Search Results (11,158)

Curriculum redesign in Computer Science and Information Technology has become increasingly complex due to rapid technological advancements, interdisciplinary knowledge requirements, and evolving industry expectations. Recent progress in artificial intelligence, particularly Transformer-based language models, offers new opportunities for data-driven and scalable curriculum analysis. This study utilizes syllabus-level textual datasets collected from multiple universities, comprising structured and unstructured course descriptions across diverse CS and IT programs. The dataset enables semantic representation learning and prerequisite inference while supporting cross-institutional curriculum analysis. We propose a hybrid framework that combines Transformer-based semantic encoding with graph-based prerequisite optimization and constraint-aware curriculum sequencing. The novelty of this work lies in integrating semantic prerequisite discovery, optimization-driven curriculum structuring, and explainable AI within a unified decision-support framework. Experimental results demonstrate that the proposed approach consistently outperforms existing machine learning and deep learning baselines, achieving higher prerequisite prediction accuracy, improved curriculum feasibility, and more coherent course sequencing, thereby offering a scalable and interpretable solution for evidence-based curriculum redesign in higher education. Full article

With the rapid development of new energy vehicles and energy storage systems, accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is of great importance for predictive maintenance and operational safety. However, battery degradation during cycling usually exhibits multi-scale characteristics, including long-term degradation trends, stage-wise drifts, and stochastic disturbances, which makes existing methods still face significant challenges in multi-step forecasting and cross-domain generalization. To address this issue, this paper proposes a time–frequency fusion model for multi-step RUL prediction, termed TF-RULNet (Time-Frequency RUL Network). The model takes cycle-level feature sequences as input and consists of three components: a multi-scale temporal convolution encoder (MSTC) for parallel extraction of degradation cues at different temporal scales; a multi-head spectral interaction module (MHSI), which performs 1D-FFT along the temporal dimension for each head and further applies adaptive band-wise mask refinement to capture local spectral structures and hierarchical band patterns with a computational complexity of $O(LlogL)$; and a cross-gated fusion module (CGF), which generates gating signals from the summary of one domain to modulate the features of the other domain, thereby enabling dynamic balancing and complementary enhancement of time–frequency information. Experiments are conducted on the NASA dataset (B005/B007) for in-domain evaluation, and further cross-dataset tests from NASA to the Maryland dataset (CS-35/CS-37) are carried out to verify the robustness of the proposed model under distribution shifts. The results show that, compared with the strongest baseline PatchTST, TF-RULNet reduces RMSE and MAE by more than 38.23% and 50.51%, respectively, in cross-dataset generalization, while achieving an additional RMSE reduction of about 24% in in-domain prediction. In summary, TF-RULNet can effectively characterize the multi-scale time–frequency degradation patterns of batteries and improve cross-domain generalization, providing a high-accuracy and scalable modeling solution for practical battery health management and life prognostics. Full article

The NAC (NAM, ATAF1/2, and CUC1/2) family of transcription factors (TFs) play critical roles in regulating salt tolerance across diverse plant species. This study identified and characterized 101 NAC TFs in eggplant (Solanum melongena L.), revealing their diverse physicochemical properties, chromosomal distributions, and evolutionary relationships. Based on its salt stress-induced expression pattern and homology to known salt-responsive NAC factors, SmNAC28 was selected as a key candidate for functional investigation of salt tolerance. Expression profiling indicated that SmNAC28 is preferentially expressed in roots and stems, and its transcript levels are modulated by salt stress. Subcellular localization confirmed that SmNAC28 localizes to both the plasma membrane and nucleus, a dynamic distribution regulated by S-palmitoylation. Under normal conditions, SmNAC28 is anchored to the plasma membrane and nucleus via S-palmitoylation; upon salt stress exposure, it undergoes depalmitoylation and translocates to the nucleus. Using a hairy root transformation system in eggplant, we demonstrated that overexpression of SmNAC28 in roots significantly enhanced salt tolerance by mitigating oxidative damage, maintaining ion homeostasis, and promoting osmotic adjustment. Analysis of transcript levels further revealed that SmNAC28 overexpression upregulated ion transporter genes (NHX2, CHXs), signaling genes (CIPKs), and the proline biosynthesis gene (P5CS), which demonstrated that SmNAC28 integrates antioxidant defense, ion homeostasis, and osmotic regulation to confer salt tolerance. This study reveals the response mechanism of SmNAC28 to salt stress of the eggplant transcription factor SmNAC28 under salt stress, and provided a research foundation for salt tolerance breeding. Full article

