Search Results (20,476)

This study proposes a novel approach to constructing composite indicators, utilizing discriminant analysis to identify the determining factors for the diagnosis of multidimensional depression and to provide an index that represents the multidimensionality of this construct. By focusing solely on factors that significantly correlate with the diagnosis of multidimensional depression, this method provides a more precise and objective representation of the problem. The application of the method to the 2019 Brazilian Health Survey data demonstrated its effectiveness, resulting in a composite indicator that separates individuals who self-declare as having depression from individuals who self-declare as not having depression. The results highlight individuals who have a limiting chronic condition, high levels of education, less support from friends and family, perform unhealthy work, and are male. In contrast, individuals with the opposite characteristics are associated with a negative multidimensional depression diagnosis. The proposed composite indicator not only improves the measurement accuracy but also offers a new means of visualizing and understanding the multidimensional nature of depression diagnosis, providing valuable information for the formulation of targeted public health policies aimed at reducing the time for which people show symptoms of depression. Full article

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm⁻¹ (C–H stretching in lipids), ~1650 cm⁻¹ (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm⁻¹ range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products. Full article

Advanced Non-Intrusive Load Monitoring (NILM) research is important to help reduce energy consumption. Very-low-frequency approaches have traditionally faced challenges in separating appliance uses due to low discriminative information. The richer signatures available in high-frequency electrical data include many harmonic orders that have the potential to advance disaggregation. This has been explored to some extent, but not comprehensively due to a lack of an appropriate public dataset. This paper presents the development of a cost-effective energy monitoring system scalable for multiple entries while producing detailed measurements. We will detail our approach to creating a NILM dataset comprising both aggregate loads and individual appliance measurements, all while ensuring that the dataset is reproducible and accessible. Ultimately, the dataset can be used to validate NILM, and we show through the use of machine learning techniques that high-frequency features improve disaggregation accuracy when compared with traditional methods. This work addresses a critical gap in NILM research by detailing the design and implementation of a data acquisition system capable of generating rich and structured datasets that support precise energy consumption analysis and prepare the essential materials for advanced, real-time energy disaggregation and smart energy management applications. Full article

Renicolid digeneans parasitise aquatic birds. In molecular trees, they are divided into three clades, one of which, the ‘duck clade’, parasitises anatids. Renicola mollissima, a member of this clade, parasitises sea ducks, mainly eiders. Its life cycle remains unknown. We verified the diagnosis of R. mollissima using integrated morphological and molecular data and provided the first information on its life cycle in northern Palaearctic. We proved that intramolluscan stages of R. mollissima, previously known as Cercaria pacifica 2, develop in intertidal snails Littorina squalida and L. saxatilis. We provided a detailed morphological description of cercariae and adults of R. mollissima and a discriminative analysis with closely related species. Molecular data demonstrated an amphiboreal distribution of R. mollissima and the existence of a single population in Europe and the North Pacific. Using molecular methods, we also found metacercariae of an unknown renicolid species from the ‘duck clade’, designated as Cercaria cf. nordica I, in subtidal mussels of the Barents Sea. All individuals of C. cf. nordica I examined in our study were represented by the same haplotype. We discuss possible ways of formation of this phylogeographic structure, the composition of the ‘duck clade’ and the evolutionary pathways of the family Renicolidae. Full article

Olive tree leaf is a phenol-rich, high-potential-value biomass that can be used to formulate food additives and supplements. Leaf phenolic content varies depending on numerous factors, like cultivar, geographical origin, year, and season of harvest. The aim of this research was to evaluate the variations in phenolic profile of four major Tuscan cultivars (Frantoio, Leccio del Corno, Leccino, and Moraiolo) over four different phenological phases and across two years. All 96 olive leaf samples were harvested from trees grown in the same orchard located in Florence. After drying, their phenolic profile was characterized using HPLC-DAD-MS, and the obtained data were processed by ANOVA, GA-LDA, and RF methods. A total of 25 phenolic derivatives were analyzed, with total contents ranging 16,674.0–50,594.3 mg/kg and oleuropein (4570.0–27,547.7 mg/kg) being the predominant compound regardless of cultivar, year, and season of harvest. Oleuropein and hydroxytyrosol glucoside showed inverse proportionality and similar behavior across years in all cultivars, and therefore were highlighted as main phenolic compounds correlated with the seasonal variability in studied cultivars. Interesting behavior was also pointed out for apigenin rutinoside. Application of GA-LDA and RF methods allowed pointing out the excellent performance of leaf phenols in discriminating samples based on cultivar, harvest year, and harvesting season. Full article

