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The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method.

In recent times, electronic portfolios (e-portfolios) are being increasingly used by students and lifelong learners as digital online multimedia résumés that showcase their skill sets and achievements. E-portfolios require secure, reliable, and privacy-preserving credential issuance and verification mechanisms to prove learning achievements. However, existing systems provide private institution-wide centralized solutions that primarily rely on trusted third parties to issue and verify credentials. Furthermore, they do not enable learners to own, control, and share their e-portfolio information across organizations, which increases the risk of forged and fraudulent credentials. Therefore, we propose a consortium blockchain-based e-portfolio management scheme that is decentralized, secure, and trustworthy. Smart contracts are leveraged to enable learners to completely own, publish, and manage their e-portfolios, and also enable potential employers to verify e-portfolio credentials and artifacts without relying on trusted third parties. Blockchain is used as an immutable distributed ledger that records all transactions and logs for tamper-proof trusted data provenance, accountability, and traceability. This system guarantees the authenticity and integrity of user credentials and e-portfolio data. Decentralized identifiers and verifiable credentials are used for user profile identification, authentication, and authorization, whereas verifiable claims are used for e-portfolio credential proof authentication and verification. We have designed and implemented a prototype of the proposed scheme using a Quorum consortium blockchain network. Based on the evaluations, our solution is feasible, secure, and privacy-preserving. It offers excellent performance.

With the swift evolution of wireless technologies, the demand for the Internet of Things (IoT) security is rising immensely. Elliptic curve cryptography (ECC) provides an attractive solution to fulfill this demand. In recent years, Edwards curves have gained widespread acceptance in digital signatures and ECC due to their faster group operations and higher resistance against side-channel attacks (SCAs) than that of the Weierstrass form of elliptic curves. In this paper, we propose a high-speed, low-area, simple power analysis (SPA)-resistant field-programmable gate array (FPGA) implementation of ECC processor with unified point addition on a twisted Edwards curve, namely Edwards25519. Efficient hardware architectures for modular multiplication, modular inversion, unified point addition, and elliptic curve point multiplication (ECPM) are proposed. To reduce the computational complexity of ECPM, the ECPM scheme is designed in projective coordinates instead of affine coordinates. The proposed ECC processor performs 256-bit point multiplication over a prime field in 198,715 clock cycles and takes 1.9 ms with a throughput of 134.5 kbps, occupying only 6543 slices on Xilinx Virtex-7 FPGA platform. It supports high-speed public-key generation using fewer hardware resources without compromising the security level, which is a challenging requirement for IoT security.

The present study has been a pioneering effort examining the role of an annual flood as a potent stimulus inducing changes in channel geomorphology of the Mayurakshi River, India. Twenty cross sections have been considered for the measurement of various hydro-geomorphic attributes of the river in both the pre- and post-flood conditions in 2018. The study sensed an escalating trend for channel width, width/depth ratio, and wetted perimeter while the reverse was also detected for average depth, maximum depth, cross-sectional area, and hydraulic radius. For example, the width/depth ratio recorded an increase of ~ 11%, and the hydraulic radius depicted a decrease of ~ 8%. Furthermore, channel asymmetry, bed asymmetry and bed relief index experienced a decrease after the flood. The sudden hydraulic impulse during monsoon flood as manifested in velocity, discharge, specific stream power, Reynolds number, Froude number increases the erosivity of the fluid. Besides the hydraulic factors, bank material (massive sandbank susceptible to hydraulic action and mixed bank constituted by alternate bands of sand and silt, and vulnerable to failure by piping action) brings substantial changes in channel morphology. Moreover, anthropogenic interventions such as sand mining are found to play a significant role in channel behaviour. The role of the multiple factors driving the morphological changes of the cross sections has been unpacked using canonical component analysis.

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.

Rapid urbanization has brought about rapid changes in land-use and land-cover (LULC) patterns, significantly affecting land surface temperatures (LST). This study uses GIS and remote sensing techniques to assess changes in LULC classes and their impacts on LST at the Barishal City Corporation (BCC) of Bangladesh. As summer is the season with the highest temperature, the study considered the months from March to July from 1998–2024. The key findings of this study reveal a substantial increase in the mean land surface temperature, increasing by 5.75°C from 1998–2024, with the highest temperature reaching 42°C in 2024. This increase in temperature was linked to an 11.29% expansion in built-up areas and a reduction in vegetation (4.8%) and agricultural land (3.8%). The correlation analysis results support these findings, as the NDBI is positively correlated with the LST, indicating that built-up areas tend to increase surface temperatures. In contrast, the NDWI is negatively correlated with the LST, suggesting that water presence helps lower temperatures. The relationship between the NDVI and LST is predominantly negative in the absence of water bodies, whereas the presence of water bodies tends to result in a positive correlation. Most built-up areas presented the highest temperatures over the years, ranging from 34°C to 42°C. Future projections indicate that if the current trend of urban expansion remains, approximately 5.89% of the BCC area is projected to experience temperatures exceeding 38°C by 2033, increasing to 7.93% of the area by 2042. In terms of health impacts, the study identified common risks such as heat stroke, respiratory problems, heat exhaustion, dehydration, diarrhea, and skin rashes, among which heat exhaustion (66.93%) was most prominent. Furthermore, in urban areas, children, elderly people, women, outdoor workers, and people living in tin-shed houses are more vulnerable to high temperatures. This study will help city planners and future stakeholders control the urban heat island effect and understand the side effects of concentrated settlements in the coastal area of Bangladesh.

