience, including transformative experiences.

Rare diseases are characterized by a wide diversity of signs and symptoms and vary not only from disease to disease, but also from person to person, and living with a disease leads patients to peculiar experiences and treatments, without limits of time and space, as they extend to various environments and relationships of their lives. The objective of this study is the theoretical interaction between Value Co-creation (VC) and the Stakeholder Theory (ST) with the Shared Decision Making (SDM) health care theory. It is configured as a multiparadigmatic proposal by enabling the analysis of multiple perspectives of different stakeholders in health care. Thus, Co-created Decision Making (CDM) emerges in a logic dominated by service, with emphasis on intangible aspects and the interactivity of the relationships. It goes beyond the clinical office and the doctor-patient relationships, as studied in SDM, extending to all environments and interactions that add value to the patient's treatment. It was concluded that the essence of this new theory proposed here is neither in patient-centered care nor in patient self-care, but in co-created relationships with and between stakeholders in both directions, including non-health care environments that are important to the patient, such as relationships with friends, family, other patients with the same disease, social media, public policies, and the practice of pleasurable activities.

Decision making is a complex process involving various parts of the brain which are active during different times. It is challenging to measure externally the exact instance when any given region becomes active during the decision-making process. Here we try to extract and visualize the dynamic functional brain activation information from the observed fMRI data. We propose the use of a regularized deconvolution model to simultaneously map various activation regions within the brain and track how different activation regions changes with time. Thus, providing both spatial and temporal brain activation information. The activation information can then be further analyzed as per need and requirements. The proposed technique was validated using simulated data and then applied to a simple decision-making task for identification of various brain regions involved in different stages of decision making. The visualization aspect of the algorithm allows us to actually see the flow of activation (and deactivation) in form of a motion picture. The dynamic estimate may aid in understanding the causality of activation between various brain regions in a better way.

Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

A crop-weed simulation model is presented to compare and evaluate Integrated Weed Management (IWM) strategies. Specifically, the model was parameterized for soybean crops in competition with Euphorbia davidii Subils. We used both, weed and crop demographic data surveyed in agronomic fields of the central zone of the Buenos Aires province, Argentina, throughout two crop cycles (2011/2012 and 2013/2014). The proposed model underwent a calibration process and subsequent validation with 70/30 % data split respectively (N=37). Two annual-based and one multiannual-based case studies were simulated to demonstrate the performance of the model. Different IWM strategies were compared under both, operational and tactical planning horizons through the evaluation of different model outcomes (i.e. crop yield, interspecific competition, economic return, and environmental impact). Our case studies results suggest that the inclusion of cultural management practices could reduce weed interspecific competition by 46 to 97 % and weed seed production by 40 to 89%. An increment in both expected crop yield, by 6 to 20%, and annual gross margin, by 44 to 199 US$.ha-1, were obtained in silico for similar levels of environmental impact.

This article is a theoretical contribution to mental model research, which currently has different threads. Whereas some researchers focus on the perceived causal structure, others also include decision policies and decisions. We focus on the link between recognized causal structure (“mental models of dynamic systems”) and policies, proposing Johnson-Laird’s theory of mental models as the link. The resulting framework hypothesizes two types of systematic mental model errors: (1) misrepresentation of the system’s structure and (2) failure to deploy relevant mental models of possibilities. Examination of three experiments through this lens reveals errors of both types. Therefore, we propose that the cognitive theory of mental models opens a path to better understand how people construct their decision policies and develop interventions to reduce such mental model errors. The article closes by raising several questions for empirical studies of the reasoning process leading from mental models of dynamic systems to decision policies.

This article describes the Analytic Hierarchy Process (AHP) method which can be calculated by several methods. The AHP method is essential for the managerial decision-making to recognize which method is efficient for the calculation and to determine the proper order of criteria. In the article there are included methods that can be used in order to calculate the matrix in the AHP process for setting criteria such as Geometric mean, Arithmetic mean, Row sum of the adjusted the Saaty matrix, Reverse sums of the Saaty matrix columns and Row sums of the Saaty matrix. The paper is focused on the accuracy of the methods used. The results show that the most accurate method is the Saaty method, and the second most accurate method is the geometric mean in order to determine the ranking. This method is easier to use when the same order is achieved as in the Saaty method, followed by Geometric mean, which is favourable for fast and easy determination of alternatives using in the AHP process. The survey carried out among managers graduated from Universities and Colleges in Slovakia showed that the respondents considered the Saaty method as the most complex and also the most difficult method and the geometric mean average method the simplest method. 44% of respondents stated that they are capable to use a program to calculate the AHP. 46% of respondents said they had experience with some method related to the managerial decision-making process. 72% of managers regarded as important to manage some method for decision-making in their managerial position.

This article mainly proposes the point of view that financial literacy curriculum aims to make kinds of people, to make people with financial creativity of resource integration thinking and continuous ‘to conscience’ of morality, which is cultivate a way of thinking that can maximize benefits through the use of resource integration and constantly ‘to conscience’ to achieve goals for participants in financial structures with balanced interests. In this sense, this article argues that financial literacy is to make kinds of people with a financial mindset and constantly strengthen ‘to conscience’ through uniting of knowledge and action to achieve human well-being and benign operation of society. In this sense, this article attempts to determine what is really taught in financial literacy education in terms of curriculum theory. That is to improve creativity by cultivating financial thinking with constant ‘to conscience’. The views of this paper provide a new way of thinking about financial literacy education assessment. This changes the evaluation criteria of financial literacy education from the result-oriented of wealth growth to the process-oriented of financial creativity growth, trying to move the criticism of financial literacy education curriculum and evaluation from controversy to unity.

Several review articles about megachurch studies have been published recently concentrating their work on USA, Europe, and other parts of the world, with just a few references about Latin American megachurches. For that reason, this article aims to identify some of the characteristics of Latin American Evangelical megachurches by looking at relevant literature, especially that produced in the region, in Spanish and Portuguese, which is usually overlooked by researchers in Global North. Since this research field is still limited in Latin America, areas, where further work is necessary, are identified. Three general catalysts for the emergence of megachurches in the region, church growth methodologies, Pentecostalization, and theologies of growth, serve as guides to organize the review process. The discussion shows several potential areas of research in a variety of fields such as theology, ecclesiology, organizational theory, leadership, gender studies, and ethics, are proposed from the review.

