New lactobionamidoalkyl thiol-disulfide compounds were synthesized using lactobionolactone and aminoalkylthiols via amidation ring opening of the cyclic lactobionolactone to give compounds di(n-lactobionamidoalkyl) disulfides (2a-4a), and nlactobionamidoalkyl thiols (2c-4c). These compounds were acetylated to form acetylated di(n-lactobionamidoalkyl) disulfides (2b-4b), and acetylated n-lactobionamidoalkyl thiols (2d-4d), and subsequently utilized to stabilized gold nanoparticles (LAuNPs1-17). These compounds were characterized by spectroscopic methods (NMR, Infrared and GCMS), microanalysis, Transmission Electron Microscope (TEM) and Zetasizer. The lactobionamidoalkyl thiol and disulfide compounds were effective capping agents of gold nanoparticles via direct and substitution methods except substitution with unacetylated thiols (2c-4c) and disulfides (2a-4a) which decomposed within minutes. In-vitro anticancer screening of selected LAuNPs using acetylated and un-acetylated demonstrated moderate activity against human lung adenocarcinoma epithelial cell line and weak activity against human normal peripheral blood mononuclear cells, except LAuNPs 11 which showed moderate activities towards both cells lines with IC50s of 14.79±3.84 µg/mL and 13.26±6.48 µg/mL respectively.

Goal – This study proposes an integrated model for improving equipment availability in maintenance management by considering the relationship between Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR). These indicators are typically evaluated individually for decision-making in maintenance management. Methodology – The proposed integrated method uses widely adopted tools in industrial management, such as reliability statistical analysis, Lean Manufacturing, M-VSM (Material and Value Stream Mapping), FMEA (Failure Mode and Effects Analysis), and criticality analysis. Results – The proposed integrated method has proven effective after implementation in the fertilizer industry, increasing the availability of the prioritized equipment as determined by reliability statistical analysis. The improvement in equipment availability was achieved through a 15.5% reduction in MTTR and a 20.6% increase in MTBF, assuming the correct application of the proposed tools. Limitations – The article was limited to investigating the MTBF and MTTR indicators, as the determination of MTTF was impossible due to data unavailability. Practical implications – The article results can be used as a reference for the strategic decision making in maintenance management within industry. Originality – The study presents the modeling of an integrated method that utilizes consolidated tools and validates it through a case study, demonstrating practical results in a company.

The efficiency of an 8-bit flash analog-to-digital converter (ADC) is influenced by factors such as power consumption, area, and timing. As technology advances, the demand for better performance and higher standards in chip design technologies is increasing. As technology continues to advance, the standards for chip performance will likely rise, motivating researchers and manufacturers to strive for 5G and even greater achievements. The aim of this work is to study and analyse the performance requirements in terms of area, power consumption, and speed of 8-bit flash analogto-Digital Converter. Compared to other kind of analog-to-digital converters, flash converters have a significantly greater conversion rate. The number of comparators needed for flash converters is more 2 N -1 for N-bit in the conventional design architecture and consumes more power and area. In order to reduce the design complexity, power, area and noise for high bit resolution in the high-speed flash analog-to-digital converter architecture, this new design focuses on employing multiplexers to reduce the number of comparators and parameters. The traditional Flash type ADC uses more power and takes up more chip space since it needs 255 comparators for 8-bit flash. Therefore, reducing the number of comparators and noise are the design's primary goal. Only 65 comparators are needed for the construction of the 8-bit resolution Flash ADC employing multiplexers with evolution parameters such power dissipation 21uW, maximum noise 1.251nv**2/Hz, offset voltage 69.2103mv, SNDR 49.2dB, SFDR 61.3dB, differential non-linearity 0.41 LSB and INL (Integral non-linearity) 0.5 LSB. The design simulation carried out using cadence virtuoso. The work focused on backend design using 180nm CMOS process Technology.

The use of information technology -in agriculture- plays an important role in Smart-Pest Management. Particularly, Artificial intelligence helps to identify, monitor, control and make decisions about pests in crops. In this paper, we present a new metacognitive architecture, called Metacognitive Architecture for a SmartPest Management of Cotton. Especially, this paper presents several contributions: (1) a new architecture that implements several metacognitive tasks (meta-memory, meta-learning, meta-reasoning, meta-comprehension, meta-knowledge); (2) a case study of the architecture for predictive and prescriptive problems, in the context of integrated pest management in cotton; (3) an integrated approach of data analytics and metacognition in smart systems.

