Search Results (2,285)

Deep learning-powered Street View Imagery (SVI) analytics provides a critical mechanism for smart city perception within the framework of Sustainable Development Goal 11 (SDG 11), effectively bridging the gap left by traditional remote sensing in fine-grained street-level observation. Over the years, deep learning-based semantic segmentation of urban streetscapes has become the dominant paradigm. However, when scaling to megacity measurements, current research faces the dual bottlenecks of “computational redundancy” and the “geographical domain shift” caused by the blind application of pre-trained models based on Western datasets. To address these challenges, this study is the first to systematically quantify the performance trade-off between Multi-Task Learning (MTL) and Single-Task Learning (STL) in megacity scenarios. Using this as a baseline, we constructed and validated a “low-computation, high-robustness” framework for streetscape semantic perception and spatial measurement. Relying on an integrated ResNeXt101-FPN MTL architecture and an ultra-low-cost fine-tuning strategy to overcome geographical domain shift, we extracted and analyzed the spatial heterogeneity of five core semantic elements—vegetation, sky, building, road, and vehicle—across the road network within Beijing’s 5th Ring Road. The results indicate the following: (1) We explicitly defined the computation-accuracy trade-off of MTL and STL in megacity perception. While utilizing only 1/5 of the parameters of STL, the MTL framework achieved a 5.34-fold increase in inference speed with a negligible 0.1% loss in overall mean Intersection over Union (mIoU); however, a 27.13% decrease in boundary segmentation accuracy was observed. (2) We established a low-cost, localized correction paradigm to overcome domain shift. Utilizing a minimal annotation cost (only 200 local images) significantly improved cross-domain adaptability, boosting the overall mIoU by 8.92% and significantly mitigating the geographical domain shift problem. (3) Multi-dimensional measurement and spatial analysis revealed a significant spatial decoupling pattern in Beijing’s streetscapes. The visual proportion of vegetation exhibited a pronounced “north-high, south-low” spatial differentiation, whereas built environment elements (e.g., building and road) displayed a typical “center-periphery” concentric gradient. This objectively reflects the spatial inequality of urban street greenery resources and the monocentric development characteristics of the built environment. The proposed framework therefore serves as a low-cost, AI-driven computational paradigm for smart city perception in resource-constrained regions. Furthermore, the revealed spatial heterogeneity offers data-driven insights for formulating sustainable urban renewal policies aligned with SDG 11. Full article

Background/Objectives: Frailty is a validated predictor of waitlist mortality and perioperative risk in liver transplant candidates, but its association with early post-transplant outcomes in large real-world cohorts remains incompletely characterized. This study evaluated the association between administratively defined pre-transplant frailty and early clinical outcomes following liver transplantation. Methods: We conducted a retrospective cohort study using the TriNetX US Collaborative Research Network. Adults undergoing first-time isolated liver transplantation through February 2026 were included. Frailty was identified using ICD-10-CM codes for frailty, sarcopenia, cachexia, weakness, abnormal gait/mobility, or reduced mobility documented within 12 months before transplantation; patients coded only for nonspecific weakness were excluded from the frailty cohort. Patients underwent 1:1 propensity score matching using 18 baseline covariates, including demographics, comorbidities, laboratory values, albumin, and MELD-Na. The primary outcome was all-cause mortality at 7, 30, and 90 days. Secondary outcomes included acute kidney injury, prolonged mechanical ventilation, vasopressor requirement/hemodynamic instability, renal replacement therapy, ICU and hospital length of stay, and 90-day readmission. Sensitivity analyses used a restrictive ≥ 2-code frailty definition and substituted MELD 3.0 for MELD-Na in the propensity model. Results: Among 4860 eligible recipients, 742 had administratively defined frailty and 4118 did not. After matching, 730 patients remained in each group with well-balanced covariates. Administratively defined frailty was associated with higher mortality at 7, 30, and 90 days, with numerically smaller relative risks at later time points. It was also associated with higher risks of acute kidney injury, prolonged mechanical ventilation, vasopressor requirement/hemodynamic instability, renal replacement therapy, longer ICU and hospital stays, and 90-day readmission. Findings were directionally consistent in both sensitivity analyses. Etiology-stratified analyses were exploratory and showed no statistically significant heterogeneity across liver disease etiologies. Conclusions: In this large propensity-matched multicenter cohort, administratively defined pre-transplant frailty was associated with worse early outcomes after liver transplantation. Because frailty and several outcomes were identified using structured EHR and administrative data, findings should be interpreted as associative and hypothesis-generating. Prospective studies using validated frailty instruments and granular donor, intraoperative, and center-level variables are needed to confirm these findings. Full article

