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Additional file 4: Fig. S4. Binding of 1E5 was not enhanced by sCD4 or CD4mc. HEK 293T cells expressing EGFP and HIV-1 Env of CRF01_AG, CRF02_AE and subtype B were incubated with sCD4 (2 µg/ml) or YIR-821 (10 µM), and stained with primary antibody (5 µg/ml). Then, IgG binding was detected by APC-conjugated anti-human IgG secondary Ab. The MFI values of APC in EGFP+ cells are shown.

Additional file 5: Fig. S5. Analysis of the competitive binding between 1E5 and A32. HEK 293T cells expressing EGFP and AG-257 Env were stained with 3 µg/ml of biotinylated 1E5 (a) or A32 (b) in the presence of serially-diluted non-biotinylated competitors, A32 and 1E5, respectively. Biotinylated antibodies were detected by APC-conjugated streptavidin. The MFI values of APC in EGFP+ cells are shown. The MFI value in the absence of competitor is shown by dotted line.

This paper represents an expert system to presume systemic lupus erythematosus (SLE) at its early development stage. SLE is an autoimmune disease that assaults almost every organ immediately and puts the affected individual to death in the briefest time frame. Statistical data shows a large number of people lose their lives to SLE due to its deferred suspicion as well as detection. Researchers in their discourse accentuate the speculating SLE at its underlying stage, which can be followed by some particular signs and manifestations. The proposed system considers all the potential signs and symptoms of SLE to develop its knowledge base. The system has been developed by using the belief rule-based inference methodology using the evidential reasoning (RIMER) approach. The domain knowledge and additional knowledge representation parameters are collected from some domain experts. The system has been tested over thousands of patients and found accurate on an average of 99.67%.

Additional file 7: Fig. S7. Anti-CoRBS antibodies failed to increase the binding of anti-cluster A antibodies to AG-252 Env. HEK 293T cells expressing both EGFP and AG-252 Env were stained with biotinylated test IgG (1E5-IgG1, 1E5-IgG3 and A32 at 10 µg/ml) alone or with anti-CoRBS antibody (17b or 4E9C, 5 µg/ml) and CD4mc YIR-821 (20 µM), and the binding was detected by APC-conjugated streptavidin. Histograms of APC in EGFP+ cells are shown.

From the farmers’ perspective, it is very important to detect the beneficial and non-beneficial paddy pests for reducing the use of pesticides with increased productivity. In this chapter, a region-based deep convolutional neural network (Faster R-CNN) where a deep CNN is connected with a region proposal network (RPN) is used to perform the detection and identification paddy pests from the images. The main focus of this chapter is to find out not only harmful pests but also beneficial pests of paddy field so that farmers can make decisions when to use pesticides and when not to. A dataset with a large number of beneficial and harmful pests’ images from the paddy field was created for experimentation. Three models of Faster R-CNN based on ResNet-101, VGG-16, and MobileNet have been applied, and it is found that ResNet-101 gives the highest accuracy of 96.93% for beneficial and 95.07% for non-beneficial pest detection and identification compared to other networks.

Additional file 6: Fig. S6. 1E5 is a non-neutralizing antibody. (a) The neutralization activity of 1E5-IgG1 was tested in TZM-bl assays against Env-pseudotyped viruses from subtype A, CRF01_AE and CRF02_AG of HIV-1. (b) Neutralization activity of 1E5-IgG3 was examined alone or with anti-CoRBS Ab (4E9C) and CD4mc (YIR-821) against pseudotyped virus with AG-257 Env.

Additional file 3: Fig. S3. 1E5 did not recognize a previously well-defined epitope. HEK 293T cells were transfected with plasmids encoding both EGFP and HIV-1 Env (subtype A and CRF01_AE) with point mutations at previously described positions in V2, CD4i, CD4bs and glycosylation sites. Cells were stained with 1E5-IgG or VRC01, and binding was detected by an APC-conjugated anti-human IgG secondary Ab. The MFI values of APC in EGFP+ cells are shown.

While looking for jobs, job seekers like to register themselves in different job portal that has a huge number of job circulars. As from all the job circulars, it is easier to look for the job of their liking. At the same time, these job portals gather some information on the jobseekers that they use to apply to different jobs. Whenever a new job circular is posted in the portal, it is advertised to the job seekers through email notification. To make the email notification more personalized and relevant to the job seekers, a decision tree algorithm is implemented. This decision tree makes the decision of which job circular should be advertised to which job seekers based on the job seeker's field of interest, expected salary and experience. This helps jobseekers relate to the notification and be more engaged in the job portal.

