Search Results (48,201)

The quality of oysters is reflected by volatile organic components. To rapidly assess the freshness level of oysters and elucidate the changes in flavor substances during storage, the volatile compounds of oysters stored at 4, 12, 20, and 28 °C over varying durations were analyzed using GC-MS and an electronic nose. Data from both GC-MS and electronic nose analyses revealed that alcohols, acids, and aldehydes are the primary contributors to the rancidity of oysters. Notably, the relative and absolute contents of Cis-2-(2-Pentenyl) furan and other heterocyclic compounds exhibited an upward trend. This observation suggests the potential for developing a simpler test for oyster freshness based on these compounds. Linear Discriminant Analysis (LDA) demonstrated superior performance compared to Principal Component Analysis (PCA) in differentiating oyster samples at various storage times. At 4 °C, the classification accuracy of the optimal support vector machine (SVM) and random forest (RF) models exceeded 90%. At 12 °C, 20 °C, and 28 °C, the classification accuracy of the best SVM and RF models surpassed 95%. Pearson correlation analysis of the concentrations of various volatile compounds and characteristic markers with the sensor response values indicated that the selected sensors were more aligned with the volatiles emitted by oysters. Consequently, the volatile compounds in oysters during storage can be predicted based on the response information from the sensors in the detection system. This study also demonstrates that the detection system serves as a viable alternative to GC-MS for evaluating oysters of varying freshness grades. Full article

Binary neural networks (BNNs) that use 1-bit weights and activations have garnered interest as extreme quantization provides low power dissipation. By implementing BNNs as computation-in-memory (CIM), which computes multiplication and accumulations on memory arrays in an analog fashion, namely, analog CIM, we can further improve the energy efficiency to process neural networks. However, analog CIMs are susceptible to process variation, which refers to the variability in manufacturing that causes fluctuations in the electrical properties of transistors, resulting in significant degradation in BNN accuracy. Our Monte Carlo simulations demonstrate that in an SRAM-based analog CIM implementing the VGG-9 BNN model, the classification accuracy on the CIFAR-10 image dataset is degraded to below 50% under process variations in a 28 nm FD-SOI technology. To overcome this problem, we present a variation-aware BNN framework. The proposed framework is developed for SRAM-based BNN CIMs since SRAM is most widely used as on-chip memory; however , it is easily extensible to BNN CIMs based on other memories. Our extensive experimental results demonstrate that under process variation of 28 nm FD-SOI, with an SRAM array size of $128\times 128$, our framework significantly enhances classification accuracies on both the MNIST hand-written digit dataset and the CIFAR-10 image dataset. Specifically, for the CONVNET BNN model on MNIST, accuracy improves from 60.24% to 92.33%, while for the VGG-9 BNN model on CIFAR-10, accuracy increases from 45.23% to 78.22%. Full article

Venous hemangiomas within the central nervous system (CNS) represent a rare pathological entity described by sporadic case reports so far. Comprehensive insights into their histological and imaging features, pathogenesis, natural course, and therapeutic modalities are lacking. This review article presents two patients with contrast-enhancing cerebellar lesions near the tentorium cerebelli lacking edema or diffusion restriction. Despite meticulous preoperative neuroradiological examination, diagnostic classification remained inconclusive. Confronted with both—progressive size and diagnostic uncertainty—surgical intervention was undertaken, resulting in uneventful and complete resection of the lesions. Histopathological analyses subsequently revealed a venous hemangioma in each case. In the literature, the term “hemangioma” is often misapplied and inaccurately used to describe a broad spectrum of vascular anomalies. Therefore, a precise identification is essential since the particular type of vascular anomaly affects its natural course and the treatment options available. We aim to contribute to the understanding of this diagnostically intricate entity by presenting the two cases and by providing a detailed overview of radiological and histopathological features of venous hemangiomas. Full article