Objectives: To characterize heterozygous pathogenic COCH variants in a French cohort with non-syndromic sensorineural hearing loss (NSHL) and assess genotype–phenotype correlations in autosomal dominant NSHL (DFNA9). Setting: National Reference Center for Genetic Hearing Loss, Necker–Enfants Malades Hospital, Paris, France. Methods: This retrospective observational study included 69 individuals from 20 unrelated families diagnosed with DFNA9 (2005–2025). All individuals underwent clinical and audiological evaluations and genetic testing via targeted COCH Sanger sequencing or next-generation sequencing (NGS) panels. Variants were interpreted according to ACMG guidelines. Audiometric profiles and vestibular data were collected. Results: Seven known pathogenic COCH variants were found in ten families, and ten novel likely pathogenic variants in the others. Variants in vWFA domains were associated with early or late onset, progressive, bilateral and symmetrical hearing loss. Three variants (p.Gln410Arg, p.Ile450Val, p.Cys542Arg) were associated with congenital or prelingual onset, an atypical DFNA9 presentation. Variants in the LCCL domain were associated with later-onset hearing loss and more frequent vestibular dysfunction. Vestibular abnormalities were observed in about half of early-onset cases. Conclusions:COCH-related hearing loss is a rare cause of autosomal dominant NSHL, with only 20 families identified over two decades within the French network. This study expands the mutational spectrum of COCH by reporting ten novel variants and supports a domain-specific genotype–phenotype correlation. These findings improve the understanding of DFNA9 variability and have direct implications for clinical diagnosis, prognosis, and genetic counseling. Full article

Multiple myeloma (MM) is a biologically heterogeneous plasma cell malignancy in which prognosis is strongly influenced by cytogenetic abnormalities. Recent updates from the International Myeloma Working Group (IMWG), along with the European Hematology Association (EHA) and European Myeloma Network (EMN), have refined the definition of high-risk (HR) disease by integrating TP53 alterations, chromosome 1 abnormalities, and specific combinations of cytogenetic lesions. However, validation of these criteria in real-world patient populations remains limited. We conducted a retrospective, single-center study including 738 patients diagnosed with MM between 2017 and 2025, of whom 408 had available fluorescence in situ hybridization (FISH) data at diagnosis. Patients were reclassified according to the latest IMWG/IMS high-risk criteria proposed in international literature. Cytogenetic abnormalities, treatment patterns, and clinical outcomes, including overall survival (OS), progression-free survival (PFS), response rates, and relapse, were analyzed. Survival was estimated using the Kaplan–Meier method. A total of 103 patients (25%) were reclassified as high-risk according to IMWG/IMS high-risk criteria. Cytogenetic HR abnormalities were identified in 17.2% of cases, with del(17p) being the most frequent (14.7%). Median OS and PFS in HR patients were 52.4 months and 16 months, respectively, compared with 68.4 months and 28 months in standard-risk patients (log-rank test p values of 0.0197 and 0.0004, respectively). Although overall response rates were high (83% in HR vs. 91% in standard-risk), relapse remained frequent in HR patients. Outcomes varied significantly according to cytogenetic complexity. Isolated del(17p) was associated with improved survival compared with cases harboring additional abnormalities, while double-hit and triple-hit profiles demonstrated inferior outcomes. The presence of chromosome 1 abnormalities, particularly in combination with IGH translocations, further worsened prognosis. Among HR patients, 44% underwent autologous stem cell transplantation (ASCT), including 10 cases of TANDEM ASCT. No survival benefit was observed for TANDEM compared with single ASCT, with median OS of 52.9 vs. 78.3 months, respectively (log-rank test p values of 0.2516). Our real-world analysis supports the prognostic relevance of the updated IMS/IMWG high-risk criteria in MM. Cytogenetic complexity, rather than individual abnormalities alone, is a key determinant of outcome. Despite high response rates achieved with modern therapies, survival remains inferior in HR patients. TANDEM ASCT did not confer additional benefit in this cohort, supporting a more individualized approach to treatment intensification. Full article

