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Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP) has been shown to increase all-cause mortality and risk of cardiomyopathy in patients with solid malignancies. CHIP has also been shown to increase T cell activation in heart failure patients. It is unclear whether CHIP can affect the risk of immune checkpoint inhibitor (ICI) myocarditis in patients with cancer treated with immunotherapy. Methods We enrolled patients with solid tumors in a prospective study, determined CHIP status at time of enrollment through blood whole exome sequencing, and assessed incidence of ICI myocarditis from time of enrollment through December 1st, 2023. We performed a competing risk cox regression to evaluate the role of CHIP in ICI myocarditis, accounting for patient demographics, cardiac comorbidities, cardiotoxic cancer therapy, and dual ICI use in our covariates. We also generated cumulative incidence curves using subdistribution hazards to evaluate development of ICI myocarditis stratified by CHIP vs no CHIP. Chart review was performed to evaluate patient co-morbidities, lab values, imaging findings and outcomes. Results Among the 88 patients receiving ICI therapy, average age was 67 ± 14 years, of which 50% harbored CHIP variants. Among all comorbidities, including diabetes, heart failure and obstructive coronary artery disease, only coronary artery calcifications were significantly increased in patients with CHIP. There were no statistically significant differences in cancer therapy or cardiovascular drugs between patients with and without CHIP. Among examined outcomes, patients with CHIP had a statistically higher rate of ICI myocarditis (overall: 57%, CHIP: 73% (32/44), no CHIP: 41% (18/44), p = 0.003) and death (CHIP: 60%, no CHIP 31%, p = 0.011). In a multivariate competing risk analysis, CHIP status doubled the risk of developing ICI myocarditis, similar to the risk of dual ICI use (CHIP status HR 2.74, 95% CI: 1.44–5.22, p = 0.002 vs dual ICI use HR 2.39, 95% CI: 1.11–5.14, p = 0.026). Conclusions This study is the first to show that CHIP independently increases risk of ICI myocarditis, with implications for risk stratification of patients prior to ICI initiation and frequency of cardiac monitoring.

Abstract Background Rapid urbanisation without concomitant infrastructure development has led to the creation of urban slums throughout sub-Saharan Africa. People living in urban slums are particularly vulnerable to food insecurity due to the lack of physical and economic accessibility to food. Hence, it is important to explore how vulnerable groups living in slums interact with the food environment. This study assessed the relationships between food insecurity, including restrictive coping strategies, food purchasing patterns and perceptions about the food environment among dwellers of selected urban slums in Ibadan, Nigeria. Methods This community-based cross-sectional study was conducted with people responsible for food procurement from 590 randomly selected households in two urban slums in Ibadan. Food insecurity and restrictive coping strategies were assessed using the Household Food Insecurity Access Scale and the Coping Strategy Index, respectively. We examined purchasing patterns of participants by assessing the procurement of household foodstuffs in different categories, as well as by vendor type. Participants’ perceptions of the food environment were derived through a five-item composite score measuring food availability, affordability and quality. Chi-square tests and logistic regression models analysed associations between food insecurity, purchasing patterns and perceptions of the food environment. Results The prevalence of food insecurity in the sample was 88%, with 40.2% of the households experiencing severe food insecurity. Nearly a third (32.5%) of the households used restrictive coping strategies such as limiting the size of food portions at mealtimes, while 28.8% reduced the frequency of their daily meals. Participants purchased food multiple times a week, primarily from formal and informal food markets rather than from wholesalers and supermarkets. Only a few households grew food or had livestock (3.2%). Food insecure households had a lower perceived access to the food environment, with an approximate 10% increase in access score per one-unit decrease in food insecurity (AOR = 0.90, 95% CI: 0.84, 0.96). The most procured foods among all households were fish (72.5%), bread (60.3%), rice (56.3%), yam and cassava flours (50.2%). Food-secure households procured fruit, dairy and vegetable proteins more frequently. Conclusion Food insecurity remains a serious public health challenge in the urban slums of Ibadan. Perceptions of greater access to the food environment was associated with increasing food security. Interventions should focus on creating more robust social and financial protections, with efforts to improve livelihoods to ensure food security among urban slum-dwellers.

