Search Results (491)

Accurately mapping agricultural land use in fragmented hilly and mountainous areas is crucial for resource management but is severely challenged by spatial heterogeneity. While high-resolution (HR) images excel at delineating fine parcel boundaries, their limited spectral and temporal information often leads to spectral confusion among diverse agricultural types. To address this limitation, this study proposes a novel spatiotemporal feature-driven geographical zoning method integrating vegetation phenology, topography, and human activity. This zoning strategy decouples the complex global classification task into relatively simple local problems, providing explicit geoscientific constraints for subsequent classification. The proposed method was validated by classifying plain open-field croplands, sloping croplands, terraces, and greenhouses in the hilly and mountainous areas of Beijing using 2 m resolution satellite images. Compared to traditional global classification methods, the proposed zoning-based method increased the overall accuracy from 84.81% to 90.81%, the Kappa coefficient from 0.74 to 0.85, and the Intersection over Union (IoU) from 77.85% to 90.85%. The advantages of geographic zoning were particularly evident in mitigating spatial heterogeneity and enhancing boundary precision. These findings indicate that integrating dynamic geographical zoning as a priori knowledge successfully bridges the gap between HR spatial details and environmental contexts, offering a robust solution for mapping fragmented agricultural landscapes. Full article

A difficult NP-hard optimization problem, designing cryptographically robust substitution-boxes (S-boxes) necessitates a careful balancing act between several conflicting properties, such as differential uniformity and nonlinearity. Genetic Algorithms (GAs) have been widely used for this task; however, their performance is often limited by premature convergence and insufficient diversity during crossover operations. This primarily occurs because genetic algorithms commence with limited a priori knowledge. This sort of “blindness” and failure to utilize local knowledge results in diminished performance. In GA, the crossover operations facilitate the dissemination of robust candidates within the population. Conventionally, GA implements crossover for each pair of parents for diversity and a robust solution. However, this is not invariably the situation. To enhance children’s candidacy, parental diversity is quite crucial. This paper proposes a similarity-aware crossover strategy, integrated with a Siamese learning framework, to guide the genetic algorithm for improved S-box optimization with better diversity and faster convergence by utilizing parental local information. The proposed model is similarity-aware to guarantee that the GA improves parental diversity. When the parents exhibit excessive similarity, a “regressive” crossover is opted, which ensures the propagation of a parental couple with sufficient diversity to produce superior offspring. The proposed similarity-aware GA model is applied and evaluated to generate cryptographically robust and optimized S-boxes. To verify the robustness in terms of diversity, the model has been tested using three different loss functions: contrastive loss, KL divergence loss, and the suggested method of combining both loss functions to form a hybrid loss function. The effectiveness of the proposed approach is demonstrated through the generation of high-quality S-boxes with strong cryptographic properties. Full article

In robotic fine-grained tasks (e.g., grasping and assembly), precise interaction requires a detailed understanding of object components. While Visual Language Models (VLMs) excel at object-level recognition, they struggle with part-level segmentation (e.g., knife handles), limiting performance in complex scenarios. VLMs face three key challenges: (1) Visual granularity mismatch—object-level features lack part-level details; (2) Semantic hierarchy gaps—parts and objects differ significantly in semantics; (3) Cross-modal bias—CLIP’s text–image alignment favors global over local features. To address these, we propose a one-stage VLM-based part segmentation method. First, the Hierarchy-Aware Feature Selection mechanism analyzes Transformer features in different hierarchies to enhance spatial and semantic precision for part segmentation. Second, the Multi-Hierarchy Feature Adapter bridges object-to-part feature granularity via the hierarchical adaptation. Finally, the Hierarchical Multimodal Alignment Module harmonizes classification accuracy and mask integrity via hierarchical alignment of vision–language, mitigating the bias of CLIP’s object-level priori knowledge. Experiments show the proposed method improves part segmentation performance for Zero-Shot, achieving 25.86% on Pascal-Part and 13.09% on ADE20K-Part (gains of +0.81% hIoU and +2.96% hIoU over baseline). This work advances robotic visual perception, with applications in intelligent manufacturing and intelligent service. Full article

