This study analyzed the relationship between contextual factors, personal factors, and entrepreneurial intentions of business area students from the perspective of the Theory of Planned Behavior. Data were collected using a questionnaire applied to 229 entering the courses, and the analysis was carried out using Structural Equation Modeling (PLS-SEM). The results demonstrated that: a) H1a: education had a positive influence on the choice of an entrepreneurial career, and b) H1b: business-friendly environments reinforce this choice, influenced by broader social and cultural support. This demonstrates that environmental, factors enhance cognitive factors and the intention to undertake. The results also showed that: c) H2c: the perception of abilities to start a business, and d) H2a: the favorable evaluation of choosing an entrepreneurial career, are determining elements of entrepreneurial intentions in the studied context. However, the negative relationship between e) H1c: government incentives and the ability to start a business, and f) H2b: the influence of social and environmental on entrepreneurial intentions requires attention from universities and governments in promoting environments conducive to entrepreneurship, the choice of an entrepreneurial career and the decision of young people to get involved in sustainable business projects. In conclusion, environmental and psychological aspects have positive and significant effects on the entrepreneurial career intentions of higher education students, opening space for governments and universities to promote student entrepreneurship with the sustainability business as beliefs and values.

Every firm is devoted to polishing and fine-tuning its maintenance strategy. More exactly, when we develop robust and efficient preventive and conditional maintenance approaches, our objective is to combine performance and robustness to increase maintenance decision-making. This, in turn, leads to a reduction in system failures and a subsequent drop in related expenses. This impulse drives the origin of this essay: to provide fresh criteria that allow us to improve and optimize maintenance techniques. Furthermore, our study has been realistically utilized via the deployment of condition-based maintenance (CBM) techniques, which are commonly regarded as a critical lever that allows firms to gain a competitive advantage in the context of the fourth industrial revolution. Combining both perfect and imperfect maintenance actions into CBM strategies appears to be an effective technique to obtain high economic performance and robustness in the maintenance strategies of the companies. Following this idea, we propose and optimize in this paper a set of criteria enabling the combined assessment of the mean economic performance and the resilience of various kinds of maintenance techniques. The advantage of the proposed criterion is that it adapts to different types of maintenance strategies and provides access to a simple and relevant evaluation model. Thanks to this new criterion, we will be able to choose the better maintenance strategies that have more performance and robustness between these strategies for a system with more or less stable behavior. The Monte Carlo Method is incorporated into the comparative assessment of maintenance methods, adding a layer to the evaluation of the performance and robustness of each adaptation in decision-making.

This research challenges the traditional perspective on consumer behavior, which focuses on external factors shaping the sequential stages of decision-making. Instead, it proposes a paradigm shift by treating the decision-making process itself as an independent variable, exploring its impact on various outcomes like purchasing behavior, brand loyalty, and post-purchase satisfaction. This novel approach offers deeper insights into the intricacies of consumer behavior, particularly in the digital age. By uncovering the reciprocal relationships between the decision-making process and other variables, the research provides a more nuanced understanding of how consumers navigate today's complex landscape. It further delves into how variations in the decision-making process across diverse consumer groups influence various outcomes, recognizing the unique characteristics and needs of different demographics. The research also acknowledges the interplay of psychological, social, and contextual factors that shape both the decision-making process and its downstream effects, offering a comprehensive understanding of the multifaceted influences on consumer behavior. Ultimately, by studying the decision-making process as an independent variable, this research aims to equip businesses and researchers with valuable insights, enabling them to better navigate the ever-evolving world of consumer behavior.

