Search Results (7,860)

Background: This study aims to verify whether the blood flow velocity and the diameter size, measured through intra-carotid artery Doppler measurements performed on sepsis patients visiting the emergency department, are useful as tools for predicting the risk of early death. Methods: As a prospective study, this research was performed on sepsis patients who visited a local emergency medical center from August 2021 to February 2023. The sepsis patients’ carotid artery was measured using Doppler imaging, and they were divided into patients measured for the size of systolic and diastolic mean blood flow velocity and diameter size: those measured for their qSOFA (quick sequential organ failure assessment) score and those measured using the SIRS (systemic inflammatory response syndrome) criteria. By measuring and comparing their mortality prediction accuracies, this study sought to verify the usefulness of blood flow velocity and the diameter size of the intra-carotid artery as tools to predict early death. Results: This study was conducted on 1026 patients, excluding 45 patients out of the total of 1071 patients. All sepsis patients were measured using systolic and diastolic blood flow velocity and diameter by Doppler imaging of the intra-carotid artery, assessed using qSOFA and evaluated using SIRS criteria. The results of the analysis performed to compare the mortality prediction accuracy were as follows. First, the hazard ratio (95% CI) of the intra-carotid artery was significant (p < 0.05), at 1.020 (1.004–1.036); the hazard ratio (95% CI) of qSOFA was significant (p < 0.05), at 3.871 (2.526–5.931); and the hazard ratio (95% CI) of SIRS showed no significant difference, at 1.002 (0.995–1.009). After 2 h of infusion treatment, the diameter size was 4.72 ± 1.23, showing a significant difference (p < 0.05). After 2 h of fluid treatment, the blood flow velocity was 101 m/s ± 21.12, which showed a significant difference (p < 0.05). Conclusions: Measuring the mean blood flow velocity in the intra-carotid arteries of sepsis patients who visit the emergency department is useful for predicting the risk of death at an early stage. And this study showed that Doppler measurement of the diameter size of the carotid artery significantly increased after performing fluid treatment after early recognition. Full article

Marfan syndrome (MFS) is an autosomal dominant connective-tissue disorder affecting multiple systems, such as skeletal, cardiovascular, and ocular systems. MFS is predominantly caused by mutations in the FBN1 gene, which encodes the fibrillin-1 protein, crucial for connective-tissue integrity. FBN1 mutations lead to defective fibrillin, resulting in structurally compromised connective tissues. Additionally, these mutations cause aberrant TGF-β expression, contributing to vascular issues and increased susceptibility to radiation-induced fibrosis. Studies about the potential radiosensitivity of MFS are rare and generally limited to case reports. Here, we aimed to investigate the radiation-induced ATM nucleo-shuttling (RIANS) model to explore the molecular and cellular radiation response in fibroblasts from MFS patients. The results showed that the MFS fibroblast cell lines tested are associated with moderate but significant radiosensitivity, high yield of micronuclei, and impaired recognition of DNA double-strand breaks (DSBs) caused by a diminished RIANS. The diminished RIANS is supported by the sequestration of ATM protein in the cytoplasm not only by mutated FBN1 protein but also by overexpressed TGF-β. This report is the first molecular and cellular characterization of the radiation response of MFS fibroblasts and highlights the importance of the FBN1-TGF-β complex after irradiation. Full article

The accuracy of data perception in Internet of Things (IoT) systems is fundamental to achieving scientific decision-making and intelligent control. Given the frequent occurrence of sensor failures in complex environments, a rapid and accurate fault diagnosis and handling mechanism is crucial for ensuring the stable operation of the system. Addressing the challenges of insufficient feature extraction and sparse sample data that lead to low fault diagnosis accuracy, this study explores the construction of a fault diagnosis model tailored for agricultural sensors, with the aim of accurately identifying and analyzing various sensor fault modes, including but not limited to bias, drift, accuracy degradation, and complete failure. This study proposes an improved dung beetle optimization–support vector machine (IDBO-SVM) diagnostic model, leveraging the optimization capabilities of the former to finely tune the parameters of the Support Vector Machine (SVM) to enhance fault recognition under conditions of limited sample data. Case analyses were conducted using temperature and humidity sensors in air and soil, with comprehensive performance comparisons made against mainstream algorithms such as the Backpropagation (BP) neural network, Sparrow Search Algorithm–Support Vector Machine (SSA-SVM), and Elman neural network. The results demonstrate that the proposed model achieved an average diagnostic accuracy of 94.91%, significantly outperforming other comparative models. This finding fully validates the model’s potential in enhancing the stability and reliability of control systems. The research results not only provide new ideas and methods for fault diagnosis in IoT systems but also lay a foundation for achieving more precise, efficient intelligent control and scientific decision-making. Full article

Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of group-reared pigs to detect health problems and improve animal welfare promptly. We have developed the method named Pig-ByteTrack. Our approach addresses target detection, Multi-Object Tracking (MOT), and behavioral time computation for each pig. The YOLOX-X detection model is employed for pig detection and behavior recognition, followed by Pig-ByteTrack for tracking behavioral information. In 1 min videos, the Pig-ByteTrack algorithm achieved Higher Order Tracking Accuracy (HOTA) of 72.9%, Multi-Object Tracking Accuracy (MOTA) of 91.7%, identification F1 Score (IDF1) of 89.0%, and ID switches (IDs) of 41. Compared with ByteTrack and TransTrack, the Pig-ByteTrack achieved significant improvements in HOTA, IDF1, MOTA, and IDs. In 10 min videos, the Pig-ByteTrack achieved the results with 59.3% of HOTA, 89.6% of MOTA, 53.0% of IDF1, and 198 of IDs, respectively. Experiments on video datasets demonstrate the method’s efficacy in behavior recognition and tracking, offering technical support for health and welfare monitoring of pig herds. Full article

Peptidoglycan recognition proteins (PGRPs) are a class of pattern recognition receptors (PRRs) that activate the innate immune system in response to microbial infection by detection of peptidoglycan, a distinct component of bacterial cell walls. Bioinformatic studies have revealed four PGRPs in the red imported fire ant Solenopsis invicta; nonetheless, the mechanism of the immune response of S. invicta induced by pathogens is still poorly understood. The peptidoglycan recognition protein full-length cDNA (designated as SiPGRP-S1/S2/S3/L) from S. invicta was used in this investigation. According to the sequencing analysis, there was a significant degree of homology between the anticipated amino acid sequence of SiPGRPs and other members of the PGRPs superfamily. Molecular docking studies demonstrated that SiPGRPs show strong binding affinity for a variety of PGN substrates. Additionally, tissue distribution analysis indicated that SiPGRPs are primarily expressed in several tissues of naïve larvae, including fat body, hemocytes, head, and thorax, as detected by quantitative real-time PCR (RT-qPCR). Microbial challenges resulted in variable changes in mRNA levels across different tissues. Furthermore, the antibacterial effects of antimicrobial peptides (AMPs) produced by major ants infected with Metarhizium anisopliae were assessed. These AMPs demonstrated inhibitory effects against M. anisopliae, Staphylococcus aureus, and Escherichia coli, with the most pronounced effect observed against E. coli. In conclusion, SiPGRPs act as pattern recognition receptors (PRRs) that identify pathogens and initiate the expression of AMPs in S. invicta, this mechanism contributes to the development of biopesticides designed for the targeted control of invasive agricultural pests. Full article

The presence of gray mold can seriously affect the yield and quality of strawberries. Due to their susceptibility and the rapid spread of this disease, it is important to develop early, accurate, rapid, and non-destructive disease identification strategies. In this study, the early detection of strawberry leaf diseases was performed using hyperspectral imaging combining multi-dimensional features like spectral fingerprints and vegetation indices. Firstly, hyperspectral images of healthy and early affected leaves (24 h) were acquired using a hyperspectral imaging system. Then, spectral reflectance (616) and vegetation index (40) were extracted. Next, the CARS algorithm was used to extract spectral fingerprint features (17). Pearson correlation analysis combined with the SPA method was used to select five significant vegetation indices. Finally, we used five deep learning methods (LSTMs, CNNs, BPFs, and KNNs) to build disease detection models for strawberries based on individual and fusion characteristics. The results showed that the accuracy of the recognition model based on fused features ranged from 88.9% to 96.6%. The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm. Full article

This study focuses on the development of a wearable real-time Humming Detector Device (HDD) aimed at enhancing the control of assistive devices through humming. As the need for portable user-friendly tools in assistive technology grows, the HDD offers a non-invasive solution to detect vocal cord vibrations. Vibrations, detected thanks to an accelerometer worn on the neck, are processed in real time using a Fast Fourier Transform (FFT) to identify specific humming frequencies, which are then translated into commands for controlling assistive devices via Bluetooth Low Energy (BLE) transmission. The device was tested with 13 healthy subjects to validate its potential and determine the optimal number of distinct commands that users can achieve. The HDD’s portability and precision make it a promising alternative to traditional voice recognition systems, particularly for individuals with speech impairments. Full article

