Search Results (12,116)

Background: The tumor microenvironment offers a new perspective in gynecologic oncology. In ovarian cancer, numerous preclinical studies, especially organoid models, have highlighted cellular, immune, and biochemical mechanisms. Beyond these sophisticated findings, more practical aspects require attention, such as the role of vaginal microbiota, which represents an interplay between external agents and internal genitalia, and its potential profiling role in early detection beyond the promise of microbiota-targeted therapies. Objectives: This review aims to assess whether such a correlation is speculative or scientifically grounded. Methods: A focused literature search was conducted on vaginal microbiota and its correlation with ovarian cancer to define the current state of knowledge. Results: Mixed outcomes have been reported, yet there is a rational and scientific basis supporting further investigation. Clinical approaches increasingly consider vaginal microbiota as relevant. However, we have to say that most available evidence is still preliminary and largely preclinical to set realistic expectations for readers. Although additional studies are needed, emerging insights highlight its importance and practical implications. We present a diagnostic–therapeutic management flowchart summarizing current evidence). Discussion: Most links between the vaginal microbiota and ovarian cancer are correlational rather than causal. The idea that microbes ascend from the vagina to the ovaries is proposed but still definitely not demonstrated. Confounding factors like age, hormones, and BRCA status complicate interpretation, and ovarian cancer itself could secondarily alter the microbiota. Mechanistic studies and longitudinal data are still needed to clarify whether dysbiosis contributes to carcinogenesis or is merely a consequence. As gynecologists, we summarize key aspects and emphasize to colleagues the importance of incorporating these findings into daily clinical practice. Vaginal dysbiosis should be considered not only a local imbalance but also a potential strategy for primary cancer prevention. Conclusions: Future research on the tumor microenvironment and vaginal microbiota will expand scientific knowledge and guide innovative preventive and therapeutic strategies. Full article

With the proliferation of the Internet of Things (IoT), gesture recognition has attracted attention as a core technology in human–computer interaction (HCI). In particular, mmWave frequency-modulated continuous-wave (FMCW) radar has emerged as an alternative to vision-based approaches due to its robustness to illumination changes and advantages in privacy. However, in real-world human–machine interface (HMI) environments, hand gestures are inevitably accompanied by torso- and arm-related reflections, which can also contain gesture-relevant variations. To effectively capture these variations without discarding them, we propose a preprocessing method called Adaptive Top-K Selection, which leverages vector entropy to summarize and preserve informative signals from both hand and body reflections. In addition, we present a Multi-Stream EfficientNetV2 architecture that jointly exploits temporal range and Doppler trajectories, together with radar-specific data augmentation and a training optimization strategy. In experiments on the publicly available FMCW gesture dataset released by the Karlsruhe Institute of Technology, the proposed method achieved an average accuracy of 99.5%. These results show that the proposed approach enables accurate and reliable gesture recognition even in realistic HMI environments with co-existing body reflections. Full article

The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval of structured patterns. We examine the underlying types of data and pattern outputs, common retrieval operations, and the variety of query types encountered in practice. Key indexing structures are surveyed, including prefix trees, inverted indices, hash-based approaches, and bitmap-based methods, each suited to different pattern representations and workloads. Storage designs are discussed with attention to metadata annotation, format choices, and redundancy mitigation. Query optimisation strategies are reviewed, emphasising index-aware traversal, caching, and ranking mechanisms. This paper also explores scalability through parallel, distributed, and streaming architectures, and surveys current systems and tools, which integrate mining and retrieval capabilities. Finally, we outline pressing challenges and emerging directions, such as supporting real-time and uncertainty-aware retrieval, and enabling semantic, cross-domain pattern access. Additional frontiers include privacy-preserving indexing and secure query execution, along with integration of repositories into machine learning pipelines for hybrid symbolic–statistical workflows. We further highlight the need for dynamic repositories, probabilistic semantics, and community benchmarks to ensure that progress is measurable and reproducible across domains. This review provides a comprehensive foundation for designing next-generation pattern retrieval systems, which are scalable, flexible, and tightly integrated into analytic workflows. The analysis and roadmap offered are relevant across application areas including finance, healthcare, cybersecurity, and retail, where robust and interpretable retrieval is essential. Full article

