Search Results (12,861)

The increasing emphasis on responsible research assessment has renewed the need for conceptual tools that help communicate the complementary roles of quantitative and qualitative evaluation. This essay proposes an interpretative metaphor that frames bibliometric indicators as the “blood tests” of research systems—heuristic devices that reveal multidimensional aspects of system vitality, balance, and dysfunction. The metaphor, grounded in standard categories of clinical diagnostics (hematological, hepatic, renal, lipidic, and cardiovascular panels), provides an accessible language for scholars and policymakers in research. Each bibliometric technique—ranging from publication and citation counts to patent analysis, altmetrics, and topic modelling—is associated with a diagnostic function such as screening, monitoring, or early risk detection. By linking established principles of responsible metrics (DORA, Leiden Manifesto, Metric Tide, CoARA) with the professionalization of evaluators, the essay situates the metaphor within current debates on bibliometric literacy and the ethical interpretation of indicators. Rather than prescribing metrics or decision rules, the contribution invites reflection on how evaluators can interpret bibliometric signals diagnostically—as contextual evidence for institutional learning, strategic decision-making, and the cultivation of healthy, adaptive research systems. Consistent with the essay format, this contribution does not propose a new evaluative methodology nor empirical validation. Instead, it advances a heuristic and communicative framework intended to emphasize the holistic, contextual, and professionally informed interpretation of quantitative indicators in the evaluation of research activity. Full article

This study benchmarks two nanocellulose (NC) production architectures: sulfuric-acid hydrolysis of pineapple peel biomass to obtain hydrolyzed nanocellulose (HNC) and microbial biosynthesis of bacterial nanocellulose (BNC) by Rhizobium leguminosarum biovar trifolii in defined media. HNC and BNC were characterized by SEM, FTIR, AFM, and ζ-potential, and the routes were compared using a sustainability-focused multicriteria framework. The Visual Integration of Multicriteria Evaluation (VIME) (radar chart + weighted decision matrix) yielded a higher overall score for BNC (66) than HNC (51), driven primarily by lower downstream washing/neutralization water demand (~0.3 L vs. ~14 L per batch), fewer purification stages (~2 vs. ~5), and lower waste hazard. In contrast, HNC performed better in calendar time (~7 vs. ~18 days). AFM revealed route-dependent morphologies: BNC formed a homogeneous nanofiber network (37 ± 9 nm), while HNC formed heterogeneous lamellar fragments (70 ± 12 nm). Route-specific yields were 3.15% (w/w, dry biomass basis) for HNC and 1.065 g/L (culture-volume basis) for BNC. Although a full ISO-compliant Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) are beyond the scope of this laboratory-scale study, the defined system boundaries and reported process inventories provide an LCA/TEA-ready template for future mass- and cost-balanced comparisons. Full article

Accurate prediction of postoperative complications, such as acute myocardial injury (AMI) and acute kidney injury (AKI), is essential for informed clinical decision-making and improved patient outcomes. However, conventional machine learning approaches often exhibit degraded performance in this setting due to severe class imbalance and the need for extensive feature preprocessing. To address these challenges, we propose a retrieval-based disease prediction (RBD) framework that leverages language models for postoperative risk assessment. The proposed framework converts heterogeneous preoperative and intraoperative clinical data into textual representations and retrieves relevant disease information by comparing patient-specific descriptions with predefined disease definitions of AMI and AKI. This retrieval-based formulation reduces the dependence on complex data normalization and resampling strategies commonly required by traditional models. Experimental results demonstrate that the RBD framework consistently outperforms existing machine learning methods in predicting postoperative complications under imbalanced data conditions. These findings indicate that retrieval-based language model analytics provide a promising approach for clinical decision support in postoperative care. Full article

