Understanding customer emotions and preferences is paramount for success in the dynamic product design landscape. This paper presents a study to develop a prediction pipeline to detect the aspect and perform sentiment analysis on review data. The pre-trained Bidirectional Encoder Representation from Transformers (BERT) model and the Text-to-Text Transfer Transformer (T5) are deployed to predict customer emotions. These models were trained on synthetically generated and manually labeled datasets to detect the specific features from review data, then sentiment analysis was performed to classify the data into positive, negative, and neutral reviews concerning their aspects. This research focused on eco-friendly products to analyze the customer emotions in this category. The BERT and T5 models were finely tuned for the aspect detection job and achieved 92% and 91% accuracy, respectively. The best-performing model will be selected, calculating the evaluation metrics precision, recall, F1-score, and computational efficiency. In these calculations, the BERT model outperforms T5 and is chosen as a classifier for the prediction pipeline to predict the aspect. By detecting aspects and sentiments of input data using the pre-trained BERT model, our study demonstrates its capability to comprehend and analyze customer reviews effectively. These findings can empower product designers and research developers with data-driven insights to shape exceptional products that resonate with customer expectations.

Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.

Monitoring flow-based systems (FBS) (e.g., water distribution systems, oil and gas pipelines, the human cardiovascular system) is of paramount importance considering their economic and health impacts. FBS monitoring typically has been achieved by costly, complex, static sensors that are strategically placed. To reduce the cost of monitoring, we propose a mobile wireless sensor network (WSN) system comprised of mobile sensors (their movement aided by the inherent flow in the FBS) and static beacons that aid in locating sensors. This article presents the first complete architectural design, algorithms, and protocols for optimal monitoring of FBS. Our proposed solution includes sensing and communication models, MAC and group management protocols for sensor and beacon communication, and algorithms for sensor and beacon placement. We compare our proposed solution with the state of the art through extensive simulations and a proof-of-concept system implementation. We demonstrate performance improvements, such as a dramatic reduction (a factor of 91) in the number of sensors when the sensing range is marginally (2.5 times) increased.

Leak and backflow detection are essential aspects of Water Distribution System (WDS) monitoring. Most existing solutions for leak/backflow detection in WDSs focus on the placement of expensive static sensors located strategically. In contrast to these, we propose a solution whereby mobile sensors (i.e., their movement aided only by the inherent water flow in the system) detect leaks/backflow. Information about the leaks/backflow is collected from the sensors either by physically capturing them, or through wireless communication. Specifically, we propose models to maximize leak/backflow detection given a cost constraint (a limit on the number of sensors). Through extensive simulations, we demonstrate the superior performance of our proposed solution when compared with the state of the art solutions (e.g., algorithms/protocols and analysis).