β-glucosidase is a kind of enzyme that can hydrolyze β-glucosidase bonds, and it plays a key role in many fields, such as lignocellulose degradation and wine brewing. The global β-glucosidase market is currently estimated to be USD 40 billion, and more is expected in the future. This trend is mainly due to the demand for enzymes in biofuel processing. At present, β-glucosidase is mainly derived from microorganisms, animals, plants and so on. It has received great attention due to its ease of production, catalytic efficiency and versatility, which have promoted its biotechnology potential in different industries. With the increasing demand for β-glucosidases, various cost-effective methods are being explored to discover, redesign and enhance their production and functional properties. Therefore, this paper reviews the latest progress in the application of β-glucosidase in industry. In this regard, the focus is on the use of recombinant technology, protein engineering and immobilization technology to improve the industrial applicability of the enzyme. In addition, the application status of β-glucosidase in production and life was analyzed. Full article

Background: Urban freight transport is essential for the functioning of cities. The COVID-19 pandemic accelerated the growth of e-commerce, creating new challenges for courier services. While consumer satisfaction has been extensively studied, little attention has been paid to courier drivers’ own perceptions and preferences. This study aims to fill that gap. Methods: A questionnaire survey was conducted among 139 drivers employed in eight courier companies in Serbia. Data were analyzed using parametric statistical methods (Independent Samples T-Test, Paired-Samples T-Test, and One-way ANOVA), with additional post hoc tests to explore group differences. Results: Statistically significant differences were observed across demographic, operational, and safety-related factors (gender, age, residence, occupation, license ownership, delivery area, and type of goods). A strong preference emerged for passenger vehicles as the safest mode of delivery, highlighting a misalignment between current operational practices and drivers’ safety perceptions. Conclusions: The findings emphasize the importance of tailoring delivery strategies to demographic and operational contexts. Practical recommendations include improving transport safety, optimizing delivery zones, and addressing driver satisfaction as a determinant of service quality. The study contributes new insights into last-mile delivery by focusing on the perspectives of courier drivers rather than consumers. Full article

Polypropylene (PP) exhibits high flammability (LOI ≈ 17.5%), which limits its industrial applications. Previous studies have primarily focused on the flame-retardant mechanisms of intumescent flame-retardant (IFR) systems, while less attention has been given to the role of inorganic synergistic additives in balancing flame retardancy with mechanical performance—an aspect crucial for commercial applications This study investigated the effect of small additions of zinc oxide (ZnO) and manganese oxide (MnO) on the flame-retardant, mechanical, and thermal properties of PP/IFR (APP + PER) composites. The oxide content was varied between 0 and 2 wt.%. LOI and UL-94 tests showed that as little as 0.25 wt.% increased LOI to 30% and enabled all materials to achieve a UL-94 V-0 classification. The highest performance was observed for ZnO (LOI = 43.7% at 1.5 wt.%), while MnO induced a linear increase up to 38.6% at 2 wt.%. SEM analysis confirmed the formation of a compact, foamed char layer. Mechanical testing revealed improved stiffness (~15%) and flexural strength (~20%), with unchanged tensile strength but reduced impact strength (−50% for ZnO, −30% for MnO). The HDT increased from 55 °C to 65 °C. These findings demonstrate that small amounts of ZnO and MnO act as effective and economically viable IFR synergists in PP composites. Full article

The random thermal switching of domain walls (DWs) in perpendicularly magnetized anisotropy nanowires (PMA) poses a significant challenge for the reliability of spintronic storage devices. In this work, we study the thermal nucleation and dynamics of DWs in PMA nanowires using micromagnetic simulations. The focus is on the effect of device temperature, with attention to uniaxial anisotropy energy (K_u), saturation magnetization (M_s), and nanowire geometry. The results show that larger K_u or M_s reduces DW thermal switching, thereby enhancing DW thermal stability and increasing the DW nucleation temperature (T_n). A wider or thicker nanowire also lowers the probability of thermally induced DW creation, further improving stability. In addition, DW velocity rises with temperature, showing a thermally assisted motion. These results provide useful guidance for designing PMA-based memory devices with improved resistance to thermal fluctuations. Full article