Background: Since 2000, vaccine coverage in Sub-Saharan Africa (SSA) has surpassed multiple milestones. Its contribution to global health, especially in low-middle-income countries is one of the achievements in global governance of modern medicine, averting 2-3 million child deaths every year. However, in Nigeria, vaccine-preventable-diseases still account for one in eight child deaths before their fifth-year birthday and remains one of the ten countries where 4.3 million children under five are without complete immunization. The reasons for declining childhood vaccine demand are unclear. Therefore, the goal of this contribution is to shed light on the reasons to set a foundation for future interventions. Methods: Four focus group discussions were conducted. The primary targets were mothers of children 0 – 12 months old in Nigeria. A simplified quota sampling approach was used to select mothers in four geographical clusters of Nigeria’s Federal Capital Territory. At least six mothers from each cluster were randomly included, giving a total of 24 participants. An interview guide developed from the 5C psychological antecedence model was used (assessing confidence, complacency, calculation, constraints, collective responsibility); two additional variables were included that had proved meaningful in previous work (religion and masculinity). The data were analyzed using meta-aggregation approach such as framework synthesis, which summarized data in a stepwise fashion. Results: The sample was generally relatively positive towards vaccination. Still, mothers reported low trust in vaccine safety and the healthcare system (confidence). Yet, they had great interest in seeking additional information during antenatal visits (calculation), difficulties in prioritizing vaccination over other equally competing priorities (constraints) and were aware that vaccination translates into overall community health and wellbeing (collective responsibility). They had a bias towards God as ultimate giver of good health (religion) and reported that their husbands played a dominant role in vaccination decision-making (masculinity). Mothers perceived their children vulnerable to disease outbreaks, which motivated them to get them vaccinated (complacency). Conclusion: The 5C model and the added determinants provided a useful qualitative tool for understanding mothers’ vaccination decision-making in low resources settings.

This paper looks at key elements of global culture that are driving a new paradigm shift in museums causing them to question their raison d'être, their design and physical space, recognizing the need to accommodate visitor interaction and participation, and to reprioritize institutional outcomes and goals reexamining their priorities. As heritage sharing in online spaces reaches across national, political, and social boundaries on platforms and networks, this has been driven by museum engagement with Internet life during the pandemic. Museum relationships and interactions with communities both local and global, continue to challenge core values and precepts, leading to radical changes in how museums define their roles and responsibilities. In this new cultural landscape, museums are responding to human digital identity in a tidal wave of human interactions on the Internet, from social media to online sharing of images and videos. This is revealing shared perspectives on cultural conflict as being tied to freedom of expression of one’s heritage embedded in digital identity.

We propose a phenomenological theory of spin behavior in a magnetic field, which explains from the point of view of classical physics the two-valued result of the Stern-Gerlach experiment. The behavior of the spin and intrinsic magnetic moment of an electron wave of an atom in an external magnetic field is considered. We show that in a weak magnetic field, the intrinsic magnetic moment of an electron wave is always oriented parallel to the magnetic field strength vector, while in a strong magnetic field, depending on the initial orientation of the intrinsic magnetic moment, two orientations are realized: either parallel or antiparallel to the magnetic field strength vector. Within the framework of classical electrodynamics, the calculation of the motion of an atomic beam in an inhomogeneous magnetic field is carried out, which reproduces the results of the Stern-Gerlach experiment.

Information is commonly considered as a mathematical quantity that forms the basis of computing. In mathematics, information can propagate instantly, so its transfer speed is not the subject of information science. In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so the information’s propagation speed cannot exceed the speed of the carrier. Because of this limitation, for any implementation, one must consider the transfer time between computing units. We need a different mathematical method to take this limitation into account: classic mathematics can only describe infinitely fast and infinitely small computing system implementations. The difference between the mathematical handling methods leads to different descriptions of the behavior of the systems. The correct handling also explains why biological implementations can have lifelong learning and technological ones cannot. The conclusion about learning evidences matches others’ experimental evidence, both in technological and biological computing.

Let b∈Cn∖{0} be a fixed direction. We consider slice holomorphic functions of several complex variables in the unit ball, i.e. we study functions which are analytic in intersection of every slice {z0+tb:t∈C} with the unit ball Bn={z∈C:|z|:=|z|12+…+|zn|2<1} for any z0∈Bn. For this class of functions there is introduced a concept of boundedness of L-index in the direction b where L:Bn→R+ is a positive continuous function such that L(z)>β|b|1−|z|, where β>1 is some constant. For functions from this class we describe local behavior of modulus of directional derivatives on every ’circle’ {z+tb:|t|=r/L(z)} with r∈(0;β],t∈C,z∈Cn. It is estimated by value of the function at center of the circle. Other propositions concern a connection between boundedness of L-index in the direction b of the slice holomorphic function F and boundedness of lz-index of the slice function gz(t)=F(z+tb) with lz(t)=L(z+tb). Also we show that every slice holomorphic and joint continuous function in the unit ball has bounded L-index in direction in any domain compactly embedded in the unit ball and for any continuous function L:Bn→R+.

