Search

A steady supply of hosts at the susceptible stage for parasitism is a major component of mass rearing parasitoids for biological control programs. Here we describe the effects of storing 5th instar Plodia interpunctella larvae in dormancy on subsequent host development in the context of host colony maintenance and effects of the duration of host dormancy on the development of Habrobracon hebetor parasitoids reared from dormant hosts. We induced dormancy with a combination of short daylength (12L:12D) and lower temperature (15°C), conditions known to induce diapause in this species, and held 5th instar larvae of P. interpunctella for a series of dormancy durations ranging from 15 to 105 days. Extended storage of dormant 5th instar larvae had no significant impacts on survival, development, or reproductive potential of P. interpunctella, reinforcing that dormant hosts have a substantial shelf life. This ability to store hosts in dormancy for more than 3 months at a time without strong negative consequences reinforces the promise of using dormancy to maintain host colonies. The proportion of hosts parasitized by H. hebetor did not vary significantly between non-dormant host larvae and dormant host larvae stored for periods as long as 105 days. Concordant with a prior study, H. hebetor adult progeny production from dormant host larvae was higher than the number of progeny produced on non-dormant host larvae. There were no differences in size, sex ratio, or reproductive output of parasitoids reared on dormant hosts compared to non-dormant hosts stored for up to 105 days. Larval development times of H. hebetor were however longer when reared on dormant hosts compared to non-dormant hosts. Our results agree with other studies showing using dormant hosts can improve parasitoid mass rearing, and we show benefits for parasitoid rearing even after 3 months of host dormancy.

Background Following the mass influx of Rohingya refugees into Cox’s Bazaar, Bangladesh in 2017, makeshift settlement camps in Ukhiya and Teknaf have been overburdened, leading to livelihood challenges for both Rohingya and host communities. The humanitarian crisis has had adverse effects on vulnerable populations, which include the elderly, persons with disabilities, adolescents, and single female household heads. Using a subset of a larger dataset on households with most vulnerable groups in both communities, we analysed the effect of the pandemic and lockdown on the livelihood of single female household (HH) heads. Methods A cross-sectional household roster survey was designed to collect data from households with most vulnerable groups (MVGs) of host and Rohingya communities from December 2020 to March 2021; 11 host community villages and 10 Rohingya camps purposively selected as per the affiliated intervention of the project. The paper analysed quantitative and qualitative data from the sub-group of single female household heads without no income/low income. Participants were surveyed for their socio-demographic characteristics, COVID-19 experiences and knowledge, and food security situation, social experiences and mental health. Results We surveyed 432 single female HH heads. Support during the pandemic was reported to be low, with less than 50% of HHs reporting relief meeting their needs; only 36% and 15% of these HHs received rations in camps and host communities respectively. Loan facilities were mostly unavailable and there were reported insufficiencies in food consumption. Over 50% of respondents tested positive on the PHQ-2. Further analyses indicates that having a chronic health issue (OR 2.2, 95% CI 1.33–3.66) was positively associated with the PHQ-2 score Rohingya single females. For host single females, having an ill member in the HH (OR 1.46, 95% CI 1.02–2.08) and the inability to save before the pandemic (OR 1.57 95% CI 1.11–2.23) increased the odds of testing positive. Conclusion Our study findings revealed insufficiencies with economic opportunities and food security for single female headed households, as well as a high rate of positive screening for depression amongst this population. These findings call for a more in-depth understanding of the needs of this group.

A steady supply of hosts at the susceptible stage for parasitism is a major component of mass rearing parasitoids for biological control programs. Here we describe the effects of storing 5th instar

Sandwich-structured composites are extensively used in aerospace, transportation, marine, and automotive applications where mechanical performance and weight-saving features are extremely critical. Consequently, analyzing the mechanical properties including the elastic behavior of the sandwich structure is incumbent for the precise application of such composites. This paper deals with the stress analysis of a two-dimensional (2-D) rectangular sandwich-structured composite with the help of a robust numerical model. A finite difference (FD) scheme based on the displacement potential function is developed to investigate the stress-strain behavior of the sandwich structure under different loading conditions. The 2-D mixed-boundary-value elastic problem is solved by implementing special FD formulations inside the domain and at the boundaries. The mathematical intricacy of the interfaces of the sandwich structure is handled by employing modified FD formula structures. Results obtained from the FD model show that the two interfacial regions constitute the critical stress zones, and the material having a higher modulus of elasticity experiences higher stress. The proposed FD model is validated by comparing the FD results for stress distribution and displacement fields with the results obtained from a commercial finite element (FE) package. The comparison between the FE and FD results shows a good agreement which constructively establishes the proposed FD scheme as an accurate and reliable technique for conducting the structural analysis.

This paper explored the use of Firth's penalized method in the Cox PH framework, which was originally proposed for solving the problem of separation, for developing prediction model for sparse or h...

The growing demand for rich content services and developments of industrial internet of things and vehicle-to-everything communications pose challenging requirements for the next-generation fog radio access networks (F-RANs). Though F-RANs are promising to support these enabling technologies by leveraging edge caching and edge computing, delay performance is still straightforward and should be optimized. A latency optimization problem for F-RANs is formulated, and to solve the problem, a deep reinforcement learning (DRL) based joint proactive cache placement and power allocation strategy is proposed in this paper. Furthermore, to enhance the content serving capability at the edge, we rigorously consider that a set of F-RAN nodes cooperatively serve the content request. The user's demand can be adaptively satisfied either through fog access point mode at the network edge or by centralized cloud computing mode at the cloud tier. The key idea of the proposal is to learn the user's demand and make an intelligent decision for caching appropriate content and allocating a significant amount of power resources. Simulation results show the effectiveness and performance gains of the proposal under maintaining throughput compared with other baselines.