Non-dilute emulsions are emulsions where the concentration of the droplets is high enough for the neighbouring droplets to interact with each other hydrodynamically but is still smaller than the packed bed concentration where the droplets are packed and deformed against each other. Thus, they cover a broad range of droplet concentrations. Many emulsions encountered in industrial applications fall under this category. Non-dilute emulsions exhibit rich rheological behaviour, from a simple Newtonian fluid to a highly non-Newtonian fluid, reflecting shear-thinning, shear-thickening, yield stress, viscoelasticity, etc. In this article, the rheology of non-dilute emulsions is reviewed comprehensively. Emulsions of hard-sphere-type droplets and deformable droplets, with and without surfactants, are covered. The mathematical models describing the rheological behaviour of non-dilute emulsions are discussed. The influences of electric charge and interfacial rheology on the rheological behaviour of emulsions are covered in detail. The flocculation of droplets caused by different mechanisms, such as depletion and bridging induced by additives, and their effect on emulsion rheology are investigated thoroughly. Finally, the dynamic rheology of non-dilute emulsions is discussed, covering both pure oil–water interfaces and additive-laden interfaces. The mathematical models describing the dynamic rheological behaviour of non-dilute emulsions are described. Based on the existing theoretical and empirical models, it is possible to a priori predict the rheology of non-dilute emulsions. However, serious gaps in the existing knowledge on non-dilute emulsion rheology remain. This review identifies the gaps in existing knowledge and points out future directions in research related to non-dilute emulsion rheology. Full article

Accurate radiation thermometry of real objects critically depends on knowledge of surface emissivity, which is rarely known a priori and often varies with surface condition, temperature, and environment. Although theoretical models for spectral emissivity evaluation exist, their practical validation under application-relevant conditions remains limited. In this study, spectral and radiometric emissivity evaluation methods are compared on metallic samples up to 350 °C. The spectral method derives effective emissivity from spectroscopy-measured spectral emissivity using instrument-specific spectral sensitivity (responsivity), while the radiometric method evaluates emissivity directly from radiance measurements using a radiation thermometer and a reference contact temperature. The radiometric method is treated as an application-level reference. Stable and homogeneous chromium nitride (CrN)-coated samples show good agreement between the two methods, whereas raw metals and polysiloxane-coated samples highlight practical limitations related to sample surface instability and inhomogeneity. The results demonstrate that spectral emissivity evaluation is valid in practice when its underlying method assumptions are fulfilled, while radiometric evaluation remains preferable for in situ infrared thermometry. Full article

Background/Objectives: Nutritional medicine remains underrepresented in medical education despite its relevance across specialties. Online learning offers a resource-efficient option to address this gap, yet evidence on the effectiveness and acceptability of online learning modules (OLMs) is limited. Methods: In this exploratory randomized controlled single post-test trial, medical students were assigned to either an OLM or an in-person lecture (IPL) on nutritional medicine (n = 91, no a priori sample size calculation performed). After course completion, students took a knowledge test and completed a questionnaire on their learning experience. Group differences were analyzed using permutation Welch t-tests, Wilcoxon–Mann–Whitney tests, or Fisher’s exact tests, depending on variable characteristics, with α = 0.05. Results: OLM students achieved significantly higher test scores than IPL students (mean difference: 2.4 points on a 0–40 scale), resulting in differences in grade classification (p < 0.05). OLM was further rated more favorably regarding content delivery, overall course evaluation, and exam preparation (all p < 0.05), while self-reported attention, concentration, and involvement did not differ between groups. Flexibility, time savings, and convenience were the most frequently reported advantages of OLM over IPL. Conclusions: This study suggests that OLM in nutritional medicine may be associated with higher test performance and more favorable student evaluations compared to IPL. These findings highlight the potential of online learning as a scalable, resource-efficient approach that may help address persistent gaps in nutritional medicine education. Building on this evidence, future work should examine how such modules can be optimally integrated into medical curricula to complement existing teaching structures. Full article

Electroencephalogram (EEG) methods for the diagnosis of Benign Epilepsy with Centrotemporal Spikes (BECTS) are reviewed. The focus is on procedures reported for EEG analysis and diagnosis in BECTS, since some recent and potential applications of artificial intelligence (AI) aim to enhance the diagnostic accuracy and time reduction process, thereby moving a step closer to advancing our knowledge of the electrical nuclei sources and dynamics of energy distribution through the scalp in patients with epilepsy. The advantages of AI classification techniques have an increasing publication rate in the specialist literature, with no clear agreement on methodology. Hence, a better understanding of the procedures, arguments, and achievements is needed. To achieve this goal, (1) we review the background knowledge of the clinical characteristics of BECTS, (2) we analyze the results and advantages of computational processing methods for source and connectivity analyses of EEG in BECTS, and finally, (3) we explore the AI methods published in specialized journals for BECTS analysis. In conclusion, we argue in favor of the combined use of a priori information, which is the basis of the clinical visual analysis of EEG, as a potential feature to be included in AI methods for the classification of epileptiform graphoelements in EEG in BECTS diagnosis. Full article

