Search Results (35,338)

The development of porous carbon materials that meet the demands of commercial supercapacitors is challenging, primarily due to the requirements for high energy and power density, as well as large-scale manufacturing capabilities. Herein, we present a sustainable and cost-effective method for synthesizing N–O–S co-doped hierarchical porous carbons (designated as ALK_x) from ammonium lignosulfonate (AL), an industrial by–product. This process employs a low KOH/AL mass ratio (x ≤ 0.75) and a carbonization temperature of 900 °C. The resulting materials, ALK₀.₅₀ and ALK_0.75, exhibit an exceptionally high specific surface area (>2000 m² g⁻¹), a well-balanced micro-mesoporous structure, and tunable heteroatom content, which collectively enhance their electrochemical performance in both aqueous and ionic liquid electrolytes. Notably, ALK_0.75 features a heteroatom content of 13.2 at.% and a specific surface area of 2406 m² g⁻¹, owing to its abundant small mesopores. When tested as an electrode in a two–electrode supercapacitor utilizing a 6 M KOH electrolyte, it achieves a high specific capacitance of 250 F g⁻¹ at a current density of 0.25 A g⁻¹ and retains 197 F g⁻¹ even at 50 A g⁻¹, demonstrating remarkable rate capability. In contrast, ALK_0.50, characterized by a lower heteroatom content and an optimized pore structure, exhibits superior compatibility with the ionic liquid electrolyte EMIMBF₄. A symmetric supercapacitor constructed with ALK_0.50 electrodes attains a high energy density of 90.2 Wh kg⁻¹ at a power density of 885.5 W kg⁻¹ (discharge time of 60 s). These findings provide valuable insights into heteroatom doping and the targeted regulation of pore structures in carbon materials, while also highlighting new opportunities for the high-value utilization of AL. Full article

Image-based geo-localization is a challenging task that aims to determine the geographic location of a ground-level query image captured by an Unmanned Ground Vehicle (UGV) by matching it to geo-tagged nadir-view (top-down) images from an Unmanned Aerial Vehicle (UAV) stored in a reference database. The challenge comes from the perspective inconsistency between matched objects. In this work, we propose a novel metric learning scheme for hard exemplar mining to improve the performance of cross-view geo-localization. Specifically, we introduce a Dynamic Online Cross-Batch (DOCB) hard exemplar mining scheme that solves the problem of the lack of hard exemplars in mini-batches in the middle and late stages of training, which leads to training stagnation. It mines cross-batch hard negative exemplars according to the current network state and reloads them into the network to make the gradient of negative exemplars participating in back-propagation. Since the feature representation of cross-batch negative examples adapts to the current network state, the triplet loss calculation becomes more accurate. Compared with methods only considering the gradient of anchors and positives, adding the gradient of negative exemplars helps us to obtain the correct gradient direction. Therefore, our DOCB scheme can better guide the network to learn valuable metric information. Moreover, we design a simple Siamese-like network called multi-scale feature aggregation (MSFA), which can generate multi-scale feature aggregation by learning and fusing multiple local spatial embeddings. The experimental results demonstrate that our DOCB scheme and MSFA network achieve an accuracy of 95.78% on the CVUSA dataset and 86.34% on the CVACT_val dataset, which outperforms those of other existing methods in the field. Full article