Vaccines decrease morbidity and mortality. Nevertheless, their benefits depend on public response. During COVID-19, vaccine hesitancy and refusal were rampant, threatening public health. A thorough understand-ing of opponents' arguments is required to address the diffusion of unreliable information on social media and prevent vaccine hesitancy from developing into vaccine refusal. Accordingly, the article examines vac-cine opponents' justifications on social media. Textual content analysis of reader comments on three health-related Israeli Facebook pages was conducted. Data collection encompassed the Israeli COVID-19 vaccination period from October 2020 to May 2022. The comments were analyzed according to the health beliefs model (HBM). We found that vaccine opponents were characterized by low perceptions of the severity of the disease combined with high perceptions of the damages of the vaccine; low perceived benefits of vac-cine compliance; vaccine hesitancy and fear along with public distrust as barriers to change; and call for ac-tion to resist the vaccine and spread related anti-establishment views on the web. As vaccination hesitancy was found to develop into public distrust in the state systems and escalate into conspiracy beliefs and an-ti-vaccination activism, its early detection may prevent future vaccine resistance.

The interrelatedness of social-structural aspects and psychological features with the vaccination intention provides the context to explore personal psychological fea-tures related to general decision-making and vaccine-related dispositions, and their contribution to the intention to vaccinate, within post-pandemic circumstances, after the imposed possibility of choosing a vaccine brand. The aim of our study was to map the function (promotive, protective, risk, vulnerability) of a set of psychological aspects in the intention to vaccinate, in people with different social roles regarding the vaccination. We surveyed three samples of people: healthcare providers (HPs), parents, and lay people, within the context of the after-pandemic. Negative vaccine attitudes lower intention to vaccinate in all regression models (all βs ranging from -.128 to -.983, all ps < .01). The main results indicate that, regardless of the sam-ple/social role, there is a shared attitudinal core for positive vaccination intention. This core consists of [high] trust in large corporations, government, and healthcare systems, as well as perceived consensus on vaccine safety/efficacy and experience of freedom (protective factors), and [low] vaccination conspiracy beliefs, trust in social media, and choice overload (risk and vulnerability factors, respectively). There are no common promotive factors of intention to vaccinate; for parents, perceived consensus on vaccines, and trust in corporations and the healthcare system play such roles, for HPs experience of freedom is obtained as a unique promotive factor. In contrast, for laypeople, no unique promotive factors were found. These results and guidelines for the promotion of positive vaccination behavior are further discussed.

Interference fits are a very common shaft-hub connection due to their low manufacturing cost and excellent technical properties. Plastic Conditioning of this machine element is a new and not very well known method. By exploiting residual stresses and the associated increase in the yield point, these components can absorb operating loads such as rotating bending moments, torsion, temperature changes and centrifugal forces purely elastically and avoid plastic deformation during operation. Compared to conventionally joined interference fits, the load bearing capacity in the elastic range can be increased by almost 200% and a specifically defined additional safety against plastic deformation in the elastic-plastic range can be ensured. This paper examines the effects of Reverse Yielding on the technology of Plastic Conditioning of Interference Fits in Power Transmission Engineering. Based on the Shear Stress Hypothesis (SH), the Plane Stress State (PSS) and the ideal plastic behavior of materials, the stress-mechanical relationships are explained, the influencing parameters are examined and conclusions are drawn for the plastic conditioning process. Taking into account the theoretical knowledge according to the state of the art, modified material behavior assumptions (isotropic hardening) and the Von Mises Yield Criterion (VMYC) are also considered. In addition, the method of plastic conditioning of interference fits is introduced and its basic principles are briefly explained. Along with computational suggestions to avoid Reverse Yielding, open issues requiring further research are identified.

