Search Results (627)

Wind power plants (WPPs), as large-scale cyber–physical systems (CPSs), have become essential to renewable energy generation but are increasingly exposed to cyber threats. Attacks on supervisory control and data acquisition (SCADA) networks can cause cascading physical and economic impacts. The systematic synthesis of cyber risk analysis methods specific to WPPs and cyber–physical energy systems (CPESs) is a need of the hour to identify research gaps and guide the development of resilient protection frameworks. This study employs a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to review the state of the art in this area. Peer-reviewed studies published between January 2010 and January 2025 were taken from four major journals using a structured set of nine search queries. After removing duplicates, applying inclusion and exclusion criteria, and screening titles and abstracts, 62 studies were examined for analysis on the basis of a synthesis framework. The studies were classified along three methodological dimensions, qualitative vs. quantitative, model-based vs. data-driven, and informal vs. formal, giving us a unified taxonomy of cyber risk analysis approaches. Among the included studies, 45% appeared to be qualitative or semi-quantitative frameworks such as STRIDE, DREAD, or MITRE ATT&CK; 35% were classified as quantitative or model-based techniques such as Bayesian networks, Markov decision processes, and Petri nets; and 20% adopted data-driven or hybrid AI/ML methods. Only 28% implemented formal verification, and fewer than 10% explicitly linked cyber vulnerabilities to safety consequences. Key research gaps include limited integration of safety–security interdependencies, scarce operational datasets, and inadequate modelling of environmental factors in WPPs. This systematic review highlights a predominance of qualitative approaches and a shortage of data-driven and formally verified frameworks for WPP cybersecurity. Future research should prioritise hybrid methods that integrate formal modelling, synthetic data generation, and machine learning-based risk prioritisation to enhance resilience and operational safety of renewable-energy infrastructures. Full article

The performance of conventional precipitation-strengthened copper alloys drastically degrades at temperatures exceeding 500 °C, hindering their application under extreme conditions like those in nuclear fusion reactors. Oxide dispersion–strengthened copper (ODS–Cu) alloy surmounts these constraints by incorporating thermally stable, nanoscale oxide dispersoids that simultaneously confer strengthening, microstructural stabilization, and enhanced irradiation tolerance, while preserving high thermal conductivity. This review comprehensively examines the state of the art in ODS–Cu alloy from a “processing–microstructure–property” perspective. We critically assess established and emerging fabrication routes, including internal oxidation, mechanical alloying, wet chemical synthesis, reactive spray deposition, and additive manufacturing, to evaluate their efficacy in achieving uniform dispersions of coherent/semi-coherent nano-oxides at engineering-relevant scales. The underlying strengthening mechanisms and performance trade-offs are quantitatively analyzed. The review also outlines strategies for joining and manufacturing complex components, highlights key gaps in metrology and reproducibility, and proposes a roadmap for research and standardization to accelerate industrial deployment in plasma-facing components. Full article

Agrivoltaics is a rapidly expanding technology thanks to its energy, agronomic, and microclimatic benefits, which have been demonstrated in a variety of climatic contexts around the world. This study presents the first systematic review exclusively focused on experimental agrivoltaics field studies, based on the analysis of 82 peer-reviewed articles. The aim is to provide a cross-study comparable synthesis of how shading from different photovoltaic (PV) technologies affects microclimate, crop yield, and crop quality. The reviewed systems include four main categories of PV modules: conventional, bifacial, semi-transparent/transparent, including spectrally selectivity modules and concentrating photovoltaic systems (CPV). To handle heterogeneity and improve comparability, results were normalised against open-field controls as relative percentage variations. The analysis reveals a high variability in results, strongly influenced by crop type, climate, level of shading, and reduction in PAR (Photosynthetically Active Radiation). Studies conducted with the same shade intensity but under different climatic conditions show contrasting results, suggesting that there is no universally optimal agrivoltaics configuration. Nevertheless, the review allows us to identify recurring patterns of compatibility between crops and photovoltaic technologies, providing useful guidance for choosing the most suitable technology based on climate, crop physiology, and production objectives. Full article

