Search Results (14,194)

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Refined urban flood risk assessment serves as a fundamental safeguard for urban sustainability. However, most studies based on scenario analysis method tend to rely on a single risk evaluation criterion, with limited consideration of applicability differences arising from underlying computational principles. Furthermore, as flood events are inherently dynamic spatial–temporal processes, most studies often overlook the three-dimensional characteristics of flood risk, particularly the connectivity of risk in physically adjacent spaces. To address these issues, this paper proposes a comprehensive flood risk assessment framework that integrates the spatial–temporal characteristics of disaster-causing factors. An improved analysis method for grid-scale flood assessment is proposed based on the comprehensive mechanical analysis method and the drowning factor. In addition, a quantitative approach for characterizing the spatial aggregation of urban flood risk is established using risk thresholds and aggregation area thresholds. These methods are then integrated through a combination weighting–cluster analysis framework for comprehensive flood risk assessment. The results show that the improved analysis method can better reflect the change in risk of flow velocity and water depth combined. Spatiotemporally, the Yinshan Road and western section of the Dongzhong Road, exhibiting high localized risk, moderate overall risk, high risk on the time scale and high spatial agglomeration status, are comprehensively assessed as extremely high-risk flooded zones. The proposed framework effectively characterizes the spatial–temporal distribution of disaster-causing factors, providing a scientific basis for disaster prevention and contributing to urban sustainability. Full article

Saudi Arabia experienced rapid convergence in women’s financial inclusion between 2014 and 2024, a period marked by the 2018–2019 reforms expanding women’s economic rights and the accelerated deployment of digital payment infrastructure. Using four waves of Global Findex microdata (2014, 2017, 2021, and 2024), this study estimates probability-weighted logit models with average marginal effects and decomposes gender gaps using nonlinear Kitagawa and Blinder–Oaxaca methods. Reform-era dynamics are examined by tracing changes in the gender gap across survey waves. The findings indicate that aggregate gender gaps in account ownership and digital payment usage narrowed substantially by 2024, with conditional gaps among employed adults no longer statistically significant, while sizable disparities persist among individuals outside the workforce. Decomposition results highlight increased female labor force participation as a key correlate of convergence, consistent with labor market integration playing a central role in women’s financial inclusion during the reform era. Full article

Transcriptome profiling is a cornerstone of functional genomics, enabling the detailed characterization of gene expression in health and disease. Bulk RNA sequencing (bulk RNAseq) remains the most widely used approach in clinical and large-cohort studies due to its cost-effectiveness, robustness, and comprehensive transcriptome coverage. However, bulk RNAseq inherently averages gene expression signals across heterogeneous cell populations, thereby masking cellular diversity and obscuring rare cell types. In contrast, single-cell RNA sequencing (scRNAseq) enables a high-resolution analysis of cellular heterogeneity, allowing the identification of distinct cell types, transitional states, and developmental trajectories. Nevertheless, scRNAseq is associated with higher cost, limited scalability, increased technical noise, sparse expression matrices, and protocol-dependent biases introduced during tissue dissociation or nuclear isolation. In this review, we summarize the conceptual and methodological foundations of integrating bulk RNAseq and scRNAseq data, emphasizing their complementary strengths and limitations. We discuss how scRNAseq-derived cell-type atlases can serve as reference matrices for computational reconstruction (deconvolution) of bulk RNAseq profiles and examine key sources of technical and biological variability. Furthermore, we outline major integration strategies, including reference-based deconvolution, pseudobulk aggregation, and Bayesian joint modeling to provide an overview of widely used analytical tools and essential components of scRNAseq data processing workflows. Full article

