Hajee Mohammad Danesh Science and Technology University
Recent publications
Increasing atmospheric nitrogen (N) and phosphorus (P) deposition affect soil nutrient availability and biogeochemical cycles, yet the impacts on the regulatory processes of soil P in P-poor tropical rainforests are not well understood. Based on a ten-year N and P addition experiment in primary and secondary rainforests in Hainan, China, we investigated the effects of N and P additions on four soil P fractions, acid phosphatase (ACP) activity, kinetic characteristics and arbuscular mycorrhizal (AM) diversity. Our results showed that P addition significantly increased soil available P (AP), but had no significant effects on microbial P and microbial N:P ratio in both rainforests. Late-successional primary rainforest had higher microbial P and lower N:P ratio compared to early-successional secondary rainforest, suggesting that forest succession would not aggravate microbial P limitation. Plants and microorganisms accessed most inorganic P by releasing hydrion and organic acids to mobilize soil P, whereas inorganic P accessed by root interception and organic P readily mineralized by ACP and phytase enzymes were relatively low. In the primary rainforest, ACP and kinetic parameters were significantly increased under low N addition, while P addition remarkedly declined ACP activity and AM diversity in both rainforests. Additionally, CaCl2 extractable P, citrate extractable P, and HCl extractable P in the two forests were significantly positively associated with soil AP, but negatively associated with ACP activity, kinetic parameters and AM diversity. Therefore, the transformations of organic P were inhibited after the exogenous addition of inorganic P. Overall, our study evaluates the soil P acquisition strategies and regulatory processes in tropical rainforests with different successional stages, which can be used to predict the effects of long-term N and P deposition on soil P cycling in P-poor tropical rainforests.
Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach.
The proportion of phishing attacks has soared worldwide amid the Covid-19 crisis since people started using the internet more actively. Browsing phishing websites can cause immense damage to user privacy. In this article, investigating the attributes of URLs to detect the possible legitimate and phishing websites, we presented a feature selection framework that improves the efficacy of machine learning models. In feature selection, considering the filter and wrapper method, we introduced an empirical hybrid framework that comprises two phases. To derive the accumulative feature subset, in the early stage, we performed a function perturbation ensemble using four filter techniques. Finally, to select the best features, we employed the wrapper method, in which the feature subset is passed into a statistical model to perform a p-value test (conforming 95% confidence). We used two phishing datasets, and applying this proposed hybrid ensemble framework, we derived only 45.95% of the initial features from each dataset. Thereafter, the optimized (hyperparameters) models such as Artificial Neural Network, XGBoost Classifier, Random Forest Classifier are applied to conduct 10-folds cross-validation on Data-I, the XGBoost Classifier outran with the accuracy of 96.08%. Besides, the XGBoost model performed prediction on Data-II, achieved a notable accuracy of 97.29%.KeywordsPhishing detectionEmpirical feature selectionMachine learning
Agricultural products cause the emission of certain significant amount of greenhouse gases. Carbon dioxide (CO2) is one of the most important greenhouse gases and its emissions are increasing day by day as a result of the increase in agricultural productivity. This study aims to pinpoint the most environmentally friendly crops and fruits that are sources of good nutrients and emits less CO2 throughout their life cycles. Relation between nutrient availability and CO2 emissions from staple foods namely; wheat, maize, rice, potato, sugarcane, sugar beet, soybean, palm oil, sunflower, rapeseed, banana, apple and grape are investigated in this study. Secondary data was collected from dataset's website. Spearman's rank and diagram interpretation technique are used to find out the correlation between nutrient availability and CO2 emissions. Among carbohydrate diets, rice emits 4 kg CO2 kg-1 of crops, which is significantly higher than that of wheat, maize and potato. However, the amount of carbohydrates in rice (0.26%) is less than those carbohydrate diets. Similarly, sugarcane emits more CO2 as 2.6 kg kg-1 of crops than sugar beet (1.4 kg kg-1 of crops) among sugar crops. Soybean and palm oil emit more CO2 as 6 kg kg-1 and 7.2 kg kg-1 of crops, respectively, as compared to other oilseed crops, but every oilseed crop has the same food value. Among fruits, bananas emit less CO2 (1.1 kg kg-1 of crops) and have a higher content of carbohydrates (0.23%) than other selected fruits. Proper crop selection based on nutrient content can lead to lower CO2 emissions than at present and a consistent balance between environmental and nutritional needs in the future.
