Enhancing Data Integrity in Blockchain through Fuzzy Augmented Lagrangian Optimization and Compact Blocks to Minimize Redundancy

Blockchain is a method of storing data that makes it difficult or impossible to modify, steal, or swindle the system. Every block in a blockchain has its header with the unique nonce, timestamp, hash, the previous hash, transaction data, and the Merkle root. The Merkle tree is crucial in a block for consolidating data into a single hash, but it can suffer from data redundancy concerns during its structure formation. The central focus of the paper revolves around data redundancy and presents a novel approach for ensuring data integrity in blockchain with a compactness technique. Compactness is accomplished using Fuzzy Augmented Lagrangian Optimization to reduce data redundancy (FALORR). We integrate compact blocks into regular blockchain setup, bringing out a faster and more efficient way to reduce memory requirements. This effectual transaction verification structure improves the overall security and efficiency of the blockchain network by detecting and preventing malicious activities. To evaluate the effectiveness of the proposed system, we employed Hyperledger Caliper, a specialized benchmarking tool tailored for gauging the performance of blockchain solutions. The results of our implementation and evaluation demonstrate the effectiveness of the proposed structure in minimizing data redundancy and maintaining the data integrity of transactions in the blockchain system

Revolutionizing Electronics E-Commerce: Harnessing The Power Of Artificial Intelligence In E-Marketing Strategies

AI-based marketing refers to the use of artificial intelligence (AI) technologies and techniques in various aspects of the marketing process to enhance efficiency, effectiveness, and personalization. AI-based marketing can be applied across multiple channels, including digital advertising, content creation, customer segmentation, personalized recommendations, and customer experience management. This study aims to explore the demographic profile of ecommerce electronics products industry workers, evaluate the relationship between demographic traits and their influence on E-Marketing, and pinpoint the key performance metrics that influence Artificial Intelligence in ecommerce's E-Marketing. This study was designed using the descriptive technique. The study only included participants who were familiar with E-Marketing and Artificial Intelligence as they related to the electronic product based ecommerce industries, either as working professionals or as students. Five main variables were used to build the structured questionnaire with regard to E-Marketing, including respondents' beliefs, the use of Artificial Intelligence (AI) as a powerful tool, AI implementation, a focus on content generation, and deal closure by the marketing team. An online survey that was converted into a Google form by emailing the URL was used to collect data. To collect the data, a judgmental sampling strategy was employed. Link was sent to more than 350 people and followed them continuously for getting the response. Data collection process was stopped after reaching the 175 responses. Around 14 responses were removed due to poor and incomplete responses and finally 161 responses were chosen for the purpose of data analysis. Statistical analysis was carried out using the SPSS software package, version 22. To achieve the goals of this investigation, a variety of statistical analyses, including regression analysis, chi-square analysis, ANOVA, and frequency analysis, were recommended. The study's conclusions have significant ramifications for enhancing Artificial Intelligence integration and marketing strategy adjustment in order to preserve competitive advantage in the erratic digital market

Exploring the Impact of COVID on Global Telecommunication Networks and ICT Solutions

The emergence of COVID-19 and its spread all over the world caused a significant increase in network traffic. Therefore, for more secure and consistent communications, it is crucial to investigate the factors that affect network traffic. In this study, the review of COVID-19 consequences on global telecommunication networks with an emphasis on network traffic is presented. The impact of lockdown on digital telecommunication networks in various countries around the globe is investigated. The rapid expansion of the virus forced countries to set up lockdown measures, and this caused people to stay at home; therefore, network traffic increased significantly from March 2020 to the middle of April, and then it slightly changed to be more stabilized until the middle of May 2020. Such increased network traffic has affected many aspects, such as mobile networks, roaming factors, and economic situations. In this research, supporting programs to protect network connectivity are studied around the globe. In a situation where people are mostly working remotely, security is a challenging issue that should be taken into careful consideration. This study provides a broad understanding of how COVID-19 affected digital communications and how governments responded to unprecedented crises

Automatic detection of visual faults on photovoltaic modules using deep ensemble learning network

The present study proposes an ensemble-based deep neural network (DNN) model for autonomous detection of visual faults such as glass breakage, burn marks, snail trail, and discoloration, delamination on various photovoltaic modules (PVM). The proposed technique utilizes an image dataset captured by RGB (Red, Green, Blue) camera mounted on an unmanned aerial vehicle (UAV). In the first step, the images are preprocessed by deriving spatial and frequency domain features, such as discrete wavelet transform (DWT), texture, grey level co-occurrence matrix (GLCM), fast Fourier transform (FFT), and grey level difference method (GLDM). The processed images are inserted as input in the proposed ensemble-based deep neural network (DNN) model in order to detect any visual faults on the photovoltaic modules (PVM). The performance of the proposed model is evaluated through classification accuracy, receiver operating characteristic (ROC) curve, and confusion matrix. The results demonstrate that the proposed ensemble-based deep neural network (DNN) model, along with the random forest classifier, achieved a classification accuracy of 99.68% for detecting visual faults on the PV modules. To verify the performance and robustness of the proposed model, we compare our model’s results to those of various classification approaches described in the literature. The suggested approach is compatible with the commercial unmanned aerial vehicle (UAV) embedded flight management system for fault detection

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Integrated genomic characterization of endometrial carcinoma

SummaryWe performed an integrated genomic, transcriptomic, and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumors and ~25% of high-grade endometrioid tumors have extensive copy number alterations, few DNA methylation changes, low ER/PR levels, and frequent TP53 mutations. Most endometrioid tumors have few copy number alterations or TP53 mutations but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A, KRAS and novel mutations in the SWI/SNF gene ARID5B. A subset of endometrioid tumors we identified had a dramatically increased transversion mutation frequency, and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy number low, and copy number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may impact post-surgical adjuvant treatment for women with aggressive tumors

Integrated Molecular Characterization of Uterine Carcinosarcoma

SummaryWe performed genomic, epigenomic, transcriptomic, and proteomic characterizations of uterine carcinosarcomas (UCSs). Cohort samples had extensive copy-number alterations and highly recurrent somatic mutations. Frequent mutations were found in TP53, PTEN, PIK3CA, PPP2R1A, FBXW7, and KRAS, similar to endometrioid and serous uterine carcinomas. Transcriptome sequencing identified a strong epithelial-to-mesenchymal transition (EMT) gene signature in a subset of cases that was attributable to epigenetic alterations at microRNA promoters. The range of EMT scores in UCS was the largest among all tumor types studied via The Cancer Genome Atlas. UCSs shared proteomic features with gynecologic carcinomas and sarcomas with intermediate EMT features. Multiple somatic mutations and copy-number alterations in genes that are therapeutic targets were identified