Patients with chronic kidney disease (CKD) have a markedly increased cardiovascular risk that is not fully explained by traditional risk factors. Gut-derived uremic toxins, indoxyl sulfate (IS), indole-3-acetic acid (IAA), and p-cresyl sulfate (pCS), are poorly cleared by dialysis and may contribute to vascular damage. This cross-sectional observational study included 70 patients with CKD under different clinical conditions (pre-dialysis, peritoneal dialysis, hemodialysis, and kidney transplantation) and 17 healthy controls. Serum levels of IS, IAA, pCS and Klotho were measured, and vascular damage was assessed by carotid intima–media thickness (IMT) using ultrasound. CKD patients showed higher concentrations of IS, IAA, and pCS compared with controls, with the highest levels observed in hemodialysis patients. Peritoneal dialysis was associated with elevated IS and pCS, whereas in kidney transplantation, IS and IAA levels did not differ significantly from controls, and pCS remained elevated. Carotid IMT was higher in patients with diabetes and those undergoing hemodialysis. IAA correlated significantly with left/mean IMT, and mean IMT was the only parameter associated with previous cardiovascular events. These findings suggest that gut-derived uremic toxins, particularly IAA, might be associated with subclinical vascular damage in advanced CKD, although larger studies are needed to confirm these associations. Full article

All-inorganic cesium lead iodide (CsPbI₃) has been investigated for more than a decade as an absorber for perovskite photovoltaics thanks to its attractive bandgap, thermal robustness compared with hybrid perovskites, and compatibility with tandem concepts. Yet, despite remarkable efficiency progress, CsPbI₃ remains far from widespread commercialization. The core roadblock is the metastability of the photoactive black perovskite phases (α/γ/β) against transformation to the photoinactive yellow δ-phase under realistic conditions, amplified by defect chemistry, ion migration, and interfacial reactions. Additional barriers arise from scale-up constraints (film uniformity, throughput, solvent management), long-term operational stability (humidity, heat, UV, bias), and environmental/safety requirements, especially lead containment, sequestration, and end-of-life strategies. This review critically analyzes the intertwined physical, chemical, and engineering factors that still limit CsPbI₃ deployment, with emphasis on how solutions in one domain can fail without co-design in others. This review summarizes state-of-the-art stabilization strategies (size/strain engineering, additive/doping routes, surface/interface passivation, and encapsulation), highlight scalable manufacturing pathways including solvent-minimized and vacuum-assisted approaches, and discuss lead-mitigation technologies such as Pb-adsorbing functional layers. Finally, I argue that artificial intelligence (AI)—from machine-learning stability models to process monitoring, robotic optimization, and digital twins—has become essential to navigate the enormous parameter space of CsPbI₃ materials and manufacturing. It concludes with actionable recommendations and future directions toward bankable, scalable, and sustainable CsPbI₃ photovoltaics. Full article