Dementia is the most critical neurodegenerative disease that gradually destroys memory and other cognitive functions. Therefore, early detection is essential, and to build an effective detection model, it is required to understand its type, symptoms, stages and causes, and diagnosis methodologies. This paper presents a novel approach to classify dementia based on a data set with some relevant patient features. The classification methodology employs non-singleton general type-2 fuzzy sets, non-singleton interval type-2 fuzzy sets, and non-singleton type 1 fuzzy sets. These advanced fuzzy sets are compared with traditional singleton fuzzy sets to evaluate their performance. The Takagi–Sugeno–Kang TSK inference method is used to handle fuzzy reasoning. In the process, the parameters of the membership functions (MFs) and rules are obtained using ANFIS, and non-singleton MFs are optimized with PSO. The results demonstrate that non-singleton general type-2 fuzzy sets improve classification accuracy compared to singleton fuzzy sets, demonstrating their ability to model the uncertainties inherent in the diagnosis of dementia. This improvement suggests that non-singleton fuzzy systems offer a more robust framework for developing effective diagnostic tools in the medical domain. Accurate classification of dementia is of utmost importance to improve patient care and advance medical research. Full article

This study aimed to investigate the effect of an essential oil/fumaric combination, mannan-oligosaccharide, galactooligosaccharide, and a mannan-oligosaccharide/galactooligosaccharide combination on the dry matter disappearance (DMD), gas production, greenhouse gasses, volatile fatty acid, and microbial community of a total mixed ration using a 24 h in vitro batch culture technique. The study design was a completely randomized design with four treatments as follows: a control treatment without any additives, the control treatment supplemented with galactooligosaccharide at 3% (Gos treatment), a galactooligosaccharide and mannan-oligosaccharide mixture at 1:1 at 3% (Gosmos treatment), or an essential oil blend (200 μL/g feed) and fumaric acid at 3% combination (Eofumaric treatment). The Gosmos treatment had the highest (p < 0.05) DMD (63.8%) and the numerical lowest acetate–propionate ratio (p = 0.207), which was 36.9% higher compared to the control. The lowest Shannon index, Simpson’s index, and all the diversity indices were recorded for the Eofumaric treatment, while the other treatments had similar Shannon index, Simpson’s index, and diversity index. The Z-score differential abundance between the Eofumaric and the control indicated that the inclusion of the Eofumaric treatment differentially increased the abundance of Patescibacteria, Synergistota, Chloroflexi, Actinobacteriota, Firmicutes, and Euryarchaeota while Verrucomicrobiota, WPS-2, Fibrobacterota, and Spirochaetota were decreased. The Random Forest Classification showed that the lower relative abundance of Fibrobacterota, Spirochaetota, and Elusimicrobiota and the higher relative abundance of Firmicutes and Chloroflexi were most impactful in explaining the microbial community data. Overall, the essential oil blend showed great potential as a methane gas mitigation strategy by modifying rumen fermentation through changes in the microbial community dynamics. Full article

Lung and colon cancer (LCC) is a dominant life-threatening disease that needs timely attention and precise diagnosis for efficient treatment. The conventional diagnostic techniques for LCC regularly encounter constraints in terms of efficiency and accuracy, thus causing challenges in primary recognition and treatment. Early diagnosis of the disease can immensely reduce the probability of death. In medical practice, the histopathological study of the tissue samples generally uses a classical model. Still, the automated devices that exploit artificial intelligence (AI) techniques produce efficient results in disease diagnosis. In histopathology, both machine learning (ML) and deep learning (DL) approaches can be deployed owing to their latent ability in analyzing and predicting physically accurate molecular phenotypes and microsatellite uncertainty. In this background, this study presents a novel technique called Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI). The proposed LCCST-EMHI method focuses on designing a DL model for the diagnosis and classification of the LCC using histopathological images (HI). In order to achieve this, the LCCST-EMHI model utilizes the bilateral filtering (BF) technique to get rid of the noise. Further, the Swin Transformer (ST) model is also employed for the purpose of feature extraction. For the LCC detection and classification process, an ensemble deep learning classifier is used with three techniques: bidirectional long short-term memory with multi-head attention (BiLSTM-MHA), Double Deep Q-Network (DDQN), and sparse stacked autoencoder (SSAE). Eventually, the hyperparameter selection of the three DL models can be implemented utilizing the walrus optimization algorithm (WaOA) method. In order to illustrate the promising performance of the LCCST-EMHI approach, an extensive range of simulation analyses was conducted on a benchmark dataset. The experimentation results demonstrated the promising performance of the LCCST-EMHI approach over other recent methods. Full article

