Search Results (16,238)

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality globally, with low-resource settings, including Ethiopia facing challenges due to limited early diagnostic services. AI-powered electrocardiography (ECG) interpretation has the potential to improve diagnostic accuracy, decentralize care, and support timely clinical decisions, but evidence on healthcare providers’ perspectives and adoption determinants is limited. This exploratory descriptive qualitative study employed 31 in-depth interviews with healthcare providers. Healthcare providers (cardiologists, internists, cardiac and critical care nurses, critical care specialists, and general practitioners) were purposively selected through maximum variation sampling from ten hospitals in four regions of Ethiopia. Data were transcribed verbatim, coded inductively, and analyzed thematically. The data analysis identified six themes: perceived benefit of AI-powered ECG interpretation CDSS, trust development, workflow integration, ethical concerns, functionality, and adoption determinants. Participants emphasized AI’s potential to enhance accessibility, consistency, and diagnostic accuracy while reducing subjectivity and unnecessary referrals. Acceptance relied on high accuracy, reliable data, and rigorous validation, with the technology seen as supportive rather than replacing clinicians. Material resources, human resource readiness, and leadership engagement were key factors for adoption. Recommendations included phased implementation, continuous training, and model expansion to ensure sustainability and clinical utility. The AI-powered ECG interpretation CDSS was viewed as a valuable adjunct for strengthening cardiovascular care in Ethiopia, highlighting the need for context-sensitive strategies, ethical safeguards, and multi-level system readiness for successful adoption. Full article

Based on the waterflooding development practice of thick carbonate reservoirs in the Middle East, aiming at the practical problems such as complex water invasion types, rapid water breakthrough of oil wells and poor development performance in such reservoirs, this study takes the MB1 reservoir of H Oilfield as the research object and establishes a multi-scale hierarchical screening scheme for the main controlling factors of water cut rise covering the reservoir-block-well group levels. Firstly, the target reservoir is divided into several typical development blocks by means of numerical simulation technology. On this basis, the dynamic development characteristics of the reservoir, typical blocks and well groups are analyzed respectively. The multi-sequence grey correlation method is adopted to screen the common influencing factors of water cut rise in typical blocks, and then the multi-factor sensitivity analysis of the screened key factors is carried out by numerical simulation. Finally, it is determined that the main controlling factors affecting the water cut rise in the reservoir are the development degree of high-permeability layers, the rationality of well pattern layout, and the injection–production intensity, and the corresponding development adjustment strategies are proposed accordingly. Guided by the multi-scale hierarchical screening of main controlling factors for water cut rise, this study improves the traditional grey correlation method and proposes a multi-sequence grey correlation analysis method. This method for determining the controlling factors, which combines mathematical approaches with reservoir numerical simulation techniques, gives full play to the advantages of both. It reduces the range of variables in numerical simulation analysis, avoids the sharp increase in simulation complexity caused by multi-factor coupling, and greatly improves work efficiency while ensuring research depth. Full article

The growing need for lightweight, multifunctional, and high-performance structures in the automotive, aerospace, electronics, and medical industries has driven the development of advanced joining technologies for polymers and metal-polymer combinations. Among these, ultrasonic welding (USW) and friction stir spot welding (FSSW) have emerged as promising solid-state techniques capable of producing reliable joints with minimal thermal degradation and enhanced interfacial bonding. This review focuses on recent developments in USW and FSSW of thermoplastics, fiber-reinforced composites, and hybrid metal–polymer systems, with a particular emphasis on process mechanics, microstructural evolution, and joint performance. The mechanisms of heat generation, material flow behavior, and consolidation are discussed in relation to key process parameters, including applied pressure, rotational speed, vibration amplitude, plunge depth, and dwell time. Microstructural transformations such as polymer chain orientation, recrystallization, interfacial diffusion, and defect formation are analyzed to establish process–structure–property relationships. Mechanical performance metrics, including lap shear strength, fatigue resistance, impact behavior, and environmental durability, are critically compared across different materials and welding methods. Furthermore, recent advances in numerical and thermo-mechanical modeling, in situ process monitoring, and data-driven optimization are discussed to highlight pathways toward predictive and scalable manufacturing. Current industrial applications and existing limitations such as challenges in automation, thickness constraints, and hybrid material compatibility are also evaluated. Finally, key research gaps and future directions are identified to improve joint reliability, sustainability, and broader industrial adoption of advanced solid-state welding technologies. Full article