In the current scenario of universal accessibility of data, organisations face highly complex challenges related to integrating and processing diverse sets of data in order to meet their analytical needs. This review paper analyses traditional and innovative methods used for data storage and integration, with particular focus on their implications for scalability, consistency, and interoperability within an analytical ecosystem. In particular, it contributes a cross-layer taxonomy linking integration mechanisms (schema matching, entity resolution, and semantic enrichment) to storage/query substrates (row/column stores, NoSQL, lakehouse, and federation), together with comparative tables and figures that synthesise trade-offs and performance/governance levers. Through schema mapping solutions addressing the challenges brought about by structural heterogeneity, storage architectures varying from traditional storage solutions all the way to cloud storage solutions, and ETL pipeline integration using federated query processors, the research provides specific attention for the application of metadata management, with a focus on semantic enrichment using ontologies and lineage management to enable end-to-end traceability and governance. It also covers performance hotspots and caching techniques, along with consistency trade-offs arising out of distributed systems. Empirical case studies from real applications in enterprise lakehouses, scientific exploration activities, and public governance applications serve to invoke this review. Following this work is the possibility of future directions in convergent analytical platforms with support for multiple workloads, along with metadata-centric orchestration with provisions for AI-based integration. Combining technological advancement with practical considerations results in an enabling resource for researchers and practitioners seeking the creation of fault-tolerant, reliable, and future-ready data infrastructure. This review is primarily aimed at researchers, system architects, and advanced practitioners who design and evaluate heterogeneous analytical platforms. It also offers value to graduate students by serving as a structured overview of contemporary methods, thereby bridging academic knowledge with industrial practice. Full article

The integration of artificial intelligence (AI) and edge computing gives rise to edge intelligence (EI), which offers effective solutions to the limitations of traditional cloud-based AI; however, deploying models across distributed edge platforms raises concerns regarding authenticity, thereby necessitating robust mechanisms for ownership verification. Currently, backdoor-based model watermarking techniques represent a state-of-the-art approach for ownership verification; however, their reliance on model poisoning introduces potential security risks and unintended behaviors. To solve this challenge, we propose BIMW, a blockchain-enabled innocuous model watermarking framework that ensures secure and trustworthy AI model deployment and sharing in distributed edge computing environments. Unlike widely applied backdoor-based watermarking methods, BIMW adopts a novel innocuous model watermarking method called interpretable watermarking (IW), which embeds ownership information without compromising model integrity or functionality. In addition, BIMW integrates a blockchain security fabric to ensure the integrity and auditability of watermarked data during storage and sharing. Extensive experiments were conducted on a Jetson Orin Nano board, which simulates edge computing environments. The numerical results show that our framework outperforms baselines in terms of predicate accuracy, p-value, watermark success rate (WSR), and harmlessness H. Our framework demonstrates resilience against watermarking removal attacks, and it introduces limited latency through the blockchain fabric. Full article

Background: Dementia refers to a spectrum of clinical conditions characterized by impairments in memory, language, and cognitive function. Alzheimer’s Disease (AD) is the most common cause of dementia and it accounted for 60–70% of the estimated 57 million cases worldwide as of 2021. The exact pathology of this neurodegenerative condition is not fully understood. While it is currently incurable, progression to more critical stages can be slowed, and early diagnosis is crucial to alleviate and manage some of its symptoms. Contemporary diagnostic practices hinder early detection due to the high costs and inaccessibility of advanced neuroimaging tools and specialists, particularly for populations with resource-constrained clinical settings. Methods: This paper addresses this challenge by developing and evaluating computationally efficient lightweight deep learning models, MobileNetV2 and EfficientNetV2B0, for early AD detection from 2D slices sourced from standard structural magnetic resonance imaging (MRI). Results: For the challenging multi-class task of distinguishing between Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), and Late Mild Cognitive Impairment (LMCI), our best model, EfficientNetV2B0, achieved 88.0% mean accuracy across a 5-fold stratified cross-validation (std = 1.0%). To enhance clinical interpretability and build trust, we integrated explainability methods, Grad-CAM++ and Guided Grad-CAM++, to visualize the anatomical basis for the models’ predictions. Conclusions: This work delivers an accessible and interpretable neuroimaging tool to support early AD diagnosis and extend expert-level capabilities to routine clinical practice. Full article

