Risk factors associated with overweight and obesity among urban school children and adolescents in Bangladesh: a case–control study

Background Childhood obesity has become an emerging urban health problem in urban cities in Bangladesh, particularly in affluent families. Risk factors for obesity in this context have not been explored yet. The objective of this study was to identify the risk factors associated with overweight and obesity among school children and adolescents in Dhaka, Bangladesh. Methods From October through November 2007, we conducted a case–control study among children aged 10–15 years in seven schools in Dhaka. We assessed body mass index (weight in kg/height in sq. meter) to identify the cases (overweight/obese) and controls (healthy/normal weight) following the Centers for Disease Control and Prevention age and sex specific growth chart. We used a structured questionnaire to collect demographic information and respondent’s exposure to several risk factors such as daily physical activity at home and in school, hours spent on computer games and television watching, maternal education level and parents’ weight and height. Results We enrolled 198 children: 99 cases, 99 controls. Multiple logistic regression analysis revealed that having at least one overweight parent (OR = 2.8, p = 0.001) and engaging in sedentary activities for >4 hours a day (OR = 2.0, p = 0.02) were independent risk factors for childhood overweight and/or obesity while exercising ≥ 30 minutes a day at home was a protective factor (OR = 0.4, p = 0.02). There were no significant associations between childhood overweight and sex, maternal education or physical activity at school. Conclusion Having overweight parents along with limited exercise and high levels of sedentary activities lead to obesity among school children in urban cities in Bangladesh. Public health programs are needed to increase awareness on risk factors for overweight and obesity among children and adolescents in order to reduce the future burden of obesity-associated chronic diseases.</p

A Doubly-Fed Induction Generator (DFIG)-Based Wind-Power System with Integrated Energy Storage for Remote Electrification

Electrification of off-grid remote communities is commonly accomplished through diesel generators. The method may even be employed in cases where there exists an un­ reliable connection to the power grid. Regardless, the method is environmentally-hostile, typically costly, and likely risky. Therefore, to mitigate the reliance on diesel fuel, uti­ lization of renewable energy resources has been considered in recent years. This thesis investigates the feasibility of and technical considerations involved in the employment of a specific class of variable-speed wind-power systems, integrated with battery energy stor­ age, for remote electrification applications. The wind-power system under consideration is based on the doubly-fed induction gen­ erator (DFIG) technology, which features a number of characteristics that render it at­ tractive for the incorporation of battery energy storage. This thesis identifies the control strategy, different control sub-functions, and the controllers structures/parametes required to accommodate the battery energy storage. The developed control strategy enables the operation of the wind-power/storage system in the off-grid (islanded) mode of operation, as well as the grid-connected mode of operation. Under the developed control strategy, the wind-power/storage system can operate in parallel with constant-speed wind-power units, passive loads, and induction motor loads. The effectiveness of the proposed control strategy has been demonstrated through comprehensive simulation studies enabled by the commercial software package PSCAD/EMTDC. In addition to the control aspects, this thesis studies the reliability aspects of the pro­ posed wind-power/storage system, for an example remote electrification system. Thus, a new reliability assessment method has been developed in this thesis, which combines the existing analytical and simulation-based probabilistic approaches. The reliability analysis conducted indicates that the battery energy storage capacity, the wind magnitude and pro­ file, and the load profile impose remarkable impacts on the reliability of the electrification system. It also indicates that a connection to the power grid, however unreliable, signifi­ cantly mitigates the need for a large battery to achieve a given degree of reliability

Monolayer Sorption Isotherm Model For Surface Tension Of Aqueous Surfactant Solutions Containing Electrolytes And Organic Mixtures

The surface tensions and compositions of aqueous aerosols dictate surface-mediated processes, especially the growth of aerosol particles into cloud nuclei and interfacial chemical reactions. Surface tension is also an indirect proxy for the partitioning of organics to the surface due to enhanced organic activity, driven by a reduction in solubility. Model predictions are generally not available for most surfactant-laden multi-component aqueous solutions, despite the importance of surface tension. Our recent multi-component surface tension model uses competitive adsorption at the interface, but these results are not consistent with salting out.1 We have now applied these techniques to surfactants in pure water and aqueous solutions containing either NaCl or glutaric acid based on our previously derived two-parameter surface tension model from a monolayer adsorption framework2 developed for binary solutions. New model expressions incorporate the measured Critical Micelle Concentrations (CMCs) to reduce an empirical parameter. Further parameter reduction was achieved through correlations to the surface tension value at the CMC. The calculated model parameters for pure surfactant solutions are used to determine Setschenow constants in salty solutions to quantify the salting out effects at different surfactant concentrations. The model parameters and predictions we present improve organic surface-bulk partitioning predictions in aqueous aerosols, which has important implications for aerosol particle processing in the atmosphere

Forensic Iris Image Synthesis

Post-mortem iris recognition is an emerging application of iris-based human identification in a forensic setup, able to correctly identify deceased subjects even three weeks post-mortem. This technique thus is considered as an important component of future forensic toolkits. The current advancements in this field are seriously slowed down by exceptionally difficult data collection, which can happen in mortuary conditions, at crime scenes, or in ``body farm'' facilities. This paper makes a novel contribution to facilitate progress in post-mortem iris recognition by offering a conditional StyleGAN-based iris synthesis model, trained on the largest-available dataset of post-mortem iris samples acquired from more than 350 subjects, generating -- through appropriate exploration of StyleGAN latent space -- multiple within-class (same identity) and between-class (different new identities) post-mortem iris images, compliant with ISO/IEC 29794-6, and with decomposition deformations controlled by the requested PMI (post mortem interval). Besides an obvious application to enhance the existing, very sparse, post-mortem iris datasets to advance -- among others -- iris presentation attack endeavors, we anticipate it may be useful to generate samples that would expose professional forensic human examiners to never-seen-before deformations for various PMIs, increasing their training effectiveness. The source codes and model weights are made available with the paper

An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometry

Metal Additive Manufacturing (MAM) has reshaped the manufacturing industry, offering benefits like intricate design, minimal waste, rapid prototyping, material versatility, and customized solutions. However, its full industry adoption faces hurdles, particularly in achieving consistent product quality. A crucial aspect for MAM's success is understanding the relationship between process parameters and melt pool characteristics. Integrating Artificial Intelligence (AI) into MAM is essential. Traditional machine learning (ML) methods, while effective, depend on large datasets to capture complex relationships, a significant challenge in MAM due to the extensive time and resources required for dataset creation. Our study introduces a novel surprise-guided sequential learning framework, SurpriseAF-BO, signaling a significant shift in MAM. This framework uses an iterative, adaptive learning process, modeling the dynamics between process parameters and melt pool characteristics with limited data, a key benefit in MAM's cyber manufacturing context. Compared to traditional ML models, our sequential learning method shows enhanced predictive accuracy for melt pool dimensions. Further improving our approach, we integrated a Conditional Tabular Generative Adversarial Network (CTGAN) into our framework, forming the CT-SurpriseAF-BO. This produces synthetic data resembling real experimental data, improving learning effectiveness. This enhancement boosts predictive precision without requiring additional physical experiments. Our study demonstrates the power of advanced data-driven techniques in cyber manufacturing and the substantial impact of sequential AI and ML, particularly in overcoming MAM's traditional challenges