This paper is mainly focused on the low cost technique to measure both AC and DC voltage along with current by using very low cost components. The system can also be easily monitored via a smartphone. The work is intended for the engineering students as most of the Voltmeter/Ammeter is very expensive. Not only that, the acquired data can be stored as per the requirements. New features can be added very easily with this ammeter/voltmeter as well. Whole task was implemented with the help of popular micro-controller ATmega328p to reduce the cost.

The world is grappling with Covid-19, a dire public health crisis. Preventive and control measures are adopted to reduce the spread of COVID-19. It is important to know the knowledge, attitude, and practice (KAP) of people towards this pandemic to suggest appropriate coping strategies. The aim of this study was to assess the KAP of Bangladeshi people towards Covid-19 and determinants of those KAPs. We conducted a cross-sectional survey of 492 Bangladeshi people aged above 18 years from May 7 to 29, 2020 throughout the country. Simple and multiple logistic regression analyses were conducted to identify the factors associated with KAP on COVID-19. About 45% of respondents had good knowledge, 49% of respondents expressed positive attitude towards controlling of COVID-19 and 24% of respondents had favorable practice towards COVID-19. Almost three fourths of the respondents went outside home during the lockdown period. Furthermore, the study found that good knowledge and attitude were associated with better practice of COVID-19 health measures. An evidence informed and context specific risk communication and community engagement, and a social and behavior change communication strategy against COVID-19 should be developed in Bangladesh, based on the findings of this study, targeting different socio-economic groups.

Weather forecasting has several effects in our everyday life from farming to event planning. In the northwestern part of Bangladesh, various natural calamities cause the tragic death of many people and economic loss which impacts the total economic growth in Bangladesh. However, the nonlinear relationship between the input parameters and output data in the weather forecasting system makes it more complex. This study investigates the machine learning-based weather forecasting model for the north-western part of Bangladesh to enhance the accuracy of forecasting results in short periods. Artificial neural networks and extreme learning machine algorithms were used for a strong weather prediction purpose. In this experiment, thirty years of historical weather data of temperature, rain, wind, and humidity from seven weather stations in the northwestern part were collected from the Bangladesh Meteorological Department (BMD). The Extreme Machine Learning (ELM) model performs better than Artificial Neural Network and the accuracy rate is 95%.

A burn is a devastating phenomenon, especially in the developing countries. A bulk amount of medical resources is required to fulfill the demand of burned patients. So, it is very important to find another alternative for the treatment of burned patients. Recently, human amniotic membrane (AM) and Moringa oleifera (MO) are being used separately to treat wounds and burn. In this study, we tried to use a mix of AM and MO formulated to a gel with sodium carboxymethylcellulose (CMC-Na), propylparaben, glycerin, distilled water, and triethanolamine and applied on the burned skin of Wistar rats. Second-degree burns were created using pieces of aluminum (2.5-cm diameter). Skin irritation study, physiological properties, the percentage of wound contraction, and epithelialization period were measured for all groups. AM+MO-treated group had maximum average body weight, food intake, and water intake. At day 24, AM+MO group showed a greater response (p

The effects of M atomic species mixing on the structural, elastic, electronic, and thermodynamic properties of newly synthesized MAX phase (Zr1-xTix)2AlC (0 ≤ x ≤ 1) solid solutions have been studied for the first time by means of density functional theory (DFT) based first principles calculations. The lattice constants are in good accord with the experimental results and found to decrease with Ti contents. The elastic constants, Cij, and the other polycrystalline elastic moduli have been calculated. The elastic constants satisfy the mechanical stability conditions of these solid solutions. The constants, C11, C33 and C44, are found to increase with Ti contents up to x = 0.67, thereafter they decrease slightly. A reverse trend is roughly followed by C12 and C13. The elastic moduli are also found to increase up to x = 0.67, beyond which these moduli go down slightly. Pugh's ratio and Poisson's ratio both confirm the brittleness of (Zr1-xTix)2AlC. Different anisotropy factors revealed the mechanically anisotropic character of these solid solutions. A non-vanishing value of the electronic energy density of states (EDOS) at the Fermi level suggests that (Zr1-xTix)2AlC are metallic in nature. A mixture of covalent, ionic and metallic bonding has been found from the electronic structure with dominant covalent bonding due to hybridization of Zr-4d states and C-2p states. This is also supported by the charge density distribution. The variation of elastic stiffness and elastic parameters with x is seen to be correlated with the partial DOS (PDOS) and charge density distribution. The calculated Debye temperature and minimum thermal conductivity are found to increase with Ti contents, while melting temperature is the highest for x = 0.67. The solid solution with x = 0.67 shows improved mechanical and thermal properties compared to that of the two end members Zr2AlC and Ti2AlC.

