Search Results (7,860)

Off-axis reflective telescopes are prone to component misalignment due to external environmental factors and mechanical vibrations. This misalignment introduces low-order aberrations, which severely degrade imaging quality. Thus, active misalignment correction is crucial for maintaining the imaging performance of off-axis reflective telescopes. Current computer-aided alignment technologies for optical systems mostly rely on wavefront sensors to acquire aberrations at multiple fixed fields of view (FOVs) or even the full FOV. This significantly increases system complexity and hinders practical engineering applications. To address this issue, this study first conducts sensitivity analysis of misaligned degrees of freedom (DOFs) using a mode truncation algorithm based on singular value decomposition (SVD). A compensation strategy is proposed to avoid the aberration coupling effect. Furthermore, two novel misalignment aberration compensation methods for off-axis reflective telescopes are presented. These methods require only a single focal spot image and eliminate the need for aberration detection and iterative calculations. One method directly solves component misalignment errors using a convolutional neural network (CNN) based on the system’s point spread function (PSF). To further improve compensation performance, an improved method fusing spot images and Zernike coefficients is proposed. In practical misalignment correction, both methods input a single acquired focal spot image into a well-trained model to obtain the misalignment compensation amount. Simulation experiments demonstrate that the improved method, which uses Zernike polynomial coefficients as an intermediate feature bridge, effectively establishes the mapping relationship between spot images and misalignment amounts. It achieves higher solution accuracy and better aberration compensation effect compared to the direct CNN method. This verifies the necessity of extracting Zernike polynomial coefficient features from spot images. Comparative experiments with the traditional sensitivity matrix method show that the two proposed methods outperform the sensitivity matrix method in aberration compensation accuracy over a large misalignment range. Comprehensive simulation results confirm the feasibility and effectiveness of the proposed methods. They overcome the limitations of existing methods, such as complex structure, high cost, and low efficiency, to a certain extent. Full article

Background/Objectives: Pediatric-onset multiple sclerosis (POMS), defined as onset before age 18, is increasingly recognized as a distinct entity, often associated with a more burdensome disease course and earlier disability milestones than adult-onset MS. Although comorbidities may significantly affect disease progression and outcomes, their prevalence, incidence, risk, and characteristics in POMS remain poorly understood. To date, no systematic review has comprehensively evaluated comorbidities in POMS. The primary aim is to systematically identify and synthesize available evidence on the prevalence, incidence, risk, and characteristics of these comorbidities in POMS populations, as well as any reported effects on disease course, treatment outcomes, and overall clinical management. Methods: We will conduct a systematic review and meta-analysis following a hierarchical and pragmatic analytical strategy tailored to the expected heterogeneity and limited evidence base in POMS. MEDLINE (via PubMed) and Embase (produced by Elsevier) will be searched without date restrictions, combining controlled vocabulary terms (MeSH/Emtree) and relevant keywords for POMS and 15 predefined comorbidity categories. Study selection, abstract and full-text screening, and data extraction will be performed independently by two reviewers using predefined criteria and standardized forms. The primary quantitative outcome will be the pooled prevalence of comorbidities. Where study design and reporting permit, incidence rates will be assessed as secondary outcomes, and risk estimates (e.g., odds ratios) will be evaluated only in studies with appropriate comparator groups. Meta-analyses will be conducted using random-effects models when pooling is feasible. Heterogeneity will be assessed using the I² statistic and Cochran’s Q test, with sensitivity and subgroup analyses performed only when sufficient data are available. When quantitative synthesis is not appropriate due to limited data or substantial heterogeneity, findings will be summarized descriptively. Publication bias will be evaluated using funnel plots and, where applicable, Egger’s and Begg’s tests. This protocol adheres to PRISMA and PRISMA-P guidelines. Discussion: A systematic quantification of comorbidity prevalence, incidence (where available), and risk, together with POMS-specific characteristics and any reported impact on clinical outcomes, is anticipated to provide a crucial evidence base for guiding screening, refining management strategies, and informing future research directions. Ultimately, these findings may improve clinical outcomes and quality of life for children and adolescents with MS. Full article