Background Separation or monotone likelihood may exist in fitting process of the accelerated failure time (AFT) model using maximum likelihood approach when sample size is small and/or rate of censoring is high (rare event) or there is at least one strong covariate in the model, resulting in infinite estimates of at least one regression coefficient. Methods This paper investigated the properties of the maximum likelihood estimator (MLE) of the regression parameters of the AFT models for small sample and/or rare-event situation and addressed the problems by introducing a penalized likelihood approach. The penalized likelihood function and the corresponding score equation is derived by adding a penalty term to the existing likelihood function, which was originally proposed by Firth (Biometrika, 1993) for the exponential family models. Further, a post-hoc adjustment of intercept and scale parameters is discussed keeping them out of penalization to ensure accurate prediction of survival probability. The penalized method was illustrated for the widely used log-location-scale family models such as Weibull, Log-normal and Log-logistic distributions and compared the models and methods uisng an extensive simulation study. Results The simulation study, performed separately for each of the log-location-scale models, showed that Firth’s penalized likelihood succeeded to solve the problem of separation and achieve convergence, providing finite estimates of the regression coefficients, which are not often possible by the MLE. Furthermore, the proposed penalized method showed substantial improvement over MLE by providing smaller amount of bias, mean squared error (MSE), narrower confidence interval and reasonably accurate prediction of survival probabilities. The methods are illustrated using prostate cancer data with existence of separation, and results supported the simulation findings. Conclusion When sample size is small (≤ 50) or event is rare (i.e., censoring proportion is high) and/or there is any evidence of separation in the data, we recommend to use Firth’s penalized likelihood method for fitting AFT model.

One of the primary reasons for hesitancy in taking COVID-19 vaccines is the fear of side effects. This study primarily aimed to inspect the potential side effects of the COVID-19 vaccines circulated in Bangladesh.Design and Settings.The study was based on a cross-sectional anonymous online survey conducted in December 2021 across Bangladesh.Participants.The study included consenting Bangladeshi individuals aged 12 and above who had received at least one dose of the COVID-19 vaccines.Main Outcome.Analyses were carried out through exploratory analysis, Chi-square test, and logistic regression to investigate potential side effects of the COVID-19 vaccines.A total of 1,180 vaccinated people participated in the study. Only 39.48% of the participants reported at least one side effect after receiving their COVID-19 vaccine. Injection-site pain, fever, headache, redness/swelling at the injection site, and lethargy were the most commonly reported adverse effects, all of which were mild and lasted 1-3 days. Side effects were most prevalent (about 80%) among individuals who received Pfizer-BioNTech and Moderna vaccines and were least common among those who received Sinopharm and Sinovac vaccines (21%-28%). When compared to the Sinopharm vaccines, the OxfordAstraZeneca, Pfizer-BioNTech, and Moderna vaccines were 4.51 times (95% CI: 2.53-8.04), 5.37 times (95% CI: 2.57-11.22), and 4.28 times (95% CI: 2.28-8.05) likelier to produce side effects. Furthermore, males, those over 50 years old, urban dwellers, smokers, and those with underlying health issues had a considerably increased risk of developing side effects. A lack of confidence in vaccines' efficacy and a substantial level of hesitancy in allowing children (age five years or over) and older people (70 years or over) to receive COVID-19 vaccines were also observed.Side effects of COVID-19 vaccines are minimal, demonstrating their safety. Efforts should be made to disseminate such findings worldwide to increase vaccine uptake.

IntroductionOne of the primary reasons for hesitancy in taking COVID-19 vaccines is the fear of side effects. This study primarily aims to inspect the potential side effects of the COVID-19 vaccines circulated in Bangladesh.MethodsThe study was a cross-sectional anonymous online survey conducted across Bangladesh. Data were collected from December 2 to December 26, 2021. The study included consenting (informed) Bangladeshi individuals aged 12 and above who had received at least one dose of the COVID-19 vaccines. Analyses were carried out through exploratory analysis, Chi-square test, and logistic regression.ResultsA total of 1,180 (males-63.89%, age 50 years or over-65.4%, rural-52.86%) vaccinated people participated in the study. Less than half of the participants (39.48%) reported at least one side effect after receiving their COVID-19 vaccine. Injection-site pain, fever, headache, redness/swelling at the injection site, and lethargy were the most commonly reported adverse effects, all of which were mild and lasted 1-3 days. Side effects were most prevalent (about 80%) among individuals who received Pfizer-BioNTech and Moderna vaccines and were least common among those who received Sinopharm and Sinovac vaccines (21%-28%). When compared to the Sinopharm vaccines, the OxfordAstraZeneca, Pfizer-BioNTech, and Moderna vaccines were 4.51 (95% CI: 2.53-8.04) times, 5.37 (95% CI: 2.57-11.22) times, and 4.28 (95% CI: 2.28-8.05) times likelier to produce side effects. Furthermore, males, those over 50 years old, urban dwellers, smokers, and those with underlying health issues had a considerably increased risk of developing side effects. A lack of confidence in vaccines’ efficacy and a substantial level of hesitancy in allowing children (age five years or over) and older people (70 years or over) to receive COVID-19 vaccines were also observed.ConclusionSide effects of COVID-19 vaccines are minimal, demonstrating their safety. Further studies are required to establish the efficacy of the vaccines.What is already known?Significant COVID-19 vaccine hesitancy has been observed globally, mainly due to vaccine safety and efficacy concerns. Until now, most of the data on COVID-19 vaccine safety and efficacy have been published in manufacturer-funded trials that adhere to regulatory criteria and are monitored by third parties. A lack of independent studies on vaccine safety may have a detrimental effect on vaccine acceptance, which should be intensified to combat the deadly virus.What are the new findings?After receiving the COVID-19 vaccines, less than half of the subjects experienced at least one side effect. The side effects were mild and regular and lasted 1-3 days only. Side effects were most prevalent among individuals who received Pfizer-BioNTech and Moderna vaccines and were least common among those who received Sinopharm and Sinovac vaccines. A lack of confidence in vaccines’ efficacy and a substantial level of hesitancy in allowing children (age five years or over) and older people (70 years or over) to receive COVID-19 vaccines were also observed.What do the new findings imply?Side effects of COVID-19 vaccines are minimal and regular, demonstrating their safety. Efforts should be made to disseminate such findings among marginalized people worldwide who showed greater vaccine hesitancy.