Ferroelectret nanogenerators (FENGs) have garnered attention due to their unique porous structure and excellent piezoelectric performance. However, most existing FENGs lack sufficient stretchability and flexibility, limiting their application in the field of wearable electronics. In this regard, we have focused on the development of an ultrathin, stretchable, and twistable ferroelectret nanogenerator (UST-FENG) based on Ecoflex, which is made up of graphene, Ecoflex, and anhydrous ethanol, with controllable pore shape and density. The UST-FENG has a thickness of only 860 µm, a fracture elongation rate of up to 574%, and a Young’s modulus of only 0.2 MPa, exhibiting outstanding thinness and excellent stretchability. Its quasi-static piezoelectric coefficient is approximately 38 pC/N. Utilizing this UST-FENG device can enable the recognition of facial muscle movements such as blinking and speaking, thereby helping to monitor people’s facial conditions and improve their quality of life. The successful application of the UST-FENG in facial muscle recognition represents an important step forward in the field of wearable systems for the human face. Full article

The integration of communication and sensing (ICAS) in optical networks is an inevitable trend in building intelligent, multi-scenario, application-converged communication systems. However, due to the impact of nonlinear effects, co-fiber transmission of sensing signals and communication signals can cause interference to the communication signals, leading to an increased bit error rate (BER). This paper proposes a noncoherent solution based on the alternate polarization chirped return-to-zero frequency shift keying (Apol-CRZ-FSK) modulation format to realize a 4 × 100 Gbps dense wavelength division multiplexing (DWDM) optical network. Simulation results show that compared to traditional modulation formats, such as chirped return-to-zero frequency shift keying (CRZ-FSK) and differential quadrature phase shift keying (DQPSK), this solution demonstrates superior resistance to nonlinear effects, enabling longer transmission distances and better transmission performance. Moreover, to meet the transmission requirements and signal sensing and recognition needs in future optical networks, this study employs the Inception-ResNet-v2 convolutional neural network model to identify three modulation formats. Compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, it achieves the highest performance. This research provides a low-cost, low-complexity, and high-performance solution for signal transmission and signal recognition in high-speed optical networks designed for integrated communication and sensing. Full article

Abnormal behavior of crew members is an important cause of frequent ship safety accidents. The existing abnormal crew recognition algorithms are affected by complex ship environments and have low performance in real and open shipborne environments. This paper proposes an abnormal crew detection network for complex ship scenarios (ACD-Net), which uses a two-stage algorithm to detect and identify abnormal crew members in real-time. An improved YOLOv5s model based on a transformer and CBAM mechanism (YOLO-TRCA) is proposed with a C3-TransformerBlock module to enhance the feature extraction ability of crew members in complex scenes. The CBAM attention mechanism is introduced to reduce the interference of background features and improve the accuracy of real-time detection of crew abnormal behavior. The crew identification algorithm (CFA) tracks and detects abnormal crew members’ faces in real-time in an open environment (CenterFace), continuously conducts face quality assessment (Filter), and selects high-quality facial images for identity recognition (ArcFace). The CFA effectively reduces system computational overhead and improves the success rate of identity recognition. Experimental results indicate that ACD-Net achieves 92.3% accuracy in detecting abnormal behavior and a 69.6% matching rate for identity recognition, with a processing time of under 39.5 ms per frame at a 1080P resolution. Full article

Arabic raw audio datasets were initially gathered to produce a corresponding signal spectrum, which was further used to extract the Mel-Frequency Cepstral Coefficients (MFCCs). The pronunciation dictionary, language model, and acoustic model were further derived from the MFCCs’ features. These output data were processed into Baidu’s Deep Speech model (ASR system) to attain the text corpus. Baidu’s Deep Speech model was implemented to precisely identify the global optimal value rapidly while preserving a low word and character discrepancy rate by attaining an excellent performance in isolated and end-to-end speech recognition. The desired outcome in this work is to forecast the next word and character in a sequential and systematic order that applies under natural language processing (NLP). This work combines the trained Arabic language model ARABERT with the potential of Long Short-Term Memory (LSTM) networks to predict the next word and character in an Arabic text. We used the pre-trained ARABERT embedding to improve the model’s capacity and, to capture semantic relationships within the language, we educated LSTM + CNN and Markov models on Arabic text data to assess the efficacy of this model. Python libraries such as TensorFlow, Pickle, Keras, and NumPy were used to effectively design our development model. We extensively assessed the model’s performance using new Arabic text, focusing on evaluation metrics like accuracy, word error rate, character error rate, BLEU score, and perplexity. The results show how well the combined LSTM + ARABERT and Markov models have outperformed the baseline models in envisaging the next word or character in the Arabic text. The accuracy rates of 64.9% for LSTM, 74.6% for ARABERT + LSTM, and 78% for Markov chain models were achieved in predicting the next word, and the accuracy rates of 72% for LSTM, 72.22% for LSTM + CNN, and 73% for ARABERET + LSTM models were achieved for the next-character prediction. This work unveils a novelty in Arabic natural language processing tasks, estimating a potential future expansion in deriving a precise next-word and next-character forecasting, which can be an efficient utility for text generation and machine translation applications. Full article

