Search Results (966)

Li-ion batteries used in portable electronic devices and electric vehicles require high safety standards, necessitating the use of high-performance structural materials for battery casings. Super duplex stainless steel (SDSS) is a structural material suitable for portable electronic products owing to its excellent strength and corrosion resistance. SDSS AISI2507 was used to construct a Li-ion battery casing, a Ag coating was applied via physical vapor deposition (PVD) after the heat treatment of AISI2507 with or without a secondary phase, and the coating thickness was controlled by varying the PVD time. The thickness of the Ag coating layer increased proportionally with time, thereby enhancing the electrical conductivity. The structure and coating behavior were confirmed using FE-SEM, XRD, and GDS. The secondary phase was crystallized by the segregation of the alloy and formed a BCC structure. The FCC lattice structure exhibited excellent coating behavior on the austenite (FCC structure) of AISI2507. Conversely, the secondary phase exhibited low adhesion owing to differences in composition and crystal structure. However, the Ag coating layer on AISI2507 exhibited excellent electrical conductivity, outperforming conventional Ni-plated Li-ion battery casings comprising AISI304. However, the precipitation of the secondary phase must be controlled, as the formation of the secondary phase acts as a factor that decreases electrical conductivity from 58.8 to 53.6 (ICAS) %. The excellent performance of Ag-coated AISI2507 makes it suitable for the fabrication of enhanced Li-ion battery casings. Full article

Background and Purpose: This study aimed to identify the imaging characteristics and discriminate the etiology of acute internal carotid artery occlusion (ICAO) on computed tomography angiography (CTA) in patients with acute ischemic stroke. Materials and Methods: We retrospectively evaluated consecutive patients who underwent endovascular thrombectomy for acute ICAO. Contrast filling of the extracranial ICA in preprocedural CTA was considered apparent ICAO. Non-contrast filling of the extracranial ICA was evaluated according to the contrast-filled lumen configuration, lumen margin and location, Hounsfield units of the non-attenuating segment, and presence of calcification or an intimal flap. Digital subtraction angiography findings were the reference standard for ICAO etiology and the occlusion site. A diagnostic tree was derived using significant variables according to pseudo-occlusion, atherosclerotic vascular disease (ASVD), thrombotic occlusion, and dissection. Results: A total of 114 patients showed apparent ICAO (n = 21), pseudo-occlusion (n = 51), ASVD (n = 27), thrombotic occlusion (n = 9), or dissection (n = 6). Most pseudo-occlusions (50/51, 98.0%) showed dependent locations with ill-defined contrast column margins and classic flame or beak shapes. The most common occlusion site of pseudo-occlusion was the petro-cavernous ICA (n = 32, 62.7%). Apparent ICAO mainly appeared in cases with occlusion distal to the posterior communicating artery orifice. ASVD showed beak or blunt shapes in the presence of low-density plaques or dense calcifications. Dissection revealed flame- or beak-shaped appearances with circumscribed margins. Thrombotic occlusions tended to appear blunt-shaped. The decision-tree model showed a 92.5% overall accuracy. Conclusions: CTA characteristics may help diagnose ICAO etiology. We provide a simple and easy decision-making model to inform endovascular thrombectomy. Full article

Air traffic controllers’ mental workload significantly impacts their operational efficiency and safety. Detecting their mental workload rapidly and accurately is crucial for preventing aviation accidents. This study introduces a mental workload detection model for controllers based on power spectrum features related to gamma waves. The model selects the feature with the highest classification accuracy, β + θ + α + γ, and utilizes the mRMR (Max-Relevance and Min-Redundancy) algorithm for channel selection. Furthermore, the channels that were less affected by ICA processing were identified, and the reliability of this result was demonstrated by artifact analysis brought about by EMG, ECG, etc. Finally, a model for rapid mental workload detection for controllers was developed and the detection rate for the 34 subjects reached 1, and the accuracy for the remaining subjects was as low as 0.986. In conclusion, we validated the usability of the mRMR algorithm in channel selection and proposed a rapid method for detecting mental workload in air traffic controllers using only three EEG channels. By reducing the number of EEG channels and shortening the data processing time, this approach simplifies equipment application and maintains detection accuracy, enhancing practical usability. Full article

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios. Full article

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter’s reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful. Full article