In this paper, we present a system built on Hyperledger Fabric (HLF) that leverages Confidential Computing (CC) technologies to strengthen data privacy guarantees beyond those achievable through application-level mechanisms alone. While HLF natively supports data confidentiality through Private Collections (PCs), which restrict data visibility to a subset of authorized network participants, these mechanisms do not protect data at the hardware level: a privileged or compromised hosting platform can access plaintext data in memory and on the filesystem irrespective of HLF access control policies. To address this limitation, we integrate CC into HLF by adopting Intel Software Guard Extensions (SGX) in conjunction with the Gramine framework. This integration enables the execution of HLF components—peer nodes, orderers, Chaincodes and client applications—within Trusted Execution Environments (TEEs). Furthermore, to securely grant access to selected data to a trusted third-party software (TPS) external to the blockchain network, we leverage the Remote Attestation (RA) feature provided by CC, as streamlined by Gramine and enforced on a per-request basis, ensuring that only verified enclaves (or “SGX enclaves”) with expected measurements may access private data. In addition, the Sealing mechanism is employed to persistently store cryptographic material required by HLF components on the filesystem while preserving both confidentiality and integrity. Together, PCs, RA, Sealing, and enclave-based execution establish a layered privacy guarantee: PCs enforce application-level data segregation among channel participants; RA provides measurement-based access control for an external TPS; Sealing ensures that cryptographic material and blockchain state remain encrypted on the filesystem; and enclave-based execution protects data in use through hardware-level memory encryption. The proposed system has been applied and experimentally validated in a logistics use case in the Port of Genoa: benchmarks against an experimental HLF deployment demonstrate an average 95th-percentile (p95) performance overhead of approximately 1.3× attributable to SGX memory encryption and Gramine-based enclave execution, whereas an elevated memory usage footprint (33–35 GB per organization) has been measured, mainly due to the Gramine environment: this remains an open direction for future work. Full article

This study investigates the evolving relationship between supply chain integration (SCI) and firm performance through a comprehensive bibliometric analysis of 148 publications retrieved from the Scopus database. Using VOSviewer 1.6.20 software, the research maps the intellectual structure of the field, highlighting influential authors, journals, and thematic developments. Findings reveal that SCI conceptualized across internal, supplier, and customer integration has consistently been linked to improved operational efficiency, responsiveness, and competitive advantage. However, empirical evidence also indicates mixed outcomes, particularly under conditions of environmental uncertainty and excessive dependence on partners. Recent scholarship demonstrates a notable shift toward sustainability-oriented integration and the adoption of digital technologies such as blockchain, big data analytics, and artificial intelligence, which collectively enhance resilience and adaptability. The analysis underscores gaps in research across developing economies and service industries, suggesting opportunities for future inquiry. Overall, the study deepens understanding of SCI’s role in shaping resilient, sustainable, and technologically enabled supply chains. Full article

Background: Laryngeal cancer (LC), a common subtype of head and neck cancers (HNC), is most frequently represented by laryngeal squamous cell carcinoma (LSCC). Prognosis largely depends on early detection; however, traditional prognostic tools, including tumor-node-metastasis (TNM) staging, often show limited predictive accuracy. Artificial intelligence (AI), including machine learning (ML), natural language processing, and deep learning (DL), has emerged as a promising approach to improving cancer diagnosis, prognosis, and treatment planning by analyzing clinical data and medical imaging. Objective: This systematic review assesses the role of AI in prognosis, recurrence prediction, and treatment outcomes in LC. Methods: PubMed, MEDLINE, Scopus, Web of Science, IEEE Xplore, and ScienceDirect were searched up to January 2025. A total of 1062 records were identified; after title/abstract screening and full-text assessment, 29 studies were included. Eligible studies involved adult patients with LC and applied AI to diagnose, prognose, predict recurrence, or assess treatment outcomes using human datasets. Study quality and risk of bias were evaluated using the QUADAS-2 and QUIPS. Results: The 29 included studies were mostly retrospective, with sample sizes ranging from 10 to 63,000 patients. Most focused on LSCC, with a higher prevalence in males. The studies utilized various AI techniques, including deep learning models such as convolutional neural networks (CNNs) and DeepSurv, as well as ML algorithms like random survival forest, gradient boosting machines, random forest, k-nearest neighbors, naïve Bayes, and decision trees. AI models demonstrated strong prognostic performance, surpassing Cox regression and TNM staging in predicting survival and recurrence. Several studies reported outcomes related to treatment, such as chemotherapy response, occult lymph node metastasis, and the need for salvage surgery. Methodological quality varied, with biases related to patient selection and confounding factors. Conclusions: AI has the potential to improve prognosis estimation, recurrence prediction, and treatment outcome assessment in LC. However, although AI can be a helpful addition to clinical decision-making, more prospective studies, external validation, and standardized evaluation are necessary before these technologies can be confidently adopted in everyday clinical practice. Full article

