Search Results (2,116)

Traffic congestion is a persistent challenge in rapidly urbanizing regions, including Penang Island, Malaysia, where growing vehicle demand increasingly exceeds road capacity. Quantitative understanding of traffic dynamics is therefore essential for evidence-based traffic management and infrastructure planning. While previous studies have typically focused on either traffic detection, forecasting, or congestion assessment separately, limited attention has been given to integrating these components into a unified framework for continuous traffic-condition evaluation. This study integrates computer vision-based data extraction with statistical modeling to analyze and predict traffic behavior. Traffic volume is automatically obtained from video streams using a deep learning detection framework and subsequently processed for time-series modeling. Seasonal Autoregressive Integrated Moving Average (SARIMA) is employed to forecast traffic flow, while Ordinary Least Squares (OLS) regression is used to identify factors associated with congestion. Road performance is evaluated using the Volume-to-Capacity ($V/C$) ratio and Level of Service (LOS) indicators. The forecasting model captures daily and weekly traffic patterns with stable predictive performance across observation periods. Regression analysis indicates significant differences in traffic counts across road segments and vehicle types, while temporal traffic analysis identifies recurring peak-period traffic patterns. High $V/C$ ratios consistently correspond to degraded LOS conditions, allowing identification of recurring bottleneck segments within the network. The combined framework demonstrates how automated sensing, statistical prediction, and engineering performance indicators can be jointly used to monitor and interpret urban traffic conditions. The approach provides a reproducible methodology for continuous congestion assessment and supports data-driven planning decisions in medium-sized urban road networks. Full article

Emergency, remote, and infrastructure-constrained environments cannot always rely on cellular networks or continuous Internet connectivity. LoRa offers an alternative for direct, long-range communication between low-power devices without depending on nearby cellular infrastructure, but its low data rate and variable transmission time create new coordination challenges. When multiple LoRa nodes must agree on information, fixed timing can either introduce unnecessary delay or cause missed deadlines as radio conditions change. This paper introduces a cross-layer controller that adapts mesh coordination timing to LoRa time-on-air at runtime. A MAPE-K loop maps estimated airtime into transmission-slot spacing $S\left(c\right)$ and deadlines $D\left(c\right)$, coupled with quorum or full-participation finalisation and a bounded single-retry mechanism. The framework was implemented on four SX1276 nodes at 915 MHz and evaluated over 2514 coordination rounds under crash and omission faults. Three-of-four quorum coordination achieved a median latency of 2.1 s, compared with 5.3 s for full participation. Overall, 88.6% of rounds met the first adaptive deadline, 11.4% required one retry, and none exhausted the retry budget or violated the evaluated safety invariants. A replay-derived fixed-SF12 schedule produced a median completion time of 11.3 s, compared with 3.4 s for adaptive execution. These results show that LoRa airtime can serve as a practical runtime signal for adaptive coordination on constrained mesh nodes. Full article

Background/Objectives: Stroke–heart syndrome (SHS) involves myocardial injury, cardiac dysfunction, and arrhythmias after acute stroke, playing a key role in prognosis. Most evidence comes from acute ischaemic stroke (IS), while cardiac involvement after haemorrhagic stroke (HS) is less understood. Additionally, the prognostic roles of cardiac biomarkers, neurological severity, and cardiac systolic function on early outcomes are not well defined. Methods: We retrospectively analysed 586 patients with stroke, assessing admission and peak high-sensitivity cardiac troponin I (hs-cTnI) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), left ventricular ejection fraction (LVEF), length of hospital stay (LOS), and in-hospital mortality. Univariable and multivariable regression analyses evaluated factors associated with mortality and LOS. Results: The cohort comprised 586 patients with acute cerebrovascular events, including 560 (95.6%) with acute ischaemic cerebrovascular disease (ischaemic ACVD) and 26 (4.4%) with HS. HS patients had higher hs-cTnI elevations and concentrations, whereas NT-proBNP did not differ between stroke subtypes. In univariable analyses, age, hs-cTnI, NT-proBNP, LVEF, National Institutes of Health Stroke Scale (NIHSS), and modified Rankin Scale (mRS) were associated with in-hospital mortality. In multivariable analysis, peak NT-proBNP remained independently associated with mortality even after logarithmic transformation. Older age, higher NIHSS and mRS scores, and lower LVEF were associated with longer LOS only in univariable analyses. Conclusions: Cardiac and neurological abnormalities provide complementary prognostic roles after acute stroke. HS was associated with a more pronounced myocardial injury profile, whereas peak NT-proBNP was independently associated with in-hospital mortality. These findings support integrated neurological and cardiovascular risk assessment after acute stroke. Full article