Bone marrow cellular therapy has undergone a remarkable evolution, significantly impacting the treatment of musculoskeletal disorders. This review traces the historical trajectory from early mythological references to contemporary scientific advancements. The groundbreaking work of Friedenstein in 1968, identifying fibroblast colony-forming cells in bone marrow, laid the foundation for future studies. Caplan’s subsequent identification of mesenchymal stem cells (MSCs) in 1991 highlighted their differentiation potential and immunomodulatory properties, establishing them as key players in regenerative medicine. Contemporary research has focused on refining techniques for isolating and applying bone marrow-derived MSCs. These cells have shown promise in treating conditions like osteonecrosis, osteoarthritis, and tendon injuries thanks to their ability to promote tissue repair, modulate immune responses, and enhance angiogenesis. Clinical studies have demonstrated significant improvements in pain relief, functional recovery, and tissue regeneration. Innovations such as the ACH classification system and advancements in bone marrow aspiration methods have standardized practices, improving the consistency and efficacy of these therapies. Recent clinical trials have validated the therapeutic potential of bone marrow-derived products, highlighting their advantages in both surgical and non-surgical applications. Studies have shown that MSCs can reduce inflammation, support bone healing, and enhance cartilage repair. However, challenges remain, including the need for rigorous characterization of cell populations and standardized reporting in clinical trials. Addressing these issues is crucial for advancing the field and ensuring the reliable application of these therapies. Looking ahead, future research should focus on integrating bone marrow-derived products with other regenerative techniques and exploring non-surgical interventions. The continued innovation and refinement of these therapies hold promise for revolutionizing the treatment of musculoskeletal disorders, offering improved patient outcomes, and advancing the boundaries of medical science. Full article

Background: Mental health issues are increasingly prominent worldwide, posing significant threats to patients and deeply affecting their families and social relationships. Traditional diagnostic methods are subjective and delayed, indicating the need for an objective and effective early diagnosis method. Methods: To this end, this paper proposes a lightweight detection method for multi-mental disorders with fewer data sources, aiming to improve diagnostic procedures and enable early patient detection. First, the proposed method takes Electroencephalography (EEG) signals as sources, acquires brain rhythms through Discrete Wavelet Decomposition (DWT), and extracts their approximate entropy, fuzzy entropy, permutation entropy, and sample entropy to establish the entropy-based matrix. Then, six kinds of conventional machine learning classifiers, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), Generalized Additive Model (GAM), Linear Discriminant Analysis (LDA), and Decision Tree (DT), are adopted for the entropy-based matrix to achieve the detection task. Their performances are assessed by accuracy, sensitivity, specificity, and F1-score. Concerning these experiments, three public datasets of schizophrenia, epilepsy, and depression are utilized for method validation. Results: The analysis of the results from these datasets identifies the representative single-channel signals (schizophrenia: O1, epilepsy: F3, depression: O2), satisfying classification accuracies (88.10%, 75.47%, and 89.92%, respectively) with minimal input. Conclusions: Such performances are impressive when considering fewer data sources as a concern, which also improves the interpretability of the entropy features in EEG, providing a reliable detection approach for multi-mental disorders and advancing insights into their underlying mechanisms and pathological states. Full article

