Search Results (498)

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach. Full article

Background: Core laboratory chemistry analyzers typically use plasma and serum samples, while blood gas instruments use whole blood for electrolyte and metabolite tests. Due to high costs to back up the core lab chemistry analyzers, especially in the remote small community hospitals, we have verified the interchangeability of serum/plasma electrolytes and metabolites on blood gas instruments (GEM4000 and Radiometer ABL90) vs. chemistry analyzers. In this study, we sought to extend the investigation to another blood gas device—Radiometer ABL837. Methods: One plasma separator tube and one serum separator tube were drawn from 20 apparently healthy individuals and outpatients and 20 intensive care unit patients. All the samples were run on Roche Cobas8000, and then were run on three Radiometer ABL837 analyzers for sodium (Na⁺), potassium (K⁺), chloride (Cl⁻), glucose, lactate (plasma only), and creatinine parameters. Paired measurements between the ABL837 and Cobas8000 were compared, and their difference were assessed for statistical and clinical significance. Results: ABL837 demonstrated statistical significance (p < 0.05) vs. Cobas8000 on all the plasma and serum parameters. However, no parameter differences were found when comparing the plasma/serum results on ABL837 to those on Cobas8000, indicating that none were clinically significant. ABL837 also demonstrated good–excellent correlations with Cobas8000 on all the parameters. Conclusions: When comparing metabolite and electrolyte values with plasma and serum sample types, the ABL837 blood gas instruments and Cobas 8000 chemistry analyzer are interchangeable. These data proves that ABL837 can be used as a backup for a chemistry analyzer in measuring plasma and serum electrolyte and metabolite concentrations. Full article

In the traditional classification system of the Lauraceae family based on morphology and anatomy, the phylogenetic position of the genus Sassafras has long been controversial. Chloroplast (cp) evolution of Sassafras has not yet been illuminated. In this study, we first sequenced and assembled the complete cp genomes of Sassafras, and conducted the comparative cp genomics, phylogenomics, and divergence time estimation of this ecological and economic important genus. The whole length of cp genomes of the 10 Sassafras ranged from 151,970 bp to 154,011 bp with typical quadripartite structure, conserved gene arrangements and contents. Variations in length of cp were observed in the inverted repeat regions (IRs) and a relatively high usage frequency of codons ending with T/A was detected. Four hypervariable intergenic regions (ccsA-ndhD, trnH-psbA, rps15-ycf1, and petA-psbJ) and 672 cp microsatellites were identified for Sassafras. Phylogenetic analysis based on 106 cp genomes from 30 genera within the Lauraceae family demonstrated that Sassafras constituted a monophyletic clade and grouped a sister branch with the Cinnamomum sect. Camphora within the tribe Cinnamomeae. Divergence time between S. albidum and its East Asian siblings was estimated at the Middle Miocene (16.98 Mya), S. tzumu diverged from S. randaiense at the Pleistocene epoch (3.63 Mya). Combined with fossil evidence, our results further revealed the crucial role of the Bering Land Bridge and glacial refugia in the speciation and differentiation of Sassafras. Overall, our study clarified the evolution pattern of Sassafras cp genomes and elucidated the phylogenetic position and divergence time framework of Sassafras. Full article

This study investigates the quantitative relationship between material composition and the performance of foam concrete based on 170 validated experimental datasets extracted from the existing literature. The statistical approach combined with machine learning modeling was employed to systematically analyze and predict key performance indicators. Pearson correlation analysis was used to identify the parameters affecting mechanical and thermal properties. The analysis revealed that the water-to-cement ratio (W/C) and cement content were the most influential factors for mechanical properties, while density and the coarse-to-fine aggregate ratio (Cag/Fag) had the greatest impact on thermal conductivity. To overcome the limitations of traditional empirical models in capturing complex nonlinear relationships, a predictive framework with eight machine learning algorithms was established. Among these, Neural Network Regression exhibited the highest accuracy for mechanical property prediction, with a coefficient of determination of R² = 0.987 for compressive strength and R² = 0.932 for flexural strength. For thermal conductivity, support vector regression achieved the best predictive performance with R² = 0.933. Error analysis demonstrated significant differences in prediction accuracy across performance indicators: compressive strength was the easiest to predict, followed by flexural strength, while thermal conductivity was the most challenging. Based on practical engineering requirements, a hierarchical model selection strategy was proposed. Specifically, Neural Network Regression is prioritized for mechanical properties, and support vector regression is prioritized for thermal properties. Decision Tree Regression is recommended as a general-purpose model. The predictive model used in this study provides reliable technical support for the optimization and engineering application of foam concrete, enhancing both prediction accuracy and practical efficiency. Full article

