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On Wednesday, January 20, 2021, Kamala Harris broke through the gender and racial barrier that has kept men at the top ranks of American politics for over two centuries. This moment singlehandedly challenged the definition of leadership and who “fits” in that traditional model. Reyes examines the challenges and highlights three lessons of leading in color in academia today, being the first Latina to chair her department. Her journey reflects similar experiences of women of color who are burdened by the emotional toll that comes with being a part of systemic change, by virtue of leading while of color. To begin dismantling systemic racism and sexism, organizations must commit to addressing the issues head-on by reexamining policies, practices, and work environments that have perpetuated systemic inequalities. Greater supports are needed for women of color to be effective as their contributions are invaluable in achieving true systemic change.

Being a millennial person-of-color in the twenty-first century is exhaustive in itself—adding the vulnerabilities of a global pandemic magnified that undertaking. As women, we bear the responsibilities of balancing our professional life with our family life; our role as a partner or friend with our role as an individual being; and our mental health with our social health. “Grow Through What You Go Through” explores the value of saying “Yes, I can do this too” when COVID-19 gave many a reason to say, “I’ve had enough.” Between being a supportive partner and family member to many who experienced financial and educational setbacks and being an important part of preparing the next set of future nurses to lead and excel during a global pandemic, COVID-19 enlightened my perspective on who I was at the start of COVID-19 and the person I would turn out to be by the end of it.

As a tenured professor at a predominantly white institution (PWI), I am aware of the hardships we face in the academy; as a mother of three Black girls, a wife to a Black man, I am full. And yet, I must make space to see my own self clearly. Each day as a Black woman, I must set the intention to save my own life.

This chapter provides a brief autobiographical account of her multiethnic and racial background as a Louisiana Creole in south Louisiana. The author points out that among all racial and ethnic groups in the United States, Indigenous people are the only ones that require some form of lineal Native American descent or blood quantum. The chapter provides an ancestry account of two American Indian tribes along the bayous of south Louisiana, the Chitimacha, and the United Houma Nation. While one federally recognized tribe has attempted to remove all relations to individuals of Black, Negro, or African American descent, the other has closed enrollment to new members (despite lineal descent), with the unifying factor among these communities being establishing progenitors. Finally, the author articulates how lack of access to resources related to issues such as COVID-19 has perpetuated the historical legacy of medical racism in tribal and underserved communities in the United States.

In this paper, we focus on improving the age estimation accuracy on smartphones. Estimating a smartphone user’s age has several applications such as protecting our children online by filtering age-inappropriate contents, providing a customized e-commerce experience, etc. However, accuracy of the the state-of-the-art age estimation techniques that use touch behavior on smartphones is still limited because of the lack of sufficient amount of training data. We perform rigorous experiments using zoom gestures on smartphones and demonstrate that increasing the amount of training data can significantly improve the age estimation accuracy. Based on the findings in this study, we recommend creating a large touch dynamics-based age estimation data set so that more accurate age estimation models can be built and in turn, can be used more confidently.

Over recent decades, research in Artificial Intelligence (AI) has developed a broad range of approaches and methods that can be utilized or adapted to address complex optimization problems. As real-world problems get increasingly complicated, this requires an effective optimization method. Various meta-heuristic algorithms have been developed and applied in the optimization domain. This paper used and ameliorated a promising meta-heuristic approach named Crow Search Algorithm (CSA) to address numerical optimization problems. Although CSA can efficiently optimize many problems, it needs more searchability and early convergence. Its positioning updating process was improved by supporting two adaptive parameters: flight length (fl) and awareness probability (AP) to tackle these curbs. This is to manage the exploration and exploitation conducts of CSA in the search space. This process takes advantage of the randomization of crows in CSA and the adoption of well-known growth functions. These functions were recognized as exponential, power, and S-shaped functions to develop three different improved versions of CSA, referred to as Exponential CSA (ECSA), Power CSA (PCSA), and S-shaped CSA (SCSA). In each of these variants, two different functions were used to amend the values of fl and AP. A new dominant parameter was added to the positioning updating process of these algorithms to enhance exploration and exploitation behaviors further. The reliability of the proposed algorithms was evaluated on 67 benchmark functions, and their performance was quantified using relevant assessment criteria. The functionality of these algorithms was illustrated by tackling four engineering design problems. A comparative study was made to explore the efficacy of the proposed algorithms over the standard one and other methods. Overall results showed that ECSA, PCSA, and SCSA have convincing merits with superior performance compared to the others.