Combining various viewpoints to produce coherent and cohesive results requires decision fusion. These methodologies are essential for synthesizing data from multiple sensors in remote sensing classification in order to make conclusive decisions. Using fully polarimetric Synthetic Aperture Radar (PolSAR) imagery, our study combines the benefits of both approaches for detection by extracting Pauli’s and Krogager’s decomposition components. The Local Pattern Differences (LPD) method was employed on every decomposition component for pixel-level texture feature extraction. These extracted features were utilized to train three independent classifiers. Ultimately, these findings were handled as independent decisions for each land cover type and were fused together using a decision fusion rule to produce complete and enhanced classification results. As part of our approach, after a thorough examination, the most appropriate classifiers and decision rules were exploited, as well as the mathematical foundations required for effective decision fusion. Incorporating qualitative and quantitative information into the decision fusion process ensures robust and reliable classification results. The innovation of our approach lies in the dual use of decomposition methods and the application of a simple but effective decision fusion strategy. Full article

Abstract: The Gedeo zone agroforestry systems are the main source of Ethiopia’s coffee beans. However, land-use and suitability analyses are not well documented due to complex topography, heterogeneous agroforestry, and lack of information. This research aimed to map the coffee coverage and identify land suitability for coffee plantations using remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) in the Gedeo zone, Southern Ethiopia. Remote sensing classifiers often confuse agroforestry and plantations like coffee cover with forest cover because of their similar spectral signatures. Mapping shaded coffee in Gedeo agroforestry using optical or multispectral remote sensing is challenging. To address this, the study identified and mapped coffee coverage from Sentinel-1 data with a decibel (dB) value matched to actual coffee coverage. The actual field data were overlaid on Sentinel-1, which was used to extract the raster value. Pre-processing, classification, standardization, and reclassification of thematic layers were performed to find potential areas for coffee plantation. Hierarchy levels of the main criteria were formed based on climatological, edaphological, physiographic, and socioeconomic factors. These criteria were divided into 14 sub-criteria, reclassified based on their impact on coffee growing, with their relative weights derived using AHP. From the total study area of 1356.2 km², the mapped coffee coverage is 583 km². The outcome of the final computed factor weight indicated that average annual temperature and mean annual rainfall are the primary factors, followed by annual mean maximum temperature, elevation, annual mean minimum temperature, soil pH, Land Use/Land Cover (LULC), soil texture, Cation Exchange Capacity (CEC), slope, Soil Organic Matter (SOM), aspect, distance to roads, and distance to water, respectively. The identified coffee plantation potential land suitability reveals unsuitable (413 km²), sub-suitable (596.1 km²), and suitable (347.1 km²) areas. This study provides comprehensive spatial details for Ethiopian cultivators, government officials, and agricultural extension specialists to select optimal coffee farming locations, enhancing food security and economic prosperity. Full article

The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It is highly relevant in industries like textiles, where speed and accuracy are critical. In the textile industry, accurate fabric type detection and classification are essential for improving quality control, optimizing inventory management, and enhancing customer satisfaction. This paper proposes a new approach using the YOLOv10 model, which offers enhanced detection accuracy, processing speed, and detection on the torn path of each type of fabric. We developed and utilized a specialized, annotated dataset featuring diverse textile samples, including cotton, hanbok, cotton yarn-dyed, and cotton blend plain fabrics, to detect the torn path in fabric. The YOLOv10 model was selected for its superior performance, leveraging advancements in deep learning architecture and applying data augmentation techniques to improve adaptability and generalization to the various textile patterns and textures. Through comprehensive experiments, we demonstrate the effectiveness of YOLOv10, which achieved an accuracy of 85.6% and outperformed previous YOLO variants in both precision and processing speed. Specifically, YOLOv10 showed a 2.4% improvement over YOLOv9, 1.8% over YOLOv8, 6.8% over YOLOv7, 5.6% over YOLOv6, and 6.2% over YOLOv5. These results underscore the significant potential of YOLOv10 in automating fabric detection processes, thereby enhancing operational efficiency and productivity in textile manufacturing and retail. Full article