Grain number per spike (GNS) is a key component of wheat yield, yet its genetic architecture remains incompletely understood. This study phenotyped 610 wheat accessions for GNS in four environments and genotyped them using 429,721 single nucleotide polymorphisms (SNPs). The phenotypes were associated with the SNPs using a three-variance multi-locus random-SNP-effect mixed linear model (3VmrMLM) to identify quantitative trait nucleotides (QTNs), as well as QTN-by-environment (QEI) and QTN-by-QTN (QQI) interactions. These genetic components and residual error explained approximately 18%, 31%, 28%, and 23% of the phenotypic variance, respectively. Two and one previously reported genes were found around QTNs and QEIs, respectively. Bioinformatics and haplotype analyses subsequently yielded 25 candidate genes, 22 gene-by-environment interactions (GEIs), and 24 gene-by-gene interactions (GGIs) around the QTNs, QEIs, and QQIs, respectively. Notably, TraesCS1D01G280000, the wheat homolog of OsRopGEF10, was located near a major QTN explaining over 10% of the total phenotypic variation. A gene interaction network constructed from all identified genes highlighted the central role of GGIs in GNS regulation. Environmental variation may reshape the regulatory network through GEIs. Furthermore, superior haplotypes of 12 candidate genes were identified, providing valuable targets for improving wheat yield. Overall, this study dissects the genetic architecture of GNS and offers practical resources for wheat molecular breeding. Full article

Pozzolanic concrete demonstrates intricate, highly nonlinear material interactions that pose significant challenges for the accurate prediction of compressive strength (CS). This study introduces a novel, interpretable ensemble machine learning (ML) framework for predicting CS based on 759 mixture records encompassing cement, aggregates, supplementary cementitious materials (pozzolans), water/binder (W/B), superplasticizer, water, and curing age. Descriptive analysis and ANOVA were used to identify key predictors, followed by an 80/20 train–test split with 10-fold cross-validation to ensure robust and generalizable modeling. To further enhance model reliability, 5% of outliers were removed using an isolation forest algorithm, after which data were normalized and ensemble hyperparameters optimized. Among the evaluated models, the extra trees algorithm with standard scaling demonstrated the most stable generalization, achieving a coefficient of determination (R²) of 0.978 and a root mean square error (RMSE) of 4.197 MPa on the test set, and R² = 0.966 (RMSE = 5.053 MPa) under 10-fold cross-validation. Feature importance, SHAP, and partial dependence analyses consistently demonstrated that W/B, curing age, and cement are the principal determinants of CS. Finally, multi-objective optimization generated high-strength, low-impact mixtures, confirming the framework’s effectiveness as a transparent decision-support tool for performance- and sustainability-oriented pozzolanic concrete design. This study is novel in combining interpretable ensemble ML with multi-objective optimization to simultaneously achieve precise CS prediction and the formulation of sustainable, performance-optimized pozzolanic concrete mixtures. Full article

As a core area for soil and water conservation on the Loess Plateau and a national primary shale oil production zone, Qingyang City faces an increasingly acute contradiction between its inherently fragile ecological base and energy development activities. From the dual perspectives of ecological regulating services and production-supporting services, this study selected six key ecosystem services—habitat quality (HQ), soil retention (SR), carbon storage (CS), water yield (WY), food supply (FS), and grassland forage supply (GS)—to comprehensively assess their spatiotemporal evolution, trade-off/synergy relationships, and driving mechanisms from 2000 to 2020. The results indicate: (1) Significant changes occurred in the total amounts and spatial patterns of all ecosystem services during 2000–2020. HQ showed a fluctuating upward trend, while SR, FS, and GS increased overall; by contrast, CS and WY generally declined. (2) Ecosystem services exhibited a differentiated pattern characterized by “intra-category synergy and inter-category trade-off.” Regulating and supporting services were generally dominated by synergistic relationships, although clear differences remained among specific service pairs; provisioning services generally showed trade-offs with regulating services, among which the trade-offs between FS–HQ and between FS–GS were the most pronounced, whereas FS–CS showed a certain degree of synergy. (3) Driving force analysis revealed a continuous decline in the influence of natural factors and a sharp intensification of human activity factors. Groundwater level and land-use intensity became core drivers of pattern shifts, with their explanatory power increasing significantly. The study reveals that ecosystem services in Qingyang have rapidly transitioned from being dominated by natural hydrothermal conditions to being profoundly reshaped by energy development activities, exposing the region to the ecological risk of a “resource curse.” These findings provide a scientific basis and management insights for achieving coordinated development between resource exploitation and ecological conservation in ecologically fragile areas of the Loess Plateau. Full article