Antibiotic-resistance genes carried by coliforms in drinking water is a concerning issue for public health in Bangladesh. This research was carried out to identify coliforms in drinking water and to understand the importance of the int1 gene of coliforms in the spread of resistance to bacterial antibiotics through consumption of contaminated water. A total of 31 drinking water samples were collected from restaurants ( n = 18), health center ( n = 9), and residences ( n = 4) located in Chattogram City, Bangladesh. The isolation and identification of coliforms was performed on selective media with a combination of biochemical and molecular analysis. PCR amplification of the LacZ, uidA and int1 genes was carried out for the identification of the coliform and fecal coliform and antibiotic resistant gene, respectively. Antimicrobial susceptibility test was performed according to the Kirby-Bauer disk diffusion method with McFarland standard against three selective antibiotics including co-trimoxazole, ciprofloxacin, and ampicillin. Of 31 drinking water samples, coliforms were detected within 32% ( n = 10) of the water samples, nine samples were collected in restaurants and one sample in a residence. But no coliform was detected in the drinking water of the health center. Among the identified coliforms, the prevalence of fecal coliforms and the int1 gene was 60% ( n = 6) and 40% ( n = 4), relatively. All isolates containing the int1 microbial-resistance gene were resistant to ampicillin.This study shows that drinking water consumed in different restaurants located in Chattogram, Bangladesh is contaminated by antibiotic-resistant gene bearing coliforms that not only increase the risk of water-borne disease, but also may be the major cause of antibiotic resistance transmission in this part of Bangladesh.

Bio-based materials have attracted ample research interest due to their intrinsic beneficial impact on human society. Microcrystalline cellulose (MCC) extracted from a variety of bioresources is one of the most prominent candidate in this regard. Herein, the MCC was synthesized from jute fiber by a facile ammonium persulfate (APS) oxidation method resulting in carboxylic functional groups on the fiber surface. The carboxylated MCC was utilized to develop polyvinyl alcohol (PVA) composite films in a solution casting method. The surface chemistry, thermal properties, and surface morphology of MCC and MCC-PVA composite films were studied by FT-IR spectroscopy, thermogravimetric analysis, and scanning electron microscopy. The SEM images confirmed the rod-like MCC with average particle diameter 4.6 μm and length in 48.4 μm. The FT-IR spectroscopy suggested the complete removal of lignin and hemicellulose from jute fiber. A peak at 1730 cm−1 was introduced in MCC due to oxidation with APS. The MCC was readily dispersible in water, and chemical interaction with –OH group of PVA was spontaneous due to the carboxylation in MCC. The absorption peak of –OH groups in MCC-PVA composite films were shifted, somewhat disappeared and weakened due to the intra and intermolecular hydrogen bonding of MCC and OH groups in PVA. Thermogravimetric analysis expressed the thermal behavior of MCC and MCC-PVA composite films. The char formation at 500°C of the two different composite films was 15% and 16%, respectively. The stability of MCC-PVA composite films at elevated temperatures is the indication of potential application as flame retardant material.

There are several pieces of research on the statistical modeling of rainfall data in Bangladesh. Since all the seasons of a year do not receive a similar amount of rainfall, hence one single statistical model might not be able to explain the pattern of rainfall at any season of a year. According to the climatologists, Bangladesh has four seasons which are Monsoon, Post-monsoon, Summer, and Winter based on the geographical characteristics of this country. This paper aims to determine the best-fitted probability distribution model for the monthly rainfall data of each particular season in the Khulna district of Bangladesh using the rainfall data of the Khulna region from 1951 to 2018. Very commonly used seven continuous distributions- Normal, Weibull, Gamma, Log-normal, Exponential, Cauchy, and Logistic distributions were used to model the data and to evaluate the performances of the distributions, three non-parametric goodness-of-fit tests were conducted, and AIC, BIC values were calculated. Parameters of the distributions were estimated by the maximum likelihood method. The best-fit result of each season was taken as the distribution with the lowest AIC and BIC values. Among the seven distributions, the Gamma distribution showed the best-fit results of the monthly rainfall data for the Monsoon, Post-Monsoon, and Winter Season, and the Weibull distribution showed the best-fit result for Summer Season.