This article deals with the nonlinear Kadomtsev-Petviashvili modified equal width (KP-mEW) equation used to model the matter-wave pulses, waves in ferromagnetic media, and long wavelength water waves with frequency dispersion and faintly nonlinear reinstating forces. The suggested model adopts a modification of the wave variable to make a single variable differential equation. Couple of competent techniques, notably the rational (G '/G)-expansion method and the improved tanh method is employed to extract wave solutions in appropriate form. Assorted typical and wide-spectral soliton solutions subject to rational, hyperbolic, and trigonometric functions and their integration are extracted successfully. To illustrate diverse soliton shapes, the obtained solutions are sketched in 3D, 2D, and contour profiles. The impact of velocity changes on soliton formation has also been the focus of this study. A comparative study of established results with existing results is performed, and the novelty of this study is shown.
Okra (Abelmoschus esculentus L. Moench) is a beneficial vegetable and oil crop that has found valuable use as food, paper, medicine, and oil. However, because of climate change, a lack of better okra cultivars that can resist drought has hampered okra production in sub-Saharan Africa (SSA) and across the world. This study aimed to investigate the economic production of this vegetable in a forest setting. The okra (A. esculentus L.) experiment was conducted underneath three forest trees (i.e. Albizia lebbeck, Melia azedarach, and Leucaena leucocephala) in the agroforestry area. A single-factor Randomised Complete Block Design (RCBD) with three replications was utilised. The sample was then separated into three groups: okra-L. leucocephala agroforestry, okra-M. azedarach agroforestry, and okra-A. lebbeck agroforestry. The findings of the study revealed that okra production had differed significantly in three agroforestry practices. The fresh okra yield was highest (10.20 ton/ha) in the okra-A. lebbeck agroforestry. The okra-L. leucocephala agroforestry (9.10 ton/ha) came in second, while the lowest yield was in the okra-M. azedarach agroforestry (8.90 ton/ha). Moreover, the okra-A. lebbeck agroforestry recorded the maximum benefit–cost ratio (3.97) for economic performance. This ratio was 21% and 14% higher compared to the respective performance of the okra-M. azedarach agroforestry and okra-L. leucocephala agroforestry. However, the highest amount of carbon sequestration (238.90 ton/ha/yr) was measured in the okra-M. azedarach agroforestry as well as (229.80 ton/ha/yr) in the okra-A. lebbeck agroforestry practice. Finally, the okra-A. lebbeck agroforestry practice would significantly increase production, touching financial benefit and ensuring sound environmental quality using the vacant woodlot space.
Hydrogen is a source of clean energy as it can produce electricity and heat with water as a by-product and no carbon content is emitted when hydrogen is used as burning fuel in a fuel cell. Hydrogen is a potential energy carrier and powerful fuel as it has high flammability, fast flame speed, no carbon content, and no emission of pollutants. Hydrogen production is possible through different technologies by utilizing several feedstock materials, but the main concern in recent years is to reduce the emission of carbon dioxide and other greenhouse gases from energy sectors. Hydrogen production by thermochemical conversion of biomass and greenhouse gases has achieved much attention as researchers have developed several novel thermochemical methods which can be operated with low cost and high efficiency in an environmentally friendly way. This review explained the novel technologies which are being developed for thermochemical hydrogen production with minimum or zero carbon emission. The main concern of this paper was to review the advancements in hydrogen production technologies and to discuss different novel catalysts and novel CO2-absorbent materials which can enhance the hydrogen production rate with zero carbon emission. Recent developments in thermochemical hydrogen production technologies were discussed in this paper. Biomass gasification and pyrolysis, steam methane reforming, and thermal plasma are promising thermochemical processes which can be further enhanced by using catalysts and sorbents. This paper also reviewed the developments and influences of different catalysts and sorbents to understand their suitability for continuous clean industrial hydrogen production.
