Investigation of Tablet Defects by AI Based Terahertz Technology

Recently a lot of medical tablets with special packets in the global market are available. For the safety and purity of the tablet, we need to scan it by developed scanner technology, which should be not more expensive and easily available in the market. The THz technology is one of them. In the proposed work, we have tasted tablet images with the help of the THz super-resolution scanner, which is already available in our lab. The AI machine learning data concept has been investigated. Good resolution of images has been obtained. Furthermore, the challenging research problems are discussed. Finally, it summarizes the recent updates in terahertz technology for drug inspection and medical applications with potential research challenges.

Towards the insights into the deactivation behavior of acetylene hydrogenation catalyst

A series of model catalysts were obtained by treating commercial fresh and spent catalysts unloaded from the factory with different methods, including green oil dipping, extraction and high-temperature regeneration;finally, the deactivation behavior of the commercial catalyst for acetylene hydrogenation were studied. The influence of various possible deactivation factors on the catalytic performance was elucidated via detailed structural characterization, surface composition analysis, and activity evaluation.The results showed that green oil, carbon deposit and sintering of active metal were the main reasons for deactivation, among which green oil and carbon deposit led to rapid deactivation, while the activity could be recovered after regeneration by high-temperature calcination. The sintering of active metal components was attributed to the high-temperature regeneration in hydrothermal conditions, which was slow but irreversible and accounted for permanent deactivation. Thus, optimizing the regeneration is expected to extend the service life of the commercial catalyst.

A critical review on solid-state welding of high entropy alloys-processing,microstructural characteristics and mechanical properties of joints

The high entropy alloys(HEAs)are the newly developed high-performance materials that have gained significant importance in defence,nuclear and aerospace sector due to their superior mechanical properties,heat resistance,high temperature strength and corrosion resistance.These alloys are manufactured by the equal mixing or larger proportions of five or more alloying elements.HEAs exhibit superior mechanical performance compared to traditional engineering alloys because of the extensive alloying composition and higher entropy of mixing.Solid state welding(SSW)techniques such as friction stir welding(FSW),rotary friction welding(RFW),diffusion bonding(DB)and explosive welding(EW)have been efficiently deployed for improving the microstructural integrity and mechanical properties of welded HEA joints.The HEA interlayers revealed greater potential in supressing the formation of deleterious intermetallic phases and maximizing the mechanical properties of HEAs joints.The similar and dissimilar joining of HEAs has been manifested to be viable for HEA systems which further expands their industrial applications.Thus,the main objective of this review paper is to present a critical review of current state of research,challenges and opportunities and main directions in SSW of HEAs mainly CoCrFeNiMn and Al_xCoCrFeNi alloys.The state of the art of problems,progress and future outlook in SSW of HEAs are critically reviewed by considering the formation of phases,microstructural evolution and mechanical properties of HEAs joints.

Numerical Investigation of Combined Production of Natural Gas Hydrate and Conventional Gas

Natural gas hydrate(NGH)is generally produced and accumulated together with the underlying conventional gas.Therefore,optimizing the production technology of these two gases should be seen as a relevant way to effectively reduce the exploitation cost of the gas hydrate.In this study,three types of models accounting for the coexistence of these gases are considered.Type A considers the upper hydrate-bearing layer(HBL)adjacent to the lower conventional gas layer(CGL);with the Type B a permeable interlayer exists between the upper HBL and the lower CGL;with the type C there is an impermeable interlayer between the upper HBL and the lower CGL.The production performances associated with the above three models are calculated under different conditions,including only a depressurized HBL(only HBL DP);only a depressurized CGL(only CGL DP);and both the HBL and the CGL being depressurized(HBL+CGL DP).The results show that for Type A and Type B coexistence accumulation models,when only HBL or CGL is depressurized,the gas from the other layer will flow into the production layer due to the pressure difference between the two layers.In the coexistence accumulation model of type C,the cumulative gas production is much lower than that of Type A and Type B,regardless of whether only HBL DP,only CGL DP,or HBL+CGL DP are considered.This indicates that the impermeable interlayer restricts the cross-flow of gas between HBL and CGL.For three different coexistence accumulation models,CGL DP has the largest gas-to-water ratio.

Investigating the Evolution of Amati Parameters with Redshift

Gamma-ray bursts(GRBs) are among the brightest objects in the Universe and, hence, can be observed up to a very high redshift. Properly calibrated empirical correlations between intensity and spectral correlations of GRBs can be used to estimate the cosmological parameters. However, the possibility of the evolution of GRBs with redshift is a long-standing puzzle. In this work, we used 162 long-duration GRBs to determine whether GRBs below and above a certain redshift have different properties. The GRBs are split into two groups, and we fit the Amati relation for each group separately. Our findings demonstrate that estimations of the Amati parameters for the two groups are substantially dissimilar. We perform simulations to investigate whether the selection effects could cause the difference. Our analysis shows that the differences may be either intrinsic or due to systematic errors in the data, and the selection effects are not their true origin. However, in-depth analysis with a new data set comprised of 119 long GRBs shows that intrinsic scatter may partly be responsible for such effects.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.

