The growing demand for renewable energy positions it as a cornerstone for climate change mitigation and greenhouse gas emissions reduction. Although renewable energy sources generate around 30% of global electricity, their production and deployment involve significant environmental challenges. This review analyzes renewable energy projects from a life cycle perspective, focusing on environmental impacts throughout the supply chain. Particular emphasis is placed on the energy-intensive nature of manufacturing phases, which account for 60% to 80% of total emissions. The extraction of critical raw materials such as neodymium, dysprosium, indium, tellurium, and silicon is associated with emission levels ranging from 0.02 to 0.09 kg of carbon dioxide equivalent per kilowatt-hour for rare earth elements, along with an estimated average land degradation of 0.2 hectares per megawatt installed. Furthermore, the production of solar-grade silicon for photovoltaic panels consumes approximately 293 kilowatt-hours of electricity per kilogram, significantly contributing to the overall environmental footprint. Through a comprehensive review of the existing literature, this study integrates life cycle assessment and sustainable supply chain management approaches to identify environmental hotspots, quantify emissions, and propose strategic improvements. The analysis provides a structured, systematized, and data-driven evaluation, highlighting the relevance of circular economy principles, advanced recycling technologies, and digital innovations to enhance sustainability, traceability, and resilience in renewable energy supply chains. This work offers actionable insights for decision-makers and policymakers to guide the low-carbon transition.

The filtered B, V, R and I light curves of four short period (P < 0.25d) eclipsing binary systems are presented and analysed; two having P ≈ 0.22d, close to the short period limit for contact binaries (CBs). New ephemerides are provided. No third light was found necessary in any case, and one or more spots were introduced to account for asymmetries in the light curves. Two systems belong to the W sub-type of the W-UMa CBs and a third one to the A sub-type of the W-UMa type CBs. The last one is a semi-detached binary with a large temperature difference between the components and a low mass ratio q = 0.191. All components of the binaries are of the K spectral type, and the secondary of the semi-detached binary is of the M spectral type. Absolute parameters of the components were calculated using different evolutionary diagrams. Interestingly, two target have estimated total masses that are smaller than the lower mass limit known for Cbs.

Abstract We present results of a theoretical analysis of an optical parametric amplification (OPA) in a collinear nearly degenerate four-wave mixing configuration close to the resonance of a two-level transition in χ ′ ′ ′ media. It is performed both for homogeneously and for inhomogeneously broadened spectral line using a standard set of Bloch equations in the semiclassical approximation. Unlike the traditional approach of a coupled propagation of the signal/idler waves, we treat OPA as a coupled propagation of the amplitude and phase modulations of the pump wave with the signal/idler waves as the modulation sidebands. We show that for low frequency off-sets between the pump and the signal/idler waves, OPA process is basically manifested in a unidirectional transformation of the input amplitude modulation of the pump wave into the output phase modulation. The frequency band of the OPA is determined by detuning the pump wave from the resonance transition. The obtained results are applied to evaluate the expected OPA gain for the acetylene-filled micro-structured fiber with the resonance vibration-rotational transitions in the 1500–1550 nm spectral range.

ABSTRACTWind turbine blades (WTB) are critical components of wind energy systems. Operating in harsh environments WTBs face significant challenges, since damage to their leading edge caused by erosion or additive surface roughness can reduce performance, and increase maintenance costs and operational downtime. One approach to detect WTB damage is to use machine learning, but properly designing a predictive system is not trivial. Auto machine learning (AutoML) can be used to simplify the design and implementation of machine learning pipelines. This work presents the first comparison of state‐of‐the‐art AutoML methods, Auto‐Sklearn, H2O‐DAI and TPOT, to detect erosion and additive roughness in WTBs. The Leading‐Edge Erosion Study database is used, which provides measurements of the pressure coefficient along the airfoil under different conditions. This is the first work to combine the pressure coefficient and AutoML systems to detect these types of damage. Results show the viability of using AutoML in this task, with H2O‐DAI producing the best results, achieving an accuracy above in many cases. However, statistical analysis shows that a standard classifier can achieve similar performance across all problems considered, based on the Friedman test and the Wilcoxon‐Holm post hoc analysis with an significance level. However, AutoML systems perform better as the complexity and difficulty of the problem increases.

Computational protein and peptide design is emerging as a transformative framework for engineering macromolecules with precise structures and functions, offering innovative solutions in medicine, biotechnology, and materials science. However, current methods predominantly rely on generative models, which are expensive to train and inflexible to modify. Here, we introduce the Key-Cutting Machine (KCM), a novel optimization-based platform that iteratively leverages structure prediction to match desired backbone geometries. KCM requires only a single GPU and enables seamless incorporation of user-defined requirements into the objective function, circumventing the high retraining costs typical of generative models while allowing straightforward assessment of measurable properties. By employing an Estimation of Distribution Algorithm, KCM optimizes sequences based on geometric, physicochemical, and energetic criteria. We benchmarked its performance on α-helices, β-sheets, and unstructured regions, demonstrating precise backbone geometry design. As a proof of concept, we applied KCM to antimicrobial peptide (AMP) design by using a template AMP as the key, yielding a candidate with potent in vitro activity against multiple bacterial strains and efficacy in a murine infection model. KCM thus emerges as a robust tool for de novo protein and peptide design, offering a flexible paradigm for replicating and extending the structure-function relationships of existing templates.

Abstract The degree to which Type Ia Supernova (SN Ia) progenitors modify their surroundings remains an open question. In this work, we explore the parameter space for circumstellar interaction in Type Ia Supernova Remnant (SNR) 0519−69.0 by comparing observed archival Chandra spectra with model SNR spectra calculated assuming different SN Ia explosion scenarios and ambient medium (AM) structures. We compared SNR models expanding into a uniform AM with those expanding into a post-common envelope cocoon generated from a planetary nebula model. We conclude that the X-ray spectra and bulk dynamics of SNR 0519−69.0 are best explained by an interaction with a planetary nebula cocoon, implying that the progenitor of this SN Ia went through a common envelope episode shortly (~10,000 yr) before the SN explosion.

Crouch gait, also referred to as flexed knee gait, is an abnormal walking pattern, characterized by an excessive flexion of the knee, and sometimes also with anomalous flexion in the hip and/or the ankle, during the stance phase of gait. Due to the fact that the amount of clinical data related to crouch gait are scarce, it is difficult to find studies addressing this problem from a data-based perspective. Consequently, in this paper we propose a gait recognition strategy using synthetic data that have been obtained using a polynomial based-generator. Furthermore, though this study, we consider datasets that correspond to different levels of crouch gait severity. The classification of the elements of the datasets into the different levels of abnormality is achieved by using different algorithms like k-nearest neighbors (KNN) and Naive Bayes (NB), among others. On the other hand, to evaluate the classification performance we consider different metrics, including accuracy (Acc) and F measure (FM). The obtained results show that the proposed strategy is able to recognize crouch gait with an accuracy of more than 92%. Thus, it is our belief that this recognition strategy may be useful during the diagnosis phase of crouch gait disease. Finally, the crouch gait recognition approach introduced here may be extended to identify other gait abnormalities.