Search Results (461)

E-commerce is quickly increasing purchasing behavior across the globe, but little is known about how psychological paradigms underscore online buying intentions for locally essential items as nomadic local foods. The primary goal of this research is to examine the effects of some important psychological constructs and motivational values on predicting consumers’ intention to purchase nomadic and local foods via online e-commerce platforms, such as Ashayershop. This study followed the Theory of Planned Behavior (TPB) and looked at direct and mediated effects of attitudes, perceived behavioral control, and subjective norms on intention to purchase. Structural Equation Modeling (SEM) was conducted, based on data collected from a representative sample of consumers who were familiar with online shopping for local foods. The results highlight that attitude towards online shopping for local foods was the strongest direct predictor of intention to purchase (β = 0.383, T = 9.487, p < 0.001). Perceived behavioral control (β = 0.220, T = 5.316, p < 0.001), hedonic value (β = 0.213, T = 4.907, p < 0.001), utilitarian value (β = 0.187, T = 3.719, p < 0.001), and subjective norms (β = 0.149, T = 3.493, p < 0.001), received a significant positive effect on intention. In addition, hedonic and utilitarian values bountifully mediated the relation between psychological antecedents (attitudes, perceived behavioral control, and subjective norms) and purchase intention. For instance, attitude indirect effect via hedonic value was β = 0.080 (T = 3.783, p < 0.01), and indirect effect via utilitarian value was β = 0.040 (T = 3.058, p < 0.01), indicating the importance of these values as mediators. This research makes a contribution to the literature by showing that motivational values serve as not only an outcome but also as cognitive–affective mediators in the behavioral process thus expanding the TPB in the context of digital food markets. In general, these results provide valuable insights to e-commerce platforms and policymakers who desire to promote consumer engagement with products stemming from culture and tradition on line by developing new integrated strategies that address the cognitive, emotional, and social components. Full article

Background/Objectives: Age-related structural changes in the human brain provide essential insights into cognitive aging and the onset of neurodegenerative diseases. This study aimed to comprehensively characterize age-related volumetric changes across multiple brain regions in a large, diverse, cognitively healthy cohort spanning adulthood (ages 21–90), integrating Korean, Information eXtraction from Images (IXI), and Alzheimer’s Disease Neuroimaging Initiative (ADNI) MRI datasets of cognitively healthy participants to characterize normative volumetric changes across adulthood using demographically diverse datasets. Methods: High resolution 3T T1-weighted MRI images from three distinct cohorts (totaling 1833 subjects) were processed through an optimized neuroimaging pipeline, combining advanced preprocessing with neural network-based segmentation. Volumetric data for 95 brain structures were segmented and analyzed across seven age bins (21–30 through 81–90). Pipeline reliability was validated against FreeSurfer using intraclass correlation coefficients (ICC) and coefficients of variation (CoV). Regression-based correction was used to correct for sex and cohort effects on brain region volume. Then, percentage change in each mean bilateral volumes of regions across the lifespan were computed to describe volumetric changes across life spans. Results: The segmentation pipeline demonstrated excellent agreement with FreeSurfer (mean ICC: 0.9965). Drastic volumetric expansions were observed in white matter hypointensities (122.6%), lateral ventricles (115.9%), and inferior lateral ventricles (116.8%). Moderate-to-notable shrinkage was found predominantly in the frontal lobe (pars triangularis: 21.5%), parietal lobe (inferior parietal: 20.4%), temporal lobe (transverse temporal: 21.6%), and cingulate cortex (caudal anterior cingulate: 16.1%). Minimal volume changes occurred in regions such as the insula (3.7%) and pallidum (2.6%). Conclusions: This study presents a comprehensive reference of normative regional brain volume changes across adulthood, highlighting substantial inter-regional variability. The findings can provide an essential foundation for differentiating normal aging patterns from early pathological alterations. Full article