Mountain orchards in southern China are characterized by fragmented and complex terrain with a wide slope variation range (5~30°), which easily leads to uneven pesticide distribution and pesticide accumulation on gentle slopes. These issues give rise to core technical bottlenecks such as low pesticide utilization rate, poor operational efficiency, and unclear atomization mechanism, hindering the optimization of pesticide application parameters, causing pesticide waste and environmental pollution, and restricting the sustainable development of the mountain fruit industry. To address this problem, this study designed a slope-classified pipeline layout and developed a high-efficiency fixed pipeline system for phytosanitary application in mountain orchards, featuring stable operation, low labor intensity, and easy intelligent transformation. Following the technical route of “theoretical design-atomization mechanism analysis-parameter optimization-laboratory verification-field application”, ruby nozzles with high wear resistance, uniform droplet distribution, and long service life were selected and optimized to meet the demand for long-term fixed pesticide application in mountain orchards. High-speed imaging technology was used to real-time capture the dynamic atomization process of nozzles, providing support for clarifying the atomization mechanism. Advanced methods such as fluorescence tracing were adopted to quantitatively evaluate key indicators including droplet deposition in canopies, and the system performance was verified through laboratory and field tests, laying a scientific foundation for its popularization and application. Field test results showed that the optimal spray pressure should not be less than 8 MPa. The XR9002 nozzle can generate fine droplets to achieve pesticide reduction while forming a stable hollow cone atomization flow. Fluorescence tracing analysis indicated that the droplet deposition on the adaxial leaf surface decreases with increasing altitude (presumably affected by wind speed), while the initial deposition on the abaxial leaf surface is low and shows no significant variation with altitude. Deposition on the adaxial leaf surface decreased with canopy height, while abaxial deposition was much lower (8.9–14.9%). This technology enables high-precision quantitative analysis of droplet deposition. The core innovations of this study are: clarifying the atomization mechanism of ruby high-pressure nozzles under pesticide application conditions in mountain orchards, constructing a slope-classified terrain-adaptive pipeline layout model, and establishing a closed-loop technical system of “atomization mechanism-pipeline layout-parameter optimization-deposition detection”. This study provides theoretical and technical support for green and precision pesticide application in mountain orchards, and has important academic value and broad application prospects for promoting the intelligent upgrading of the fruit industry in southern China. Full article

Decades of traditional fertilizer subsidies have yielded modest maize productivity gains for Malawian farmers, mainly due to the twin challenges of soil degradation and intermittent weather patterns. Increasing nitrogen intake through subsidies without addressing these structural constraints has failed to close the country’s yield gap. Although climate-smart agriculture (CSA) technologies offer options for sustainable productivity growth, low and inconsistent adoption among farmers has led to insufficient evidence. Most existing studies that have examined the complementarity between CSA and inorganic fertilizers rely on experimental plot data, with limited evidence from actual farmer-managed fields. We use farm-level data collected in 2022 from 307 smallholder farmers across central and southern Malawi to investigate whether integrating CSA technologies with subsidized inorganic fertilizers enhances maize productivity. We apply the Inverse Probability Weighted Regression Adjustment (IPWRA) model to estimate the effects of CSA adoption and its integration with subsidized fertilizer. Results indicate that CSA adoption increased maize yields by 30%, confirming significant productivity gains from technologies such as mulching, agroforestry, and organic manure. However, integrating these technologies with subsidized fertilizers produced no additional yield advantage, suggesting that farmers often substitute CSA with inorganic inputs rather than combining them effectively. These findings imply that the potential synergies between CSA and subsidy programs remain unrealized under current practices. Policy reforms under Malawi’s current farm input subsidy program (FISP) should therefore emphasize extension and incentive mechanisms that promote complementary—not substitutive—use of CSA technologies and fertilizers at recommended application rates. Full article

Ultra-wideband (UWB) technology has been widely adopted for indoor positioning due to its high temporal resolution. However, the accuracy of UWB-based indoor positioning is fundamentally limited by ranging measurement errors, particularly under non-line-of-sight (NLOS) conditions, where systematic bias and uncertainty are introduced into the measured distances. In this paper, a measurement error mitigation method is proposed to improve UWB ranging reliability in complex indoor environments. The method first identifies NLOS measurements using low-dimensional physical features and a lightweight machine learning classifier. Subsequently, an error compensation strategy is applied to correct biased ranging observations, which are then incorporated into a nonlinear least squares positioning model. Experimental results obtained in typical indoor environments demonstrate that the proposed method significantly reduces ranging errors and improves positioning accuracy compared with conventional approaches. The results indicate that the proposed framework effectively enhances measurement robustness without increasing system complexity. Full article