Kenaf (Hibiscus cannabinus) core, an abundant renewable byproduct rich in cellulose and hemicellulose, has emerged as a candidate to replace or supplement peat and coco coir in soilless culture. This review synthesizes the physical, chemical, and biological performance of ground kenaf core and benchmarks it against conventional substrates. Kenaf core exhibits low bulk density (0.06 to 0.15 g cm⁻³), high total porosity (approximately 90%), and substantial plant available water (approximately 42%), supporting root aeration and water supply. Its pH (6.0–7.2) is near optimal for most crops, whereas electrical conductivity (EC) (3.2–4.7 dS m⁻¹) can exceed recommended ranges for salt-sensitive species, which necessitates pre-leaching or blending. Growth studies show comparable shoot and root performance in blends containing 20 to 70% kenaf, with composted kenaf often outperforming raw core. Pure kenaf generally requires more frequent irrigation and may shrink at high proportions. We outline processing variables such as core purity, particle size, composting, and leaching that govern stability and plant response, identify critical data gaps (including standardized EC and pH methods, and long-term shrinkage), and frame a sustainability agenda. Practically, studies to date indicate that pre-leached kenaf core, incorporated at up to about 70% by volume into peat or coir-based blends with structurally stable components such as perlite, can maintain growth and quality for several ornamental and bedding crops under greenhouse and nursery conditions. At the same time, reports of poor performance in some conifers and early suppression in direct-sown vegetables underscore that the suitability of kenaf-based substrates remains crop specific and dependent on material processing and management. Full article

This paper proposes an integrated framework to evaluate Corporate Social Responsibility (CSR) initiatives in peacebuilding settings using the Analytic Hierarchy Process (AHP). The model is structured around six criteria: conflict sensitivity, economic resilience, social inclusion, governance, education for peace, and sustainability, each subdivided into measurable subcriteria. A key methodological innovation is the introduction of objective grouping, which ensures that each alternative project is assessed only against the subcriteria where it generates tangible impact. Unlike the traditional AHP approach, where alternatives are evaluated against all criteria, objective grouping prevents irrelevant comparisons, reduces the cognitive burden on experts, and increases consistency in judgments. The method distinguishes between direct contributions (full weight allocation) and indirect contributions (partial weight allocation), while excluding unrelated dimensions. This refinement yields more transparent and context-sensitive prioritization, particularly relevant for fragile territories where CSR interventions must be both socially legitimate and economically viable. The empirical application shows that objective grouping highlights structural levers, such as grievance redress, local supply chain integration, peace education, and project scalability, as decisive for long-term peacebuilding. The framework thus improves decision-making by combining analytical rigor and stakeholder legitimacy, enhancing both business legitimacy and long-term societal resilience. Full article

Background/Objectives: Age-related macular degeneration (AMD) is a leading cause of irreversible central vision loss in older adults. Detection of referable AMD—typically intermediate or advanced disease requiring specialist evaluation—is critical for timely intervention. Deep learning (DL) applied to color fundus photographs has emerged as a potential tool to support large-scale AMD screening. This systematic review and meta-analysis evaluated the diagnostic accuracy of DL algorithms for detecting referable AMD and compared their performance with human graders. Methods: We systematically searched PubMed, Embase, Web of Science, and IEEE Xplore through December 18, 2025. Diagnostic accuracy studies assessing DL algorithms on color fundus photographs for referable AMD in adults were included. Two reviewers independently screened studies, extracted data, and assessed risk of bias using an AI-adapted PROBAST framework. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. Clinical utility was evaluated using likelihood ratios, and paired head-to-head comparisons were synthesized using a contrast-based meta-analysis. Results: Fourteen studies were included. DL algorithms achieved a pooled sensitivity of 0.91 (95% CI: 0.86–0.94) and specificity of 0.93 (95% CI: 0.86–0.96), with substantial heterogeneity. The pooled positive and negative likelihood ratios were 12.22 and 0.10, respectively, indicating strong diagnostic utility. In direct comparisons, DL systems showed slightly lower sensitivity but higher specificity than human graders. Conclusions: Deep learning demonstrates high diagnostic accuracy for detecting referable AMD from fundus photographs and may support screening and referral workflows. Further prospective validation and standardized evaluation are needed before widespread clinical implementation. Full article