The Optical Burst Switching (OBS) network is mostly victimized to the Denial of Service (DOS) attack, referred as Burst Header Packet (BHP) flooding attack can prevent reasonable traffics from keeping the necessary resources at transitional core nodes. The attack scenario is to flood the malicious BHP without acknowledging Data Bursts (DB) which can affect low bandwidth utilization, degrade network performance, high data loss rate and ultimately DOS. Therefore, machine predicted analysis has become very promising in recent decades that can effectually identify the attack in the optical switching network. However, due to a very small number of samples of the datasets, traditional machine learning approaches such as Naive Bayes, K-Nearest Neighbour’s (KNN) and Support Vector Machine (SVM) cannot analyse the data efficiently. In this regard, we intend a Deep Convolution Neural Network (DCNN) model to automatically detect the edge nodes at an early stage. Finally, presented that proposed deep model is working enhanced rather than any other traditional model (e.g. Naive Bayes, SVM and KNN).

A culture experiment was conducted to see the growth and production performance of Vietnamese koi (Anabas testudineus) with Magur (Clarias batrachus) at different stocking densities in a farmer’s ponds under semi-intensive rearing system for a period of 120 days. The experiment was conducted in three ponds at Rahmatpur of Muktagachha Upazila of Mymensingh district from 5 August to 5 December, 2013. The size of ponds were 24 (T1), 24 (T2) and 24 (T3) decimal with an average depth of 4 feet. Three different stocking densities were tested, namely treatments T1 (400 fish /decimal, 350 Vietnamese koi and 50 magur); T2 (450 fish /decimal, 400 Vietnamese koi and 50 Magur) and T3 (550 fish /decimal, 500 Vietnamese koi and 50 Magur). Ruposhi Bangla feed was used in all treatments two times daily from the beginning of the fry stocking. The initial weight of Vietnamese koi and Magur were 5±0.00 g and 2±0.00 g respectively. The initial length of Vietnamese koi and Magur were 2.54 and 3.50 cm respectively. The average highest final weight gain of Vietnamese koi was (138.71±0.03 g) observed in T1 and followed by T2 (135.65±0.12 g) and T3 (129.29±0.49 g). Similarly, the average highest final weight gain of Magur (76.00±0.58 g) was observed in T1 and followed by T2 (68.36±0.43 g) and T3 (62.61±0.58 g) respectively. The average highest final length gain of Vietnamese koi (17.38±0.05 cm) was observed in T1 and followed by T2 (17.00±0.04 cm) and T3 (16.33±0.03 cm). Similarly, the average highest final length gain of Magur (21.30±0.09 cm) was observed in T1 and followed by T2 (17.00±0.05 cm) and T3 (15.87±0.02 cm) respectively. The survival rate of the stocking Vietnamese koi and Magur were recorded 95.14 and 72.00 % in T1; 93.25 and 64.00% in T2; 90.80 and 68.00 % in T3 respectively. Fish production in T1, T2 and T3 were 48.93; 52.79 and 60.83 kg/decimal/120 days, respectively. The total production of T3 was increased with the increasing of stocking density compare to T2 and T1. The benefit-cost ratio (BCR) was higher in T3 where BCR was 1.67 and on the other two treatments BCR was 1.53 in T2 and 1.52 in T1 although the highest production was obtained in T3 but individually growth performance of Vietnamese koi and Magur were higher in T1. Based on the result of the present experiment, farmers could be suggested to rear Vietnamese koi with Magur at the stocking density of 550 fish/decimal (500 Vietnamese koi and 50 Magur) which was the highest density tried to get more production and financial benefit. Further experiment need to be conducted by taking higher density than the 550 fish/decimal for the optimization of stocking density to get more production and benefit. Asian Australas. J. Biosci. Biotechnol. 2017, 2 (3), 226-237

Rising usage of web applications in every aspect of life has created intense need of enhancement of user experience and formation of intelligent web applications. This research work was conducted with a view to do so. This paper focuses on the reorganization of web application content based on user’s specific interests while browsing any web application by providing new algorithms and a new approach.

