Research and application of polypropylene: a review.

Polypropylene (PP) is a versatile polymer with numerous applications that has undergone substantial changes in recent years, focusing on the demand for next-generation polymers. This article provides a comprehensive review of recent research in PP and its advanced functional applications. The chronological development and fundamentals of PP are mentioned. Notably, the incorporation of nanomaterial like graphene, MXene, nano-clay, borophane, silver nanoparticles, etc., with PP for advanced applications has been tabulated with their key features and challenges. The article also conducts a detailed analysis of advancements and research gaps within three key forms of PP: fiber, membrane, and matrix. The versatile applications of PP across sectors like biomedical, automotive, aerospace, and air/water filtration are highlighted. However, challenges such as limited UV resistance, bonding issues, and flammability are noted. The study emphasizes the promising potential of PP while addressing unresolved concerns, with the goal of guiding future research and promoting innovation in polymer applications.

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation.

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multiscale weighted colored geometric subgraphs to capture structural features of biomolecules, demonstrating superior performance on three standard data sets, namely, AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 data sets. The model's efficacy extends to predicting protein thermodynamic stability in a blind test set, providing unbiased predictions for both direct and reverse mutations and showcasing notable generalization. GGL-PPI's precision in predicting changes in binding free energy and stability due to mutations enhances our comprehension of protein complexes, offering valuable insights for drug design endeavors.

Factors affecting depression among married adults: a gender-based household cross-sectional study.

BACKGROUND: Depression is a common mental health problem all over the world including Bangladesh. World Health Organization included it in the Mental Health Gap Action Programme as one of its priority conditions. Research on this issue is scanty in Bangladesh. Therefore, we designed to a gender-based household study on associated factors of depression among married adults in Rajshahi City of Bangladesh. METHODS: We conducted this household cross-sectional study from August 01 to October 31, 2019. A total of 708 married adults currently living together in Rajshahi City were recruited for this study. We applied a multi-stage random sampling technique for selecting samples and used a semi-structured questionnaire to collect necessary information from them. The Patient Health Questionnaire-9 was used for measuring depression and frequency distribution and binary logistic regression model were used for data analysis. RESULTS: The prevalence of depression (moderate to severe) was 14.4% (95% CI: 11.9-16.9) among married adults, and females (21.2%, 95% CI: 17.2-25.4) suffered more than males (7.6%, 95% CI: 4.8-10.5). A multiple binary logistic regression model established four main factors of depression among married females: (i) multiple marriage [AOR = 19.982; 95% CI: 10.081-39.610; p < 0.01]; (ii) poor relationship with spouse [AOR = 2.175; 95%CI: 1.068-4.428; p < 0.05]; (iii) chronic medical comorbidity [AOR = 1.876; 95%CI: 1.009-2.626; p < 0.05]; and (iv) 7-12 years duration of conjugal life [AOR = 2.091, 1.009-4.334; p < 0.05]. Two main factors of depression among married males were (i) multiple marriage [AOR = 24.605; 95% CI: 20.228-40.402; p < 0.01] and hard work [AOR = 4.358; 95%CI: 1.109-7.132; p < 0.05]. CONCLUSION: The prevalence of depression was significantly high among the study population, and females were the most vulnerable group. The concerned authorities and stakeholders should take appropriate measures to manage the problem with special focus on the risk factors and the vulnerable groups.

Nano Groove and Prism-Structured Triboelectric Nanogenerators.

Enhancing the output power of triboelectric nanogenerators (TENGs) requires the creation of micro or nano-features on polymeric triboelectric surfaces to increase the TENGs' effective contact area and, therefore, output power. We deploy a novel bench-top fabrication method called dynamic Scanning Probe Lithography (d-SPL) to fabricate massive arrays of uniform 1 cm long and 2.5 µm wide nano-features comprising a 600 nm deep groove (NG) and a 600 nm high triangular prism (NTP). The method creates both features simultaneously in the polymeric surface, thereby doubling the structured surface area. Six thousand pairs of NGs and NTPs were patterned on a 6×5 cm2 PMMA substrate. It was then used as a mold to structure the surface of a 200 µm thick Polydimethylsiloxane (PDMS) layer. We show that the output power of the nano-structured TENG is significantly more than that of a TENG using flat PDMS films, at 12.2 mW compared to 2.2 mW, under the same operating conditions (a base acceleration amplitude of 0.8 g).

Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction.

Understanding and accurately predicting protein-ligand binding affinity are essential in the drug design and discovery process. At present, machine learning-based methodologies are gaining popularity as a means of predicting binding affinity due to their efficiency and accuracy, as well as the increasing availability of structural and binding affinity data for protein-ligand complexes. In biomolecular studies, graph theory has been widely applied since graphs can be used to model molecules or molecular complexes in a natural manner. In the present work, we upgrade the graph-based learners for the study of protein-ligand interactions by integrating extensive atom types such as SYBYL and extended connectivity interactive features (ECIF) into multiscale weighted colored graphs (MWCG). By pairing with the gradient boosting decision tree (GBDT) machine learning algorithm, our approach results in two different methods, namely sybylGGL-Score and ecifGGL-Score. Both of our models are extensively validated in their scoring power using three commonly used benchmark datasets in the drug design area, namely CASF-2007, CASF-2013, and CASF-2016. The performance of our best model sybylGGL-Score is compared with other state-of-the-art models in the binding affinity prediction for each benchmark. While both of our models achieve state-of-the-art results, the SYBYL atom-type model sybylGGL-Score outperforms other methods by a wide margin in all benchmarks. Finally, the best-performing SYBYL atom-type model is evaluated on two test sets that are independent of CASF benchmarks.

Dietary exogenous phytase improve egg quality, reproductive hormones, and prolongs the lifetime of the aging Hy-Line brown laying hens fed nonphytate phosphorus.

The study examined how adding phytase to nonphytate phosphorus (NPP) diets affected performance, egg quality, reproductive hormones, and plasma biochemical indices in 73- to 80-wk-old laying hens. Six treatments with 5 replicates of 18 Hy-Line brown laying hens each were randomly assigned. Three isonitrogenous, isocaloric diets containing consistent calcium levels (3.8%) were formulated to contain 0.20, 0.25, and 0.30% NPP, treated with or without phytase supplementation (1,000 FYT per kg feed, Ronozyme HiPhos-L, Aspergillus oryzae 6-phytase). The results showed that the addition of phytase to the diet containing 0.20, 0.25, and 0.30% NPP increased egg production by 1.50, 1.64, and 0.97%, respectively, and improved eggshell thickness. Also, use of phytase in the diet contain 0.25, and 0.30% NPP increased the plasma concentration of albumin (ALB), high-density lipoprotein (HDL), phosphorus (P), and plasma follicle-stimulating hormone (FSH), plasma calcium (Ca), estradiol-17ß (E2ß), and luteinizing hormone (LH). In contrast, the egg weight, feed intake, egg mass, feed conversion ratio, albumen height, Haugh unit, yolk, and shell color were unaffected. It can be advisable to use phytase supplementation in an elderly laying hen's diet contain 0.25, and 0.30% NPP to improve shell quality and positively impact reproductive hormones leading to the persistence of egg production.

Nutritional status of tribal and non-tribal adults in rural Bangladesh: A comparative study.

BACKGROUND: Nutritional status is an important indicator of health status among adults. However, to date, there exists scanty information on the nutritional status of tribal populations of Bangladesh. The aim of the study was to investigate the nutritional status of tribal (T) and non-tribal (NT) adult people living in the rural area of Rajshahi district, Bangladesh. METHODS: A total of 420 (72 T and 348 NT) households were studied. The samples were selected using multistage stratified sampling with proportional allocation. The nutritional status of adults was measured using body mass index (BMI). Descriptive statistics, t-test, ANOVA and Z-proportional test were utilized for data analysis. RESULTS: The study revealed that 8.3% and 9.2% of T and NT men were suffering from under nutrition respectively, while the corresponding figures in women were 12.5% and 10.1% respectively. Overall, 11.1% and 27.0% men, and 13.9% and 29.3% women T and NT were over-nourished respectively. The rate of over nutrition among T was significantly (p<0.05) higher than NT for both sexes. The mean weight and BMI of the NT men were significantly (p<0.01) higher than T men. The mean weight, height and BMI of NT women were higher (p<0.05) than T women. ANOVA demonstrated that the variation in BMI among education levels of NT men and the variation among occupation for both ethnicities were significant (p<0.01). The variation in BMI among education levels and occupation of T and NT women were significant (p<0.05), moreover ordinal logistic regression model demonstrated that hygienic toilet facilities and father's occupation were predictors of nutritional status. The interaction effects of education and occupation, and education and household monthly income on BMI were significant (p<0.01) for T men and both T and NT women (p<0.05). CONCLUSIONS: The prevalence of over-nutrition among NT is higher than T for both sexes. Some socio-economic and demographic factors were found as predictors of malnutrition. At least 12 of the 17 Sustainable Development Goals (SDGs) contain indicators that are highly related to nutrition, our findings can help Bangladesh Government for achieving SDGs by 2030. Appropriate nutritional intervention and awareness programmes can be initiated by the Government to ameliorate the burden of malnutrition among adults in the country.

