Search Results (58,440)

This study demonstrates how Structural Topic Modeling (STM) can be used to analyze qualitative student comments in conjunction with quantitative student evaluation of teaching (SET) scores, providing a scalable framework for interpreting student evaluations of teaching. Drawing on 286,203 open-ended comments collected over fourteen years at a large U.S. research university, we identify eleven latent topics that characterize how students describe instructional experiences. Unlike traditional topic modeling methods, STM allows us to examine how topic prevalence varies with course and instructor attributes, including instructor gender, course discipline, enrollment size, and numeric SET scores. To illustrate the utility of the model, we show that topic prevalence aligns with SET ratings in expected ways and that students associate specific teaching attributes with instructor gender, though the effects are relatively small. Importantly, the direction and strength of topic–SET correlations are consistent across male and female instructors, suggesting shared student perceptions of effective teaching practices. Our findings underscore the potential of STM to contextualize qualitative feedback, support fairer teaching evaluations, inform institutional decision-making, and examine the relationship between qualitative student comments and numeric SET ratings. Full article

As direct detection methods of Borrelia burgdorferi are limited, serology plays an important role in diagnosing Lyme disease (LD). There are various types of Lyme serological tests with varying diagnostic accuracy, so it is necessary to compare and rank them. The aim of this study is to compare the accuracy of various serological diagnostic methods for LD using network meta-analysis (NMA). We searched the Cochrane Library and PubMed databases for all serological diagnostic accuracy studies published from the discovery of LD until June 2024. After screening, we assessed the quality of the included studies with QUADAS-C and extracted relevant data. We calculated the Q* index of the receiver operating characteristic curve for each diagnostic test. Meta-disc 2.0 and Stata 15.0 were used to perform traditional meta-analysis and NMA with the gold standard (the comprehensive evaluation) as a reference. We then compared the Q* index values between different methods using two-by-two comparisons and ranked them accordingly. A total of 52 studies with 181,032 participants, including 5318 patients with LD, were included. These studies covered 14 diagnostic methods. The results of the NMA suggest that modified two-tiered testing (MTTT), C6 enzyme immunoassay (EIA), and standard two-tiered testing (STTT) rank in the top three among the 14 methods in terms of Q* index, with MTTT being the highest, followed by C6 EIA and STTT. MTTT and C6 EIA have higher overall diagnostic performance, and their accuracy is not inferior to that of the widely used STTT (PROSPERO CRD42022378326). Full article

Traditional vehicle emergency braking research suffers from inaccurate maximum road adhesion coefficient identification and suboptimal wheel slip ratio control. To address these challenges in electronic hydraulic braking systems’ anti-lock braking technology, firstly, this paper proposes a CDOA-SENet-CNN neural network to precisely estimate the maximum road adhesion coefficient by monitoring and analyzing the braking process. Secondly, correlation curves between peak adhesion coefficients and ideal slip ratios are established using the Burckhardt model and CarSim 2020, and the estimated maximum adhesion coefficient from the CDOA-SENet-CNN network is used with these curves to determine the optimal slip ratio for the single-neuron integral sliding mode control (SNISMC) algorithm. Finally, an SNISMC control strategy is developed to adjust the wheel slip ratio to the optimal value, achieving stable wheel control across diverse road surfaces. Results indicate that the CDOA-SENet-CNN network rapidly and accurately estimates the peak braking surface adhesion coefficient. The SNISMC control strategy significantly enhances wheel slip ratio control, consequently increasing the effectiveness of vehicle brakes. This paper introduces an innovative, stable, and efficient solution for enhancing vehicle braking safety. Full article

Advanced therapy medicinal products (ATMPs), especially effective against cancer, remain costly due to their reliance on genetically modified T cells. Contamination during production is a major concern, as traditional quality control methods involve samplings, which can themselves introduce contaminants. It is therefore necessary to develop methods for detecting contamination without sampling and, if possible, in real time. In this article, we present a white light spectroscopy method that makes this possible. It is based on shape analysis of the absorption spectrum, which evolves from an approximately Gaussian shape to a shape modified by the 1/λ component of bacterial absorption spectra when contamination develops. A warning value based on this shape descriptor is proposed. It is demonstrated that a few hours are sufficient to detect contamination and trigger an alarm to quickly stop the production. This time-saving should reduce the cost of these new drugs, making them accessible to as many people as possible. This method can be used regardless of the type of contaminants, provided that the shape of their absorption spectrum is sufficiently different from that of pure T cells so that the shape descriptor is efficient. Full article