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support. Full article

To investigate the effects of incorporating nanomaterials—carbon nanotubes (CNTs) and graphene oxide (GO)—on the axial compressive mechanical properties of alkali-activated recycled aggregate concrete (AARAC) after high-temperature exposure, this study designed 51 sets of specimens with recycled coarse aggregate replacement rate, nanomaterial content, and temperature as the main parameters. Compression tests were conducted to analyze the failure mode and strength variation in AARAC specimens after heating. In addition, microscopic tests, including X-ray diffraction, scanning electron microscopy, and computed tomography (CT scanning), were performed to analyze the microstructural characteristics of the post-heated AARAC specimens. The results indicate that as the replacement rate of recycled coarse aggregate increased from 0% to 100%, the residual compressive strength after exposure to 600 °C decreased from 33.6 MPa to 19 MPa. When 0.1 wt% of CNTs is added, the compressive strength of AARAC after exposure to a high temperature of 600 °C increases by approximately 30.4% compared to that of AARAC without nanomaterial addition. When 0.1 wt% of CNTs and 0.05 wt% of GO are added, the compressive strength after exposure to a high temperature of 600 °C increases by approximately 44.3%, while the size of scattered fragments upon failure increased, and the failure mode appeared more complete. Microscopic test results indicate that the high-temperature treatment did not cause significant changes in the main phase composition of AARAC. The synergistic effect of the nanomaterials CNTs and GO can fully utilize their functions as nucleation sites, pore fillers, and crack bridging agents. By strengthening the Interfacial Transition Zone between the recycled coarse aggregate and the cement paste, refining the Matrix Pore Structure, dispersing local thermal stress, and suppressing the propagation of high-temperature cracks, the mechanical properties of AARAC after high-temperature exposure can be effectively maintained. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article

Microplastic pollution has emerged as a major environmental concern due to its persistence, widespread distribution and potential risks to ecosystems and human health. Among the various types of microplastics, fibrous microplastics (FMPs) account for 60% to 90% of all detected microplastic particles in surface waters, primarily originating from synthetic textile production, laundering, and wastewater discharge. Their elongated morphology, high aspect ratio, and complex surface chemistry differentiate them significantly from microplastic fragments or beads, creating unique challenges for effective removal in water treatment systems. In recent years, engineered biochar has attracted increasing attention as a promising and sustainable material for microplastic removal due to tunable pore structure, surface chemistry, and adsorption capacity. However, existing reviews largely discuss microplastic removal in general terms, with limited attention to the distinctive properties of textile FMPs and their implications for biochar design and performance. This review provides a comprehensive and focused analysis of the functional characteristics of biochar that enable the effective removal of textile FMPs in water systems. First, the environmental significance and physicochemical characteristics of textile-derived FMPs are summarized. Next, the major mechanisms governing biochar–microplastic interactions, including physical interception, adsorption, and aggregation processes, are discussed. The review then examines key functional characteristics of engineered biochar, such as pore structure, surface functional groups, hydrophobicity, and composite modifications, that enhance the sequestration of FMPs. Finally, current technological challenges, research gaps, and future directions for developing scalable biochar-based solutions for textile microplastic mitigation are discussed. By linking the unique properties of textile FMPs with the functional design of biochar, this review provides a framework to guide the development of more effective and sustainable treatment strategies for reducing microplastic contamination in aquatic environments. Full article

In the post-antibiotic era, the Bacillus-based direct-fed beneficial microorganisms are emerging as a cornerstone for sustainable animal farming. This study aimed to screen and evaluate Bacillus strains with probiotic potential for use as feed additives. A total of 394 Bacillus strains were initially screened based on their extracellular enzyme production (cellulase, protease, and amylase) and antibacterial activities against Escherichia coli, Staphylococcus aureus, and Salmonella enterica. Two strains, Bacillus velezensis FJAT-10508 and FJAT-13563, were selected and subsequently subjected to in vitro probiotic characterization, safety assessment, and whole-genome analysis. The results demonstrated that both strains exhibited α-hemolysis, acceptable antibiotic susceptibility profiles, absence of invasion and cytotoxicity effect on the Caco-2 cells, and no mobile virulence or antibiotic resistance genes, indicating their safety as probiotic candidates. High endospore-forming efficiencies (72.4–90.8%), strong auto-aggregation (74–85%) and co-aggregation abilities (52–82%) were observed. In addition, both strains showed considerable tolerance to simulated gastrointestinal conditions, with vegetative cell and endospore survival rates of 28.33–38.33% and 85–89.67% at pH 2.0, and 38.33–43.33% and 90.33–96.33% in 0.3% bile salts, respectively. Overall, B. velezensis FJAT-10508 and FJAT-13563 demonstrated robust in vitro probiotic properties, supporting their potential application as reliable Bacillus-based feed additives. Full article