Proactivity is a particularly important attribute of knowledge-intensive organizations, where the work required to increase the potential of knowledge-intensive employees is crucial. A priority challenge for these firms is also to understand the increasing importance of the functioning of cognitive mechanisms leading to increased knowledge workers' well-being following the implementation of the psychological contract. The aim of this article is to identify the relationship between inclusive leadership, the fulfillment of the psychological contract, two dimensions of well-being: workplace and life well-being and knowledge workers' proactivity. Based on survey data collected through the CAWI method from 1.000 knowledge workers employed in Polish companies in the business services sector, the research hypotheses were tested using a stepwise equation modelling (SEM) technique, leading to a model containing all the main constructs. The results obtained indicate that inclusive leadership positively impacts the fulfillment of the psychological contract. Furthermore, the fulfillment of the psychological contract positively affects proactive working behavior with and the wellbeing of knowledge workers. Along with proactive work behavior, two dimensions of well-being, were examined as outcome variables. The analysis also shows that knowledge-intensive organizations, intending to build the proactivity of their employees and nurture a high level of well-being in their lives and in the workplace, should take care to fulfil the expectations and obligations of the psychological contract. However, one way to achieve this is for managers to use an inclusive management style, which supports an atmosphere of a safe working environment in a diverse setting and leads to employees feeling comfortable sharing their opinions and ideas. The study of inclusive leadership in the context of knowledge-intensive organizations provides human resource professionals and employee man-agers with important insights into how inclusive leadership can effectively contribute to the psychological contract, which will consequently lead to proactive work behavior and also improve employees' workplace and life well-being.

In the framework of the self-consistent Maxwell-Pauli theory, the non-linear Pauli equation is obtained. Stationary and nonstationary solutions of the nonlinear Pauli equation for the hydrogen atom are studied. We show that spontaneous emission and the related rearrangement of the internal structure of an atom, which is traditionally called a spontaneous transition, have a simple and natural description in the framework of classical field theory without any quantization and additional hypotheses. The behavior of the intrinsic magnetic moment (spin) of an electron wave in an external magnetic field is considered. We show that, according to the self-consistent Maxwell-Pauli theory, in a weak magnetic field, the intrinsic magnetic moment of an electron wave is always oriented parallel to the magnetic field strength vector, while in a strong magnetic field, depending on the initial orientation of the intrinsic magnetic moment, two orientations are realized: either parallel or antiparallel to the magnetic field strength vector.

Financial Payment has evolved into mobile payment for easy access and convenience. Despite the benefits of mobile payment, the adoption rate is unsatisfactory worldwide. Therefore, understanding the customer's adoption intention factors is essential for both researchers and practitioners. This study examines mobile payment intention and actual use by adapting the integrated framework, which combines the Mobile Technology Acceptance Model and the Theory of Planned Behaviour with additional new constructs found to be related. The new model consists of 12 variables moderated by the individual's experience and is examined via a structured PLS equation mixed-mode methodology. Knowing mobile payment influence will shape the industry strategic decision and socially contribute to transforming society into a cashless society. This paper provides an opportunity to prove the relationship between technology variables and human behaviour concerning mobile payment adoption. In contrast, this study novelty adopted an integrated model that combines MTAM with the TPB.

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

Understanding behavioral factors associated with obesity is of importance in addressing this issue. This study examined the association between cognitive restraint, uncontrolled eating, emotional eating and body mass index (BMI) and amount of food plated, consumed, leftovers, and leftover food thrown into the trash (food wasted) in early adolescent girls nine to 13 years in O’ahu, Hawai’i (n = 93). Food plated, consumed, leftovers, and food wasted were estimated using a three-day mobile food record (mFR). Weight and height were measured to compute BMI (kg/m2). The three-factor eating questionnaire provided a score from 0 to 100 for cognitive restraint, uncontrolled eating, and emotional eating. Higher scores are indicative of greater cognitive restraint, uncontrolled eating, and emotional eating. Pearson’s correlation and general linear models were computed to examine the relationship between three factor eating scores, BMI, and food plated, consumed, leftovers, and food wasted. There was no clinically significant association between cognitive restraint and amount of food wasted. Cognitive restraint was positively correlated with BMI (r=0.36, p<0.001) and with BMI z score (r=0.40, p<0.001). Uncontrolled eating and emotional eating were positively correlated with amount of leftover food at dinner (r=0.30, p=0.006; r=0.33, p=0.003, respectively). Emotional eating was positively associated with percentage of leftover food at dinner (r=0.24, p=0.30). Additional research should examine the specific roles of cognitive restraint, uncontrolled eating, emotional eating and food waste in the development of obesity in adolescents.

