Experimental and simulation optimization analysis of the Whipple shields against shaped charge

Occasionally, the Whipple shields are used for the protection of a space station and a satellite against the meteoroids and orbital debris. In the Whipple shields each layer of the shield depletes part of high speed projectile en- ergy either by breaking the projectile or absorbing its energy. Similarly, this investigation uses the Whipple shields against the shaped charge to protect the light armour such as infantry fighting vehicles with a little modification in their design. The unsteady multiple interactions of shaped charge jet with the Whipple shield package against the steady homogeneous target is scrutinized to optimize the shield thickness. Sim- ulations indicate that the shield thickness of 0.75 mm offers an optimum configuration against the shaped charge. Exper- iments also support this evidence.

Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

Coronavirus 19(COVID-19)can cause severe pneumonia that may be fatal.Correct diagnosis is essential.Computed tomography(CT)usefully detects symptoms of COVID-19 infection.In this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and classification.In the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung region.In the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head.DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries.The model was trained using fine-tuned hyperparameters selected after extensive experimentation.Subsequently,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented image.The computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for classification.The performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POF Hospital datasets.The results were better than those of existing methods.

Aluminum Toxicity:A Case Study on Tobacco(Nicotiana tabacum L.)

Aluminum is an abundant metal in the earth’s crust that turns out to be toxic in acidic environments.Many plants are affected by the presence of aluminum at the whole plant level,at the organ level,and at the cellular level.Tobacco as a cash crop(Nicotiana tabacum L.)is a widely cultivated plant worldwide and is also a good model organism for research.Although there are many articles on Al-phytotoxicity in the literature,reviews on a single species that are economically and scientifically important are limited.In this article,we not only provide the biology associated with tobacco Al-toxicity,but also some essential information regarding the effects of this metal on other plant species(even animals).This review provides information on aluminum localization and uptake process by different staining techniques,as well as the effects of its toxicity at different compartment levels and the physiological consequences derived from them.In addition,molecular studies in recent years have reported specific responses to Al toxicity,such as overexpression of various protective proteins.Besides,this review discusses data on various organelle-based responses,cell death,and other mechanisms,data on tobacco plants and other kingdoms relevant to these studies.

A systematic review of urban terrorism literature:Root causes,thematic trends,and future directions

Urban terrorism is a significant global concern,prompting extensive scholarly inquiry into its underlying causes and effects.However,a comprehensive literature review summarizing this body of knowledge is notably absent.Thus,this study seeks to address this gap by conducting a thorough examination of existing literature on terrorism,particularly focusing on urban contexts,to identify key patterns and recurring themes.The study identified 515 research articles using the keywords"urban"and"terrorism"through the Web of Science and Scopus databases.A bibliometric review was conducted,which included a historical background,author keywords,country and institution,citation,and co-citation analyses.The findings revealed an increase in the number of studies on urban terrorism following the 9/11 attacks in the United States,which accounted for the highest number of publications in the country.Most studies were conducted in government law,international relations,and urban studies.Keyword analysis revealed that counterterrorism,security,and disasters were more closely linked to terrorism.Thematic analysis identified six main themes related to urban spaces and terrorism:tourism,governance,resilience,public health,economy,security,and counterterrorism.This study emphasizes the importance of involving the public in counterterrorism efforts in addition to traditional approaches to addressing urban terrorism.

An Innovative Approach Utilizing Binary-View Transformer for Speech Recognition Task

The deep learning advancements have greatly improved the performance of speech recognition systems,and most recent systems are based on the Recurrent Neural Network(RNN).Overall,the RNN works fine with the small sequence data,but suffers from the gradient vanishing problem in case of large sequence.The transformer networks have neutralized this issue and have shown state-of-the-art results on sequential or speech-related data.Generally,in speech recognition,the input audio is converted into an image using Mel-spectrogram to illustrate frequencies and intensities.The image is classified by the machine learning mechanism to generate a classification transcript.However,the audio frequency in the image has low resolution and causing inaccurate predictions.This paper presents a novel end-to-end binary view transformer-based architecture for speech recognition to cope with the frequency resolution problem.Firstly,the input audio signal is transformed into a 2D image using Mel-spectrogram.Secondly,the modified universal transformers utilize the multi-head attention to derive contextual information and derive different speech-related features.Moreover,a feedforward neural network is also deployed for classification.The proposed system has generated robust results on Google’s speech command dataset with an accuracy of 95.16%and with minimal loss.The binary-view transformer eradicates the eventuality of the over-fitting problem by deploying a multiview mechanism to diversify the input data,and multi-head attention captures multiple contexts from the data’s feature map.

Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

During COVID-19,the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand.This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals,pharmacies,and retail stores as its customers.Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers.A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customerselection criteria.These criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’domain-related knowledge using Analytical Hierarchy Process.Profit potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty,commitment,brand awareness,brand image,sustainable behavior,and risk.Subsequently,Multi Criteria Decision Analysis has been performed to prioritize the customerselection criteria and customers with respect to selection criteria.Three experts with seven and three and ten years of experience have participated in the study.Findings of the study suggest large hospitals,large pharmacies,and small retail stores are the highly preferred customers.Moreover,findings of prioritization of customer-selection criteria fromboth Principal Component Analysis and Analytical Hierarchy Process are consistent.Furthermore,this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective decision.Unlike traditional supply chain problems of supplier selection,this study considers a selection of customers and is useful for procurement and supply chain managers to prioritize customers while considering multiple selection criteria.

Recent advances in Ir/Ru-based perovskite electrocatalysts for oxygen evolution reaction

Oxygen evolution reaction(OER)is a kinetically harsh four-electron anode reaction that requires a large overpotential to provide current and is of great importance in renewable electrochemical technique.Ir/Rubased perovskite oxides hold great significance for application as OER electrocatalysts,due to that their multimetal-oxide forms can reduce the use of noble metals,and their compositional tunability can modulate the electronic structure and optimize OER performance.However,high operating potentials and corrosive environments pose a serious challenge to the development of durable Ir-based and Ru-based perovskite electrocatalysts.Tremendous efforts have been dedicated to improving the Ir/Ru-based perovskite activity to enhance the efficiency;however,progress in improving the durability of Ir/Ru-based perovskite electrocatalysts has been rather limited.In this review,the recent research progress of Ir/Ru-based perovskites is reviewed from the perspective of heteroatom doping,structural modulation,and formation of heterostructures.The dissolution mechanism studies of Ir/Ru and experimental attempts to improve the durability of Ir/Ru-based perovskite electrocatalysts are discussed.Challenges and outlooks for further developing Ru-and Irbased perovskite oxygen electrocatalysts are also presented.

Blind identification of occurrence of multi-modality in laser-feedback-based self-mixing sensor

Self-mixing interferometry(SMI)is an attractive sensing scheme that typically relies on mono-modal operation of an employed laser diode.However,change in laser modality can occur due to change in operating conditions.So,detection of occurrence of multi-modality in SMI signals is necessary to avoid erroneous metric measurements.Typically,processing of multi-modal SMI signals is a difficult task due to the diverse and complex nature of such signals.However,the proposed techniques can significantly ease this task by identifying the modal state of SMI signals with 100%success rate so that interferometric fringes can be correctly interpreted for metric sensing applications.

Temperature and Tumor Microenvironment Dual Responsive Mesoporous Magnetic Nanospheres for Magnetothermal Effect-Induced Cancer Theranostics

The development of smart drug delivery systems(SDDSs)based on engineered nanomaterials is important for clinical applications.Nevertheless,controllable administration of chemotherapeutic drugs for deep tumors and the avoidance of side effects caused by off-targeting during delivery remain a great challenge.Herein,a stimulus-responsive system of mesoporous nanospheres(composed of Cu@Fe_(2)C@mSiO_(2))with good magnetothermal effect is introduced into the tumor microenvironment.This system plays an important role in image-guided controllable targeted drug delivery that is independent of tumor depth.Aggregation-induced emission luminogen-based fluorescence imaging and magnetic resonance imaging were utilized since these techniques visualize the delivery process in real time.In addition,the degraded nanocarriers showed high catalytic activity for Fenton and Fenton-like reactions,upregulating the level of hydroxyl radicals(•OH)in cancer cells to realize chemodynamic therapy.The induced•OH led to the overexpression of pho-STAT3,activating the STAT3 signaling pathway,eventually inducing cancer cell apoptosis.Through metabolic monitoring,this SDDS is removed from the body after its degradation in vivo.The synergistically enhanced therapeutic effect was obtained in the chemo-chemodynamic therapy of 4T1 tumor-bearing mice,offering a platform for efficient cancer therapy with a personalized theranostic strategy.