The maritime sector plays a critical role in global logistics systems, acting as a key link within international supply chains. Companies in this sector generate significant employment across the logistics and global value chain. However, it is noticeable that this sector still lacks innovative ideas related to the growth of the level of sustainable development. A wider adoption of green innovations could significantly improve environmental performance and reduce the ecological impact of maritime activities. A key factor that can stimulate the development of innovations in the maritime sector is green finance solutions. Dedicated financing for the greening of the maritime sector can catalyze innovation implementation processes both on ships and in ports. This article analyzes the opportunities for investments in greening activities using specific green finance instruments. This article presents the current situation of the maritime sector in terms of innovation and opportunities for project financing and increasing the value of companies, as well as key technological solutions that increase the level of sustainability in this sector. One of the key challenges is the limited intervention of governments and international organizations in accelerating maritime decarbonization. Maritime sector companies are slow to make progress towards sustainability—there is a lack of fundamental innovation and voluntary efforts to decarbonize. This has led to a situation where a large part of the innovations created are unprofitable today. The authors of this article suggested key investment directions—digitalization and robotization solutions, modernization of old ships and greening solutions for port companies. These actions would provide a short-term breakthrough, but it is necessary to consistently invest in new types of innovations based on scientific research. Full article

To analyze the phytogenic components of honey of Amorpha fruticosa L. (AFH) and establish a targeted quantitative method, the liquid chromatography-mass spectrometry (LC-MS) based metabolomic technology was used in this study. Firstly, high performance liquid chromatography—quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) untargeted metabolomics technology was used to screen candidate markers by comparing AFH metabolites with plant chemicals of Amorpha fruticosa L. Afterward, high performance liquid chromatography—triple quadrupole tandem mass spectrometry (HPLC-QQQ-MS/MS) was used to verify and identify the candidate markers, confirming ononin as the characteristic phytogenic marker of AFH, and determining its content range in AFH as 76.84–93.27 μg/kg (absent in acacia, rape, jujube, and Galla chinensis honey). Then, network pharmacology and molecular docking techniques were adopted to explore the gastric protective mechanism of ononin, and the results showed that ononin strongly binds AKT1 (binding free energy −8.0677 kcal/mol). Using the established method, the LC-MS analytical method for ononin in honey established in this study may be used for the authenticity identification of the characteristic phytogenic markers of AFH. Full article

Agentic artificial intelligence—systems that reason, plan, and act autonomously within governed workflows—is converging with autonomous electric mobility and urban robotics to reshape how cities govern, move, and manage physical space. We argue that the simultaneous arrival of these three vectors is triggering a transformation comparable in scope to the Industrial Revolution. Cities that deploy across all three domains are becoming the new hubs of innovation: they concentrate talent, accelerate knowledge circulation, enable cross-fertilisation, and generate hybrid proposals that no single vector could produce alone. Just as Manchester, Birmingham, and the Ruhr became the defining centres of industrialisation because steam, textiles, iron, and coal recombined through the proximity of the engineers and entrepreneurs who moved between them, a small number of cities today are pulling ahead because they host the shared talent pool around which agentic governance, autonomous mobility, and urban robotics co-evolve. Conceptually, we extend the mirroring hypothesis in two directions: dynamically, arguing that organisations and urban ecosystems converge toward the configurations new technologies make possible; and ontologically, arguing that agentic AI introduces non-human agents into organisational architectures, requiring hybrid human–AI coordination. We formalise this dynamic as five propositions (P1–P5) of cumulative recursive hybridisation (CRH), operating through four reinforcing feedback loops—data, regulation, infrastructure, and talent. Together, these loops explain why the emerging urban order is path-dependent: early movers accumulate compounding advantages, while latecomers face exponentially rising costs of entry. We demarcate CRH from adjacent frameworks—general-purpose technologies, organisational complementarities, and complex adaptive systems—and test it against counterfactual evidence from failed, stalled, and Global South trajectories (Sidewalk Toronto, the Cruise rollback, Songdo, Bengaluru). We also examine its political-economy, equity, and surveillance limits. Drawing on comparative evidence from public-sector chatbot deployments, autonomous mobility ecosystems in the United States and China, and emerging urban robotics cases, we conclude that what is at stake is not incremental modernisation but the construction of a new urban order. The cities that act as innovation hubs for the agentic AI era will shape global standards, attract global talent, and define the institutional templates that others eventually adopt—much as the industrial cities of the eighteenth and nineteenth centuries did. Full article