E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations’ influence on customer clicks and buys, three target areas—customer behavior, data collection, user-interface —will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations’ true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget’s positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer’s interactions influenced by the recommendation model in the context of standard industry metrics such as Click-through rates, Buy-through rates, and Conversion revenue.

Full-order state-space models represent the starting point for the development of advanced control methods for wind turbine systems (WTSs). Regarding existing control-oriented WTS models, two research gaps must be noted: (i) There exists no full-order WTS model in form of one overall ordinary differential equation that considers all dynamical effects which significantly influence the electrical power output; (ii) all existing reduced-order WTS models are subject to rather arbitrary simplifications and are not validated against a full-order model. Therefore, in this paper, two full-order nonlinear state-space models (of 11th and 9th-order in the (a, b, c)- and (d, q)-reference frame, resp.) for variable-speed variable-pitch permanent magnet synchronous generator WTSs are derived. The full-order models cover all relevant dynamical effects with significant impact on the system’s power output, including the switching behavior of the power electronic devices. Based on the full-order models, by a step-by-step model reduction procedure, two reduced-order WTS models are deduced: A non-switching (averaging) 7th-order WTS model and a non-switching 3rd-order WTS model. Comparative simulation results reveal that all models capture the dominant system dynamics properly. The full-order models allow for a detailed analysis covering the high frequency oscillations in the instantaneous power output due to the switching in the power converters. The reduced-order models provide a time-averaged instantaneous power output (which still correctly reflects the energy produced by the WTS) and come with a drastically reduced complexity making those models appropriate for large-scale power grid controller design.

Molecular dynamics simulations were performed to examine the impact of temperature (ranging from 250K to 350K) on the structural behavior of cellulose chains when dissolved in an aqueous cuprammonium hydroxide Cuam solvent system. By monitoring the root mean square deviation (RMSD) and radius of gyration (Rg) values at each temperature, we found that the geometric deformation of the cellulose chains changed significantly at T=300K. In contrast, Rg and RMSD values remained stable at temperatures in the range of 250K, 270K and the range of 330K, 350K. Calculating the interaction energy between the solvent and cellulose chains revealed that temperature significantly influences the cellulose dissolution process in the Cuam solvent system and that T=300K is an efficient temperature for its dissolution. The number of intra- and interchain hydrogen bonds was also calculated as a function of temperature, and the analysis confirmed that there is no breakage of these bonds at temperatures below 300 K (i,e: 250K, 270K) and above 310 K (I,e, : 330K and 350K) either between two native chains or between a native chain and another derivative.

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

For a linear ordinary differential equation (ODE in short) of the third order, results are presented that supplement the theory of conjugate points and extremal solutions by W. Leighton, Z. Nehari, M. Hanan. It is especially noted the sensitivity of solutions to the initial data, which makes their numerical study difficult. Similar results were obtained for the third-order nonlinear equations of the Emden-Fowler type.

The assessment of welfare and disease progression in animal models is critical. Most tools rely on evaluating individual subjects, whereas social behaviors, also sensitive to acute illness, chronic diseases, or mental health, are scarcely monitored because of their complexity, are invasive, and time-consuming. We propose the evaluation of social nesting, a species-typical behavior naturally occurring in standard housing conditions, for such behavioral monitoring. We provide an example of its use to evaluate social deficits and the long-term effects of neonatal sensorial stimulation in male and female adult 3xTg-AD mice for Alzheimer's disease compared to sex- and age-matched NTg counterparts with normal aging. Social nesting was sensitive to genotype (worse in 3xTg-AD mice), sex (worse in males), profile, and treatment (distinct temporal patterns, time to observe the maximum score and incidence of the perfect nest). Since social nesting can be easily included in housing routines, this neuroethological approach can be useful for animal's welfare, monitoring the disease's progress, and evaluating potential risk factors and effects of preventive/therapeutical strategies. Finally, the non-invasive, painless, simple, short time and low-cost features of this home-cage monitoring are advantages that make social nesting feasible to be successfully implemented in most animal department settings.

