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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical
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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.
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Investigating the role of podocytes in proteinuric disease is imperative to address the increasing global burden of chronic kidney disease (CKD). Studies strongly implicate increased levels of monocyte chemoattractant protein-1 (MCP-1/CCL2) in proteinuric CKD. Since podocytes express the receptor for MCP-1 (i.e., CCR2),
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Investigating the role of podocytes in proteinuric disease is imperative to address the increasing global burden of chronic kidney disease (CKD). Studies strongly implicate increased levels of monocyte chemoattractant protein-1 (MCP-1/CCL2) in proteinuric CKD. Since podocytes express the receptor for MCP-1 (i.e., CCR2), we hypothesized that podocyte-specific MCP-1 production in response to stimuli could activate its receptor in an autocrine manner, leading to further podocyte injury. To test this hypothesis, we generated podocyte-specific MCP-1 knockout mice (Podo-Mcp-1fl/fl) and exposed them to proteinuric injury induced by either angiotensin II (Ang II; 1.5 mg/kg/d, osmotic minipump) or Adriamycin (Adr; 18 mg/kg, intravenous bolus). At baseline, there were no between-group differences in body weight, histology, albuminuria, and podocyte markers. After 28 days, there were no between-group differences in survival, change in body weight, albuminuria, kidney function, glomerular injury, and tubulointerstitial fibrosis. The lack of protection in the knockout mice suggests that podocyte-specific MCP-1 production is not a major contributor to either Ang II- or Adr-induced glomerular disease, implicating that another cell type is the source of pathogenic MCP-1 production in CKD.
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Detecting pedestrians in varying illumination conditions poses a significant challenge, necessitating the development of innovative solutions. In response to this, we introduce Prior-AttentionNet, a pedestrian detection model featuring a Prior-Attention mechanism. This model leverages the stark contrast between thermal objects and their backgrounds
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Detecting pedestrians in varying illumination conditions poses a significant challenge, necessitating the development of innovative solutions. In response to this, we introduce Prior-AttentionNet, a pedestrian detection model featuring a Prior-Attention mechanism. This model leverages the stark contrast between thermal objects and their backgrounds in far-infrared (FIR) images by employing saliency attention derived from FIR images via UNet. However, extracting salient regions of diverse scales from FIR images poses a challenge for saliency attention. To address this, we integrate Simple Linear Iterative Clustering (SLIC) superpixel segmentation, embedding the segmentation feature map as prior knowledge into UNet’s decoding stage for comprehensive end-to-end training and detection. This integration enhances the extraction of focused attention regions, with the synergy of segmentation prior and saliency attention forming the core of Prior-AttentionNet. Moreover, to enrich pedestrian details and contour visibility in low-light conditions, we implement multispectral image fusion. Experimental evaluations were conducted on the KAIST and OTCBVS datasets. Applying Prior-Attention mode to FIR-RGB images significantly improves the delineation and focus on multi-scale pedestrians. Prior-AttentionNet’s general detector demonstrates the capability of detecting pedestrians with minimal computational resources. The ablation studies indicate that the FIR-RGB+ Prior-Attention mode markedly enhances detection robustness over other modes. When compared to conventional multispectral pedestrian detection models, Prior-AttentionNet consistently surpasses them by achieving higher mean average precision and lower miss rates in diverse scenarios, during both day and night.
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The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized
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The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized as the fourth industrial revolution, with the aim of improving manufacturing processes and increasing the competitiveness of industry. I4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity. In addition, concepts such as Smart Factories are emerging, which use context awareness to assist people and optimize tasks based on data from the physical and virtual world. This article explores and applies the capabilities of context-aware applications in industry, with a focus on production lines. In specific, this paper proposes a context-aware application based on a microservices approach, intended for integration into a context-aware information system, with specific application in the area of manufacturing. The manuscript presents a detailed architecture for structuring the application, explaining components, functions and contributions. The discussion covers development technologies, integration and communication between the application and other services, as well as experimental findings, which demonstrate the applicability and advantages of the proposed solution.
