Abstract
The wine industry, with its high resource demands, is facing increasing pressure to improve operational sustainability by reducing water and energy use due to climate change, limited resources, and changing regulations. This study is a critical narrative review that followed a structured literature identification and screening approach to synthesise evidence on monitoring, instrumentation, and digital solutions for water and energy efficiency in vineyards and wineries. The review and critical analysis focus on sensor technologies, IoT-based systems, and smart management platforms applicable to the winemaking process. The evidence highlights the underexplored water-energy nexus within the wine sector, demonstrating bidirectional dependencies and potential co-benefits of integrated resource management, particularly in water-intensive and energy-intensive operations (e.g., cleaning/CIP, cooling/refrigeration, and pumping). Despite advancements in agriculture and industrial sectors, the integration of smart monitoring systems in wineries remains limited due to fragmented implementation, lack of standardized indicators, and socioeconomic barriers affecting scalability. Based on the synthesis, this work proposes actionable measures and implementation pathways, including baseline metering and sub-metering with clear KPIs, real-time monitoring and anomaly/leak detection, optimisation of cleaning/ CIP and cooling control, water reuse/recycling opportunities, and integrated energy management via dashboards/decision-support tools to enable data-driven operation. These findings help bridge technical innovations with practical adoption pathways, supporting the transition toward sustainable, low-impact, and climate-resilient wine production.

Abstract
In ordinal regression, where output classes exhibit a natural ordering (e.g., age groups or disease severity), two structural properties are highly desirable: unimodality—the predicted probability distribution should be single-peaked around the true class—and sparsity, meaning the probability mass should concentrate on a small subset of classes. However, existing approaches either ignore ordinality entirely or enforce unimodality while ignoring sparsity. We propose unisparse, a differentiable output layer that first uses the Pool-Adjacent-Violators Algorithm (PAVA) to obtain the exact Euclidean projection onto the unimodal cone and then applies sparsemax to project the result onto the probability simplex. The second projection is order-preserving, and thus the composition guarantees a unimodal probability vector while inducing exact zeros. Unlike parametric approaches (e.g., binomial unimodal), the method does not impose a prescribed distributional family. The composition is piecewise differentiable and supports end-to-end training. Experiments on seven benchmarks (five tabular and two vision) show that unisparse is competitive with six ordinal and multiclass baselines (Softmax, Sparsemax, UnimodalNet, CORAL, CORN, ORD-ACL, and VS-SL) in terms of accuracy and MAE while consistently producing much smaller supports. Our layer is architecture-agnostic and easy to integrate into modern deep network architectures. © 2026 by the author.

Abstract
Systematic reviews and mapping studies are critical to synthesize research, identify gaps, and guide future work, but are often labor-intensive and time-consuming. Existing tools provide partial support for specific steps, leaving much of the process manual and error-prone. We present ProfOlaf, a semi-automated tool designed to streamline systematic reviews while maintaining methodological rigor. ProfOlaf supports iterative snowballing for article collection with human-in-the-loop filtering and uses large language models to help select articles, extract key topics, and answer queries about the content of articles. By combining automation with guided manual effort, ProfOlaf enhances the efficiency, quality, and reproducibility of systematic reviews across research fields. ProfOlaf can be used both as a CLI tool and in web application format. A video demonstrating ProfOlaf is available at: https://youtu.be/R-gY4dJlN3s © 2026 Copyright held by the owner/author(s).

Abstract
The United Nations' 2030 Agenda for Sustainable Development highlights the importance of energy-efficient software to reduce the global carbon footprint. Programming languages and execution models strongly influence software energy consumption, with interpreted languages generally being less efficient than compiled ones. Lua illustrates this trade-off: despite its popularity, it is less energy-efficient than greener and faster languages such as C. This paper presents an empirical study of Lua's runtime performance and energy efficiency across 25 official interpreter versions and just-in-time (JIT) compilers. Using a comprehensive benchmark suite, we measure execution time and energy consumption to analyze Lua's evolution, the impact of JIT compilation, and comparisons with other languages. Results show that all LuaJIT compilers significantly outperform standard Lua interpreters. The most efficient LuaJIT consumes about seven times less energy and runs seven times faster than the best Lua interpreter. Moreover, LuaJIT approaches C's efficiency, using roughly six times more energy and running about eight times slower, demonstrating the substantial benefits of JIT compilation for improving both performance and energy efficiency in interpreted languages.

Abstract
The incorporation of large language models (LLMs) into clinical decision support systems (CDSS) offers potential for enhancing the quality of healthcare. Nevertheless, there is a dearth of comprehensive standards and understanding about the application of explainable and responsible AI practices in this context. The objective of this study is to present an overview of the literature on the utilization of LLMs in CDSSs, with an emphasis on Explainable AI (XAI) techniques and responsible AI principles. The present study was conducted through a systematic literature review analyzed of 36 studies on the application of LLMs in CDSS, with a focus on XAI and responsible AI. Human-centered AI is essential to design CDSSs that not only provide accurate recommendations but also align with clinicians' trust and workflow realities. The results suggest that while LLM-based CDSSs demonstrate potential in enhancing clinical decision-making, concerns regarding model interpretation into healthcare workflows persist as challenges.

Abstract
Hyperspectral image (HSI) classification presents significant challenges due to the high dimensionality of spectral data and the long-tailed distribution of available samples. Current methods often employ principal component analysis or related preprocessing techniques to reduce spectral dimensionality, which may disrupt spectral continuity and weaken frequency-aware representation learning. Furthermore, most existing long-tailed HSI classification methods primarily focus on loss function design or classifier adjustment, while the interactions among representation learning, prototype modeling, training strategy, and output calibration remain insufficiently explored. To address these limitations, we propose SPARC-Net, a Spectral-Preserving Amplitude-Phase and Reliable Correction Network for long-tailed HSI classification. Its core component is a Spectral-Preserving Amplitude-Phase (SPAP) Backbone, which p'reserves the original spectral order, jointly models amplitude-phase representations, spatial high-frequency information, and spatial-spectral frequency interactions, and constrains feature distortion during representation learning. For long-tailed decision learning, a Main-anchored Reliable Prototype Correction (MRPC) Head retains a cosine classifier as the primary decision branch and employs reliability-aware dual-anchor prototypes solely for gated and bounded auxiliary correction. A main-branch-first staged training strategy and Reversible Tail-Prior Calibration (RTPC) further stabilize prototype learning and mitigate residual head-class bias during inference. Experiments on four datasets, including controlled comparisons with Mamba-based classifiers, comparisons between PCA and original-band inputs, head/medium/tail group evaluations, and sensitivity analyses under varying imbalance ratios, demonstrate competitive performance and improved tail-class reliability. These results provide complementary evidence for the effectiveness of the SPARC-Net for long-tailed HSI classification.