The depolarization of the sarcolemma is one of the first effects of unloading on skeletal muscle. We hypothesized that unloading-induced activation of the dihydropyridine receptor (DHPR), a voltage-sensitive L-type Ca²⁺ channel, and depolarization of the sarcolemma trigger intracellular Ca²⁺ release from the sarcoplasmic reticulum and activation of Ca²⁺-dependent signaling pathways, resulting in muscle atrophy. Nifedipine, a DHPR calcium channel blocker, was used to study the role of DHPR in the regulation of signaling pathways during three days of rat soleus muscle unloading/hindlimb suspension. Inhibition of the DHPR during unloading attenuates the decrease in soleus muscle contractile properties, prevents the accumulation of ATP, ROS, and Ca²⁺ content in the sarcoplasm and the mitochondria, and blocks the decrease in PGC1alpha mRNA expression and Junctophilin-1 (JP1) proteolysis. In nifedipine-treated rats, the improvement of the unloaded soleus muscle contractile properties could be mediated by blocking the calpain-mediated degradation of the cytoskeletal proteins. DHPR blocking could be one of the future directions for the preservation of contractile properties of inactive/unloaded muscle. Full article

Closed-basin lakes are highly sensitive to climatic variability, yet for the Lake Van Basin (Türkiye), the dynamic and spatially heterogeneous linkages among atmospheric drivers and lake-level changes (particularly their lag structure and predictive directionality) remain insufficiently quantified in a unified multivariate setting. This study examines how temperature and precipitation jointly influence hydrological behavior in the Lake Van Basin using a multi-station Vector Autoregression (VAR) framework. By integrating long-term observations from multiple meteorological stations, the analysis explicitly captures the spatial heterogeneity that characterizes this complex endorheic system and provides a consistent basis for comparing station-specific dynamics. The results show strong persistence in lake-level dynamics across specifications, with lagged lake-level coefficients of 0.2595 to 0.3685 (p < 0.01), indicating a buffered endorheic response. Temperature exhibits a highly consistent seasonal dependence across stations, reflected by a uniformly negative and significant four-month temperature lag in the temperature equations (−0.34 to −0.42, p < 0.01). Granger-causality tests further indicate robust bidirectional coupling between temperature and precipitation in all station specifications (p < 0.01 and typically p ≤ 0.05), while climate-to-lake-level linkages remain spatially heterogeneous but are statistically supported across both Tatvan-based and Gevas-based specifications (Tatvan-Tatvan: p < 0.01 for both climate variables; Tatvan-Ahlat: temperature p = 0.000; Gevas-Van, Gevas-Ercis, and Gevas-Muradiye: temperature p = 0.000 and precipitation p = 0.013, 0.008, and 0.015, respectively). Distinct station-level patterns further demonstrate that topographical differences modulate the strength and direction of climate–hydrology linkages across the basin. By providing a coherent, causally consistent understanding of these interactions and explicitly incorporating season-specific VAR and Granger-causality evidence, this study offers a transferable methodological framework for analyzing climate-sensitive lake systems and highlights the need to incorporate temperature-driven processes into water-management and climate-adaptation strategies in endorheic basins. Full article