Rapidly pinpointing the origin of accidental chemical gas releases is essential for effective response. Prior vision pipelines—such as 3D CNNs, CNN–LSTMs, and Transformer-based ViViT models—can improve accuracy but often scale poorly as the temporal window grows or winds meander. We cast recursive backtracking of concentration fields as a finite-horizon, multi-step spatiotemporal sequence modelling problem and introduce Recursive Backtracking with Visual Mamba (RBVM), a Visual Mamba-inspired, directionally gated state-space backbone. Each block applies causal, depthwise sweeps along ${\mathrm{H}}^{\pm }$, ${\mathrm{W}}^{\pm }$, and ${\mathrm{T}}^{\pm }$ and then fuses them via a learned upwind gate; a lightweight MLP follows. Pre-norm LayerNorm and small LayerScale on both branches, together with a layer-indexed, depth-weighted DropPath, yield stable stacking at our chosen depth, while a 3D-Conv stem and head keep the model compact. Computation and parameter growth scale linearly with the sequence extent and the number of directions. Across a synthetic diffusion corpus and a held-out NBC_RAMS field set, RBVM consistently improves Exact and hit $\le 1$ over strong 3D CNN, CNN–LSTM, and ViViT baselines, while using fewer parameters. Finally, we show that, without retraining, a physics-motivated two-peak subtraction on the oldest reconstructed frame enables zero-shot dual-source localization. We believe RBVM provides a compact, linear-time, directionally causal backbone for inverse inference on transported fields—useful not only for gas–release source localization in CBRN response but more broadly for spatiotemporal backtracking tasks in environmental monitoring and urban analytics. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

In this paper, we utilize the Lukasiewicz t-norm to construct a novel class of fuzzy sets, termed $\zeta$-Lukasiewicz fuzzy sets, derived from a given fuzzy framework. These sets are then applied to the structure of Sheffer stroke BE-algebras (SBE-algebras). We introduce and examine the concepts of $\zeta$-Lukasiewicz fuzzy SBE-subalgebras and $\zeta$-Lukasiewicz fuzzy SBE-ideals, with a focus on their algebraic properties. Furthermore, we define three specific types of subsets, referred to as ∈-sets, q-sets, and O-sets, and investigate the necessary conditions for these subsets to constitute subalgebras or ideals within the SBE-algebraic context. Full article

Accurate node localization is essential for many Internet of Things (IoT) applications. However, incomplete and noisy distance measurements often degrade the reliability of the Euclidean Distance Matrix (EDM), which is critical for range-based localization. To address this issue, a Low-Rank Matrix Completion approach based on nonconvex rank approximation (LRMCN) is proposed to recover the true EDM. First, the observed EDM is decomposed into a low-rank matrix representing the true distances and a sparse matrix capturing noise. Second, a nonconvex surrogate function is used to approximate the matrix rank, while the $l_1$-norm is utilized to model the sparsity of the noise component. Third, the resulting optimization problem is solved using the Alternating Direction Method of Multipliers (ADMMs). This enables accurate recovery of a complete and denoised EDM from incomplete and corrupted measurements. Finally, relative node locations are estimated using classical multi-dimensional scaling, and absolute coordinates are determined based on a small set of anchor nodes with known locations. The experimental results show that the proposed method achieves superior performance in both matrix completion and localization accuracy, even in the presence of missing and corrupted data. Full article

We propose a strictly causal early–warning framework for financial crises based on topological signal extraction from multivariate return streams. Sliding windows of daily log–returns are mapped to point clouds, from which Vietoris–Rips persistence diagrams are computed and summarised by persistence landscapes. A single, interpretable indicator is obtained as the $L_2$ norm of the landscape and passed through a causal decision rule (with thresholds $\alpha ,\beta$ and run–length parameters $s,t$) that suppresses isolated spikes and collapses bursts to time–stamped warnings. On four major U.S. equity indices (S&P 500, NASDAQ, DJIA, Russell 2000) over 1999–2021, the method, at a fixed strictly causal operating point $(\alpha =\beta =3.1,s=57,t=16)$, attains a balanced precision–recall ($F_1\approx 0.50$) with an average lead time of about 34 days. It anticipates two of the four canonical crises and issues a contemporaneous signal for the 2008 global financial crisis. Sensitivity analyses confirm the qualitative robustness of the detector, while comparisons with permissive spike rules and volatility–based baselines demonstrate substantially fewer false alarms at comparable recall. The approach delivers interpretable topology–based warnings and provides a reproducible route to combining persistent homology with causal event detection in financial time series. Full article