The purpose of this study is to investigate the thermal and mechanical properties of L-PBF processed specimens at room and elevated temperatures while varying the loading conditions. The study covers finite element (FE) modeling of the tensile and fatigue behavior of the L-PBF processed Ti-6Al-4V specimens at room and high temperatures and corresponding experimental validation. The specimen used for the study is a heat-treated and post-machined standard ASTM sub-size flat dog-bone specimen. The FE modeling for the fatigue analyses is conducted for a load ratio of R = 0.1 and by applying fully-reversed cyclic loads at different frequencies. The high-temperature test condition is obtained by adjusting the convective heat transfer coefficient of surrounding air and emissivity inside the furnace. Results for the von Mises stress, strain, total deformation, fatigue life, the factor of safety, and fatigue limit are obtained at room (22 °C) and elevated temperatures (350 °C and 450 °C). The FE results show that the fatigue life decreases as the load increases. It is also found that the strength and fatigue life decrease as the temperature increases due to the development of thermal stress. The performance of the L-PBF processed specimens is also compared to the conventionally manufactured Ti-6Al-4V parts under the same magnitude of load and temperature to outline the differences in strength and fatigue resistance properties. The validation of the FE model is performed by comparing the numerical results with the experimental results under similar operating conditions.

This paper presents a supervised machine learning (ML) model to predict the melt-pool geometries of Ti-6Al-4V alloy in the laser powder-bed fusion (L-PBF) process. The ML model is developed based on the normalized values of the five key features (i.e., the laser and material parameters) — laser power, scanning speed, spot size, powder layer thickness, and powder porosity. The two target variables are the melt-pool width and depth, which define the melt-pool geometry and strongly correlate the geometry with the melt-pool dynamics. Information about the features and the corresponding target variables are compiled from an extensive literature survey. A trained data set is created with the melt-pool evolution data collected from experiments. The data set is divided into training and testing sets before any feature engineering, visualization, and analysis, to prevent any data leakage. The k-fold cross-validation technique is applied to minimize the error and find the best performance. Multiple regression methods are trained and tested to find the best model to predict the melt-pool geometry data. Extra trees regressor is found to be the model with the least amount of error using the mean absolute error function. The verification of the ML model is performed by comparing its results with the experimental and CFD modeling results for the melt-pool geometry at a given combination of the processing parameters in the L-PBF process. The melt-pool geometry outputs obtained for the ML model are consistent with the experimental and CFD modeling results.

Additive manufacturing (AM) technologies are catching the interest day by day due to their ability to produce very complex shapes or geometries. Investigating the fracture and fatigue behavior of additively manufactured parts are essential not only to understand their mechanical response but also to predict and prevent their failure. In this study, fracture and fatigue properties of additively manufactured polymers are investigated experimentally combining the servohydraulic material testing system (MTS 810) and the three-dimensional digital image correlation technology. The polymers used for the analysis are acrylonitrile styrene acrylate (ASA) and Onyx, which are readily available and more prevalent in AM applications. Both notched (i.e., pre-cracked) and unnotched specimens are printed in the edge and flat orientations by the fused deposition modeling printer in the shape of the standard ASTM sub-size flat dogbone structure. The tensile strain is studied ahead of the crack formation, and the crack propagation is observed in the photos obtained from the digital image correlation system. A finite element model of similar contemplation is generated for the validation and extended results. Results show that the orientation of manufacturing has a noticeable effect on the failure of the specimen. In the unnotched specimens, the crack occurs near the neck due to a high-stress concentration. Results for the high cycle fatigue test indicate that fatigue life decreases as the load increases for all types of specimens.

Mobile edge computing (MEC) supports the internet of things (IoT) by leveraging computation offloading. It minimizes the delay and consequently reduces the energy consumption of the IoT devices. However, the consideration of static communication mode in most of the recent work, despite varying network dynamics and resource diversity, is the main limitation. An energy-efficient computation offloading method using deep reinforcement learning (DRL) is proposed. Both delay-tolerant and non-delay tolerant scenarios are considered using capillary machine type communication (MTC). Depending upon the type of service, an intelligent MTC edge server using DRL decides either process the incoming request at the MTC edge server or sends it to the cloud server. To control communication, we draft a markov decision problem (MDP). This minimizes the long-term power consumption of the system. The formulation of the optimization problem is considered under the constraint of computing power resources and delays. Simulation results delineate the significant performance gain of 12% in computation offloading through the proposed DRL approach. The effectiveness and superiority of the proposed model are compared with other baselines and are demonstrated numerically.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance. However, due to the increasing number of users and the constraint of caching storage capacity, there exists a tradeoff between NOMA transmission performance and fronthaul saving. In this paper, a hierarchical game framework is presented to solve the joint optimization problem of user access mode selection and content popularity prediction in NOMA based F-RANs. More specifically, the access mode selection problem is formulated as an evolutionary game. The proposals’ evolutionary payoff expressions are derived by stochastic geometry tool, and the cost functions are related to the fog access point (F-AP) content placement profile as well as the fronthaul constraint. Moreover, the problem of what contents the F-AP should cache is modeled as a content popularity prediction problem, and based on both local and global user request states, a machine learning algorithm is presented to solve it. Simulation results validate the accuracy of analytical results and demonstrate our proposed algorithms can further improve the performance of NOMA based F-RANs.