TP53 is a tumor-suppressor gene involved in regulating apoptosis, DNA repair, and genomic stability. Mutations in TP53 are implicated in approximately half of all detected cancers, including breast, lung, colorectal, and ovarian cancers, making it a significant target for therapeutic interventions. Many pharmaceutical drugs aim to restore TP53 function, and there is a need for predictive tools to assess how compounds may affect TP53 expression. In this study, we propose a new ensemble machine-learning model to predict the direction of TP53 relative gene expression in response to pharmaceutical compounds. Our model utilizes molecular fingerprints, descriptors, and scaffold-based features extracted from SMILES representations of compounds concatenated into a single feature vector. Trained using our newly generated benchmark dataset based on the Connectivity Map (CMap) database and addressing class imbalance with the Synthetic Minority Over-sampling Technique (SMOTE), our model achieves 62.9%, 93.9%, 40.3%, and 0.39 in terms of accuracy, sensitivity, specificity, and Matthews Correlation Coefficient (MCC), respectively. As the first-of-its-kind TP53 gene regulation prediction, our study serves as a convincing proof-of-concept that paves the way for future investigation. GenReP as a stand-alone predictor, its source code, and our newly generated benchmark dataset are publicly available. Full article

Artificial intelligence (AI) is rapidly transforming language education. However, its pedagogical and sociocultural impacts on Japanese business writing remain underexplored. This study aims to examine how ChatGPT4o-based automated feedback functions within Japanese business writing education for adult learners in online higher education, with particular attention to both its instructional impact and learners’ sociocultural awareness. Situated in a cyber university context where the proportion of adult learners is increasing, the study explores the potential of AI-mediated feedback to address learners’ diverse educational and cultural needs. It employed a mixed-methods design, combining a survey of 27 participants and in-depth interviews with 11 participants. The interviews were transcribed and thematically coded to gain deeper insights into learners’ perceptions. The findings indicate that ChatGPT feedback contributed to learners’ planning of study strategies, the provision of immediate and personalized corrections, the reinforcement of error awareness, and the acquisition of honorific and polite expressions. On the one hand, learners reported that they could quickly understand regional language practices and communication conventions in business contexts, thereby deepening their cultural sensitivity. On the other hand, some learners expressed concern that increased reliance on AI could weaken exploratory and critical learning. These results suggest that ChatGPT can serve not merely as a correction tool but also as an educational resource that simultaneously fosters self-directed learning and sociocultural competence. However, to ensure reliability and cultural appropriateness, hybrid feedback incorporating instructor guidance is necessary. This study has academic significance in demonstrating the potential of extending AI-based feedback to Japanese business communication education, thereby constructing an integrated language and culture learning environment. Full article

Accurate determination of in situ stress is fundamental for the safe and efficient design of underground construction projects such as tunnels, caverns, and deep mining excavations. Conventional techniques—particularly overcoring and hydraulic fracturing—have been widely adopted for decades, but their practical use is often constrained by high operational cost, rigorous field requirements, and logistical limitations at depth. As engineering projects advance into deeper and more complex geological environments, these constraints have prompted growing interest in laboratory-based, core-derived stress measurement approaches. Such methods utilize the stress-relief deformation that occurs when drill cores are extracted, enabling stress estimation without extensive downhole instrumentation. This paper presents a critical review of core-based stress measurement techniques based on a structured survey of peer-reviewed literature retrieved from major scientific databases (Web of Science, Scopus, and Google Scholar), covering studies published from the 1960s to 2025. The review examines Anelastic Strain Recovery (ASR), Differential Strain Curve Analysis (DSCA), Deformation Rate Analysis (DRA), acoustic-emission-based Kaiser effect approaches, and the emerging Diametrical Core Deformation Technique (DCDT). Recent studies show that DCDT, which measures instantaneous elastic diametrical deformation of cores, provides a more direct and physically transparent link to differential in situ stress, with reduced sensitivity to time-dependent effects. The DCDT, based on precise measurement of instantaneous elastic deformation upon coring, offers high-resolution stress estimation with minimal disruption to field operations. Its compatibility with optical scanning, laser micrometers, and CT imaging highlights its potential as a practical alternative to conventional techniques. A comparative synthesis of assumptions, accuracy, and applicability is provided, and key limitations and future research needs of core-based stress measurement methods are identified. The findings of this review provide practical guidance for selecting stress measurement techniques and support the application of core-based methods, particularly DCDT, in deep underground engineering, where cost-effective and reliable stress characterization is required. Full article