The advanced industrial systems are characterized by complex event-driven processes. The specification and computer analysis are illustrated by the common problem of a servo drive with the PWM controller. A mathematical model from the class of Cauchy problems with non-liner control logic is presented to resolve the task discussed. The designed model is performed in two stages: the mathematical model specification (modeling) and implementation (simulation). Two approaches to make a model specification are considered, the first one is the traditional structural technique, and the advanced method is based on the finite state machine paradigm. Structural and finite state software models are built in progressive simulation tools. Computer implementation of the software models of hybrid systems is performed using platform built-in libraries with numerical methods and algorithms for correct event detection. The results of computational experiments for transient processes of phase variables are qualitatively identical, that proves the adequacy of the developed models. The algorithm for asymptotic event detection is considered in detail. This technique demonstrated the best results by the one-sidedness criterion, which is explained by using the author’s algorithm for the integration step determining and the event function dynamics monitoring in the vicinity of the event limits.

Obesity in adolescents combined with the increased exposure time to electronic devices, enhances a sedentary lifestyle and is linked to the risk of developing musculoskeletal injuries. The aim of this study was to identify possible correlations between anthropometric characteristics, body composition, exposure time to electronic devices or screen time (ST) and physical activity (PA) level along with muscle power and dynamic knee valgus (DKV) in young athletes to avoid future injuries. A total of 125 adolescent basketball athletes (66.4% boys), 12 to 17 years old (13.94±1.58 years) participated. Anthropometric and demographic characteristics were recorded. PA level was assessed through IPAQ and ST through a single question from the HELENA study. The Counter Movement Jump test was used for the evaluation of the lower limb muscle power, while the Single Leg Drop Jump test was used for the evaluation of the dynamic knee valgus via two-dimensional (2D) kinematic analysis. The Pearson index showed a statistically significant positive correlation between muscle power and height (r=0.788, p<0.001), while the Spearman index showed a smaller but equally statistically significant positive correlation with BMI (rs =0.651, p<0.001) and age (rs=0.579, p<0.001). The ST (rs= -0.194, p=0.03) showed a weak negative correlation with DKV, while PA (r=0.85, p<0.001) showed a high, statistically significant, positive correlation. The anthropometric characteristics of adolescent athletes can determine their ability to produce muscle power. A high level of physical activity could play a key role in compensating and thus reducing the negative effects of high exposure to screen time on the dynamic knee valgus

Eco-innovation denotes developing green practices using environmentally friendly innovative approaches or technologies. Although, eco-innovation has been broadly applied in different in-dustries, such as low-carbon production and manufacturing, how to implement such innovation in education for sustainable development (ESD) has rarely been studied. Therefore, this research considered a reflective and cooperative learning approach to science, technology, engineering, arts, and math (STEAM) education. A case study examined Wanluan Township of Pingtung County in southern Taiwan. Four departments’ students and teachers at Meiho University were involved in situated learning. Students of hospitality management played farm owners who en-gaged in organic agriculture to produce food and beverages. These agricultural products were farm-to-table, cooked and served for customers in a natural dining setting through the students’ teamwork. Students of tourism, sports & leisure management, and food science & nutrition played tourists in a self-guided travel context, who engaged in acts such as visiting buildings to understand heritage while observing that the houses were still in use a dwelling. This encouraged reflection on the importance of cultural preservation. According to the results from the role-play farm owners and tourists, eco-innovation can represent a sustainable marketing strategy for im-proving the local-community economy and can be implemented in a practical situation in STEAM. The goal of ESD for 2030 — societal transformation — is to foster students’ responsible behavior and attitudes in a personally authentic manner, thereby fostering sustainability learning and understanding.

Improving the effectiveness of health interventions is a major challenge in public health research and program development. A large body of literature has found low or no impact of health education and promotional interventions. We aim to develop a conceptual framework in support of intervention designs for preventive health behavior improvement programs and outcomes. The proposed approach is based on a narrative review of empirical literature assessing the limitations of less effective or ineffective field experiments regarding preventive health education and promotion interventions. We found three major limitations regarding the mental model’s balance of treatment and comparison groups, treatment groups’ willingness to adopt suggested behaviors, and the type, length, frequency, intensity, and sequence of treatments. To minimize the influence of these concerns, we propose a mental model-based repeated multifaceted (MRM) intervention design framework to provide an intervention design for improving health education and promotional programs.

