Search Results (892)

Background: Surgical resection of vestibular schwannomas (VS) can be responsible for single-sided deafness (SSD). Hearing restoration can be a challenge both for the otolaryngologist and the patient. Patients and Methods: In a retrospective series, we analyzed the charts of SSD patients operated on for VS from 2005–2021, checking which type of hearing rehabilitation was chosen. All patients who wanted a hearing restoration underwent a hearing in noise test (HINT) in a stereo auditorium with and without a bone-anchored hearing device (BAHD) worn with a headband on the deaf side. Then, they had a preimplantation one-month trial with the BAHD at home vs. contralateral routing of signal (CROS) or BiCROS (with contralateral signal amplification) hearing aids (HAs). Results: Among 52 charts of the included adult SSD patients, only 29 (56%) eventually chose a hearing rehabilitation device (14 BAHD). Only one BAHD patient required a device explantation for skin complications, but then asked for reimplantation. Another one swapped the BAHD for HAs 2.5 years after. Two patients only occasionally used their BAHD with a headband. Nine patients preferred HAs, mainly BiCROS. Their contralateral hearing was significantly less than BAHD patients (p < 0.05), and only three used their HAs every day. Conclusions: Hearing rehabilitation in SSD patients after VS surgical resection is chosen in about 50% of cases. In complement of HINT, a real-life comparative hearing trial helps patients chose the best device, with good long-term results when a BAHD is chosen. HAs are preferred when contralateral hearing is altered but are not always worn. Full article

Background: Dermatoglyphic pattern deviances have been associated with schizophrenia-spectrum disorders (SSD) and are considered neurodevelopment vulnerability markers based on the shared ectodermal origin of the epidermis and the central nervous system. The endocannabinoid system participates in epidermal differentiation, is sensitive to prenatal insults and is associated with SSD. Objective: We aimed to investigate whether the Cannabinoid Receptor 1 gene (CNR1) modulates the dermatoglyphics–SSD association. Methods: In a sample of 112 controls and 97 patients with SSD, three dermatoglyphic markers were assessed: the total palmar a-b ridge count (TABRC), the a-b ridge count fluctuating asymmetry (ABRC-FA), and the pattern intensity index (PII). Two CNR1 polymorphisms were genotyped: rs2023239-T/C and rs806379-A/T. We tested: (i) the CNR1 association with SSD and dermatoglyphic variability within groups; and (ii) the CNR1 × dermatoglyphic measures interaction on SSD susceptibility. Results: Both polymorphisms were associated with SSD. The polymorphism rs2023239 modulated the relationship between PII and SSD: a high PII score was associated with a lower SSD risk within C-allele carriers and a higher SSD risk within TT-homozygotes. This result indicates an inverse relationship between the PII and the SSD predicted probability conditional to the rs2023239 genotype. Conclusions: These novel findings suggest the endocannabinoid system’s role in the development and variability of dermatoglyphic patterns. The identified interaction encourages combining genetic and dermatoglyphics to assess neurodevelopmental alterations predisposing to SSD in future studies. Full article

Pest infestations in tea gardens are one of the common issues encountered during tea cultivation. This study introduces an improved YOLOv8 network model for the detection of tea pests to facilitate the rapid and accurate identification of early-stage micro-pests, addressing challenges such as small datasets and the difficulty of extracting phenotypic features of target pests in tea pest detection. Based on the original YOLOv8 network framework, this study adopts the SIoU optimized loss function to enhance the model’s learning ability for pest samples. AKConv is introduced to replace certain network structures, enhancing feature extraction capabilities and reducing the number of model parameters. Vision Transformer with Bi-Level Routing Attention is embedded to provide the model with a more flexible computation allocation and improve its ability to capture target position information. Experimental results show that the improved YOLOv8 network achieves a detection accuracy of 98.16% for tea pest detection, which is a 2.62% improvement over the original YOLOv8 network. Compared with the YOLOv10, YOLOv9, YOLOv7, Faster RCNN, and SSD models, the improved YOLOv8 network has increased the mAP value by 3.12%, 4.34%, 5.44%, 16.54%, and 11.29%, respectively, enabling fast and accurate identification of early-stage micro pests in tea gardens. This study proposes an improved YOLOv8 network model based on deep learning for the detection of micro-pests in tea, providing a viable research method and significant reference for addressing the identification of micro-pests in tea. It offers an effective pathway for the high-quality development of Yunnan’s ecological tea industry and ensures the healthy growth of the tea industry. Full article