Sect. Tuberculata Chang in the genus Camellia (Theaceae Mirb.) is named after the “tubercle-like projections on the surface of the capsule and ovary”. Due to complex morphological variations in these taxon and insufficient field investigations, the interspecies relationships are unclear, the species’ definitions are vague, and the names are confusing. This is not conducive to the conservation and study of these species. Therefore, herein, we systematically explore the taxonomic status of five sect. Tuberculata species using morphological, pollen morphological, and molecular phylogenetic methods. The results showed that (1) the morphological characteristics of the flower, fruit, and leaves of C. anlungensis and C. leyeensis are similar. Furthermore, the pollen characteristics and pollen wall ornamentation show that there is no significant difference between the two species; (2) there are significant differences between C. acutiperulata and C. anlungensis in terms of leaf shape (elliptic vs. obovate), calyx characteristics (sepal apex pointed vs. sepal oblong), and fruit shape (subglobose folds with shallow verruculose vs. flat folds and verruculose protuberances with pronounced internal cleavage); (3) C. pyxidiacea and C. rubituberculata differ in flower color (white or light color vs. red) and fruit verrucae (obviously deeply cleft vs. shallowly uncracked); (4) a phylogenetic tree based on the chloroplast genome shows that C. anlungensis and C. leyeensis form a single clade (BS = 100%, PP = 1.0) and are on a different branch, with C. acutiperulata on clade II (BS = 100%, PP = 1.0), and C. pyxidiacea and C. rubituberculata clustered on different branches of clade I (BS = 99%, PP = 1.00). Considering the above results together, we propose that C. leyeensis should be treated as a homonym of C. anlungensis, and C. acutiperulata, C. pyxidiacea, and C. rubituberculata should be considered as separate species. Clarifying the taxonomic status of these five species not only advances our understanding of the significance and complexity of the systematic classification of the genus Camellia but also has important implications for diversity conservation and population genetics. Full article

This paper reviews natural capital (NC) valuation approaches in the context of woodland, forest, and riparian ecosystems, emphasising the need for participatory methods to take priority over neoclassical economics approaches. Focusing on research carried out in Scotland, the study analyses findings according to a classification of natural capital initiatives that we have developed, building on ideas proposed by the UK ENCA initiative, a guideline proposed to help researchers and practitioners understand NC and take it into account in valuation, decision-making and policy. We have found that landscape-scale initiatives that address the relationships between people and place to inform value and decision-making beyond the economic (monetary) benefits generated by ecosystem services (ES) are becoming popular. For instance, recent methods employed to capture stakeholders’ non-utilitarian preferences include the use of participatory GIS mapping, scenario planning, and other participatory methods to identify, explore and quantify less tangible cultural ecosystem services (CES). The review shows that many studies provide information relevant to the formulation of a place-based NC approach, working towards the integration of contextual and relational values into land management decisions to help formulate management strategies that maximise ES delivery. Conversely, we have not found evidence of the integration of shared values arising from an eco-centric perspective of nature valuation into the more classical, instrumental value lens. Such an approach would help inform broader, overarching aspects of woodland and forest management that may foster more effective conservation and help to manage conflicts. Full article

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: body mass index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 12 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33, 0.11–0.87) but increased risk in the BFP model (OR: 1.66, 1.01–2.76). The BMI model demonstrates exceptional predictive ability (AUC = 0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions: BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model. Full article

Current research on game difficulty prediction mainly uses heuristic functions or physiological signals. The former does not consider user data, while the latter easily causes interference to the user. This paper proposes a difficulty prediction method based on multiple facial cues and game performance. Specifically, we first utilize various computer vision methods to detect players’ facial expressions, gaze directions, and head poses. Then, we build a dataset by combining these three kinds of data and game performance as inputs, with the subjective difficulty ratings as labels. Finally, we compare the performance of several machine learning methods on this dataset using two classification tasks. The experimental results showed that the multilayer perceptron classifier (abbreviated as MLP) achieved the highest performance on these tasks, and its accuracy increased with the increase in input feature dimensions. These results demonstrate the effectiveness of our method. The proposed method could assist in improving game design and user experience. Full article