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In relativistic heavy-ion collisions, a global spin polarization, PH, of Λ and ¯¯¯Λ hyperons along the direction of the system angular momentum was discovered and measured across a broad range of collision energies and demonstrated a trend of increasing PH with decreasing √sNN. A splitting between Λ and ¯¯¯Λ polarization may be possible due to their different magnetic moments in a late-stage magnetic field sustained by the quark-gluon plasma which is formed in the collision. The results presented in this study find no significant splitting at the collision energies of √sNN=19.6 and 27 GeV in the BNL Relativistic Heavy Ion Collisions Beam Energy Scan Phase II using the STAR detector, with an upper limit of P¯¯¯Λ−PΛ<0.24% and P¯¯¯Λ−PΛ<0.35%, respectively, at a 95% confidence level. We derive an upper limit on the naive extraction of the late-stage magnetic field of B<9.4×1012 T and B<1.4×1013 T at √sNN=19.6 and 27 GeV, respectively, although more thorough derivations are needed. Differential measurements of PH were performed with respect to collision centrality, transverse momentum, and rapidity. With our current acceptance of |y|<1 and uncertainties, we observe no dependence on transverse momentum and rapidity in this analysis. These results challenge multiple existing model calculations following a variety of different assumptions which have each predicted a strong dependence on rapidity in this collision-energy range.

COVID-19 caused an increase in the demand for medications, which led to an increase in pharmaceutical waste and there is no doubt that this contributes to environmental pollution. Hence, it became necessary to search for how to protect and improve the environment by encouraging the behavior of medication waste reduction. Accordingly, this study aims to investigate the factors affecting intentions to reduce medication waste. Considering this, we develop an extended theory of planned behavior (TPB) framework by incorporating the constructs of moral obligation, environmental awareness, and medication waste risk perception. Using the convenience sampling method and based on a self-administered questionnaire, a total of 225 usable responses were collected in five Algerian cities. The results showed that positive attitudes (β = 0.316, p < 0.001), moral obligation (β = 0.291, p < 0.001), environmental awareness (β = 0.227, p < 0.001), perceived behavior control (PBC) (β = 0.151, p = 0.001), greater perceived risks (β = 0.127, p < 0.001), and subjective norm (β = 0.096, p < 0.05) significantly and positively influence the medication waste minimization intention. Furthermore, our analyses revealed that the extended TPB model explained 73.40% of the intention variance. In conclusion, we have explored the intentions, and there may be a gap between intent and actual behavior. Therefore, we recommend future studies to examine the factors affecting the actual behavior of medication waste reduction and to investigate environmental ethics and religious commitment as predictors of waste reduction intentions.

Monastic tonsure of citizens of the Russian Empire abroad became a particularly complex issue during the latter half of the nineteenth century, when Mt. Athos became a favorite destination of those interested in assuming the monastic habit outside the empire. The Russian authorities sought to control and regulate such tonsures by establishing procedures for checking their validity, and by ruling out automatic recognition of them in the empire. Individuals who were tonsured as monks abroad manipulated or tried to manipulate such regulations in order to facilitate travel back and forth from the Russian Empire for their own purposes. The result was that the real and apparent ambiguities that such rules allowed for were exploited by both state authorities and by real or fake monks themselves. The essay seeks to offer some perspectives on this phenomenon by focusing on four cases preserved in archival records. © 2023 Ltd "Integration: Education and Science". All rights reserved.

This study explores the social media engagement of human service nonprofits during the early phase of the COVID-19 pandemic. We analyzed 2,726 tweets from 15 food banks in Texas to understand their communication and audience engagement on social media. During the early outbreak, food banks were more active on social media, and these organizations primarily shared community-building and informational content. Before the pandemic, community-building content led to the highest engagement level. However, action and informational content saw greater engagement during the crisis. Our findings reveal that online audiences perceive the importance of each content type differently in varying circumstances. By understanding preferences and needs of online audiences, human service nonprofits can harness the power of social media to amplify their impact while operating during times of crisis.

This paper introduces an enhanced version of the Capuchin Search Algorithm (CapSA) called ECapSA. CapSA draws inspiration from the collective intelligence of Capuchin monkeys and has shown success in solving real-world problems. However, it may encounter challenges handling complex optimization tasks, such as premature convergence or being trapped in local optima. ECapSA employs a local escaping mechanism operating the abandonment limit concept to exploit potential solutions and introduce diversification trends. Additionally, the ECapSA algorithm is improved by integrating the principles of the cooperative island model, resulting in the iECapSA. This modification enables better management of population diversity and a more optimal balance between exploration and exploitation. The efficiency of iECapSA is validated through a series of experiments, including the IEEE-CEC2014 benchmark functions and training the feedforward neural network (FNN) on seven biomedical datasets. The performance of iECapSA is compared to other metaheuristic techniques, namely differential evolution (DE), sine cosine algorithm (SCA), and whale optimization algorithm (WOA). The results of the comparative study demonstrate that iECapSA is a strong contender and surpasses other training algorithms in most datasets, particularly in terms of its ability to avoid local optima and its improved convergence speed.