Surface defects in high-temperature continuous cast billets are critical factors affecting the quality of steel products. Owing to high-temperature radiation, heavy dust contamination, varying billet specifications, and background interference from oxide scales and water stains, existing online surface defect detection technologies for high-temperature continuous cast billets still suffer from limitations including high false-positive rates, inefficient identification of pseudo-defects, and the inability to simultaneously detect three-dimensional (3D) depth information alongside two-dimensional (2D) features. To solve these problems, this paper proposes a multi-dimensional online detection technology for surface defects in high-temperature continuous cast billets based on multi-information fusion. A four-channel multispectral image sensor and a corresponding three-light-source imaging system were developed. Furthermore, a defect sample augmentation method, a deep learning-based 2D recognition method, and a photometric stereo-based 3D reconstruction method were designed to mitigate problems of low detection accuracy and poor robustness caused by sample imbalance among different defect types. Finally, industrial applications were conducted on large-section continuous cast billets, beam blanks, and billets during the grinding process. According to the surface defect detection requirements of different continuous cast billets, multispectral multi-information fusion and traditional 2D defect imaging methods were adopted respectively. The results demonstrate high-precision online detection of surface defects in continuous cast billets, with favorable practical application effects. Full article

The shift to renewable energy sources is widely seen as a promising way to reduce carbon emissions and mitigate the impacts of climate change. The abundance of renewable energy resources in Africa has enormous potential to reduce greenhouse gas emissions and promote climate resilience. This study conducted a bibliometric analysis of research trends in the adoption of renewable energy systems for climate change mitigation in Africa from 1993 to the first quarter of 2025. The results showed a steady growth in publications during the 2000s, with a growing annual rate of approximately 12.7%, reaching a peak in 2024, indicating increasing research interest in Africa. The thematic analysis highlights key but underdeveloped and emerging themes, including climate change mitigation, renewable energy sources, greenhouse gas assessment, climate change, energy policy, economic growth, carbon emissions, energy consumption, rural electrification, and energy transformation for further investigation. These findings also revealed regional disparities, highlighting the need to strengthen institutional capacity, develop clear long-term policies, and develop innovative financing mechanisms to expedite the deployment of renewable energy. Additionally, results from network analysis and emerging keyword detection revealed that enhanced regional and international cooperation, grid modernization, and technological innovation, such as energy storage and digital solutions, are vital in the developmental efforts to enhance optimized resource utilization and ensure energy access and security. The study thus provides insights into existing research gaps and future research directions, which will benefit policymakers, academics, and related stakeholders in their efforts to utilize Africa’s renewable energy potential to mitigate climate change, enable sustainable development, and achieve energy security throughout the continent. Full article

Atmospheric pollutants and CO₂ share common origins in fossil fuel combustion, raising the question of whether fiscal incentives targeting air quality alone can indirectly reduce carbon emissions. This study examines this question by evaluating China’s air quality ecological compensation policy, a provincial-level horizontal fiscal transfer mechanism under which cities are rewarded or penalized according to changes in ambient air quality indicators, without incorporating any explicit carbon-related assessment criteria. Using panel data from 268 prefecture-level cities over 2007–2023 and a multi-period difference-in-differences design, we find that the policy significantly reduces the composite pollution carbon index (β = −0.213, p < 0.01), with the effect confirmed by an alternative weighted-average specification (β = −0.153, p < 0.01) and robust to propensity score matching, one-period lagged regression, exclusion of provincial-level municipalities, and exclusion of the COVID-19 period. A two-step mechanism analysis, adopted to avoid post-treatment bias from “bad controls,” reveals that the policy promotes industrial structure upgrading (β = 0.253, p < 0.01), enhances green technological innovation capacity (β = 0.047, p < 0.10), and reduces energy consumption intensity (β = −0.012, p < 0.01). Heterogeneity analysis based on quartile subsamples shows that the synergistic benefits concentrate in cities with stronger fiscal capacity (β = −0.349, p < 0.01 versus insignificant for low-support cities), higher economic development, and greater urbanization (β = −1.558, p < 0.01 for highly urbanized cities), while the policy effect is statistically insignificant in the least-advantaged subgroups across these three dimensions. In contrast, the green coverage dimension reveals an opposite pattern: the effect is strongest in cities with lower green coverage (β = −0.378, p < 0.05) and insignificant in high-coverage cities, indicating diminishing marginal returns where environmental baselines are already favorable. These findings highlight the need for differentiated compensation standards, including tiered compensation coefficients and targeted fiscal support for resource-constrained regions, to ensure equitable governance outcomes. Full article