As artificial intelligence (AI) becomes increasingly integrated into organizational processes to enhance efficiency, decision-making, and innovation, aligning AI systems with human teams remains a major challenge to realizing their full potential. Although academic interest is growing, the conceptual landscape of human–AI relationships remains fragmented. This study employs a bibliometric co-word analysis of 4093 peer-reviewed documents indexed in Scopus to map the intellectual structure of the field. Using a strategic diagram, we assess the relevance and maturity of five major thematic clusters identified in the field. Results highlight the structural dominance of Human–AI Interactions (Centrality: 1595), Human–AI Collaboration (1150), and Teaming and Augmentation (1131) as foundational themes, while Conversational AI (655), and Ethics and Responsibility (431) emerge as specialized domains. Based on the analysis, we propose a conceptual framework that classifies human–AI relationships into four categories—symbiotic, augmented, assisted, and substituted intelligence—according to the level of AI autonomy and human involvement. Rather than providing prescriptive guidance for practitioners, this framework is intended primarily as a scholarly contribution that clarifies the conceptual landscape and supports future theoretical and empirical work. While potential implications for organizational contexts can be inferred, these are secondary to the study’s main goal of offering a research-based synthesis of the field. Ultimately, our work contributes to academic consolidation by offering conceptual clarity and highlighting opportunities for future research, while underscoring the critical need for ethical alignment and interdisciplinary dialogue to guide future AI adoption. Full article

With the expansion of interconnection in power systems and the extensive adoption of phasor measurement units (PMUs), the secure operation of power systems has been increasingly covered in research. In this article, a unified online framework for pre-fault and post-fault dynamic security assessment (DSA) is proposed. First, maximum mutual information (MIC) and the random subspace method (RSM) are employed to select the key variables and enhance the diversity of input data, serving as feature engineering. Then, a deep forest (DF) regressor and classifier are utilized respectively to predict security margin (SM) and security state (SS) during online pre-fault and post-fault DSA based on the selected variables. In pre-fault DSA, scenarios with high SM are identified as stable, while those with low SM are forwarded to post-fault DSA. In addition, a time self-adaptive scheme is employed to balance low response time and high prediction accuracy. This approach prevents the misclassification of unstable scenarios as stable by either outputting high-credibility predictions of unstable SS or deferring decisions on SS until the end of the decision-making period. The unified framework, tested on an IEEE 39-bus system and a practical 1648-bus system provided by the PSS/E version 35 software, demonstrates significantly improved assessment accuracy and response times. Specifically, it achieves an average response time (ART) of 2.66 cycles for the IEEE 39-bus system and 3.13 cycles for the 1648-bus system while maintaining an accuracy exceeding 98%, surpassing the performance of currently widely used deep learning models. Full article

Conservative management of rotator cuff disorders remains challenging, with no comprehensive, evidence-based framework integrating diagnosis, prognosis, rehabilitation, and biological therapies. Existing recommendations usually address isolated components of care, leading to inconsistent treatment strategies. This article proposes a global, pragmatic protocol for the non-surgical management of rotator cuff lesions, from initial assessment to long-term follow-up. Drawing on clinical expertise supported by recent literature, we outline a stepwise approach that begins with a comprehensive diagnostic process that combines history, clinical examination, and targeted imaging. Based on lesion type, associated shoulder or neurogenic conditions, and patient profile, rotator cuff disorders are stratified into three prognostic categories under conservative care: good, borderline, and poor prognosis, highlighting factors that require treatment adaptation or early surgical consideration. Rehabilitation objectives are structured around four domains: (1) inflammation and pain control, (2) mobility and scapular kinematics, (3) strengthening and motor control with tendon-sparing strategies, and (4) preservation or restoration of anatomy. For each prognostic category, we define a monitoring plan integrating clinical reassessment, ultrasound follow-up, and functional milestones, including return-to-play criteria for athletes. This comprehensive narrative review demonstrates that precise diagnosis and individualized rehabilitation can optimize medical follow-up, active strengthening, and complementary or regenerative therapies. Aligning therapeutic decisions with prognostic and functional goals allows clinicians to optimize patient satisfaction and recovery, providing a clear, evidence-informed roadmap for conservative management of rotator cuff disorders. Full article