Multi-robot systems (MRS) have attracted a lot of attention from researchers due to their widespread application in various environments. However, in multi-robot collaborative search tasks, two problems often arise: sparse rewards for capturing targets and control oscillations. To address these issues, this paper proposes the hierarchical active perception multi-agent deep deterministic policy gradient (HAP-MADDPG) framework. This framework guides robots to efficiently explore maps and discover targets through global utility planning based on global exploration rate and local information aggregation based on local exploration rate. A stability control mechanism, which includes hysteresis logic and reward decay, is introduced to suppress control oscillations. Experimental results show that the HAP-MADDPG framework achieves a success rate of 96.25% and an average search time of 216.3 steps. The path trajectories are smooth, demonstrating the effectiveness of the proposed approach. Full article

Large-scale green tides dominated by Ulva species have recurred annually in the Southern Yellow Sea for nearly two decades, yet early detection remains challenging due to the patchy distribution of incipient floating macroalgae. This study investigated the spatiotemporal dynamics of Ulva micropropagules during the 2020 outbreak using a systematic cultivation assay. Seawater samples were collected from 23 stations across the Subei Shoal and adjacent waters in April, May, and July, and incubated under controlled laboratory conditions to enumerate Ulva germling densities. Results revealed that Ulva micropropagule abundance peaked in April, with high-density foci concentrated in the Subei Shoal region—particularly in aquaculture areas of Neopyropia J. Brodie & L.-E. Yang, 2020—confirming this zone as one of the important sources. Abundance declined progressively through May and July as macroalgae drifted northward under wind and current forcing. This method effectively identified putative source regions and reconstructed initial dispersal patterns prior to satellite-detectable macroalgal aggregation. These findings demonstrate that Ulva micropropagule monitoring provides a cost-effective, sensitive tool for early warning and Ulva source tracking, offering finer-scale propagule distribution data to inform precision management strategies for mitigating green tide impacts on coastal marine ecosystems. Future research should expand investigations into Ulva micropropagule dynamics to elucidate their mechanistic processes and ecological significance in green tide initiation and development. Full article

Compost is widely used to improve soil fertility and structure, yet its effects on soil physical properties in Vertosols remain insufficiently documented. This study evaluated the effects of repeat compost application on soil carbon and nitrogen contents and selected soil physical properties in Vertosols from three farms in Queensland, Australia (Roma, Dalby, and Goovigen). Compost had been applied at rates between 5 and 22 Mg ha⁻¹ yr⁻¹ for periods ranging from 3 to 11 years, depending on the site. Intact and disturbed soil samples from the top 0–8 cm were analyzed for bulk density, water retention, hydraulic properties, aggregate stability, and water repellence. Aggregate stability was assessed using laser diffraction before and after ultrasonic dispersion. Compost application significantly increased total carbon and nitrogen contents at all sites (p ≤ 0.01), although effects on soil physical properties varied by site. In Dalby, compost improved water retention and aggregate stability; in Goovigen, it resulted in lower Disaggregation Ratios. Compost did not induce soil water repellence at any site. The results indicate that compost amendments improve soil carbon and nitrogen concentrations and can modify soil physical properties in Vertosols, although responses depend on site conditions and management history. Full article