Nowadays the freedom to make choices is considered of vital importance for decision-makers. Researchers investigating the psychological effects of choice provided empirical evidence about the many advantages of choice (i.e., increased intrinsic motivation, greater perception of self-determination, better performance, and greater life satisfaction). However, more recent studies demonstrated the downsides of choice. When the decision task difficulty exceeds the natural cognitive resources of human mind, the possibility to choose becomes more a source of unhappiness and dissatisfaction than an opportunity for a greater well-being. We here discuss in a detailed fashion the main findings of research in psychology and consumer behavior showing the bright sides and the downsides of choice.

Citizen participation has always been of vital importance in decision-making processes and, likewise, joint work in the formulation and execution of projects that respond to territorial needs. This in order to ensure the best impact of public or private investment, optimizing the result and community articulation. For this reason, this investigation proposes a methodology of evaluation and prioritization of projects based on an approach integrated between the modelling in dynamics systems (SDM) and the Analytical process network (ANP), in which the citizen factors are used as qualitative and quantitative variables inside the posed balance sheet model. This is how the model developed by the Stella Architect software, it offers to the decision makers approximate information about the reality of the community, especially those who were affected by the Colombian conflict and where the expectations of a comprehensive repair could disrupt the socioeconomic system and reveal more pressing interests than those of the central government. The methodology is described by the information gotten through the participatory workshops done in the California county that belongs to the Magdalena region, this is a very affected region by the conflict in Colombia. The investigation results confirm the useful of, first of all, the use of the modelling as an information-generating tool by allowing the simulation of relevant variables in the evaluation and prioritization of projects at different moments of time and, in second place, use the knowledge of the experts that expose the ANP method for establishing the importance of each variable compared with other variables and, likewise of each project compared to the rest of the alternatives in each of the different instants of time in which the evaluation is carried out, given that the results could change in any time, the official ranking is showed after an optimization process with partial results.

The notion of free will seems so intuitive to us that it would be hard, even impossible, to imagine that we live in a world without ever exerting any willpower. This view of reality is not only hindrance to inspiration, it poses a serious threat to our moral and social responsibilities. Nonetheless, many scientific and philosophical schools of thought such as determinism purport free will as a mere illusion. As an attempt to rescue free will put forward by libertarianism, compatibilism or physical indeterminism that either exempts our mind from the universal rules of cause and effect by offering our minds a metaphysical status or substitute free will with random will rooted in the laws of quantum mechanics. This manuscript offers an alternative perspective under a new paradigm of consciousness called physical libertarianism that explicates true free will through the unwavering laws of cause and effect. Based on this paradigm, consciousness is the result of interaction of awareness and decision-making process. By applying awareness to the process of decision-making awareness-based choice selection or true free will is conceived. In return, by assigning the power of decision making to the process of awareness discretionary selection of information for attention or intentional attention is emerged. Through integration of these two mental functions, an independent entity called “I” is formed that differentiates natural intelligence from artificial intelligence. While determinism can aptly describe the world of inanimate objects and artificial intelligence, because of “I,” determinism has no jurisdiction over the realm of natural intelligence.

Background: Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. Objectives: To reach a deeper understanding of how patients themselves contribute to the diagnostic process. To describe the genesis of the LD hypothesis in care pathways. Methods: In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were : suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. Results: The diagnosis of Lyme disease was primarily triggered by identification with personal testimonies found on the internet. Most of patients were leading their own diagnostic investigation. The majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. Conclusion: GPs should first systematically explore patients’ etiologic representations in order to improve adherence to the diagnosis especially in the management of medically unexplained symptoms. Long COVID-19 syndrome challenge offers an opportunity to promote active patient involvement in diagnosis.

Financial statement analysis is an analysis that uses analytical procedures to evaluate a company's financial health, risks, performance, and future growth potential. This article aims to find out the application of analysis of financial statements of PT. Astra Internasional, Tbk towards the decision-making of novice investors in the Indonesian capital market. This article is qualitative descriptive with literature studies. The results of this article can be concluded that the analysis of the financial statements of PT. Astra Internasional, Tbk can help novice investors in the Indonesian capital market in making investment decisions.

Petrochemicals, which convert oil and gas into products such as plastics, are fundamental to modern societies. Chemists recognize their role in the design of materials and the adverse effects that these may have on the environment, preventing sustainable development. Several methodological frameworks and sustainability assessment approaches have been developed to evaluate the resources used in the petrochemical sector in terms of environmental costs. A combination of Life Cycle Assessment and Emergy Accounting - to assess the environmental support for resource use - is applied in this study of the PET production chain in Europe. The Unit Emergy Values of several intermediates are calculated or updated to facilitate discernment of the quality of energy used and the processes' efficiency. Several routes for synthesizing renewable para-xylene and ethylene glycol from biomass are discussed and confronted with the efforts focused on recycling and recovering the final product providing concurrently a procedure and a valuable data set for future CP actions. The results show that understanding the efficiencies changing across the production chain may help stakeholders make wise choices as to where and when interventions to promote a circular economy are most effective along a petrochemical production chain.

Cities are undergoing huge shifts in technology and operations in recent days, and `data science' is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting insights or actionable knowledge from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the study and analysis of actual happenings with historical data using a variety of scientific methodology, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ``Smart City Data Science", where city data collected from various sources like sensors and Internet-connected devices, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, various machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world services of today's cities. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, professionals, as well as policy-makers of a country, particularly, from the technological point of view.

Big data analytics tools are the use of advanced analytic techniques targeting large and diverse volumes of data that include structured, semi-structured, and unstructured data from different sources and in different sizes from terabytes to zetabytes. The health sector is faced with the need to generate and manage large data sets from various health systems, such as electronic health records and clinical decision support systems. This data can be used by providers, clinicians, and policymakers to plan and implement interventions, detect disease more quickly, predict outcomes, and personalize care delivery. However, little attention is paid to the connection between big data analytics tools and the health sector. Thus, a systematic review of the bibliometric literature (LRSB) was developed to study how the adoption of big data analytics tools and infrastructures will revolutionize the healthcare industry. The review integrated 77 scientific and/or academic documents indexed in SCOPUS presenting up‐to‐date knowledge on current insights on how big data analytics technologies influence the healthcare sector and the different big data analytical tools used. The LRSB provides findings related to the impact of Big Data analytics on the health sector by introducing opportunities and technologies that provide practical solutions to various challenges.