Mango is a high-value agricultural commodity, and accurate and efficient appearance quality grading and defect inspection are critical for export-oriented markets. This study proposes a dual-pipeline deep learning framework for automated mango assessment, in which surface defect classification and quality grading are jointly implemented within a unified inspection system. For defect assessment, the task is formulated as a multi-label classification problem involving five surface defect categories, eliminating the need for costly bounding box annotations required by conventional object detection models. To address the severe class imbalance commonly encountered in agricultural datasets, a copy–paste-based image synthesis strategy is employed to augment scarce defect samples. For quality grading, mangoes are categorized into three quality levels. Unlike conventional CNN-based approaches relying solely on spatial-domain information, the proposed framework integrates decision-level fusion of spatial-domain and frequency-domain representations to enhance grading stability. In addition, image preprocessing is investigated, showing that adaptive contrast enhancement effectively emphasizes surface textures critical for quality discrimination. Experimental evaluations demonstrate that the proposed framework achieves superior performance in both defect classification and quality grading compared with existing detection-based approaches. The proposed classification-oriented system provides an efficient and practical integrated solution for automated mango assessment. Full article

Reservoir bank collapse is a critical geological hazard during the operation of large-scale water conservancy projects, controlled by unique hydrodynamic mechanisms induced by reservoir impoundment, and differs significantly from ordinary landslides. Traditional risk assessment methods, however, often struggle to achieve effective integration between macro-regional zoning and micro-mechanical analysis. Against this limitation, this study proposes a GIS-integrated multi-scale risk screening framework to achieve the preliminary integration of qualitative regional evaluation and quantitative site-specific analysis. Compared with traditional multi-scale studies, the innovations of this research are as follows: (1) a customized GIS component was developed to realize semi-automatic profile extraction from high-resolution DEMs and batch Bishop stability calculations, overcoming the bottleneck of spatializing micro-models over large areas; (2) a “bottom-up” dynamic feedback mechanism was established, utilizing the quantitative safety factor from site-specific evaluations as an explicit indicator for the conservative screening correction of the macro-regional risk map. Applied to the Xiluodu Reservoir, this framework illustrates a potential multi-scale approach for cross-scale risk screening driven by physical–mechanical mechanisms. This provides both a global perspective and a localized physical basis, offering a strategic screening tool for reservoir management. By linking failure mechanisms directly to spatial impacts, the framework provides a plausible conservative feedback rule for risk-informed decision-making in complex reservoir settings. Full article

This study develops an integrated methodological framework coupling CMIP6 climate projections with a socio-economic-hydrological System Dynamics (SD) model to evaluate adaptation strategies for agricultural resilience. Applied to the Qazvin Plain aquifer in Iran, the model demonstrates high fidelity in capturing hydrological–human interactions, evidenced by a 97% correlation between simulated and observed groundwater levels. The system was developed using long-term meteorological drivers (1993–2024) and calibrated against observed socio-hydrological data for the period 2006–2024 and projected to 2062 under multiple CMIP6 scenarios, identifying SSP245 and SSP126 as the most accurate predictors for regional precipitation and temperature, respectively. Modeling outcomes indicate that aridity will intensify across all scenarios; specifically, under current water-use patterns, groundwater storage is projected to decline by 24.5%, 25.4%, and 27.6% by 2041 under SSP126, SSP245, and SSP585, respectively. However, the simulation reveals that integrating demand-side management with crop pattern optimization can stabilize the aquifer and boost agricultural value added by 7.4%. The findings further highlight that a 48% reduction in current groundwater withdrawals is essential to reach a sustainable threshold of 781 million m³. These quantitative insights suggest that while climatic pressures are increasing, human-driven management remains the decisive factor, provided that economic tools and smart monitoring are prioritized for long-term sustainability. Full article