Prescriptive grammars of Netherlandic Dutch usually explicitly warn against mixing T- and V-pronouns. Although the prescriptive norm opposes mixing, pronoun mixing does occur, and its use can often be interpreted as strategic, in the sense that mixing pronouns might help to balance conflicting needs such as signaling respect and formality to the addressee on the one hand as well as expressing closeness on the other hand. This article explores the perception of pronoun mixing among high school students who were in the process of acquiring the norm. As part of a student science project, we asked students to categorize real-world examples of pronoun mixing that they themselves had gathered as a strategy or as a mistake. Based on the students’ responses, we extrapolated that the most acceptable forms of mixing were brief switches to V in a T-context to express humor or urgency and—if there was no clear default pronoun—that mixing was most acceptable (1) when the text was free of spelling errors and other signs of sloppiness, (2) when the mixing was intersentential, (3) when the number of switches was infrequent, and (4) when there was a clear division of tasks between the pronouns. As an offshoot of this student science project, we designed a brief follow-up survey to gain insight into domains and consensus and variation among the students’ perceptions of pronoun mixing. This follow-up survey revealed that if not explicitly asked, most students do not notice pronoun mixing. We asked students to rank four real-life examples of address pronoun mixing that they had gathered during the student science project. We expected that with respect to their perception of the mixing of address pronouns all students would rank examples of mixing in the same order. A primary result of this part of our exploration was that there were large individual differences in the perception of mixing and that there was variation in the ranking of examples among the students. Intersentential mixing yielded the most neutral evaluations by the students, but intrasentential mixing showed the most extreme evaluations. It was disliked most strongly by students who had a general dislike of mixing and liked best by students who appreciated mixing as a style. Briefly switching to V in contexts associated with the T-pronoun was perceived to be humorous by a quarter of the students, and half of the students perceived a switch to the petrified abbreviation AUB (‘if you-V please’) as expressing urgency. Full article

Background/Objectives: Magnetic resonance elastography (MRE) is a noninvasive imaging technique that quantitatively characterizes tissue mechanical properties. This study aimed to establish and validate a feasible parotid MRE protocol using 3T MRI, with potential relevance for oral and maxillofacial surgery (OMFS) and otorhinolaryngology practice. Methods: This study included 21 healthy volunteers (18 women, 3 men; mean age, 49 years) examined between January and May 2024. MRE was performed using a 3.0 Tesla MRI system (Discovery MR750w, GE Healthcare, Waukesha, WI, USA) with a passive driver positioned over the parotid gland in a 16-channel head/neck coil. Two radiologists independently analyzed axial magnitude images drawing regions of interest (ROIs) encompassing the entire gland while excluding intraparotid lymph nodes and vascular structures. Mean and maximum stiffness values (kPa) were recorded for each gland. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland–Altman analysis. Results: Mean stiffness was 1.209 ± 0.240 kPa (Radiologist 1) and 1.146 ± 0.233 kPa (Radiologist 2); maximum stiffness was 1.595 ± 0.532 kPa and 1.563 ± 0.528 kPa, respectively. ICCs were 0.638 for mean stiffness and 0.918 for maximum stiffness, indicating moderate-to-excellent agreement. Conclusions: MRE is a technically feasible and reproducible method for evaluating parotid stiffness using standard imaging infrastructure. This feasibility study in healthy volunteers provides normative stiffness values for the parotid gland and supports MRE as a potential tool for “radiological palpation” to aid in the differentiation of salivary gland lesions and post-treatment assessment in OMFS and otorhinolaryngology practice. Full article