The rise in the number of cancer cases and the dissemination of strains with multiple drug resistance in the world pose a serious threat to public health care and human well-being. The design and study of new chemotherapeutic agents for cancer and infectious diseases are hot topics in science. Hydrazones, a versatile and diverse class of chemical compounds, gained a lot of attention as a promising base for future drugs. In this paper, we report on the synthesis of eight new gold(III) complexes with hydrazones derived from pyridoxal-5′-phosphate and pyridoxal. The complexes are thoroughly characterized using IR, ¹H, ³¹P NMR, and mass spectroscopy. The cytotoxic effect of twelve various hydrazones derived from pyridoxal 5′-phosphate on both immortalized (HEK293T) and tumor (HCT116) human cell lines was estimated using the MTT assay. In addition, this contribution describes the antibacterial action of complexes of gold(III) and pyridoxal and pyridoxal 5′-phosphate-derived hydrazones, as well as the mixtures of the solutions containing tetrachloroaurate(III) and hydrazones, using the zone of inhibition test. Gold(III) complexes exhibit moderate antibacterial activity against both Gram-positive and Gram-negative bacteria, while free hydrazones show low cytotoxicity and thus could be considered relatively safe for humans. Full article

Direct thermal decomposition of rare-earth chlorides into rare-earth oxides (REOs) in a single step presents a short-process, wastewater-free, and environmentally friendly alternative to the conventional precipitation–calcination method, which produces large amounts of saline wastewater. While earlier reviews have primarily focused on summarizing reaction conditions and thermodynamic parameters, they have seldom discussed the critical variations in pyrolysis behavior across different rare-earth elements. This review highlights a novel classification of rare-earth chlorides into fixed-valence and variable-valence groups, revealing how their respective oxidation states govern thermodynamic stability, reaction pathways, and chlorine release behavior. Furthermore, a systematic comparison is provided on the effects of additives, temperature, and gas partial pressure on product purity, particle size, and microstructure, with particular attention to the mechanisms underlying oxychloride intermediate formation. Beyond fundamental reaction principles, this work uniquely evaluates the design and performance of existing pyrolysis reactors, outlining both opportunities and challenges in scaling up direct rare-earth chloride (RECl_x) pyrolysis for industrial REO production. By integrating mechanistic insights with reactor engineering considerations, this review offers advancements over previous descriptive summaries and proposes a strategic pathway toward sustainable rare-earth processing. Full article

Time-domain signal models have been widely applied to single-channel music source separation tasks due to their ability to overcome the limitations of fixed spectral representations and phase information loss. However, the high acoustic similarity and synchronous temporal evolution between vocals and accompaniment make accurate separation challenging for existing time-domain models. These challenges are mainly reflected in two aspects: (1) the lack of a dynamic mechanism to evaluate the contribution of each source during feature fusion, and (2) difficulty in capturing fine-grained temporal details, often resulting in local artifacts in the output. To address these issues, we propose an attention-driven time-domain convolutional network for vocal and accompaniment source separation. Specifically, we design an embedding attention module to perform adaptive source weighting, enabling the network to emphasize components more relevant to the target mask during training. In addition, an efficient convolutional block attention module is developed to enhance local feature extraction. This module integrates an efficient channel attention mechanism based on one-dimensional convolution while preserving spatial attention, thereby improving the ability to learn discriminative features from the target audio. Comprehensive evaluations on public music datasets demonstrate the effectiveness of the proposed model and its significant improvements over existing approaches. Full article

Accurate short-term photovoltaic (PV) power generation forecasting is critical for the stable integration of renewable energy into the grid. This study proposes a Transformer model enhanced with an adaptive sparse self-attention (ASSA) mechanism for PV power forecasting. The ASSA framework employs a dual-branch attention structure that combines sparse and dense attention paths with adaptive weighting to effectively filter noise while preserving essential spatiotemporal features. This design addresses the critical issues of computational redundancy and noise amplification in standard self-attention by adaptively filtering irrelevant interactions while maintaining global dependencies in Transformer-based PV forecasting. In addition, a deep feedforward network and a feature refinement feedforward network (FRFN) adapted from the ASSA–Transformer are incorporated to further improve feature extraction. The proposed algorithms are evaluated using time-series data from the Desert Knowledge Australia Solar Centre (DKASC), with input features including temperature, relative humidity, and other environmental variables. Comprehensive experiments demonstrate that the ASSA models’ accuracy in short-term PV power forecasting increases with longer forecast horizons. For 1 h ahead forecasts, it achieves an R² of 0.9115, outperforming all other models. Under challenging rainfall conditions, the model maintains a high prediction accuracy, with an R² of 0.7463, a mean absolute error of 0.4416, and a root mean square error of 0.6767, surpassing all compared models. The ASSA attention mechanism enhances the accuracy and stability in short-term PV power forecasting with minimal computational overhead, increasing the training time by only 1.2% compared to that for the standard Transformer. Full article

