Search Results (457)

Urban floods have become a major systemic risk to sustainable urban development under climate change and increasingly frequent extreme hydro-meteorological events. Yet evidence on the long-term evolution of urban flood resilience (UFR) and its structural constraints at the provincial scale remains limited. This study develops a PSR-based framework to assess UFR and diagnose its dominant obstacles using data for 21 prefecture-level cities in Sichuan Province from 2011 to 2023, including meteorological, geomorphological, socioeconomic, infrastructure, environmental, and public service indicators. A combined AHP–EWM is used to integrate subjective and objective information, TOPSIS is applied to derive a composite UFR index and subsystem scores, and an obstacle degree model is employed to identify key constraints and their temporal evolution. Results show that: (1) UFR in Sichuan Province fluctuated but increased overall during 2011–2023, reaching its highest level in 2023; (2) resilience improvement was driven mainly by the response subsystem, while the pressure subsystem showed the greatest interannual variability; and (3) the annual top five obstacles were highly persistent and insufficient response capacity was the dominant long-term constraint on resilience enhancement. These findings underscore that improving the adequacy, institutional robustness, and operational stability of response systems is central to enhancing UFR. This study provides empirical support for the assessment of provincial-scale resilience and policy-oriented flood risk governance. Full article

The construction sector is one of the primary areas that underpin a country’s economic development. However, this sector is characterized by various types of obstacles, including the participation of numerous stakeholders, strict schedules, limited resources, and the management of vast amounts of data throughout the project lifecycle. Building Information Modeling (BIM) has emerged as a promising technology for centralizing and managing construction data throughout the project lifecycle. However, having the ability to extract real-time, decision-oriented insights from BIM models remains a challenge for project stakeholders. To address this limitation, this research paper explores the integration of BIM with Business Intelligence (BI) to enhance control and management of construction projects throughout the development of a customized Power BI dashboard. The proposed framework of the paper utilizes BIM’s data-rich environment and Power BI’s advanced analytical and visualization capabilities to deliver real-time and interactive insights about project performance and progress. The customized dashboard enables stakeholders, especially project managers, to monitor key performance indicators of the project that are related to cost and schedule. It also supports progress tracking, early identification of inefficiencies, and data-driven decision-making. To demonstrate the practical application of the proposed framework, a case study was conducted. The results indicate that integrating BIM with BI helps in enhancing project control, improving transparency, and facilitating collaboration between stakeholders through a centralized cloud platform that can be easily accessed through desktop and mobile devices. Full article

A multi-dimensional quantitative assessment of high-quality development (HQD) in China’s livestock industry and the identification of its main constraints are essential to understanding its current stage and future direction. Guided by global sustainability targets and the United Nations’ Sustainable Development Goals (SDGs), an evaluation system was constructed by this study. This system integrates five key aspects: product safety, output efficiency, resource conservation, environmental friendliness, and regulatory effectiveness. Using provincial panel data from China for 2013–2022, this research applies the entropy-weighted TOPSIS method, kernel density estimation (KDE), and an obstacle degree model for analysis, the goal is to support food security and foster environmentally sustainable growth. The findings indicate the following: (1) Notable inter-provincial disparities exist in the HQD of China’s livestock industry, revealing a spatial pattern of “leading in the east, stable in the center, and lagging in the west.” (2) The nationwide evolution exhibits a “convergence followed by divergence” pattern: from 2013 to 2017, the primary peak of the KDE rose and its width narrowed; from 2018 to 2022, the primary peak declined and its width widened, indicating that inter-provincial disparities first narrowed and then expanded. At the regional level, the development pattern is characterized by eastern polarization, central stability, and western lock-in. (3) Obstacle factor analysis identifies product safety and environmental friendliness as the principal constraints on HQD in the livestock industry. Addressing these bottlenecks is crucial for ensuring the supply of livestock products (SDG 2: Zero Hunger), promoting resource conservation and green production (SDG 12: Responsible Consumption and Production), and alleviating the ecological and environmental pressures of the livestock industry (SDG 15: Protection of Terrestrial Ecosystems). The challenges related to resources, the environment, and quality safety confronting China’s livestock industry are common among developing countries. Consequently, the evaluation framework established in this study can offer methodological references for relevant nations. Full article

