Search Results (12,689)

Against the backdrop of the coordinated advancement of the dual carbon goals and new-type urbanization, the traditional high-carbon development model has become a major constraint on urban green transformation. As a critical vehicle for digital technologies to empower low-carbon governance, smart cities have yet to receive a systematic evaluation of their long-term policy effects based on quasi-natural experiments. Using panel data from 280 prefecture-level cities from 2003 to 2023, this study takes the smart city pilot policy as a quasi-natural experiment. It adopts Interpretive Structural Modeling (ISM) to identify the key influencing factors and transmission paths of carbon emissions, and employs the progressive difference-in-differences (DID) model to assess the carbon emission reduction effects, dynamic evolutionary characteristics and urban heterogeneity of smart city construction. Furthermore, the mediation effect model is applied to clarify its underlying mechanisms. The empirical results show that smart city construction significantly curbs urban carbon emissions, and this finding remains valid after a series of robustness tests, including the parallel trend test, placebo test and PSM-DID. The emission reduction effect of the policy exhibits an obvious time lag: the effect is insignificant in the first and second years after policy implementation but turns significantly negative and continues to strengthen starting from the third year. Noticeable urban heterogeneity is also observed, with a more prominent emission reduction effect in eastern regions, central cities with high administrative ranks and large-sized cities. Mechanism analysis reveals that the conventional industrial pollution reduction pathway does not serve as the primary transmission channel. Instead, a suppression effect is identified, suggesting that smart cities achieve carbon abatement primarily through the digital empowerment of energy allocation efficiency—a pathway distinct from traditional end-of-pipe governance approaches. Unlike previous studies, this study combines ISM with a staggered DID framework to reveal the dynamic effects and transmission mechanisms of smart city policies. Full article

Radon (²²²Rn) is a naturally occurring radioactive noble gas and a leading cause of lung cancer after tobacco smoking. Elevated concentrations in groundwater, mainly from uranium-rich geological formations, pose significant health risks in regions reliant on groundwater. This review critically examines current tools and techniques for detecting radon in drinking water, focusing on how methodological choices affect reported levels and risk assessments. A structured literature review from major scientific databases covers established laboratory methods like liquid scintillation counting (LSC), alpha/gamma spectrometry, Lucas cells, and electret chambers, alongside field methods such as the RAD7, and new technologies including IoT sensors and AI analytics. The review finds that accurate radon measurement is limited more by sampling losses due to volatility, radioactive decay (half-life: 3.82 days), and the heterogeneity of fractured aquifers, than by instrument precision. While LSC remains the regulation standard for its traceability and low detection limits, portable methods are key for quick field screening, and automated systems are increasingly important for capturing transient changes in treatment settings and high-risk aquifers. A decision matrix is provided to help choose methods based on monitoring goals, infrastructure, and needed confidence levels. The study emphasizes that improving data quality and comparability requires not just technological progress but also harmonized sampling protocols, rigorous quality assurance, transparent uncertainty reporting, and the inclusion of hydrogeological context in monitoring design. This comprehensive approach is crucial for translating measurements into effective exposure assessments, regulatory decisions, and public health protections. Full article

Shale gas development in China is dominated by high-pressure gas reservoirs occurring in deep horizons in Sichuan Basin and its peripheral areas. With the progress of exploration and development technologies, medium-shallow normal-pressure shale gas represented by Anchang Syncline in Guizhou has also realized commercial operation. To address the problems of overall low production and high gas breakthrough flowback ratio of medium-shallow normal-pressure shale gas, this paper systematically analyzes its development performance laws based on the complex tectonic setting and the reservoir characteristics of “four lows and one high” of the Anchang Syncline. A series of key technologies were developed, including the static–dynamic iterative identification technology for faults and micro-amplitude structures in complex tectonic areas, the coupled iterative fine modeling technology of geology-development dual chain, the iterative optimization technology of fractures and simulation parameters, as well as low-cost drilling-completion and drainage-production process technology. The application of the above technical suite achieved commercial development of normal-pressure shale gas. By the end of 2024, 66 wells had produced 6.5 × 10⁸ m³ cumulatively, and the well Estimated Ultimate Recovery (EUR) is 2000–4000 × 10⁴ m³. It was clarified that geological conditions constitute the intrinsic basis of gas well production capacity, and engineering factors such as horizontal section length, well type selection, fracturing matching degree, and drainage-production timing are the key to production enhancement. The established development technical system and practical experience for normal-pressure shale gas provide an important reference for the efficient development of analogous gas reservoirs. Full article

