There is a need for energy storage to improve the efficiency and effectiveness of energy distribution with the increasing penetration of renewable energy sources. Among the various energy storage technologies being developed, ‘power-to-gas’ is one such concept which has gained interest due to its ability to provide long term energy storage and recover the energy stored through different energy recovery pathways. Incorporation of such systems within the energy infrastructure requires analysis of the key factors influencing the operation of electrolyzers and hydrogen storage. This study focusses on assessing the benefits power-to-gas energy storage while accounting for uncertainty in the following three key parameters that could influence the operation of the energy system: (1) hourly electricity price; (2) the number of fuel cell vehicles serviced; and (3) the amount of hydrogen refueled. An hourly time index is adopted to analyze how the energy hub should operate under uncertainty. The results show that there is a potential economic benefit for the power-to-gas system if it is modeled using the two-stage stochastic programming approach in comparison to a deterministic optimization study. The power-to-gas system also offers environmental benefits both from the perspective of the producer and end user of hydrogen.

Temporal properties define the order of occurrence and timing constraints on event occurrence. Such specifications are important for safety-critical real-time systems. We propose a framework for automatically mining temporal properties that are in the form of timed regular expressions (TREs) from system traces. Using an abstract structure of the property, the framework constructs a finite state machine to serve as an acceptor. We analytically derive speedup for the fragment and confirm the speedup using empirical validation with synthetic traces. The framework is evaluated on industrial-strength safety-critical real-time applications using traces with more than 1 million entries.

Adverse childhood experiences (ACEs) encompass negative, stressful, and potentially traumatic events during childhood, impacting physical and mental health outcomes in adulthood. Limited studies suggest ACEs can have short-term effects on children’s gut microbiomes and adult cognitive performance under stress. Nevertheless, the long-term effects of ACEs experienced during adulthood remain unexplored. Thus, this study aimed to assess the long-term effects of ACEs on the gut microbiota of adult nursing students. We employed a multidimensional approach, combining 16S rRNA sequencing, bioinformatics tools, and machine learning to predict functional capabilities. High-ACE individuals had an increased abundance of Butyricimonas spp. and Prevotella spp. and decreased levels of Clostridiales, and Lachnospira spp. Prevotella abundance correlated negatively with L-glutamate and L-glutamine biosynthesis, potentially impacting intestinal tissue integrity. While nursing students with high ACE reported increased depression, evidence for a direct gut microbiota–depression relationship was inconclusive. High-ACE individuals also experienced a higher prevalence of diarrhea. These findings highlight the long-lasting impact of ACEs on the gut microbiota and its functions in adulthood, particularly among nursing students. Further research is warranted to develop targeted interventions and strategies for healthcare professionals, optimizing overall health outcomes.

Convolutional Neural Networks have become an integral part of anomaly detection in Cyber-Physical Systems (CPS). Although highly accurate, the advent of adversarial patches exposed the vulnerability of CNNs, posing a security concern for safety-critical CPS. The current form of patch attacks often involves only a single adversarial patch. Using multiple patches enables the attacker to craft a stronger adversary by utilizing various combinations of the patches and their respective locations. Moreover, mitigating multiple patches is a challenging task in practice due to the nascence of the domain. In this work, we present three novel ways to perform an attack with multiple patches: Split, Mono-Multi, and Poly-Multi attacks. We also propose a search method named 'Boundary Space Search (BSS)' for the placement of patches to enhance the attack's efficacy further, experimenting on EuroSAT, Imagenette, and CIFAR10 datasets for various perturbation levels across diverse model architectures. The results show that the Poly-Multi attack outperforms other multi-patch and single-patch attacks and the best perception stealth to surpass the detection. We also highlight the trade-off between the number of patches and the patch size in a Multi-Patch attack. In the end, we analyze the ability of the Multi-Patch attack to overcome state-of-the-art defenses designed for single patch attacks.

Objective: The microscopic analysis of biopsied lung nodules represents the gold-standard for definitive diagnosis of lung cancer. Deep learning has achieved pathologist-level classification of non-small cell lung cancer histopathology images at high resolutions (0.5–2 µm/px), and recent studies have revealed tomography–histology relationships at lower spatial resolutions. Thus, we tested whether patterns for histological classification of lung cancer could be detected at spatial resolutions such as those offered by ultra-high-resolution CT. Methods: We investigated the performance of a deep convolutional neural network (inception-v3) to classify lung histopathology images at lower spatial resolutions than that of typical pathology. Models were trained on 2167 histopathology slides from The Cancer Genome Atlas to differentiate between lung cancer tissues (adenocarcinoma (LUAD) and squamous-cell carcinoma (LUSC)), and normal dense tissue. Slides were accessed at 2.5 × magnification (4 µm/px) and reduced resolutions of 8, 16, 32, 64, and 128 µm/px were simulated by applying digital low-pass filters. Results: The classifier achieved area under the curve ≥0.95 for all classes at spatial resolutions of 4–16 µm/px, and area under the curve ≥0.95 for differentiating normal tissue from the two cancer types at 128 µm/px. Conclusions: Features for tissue classification by deep learning exist at spatial resolutions below what is typically viewed by pathologists. Advances in knowledge: We demonstrated that a deep convolutional network could differentiate normal and cancerous lung tissue at spatial resolutions as low as 128 µm/px and LUAD, LUSC, and normal tissue as low as 16 µm/px. Our data, and results of tomography–histology studies, indicate that these patterns should also be detectable within tomographic data at these resolutions.

In discrete sequence based Generative Adversarial Networks (GANs), it is important to both land the samples in the initial distribution and drive the generation towards desirable properties. However, in the case of longer molecules, the existing models seem to under-perform in producing new molecules. In this work, we propose the use of Self-Attention mechanism for Generative Adversarial Networks to allow long range dependencies. Self-Attention mechanism has produced improved rewards in novelty and promising results in generating molecules.