Sustainable hybrid composites, made of two different natural plant fiber types, are increasingly being attracted by composite researchers, for their cost effectiveness and ability to control mechanical performances through varying weight ratios of different fibers. In contrast, their lower mechanical properties are reported in the literature, because of strength variations of different fiber types and an improper fiber‐matrix stress distribution. Therefore, it is aimed to develop sustainable hybrid composites from two dry fiber preforms—woven fabric and short fiber preform—originated from same fiber type (jute). A highly packed short fiber preform is used as the core layer, while woven fabrics (plain/twill–rib/twill–diamond) are used in the skin layers for producing sandwiched hybrid jute composites. Mechanical tests and scanning electron microscopy images show that hybridized plain fabric/short fiber preform composites have better mechanical properties (≈58 MPa tensile strength/≈117 MPa flexural strength/≈112.12 kJm−2 impact strength with an ≈487.4% improvement) compared to other fabric structures hybrid/nonhybrid composites. This enhancement is related to the interlocking of short fibers with long plain fabric leading to a strong fiber‐matrix interfacial bonding. Thus, this developed hybrid composites, can be applied in many semi‐structural applications, wherein composites’ low cost and mechanical performances are primary concerns. Sustainable hybrid composites are developed using two different dry‐fiber preforms from the similar jute fiber type. Woven fabric and highly packed short jute fiber preform are used as skin and core layers respectively, in these sandwiched hybrid jute composites. Plain fabric/short fiber preform hybrid composites show improved mechanical properties. They are cost‐effective and can be used in semistructural composite applications.

While studies on fertility and contraceptives issues are available, until recently adolescent motherhood has not received enough attention among policy makers in understanding adolescent motherhood in Bangladesh. We aimed to examine the trends and determinants of adolescent motherhood among women aged 15-49 years. For trend analysis we used all the 7 waves of Bangladesh Demographic and Health Survey (BDHS, 1993-2014) data but for multivariate analysis 4 waves of BDHS (2004-2014). Two separate analyses were carried out on ever married women aged 15-49: (1) teenage girls aged 15-19 and (2) adult women aged 20 and above. The prevalence of adolescent motherhood had declined to a slower pace from 1993 to2014 (from 33.0% to 30.8%). Lower spousal age gap and higher education were found to be associated with lower likelihood of adolescent motherhood both among teenage girls [OR 0.447 (0.374-0.533)] and adult women [OR 0.451 (0.420-0.484)]. Teenage girls in the poorest wealth quintile [OR 1.712 [1.350-2.173] were more likely to experience adolescent motherhood than the richest wealth quintile. Teenage girls who had no education were found to have 2.76 times higher odds of adolescent motherhood than their counterparts who had higher than secondary education. Concerning the time effect, the odds of adolescent motherhood among adult women was found to decline overtime. Despite substantial decrease in total fertility rate in Bangladesh adolescent motherhood is still highly prevalent though declining from 1993 to 2014. Social policies including those addressing poverty, ensuring greater emphasis on education for women; and adolescent mothers in rural areas are needed.

Sign language is undoubtedly a common way of communication among deaf and non-verbal people. But it is not common among hearing people to use sign language to express feelings or share information in everyday life. Therefore, a significant communication gap exists between deaf and hearing individuals, despite both groups experiencing similar emotions and sentiments. In this paper, we developed a convolutional neural network–squeeze excitation network to predict the sign language signs and developed a smartphone application to provide access to the ML model to use it. The SE block provides attention to the channel of the image, thus improving the performance of the model. On the other hand, the smartphone application brings the ML model close to people so that everyone can benefit from it. In addition, we used the Shapley additive explanation to interpret the black box nature of the ML model and understand the models working from within. Using our ML model, we achieved an accuracy of 99.86% on the KU-BdSL dataset. The SHAP analysis shows that the model primarily relies on hand-related visual cues to predict sign language signs, aligning with human communication patterns.

Lightning search algorithm (LSA) is a novel nature-inspired optimization algorithm based on the phenomenon of lighting. This optimization algorithm is generalized from the mechanism of step leader propagation. In this study, a variant of LSA for solving binary optimization problems called as binary LSA (BLSA) is presented. It is done by introducing some modification and introducing tangent hyperbolic sigmoid activation function in updating process of the original version of LSA. To evaluate the quality, convergence rate and robustness of the algorithm, the BLSA is tested with a set of well-utilized 24 benchmark functions. Furthermore, a comparative study with other four well-known binary optimization methods is given for validation of the BLSA performance. The results affirm that the proposed BLSA outperforms the other binary optimization algorithms in multidimensional search space in terms of search accuracy and convergence.