Abstract:The purposes of this paper are to explore the major deadly incidents of the apparel industry in Bangladesh and to illustrate the causes for these deadly incidents with the effect of these incidents. Different types of deadly incidents of the recent decade have made Bangladeshi apparel industry questionable. This paper will explore the causes of the different incidents and will show the precautionary measure of these problems of apparel industry which affected their sustainability and profitability.

The species of Trichoderma are one of the most frequently used natural biocontrol agents to mitigate plant diseases and improve crop yields. In this study, sixteen Trichoderma spp. were isolated from soil of different regions of China. However, we identified Trichoderma. asperellum HbGT6-07 by initial fungal growth inhibition assay and molecular approach and also evaluated the antimicrobial effects. Tested 10% concentrated culture filtrate of T. asperellum HbGT6-07 inhibited 93 % of colony radial growth in Botrytis cinerea (B05.10) as well as 91 % of Sclerotinia sclerotiorum (A367). VOCs emitted from HbGT6-07 have antimicrobial properties against Botrytis cinerea (B05.10) and Sclerotinia sclerotiorum (A367). In in-vitro DwD method, The T. asperellum HbGT6-07 volatile organic compounds (VOCs) effectively reduced colonial diameter, mycelial growth rate and sclerotia production by two virulent fungal pathogens. The GC-MS analysis identified thirty-two VOCs derived from HbGT6-07 isolates. Moreover, the hyphal fragments of the T. asperellum HbGT6-07 demonstrated successful mycelia growth suppression of two virulent fungal agents by competing toward the invasion on oilseed rape leaves. The above findings indicated that T. asperellum HbGT6-07 could attain competitive progress via volatile antifungal compound production and comprehensive mycelial growth. This study provided an outlook of using T. asperellum HbGT6-07 to control virulent pathogens of B. cinerea and S. sclerotiorum.

Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading to blindness. Computer-Aided Diagnosis systems play an essential role in detecting features in fundus images. Fundus images may include blood vessels, exudates, micro-aneurysm, hemorrhages, and neovascularization. In this paper, our model combines automatic detection for the diabetic retinopathy classification with localization methods depending on weakly-supervised learning. The model has four stages; in stage one, various preprocessing techniques are applied to smooth the data set. In stage two, the network had gotten deeply to the optic disk segment for eliminating any exudate's false prediction because the exudates had the same color pixel as the optic disk. In stage three, the network is fed through training data to classify each label. Finally, the layers of the convolution neural network are re-edited, and used to localize the impact of DR on the patient's eye. The framework tackles the matching technique between two essential concepts where the classification problem depends on the supervised learning method. While the localization problem was obtained by the weakly supervised method. An additional layer known as weakly supervised sensitive heat map (WSSH) was added to detect the ROI of the lesion at a test accuracy of 98.65%, while comparing with Class Activation Map that involved weakly supervised technology achieved 0.954. The main purpose is to learn a representation that collect the central localization of discriminative features in a retina image. CNN-WSSH model is able to highlight decisive features in a single forward pass for getting the best detection of lesions.

The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways.

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