Additional file 1. Supplementary tables and figures featuring additional subgroup comparisons and costs in 2018 Bangladeshi Takas (BDT).

Machine learning methods such as deep neural networks have remarkably improved plant species classification in recent years. It is very challenging task to classify plant species based on their categories. In this work, deep learning approach is explained to identify and classify succulent plant species using VGG19, three layers CNN and five layers CNN network on our dataset. The proposed architecture achieved a significant result from VGG19 and three layers CNN model. In succulent plant image dataset, there are 10 different classes of succulent and non-succulent plants. The dataset consists of 3632 succulent plant images and 200 non-succulent plant images. The model achieved 99.77% accuracy which performs better than VGG19 and three layers CNN model.

This paper represents a novel strategy for developing a disease diagnosis gadget from a patient’s statement. For that, the system solely accepts patients’ statements in a natural language like English and analyzes the patients’ statements to prognosis the symptoms the affected person is presently suffering from. The framework forms the patients’ discourse and afterward utilizes Term Frequency (TF) to find the indications of a malady. Cosine Similarity is utilized to settle on a final decision with respect to regarding disease diagnosis task. Cosine Similarity quantifies the similitude between two non-zero vectors in a vector space model where one of the vectors is constructed with the symptoms the patient is encountering and the rest is developed during knowledge base setup. The framework is tested over 1013 patients with various ailments and its accuracy up to 98.3%.

Visually impaired person cannot perform their activities like ordinary person. It becomes more difficult when a person is visually impaired along with deafness. Sometimes, it is quite impossible to communicate with such people whether in speaking or writing format. Therefore, Expert Braille Communicating System (EBCS) is a promising solution which provides braille writing format from voice. Unfortunately, people of underdeveloped countries are not getting the facilities of advanced electronic braille system for overpricing. In this work, an attempt has taken on designing a modified braille device which is economical and more compelling than previous. The device takes voice as input from an android apps and can convert the voice signal into text format of braille and prints the braille letter in paper. EBCS is trained with 1 epoch and the accuracy is achieved of 97.6%. By implementing the considered Braille system, visually impaired person can entirely engage themselves in the society.

Security has emerged to be a concerning issue in cloud computing as numerous sensitive data are processed and transferred over the cloud servers. In this paper, a variable length key based security mechanism has been designed, developed, and implemented by using the Advanced Encryption Standard (AES) protocol for the secured delivery of cloud data which also works as SaaS (Software as a Services). The length of the used key is given as input by the user followed by the selection of a secret key which is fed into the AES cryptographic system with the desired cloud data message thus producing the Cipher text that is to be transmitted to the destination. In the receiver end, the reverse process is performed with the same key on the received Cipher text and the plaintext is retrieved. Using Python to implement and validate the security process, several messages from cloud users are used and the result for each input is analyzed. The result of our study is then compared with the two existing approaches which clearly shows the advancement of the proposed approach. This process can be applied in any secured electronic message transactions for cloud data.

This paper presents coherency-based reduction of a large power system network in PSS/E. It consists of methodology of network reduction in PSS/E. Reduction has been done on New England IEEE 39-Bus System. Coherent generators are identified by applying fault and noticing their swing equation. Static reduction has been done by aggregating the coherent generators in PSS/E. For dynamic reduction, generator parameters have been modified manually. Results of static reduction and dynamic reduction have been compared with the exact model. The reduced equivalent model will give a better understanding on a large power system and analysis will be easier.

The aim of this study is to build a classifier for predicting a disease existence by learning a least conventional set of features extracted from the clinical dataset. Rough set, indiscernibility relation method along with a feedforward neural network is applied and divided the whole process into two parts. At first part, the rough set method is considered as a reduction of features and selected as subset of attributes. In the next part, classification via feedforward artificial neural network is applied to the selected reduction on the dataset. Obtaining datasets of skin cancer disease from the Engineering in Medicine and Biology Society (EMBC) has been used to test the classifier. Our proposed method obtained 95% accuracy for melanoma skin cancer detection. In this regard, this (ANN) model is proposed intended for detecting automatically the cancer patients at a primary stage. Finally, our proposed model is working improved as opposed to some other conventional model (for example RF and SVM).

