This study examined the contribution of GltB on biofilm formation and biocontrol efficiency of B. subtilis Bs916. The gltB gene was identified through a biofilm phenotype screen and a bioinformatics analysis of serious biofilm formation defects, and then a gltB single knockout mutant was constructed using homologous recombination. This mutant demonstrated severe deficits in biofilm formation and colonisation along with significantly altered production ofγ-polyglutamate (γ-PGA) and three lipopeptide antibiotics (LPs) as measured by a transcriptional analysis of both the wild type B. subtilis Bs916 and the gltB mutant. Consequently, the mutant strain retained almost no antifungal activity against Rhizoctonia solani and exhibited decreased biocontrol efficiency against rice sheath blight. Very few gltB mutant cells colonised the rice stem, and they exhibited no significant nutrient chemotaxis compared to the wild type B. subtilis Bs916. The mechanism underlying these deficits in the gltB mutant appears to be decreased significantly in production of γ-PGA and a reduction in the production of both bacillomycin L and fengycin. Biofilm restoration of gltB mutant by additionγ-PGA in the EM medium demonstrated that biofilm formation was able to restore significantly at 20 g/L. GltB regulates biofilm formation by altering the production ofγ-PGA, the LPs bacillomycin L and fengcin and influences bacterial colonisation on the rice stem, which consequently leads to poor biocontrol efficiency against rice sheath blight. This is the first report of a key regulatory protein (GltB) that is involved in biofilm regulation and its regulation mechanism and biocontrol efficiency by B. subtilis.

Anomalous behavior detection in business processes inspects abnormal situations, such as errors and missing values in system execution records, to facilitate safe system operation. Since anomaly information hinders the insightful investigation of event logs, many approaches have contributed to anomaly detection in either the business process domain or the data mining domain. However, most of them ignore the impact brought by the interaction between activities and their related attributes. Based on this, a method is constructed to integrate the consistency degree of multi-perspective log features and use it in an isolation forest model for anomaly detection. First, a reference model is captured from the event logs using process discovery. After that, the similarity between behaviors is analyzed based on the neighborhood distance between the logs and the reference model, and the data flow similarity is measured based on the matching relationship of the process activity attributes. Then, the integration consistency measure is constructed. Based on this, the composite log feature vectors are produced by combining the activity sequences and attribute sequences in the event logs and are fed to the isolation forest model for training. Subsequently, anomaly scores are calculated and anomalous behavior is determined based on different threshold-setting strategies. Finally, the proposed algorithm is implemented using the Scikit-learn framework and evaluated in real logs regarding anomalous behavior recognition rate and model quality improvement. The experimental results show that the algorithm can detect abnormal behaviors in event logs and improve the model quality.

For the reliable prediction and analysis of large amounts of data, big data analytics may be applied in many disciplines. They facilitate the discovery of important information in large amounts of data that would otherwise be obscured. Almost all organizations stored their data in the cloud as event logs over the last few decades. These data can be utilized to extract useful information, which can be used to boost an organization’s productivity and effectiveness by identifying, monitoring, and optimizing its processes. Supporting operations, recognizing faults in running processes, predicting event length, and predicting the next activity are all ways of accomplishing this. As part of our strategy, we provide a data collection and machine learning technique. Process mining can help you achieve these objectives. The major enabler of data-driven approaches in process mining is predictive process monitoring. Deep learning has been used in the realm of predictive monitoring to provide accurate future activity predictions in a running trace by analyzing data from previous events. Using image-based data engineering and convolutional neural networks, the next activity in a business process has been forecast in this paper (CNN). The use of CNN in process mining and data analytics guarantees that the proposed system has high accuracy in predicting the next activity in a business process. The experimental evaluation shows that the proposed CNN algorithm is faster at training and inference than the Long Short-Term Memory (LSTM) approach, even when the process has longer traces.

The Social Opportunistic Internet of Things (SO-IoT) is a rapidly emerging paradigm that enables mobile, ad-hoc device communication based on both physical and social interactions. In such networks, routing decisions heavily depend on the selection of intermediate nodes to ensure secure and efficient data dissemination. Traditional approaches relying solely on reliability or social interest fail to capture the multifaceted trustworthiness of nodes in dynamic SO-IoT environments. This paper proposes a trust-based route optimization framework that integrates social interest and behavioral reliability using Bayesian inference and Jeffrey’s conditioning. A composite trust level is computed for each intermediate node to determine its suitability for data forwarding. To validate the framework, we conduct a two-phase simulation-based analysis: a scenario-driven evaluation that demonstrates the model’s behavior in controlled settings, and a large-scale NS-3-based simulation comparing our method with benchmark routing schemes, including random, greedy, and AI-based protocols. Results confirm that our proposed model achieves up to an $88.9\%$ delivery ratio with minimal energy consumption and the highest trust accuracy ($86.5\%$), demonstrating its robustness and scalability in real-world-inspired IoT environments.