Purpose: This research aims to model a police agency ordering problem based on criteria that measure the performance of operational and logistical variables, using a multicriteria method. Design/methodology/approach: The multicriteria method PROMETHEE II was used, which with the aid of the Visual PROMETHEE software, emulated the systematized data in the impact matrix and produced the final ordering of the most efficient police agencies in equalizing the operational, logistical, financial, and human resources applied in the combating crime and reducing criminal indices. Findings: The results confirmed that the resources made available to police agencies located in different municipalities in the metropolitan region of the state of Rio de Janeiro are better applied by managers compared to police units in the capital of Rio de Janeiro. Research limitations/implications: As implications of the research, it can be inferred that the use of multicriteria methods in modelling problems in ​​public security can contribute to the rationalization of the use of available resources to fight crime in large cities. Research has shown that it is possible to use the multicriteria methodology in ordering police agencies that best equalize the available resources. Practical implications: The practical impact of this research lies in optimizing the resources available to law enforcement agencies in the fight against crime in general. Social implications: The results can influence the decision making of the local government in the allocation of resources, as well as offering sectors of the economy information relevant to local development. Originality/value: The results of the MCDA analysis can be used to help police agencies in Rio de Janeiro, Brazil to be more efficient. In addition, the application of MCDA can be a new approach to measuring the efficiency of police services.

Interdisciplinary research into the underlying neural processes of music therapy (MT) and subjective experiences of patients and therapists are largely lacking. The aim of the current study was to assess the feasibility of newly developed procedures (including EEG/ECG hyperscanning, synchronous audio-video monitoring, and qualitative interviews) to study the personal experiences and neuronal dynamics of moments of interest during MT with stroke survivors. The feasibility of our mobile set-up and procedures as well as their clinical implementation in a rehabilitation centre and an acute hospital ward were tested with four phase C patients. Protocols and interviews were used for the documentation and analysis of the feasibility. Recruiting patients for MT sessions was feasible, although data collection on three consecutive weeks was not always possible due to organisational constraints, especially in the hospital with acute ward routines. Research procedures were successfully implemented, and according to interviews, none of the patients reported any burden, tiredness or increased stress due to the research procedures, which lasted approx. 3 hours (ranging from 135min to 209min) for each patient. Implementing the research procedures in a rehabilitation unit with stroke patients was feasible and only small adaptations were made for further research.

The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning methods such as classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The findings showed that the CART method is most accurate method (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

Considering increasingly ambitious pledges by countries, pressure from society, investors, and clients further up the supply chain, the question for companies is not so much whether to take decarbonisation action, but what action and by when. However, determining an ideal mix of measures to apply ‘decarbonisation efficiency’ requires more than knowledge of technically feasible measures and how to combine them to achieve the most economic outcome: In this paper, the author describes seven aspects which heavily influence the composition of an ‘ideal mix’ that executive leadership needs to take a (strategic) position on. These aspects consider underlying motivations and span across (socio-)economic, technical, regulatory, strategic, corporate culture and environmental factors and further underline the necessity of clarity of definitions. How these decisions influence the determination of the decarbonisation-efficient ideal mix of measures, both in principle but also in terms of specific impact is further explored by providing examples. What choices are taken by German manufacturers in several of the aspects is disclosed by insights from about 850 responses to the ‘Energy Efficiency Index of German Industry’. Knowledge of the status quo, and clarity in definitions, objectives, time frames and scope are key.

Stemming from the Bulgarian case study developed within a European research project (ECHOES, Horizon 2020), the paper discusses the links between: (a) the urgent need to operationalize EU energy transition policy in the housing domain; (b) the complexity of factors influencing the policy implementation in different contexts – geographical, economic, and technical but also social and cultural; and (c) the important role of the urban level in policy implementation. Under the specific spatial planning context of Bulgaria, the local collective energy-related decision-making in the housing field evolves through the interaction – formal (at the municipal level of governance) and informal (individuals, households and homeowners’ associations taking decisions on self-organization and collective action). The authors claim that interdisciplinary context-sensitive research would contribute to a better understanding for the ongoing energy-related decision-making processes at the local level and would enable the development and implementation of effective and efficient policy instruments in support of energy transition in the housing sector in Europe.

Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further break down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

Power law relationships, which describe scaling relationships of data, are powerful information theoretic descriptive tools in many empirical contexts, including that of music. Zipf’s law (pink noise) describes the optimum case of a power law relation where observations are exactly inversely proportional to their rank. Descriptions that approximate a pink noise signature can be said to maximize the amount of information in a signal, and is thus suggestive of a richness of an understanding. This information density of pink noise signatures is not, however, necessarily a desirable quality for explanations in general, which, by definition, “flattens out” some data while highlighting others, ideally those most relevant to an interpretation. The privileging of data most relevant to comprehension corresponds to a red noise relationship, as opposed to the pink noise of Zipf’s law or the white noise of a description that highlights nothing in particular. Here, I explore and evaluate this concept of red noise explanations in the form of analyses of three comparable short piano works: Robert Schumann’s “Von fremden Ländern und Menschen” (no.1 from Kinderszenen op. 15, 1838), Frédéric Chopin’s Prelude op. 28 no. 20 (1838-9), and Felix Mendelssohn’s “Venetianisches Gondellied” (no. 6 from Lieder ohne Worte op. 19b, 1829-30).

Trace amine-associated receptors (TAARs) are a family of G protein-coupled receptors present in mammals in the brain and in several peripheral organs. Apart from its olfactory role, TAAR5 is expressed in the major limbic brain areas and regulates brain serotonin functions and emotional behaviors. However, most of its functions remain undiscovered. Given the role of serotonin and limbic regions in some aspects of cognition, we used a temporal decision-making task to unveil a possible role of TAAR5 in cognitive processes. We found that TAAR5 knock-out (KO) mice showed a generally better performance due to a reduced number of errors and displayed a greater rate of improvement at the task than WT littermates. However, task-related parameters, such as time accuracy and uncertainty have not changed significantly. Overall, we show that TAAR5 modulates specific domains of cognition, highlighting a new role in brain physiology.

The introduction of social commerce ushered in a new era in business-consumer interaction. As a result, more power has passed from the vendor to the buyer, primarily fueling e-commerce acceptance. As a result, understanding consumer behaviour in the context of social commerce adoption has become essential for businesses looking to persuade customers by using the power of social ties and support.Furthermore, such social ties will facilitate trust as the most promising benefit while reducing perceived risk, which has always been a critical problem with online commerce. This study proposes a paradigm for understanding the impact of social commerce on the stages of the consumer decision-making process: need recognition, information search, alternative evaluation, purchase decision, and post-purchase behaviour, with a focus on social support. In this respect, relevant literature in the subject of social commerce either (1) lacks an adequate explanatory model, (2) has a solid theoretical base, or (3) contains practically complex theories with insufficient empirical data. The research model applies the Social Commerce Constructs (SCC): recommendations and referrals, forums and communities, and ratings and reviews to study the respective influence on the consumer decision-making process phases. This paper aims to understand the influence of social commerce on an integrative model that incorporates all customer choice phases while expecting new knowledge. Furthermore, it is advised that this conceptual model be empirically verified to evaluate the practical consequences.