When looking towards the goal of reducing greenhouse gas (GHG) emissions, automotive manufacturers face several challenges when planning future vehicle offerings in different markets. The planned vehicle offerings must cope with uncertainties in the supply chains of critical materials and adhere to regulatory requirements in different regions, all while appealing to customer preferences and maintaining low cost. Regulatory requirements, which are often based on tailpipe GHG emissions, do not necessarily align with Lifecycle Analysis (LCA) of GHG emissions, which becomes yet another challenge towards attaining sustainability goals. Planning the future mix of vehicles to be manufactured under all such considerations can be a complex task, often relying on methods with poor transparency, unguaranteed optimality, or requiring difficult-to-predict a priori knowledge. This paper considers the special case of a short time window (one future model–year), which allows for modelling the future planning decisions as a linear programming (LP) problem, which in turn, can be solved to global optimality via well-established algorithms, such as Dual-Simplex. The proposed formulation is demonstrated via one simple example, as well as a scaled-up study with two regions, two vehicle size categories, and four powertrain configurations. A key insight that the proposed formulation is able to demonstrate in the scaled-up study is how the optimum (lowest) LCA GHG solution depends on the availability of battery materials, ranging from an increased share of hybrids under low battery supply to an increased share of electric vehicles for abundant battery supply. Full article

In communication-centric integrated sensing and communication (ISAC) systems, passive radars exploit existing communication signals of opportunity for sensing. To compute delay-Doppler or range–velocity maps (DDMs and RVMs, respectively), modern orthogonal frequency division multiplexing (OFDM)-based sensing systems use the channel frequency response (CFR) originally estimated in communication receivers for equalization. In OFDM-based passive radars utilizing 4G LTE or 5G NR waveforms, CFR estimation typically relies only on reference signals. However, simulation-based studies that assume a priori knowledge of user data symbols indicate potential performance gains when incorporating user data and other downlink channels. In this work, we present an experimental evaluation of an OFDM-based passive radar that jointly utilizes all commonly present components of the 5G NR downlink waveform: synchronization signals (PSS and SSS), broadcast and control channels (PBCHs and PDCCHs, respectively), data channels (PDSCHs), and reference signals (PBCH DM-RSs, PDCCH DM-RSs, PDSCH DM-RSs, and CSI-RSs). Our results show that utilizing user data from fully occupied 5G downlink signals, under the assumption of full knowledge of PDSCH locations, significantly improves both the probability of detection (POD) and the peak height, measured by the peak-to-noise-floor ratio (PNFR), compared with pilot-only sensing. Since perfect knowledge of the user data payload is not assumed, we estimate the transmission bit error rate (BER) and analyze its impact on sensing performance. Finally, we investigate more realistic scenarios in which only a subset of PDSCH resource element locations is known, as in practical 5G deployments, and evaluate how partial data location knowledge affects the POD and PNFR under different BER conditions. Full article

Adolescent tobacco use remains a critical public health challenge, requiring innovative early prevention strategies. This study participatively evaluated a medium-fidelity prototype of the No le entres app, designed to prevent tobacco use among adolescents. The prototype was developed based on user-centered design and gamification frameworks, with the aim of ensuring cultural relevance and active user engagement. Qualitative design with content analysis was employed. Four focus groups were conducted in Xalapa, Veracruz: two with health professionals from Medical Specialty Units—Community Mental Health and Addiction Centers (UNEME CECOSAMA), and two with secondary school students. Nineteen professionals and twenty-two adolescents participated. Data were analyzed using MAXQDA 2022, applying both a priori and emergent categories. Professionals valued the app’s innovative approach but recommended improvements in navigation speed, visual design, message clarity, and cultural validation. Adolescents emphasized the need for more engaging features, such as music, rewards, team competitions, and updated graphics. Both groups highlighted the importance of interactivity, personalization, and contextualized content. Findings underscore the value of participatory methods in designing digital health interventions and confirm that involving end users enhances usability and acceptability. The app demonstrates potential for integration into school settings as a preventive tool, with implications for influencing adolescent knowledge, attitudes, and behaviors regarding tobacco use. Full article

Background and Objectives: Recurrent urinary tract infection (rUTI) remains common and burdensome, with growing emphasis on antibiotic stewardship and non-antibiotic prevention. We characterized what women with rUTI know, do, and receive in everyday care and identified gaps between patient understanding and guideline-concordant management. Materials and Methods: We conducted a single-center, cross-sectional survey of consecutive adult women presenting with rUTI to a urology clinic in Poland. A structured questionnaire captured demographics, knowledge, symptoms and triggers, diagnostics, treatments and prevention, and satisfaction. Responses were standardized a priori; descriptive statistics and exploratory comparisons were performed (N = 36). Results: The mean age was 53.0 years (SD: 14.8). Only 36.1% identified the correct rUTI definition, while 83.3% recognized bacteria as the common cause. The symptom profile was dominated by frequency and dysuria (each 88.9%); 27.8% reported intercourse as a precipitant, and this was more frequent among sexually active women (43.5% vs. 7.7%; p = 0.031). Over half (55.6%) perceived no seasonality. The median number of episodes in the prior year was five (IQR 4–6). Urine culture was obtained before treatment in 38.9% and after treatment in 13.9%. The overall satisfaction with care was low to moderate (13.9% were very satisfied, 61.1% were moderately satisfied, and 25.0% were dissatisfied). Prior antibiotic exposure included ciprofloxacin (55.6%), furazidin (75.0%), and fosfomycin (47.2%). The uptake of preventive options was uneven: immunoactive vaccines accounted for 19.4%, methenamine hippurate for 16.7%, pelvic floor physiotherapy for 33.3%, and vaginal estrogen for 5.6% overall (9.5% among women ≥ 50 years). Conclusions: In this clinic-referred cohort, rUTI was frequent and disruptive, factual knowledge was limited, urine culture use was inconsistent, and fluoroquinolone exposure remained common. Preventive care was misaligned with guidelines, with underuse of vaginal estrogen and variable adoption of non-antibiotic strategies. Targeted education, stewardship, and structured access to evidence-based prevention may improve outcomes. Full article

