Unplanned machine failures may interrupt regular operations resulting in production losses. These incur financial losses as well as additional time required to service, procure, and maintain components. Industry 4.0 revolution embodies the use of modern tools and technologies to establish predictive maintenance (PdM), where integrating sensors, IoT devices, and Machine Learning (ML) capabilities and enable fault prediction, diagnosis, and maintenance optimization can be achieved. However, only a fraction of industries currently deploy predictive measures due to its complexity, accuracy, and explainability limitations. This research presents a two-step approach for PdM, first, industrial sensors record motor shaft vibrations, misalignment, and equipment temperature to model faults. Several ML models were trained on this data to accurately classify the fault. Next, the Explainable AI (XAI) model SHapley Additive exPlanations (SHAP) was used to explain the fault nature, and the selection of most important features used by the model. This was further used to tune the Long-Section Term Memory Auto-Encoder (LSTM-AE) Network model accordingly, resulting in 98. 4% precision. Finally, the models were evaluated on several datasets at various motor speeds and shaft alignments, resulting in a consistent accuracy score.

Given the sudden rise of the emerging post-quantum encryption field, this comprehensive study has a purpose of delving into the intricate realm of network protocols, focusing in depth on the transition from classical public key encryption. To culminate through various stages in the evaluation of key encapsulation mechanisms, where the investigation systematically progresses from rigorously exploring the mathematical foundations and theoretical background, to practical frameworks implementation, scrutinizing protocols such as SSL and IPsec, while insuring optimal performance within these protocols by addressing load balancing. To enhance the efficiency of embedded systems for data encryption, one must take in consideration their computational resources and power. Finally, this article presents an account of the implementation architecture for MQTT communications with adaptive and lightweight algorithms on embedded platforms.

Recent advancements in technology have led to the emergence of Cyber-Physical Systems (CPS), which seamlessly integrate the cyber and physical domains in various sectors such as agriculture, autonomous systems, and healthcare. This integration presents opportunities for enhanced efficiency and automation through the utilization of Artificial Intelligence (AI) and Machine Learning (ML). However, the complexity of CPS brings forth challenges related to transparency, bias, and trust in AI-enabled decision-making processes. This research explores the significance of AI and ML in enabling CPS in these domains and addresses the challenges associated with interpreting and trusting AI systems within CPS. Specifically, the role of Explainable AI (XAI) in enhancing trustworthiness and reliability in AI-enabled decision-making processes is discussed. Key challenges such as transparency, security, and privacy are identified, along with the necessity of building trust through transparency, accountability, and ethical considerations.

BACKGROUND: Transcription factor (TF) binding motifs are identified by high throughput sequencing technologies as means to capture Protein-DNA interactions. These motifs are often represented by consensus sequences in form of position weight matrices (PWMs). With ever-increasing pool of TF binding motifs from multiple sources, redundancy issues are difficult to avoid, especially when every source maintains its own database for collection. One solution can be to cluster biologically relevant or similar PWMs, whether coming from experimental detection or in silico predictions. However, there is a lack of efficient tools to cluster PWMs. Assessing quality of PWM clusters is yet another challenge. Therefore, new methods and tools are required to efficiently cluster PWMs and assess quality of clusters. RESULTS: A new Python package Affinity Based Clustering for Position Weight Matrices (abc4pwm) was developed. It efficiently clustered PWMs from multiple sources with or without using DNA-Binding Domain (DBD) information, generated a representative motif for each cluster, evaluated the clustering quality automatically, and filtered out incorrectly clustered PWMs. Additionally, it was able to update human DBD family database automatically, classified known human TF PWMs to the respective DBD family, and performed TF motif searching and motif discovery by a new ensemble learning approach. CONCLUSION: This work demonstrates applications of abc4pwm in the DNA sequence analysis for various high throughput sequencing data using ~ 1770 human TF PWMs. It recovered known TF motifs at gene promoters based on gene expression profiles (RNA-seq) and identified true TF binding targets for motifs predicted from ChIP-seq experiments. Abc4pwm is a useful tool for TF motif searching, clustering, quality assessment and integration in multiple types of sequence data analysis including RNA-seq, ChIP-seq and ATAC-seq.

Frequent itemsets mining is an active research problem in the domain of data mining and knowledge discovery. With the advances in database technology and an exponential increase in data to be stored, there is a need for efficient approaches that can quickly extract useful information from such large datasets. Frequent Itemsets (FIs) mining is a data mining task to find itemsets in a transactional database which occur together above a certain frequency. Finding these FIs usually requires multiple passes over the databases; therefore, making efficient algorithms crucial for mining FIs. This work presents a graph-based approach for representing a complete transactional database. The proposed graph-based representation enables the storing of all relevant information (for extracting FIs) of the database in one pass. Later, an algorithm that extracts the FIs from the graph-based structure is presented. Experimental results are reported comparing the proposed approach with 17 related FIs mining methods using six benchmark datasets. Results show that the proposed approach performs better than others in terms of time.