Background: B-cell malignancies have been effectively treated using chimeric antigen receptor-T (CAR-T) treatment employing traditional αβT cells. However, because of several obstacles, application in acute myeloid leukemia (AML) is still restricted. A safer “off-the-shelf” alternative can be supplied by CAR-γδT cells, which have major histocompatibility complex (MHC)-independent tumor identification capabilities and a decreased risk of graft versus host disease (GvHD). This study aimed to develop FLT3-targeted CAR-γδT cells that co-express cytokines (IL-2 or IL-7) to increase their anti-AML persistence and therapeutic efficacy. Methods: FLT3-CAR-γδT cells, FLT3-IL2-CAR-γδT cells, and FLT3-IL7-CAR-γδT cells were constructed. Their antitumor potency was comprehensively assessed through cytotoxicity assays, cytokine release, and persistence evaluation in vitro (using AML cell lines and primary AML cells) and in vivo (via mouse model). Results: Superior cytotoxicity against AML cell lines (OCI-AML3, MOLM-13, THP-1, and MV4-11) was demonstrated by FLT3-IL2-CAR-γδT cells, which also released higher levels of granzyme B, interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α). FLT3-IL2-CAR-γδT cells exhibited cytotoxicity in some primary AML cells in vitro. During the antigen-repeated stimulation assay, FLT3-IL2-CAR-γδT cells preserved the stem cell-like memory T (T_SCM) cell subsets, sustained cytokine release, and maintained excellent viability. FLT3-IL2-CAR-γδT cells considerably slowed the development of AML in vivo and extended the existence (>68 days) of mice. Conclusions: FLT3-IL2-CAR-γδT cells exhibit potent and durable anti-AML activity, providing a novel strategy for clinical AML immunotherapy. Full article

Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to link microstructural features with engineering-scale mechanical behaviour. Their practical use, however, is limited by the high computational cost of physics-based simulations and the labour-intensive nature of parameter calibration, challenges that are amplified in additively manufactured materials with location-dependent properties. To address these obstacles, we first developed deep neural network (DNN) surrogate models of physics simulations to predict the stress–strain response of an additively manufactured AlSi10Mg alloy. Twenty-five experimentally derived scenarios (five microstructures × five sets of grain orientations) were used for training 25 separate DNNs, with datasets for validated material behaviour generated using the Düsseldorf Advanced Material Simulation Kit (DAMASK) platform and a Fast Fourier Transform (FFT)-based solver. Once trained, the DNNs produced stress–strain curves almost instantaneously, enabling an exhaustive grid-search exploration of a vast parameter space. Our approach yielded significant efficiency gains, which were comprehensively quantified. The best-fit CP parameters obtained through this approach are expected to be more accurate than those derived from conventional trial-and-error calibration, which is restricted to a limited number of candidate values. In addition, the minimum number of CP-FFT simulations required to train the DNNs with sufficient accuracy was identified, reducing the need for costly physics simulations in future studies. The proposed framework enhances the practical utility of CP models for simulation-informed materials engineering and optimisation and is broadly applicable to parameter identification in phenomenological models of other domains. Full article

Addressing meso-scale sensing voids and resource misallocations, this study constructs an integrated “Performance Sensing–Bottleneck Diagnosis–Configuration Identification” framework to evaluate the spatiotemporal evolution of resilience across Xi’an’s districts (2018–2023). This research operationalizes a diagnostic-driven analytical pipeline coupling multi-source parameters with the CRITIC method to complement static stock accounting with dynamic performance sensing. This logic integrates Dagum Gini decomposition to pinpoint spatiotemporal bottlenecks and fuzzy-set QCA (fsQCA) to uncover driving pathways, utilizing an “Obstacle–Correlation” matrix to provide an objective basis for antecedent selection. The results show the following: (1) A “V-shaped” spatiotemporal trajectory and 2020 “resilience inversion” (dipping to 0.364) highlight the sensitivity of dynamic performance sensing in exposing latent vulnerabilities. (2) Persistent “center-periphery” gradients exist, with administrative siphoning driving 66.7% of inequality; diagnosis identifies distinct spatiotemporal pathologies: rigid spatial constraints in urban cores versus service imbalances in expansion zones. (3) Three equifinal pathways and an “asymmetric cancellation” effect prove that resilience hinges on configurational fit rather than linear stacking, where extreme single-dimension shortfalls neutralize collective gains. By bridging situational pathologies and governance pathways, this framework provides a robust empirical basis for the refined allocation of resources in complex environments. Full article