Performance and Emission Characteristics of a CRDI Diesel Engine Fuelled by SiO_(2) Nanoparticle-Waste Fat Biodiesel Blends

This study investigates the use of waste fat biodiesel(WFB)from the leather industry as a substitute for diesel fuel.Specifically,it examines the diesel engine performance of WFB,a blend of WFB and diesel(B50),and different blends of WFB and silicon dioxide(SiO_(2))nanoparticles(B50SiO_(2)40,B50SiO_(2)80,and B50SiO_(2)120μg/g).The results indicate that the B50SiO_(2)120 blend increases brake thermal efficiency by 10.03%compared to pure biodiesel but falls 1.93%short of neat diesel.Furthermore,the B50SiO_(2)120 mixture reduces smoke,hydrocarbon,and carbon monoxide emissions by 31.87%,34.14%,and 43.97%respectively,compared to diesel.However,the B50SiO_(2)120 blend shows a 4.91%increase in nitrogen oxide emissions compared to diesel.

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter(STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate(K) and the moment(t).Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of Scrless than 1.761.

Isolation of Microcrystalline Cellulose from Wood and Fabrication of Polylactic Acid(PLA)Based Green Biocomposites

An innovative microcrystalline cellulose(MCC)natural fibre powder-reinforced PLA biocomposite was investigated using the hand lay-up technique.The polymer matrix composite(PMC)samples were prepared by varying the weight percentages(wt.%)of both PLA matrix and MCC reinforcement:pure PLA/100:0,90:10,80:20,70:30,60:40 and 50:50 wt.%,respectively.From the results obtained,MCC powder,with its impressive aspect ratio,proved to be an ideal reinforcement for the PLA,exhibiting exceptional mechanical properties.It was evident that the 80:20 wt.%biocomposite sample exhibited the maximum improvement in the tensile,flexural,notched impact,compressive strength and hardness by 28.85%,20.00%,91.66%,21.53%and 35.82%,respectively compared to the pure PLA sample.Similarly,during the thermogravimetric analysis(TGA),the same 80:20 wt.%biocomposite sample showed a minimum weight loss of 20%at 400℃,among others.The morphological study using Field Emission Scanning Electron Microscopy(FE-SEM)revealed that the uniform distribution of cellulose reinforcement in the PLA matrix actively improved the mechanical properties of the biocomposites,especially the optimal 80:20 wt.%sample.Importantly,it was evident that the optimal PLA/cellulose biocomposite sample could be a suitable and alternative sustainable,environmentally friendly and biodegradable material for semi/structural applications,replacing synthetic and traditional components.

Federated Learning on Internet of Things:Extensive and Systematic Review

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.

Research on technology of automatic welding machine and procedure for pile pipes of offshore jacket platform

Because of the high requirement of quality,vile working environment and great difficulty of automatic welding,MMAW is usually adopted for the welding of the platform jacket and the pile pipe.Its low efficiency and hard working is one of the choke points for construction progress. In this article,an up-to-date technology for platform pile pipe welding was introduced. Cored with FCW-S and combined with the characters and requirements of platform construction,the special automatic horizontal welding equipment and process were developed. Further more,the offshore application were introduced too.

A Survey of High Performance Concrete Developments in Civil Engineering Field

High Performance concrete (HPC) has received increased attention in the development of infrastructure Viz., Buildings, Industrial Structures, Hydraulic Structures, Bridges and Highways etc. leading to utilization of large quantity of concrete. This paper presents a comprehensive coverage of High Performance concrete developments in civil engineering field. It highlights the High Performance concrete features and requirements over conventional concrete. Furthermore, recent trends with regard to High Performance Concrete development in this area are explored. This paper also includes effect of Mineral and Chemical Admixtures used to improve performance of concrete.

Effects of microstructure characteristics on the tensile properties and fracture toughness of TA15 alloy fabricated by hot isostatic pressing

Powder hot isostatic pressing(HIP) is an effective method to achieve near-net-shape manufacturing of high-quality complex thinwalled titanium alloy parts, and it has received extensive attention in recent years. However, there are few reports about the microstructure characteristics on the strengthening and toughening mechanisms of powder hot isostatic pressed(HIPed) titanium alloys. Therefore, TA15powder was prepared into alloy by HIP approach, which was used to explore the microstructure characteristics at different HIP temperatures and the corresponding tensile properties and fracture toughness. Results show that the fabricated alloy has a “basket-like structure” when the HIP temperature is below 950℃, consisting of lath clusters and surrounding small equiaxed grains belts. When the HIP temperature is higher than 950℃, the microstructure gradually transforms into the Widmanstatten structure, accompanied by a significant increase in grain size. The tensile strength and elongation are reduced from 948 MPa and 17.3% for the 910℃ specimen to 861 MPa and 10% for the 970℃ specimen.The corresponding tensile fracture mode changes from transcrystalline plastic fracture to mixed fracture including intercrystalline cleavage.The fracture toughness of the specimens increases from 82.64 MPa·m^(1/2)for the 910℃ specimen to 140.18 MPa·m^(1/2)for the 970℃ specimen.Specimens below 950℃ tend to form holes due to the prior particle boundaries(PPBs), which is not conducive to toughening. Specimens above 950℃ have high fracture toughness due to the crack deflection, crack branching, and shear plastic deformation of the Widmanstatten structure. This study provides a valid reference for the development of powder HIPed titanium alloy.

Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.

Effect of deep cryogenic treatment on the microstructural,mechanical and ballistic properties of AA7075-T6 aluminum alloy

The study focused on investigating the effect of Deep Cryogenic Treatment(DCT)on the mechanical and ballistic properties of AA7075-T6 aluminum alloy.The microstructure,microhardness,tensile strength,and impact strength of the Base Material(BM)and DCT-treated 7075 samples were analyzed through metallographic analysis and mechanical tests.The microstructure of the DCT-treated 7075 samples revealed fine grains and a distribution of secondary phase particles.The tensile strength,impact strength,and microhardness of DCT-treated samples increased by 7.41%,4%,and 9.68%,respectively,compared to the BM samples.The fractography analysis of the tensile samples showed cleavage facets,microvoids,and dimples in both the samples.The ballistic behavior of the BM and DCT target plates were studied by impacting hard steel core projectiles at a velocity of 750±10 m/s.The target plates failed due to petaling and ductile hole enlargement,and the depth of penetration(DOP)of the DCT target was less than that of the BM target,indicating a higher ballistic resistance.The post-ballistic microstructure examination of the target plates showed the formation of an Adiabatic Shear Band(ASB)without any cracks.It was concluded that the DCT treatment improved the mechanical and ballistic properties of the aluminum alloy due to grain refinement and high dislocation density.

In Vitro Evaluation of Polyurethane-Chitosan Scaffolds for Tissue Engineering

In this work the use of Polyurethane (PU)-Chitosan(CH) scaffolds prepared by thermal induced phase separation (TIPS) for osteoblast proliferation and bone mineralization is described. Primary rat calvaria osteoblasts were seeded in the scaffolds and it was shown that supported cell adhesion and growth. The behavior osteoblast cells growing in the scaffold in function of the different ratio of PU and CH is presented. The results showed that TIPS is an appropriate technique for the production of PU-CH scaffolds with high potential for application as cell scaffolds in bone tissue engineering.

Contribution of technology in language learning： A focus

The emerging technologies in various fields are affecting all walks of life including learning, teaching, education, etc.. The present learning in engineering colleges in the Indian scenario and their effect on general language proficiency is discussed. In the present era of job-oriented education how the accurate language learning and strengthening of skills facilitate one＇s career building is discussed here. The present paper focuses on independent, collaborative and supportive learning environment in the class to mould the reluctant learners. It also brings out the differences between conventional learning and technology assisted learning. In the midst of thousands of soft ware available, it is difficult for one to choose which is most suitable, to use friendly English learning process and which will be specific, more and more learners center and the need for such soft wares is given specific high light here. How the assessment should be carried through language modules is discussed here with a special attention to the learners and product assessment separately.

Development of Technology for Producing a Fat Composition Based on Ester for Fattening Leather

An ester based on the secondary and by-products of oil and fat and alcohol hydrolysis is obtained for fattening the skin. Distilled fatty acids are used as a secondary product for the preparation of the ester, and fusel oil as a by-product. The main physicochemical properties of the ester obtained were determined. An ester-based formulation has been developed to fatten clothing skins from fine raw materials. The recommended is technological scheme of obtaining a composition for fattening leather.

Survey on <i>Essential and Accidental Real-Time Issues</i>in Software Engineering

Software product lines have recently been presented as one of the best promis-ing improvements for the efficient software development. Different research works contribute supportive parameters and negotiations regarding the prob-lems of producing a perfect software scheme. Traditional approaches or recy-cling software are not effective to solve the problems concerning software competence. Since fast developments with software engineering in the past few years, studies show that some approaches are getting extensive attention in both industries and universities. This method is categorized as the software product line improvement;that supports reusing of software in big organizations. Different industries are adopting product lines to enhance efficiency and reduce operational expenses by way of emerging product developments. This research paper is formed to offer in-depth study regarding the software engineering issues such as complexity, conformity, changeability, invisibility, time constraints, budget constraints, and security. We have conducted various research surveys by visiting different professional software development organizations and took feedback from the professional software engineers to analyze the real-time problems that they are facing during the development process of software systems. Survey results proved that complexity is a most occurring issue that most software developers face while developing software applications. Moreover, invisibility is the problem that rarely happens according to the survey.

Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction tasks.To prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were compared.The evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.