The Max+Sum Spanning Tree (MSST) problem, with applications in secure communication systems, seeks a spanning tree T minimizing ${max}_{e\in T}w\left(e\right)+{\sum }_{e\in T}c\left(e\right)$ on a given edge-weighted undirected network $G(V,E,c,w)$, where the sets V and E are the sets of vertices and edges, respectively. The functions c and w are defined on the edge set, representing transmission cost and verification delay in secure communication systems, respectively. This problem can be solved within $O\left(\right|E|log|V\left|\right)$ time. We investigate its interdiction (MSSTID) and improvement (MSSTIP) problems under the weighted $l_{\infty }$ norm. MSSTID seeks minimal edge weight adjustments (to either c or w) to degrade network performance by ensuring the optimal MSST’s weight is at least K, while MSSTIP similarly aims to enhance performance by making the optimal MSST’s weight at most K through minimal weight modifications. These problems naturally arise in adversarial and proactive performance enhancement scenarios, respectively, where network robustness or efficiency must be guaranteed through constrained resource allocation. We first establish their mathematical models. Subsequently, we analyze the properties of the optimal value to determine the relationship between the magnitude of a given number and the optimal value. Then, utilizing binary search methods and greedy techniques, we design four algorithms with time complexity $O\left(\right|E{|}^{2}log\left|V\right|)$ to solve the above problems by modifying w or c. Finally, numerical experiments are conducted to demonstrate the effectiveness of the algorithms. Full article

(1) This study aims to quantify differences between platforms and countries by comparing Hilton’s ratings on Booking.com and Google.com in the V4 countries. (2) Data were collected directly from Booking.com and Google.com for selected Hilton hotels. Descriptive statistics were used to process and analyze the data, and a paired Student’s T-test was used to compare standard deviations between platforms. (3) The analysis showed that these differences can be applied not only to subjective preferences but also to sociotechnical devices, including cultural platforms and their associated norms and user expectations. Additionally, factors such as price, food quality, and atmosphere were shown to influence overall guest satisfaction, with ratings approximately indicating satisfaction or dissatisfaction. (4) From a practical perspective, these insights can help hotel managers optimize their online communication strategy, tailor content to different platforms, and manage their reputation more effectively. Overall, the findings highlight that effectively managing online reviews is key to establishing trust, increasing satisfaction, and ensuring the long-term success of the Hilton brand in diverse sociocultural contexts. Future research should focus on sociotechnical aspects and the impact of seasonal or marketing campaigns on reviews to gain a more comprehensive understanding of the dynamics of online reviews in the hospitality industry. Full article

We investigate a numerical method for approximating stochastic Caputo fractional differential equations driven by multiplicative noise. The nonlinear functions f and g are assumed to satisfy the global Lipschitz conditions as well as the linear growth conditions. The noise is approximated by a piecewise constant function, yielding a regularized stochastic fractional differential equation. We prove that the error between the exact solution and the solution of the regularized equation converges in the $L^2((0,T)\times \mathrm{\Omega })$ norm with an order of $O(\Delta t^{\alpha -1/2})$, where $\alpha \in (1/2,1]$ is the order of the Caputo fractional derivative, and $\Delta t$ is the time step size. Numerical experiments are provided to confirm that the simulation results are consistent with the theoretical convergence order. Full article