Background: Ventricular septal rupture (VSR) following acute myocardial infarction (AMI) creates an abrupt left-to-right shunt that can progress to cardiogenic shock (CS). Once CS develops, mortality increases dramatically and delayed repair becomes less feasible. Intra-aortic balloon pumps (IABPs) are widely used to facilitate delayed repair; however, whether initiating IABP before CS onset improves survival remains unclear. Methods: We retrospectively analyzed 124 patients with AMI-related VSR (2009–2024), categorized by IABP timing relative to CS onset (defined as first catecholamine administration) into pre-CS, post-CS, and no-IABP groups. The primary outcome was all-cause mortality within 90 days after AMI onset. Kaplan–Meier curves and Cox proportional hazards models were applied, with subgroup analyses by CS status. Results: The 90-day survival rate was 68.2% in the pre-CS IABP group, 14.3% in the post-CS group, and 35.1% in the no-IABP group. Pre-CS IABP was associated with significantly lower mortality compared with no-IABP (adjusted HR = 0.401, 95% CI 0.174–0.925, p = 0.032) and post-CS IABP (adjusted HR = 0.369, 95% CI 0.149–0.910, p = 0.030). In the CS subgroup, IABP use did not improve survival (19.4% vs. 17.6%, p = 0.365). Among non-CS patients, IABP use was independently associated with lower mortality (85.7% vs. 50.0%, p = 0.027; adjusted HR = 0.178, 95% CI 0.040–0.801, p = 0.025). Conclusions: Given the retrospective design and limited sample size, these findings are hypothesis-generating. Early IABP use was associated with improved short-term survival, an effect not observed once CS had developed. These findings support early risk stratification to identify high-risk patients who may benefit from timely hemodynamic support. Full article

Background/Objectives: Inflammatory mediators within the tumor microenvironment contribute to lung cancer progression by enhancing cellular motility and invasive capacity through cytokine-dependent signaling networks. Modulation of these inflammation-associated pathways by dietary bioactive compounds may provide complementary strategies for limiting cancer aggressiveness. Our objective was to examine the inhibitory effects of a cyanidin-3-O-glucoside (C3G)-rich fraction from Kum Akha pigmented black rice (CKAB-P1) on inflammation-stimulated A549 cancer cell progression. Methods: CKAB-P1 was obtained through solvent-partition extraction and chemically characterized using the pH differential method and high-performance liquid chromatography. A549 cells were pretreated with CKAB-P1 or C3G, followed by stimulation with conditioned medium predominantly containing IL-6 and IL-1β derived from LPS-exposed THP-1 macrophages (THP-1-CS). Effects on cancer cell migration and invasion were evaluated using wound-healing, Transwell invasion, gelatin zymography, and Western blot analyses. Results: CKAB-P1 contained 106.62 ± 3.54 mg/g extract of total anthocyanins, with C3G representing the major constituent (59.42 ± 2.54 mg/g extract). Exposure of THP-1-CS stimulated migration and invasion of A549 lung cancer, and neutralization of IL-6 and IL-1β reduced these pro-migratory effects, confirming cytokine involvement. Treatment with CKAB-P1 (10–40 μg/mL) or C3G (2.5–20 μg/mL) markedly attenuated inflammation-enhanced migration and invasion (p < 0.05). A reduction in MMP-2 and MMP-9 activity, along with decreased expression of invasion-associated protein expressions (uPA, uPAR, and MT1-MMP), was observed. Furthermore, both CKAB-P1 and C3G attenuated phosphorylation of JAK1 and STAT3. Conclusions: These findings suggest that anthocyanin-enriched black rice fraction may limit inflammation-driven A549 lung cancer cell aggressiveness through modulation of the cytokine-driven JAK1/STAT3 signaling cascade, indicating its potential relevance as a bioactive dietary component targeting tumor-associated inflammatory signaling. Full article

