Inorganic halide perovskites, particularly CsPbBr3, have emerged in recent years as promising materials for optoelectronic applications due to their easily tunable bandgap, high charge carrier mobility, and radiation sensing capabilities. This study describes an efficient and straightforward method for growing high-quality CsPbBr3 single crystals using the antisolvent vapor-assisted method, using nitromethane as the antisolvent within a temperature range of 25-45 degrees C. The resulting crystal has an optical bandgap of about 2.28 eV. X-ray structural analysis confirms the orthorhombic phase. Optical and photoelectrical measurements reveal a clear photocurrent response under UV irradiation at 365 nm, marked by a evident increase in photocurrent density. In addition, the charge carrier mobility-lifetime product extracted from charge collection efficiency analysis reaches similar to 0.8 & times; 10-3 cm2 V-1, indicating efficient charge transport. Under visible-light illumination at 530 nm, the crystals demonstrate a responsivity of 6.24 & times; 10-3 A W-1 and a specific detectivity of similar to 1.3 & times; 1010 Jones. Surface morphology assessed by scanning electron microscopy (SEM) confirms the high surface quality of the crystals. These findings highlight the potential of this modified growth method for producing CsPbBr3 single crystals suitable for next-generation photodetectors and other optoelectronic devices.
Two-handed dynamic gesture recognition represents a fundamental component of sign language interpretation involving the modeling of temporal dependencies and inter-hand coordination. In this task, a major challenge is modeling asymmetric motion patterns, as well as bidirectional and long-range temporal dependencies. Most existing frameworks rely on early fusion strategies that merge joints, keypoints or landmarks from both hands in early processing stages, primarily to reduce model complexity and enforce a unified representation. In this work, a novel dual-stream BiLSTM-Transformer model architecture is proposed for two-handed dynamic sign language recognition, where parallel encoders process the trajectories of each hand independently. To capture spatial and temporal dependencies for each hand, an attention-based cross-hand fusion mechanism is employed, with hand landmarks extracted by the MediaPipe Hands framework as a preprocessing step to enable real-time CPU-based inference. Experimental evaluation conducted on custom Romanian Sign Language dynamic gesture datasets indicates that the proposed dual-stream-based system outperforms single-handed baselines, achieving improvements in high recognition accuracy for asymmetric gestures and consistent performance gains for synchronized two-handed gestures. The proposed architecture represents an efficient and lightweight solution suitable for real-time sign language recognition and interpretation.
This review highlights the anti-inflammatory and antioxidant effects of probiotics and their complex health-related impacts. The main health areas targeted are gastrointestinal inflammation, neuroinflammation, systemic metabolic disorders, and liver conditions. Probiotics work mechanistically to regulate key inflammatory pathways by suppressing nuclear factor (NF-κb) and mitogen-activated protein kinase (MAPK) pathways and activating antioxidant defenses through nuclear erythroid 2-related factor (Nrf2). They stimulate anti-inflammatory cytokines (including interleukin 10 (IL-10) and inhibit pro-inflammatory mediators such as tumor necrosis factor-α (TNF-α), partly through the regulation of T cells. Probiotics also produce antioxidant metabolites (e.g., exopolysaccharides and short-chain fatty acids), which enhance the host's resistance to oxidative stress. Supplementation with probiotics improves intestinal inflammation and oxidative injury in gut disorders. Clinical trials suggest that probiotic supplements may reduce neuroinflammation and oxidative stress, while improving cognitive or behavioral outcomes in neurodegenerative disorders. Overall, this review underscores that probiotics have potent anti-inflammatory and antioxidant effects within the gut-brain axis and across various organ systems, supporting their use as valuable adjunctive therapies for inflammatory and oxidative stress-related conditions. It further emphasizes that additional mechanistic research and controlled clinical trials are essential to translate these findings into the most effective therapeutic strategies.
Forest restoration in Europe has a complex history strongly influenced by various social, policy and economic factors. Understanding these influences is essential for shaping effective restoration strategies and avoiding past mistakes, particularly in light of meeting ambitious targets outlined in initiatives such as the EU Nature Restoration Regulation. Here we identify the key social, policy and economic drivers, barriers and enablers that have historically shaped forest restoration across Europe. We analyzed and synthesized detailed information from historical national narratives on forest restoration provided by experts from 18 European countries. Our work details how wars, changes in governance (centralization vs. decentralization) and forest tenure (privatization vs. nationalization), different policy instruments (regulatory, financial, persuasive and organizational), market fluctuations and sociodemographic changes (e.g., rural abandonment, changes in public opinion) have driven the development of forest restoration in Europe. The findings underscore the need to use inclusive and innovative governance mechanisms to reconcile diverging societal paradigms (e.g., rural vs. urban, conservation vs. forestry) partly reflected in incoherent forest-related policies, as well as to address the fragmentation resulting from forest privatization. Ensuring stable funding mechanisms (e.g., remuneration systems for forest ecosystem services) alongside favorable regulatory frameworks will also be key for successful large-scale forest restoration efforts. Policy recommendations are made to ensure the effective implementation of the EU Nature Restoration Regulation, including a hybrid governance model that balances strong national regulatory frameworks with local adaptability to diverse socioecological contexts, integrating socioeconomic metrics, strengthening public engagement, and leveraging market-based and green tax incentives.
Caregivers of children with Autism Spectrum Disorder (ASD) frequently experience chronic psychological stress, thereby necessitating accessible support. Although artificial intelligence (AI)-based assisted technologies have the potential to reduce caregiver workload, most existing solutions lack robust privacy control and clinical interoperability, which significantly limits their adoption in regulated healthcare environments. To address these challenges, this paper proposes a Privacy-by-Design (PbD) multi-agent architecture that enables consent-aware, auditable, and privacy-preserving AI-assisted support for caregivers of children with ASD. The effectiveness of the proposed architecture was evaluated using two datasets: one focusing on clinically grounded autism-related knowledge and another reflecting naturalistic caregiver observation language. System performance was assessed using a Retrieval-Augmented Generation Assessment (RAGAs)-based framework with a Large Language Model (LLM)-as-a-Judge approach implemented via a locally deployed Llama 3 8B model. The system achieved answer relevancy scores of 0.767 for the clinical dataset and 0.750 for the observational dataset, with corresponding Recall@K values of 0.400 and 0.742, respectively. Context precision ranged from 0.599 to 0.631, and no harmful content was detected. Overall, the proposed architecture demonstrates secure caregiver-specialist collaboration through consent-aware routing, anonymised data storage, and controlled data reconstruction, providing a regulation-aligned design option for privacy-preserving AI integration in assisted care platforms.