This chapter introduces the 5 ‘pillars’ through which to (re-)build a shared sustainable prosperity. The five pillars aim to provide a framework through which policymakers, decision-makers, politicians, community groups and the corporate sphere might begin to consider, map out and plan for just transitions in their domains. The theoretical framing combines socio-technical transitions, social justice and just transitions perspectives; the novelty of our proposed approach here is the further inclusion of resilience perspectives, to account for the shifting relations between sustainability and resilience. Our understanding of sustainability aligns with a ‘strong sustainability’ perspective, whereby ecological limits represent ‘hard’ limits to development, limits which need to be acknowledged and respected.

Aphasia is a language disorder that occurs as a result of brain damage. It affects auditory comprehension, verbal expression, reading, and writing due to the disruption of specific language components such as phonology, semantics, and/or syntax. A hallmark characteristic of aphasia is anomia, or impaired word retrieval. This may occur in the context of fluent speech (normal rate, rhythm and prosody) or nonfluent speech (hesitant and agrammatic verbal output). Other characteristics of aphasia vary depending on the specific location and extent of brain damage. Alexia and agraphia are acquired impairments in reading and writing, respectively. These deficits may occur in isolation but more commonly co-occur with aphasia. Assessment and treatment of aphasia, alexia, and agraphia focuses on identifying and restoring impaired function as well as determining the impact of the impairment of quality of life and developing compensatory strategies to manage persisting deficits. This article describes the symptoms associated with aphasia, alexia, and agraphia, the subtypes and neurological correlates of each impairment, the general trajectory of recovery, and assessment and treatment approaches. © 2023 Elsevier Inc. All rights reserved.

Global polarizations (P) of Λ (¯¯¯Λ) hyperons have been observed in noncentral heavy-ion collisions. The strong magnetic field primarily created by the spectator protons in such collisions would split the Λ and ¯¯¯Λ global polarizations (ΔP=PΛ−P¯¯¯Λ<0). Additionally, quantum chromodynamics predicts topological charge fluctuations in vacuum, resulting in a chirality imbalance or parity violation in a local domain. This would give rise to an imbalance (Δn=NL−NR⟨NL+NR⟩≠0) between left- and right-handed Λ (¯¯¯Λ) as well as a charge separation along the magnetic field, referred to as the chiral magnetic effect (CME). This charge separation can be characterized by the parity-even azimuthal correlator (Δγ) and parity-odd azimuthal harmonic observable (Δa1). Measurements of ΔP, Δγ, and Δa1 have not led to definitive conclusions concerning the CME or the magnetic field, and Δn has not been measured previously. Correlations among these observables may reveal new insights. This paper reports measurements of correlation between Δn and Δa1, which is sensitive to chirality fluctuations, and correlation between ΔP and Δγ sensitive to magnetic field in Au+Au collisions at 27 GeV. For both measurements, no correlations have been observed beyond statistical fluctuations.

The chiral magnetic wave (CMW) has been theorized to propagate in the deconfined nuclear medium formed in high-energy heavy-ion collisions and to cause a difference in elliptic flow (v2) between negatively and positively charged hadrons. Experimental data consistent with the CMW have been reported by the STAR Collaboration at the Relativistic Heavy Ion Collider (RHIC), based on the charge asymmetry dependence of the pion v2 from Au+Au collisions at √sNN=27 to 200 GeV. In this comprehensive study, we present the STAR measurements of elliptic flow and triangular flow of charged pions, along with the v2 of charged kaons and protons, as a function of charge asymmetry in Au+Au collisions at √sNN=27, 39, 62.4, and 200 GeV. The slope parameters extracted from the linear dependence of the v2 difference on charge asymmetry for different particle species are reported and compared in different centrality intervals. In addition, the slopes of v2 for charged pions in small systems, i.e., p+Au and d+Au at √sNN=200 GeV, are also presented and compared with those in large systems, i.e., Au+Au at √sNN=200 GeV and U+U at 193 GeV. Our results provide new insights for the possible existence of the CMW and further constrain the background contributions in heavy-ion collisions at RHIC energies.

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).