The dense passivation film (DPF) formed on the surface of martensitic stainless steel effectively improves corrosion resistance, but it also hinders the adsorption and diffusion of active nitrogen atoms during gas nitriding. In this work, the influence of the DPF of 1Cr11Ni2W2MoV stainless steel on gas nitriding was overcome by controlling the cooling rate during stainless steel solution treatment, thereby enabling the successful formation of a nitrided layer. The effects of nitrogen potential on the microstructure, phase constitution, and tribological performance of the nitrided layer were systematically investigated. A dense passivation film formed at a solid-solution cooling rate of 110 ± 5 °C/s effectively inhibited nitrogen diffusion, resulting in the absence of a nitrided layer. However, when the cooling rate during solid solution was reduced to 80 ± 5 °C/s, the precipitation of chromium carbide along the grain boundaries damaged the density and integrity of the DPF, thereby enabling the formation of a nitrided layer during gas nitriding. A high nitrogen potential enhanced nitrogen diffusion and increased the nitrided layer thickness. However, an excessively high nitrogen potential led to nitrogen enrichment along grain boundaries, resulting in microcracking and reduced mechanical integrity of the compound layer. When the nitrogen potential was 1.0, a uniform and crack-free nitrided layer with a surface hardness exceeding 1000 HV_0.1 was obtained. Tribological tests combined with SEM observations of the worn surfaces showed that gas nitriding significantly reduced the friction coefficient and wear rate compared with the matrix sample. Among the nitrided samples, H-10 exhibited the lowest friction coefficient and wear rate, whereas H-23 showed relatively inferior wear resistance due to microcrack-related brittleness. The dominant wear mechanism changed from severe abrasive–adhesive wear in the matrix sample to mild abrasive wear in the nitrided samples. These results indicate that regulating passivation film integrity through heat treatment, together with optimizing nitrogen potential, is an effective strategy for achieving high-quality gas nitriding and improved tribological performance in martensitic stainless steel. Full article

Selenium nanoparticles (SeNPs) represent promising bioactive agents due to their reduced toxicity and multifunctional biological properties. In this study, SeNPs were synthesized via an eco-friendly phytosynthesis approach using Curcuma longa extract, yielding curcumin-functionalized selenium nanoparticles (cur–SeNPs). The composites (cur–SeNPs), either in native extract form or isolated, were incorporated into siloxane hybrid matrices prepared by the sol–gel method from tetraethyl orthosilicate: dimethyldimethoxysilane precursors, with polyvinylpyrrolidone (PVP) as a structural modifier. The host matrices were differentiated by the ratios between the precursors of the siloxane network, 3:1 for CS0–CS4, respectively, 1:1 for CS5, modified with PVP in the case of CS2 and CS3. These were loaded with cur–SeNPs–T in the cases of CS1, CS2, CS5 or with cur–SeNPs for CS3 and CS4. FTIR, XRD, SEM, and EDX analyses confirmed the formation of amorphous siloxane networks with well-dispersed SeNPs (up to ~12 wt%). PVP incorporation generated ordered mesoporous structures, increasing total pore volume sixfold and enlarging the average pore diameter to 9.26 nm. Studies about selenium ion release demonstrate that mesoporosity significantly enhances diffusion-controlled release. Antimicrobial assays against Staphylococcus aureus, Escherichia coli, and Candida albicans reveal a synergistic effect between curcuminoids and SeNPs, particularly in matrices with higher nanoparticle loading. The sol–gel technique for obtaining hybrid materials is very versatile regarding the supports on which the resulting materials or the compounds hosted in these host networks can be deposited. The dynamics of the development of hybrid materials is also reflected in the multitude of applications in various fields such as bio-medical, electronics, agriculture or food. Results obtained in this work highlight the potential of the developed systems for antimicrobial coatings on glass substrates and targeted delivery applications. Full article