Plantations are not inherently normal, yet they have been normalized within traditional agricultural landscapes. This is the premise through which we explore why plantations thrive despite numerous social and ecological drawbacks. Accordingly, the aim of this paper is to present a framework to elucidate why Hass avocado plantations succeed, using Salamina, Colombia as a case study. We argue that these plantations prosper through a process of normalization, driven by the dynamic interplay between social structures and human agency in agriculture. Our theoretical framework regarding normalization unfolds in three stages: prescription, implementation embeddedness, and integration. To reach this outcome, we first build a theoretical foundation based on realist social theory and subsequently conduct a primarily qualitative case study, focusing on neighboring respondents to plantations for understanding the process of introduction, development, and persistence of these plantations in the landscape. Additionally, we consider supplementary interviews and secondary information to understand the context of Hass avocado expansion. We found that while normalization may appear to involve passive conformity, our analysis highlights the critical role of human agency. As our study demonstrates, agency fosters reflection and sustains various forms of resistance and counterbalance against systemic pressures. This recognition underscores the potential for proactive engagement and transformative action within agricultural systems, challenging and reshaping the prevailing norms. Full article

Sudden failures of measurement and control circuits in hydropower plants may lead to unplanned shutdowns of generating units. Therefore, the diagnosis of hydropower station measurement and control system poses a great challenge. Existing fault diagnosis methods suffer from long fault identification time, inaccurate positioning, and low diagnostic efficiency. In order to improve the accuracy of fault diagnosis, this paper proposes a fault diagnosis method for hydropower station measurement and control system that combines variational modal decomposition (VMD), Pearson’s correlation coefficient, a one-dimensional convolutional neural network, and a bi-directional long and short-term memory network (1DCNN-BiLSTM). Firstly, the VMD parameters are optimised by the Improved Sparrow Search Algorithm (ISSA). Secondly, signal decomposition of the original fault signals is carried out by using ISSA-VMD, and meanwhile, the optimal intrinsic modal components (IMFs) are screened out by using Pearson’s correlation coefficient, and the optimal set of components is subjected to signal reconstruction in order to obtain the new signal sequences. Then, the 1DCNN-BiLSTM-based fault diagnosis model is proposed, which achieves accurate diagnosis of the faults of hydropower station measurement and control system. Finally, experimental verification reveals that, in comparison with other methods such as 1DCNN, BiLSTM, ELM, BP neural network, SVM, and DBN, the proposed approach in this paper achieves an exceptionally high average recognition accuracy of 99.8% in both simulation and example analysis. Additionally, it demonstrates faster convergence speed, indicating not only its superior diagnostic precision but also its high application value. Full article

Background: Family and Community Nurse specialists are advocates of a holistic model of care in multidisciplinary primary care teams. This study aims to describe the experiences and perceptions of nurses specialising in Family and Community Nursing regarding their working conditions in primary care in Spain. Methods: A qualitative descriptive study was conducted. Eighteen family and community specialist nurses from different autonomous communities in Spain participated. Individual interviews and a focus group were conducted. Results: The results identified two main themes: The current work situation of the Family and Community Nursing specialist and Support network and system of rejection with four sub-themes highlighting the lack of social and work recognition, the advantages of working with Family and Community Nursing specialists, systematic ambivalence towards Family and Community Nursing, and the need for institutional support. The inclusion of Family and Community Nursing specialists in primary care teams favours the nurse–patient bond, increases and/or maintains the quality of life of patients, and strengthens their empowerment; however, there is an absence of specific job vacancies. Conclusions: The institutional and social lack of awareness about the roles of Family and Community nurse practitioners and their impact on health care systems limits the quality of patient care in primary care. Full article

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies. Full article