Abstract Background Malnutrition remains a pressing public health concern for mothers and children in South Africa. Despite the government’s multisectoral response, unaddressed social needs prevent some mothers getting full benefit from interventions, spanning financial planning, income stability, housing, access to government services, social support, and provision of affordable, nutritious foods. Engaging with mothers and prioritising their concerns is important if we wish to overcome obstacles to women benefiting from government nutrition interventions. This study aimed to identify the programmes that women perceived as a priority in addressing the social needs of mothers of young infants and pregnant women to enhance nutrition in a resource-constrained urban township in South Africa. Methods A cross-sectional study employed a quantitative preference elicitation survey, administered to 210 mothers and pregnant women from five primary healthcare facilities in Soweto. The survey tool was developed with the community to identify unmet social needs and potential solutions, which were synthesised with findings from the literature. The survey described 15 programmes, grouped into three delivery levels: clinics, community, and government. Participants were required to rank programme options in two stages. First, they selected their top two programmes within each delivery level. Subsequently, they allocated stickers to indicate the strength of their preference among the top programmes across the levels. Rankings were analysed using descriptive statistics. Results The highest priority was given to five programmes. Two delivered at the community level: Women’s economic empowerment groups and Job search assistance, two at the clinic level: Social needs assessment and referral, and Prescription-based food, and one at the government level: Free quality childcare. The lowest-ranked programmes were two clinic-based programmes, specifically Maternal nutrition groups and Couple antenatal education. Conclusion Women expressed strong views about which programmes should be prioritised to support mothers and pregnant women in addressing their social needs and improving nutrition. Key areas included providing support with job searching and entrepreneurship, accessing childcare and the healthy foods recommended at clinics, as well as finding information on available community and government services. Leveraging multisectoral collaboration, aligned policy objectives, efficient public financing, and strengthened implementation capacity will be pivotal in delivering these programmes.

Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

Maternal and child malnutrition persists globally, despite existing healthcare and social protection systems. Socio-economic disadvantages contribute to high malnutrition rates, particularly in poor urban communities where many disadvantaged mothers cannot fully benefit from services. To address these disparities, a novel social needs framework has been proposed, emphasising the importance of addressing individuals’ unmet needs to enhance the benefits of nutrition services. This study investigates the perceived impact of community-based organisations (CBOs) in addressing the social needs of mothers in a resource-constrained urban township in South Africa. Interviews were conducted with 18 employees from 10 CBOs working on maternal and child health, food security and social support in Soweto. Thematic analysis revealed 23 services and four pathways through which CBOs believed to address unmet social needs of beneficiaries. Services were small-scale, including food aid, learning support, and social protection assistance, available to a few in dire need. CBO services partially addressed social needs of mothers due to scale, coverage, and sustainability limitations. The South African government should reaffirm its commitment to financially supporting the non-profit sector and integrating it into government sectors to provide tailored services and resources to address diverse social needs and mitigate nutrition inequalities among mothers and children.

Osteoarthritic (OA) pain affects 18% of females and 9.6% of males aged over 60 worldwide, with 62% of all OA patients being women. The molecular drivers of sex-based differences in OA are unknown. Bone is intricately coupled with the sensory nervous system and one of the only joint tissues known to show changes that correlate with patient pain in OA. There are fundamental sex differences in pain sensation and bone biology which may be intrinsic to OA disease progression, however these differences are vastly under researched. We have utilised three data sets to investigate the hypothesis that potential mediators responsible for sex dependent pain mechanisms displayed in OA are derived from mechanically stimulated osteocytes. Our published dataset of the in vitro human osteocyte mechanosome was independently compared with published data from, sex-based gene expression differences in human long bone, the sex-based gene expression differences during the skeletal maturation of the mouse osteocyte transcriptome and sex specific OA risk factors and effector genes in a large human GWAS. 80 of the 377 sex-specific genes identified in the mouse osteocyte transcriptome were mechanically regulated in osteocytes with enrichment associated with neural crest migration and axon extension, and DISEASES analysis enrichment for the rheumatoid arthritis pathway. 3861 mechanically regulated osteocytic genes displayed sex-specific differences in human long bone with enrichment for genes associated with the synapse, sensory perception of pain, axon guidance, immune responses, distal peripheral sensory neuropathy, sensory neuropathy, and poor wound healing. 32 of 77 effector genes and 1 of 3 female specific OA risk factor genes identified in the human GWAS were differentially expressed in the osteocyte mechanosome and male and female bone. This analysis lends support to the hypothesis that mechanically regulated genes in osteocytes could influence sex specific differences in osteoarthritic pain and highlights pain pathways with approved drugs that could potentially treat elevated pain susceptibility in females with OA.