Green tea and tulsi extracts as efficient green corrosion inhibitor for aluminum alloy in alkaline medium.

Corrosion is a major issue in every industrial system. As a result of its widespread application, aluminum suffers enormous annual losses due to corrosion. Scientists are continually on the lookout for effective anti-corrosion strategies. Corrosion may be reduced in a number of ways, but many of them are harmful to the environment, so it's important to find a green alternative. Corrosion inhibitors in aluminum alloys can be found in green tea and tulsi extract. In this research, we found that aluminum alloy 1100 (Al-1100) ina 10% NaOH solution was inhibited by both green tea and Tulsi extract. Samples of AL alloy are submerged in 10% NaOH solutions with and without an inhibitor for a total of 25 days. The weight-loss technique is used to determine the effectiveness of an inhibitor, with tulsi extract far outperforming green tea with the best efficiency of 83.93% compared to the greatest efficiency of 14.29% for green tea. After being submerged in an inhibitory solution, an aluminum alloy surface developed an adsorbed protective layer, which is chemical adsorption, as seen by FTIR (Fourier-Transform Infrared Spectroscopy) spectroscopy. Green inhibitors those are present on the surface of the aluminum alloys are less corrosive confirmed by the SEM (Scanning Electron Microscopy) analysis. The chemical particles were found to be present as a coating over AL alloy surfaces, as determined by EDS (Energy Dispersion Spectroscopy) testing. In a10% NaOH solution, Al-1100 is inhibited more effectively by tulsi extracts than by green tea extracts.

Occurrences, sources, fate and impacts of plastic on aquatic organisms and human health in global perspectives: What Bangladesh can do in future?

Bangladesh is not an exception to the growing global environmental problem of plastic pollution. Plastics have been deemed a blessing for today's world thanks to their inexpensive production costs, low weight, toughness, and flexibility, but poor biodegradability and massive misuse of plastics are to blame for widespread contamination of the environmental components. Plastic as well as microplastic pollution and its adverse consequences have attracted significant investigative attention all over the world. Plastic pollution is a rising concern in Bangladesh, but scientific studies, data, and related information are very scarce in numerous areas of the plastic pollution problem. The current study examined the effects of plastic and microplastic pollution on the environment and human health, and it examined Bangladesh's existing knowledge of plastic pollution in aquatic ecosystems in light of the rapidly expanding international research in this field. We also made an effort to investigate the current shortcomings in Bangladesh's assessment of plastic pollution. This study proposed several management approaches to the persistent plastic pollution problem by analyzing studies from industrialized and emerging countries. Finally, this work pushed investigators to investigate Bangladesh's plastic contamination thoroughly and develop guidelines and policies to address the issue.

Control of Halogen Atom in Inorganic Metal-Halide Perovskites Enables Large Piezoelectricity for Electromechanical Energy Generation.

Regulating the strain of inorganic perovskites has emerged as a critical approach to control their electronic and optical properties. Here, an alternative strategy to further control the piezoelectric properties by substituting the halogen atom (I/Br) in the CsPbX3 perovskite (X = Cl, Br) structure is adopted. A series of piezoelectric materials with excellent piezoelectric coefficients (d33 ) are unveiled. Iodine-incorporated CsPbBr2 I demonstrates the record intrinsic piezoelectric response (d33 ≈47 pC N-1 ) among all inorganic metal halide perovskites. This leads to an excellent electrical output power of ≈ 0.375 mW (24.8 µW cm-2 N-1 ) in the piezoelectric energy generator (PEG) which is higher than those of the pristine/mixed perovskite references with CsPbX3 (X = I, Br, Cl). With its structural phase remaining unchanged, the strained CsPbBr2 I retains its superior piezoelectricity in both thin film and nanocrystal powder forms, further demonstrating its repeatability and versatility of applications. The origin of high piezoelectricity is found to be due to halogen-induced anisotropic lattice strain in the unit-cell along the c-axis, and octahedral distortion. This study reveals an avenue to design new piezoelectric materials by modifying their halide constituents and paves the way to design efficient PEGs for improved electromechanical energy conversion.