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Lithium-ion batteries (LIBs) have the advantage of high energy density, which has attracted the wide attention of researchers. Nevertheless, the growth of lithium dendrites on the anode surface causes short life and poor safety, which limits their application. Therefore, it is necessary to
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Lithium-ion batteries (LIBs) have the advantage of high energy density, which has attracted the wide attention of researchers. Nevertheless, the growth of lithium dendrites on the anode surface causes short life and poor safety, which limits their application. Therefore, it is necessary to deeply understand the growth mechanism of lithium dendrites. Here, the growth mechanism of lithium dendrites is briefly summarized, and the real-time monitoring technologies of lithium dendrite growth in recent years are reviewed. The real-time monitoring technologies summarized here include in situ X-ray, in situ Raman, in situ resonance, in situ microscopy, in situ neutrons, and sensors, and their representative studies are summarized. This paper is expected to provide some guidance for the research of lithium dendrites, so as to promote the development of LIBs.
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The realization of the value of ecological products has led to an economic means for reducing carbon emissions in China. Tea is one of the most important cash crops and one of the most popular beverages in the world. Due to the complex
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The realization of the value of ecological products has led to an economic means for reducing carbon emissions in China. Tea is one of the most important cash crops and one of the most popular beverages in the world. Due to the complex the tea industrial chain, it is considered to be an industry with high carbon emissions. Ecological tea products with low-carbon attributes can be considered a linkage of ecology, economy, and society. Based on this, this paper presents research on low-carbon ecological tea (LCT). Herein, we construct the formational logic of low-carbon ecological products, explore the connotations of LCT, and form a conceptual pathway for realizing LCT to contribute to climate change mitigation and adaptation. This paper starts from the upstream, midstream, and downstream of the industrial chain; it establishes three value realization pathways that keep, as a priority, the promotion of ecological industrialization, focus on restoration to improve the ecology of the industrial chain, and innovate technology to expand the industrial chain. The pathways are a set of low-emission production solutions that use techniques to enhance carbon sequestration in soil, reduce the use of fertilizers and pesticides, and help shift to clean energy from low-emission sources in the stages of plantation, processing, and distribution. In the process of realizing LCT, the government plays an important role, and its support and guidance are needed. Based on stakeholder theory, this paper builds an implementation mechanism that focuses on the micro perspective (users, organizations), integrates the mesoscopic perspective (industry), and relies on the macro perspective (government).
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Based on the quasi-static tests of 12 corroded RC (reinforced concrete) shear walls, it was found that reinforcement corrosion has a great influence on the skeleton curve of RC shear walls. With an increase in the degree of corrosion, the bearing capacity of
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Based on the quasi-static tests of 12 corroded RC (reinforced concrete) shear walls, it was found that reinforcement corrosion has a great influence on the skeleton curve of RC shear walls. With an increase in the degree of corrosion, the bearing capacity of specimens decreases, and the deformation capacity worsens. Increasing the diameter of longitudinal reinforcements can significantly improve the bearing capacity of corroded RC shear walls, while the deformation capacity of corroded specimens can be improved by increasing the lateral distributed reinforcement or the transverse reinforcement in the embedded column. In order to accurately evaluate the seismic performance of corroded RC shear walls, we considered descent segments of four broken-line models to estimate the skeleton curve. After considering the influence of corrosion on the parameters of the characteristic point for the skeleton curve, the calculation formulas of the characteristic point parameters of the skeleton curve for the corroded RC shear wall were determined based on the test data fitting. It was proven that the formula for the characteristic point parameters for the skeleton curve of corroded RC shear walls has good applicability. This study lays a theoretical foundation for the seismic performance evaluation of an RC shear wall structure in a salt fog environment. It provides a theoretical basis for further improving the life-cycle seismic capacity evaluation system for RC structures.
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Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a
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Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings.