Wi-Fi sensing leverages variations in CSI (channel state information) to infer human activities in a contactless and low-cost manner, with growing applications in smart homes, healthcare, and security. While deep learning has advanced macro-motion sensing tasks, micro-motion sensing such as keypad stroke recognition remains underexplored due to subtle inter-class CSI variations and significant intra-class variance. These challenges make it difficult for existing deep learning models typically relying on fully connected MLPs to accurately recognize keypad inputs. To address the issue, we propose a novel approach that combines a discriminative feature extractor with a Kolmogorov–Arnold Network (KAN)-based classifier. The combined model is trained to reduce intra-class variability by clustering features around class-specific centers. The KAN classifier learns nonlinear spline functions to efficiently delineate the complex decision boundaries between different keypad inputs with fewer parameters. To validate our method, we collect a CSI dataset with low-cost Wi-Fi devices (ESP8266 and Raspberry Pi 4) in a real-world keypad sensing environment. Experimental results verify the effectiveness and practicality of our method for keypad input sensing applications in that it outperforms existing approaches in sensing accuracy while requiring fewer parameters. Full article

This study investigates the application of the Analytic Hierarchy Process (AHP) as a decision-support mechanism for managing complex sustainability issues in industrial settings, specifically within the framework of circular economy principles. Focusing on a case from the brewery sector, developed under the EU ECOFACT initiative, this research evaluates ten distinct configurations for the must cooling process. These alternatives are assessed using environmental, economic, and technical criteria, drawing on data from life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The findings indicate that selecting an optimal scenario involves balancing trade-offs among electricity and water consumption, operational efficiency, and overall environmental impacts. Notably, Scenario 3 emerges as the most balanced option, consistently demonstrating superior performance across the primary evaluation criteria. The use of AHP in this context proves valuable by introducing structure and transparency to a multifaceted decision-making process where quantitative metrics and sustainability objectives intersect. By integrating empirical industrial data with an established multi-criteria decision approach, this study highlights both the practical utility and existing limitations of conventional AHP, particularly its diminished ability to discriminate between alternatives when their scores are closely aligned. These insights suggest that hybrid or advanced AHP methodologies may be necessary to facilitate more nuanced decision-making for circular economy transitions in industrial environments. Full article

Early diagnosis is critical for the effective management of neurodegenerative disorders, and retinal alterations have emerged as promising early biomarkers due to the retina’s close developmental and functional link to the brain. The zebrafish (Danio rerio), with its rapid development, transparent embryos, and evolutionarily conserved visual system, represents a powerful and versatile model for studying retinal degeneration. This review discusses a range of behavioral assays—including visual adaptation, motion detection, and color discrimination—that are employed to evaluate retinal function in zebrafish. These methods enable the detection of subtle visual deficits that may precede overt anatomical damage, providing a non-invasive, efficient strategy for early diagnosis and high-throughput drug screening. Importantly, these behavioral tests also serve as sensitive functional readouts to evaluate the efficacy of pharmacological treatments over time. Compared to traditional murine models, zebrafish offer advantages such as lower maintenance costs, faster development, optical transparency for live imaging, and ethical benefits due to reduced use of higher vertebrates. However, variability in experimental protocols highlights the need for standardization to ensure reliability and reproducibility. Full article