Donkey milk contains bioactive components with immunomodulatory potential, but preservation technologies must retain these effects to support its use as a functional food ingredient. This study investigated whether spray drying preserves the in vivo biological functionality of donkey milk. Forty-five female Pannon White rabbits subjected to a standardized vaccine-induced immune challenge were randomly allocated to three groups: one Control group and two groups receiving raw or reconstituted spray-dried donkey milk (2 mL/day; n = 15/group) for four weeks. Serum lysozyme and total immunoglobulins were monitored longitudinally, while biochemical, hematological, and integrated multivariate profiles were assessed. Lysozyme increased progressively in both supplemented groups, reaching 29.4% and 33.1% above baseline with raw and spray-dried milk, respectively; cumulative responses were statistically equivalent. Total immunoglobulins recovered earlier after both treatments, reaching 31.31 and 28.92 mg/mL, respectively, versus 23.04 mg/mL in Control; their mean response profiles were closely aligned, although inter-individual variability reduced the precision of the equivalence assessment. Biochemical and hematological assessments revealed no coordinated adverse response to either milk preparation. Moreover, the integrated immune, biochemical, and hematological profiles showed strong concordance between Raw and Spray. Overall, spray-dried donkey milk retained the principal biological responses elicited by raw milk, supporting its potential development as a stable bioactive dairy ingredient. Full article

Accurate and efficient early pregnancy diagnosis (EPD) in livestock is crucial for optimizing breeding management and enhancing productivity in modern animal husbandry. Over the past century, EPD technology has evolved from empirical methods to sophisticated techniques, encompassing biochemical marker detection, ultrasonic imaging, and further extending to emerging non-invasive approaches such as infrared thermography (IRT) and spectroscopic analysis. These advancements have not only improved diagnostic accuracy but also broadened the research scope to include small livestock and multiple species. This review critically examines the historical evolution, current methodologies, and applications of EPD technology, with a focus on analyzing the advantages and limitations of both traditional and emerging techniques. Additionally, it explores the potential of multimodal fusion strategies and artificial intelligence (AI) in EPD. At present, machine vision, wearable monitoring, and several AI applications remain prospective approaches rather than validated tools for routine EPD. The conclusion highlights that, despite significant progress, current technologies still face limitations in achieving in situ, non-contact, and high-throughput detection. Looking ahead, the integration of cutting-edge technologies, such as AI, small wearable sensors, and physiological time-series data analysis, holds promise for overcoming these bottlenecks, enabling more intelligent and efficient pregnancy diagnosis, and providing scientific support for modern animal husbandry. Full article

Laser-directed energy deposition (L-DED) is a key technology for fabricating wear-resistant Stellite-6 coatings on power-plant components. This study aims to clarify the surface cracking mechanism of L-DED Stellite-6 coatings deposited on 22Cr15Ni3.5CuNbN (SP2215) boiler tubes during long-term high-temperature service. The coatings were exposed at 650 °C for up to 5000 h, and their microstructural evolution and mechanical degradation were systematically characterized using SEM/EDS, TEM, EBSD, microhardness testing, and impact testing. High-temperature service induces the decomposition of M₂₃C₆ precipitates at dendrite boundaries, releasing Cr, C, and W atoms that subsequently migrate toward the coating surface. Owing to the rapid interstitial diffusion of C, a carbon-enriched surface region preferentially develops, accompanied by progressively increasing coverage of surface Cr₂O₃ and subsurface M₂₃C₆. Consequently, the surface hardness increases from 441.6 HV0.1 at 0 h to 613.1 HV0.1 after 5000 h, whereas the impact-absorbed energy decreases from 82.8 ± 4.2 J to 5.9 ± 2.5 J. An increase of 1 HV0.1 in hardness corresponds to an approximately 0.448 J reduction in impact-absorbed energy. Mechanistically, local stress concentration associated with Cr₂O₃ formation, interfacial sliding promoted by lattice mismatch, and crack nuclei originating from pores between chain-like M₂₃C₆ precipitates collectively promote crack initiation and propagation. These results demonstrate that precipitate decomposition, elemental redistribution, and subsequent oxide/carbide evolution govern the progressive surface embrittlement and cracking of L-DED Stellite-6 coatings during long-term high-temperature service. This study provides mechanistic insight into the coupling between microstructural evolution and surface failure and offers a theoretical basis for microstructural regulation and long-term reliability assessment of wear-resistant Co-based coatings used in power-plant components. Full article