Accurate and energy-efficient localization of autonomous underwater vehicles (AUVs) remains a fundamental challenge due to the complex, bandwidth-limited, and highly dynamic nature of underwater acoustic environments. This paper proposes a fully adaptive deep reinforcement learning (DRL)-driven localization framework for AUVs operating in Underwater Acoustic Sensor Networks (UAWSNs). The localization problem is formulated as a Markov Decision Process (MDP) in which an intelligent agent jointly optimizes beacon selection and transmit power allocation to minimize long-term localization error and energy consumption. A hierarchical learning architecture is developed by integrating four actor–critic algorithms, which are (i) Twin Delayed Deep Deterministic Policy Gradient (TD3), (ii) Soft Actor–Critic (SAC), (iii) Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and (iv) Distributed DDPG (D2DPG), enabling robust learning under non-stationary channels, cooperative multi-AUV scenarios, and large-scale deployments. A round-trip time (RTT)-based geometric localization model incorporating a depth-dependent sound speed gradient is employed to accurately capture realistic underwater acoustic propagation effects. A multi-objective reward function jointly balances localization accuracy, energy efficiency, and ranging reliability through a risk-aware metric. Furthermore, the Cramér–Rao Lower Bound (CRLB) is derived to characterize the theoretical performance limits, and a comprehensive complexity analysis is performed to demonstrate the scalability of the proposed framework. Extensive Monte Carlo simulations show that the proposed DRL-based methods achieve significantly lower localization error, lower energy consumption, faster convergence, and higher overall system utility than classical TD3. These results confirm the effectiveness and robustness of DRL for next-generation adaptive underwater localization systems. Full article

The development of Geospatial Artificial Intelligence (GeoAI), combining deep learning and remote sensing imagery, is of great interest for automated spatial inference and decision-making support. In this paper, a GeoAI-based efficient object detection framework named RS-YOLO is introduced by adopting the YOLOv11 architecture. The model integrates Dynamic Convolution for adaptive receptive field adjustment, Selective Kernel Attention for multi-path feature aggregation, and the MPDIoU loss function for geometry-aware localization. The proposed approach outperforms in experimental results on the TGRS-HRRSD dataset of 13 scenes from different geospatial scenarios, giving an 89.0% mAP and an 87 F1-score. Beyond algorithmic advancement, RS-YOLO provides a GeoAI-based analytical tool for applications such as urban infrastructure monitoring, land use management, and transportation facility recognition, enabling spatially informed and sustainable decision-making in complex remote sensing environments. Full article

The growing miniaturization of electronic systems and the expansion of sustainable, autonomous IoT technologies emphasize the need for efficient, ultra-low-power energy harvesting devices. This study evaluates fifteen devices from five industry leaders for use in small-scale autonomous seed germination systems. Its novelty lies in applying a competitive profile matrix within a flexible multicriteria evaluation framework based on the simple additive weighting (SAW) method that uses a comprehensive set of competitive technology factors (CTFs). The results demonstrate that a transparent and structured methodology can generate prioritized lists of suitable energy harvesters while accounting for technical, economic, and environmental trade-offs. The study also shows that device rankings depend on the scope and objectives of the project. If these change, then the CTF selection, classification, and weighting adjust accordingly. Therefore, the relevance of this study lies in the adaptability, replicability, and audibility of the proposed framework, which supports the selection of informed technology for autonomous, IoT-based germination systems and other technological projects. Full article