The K-12 health and physical education professions are largely guided and supported by three entities. They include state departments of education, national and state professional associations, and teacher education programs (i.e., health education teacher education [HETE] and physical education teacher education [PETE]). From the outside looking in, it may seem that HETE and PETE programs are not dissimilar, yet both disciplines continue to operate largely within individual vacuums, each being historically undermined and marginalized. Therefore, questions regarding the purpose and relevance of K-12 health and physical education programs are continually raised. These common challenges may have a negative impact on recruitment of health and physical education teachers, retention of HETE and PETE programs, and ultimately the learning outcomes of youth in schools. In this article, we review critical challenges faced by both HETE and PETE programs with the hope that this can lead to collaboration, advocacy, and meaningful change.

In this study, we conducted experiments to model the temperature of two manufacturing processes using various metaheuristic search algorithms. The two processes adopted were the P05 horny steel tool and the AISI304 stainless steel castings machines. Our approach involves building a data-driven model, as traditional search methods for modeling manufac-turing problems often need help finding the global optimum when faced with a complex objective function and numerous decision variables. Bio-inspired metaheuristic search algorithms have shown promising performance in handling multi-model optimization functions, and efficiently exploring the search space to attain more global results. We applied several metaheuristic search algorithms to find the optimal tuning parameters of a temperature-based model. The results from the case studies demonstrate that Particle Swarm Optimization (PSO) provided the best performance in tuning model parameters, resulting in minimum modeling error.

Sleep apnea is a sleeping disorder affecting more than 20 % of all American adults, associated with intermittent air passageway obstruction during sleep. This results in intermittent hypoxia, sympathetic activation, and an interruption of sleep with various health consequences. The diagnosis of sleep apnea traditionally involves the performance of overnight polysomnography, where oxygen, heart rate, and breathing, among other physiologic variables, are continuously monitored during sleep at a sleep center. However, these sleep studies are expensive and impose access issues, given the number of patients who need to be diagnosed. There is hence utility in having an effective triage system to screen for OSA to utilize polysomnography better. In this study, we plan to explore using several machine learning algorithms to utilize pre-screening symptoms to diagnose obstructive sleep apnea (OSA). Per our experimental results, it was found that Decision Tree Classifier (DTC) and Random Forest (RF) provided the highest classification accuracies compared to other algorithms such as Logistic Regression (LR), Support Vector Machines (SVM), Gradient Boosting Classifier (GBC), Gaussian Naive Bayes (GNB), K Neighbors Classifier (KNC), and Artificial Neural Networks (ANN).

The growing diversity of students in schools has raised important questions regarding how students learning English and discipline-content simultaneously succeed academically. This challenge has been exacerbated by disparities in resources among multilingual communities throughout the COVID-19 pande...

We describe the rationale and study design for “TRUsted rEsidents and Housing Assistance to decrease Violence Exposure in New Haven (TRUE HAVEN),” a prospective type 1 hybrid effectiveness/implementation study of a multi-level intervention using a stepped wedge design. TRUE HAVEN aims to lower rates of community gun violence by fostering the stability, wealth, and well-being of individuals and families directly impacted by incarceration through the provision of stable housing and by breaking the cycle of trauma.

Identification of the optimal subset of features for Feature Selection (FS) problems is a demanding problem in machine learning and data mining. A trustworthy optimization approach is required to cope with the concerns involved in such a problem. Here, a Binary version of the Capuchin Search Algorithm (CSA), referred to as BCSA, was developed to select the optimal feature combination. Owing to the imbalance of parameters and random nature of BCSA, it may sometimes fall into the trap of an issue called local maxima. To beat this problem, the BCSA could be further improved with the resettlement of its individuals by adopting some methods of repopulating the individuals during foraging. Lévy flight was applied to augment the exploitation and exploration abilities of BCSA, a method referred to as LBCSA. A Chaotic strategy is used to reinforce search behavior for both exploration and exploitation potentials of BCSA, which is referred to as CBCSA. Finally, Lévy flight and chaotic sequence are integrated with BCSA, referred to as LCBCSA, to increase solution diversity and boost the openings of finding the global optimal solutions. The proposed methods were assessed on twenty-six datasets collected from the UCI repository. The results of these methods were compared with those of other FS methods. Overall results show that the proposed methods render more precise solutions in terms of accuracy rates and fitness scores than other methods.