Clostridioides difficile infection (CDI) is a major cause of antibiotic-associated diarrhea and pseudomembranous colitis and remains one of the most common healthcare-associated infections. Clostridioides difficile sequence types 1 and 11 (ST1 and ST11), which belong to multilocus sequence typing (MLST) clades 2 and 5, respectively, are widely recognized as epidemic-associated lineages. Both lineages harbor virulence-associated genetic features, including truncating variants in tcdC and carriage of the binary toxin locus (CdtLoc). ST37 (clade 4) has been an epidemic lineage in East Asia, whereas ST17 (clade 1) has been reported as a predominant lineage in the Republic of Korea and Japan. We performed short-read whole-genome sequencing (WGS) on 61 clinical isolates collected at a university-affiliated hospital in the Republic of Korea between February 2016 and April 2019. Long-read sequencing was additionally performed for 24 isolates, generating complete genomes by hybrid assembly. ST17 was the most prevalent sequence type (n = 16, 26.2%), followed by ST1 (n = 10, 16.4%). ST1 and ST11 isolates harbored the genetic features reported for these lineages, including truncating variants in tcdC. Genomic profiling further identified virulence-associated genes and antimicrobial resistance (AMR) determinants in diverse lineages. These findings characterize the genomic features of co-circulating C. difficile lineages and reveal the local presence of epidemic-associated strains. Full article

Hybrid lightweight structures increasingly combine cast, wrought, and additively manufactured aluminum alloys, but the mechanical response of their welded joints remains strongly material-dependent. In this study, cast EN AC-42100-T6, wrought EN AW-6082-T6, and laser powder bed fusion (LPBF) AlSi10Mg stress-relieved plates were welded in 4 and 8 mm configurations using cold metal transfer and pulsed multi-control processes over two campaigns. Weld defects, microstructure, hardness profiles, and tensile properties were analyzed using a framework combining a hardness derived local yield-stress descriptor (σ_y,loc), a cumulative hardness deficit (ID_HV), and a porosity increment metric (ΔP). The second campaign eliminated fusion and penetration related defects, but did not mitigate fusion zone porosity in LPBF-related joints, which reached 13.7% in the 8 mm LPBF–Cast joint. Hardness analysis showed the widest hardness-affected region in the wrought alloy, an intermediate response in the cast alloy, and localized alteration in LPBF. Tensile results identified three degradation modes: strength loss associated with heat-affected zone (HAZ) softening in wrought-containing joints, ductility limitation associated with the initial cast condition, and porosity-associated ductility loss in LPBF-related joints. The lower yield strength reduction in LPBF-related joints does not imply improved performance, as fracture remains strongly influenced by fusion zone porosity. The results support material specific mitigation and design strategies for hybrid welded aluminum structures. Full article