Rhabditis (Rhabditella) axei is a predominantly free-living nematode commonly found in sewage systems and decomposing organic matter. While primarily saprophytic, it has been documented as an opportunistic pathogen in human urinary and gastrointestinal tracts. The Chinese red panda (Ailurus styani), a rare and protected species in China, has not previously been reported as a host for Rhabditis (Rhabditella) spp. infections. This study reports the first documented occurrence of R. axei in red panda feces, unambiguously confirmed through integrative taxonomic approaches combining morphological and molecular analyses. The nematodes exhibited key morphological features consistent with R. axei, including a cylindrical rhabditiform esophagus, sexually dimorphic tail structures, and diagnostic spicule morphology. Molecular analysis based on 18S-ITS-28S rDNA sequencing confirmed their identity, showing >99% sequence similarity to R. axei reference strains (GenBank: PP135624.1, PP135622.1). Phylogenetic reconstruction using 18S rDNA and ITS rDNA sequences placed the isolate within a well-supported R. axei clade, clearly distinguishing it from related species such as R. blumi and R. brassicae. The findings demonstrate the ecological plasticity of R. axei as a facultative parasite capable of infecting non-traditional hosts and further highlight potential zoonotic risks associated with environmental exposure in captive wildlife populations. Our results emphasize the indispensable role of molecular diagnostics in accurately distinguishing morphologically similar nematodes within the Rhabditidae family, while providing essential baseline data for health monitoring in both in situ and ex situ conservation programs for this endangered species. Full article

This study presents a comprehensive computational analysis of flow rate efficiency during uranium extraction via the In Situ Recovery method. Using field data from a deposit located in Southern Kazakhstan, a series of mathematical models were developed to evaluate the distribution and balance of leaching solution. A reactive transport model incorporating uranium dissolution kinetics and acid–rock interactions were utilized to assess the accuracy of both traditional and proposed methods. The results reveal a significant spatial imbalance in sulfuric acid distribution, with up to 239.1 tons of acid migrating beyond the block boundaries. To reduce computational demands while maintaining predictive accuracy, two alternative methods, a streamline-based and a trajectory-based approach were proposed and verified. The streamline method showed close agreement with reactive transport modeling and was able to effectively identify the presence of intra-block reagent imbalance. The trajectory-based method provided detailed insight into flow dynamics but tended to overestimate acid overflow outside the block. Both alternative methods outperformed the conventional approach in terms of accuracy by accounting for geological heterogeneity and well spacing. The proposed methods have significantly lower computational costs, as they do not require solving complex systems of partial differential equations involved in reactive transport simulations. The proposed approaches can be used to analyze the efficiency of mineral In Situ Recovery at both the design and operational stages, as well as to determine optimal production regimes for reducing economic expenditures in a timely manner. Full article

This study investigates the production and economic feasibility of biochar for smallholder and family farms in Central Amazonia, with potential implications for other tropical regions. The costs of construction of a prototype mobile kiln and biochar production were evaluated, using small-sized biomass from acai (Euterpe oleracea Mart.) agro-industrial residues as feedstock. The biochar produced was characterised in terms of its liming capacity (calcium carbonate equivalence, CaCO_3eq), nutrient content via organic fertilisation methods, and ash analysis by ICP-OES. Field trials with cowpea assessed economic outcomes, as well scenarios of fractional biochar application and cost comparison between biochar production in the prototype kiln and a traditional earth-brick kiln. The prototype kiln showed production costs of USD 0.87–2.06 kg⁻¹, whereas traditional kiln significantly reduced costs (USD 0.03–0.08 kg⁻¹). Biochar application alone increased cowpea revenue by 34%, while combining biochar and lime raised cowpea revenues by up to 84.6%. Owing to high input costs and the low value of the crop, the control treatment generated greater net revenue compared to treatments using lime alone. Moreover, biochar produced in traditional kilns provided a 94% increase in net revenue compared to liming. The estimated externalities indicated that carbon credits represented the most significant potential source of income (USD 2217 ha⁻¹). Finally, fractional biochar application in ten years can retain over 97% of soil carbon content, demonstrating potential for sustainable agriculture and carbon sequestration and a potential further motivation for farmers if integrated into carbon markets. Public policies and technological adaptations are essential for facilitating biochar adoption by small-scale tropical farmers. Full article