Diabetes mellitus (DM) is a global health challenge that contributes to numerous complications. As a chronic metabolic disorder, DM leads to persistent microvascular and macrovascular damage, ultimately impairing the function of multiple organ systems. Cardiovascular diseases (CVD), including heart failure (HF), are among the most serious diabetes-related outcomes, accounting for substantial morbidity and mortality worldwide. Traditionally, diabetic HF has been attributed to coexisting conditions such as hypertensive heart disease or coronary artery disease. However, a high prevalence of HF is observed in individuals with DM even in the absence of these comorbidities. In recent years, the phenomenon of diabetes-induced HF has attracted considerable scientific interest. Gaining insight into the mechanisms by which diabetes elevates HF risk and drives key molecular and cellular alterations is essential for developing effective strategies to prevent or reverse these pathological changes. This review consolidates current evidence and recent advances regarding the cellular and molecular pathways underlying diabetes-related HF. Full article

Current travel planning tools suffer from information fragmentation, requiring users to switch between multiple apps for maps, weather, hotels, and other services, which creates a disjointed user experience. While Large Language Models (LLMs) show promise in addressing these challenges through unified interfaces, they still face issues with hallucinations and accurate intent recognition that require further research. To overcome these limitations, we propose a multi-layer prompt engineering framework for enhanced intent recognition that progressively guides the model to understand user needs while integrating real-time data APIs to verify content accuracy and reduce hallucinations. Our experimental results demonstrate significant improvements in intent recognition accuracy compared to traditional approaches. Based on this algorithm, we developed a Flask-based travel planning assistant application that provides users with a comprehensive one-stop service, effectively validating our method’s practical applicability and superior performance in real-world scenarios. Full article

The escalating challenges of climate change, soil degradation, and the need to ensure global food security are driving the transition towards more sustainable agricultural practices. Biostimulants, a diverse category of substances and microorganisms, have emerged as promising tools to enhance crop resilience, improve nutrient use efficiency (NUE), and support sustainable intensification. However, their widespread adoption is hampered by significant variability in efficacy and a lack of consensus on their optimal use. This comprehensive review synthesizes current scientific knowledge to critically evaluate the performance of biostimulants within sustainable agricultural systems. It aims to move beyond isolated case studies to provide a holistic analysis of their modes of action, efficacy under stress, and interactions with the environment. The analysis confirms that biostimulant efficacy is inherently context-dependent, governed by a complex interplay of biological, environmental, and management factors. Performance variability is explained by four core principles: the Limiting Factor Principle, the Biological Competition Axiom, the Stress Gradient Hypothesis, and the Formulation and Viability Imperative. A significant disconnect exists between promising controlled-environment studies and variable field results, highlighting the danger of extrapolating data without accounting for real-world agroecosystem complexity. Biostimulants are not universal solutions but are sophisticated tools whose value is realized through context-specific application. Their successful integration requires a precision-based approach aligned with specific agronomic challenges. We recommend that growers adopt diagnostic tools and on-farm trials, while producers must provide transparent multi-location field data and invest in advanced formulations. Future research must prioritize field validation, mechanistic studies using omics tools, and the development of crop-specific protocols and industry-wide standards to fully unlock the potential of biostimulants for building resilient and productive agricultural systems. Full article

Cancer remains a primary global health concern, with treatment-related side effects and malnutrition posing significant challenges to patient care and recovery. In recent years, there has been growing interest in the therapeutic potential of functional food components, especially whey proteins (WPs), due to their notable antioxidant, immunomodulatory, and anticancer properties. This systematic review explores the effects of WPs across various cancer types and assesses their value as supportive nutritional agents. A thorough literature search was conducted in PubMed, Scopus, and Web of Science databases, identifying 24 relevant studies published between 2000 and 2024. The selection process followed PRISMA guidelines. The evidence, drawn from both laboratory and clinical research, suggests that WPs may exert anticancer effects by inhibiting tumor cell growth, promoting apoptosis, enhancing antioxidant defenses, modulating immune activity, and influencing signaling pathways such as the PI3K/Akt, mTOR, and Wnt/β-catenin pathways. Colorectal, breast, and liver cancers emerged as the most extensively studied types. Additionally, the form of WP used—whether concentrate, isolate, or hydrolysate—appeared to influence both biological activity and clinical outcomes. Clinical findings suggest that WP supplementation may support nutritional status, mitigate the adverse effects of chemotherapy, and enhance the quality of life in cancer patients. While the preclinical data are compelling, further high-quality randomized controlled trials are needed to confirm these benefits and determine optimal use in clinical practice. This review highlights WPs as promising, well-tolerated nutritional agents with potential to enhance current cancer care strategies. Full article