This paper pioneers a novel exploration of environmental impacts in livestock farming, with a focus on pig farming's intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs, aged 86 to 108 days, using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores potential detection of gait abnormalities, correlations between pigs' age and their gait or activity patterns, and the evolution of pigs' walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time, and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs' physiological parameters. Sensor data is further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems.

This research explores the predictive capabilities of autonomous driving systems by integrating human factor considerations within the context of safety-critical events. Recognizing the significance of human behaviors in influencing driving dynamics, the study employs advanced modeling techniques to enhance the accuracy of predictions in scenarios that demand heightened safety measures. Traditional rule-based systems and monotonic logic often fall short in addressing the complexities of safety-critical events. To overcome these limitations, the research proposes the application of non-monotonic logic, allowing for flexible and adaptive reasoning that accommodates exceptions and context-specific information.The study emphasizes the importance of incorporating individual differences among drivers, such as risk-taking tendencies, reaction times, decision-making processes, and driving styles. By considering these human factors, the research aims to develop realistic and accurate autonomous driving models that capture the nuances of real-world driving scenarios, especially in safety-critical situations. The predictive model takes into account both internal and external factors, enabling the autonomous system to anticipate and respond effectively to unforeseen events.The primary goal is to provide autonomous vehicles with the capability to make plausible inferences, handle conflicting data, and adapt their behavior in real-time during safety-critical events. The proposed model integrates personalized cognitive agents for each driver, incorporating their unique preferences, characteristics, and needs. This personalized approach aims to optimize the safety and efficiency of autonomous driving, contributing to the ongoing evolution of intelligent transportation systems.In conclusion, this research contributes to advancing the field of autonomous driving by introducing a predictive model that leverages human factor considerations to enhance safety in safety-critical events. The incorporation of non-monotonic logic and individualized cognitive agents signifies a comprehensive approach to address the challenges associated with predicting autonomous driving behavior, paving the way for safer and more reliable autonomous vehicles in dynamic and unpredictable environments.

Engineering application of additively manufactured (AM) metallic materials is quite limited by their fatigue behaviors which are very inconsistent with that of conventionally wrought or cast ones. Here, based on advanced material characterization techniques, such as focused ion beam (FIB), scanning electron microscopy (SEM) and transmission electron microscopy (TEM), the mi-crostructures underneath fracture surfaces were thoroughly investigated in an AM aluminium (AlSi10Mg) alloy with horizontal and vertical building orientation endured very-high-cycle fa-tigue (VHCF) loading under stress ratios R = –1, 0 and 0.5. Two VHCF failure specimens A and B were representatively selected to further SEM examine and TEM samples preparation via FIB milling. Specimen A was horizontally printed and failed at R = –1; specimen B was vertically printed and failed at R = 0. TEM samples A1 and B1 were lifted from locations near the crack ini-tiation sites on the fracture surfaces of specimens A and B; The locations of TEM samples A2 and B2 kept away from the crack origin sites but still within the “fish-eye” region of crack steady growth. TEM observations show that there was no characteristic microstructure induced by VHCF in different oriented specimens and under various R values.

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%.

The difficult current environmental situation, caused by construction industry residues containing ceramic materials could be improved by using these materials as recycled aggregates in mortars, with their processing causing a reduction in their use in landfill, contributing to recycling and also minimizing the consumption of virgin materials. Although some research is currently being carried out into recycled mortars, little is known about their stress-strain (σ-ε); therefore this work will provide the experimental results obtained from recycled mortars with recycled ceramic aggregates (with contents of 0, 10, 20, 30, 50 and 100%), such as: the density, the compression strength, as well as the σ-ε curves representative of their behavior. The values obtained from the analysis process of the results are those of: σ (elastic ranges and failure maximum), ε (elastic ranges and failure maximum), and Resilience and Toughness; in order to finally obtain, through numerical analysis, the equations to predict their behavior (related to their recycled content). At the end of the investigation it is established that mortars with recycled ceramic aggregate contents of up to 20% could be assimilated just like mortars with the usual aggregates, and the prediction equations produced could be used in cases of similar applications.