Grapevine (Vitis vinifera L.) cultivation is an important agricultural sector worldwide. Its expansion into new areas, like Kazakhstan, brings significant phytosanitary risks. Viroids, such as grapevine yellow speckle viroid 1 (GYSVd-1) and hop stunt viroid (HSVd), are RNA pathogens that threaten vineyard productivity. They can cause a progressive decline through latent infections. Traditional diagnostic methods are usually targeted and therefore not suitable for thorough surveillance. In contrast, modern high-throughput sequencing (HTS) methods often face challenges due to their high costs and complicated sample preparation, such as ribosomal RNA (rRNA) depletion. This study introduces a simplified diagnostic workflow that overcomes these barriers. We utilized the latest Oxford Nanopore V14 cDNA chemistry, which is designed to prevent internal priming, by substituting a targeted oligo(dT)VN priming strategy to facilitate the sequencing of non-polyadenylated viroids from total RNA extracts, completely bypassing the rRNA depletion step and use of random oligonucleotides for c DNA synthesis. This method effectively detects and identifies both GYSVd-1 and HSVd. This workflow significantly reduces the time, cost, and complexity of HTS-based diagnostics. It provides a powerful and scalable tool for establishing strong genomic surveillance and phytosanitary certification programs, which are essential for supporting the growing viticulture industry in Kazakhstan. Full article

Fuzzy signature (${\mathsf{SIG}}_{\mathsf{F}}$) is a type of digital signature that preserves the core functionalities of traditional signatures, while accommodating variations and non-uniformity in the signing key. This property enables the direct use of high-entropy fuzzy data, such as biometric information, as the signing key. In this paper, we define the m-existentially unforgeable under chosen message attack ($\mathfrak{m}{-\mathrm{EUF}-\mathrm{CMA}}^{\ast }$) security of fuzzy signature. Furthermore, we propose a generic construction of fuzzy signature, which is composed of a homomorphic secure sketch ($\mathsf{SS}$) with an error-recoverable property, a homomorphic average-case strong extractor ($\mathsf{Ext}$), and a homomorphic and key-shift* secure signature scheme ($\mathsf{SIG}$). By instantiating the foundational components, we present a $\mathfrak{m}{-\mathrm{EUF}-\mathrm{CMA}}^{\ast }$ secure fuzzy signature instantiation based on the Computational Diffie–Hellman (CDH) assumption over bilinear groups in the standard model. Full article

Adulteration detection in medicinal plant powders remains a critical challenge in quality control. In this study, we propose a hyperspectral imaging (HSI)-based method combined with deep learning models to quantitatively analyze adulteration levels in Anoectochilus roxburghii powder. After preprocessing the spectral data using raw, first-order, and second-order Savitzky–Golay derivatives, we systematically evaluated the performance of traditional machine learning models (Random Forest, Support Vector Regression, Partial Least Squares Regression) and deep learning architectures. While traditional models achieved reasonable accuracy (R² up to 0.885), their performance was limited by feature extraction and generalization ability. A single-channel convolutional neural network (CNN) utilizing individual spectral representations improved performance marginally (maximum R² = 0.882), but still failed to fully capture the multi-scale spectral features. To overcome this, we developed a multi-channel CNN that simultaneously integrates raw, SG-1, and SG-2 spectra, effectively leveraging complementary spectral information. This architecture achieved a significantly higher prediction accuracy (R² = 0.964, MSE = 0.005), demonstrating superior robustness and generalization. The findings highlight the potential of multi-channel deep learning models in enhancing quantitative adulteration detection and ensuring the authenticity of herbal products. Full article

