This scholarly investigation conducts a rigorous exploration of Bangladesh's textile sector spanning the years 2011 to 2022, centering its attention primarily on the development of an intricate econometric framework. The study unveils profound insights into the sector's growth trajectories, the intricate dynamics of global demand, the undulating fluctuations of interest rates, and other pivotal economic gauges. The core component of this research, the econometric model, adeptly prognosticates textile exports through the incorporation of multifarious variables, encompassing the count of garment establishments, the magnitude of the workforce, market penetration, worldwide demand patterns, currency exchange rates, and interest rate fluctuations. Notably, the model attains a lofty degree of explicative potency, with an R-squared coefficient approximating 0.756, thereby attesting to its remarkable capacity to elucidate variances in textile export values. These discoveries carry substantial consequences for policymakers and stakeholders within the industry, as they bestow upon them a potent instrument for judicious decision-making and strategic blueprinting within Bangladesh's textile domain. The model accentuates the paramount significance of global demand and market share, concurrently accentuating the latent repercussions posed by fluctuations in interest rates.This research provides valuable insights into promoting the industry's sustainable growth, diversification, and resilience in the face of economic challenges.

Recent advancements in Software Defined Networking (SDN) makes it possible to overcome the management challenges of traditional network by logically centralizing control plane and decoupling it from forwarding plane. Through centralized controllers, SDN can prevent security breach, but it also brings in new threats and vulnerabilities. Central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this paper, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with Random Forest classifier using Gain Ratio feature selection evaluator. In the later phase, the second approach is combined with Gated Recurrent Unit Long Short-Term Memory based intrusion detection model based on Deep Neural Network (DNN) where we applied an appropriate ANOVA F-Test and Recursive Feature Elimination feature selection method to improve the classifier performance and achieved an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.

Salinity is abiotic stress, which causes adverse environmental conditions for rice cultivation. In particular, local aromatic rice cultivation is heavily influenced by soil salinity stress, which has an impact on global food security. This study aimed to screen local aromatic rice genotypes in a hydroponics experiment using Yoshida solutions to evaluate the effect of increasing NaCl concentrations on the early growth stages of rice seedlings. Genetic diversity along with phylogenetic relationship was assessed using the random amplified polymorphic DNA (RAPD) markers. Out of 20 RAPD markers, 17 markers produced reproducible polymorphic bands. Individuals of all genotypes shared 88 (89.80%) of the 98 total RAPD elements amplified. The genetic distance-focused similarity index ranged from 0.05 to 0.94. The highest genetic distance (0.94) was discovered between genotypes Nayanmoni and Kalijira Barisal, and the lowest was between Badshabhog and Kataribhog (0.05). In addition, the OPS 3(510bp) and OPA 14(1100bp) markers could be used to identify salt-tolerant genotypes. According to genetic distance, the salt stress tolerant check genotype, Pokkali was genetically related to Chinigura as well as Kalijira Barisal. This study established a simple and consistent method for evaluating variability across various aromatic rice genotypes, which will benefit in genotype selection for breeding salinity stress tolerant aromatic rice varieties in Bangladesh.

Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

Application of chemical pesticides to protect agricultural crops from pests and diseases is discouraged due to their harmful effects on human and environment. Therefore, alternative approaches for crop pro-tection through microbial or microbe originated pesticides have been gaining momentum. Wheat blast is a destructive fungal disease caused by Magnaporthe oryzae Triticum (MoT) pathotype, which poses a seri-ous threat to global food security. Screening of secondary metabolites against MoT revealed that antimy-cin A isolated from a marine Streptomyces sp. had significant inhibitory effect on mycelial growth in vitro. This study aimed to investigate the inhibitory effects of antimycin A on some critical life stages of MoT and evaluate the efficacy of wheat blast disease control by this natural product. Bioassay indicated that antimycin A suppressed mycelial growth, conidiogenesis, germination of conidia and formation of ap-pressoria in germinated conidia of MoT in a dose-dependent manner with minimum inhibitory concen-tration 0.005 μg/disk. If germinated, antimycin A induced abnormal germ tubes (4.8%) and suppressed the formation of appressoria. Interestingly, application of antimycin A significantly suppressed wheat blast disease in both seedling and heading stages of wheat supporting the results from invitro study. This is the first report on inhibition of mycelial growth, conidiogenesis, conidia germination, detrimental morphological alterations in germinated conidia, and suppression of wheat blast disease caused by a Triticum pathotype of M. Oryzae. Further study is required to unravel the precise mode of action of this promising natural compound for considering it as a biopesticide to combat wheat blast.

Wheat is threatened by global warming. This study aimed to characterize based on DNA profile and identify heat-tolerant wheat genotypes. Fifteen wheat genotypes were sown in Nov 21 and Dec 21 and evaluated within 13 SSR markers. A dendrogram classified the genotypes into two main groups. The highest genetic distances (0.93) were drawn between BAW 1290 and BARI Gom 28, and the lowest one between Nadi 2 and BAW 1147. Heat susceptibility index (HSI) values for 1000-grain weight (TGW) and grain yield indicated moderate heat tolerance in Cluster I genotypes (HSI 0.50-1.00), closely matching Group A's Cluster II members. In Group B, Cluster III genotypes had higher HSI (1.04-1.23) and significant reductions in TGW and grain yield under heat stress, indicating heat susceptibility. The varieties BARI Gom 25, 26, 27, 28, 29, and 30 were identified as heat-stress tolerant. This research provides insights for future heat stress-molecular breeding programs.

Bangladesh faces significant wheat grain deficiency compared to its demand due to the lengthy process (10-12 years) required to release a variety through a conventional breeding approach. Traditional breeding methods involve several years of various types of crosses between male and female parents and/or progenies, along with recurrent selection-crossing to obtain a homozygous line (F₁ to F₆ generations) with desired traits within almost 10 years. The double haploid (DH) method is also a time-saving, easier, and more reliable technique to achieve the desired traits in homozygous lines within 2-3 years. DH lines were produced by crossing two F₁ hybrids of spring wheat with maize. 0.1% solution of colchicine was applied to produce DH of F₂ progeny of wheat in the study. 80.8% (340) of the 421 pollinated florets from the two F₁ wheat hybrids were produced as green parthenocarpic caryopses (GPCs) (F₂). 35 of the 70 rescued embryos (or 20.6% of the GPCs) germinated. The germinated embryos regenerated 10 haploid green plants (14.3% of total embryos). After being applied colchicine, 8 of the regenerated haploid (H₁) plants made it. Out of the 8 plants that made it through, only one developed homozygous double haploid seed (DH₁ seed). These seeds were multiplied to produce DH₂, used in the preliminary yield in the next year. The results demonstrated the potential of this method to produce a homozygous line within a short period (2-3 years) by doubling the haploid line generated through pollination of spring bread wheat F₁ with maize.