Brain stroke become a serious cardiovascular and cerebral disease causes of human death. Precisely predicting stroke effect from a set of predictive attributes may classify high-risk patients and guide cure approaches, leading to reduce relative incidence. In respect to, we have collected the information regarding brain stroke patient’s data from five renowned hospitals in Bangladesh with connectivity in patients with acute thalamic ischemic stroke (melanoma), Atypical Nevus (cancer risk) and Common Nevus (No cancer risk). In this work, we propose an ensemble based Modified Bootstrap Aggregating (Bagging) technique for pattern classification. Existing bagging algorithm, can usually progress the performance of a single classifier. However, they typically need larger space as well as quite time-consuming predictions. However, our proposed accuracy based pruning bagging method can improve the classification performance and reduce ensemble size. In general, our proposed modified bagging technique is more appropriate than traditional bagging technique for the prediction of brain stroke disease patients with greater accuracy of 96%.

Background In recent years, developing countries like Bangladesh are facing a higher burden of non-communicable diseases such as hypertension as a result of demographic transition. Prevalence of hypertension is often studied in this setting. However, evidence on undiagnosed hypertension is not widely available in the existing literature. Therefore, the current study focuses on inequalities in the prevalence of undiagnosed hypertension in Bangladesh. Methods A total of 8835 participants aged 35+ years were included in this study using nationally representative Bangladesh Demographic and Health Survey 2011 (BDHS). In the survey, systolic blood pressure (SBP) and diastolic blood pressure (DBP) of these participants were measured three times with approximately 10 minutes of an interval between each measurement. Any respondent with either SBP ≥ 140 mmHg or DBP ≥ 90 mmHg was considered as patient with hypertension as per the guidelines from American Heart Association. Among the participants, undiagnosed hypertension was defined as having SBP > =140 mmHg or DBP > =90 mmHg and never taking prescribed medicine or being told by health professionals to lower/control blood pressure. Multiple logistic regression analysis was applied for identifying factors associated with undiagnosed hypertension. Further, socioeconomic inequalities in the prevalence of undiagnosed hypertension were estimated using Concentration Index (C). Results We found 978 (59.9% of the total) were undiagnosed among 1685 hypertensive patients studied. Regression analysis showed individuals with being underweight, having poor socioeconomic conditions, and lower educational qualifications were more likely to have undiagnosed hypertension. A similar association between undiagnosed hypertension and socioeconomic quintiles was observed using concentration index (C = − 0.07). On the other hand, individuals from higher age group (50–64 or above), female sex, and Sylhet region were at lower risk of undiagnosed hypertension. Conclusions This study showed that a large proportion of the cases with hypertension are remained undiagnosed in Bangladesh, especially among the poor and low educated population. Screening and awareness building initiatives on hypertension should be taken for this group of population to reduce the burden of undiagnosed hypertension.

In grid connected system for monitoring single phase voltage parameters such as voltage amplitude, frequency and phase accurately variety of PLL methods are being used recently. In this paper a modified phase locked loop (PLL) system is represented for single phase operations. The mentioned technique offers a modified orthogonal system generation based on second order generalized integrator (SOGI). In this work Park transformation based phase comparator is used where only the quadrature axis component is tuned through PI controller to maintain almost zero phase difference between input and output voltage. This PLL method shows better performance in different abnormal conditions of grid like harmonics, noise, DC offset, voltage sag and so on. For harmonics suppression from input voltage signal, a filter is utilized at the input side of this PLL structure. The obtained simulation results are provided to evaluate the efficiency and accuracy of the mentioned PLL method.

Cloud computing has become the latest technological computing area providing a large number of advantage to the different organization with its different business model at low cost. Nevertheless, there is always a security concern when uploading sensitive data in the cloud server. Client-side encryption is a common and better solution for ensuring end users that a third party user cannot access the uploading data. Cloud service providers maintain different techniques to protect data but like google drive, most of the company do not use client- side encryption. In this paper, we propose a way to protect the data from hacking or losing when storing or uploading the data in the cloud server using the combination of Advanced Encryption Standard and Secure Hash Algorithm with Initial Vector.