Background/Objectives: To assess associations between quantitative computed tomography (CT) features, inflammatory markers, and length of hospital stay (LOS) in acute pyelonephritis (APN). Methods: This retrospective single-center study included 82 patients with CT-confirmed APN. Two radiologists quantified renal perfusion-deficit volume and percentage and perirenal fat-stranding (PFS) area and attenuation. Associations with C-reactive protein (CRP), white blood cell (WBC) count, procalcitonin, and LOS were assessed using Spearman correlation and regression analyses. Receiver operating characteristic (ROC) curve analysis evaluated LOS ≥ 10 days. Results: Perfusion-deficit volume and percentage correlated strongly with CRP (ρ = 0.763 and 0.714; both p < 0.001) and weakly with WBC count (ρ = 0.379 and 0.374; both p < 0.001). PFS area correlated moderately with procalcitonin (ρ = 0.482, p = 0.001; n = 42). Both perfusion-deficit measures correlated moderately with LOS (ρ = 0.538 and 0.536; both p < 0.001). In adjusted linear regression, CRP remained associated with LOS (Coefficient = 0.523 days per 10 mg/L; 95% CI, 0.345–0.692; p < 0.001), whereas perfusion-deficit percentage did not (p = 0.430). In adjusted logistic regression, CRP, age, and male sex were associated with LOS ≥ 10 days. The multivariable model yielded an AUC of 0.883 (95% CI, 0.81–0.95). Results were unchanged in a sensitivity analysis excluding the seven outpatients, and bootstrap internal validation yielded an optimism-corrected AUC of 0.86. Conclusions: Quantitative renal perfusion deficits were associated with inflammatory burden and LOS in univariable analyses. CRP showed the most consistent independent association, whereas the incremental value of CT metrics requires external validation. Full article

Rice (Oryza sativa L.) is a staple food crop globally, and identifying genes governing grain yield is critical for food security. Although heat shock proteins (HSPs) are known for their roles in stress tolerance, the molecular mechanisms by which they regulate yield formation remain unclear. In this study, we generated knockout and overexpression lines for OsHSP20 which is encoding a member of the Hsp20/alpha crystallin family, LOC_Os10g30162.1 (It is also one of the four candidate genes discovered during our fine mapping of major QTLs for photosynthetic rate in rice.), and performed integrated analyses combining field phenotyping, multi-stress assays, and transcriptomics. Phenotypic analyses revealed that OsHSP20 deficiency resulted in compromised plant architecture, leaf morphology, tillering, and panicle development, leading to a significant reduction in grain setting rate. Conversely, OsHSP20 overexpression enhanced drought tolerance. Mechanistically, transcriptomic and functional analyses demonstrated that OsHSP20 maintains protein homeostasis under drought stress via its chaperone activity; this function orchestrates a coordinated regulatory network involving lipid barrier formation, antioxidant defense, and carbon allocation. Our findings establish OsHSP20 as a positive regulator of both yield and drought resilience, improving crop adaptability by balancing growth and stress responses. In addition, our previous research has shown that OsHSP20 is actually one of the important components of the main QTL for rice photosynthetic rate. Therefore, this study can provide new genetic resources and a theoretical basis for cultivating rice varieties with high yield, stress resistant, and a high photosynthetic rate. Full article

Potato tubers exhibit prominent spatial heterogeneity in starch accumulation across distinct anatomical zones, yet the underlying transcriptional and metabolic regulatory mechanisms remain unclear, especially the molecular basis for divergent starch deposition between high- and low-starch tetraploid cultivars. In this study, the high-starch cultivar Atlantic (DX) and low-starch cultivar Dingshu No. 1 (D) were used as experimental materials. Dynamic starch contents of the tuber cortex (CR), perimedullary region (PMR), and inner medullary region (IMR) were measured throughout starch biosynthetic stages. Integrated untargeted metabolomics and RNA-seq transcriptomics were performed to compare transcriptional and metabolic differentiation between CR and IMR with the largest starch gap, followed by qRT-PCR verification of hub genes. Physiological results showed that starch content in both cultivars followed the gradient CR > PMR > IMR, and inter-regional starch disparities expanded continuously with tuber development; low-starch D had lower overall starch levels and milder tissue differences than DX. Transcriptomic analysis revealed massive transcriptional activation in the cortex of both cultivars, while D displayed far more dramatic transcriptional divergence between CR and IMR than DX. Core transcription factors including AP2/ERF, bHLH and MYB were identified to mediate tissue-specific starch synthesis. Metabolomic data demonstrated severe carbon metabolic polarization in D (hyperactive cortex, weak inner medulla), whereas DX maintained balanced sugar metabolism across whole tuber tissues. Integrated multi-omics analysis of the starch and sucrose pathway (ko00500) screened 18 key structural genes. High-starch DX upregulated starch synthetic and sugar transport genes while repressing starch hydrolysis and carbon diversion genes, realizing coordinated whole-tuber carbon flux toward starch production. In contrast, D showed disrupted regulatory networks, excessive carbon shunting to secondary metabolism and aggravated starch degradation, limiting starch accumulation. Three conserved negative regulatory candidate genes, LOC102593331 (TPS), LOC102584887 (AMY), and LOC102580651 (EN), were characterized via tissue expression profiling: TPS dominates upstream carbon flux allocation, EN competes for starch precursors during cell wall synthesis, and AMY accelerates starch breakdown. This study systematically uncovered the molecular framework of spatial starch heterogeneity in tetraploid potato tubers, clarified the essential distinctions in carbon partitioning strategies between high- and low-starch cultivars, and provided valuable gene resources and theoretical support for high-starch potato molecular design breeding. Full article

