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Food insecurity is widespread in the United States. The COVID-19 pandemic intensified the need for food assistance and created opportunities for collaboration among historically-siloed organizations. Research has demonstrated the importance of coalition building and community organizing in Policy, Systems, and Environmental (PSE) change and its potential to address equitable access to food, ultimately improving population health outcomes. In New Haven, community partners formed a coalition to address systems-level issues in the local food assistance system through the Greater New Haven Coordinated Food Assistance Network (CFAN). Organizing the development of CFAN within the framework of Collaborating for Equity and Justice (CEJ) reveals a new way of collaborating with communities for social change with an explicit focus on equity and justice. A document review exploring the initiation and growth of the network found that 165 individuals, representing 63 organizations, participated in CFAN since its inception and collaborated on 50 actions that promote food access and overall health. Eighty-one percent of these actions advanced equitable resource distribution across the food system, with forty-five percent focused on coordinating food programs to meet the needs of underserved communities. With the goal of improving access to food while addressing overall equity within the system, the authors describe CFAN as a potential community organizing model in food assistance systems. © 2022 by the authors.

Online markets offer sellers access to buyers’ information and, thus, the potential to alter prices and products accordingly. In light of this, we undertook an empirical analysis to test for individualization on Amazon.com. We collect data from individuals recruited to shop for household items. Our results indicate evidence of individualization of search results and net prices (via coupons). We found, contrary to what was expected, that demographic, geolocation, and account information play an insignificant role in individualization of search results. Thus, we conclude that individualization is based on more dynamic information, e.g., online browsing behavior. This highlights the fact that sellers’ need for (and use of) buyer information goes beyond the simple information accessible from the buyers’ accounts to a more rigorous monitoring of buyers’ online behavior.

Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. Bone mineral density (BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and osteopenia (OPN). The best method for determining bone mineral density and OP/OPN risk is via dual energy X-ray absorptiometry (DXA) technology. The risk of osteoporosis-related fracture may increase for people with diabetes. Therefore, it is necessary to develop a system that can support the early detection of OP/OPN in diabetic patients. In this study, we analyzed Qatar diabetic cohorts including 500 subjects, among which 68 were OP/OPN (target) subjects and 432 were without osteoporosis/osteopenia (control) subjects. The objective of this study is to develop an ML model to distinguish diabetic OP/OPN patients from diabetic non-OP/non-OPN subjects based on their bone health indicators from full body DXA scan measurements. Based on our experiments, AdaBoost model performed the best for classifying the target group from the control group. 10-fold cross validation-based results indicate that the proposed ML model was able to distinguish the target group from the control group at 80% sensitivity, 96% specificity. To the best of our knowledge, our study is the first ML-based approach to detect the early onset of OP/OPN in diabetic cohort from Qatar. Our analyses revealed the higher level of lean mass, fat mass and bone mass for the control group compared to the target group. Higher levels of BMC, BMD from different body parts in the control group compared to the osteoporosis/osteopenia group indicate the protective effects of obesity on bone health in the Qatari diabetic cohort. Moreover, higher value of anthropometric measurements in troch, lumbar spine (L1, L2, L3, L4), pelvis and other body parts in the control group indicates that the WHO guideline can be applied to the Qatari diabetic cohort for the early detection of OP/OPN based on the proposed ML model. Further research on OP/OPN in diabetic patients is warranted in future to confirm the role of DM on bone health.

Breastfeeding has health benefits for both infants and mothers, yet Black mothers and infants are less likely to receive these benefits. Despite research showing no difference in breastfeeding intentions by race or ethnicity, inequities in breastfeeding rates persist, suggesting that Black mothers face unique barriers to meeting their breastfeeding intentions. The aim of this study is to identify barriers and facilitators that Black women perceive as important determinants of exclusively breastfeeding their children for at least 3 months after birth. Utilizing a Barrier Analysis approach, we conducted six focus group discussions, hearing from Black mothers who exclusively breastfed for 3 months and those who did not. Transcripts were coded starting with a priori parent codes based on theory-derived determinants mapped onto the Socioecological Model; themes were analysed for differences between groups. Facilitators found to be important specifically for women who exclusively breastfed for 3 months include self-efficacy, lactation support, appropriate lactation supplies, support of mothers and partners, prior knowledge of breastfeeding, strong intention before birth and perceptions of breastfeeding as money-saving. Barriers that arose more often among those who did not exclusively breastfeed for 3 months include inaccessible lactation support and supplies, difficulties with pumping, latching issues and perceptions of breastfeeding as time-consuming. Lack of access to and knowledge of breastfeeding laws and policies, as well as negative cultural norms or stigma, were important barriers across groups. This study supports the use of the Socioecological Model to design multicomponent interventions to increase exclusive breastfeeding outcomes for Black women.