This article examines the accessibility of public space for individuals with disabilities in the Netherlands, as well as the relevant legal frameworks intended to promote accessibility. It discusses the Convention on the Rights of Persons with Disabilities (UN CRPD) and efforts to implement its provisions at the local level. The article first provides an overview of Dutch legislation and regulations concerning accessibility in public spaces. It then presents an analysis of the experiences of individuals with disabilities in navigating streets and pavements in two Dutch cities, Utrecht and Almere. The central question is to what extent equal participation in public space has been realised. The findings indicate that national legislation remains inadequate in addressing the accessibility of streets and pavements. Despite the constitutional amendment in January 2023, which prohibits discrimination on the grounds of disability, substantive equality is largely dependent on the individual policies and bylaws of the 342 municipalities. The involvement of individuals with disabilities in shaping the inclusive use of public space is therefore crucial at the local level. This article highlights local initiatives that have successfully drawn the attention of municipal policymakers and civil servants to the importance of accessible streets. Full article

Accurate segmentation of densely stacked and weakly textured objects remains a core challenge in robotic depalletizing for industrial applications. To address this, we propose MaskNet, an instance segmentation network tailored for RGB-D input, designed to enhance recognition performance under occlusion and low-texture conditions. Built upon a Vision Transformer backbone, MaskNet adopts a dual-branch architecture for RGB and depth modalities and integrates multi-modal features using an attention-based fusion module. Further, spatial and channel attention mechanisms are employed to refine feature representation and improve instance-level discrimination. The segmentation outputs are used in conjunction with regional depth to optimize the grasping sequence. Experimental evaluations on camshaft depalletizing tasks demonstrate that MaskNet achieves a precision of 0.980, a recall of 0.971, and an F1-score of 0.975, outperforming a YOLO11-based baseline. In an actual scenario, with a self-designed flexible magnetic gripper, the system maintains a maximum grasping error of 9.85 mm and a 98% task success rate across multiple camshaft types. These results validate the effectiveness of MaskNet in enabling fine-grained perception for robotic manipulation in cluttered, real-world scenarios. Full article

We propose a high-speed all-optical logic gate design based on silicon racetrack and ring resonators patterned on a silica substrate. The architecture features racetrack resonators at both the input and output, with a central ring resonator enabling the required phase-sensitive interference for logic processing. Logic operations are achieved through the interplay of constructive and destructive interference induced by phase-shifted input beams. Using the finite-difference time-domain (FDTD) method in Lumerical software, we simulate and demonstrate seven fundamental Boolean logic functions, namely XOR, AND, OR, NOT, NOR, NAND, and XNOR, at an operating wavelength of 1.33 µm. The system supports a data rate of 47.94 Gb/s, suitable for ultrafast optical computing. The performance is quantitatively evaluated using the contrast ratio (CR) as the reference metric, with more than acceptable values of 13.09 dB (XOR), 13.84 dB (AND), 13.14 dB (OR), 13.80 dB (NOT), 14.53 dB (NOR), 13.80 dB (NAND), and 14.67 dB (XNOR), confirming strong logic level discrimination. Comparative analysis with existing optical gate designs underscores the advantages of our compact silicon-on-silica structure in terms of speed, CR performance, and integration potential. This study validates the effectiveness of racetrack–ring configurations for next-generation all-optical logic circuits. Full article