This two part study investigated the deicing performance of a GLAss fiber REinforced aluminum laminate and the influence of its surface curvature on the ice-to-water phase change. Part-1 of this study described the numerical model and the experiments conducted to validate the numerical model. In companion, Part-2 focused on interrogating the influence of substrate curvature on the phase transition based on a numerical model, in which an ice-to-water phase change method in a Lagrangian numerical framework predicted the deicing of a rectangular flat- and a curved GLAss fiber REinforced aluminum 5A-3/2 laminate embedded with a recurring S-shaped copper-mesh. As seen, deicing of the rectangular flat GLAss fiber REinforced aluminum 5A-3/2 model demanded a surface power density of 14.62 kW/m2 at an ambient temperature of 267.15 K. The numerically predicted deicing time, surface power density and deicing temperature of the rectangular flat GLAss fiber REinforced aluminum model were in excellent agreement with that of the rectangular flat GLAss fiber REinforced aluminum specimen of the experiment. Using the identical surface power density and power pulse duration, the curved GLAss fiber REinforced aluminum model required a deicing time 9 s shorter than that needed by the rectangular flat GLAss fiber REinforced aluminum model. After the melting of the entire ice-aluminum interface, temperature of the curved GLAss fiber REinforced aluminum model exhibited a steep increase and surpassed the temperature of the rectangular flat counterpart. A higher temperature of GLAss fiber REinforced aluminum is appreciated for efficient deicing, if the cyclic change of temperature does not induce thermal fatigue.

Throughout this study, we have investigated Sb doping’s effects on the physical metallurgy of perovskite BaSnO3 by employing the first-principles calculations based on the density functional theory (DFT). We observed an increasing trend of lattice constants with an increasing doping concentration of Sb on perovskite BaSnO3. The calculated formation enthalpy and elastic constants ensured the structural stability of our system even under varied doping of Sb. Our band structure calculations suggested that it would be possible to tune the band structure of BaSnO3 by partially substituting Sb at the Sn-site, which reduced the band gap at low doping levels and transformed the compound from semiconducting to metallic at Sb ≥ 12.5%. The mechanical properties exhibited the prominent effects of Sb doping. In addition, the anisotropy of our system was significantly dependent on the elastic constant Cij and varied as the doping concentration of Sb is increased. Therefore, our simulation outputs clearly illustrated the importance of taking into account the Sb doping influence on the physical properties of BaSnO3 perovskite material.

This study uses density functional theory to investigate the structural, mechanical, electronic, optical, and thermodynamic properties of tetragonal LaRhGe3, LaIrGe3, and LaPdGe3 compounds. The investigated lattice parameter showed similar results to the experimental data, justifying the accuracy of our calculations. The negative values of formation enthalpy confirmed the thermodynamic stability of LaTGe3 (T = Rh, Ir, or Pd). The mechanical stability of these compounds was also verified by their single independent elastic constants. Poisson’s and Pugh’s ratios revealed that all the compounds have a ductile nature. The metallic nature of these phases was found from their band structure calculations. The study of Mulliken bond populations and charge density maps ensured the existence of a mixed character of ionic, covalent, and metallic nature in LaRhGe3, LaIrGe3, and LaPdGe3 compounds. Detailed investigation was also performed on optical properties, and the dielectric function, absorption, and conductivity again ensured the metallic feature of all these phases. The calculated optical functions suggested their potential application in quantum-dot light emitting diodes, organic light emitting diodes, solar cells, waveguides, and solar heating reduction. Moreover, the very low values of minimum thermal conductivity and the Debye temperature are indicative of their suitability for thermal barrier coating materials.

First-principles explorations were carried out on the structural, mechanical, thermal, and optical properties of Cr2CoZ (Z = Al, In) inverse-Heuslers for the first time using density functional theory (DFT). The formation enthalpy and elastic constants (satisfied Born criteria), respectively, ensured the thermodynamic and mechanical stability of both compounds. The dynamical stability of Cr2CoIn was also confirmed by the phonon dispersion curve. The compound Cr2CoAl switched from brittle to ductile when Al is replaced by In, which in turn enhanced its machinability for industrial applications. In addition, these compounds showed themselves to be anisotropic, and the anisotropy decreased when Al was replaced with In. The Debye temperature and thermal conductivity of Cr2CoAl were also reduced due to the replacement of Al by In, which made it promising thermal barrier coating (TBC) material. Moreover, the deeper optical insights revealed the potential applications of our compounds in numerous optoelectronic and electronic device applications.