Background: This study aims to establish standardized reference values for myocardial T1 and T2 relaxation times in a clinically and imaging-defined real-world patient cohort, evaluating their variability in relation to age, sex, and comorbidities. By identifying key physiological and pathological influences, this investigation seeks to enhance CMR-based myocardial mapping for improved differentiation between normal and pathological myocardial conditions. Methods: This retrospective observational study analyzed T1 and T2 relaxation times using CMR at 1.5 Tesla in a cohort of 491 subjects. T1 and T2 times were measured using MOLLI and GRASE sequences, and statistical analyses assessed intra- and interindividual variations, including the influence of age, sex, and comorbidities, to establish reference values and improve myocardial tissue characterization. Results: T1 and T2 relaxation times were analyzed in 291 and 200 participants, respectively. The mean global T1 time was 1004.7 ± 49.8 ms, with no significant differences between age groups (p = 0.81) or sexes (p = 0.58). However, atrial fibrillation (AF) and mitral regurgitation (MR) were associated with significantly prolonged T1 times (p < 0.05). The mean global T2 time was 67.4 ± 8.6 ms, with age-related prolongation (p < 0.05), but no sex differences (p = 0.46). Comorbidities did not significantly influence T2 times, except for NYHA Class III–IV patients, who exhibited prolonged T2 values (p < 0.05). Conclusions: Standardized T1 and T2 reference values are essential to improve diagnostic accuracy and risk stratification in CMR-based myocardial tissue characterization. Future research should focus on multicenter validation, AI-driven analysis, and the development of age- and comorbidity-adjusted normative databases to enhance individualized cardiovascular care. Full article

Multi-UAV cooperative missions demand millisecond-level coordination across three key resource dimensions—battery energy, wireless bandwidth, and onboard computing power—where traditional Min or linearly weighted schedulers struggle to balance safety with efficiency. We propose a prediction-enhanced two-stage T-norm–Choquet–OWA resource aggregator. First, an LSTM-EMA model forecasts resource trajectories 3 s ahead; next, a first-stage T-norm (min) pinpoints the bottleneck resource, and a second-stage Choquet–OWA, driven by an adaptive interaction measure $\varphi$, elastically compensates according to instantaneous power usage, achieving a “bottleneck-first, efficiency-recovery” coordination strategy. Theoretical analysis establishes monotonicity, tight bounds, bottleneck prioritization, and Lyapunov stability, with node-level complexity of only $O\left(1\right)$. In joint simulations involving 360 UAVs, the method holds the average round-trip time (RTT) at 55 ms, cutting latency by 5%, 10%, 15%, and 20% relative to Min, DRL-PPO, single-layer OWA, and WSM, respectively. Jitter remains within 11 ms, the packet-loss rate stays below 0.03%, and residual battery increases by about 12% over the best heuristic baseline. These results confirm the low-latency, high-stability benefits of the prediction-based peak-shaving plus two-stage fuzzy aggregation approach for large-scale UAV swarms. Full article

Background/Objectives: The emergence of artificial intelligence (AI) has revolutionized the healthcare industry. However, its integration into clinical practices raises ethical and practical concerns. This study aims to explore ethical and practical considerations perceived by physicians and nurses in Saudi Arabia. Methods: It employed a cross-sectional design with 400 physicians and nurses, using a pre-established online questionnaire. Descriptive data were analyzed through means and standard deviations, while inferential statistics were performed using the independent samples t-test. Results: Most participants were male (57%) and physicians (73.8%), with most employed in governmental organizations (87%). The participants’ use and awareness of AI was low, as 34.0% said they had never used it, but 74.5% of respondents were willing to use AI in clinical practices. Also, 80.5% of participants were aware of the AI benefits, and 71.0% had background knowledge about the ethical concerns related to AI’s implementation in their clinical practices. Moreover, (62.0%) of respondents recognized the applicability of AI in their specialty. Key findings revealed significant concerns: participants perceived a lack of skills to effectively utilize AI in clinical practice (mean = 4.04) and security risks such as AI manipulation or hacking (mean = 3.83). The most pressing ethical challenges included AI’s potential incompatibility with all populations and cultural norms (mean = 3.90) and uncertainty regarding responsibility for AI-related errors (mean = 3.84). Conclusions: These findings highlight substantial barriers that hinder the effective integration of AI in clinical practices in Saudi Arabia. Addressing these challenges requires leadership support, specific training initiatives, and developing practical strategies tailored to the local context. Future research should include other healthcare professionals and qualitatively explore further underlying factors influencing AI adoption. Full article