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Non-small-cell lung cancer (NSCLC) is the most prevalent type of lung cancer, with extensively characterized mutational spectra. Several biomarkers (such as EGFR, BRAF, KRAS gene mutations, etc.) have emerged as predictive and prognostic markers for NSCLC. Unfortunately, the quality of the
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Non-small-cell lung cancer (NSCLC) is the most prevalent type of lung cancer, with extensively characterized mutational spectra. Several biomarkers (such as EGFR, BRAF, KRAS gene mutations, etc.) have emerged as predictive and prognostic markers for NSCLC. Unfortunately, the quality of the available tumor biopsy and/or cytology material is not always adequate to perform the necessary molecular testing, prompting the search for alternatives. Cell-free DNA (cfDNA) found in plasma is emerging as a highly promising avenue or a supplementary method for assessing the efficacy of cancer treatments. This is especially valuable in instances where conventional biopsy specimens, like formalin-fixed, paraffin-embedded (FFPE), or freshly frozen tumor tissues prove inadequate for conducting molecular pathology analyses subsequent to the initial diagnostic procedures. By leveraging cfDNA from plasma, clinicians gain an additional tool to gauge the effectiveness of cancer therapies, thereby enhancing their ability to optimize tailored treatment strategies. In this study, 51 Lithuanian females with NSCLC were analyzed, with adenocarcinoma being the predominant pathology diagnosis in 40 cases (78%). Target mutations were identified in 38 out of 51 patients (74.5%) in tumor tissue samples, while in plasma samples, they were identified in only 10 patients’ samples (19.6%). Even though we did not have enough voluminous plasma samples in our study, gene mutations were detected in plasma from ten women, three of whom were diagnosed with early stages of lung cancer (stages I and II). For these patients, the following mutations were detected: deletion in exon 19 of the EGFR gene and single nucleotide polymorphisms in the TP53 and MET genes. All other women were diagnosed with stages III or IV of lung cancer. This indicates that the later stages of cancer contribute more cfDNA in plasma, making extraction less complicated.
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Hyaluronic acid (HA) is a new functional food additive which has the potential to ameliorate persistent inflammation, immunosuppression and catabolism syndrome (PICS), but the biological effects of HA with various molecular weights differ dramatically. To systematically investigate the efficacy of HA in altering
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Hyaluronic acid (HA) is a new functional food additive which has the potential to ameliorate persistent inflammation, immunosuppression and catabolism syndrome (PICS), but the biological effects of HA with various molecular weights differ dramatically. To systematically investigate the efficacy of HA in altering PICS symptoms, medium-molecular-weight (MMW) HA was specifically selected to test its intervention effect on a PICS mouse model induced by CLP through oral administration, with high-molecular-weight (HMW) and low-molecular-weight (LMW) HA also participating in the experimental validation process. The results of pathological observations and gut flora showed that MMW HA rapidly alleviated lung lesions and intestinal structural changes in PICS mice in the short term. However, although long-term MMW HA administration significantly reduced the proportions of harmful bacteria in gut flora, inflammatory responses in the intestines and lungs of PICS mice were significantly higher in the MMW HA group than in the HMW HA and LMW HA groups. The use of HMW HA not only rapidly reduced the mortality rate of PICS mice but also improved their grip strength and the recovery of spleen and thymus indices. Furthermore, it consistently promoted the recovery of lung and intestinal tissues in PICS mice, and it also assisted in the sustained restoration of their gut microbiota. These effects were superior to those of LMW HA and MMW HA. The experimental results indicate that HMW weight HA has the greatest potential to be an adjunct in alleviating PICS as a food additive, while the safety of other HAs requires further attention.
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Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select
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Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III–IV(A) and BCI competition IV–I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.
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We explored the functional redundancy of three structurally related KCTD (Potassium Channel Tetramerization Domain) proteins, KCTD2, KCTD5, and KCTD17, by progressively knocking them out in HEK 293 cells using CRISPR/Cas9 genome editing. After validating the knockout, we assessed the effects of progressive knockout
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We explored the functional redundancy of three structurally related KCTD (Potassium Channel Tetramerization Domain) proteins, KCTD2, KCTD5, and KCTD17, by progressively knocking them out in HEK 293 cells using CRISPR/Cas9 genome editing. After validating the knockout, we assessed the effects of progressive knockout on cell growth and gene expression. We noted that the progressive effects of knockout of KCTD isoforms on cell growth were most pervasive when all three isoforms were deleted, suggesting some functions were conserved between them. This was also reflected in progressive changes in gene expression. Our previous work indicated that Gβ1 was involved in the transcriptional control of gene expression, so we compared the gene expression patterns between GNB1 and KCTD KO. Knockout of GNB1 led to numerous changes in the expression levels of other G protein subunit genes, while knockout of KCTD isoforms had the opposite effect, presumably because of their role in regulating levels of Gβ1. Our work demonstrates a unique relationship between KCTD proteins and Gβ1 and a global role for this subfamily of KCTD proteins in maintaining the ability of cells to survive and proliferate.