The 3 Screen ICA ELISA is a novel assay capable of simultaneously measuring autoantibodies to glutamic acid decarboxylase (GADA), insulinoma-associated antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A), making it a valuable tool for screening type 1 diabetes. Despite its advantages, it cannot specify which individual autoantibodies are positive or negative. This study aimed to estimate individual positive autoantibodies based on the 3 Screen ICA titer. Six hundred seventeen patients with type 1 diabetes, simultaneously measured for 3 Screen ICA and three individual autoantibodies, were divided into five groups based on their 3 Screen ICA titer. The sensitivities and contribution rates of the individual autoantibodies were then examined. The study had a cross-sectional design. Sixty-nine percent (424 of 617) of patients with type 1 diabetes had 3 Screen ICA titers exceeding the 99th percentile cut-off level (20 index). The prevalence of GADA ranged from 80% to 100% in patients with a 3 Screen ICA over 30 index and 97% of patients with a 3 Screen ICA ≥300 index. Furthermore, the prevalence of all individual autoantibodies being positive was 0% for ≤80 index and as high as 92% for ≥300 index. Significant associations were observed in specific titer groups: the 20–29.9 index group when all the individual autoantibodies were negative, the 30–79.9 index group when positive for GADA alone or IA-2A alone, the 30–299.9 index group when positive for ZnT8A alone, the 80–299.9 index group when positive for both IA-2A and ZnT8A, the 300–499.9 index group when positive for both GADA and ZnT8A, and the ≥300 index group when positive for all individual autoantibodies. These results suggest that the 3 Screen ICA titer may be helpful in estimating individual positive autoantibodies. Full article

GRT-X, which targets both the mitochondrial translocator protein (TSPO) and the Kv7.2/3 (KCNQ2/3) potassium channels, has been shown to efficiently promote recovery from cervical spine injury. In the present work, we investigate the role of GRT-X and its two targets in the axonal growth of dorsal root ganglion (DRG) neurons. Neurite outgrowth was quantified in DRG explant cultures prepared from wild-type C57BL6/J and TSPO-KO mice. TSPO was pharmacologically targeted with the agonist XBD173 and the Kv7 channels with the activator ICA-27243 and the inhibitor XE991. GRT-X efficiently stimulated DRG axonal growth at 4 and 8 days after its single administration. XBD173 also promoted axonal elongation, but only after 8 days and its repeated administration. In contrast, both ICA27243 and XE991 tended to decrease axonal elongation. In dissociated DRG neuron/Schwann cell co-cultures, GRT-X upregulated the expression of genes associated with axonal growth and myelination. In the TSPO-KO DRG cultures, the stimulatory effect of GRT-X on axonal growth was completely lost. However, GRT-X and XBD173 activated neuronal and Schwann cell gene expression after TSPO knockout, indicating the presence of additional targets warranting further investigation. These findings uncover a key role of the dual mode of action of GRT-X in the axonal elongation of DRG neurons. Full article

Background and Objectives: Laparoscopic right hemicolectomy (LRHC) is commonly performed for patients with colon cancer, selecting between intracorporeal anastomosis (ICA) or extracorporeal anastomosis (ECA). However, the impact of ICA versus ECA on patient outcomes remains debatable. The varying levels of experience among surgeons may influence the outcomes. Therefore, this study sought to compare the short- and long-term outcomes of LRHC using ICA versus ECA. Materials and Methods: This retrospective study extracted patient data from the medical records database of Changhua Christian Hospital, Taiwan, from 2017 to 2020. Patients with colon cancer who underwent LRHC with either ICA or ECA were included. Primary outcomes were post-surgical outcomes and secondary outcomes were recurrence rate, overall survival (OS), and cancer-specific survival (CSS). Between-group differences were compared using chi-square, t-tests, and Fisher’s exact tests and Mann–Whitney U tests. Associations between study variables, OS, and CSS were determined using Cox analyses. Results: Data of 240 patients (61 of ICA and 179 of ECA) with a mean age of 65.0 years and median follow-up of 49.3 months were collected. No recognized difference was found in patient characteristics between these two groups. The ICA group had a significantly shorter operation duration (median (IQR): 120 (110–155) vs. 150 (130–180) min) and less blood loss (median (IQR): 30 (10–30) vs. 30 (30–50) mL) than the ECA group (p < 0.001). No significant differences were found in 30-day readmission (ICA vs. ECA: 1.6% vs. 2.2%, p > 0.999) or recurrence (18.0% vs. 13.4%, p = 0.377) between the two groups. Multivariable analyses revealed no significant differences in OS (adjusted hazard ratio (aHR): 0.65; 95% confidence interval (CI): 0.25–1.44) or CSS (adjusted sub-hazard ratio (aSHR): 0.41, 95% CI: 0.10–1.66) between groups. Conclusions: Both ICA and ECA in LRHC for colon cancer had similar outcomes without statistically significant differences in post-surgical complications, 30-day readmission rates, recurrence rate, and long-term survival outcomes. However, ICA may offer two advantages in terms of a shorter operative duration and reduced blood loss. Full article