The Social Internet of Things (SIoT) enables collaborative service provisioning among interconnected devices by leveraging socially inspired trust relationships. This paper proposes a socially driven SIoT protocol for trust-aware service selection, enabling dynamic friendship formation and ranking among distributed service-providing devices based on observed execution behavior. The protocol integrates detection accuracy, round-trip time (RTT), processing time, and device characteristics within a graph-based friendship model and employs PageRank-based scoring to guide service selection. Industrial computer vision workloads are used as a representative testbed to evaluate the proposed SIoT trust-evaluation framework under realistic execution and network constraints. In homogeneous environments with comparable service-provider capabilities, friendship scores consistently favor higher-accuracy detection pipelines, with F1-scores in the range of approximately 0.25–0.28, while latency and processing-time variations remain limited. In heterogeneous environments comprising resource-diverse devices, trust differentiation reflects the combined influence of algorithm accuracy and execution feasibility, resulting in clear service-provider ranking under high-resolution and high-frame-rate workloads. Experimental results further show that reducing available network bandwidth from 100 Mbps to 10 Mbps increases round-trip communication latency by approximately one order of magnitude, while detection accuracy remains largely invariant. The evaluation is conducted on a physical SIoT testbed with three interconnected devices, forming an 11-node, 22-edge logical trust graph, and on synthetic trust graphs with up to 50 service-providing nodes. Across all settings, service-selection decisions remain stable, and PageRank-based friendship scoring is completed in approximately 20 ms, incurring negligible overhead relative to inference and communication latency. Full article

Wave Energy Converter (WEC) buoys operate in aggressive marine environments that impose demanding requirements on structural materials, particularly in terms of moisture resistance, mechanical reliability, and long-term durability. Conventional glass fiber reinforced composites meet these performance requirements but raise sustainability concerns due to their high environmental footprint and limited recyclability. This study addresses this challenge by introducing a systematic, application-driven multi-criteria decision-making (MCDM) framework specifically tailored for material selection in marine renewable energy devices. The novelty of this work lies in the integration of marine durability-dominated criteria weighting with sustainability metrics, moving beyond cost-driven selection approaches commonly reported in the literature. Four cellulosic natural fibers, flax, hemp, kenaf, and sisal, are evaluated as reinforcements for polymer composites intended for point-absorber WEC buoy structures, using conventional E-glass as a baseline reference. Ten performance criteria covering mechanical properties, environmental durability, manufacturing feasibility, and sustainability are defined and objectively weighted using the entropy method to minimize subjective bias. Moisture resistance emerges as the most influential criterion with a weight of 0.142, underscoring its role as a primary degradation mechanism in marine environments, while material cost receives the lowest weight of 0.057, reflecting the prioritization of long-term performance over initial cost. The results identify flax as optimal reinforcement, achieving the highest aggregated score of 4.022 by effectively balancing mechanical performance, resistance to marine exposure, and environmental sustainability. This work introduces a novel decision-support tool for the sustainable design of buoy structures using natural fiber-reinforced composites and establishes a foundation for future optimization of such composites in wave energy applications. Full article

Humans have an inherent connection to nature, and exposure to natural elements has been shown to reduce stress, improve mood, and support cognitive performance, forming the basis of biophilic design in the built environment. However, existing biophilic design guidance remains largely conceptual and offers limited evidence-based direction on how design properties should be applied. This scoping review addresses this gap by systematically mapping and synthesizing empirical evidence on indoor biophilic design interventions and their properties. Following PRISMA-ScR guidelines, 136 studies published between 2000 and 2025 were reviewed across seven intervention types, including green walls, indoor plants, window views, natural light, natural materials, water features, and nature-inspired visual references. Cross-category analyses identified design properties most consistently associated with restorative outcomes and human cognitive and physiological responses. The findings highlight the importance of moderate greenery levels, high-visibility placement, multi-sensory integration, and the enhanced restorative effects of combining multiple interventions. Contextual factors such as exposure duration and user characteristics were found to influence effectiveness. Based on these findings, the study introduces the Biophilic Intensity Matrix (BIMx), a matrix-based decision-support framework that supports early-stage design by helping designers select biophilic intervention types and compare their relative scale and intensity ranges according to exposure duration. Full article