The air-conditioner noise in a rehabilitation room can seriously affect the mental state of patients. However, the existing single-layer active noise control (ANC) filters may fail to attenuate the complicated harmonic noise, and the deep recursive ANC method may fail to work in real time. To solve the problem, in a bending-pipe model, a new hybrid adaptive self-loading filtered-x least-mean-square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. At first, based on the recursive GRU translation core, an improved CNN-GRU network with multi-head attention layers is proposed. Especially for complicated harmonic noises with more or fewer frequencies than harmonic models, the attenuation performance will be improved. In addition, its structure is optimized to decrease the computing load. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of complicated harmonic noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. Full article

Hericium erinaceus, a rare edible–medicinal fungus, has attracted great attention in food and pharmaceutical fields due to its rich nutritional and bioactive components. However, its traditional cultivation relies heavily on wood chip substrates, causing resource unsustainability. The “wood-replacing-with-grass” technology can address this issue, contributing to ecological conservation and alleviating resource conflicts between edible fungus cultivation and forestry development. This study focused on straw substitution for wood chips, initially screening suitable straw types and optimal addition ratios from 7 straw varieties, and systematically investigating the agronomic trait variations in H. erinaceus under different substrate formulations via cultivation experiments. Results showed the following: (1) Rapeseed straw, soybean straw, and corn straw substituting 20%, 30%, and 40% of wood chips, respectively, promoted better mycelial growth of H. erinaceus. (2) All screened straw formulations enabled fruiting. With increased straw addition, the mycelial full colonization time shortened (up to 5 days shorter in 40% corn/soybean straw treatments). The 20% corn straw treatment showed significantly higher biological efficiency and average fresh weight than the control (CK); the 20% soybean straw treatment had no significant difference in biological efficiency but significantly higher average fresh weight than CK; and the 20% rapeseed straw treatment showed no significant differences in both indexes from CK. However, when straw addition exceeded 20%, fruiting body firmness, yield, and biological efficiency decreased progressively. (3) The 40% soybean straw treatment yielded fruiting bodies with the highest crude protein, manganese, and iron contents, while the 40% rapeseed straw treatment had the highest crude fat, potassium, phosphorus, calcium, zinc, and selenium contents. These findings provide a theoretical basis and practical reference for optimizing H. erinaceus cultivation substrate formulations, improving product quality, and promoting sustainable industrial development. Full article

To address the global challenge of sustainable energy transition in cities, there is a growing demand for innovative solutions to provide flexible, low-carbon, and socio-economically profitable energy systems. In this context, there is a need for holistic evaluation frameworks for the prioritization and economic optimization of interventions. This paper provides a literature review on sustainable planning and economic impact assessment of innovative urban areas, such as Positive Energy Districts (PEDs), to analyze research trends in terms of evaluation methods, impacts, system boundaries, and identify conceptual and methodological gaps. A dedicated search was conducted in the Scopus database using several query strings to conduct a systematic review. At the end, 57 documents were collected and categorized by analysis approach, indicators, project interventions, and other factors. The review shows that the Cost–Benefit Analysis (CBA) is the most frequently adopted method, while Life Cycle Costing and Multi-Criteria Analysis result in a more limited application. Only in a few cases is the reduction in GHG emissions and disposal costs a part of the economic model. Furthermore, cost assessments usually do not consider the integration of the district into the wider energy network, such as the interaction with energy markets. From a more holistic perspective, additional costs and benefits should be included in the analysis and monetized, such as the co-impact on the social and environmental dimensions (e.g., social well-being, thermal comfort improvement, and biodiversity preservation) and other operational benefits (e.g., increase in property value, revenues from Demand Response, and Peer-To-Peer schemes) and disposal costs, considering specific discount rates. By adopting this multi-criteria thinking, future research should also deepen the synergies between urban sectors by focusing more attention on mobility, urban waste and green management, and the integration of district heating networks. According to this vision, investments in PEDs can generate a better social return and favour the development of shared interdisciplinary solutions. Full article

Prenylated chalcones, a subclass of chalcones distinguished by the addition of one or more prenyl (3-methylbut-2-enyl) groups, have attracted significant attention due to their promising biological activities. The origins, chemical diversity, and synthetic routes used to prepare naturally occurring and synthetic prenylated chalcones are discussed in this review paper, alongside their diverse pharmacological properties, as reported over the past 10 years (2015–2025), mainly emphasising their strong anti-cancer, anti-inflammatory, anti-bacterial, anti-fungal, anti-parasitic, and anti-malarial effects. We address their structure–activity relationships (SARs) to interrogate how prenylation affects the pharmacological activity of these chalcones. Full article