Canola (Brassica napus L.) is an oilseed crop that is currently being impacted by climate change. Heat stress risks production by impacting yield, oil, protein, and fatty acid profile. The purpose of this literature review was to assess the impact of heat stress on canola while briefly evaluating other abiotic stresses, and to address the following research questions: (1) What is the impact of heat stress on canola yield?, (2) What is the impact of heat stress on canola oil and protein content?, and (3) What is the impact of heat stress on the fatty acid profile of canola? Forty papers were selected in relation to B. napus heat stress and impact on yield, oil content, or fatty acid profile, from 1978 to 2025. Key findings revealed that heat stress negatively impacted yield and oil, while significant variation was observed within the fatty acid profile. Genotype, heat stress condition, and growth stage significantly impacted results. Certain genotypes were identified as having potential heat-tolerant traits, providing a basis for future breeding programs. Future field studies with controlled irrigation may better explain variations between controlled environment and field studies when water stress is not a concern. A better understanding of the impact of combined stresses, particularly heat and drought, is also required for breeding tolerant lines in regions with minimal irrigation. Full article

Adolescents’ performance in high-stakes standardized examinations plays a pivotal role in shaping their educational trajectories. This longitudinal study investigated whether parental attendance at parent–teacher conferences (PTCs) during early adolescence is associated with students’ performance in standardized examinations required for high school and college entrance. Drawing on data from the Taiwan Youth Project, we analyzed responses from 1294 ninth-grade students and 524 twelfth-grade students with available exam results. Parental participation in PTCs was recorded in both seventh and eighth grades, along with two other types of school-based involvement and covariates, such as parental education level, household income, students’ birth order, prior academic rank, peer relationships, parental support, and parental expectations. Hierarchical linear modeling was employed to control for individual and school-level variables. The results showed that parental attendance at PTCs in eighth grade was associated with higher scores on high school entrance exams in ninth grade. Furthermore, attending PTCs in both seventh and eighth grades was significantly associated with better performance in college entrance exams in twelfth grade (β = 3.02, p < 0.01). These findings suggest that sustained parental engagement in PTCs contributes to improved academic performance in adolescence. Policies that promote equitable and continued parent–teacher collaboration may support long-term student success. Full article

To understand the impact of falls on gait in those with poor sight, we examined how gait changed after falls in older adults with varying degrees of visual impairment from glaucoma. Participants were classified as fallers or non-fallers based on prospective falls data from the first study year. Injurious fallers were those who suffered injuries from falls. The GAITRite Electronic Walkway characterized gait at baseline and three annual follow-ups. Parameters examined included stride length, variability in stride length (CV), stride velocity, stride velocity CV, base of support, base of support CV, and cadence. Longitudinal gait changes were assessed using generalized estimating equation models. Stride length significantly decreased in both fallers (β = −0.09 z-score unit/year) and non-fallers (β = −0.08 z-score unit/year), stride velocity slowed only among fallers (β = −0.08 z-score unit/year), and, in contrast, stride velocity CV decreased only among non-fallers (β = −0.07 z-score unit/year). No longitudinal differences were noted between groups. Additionally, no significant differences in gait metrics were observed between non-fallers, one-time fallers, and multiple fallers, nor between those with and without an injurious fall. Amongst older adults, and enriched for those with visual impairment, fallers and non-fallers adopted a more cautious gait over time, with similar gait changes across groups. Our results suggest that, in visual impairment, many falls may not lead to significant changes in gait. Full article