Background: Suicidality continues to rise, while mental health services face obstacles of access, availability, and affordability. Digital peer support (DPS) may help bridge these gaps and facilitate early identification of suicidal ideation (SI). Objective: This study examined (1) the effectiveness of a hybrid solution combining a proprietary AI-based SI detection with real-time human moderation within DPS, (2) distribution of SI, (3) active SI referral, (4) linguistic differences in SI, (5) sentiment changes among users, and (6) the effects of peer SI disclosure. Methods: We retrospectively analyzed 169,181 live-chat transcripts encompassing 449,946 user visits (January–December 2024) from a DPS provider, Supportiv. Passive and active SI were identified using a hybrid AI and human moderator solution with post hoc LLM verification. Sentiment analysis and ANCOVA compared changes in sentiment across three propensity-matched user groups: passive SI users, non-SI users exposed to peer SI, and non-SI users not exposed to SI. Results: SI occurred in 3.19% of live chats. The AI model identified SI faster than humans (in 77.52% passive and 81.26% active cases), with 90.3% agreement. Moderators followed up 71.3 s after AI alerts and referred 5472 active SI users (1.21%) to crisis care. All users significantly benefited from DPS, with reductions up to 29.3% in depression, 26.8% in loneliness, 25.3% in despair, and 22.3% in helplessness, with optimism increasing up to 40.4%. Conclusions: AI-integrated, human-moderated DPS offers scalable and effective support for high-risk populations. The proprietary SI detection AI model accurately detects suicidality, allowing for human-moderated DPS to improve the mental well-being of users with and without SI, and maintains peer safety. Full article

In the information society, open data is an important resource for creating economic value. This study conducts a systematic review, following the PRISMA methodology, of articles published between 2000 and 2025 in Scopus and Web of Science that include the terms Open Data and Spain (in Spanish or English) in their title and/or abstract, with the aim of assessing how Law 37/2007 on the reuse of public sector information has influenced the publications analyzed. After identifying 240 articles in Scopus and 109 in Web of Science and applying the exclusion criteria, we observe that 37 studies use the Diamond Open-Access publishing route. The results are organized into four categories corresponding to the research questions, which represent a meaningful theoretical contribution and enhance current knowledge on open data research in Spain. The identification of obstacles to the effective use of open data—such as the lack of standardization, poor information quality, and the vague definition of reuse conditions—entails practical implications of significant value for managers of open data portals seeking to improve their initiatives. Full article

Targeting the Warburg effect (aerobic glycolysis) in tumor cells represents a promising metabolic therapeutic strategy in cancer research. This review analyzes the regulatory mechanisms and therapeutic potential of key glycolysis pathway components, including glucose transporters (GLUTs) and glycolytic enzymes such as hexokinase 2 (HK2), phosphofructokinase (PFK), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), pyruvate kinase M2 (PKM2), and lactate dehydrogenase A (LDHA). We evaluate the molecular mechanisms of various inhibitors and the current clinical development landscape, noting that limitations of monotherapy stem not only from tumor metabolic plasticity but also largely from the unacceptable toxicity of many inhibitors due to the essential role of glycolysis in normal cell metabolism. Furthermore, we explore the molecular basis of synergistic interactions between glycolysis inhibitors and chemotherapy, radiotherapy, immunotherapy, photothermal therapy, and targeted therapy, proposing that rational combination strategies may help overcome resistance and improve therapeutic efficacy. Finally, the review outlines future challenges and directions, emphasizing that the primary obstacle in metabolic treatments is achieving selective inhibition of glycolytic enzymes in cancer cells while sparing normal cells. To address this challenge, the development of high-selectivity agents, cancer-specific nanodelivery systems, precise biomarker identification, and innovative combination regimens based on metabolic-immune regulation is crucial for advancing glycolysis-targeted therapy toward clinical translation. Full article

With the increasing demand for perception in complex road environments in intelligent driving, rapid and accurate identification of paved-road obstacles has become a critical prerequisite for driving safety and comfort. Various types of road obstacles can significantly affect vehicle stability and ride quality. To address this challenge, a lightweight and efficient vision-based obstacle detection framework, termed GroupScale-YOLO, is proposed, in which detection accuracy and computational efficiency are jointly enhanced through the collaborative design of multiple novel modules. First, a dedicated dataset targeting common paved-road obstacles is constructed, and six data augmentation strategies are employed to mitigate the adverse effects of road surface undulations and illumination variations on visual perception. Second, to overcome the limitations of YOLOv11n in paved-road obstacle detection tasks, targeted optimizations are introduced to the backbone network, convolutional blocks, and detection head. Experimental results indicate that GroupScale-YOLO achieves a 29.95% reduction in model parameters while simultaneously increasing mAP@0.5 by 0.6% on the self-built dataset, demonstrating its suitability for deployment in resource-constrained scenarios. Furthermore, real-vehicle road tests confirm that the proposed method maintains stable and accurate obstacle detection performance under practical driving conditions, offering a reliable solution for intelligent vehicle environmental perception. Full article