The detection of circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) is a minimally invasive approach for diagnosing and monitoring cancer. These liquid biopsy-based strategies enable the identification of primary or metastatic tumors and provide valuable information on tumor biology and treatment response. A variety of techniques are employed to analyze components obtained through liquid biopsy. While CTCs are typically detected using cell-based methods, ctDNA is commonly analyzed using highly sensitive molecular approaches, including quantitative PCR (qPCR), digital PCR (dPCR), and next-generation sequencing (NGS). Extracellular vesicles (EVs), which carry tumor-derived nucleic acids, proteins, and other biomolecules, are increasingly investigated as potential biomarkers, while tumor-educated platelets (TEPs) can reflect tumor-associated molecular changes and may provide additional information for cancer detection and monitoring. Furthermore, emerging multi-cancer early detection (MCED) approaches aim to identify molecular signatures associated with multiple cancer types from a single blood sample, highlighting the broader diagnostic potential of liquid biopsy. Due to the high specificity of tumors and somatic mutations, ctDNA serves as a real-time biomarker for tracking cancer progression. The advantages of ctDNA diagnostics include its low invasiveness and its capacity to detect cancer at early stages. In addition to confirming the presence of a tumor, liquid biopsy approaches facilitate the assessment of malignancy and the evaluation of treatment response. In the field of human medicine, ctDNA plays a pivotal role in diagnostic procedures. It is utilized not only for screening tests that detect the presence of cancer but also for the development of targeted treatment protocols for specific patients. These protocols are informed by the detection of genomic alterations and are designed to monitor the response to therapy over the course of treatment. Similarly, CTC, EV, TEP, and MCED approaches are being investigated as complementary tools for cancer detection, molecular characterization, prognosis, and longitudinal disease monitoring. In the domain of veterinary medicine, ctDNA has been instrumental in the diagnosis of various neoplasms, including osteosarcomas, hemangiosarcomas, lymphomas, canine mammary tumors, and melanomas. Other liquid biopsy components, including CTCs and EVs, also show promise for the detection and characterization of tumors in companion animals, although their clinical application remains less developed than in human medicine. Another significant application is in the detection of minimal residual disease (MRD), where a small number of cancer cells remain undetectable by conventional tests, such as blood counts. This underscores the significance of advanced ctDNA analysis. However, challenges persist, including the low concentration of ctDNA in blood, the low ratio of mutated to normal DNA fragments, potential contamination, biological variability, and the need for standardized protocols. This review synthesizes the current knowledge on liquid biopsy, including ctDNA, CTCs, EVs, TEPs, and emerging MCED approaches, and the potential for transferring these technologies from human medicine to animal medicine. Continued research is necessary to enhance the sensitivity and specificity of detection, which could facilitate early cancer diagnosis in animals and improve survival through timely treatment. Full article

Gastrointestinal (GI) malignancies account for approximately one-quarter of new cancer diagnoses and more than one-third of cancer-related deaths worldwide, yet as of August 2026, only one CAR-T therapy has received regulatory approval for a solid tumor indication anywhere in the world. The recent CT041-ST-01 phase II trial of satricabtagene autoleucel, the first randomized CAR-T trial conducted in a solid tumor, demonstrated a significant improvement in progression-free survival for patients with advanced gastric cancer (median 3.25 vs. 1.77 months; hazard ratio (HR) 0.37, p < 0.001). While this landmark study established the clinical feasibility of CAR-T therapy in solid tumors, it also underscored the biological barriers that continue to limit durable responses. GI tumors are characterized by heterogeneous antigen expression, dense desmoplastic stroma, inefficient immune-cell trafficking, profoundly immunosuppressive tumor microenvironments, and progressive T-cell dysfunction, all of which are further compounded by the logistical and economic challenges of autologous cell manufacturing. In this narrative review, we organize these obstacles within a unified four-barrier engineering framework and critically examine the strategies being developed to overcome each of them. We discuss advances in multi-antigen and logic-gated CAR architectures, stromal remodeling through fibroblast activation protein (FAP)-targeted approaches and extracellular matrix-degrading enzymes, chemokine receptor engineering, regional delivery, hypoxia-responsive CARs, cytokine-armored and persistence-enhanced constructs, dominant-negative and switch receptors, metabolic reprogramming, and intrinsic checkpoint disruption. We also review emerging manufacturing platforms, including allogeneic CAR-T and CAR-natural killer (CAR-NK) cells, induced pluripotent stem cell-derived products, CAR-macrophages, and in vivo CAR generation, together with engineering strategies designed to improve safety and scalability. Rather than relying on a single technological advance, the future of CAR-based therapy for GI malignancies will likely depend on integrating multiple engineering approaches to address the diverse biological barriers within the tumor microenvironment. By synthesizing current preclinical and early clinical evidence, this review provides a translational framework for the next generation of CAR-based cellular therapies in gastrointestinal oncology. Full article