The influence of alginate edible coatings enriched with black cumin (BC) extract was investigated to preserve the quality of guava fruits for 16 days at 11 ± 1 °C and 85 ± 2% relative humidity. The analysis of polyphenolic compounds in BC extract confirmed the TPC (28.43 ± 1.11 mg GAE/g DM) and TFC (4.83 ± 0.17 mg QE/g DM) with strong antioxidant activity (161.69 ± 2.31 µM Trolox/g DM in DPPH and 889.19 ± 36.45 µM Fe (II)/g DM in FRAP assays). The antibacterial activity of BC extract was also proved against Staphylococcus hominis and Escherichia coli with the inhibition zone diameter. The application of alginate coatings enriched with BC extracts suppressed the respiration rate, weight loss, firmness loss, and changes in the skin color of guavas. Fruits treated with alginate coating in a combination of BC extract retarded the ripening index of guavas till the end of the storage period compared to control samples (fruits treated with distilled water). The content of vitamin C, total phenolics, and total flavonoid in fruits treated with BC extract-loaded alginate coating was significantly higher than control, alginate with CaCl 2 , and alginate itself treatments. The antioxidant and antidiabetic activities in guava fruits coated with BC extract-based alginate coating were also comparatively higher than control and other treatments. The application of alginate coating enriched with BC extract was significantly delayed in total carotenoid formation in guavas since it delayed the ripening of fruits. Moreover, the concentrations of BC extract were worked in a dose-dependent manner in the coating systems in retarding respiration rate, weight loss, firmness loss, and ripening processes. These results proved that BC extract as a novel functional ingredient in alginate coatings was efficient in improving the quality of guava fruit and prolonging its shelf life.
The performance parameters of molybdenum disulfide (MoS2) solar cell with antimony trisulfide (Sb2S3) hole transport layer (HTL) have been studied by One Dimension Solar Capacitance Simulator software program (SCAPS-1D). The detailed numerical analysis of the influence of band alignment, defect density, absorber layer thickness, electron affinity of HTL on open circuit voltage (Voc), short circuit current (Jsc), fill factor (FF) and efficiency (ɳ) have been investigated. The impact of interface defect density at Sb2S3/MoS2 and CdS/MoS2 has also been analyzed. The insertion of Sb2S3 HTL into the newly designed hetero-structure (Al/FTO/CdS/MoS2/Sb2S3/Ni) solar cell enhances Voc, and ɳ by creating appropriate band alignment as well as reducing recombination loss at rear surface. The effects of surface recombination velocity, shunt and series resistance, temperature on photovoltaic efficiency parameters have also been investigated. The determined value of ɳ is 27.96% along with Voc of 0.92 V, Jsc of 35.20 mA/cm² and FF of 85.51% at 1.0 μm optimized thickness of MoS2 with doping density 1 × 10¹⁵ cm⁻². These results may provide an insightful approach to fabricate low cost and superior performance solar cells with the Sb2S3 HTL layer.