The purpose of this work is to study machine-learning-based model predictive control of nonlinear systems with time-delays. The proposed approach involves initially building a machine learning model (i.e., Long Short Term Memory (LSTM)) to capture the process dynamics in the absence of time delays. Then, an LSTM-based model predictive controller (MPC) is designed to stabilize the nonlinear system without time delays. Closed-loop stability results are then presented, establishing robustness of this LSTM-based MPC towards small time-delays in the states. To handle input delays, we design an LSTM-based MPC with an LSTM-based predictor that compensates for the effect of input delays. The predictor is used to predict future states using the process measurement, and then the predicted states are used to initialize the LSTM-based MPC. Stabilization of the time-delay system with both state and input delays around the steady state is achieved through the featured design. The approach is applied to a chemical process example, and its performance and robustness properties are evaluated via simulations.

Since 1990 extensive funds have been spent on research in climate change. Although Earth Sciences, including climatology and hydrology, have benefited significantly, progress has proved incommensurate with the effort and funds, perhaps because these disciplines were perceived as "tools" subservient to the needs of the climate change enterprise rather than autonomous sciences. At the same time, research was misleadingly focused more on the "symptom", i.e. the emission of greenhouse gases, than on the "illness", i.e. the unsustainability of fossil fuel-based energy production. Unless energy saving and use of renewable resources become the norm, there is a real risk of severe socioeconomic crisis in the not-too-distant future. A framework for drastic paradigm change is needed, in which water plays a central role, due to its unique link to all forms of renewable energy, from production (hydro and wave power) to storage (for time-varying wind and solar sources), to biofuel production (irrigation). The extended role of water should be considered in parallel to its other uses, domestic, agricultural and industrial. Hydrology, the science of water on Earth, must move towards this new paradigm by radically rethinking its fundamentals, which are unjustifiably trapped in the 19th-century myths of deterministic theories and the zeal to eliminate uncertainty. Guidance is offered by modern statistical and quantum physics, which reveal the intrinsic character of uncertainty/entropy in nature, thus advancing towards a new understanding and modelling of physical processes, which is central to the effective use of renewable energy and water resources.

The possible water management of the Plastiras Lake, an artificial reservoir in central Greece, is examined. The lake and surrounding landscape are aesthetically degraded when the water level drops, and the requirement of maintaining a high quality of the scenery constitutes one of the several conflicting water uses, the other ones being irrigation, water supply, and power production. This environmental water use, and, to a lesser extent, the requirement for adequate water quality, results in constraining the annual release. Thus, the allowed fluctuation of reservoir stage is not defined by the physical and technical characteristics of the reservoir, but by a multi-criteria decision, the three criteria being maximising water release, ensuring adequate water quality, and maintaining a high quality of the natural landscape. Each of these criteria is analyzed separately. The results are then put together in a multicriterion tableau, which helps understand the implications of the possible alternative decisions. Several conflict resolution methods are overviewed, namely willingness to pay, hedonic prices, and multi-criteria decision analysis. All these methods attempt to quantify non-quantifiable qualities, and it is concluded that they don't necessarily offer any advantage over merely making a choice based on understanding.

Background In South Africa, women disproportionately bear the burden of intimate partner violence (IPV), HIV or AIDS, and poor mental health.Objective This study investigated parenting practices among women affected by IPV, HIV and poor mental health syndemics.Study setting The study was conducted in two sites, a peri-urban area and a rural area in Mpumalanga, South Africa.Study design A qualitative research design using a narrative approach with in-depth interviews supported by arts-based methods was used. Data were analysed thematically using MAXQDA (2022).Participants 20 women aged 20–60 who screened positive for HIV, IPV and/or poor mental health in a larger three-generational cohort study were selected.Results Living with the syndemics exacerbated socioeconomic challenges that often translated into an inability to meet basic child needs. Socioeconomic challenges also led to more harsh parenting practices among women living with IPV-Mental Health and HIV-Mental Health syndemics. Due to lack of trust from family members, women living with the HIV-Mental Health-IPV syndemic were often separated from their children. These women exhibited less harsh parenting practices than the women in the other syndemic groups when they did see their children. A history of childhood trauma, leading to overprotective parenting, was common across the groups except for the IPV-Mental Health group. Women in the IPV-Mental Health group often had strained relations with their children’s fathers, affecting their engagement and connection with their children.Conclusion The study underlines challenges experienced by women with IPV-Mental Health, HIV-Mental Health and HIV-Mental Health-IPV syndemics. The overlap of these epidemics strains women’s relationships and affects women’s parenting practices detrimentally resulting in an inadequate provision for children’s needs.