Maintaining the excellent state of the road is critical to secure driving and is an obligation of both transportation and regulatory maintenance authorities. For a safe driving environment, it is essential to inspect road surfaces for defects or degradation frequently. This process is found to be labor-intensive and necessitates primary expertise. Therefore, it is challenging to examine road cracks visually; thus, we must effectively employ computer visualization and robotics tools to support this mission. This research provides our initial idea of simulating an Autonomous Robot System (ARS) to perform pavement assessments. The ARS for crack inspection is a camera-equipped mobile robot (i.e., an Android phone) to collect images on the road. The proposed system is simulated using an mBot robot armed with an Android phone that gathers video streams to be processed on a server that has a pre-training Convolutional Neural Networks (CNN) that can recognize crack existence. The proposed CNN model attained 99.0% accuracy in the training case and 97.5% in the testing case. The results of this research are suitable for application with a commercial mobile robot as an autonomous platform for pavement inspections. © 2022 Little Lion Scientific.

Water the color of oolong reflects her shape vanishing.

Let Mφ be a surface bundle over a circle with monodromy φ: S → S. We study deformations of certain reducible representations of π1(Mφ)intoSL(n, C), obtained by composing a reducible representation into SL(2, C) with the irreducible representation SL(2, C) → SL(n, C). In particular, we show that under certain conditions on the eigenvalues of φ∗, the reducible representation is contained in a (n + 1 + k)(n − 1) dimensional component of the representation variety, where k is the number of components of ∂Mφ . This result applies to mapping tori of pseudo-Anosov maps with orientable invariant foliations whenever 1 is not an eigenvalue of the induced map on homology, where the reducible representation is also a limit of irreducible representations. © 2022, Osaka University. All rights reserved.

The sharpest optical images of the R136 cluster in the Large Magellanic Cloud are presented, allowing us for the first time to resolve members of the central core, including R136a1, the most-massive star known. These data were taken using the Gemini speckle imager Zorro in medium-band filters with effective wavelengths similar to BVRI achieving angular resolutions between 30-40 mas. All stars previously known in the literature, having V < 16 mag within the central 2″ × 2″, were recovered. Visual companions (≥40 mas; 2000 au) were detected for the WN5h stars R136 a1 and a3. Photometry of the visual companion of a1 suggests it is of mid-O spectral type. Based on new photometric luminosities using the resolved Zorro imaging, the masses of the individual WN5h stars are estimated to be between 150 and 200 M ⊙, lowering significantly the present-day masses of some of the most-massive stars known. These mass estimates are critical anchor points for establishing the stellar upper-mass function. © 2022. The Author(s). Published by the American Astronomical Society.

The STAR Collaboration reports measurements of back-to-back azimuthal correlations of di-π0s produced at forward pseudorapidities (2.6<η<4.0) in p+p, p+Al, and p+Au collisions at a center-of-mass energy of 200 GeV. We observe a clear suppression of the correlated yields of back-to-back π0 pairs in p+Al and p+Au collisions compared to the p+p data. The observed suppression of back-to-back pairs as a function of transverse momentum suggests nonlinear gluon dynamics arising at high parton densities. The larger suppression found in p+Au relative to p+Al collisions exhibits a dependence of the saturation scale Q2s on the mass number A. A linear scaling of the suppression with A1/3 is observed with a slope of −0.09±0.01.