Following hygiene standards in milk production is essential for making high-quality sour cream, especially when using traditional methods that rely on raw milk. The aim of this study was to determine the physicochemical, microbiological, and sensory quality of artisanal sour cream samples collected from major marketplaces in the wider Zagreb area. On average, the samples contained 27.99% milk fat, 3.30% protein, 34.29% dry matter, 6.51% fat-free dry matter and 3.00% lactose, with considerable variability observed across all components. Microbiological analysis revealed the presence of Staphylococcus aureus in 35.30% of the samples, Enterobacteriaceae in 76.47%, Escherichia coli in 94.11%, Bacillus spp. in 23.53%, and yeasts in 100% of the samples. Listeria monocytogenes and Salmonella spp. were not detected. The sensory analysis of the textural properties showed significant variability in firmness, adhesiveness, viscosity, creaminess, and fizziness. Samples with higher milk fat and dry matter content were rated better for creaminess, viscosity and mouth firmness. Flavour assessments, particularly for cream and diacetyl notes, also varied widely among samples. These findings highlight the complexity of sour cream’s sensory attributes and the significant influence of ingredient composition and processing techniques on appearance, aroma, texture, taste, and flavour. Principal component analysis (PCA) with Varimax rotation simplified the data structure and identified key dimensions of quality variation. Principal component analysis (PCA) revealed that the first principal component (PC1) effectively discriminated the cream samples based on sensory attractiveness and indicators of spoilage and highlighted the association between off-flavour and microbial contamination with inferior characteristics. The second principal component (PC2) captured the differences in physicochemical characteristics and showed a gradient from richer, creamier samples with higher fat content to those with lower acidity and higher freshness. Full article

Resource-constrained Internet of Things (IoT) devices demand efficient and robust intrusion detection systems (IDSs) to counter evolving cyber threats. The traditional IDS models, however, struggle with high computational complexity and inadequate feature extraction, limiting their accuracy and generalizability in IoT environments. To address this, we propose FFT-RDNet, a lightweight IDS framework leveraging depthwise separable convolution and frequency-domain feature fusion. An ADASYN-Tomek Links hybrid strategy first addresses class imbalances. The core innovation of FFT-RDNet lies in its novel two-dimensional spatial feature modeling approach, realized through a dedicated dual-path feature embedding module. One branch extracts discriminative statistical features in the time domain, while the other branch transforms the data into the frequency domain via Fast Fourier Transform (FFT) to capture the essential energy distribution characteristics. These time–frequency domain features are fused to construct a two-dimensional feature space, which is then processed by a streamlined residual network using depthwise separable convolution. This network effectively captures complex periodic attack patterns with minimal computational overhead. Comprehensive evaluation on the NSL-KDD and CIC-IDS2018 datasets shows that FFT-RDNet outperforms state-of-the-art neural network IDSs across accuracy, precision, recall, and F1 score (improvements: 0.22–1%). Crucially, it achieves superior accuracy with a significantly reduced computational complexity, demonstrating high efficiency for resource-constrained IoT security deployments. Full article

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this thanks to three key newly designed modules—a Self-Perception Coordinated Global Attention (SPCGA) module, a High/Low-Frequency Feature Enhancement (HLFFE) module, and a Local Attention Enhancement (LAE) module. The SPCGA module improves the model’s ability to perceive fine-grained targets by fusing multiple attention mechanisms. The HLFFE module adopts a frequency domain separation strategy to strengthen edge delineation and structural detail representation in affected areas. The LAE module effectively improves the model’s discrimination ability between targets and backgrounds through local importance calculation and intensity adjustment mechanisms. Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in $precision$, recall, $F1score$, and $mAP@50$, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value. Full article

Healthcare abuse is a critical human rights and public health issue, particularly impacting medical interns and trainees who are vulnerable to mistreatment during their formative professional years. This cross-sectional study, conducted from February to June 2024, evaluated the prevalence and psychological impact of harassment and discrimination among 463 healthcare interns in Saudi Arabia from various specialties, including medicine, nursing, pharmacy, and dentistry. Using a self-administered online questionnaire, we found that mistreatment was widely reported, with female interns experiencing significantly higher rates of sexual harassment and gender-based discrimination. Common perpetrators included residents, lecturers, professors, nurses, and patients, with incidents most frequently occurring in surgical and internal medicine departments. Despite high prevalence, only 9% of interns reported the abuse due to mistrust in reporting systems or failure to recognize the behavior as abuse. These experiences were associated with significant psychological distress, including frustration, reduced motivation to learn, and higher DASS scores, particularly among female interns. The study underscores the need for institutional reforms, including policy development, cultural change, and effective reporting systems to ensure a safe and supportive learning environment for future healthcare professionals. Addressing abuse in medical training is essential for individual well-being and the sustainability and integrity of healthcare systems. Full article