Asbestos research, identification, and quantification have been performed over the years, and the relationship between fiber inhalation and lung disease development is well defined. The same cannot be said for the gastroenteric system: the International Agency for Research on Cancer (IARC) believes that colorectal cancer (CRC) could be associated with asbestos exposure, but research has not demonstrated a casual nexus between exposure and CRC, despite highlighting an association tendency. The combination of scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) is the most applied technique in asbestos fiber identification in tissues and intestinal mucosa. In this study, SEM/EDS was applied to evaluate the presence of asbestos fibers and bodies (ABs) inside the tissue of eleven patients affected by CRC who had undergone environmental exposure due to living in an asbestos-polluted area where an Eternit plant had been active in the past. This technique was coupled with optical microscopy (OM) to verify whether the latter could be applied to evaluate the presence of these mineral phases, with the goal of understanding its suitability for identifying fibers and ABs in colon tissues. In addition to verifying the presence of fibers, this study allowed us to identify the deposition site of said fibers within the sample and possibly detect associated tissue reactions using OM, over a shorter time and at lower costs. Despite being a preliminary and descriptive work, the obtained results allowed us to propose a method involving first-sample OM observation to identify regulated (fibers with a length ≥ 5 μm, a thickness ≤ 3 μm, and a length/thickness ratio > 3) asbestos phases and ABs in the extra-respiratory system. In fact, OM and SEM/EDS provided similar information: no asbestiform morphology or ABs were found, but phyllosilicates and other inorganic materials were identified. This research needs to be continued using higher-resolution techniques to definitively rule out the presence of these fibers inside tissues whilst also increasing the number of patients involved. Full article

China’s Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science. Full article

Star anise, a widely popular spice, benefits from classification that enhances its economic value. In response to the low identification efficiency and accuracy of star anise varieties in the market, as well as the scarcity of related research, this study proposes an efficient identification method based on non-similarity augmentation and a lightweight cascaded neural network. Specifically, this approach utilizes a Siamese enhanced data network and a front-end SRGAN network to address sample imbalance and the challenge of identifying blurred images. The YOLOv8 model is further lightweight to reduce memory usage and increase detection speed, followed by optimization of the weight parameters through an extended training strategy. Additionally, a diversified fusion dataset of star anise, incorporating open data, was constructed to further validate the feasibility and effectiveness of this method. Testing showed that the SA-SRYOLOv8 detection model achieved an average detection precision (mAP) of 96.37%, with a detection speed of 146 FPS. Ablation experiment results showed that compared to the original YOLOv8 and the improved YOLOv8, the cascade model’s mAP increased by 0.09 to 0.81 percentage points. Additionally, when compared to mainstream detection models such as SSD, Fast R-CNN, YOLOv3, YOLOv5, YOLOX, and YOLOv7, the cascade model’s mAP increased by 1.81 to 19.7 percentage points. Furthermore, the model was significantly lighter, at only about 7.4% of the weight of YOLOv3, and operated at twice the speed of YOLOv7. Visualization results demonstrated that the cascade model accurately detected multiple star anise varieties across different scenarios, achieving high-precision detection targets. The model proposed in this study can provide new theoretical frameworks and ideas for constructing real-time star anise detection systems, offering new technological applications for smart agriculture. Full article

Polyolefin separators with worse porous structures and compatibilities mismatch the internal environment and deteriorate lithium-ion battery (LIB) combination properties. In this study, a sulfonated SiO₂ (SSD) composited polypropylene separator (PP@SSD) is prepared to homogenize pore sizes and in situ-built SSD coatings on porous skeletons. Imported SSD uniformizes pore sizes owing to centralized interface distributions within casting films. Meanwhile, abundant cavitations enable the in situ SSD coating to facilely fix onto porous skeleton surfaces during separator fabrications, which feature simple techniques, low cost, environmental friendliness, and the capability for continuous fabrications. A sturdy SSD coating on the porous skeleton confines thermal shrinkages and offers a superior safety guarantee for LIBs. The abundant sulfonic acid groups of SSD endow PP@SSD with excellent electrolyte affinity, which lowers Li⁺ transfer barriers and optimizes interfacial compatibility. Therefore, assembled LIBs give the optimal C-rate capacity and cycling stability, holding a capacity retention of 82.7% after the 400th cycle at 0.5 C. Full article