The last few decades have witnessed a surge of interest in adventure sports, and an emerging research focus on these activities. However, recent conceptual analyses and scientific reviews have highlighted a major, fundamental question that remains unresolved: what constitutes an adventure sport (and are they ‘sports’ at all)? Despite several proposals for definitions, the field still seems to lack a shared conceptualization. This deficit may be a serious limitation for research and practice, restricting the development of a more nuanced theoretical explanation of participation and prac-tical implications within and across adventure sports. In this article we address another crucial question, how can adventure sports be better understood for research and practice? We briefly summarize previous definitions to address evident confusion and lack of conceptual clarity in the discourse. Alternatively, we propose how an ecological perspective on human behaviors, as in-teractions with the environment, may provide an appropriate conceptualization to guide and enhance future research and practice, using examples from activities such as freeride skiing / snowboarding, white-water kayaking, climbing, mountaineering and the fields of sport science, psychology and avalanche research and education. We draw on ecological dynamics as a trans-disciplinary approach to discuss how this holistic framework presents a more detailed, nuanced, and precise understanding of adventure sports.

Among many research areas to which Ron Yager contributed are decision making under uncertainty (in particular, under interval and fuzzy uncertainty) and aggregation – where he proposed, analyzed, and utilized the use of Ordered Weighted Averaging (OWA). The OWA algorithm itself provides only a specific type of data aggregation. However, it turns out that if we allows several OWA stages one after another, we get a scheme with a universal approximation property – moreover, a scheme which is perfectly equivalent to deep neural networks. In this sense, Ron Yager can be viewed as a (grand)father of deep learning. We also show that the existing schemes for decision making under uncertainty are also naturally interpretable in OWA terms.

While the link between Sensory-processing sensitivity (SPS) and internalizing symptoms has been well-established, a link to externalizing problems is still to be explored. This study aimed to further examine the relation between SPS and behavioral problems by testing the potential mediating roles of trait emotional intelligence (TEI) and decision-making styles. Pathway analyses were conducted on data from 268 community sample participants (Mage= 25.81, SD=2.41, 61.2% females). Results indicated gender differences in the pathway level outcomes of SPS, as well as potential partial mediators in men and women. SPS both directly and via the mediating effects of well-being factor (TEI) and avoidant decision-making influenced depression, regardless of gender. Direct effects on aggression were, however, obtained only in the male sample. Indirect effects of SPS on aggression were found in spontaneous decision-making for men, and in self-control and sociability factors of TEI for women. Directions for future research were discussed. sensory-processing sensitivity, depression, aggression, emotional intelligence, decision-making style, gender differences

Instead of the conventional 0 and 1 values, bipolar reasoning uses -1, 0, +1 to describe double-sided judgements in which neutral elements are halfway between positive and negative evaluations (e.g., “uncertain” lies between “impossible” and “totally sure”). We discuss the state-of-the-art in bipolar logics and recall two medieval forerunners, i.e., William of Ockham and Nicholas of Autrecourt, who embodied a bipolar mode of thought that is eminently modern. Starting from the trivial observation that “once a wheat sheaf is sealed and tied up, the packed down straws display the same orientation”, we work up a new theory of the bipolar nature of networks, suggesting that orthodromic (i.e., feedforward, bottom-up) projections might be functionally coupled with antidromic (i.e., feedback, top-down) projections via the mathematical apparatus of presheaves/globular sets. When an entrained oscillation such as a neuronal spike propagates from A to B, changes in B might lead to changes in A, providing unexpected antidromic effects. Our account points towards the methodological feasibility of novel neural networks in which message feedback is guaranteed by backpropagation mechanisms endowed in the same feedforward circuits. Bottom-up/top-down transmission at various coarse-grained network levels provides fresh insights in far-flung scientific fields such as object persistence, memory reinforcement, visual recognition, Bayesian inferential circuits and multidimensional activity of the brain. Implying that axonal stimulation by external sources might backpropagate and modify neuronal electric oscillations, our theory also suggests testable previsions concerning the optimal location of transcranial magnetic stimulation’s coils in patients affected by drug-resistant epilepsy.

The lymphatic system of the brain meninges and head plays a crucial role in the clearance of amyloid-β protein (Aβ), a peptide thought to be pathogenic in Alzheimer’s disease (AD), from the brain. The development of methods to modulate lymphatic clearance of Aβ from the brain coild be a revolutionary step in the therapy of AD. The opening of the blood-brain barrier (OBBB) by focused ultrasound is considered as a possible tool for stimulation of clearance of Aβ from the brain of humans and animals. Here, we propose an alternative method of non-invasive music-induced OBBB that is accompanied by the activation of clearance of fluorescent Aβ (Fαβ) from the mouse brain. Using confocal imaging, fluorescence microscopy and magnetic resonance tomography, we clearly demonstrate that OBBB by music stimulates the movement of Fαβ and Omniscan in the cerebrospinal fluid and lymphatic clearance of Fαβ from the brain. We propose the extended detrended fluctuation analysis (EDFA) as a promising method for the identification of OBBB markers in the electroencephalographic (EEG) patterns. These pilot results suggest that music-induced OBBB and the EDFA analysis of EEG can be a non-invasive, low cost, labelling free, clinical perspective and completely new approach for the treatment and monitoring of AD.

Decision-making is a complex process of selecting an option from the given choices by analyzing the background information like risk, loss, and gain within the alternative options presented. It has been observed in earlier studies that people are prompt to make less rational decisions when choices are given in a language less known to them. Therefore, to understand the effect of languages on decision-making, we have questioned native Hindi speakers in French and English. French being the foreign language, and English as their second language. Thus, this effect of a non-native language brings to light the important role that the native language plays routinely in judgment and decision-making. In this paper, we developed a Neuropsychological assessment to decipher the effects on decision-making between choices when given in foreign language and second language in comparison with the native language of an individual, which is termed as foreign language effect(Fle). We have explored various possible situations to understand the foreign language effect(Fle) in decision-making and does this change translates when the decision is to be made in the second language. Our study concludes that the Foreign language is least affected by the intuitive biases, followed by the second language, and the native language is most affected by it.