Last-mile logistics is a critical operational and environmental challenge in urban areas. This paper introduces an intelligent path planning system using the Resistive Grid Path Planning Methodology (RGPPM) to optimize distribution based on energy and environmental metrics. The foundational innovation is the integration of electrical-circuit analogies, modeling the distribution network as a resistive grid where optimal routes emerge naturally as current flows, offering a paradigm shift from conventional algorithms. Using a multi-connected grid with georeferenced resistances, RGPPM estimates minimum and maximum paths for various starting points and multi-agent scenarios. We introduce five key performance indicators (KPIs)—Percentage of Distance Savings (PDS), Coefficient of Savings (CS), Coefficient of Global Savings (CGS), Percentage of Load Imbalance (PLI), and Percentage of Deviation with Multi-Agent (PDM)—to evaluate system performance. Simulations for textbook delivery to 129 schools in the Veracruz–Boca del Río area show that RGPPM significantly reduces travel distances. This leads to substantial savings in energy consumption, CO₂ emissions, and operating costs, particularly with electric vehicles. Finally, the results validate RGPPM as a flexible and scalable strategy for sustainable urban logistics. Full article

Human papillomavirus (HPV)-related oropharyngeal squamous cell carcinomas (OPSCC) represent a distinct subgroup of head and neck squamous cell carcinoma (HNSCC) characterized by better prognosis and increased radiosensitivity compared to HPV-negative OPSCC. However, current diagnostic and monitoring methods, including tissue biopsies and imaging, are insufficient for precise risk stratification and early detection of recurrence, leading to challenges in treatment de-escalation and surveillance strategies. Circulating tumor HPV DNA (ctHPV-DNA) has emerged as a promising minimally invasive biomarker that offers tumor-specific detection and monitoring capabilities, potentially transforming the management of HPV-related OPSCC through early disease detection, treatment response assessment, recurrence surveillance stratification, and disease monitoring. Despite encouraging results from early clinical studies, current use is limited to trial settings. Large-scale prospective studies are needed to validate its clinical utility and determine whether early ctHPV-DNA testing can improve patient outcome while reducing treatment related morbidity. This review outlines the biological rationale, technological approaches, and current clinical evidence for ctHPV-DNA in HPV-related OPSCC, emphasizing its potential role in treatment monitoring and surveillance. Full article

LTP allergy and its accurate diagnosis remain a challenge in modern allergology. Patients sensitized to lipid transfer proteins (LTPs) present a wide range of symptoms, from mild manifestations—such as oral allergy syndrome, urticaria, and angioedema—to severe systemic reactions, including anaphylaxis. Oral food challenges (OFCs), the gold standard in food allergy diagnostics, are problematic in this group of patients due to the high risk of life-threatening reactions during the procedure. The basophil activation test (BAT), a functional assay based on flow cytometry, is a promising diagnostic tool that may benefit many food-allergic patients by reducing the need for OFCs. In 2023, BAT was incorporated into selected diagnostic pathways for food sensitization in the guidelines issued by the European Academy of Allergy and Clinical Immunology (EAACI). While many studies have investigated BAT in the context of peanut allergy, evidence regarding its application in LTP allergy remains limited. In this systematic review, we analyzed the currently available studies on the use of BAT in the diagnosis of LTP sensitization and evaluated its potential to supplement or even replace OFCs in specific clinical scenarios. Full article