In-tube solid-phase microextraction (IT-SPME) is an advanced microextraction technique in which a sample solution flows through a capillary containing an internal stationary phase, enabling efficient extraction and preconcentration of target analytes. The online coupling to liquid chromatography is a key advantage of this technique, enabling full automation and high analytical throughput, both of which are significant for food analysis. Recent advances have focused on developing novel sorbent materials that respond to external stimuli (e.g., magnetic, electrical, or thermal) and on integrating them into emerging chromatographic platforms. Moreover, key operational parameters, including sample volume, pH, phase thickness, and the capillary’s dimensions (length and inner diameter), must be optimized to achieve enhanced selectivity, speed, and sensitivity. Despite this, the literature still lacks updated reviews of SPME concepts and their innovations for versatile applications in food matrices. Hence, this review outlines the fundamental principles of IT-SPME while highlighting key parameters that affect analytical performance. Finally, we provide a literature review of SPME applications in food analysis over the past 6 years, while exploring current trends and future directions for SPME development and enhanced applications in food science. Full article

Land-use and land-cover change plays a critical role in shaping urban expansion patterns and modifying near-surface soil conditions, hydrological behaviour, and geomorphological stability in rapidly developing regions. This study presents a comparative modelling framework to analyze long-term land-use change and its implications for regional-scale geotechnical risk screening by integrating historical land-use classification, Markov transition analysis, and machine learning–based spatial simulation. Landsat imagery from 1985 and 2024 was classified using a Support Vector Machine approach, and future land-use projections for 2063 were generated using both the TerrSet Land Change Modeler (LCM) and the GeoFLUS model under identical transition demands. Spatial driving variables included topographic, hydrological, and accessibility-related factors that influence soil behaviour and urban suitability. The results reveal sustained urban expansion primarily driven by the systematic conversion of agricultural land into built-up surfaces, while forested areas and water bodies exhibit high class persistence, as indicated by dominant diagonal values in the Markov transition matrix. Although both models reproduce consistent directional trends, they generate distinct spatial allocation patterns, with LCM producing compact and centralized growth and GeoFLUS generating more spatially dispersed expansion. These differences lead to contrasting implications for potential settlement, flooding, and slope instability zones. By treating future land-use maps as alternative geotechnical screening scenarios rather than fixed predictions, this study demonstrates how model uncertainty can be incorporated into hazard-sensitive planning. The proposed framework supports preliminary geotechnical zoning and infrastructure planning by identifying robust development corridors and spatial uncertainty zones where detailed site investigations may be prioritized. The methodology is transferable to other rapidly urbanizing regions facing complex soil and geomorphological constraints. Full article

The global expansion of genetically modified (GM) crop cultivation has increased the demand for analytical platforms that can provide rapid, reliable, and cost-effective detec-tion of GM-derived ingredients to support traceability, regulatory compliance, and accu-rate labeling. Conventional molecular assays such as polymerase chain reaction (PCR) and isothermal amplification are highly sensitive and specific but depend on sophisticated instrumentation and trained personnel, limiting their applicability in field settings. Here, we present a label-free and amplification-free nanobiosensor based on citrate-capped gold nanoparticles (AuNPs) for the direct colorimetric detection of the Cry1Ac gene associated with the MON87701 soybean event, without the use of polymerase chain reaction (PCR) or any enzymatic nucleic acid amplification step. The assay relies on the localized surface plasmon resonance (LSPR) of AuNPs, which induces a red-to-purple color transition upon hybridization between complementary DNA strands. Critical reaction parameters, including NaCl concentration, AuNP size, and ionic strength, were optimized to enable selective and reproducible aggregation. Integration with a Support Vector Machine (SVM) algorithm enabled automated spectral classification and semi-quantitative discrimination of GM content levels. The optimized AuNP–SVM system achieved high sensitivity (limit of detection ≈ 2.5 ng μL⁻¹, depending on nanoparticle batch), strong specificity toward Cry1Ac-positive sequences, and reproducible classification accuracies exceeding 90%. By eliminating enzymatic amplification steps, the proposed platform significantly reduces assay time, operational complexity, and instrumentation requirements, making it suitable for rapid on-site GMO screening. Full article