Background: Liquid biopsy, particularly through the analysis of circulating tumor DNA (ctDNA), represents a significant advancement in oncology. Unlike traditional tissue biopsies, ctDNA offers a minimally invasive, real-time approach to cancer management. It has demonstrated considerable potential in early cancer detection, monitoring of therapeutic responses, and assessing minimal residual disease (MRD) to predict recurrence. By enabling comprehensive molecular profiling through a simple blood test, ctDNA supports the core principles of precision oncology, facilitating more personalized and adaptive treatment strategies. Methods: In the following article we describe the recent developments focused on refining ctDNA detection assays to improve sensitivity and specificity. Advanced technologies, including next-generation sequencing (NGS) and digital PCR, are commonly employed. The integration of artificial intelligence (AI) and multi-omics approaches—such as combining genomic, epigenomic, and transcriptomic data—has further enhanced the analytical power of ctDNA assays. Results: Emerging evidence shows that ctDNA-based liquid biopsy enables dynamic, real-time tracking of tumor evolution and therapeutic resistance. Clinical studies have demonstrated its efficacy in detecting early-stage cancers, guiding treatment selection, and predicting relapse with higher accuracy than some conventional methods. Moreover, AI-enhanced algorithms have improved signal detection, allowing for more precise and earlier identification of actionable mutations and MRD. Conclusions: ctDNA analysis via liquid biopsy is poised to revolutionize cancer care by offering a non-invasive, precise, and adaptive tool for tumor characterization and monitoring. Although obstacles remain—particularly regarding assay sensitivity, standardization, and economic feasibility—ongoing technological innovations and multi-omics integration are rapidly advancing its clinical viability. With continued progress, ctDNA-based liquid biopsy is likely to become a cornerstone of routine oncology practice. Full article

Background: Traditional machine-learning approaches face challenges when attempting to integrate diverse biological information for predicting metabolite–disease relationships. The intricate connections linking metabolites, diseases, proteins, and Gene Ontology (GO) annotations present substantial obstacles for conventional pairwise graph representations, which prove inadequate for modeling such complex multi-way interactions. Methods: An innovative hypergraph-based framework (DHG-LGB) was developed to exploit this complexity through conceptualizing diseases as hyperedges. Within this architecture, individual hyperedges link multiple vertices including metabolites, proteins, and GO annotations, thereby enabling richer representation of the biological networks underlying metabolite–disease relationships. Metabolite–disease relationships were encoded as low-dimensional vectors through hypergraph neural network (HGNN) operations incorporating Laplacian smoothing and message propagation mechanisms. LightGBM (LGB) was used to construct a model for identifying the potential metabolite–disease associations. Results: Under 5-fold cross-validation, DHG-LGB achieved 98.87% accuracy, 91.77% sensitivity, 99.58% specificity, 95.60% precision, Matthews correlation coefficient (MCC) of 0.9305, receiver operating characteristic area under curve (AUC) of 0.9983, and precision-recall area under curve (AUPRC) of 0.9860. The framework maintained strong performance when tested with varying positive-to-negative ratios (spanning 1:1 through 1:10), consistently achieving AUC values exceeding 0.9954 and AUPRC values above 0.9820, thereby confirming excellent robustness and generalization capability. Comparative evaluations against existing methodologies verified the superiority of DHG-LGB. Conclusions: The DHG-LGB framework delivers more comprehensive modeling of biological interactions relative to conventional approaches and substantially enhances predictive accuracy for metabolite–disease relationships. It is foreseeable that it will be a valuable computational tool for biomarker identification and precision medicine initiatives. Full article