Accurate Land Use and Land Cover (LULC) mapping in high-resolution alpine environments is challenging due to complex terrain, heterogeneous vegetation, seasonal snow and ice cover, and the limited spectral information provided by conventional aerial imagery. Although foundation models such as the Segment Anything Model (SAM) effectively capture structural features, their class-agnostic design, limits fine-grained semantic discrimination and typically requires large annotated datasets. This study proposes a multi-modal deep learning framework for alpine LULC mapping using sparse annotations, which would fall under the category of weakly supervised learning. The framework employs a dual-encoder architecture that integrates RGB imagery, six-band multispectral imagery, and custom adapters for spectral indices, and Digital Surface Models (DSMs). A SAM-based encoder extracts geometric and contextual features from RGB imagery, while a dedicated encoder learns complementary spectral representations from multispectral data. To address the boundary uncertainty introduced by sparse supervision, we propose post inference hybrid refinement strategy that combines a Canopy Height Model (CHM) derived from the DSM to improve tree crown delineation with edge-based refinement for low vegetation classes, such as shrubs, and mathematical methods to refine road and building edge delineation with DSMs. Experimental results across fourteen alpine classes highlight that the framework achieves a validation mIoU of 0.777, macro-averaged over the fourteen classes. For the present sensor choice and classification scheme, no comparable multi-modal baseline exists. Thus, results are reported in absolute terms. The present multi-modal data fusion sets the baseline for future scalable alpine LULC mapping applications. Full article

Objectives: We investigate the relationship between the loss of consciousness (LOC) and the state of default mode network (DMN) connectivity in diffuse axonal injury (DAI) patients, using diffusion tensor tractography (DTT). Methods: Twenty-one consecutive patients with DAI were recruited in this study. The DMN connectivity [medial prefrontal cortex (mPFC)—posterior cingulate cortex (PCC)/precuneus and retrosplenial cortex (RSC)—medial temporal lobe (MTL)] was reconstructed using DTT. Fractional anisotropy (FA) value, mean diffusivity (MD) value, and tract volume (TV) of the DMN connectivity at the chronic stage were measured in this study. Results: After adjusting for covariates, none of the DTT parameters reached definitive statistical significance at the p < 0.05 level. However, the LOC duration revealed a marginal trend toward a negative correlation with the TV of the mPFC-PCC/precuneus neural connectivity (partial r = −0.466, p = 0.060) and a marginal trend toward a positive correlation with the MD value of the MTL-RSC neural connectivity (partial r = 0.467, p = 0.059). The FA and MD of the mPFC-PCC/precuneus, and the FA and TV of the MTL-RSC, showed no significant associations (p > 0.05). Conclusions: This exploratory study identified a marginal trend between the LOC duration and the state of DMN connectivity (mPFC-PCC/precuneus and MTL-RSC) at the chronic stage in DAI patients. Chronic-stage microstructural changes in the DMN are likely influenced by several factors, including the severity of the initial injury and the time since injury. Therefore, although the LOC duration provides useful clinical information, it should not be considered a direct predictor of chronic DMN deterioration in patients with DAI. Full article