Parental and family involvement in schools has been a concern for educators and administrators. The authors set out to assess the path directions and significance of the interrelationships between Performance Feedback (PF), Academic Performance (AP) on Parent–Family Involvement (PFI), and Parent Satisfaction (PS) in schools. This study utilizes data from the PFI in Education Survey 2019 under the National Household Education Surveys program done by the US Department of Education. The sample for this research is 954 parents. Structural equation modeling was employed using AMOS. Results establish the three research propositions: influence of PFI on PS with the mediation of AP and PF, influence of AP on PS is moderated by PF, influence of AP on PFI is moderated by PF. The findings are important for school administrators and all stakeholders for ensuring greater PFI, improved PF and AP of students, and higher PS. This study is unique in assessing the interactional effects of the variables considered. The study also establishes mediating and moderating influences and offers new insights in understanding the influences on PFI and PS and some bidirectional effects. © 2023 Wiley Periodicals LLC.

The following research examines the acculturation processes of migrants, working in Switzerland, particularly in the hospitality industry. Due to rapid globalisation and lack of investigation on worker’s adaptation, there remains a gap in the research based on the employees’ cultural clash occurrence. The study explores potential tools which might simplify cultural adaptation and provide a better understanding of international communication for any future employees coming to live and work in Switzerland. Semi-structured interviews with participants from different nationalities were conducted to identify the importance of effective acculturation, the adaptation issues, and the influence of those on work and life in Switzerland. The results demonstrate that the most efficient way to acculturate in Switzerland is integration to the culture and traditions of the country and its history through understanding their values, norms, practices. Moreover, Switzerland was viewed as a very conservative and strict country where one must work hard to be accepted. Those participants who tried to separate, assimilate, or marginalise tend to experience tougher acculturation. Language, namely Swiss German or German, was highlighted as the most essential to learn or to be provided by the workplace. Lastly, current research identified a link between successful acculturation with job opportunities and development. Since salaries and job opportunities are not the same for the foreign workers in comparison to Swiss employees on the same position there might be a potential recommendation for improvement. Little to no link has been found between acculturation and psychological well-being, financial status, and health conditions. © 2023 AJHTL /Author(s)

Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players’ physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players’ performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders’ role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15–30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.

Audiovisual speech perception includes the simultaneous processing of auditory and visual speech. Deficits in audiovisual speech perception are reported in autistic individuals; however, less is known regarding audiovisual speech perception within the broader autism phenotype (BAP), which includes individuals with elevated, yet subclinical, levels of autistic traits. We investigate the neural indices of audiovisual speech perception in adults exhibiting a range of autism-like traits using event-related potentials (ERPs) in a phonemic restoration paradigm. In this paradigm, we consider conditions where speech articulators (mouth and jaw) are present (AV condition) and obscured by a pixelated mask (PX condition). These two face conditions were included in both passive (simply viewing a speaking face) and active (participants were required to press a button for a specific consonant–vowel stimulus) experiments. The results revealed an N100 ERP component which was present for all listening contexts and conditions; however, it was attenuated in the active AV condition where participants were able to view the speaker’s face, including the mouth and jaw. The P300 ERP component was present within the active experiment only, and significantly greater within the AV condition compared to the PX condition. This suggests increased neural effort for detecting deviant stimuli when visible articulation was present and visual influence on perception. Finally, the P300 response was negatively correlated with autism-like traits, suggesting that higher autistic traits were associated with generally smaller P300 responses in the active AV and PX conditions. The conclusions support the finding that atypical audiovisual processing may be characteristic of the BAP in adults.

The aim of this study is to describe a measured return to instrumental dysphagia assessments for our vulnerable surgical patient population, such that best practice patterns could be resumed and our staff kept safe from transmission of COVID-19. A retrospective medical record review provided data on clinical practice patterns of swallowing assessment in an at-risk surgical patient population. Outcomes of this study support protocols that allow clinicians to safely resume the use of instrumental assessment and return to best practice in dysphagia assessment for our surgical patient population.

Psychoeducation, where clinicians teach problem-solving skills in a supportive environment, can help address families’ social vulnerabilities and promote well-being. Group well-child care (GWCC) may provide unique opportunities for pediatric residents to improve their skills in psychoeducation. Our aim was to characterize pediatric residents’ perspectives and experiences of communication while conducting both individual well-child care and GWCC. We used a longitudinal qualitative study design to conduct 15 semistructured interviews with five pediatric residents who facilitated GWCC. Using the constant comparative method, we characterized pediatric residents’ perspectives and experiences of communication while conducting both individual well-child care and GWCC. Four themes emerged. Residents perceived that GWCC (i) enabled families to honestly share their knowledge and parenting practices, (ii) allowed time and a space for families to share personal stories and scenarios, (iii) facilitated discussions of maternal health and psychosocial matters, toward which residents felt ambivalence, and (iv) fostered skills in psychoeducation that transferred to the rest of their clinical practice. When pediatric residents lead GWCC, they perceive that they can facilitate key aspects of psychoeducation, enabling them to assist families in meeting complex social needs. Residents describe that they transfer psychoeducation skills learned in GWCC to the rest of their practice.