Chondroitin sulfate is a glycosaminoglycan polysaccharide, playing key roles in a plethora of physiopathological processes typical of higher animals. The position of sulfate groups within CS disaccharide subunits composing the polysaccharide chain is able to encode specific functional information. In order to expand such a “sulfation code”, access to non-natural CS variants and mimics thereof can be pursued. In this context, an interesting topic concerns phosphorylated analogs of CS polysaccharides, as the replacement of sulfate groups with phosphates can lead to unreported activities of phosphorylated CS. In light of this, the phosphorylation reaction of a microbial-sourced, unsulfated chondroitin polysaccharide with phosphoric acid is reported in the present study, testing different microwave irradiation conditions and comparing them with conventional heating procedures. The obtained products were subjected to a detailed characterization, in terms of chemical structure and hydrodynamic properties, by 1D- and 2D-NMR spectroscopy and HP-SEC-TDA analysis, respectively. The characterization study showed how different reaction conditions can not only influence the regioselectivity and degree of phosphorylation but also trigger the formation of phosphate diester functionalities acting as cross-linkers between polysaccharide chains. The results from the screening presented in this work could be interesting for any research devoted to the regioselective phosphorylation of a polysaccharide. Full article

Payments for Ecosystem Services (PESs) have become a central instrument in global biodiversity finance, yet endangered species-specific PESs remain rare and poorly understood in implementation terms. Taiwan provides a revealing case: a three-year program paying farmers to conserve four threatened species—Prionailurus bengalensis, Lutra lutra, Tyto longimembris, and Hydrophasianus chirurgus—in working farmland across Taiwan and Kinmen island. Through semi-structured interviews with farmers, residents, and local conservation actors, we examine how payments are interpreted, rationalized, enacted, and emotionally experienced at the ground level. This study adopts Colaizzi’s data analysis method, the primary advantage of which lies in its ability to systematically transform fragmented and emotive interview narratives into a logically structured essential description. This is achieved through the rigorous extraction of significant statements and the subsequent synthesis of thematic clusters. Participants reported willingness to continue not only because subsidies offset losses, but because rarity, community pride, and the visible arc of “we helped this creature survive” became internalized rewards. NGOs amplified this shift by translating science into farm practice and “normalizing” coexistence. In practice, conservation work became a social project—identifying threats, altering routines, and defending habitat as a shared civic act. This study does not estimate treatment-effect size; instead, it delivers mechanistic insight at a live policy moment, as Taiwan expands PESs and the OECD pushes incentive reform. The finding is simple and strategically important: endangered-species PESs work best where payments trigger meaning—not where payments replace it. Full article

Hexahydrocannabinol (HHC), a hydrogenated derivative of Δ⁹-tetrahydrocannabinol (Δ⁹-THC), is a semi-synthetic cannabinoid marketed as an alternative to Δ⁹-THC. Its hydroxylated metabolite, 8-hydroxyhexahydrocannabinol (8-OH-HHC), exists as four stereoisomers: (6aR,8R,9R,10aR), (6aR,8S,9S,10aR), (6aR,8S,9R,10aR), and (6aR,8R,9S,10aR). However, the lack of reference standards has hindered pharmacokinetic and forensic studies. This work reports the first stereoselective synthesis and structural confirmation of all four 8-OH-HHC stereoisomers. Two strategies were employed: hydroboration–oxidation and epoxidation–reduction. Hydroboration of Δ⁸-THC with BH₃·THF followed by oxidation predominantly produced anti-isomers (6aR,8R,9R,10aR) and (6aR,8S,9S,10aR) in moderate yields, along with small amounts of syn-isomer (6aR,8S,9R,10aR), suggesting an atypical mechanistic pathway. In contrast, syn-isomers (6aR,8S,9R,10aR) and (6aR,8R,9S,10aR) were accessed via epoxidation of Δ⁸-THC acetate using mCPBA and subsequent reduction with NaBH₃CN/BF₃·OEt₂, affording the desired products with moderate selectivity. Absolute configurations were confirmed by nuclear Overhauser effect spectroscopy (NOESY). These methods will facilitate future pharmacokinetic and forensic research and support the development of improved detection strategies. Full article

The diterpene sessein, isolated from Salvia sessei, is a metabolite of interest due to its conjugated p-quinone system, δ-lactone ring, and phenolic hydroxyl in C-12. These functionalities make it an ideal starting point for reactivity studies and semi-synthetic derivatization. In this work, we report the obtainment of two derivatives by selective esterification of phenolic hydroxyl in C-12, through acetylation and benzoylation reactions under mild conditions and with high yields. The structures were characterized by UPLC-MS, FTIR, and NMR spectroscopy ¹H, ¹³C, and 2D, which allowed to precisely confirm the modifications made in the derivatives. These results confirm that hydroxyl in C-12 constitutes a privileged site of reactivity within the royleanone family, consolidating sessein as a versatile nucleus for the generation of derivatives. Finally, the preliminary evaluation of the antimicrobial activity showed that sessein shows a broad spectrum of action against Gram-positive, Gram-negative, and Candida albicans strains. The acetylated derivative showed an increase in activity against gram-negative bacteria, while the benzoyl derivative had a loss of effect at the concentrations evaluated. These findings demonstrate that structural modifications influence the properties of the derivatives with respect to the compound sessein. Full article