Regional digital innovation ecosystems are gradually becoming the core engine driving regional innovation and economic development, and are increasingly a critical strategic choice for countries seeking to secure a leading position in the global innovation competition. In addition to promoting economic growth, the RDIES can also provide crucial support for regional sustainable development by optimizing the allocation of digital innovation resources and strengthening collaborative innovation among multiple entities. Based on niche theory, this study constructs an evaluation index system and model to assess the niche fitness of RDIES and conducts an empirical evaluation of digital innovation ecosystems across 30 provinces and municipalities in China from 2013 to 2023. The results indicate that the overall niche fitness level of China’s RDIES remained relatively low during the study period, with significant disparities observed among provinces. Nevertheless, most provinces demonstrated a positive evolutionary trend in the niche fitness of their digital innovation ecosystems, suggesting that China’s RDIES as a whole is developing healthily and progressively. Furthermore, by employing the FFAGM(1,1) model optimized with a Fourier series, this study forecasts the niche fitness of China’s RDIES from 2024 to 2029. The prediction results show that the niche fitness values of the innovation community show a slight fluctuating decline; the niche fitness values of the resource niche fluctuate moderately and increase slightly; the niche fitness values of the technology niche remain relatively stable; the niche fitness values of the habitat niche continue to steadily improve; and the niche fitness of the overall RDIES shows a gradually upward trend. This study provides a framework for evaluating and forecasting the niche fitness of the RDIES, enriches research on grey prediction models, and offers theoretical and policy support for fostering resilient and resource-efficient digital innovation ecosystems. Full article

Medicinal plants are major sources of therapeutic natural products, yet the cell-type-specific organization that governs metabolite biosynthesis, transport, and storage remains imperfectly resolved by organ-level omics. This review synthesizes studies published up to June 2026 that used single-cell, single-nucleus, spatial, metabolomic, and epigenomic approaches to medicinal plant systems, following a PRISMA-guided literature search across PubMed, Web of Science, Scopus, and CNKI. Emerging evidence shows that specialized metabolism is organized through discrete and often rare cell populations, including idioblasts, laticifers, glandular trichomes, secretory epidermal cells, internal phloem-associated parenchyma, cork and periderm cells, mesophyll cells, and other biosynthetic niches. Single-cell RNA sequencing has defined these populations and reconstructed developmental trajectories, whereas single-cell metabolomics and mass spectrometry imaging reveal that metabolite accumulation frequently diverges from biosynthetic gene expression because of intercellular transport, storage capacity, and subcellular compartmentation. Single-cell ATAC-seq and multiome profiling further identify cell-type-specific regulatory regions, transcription factors, and candidate promoters controlling metabolic competence. Together, these technologies are reshaping medicinal plant biology from pathway-centric catalogs into spatially and developmentally resolved cellular maps. We highlight how artificial intelligence (AI)-assisted integration can accelerate cell annotation, regulatory network inference, metabolite assignment, and prioritization of biosynthetic genes, transporters, and engineering targets. Future progress will depend on comparative medicinal plant atlases, improved recovery of recalcitrant tissues, matched transcriptomic, metabolomic, and spatial designs, and functional validation of cell-type-specific mechanisms. Full article

Microorganisms are ubiquitous in nature, and microbial activities are closely intertwined with the entire life cycle system and human life. Developing novel technologies for the detection, characterization and manipulation of microorganisms promotes their applications in clinical, environmental and industrial areas. Over the last two decades, terahertz (THz) technology has emerged as a new optical tool for microbiology. The great potential originates from the unique advantages of THz waves including the high sensitivity to water and inter-/intra-molecular motions, the non-invasive and label-free detecting scheme, and their low photon energy. THz waves have been utilized as a stimulus to alter microbial functions or as a sensing approach for quantitative measurement and qualitative differentiation. This review specifically focuses on recent research progress of THz technology applied in the field of microbiology, including two major parts of THz biological effects and the microbial detection applications. At the end of this paper, we summarize the research progress and discuss the challenges currently faced by THz technology in microbiology, along with potential solutions. We also provide a perspective on future development directions. This review aims to build a bridge between THz photonics and microbiology, promoting both fundamental research and application development in this interdisciplinary field. Full article