Major depressive disorder (MDD) is a prevalent mental illness globally, yet its etiology remains largely elusive. Recent interest in the scientific community has focused on the correlation between the disruption of iron homeostasis and MDD. Prior studies have revealed anomalous levels of iron in both peripheral blood and the brain of MDD patients; however, these findings are not consistent. This study involved 95 MDD patients aged 18–35 and 66 sex- and age-matched healthy controls (HCs) who underwent 3D-T1 and quantitative susceptibility mapping (QSM) sequence scans to assess grey matter volume (GMV) and brain iron concentration, respectively. Plasma ferritin (pF) levels were measured in a subset of 49 MDD individuals and 41 HCs using the Enzyme-linked immunosorbent assay (ELISA), whose blood data were simultaneously collected. We hypothesize that morphological brain changes in MDD patients are related to abnormal regulation of iron levels in the brain and periphery. Multimodal canonical correlation analysis plus joint independent component analysis (MCCA+jICA) algorithm was mainly used to investigate the covariation patterns between the brain iron concentration and GMV. The results of “MCCA+jICA” showed that the QSM values in bilateral globus pallidus and caudate nucleus of MDD patients were lower than HCs. While in the bilateral thalamus and putamen, the QSM values in MDD patients were higher than in HCs. The GMV values of these brain regions showed a significant positive correlation with QSM. The GMV values of bilateral putamen were found to be increased in MDD patients compared with HCs. A small portion of the thalamus showed reduced GMV values in MDD patients compared to HCs. Furthermore, the region of interest (ROI)-based comparison results in the basal ganglia structures align with the outcomes obtained from the “MCCA+jICA” analysis. The ELISA results indicated that the levels of pF in MDD patients were higher than those in HCs. Correlation analysis revealed that the increase in pF was positively correlated with the iron content in the left thalamus. Finally, the covariation patterns obtained from “MCCA+jICA” analysis as classification features effectively differentiated MDD patients from HCs in the support vector machine (SVM) model. Our findings indicate that elevated peripheral ferritin in MDD patients may disrupt the normal metabolism of iron in the brain, leading to abnormal changes in brain iron levels and GMV. Full article

Since the reliability of the avionics module is crucial for aircraft safety, the fault diagnosis and health management of this module are particularly significant. While deep learning-based prognostics and health management (PHM) methods exhibit highly accurate fault diagnosis, they have disadvantages such as inefficient data feature extraction and insufficient generalization capability, as well as a lack of avionics module fault data. Consequently, this study first employs fault injection to simulate various fault types of the avionics module and performs data enhancement to construct the P2020 communications processor fault dataset. Subsequently, a multichannel fault diagnosis method, the Hybrid Attention Adaptive Multi-scale Temporal Convolution Network (HAAMTCN) for the integrated functional circuit module of the avionics module, is proposed, which adaptively constructs the optimal size of the convolutional kernel to efficiently extract features of avionics module fault signals with large information entropy. Further, the combined use of the Interaction Channel Attention (ICA) module and the Hierarchical Block Temporal Attention (HBTA) module results in the HAAMTCN to pay more attention to the critical information in the channel dimension and time step dimension. The experimental results show that the HAAMTCN achieves an accuracy of 99.64% in the avionics module fault classification task which proves our method achieves better performance in comparison with existing methods. Full article

Glanzmann Thrombasthenia (GT) is an inherited platelet disorder caused by defects in platelet integrin α_IIbβ₃ (GPIIb/IIIa), which is a platelet receptor essential for the binding of fibrinogen. This can lead to severe bleeding, especially after trauma or perioperatively, and to microcytic anemia because of chronic blood loss. We report on a 40-year-old female patient with extensive bleeding complications and platelet antibody formation who presented in Homburg and Freiburg for extensive platelet function analyses and molecular genetic analyses. According to platelet aggregometry, the patient had previously been diagnosed with Glanzmann Thrombasthenia (GT). In addition, an MRI scan had been performed due to an unsteady gait and had revealed bilateral para-ophthalmic aneurysms of both internal carotid arteries (ICAs). Assuming a 5% rupture risk per 5 years for each aneurysm, the patient was offered and accepted endovascular treatment. Next-generation sequencing (NGS) panel analysis identified a previously undescribed homozygous one-base-pair deletion in ITGA2B, which leads to a loss of function of the α_IIb-subunit of the receptor. This case illustrates the difficulties that can arise regarding the treatment of patients with rare platelet bleeding disorders, and supports the importance of continuous medical care by a specialized hemophilia center for these patients. Full article