The accurate modeling of dynamic soil–structure interaction processes under impact loading is critical for advancing the design of soil-embedded barrier systems. Full-scale crash testing remains the benchmark for evaluating barrier performance; however, such tests are costly, logistically demanding, and subject to variability that limits repeatability. Recent advancements in computational methods, particularly the development of large-deformation numerical schemes, such as the multi-material arbitrary Lagrangian–Eulerian (MM-ALE) and smoothed particle hydrodynamics (SPH) approaches, offer viable alternatives for simulating soil behavior under impact loading. These methods have enabled a more realistic representation of granular soil dynamics, particularly that of the Manual for Assessing Safety Hardware (MASH) strong soil, a well-graded gravelly soil commonly used in crash testing of soil-embedded barriers and safety features. This soil exhibits complex mechanical responses governed by inter-particle friction, dilatancy, confining pressure, and moisture content. Nonetheless, the predictive fidelity of these simulations is governed by the selection and implementation of soil constitutive models, which must capture the nonlinear, dilatant, and pressure-sensitive behavior of granular materials under high strain rate loading. This review critically examines the theoretical foundations and practical applications of a range of soil constitutive models embedded in the LS-DYNA hydrocode, including elastic, elastoplastic, elasto-viscoplastic, and multi-yield surface formulations. Emphasis is placed on the unique behaviors of MASH strong soil, such as confining-pressure dependence, limited elastic range, and strong dilatancy, which must be accurately represented to model the soil’s transition between solid-like and fluid-like states during impact loading. This paper addresses existing gaps in the literature by offering a structured basis for selecting and evaluating constitutive models in simulations of high-energy vehicular impact events involving soil–structure systems. This framework supports researchers working to improve the numerical analysis of impact-induced responses in soil-embedded structural systems. Full article

Understanding the potential distribution patterns and habitat suitability of threatened species under climate change scenarios is essential for conservation efforts. This study aimed to assess the current and future distribution patterns of the endangered Cycas taiwaniana in China using the MaxEnt model under two contrasting climate change scenarios: SSP1-2.6 (low emissions) and SSP3-7.0 (high emissions), projected for the 2050s and 2070s periods. The model identified key bioclimatic variables influencing habitat suitability, including Annual Mean Temperature, Mean Diurnal Range, and Temperature Seasonality. Under current climate conditions, the species’ most suitable habitats are primarily located in southern coastal regions, with Hainan Island showing exceptional suitability. However, future projections under the moderate emission (SSP1-2.6) scenario suggest a significant shrinking of suitable habitat areas, particularly a 27.5% decline in excellent and a 35% decrease in good categories by the 2070s. In contrast, under the high-emission scenario (SSP3-7.0), while an initial decline in suitable habitats is projected, the model predicts an unexpected expansion of highly suitable areas by 2070, particularly in Guangxi, Guangdong, and Fujian coastal regions. The results highlight the vulnerability of C. taiwaniana to climate change and underscore the importance of developing adaptive conservation strategies to mitigate potential habitat loss. The findings also emphasize the need for further research on species-specific responses to climate change and the development of proactive measures to safeguard the future distribution of this threatened species. Full article

Background/Objectives: Previous work in our lab demonstrated that a 3D scaffold containing lysine-modified chitosan (K-chitosan) and decorated with Matrilin-2 (MATN2) enhanced Schwann cell (SC) migration and axonal outgrowth in vitro and ex vivo. This study aimed to assess the regenerative effect of this scaffold compared to that of a collagen conduit and an autograft using a segmental rat sciatic nerve injury model. Methods: A total of 30 Lewis Rats were assigned into three groups: an untreated collagen conduit (UC) group, a collagen conduit treated with MATN2 K-chitosan (TC) group, and a reverse autograft (RA) group. Walking force measurements, compound muscle action potential (CMAP), the wet muscle weight of the tibialis anterior and the gastrocnemius, and axonal histomorphometry were assessed. Results: The walking force and CMAP were significantly higher in the TC group compared to those in the UC group, with no significant difference between the TC and RA groups. The muscle weights were significantly greater in the TC group compared to those in the UC group but smaller than those in the RA group. The TC group experienced significantly greater axonal regeneration compared to that with the UC, and no differences were found with the RA. The TC group further demonstrated significantly greater cell counts than those in the UC group and greater affinity of the Schwann cells towards nerve reconstruction. Conclusion: The MATN2 K-chitosan scaffold significantly improved nerve regeneration and was comparable to the RA, supporting the development of a novel bio-conductive scaffold conduit. Full article