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. This study aims to compare the performance of three deep learning object detection models—Faster R-CNN, YOLO V2, and SSD—using different ResNet architectures (ResNet-18, ResNet-50, and ResNet-101) as feature extractors for detecting and classifying third molar angles in panoramic X-rays according to Winter’s classification criterion. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using panoramic X-rays by using Winter’s classification criterion. This criterion characterizes the third molar’s position relative to the second molar’s longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-rays. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic radiographs represents a promising solution in dental applications. Full article

The pathological process of prion diseases implicates that the normal physiological cellular prion protein (PrP^C) converts into misfolded abnormal scrapie prion (PrP^Sc) through post-translational modifications that increase β-sheet conformation. We recently demonstrated that HuPrP(90–231) thermal unfolding is partially irreversible and characterized by an intermediate state (β-PrPI), which has been revealed to be involved in the initial stages of PrP^C fibrillation, with a seeding activity comparable to that of human infectious prions. In this study, we report the thermal unfolding characterization, in cell-mimicking conditions, of the truncated (HuPrP(90–231)) and full-length (HuPrP(23–231)) human prion protein by means of CD and NMR spectroscopy, revealing that HuPrP(90–231) thermal unfolding is characterized by two successive transitions, as in buffer solution. The amyloidogenic propensity of HuPrP(90–231) under crowded conditions has also been investigated. Our findings show that although the prion intermediate, structurally very similar to β-PrPI, forms at a lower temperature compared to when it is dissolved in buffer solution, in cell-mimicking conditions, the formation of prion fibrils requires a longer incubation time, outlining how molecular crowding influences both the equilibrium states of PrP and its kinetic pathways of folding and aggregation. Full article

Aiming at the problems of a large volume, slow processing speed, and difficult deployment in the edge terminal, this paper proposes a lightweight insulator detection algorithm based on an improved SSD. Firstly, the original feature extraction network VGG-16 is replaced by a lightweight Ghost Module network to initially achieve the lightweight model. A Feature Pyramid structure and Feature Pyramid Network (FPN+PAN) are integrated into the Neck part and a Simplified Spatial Pyramid Pooling Fast (SimSPPF) module is introduced to realize the integration of local features and global features. Secondly, multiple Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanisms are introduced in the Neck part to make the model pay more attention to the channels containing important feature information. The original six detection heads are reduced to four to improve the inference speed of the network. In order to improve the recognition performance of occluded and overlapping targets, DIoU-NMS was used to replace the original non-maximum suppression (NMS). Furthermore, the channel pruning strategy is used to reduce the unimportant weight matrix of the model, and the knowledge distillation strategy is used to fine-adjust the network model after pruning, so as to ensure the detection accuracy. The experimental results show that the parameter number of the proposed model is reduced from 26.15 M to 0.61 M, the computational load is reduced from 118.95 G to 1.49 G, and the mAP is increased from 96.8% to 98%. Compared with other models, the proposed model not only guarantees the detection accuracy of the algorithm, but also greatly reduces the model volume, which provides support for the realization of visible light insulator target detection based on edge intelligence. Full article

In order to extend the lifespan of SSDs, it is essential to achieve wear leveling that evenly distributes the accumulated erase counts of NAND blocks, thereby delaying the occurrence of bad blocks as much as possible. This paper proposes the Greedy-MP policy, integrating static and dynamic wear leveling. When a specific block exhibits excessive erasures surpassing a defined threshold, Greedy-MP initiates the migration of cold data, expected to undergo infrequent modifications, to that block. Additionally, migrated blocks are excluded as candidates for garbage collection until their erase counts reach a similar level to others, preventing premature transition into bad blocks. Performance evaluations demonstrate that Greedy-MP achieves the longest lifespan across all test scenarios. Compared to policies solely utilizing static wear leveling like PWL, it extends the lifespan by up to 1.72 times. Moreover, when integrated with dynamic wear leveling policies such as CB alongside static wear leveling like PWL, it extends the lifespan by up to 1.99 times. Importantly, these extensions are achieved without sacrificing performance. By preserving garbage collection efficiency, Greedy-MP delivers the shortest average response time for I/O requests. Full article