This review focuses on research outputs of water purification, wastewater treatment, metallic remediation, and sorption-based experimental studies. It aims to identify the leading nations contributing to these areas and identify the journals that have published the highest number of papers from 2010 to 2025, and centers on yearly publication trends. A thorough quantitative analysis was carried out to examine key characteristics of adsorbents derived from various materials, as well as symmetry and asymmetry of wastewater treatment for the removal of metallic pollutants. Key adsorption mechanisms—including ion exchange, surface complexation, electrostatic attraction, and pore filling—are discussed alongside the structural roles of symmetric (ordered) and asymmetric (heterogeneous) adsorbent architectures. Data was collected from the Scopus database, focusing on specific keywords like “metal,” “water,” “removal,” “adsorption,” “purification,” “drinking water,” “nano adsorbent,” etc. Among approximately 29,598 publications encompassing research papers, reviews, short communications, conference papers, and book chapters, China emerged as the leading publisher with 11,957 papers, trailed by India (4324 papers), the USA (1825 papers), Iran (1739 papers), Saudi Arabia (1484 papers), Egypt (1318 papers), and Republic of Korea (1194 papers). The bibliometric mapping of conventional adsorbents and nanomaterials used in sorption-based technologies was analyzed using VOSviewer, revealing major research clusters, research hotspots, networks, and evolutionary patterns in wastewater treatment and sorption-based water purification. This study indicates that several journals from Elsevier Ltd. and Springer Nature are leading the field with a large number of publications per year. The analysis reveals a consistent upward trend in the number of research publications in recent years. In sum, the bibliometric data provided highlights the growing relevance of these areas among academicians and acts as a catalyst for further research, motivating researchers to investigate new adsorbents or modifications that could improve adsorption performance while maintaining economic viability and efficiency. Full article

Hyperspectral anomaly detection (HAD) aims to identify rare targets without relying on prior target knowledge. However, background spectra in hyperspectral images often lie on highly complex and nonlinear manifolds, making accurate modeling challenging. Although models with strong nonlinear approximation capabilities, such as Kolmogorov–Arnold Networks (KANs), provide a promising solution for capturing such complexity, self-supervised reconstruction-based HAD methods still suffer from a fundamental issue known as anomaly leakage. When the model has high representation capacity, anomalous signatures tend to be partially reconstructed, which reduces residual contrast and degrades detection performance. To address this issue, we propose a Blind-Spot KAN-based background reconstruction network with prior purification (BKP-Net), which mitigates anomaly leakage from both data and model perspectives. Specifically, we first introduce a Background Prior Purification (BPP) module to construct a cleaner background prior. This module suppresses and replaces potential outlier pixels through spatial clustering and robust weighted mean estimation. We then design a Blind-Spot KAN-based Reconstruction backbone (BKCN) to model complex nonlinear background characteristics while preventing direct information flow from the center pixel, thereby reducing anomaly leakage during reconstruction. In addition, separable convolutions are employed to enhance spatial–spectral feature representation, followed by an attention-guided fusion mechanism to suppress cross-domain interference. Furthermore, a band-wise Guided Reconstruction Refinement (GRR) strategy is introduced in the detection phase to improve structural consistency between the reconstructed background and the original hyperspectral image, leading to more reliable anomaly discrimination. Experimental results on four hyperspectral datasets demonstrate that the proposed method achieves competitive performance compared with several representative traditional and deep learning-based detectors. Full article