Ageing is a key post-mortem process affecting beef quality; however, alternative fat-coating methods remain insufficiently understood. This study compared wet ageing (WA), dry ageing (DA), butter ageing (BA), and tallow ageing (TA) in terms of their effects on physicochemical, technological, and sensory characteristics. Longissimus lumborum muscles from Fleckvieh heifers were aged for 50 days under controlled conditions: WA samples were vacuum-packed, DA samples were stored bone-in, and BA and TA samples were coated with butter or rendered tallow. Instrumental quality analyses and descriptive sensory evaluation by a trained panel were performed. Ageing significantly improved tenderness in all treatments, reducing the Warner–Bratzler shear force (WBSF) from 55.8 N in fresh meat to 35.1–39.9 N after 50 days of ageing. However, tenderness did not differ significantly among the ageing methods. Meanwhile, pH increased and myofibrillar fragmentation progressed during ageing. The BA and TA samples exhibited higher scores for sour and atypical sensory notes. DA samples were also perceived as juicier than the other treatments. Overall, although all methods produced acceptable tenderness, conventional ageing, particularly dry ageing, provided superior sensory quality. Utilising butter or tallow coating during beef ageing did not enhance flavour development, and may negatively affect sensory profiles. Full article

The rapid expansion of the Internet of Medical Things (IoMT) and intelligent assistive technologies has intensified the need for resource-optimized cryptographic hardware to protect sensitive biometric data. Cryptographic hardware performance relies primarily on modular arithmetic operations, especially field multiplication. Although the binary extension field provides carry-less arithmetic ideal for battery-powered devices, standard general-purpose processors lack the dedicated hardware required to execute these operations efficiently. This paper proposes a novel multicore-based modular multiplication algorithm that bridges this gap by exploiting the parallel coordination fabric and high-bandwidth interconnects of modern embedded multicore systems. Central to this work is the Progressive Product Reduction (PPR) paradigm, which optimizes hardware efficiency by integrating the multiplication and field reduction phases into a unified process, thereby minimizing intermediate data storage and computational depth. We introduce and analyze two distinct architectural strategies—Progressive Product Reduction with Column Division (PPR-CD) and Progressive Product Reduction with Row Division (PPR-RD)—and establish rigorous mathematical models to estimate hardware area, critical path delay, and exact operational latency across various core configurations. Our performance evaluation demonstrates that the PPR-RD architecture achieves superior Area-Delay Product (ADP) and energy efficiency, providing a scalable framework for securing sensitive biometric data in next-generation assistive devices. This implementation ensures robust cryptographic protection for assistive devices while maintaining energy autonomy and computational resilience essential for sustaining consumer trust, advancing global health equity, and driving financial stability in future digital economies. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and functional deterioration. With the rapid growth of the aging population, AD has become a major global health challenge, imposing substantial burdens on patients, families, and healthcare systems. Despite extensive research, early and accurate diagnosis of AD remains challenging due to disease heterogeneity, overlapping clinical manifestations, and the lack of easily accessible, highly sensitive, and specific diagnostic markers. Recent advances in biomedical technologies, including neuroimaging, multi-omics profiling, electronic health records, and digital health tools, have generated large-scale and heterogeneous datasets, providing new opportunities for improving AD diagnosis. However, extracting clinically meaningful information from these complex data sources remains difficult using conventional statistical approaches. Artificial intelligence (AI) has progressively transformed AD diagnosis by evolving from traditional machine learning (ML) approaches based on handcrafted feature engineering to deep learning (DL) models capable of automated representation learning and multimodal information integration. More recently, large language models (LLMs) have further expanded the scope of AI-driven AD diagnosis by enabling contextual understanding of unstructured clinical information, knowledge-guided reasoning, and integration of multimodal biomedical evidence. This transition reflects a shift from feature-based prediction toward more flexible and intelligent diagnostic frameworks. This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs. Particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and enabling multimodal frameworks for AD assessment. Full article

Waste prevention begins upstream, in decisions made long before a product becomes waste. This article examines product lifetime management as an upstream environmental strategy and asks when longer product use can reduce waste generation, resource consumption and environmental impacts across the life cycle. An integrative literature review combined exploratory searches in Google Scholar and citation tracking with a structured search of Scopus. Records were screened by title, abstract and keywords. Publications in English were considered without a date limit and selected according to their relevance to product lifetime, premature replacement, repair and lifetime extension, consumer and business decisions, regulation, waste prevention and life cycle assessment. The selected studies were supplemented with relevant legal, regulatory, institutional and standardisation documents, producing a final reference base of 240 sources. The review shows that actual product lifetime is shaped not only by technical durability, but also by repairability, access to spare parts and information, continued software and service support, opportunities for upgrading and reuse, business incentives and consumer behaviour. Extending product use can reduce waste and resource demand, but environmental benefits are not automatic. They depend on product characteristics, energy consumption, technological progress and, above all, whether prolonged use genuinely replaces the production and purchase of new products. Product lifetime management should therefore be understood as a coordinated upstream strategy linking design, repair, reuse, business models, regulation and consumption with environmental assessment across the life cycle. Full article