Background/Objectives: Caffeic acid is a hydroxycinnamic acid that has a wide range of applications in the medical field. The synthesis of caffeic acid using microbial fermentation technology is an environmentally friendly method. Methods: By engaging various enzymes, specifically 4-hydroxyphenylacetate 3-monooxygenase (HpaB), sourced from diverse bacterial strains, we successfully engineered a functional version of this enzyme within Escherichia coli, enabling the production of caffeic acid. In addition to the two common tyrosine ammonia lyases (TAL) and HpaC, different combinations of HpaB demonstrated varying abilities in converting the substrate L-tyrosine into the desired product, caffeic acid. Results: Under shake-flask culture conditions, the highest yield of caffeic acid was achieved with an enzyme mixture containing HpaB from Escherichia coli, reaching 75.88 mg/L. Enhancing the activity of the rate-limiting enzyme through engineering could potentially increase caffeic acid titer. This study aims to conduct a semi-rational design of HpaB through structure-based approaches to screen for mutants that can enhance the production of caffeic acid. Initially, the predicted three-dimensional structure of HpaB was generated using AlphaFold2, and subsequent analysis was conducted to pinpoint the critical mutation sites within the substrate-binding pocket. Five key amino acid residues (R113, Y117, H155, S210 and Y461) located in the vicinity of the flavin adenine dinucleotide binding domain in HpaB from Escherichia coli could be instrumental in modulating enzyme activity. Subsequently, the mutant S210G/Y117A was obtained by iterative saturation mutagenesis, which increased the titer of caffeic acid by 1.68-fold. The caffeic acid titer was further improved to 2335.48 mg/L in a 5 L fermenter. The findings show that the yield of caffeic acid was significantly enhanced through the integration of semi-rational design and fermentation process optimization. Full article

This entry explores the implications of generative AI for the underlying foundational premises of copyright law and the potential threat it poses to human creativity. It identifies the gaps and inconsistencies in legal frameworks as regards authorship, training-data use, moral rights, and human originality in the context of AI systems that are capable of imitating human expression at both syntactic and semantic levels. The entry includes: (i) a comparative analysis of the legal frameworks of the United Kingdom, United States, and Germany, using the Berne Convention as a harmonising baseline, (ii) a systematic synthesis of the relevant academic literature, and (iii) insights gained from semi-structured interviews with legal scholars, AI developers, industry stakeholders, and creators. Evidence suggests that existing laws are ill-equipped for semantic and stylistic reproduction; there is no agreement on authorship, no clear licensing model for training data, and inadequate protection for the moral identity of creators—especially posthumously, where explicit protections for likeness, voice, and style are fragmented. The entry puts forward a draft global framework to restore legal certainty and cultural value, incorporating a semantics-aware definition of the term “work”, and encompassing licensing and remuneration of training data, enhanced moral and posthumous rights, as well as enforceable transparency. At the same time, parallel personality-based safeguards, including rights of publicity, image, or likeness, although present in all three jurisdictions studied, are not subject to the same copyright and thus do not offer any coherent or adequate protection against semantic or stylistic imitation, which once again highlights the need for a more unified and robust copyright strategy. Full article