Since 1992, we have promoted the use of descriptions from ethnographic data, including ancient, surviving oral traditions, to aid in explaining the iconography portrayed in pictographs and petroglyphs found in Missouri, particularly those at Picture Cave. The literature to which we refer is from American Indian groups related linguistically and connected to the pre-Columbian inhabitants of Missouri. In addition, we have had on-going conversations with many elder tribal members of the Dhegiha Sioux language group (including the Osage, Quapaw, and Kansa (the Ponca and Omaha are also part of this cognate linguistic group)). With the copious collections of southern Siouan ethnographic accounts, we have been able to explain salient features in the iconography of several of the detailed rock art motifs and vignettes, and propose interpretations. This Midwest region is part of the Cahokia interaction sphere, an area that displays western Mississippian symbolism associated with that found in Missouri rock art as well as on pottery, shell, and copper. Full article

This study examines the hydrodynamic and acoustic performance of plain NACA0018 hydrofoil and modified NACA0018 hydrofoils (foil with a cavity on suction surface) at a Reynolds number ($Re$) of $40,000$, which is indicative of small-scale turbines and marine applications. A cavity was created on suction side surface at 40–50% of the chord length, which is chosen for its efficacy in cavitation control. The present analysis examines the impact of the cavity on lift-to-drag-ratio ($L/D$) and cavity length at three cavitation numbers ($1.7$, $1.2$, and $0.93$) for plain and modified hydrofoils. Simulations demonstrate a significant enhancement of 7% in the lift-to-drag ratio relative to traditional designed foils. Contrary to earlier observations, the cavity length increases instead of decreasing for the modified hydrofoil. Both periodic steady and turbulent inflow conditions are captured that simulate the complex cavity dynamics and flow–acoustic interactions. It is found that a reduction in RMS velocity with modified blade suggests flow stabilization. Spectral analysis using Mel-frequency techniques confirms the cavity’s potential to reduce low-frequency flow-induced noise. These findings offer new insights for designing quieter and more efficient hydrofoils and turbine blades. Full article

Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article

Sustainability education has become a global priority in educational systems, aiming to equip learners with the knowledge, values, and skills necessary to address complex environmental and social challenges. This study specifically aims to understand the role of science education in promoting students’ awareness of sustainability and their understanding of the interconnected relationship between humans and the environment, based on the perceptions and practices of primary science teachers in Al-Ahsa, Saudi Arabia. A qualitative approach was utilized, which included semi-structured interviews complemented by classroom observations as primary data collection instruments. The targeted participants comprised a purposive sample consisting of forty-nine primary-level science instructors from the Al-Ahsa district, located in eastern Saudi Arabia. Emergent concepts from open and axial coding processes by using grounded theory were developed with the gathered data. Based on the findings, teachers perceive science teaching not only as knowledge delivery but as an opportunity to cultivate critical thinking and nurture eco-friendly actions among pupils. Classroom practices that underscore environmental values and principles of sustainability foster a transformative view of the teacher’s role beyond traditional boundaries. The data also highlighted classroom practices that integrate environmental values and sustainability principles, reflecting a transformative perspective on the teacher’s educational role. Full article

Modern cloud-based Internet of Things (IoT) infrastructures face increasingly sophisticated and diverse cyber threats that challenge traditional detection systems in terms of scalability, adaptability, and explainability. In this paper, we present (H-DIR)², a hybrid entropy-based framework designed to detect and mitigate anomalies in large-scale heterogeneous networks. The framework combines Shannon entropy analysis with Associated Random Neural Networks (ARNNs) and integrates semantic reasoning through RDF/SPARQL, all embedded within a distributed Apache Spark 3.5.0 pipeline. We validate (H-DIR)² across three critical attack scenarios—SYN Flood (TCP), DAO-DIO (RPL), and NTP amplification (UDP)—using real-world datasets. The system achieves a mean detection latency of 247 ms and an AUC of 0.978 for SYN floods. For DAO-DIO manipulations, it increases the packet delivery ratio from 81.2% to 96.4% (p < 0.01), and for NTP amplification, it reduces the peak load by 88%. The framework achieves vertical scalability across millions of endpoints and horizontal scalability on datasets exceeding 10 TB. All code, datasets, and Docker images are provided to ensure full reproducibility. By coupling adaptive neural inference with semantic explainability, (H-DIR)² offers a transparent and scalable solution for cloud–IoT cybersecurity, establishing a robust baseline for future developments in edge-aware and zero-day threat detection. Full article