Hybridization in vehicle powertrains extends beyond the aggregate system level and can target individual components to enhance engine performance. While prior studies have highlighted the performance benefits of electrified turbochargers, this work focuses on mitigating engine-out emissions for a medium- to heavy-duty diesel engine with an electrified airpath. Unlike conventional engines and actuators, the alternative engine architecture with an electrified airpath provided superior airpath control. This is critical for fuel-led diesel engines, where the initial combustion cycles during the tip-in phase of a transient operate at a rich equivalence ratio. In this work, a 3.2 L two-cylinder opposed piston two-stroke (OP2S) engine equipped with an Electrically Assisted Turbocharger (EAT) and an electrically operated EGR pump was experimentally tested in a Hardware in the Loop (HIL) setup under transient conditions. Actuator positions were varied to identify strategies that mitigate soot and NO_x without compromising transient response. The experiments are discussed case-wise, where the effects of each airpath actuator, including fuel rate shaping, are analyzed, showing to what extent each strategy mitigates emissions. At the end, an optimized case is presented to the readers for their perusal. The electrified airpath, along with fuel rate shaping, demonstrated cumulative soot reduction up to 92% and NO_x emissions by 77% for a transient load step between 3 and 13 bar BMEP at a mid-engine speed of 1250 rpm. Full article

This study examines how firms’ financial heterogeneity shapes the transmission of monetary policy to investment in Latin American economies. It develops an extended theoretical model with heterogeneous firms, calibrated for Latin American economies, and validates it empirically through local projection models. These projections are applied to both a dataset of 72 of the most representative firms from the six analyzed Latin American economies and simulated data from the theoretical model, enabling direct comparison of the results. The research yields three main findings. First, it shows that financial heterogeneity is crucial and determines how firms respond to a monetary shock. Firms with fragile structures or high levels of indebtedness tend to restrict investment following monetary expansions, whereas firms with stronger financial positions or greater distance to default tend to increase it. The aggregate effect depends on the distribution of financial structures in the economy and which group dominates. Second, a transmission mechanism is identified via a financial channel based on a price–quantity sequence. The drop in the real rate compresses spreads and raises the price of capital; if financial constraints are active, the monetary relief is used to repair balance sheets rather than to invest; otherwise, the stimulus quickly translates into investment. Finally, the study shows that ignoring heterogeneity—as in representative–agent models—leads to a significant overestimation of both the magnitude and persistence of investment responses to monetary policy shocks. Full article

For the automation of Agrobacterium-mediated genetic transformation of tissue-cultured plantlets, accurate leaf vein segmentation is essential. The thin, low-contrast structure of leaf veins frequently leads to fragmented segmentation outputs, despite the proposal of various methodologies for vein segmentation. To address this issue, we propose Direction-Aware U-Net (DA-UNet), an improved U-Net architecture that incorporates a Direction-Aware Context Pooling (DACPool) module and Topology-aware Segmentation loss (TopoSeg loss). The DACPool module explicitly exploits vein orientation to aggregate directional contextual information, while the TopoSeg loss jointly optimizes pixel-level accuracy and topological continuity. DA-UNet achieves efficient leaf vein segmentation with improved continuity and structural integrity, according to evaluations on the self-constructed Tissue-Cultured Plantlet Vein Dataset 2025 (TCPVD2025). Comparative experiment results show that the improved model outperforms PSPNet, DeepLabV3+, U-Net, TransUNet, Swin-UNet, CCNet, and SegNeXt, as evidenced by Recall, Dice, and CONNECT scores of 71.35%, 69.08%, and −2.25, while maintaining competitive Precision of 66.98%. Ablation experiment results provide further evidence for the efficacy of the TopoSeg loss and the DACPool module. The results demonstrate the effectiveness of the proposed vein segmentation framework for generating outputs that are both accurate and structurally consistent, thus enabling reliable automated processes for plant genetic transformation. Full article