We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckions. These material particles interact indirectly, and have very strong restoring forces keeping them a finite distance apart from each other within their respective species. Because of their mass compensating effect, the vacuum appears massless, charge-less, without pressure, net energy density or entropy. In addition, we consider two variable models, where, , is Newton’s constant, and, , increases with an increase in cosmological time. We argue that there are at least two competing models for the quantum vacuum within such a framework. The first follows a strict extension of Winterberg’s model. This leads to nonsensible results, if increases, going back in cosmological time, as the length scale inherent in such a model will not scale properly. The second model introduces a different length scale, which does scale properly, but keeps the mass of the Planck particle as, the Planck mass. Moreover we establish a connection between ordinary matter, dark matter, and dark energy, where all three mass densities within the Friedman equation must be interpreted as residual vacuum energies, which only surface, once aggregate matter has formed, at relatively low temperatures. The symmetry of the vacuum will be shown to be broken, because of the different scaling laws, beginning with the formation of elementary particles. Much like waves on an ocean where positive and negative planckion mass densities effectively cancel each other out and form a zero vacuum energy density/ zero vacuum pressure surface, these positive mass densities are very small perturbations (anomalies) about the mean. This greatly alleviates, i.e., minimizes the cosmological constant problem, a long standing problem associated with the vacuum.

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.

Gigantism in theropods represents one of the most intriguing phenomena in dinosaur evolution, showcasing extreme adaptations in body size and ecological roles. This literature review focuses on Giganotosaurus carolinii, one of the largest known theropods to have ever existed, to explore its unique aspects of gigantism and the subsequent ecological impacts. By analyzing features such as its skeletal morphology, comparative anatomy, and paleoenvironmental data, we assess the evolutionary pressures and biological constraints that shaped the gigantism observed in Giganotosaurus. We discover that the species exhibited a combination of physiological and ecological adaptations that allowed it to thrive as an apex predator in its Cretaceous environment. We will also further examine the implications of its size on prey dynamics, competition with other large theropods, and the overall ecosystem structure. Through a thorough, multidisciplinary approach, this research enhances our understanding of how gigantism influenced theropod behavior, interspecies interactions, and the broader paleoecological landscape.

We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.

This literature review provides a comprehensive analysis of the functional morphology and paleoecology of Pteranodon, a prominent genus of Pterosaur from the Late Cretaceous period. By synthesizing recent research findings with classic studies, this paper elucidates the aerodynamic and ecological adaptations of Pteranodon, which was one of the largest pterosaurs to ever soar the skies. The review examines the distinctive cranial features, including its elongated cranial crest and its implications for sexual dimorphism and species identification. Detailed analysis of wing morphology and limb proportions reveals insights into its flight mechanics, suggesting that Pteranodon was adapted for dynamic soaring, leveraging its expansive wingspan for efficient long-distance travel. Additionally, the paper explores Pteranodon's feeding habits and dietary preferences based on dental and jaw structure, indicating a diet primarily consisting of fish and other aquatic prey. The review also integrates fossil evidence with ecological models to reconstruct the paleoenvironment of the Late Cretaceous, highlighting the role of Pteranodon in its ecosystem as both a predator and a competitor. This synthesis not only enhances our understanding of Pteranodon’s physiological and ecological roles but also contributes to broader discussions on pterosaur evolution and behavior.

This literature review investigates how the diverse habitats of Baryonyx walkeri influenced its hunting strategies, with a focus on adaptations for piscivory and generalist predation. The spinosaurid theropod Baryonyx walkeri, known for its distinctive elongated snout and conical teeth, inhabited a variety of environments during the Early Cretaceous period. Fossil evidence suggests that Baryonyx frequented fluvial and lacustrine environments, leading to the hypothesis that its primary diet consisted of fish and other aquatic organisms. Detailed analysis of its cranial morphology reveals a suite of adaptations, including robust forelimbs with a large, sickle-shaped claw, ideal for catching slippery prey. Isotopic analysis of oxygen in Baryonyx indicates a significant reliance on fish and other similar organisms. Comparisons with modern analogs, such as crocodilians, provide further insight into the behavioral ecology of Baryonyx. This review integrates paleontological data with modern ecological analogs to reconstruct the hunting strategies of Baryonyx walkeri. The findings underscore the importance of habitat diversity in shaping the evolutionary pathways of predatory dinosaurs and contribute to a broader understanding of spinosaurid ecology and behavior.