Effects of drying temperature (60, 70, and 80 °C) and air velocity (3.0, 6.0, and 9.0 m/s) on thin layer and color kinetics of maize grain during mixed flow dryer (MFD) were investigated for the present study. Five thin layer grain drying models (Henderson and Pabis, Page, Lewis, Midilli et al. and Wang and Singh) as well as Zero order reaction rate were applied with the experimental data to predict the drying and color kinetics of maize, respectively. Increased drying temperature had significant leverage on drying time whereas proliferation of air velocity (>6.0 m/s) is scarcely imperative. Model fitting analysis revealed that Lewis and Page model is aptly applied in evaluating drying kinetics for mixed flow drying of maize as coefficient of determination (R²) is higher. Effective moisture diffusivity (4.92 × 10⁻⁷ to 8.38 × 10⁻⁷ m²/s) was maximum both for higher temperature and air flow whereas specific energy consumption was lowest (2.75 MJ/kg) for lower temperature and air flow. Regression analysis of color attributes (L*, a*, and b*) designates that color kinetics of maize grain occurred according to the zero order reaction. L* and b* indices of color decreases while a* and color difference (ΔE) increases with drying temperature and time. Thin layer and kinetic constant (k) is proportionate with drying temperature while adverse results were noticed for color attributes. Drying and color degradation kinetics of maize can readily be described by Lewis and Page model and zero order reaction, respectively for mixed flow drying.
Probiotic bacteria were isolated from yogurt and cheese whey and used to prepare encapsulated probiotic bacteria with whey protein (WP), maltodextrin (MD), and gum Arabic (GA) as single, binary, and ternary encapsulation coating materials by freeze‐drying. Probiotic powders were characterized using FTIR, SEM, encapsulation efficiency, and survivability under simulated gastrointestinal conditions. Limosilactobacillus fermentum strain LF‐HSTU‐FPP and Streptococcus thermophilus strain ST‐HSTU‐FPP were identified by 16S RNA. Encapsulation with WP and GA had a high encapsulation efficiency of 94.69% and the least injury of cell viability of 2.8715 Log CFU/gm and 2.85 Log CFU/gm in simulated gastric juice and stimulated intestinal juice, respectively. Microcapsules showed broken glass, porous, and irregularly shaped structures. The stability of probiotic bacteria was confirmed by FTIR analysis of amide group I and II peak alterations. Binary encapsulation (WP and GA) was a suitable coating material for the stability of probiotic bacteria in the gastrointestinal tract during storage.
Bacterial nanocellulose (BNC) and polyhydroxyalkanoate (PHA) biopolymers were extracted using Bacillus velezensis BV-HSTU-FPP and Bacillus subtilis BS-HSTU-FPP strains which were isolated from fermented coconut water. Subsequently, bacterial nanocellulose and polyhydroxyalkanoate were used to synthesize biopolymer films along with CMC, gelatin and tween-80. 16S-rRNA sequencing was used to identify bacterial strains and structural properties of biopolymer films were determined using SEM, XRD, FTIR and TGA techniques. Functional and barrier properties of the films were determined using solubility, swelling degree, WVP, WCA, transparency and mechanical strength. XRD analysis showed that BNC (77.25%) and PHA (68.93%) films had higher crystalline properties than biopolymer-free film (57.91%). Biopolymer films containing BNC showed high peak absorption at the O-H group, whereas PHA film was at -COO group as compared to biopolymer-free film. Additionally, films containing BNC and PHA increased thickness, WCA, tensile strength and elongation while decreased solubility, swelling degree and WVP than those of biopolymer free film. SEM analysis revealed biopolymer aggregation in both BNC and PHA films.Thermal stability and transparency of the biopolymer and non-biopolymer films were also comparable. The findings indicated that the BNC and PHA biopolymer-based films could have improved film properties that would be used to formulate sustainable packaging films
We present an excellent design of five layers of decagonal shape in the cladding area and two elliptical shapes of core area based photonic crystal fiber (PCF) for many types of communiqué arenas in the THz wave pulse in this study. The Finite Element Method with perfectly matched layers used the optical parameters of our proposed D-PCF structure numerically to design and analyze. Therefore, D-PCF shows a low effective material loss of 0.0079 cm−1, an increase in effective area of 3.49 × 10–8 m2, a core power fraction of 85%, a low confinement and scattering loss, of 3.35 × 10–16 and 1.27 × 10–10 dB/km respectively at 1 THz of frequency. After analyses all the graphical results, our proposed D-PCF will be highly suitable for communiqué parts in the THz regions.