Understanding how tacit knowledge embedded in visual materials is accessed and utilized during evaluation tasks remains a key challenge in human–computer interaction and visual expertise research. Although eye-tracking studies have identified systematic differences between experts and novices, findings remain inconsistent, particularly in art-related visual evaluation contexts. This study examines whether tacit aspects of visual evaluation can be inferred from gaze behavior by comparing individuals with and without formal art education. Visual evaluation was assessed using a structured, prompt-based task in which participants inspected artistic images and responded to items targeting specific visual elements. Eye movements were recorded using a screen-based eye-tracking system. Areas of Interest (AOIs) corresponding to correct-answer regions were defined a priori based on expert judgment and item prompts. Both AOI-level metrics (e.g., fixation count, mean, and total visit and gaze durations) and image-level metrics (e.g., fixation count, saccade count, and pupil size) were analyzed using appropriate parametric and non-parametric statistical tests. The results showed that participants with an art-education background produced more fixations within AOIs, exhibited longer mean and total AOI visit and gaze durations, and demonstrated lower saccade counts than participants without art education. These patterns indicate more systematic and goal-directed gaze behavior during visual evaluation, suggesting that formal art education may shape tacit visual evaluation strategies. The findings also highlight the potential of eye tracking as a methodological tool for studying expertise-related differences in visual evaluation processes. Full article

Despite the extensive application of machine learning (ML) methods to educational datasets, few studies have provided a systematic benchmarking of the available algorithms with respect to both predictive performance and interpretability of the resulting models. In this work, we address this gap by comparing a range of supervised learning methods on a freely available dataset concerning two high schools, where the goal is to predict student performance by modeling it as a binary classification task. Given the high feature-to-sample ratio, the problem falls within the small-data learning regime, which often challenges ML models by diluting informative features among many irrelevant ones. The experimental results show that several algorithms can achieve robust predictive performance, even in this scenario and in the presence of class imbalance. Moreover, we show how the output of ML algorithms can be interpreted and used to identify the most relevant predictors, without any a priori assumption about their impact. Finally, we perform additional experiments by removing the two most dominant features, revealing that ML models can still uncover alternative predictive patterns, thus demonstrating their adaptability and capacity for knowledge extraction under small-data conditions. Future work could benefit from richer datasets, including longitudinal data and psychological features, to better profile students and improve the identification of at-risk individuals. Full article

Multiple Myeloma (MM) is an incurable malignancy that progresses from asymptomatic precursor stages—Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smouldering Multiple Myeloma (SMM)—to active disease. Despite ongoing research, the molecular mechanisms driving this progression remain poorly understood. In this study, we aimed to uncover key regulatory factors involved in MM progression by integrating single-cell RNA sequencing (scRNA-seq) data with curated a priori biological knowledge of MM. To this end, we first integrated a priori knowledge from databases in a synthetic gene network map to play the role of an MM-related backbone to project findings from scRNA analysis on CD138⁺ Plasma Cells. This was followed by stage-specific regulatory network construction and analysis using Integrated Value of Influence (IVI) metrics to identify the most influential genes across disease stages. Our findings revealed GSK3B, RELA, CDKN1A, and PCK2 as central regulators shared across multiple stages of the disease. Notably, several of these genes had not previously been included in established MM gene sets, highlighting them as prime candidates for biomarkers and drug targets. Full article

The Lagrangian formalism has provided a powerful and elegant framework for obtaining governing equations for classical and quantum systems. It is based on the concept of action, which involves Lagrangians, whose a priori knowledge is required. There are different methods to obtain Lagrangians for given equations of motion, and a brief review of these methods is presented. However, the main purpose of this review paper is to describe the so-called null Lagrangians and their gauge functions, and discuss their physical applications. The paper also reviews some recent results, which demonstrate that gauge functions play the most fundamental roles in classical dynamics as they can be used to predict the future states of dynamical systems, without solving the equations of motion, as well as to construct their Lagrangians. Full article