We are of the opinion that during the design of a binary classifier one ought to consider adding an “I don’t know” answer. We provide the case for the introduction of this third category when a human needs to make a decision based on the answer from a binary classifier. We discuss the costs and potential benefits of its introduction. Colloquially, we have used the term “I don’t know”, but formally we refer to it as NotAvailable. A procedure to define NotAvailable predictions in binary classifiers, called all leave-one-out models (ALOOM), is presented as proof of the concept. Furthermore, we discuss the potential benefits of applying ALOOM in real life applications.

This study focused on the characterization of lactic acid bacteria (LAB) in a renowned traditional Italian cheese, Pecorino di Farindola, to select an autochthonous culture and investigate its po-tential for the improvement of safety and functional properties. Two hundred and six LAB isolated throughout production and maturation from nine cheese lots of three farms were identified by 16S rRNA gene sequencing and tested for the presence of genes encoding virulence factors, vancomycin resistance (for enterococci), biogenic amines (BAs) and bacteriocin production, for antimicrobial activity, and for the capacity to survive in the gastro-intestinal tract (GIT) based on tolerance to low pH and bile salts and adhesion to CaCo-2 cells. A Lacticaseibacillus paracasei isolate was used in cheese making and determined a decline of spiked Listeria monocytogenes and Escherichia coli O157 faster than in control cheese. The autochthonous bacterial groups were numerically unaffected, apart from lactobacilli that were recovered in higher numbers in the cheese with added L. paracasei. Based on Repetitive Extragenic Palyndrome (Rep) PCR profiles, the added strain possibly dominated until day 88 in cheese. Results encourage further trials with autochthonous cultures to efficiently inhibit hazardous bacteria and enrich a functional microbiota in Pecorino di Farindola and similar cheeses.

There is a systematic exclusion of gender-based violence, safe abortion, reproductive cancers, infertility services, comprehensive sexuality education, sexuality services, and STI’s other than HIV in essential health packages in LMICs. To accelerate progress on sexual reproductive health (SRH), the Guttmacher–Lancet Commission proposed the adoption of these interventions into an essential health package of SRH services that should be universally available. In this commentary, we use a healthcare priority-setting processes lens to review the importance of these services for universal health coverage. We isolate inherent challenges in social value judgments for terminal, process and content evidence for their healthcare priority-setting. We then advance promising emerging practical examples from low to middle-income countries on evidence-informed decision-making processes. We recommend capacity development through regional support, generating equity and efficiency evidence and strengthening political and publicly acceptable processes to institutionalise and operationalise evidence-informed decision-making.

Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 minutes) and cognitive (~10 minutes) fatigue on one healthy participant. Physical load was a 3.8 km, 200 m vertical gain, trail run with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery including the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R2 0.71) and Jump Height (R2 0.78) were the most sensitive while the other tests were less sensitive (R2 values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). Best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for ‘walk up’ (MAE200 12.5%) and range of absolute error for ‘run down‘ (RAE200 16.7%). Conclusion: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.

Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20 x 20 m convex and non-convex environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment and at lower computational complexity per action. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.

Little is known about fertility preservation within the TGD community. Few receive adequate counseling placing them at risk for decision regret. The goal of this project was to develop, and pilot test the Transgender Fertility Preservation Knowledge Scale (TFPKS) to support the development and evaluation of health education resources. A community engaged; cross sectional retrospective design was used. Participants (n=189) provided information describing demographics, healthcare decision-making preferences, experiences/knowledge of fertility preservation, and treatment decision regret. The sample included 189 TGD adults. Most were white and aged 26-35 (33.3%) and not offered a consultation (73.0%). Many (41.2%) report they would have participated if offered. Knowledge regarding fertility preservation to support this desire was low. Most participants identified a patient-centered (69.4%) decision making preference. Much remains to address the healthcare inequities within the TGD population regarding fertility preservation. Overall participants had low levels of knowledge to support decision making. Further, healthcare system and individual barriers to fertility preservation remain prevalent. A foundational step towards addressing these disparities, is the identification of a valid and reliable instrument to measure TGD knowledge of fertility preservation.

This article aims to analyze music from a philosophical and theological perspective, using the principles of multi- and transdisciplinary methodology. After a brief introduction, which presents the main moments in the history of the musical phenomenon, a first chapter addresses the metaphysical dimension of music in classical composers. The second chapter shows the position of philosophers towards music, starting with Pythagoras and ending with Schopenhauer. The third chapter focuses on music theology in general, but also on the theology and metaphysics of music to the French philosopher of Romanian origin, Emil Cioran, who, after Augustin and Schopenhauer, wrote probably the deepest pages on the ontology of music. The last chapter refers to to the archetypal character of music.

The last half of the 20th century was characterized by the most impactful ecosystem changes throughout regulations and guidelines minimalizing environmental harms. Many of them emphasize the importance of enterprise sustainability for renewable energy policy. When dealing with numerous alternatives for the implementation of renewables-based policy, decision-makers need to analyze their impacts on environmental, economic, and social dimensions from a long-term perspective. The aim of this paper is to highlight a challenge to apply the multi-criteria sustainability decision-making framework (MDCA) to mainstream energy policy. The integrative framework covers an approach for the needs of analyzing factors influencing the energy technology selection. In this study, the MDCA-approach combines lifecycle-based methods, Analytical Hierarchy Process, PESTEL (Political, Economic, Social, Technological, Environment and Legal). To demonstrate the applicability and usability of the method three photovoltaic technologies are assessed. The results showed that string ribbon technology is the most sustainable along its life cycle, with a 0.503 sustainability score. The study highlighted the need to embody robust methodologies assessing sustainability-oriented technologies, not used anymore for supporting energy policies. This challenge regarding example evidence can give energy decision-makers a tool to realize their energy policy and in consequence, enhance enterprise sustainability.

In this study, the effects of music therapy on anxiety for patients undergoing regional anaesthesia in an operating room was succinctly investigated. This investigation was largely based on the adapted Hospital Anxiety and Depression Score (HADS) and Spielberger State-Trait Anxiety Inventory (STAI-S), of patients undergoing regional anaesthesia in an operating room. A randomized control trial was performed on 90 patients due for surgery. The selected patients for regional anaesthesia were allocated to either the music therapy group who listened to music using headphones for the entire surgery or the no-treatment control group. Based on the findings, it has been conclusively demonstrated that music can decrease the patient's anxiety level. According to the socio-demographic evaluation, elderly patients have the highest stress hormones levels when compared to young patients. Although elderly patients are more likely to choose religious songs to help them relax, cortisol analysis revealed an increase in cortisol levels among the elderly compared with younger patients. As a result, music is especially important to be delivered to elderly patients. Nonetheless, there is no restriction against administering music to elderly patients because evidence from the Hospital Anxiety and Depression Score (HADS) and State-Trait Anxiety Inventory (STAI-S) has shown that music helps to shift their attention away from pain and complications and makes them feel tranquil. Similarly, the HADS and modified Spielberger STAI (STAI-S) analyses demonstrate a substantial outcome for both groups, with respondents responding positively. The study found that listening to music during regional anaesthesia might help people feel less worried.