A performance comparison of heterostructure surface plasmon resonance biosensor for the diagnosis of novel coronavirus SARS-CoV-2.

This paper presents a performance comparison of heterostructure surface plasmon resonance (SPR) biosensors for the application of Novel Coronavirus SARS-CoV-2 diagnosis. The comparison is performed and compared with the existing literature based on the performance parameters in terms of several prisms such as BaF2, BK7, CaF2, CsF, SF6, and SiO2, several adhesion layers such as TiO2, Chromium, plasmonic metals such as Ag, Au, and two-dimensional (2D) transition metal dichalcogenides materials such as BP, Graphene, PtSe2 MoS2, MoSe2, WS2, WSe2. To study the performance of the heterostructure SPR sensor, the transfer matrix method is applied, and to analyses, the electric field intensity near the graphene-sensing layer contact, the finite-difference time-domain approach is utilized. Numerical results show that the heterostructure comprised of CaF2/TiO2/Ag/BP/Graphene/Sensing-layer has the best sensitivity and detection accuracy. The proposed sensor has an angle shift sensitivity of 390°/refractive index unit (RIU). Furthermore, the sensor achieved a detection accuracy of 0.464, a quality factor of 92.86/RIU, a figure of merit of 87.95, and a combined sensitive factor of 85.28. Furthermore, varied concentrations (0-1000 nM) of biomolecule binding interactions between ligands and analytes have been observed for the prospects of diagnosis of the SARS-CoV-2 virus. Results demonstrate that the proposed sensor is well suited for real-time and label-free detection particularly SARS-CoV-2 virus detection.

Development and characterization of natural sourced bioplastic for food packaging applications.

Climate change and increased pollution caused by traditional petrochemical plastics made the biodegradable environment-friendly plastic (bioplastic) research more popular. Bioplastics can be manufactured from natural renewable ingredients and used as food packaging material without harming the environment. This research work focuses on developing bioplastic films from natural ingredients such as starch of tamarind seeds, and berry seeds, with licorice root. Attention has been paid to characterizing the material by biodegradability, mechanical testing, Fourier Transformed Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC), antimicrobial analysis tests. Phenolic compounds present in the berry seeds starch increased the soil biodegradability as well as the mechanical and thermal properties of the bioplastic films. The FTIR spectra confirmed the presence of various bio-molecules. Improved antimicrobial performance is also obtained. The results of this research confirm that the prepared bioplastic samples can be used in packaging applications.

Green synthesis and characterization of zirconium nanoparticlefor dental implant applications.

Green synthesis is a promising and cost-effective technique to synthesize nanoparticles from plant extract. The present study shows the green synthesis of zirconium nanoparticles using the extract of ginger, garlic, and zirconium nitride. The obtained nanoparticles were studied for potential dental implant applications. The synthesized nanoparticles were characterized by Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-Ray Spectroscopy (EDX), X-Ray diffraction analysis (XRD), and antibacterial analysis. FTIR analysis confirmed the presence of various organic compounds in the synthesized nanoparticles. The synthesized nanoparticles were spherical, triangular, and irregular, with varying sizes confirmed by FESEM analysis. The nanoparticles synthesized from the combination of garlic and ginger, and zirconium exhibited potent antibacterial activity against S. aureus. Anti-biofilm, anti-microbial activity, biointegration formation, and cell mechanism survival are also mentioned. Thus, the synthesized nanoparticles can be a good candidate for a dental implant because of their excellent antimicrobial properties.

Physicochemical and Nutritional Characteristics of Cookies Prepared with Untapped Seaweed Ulva intestinalis: An Approach to Value Addition as a Functional Food.