Headspace (HS) in anaerobic batch biodigesters is a critical design parameter that modulates pressure stability, gas–liquid equilibrium, and methanogenic productivity. This systematic review, guided by PRISMA 2020, analyzed 84 studies published between 2015 and 2025, of which 64 were included in the qualitative and quantitative synthesis. The interplay between headspace volume fraction ${\mathrm{V}}_{\mathrm{H}\mathrm{S}}/{\mathrm{V}}_{\mathrm{t}\mathrm{o}\mathrm{t}}$, operating pressure, and normalized methane yield was assessed, explicitly integrating safety and instrumentation requirements. In laboratory settings, maintaining a headspace volume fraction (HSVF) of 0.30–0.50 with continuous pressure monitoring P(t) and gas chromatography reduces volumetric uncertainty to below 5–8% and establishes reference yields of 300–430 NmL CH₄ g⁻¹ VS at 35 °C. At the pilot scale, operation at 3–4 bar absolute increases the CH₄ fraction by 10–20 percentage points relative to ~1 bar, while maintaining yields of 0.28–0.35 L CH₄ g COD⁻¹ and production rates of 0.8–1.5 Nm³ CH₄ m⁻³ d⁻¹ under OLRs of 4–30 kg COD m⁻³ d⁻¹, provided pH stabilizes at 7.2–7.6 and the free NH₃ fraction remains below inhibitory thresholds. At full scale, gas domes sized to buffer pressure peaks and equipped with continuous pressure and flow monitoring feed predictive models (AUC > 0.85) that reduce the incidence of foaming and unplanned shutdowns, while the integration of desulfurization and condensate management keep corrosion at acceptable levels. Rational sizing of HS is essential to standardize BMP tests, correctly interpret the physicochemical effects of HS on CO₂ solubility, and distinguish them from intrinsic methanogenesis. We recommend explicitly reporting standardized metrics (Nm³ CH₄ m⁻³ d⁻¹, NmL CH₄ g⁻¹ VS, L CH₄ g COD⁻¹), absolute or relative pressure, HSVF, and the analytical method as a basis for comparability and coupled thermodynamic modeling. While this review primarily focuses on batch (discontinuous) anaerobic digesters, insights from semi-continuous and continuous systems are cited for context where relevant to scale-up and headspace dynamics, without expanding the main scope beyond batch systems. Full article

Background and Objectives: Paediatric palliative care in Romania is underdeveloped and unevenly distributed, which creates major difficulties in accessing services for children with life-limiting illnesses and their families. The lack of a dedicated national strategy, the shortage of specialised staff, and socio-economic barriers exacerbate the vulnerability of these groups. This study aimed to explore parents’ and caregivers’ experiences, to analyse the perspectives of public institutions and NGOs involved in supporting these children, and to identify the main barriers and facilitators in accessing paediatric palliative care. Materials and Methods: Given that all data were collected exclusively through focus group discussions, this study employed a qualitative design based on three focus groups guided by a semi-structured interview guide. The analysis was conducted using MAXQDA software, which enabled the coding and synthesis of emerging themes. Participants were parents/caregivers of children with life-limiting illnesses, representatives of public institutions, and members of relevant NGOs in Bacău County, Romania. Purposive sampling was used to capture diverse and experience-based perspectives, resulting in a total of 24 participants across three focus groups—parents and caregivers (n = 11), public institution representatives (n = 7), and NGO representatives (n = 6). No individual semi-structured interviews were conducted. Results: The analysis highlighted a complex typology of medical, emotional, social, educational, and spiritual needs of children and their families. Parents reported experiences of abandonment in the curative system, emphasising the importance of pain control, safety, and community support. Public institutions acknowledged the lack of skills and inter-sectoral coordination, while NGOs pointed to structural barriers and the low visibility of these children. Major needs include access to specialised care, psychological support, respite services for carers, financial and administrative assistance, education, and spiritual counselling. A significant obstacle is the lack of up-to-date statistical data needed to inform public policy. Conclusions: Paediatric palliative care should be considered a national priority through the development of a dedicated strategy, the expansion of specialised services, and the strengthening of partnerships between the public and non-governmental sectors. An integrated, child- and family-centred approach addressing the medical, social, emotional, and spiritual dimensions of care is essential. The results highlight the need for continuous staff training, information campaigns, and community support mechanisms to reduce inequalities and improve the quality of life of children with life-limiting illnesses. Full article