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This work focuses on the modeling of a zero-emissions, high-speed catamaran ferry employing a full-electric propulsion system. It addresses the global emphasis on full-electric vessels to align with IMO regulations regarding ship emissions and energy efficiency improvement. Using the AVL Cruise-M software, this
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This work focuses on the modeling of a zero-emissions, high-speed catamaran ferry employing a full-electric propulsion system. It addresses the global emphasis on full-electric vessels to align with IMO regulations regarding ship emissions and energy efficiency improvement. Using the AVL Cruise-M software, this research verified the implementation of an onboard fuel cell power-generating system integrated with a propulsion plant, aiming to assess its dynamic performance under load variations. The catamaran was 30 m long and 10 m wide with a cruise speed of 20 knots. The power system consisted of a proton-exchange membrane fuel cell (PEM) system, with a nominal power of 1600 kWe, a battery pack with a capacity of 2 kWh, two 777 kW electric motors, and their relative balance of the plant (BoP) subsystems. The simulation results show that the battery effectively supported the PEM during the maneuvering phase, enhancing its overall performance and energy economy.
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This article presents a detailed study on the diagnosis of rotor faults in an Interior Permanent Magnet Machine based on a mathematical model. The authors provided a wide literature review, mentioning the fault diagnosis methods used for Permanent Magnet Machines. The research emphasizes
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This article presents a detailed study on the diagnosis of rotor faults in an Interior Permanent Magnet Machine based on a mathematical model. The authors provided a wide literature review, mentioning the fault diagnosis methods used for Permanent Magnet Machines. The research emphasizes the necessity of precise assumptions regarding winding construction to accurately analyze the additional harmonics appearing in rotor faults caused by electromotive force (EMF), i.e., rotor eccentricity and magnet damage. The article also discusses specific features appearing in the spectrum of air gap permeance functions and the impact of rotor eccentricity and magnet damage on PM flux density distribution and as a consequence on EMF stator windings. The novelty of the presented content is the analysis of induced EMFs for cases of the simultaneous occurrence of rotor eccentricity and PM damage. The findings of this study provide valuable insights for the diagnosis and understanding of internal asymmetries in Interior PM Machines.
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by
Pritha Mukherjee, Xin Zhou, Susana Galli, Bruce Davidson, Lihua Zhang, Jaeil Ahn, Reem Aljuhani, Julius Benicky, Laurie Ailles, Vitor H. Pomin, Mark Olsen and Radoslav Goldman
Int. J. Mol. Sci.2024, 25(9), 4998; https://doi.org/10.3390/ijms25094998 (registering DOI) - 03 May 2024
Aspartate β-hydroxylase (ASPH) is a protein associated with malignancy in a wide range of tumors. We hypothesize that inhibition of ASPH activity could have anti-tumor properties in patients with head and neck cancer. In this study, we screened tumor tissues of 155 head
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Aspartate β-hydroxylase (ASPH) is a protein associated with malignancy in a wide range of tumors. We hypothesize that inhibition of ASPH activity could have anti-tumor properties in patients with head and neck cancer. In this study, we screened tumor tissues of 155 head and neck squamous cell carcinoma (HNSCC) patients for the expression of ASPH using immunohistochemistry. We used an ASPH inhibitor, MO-I-1151, known to inhibit the catalytic activity of ASPH in the endoplasmic reticulum, to show its inhibitory effect on the migration of SCC35 head and neck cancer cells in cell monolayers and in matrix-embedded spheroid co-cultures with primary cancer-associated fibroblast (CAF) CAF 61137 of head and neck origin. We also studied a combined effect of MO-I-1151 and HfFucCS, an inhibitor of invasion-blocking heparan 6-O-endosulfatase activity. We found ASPH was upregulated in HNSCC tumors compared to the adjacent normal tissues. ASPH was uniformly high in expression, irrespective of tumor stage. High expression of ASPH in tumors led us to consider it as a therapeutic target in cell line models. ASPH inhibitor MO-I-1151 had significant effects on reducing migration and invasion of head and neck cancer cells, both in monolayers and matrix-embedded spheroids. The combination of the two enzyme inhibitors showed an additive effect on restricting invasion in the HNSCC cell monolayers and in the CAF-containing co-culture spheroids. We identify ASPH as an abundant protein in HNSCC tumors. Targeting ASPH with inhibitor MO-I-1151 effectively reduces CAF-mediated cellular invasion in cancer cell models. We propose that the additive effect of MO-I-1151 with HfFucCS, an inhibitor of heparan 6-O-endosulfatases, on HNSCC cells could improve interventions and needs to be further explored.