Carbapenem resistance in Pseudomonas aeruginosa is frequently mediated by the mutational inactivation or complete loss of the oprD porin gene. However, extreme genetic polymorphism within oprD limits traditional PCR amplification, a bottleneck for molecular surveillance. This study aimed to design and validate a novel quantitative PCR (qPCR) primer set flanking the 5′ and 3′ untranslated regions to reliably amplify the complete oprD cassettes across divergent clinical isolates. The assay demonstrated optimal amplification at 54 °C and an analytical limit of detection (LoD) between 10⁻⁴ and 10⁻⁵ ng of genomic DNA mass. This assay successfully matched 94.09% (1608/1709) of globally distributed P. aeruginosa strains via in silico BLASTn analysis. In a blinded validation cohort of 100 clinical isolates (50 P. aeruginosa and 50 non-P. aeruginosa controls), the qPCR assay achieved 100.0% analytical specificity without cross-reactivity. Among the 50 P. aeruginosa isolates including 40 carbapenem-resistant (CRPA) and 10 carbapenem-susceptible (CSPA), the qPCR assay exhibited 96.0% sensitivity, successfully amplifying oprD cassettes in 95.0% (38/40) of CRPA and 100.0% (10/10) of CSPA strains without phenotypic bias (p > 0.9999). Notably, when combined with sequencing, the two qPCR-negative CRPA isolates were confirmed to harbor true biological oprD deletions. This optimized assay provides a robust molecular surveillance tool for detecting oprD cassettes to facilitate the epidemiological genetic characterization in both CRPA and CSPA isolates. Full article

Soil salinity is a major abiotic stress limiting rice growth and grain productivity worldwide. Basic leucine zipper (bZIP) transcription factors serve as central regulators of plant environmental stress responses, yet the biological function and molecular regulatory mechanism of rice OsbZIP60 (LOC_Os07g44950) underlying salinity tolerance remain largely uncharacterized. In this study, we systematically characterized the salt-stress regulatory function of OsbZIP60 in rice. Tissue expression profiling revealed that OsbZIP60 was ubiquitously transcribed across all examined rice tissues, and its encoded protein predominantly localizes to the cell nucleus. Transcript abundance of OsbZIP60 was significantly induced by salt, the osmotic phase, abscisic acid (ABA), and oxidative stress signals. Phenotypic assays demonstrated that overexpression of OsbZIP60 substantially enhanced rice salt tolerance, whereas the bzip60 knockout mutant exhibited aggravated salt hypersensitivity. Physiological quantification revealed that OsbZIP60 promoted the accumulation of osmoprotectants, alleviated salt-triggered oxidative damage, and elevated the activities of core antioxidant enzymes under saline conditions. Moreover, OsbZIP60 maintained intracellular Na⁺/K⁺ homeostasis and positively modulated the transcript levels of a series of salt-responsive downstream genes. Collectively, our results demonstrate that OsbZIP60 acts as a positive regulatory hub that coordinates osmotic adjustment, antioxidant defense, and ion balance to confer salt tolerance in rice, providing a promising genetic target for molecular breeding of salt-tolerant rice varieties. Full article

Single-photon avalanche diode (SPAD) arrays enable photon-starved applications, including time-of-flight imaging and stellar intensity interferometry. Their astronomical use remains scarcely explored, as the bottleneck is usually not detection but rather acquisition electronics for high-rate event streams. This work presents a modular, event-driven acquisition system for a 64 × 64 SPAD array within the La Palma Quantum Interferometer (LPQI) project, repurposing a LiDAR detector for multi-telescope interferometry. Two stages are used: a Raspberry Pi 5 with a custom board for validation, and an AMD Kria KR260 (Zynq UltraScale+ MPSoC) implementing the Address-Event Representation (AER) handshake in hardware at 100 MHz. The system streams AER events without per-event timestamping; sub-nanosecond time-tagging is left for a future stage based on the White Rabbit protocol. Optical bench tests confirmed spatial detection and localization of photons at a measured throughput of up to ≈124 keps under the highest illumination condition tested, and dark-count-rate characterization showed a rate below 10 Hz for most pixels (median: 1.68 Hz at 27.8 °C); raw per-pixel event-count maps further confirmed, for the first time on this array, the expected 2 × 2 spatial pattern of inter-pixel crosstalk from its shared-cathode pixel groups. The results demonstrate the feasibility of repurposing a LiDAR SPAD sensor and establish an acquisition-electronics baseline to aid the development of a timestamped, multi-telescope system for deployment on five telescopes of the Roque de los Muchachos Observatory. Full article