This research determined the causes and effects of trade fragmentation and how to reconfigure the global value chain by looking at the challenges and opportunities for the African automotive sector to produce electric vehicle batteries. Mixed method research methodology made use of journal articles from diverse online repositories. The data were analysed using documentary analysis and descriptive statistics. The results indicated that trade fragmentation is caused by geopolitical conflicts, policy concerns, trade prohibitions, and weak bilateral and international institutions. The findings indicated that trade interruptions, economic volatility, technology decoupling, company closures, job losses, welfare losses, and a decline in global output are all consequences of trade fragmentation. Opportunities are available to be explored by the African automotive sector, it was established that African economies can use the abundant minerals needed as raw materials to produce EV batteries. This, however, should be accompanied by effective industrial policies, capital, research and development, as well as harnessing much-needed modern technology. In terms of policy recommendations, it is suggested that there is a need to develop and implement an African automotive policy framework, promote business partnerships, and mobilise financial resources to enable the setting up of the EV battery industry in Africa.

Utilizing energy efficiency to prolong the life and sustainability of Wireless Sensor Networks (WSN) is still a challenge. While clustering has improved energy productivity by allowing for the classification of cluster heads, its \nexecution continues to be difficult. Existing criteria for cluster head selection begin by identifying desirable cluster head locations. Most strongly associated with these locations, the cluster heads are then chosen from the nodes. This location-based model adopts difficulties such as enhanced computation, decreased selection precision, and redundant node selection. This manuscript addresses these issues through the development of the sampling based Efficient Spider Monkey Optimization (ESMO) technique. If the nodes in the sample population are diverse, network nodes are chosen from the nodes. The issues associated with dispersed nodes and cluster heads have \nbeen resolved. This article demonstrates how to extend the lifetime and stability of WSNs using an ESMO and efficient CH selection. The purpose of this study is to discuss how basic SMO and sampling-based SMO are used to select cluster heads. The obtained measurements are compared to those obtained in homogeneous and heterogeneous setups using comparable procedures, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and ESMO enhanced routing protocol. ESMO increases lifetime of the network and steadiness periods by an estimate of 12.22 percent, 6.92 percent, 32.652 \npercent, and 1.22 percent, respectively, in these implementations.

Recent years have seen a surge in curiosity in machine-to-machine (M2M) collaborations between academics and industry. Machine-to-machine communication devices (MTCDs) are able to communicate automatically and with minimum human intervention in M2M communications infrastructure. While MTCDs are anticipated to deliver a range of services, resource allocation and clustering approaches in M2M transceivers face issues and limits due to the diverse quality of service (QoS) needs on various network conditions. A major issue in M2M communication systems is how to distribute and cluster resources. This article presents a clustering technique and collaborative resource allocation for MTCD resource management. The clustering and integrated resource allocation challenge is characterized as a maximization of energy efficiency problem. We separate the issue into two sub problems as a consequence of the original optimization model\'s inability to tackle nonlinear fractional utilizations: power redistribution and cluster. We begin by obtaining the optimal power distribution plan through an iterative technique energy efficiency maximization algorithm, and then offer a modified K-means technique for clustering. The effectiveness of the proposed approach is shown by numerical solution.

Generalization of intuitionistic fuzzy sets (IFSs) and picture fuzzy sets (PFSs), T-spherical fuzzy sets (TSFSs) can deal with blurred, hesitant, deficient and conflicting information. Similarity measures (SMs) are the most appropriate method to find similar data. In this article we proposed a new SM based on a relationship matrix, which is composed of the values relationship between membership grade (MG), refusal grade (RG), non-membership grade (NMG) and hesitance grade (HG). The proposed SM satisfies the axiomatic definition. We applied the developed SM in multi-attribute decision making (MADM) and pattern recognition (PR) problem. We support our proposed work with numerical example. It can be seen from the given example that developed SM is more effective. Further we made a comparative study with some existing SMs and discuss the advantages of our proposed work.

Social Networking platforms facilitate the sharing of information, ideas, and thoughts through constructing the virtual communities. Therefore, finding people in given social networking who are really have the power to influence others is more critical. For example, finding the right person who has an impact to support or contradict any opinion or bring more profits when he/she publishes an advertisement for a certain business or product. This problem can be modelled as finding influential users in social networking. There are different ways to find the influential users in these platforms and there are some criteria that should be considered. In this article, we propose a solution named Muatheer which help to determine the influential users in Instagram by scraping data using Instagram API and applying the sentiment analysis algorithm to classify the comments whether it is positive or negative, as well as using the unigram as a feature extraction method. Then, the sentiment analysis result combines with other factors to calculate the “Influence Ratio” that proposes to determine the actual influencer in a specific domain. The experiments were conducted using set of training datasets and the proposed algorithm gives a high accurate result using some metrices.