Currently, the resource wastage and safety hazards caused by fruit and vegetable spoilage are becoming increasingly prominent. Developing green, efficient, and non-toxic novel preservation materials has emerged as a hot spot in fruit and vegetable research. Based on this, this study utilized pomegranate peel as a raw material to prepare spherical multifunctional carbon dots (P-CDs) with an average particle size of 1.98 ± 0.58 nm through a one-step hydrothermal reaction. Subsequently, P-CDs were co-incorporated with L-cysteine (L-Cys) into a polyvinyl alcohol (PVA) and chitosan (CS) matrix to construct a novel composite coating material with combined antibacterial, antioxidant, and preservation functions. Experimental results demonstrate that P-CDs exhibit outstanding antioxidant activity and antibacterial performance. Compared to PVA/CS film, the P-CDs/L-Cys/PVA/CS film exhibited a 6.55 MPa increase in tensile strength and significantly enhanced thermal stability. Furthermore, the incorporation of P-CDs and L-Cys markedly boosted the PVA/CS film’s antioxidant activity (97% for ABTS; 85.69% for DPPH), antibacterial performance, and ultraviolet (UV) shielding capability. Coating cherry tomatoes with the P-CDs/L-cysteine/PVA/CS composite extended their shelf life by 6 days. This composite coating material exhibits preliminary biocompatibility and eco-friendly properties, aligning with green sustainable development needs and offering a novel potential solution for food preservation technology, while its practical applicability to food safety requires further comprehensive verification. Full article

The new compound Cs₂Cu₂[V₄O₁₂]Br₂ was synthesized by the chemical vapor transport reaction method. Structural data obtained by single-crystal X-ray diffraction in the temperature range 100–700 K revealed three successive (with decreasing temperature) structural phase transitions: from the high-temperature aristotype structure I4/mmm (>550 K) to the polymorph P4/mnc (550–340 K), then to P4/m (340–300 K), and finally to the low-temperature phase I4/m (<300 K). The crystal structure of the new compound is based upon the Cu₂[V₄O₁₂]⁰ layers, consisting of four-membered rings of corner-sharing vanadate tetrahedra linked by CuO₄ squares. Analysis of the structural evolution with increasing temperature shows that the entire sequence of phase transitions is governed by the rotation of the [V₄O₁₂]⁴⁻ rings about the z axis. Full article

This study investigates the influence of bottom ash (BA) and marble dust powder (MD) as partial replacements for Ordinary Portland Cement (OPC) on the physical, mechanical, and mass loss performance of cement pastes under cyclic seawater exposure and their economic feasibility. Mixtures containing 0–20% BA and 0–20% MP were tested to evaluate their workability, strength, porosity, durability, and cost efficiency. The results indicate that BA reduces workability, which is reflected in the lower slump values of mixtures with a higher BA content, whereas MD enhances fluidity by filling the voids between particles and improving the packing density of the mixture, which results in better workability. The optimal composition, which was 15% bottom ash and 10% marble dust powder, achieved a superior mechanical performance, with compressive strength (CS) and flexural strength (FS) increases of 2.2% and 38.7%, respectively, at 28 days compared to the control. Increasing the BA and MD content up to a total of 35% of the binder generally led to a moderate reduction in early-age strength, while mixtures with 20% replacement exhibited comparable or improved long-term strength at 90 days. This led to decreased porosity and improved long-term mass loss performance under cyclic seawater exposure. The incorporation of BA and MD also reduced water absorption, indicating enhanced durability, with these beneficial effects becoming more pronounced at later ages. Economically, cement substitution with BA and MD reduced production costs by up to 39.6%. In summary, moderate incorporation of BA and MD enhances performance, reduces cost, and supports the sustainable utilization of industrial waste in cementitious materials. The mixture proportions investigated in this study offer a promising alternative binder for use in the sustainable building sector. Full article