Composites of MnO2/multi-wall carbon nanotubes (MWCNTs) were prepared using different weight ratios of MWCNTs: KMnO4 (1:2, 1:5, 1:10, 1:15, 1:20, and 1:25) using a one-pot hydrothermal method. The synthesized materials were physically characterized by x-ray diffraction, transmission electron microscopy (TEM), field emission-scanning electron microscopy (FE-SEM), (Brunauer–Emmett–Teller) BET, and thermogravimetric analysis. TEM and SEM studies indicate that MnO2 is homogeneously entangled with MWCNTs. The electrochemical performance evaluation was performed in a 3-electrode system using MnO2/MWCNT electrodes coated onto a Ni mesh as the working electrode, a Pt foil as the counter electrode, and Ag/AgCl as the reference electrode. The specific capacitance was obtained from charge–discharge studies at varying current densities between 0.5 and 5 A/g. The specific capacitance of MWCNT-KMnO4 (1:10, 1:15, and 1:25) samples was obtained as 114, 164, and 100 F/g, respectively, at a current density of 1 A/g.

Paraeducators often support students with the most intensive academic, life, and behavioral needs, which includes students with intellectual and other developmental disabilities (IDD; e.g., autism spectrum disorder; ASD), yet they typically enter the classroom with inadequate preparation to perform their roles effectively. Using a multiple-baseline research design replicated across participants, we evaluated the effects of job-embedded bug-in-ear (BIE) coaching delivered by the teacher on paraeducators’ use of behavior specific praise (BSP) while teaching transition-age students with ASD. Findings confirmed each of the three paraeducators immediately increased the percentage of occurrence and rate per minute in which they offered BSP. They sustained these high levels during fading. Further, the special education teacher, who served as the eCoach, and the paraeducators reported BIE was an effective form of paraeducator professional development. Finally, changes in expressive social and communicative behaviors were observed in student participants as a result of the intervention. These results extend literature on BSP and also help establish BIE coaching as an evidence-based practice for paraeducators. © Division on Autism and Developmental Disabilities.

Economic load dispatch (ELD) is a challenge optimization problem to minimize the total cost of the thermally generated power that satisfies a set of equality and inequality constraints. We need to maximize the power network load under several operational constraints to solve this problem. Meanwhile, we need to minimize the cost of power generation and minimize the loss in the network transmission. Traditional optimization methods were used to solve such problems as linear programming. Meta-heuristic search algorithms have shown encouraging performance in solving various real-life engineering problems. This paper attempts to provide a comprehensive comparison between nine meta-heuristic search algorithms, including Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Crow Search Algorithm (CSA), Differential Evolution (DE), Salp Swarm Algorithm (SSA), Harmony Search (HS), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), and Moth-Flame Optimization Algorithm (MFO) for solving the economic load dispatch problem. Our developed results demonstrated that meta-heuristics search algorithms (i.e., CSA and DE) offer the optimal power set for each power station. These computed power fulfill the supply needs and maintain both minimum power costs and power losses in power transmission.

Lebanon is a diverse and dynamic nation of six million people that has experienced considerable disruption for the last two decades. The Syrian Civil War, which began in 2011, resulted in the displacement of 1.1 million Syrians to Lebanon. Today, Lebanon is the country with the largest per capita number of refugees in the world. In addition, the country experienced a social, economic, and political crisis in 2019 that destabilized the entire society-circumstances that were further complicated by COVID-19 pandemic. With all of the competing calamities in Lebanon, there has been limited scientific investigation into substance use and the risk of HIV infection among the country's population. To address this gap in knowledge, a qualitative rapid situational assessment (RSA) of substance use and risk of HIV infection in and around Beirut, the nation's capital, was conducted. The goal of this analysis is to describe the demographics and drug use patterns of this population, explore their HIV knowledge and risks, and build knowledge about their perceptions of and access to substance use treatment and other social services.