Graph representation learning (GRL) has emerged as a unifying paradigm for modeling the relational and heterogeneous nature of battery energy storage systems (BESS), yet a systematic synthesis focused on data-scarce (few-shot) battery scenarios is still lacking. Graph representation learning offers a natural way to describe the structure and interaction of battery cells, modules and packs. At the same time, battery applications often suffer from very limited labeled data, especially for new chemistries, extreme operating conditions and second-life use. This review analyzes how graph representation learning can be combined with few-shot learning to support key battery management tasks under such data-scarce conditions. We first introduce the basic ideas of graph representation learning, including models based on neighborhood aggregation, contrastive learning, autoencoders and transfer learning, and discuss typical data, model and algorithm challenges in few-shot scenarios. We then connect these methods to battery state estimation problems, covering state of charge, state of health, remaining useful life and capacity. Particular attention is given to approaches that use graph neural models, meta-learning, semi-supervised and self-supervised learning, Bayesian deep networks, and federated learning to extract transferable features from early-cycle data, partial charge–discharge curves and large unlabeled field datasets. Reported studies show that, with only a small fraction of labeled samples or a few initial cycles, these methods can achieve state and life prediction errors that are comparable to or better than conventional models trained on full datasets, while also improving robustness and, in some cases, providing uncertainty estimates. Based on this evidence, we summarize the main technical routes for few-shot battery scenarios and identify open problems in data preparation, cross-domain generalization, uncertainty quantification and deployment on real battery management systems. The review concludes with a research outlook, highlighting the need for pack-level graph models, physics-guided and probabilistic learning, and unified benchmarks to advance reliable graph-based few-shot methods for next-generation intelligent battery management. Full article

We study a minimum-time (time-optimal) control problem for a nonlinear pendulum-type oscillator, in which the control input is the system’s natural frequency constrained to a prescribed interval. The objective is to transfer the oscillator from a given initial state to a prescribed terminal state in the shortest possible time. Our approach combines Pontryagin’s maximum principle with Bellman’s principle of optimality. First, we decompose the original problem into a sequence of auxiliary problems, each corresponding to a single semi-oscillation. For every such subproblem, we obtain a complete analytical solution by applying Pontryagin’s maximum principle. These results allow us to reduce the global problem of minimizing the transfer time between the prescribed states to a finite-dimensional optimization problem over a sequence of intermediate amplitudes, which is then solved numerically by dynamic programming. Numerical experiments reveal characteristic features of optimal trajectories in the nonlinear regime, including a non-periodic switching structure, non-uniform semi-oscillation durations, and significant deviations from the behavior of the corresponding linearized system. The proposed framework provides a basis for the synthesis of fast oscillatory regimes in systems with controllable frequency, such as pendulum and crane systems and robotic manipulators. Full article

Artificial Intelligence in Education is expanding rapidly, yet the adaptation of chatbots to specific reading-comprehension levels remains underexplored. This mixed-methods study presents Lectobot, a conversational agent designed to provide personalized scaffolding across three levels of reading comprehension (literal, inferential, and critical). First, we conducted a diagnostic assessment with first-year undergraduates (N = 58) using validated instruments: COMPLECsec (reading comprehension), EMA (Academic Motivation Scale), and MARSI (Metacognitive Strategies). Non-parametric analyses (Kolmogorov–Smirnov; Mann–Whitney U with Benjamini–Hochberg adjustment) indicated wide heterogeneity in comprehension (median global accuracy ≈ 55%) and a predominance of extrinsic motivation, with selective use of problem-solving strategies. These findings informed design rules for Lectobot (text selection, adaptive task difficulty, and strategy prompts). In a five-week implementation with a focus group (n = 8), semi-structured interviews were transcribed and coded in MAXQDA, guided by the Technology Acceptance Model (perceived usefulness and ease of use). Students perceived Lectobot as useful for text understanding and synthesis and moderately easy to use; reported difficulties were mainly technical (access and session continuity), leading to actionable design improvements. We discuss ethical and practical implications for personalized scaffolding in higher education and outline avenues for larger-scale evaluations and broader grade levels. Full article

In practical applications, steel storage racks include a wide range of beam-to-column connections (BCCs), which have a significant impact on their structural stability, particularly under various loading conditions. This systematic review focuses on the application of the finite element method (FEM) as a complementary tool to evaluate the mechanical behavior of these connections. Key parameters that influence connection performance include the connector’s class and hook configuration, column thickness, beam height and weld position on the connector. Although the Eurocode 3 standard provides design guidelines for connections, experimental testing remains the most reliable method due to the complexity of semi-rigid connections, particularly in the context of pallet racks. Validated FEM analysis emerges as a dependable and cost-effective alternative to experiments, enabling more detailed parametric studies and improving the prediction of structural response. This review focuses on the advantages of FEM integration into design workflows via quantitative synthesis, while also emphasizing the role of contact formulations in modeling accuracy. To establish FEM as an independent predictive tool for the design and optimization of steel storage racks, future research should focus on cohesive zone modeling, ductile damage criteria, advanced contact strategies and additional machine learning (ML) techniques. Full article