Music has the ability to evoke a wide variety of emotions in human listeners. Research has shown that treatment for depression and mental health disorders is significantly more effective when it is complemented by music therapy. However, because each human experiences music-induced emotions differently, there is no systematic way to accurately predict how people will respond to different types of music at an individual level. In this experiment, a model is created to predict humans’ emotional responses to music from both their electroencephalographic data (EEG) and the acoustic features of the music. By using recursive feature elimination (RFE) to extract the most relevant and performing features from the EEG and music, a regression model is fit and accurately correlates the patient’s actual music-induced emotional responses and model’s predicted responses. By reaching a mean correlation of r = 0.788, this model is significantly more accurate than previous works attempting to predict music-induced emotions (e.g. a 370% increase in accuracy as compared to Daly et al. (2015)). The results of this regression fit suggest that accurately predicting how people respond to music from brain activity is possible. Furthermore, by testing this model on specific features extracted from any musical clip, music that is most likely to evoke a happier and pleasant emotional state in an individual can be determined. This may allow music therapy practitioners, as well as music-listeners more broadly, to select music that will improve mood and mental health.

Outcomes of repeated decision--making processes may be affected by adversarial actors, without being noticed. Adversaries may try to gain knowledge about a particular decision--making process, identify its decision--makers, and guess which underlying decision support model is used. Then they can simulate the process, and craft different scenarios to affect its decision outcomes. Therefore, designers of decision support systems need to incorporate this in the decision modeling phase. The purpose of this study is to demonstrate this for the repeated decision--making in a patent application process. In this process, two sequential decision outcomes can be affected by adversarial actors: a company's decision to which type of patent office to send a patent request to, and the decision of a specialized patent officer to grant an application, or not. It is motivated that the company's decision--maker is \emph{bounded} rational. A theory for information--theoretic bounded rational decision--making under uncertainty proposed by Ortega et al.\ is adopted to model this type of decision--maker. A framework is provided to simulate a number of scenarios that adversaries may deploy to affect decision outcomes of a repeated patent application decision--making process. The framework is also utilized for statistically testing the presence of the scenarios, and to demonstrate how to discourage adversaries from deploying them.

As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analyzed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.

Results from a behavioral economic laboratory experiment are used to enhance our understanding of public health decisions made during the COVID-19 pandemic. The identification of systematic biases from optimal decision theory found in controlled experiments could help inform public policy design for future public health crises. The laboratory and the shelter-in-place decisions made during COVID-19 included elements of risk, uncertainty and ambiguity. The lab findings found individuals adopt different decision rules depending on both personal attributes and on the context and environment in which the decision task is conducted. Key observations to consider in the context of the COVID-19 decision environment include the importance of past experience, the ability to understand and calculate the odds of each action, the size and differences in economic payoffs given the choice, the value of information received, and how past statistical independent outcomes influence future decisions. The academic space encompassing both public health and behavioral economics is small, yet important, particularly in the current crisis. The objective of continued research in this area would be to develop a more representative model of decision-making processes, particularly during crisis, that would serve to enhance future public health policy design.

This article addresses the generic dynamic decision problem of how to achieve sustained market growth by increasing two interdependent organizational resources needed (1) to increase and (2) to sustain demand. The speed and costs of increasing each resource are different. Failure to account for this difference has been reported to lead to policies that drive a quick increase of demand followed by decline. Three generic policies derived from the literature have been implemented in a system dynamics model. Simulation shows that all three policies can generate sustained exponential growth but differ in performance. These results suggest that even policies which risk generating overshoot and collapse can avoid it. This poses two questions for further research: (1) what is the reasoning of human decision-makers when choosing between these policies and (2) how can the important but easily overlooked features of such decision situations be made sufficiently salient to be accounted for?

This paper reviews the management of the COVID-19 crisis and the difficulty of cost estimation model, comparing centralized management or bureaucratic government coaction and the agile market alternative or spontaneous social coordination. This is a study of Political Economy and Health Economics from the perspective of Austrian Economics. We describe and compare the al-ternative models, which are adapted to the current crisis. The analysis is based on the theorem of the impossibility of the economic calculation under coactive systems, and other principles of economy. In this context we pay also attention to collateral problems of the centralized and coac-tive management. Finally we propose a solution based on dynamic efficiency and the constitutions of wellbeing economics

Background: Acceleration of mass vaccination strategies is the only pathway to overcome the COVID-19 pandemic. Healthcare professionals and students have a key role in shaping public opinion about vaccines. This study aimed to evaluate the attitudes of dental students globally towards COVID-19 vaccines and explore the potential drivers for students' acceptance levels; Methods: A global cross-sectional study was carried out in February 2021 using an online ques-tionnaire. The study was liaised by the scientific committee of the International Association of Dental Students (IADS), and data was collected through the national and local coordinators of IADS member organizations. The dependent variable was the willingness to take the COVID-19 vaccine, and the independent variables included demographic characteristics, COVID-19-related experi-ence, and the drivers of COVID-19 vaccine-related attitude suggested by the WHO-SAGE; Results: A total of 6639 students from 22 countries representing all world regions responded to the ques-tionnaire properly. Their mean age was 22.06 ± 2.79 (17-40) years, and the majority were females (70.5%), in clinical years (66.8%), and from upper-middle-income economies (45.7%). In general, 22.5% of dental students worldwide were hesitant, and 13.9% rejected COVID-19 vaccines. The students in low- and lower-middle-income (LLMI) economies had significantly higher levels of vaccine hesitancy compared to their peers in upper-middle- and high-income (UMHI) economies (30.4% vs 19.8%; p < 0.001); Conclusions: The global acceptance level of dental students for COVID-19 vaccines was suboptimal, and their worrisome level of vaccine hesitancy was influenced by the socioeconomic context where the dental students live and study. The media and social media, public figures, insufficient knowledge about vaccines, and mistrust of governments and the pharmaceutical industry were barriers to vaccination. The findings of this study call for further implementation of epidemiology (infectious diseases) education within undergraduate dental curricula.