Concordance statistic (C-statistic), which is equivalent to the area under a receiver operating characteristic curve (AUC), is frequently used to quantify the discriminatory power (the ability of the model to distinguish low and high risk patient) of a risk prediction model developed in the logistic regression framework. Several methods for estimating concordance statistics including both non-parametric and parametric have been proposed in the literature. Despite the several proposals of the C-statistic, it is still unclear to the practical users which approaches should be applied in practice. This paper reviewed and evaluated some commonly used C-statistics by illustrating them using two datasets with different prognostic abilities and an extensive simulation study and compared their results to make some practical recommendations. Several simulation scenarios were considered by varying the sample size, prevalence of the binary outcome, and distribution of prognostic index (or log-odds) derived from the model, to mimic the scenarios in practice. The results revealed that both non-parametric and Kernel-smoothing based methods showed comparable results in most simulation scenarios but performed better than the parametric approach particularly for small sample situation and skewed distribution of the prognostic index. Based on the findings of the study, some practical recommendations are discussed.

The performance of the small-scale stand-alone energy harvesters can be improved by implementing a hybrid energy harvesting technique. This paper aims at presenting the design and characterization of a hybrid energy harvester that can simultaneously harvest energy from mechanical vibration and solar radiation by combining piezoelectric, electromagnetic, electrostatic, and photovoltaic mechanisms. The hybrid device consists of a small high-efficiency solar panel and a bimorph PZT cantilever beam having a cylindrical tip magnet and two sets of capacitors (comb electrodes) attached on two sides of an ASTM 6061 T-6 Aluminum substrate. All the transducing sections of the configuration are interconnected by a smart hybrid electric circuit having a common optimum load resistance, an energy storage, and a microcontroller to generate and store combined power output when subjected to transverse vibration and solar radiation. The initial bias-voltage input required for the electrostatic mechanism is either obtained from the photovoltaic system or taken from the storage through the microcontroller. Results for the maximum power output are obtained at the fundamental resonance frequency of the vibrating cantilever beam. As the hybrid design allows a combined power harvesting method, more power is generated with better conversion efficiency than those obtained by stand-alone mechanisms. In addition to the power calculation, the study includes a stress and fatigue analysis of the cantilever beam using the finite element method to investigate the stress-life criteria of the hybrid structure.

At present a large inventory of discrete as well as continuous probability distributions are available. Mean, variance, skewness, and kurtosis are a set of constants of a probability distribution, which are useful for describing its properties and under certain circumstances for specifying it. These constants depend on the distribution of the random variable. Extensive knowledge on integration, differentiation and summation are required to find mean, variance, skewness and kurtosis of any probability distribution. It is difficult to explain these to students with limited knowledge in mathematics. This book derived and applied general formulae to directly find mean, variance, skewness and kurtosis, instead of using integration or differentiation or summation methods. General formulae for the determination of characteristic function, Laplace transform and moments of various distributions belonging to the transformed Chi-square family, generalized gamma family, generalized family of discrete distributions and log type distributions are derived and applied. This book also derives general formulae for finding point and interval estimators of function of parameters, identification of best population in the transformed Chi-square family including probability of correct identification. This book introduced and applied an easy method for selecting null and alternative hypotheses, finding p-values and taking decisions.

The tensile and fatigue properties of laser-powder-bed-fusion (L-PBF) processed Ti-6Al-4V specimens are investigated at different loading conditions. Two types of as-built and post-machined L-PBF processed dogbone specimens are considered for the study, one is an ASTME8M round specimen and the other one is a customized small-scale flat structure. The tensile and fatigue behavior of the specimens are investigated numerically using the finite element (FE) method. The FE modeling considers both low cycle fatigue (LCF) and high cycle fatigue (HCF) test conditions by applying cyclic loads in fully-reversed and stress ratio R = 0.1 conditions. The FE results for the von Mises stress, strain, total deformation, fatigue life, factor of safety, and fatigue limit of the Ti-6Al-4V specimens are obtained at room temperature (295 K). Results obtained from the model show that the fatigue life decreases as the load increases. It is also found that fatigue life does not vary with the change of the test frequency under a specific fatigue load. The comparison of mechanical properties of the L-PBF processed specimens with conventionally manufactured Ti-6Al-4V parts is also shown to understand the differences in the tensile and fatigue behavior. The validation of the FE model is performed by comparing the numerical results for the yield stress and fatigue limit with the experimental results found from the literature. The overall study contains a detailed analysis of the tensile and fatigue behavior of additively manufactured Ti-6Al-4V parts and provides a guide to investigating the similar properties for other functional materials used in the L-PBF process.