The study’s goal is to analyse and predict customer reviews of insurance products using various machine learning techniques. We gathered consumer rating data from the Yelp website and filtered the initial data set to only include insurance reviews. Following cleaning, the filtered summary texts were graded as positive, neutral or negative sentiments, and the AFINN and Valence Aware Dictionary for Sentiment Reasoning (VADER) sentiment algorithms were used to rate those sentiments. Furthermore, the current investigation employs five supervised machine learning approaches to divide customer ratings of insurance companies into three sentiment groups. The results of the current study revealed that the majority of customer reviews for the insurance products were negative, with the average number of words with negative sentiment being higher. In addition, current research discovered that while all of the approaches (decision tree, K Neighbours classifier, support vector machine (SVM), logistic regression and random forest classifier) can correctly classify review text into sentiment class, logistic regression outperforms in high accuracy. We analysed and predicted customer review messages using a variety of machine learning methods, which could help companies better understand how customers respond to their products and services. As a result, companies can learn how to use machine learning methods to better understand the behaviour of their customers.
The key objective of the study was to determine the extent of attitude of the women farmers towards organic farming. The study was conducted in two union of Nilphamari Sadar upazila such as Lakshmi Chap and Palashbari under Nilphamari district. Total 100 women farmers were selected from the study area as the population and random sample techniques was used to comprised of 80 constituted the sample of the study. Data were collected by a pre-tested interview schedule during 25 April to 25 May 2020. Simple and direct questions with different scales were used to obtain information. Attitude of the women farmers towards organic farming was measured by Likert scale. Descriptive statistics, multiple regression was used for analysis. Slightly above three-fifths (63.7 percent) of the women farmers had moderately favorable attitude towards organic farming while 21.3 percent slightly favorable attitude and 15.0 percent of women farmers under highly favorable attitude towards organic farming. It is noticed that the majority (85.0 percent) of the women farmers showed slightly favorable to moderately favorable attitude towards organic farming. Educational qualification, training experience, and access to extension contact of the respondents had significant positive contribution with their attitude towards organic farming. The most important problem (77.50 percent) faced by the women farmers was “higher amount of insect pest and diseases”. The foremost (67.50 percent) suggestion offered by the women farmers was “Developing organic pesticide company through private and government initiatives”. Int. J. Agril. Res. Innov. Tech. 12(1): 174-181, June 2022
The volume of the environmental risk disclosure in the annual reports of firms in the pharmaceutical and chemical, tannery, telecommunications, and paper and printing industries listed on the Dhaka Stock Exchange (DSE) in Bangladesh was analyzed in this paper. The research used a content analysis of the annual reports of 43 companies that represented four DSE sectors. To quantify the level of environmental risk disclosure reporting practiced by corporations in their annual reports, the authors established the ERDIPCI for the pharmaceutical and chemical industry, the ERDITI for the tannery industry, the ERDITeI for the telecommunications industry, and the ERDIPPI for the paper and printing industry. Similarly, the machine learning clustering algorithm, k-means clustering, is used to cluster the companies based on the completion of different environmental indices. It is observed that from four sectors, the highest number of companies from the pharmaceutical and chemical industry disclosed environmental risk disclosures, and the lowest number of companies was from the tannery industry, followed by the telecommunications and the paper and printing industries. The enterprises differ significantly in their environmental risk disclosures, and the overall scenarios of the environmental reporting practices by companies in Bangladesh are quite poor. It also shows that among the 43 companies, a limited number of enterprises are placed first. The majority of the businesses are in the midst of a cluster that reflects the increasing order of indices fulfillment. This paper provided a few specific proposals to the relevant authorities in order to establish a regularity framework in which all the firms listed on the DSE in Bangladesh will be expected to address environmental risk disclosures and conservation actions in their annual reports towards adaptation to climate change and achieving environmental sustainability.