Background: Timely detection of oesophageal injuries post-penetrating neck trauma is imperative because of the associated high morbidity and mortality. Patients commonly undergo both CT angiography (CTA) and contrast swallow studies (fluoroscopic oesophagography) when oesophageal injury is suspected. Objectives: To determine the radiological findings of oesophageal injury after penetrating neck trauma comparing CTA and fluoroscopic oesophagography at a single tertiary centre. Method: The study retrospectively reviewed the data from CTA and fluoroscopic oesophagography reports of patients suspected of oesophageal injuries secondary to penetrating neck trauma at a tertiary hospital in South Africa from January 2018 to December 2022. Results: A total of 76 records were reviewed. The mean age for the participants was 31.5 years, ranging from 0.75–66 years. In this study 6/76 (8%) patients had confirmed oesophageal injury on fluoroscopy, which is considered the gold standard. The majority of penetrating neck injuries were in the 20–29 year age group, with 33/76 (43%) injuries. Stab wounds as the mechanism of injury accounted for 57/76 (75%). Dysphagia was experienced by 10/76 (13%) of those who had injuries. Zone I injuries accounted for 33/76 (43%) of the injuries. Conclusion: The incidence of oesophageal injuries secondary to penetrating neck injuries is comparable to previous studies. This study determined that CTA has a high sensitivity but low specificity. Contribution: Fluoroscopic oesophagography should, therefore, be performed in patients who have an abnormal CTA coupled with clinical signs and symptoms of oesophageal injury.

Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional–integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.

Background: Botswana is one of the countries severely impacted by the HIV/AIDS pandemic. Despite an extensive HIV prevention campaign, the incidence of HIV, particularly among women, remains high. Condoms play a significant role in preventing new HIV infections, although men and women do not consistently use them. Objective: The study assessed the individual, relationship and community factors associated with consistent condom use among women in Botswana. Design: A cross-sectional study using secondary data drawn from a national survey on Gender-Based Violence Indicators in 2012. Methods: The primary survey sampled 639 women, aged 18 years and older, using a multistage procedure. The final sample size for the secondary analysis included a total of 480 women who were sexually experienced and had reported using condoms with their male partners. Multivariate logistic regression analysis was employed to assess the association between consistent condom use and the explanatory variables. The multivariate logistic regression adjusted for cluster/community random effects. Results: About 43% of the women used condoms consistently in the past year. Consistent condom use was more likely among women who were employed in the past year (adjusted odds ratio = 1.77; 95% confidence interval = 1.25-2.50). While, women who lived with their partners (adjusted odds ratio = 0.46; 95% confidence interval = 0.28–0.74), had non-Christian beliefs (adjusted odds ratio = 0.52; 95% confidence interval = 0.29–0.92), perceived that their partners would be angry if they ask to use a condom (adjusted odds ratio = 0.19; 95% confidence interval = 0.06–0.58) and perceived that their community says women need their husband’s permission to do paid work (adjusted odds ratio = 0.56; 95% confidence interval = 0.38–0.83) were less likely to use condoms consistently. Conclusion: Consistent condom use among Botswana’s female population is suboptimal. Consistent condom use was higher among women with employment, and lower among women who lived with their partners, had non-Christian beliefs, feared their partners’ reaction upon asking for condom use and held inequitable community gender beliefs. To enhance women’s consistent use of condoms, friendly condom use information, female economic empowerment strategies and programmes that involve religious leaders and promote progressive and healthy masculine practices in Sexual Reproductive Health/HIV interventions should be considered.