Grapevine (Vitis vinifera L.) leaves are an abundant agricultural by-product and an underexploited source of bioactive polyphenols. This study characterized hydroalcoholic leaf extracts from autochthonous Sardinian cultivars and evaluated their effects in RKO human colorectal carcinoma cells. LC–HRMS revealed flavonol-rich profiles dominated by glycosylated derivatives of quercetin, kaempferol, and isorhamnetin, with marked cultivar-dependent differences and higher total flavonol abundance in Cannonau and Nieddera. Cyclic voltammetry showed distinct redox reactivity among extracts, with Vermentino and Martellada Bianca displaying the strongest electrochemical responses, consistent with their modulation of intracellular reactive oxygen species. In RKO cells, the extracts reduced metabolic activity in a dose-dependent manner, with IC50 values ranging from 100.3 to 251.8 µg/mL. At the molecular level, treatments modulated c-myb expression and shifted the bax/bcl2 balance toward a pro-apoptotic profile. These effects were supported by DAPI staining, showing chromatin condensation and nuclear fragmentation, and by moderate activation of caspase-3/7. Overall, Sardinian grapevine leaf extracts showed cultivar-specific phenolic composition, redox activity, and pro-apoptotic effects in an in vitro colorectal cancer model, supporting their potential valorization as sustainable sources of bioactive compounds. Further studies addressing bioavailability, metabolism, and in vivo effects are required to assess their physiological relevance and translational potential. Full article

The integration of zeolitic imidazolate framework-8 (ZIF-8) into polymer matrices has created a versatile class of nanocomposites with potential applications in gas separation, water purification, food packaging, sensing, catalysis, energy systems, and environmental remediation. However, their performance is governed by complex and strongly coupled variables, including ZIF-8 particle size, morphology, defect density, surface chemistry, filler loading, polymer compatibility, interfacial adhesion, dispersion state, and processing conditions. To organize this complexity, the review introduces a hierarchical design framework that distinguishes controllable synthesis and processing inputs, experimentally measurable intermediate material states, and condition-dependent performance outputs, thereby providing a structured basis for machine-learning-ready data representation. Conventional trial-and-error approaches are therefore often inefficient and provide limited capacity to identify transferable structure–processing–property relationships. This review examines the emerging role of machine learning (ML) in the rational design and optimization of ZIF-8/polymer nanocomposites for sustainable applications. Particular attention is given to the construction of material descriptors, selection of predictive algorithms, interpretation of feature importance, optimization of synthesis and processing parameters, and prediction of mechanical, thermal, barrier, transport, adsorption, catalytic, and antimicrobial properties. The review further discusses how supervised learning, explainable artificial intelligence, active learning, Bayesian optimization, transfer learning, and physics-informed models can support material screening and multi-objective optimization across performance, cost, energy consumption, environmental impact, and end-of-life considerations. Current limitations, including small and heterogeneous datasets, inconsistent reporting, insufficient negative results, limited model interpretability, and weak experimental validation, are critically evaluated. A future framework is proposed that integrates standardized databases, high-throughput experimentation, multiscale characterization, life-cycle indicators, uncertainty quantification, and closed-loop machine learning. Such an approach could accelerate the transition from empirical formulation toward data-driven, interpretable, and sustainability-oriented design of ZIF-8/polymer nanocomposites. Full article