: The global e�ort to develop herd immunity in the general public against the COVID-19 pandemic is currently ongoing. However, to the best of our knowledge, there have been no studies on how the COVID-19 vaccine a�ects mental health in the context of the COVID-19 pandemic in Bangladesh. The present study investigated the psychological e�ects and associated factors among vaccinated and unvaccinated general populations against COVID-19 infection in Bangladesh
Nonlinear models of fractional order have elaborately been taken place in the research field for their importance bearing the significant roles to depict the interior mechanisms of complicated phenomena belonging to the nature. This present exploration deals with the competent approach namely rational (G′/G)-expansion scheme to extract accurate wave solutions of two arbitrary order nonlinear Schrodinger models. The implementation of the advised technique combining with Cole–Hopf transformation purvey a heap of wave solutions in appropriate form. The achieved solutions are presented graphically in contour shape as well as in three- and two-dimensional frames. The wave structures in various profiles such as periodic, kink, anti-kink, bell, anti-bell, compacton etc. are appeared. The gained solutions are compared with the previous established results to exhibit diversity and novelty. The governing models are interesting and significant as they are related to logarithm law, Kerr law media, saturable law, triple-power law, dual-power law, power law and polynomial law.
Noni (Morinda citrifolia) is a climatic small evergreen plant that belongs to the Rubiaceae or coffee family, available throughout the year. This study aimed to identify the nutritional contents, extraction of noni seed oil (NSO) and their physicochemical properties, antioxidants, specifically total phenolic content (TPC) and ferric reducing antioxidant power (FRAP) assay of noni seed (NS) and NSO cultivated in Sabah, Malaysia. The results showed 8.37 g/100g moisture, 10.55 g/100g crude oil, 7.1 g/100g protein, 1.29 g/100g ash, and 25.20 g/100g carbohydrates indicating NS has considerable biomolecules that can be utilized as excellent resources of biomolecules. NSO showed a high iodine value of 125.90 g I2/100g, peroxide value of 10.60 mEq/g, and free fatty acid (FFA) of 1.07%. The fatty acids composition revealed that NSO dominated by linoleic acid (omega-6) (71.74%) which makes it beneficial for human health. The TPCs for NS powder and NSO were 22.65 mg GAE/g and 48.85 mg GAE/g, respectively. Moreover, NS powder's FRAP was 73.15 mM/100g. These results suggest that NS and NSO have the potency to be a healthier source of edible functional food, cosmetic and pharmaceutical products, and vegetable oil due to considerably high omega-6 fatty acid, carbohydrates, low FFA, and antioxidant activities.
Background: Deep-fat fried foods are widely popular due to their distinct organoleptic and sensory characteristics. Deep-fat frying allows physical and chemical structural changes at the macro and micro levels. One of the greatest concerns regarding fried foods is their high oil content, and the excessive consumption of these foods has been linked to a range of metabolic diseases, including heart ailments, obesity, high cholesterol, and high blood pressure. Scope and approach: This review paper presents a comprehensive and up-to-date review of various innovative frying processes, such as vacuum frying (VF), microwave frying, microwave-assisted vacuum frying (MVF), ultrasound combined microwave vacuum frying (UMVF), air frying, and radiant frying. Additionally, it explains the oil uptake mechanism, oil quality, and the effects of different pretreatments and post-frying treatments on the frying process. Key findings and conclusions: Innovative frying processes are considered promising and capable of producing healthier fried foods compared to traditional frying; they reduce oil uptake without deteriorating organoleptic and sensory characteristics. Specifically, vacuum-assisted frying technologies, such as VF, MVF, and UMVF have significantly reduced oil uptake and acrylamide production while preserving oil quality. In addition, appropriate pretreatment and post-frying techniques play an essential role in reducing oil uptake and optimizing the frying process by saving energy.
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569 members
M S Islam
  • Agronomy
Begum Fatema Zohara
  • Medicine, Surgery and Obstetrics
Imran Parvez
  • Fisheries Biology and Genetics
Md. Rashedul Islam
  • Genetics and Animal Breeding
Md Arifuzzaman
  • Genetics & Plant Breeding
Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, 5200, Dinājpur, Bangladesh
Head of institution
Vice- Chancellor, HSTU