Background/Objectives: The inherent low aqueous solubility of lipophilic drugs, belonging to Class II based on Biopharmaceutical classification system, negatively impacts their oral bioavailability. However, the manufacturing of pharmaceutical dosage forms for these drugs faces challenges related to environmental impact and production complexity. Herein, the surfactant-enriched cross-linked scaffold addresses the limitations of conventional approaches, such as the use of organic solvents, energy-intensive processing, and the demand for sophisticated equipment. Methods: Scaffold former (Pluronic F68) and scaffold trigger agent (propylene glycol) were used to prepare cross-linked scaffold loaded with candesartan cilexetil as a model for lipophilic drugs. Moreover, surfactants were selected based on the measured solubility to enhance formulation loading capacity. Design-Expert was used to study the impact of Tween 80, propylene glycol, and Pluronic F68 concentrations on the measured responses. In addition, in vitro dissolution study was implemented to investigate the drug release profile. The current approach was assessed against the limitations of conventional approach in terms of environmental and manufacturing feasibility. Results: The optimized formulation (59.27% Tween 80, 30% propylene glycol, 10.73% Pluronic F68) demonstrated a superior drug loading capacity (19.3 mg/g) and exhibited a solid-to-liquid phase transition at 35.5 °C. Moreover, it exhibited a rapid duration of solid-to-liquid transition within about 3 min. In vitro dissolution study revealed a remarkable enhancement in dissolution with 92.87% dissolution efficiency compared to 1.78% for the raw drug. Conclusions: Surfactant-enriched cross-linked scaffold reduced environmental impact by eliminating organic solvents usage and reducing energy consumption. Moreover, it offers significant manufacturing advantages through simplified production processing. Full article

The production environments of coal mines are inherently complex, with interrelated disaster risks that challenge safety management. Current prediction systems struggle with fragmented data, limited mechanistic understanding, and inadequate early warnings, falling short of modern coal mine safety needs. This paper advances the thesis that artificial intelligence, including machine learning, deep learning, and Large Language Model, provides essential tools for overcoming these prediction challenges in coal mining. We review AI-based approaches for forecasting coal and gas outbursts, mine fires, water disasters, roof collapses, and dust disasters, analyzing them through technical principles, application scenarios, and empirical outcomes. The analysis clarifies how AI improves risk prediction accuracy, enhances data integration, and enables smarter decision-making for safety. By examining the five major hazards, we highlight ongoing challenges in AI implementation and outline pathways for future development, emphasizing the importance of large models and autonomous agents. Our findings support the creation of advanced AI-driven safety and early warning systems for coal mines. Full article

Despite continuous advances in the development of methodologies for the diagnosis and therapeutic treatment of cancer, the disease remains a primary cause of mortality worldwide. A comprehensive understanding of the molecular mechanisms underlying cancer could ultimately lead to increasingly effective therapeutic interventions. One approach that could be adopted is to formulate methodologies that impede cell signalling and/or the expression of genes pivotal to carcinogenesis. A notable example of this strategy is the focus on the estrogen receptor, a key player in the development of various types of cancer. The deregulation of this receptor, and the subsequent impact on cell function, is a critical factor in the progression of these diseases. This renders it a significant therapeutic target. Furthermore, the microenvironment has been demonstrated to exert a significant influence on the development of cancers. A mounting body of evidence indicates that the abnormal physical properties of the tumour microenvironment can induce widespread changes, leading to the selection of characteristic tumour cell abilities and subsequent clonal proliferation. This process is accompanied by an increased capacity for invasive growth and, notably, the induction of multidrug resistance. The present article focuses on presenting the structure and role of the estrogen receptor in selected hormone-dependent cancers, its involvement in the formation of the tumor microenvironment, currently used therapeutic methods in the treatment of these cancers, and the challenges associated with them. Each new discovery in the field of cancer biology offers the prospect of developing new potential treatments, including targeted therapies aimed at improving the survival of patients suffering from hormone-dependent malignant tumours. Although the role of the estrogen receptor in their development is well established, further research is required to develop a detailed understanding of how its specific isoforms act in different types of cancer. Full article