We live in the time of profound transformations commonly labelled with the word “globalization”. The rise of one ecological-technological-social system encompassing our whole planet is an important element of these processes. Solving big global problems demands knowledge of two complementary sorts: on the one hand – going “in depth”, on the other – going “in breadth”. The present paper assumes the second (in a sense: philosophical) perspective. It tries to analyze some relations between the development of technology (IT) and the development of democracy. The notion of democracy, its various forms and axiological reasons for it are considered first. In the subsequent chapter different consequences (both positive and negative) the IT development has for contemporary democracy are discussed. In the next chapter the evolutionary nature of the technological development is debated as well as the question of (democratic) control of this process. The development of Artificial General Intelligence is presented as a challenge for democracy

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Offshore vessels (OVs) often requires precise station-keeping and some vessels, for example, vessel involves in geotechnical drilling generally use Spread Mooring (SM) or Dynamic Positioning (DP) systems. Most of these vessels are equipped with both systems to cover all ranges of water depths. However, determining which systems to use for a particular operational scenario depends on many factors and requires significant balancing in terms of cost-benefit. Therefore, this research aims to develop a platform that will determine the cost factors for both the SM and DP station keeping systems. Operational information and cost data are collected for several field operations, and Artificial Neural Networks (ANN) is trained using those data samples. After that, the trained ANN is used to predict the components of cost for any given environmental situation, fieldwork duration and water depth. Later, the total cost is investigated against water depth for both DP and SM systems to determine the most cost-effective option. The results are validated using two operational scenarios for a specific geotechnical vessel. This decision-making algorithm can be further developed by adding up more operational data for various vessels and can be applied in the development of sustainable decision-making business models for OVs operators.

Multi-criteria decision-making (MCDM) methods and techniques have been applied to many real-world problems in different fields of engineering science and technology. The evaluation based on distance from average solution (EDAS) method is a new and efficient MCDM method. The aim of this study is to propose a modification to address two exceptional cases in which the EDAS method fails to solve an MCDM problem.

Business school curriculums are designed to improve business skills and a student’s eventual workplace performance. In addition to these business skill sets the emerging business environment demands softer skills associated with ethical decision-making and sustainable business practices. Understanding the key influencers of ethical orientation and attitudes towards the environment is the first critical step for curriculum planning designed to develop both ethical decision-making and environmental sensibilities of students in business schools. Using a bivariate regression analysis (OLS) that compared the established New Ecological Paradigm (NEP) scale and the newly introduced Ethical Orientation Scale (EOS), this study assesses environmental eco-consciousness and ethical orientation over time and across varying socio-demographic variables. The study shows first, that in addition to socio-cultural variables, situational factors influence ethical decision-making. Secondly, it illuminates that ethical orientations as measured by the EOS predicts beliefs about the environment as measured by the NEP scale. It further provides evidence of the ethical underpinnings of the New Ecological Paradigm as well as provides initial validation for the new EOS. These outcomes provide additional levers to assist business educators in the creation of high impact teaching strategies to measure and encourage ethical decision-making and sustainable business practices that protect the environment.

The new Post Accelerating Data and Knowledge Online Society, or ‘Padkos’ requires a new model of decision making. This introductory paper proposes a model where decision making and learning are a single symbiotic process, incorporating man and machine, as well as the AADD (ánthrōpos, apparatus, decider, doctrina) diamond model of individual and organizational decision-making and learning processes. The learning is incorporated by using a newly proposed quadruple loop learning model. This model allows for controlled changes of identity, the process of creating and the sense making of new mental models and assumption, and reflections. The model also incorporates the recently proposed model of quantum decision-making, where time collapse of the opted past and the anticipated future (explicitly including its time horizon) into the present play a key role in the process, leveraging decision-making and learning by human as well as Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The paper closes with conclusions.

Background: In the midst of a worldwide COVID-19 pandemic music therapists previously not involved in telehealth had to develop effective remote forms of music therapy. The objective of this review was to systematically explore how music therapists previously working in-person adapted to the transfer to remote forms of therapy in the context of the coronavirus outbreak. Methods: We searched Scopus, Web of Science Core Collection, CINAHL, Medline, ProQuest Central, PubMed, EMBASE, PsycINFO and PsyARTICLES, grey literature (October 2020), and websites of professional organizations. We followed the JBI methodology for scoping reviews. Results: Out of the 194 screened texts, we included ten very heterogenous articles with an overall very low quality. Most texts described remote therapy in the form of synchronous video calls using the Internet, one paper described a concert in a patio of a residential home. We report the authors´ experience with the adaptation and activities, challenges and benefits of remote forms of therapy, recommendations of organizations, and examples and tips for online therapies. Conclusions: Music therapists have adapted the musical instruments, the hours, the technology used, the therapeutic goals, the way they prepared their clients for sessions, and other aspects. They needed to be more flexible, consult with colleagues more, and mind the client-therapist relationship's boundaries. It seems, when taken as a necessary short-term measure, online music therapy works sufficiently well. The majority of papers stated that benefits outweighed the challenges, although many benefits were directly linked with the pandemic context.

The purpose of the study was to provide support in the decision-making process for architects and engineers regarding large-spans structures for the presentation of archaeological sites in situ - construction of roofs, shelters, and bridges. We examined existing practice and analyzed their engineering classifications looking for a pattern in their application regarding sustainability and relation between type of cultural heritage site and type of applied large span structure. Contemporary engineering structures at built heritage sites create a sharp contrast between old and new. A presentation of cultural heritage in situ requires an understanding of heritage theory and internationally accepted doctrine which exceeds common engineering education. Nevertheless, application of large-span structures, which often take advantages of state-of-art construction-technologies nowadays, is also an aesthetical statement that affects the appearance of the site. Therefore, we gave an overview of the theoretical background of aesthetical issues and the overall ethics of the decision-making process in such sensitive cases. Within the framework of heritage-presentation, engineering and architectural issues, and selected case studies, we concluded in favor of the application of large-span structures under certain conditions.

We present the mathematical model of cooperative functioning of unconscious and consciousness. The model is based on the theory of open quantum systems. Unconscious and consciousness are treated as bio-information systems. The latter plays the role a measurement apparatus for the former. States of both systems are represented in Hilbert spaces. Consciousness performs measurements on the states which are generated in unconscious. This process of unconscious-conscious interaction is described by the scheme of indirect measurements. This scheme is widely used in quantum information theory and it leads to the theory of quantum instruments (Davis-Lewis-Ozawa). Our approach is known as quantum-like modeling. It should be sharply distinguished from modeling of genuine quantum physical processes in biosystems, in particular, in the brain. In the quantum-like framework, the brain is a black box processing information in the accordance with the laws of quantum theory. During the last 10-15 years this framework has been actively used in cognition, psychology, decision making, social and political sciences. The quantum-like scheme of unconscious-consciousness functioning has already been explored for sensation-perception modeling.