This paper presents a radio frequency (RF) energy harvesting (RFEH) system with a multiband antenna configuration that can simultaneously harvest energy from the sub-6 GHz and 5G millimeter-wave (mm-Wave) frequency bands. The performance of the RFEH system is studied from −25 dBm to 5 dBm input power levels underlying the maximization of the overall efficiency and possible optimization strategies. The maximum achievable power conversion efficiency (PCE) is formulated as a mathematical programming problem and solved by optimizing the design factors including antenna geometry, operational frequencies, rectifier topologies, and rectifier parameters. An array of broadband high gain patch antennas with reconfigurable rectifiers, an impedance matching network, and a voltage-multiplier circuit are employed in the system to maximize the PCE. The voltage standing wave ratio (VSWR) and reflection coefficient (S11) of the antenna are estimated and optimized by numerical method. Simulations are conducted to evaluate the performances of the rectenna and the voltage-multiplier circuit. Results for radiation pattern, wave absorption, input impedance, voltage, and power across the load resistance as a function of frequency are obtained for the optimized configuration. The overall efficiency of the optimized RFEH system is measured at various power inputs and load resistances.

Laser Powder-bed Fusion (L-PBF) is a promising additive manufacturing (AM) process that is capable of speedily fabricating functional components typically used for aerospace, automotive, microelectronics, and biomedical applications. In this study, a 3-D computational fluid dynamics model with Ti-6Al-4V powder-bed is developed incorporating a moving conical volumetric heat source and thermophysical properties of Ti-6Al-4V alloy to conduct the simulations of the L-PBF process. The melt-pool dynamics and its thermal behavior are investigated numerically and results for temperature profile, cooling rate, variation in density, thermal conductivity, specific heat capacity, and enthalpy are obtained for different laser irradiation conditions. Simulation results show that the maximum temperature, dimensions, and liquid lifetime of the melt pool increase with the increase of the laser power and decrease of the scanning speed. A custom ytterbium fiber laser system is applied to perform laser melting experiments with a solid Ti-6Al-4V specimen. The microstructures of the cross-sectional areas are examined by a scanning electron microscope to observe the melt-pool dimensions and the heat-affected zones. Finally, the simulation results for melt-pool geometry are compared with the experimental results to validate the numerical model for the L-PBF process.

Powder-bed fusion (PBF) process is a subdivision of additive manufacturing (AM) technology where a heat source at a controlled speed selectively fuses regions of a powder-bed material to form three-dimensional (3D) parts in a layer-by-layer fashion. Two of the most commercialized and powerful PBF methods for fabricating full-density metallic parts are the laser PBF (L-PBF) and electron beam PBF (E-PBF) processes. In this study, a multiphysics-based 3D numerical model is developed to compare the thermo-fluid properties of Ti-6Al-4V melt pools formed by the L-PBF and E-PBF processes. The temperature-dependent properties of Ti-6Al-4V alloy and the parameters for the laser and electron beams are incorporated in the model as the user-defined functions (UDFs). The melt-pool geometry and its thermo-fluid behavior are investigated using the finite volume (FV) method, and results for the variations of temperature, thermo-physical properties, velocity, geometry of the melt pool, and cooling rate in the two processes are compared under similar irradiation conditions. For an irradiance level of 26 J/mm3 and a beam interaction time of 1.212 ms, simulation results show that the L-PBF process gives a faster cooling rate (1. 5 K/μs) than that in the E-PBF process (0.74 K/μs). The magnitude of liquid velocity in the melt pool is also higher in L-PBF than that in E-PBF. The numerical model is validated by comparing the simulation results for the melt-pool geometry with the PBF experimental results and comparing the numerical melt-front position with the analytical solution for the classical Stephan problem of melting of a phase-change material (PCM).

ObjectiveTo investigate the sociodemographic and geographical variation in under- and overnutrition prevalence among children and mothers.DesignData from the 2014 Bangladesh Demographic and Health Survey were analysed. Stunting and wasting for children and BMI2for mothers were considered as undernutrition; overweight was considered as overnutrition for both children and mothers. We estimated the prevalence and performed simple logistic regression analyses to assess the associations between outcome variables and predictors. Bayesian spatial models were applied to estimate region-level prevalence to identify the regions (districts) prone to under- and overnutrition.Settings/SubjectsChildren agedResultsA significant difference (PConclusionsObserved sociodemographic and geographical inequalities imply slow progress in the overall improvement of both under- and overnutrition. Therefore, effective intervention programmes and policies need to be designed urgently targeting the grass-roots level of such regions.

Multivariate binary responses from the same subject are usually correlated. For example, malnutrition of children are usually measured using ‘stunting’ (low height-for-age) and ‘wasting’ (low weight-for-age) calculated from their height, weight and age, and hence the status of being stunted may depend on the status of being wasted and vice-versa. For analyzing such malnutrition data, one needs special statistical models allowing for dependence between the responses to avoid misleading inference. The problem of dependence in multivariate binary responses is generally addressed by using marginal models with generalized estimating equation. However, using the marginal models alone, it is difficult to specify the measures of dependence between the responses precisely. Islam et al. (J Appl Stat 40(5):1064–1075, 2013) proposed a joint modeling approach for bivariate binary responses using both the conditional and marginal models where the dependence between the responses can be measured and tested using a link function of the models. However, the author didn’t examine the properties of the regression coefficient except for the dependence parameter. This paper has given further insight into the joint model and investigated the properties of regression coefficients using an extensive simulation study. The simulation results showed that the maximum likelihood estimators (MLEs) of the regression coefficients of the joint model showed well performance in terms of bias, mean squared error and coverage probability particularly when sample size large. Generally speaking, the MLEs of the parameters associated with joint models possessed the same asymptotic properties as the MLEs of those associated with standard generalized linear models, except for the interpretations. Further the paper provided an application of joint model for analyzing malnutrition data from Bangladesh demographic and health survey 2011. The results revealed that the estimates of the both marginal and condition regression coefficients of the joint model have meaningful interpretation and explanation, which will in turn help the policy makers for designing appropriate policies for improving nutrition status.