Background/Objectives: Nonacute subdural hematomas (naSDHs) are a prevalent intracranial pathology, particularly in older people, due to increased brain atrophy, fall risk, and anticoagulant use. This study examines the impact of frailty on the surgical outcomes of craniotomy for naSDH over 20 years. Methods: Data from the Nationwide Inpatient Sample (NIS) from 2000 to 2021 were analyzed, including 251,597 patients who underwent cranial decompression for naSDH. Patients were selected using specific ICD codes. Frailty was calculated using the modified frailty index (mFI-5 and mFI-11) and the Charlson Comorbidity Index (CCI). Outcomes were compared using descriptive statistics and multivariable regression models. Results: 251,597 patients underwent craniotomy, with a mean age of 69.2 years. The cohort exhibited significant comorbid conditions, reflected in a mean Charlson Comorbidity Index (CCI) of 3.8, and a high frailty prevalence, with 23.49% of patients classified as frail and 20.14% as severely frail. The CCI demonstrated the highest predictive value for adverse outcomes, with an area under the curve (AUC) of 0.6346 for mortality and 0.6804 for complications. Frailty indices (mFI-5 and mFI-11) were also strongly associated with increased mortality (p < 0.001), complications (p < 0.001), and extended length of stay (p < 0.001). Age was not a significant predictor of outcomes. Conclusions: This study highlights the moderate impact of frailty on surgical outcomes for naSDH. Full article

Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based solely on a diagnostic CT (dCT). Methods: A DL network, U-Net, was trained utilizing 46 thoracic cases from a public database to generate sCT images predicting planning CT (pCT) scans based on the latest dCT, and tested on 15 institutional patients. The sCT accuracy was evaluated against the corresponding pCT and a commercial algorithm deformed CT (MdCT) based on Mean Absolute Error (MAE) and Universal Quality Index (UQI). To determine advantageous treatment modality, clinical dose-volume histogram (DVH) metrics and Normal Tissue Complication Probability (NTCP) differences between proton and photon treatment plans were analyzed on the sCTs via concordance correlation coefficient (CCC). Results: The AI-generated sCTs closely resembled those of the commercial deformation algorithm in the tested cases. The differences in MAE and UQI values between the sCT-vs-pCT and MdCT-vs-pCT were 19.38 HU and 0.06, respectively. The mean absolute NTCP deviation between sCT and pCT was 1.54%, 0.21%, and 2.36% for esophagus perforation, lung pneumonitis, and heart pericarditis, respectively. The CCC between sCT and pCT was 0.90 for DVH metrics and 0.97 for NTCP, indicating moderate agreement for DVH metrics and substantial agreement. Conclusions: Radiation oncologists can potentially utilize this personalized sCT based approach as a clinical support tool to rapidly compare the treatment modality benefit during patient consultation and facilitate in-depth discussion on potential toxicities at a patient-specific level. Full article