This is a short introductory review on quantum-like modeling of cognition with applications to decision making and rationality. The aim of the review is twofold: a) to present briefly the apparatus of quantum information and probability theory useful for such modeling; b) to motivate applications of this apparatus in cognitive studies and artifical intelligence, psychology, decision making, social and political sciences. We define quantum rationality as decision making that is based on quantum information processing. Quantumly and classically rational agents behaves differently. A quantum-like agent can violate the Savage Sure Thing Principle, the Aumann theorem on impossibility of agreeing to disagree.Such an agent violates the basic laws of classical probability, e.g., the law of total probability and the Bayesian probability inference. In some contexts, ``irrational behavior'' (from the viewpoint of classical theory of rationality) can be profitable, especially for agents who are overloaded by a variety of information flows. Quantumly rational agents can save a lot of information processing resources. At the same time, this sort of rationality is the basis for quantum-like socio-political engineering, e.g., social laser. This rationality plays the important role in the process of decision making not only by biosystems, but even by AI-systems. The latter equipped with quantum(-like) information processors would behave irrationally, from the classical viewpoint. As for biosystems, quantum rational behavior of AI-systems has its advantages and disadvantages. Finally, we point out that quantum-like information processing in AI-systems can be based on classical physical devices, e.g., classical digital or analog computers.

The onset of coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked unprecedented change. Due to the public health guidelines imposed during the COVID-19 pandemic, there is no longer sufficient street traffic for remaining buskers to generate sufficient revenue, leading a majority of street musicians to pursue remote music production. However, real-time music production is notoriously difficult due to the excessively high latencies that current video call platforms such as Zoom and Google Meet harbor. In this paper, we propose an architecture for a platform with end-to-end, near-lossless audio transmission tailored specifically to online joint music production, called Latent Space. We discuss the usage of a recurrent autoencoder with sequence-aware encoding (RAES) and a 1D convolutional layer for audio compression, which we dub ClefNet, as well as propose a new evaluation metric for naive autoencoders (AEs), MSE-DTW loss, which combines the traditional mean square error (MSE) loss function with dynamic time warping (DTW) to prevent an increase in loss when the target sequence predicted by the AE is strictly a temporal variation of the source sequence. Moreover, we detail the logistics of a live system implementation which uses the Web Audio API to extract raw audio samples in real-time to feed into our client-side model before relaying the traffic using peer-to-peer WebRTC technology. The Latent Space platform can be accessed at https://latent-space.tech, and the code and data can be found under the MIT License at https://github.com/rvignav/ClefNet.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

Re-creation of the past of historical buildings sits at the intersection of the spatio-temporal manifestation of cultural memories, socio-cultural meanings, values and identity re-moulds and refines the existing understanding and sense of place. Digital technologies have become a popular tool in re-creation of the past by creating a new body of knowledge and historical discourse based on identifying the gaps within our written histories. Designers and policymakers around the world have been exploring various tools and technologies such as diachronic modelling yet there is a gap in evidence-based understanding regarding the actual functioning and success of application for place making. This paper, therefore, sets out to scrutinise the role of digital technologies in facilitating digital place making. To do so, it investigates the potential of a new “digital heritage” narrative in the revival of the lost architectural narrative of the Dennys Lascelles Wool Store, Geelong. The proposed paper aims to investigate the potential of a new “digital heritage” narrative and story-telling as a means towards digital place making framework. While exploring the new and unique capabilities provided by the digital narrative in capturing, simulating and disseminating ‘lost’ heritage it will further imbue a sense of place by connecting the everyday city dweller.

Clearly policy makers should consider the impacts of any decisions they might make before making them. Science can provide estimates of various economic, ecologic, environmental, and even social impacts of alternative policies, impacts that determine how effective any particular policy will be. These impact estimates can be used to compare and evaluate alternative policies in the search for identifying the best one to implement. Among all scientists providing inputs to policy making processes are analysts who develop and apply models that provide these estimated impacts and, possibly, their probabilities of occurrence. But just producing them is not a guarantee that they will be considered by policy makers. This paper discusses ways scientists, including systems analysts, can effectively contribute to and inform those involved in making water management decisions. Brief descriptions of a variety of past and on-going water management policy making processes illustrate both some successes and failures of science informing policy.

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the Multiple Criteria Decision Making (MCDM) method TOPSIS-2N to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and / or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with a MCDM method to evaluate companies to be incubated does not exist in the literature.

Background We intend to examine whether the COVID-19 outbreak influences medical decision-making (MDM) among Non-COVID patients. Method We recruit 287 patients who admit to ER department due to cardiovascular complaints. Anxiety level was measured using three questionnaires (GAD-7, Beck Inventory, and the cardiac anxiety questionnaire). A fourth survey was designed to assess MDM considerations. Results 64% of patients were male (median age 54). Almost half of the patients were found to have moderate to severe levels of anxiety.79.3% of patients reported that the outbreak influenced their MDM. 44.5% of patients sought medical care 2-3 from the onset of symptoms. Coronary artery disease was found in only 26 patients (9.1%). Almost half of the patients stated that they would have gone earlier if not for the current pandemic. Conclusion Non-COVID patients seeking medical care had a high anxiety level that directly affected decision-making and put them at unnecessary risk.

This conceptual, interdisciplinary paper will start by introducing the commencement of a new era in which human society faces continuously accelerating technological revolutions, named for short ‘Padkos’. In this context, a conceptual model of sustainable development with a focus on knowledge sharing and management will be proposed. The construct of knowledge management will be unpacked into a three-layer model with a focus on the knowledge-human and data-machine spheres. Then, each sphere will be discussed with concentrating on the learning and decision- making processes, the digital supporting systems and the human actors’ aspects. Moreover, the recombination of new knowledge development and contemporary knowledge management into one amalgamated construct will be proposed. The holistic conceptual model of knowledge sharing for sustainable development is comprised by time, cybersecurity and two alternative humanistic paradigms (Homo Technologicus and Homo Sustainabiliticus). Two additional particular models are discussed in depth. First, a recently proposed model of quantum organizational decision-making is elaborated. Next, a boundary management and learning process is particularized. The paper ends with several implications for the future based on the deliberations in the paper and the models discussed.