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Textile based pressure sensors: a review of materials, fabrication, and applications
(Journal of the Textile Institute, 2025-12-25) Md Aptabusjaman; Syed Rashedul Islam
The greatest breakthrough in modern trends is smart textiles. A magnificent development in the field of wearable and touchable electronics, specifically, for the creation of smart or intelligent textiles, the pressure sensor-based smart textile is highly needed. Designing textile-based products is very difficult, with sensitive power, straightforward production, and low cost. Therefore, this review paper has reported the substantial yarn-based triboelectric and pressure-based sensing smart textiles. The integrated spiral stainless steel yarn has been acting as the inner electrode layer, synthetic filament, and polytetrafluoroethylene filament, respectively, as both positive and negative layers are made of the woven construction. Both mechanical stability and sensing capabilities are strong points of this sensing textile. The created device, which is breathable, light, and even dyeable, can be applied to any chosen body portion to measure dynamic human motions. It can also be used to measure and keep tracking of a variety of human movements at conjunction with numerous joints, including the hand, elbow, knee, and underarms. Additionally, the sensing textile can record pulse signals in real-time and reflect the human body’s current state of health. Thus, this analysis offers a cutting-edge and potentially lucrative path for multifunctional pressure sensor textiles, which have numerous uses in smart clothing and individualized healthcare.
Enhancing E-Commerce Text Classification: A GRU-Based Approach for Improved Product Understanding.
(Lecture Notes in Electrical Engineering, 2025-04-12) Md. Nazmul Abdal; Shahanaz Islam; Shaown Khairum Islam; Rutba Aman
In the burgeoning landscape of e-commerce, the ability to accurately classify product texts is paramount for enhancing user experience and driving business success. Traditional approaches to text classification often struggle with the nuances and complexities inherent in e-commerce product descriptions. In this paper, we propose a novel approach utilizing Gated Recurrent Unit (GRU) to address these challenges and improve product understanding in e-commerce text classification tasks. Our model leverages the inherent sequential nature of product descriptions, effectively capturing long-range dependencies and semantic relationships within the text. We use a standard dataset in extended trials to demonstrate the superiority of our GRU-based approach over conventional methods in terms of classification accuracy and robustness across diverse product categories. Furthermore, we conduct comprehensive analyses to gain insights into the inner workings of our model and its ability to learn meaningful representations of e-commerce text data. The performance of the model is compared using several cutting-edge techniques, including Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) in order to show that our model is superior at correctly classifying e-commerce texts. The experimental findings show that the suggested model performs competitively in classifying e-commerce texts, surpassing other approaches with an accuracy of 98.35%. Our findings underscore the potential of GRU-based approaches for advancing the state-of-the-art in e-commerce text classification, offering promising avenues for future research and practical applications in the domain.
Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms
(Geomatics, Natural Hazards and Risk, 2025-07-12) Abu Reza Md. Towfiqul Islam; Md. Uzzal Mia; Nourin Akter Nova; Rabin Chakrabortty; Md. Sanjid Islam Khan; Bonosri Ghose; Subodh Chandra Pal; A. B. M. Mainul Bari; Edris Alam; Md Kamrul Islam; Mohammed Ali Alshehri; Hazem Ghassan Abdo; Romulus Costach
This article intends to assess flood susceptibility mapping in
Meghna River basin (MRB) and identified flood susceptible regions
using three benchmark models including random forest (RF), support vector machine (SVM) and bagging with Naïve Bayes (NB)
stacking ensemble algorithms (e.g. RF-NB; SVM-NB and BaggingNB). The flood sample was partitioned into a training set (70%), and
a validation set (30%), and the capability of prediction of floodinfluencing variables was quantified by the multi-collinearity test.
Several statistical metrics and Area Under the Receiver Operating
Characteristics (AUROC) technique were applied to evaluate the
techniques’ performance and precision. The outcomes showed that
the significant factors influencing flash floods include rainfall, distance from the river and river density. The NB-Bagging outperforms
(� a prediction accuracy of 95.1%) than other models in predicting
the risk of flooding in the MRB. Results obtained from NB-Bagging
showed that 12% and 21% of the basin were demarcated as having
high and very high flood susceptibility, respectively. This article
identified that rainfall and distance from the river were the two
most driving factors influencing flooding in the MRB. The present
work will aid decision-makers and local authorities determine flood
Fabrication of nanoparticle-reinforced composite hydrogel for improved durability, antifouling, and thrombosis-resistance in arteriovenous grafts.