In clinical research, prediction models for binary data are frequently developed in logistic regression framework to predict the risk of patient’s health status such as death and illness. However, when the outcome is rare, the maximum likelihood (ML) based standard logistic regression has been reported to show poor predictive performance by providing overfitted model. To overcome this, penalized maximum likelihood (PML) based logistic models are being widely used in risk prediction, however, their predictive performance in validation settings is not well-documented. Several validation approaches, namely split-sample, crossvalidation, bootstrap validation and its two variants 0.632 and 0.632+, have been widely used to validate the performance of a prediction model, however, it is also unclear which one of these approaches best for estimating accurate predictive performance of a rare-outcome model. This paper focused on evaluating predictive performance of PML based logistic model in such validation settings in comparison with ML based standard model and identifying the effective validation method. An extensive simulation study was performed by creating several scenarios to reflect modeling in dataset with few events. The results revealed that PML based model showed better performance by reducing overfitting to some extent and increasing discriminatory ability over ML based model, irrespective of validation methods under study. Of the validation methods, regular bootstrap and its variants 0.632 and 0.632+, particularly 0.632+, performed well by providing nearly accurate and stable estimate of the true predictive performance. We also illustrated the methods applying them to cardiac data set with few events.

Existing wireless systems do not work efficiently under changing environment. Due to its inflexible and proprietary hardware based architectural limitations it's not easy for an operator to change the network strategy under heavy loads, which enables vendors to try and implement new networking protocols. The software defined network (SDN) is proposed to bring flexibility and programmability, which allows the control plane of switch to be controlled and managed remotely using open-flow channels. Taking the advantage of SDN in wireless networks, a new SDN based Wi-Fi architecture is introduced and an access point (AP) load balance based handover algorithm is proposed. Mininet Wi-Fi emulator is used to construct the desired topology for experiments and performance analysis. Simulation results show a successful handover from an overloaded AP to a lightly loaded AP. A significant improvement observed in the throughput with low latency.

The design and analysis of a paper-based microfluidic analytical device (μPAD) are presented in this paper for the detection of fentanyl and related synthetic opioids. Fentanyl, a synthetic opioid, is an extremely fast-acting synthetic narcotic analgesic having a high potency of approximately 100 to 200 times that of morphine. Detection of fentanyl can be done by colorimetric assays, i.e., spot tests with paper strips and μPADs which offer speed, simplicity of operation, portability, and affordability. The microfluidic behavior of liquid specimen and paper in the μPADs and test strips play a significant role in drug detection methods. Therefore, the study contains the fabrication of the test device using 3D printing and analysis of microfluidic behavior of the paper-based fentanyl test device. A multiphase computational fluid dynamics (CFD) model of a 3D microchannel is developed to evaluate the microfluidic properties. The CFD model incorporates the properties of cellulose and fentanyl solution to determine the flow parameters using the volume of fluid method. Wicking in the cellulose paper is studied analytically considering the Lucas-Washburn equation and Darcy’s law. Experiments with the fabricated μPAD and commercial test-kit samples are also conducted to compare the experimental results with the results for the flow parameters found from the numerical simulation.

Selective laser melting (SLM) is a growing additive manufacturing (AM) technology which is capable of rapidly fabricating functional components in the medical and aviation industries. The thermophysical properties and melt-pool dynamics involved in the powder-bed SLM process play a crucial role to determine the part quality and process optimization. In this study, a 3-D computational fluid dynamics (CFD) model with Cu-Cr-Zr (C-18150) powder-bed is developed incorporating a moving conical volumetric heat source and temperature-dependent thermal properties to conduct the Multiphysics simulations of the SLM process. The melt-pool dynamics and its thermal behavior are investigated numerically and results for temperature profile, cooling rate, variation in density, thermal conductivity, specific heat capacity, and velocity in the melt pool are obtained for different laser beam specifications. The validation of the CFD model is conducted by comparing the simulation results for temperature and the melt-front motion with the analytical results found from the classical Stefan problem of the phase-change material. Studying the process parameters, melt-pool geometry, and thermal behavior of Cu-Cr-Zr alloy can generate valuable information to establish Cu-Cr-Zr as a low-cost engineering material in the AM industry.

Electron beam additive manufacturing (EBAM) is a powder-bed fusion additive manufacturing (AM) technology that can make full density metallic components using a layer-by-layer fabrication method. To build each layer, the EBAM process includes powder spreading, preheating, melting, and solidification. The quality of the build part, process reliability, and energy efficiency depends typically on the thermal behavior, material properties, and heat source parameters involved in the EBAM process. Therefore, characterizing those properties and understanding the correlations among the process parameters are essential to evaluate the performance of the EBAM process. In this study, a three-dimensional computational fluid dynamics (CFD) model with Ti-6Al-4V powder was developed incorporating the temperature-dependent thermal properties and a moving conical volumetric heat source with Gaussian distribution to conduct the simulations of the EBAM process. The melt pool dynamics and its thermal behavior were investigated numerically, and results for temperature profile, melt pool geometry, cooling rate and variation in density, thermal conductivity, specific heat capacity, and enthalpy were obtained for several sets of electron beam specifications. Validation of the model was performed by comparing the simulation results with the experimental results for the size of the melt pool.