Deicing is a mandatory procedure to ensure the required aerodynamic lift of aircraft. Countless experimental and numerical investigations have been conducted to determine the deicing efficiency of various ice protection systems. The literature review, however, yielded no single study that has been dedicated to investigate the deicing performance of a GLAss fiber REinforced aluminum (GLARE)-based electro-thermal ice protection system. Of notes, the upper fuselage of the front- and the rear section of Airbus A380 comprises circa 469 m2 of GLARE. This study, therefore, proposed a phase change model based on thermo-dynamics to determine the power consumption and the de-icing performance of a GLARE laminate embedded with recurring S-shaped copper heater elements. The model addressed the influence of copper mesh length and orthotropic properties of GLARE on deicing. As a test case, the power budget for deicing a GLARE 5A-3/2 laminate was predicted numerically. The numerical analysis demonstrated that a surface power density of 14.62 kW/m2 sufficed to deice a GLARE 5A-3/2 laminate covered with a 0.8 mm thick ice layer at an ambient temperature of 267.15 K. To corroborate the numerical predictions, deicing experiments had been conducted in a miniature icing wind tunnel by incorporating icing conditions comparable to that of the numerical analysis. It was found that the predicted surface power density was in excellent agreement with that determined in the deicing experiment. In the experiment, an infrared camera measured the temperature gradient of the GLARE specimen, which was also in close proximity to that of the GLARE model in the numerical analysis.

Since many decades researchers have been interrogating the re-entry physics of lunar objects in the Earth’s atmosphere. A request like ‘demise + re-entry’ in a web search engine gives hundreds if not thousands of studies concerned with the breakup and the disappearance of space flotsam in the Earth’s atmosphere. However, it is hard to comprehend a rational reason why the engineering physics involved in the thermal degradation of spacecraft structures still can be explained with inherent uncertainties. Many derelict satellites and spacecraft are orbiting the Earth. They will gradually enter the Earth’s atmosphere attributed to the gravitational force and may will not survive the aero-thermal load induced by the dense Earth’s atmosphere. This study, therefore, investigated the propensity to aero-thermal demise of a pristine and a pre-damaged spacecraft shielding structure during atmospheric re-entry. The shield was made of a GLAss fiber REinforced aluminum panel, which accommodated five quasi-isotropic S2-glass fiber reinforced FM94-epoxy laminates alternately stacked between six aluminum 2024-T3 alloy sheets. The pre-damage of the shielding system was akin to a damage can be generated by an impact of a micrometeoroid. To impart the pre-damage, a GLAss fiber REinforced aluminum plate was first impacted with a 2 mm diameter stainless steel spherical projectile at 5.49 km/s. The projectile perforated through the plate. Next, the perforated GLAss fiber REinforced aluminum plate was exposed to an air plasma-jet (oxygen dissociated) of a specific enthalpy of 5.4 × 103 kJ/kg at standard air pressure conditions. The high enthalpy plasma thermally degraded the pre-damaged plate, while debris was discharged uprange and downrange out of the plate. The dynamic pressure of the plasma flow exacerbated the debris discharge. The endothermic destruction of a pristine GLAss fiber REinforced aluminum specimen followed several steps: first, layer-after-layer oxidation and melting of the aluminum sheets; next, decomposition and sublimation of epoxy accompanied by thermal and viscous diffusion of molten aluminum and molten glass fibers; finally, the onset of a hole in the pristine specimen. By contrast, the plasma-jet that streamed through the punched hole of the pre-damaged GLAss fiber REinforced aluminum specimen, had thermally decomposed the frontal material layers of the specimen to a large extent in the initial few seconds. The punched hole, however, did not succumb to enlarge after the surface temperature of the GLAss fiber REinforced aluminum specimen reached an asymptote. To reproduce the experimental findings, a new Moving Mesh Front procedure had been developed and implemented in a Lagrangian finite element framework. The numerical analysis considered the temperature-dependent thermal properties of orthotropic S2-glass/FM94-epoxy composites and isotropic aluminum alloys. For a comparable heating condition, the agreement between the predicted and the measured temperatures of the specimen front surface was within 4.4%, while the corresponding agreement was within 6.7% for the rear surface temperature of the test specimen.

In the original article, the Discussion and Conclusions section inadvertently contained an error in the sentence “the inner Al-skins need to be thinner (max 0.2 mm thick) to reinforce global bending and energy dissipation in elastic deformation”. It is now corrected to "the inner Al-skins need to be thinner (max 0.2 mm thick) to reinforce global bending and energy conservation in elastic deformation."