(Colloids and Surfaces B: Biointerfaces, 2025-04-29) Dawit H; Mehmood S; Zahid Hussain; Syed Rashedul Islam; Zhili Wang; Yi Cao; Xingzhu Liu; Renjun Pei
Arteriovenous grafts are routinely designed to provide a deliberate connection between an artery and vein in patients during hemodialysis. The commonly used grafts present significant drawbacks such as thrombosis, bacterial infection, and biofouling which prevents their functionality. To endow hydrogels with improved anti-thrombosis, stable antifouling, and strong mechanical strength, a surface-modified nanoparticle-reinforced nanohybrid hydrogel is developed. In brief, zwitterionic sulfobetaine methacrylate (SBMA) is coated on bentonite clay (BC) nanoparticles via a simple method. BC-SBMA nanoparticles were then loaded onto sodium alginate /polyvinyl alcohol hydrogel composite. Calcium chloride (Ca2+) crosslinking is employed to form stable network and optimize polyvinyl alcohol/sodium alginate (PS) hydrogel composite. BC-SBMA particles were dispersed into PS hydrogel and crosslinked to form nanohybrid hydrogel (PS@BC-SBMA). The nanohybrid hydrogel was characterized for its morphological, mechanical, physicochemical, antibacterial, biocompatibility, antifouling, ex-vivo anti-thrombogenic, and in-vivo anti-inflammatory properties. The results revealed that the presence ofBC-SBMA particles boosted the mechanical strength and facilitated biocompatibility. The presence of zwitterionic polymers provided excellent antifouling properties toward blood platelets, unnecessary proteins, and bacterial strains. Hence, the cooperative effects of the nanohybrid hydrogel such as biocompatibility, antifouling, and mechanical properties lead to a desirable candidate for blood-contacting implants.
Green HRM practices and firm environmental performance: mediating role of employee green behavior
(Journal of Advances in Management Research, 2025-05-14) Md Sajjd Hosain; Farhana Mitu; Mohitul Mustafi
Purpose The purpose of this comprehensive empirical study is to investigate the relationships between four green human resource management (GHRM) practices [green recruitment and selection (GRS), green training and development (GTD), green compensation and benefit (GCB), and green performance management (GPM)] and firm environmental performance (FEP) of 92 Bangladeshi manufacturing firms based on the Resource Based View (RBV). Additionally, the researchers examined the mediating role of employee green behavior (EGB) on the four direct relationships. Design/methodology/approach This research was carried out utilizing survey data obtained from 242 diverse high and mid-ranking executives, including Chief Executive Officers (CEOs), department heads and project managers. The selection of respondents was purposive. Employing a cross-sectional survey design, this study engaged a quantitative deduction approach to examine the assumed hypotheses using partial least squares structural equation modeling (PLS-SEM). Descriptive statistics were conducted using IBM SPSS (Version 29) while SmartPLS 4.1.0.3 was employed to evaluate the measurement and structural models. Findings The study revealed that, first of all, GRS, GTD and GPM have significant positive relationships with FEP, while GCB was found to have an insignificant relationship with FEP. Second, GRS, GTD and GPM have significant positive relationships with EGB (the mediator) while GCB has an insignificant relationship with EGB. Third, the mediator (EGB) itself has a significant affirmative relationship with FEP. Finally, concerning the mediating effects, it was observed that EGB exhibits partial mediation on the positive relationships between GRS and FEP; GTD and FEP; and GPM and FEP. However, EGB does not mediate the insignificant relationship between GCB and FEP. Originality/value Scholarly investigations focusing on the relationships between different GHRM practices and FEP are still inadequate. Particularly, such studies on Bangladeshi manufacturing firms are very rare despite being a rising export-oriented economy. The RBV provides a theoretical lens through which the strategic implications of aligning different GHRM practices with environmental performance can be explored. The researchers of this study