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Ginés Garnés-Morales, Maria João Costa, Juan Antonio Bravo-Aranda, María José Granados-Muñoz, Vanda Salgueiro, Jesús Abril-Gago, Sol Fernández-Carvelo, Juana Andújar-Maqueda, Antonio Valenzuela, Inmaculada Foyo-Moreno, Francisco Navas-Guzmán, Lucas Alados-Arboledas, Daniele Bortoli and Juan Luis Guerrero-Rascado
This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave
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This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave radiometers, and radiosondes), the optimal ABLH determination relied on identifying the lowest refractivity gradient negative peak with a magnitude at least % times the minimum refractivity gradient magnitude, where is a fitting parameter representing the minimum peak strength relative to the absolute minimum refractivity gradient. Different values were derived accounting for the moment of the day (daytime, nighttime, or sunrise/sunset) and the underlying surface (land or sea). Results show discernible relations between ABLH and various features, notably, the land cover and latitude. On average, ABLH is higher over oceans (≈1.5 km), but extreme values (maximums > 2.5 km, and minimums < 1 km) are reached over intertropical lands. Variability is generally subtle over oceans, whereas seasonality and daily evolution are pronounced over continents, with higher ABLHs during daytime and local wintertime (summertime) in intertropical (middle) latitudes.
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This research examines the ways people acquire dogs in the US as well as the ways the dogs leave the household and the way these differ by income level in seven geographically diverse study communities. A web-based panel survey was distributed and received
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This research examines the ways people acquire dogs in the US as well as the ways the dogs leave the household and the way these differ by income level in seven geographically diverse study communities. A web-based panel survey was distributed and received 6318 responses. Individuals were asked a series of demographic and socioeconomic questions as well as how they acquired their current dogs, how previous dogs left their household, and where they left to. The results indicate that the likelihood of acquiring a dog through friends and family decreased monotonically as income increased, while the opposite was observed for adopting and purchasing a dog. The likelihood of giving a dog away to a friend or family member also decreased as income increased, as opposed to shelter surrender—a person earning over USD 100,000 annually was more than four times likelier to surrender to a shelter than a person earning under USD 15,000. The results suggest a stronger reliance on informal social networks in lower-income communities for both obtaining and placing dogs. As these dogs would otherwise end up in the shelter system, animal shelters may support low-income pet owners to help keep their dogs within their community of care.
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The potential for native proteins to serve as a platform for biocompatible, targeted, and personalized therapeutics in the context of genetic and metabolic disorders is vast. Nevertheless, their clinical application encounters challenges, particularly in overcoming biological barriers and addressing the complexities involved in
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The potential for native proteins to serve as a platform for biocompatible, targeted, and personalized therapeutics in the context of genetic and metabolic disorders is vast. Nevertheless, their clinical application encounters challenges, particularly in overcoming biological barriers and addressing the complexities involved in engineering transmembrane permeability. This study is dedicated to the development of a multifunctional nanoentity in which a model therapeutic protein is covalently linked to a cell-penetrating peptide, NickFect 55, with the objective of enhancing its intracellular delivery. Successful binding of the nanoentity fragments was achieved through the utilization of an intein-mediated protein-trans splicing reaction. Our research demonstrates that the fully assembled nanoentity-containing protein was effectively internalized by the cells, underscoring the potential of this approach in overcoming barriers associated with protein-based therapeutics for the treatment of genetic disorders.