Following an incidence of freshwater prawn Macrobrachium rosenbergii postlarvae (PL) mortality in hatcheries in summer 2012, samples from dead PL, rearing water and prawn feed from two south-west coastal districts of Bangladesh were collected to isolate, identify and characterize the agents causing PL mortality. Antibiogram profile of sixteen randomly selected bacteria, isolated from dead PL, that grew on TCBS, to 20 different antibiotics belonging to 12 major groups revealed that the drug resistance pattern varied from moderate (56% to the drugs: ciprofloxacin, cefotaxime, nitrofurantoin, kanamycin) to complete (to penicillin, ceftazidime and oxacillin) level. To identify the isolates, amplified rDNA restriction analysis (ARDRA) classified them in to four groups, and RAPD (randomly amplified polymorphic DNA) typing yielded nine different types of isolates within these four ARDRA groups. The 16S rDNA gene sequences identified that the groups were genotypically diverse belonging to the bacterial species: Enterobacter cloacae, Klebsiella pneumoniae, Exiguobacterium profundum and Enterococcus casseliflavus, respectively, that all demonstrated their killing potential to PLs in a simulated environment. The study therefore identified four different bacterial pathogens, one of which, Exiguobacterium profundum is reported for the first here in Bangladesh, that demand special consideration for disease management strategy.

The glycosyltransferases (GTs) are an important subclass of enzymes that catalyze the biosynthesis of glycosidic bonds in oligosaccharides, polysaccharides and glycoconjugates by transferring a sugar residue from a donor substrate to an acceptor substrate. The membrane-associated GTs play a vital role in the biosynthesis of bacterial cell-wall polysaccharides. Characterization and quantification of GT activities is important for studies of biosynthesis of polysaccharides, drug target development, and production of bacterial products. In this chapter, colorimetric assays for the measurement of GT activities will be presented. Assays for GTs acting on monosaccharide-derivatives are based on the cleavage of unreacted glycosyl-p-nitrophenol acceptors followed by detection of p-nitrophenolate. GT reactions coupled with phosphatases and detection of inorganic phosphate are suitable for most GTs. These assays permit convenient quantification of GT activities and kinetics without the use of radioactive sugars.

In this study, a hybrid energy harvesting device is presented whichsimultaneously harvests energy from solar radiation and mechanical vibration using the photovoltaic, piezoelectric, and electromagnetic mechanisms. The device consists of a bimorph piezoelectric cantilever beam having Lead Zirconate Titanate (PZT) crystal patches on top and bottom surfaces of an Aluminum substrate. The tip of the cantilever beam has an attached permanent magnet which oscillates within stationary coils fixed to the top and bottom of a housing inside a casing. The exterior surface of the casing is covered by silicon photovoltaic panel that can capture energy from solar illumination. All the segments are interconnected by an electric circuit to generate a combined output when subjected to solar radiation and mechanical vibration. The hybrid prototype of the harvester is optimized by using finite element analysis to find the resonance frequencies and stress distributions. Results for power output are obtained at the resonance frequency of the hybrid beam using an optimum common load resistance. As the power outputs of all the mechanisms are combined, a high power output can be achieved by the proposed hybrid energy harvester.

The glycosyltransferases (GTs) are an important subclass of enzymes that catalyze the biosynthesis of glycosidic bonds in oligosaccharides, polysaccharides and glycoconjugates by transferring a sugar residue from a donor substrate to an acceptor substrate. The membrane-associated GTs play a vital role in the biosynthesis of bacterial cell-wall polysaccharides. Characterization and quantification of GT activities is important for studies of biosynthesis of polysaccharides, drug target development, and production of bacterial products. In this chapter, colorimetric assays for the measurement of GT activities will be presented. Assays for GTs acting on monosaccharide-derivatives are based on the cleavage of unreacted glycosyl-p-nitrophenol acceptors followed by detection of p-nitrophenolate. GT reactions coupled with phosphatases and detection of inorganic phosphate are suitable for most GTs. These assays permit convenient quantification of GT activities and kinetics without the use of radioactive sugars.

Fentanyl, a synthetic opioid, is an extremely fast-acting synthetic narcotic analgesic having a high potency of approximately 100 to 200 times that of Morphine. As the synthetic opioid crisis continues to sweep across the world, detection technologies are required to be enhanced to detect, categorize, and identify synthetic opioids effectively. To detect fentanyl and its analogues, immunoassay screens are commercially available for urine specimens. Simple colorimetric assays, i.e., spot tests with paper strips, offer speed, simplicity of operation, portability, and affordability. The microfluidic behavior of the paper strips along with the properties of chemical reagents play significant role in drug detection methods. Therefore, the objectives of this study are to characterize the chemical properties of fentanyl and its analogues and to conduct microfluidic analysis for design optimization and performance evaluation of the fentanyl test strips. A computational fluid dynamics model is developed to evaluate the microfluidic properties. Analytical study and Experiments with test-kit samples are also conducted to validate the results of the numerical simulation. Finally, the performance parameters based on microfluidic analysis were reported showing the room for improvements in the detection technology of fentanyl and related synthetic opioids.

Hybrid energy harvesting is a concept that can be applied to improve the performance of the conventional standalone energy harvesters. In this study, a hybrid energy harvesting device is presented that harvests energy from solar radiation and mechanical vibration by simultaneously combining the photovoltaic, piezoelectric, electrostatic, and electromagnetic mechanisms. The device consists of a bimorph piezoelectric cantilever beam having Lead Zirconate Titanate crystal layers on top and bottom surfaces of an Aluminum substrate. Two sets of comb electrodes (capacitors) are attached on two sides of the substrate. A permanent magnet is attached at the tip which oscillates within a stationary coil inside a casing. The exterior surface of the casing is covered by organic photovoltaic panel that captures energy from illumination. All the segments are interconnected by an electric circuit to generate combined output when subjected to solar radiation and mechanical vibration. Results for power output are obtained at the first resonance frequency of the beam with a common optimum load resistance. As the power outputs of all the mechanisms are combined, a high power efficiency can be achieved by the proposed hybrid energy harvester.