The present study was investigated to know the sensory, physicochemical, nutritional and fatty acid properties of seaweed-based cookies prepared with untapped seaweed Ulva intestinalis (UI) from Bangladesh coast. The cookies were formulated with different percentages of UI inclusions both in powdered (PUI) and fragmented (FUI) forms, in order to evaluate different quality attributes in prepared value-added cookies. In sensory analysis, seaweed inclusion levels of 1% PUI, 2.5% PUI, 1% FUI, 2.5% FUI and 5% FUI to cookies were acceptable by panelists. Considering the maximum percentage of seaweed inclusions, 2.5% PUI and 5% FUI were selected for further analysis. The results of physicochemical properties such as moisture content, spread factor, baking loss, pH, cookie density, color, texture properties, volatile basic nitrogen and thiobarbituric acid reactive species were within acceptable limits. In nutritional analysis, 2.5% PUI and 5% FUI cookies showed a remarkable and significant increase in lipid and ash contents, compared to untreated controls. Being the first report on fatty acids profile by UI from Bangladesh, among 24 fatty acids identified, the amount of total saturated, mono-unsaturated, omega-3 fatty acids and omega-6 fatty acids were reported to be 641.9 (36.2%), 563.7 (31.8%), 133.8 (7.6%) and 436.3 (24.6%) µg/g DW, respectively. The results suggest that cookies with 2.5% PUI and 5% FUI can be marketed as healthy foods for consumers.

Synthesis and Characterization of Eco-Friendly Bio-Composite from Fenugreek as a Natural Resource.

The present study show the usability of starch (tamarind) based-bio-composite film reinforced by fenugreek by various percentages to replace the traditional petrochemical plastics. The prepared bio-composite films were systematically characterized using the universal testing machine (UTM), soil degradation, scanning electron microscope (SEM), X-ray diffraction (XRD), thermogravimetric analyzer (TGA), and antibacterial tests. The experiments showed that a lower percentage of fenugreek improves biodegradation and mechanical strength. More than 60% of biodegradation occurred in only 30 days. Almost 3 N/mm2 tensile strength and 6.5% tensile strain were obtained. The presence of micropores confirmed by SEM images may accelerate the biodegradation process. Antibacterial activity was observed with two samples of synthesized bio-composite, due to photoactive compounds confirmed by FTIR spectra. The glass transition temperature was shown to be higher than the room temperature, with the help of thermal analysis. The prepared bio-composite containing 5% and 10% fenugreek showed antibacterial activities.

EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction.

Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure-activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks.

COVID-19 knowledge, attitude, and practices among the Rohingya refugees in Cox's Bazar, Bangladesh.

Objectives: The Rohingya refugee population in Bangladesh has become more vulnerable to COVID-19 because of their living and environmental conditions. The aim of the study was to represent an assessment of the Rohingya people's COVID-19-related knowledge, attitude, and practices (KAP) at eight refugee camps in Cox's Bazar. Study design: Cross-sectional study. Methods: This study was completed with a total of 400 responses between July and September of 2020. A questionnaire was created to assess demographic characteristics (5 items), knowledge (10 items), attitude (5 items), practices (5 items), and information sources (1 item). Aside from the KAP scores, the scores are also presented based on demographic variables. Results: The KAP of the respondents were not satisfactory, with scores of 5.8 ± 1.8, 2.2 ± 1.0, and 0.9 ± 0.7, respectively. We found significant differences only in the knowledge scores based on education and gender. Conclusion: In conclusion, this study emphasizes the importance of COVID-19 training that focuses on behavioral changes for the Rohingya people in Bangladesh.

Development of SiC-TiO2-Graphene neem extracted antimicrobial nano membrane for enhancement of multiphysical properties and future prospect in dental implant applications.

This paper presents the coating technology on Nano membrane using SiC-TiO2-Graphene with varying percentages of Azadirachta indica (Neem) extract with an objective to develop new coating materials. The nanomembranes have been synthesized by electrospinning machine over aluminum foil paper using the raw materials PVA grain, SiC, TiO2, Graphene, and neem. The nanomembranes have been characterized by SEM, XRD, FTIR, Surface Roughness, antibacterial, and Cytotoxicity test. FTIR analysis established the presence of PVA and neem indicating the formation of different organic compounds. It also confirmed that no chemical reaction occurred during the synthesis process. The membrane's roughness analysis obtained average roughness values from 1.15 to 3.84. The formation of homogeneous and smooth membranes with the formation of micropores was confirmed by SEM analysis. Miller Indices identified different types of crystal structures in XRD analysis. Antibacterial activity increased with the increase of the percentage of neem confirmed by the antibacterial test. No toxic effects were observed from the membrane during the cytotoxicity test. The obtained data confirmed that the synthesized nanomembrane could be used in different biomedical applications.

COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.