IntroductionMinimizing postprandial glucose response is an important goal for overall diabetes management. Diabetes-specific nutritional shakes (DSNS) have been clinically shown to minimize postprandial glucose response in people with type 2 diabetes (T2DM) compared to high-glycemic foods. However, it is unknown how a high-protein, low-fat DSNS impacts the GLP-1 response.MethodsWe tested the postprandial glucose, insulin, and GLP-1 response to a high-protein, low-fat diabetes-specific nutritional shake (DSNS-HP) compared to isocaloric instant oatmeal (IOM) in a randomized, controlled, crossover study in adults with T2DM (n = 24). Participants were randomly selected to receive IOM or DSNS-HP on two test days. Glucose, insulin, and total GLP-1 concentration were measured at baseline and 15, 30, 45, 60, 90, 120, 180, and 240 min postprandially.ResultsCompared to IOM, the glucose-positive area under the curve (pAUC) was significantly lower (P = .021). DSNS-HP significantly increased GLP-1 pAUC response by 213% (P

The surge in demand for hydrogen (H2) across diverse sectors, including clean energy transportation and chemical synthesis, underscores the need for a thorough investigation into H2 production dynamics and the development of effective controllers for industrial applications. This paper focuses on an electrically heated steam methane reforming (SMR) process for H2 production, offering advantages such as enhanced environmental sustainability, compactness, efficiency, and controllability compared to conventional reforming methods. Electric heating of the entire system allows for adjustments in current to control reactor temperature, thereby impacting hydrogen production rates. However, accurately modeling hydrogen production dynamics presents a formidable challenge, as complex models with high precision are computationally unsuitable for real-time control integration. Considering these factors, an accurate and efficient first-principles-based lumped-parameter model is developed to provide a dependable estimation of hydrogen production in an electrically-heated steam methane reformer. This model is validated experimentally and then utilized in a model predictive controller (MPC). To obtain the necessary state estimate information for the MPC, an extended Luenberger observer (ELO) method is employed to estimate state variables from limited, infrequent and delayed measurements of gas-phase reactor outlet stream and frequent measurements of the reactor temperature. Simulation comparisons with a proportional-integral (PI) controller reveal a much faster response in achieving the desired H2 production rate under the estimation-based MPC. Additionally, the simulations demonstrate the robustness of the controller to process variability such as a decrease in catalyst activation energy, commonly encountered in the SMR process, highlighting its effectiveness in maintaining stable operation under varying process conditions.

BackgroundAlthough food insecurity has been associated with intimate partner violence (IPV), few studies examine it longitudinally or among male perpetrators.MethodsWe used secondary data from a trial that followed 2479 men in a peri-urban settlement in South Africa (February 2016-August 2018). Men self-completed questionnaires at baseline (T0), 12 months (T1) and 24 months (T2) on food security, household type, relationship status, childhood abuse exposure, alcohol use, and perpetration of physical and/or sexual IPV. Cross-lagged dynamic panel modelling examines the strength and direction of associations over time.ResultsAt baseline, rates of IPV perpetration (52.0%) and food insecurity (65.5%) were high. Food insecure men had significantly higher odds of IPV perpetration at T0, T1 and T2 (ORs of 1.9, 1.4 and 1.4, respectively). In longitudinal models, food insecurity predicted men's IPV perpetration 1 year later. The model had excellent fit after controlling for housing, relationship status, age, childhood abuse and potential effect of IPV on later food insecurity (standardised coefficient=0.09, p=0.031. root mean squared error of approximation=0.016, comparative fit index=0.994). IPV perpetration did not predict later food security (p=0.276).ConclusionFood insecurity had an independent, longitudinal association with men's IPV perpetration in a peri-urban South African settlement. These findings suggest food security could be a modifiable risk factor of partner violence.Trial registration numberNCT02823288.

Objectives: The objective of this review was to examine the latest literature regarding the effectiveness of monoclonal antibodies as COVID-19 prophylaxis therapy for immunocompromised patient populations. Methods: Literature review of published real-world and randomized control trials (RCTs) from 2020 to May 2023. Results: COVID-19 is highly transmissible with potentially serious health outcomes, underscoring the need for effective prevention and treatment strategies. Vaccines are highly effective at preventing COVID-19 for the general population; however, efficacy is often impaired in immunocompromised patients given insufficient response to initial exposure and/or memory for secondary exposures. Some individuals may also have contraindications to vaccination. As such, additional protective measures are needed to bolster the immune response in these populations. Monoclonal antibodies have been effective at bolstering immune system responses to COVID-19 among immunocompromised patients; however, they are proving ineffective against the most recent Omicron strains (BA.4 and BA.5). Conclusion: Several studies have investigated the efficacy of monoclonal antibodies as pre- and post-prophylaxis for COVID-19. Historical evidence is promising; however, new variants of concern are proving challenging for currently available regimens.