Designing informative descriptors for time-varying complex networks is important for characterizing structural reconfiguration in evolving graph data. This paper introduces a thermodynamics-inspired framework for dynamic graph analysis, centered on Spectral Core Entropy (SCE), node energy, internal energy, and a temperature-like reconfiguration index. These quantities provide a compact representation of how graph organization changes over time. We apply this framework to resting-state fMRI data from autism spectrum disorder (ASD) and control subjects. At the event level, the temperature index shows a statistically significant but modest association with low-SSIM reconfiguration events, indicating that it serves as a weak yet reproducible marker of rapid network change. On controlled synthetic dynamic graphs, the framework exhibits regime-dependent sensitivity: spectral-core change is more informative under rewiring, whereas the temperature index is more informative under gain modulation. At the node level, node energy highlights regional differences between ASD and control groups, providing interpretable neuroscientific context for dynamic brain connectivity. Overall, the proposed framework provides a promising and computationally tractable approach for characterizing reconfiguration patterns in dynamic brain networks and other evolving complex systems. Full article

This article highlights the nature and extent of challenges faced by students from refugee backgrounds in Australian higher education, and suggests potential cultural, institutional and policy reforms to meet these challenges. People from refugee backgrounds are less likely than other Australians to access higher education and face barriers across and beyond the student life cycle. These issues include highly unequal graduate outcomes, resulting from factors such as unconscious (and conscious) employer bias and limited social networks. However, while national census data confirm relatively poor access rates and graduate outcomes, most people from refugee backgrounds have historically been subsumed under a broader non-English speaking background (NESB) category within higher education statistics. This approach has served to mask inequities and create a largely invisibilized group of under-represented domestic students. Improving access and outcomes will require a greater focus on collection and publication of equity data, more targeted institutional policies across the life cycle, and effective advocacy. Cultural change is also required for universities to better identify, recognize, and reward diverse forms of capital possessed by students from refugee backgrounds. Equally, effective advocacy could include allyship with the original displaced people in Australia, Aboriginal and Torres Strait Islander people, whose own voices are increasingly centered and central to reform of Australian higher education. Full article

With the in-depth research of data centers, the task scheduling for sustainable data centers has garnered significant attention. Since thermal dissipation power occupies half of the energy consumption of a whole data center, a large number of sustainable data centers use Thermal-Aware Task Scheduling (TATS). However, existing approaches struggle to perform well in TATS, by ignoring the dynamic characteristics of the thermal in sustainable data centers, which is highly related to the temporal and spatial relationship between servers. It might cause inaccurate scheduling timing and poor scheduling locations, which will lead to additional scheduling costs and cooling costs. To address this problem, we propose a dynamic TATS with GNN-based deep reinforcement learning (DRLGTS) for sustainable data centers. Specifically, DRLGTS first evenly distributes the tasks according to the spatial positions of servers in data center, which is the preprocessing step of DRLGTS. Then, by calling the TranSimmethod, a dynamic thermal graph generation method based on thermal transient simulation in the entire task execution process is conducted. The thermal graph generated by TranSim represents the operating status of the servers and the temporal and spatial correlations between the servers as a graph layout. Finally, based on the feature extraction of thermal graphs using GNN, the proposed dynamic thermal-aware task scheduling using deep reinforcement learning (DRL) is executed. DRLGTS minimizes the energy consumption cost and time cost while scheduling task to adjacent servers as much as possible. Experimental results show that this architecture has good effectiveness and dynamic adaptability. Full article

Deformation monitoring is critical for structural health assessment of operational shield tunnels in urban rail transit. LiDAR point clouds in operating tunnels usually contain auxiliary facilities, occlusions, noise, and uneven point density. Conventional section-by-section ellipse fitting often leads to unstable parameter jumps between adjacent sections. This paper presents a high-precision method to extract lateral deformation from tunnel LiDAR point clouds. First, a point-wise attention Transformer network (PWAT) is proposed based on PointNet++ for lining segmentation, using k-NN adaptive sampling, geometric position encoding, and geometry-constrained multi-head self-attention. Second, a continuity-constrained RANSAC (CC-RANSAC) algorithm is developed to improve ellipse parameter stability by adding continuity penalties between neighboring sections. Experiments were carried out on a Shanghai metro shield tunnel. Results show that PWAT achieves 99.53% overall accuracy and 99.06% mIoU in six-class segmentation. CC-RANSAC reduces the mean residual to 2.0 mm and the center jump rate to 4.2%. Compared with total station data, the mean absolute error and root mean square error are 1.35 mm and 1.68 mm. The proposed method can automatically and accurately extract lateral deformation for operational shield tunnels. Full article