Our research aimed to investigate the potential learning benefits to young children of implementing digital interactive multimodal technologies that provide both visual and haptic experiences in elementary mathematics classrooms. We studied the ways in which fourth-grade students collaboratively create collective strategies for solving mathematical problems utilizing dynamic geometry software with multi-touch interfaces, a combination we call a multi-touch Dynamic Geometry Environment. We examine in-depth two case studies each illustrating how mathematical strategies, collaboration, and socially mediated metacognition emerge in the small groups of children while working on an activity using the Geometer’s Sketchpad® on the iPad to make sense of an intuitive idea of covariation. We found that children’s interactions with their peers, the interviewer, and the mDGE favored the emergence of varied collaborative behaviors and socially mediated metacognitive processes that fostered the co-construction and development of mathematical strategies over a short period of time. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Cycling of organic carbon in the ocean has the potential to mitigate or exacerbate global climate change, but major questions remain about the environmental controls on organic carbon flux in the coastal zone. Here, we used a field experiment distributed across 28° of latitude, and the entire range of 2 dominant kelp species in the northern hemisphere, to measure decomposition rates of kelp detritus on the seafloor in relation to local environmental factors. Detritus decomposition in both species were strongly related to ocean temperature and initial carbon content, with higher rates of biomass loss at lower latitudes with warmer temperatures. Our experiment showed slow overall decomposition and turnover of kelp detritus and modeling of coastal residence times at our study sites revealed that a significant portion of this production can remain intact long enough to reach deep marine sinks. The results suggest that decomposition of these kelp species could accelerate with ocean warming and that low-latitude kelp forests could experience the greatest increase in remineralization with a 9% to 42% reduced potential for transport to long-term ocean sinks under short-term (RCP4.5) and long-term (RCP8.5) warming scenarios. However, slow decomposition at high latitudes, where kelp abundance is predicted to expand, indicates potential for increasing kelp-carbon sinks in cooler (northern) regions. Our findings reveal an important latitudinal gradient in coastal ecosystem function that provides an improved capacity to predict the implications of ocean warming on carbon cycling. Broad-scale patterns in organic carbon decomposition revealed here can be used to identify hotspots of carbon sequestration potential and resolve relationships between carbon cycling processes and ocean climate at a global scale.

Motivation for bodily movement, physical activity and exercise varies from moment to moment. These motivation states may be “affectively-charged,” ranging from instances of lower tension (e.g., desires, wants) to higher tension (e.g., cravings and urges). Currently, it is not known how often these states have been investigated in clinical populations (e.g., eating disorders, exercise dependence/addiction, Restless Legs Syndrome, diabetes, obesity) vs. healthy populations (e.g., in studies of motor control; groove in music psychology). The objective of this scoping review protocol is to quantify the literature on motivation states, to determine what topical areas are represented in investigations of clinical and healthy populations, and to discover pertinent details, such as instrumentation, terminology, theories, and conceptual models, correlates and mechanisms of action. Iterative searches of scholarly databases will take place to determine which combination of search terms (e.g., “motivation states” and “physical activity”; “desire to be physically active,” etc.) captures the greatest number of relevant results. Studies will be included if motivation states for movement (e.g., desires, urges) are specifically measured or addressed. Studies will be excluded if referring to motivation as a trait. A charting data form was developed to scan all relevant documents for later data extraction. The primary outcome is simply the extent of the literature on the topic. Results will be stratified by population/condition. This scoping review will unify a diverse literature, which may result in the creation of unique models or paradigms that can be utilized to better understand motivation for bodily movement and exercise.

We incorporate deep learning techniques into capacitive images of body parts (ear, four fingers, and thumb) to improve the performance of user authentication in smartphones. Use of a capacitive touchscreen as an image sensor has several advantages, such as it is less sensitive to poor illumination conditions, occlusions, and pose variations. Also, it does not need an additional hardware like iris or fingerprint scanner. Use of capacitive images for user authentication is not new. However, the performance, specially, false reject rates (FRRs) of the state-of-the-art capacitive image-based systems are poor. In this paper, we focus on improving the performance and leverage deep learning. Deep learning techniques demonstrated spectacular performance in previous physical biometrics-based research. However, to our knowledge, effectiveness of deep learning is still unexplored in capacitive touchscreen-based user authentication. In order to bridge this research gap, we devise a multi-modal deep learning model, namely UASNet, and compare its performance with a large set of uni- and multi-modal baselines. Using the UASNet, we achieve an accuracy of 99.77%, an EER of 0.48%, and an FRR of 1.19% at FAR of 0.06%.