The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are “Computers and Electronics in Agriculture” and “Journal of Dairy Science”. It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production. Full article

Extensive use of pesticides in agricultural production has been causing serious health threats to humans and animals. Among them, phorate is a highly toxic organophosphorus insecticide that has been widely used in planting. Due to its harmful effects on human and animal health, it has been restricted for use in many countries. Analytical methods for the rapid and sensitive detection of phorate residues in agricultural products are urgently needed. In this study, a new method was developed by combining surface-enhanced Raman spectroscopy (SERS) and immunochromatography assay (ICA). Hybrid magnetic Fe₃O₄@Au@DTNB-Ab nanoprobes were prepared by modifying and growing Au nanoseeds on an Fe₃O₄ core. SERS activity of the nanoprobe was optimized by adjusting the concentration of the Au precursor. A rapid and sensitive assay was established by replacing the traditional colloidal gold-based ICA with hybrid SERS nanoprobes for SERS-ICA. After optimizing parameters including coating antibody concentrations and the composition and pH of the buffer solution, the limit of detection (LOD) for phorate could reach 1 ng/mL, with a linear range of 5~100 ng/mL. This LOD is remarkably lower than the maximum residue limit in vegetables and fruits set by the Chinese government. The feasibility of this method was further examined by conducting a spiking test with celery as the real sample. The result demonstrated that this method could serve as a promising platform for rapid and sensitive detection of phorate in agricultural products. Full article

Conventional gas well classification methods cannot provide effective support for gas well routine management, and suffer from poor timeliness. In order to guide the on-site operation in liquid loading gas wells and improve the timeliness of gas well classification, this paper proposes a production data-driven gas well classification method based on the LDA-DA (Linear Discriminant Analysis–Discriminant Analysis) combination model. In this method, considering the requirements of routine management, gas wells are evaluated from two aspects: liquid drainage capacity (LDC) and liquid production intensity (LPI), and are classified into six types. Domain knowledge is used to perform the feature engineering on the on-site production data, and five features are set up to quantitatively evaluate the gas well and to create classification samples. On this basis, in order to specify the optimal data processing flow to establish the gas well classification map, four linear dimensionality reduction techniques, LDA, PCA, LPP, and ICA, are used to reduce the dimensionality of original classification samples, and then, four classical classification algorithms, NB, DA, KNN, and SVM, are trained and evaluated on the low-dimensional samples, respectively. The results show that the LDA space achieves the optimal sample separation and is chosen as the decision space for gas well classification. The DA algorithm obtains the top performance, i.e., the highest Average Macro F1-score of 95.619%, in the chosen decision space, and is employed to determine the classification boundaries in the decision space. At this point, the LDA-DA combination model for sample data processing is developed. Based on this model, gas well classification maps can be established by data mining, and the rapid evaluation and diagnosis of gas wells can be achieved. This method realizes instant and efficient production data-driven gas well classification, and can provide timely decision-making support for gas well routine management. It introduces new ideas for performing gas well classification, expanding the content and scope of the classification work, and presenting valuable insights for further research in this field. Full article

Doñana National Park is located in the southwest of the Iberian Peninsula, where water scarcity is recurrent, together with a high heterogeneity in species and ecosystems. Monitoring carbon assimilation is essential to improve knowledge of global change in natural vegetation cover. In this work, a light use efficiency (LUE) model was applied to estimate gross primary production (GPP) in two ecosystems of Doñana, xeric shrub (drought resistant) and seasonal marsh (with grasslands dependent on water hydroperiod) and validated with in situ data from eddy covariance (EC) towers installed in both ecosystems. The model was applied in two ways: (1) using the fraction of absorbed photosynthetically active radiation (FAPAR) from Sentinel-2 and meteorological data from reanalysis (ERA5), and (2) using Sentinel-2 FAPAR, reanalysis solar radiation (ERA5) and the Sentinel-2 land surface water index (LSWI). In both cases and for both ecosystems, the error values are acceptable (below 1 gC/m²) and in both ecosystems the model using the LSWI gave better results (R² of 0.8 in marshes and 0.51 in xeric shrubs). The results also show a greater influence of the water status of the system than of the meteorological variables in this area. Full article