Future networks should provide access to cloud-based content to subscribers in mountainous region. This research proposes a network architecture incorporating mountain data centers that provide content access via caching in a capital-constrained context. It also discusses the aspects of the power system supporting the network architecture. The use of content caching reduces content access latency. The research recognizes that mountains can host computing platforms while ensuring low to moderate operational costs. Using the proposed approach also reduces the number of network hops and associated power consumption by (26–37)% and (17–25)% on average, respectively. Full article

Xylitol is a five-carbon sugar alcohol of industrial interest due to its applications as a food sweetener and sugar substitute. In this study, artificial neural networks combined with a genetic algorithm were evaluated as a data-driven approach for modeling and exploring xylitol production by Spathaspora boniae and Spathaspora brasiliensis during fermentation of sugarcane bagasse hemicellulosic hydrolysate. The dataset comprised 20 experimental points obtained from a face-centered central composite design, using urea, yeast extract, peptone, and ammonium sulfate as input variables. The neural network models showed high goodness-of-fit, with R² values of 0.9952 for S. boniae and 0.9930 for S. brasiliensis. Experimental validation of the optimized conditions resulted in xylitol production of 11.54 ± 0.52 g L⁻¹ for S. boniae and 9.29 ± 0.24 g L⁻¹ for S. brasiliensis. Comparison with response surface methodology showed that both approaches provided strong predictive performance, although the statistical model predicted the optimum conditions more accurately. For S. boniae, however, the ANN-GA approach identified an alternative condition associated with lower nitrogen supplementation and higher experimental xylitol production. Given the limited dataset, this study should be regarded as a proof-of-concept for the application of data-driven optimization tools to xylitol fermentation. The results indicate that ANN-GA can complement classical statistical methods by helping to identify alternative operating conditions in bioprocess optimization. Full article

This paper offers a comprehensive overview of academic research on sentiment analysis in urban built environments from 2000 to 2025. Based on data from the scientific database Scopus and drawing on bibliometric tools like Bibliometrix (R) and VOSviewer for performance analysis and scientific mapping, it identifies publication trends, key influential works, leading authors and institutions, funding sources, and thematic clusters. The final dataset comprises 1315 English-language documents authored by 3855 researchers across 160 sources, with a total of 14,058 citations worldwide. The academic production increased after 2009, peaking in 2025. Keyword and network analyses highlight central themes (and methodological approaches) to the study of sentiment analysis in urban built environments. These include social media platforms like Twitter/X, machine learning, smart cities, artificial intelligence, mental health, and urban planning. China, the USA, and India lead in publication output. Over the last twenty-five years, key publication outlets included Sustainability (Switzerland), Cities, and the International Journal of Environmental Research and Public Health, while the National Natural Science Foundation of China has been the main funder. The paper discusses how sentiment analysis can support urban planning and public health by linking environmental features to well-being and explores emerging methodological trends like deep learning, multimodal approaches, and context-aware models. Overall, it maps the field’s intellectual landscape and argues in future directions for human-centered, data-driven urban decision-making. Full article