Object detection in maritime environments is a challenging problem because of the continuously changing background and moving objects resulting in shearing, occlusion, noise, etc. Unluckily, this problem is of critical importance since such failure may result in significant loss of human lives and economic loss. The available object detection methods rely on radar and sonar sensors. Even with the advances in electro-optical sensors, their employment in maritime object detection is rarely considered. The proposed research aims to employ both electro-optical and near-infrared sensors for effective maritime object detection. For this, dedicated deep learning detection models are trained on electro-optical and near-infrared (NIR) sensor datasets. For this, (ResNet-50, ResNet-101, and SSD MobileNet) are utilized in both electro-optical and near-infrared space. Then, dedicated ensemble classifications are constructed on each collection of base learners from electro-optical and near-infrared spaces. After this, decisions about object detection from these spaces are combined using logical-disjunction-based final ensemble classification. This strategy is utilized to reduce false negatives effectively. To evaluate the performance of the proposed methodology, the publicly available standard Singapore Maritime Dataset is used and the results show that the proposed methodology outperforms the contemporary maritime object detection techniques with a significantly improved mean average precision. Full article

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO’s feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model’s performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs’ appropriate parameters with high precision. Full article

Multiple myeloma (MM) affects a population with a high prevalence of cardiovascular (CV) disease. These patients benefit from an accurate CV risk evaluation in order to choose the safest drug regimen. Haemodynamic forces (HDFs) analysis allows for the earlier detection of myocardial damage compared with standard markers; the role played by MM in HDFs alteration, with or without the influence of hypertension, is yet to be studied. Therefore, we aimed to identify differences in HDFs analysis in patients with MM, hypertension or both versus normotensive non-oncologic subjects. A total of 173 patients (MM hypertensive patients, MMHT; MM normotensive patients, MMNT; non-oncologic hypertensive patients, CoHT; and non-oncologic normotensive patients, CoNT) underwent transthoracic echocardiography for HDFs analysis and pulse wave velocity (PWV) assessment. Hypertensive patients (MMHT, CoHT) showed decreased ejection fraction (EF), global longitudinal strain (GLS) and HDFs values compared with CoNT, whereas ventricular mass (LVMi) and PWV increased. MMNT displayed a significant reduction in systolic HDFs (p < 0.006) and systolic ejection HDFs (p < 0.008) compared with CoNT, without significant change in EF, GLS, LVMi or PWV. In conclusion, MM leads to ventricular remodelling regardless of hypertension; HDFs application for MM patients could help detect early myocardial damage, especially in patients receiving cardiotoxic drugs. Full article

Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with diffusion models to address these challenges effectively, aligning with Sustainable Development Goal (SDG) 14: Life Below Water. The methodology leverages the Convolutional Block Attention Module (CBAM), Modified Swin Transformer Block (MSTB), and Diffusion model to enhance the quality of underwater images, thereby improving the accuracy of object detection and classification tasks. This study utilizes the TrashCan dataset, comprising diverse underwater scenes and objects, to validate the proposed method’s efficacy. This study proposes an advanced imaging technique YOLO (you only look once) network (AIT-YOLOv7) for detecting objects in underwater images. This network uses a modified U-Net, which focuses on informative features using a convolutional block channel and spatial attentions for color correction and a modified swin transformer block for resolution enhancement. A novel diffusion model proposed using modified U-Net with ResNet understands the intricate structures in images with underwater objects, which enhances detection capabilities under challenging visual conditions. Thus, AIT-YOLOv7 net precisely detects and classifies different classes of objects present in this dataset. These improvements are crucial for applications in marine ecology research, underwater archeology, and environmental monitoring, where precise identification of marine debris, biological organisms, and submerged artifacts is essential. The proposed framework advances underwater imaging technology and supports the sustainable management of marine resources and conservation efforts. The experimental results demonstrate that state-of-the-art object detection methods, namely SSD, YOLOv3, YOLOv4, and YOLOTrashCan, achieve mean accuracies ([email protected]) of 57.19%, 58.12%, 59.78%, and 65.01%, respectively, whereas the proposed AIT-YOLOv7 net reaches a mean accuracy ([email protected]) of 81.4% on the TrashCan dataset, showing a 16.39% improvement. Due to this improvement in the accuracy and efficiency of underwater object detection, this research contributes to broader marine science and technology efforts, promoting the better understanding and management of aquatic ecosystems and helping to prevent and reduce the marine pollution, as emphasized in SDG 14. Full article