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Abstract Explainable artificial intelligence (XAI) aims at addressing complex problems by coupling solutions with reasons that justify the provided answer. In the context of Answer Set Programming (ASP) the user may be interested in linking the presence or absence of an atom in an answer set to the logic rules involved in the inference of the atom. Such explanations can be given in terms of directed acyclic graphs (DAGs). This article reports on the advancements in the development of the XAI system xASP by revising the main foundational notions and by introducing new ASP encodings to compute minimal assumption sets, explanation sequences, and explanation DAGs. DAGs are shown to the user in an interactive form via the xASP navigator application, also introduced in this work. 

Abstract A three‐stage rail‐to‐rail bulk‐driven class AB OTA that operates with ±0.15 V supplies and a power dissipation of 90 nW is introduced. The first two stages use resistive local common mode feedback. The OTA uses simple phase lead compensation. It has a 36 MHz.pF/μW small signal figure of merit and a 55(V/μs) pF/μW large signal figure of merit. 

Abstract A distribution transformer's thermal operating conditions can impose a limitation on the Hosting Capacity (HC) of an electrical distribution feeder for PV interconnections in the feeder's low‐voltage network. This is undesirable as it curtails PV interconnection of both residential and commercial customers in the secondary networks at a time when there are record numbers of interconnection requests by utilities' customers. The authors analyse the limitations on HC due to transformer loading and degradation considerations. Then, the paper proposes a battery energy storage system (BESS) dispatch strategy that will mitigate the limitation on distribution feeder HC by distribution transformers. Three scenarios of HC were simulated for a test network—HC evaluation without restrictions by the distribution transformer (scenario 1), HC evaluation with restrictions by the distribution transformer (scenario 2), and HC evaluation without restriction by the distribution transformer, and with the implementation of the proposed BESS mitigation strategy (scenario 3). Simulation results show that transformer lifetime is depleted to about 6% of expected lifetime for unrestricted HC in scenario 1. Curtailing the HC by 32% in scenario 2 improves the lifetime to 149% of expected lifetime. Implementing the proposed BESS in scenario 3 improves the transformer lifetime to 127% and increases the HC by 62% above the curtailed value in scenario 2, and by 10% above the original HC in scenario 1. The BESS strategy implementation produced cost savings of 49% and 27% of the transformer cost in scenarios 2 and 3, respectively, due to deferred transformer replacement. Conversely, there is a 1600% replacement cost incurred in scenario 1, which underscores the need for a mitigation strategy. The proposed BESS strategy does not only improve the HC of a distribution feeder but also increases a distribution transformer's lifetime leading to replacement cost savings. 

Abstract It is well known that some harmful objects in the Tanner graph of low-density parity-check (LDPC) codes have a negative impact on their error correction performance under iterative message-passing decoding. Depending on the channel and the decoding algorithm, these harmful objects are different in nature and can be stopping sets, trapping sets, absorbing sets, or pseudocodewords. Differently from LDPC block codes, the design of spatially coupled LDPC codes must take into account the semi-infinite nature of the code, while still reducing the number of harmful objects as much as possible. We propose a general procedure, based onedge spreading, enabling the design of good quasi-cyclic spatially coupled LDPC (QC-SC-LDPC) codes. These codes are derived from quasi-cyclic LDPC (QC-LDPC) block codes and contain a considerably reduced number of harmful objects with respect to the original QC-LDPC block codes. We use an efficient way of enumerating harmful objects in QC-SC-LDPCCs to obtain a fast algorithm that spans the search space of potential candidates to select those minimizing the multiplicity of the target harmful objects. We validate the effectiveness of our method via numerical simulations, showing that the newly designed codes achieve better error rate performance than codes presented in previous literature. 

Abstract In the Nuclear Power Plant (NPP) control room, the operators’ performance in emergencies is impacted by the need to monitor many indicators on the control room boards, the limited time to interact with dynamic events, and the incompleteness of the operator’s knowledge. Recent research has been directed toward increasing the level of automation in the NPP system by employing modern AI techniques that support the operator’s decisions. In previous work, the authors have employed a novel AI-guided declarative approach (namely, Answer Set Programming (ASP)) to represent and reason with human qualitative knowledge. This represented knowledge is structured to form a reasoning-based operator support system that assists the operator and compensates for any knowledge incompleteness by performing reasoning to diagnose failures and recommend executing actions in real time. A general ASP code structure has been proposed and tested against simple scenarios, e.g., diagnosis of pump failures that result in loss of flow transients and generating the needed plans for resolving the issue of stuck valves in the secondary loop. In this work, we investigate the potential of the previously proposed ASP structure by applying ASP to a realistic case study of the Three Mile Island, Unit 2 (TMI-2) accident event sequence (in particular, the first 142 minutes). The TMI scenario presents many challenges for a reasoning system, including a large number of variables, the complexity of the scenario, and the misleading readings. The capability of the ASP-based reasoning system is tested for diagnosis and recommending actions throughout the scenario. This paper is the first work to test and demonstrate the capability of an automated reasoning system by applying it to a realistic nuclear accident scenario, such as the TMI-2 accident. 

Curtovirusspp., a group of widely distributed geminiviruses, are among the most significant plant pathogens in the United States. Beet curly top virus (BCTV), vectored by the beet leafhopper [Circulifer(Neoaliturus)tenellusBaker], causes severe economic losses in crops such as tomatoes, peppers, dry beans, sugar beets, melons, and leafy greens, particularly in the western United States. In New Mexico, chile (Capsicum annuum) is vulnerable, with infected plants exhibiting symptoms such as stunting, chlorotic and curled leaves, misshapen fruits, reduced yields, and plant death. Yield losses can reach up to 50% in some years. Current management strategies, including pesticide applications, provide limited protection, leaving growers with substantial losses. Research indicates beet leafhoppers prefer full sun and avoid shaded areas, suggesting potential for innovative pest management strategies. Agrivoltaic systems (AVSs), which combine photovoltaic (PV) installations with agriculture, increase field shade and could potentially deter crop pests. However, the effect of AVS on pest management and chile crop yields remains underexplored. This study evaluated the impact of AVS shade on chile yield and plant growth, beet leafhopper abundance, and BCTV incidence. In 2023 and 2024, ‘NuMex Odyssey’, a New Mexico type chile cultivar, was grown in PV module–shaded and full sun replications at New Mexico State University’s Leyendecker Plant Science Research Center. PV modules provided shade to plants during the morning hours (0630 to 1330 HR), resulting in an 11% reduction in mean light intensity compared with the full sun replications. Full sun replications had more marketable green yield, while PV shaded replications exhibited significantly lower BCTV-affected fruit in 2024 and beet leafhopper abundance in both 2023 and 2024. These findings suggest that shading can reduce beet leafhopper abundance and BCTV incidence, offering potential benefits for chile cultivation. 

Social skills training targets behaviors necessary for success in social interactions. However, traditional classroom training for such skills is often insufficient to teach effective communication — one-to-one interaction in real-world scenarios is preferred to lecture-style information delivery. This paper introduces a framework that allows instructors to collaborate with large language models to dynamically design realistic scenarios for students to communicate. Our framework uses these scenarios to enable student rehearsal, provide immediate feedback and visualize performance for both students and instructors. Unlike traditional intelligent tutoring systems, instructors can easily co-create scenarios with a large language model without technical skills. Additionally, the system generates new scenario branches in real time when existing options don't fit the student's response.