Area-selective atomic layer deposition (AS-ALD) is a beneficial procedure that facilitates self-alignment for transistor stacking by concentrating oxide growth on targeted areas of a substrate. However, AS-ALD is difficult to incorporate into semiconductor manufacturing industries due to difficulties such as minimal process data and a lack of insight into reactor design. To enable the industrial scale-up of AS-ALD, in silico modeling is necessary to characterize the process. Thus, this work proposes a multiscale computational fluid dynamics modeling framework that simultaneously describes the surface chemistry and ambient fluid behavior for an Al2O3/SiO2 substrate. The multiscale model first involves ab initio molecular dynamics simulations to optimize molecular structures involved in the AS-ALD reactions. Next, a kinetic Monte Carlo simulation is performed to describe the stochastic surface chemistry behavior to determine the surface coverage, and deposition and byproduct rates. Lastly, computational fluid dynamics is performed to study the spatiotemporal behavior of the flow. The surface and flow field simulations are carried out in an integrated fashion. Various AS-ALD discrete feed reactor configurations with differing injection plate geometries were developed to investigate their impact on the processing time to achieve full surface coverage and film uniformity. Results indicate that the multi-inlet reactor model achieves minimal processing time while producing a high-quality film with the AS-ALD process.

Steam methane reforming (SMR) is one of the most widely used hydrogen (H2) production processes. In addition to its extensive utilization in industrial sectors, hydrogen is expanding it share as a clean energy carrier, and more sustainable and efficient H2 production methods are continuously being explored and developed. One method replaces conventional fossil fuel-based heating with electrical heating through the flow of electrons across the reformer. At UCLA, an experimental setup was built of an electrically heated steam methane reforming process. This paper describes the system components, explains the digitalization of the experimental setup and introduces methods for building a first-principles-based dynamic process model using parameters estimated via data-driven methods from process experimental data. The modeling approach uses a lumped parameter approximation and employs algebraic equations to solve for gas-phase variables. The reaction parameters are calculated from steady-state experimental data, and the temperature change is modeled with respect to change in electric current using a first-order dynamic model. The overall dynamic process model is then used in a computational model predictive control (MPC) scheme to drive the process to a new H2 production set-point under unperturbed and steam flowrate disturbance cases. The performance and robustness of the proposed MPC scheme are compared to the ones of a classical proportional–integral (PI) controller and are demonstrated to be superior in terms of closed-loop response, robustness, and constraint handling.

Abstract Background Achieving universal health coverage (UHC) in the context of limited resources will require prioritising the most vulnerable and ensuring health policies and services are responsive to their needs. One way of addressing this is through the engagement of marginalised voices in the priority setting process. Public engagement approaches that enable group level deliberation as well as individual level preference capturing might be valuable in this regard, but there are limited examples of their practical application, and gaps in understanding their outcomes, especially with rural populations. Objective To address this gap, we implemented a modified priority setting tool (Choosing All Together—CHAT) that enables individuals and groups to make trade‐offs to demonstrate the type of health services packages that may be acceptable to a rural population. The paper presents the findings from the individual choices as compared to the group choices, as well as the differences among the individual choices using this tool. Methods Participants worked in groups and as individuals to allocate stickers representing the available budget to different health topics and interventions using the CHAT tool. The allocations were recorded at each stage of the study. We calculated the median and interquartile range across study participants for the topic totals. To examine differences in individual choices, we performed Wilcoxon rank sum tests. Results The results show that individual interests were mostly aligned with societal ones, and there were no statistically significant differences between the individual and group choices. However, there were some statistically significant differences between individual priorities based on demographic characteristics like age. Discussion The study demonstrates that giving individuals greater control and agency in designing health services packages can increase their participation in the priority setting process, align individual and community priorities, and potentially enhance the legitimacy and acceptability of priority setting. Methods that enable group level deliberation and individual level priority setting may be necessary to reconcile plurality. The paper also highlights the importance of capturing the details of public engagement processes and transparently reporting on these details to ensure valuable outcomes. Public Contribution The facilitator of the CHAT groups was a member from the community and underwent training from the research team. The fieldworkers were also from the community and were trained and paid to capture the data. The participants were all members of the rural community‐ the study represents their priorities.