ABB Limited

Real‐time prediction about the severity of noncommunicable diseases like cancers is a boon for early diagnosis and timely cure. Optical techniques due to their minimally invasive nature provide better alternatives in this context than the conventional techniques. The present study talks about a standalone, field portable smartphone‐based device which can classify different grades of cervical cancer on the basis of the spectral differences captured in their intrinsic fluorescence spectra with the help of AI/ML technique. In this study, a total number of 75 patients and volunteers, from hospitals at different geographical locations of India, have been tested and classified with this device. A classification approach employing a hybrid mutual information long short‐term memory model has been applied to categorize various subject groups, resulting in an average accuracy, specificity, and sensitivity of 96.56%, 96.76%, and 94.37%, respectively using 10‐fold cross‐validation. This exploratory study demonstrates the potential of combining smartphone‐based technology with fluorescence spectroscopy and artificial intelligence as a diagnostic screening approach which could enhance the detection and screening of cervical cancer.

As countries around the world commit to reducing brownfield energy generation and shifting toward clean energy, the placement of renewable energy sources (RES) optimally in the electrical distribution system remains a strenuous issue. Improperly integrating RES could have a detrimental impact on the efficient operation of the grid. This study proposes a real-time data-driven approach for optimal DERs allocation and identification of critical nodes in the electrical distribution system. A community detection clustering is performed on the IEEE 123 node feeder system to optimally cluster the nodes into two regions. Then, the Granger causal analysis is used to identify critical nodes in the system that are susceptible to failure or extreme events which may interrupt the operation of the system. Hence, strategically allocating RES to these critical nodes enhances network resilience, as validated by the computation of the percolation threshold. The findings reveal an impressive 37% boost in the system’s resilience attributed to the optimized deployment of RES.

The availability of high-fidelity time-series data is essential for distribution grid operations such as state estimation, prediction, protection and scheduling of distributed energy resources. However, disruptions in the metering systems, such as latency, equipment malfunctions, and communication congestion, could lead to missing data points. In this paper, we propose a fully data-driven approach for the distribution grid aimed at imputing the missing data from distributed-phasor measurement units (D-PMU). An advanced method is proposed named self-attention-based imputation for time series (SAITS) which is adapted to handle the complex and diverse data found in digital substations. The proposed method has the ability to accurately impute data even during fault events, thereby mitigating the potential misoperation of protection devices. This enables accurate data imputation and reconstruction of missing data points, thereby preserving the continuity and integrity of the dataset. We have validated the performance of the proposed method by comparing it with various traditional methods. By implementing the proposed imputation technique, the accuracy, and reliability are significantly improved, which enhances the decision-making processes for digital substations.

Cervical cancer is one of the most prevalent forms of cancer, with a lengthy latent period and a gradual onset phase. Conventional techniques are found to be severely lacking in real time detection of disease progression which can greatly enhance the cure rate. Due to their high sensitivity and specificity, optical techniques are emerging as reliable tools, particularly in case of cancer. It has been seen that biochemical changes are better highlighted through intrinsic fluorescence devoid of interference from absorption and scattering. Its effectiveness in in‐vivo conditions is affected by the fact that the intrinsic spectral signatures vary from patient to patient, as well as in different population groups. Here, we overcome this limitation by collectively enumerating the subtle changes in the spectral profiles and correlations through an information theory based entropic approach, which significantly amplifies the minute spectral variations. In conjunction with artificial intelligence (AI)/ machine learning (ML) tools, it yields high specificity and sensitivity with a small dataset from patients in clinical conditions, without artificial augmentation. We have used an in‐house developed handheld probe (i‐HHP) for extracting intrinsic fluorescence spectra of human cervix from 110 different subjects drawn from diverse population groups. The average classification accuracy of the proposed methodology using 10‐fold cross validation is 93.17%. A combination of polarised fluorescence spectra from i‐HHP and the proposed classifier is proven to be minimally invasive with the ability to diagnose patients in real time. This paves the way for effective use of relatively smaller sized sensitive fluorescence data with advanced AI/ML tools for early cervical cancer detection in clinics. This article is protected by copyright. All rights reserved.