Clutch persistence, defined as the ability to sustain consecutive egg-laying cycles, is a pivotal determinant of profitability in the poultry industry, particularly for aging laying hens (≥65 weeks). However, the molecular mechanisms governing this trait remain elusive, largely due to the traditional “ovary-centric” paradigm that overlooks systemic regulation by the gut microbiota. To address this knowledge gap, the present study aimed to dissect the comprehensive regulatory network governing clutch persistence using integrated multi-omics analyses. A total of 20 sixty-five-week-old Rhode Island Red (RIR) laying hens with cumulative egg production exceeding 300 eggs but distinct clutch persistence were stratified into a high-clutch persistence group (HCP, ≥25 clutches, n = 10) and a low-clutch persistence group (LCPLCP, ≤15 clutches, n = 10). Multi-omics profiling, including ovarian transcriptomics, proteomics, and metabolomics; serum metabolomics; and cecal microbiota 16S rRNA sequencing was performed. Data integration and association mining were conducted via Spearman correlation analysis with stringent thresholds (r > 0.6, p < 0.01). Integrated analyses revealed a “gut–ovary axis” regulatory model mediated by a lipid mediator network, operating through a three-tiered mechanism: (1) Gut Initiation: The HCP group exhibited enriched cecal γ-Proteobacteria, which promoted biosynthesis of lipid precursors. (2) Serum Transport: Key serum lipid mediators, most notably LysoPC (22:6) (VIP = 4.5) and cholesterol ester CE (20:4), served as critical carriers transducing gut-derived signals to the ovary. (3) Ovarian Execution: These lipid signals activated a core ovarian metabolic pathway centered on the PLA2G6-ALOX15B-AGPAT3 axis, which coordinated follicular development and ovulation by supplying steroid hormone synthesis substrates, exerting anti-inflammatory effects, and stabilizing membrane structures. Collectively, this study demonstrates that gut microbiota modulates clutch persistence in aging laying hens via lipid mediators, orchestrating a systemic “gut–serum–ovary” regulatory cascade. These findings provide a novel molecular framework for extending the economic egg-laying cycle through the targeted manipulation of intestinal microbiota or serum lipid metabolism. Full article

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airflow limitation, chronic airway inflammation, and immune dysregulation, and currently available therapies remain insufficient to effectively halt disease progression. In this study, we used an integrative, hypothesis-generating strategy to investigate the potential mechanisms of baicalein against COPD by combining multi-dataset transcriptomic analysis, single-cell transcriptomics, machine learning-based feature selection, Mendelian randomization (MR), molecular simulation, virtual knockout analysis, and in vitro validation. Putative targets of baicalein were predicted using CTD, SEA, and SwissTargetPrediction, and were intersected with COPD-related genes collected from GeneCards and OMIM. Four GEO datasets (GSE20257, GSE42057, GSE76925, and GSE130928) were integrated after batch-effect correction, yielding a combined cohort of 260 control samples and 250 COPD samples. Candidate genes were prioritized by intersecting the results of LASSO regression, random forest, and support vector machine. Immune-cell infiltration was estimated using CIBERSORT, and single-cell transcriptomic data were used to define the cellular localization of prioritized genes. Formal protein-level MR analysis was conducted for CD163 using deCODE plasma protein pQTL/GWAS summary statistics as the exposure dataset and the IEU OpenGWAS COPD dataset (ebi-a-GCST90018807) as the outcome dataset. Molecular docking, molecular dynamics simulation, and virtual knockout analysis were further used to provide structural and network-level supportive evidence. Finally, LPS-stimulated BEAS-2B cells were used as an epithelial inflammatory model to evaluate the effects of baicalein by CCK-8 assay, wound-healing assay, ELISA, and RT-qPCR. Five core genes were prioritized, namely ABCC1, CD163, CYP1B1, IKBKB, and PIK3CA. Immune infiltration and single-cell analyses suggested that macrophage-associated immune regulation may represent an important mechanistic direction. MR analysis provided supportive genetic evidence for prioritizing CD163 in COPD. Molecular simulation offered preliminary structural support for several target-compound interactions. In LPS-stimulated BEAS-2B cells, baicalein reduced inflammatory cytokine release and modulated the expression of IKBKB, PIK3CA, IL1B, IL6, and IL10, thereby providing epithelial-level support for the predicted network. Taken together, these findings suggest that baicalein may exert anti-inflammatory effects in COPD through a multi-target, immune-associated mechanism, with macrophage-related regulation and CD163 emerging as noteworthy candidate directions for further investigation. This study provides an integrative framework for target prioritization and mechanistic exploration, while the predicted macrophage-centered mechanisms still require dedicated validation in immune-cell and in vivo models. Full article