Dense foliage, severe illumination variations, and interference from multiple individuals with similar appearances in complex orchard environments pose significant challenges for vision-based following robots in maintaining persistent target perception and identity consistency, thereby compromising the stability and safety of fruit transportation operations. To address these challenges, we propose a novel framework, DeepDIMP-ReID, which integrates the Deep Implicit Model Prediction (DIMP) tracker with a person re-identification (ReID) module based on EfficientNet. This visual perception and autonomous following framework is designed for differential-drive orchard transportation robots, aiming to achieve robust target perception and reliable identity maintenance in unstructured orchard settings. The proposed framework adopts a hierarchical perception–verification–control architecture. Visual tracking and three-dimensional localization are jointly achieved using synchronized color and depth data acquired from a RealSense camera, where target regions are obtained via the discriminative model prediction (DIMP) method and refined through an elliptical-mask-based depth matching strategy. Front obstacle detection is performed using DBSCAN-based point cloud clustering techniques. To suppress erroneous following caused by occlusion, target switching, or target reappearance after occlusion, an enhanced HOReID person re-identification module with an EfficientNet backbone is integrated for identity verification at critical decision points. Based on the verified perception results, a state-driven motion control strategy is employed to ensure safe and continuous autonomous following. Extensive long-term experiments conducted in real orchard environments demonstrate that the proposed system achieves a correct tracking rate exceeding 94% under varying human walking speeds, with an average localization error of 0.071 m. In scenarios triggering re-identification, a target discrimination success rate of 93.3% is obtained. These results confirm the effectiveness and robustness of the proposed framework for autonomous fruit transportation in complex orchard environments. Full article

Non-line-of-sight (NLOS) propagation remains a major obstacle to high-accuracy ultra-wideband (UWB) indoor positioning. To address this issue, this study investigates solutions from two complementary perspectives: NLOS identification and error mitigation. First, an NLOS signal classification model is proposed based on multidimensional statistics of the channel impulse response (CIR). The model incorporates an attention mechanism and an improved snake optimization (ISO) algorithm, achieving significantly enhanced classification accuracy and robustness. For error mitigation, a UKF–BiLSTM dual-directional mutual calibration framework is proposed to dynamically compensate for NLOS errors. The framework embeds the constant turn rate and velocity (CTRV) motion model within an unscented Kalman filter (UKF) to enhance trajectory modeling. It establishes a bidirectional correction loop with a bidirectional long short-term memory (BiLSTM) network. Through the synergy of physical constraints and data-driven learning, the framework adaptively suppresses NLOS errors. Experimental results show that the proposed framework achieves state-of-the-art–comparable performance with improved model efficiency in complex indoor UWB positioning scenarios. Full article

Background: The precise segmentation and classification of dermoscopic images remain prominent obstacles in automated skin cancer evaluation due, in part, to variability in lesions, low-contrast borders, and additional artifacts in the background. There have been recent developments in foundation models, with a particular emphasis on the Segment Anything Model (SAM)—these models exhibit strong generalization potential but require domain-specific adaptation to function effectively in medical imaging. The advent of new architectures, particularly Vision Transformers (ViTs), expands the means of implementing robust lesion identification; however, their strengths are limited without spatial priors. Methods: The proposed study lays out an integrated foundation-model-based framework that utilizes SAM-Adapter-fine-tuning for lesion segmentation and a ViT-based classifier that incorporates lesion-specific cropping derived from segmentation and cross-attention fusion. The SAM encoder is kept frozen while lightweight adapters are fine-tuned only, to introduce skin surface-specific capacity. Segmentation priors are incorporated during the classification stage through fusion with patch-embeddings from the images, creating lesion-centric reasoning. The entire pipeline is trained using a joint multi-task approach using data from the ISIC 2018, HAM10000, and PH2 datasets. Results: From extensive experimentation, the proposed method outperforms the state-of-the-art segmentation and classification across the dataset. On the ISIC 2018 dataset, it achieves a Dice score of 94.27% for segmentation and an accuracy of 95.88% for classification performance. On PH2, a Dice score of 95.62% is achieved, and for HAM10000, an accuracy of 96.37% is achieved. Several ablation analyses confirm that both the SAM-Adapters and lesion-specific cropping and cross-attention fusion contribute substantially to performance. Paired t-tests are used to confirm statistical significance for all the previously stated measures where improvements over strong baselines indicate a $p<0.01$ for most comparisons and with large effect sizes. Conclusions: The results indicate that the combination of prior segmentation from foundation models, plus transformer-based classification, consistently and reliably improves the quality of lesion boundaries and diagnosis accuracy. Thus, the proposed SAM-ViT framework demonstrates a robust, generalizable, and lesion-centric automated dermoscopic analysis, and represents a promising initial step towards clinically deployable skin cancer decision-support system. Next steps will include model compression, improved pseudo-mask refinement and evaluation on real-world multi-center clinical cohorts. Full article