Selective Laser Melting (SLM) and Electron Beam Additive Manufacturing (EBAM) are two of the most promising additive manufacturing technologies that can make full density metallic components using layer-by-layer fabrication methods. In this study, three-dimensional computational fluid dynamics models with Ti-6Al-4V powder were developed to conduct numerical simulations of both the SLM and EBAM processes. A moving conical volumetric heat source with Gaussian distribution and temperature-dependent thermal properties were incorporated in the thermal modeling of both processes. The melt-pool geometry and its thermal behavior were investigated numerically and results for temperature profile, cooling rate, variation in specific heat, density, thermal conductivity, and enthalpy were obtained with similar heat source specifications. Results obtained from the two models at the same maximum temperature of the melt pool were then compared to describe their deterministic features to be considered for industrial applications. Validation of the modeling was performed by comparing the EBAM simulation results with the EBAM experimental results for melt pool geometry.

Bangladesh has been experiencing an epidemiological transition from communicable diseases to non-communicable disease (NCDs), with a rapid increase in the NCD related morbidity and mortality in the last decade. Hypertension and diabetes are two important risk factors of NCDs that significantly increase the burden of cardiovascular diseases and risk of death. While the prevalence of people with both hypertension and diabetes has been increasing dramatically over time, it is essential to identify relatively more prevalent socio-demographic groups and geographical regions (local administrative districts) to reduce the NCDs related deaths in an urgent basis. This study focused on examining the association of socio-demographic factors with both hypertension and diabetes and exploring the regional variations in their prevalence using nationally representative survey data on adult population of age over 35 years. Bayesian spatial analysis was performed for both hypertension and diabetes data separately by fitting a model, that accounts for spatial variations, using integrated nested laplace approximation. The area-specific prevalence was then estimated as weighted average of the corresponding individual level predicted probabilities of being diseased derived from the fitted model, with weight from the individual level sampling weight. Finally, the estimated area-specific prevalence estimates were sketched in country-map to explore regional variations and identify regions with relatively higher prevalence. The results revealed that people of older age, higher education, better socio-economic condition, higher BMI are at greater risk of having hypertension and diabetes. Significant regional variations were observed with prevalence for hypertension ranges between 10% and 35% and for diabetes between 6% and 19% while their national prevalence were reported as 24% and 11%, respectively. The western regions of the country including middle capital city were found to be relatively more prevalent for hypertension while the middle-east and south-east regions were observed to be more prevalent for diabetes. The capital Dhaka region was observed as the most prevalent for both diabetes and hypertension. Details explanations of the findings and evidence based policy implications were discussed.

Cellulosic biomass, the most common organic compound of primary energy source on earth, is a network of interwoven biopolymers of plant cell walls. Degradation of cellulose is important for global carbon recycling. Moreover, biofuel, a renewable fuel whose energy can be derived from cellulosic biomass by enzymatic hydrolysis of cellulases. Among cellulases are endoglucases that act synergistically for subsequent hydrolytic reactions to break down the polymeric cellulose. However, in cellulolytic enzyme activity endoglucanase plays a prominent role in initiating and sustaining the hydrolytic process. Endoglucanase randomly cleaves the cellulose polymer into smaller sugar and oligomeric polysaccharides. Characterization and quantification of endoglucanase activity is important for industry and in the overall study of cellulose degradation. All assays including those for endoglucanase fall into two broad categories either qualitative or quantitative. Quantitative assays can tell if the enzyme is present, how much and its activity. Measurement can be done indirectly using a secondary colorimetric product like (1) molybdenum blue, (2) 3-amino-5-nitrosalicylic acid, (3) bicinchoninic acid (BCA), and (4) 2-cyanoacetamide or directly using an antibody in an ELISA. In this chapter, we discuss several common protocols for the measurement of endoglucanase activity.

Cellulosic biomass, the most common organic compound of primary energy source on earth, is a network of interwoven biopolymers of plant cell walls. Degradation of cellulose is important for global carbon recycling. Moreover, biofuel, a renewable fuel whose energy can be derived from cellulosic biomass by enzymatic hydrolysis of cellulases. Among cellulases are endoglucases that act synergistically for subsequent hydrolytic reactions to break down the polymeric cellulose. However, in cellulolytic enzyme activity endoglucanase plays a prominent role in initiating and sustaining the hydrolytic process. Endoglucanase randomly cleaves the cellulose polymer into smaller sugar and oligomeric polysaccharides. Characterization and quantification of endoglucanase activity is important for industry and in the overall study of cellulose degradation. All assays including those for endoglucanase fall into two broad categories either qualitative or quantitative. Quantitative assays can tell if the enzyme is present, how much and its activity. Measurement can be done indirectly using a secondary colorimetric product like (1) molybdenum blue, (2) 3-amino-5-nitrosalicylic acid, (3) bicinchoninic acid (BCA), and (4) 2-cyanoacetamide or directly using an antibody in an ELISA. In this chapter, we discuss several common protocols for the measurement of endoglucanase activity.

This paper introduced an easy way for selecting null (H0) and alternative hypotheses (H1), finding p-values and taking decisions. Should a research hypothesis or a claim be H0 or H1? This paper proposed a new method to select H0 and H1. Considering two sets of hypotheses (a) H0: θ ≤ θ0 against H1: θ > θ0 and (b) H0: θ ≥ θ0 against H1: θ