Condition monitoring of rolling bearings is essential for ensuring the reliability of mechanical systems operating under severe or insufficient lubrication conditions. This study proposes a fault diagnosis framework that integrates tribological interpretation of wear phenomena, acoustic emission (AE) signal analysis, and machine learning, based on bearing life tests conducted under dry conditions as an accelerated wear environment to capture damage progression within a practical experimental time. Unlike conventional studies relying on artificially introduced defects, this work focuses on AE signals obtained from bearings in which damage initiates and progresses through actual wear processes. Life tests were conducted using deep groove ball bearings under two radial load conditions. The temporal evolution of the coefficient of friction, AE signals, and surface damage was analyzed. Although the coefficient of friction was the most sensitive indicator of wear progression, its direct measurement is impractical for in-service applications. Frequency-domain analysis revealed that AE counts per second and band-specific AE energy exhibit early changes consistent with the evolution of the friction coefficient. Using these physically interpretable AE features, a fully connected neural network was developed to classify bearing conditions into normal, early-stage damage, and damage progression. The proposed model achieved an average classification accuracy of approximately 85%, demonstrating the effectiveness of AE-based machine learning for bearing fault diagnosis under real wear progression conditions rather than artificial defect scenarios. Full article

Accurate derivative information is central to sensitivity analysis and optimization, yet standard finite differences can lose many digits when the step size is small because of subtractive cancellation. Complex-step differentiation largely resolves this issue for first derivatives, but robust second derivatives and mixed partials remain delicate: several practical complex-step variants for $f^{\prime \prime }$ still subtract nearly equal quantities, and quaternion-step rules are often presented as separate constructions. We develop a unified slice-based framework that extracts first and second derivatives from a single evaluation by projecting algebraic coefficients in commutative subalgebras of the complexified quaternions. First, we formulate a directional quaternion-steprule parameterized by an arbitrary unit pure quaternion u and provide an explicit projection operator that makes the underlying complex slice ${\mathbb{C}}_u\simeq \mathbb{C}$ transparent; the resulting first-derivative formula is rotation invariant and recovers classical j-step and planar $(j,k)$-step rules as special cases. Second, we construct a bicomplex double-step calculus in the commuting imaginary units $\mathrm{i}$ and u and show that one evaluation at $z+(\mathrm{i}+u)h$ separates derivative information into distinct coefficients, with the $\mathrm{i}u$-component equal to $h^2{f}^{\prime \prime }\left(z\right)+\mathcal{O}\left(h^4\right)$, giving a subtraction-free $\mathcal{O}\left(h^2\right)$ approximation of $f^{\prime \prime }$. For bivariate analytic functions we additionally derive one-shot identities for $f_x$, $f_y$, and $f_{xy}$ from $f(x+uh,y+\mathrm{i}h)$ and supply practical extraction identities, step-size guidance for $h^2$-scaled coefficients, and branch-consistency diagnostics for non-entire functions. The “cancellation-free” property here refers to avoiding the subtraction of nearly equal real quantities at the level of the differentiation formula; in floating-point arithmetic, coefficient extraction and the $1/h^2$ scaling for second-order quantities still interact with roundoff, and we quantify the resulting stable regimes numerically. Full article