In order to ensure the normal operation of electronic equipment and better guide the electromagnetic protection design of electronic equipment, a set of computational models and numerical methods are needed to analyze the electromagnetic interference problems of electronic equipment. To this end, a new field-path hybrid time domain algorithm is adopted to simulate the electromagnetic interference of broadband electromagnetic signals to electronic equipment in time domain，the results are compared with those of FDTD method to verify the correctness and efficiency of the algorithm. Numerical verification results: In the numerical simulation, the mesh size of FDTD is 2mm, and the mesh size of field-path hybrid time domain algorithm is 5mm. The average calculation time of FDTD is 478.3min, while that of field-path hybrid time domain algorithm is 46.5min. The results show that the simulation effect of the field-path hybrid time domain algorithm is consistent with that of FDTD, and saves a lot of memory and computation time, which proves the correctness and efficiency of the algorithm.

In order to solve the problem of long training time for remote sensing image super-resolution reconstruction algorithm, propose a super-resolution reconstruction method of remote sensing image based on convolutional neural network, a method combining dense convolutional network, parallel CNN structure and sub-pixel convolution, use dense convolutional networks to extract features of low-resolution images, parallel CNN reduces network parameters, and sub-pixel convolution completes feature reconstruction. Experimental results show, the final PSNR value of the black curve of the three methods in the number of iterations during the training process is as high as 27.3, the middle curve is second, and the bottom curve is the worst at 27.0. Prove that the method has extracted more features, it retains more image details and improves the reconstruction effect of the image; The parameters in the network are greatly reduced to avoid the phenomenon of over-fitting in the deep network.

In order to reduce the adverse impact of disordered charging of electric vehicles on the safe and stable operation of distribution network, a multi-objective orderly charging strategy for inter-vehicle electric vehicles was proposed. By establishing a multi-objective optimization mathematical model of EV charging, which aims to minimize the charging cost and the peak-valley difference of distribution network load, an orderly charging strategy is proposed. The results showed that: The probability distribution of users\' selection of charging mode and response behavior is corrected through the actual response data collected by the Internet of Vehicles APP，on the fifth day, the user\'s actual charging response curve approached the theoretical curve obtained by the optimization algorithm, and basically reached the expected charging effect. The calculation showed that the variance of the total load curve after charging decreased from 169.35 to 127.39, a reduction of 24.8.

Modern healthcare data comprises data from the abundant origin such as electronic health records (EHRs), genomic sequencing, medical imaging, wearable, medical devices, payor records, and pharmaceutical research. This data is not the same as conventional e-medical and human health data. It is tough to process such data in conventional databases. Big data analytics can be used to process such data by collecting, systemizing and evaluating large sets of data to explore patterns and applicable information. Nowadays Diabetes is one of the challenging chronic diseases rovering the world. Predicting diabetes is a challenge that can be enriched. Various Machine Learning (ML) algorithm helps medical practitioners to predict the disease early. The accuracy of the prediction should be good. This was already implemented by various algorithms. But still, those existing algorithms result in poor accuracy and performance. In this paper, the accuracy of various machine learning algorithms like Naïve Bayes(NB), K-Nearest Neighbour(KNN),Random Forest(RF), AdaBoost(AB), Gradient Boosting(GB) and CatBoost(CB) classifiers were improved by combining with Principal Component Analysis (PCA) and the results are compared. It was proven that Improved CatBoost – Principal Component Analysis (ICB-PCA) classifier produces the best accuracy of 96% with 0.03 Hamming loss.

Analogical inference is widely used in geology research as a crucial technique for deriving conclusions and constructing hypotheses about the geology of Earth and other planetary bodies. Deriving conclusions by analogical reasoning in geology is no trivial matter and can even be quite complex, especially when applied to processes which occurred in the distant past (deep time) or to geological processes that are taking place (or have taken place) on other planetary bodies. In such cases, analogical reasoning might lead the geologist astray, to the extent of his reaching mistaken conclusions. Many geologists, like most people, use analogy intuitively, without always being aware of how the process works or of its pitfalls. Lack of such knowledge often leads to hasty, incorrect analogical argument, and consequently to incorrect conclusions. Since it is my view that some of these mistakes can be prevented through a better understanding of analogical inferencing and the problems involved, I develop in this paper key issues relating to analogical argument, and suggest means for preventing bad analogies. It is to be hoped that the following presentation will increase awareness among geologists and perhaps thereby succeed in preventing incorrect analogical inferences.