Background/Objectives: MVI is a relevant prognostic factor among patients with hepatocellular carcinoma (HCC) receiving liver transplantation (LT). The preoperative assessment of the risk for MVI is relevant to pre-LT patient management and selection. The objective of this study was to create and validate a model to predict microvascular invasion (MVI) based on preoperative variables in the LT setting. Methods: A total of 2170 patients from 11 collaborative centers in Europe, Asia, and the US, who received transplants between 1 January 2000 and 31 December 2017, were enrolled in the study. The entire cohort was split into a training and a validation set (70/30% of the initial cohort, respectively) using random selection. Results: MVI was reported in 586 (27.0%) explanted specimens. Using the training set data, multivariable logistic regression identified three preoperative parameters associated with MVI: α-fetoprotein (lnAFP; odds ratio [OR] = 1.19; 95% confidence interval [CI] = 1.13–1.27), imaging tumor burden score (lnTBS; OR = 1.66; 95%CI = 1.39–1.99), and a fast-track approach before LT due to the availability of a live donation (OR = 1.99; 95%CI = 1.56–2.53). In the validation set, the LT-MVI c-index was 0.74, versus 0.69 for the MVI score proposed by Endo et al. (Brier Skill Score +75%). The new score had a relevant net reclassification index (overall value = 0.61). Stratifying the validation set into three risk categories (0–50th, 51st–75th, and >75th score percentiles), a very good stratification was observed in terms of disease-free (5-year: 89.3, 75.5, and 50.7%, respectively) and overall survival (5-year: 79.5, 72.6, and 53.7%, respectively). Conclusions: The preoperative assessment of MVI using the proposed score demonstrated very good accuracy in predicting MVI after LT. Full article

Modular green wall, or living wall (LW) system, has evolved worldwide over the past decades as a popular green building feature and a nature-based solution. Differential climatic conditions across the globe make the standardisation of practices inapplicable to local scenarios. LW projects with differing goals and preferences like aesthetic (such as plant healthiness), water-saving, and minimal plant growth require optimal combinations of plant species to achieve single or multiple goals. This exploratory study aimed to deploy empirical field LW data to optimise analytical models to support plant species selection and LW design. Plant growth performance and water demand data of 29 commonly used plant species in outdoor modular LW systems without irrigation were collected in subtropical Hong Kong for 3 weeks. The 29 species tested were grouped into five plant forms: herbaceous perennials (16 spp), succulents (2 spp), ferns (2 spp), shrubs (7 spp), and trees (2 spp). Plant species-specific plant height, LAI, plant health rating, and water absorption amount were recorded every 6 days, together with photo records. Total water demand varied widely among plant species, ranging from 52.5 to 342.5 mL in 3 weeks (equivalent to 2.5 to 16.3 mL per day). The random forest algorithm proved that the water demand of the species was a dominant predictor of plant health tendency, among other parameters. Hierarchical clustering grouped plant species with similar water demand and health rating tendencies into four groups. The health rating threshold approach identified the top-performing species that displayed a healthy appearance as a basic prerequisite, coupled with one or two optional objectives: (1) water-saving and (2) slow-growing. The comparison among the plant selection scenarios based on projected LW performance (water demand, plant health, and growth) provided sound evidence for the optimisation of LW design for sustainability. LW projects with multiple objectives inherited a multitude of multi-scalar properties; thus, the simulation of LW performance in this study demonstrated a novel data-driven approach to optimise plant species selection and planting design with minimal resource input. Full article

The influence of annealing temperature on the mechanical properties, microstructural evolution, and recrystallization behavior of AA5083 cold-rolled sheets with and without Sc/Zr microalloying was studied utilizing hardness tests, optical microscopy, electron backscatter diffraction, and transmission electron microscopy. The results show that a minor addition of Sc/Zr to the Al-Mg-Mn alloy can significantly improve the alloy strength and recrystallization resistance. Adding 0.1 wt.% Sc and 0.08 wt.% Zr raised the recrystallization temperature of heavily deformed sheets to 500 °C, which is 250 °C higher than for the Sc-free base alloy. The higher recrystallization resistance of the Sc-bearing alloy was mainly attributed to the presence of Al₃(Sc,Zr) nanoparticles, which enhanced the Zener drag pressure and delayed recrystallization. Grain boundary strengthening effects at various annealing temperatures were estimated using a constitutive equation. This work revealed that grain structure change and the corresponding boundary strengthening effect are key factors governing alloy strength evolution during annealing. Full article