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Plasma technology is increasing its applications in the textile industry for conferring surface functionalities through greener processes. In this study, plasma treatments are studied to improve the wettability of mycelium-based material, an emerging material with a lot of potential in the near future.
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Plasma technology is increasing its applications in the textile industry for conferring surface functionalities through greener processes. In this study, plasma treatments are studied to improve the wettability of mycelium-based material, an emerging material with a lot of potential in the near future. The plasma effect was characterized by assessing the added functionality (wettability) and inspecting surface modifications with different techniques, such as scanning electron microscopy (SEM) and X-Ray photoelectron spectroscopy (XPS). Low pressure plasma (LPP) treatments were successfully applied into the mycelium-based material and optimal power of discharge and treatment time were set for this material (750 W, 17.5 min). With the optimized LPP treatments, the water absorption capacity of mycelium-based material was improved by 2000% and some surface morphological modifications were observed by SEM analysis. On the other hand, XPS analysis demonstrated how the plasma treatment changes the surface composition.
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Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT
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Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT and countering emerging risks require constant updates and monitoring of these devices. Machine learning (ML), in combination with Explainable Artificial Intelligence (XAI), has become an essential component of the CIoT ecosystem due to its rapid advancement and impressive results across several application domains for attack detection, prevention, mitigation, and providing explanations of such decisions. These attacks exploit and steal sensitive data, disrupt the devices’ functionality, or gain unauthorized access to connected networks. This research generates a novel dataset by injecting adversarial attacks into the CICIoT2023 dataset. It presents an adversarial attack detection approach named TXAI-ADV that utilizes deep learning (Mutli-Layer Perceptron (MLP) and Deep Neural Network (DNN)) and machine learning classifiers (K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), ensemble voting, and Meta Classifier) to detect attacks and avert such situations rapidly in a CIoT. This study utilized Shapley Additive Explanations (SHAP) techniques, an XAI technique, to analyze the average impact of each class feature on the proposed models and select optimal features for the adversarial attacks dataset. The results revealed that, with a 96% accuracy rate, the proposed approach effectively detects adversarial attacks in a CIoT.
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Multiple myeloma (MM), the second most common hematologic malignancy, remains incurable, and its incidence is rising. Chimeric Antigen Receptor T-cell (CAR-T cell) therapy has emerged as a novel treatment, with the potential to improve the survival and quality of life of patients with
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Multiple myeloma (MM), the second most common hematologic malignancy, remains incurable, and its incidence is rising. Chimeric Antigen Receptor T-cell (CAR-T cell) therapy has emerged as a novel treatment, with the potential to improve the survival and quality of life of patients with relapsed/refractory multiple myeloma (rrMM). In this systematic review and meta-analysis, conducted in accordance with PRISMA guidelines, we aim to provide a concise overview of the latest developments in CAR-T therapy, assess their potential implications for clinical practice, and evaluate their efficacy and safety outcomes based on the most up-to-date evidence. A literature search conducted from 1 January 2019 to 12 July 2023 on Medline/PubMed, Scopus, and Web of Science identified 2273 articles, of which 29 fulfilled the specified criteria for inclusion. Our results offer robust evidence supporting CAR-T cell therapy’s efficacy in rrMM patients, with an encouraging 83.21% overall response rate (ORR). A generally safe profile was observed, with grade ≥ 3 cytokine release syndrome (CRS) at 7.12% and grade ≥ 3 neurotoxicity at 1.37%. A subgroup analysis revealed a significantly increased ORR in patients with fewer antimyeloma regimens, while grade ≥ 3 CRS was more common in those with a higher proportion of high-risk cytogenetics and prior exposure to BCMA therapy.
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At the moment, there are two neutron star X-ray binaries with massive red supergiants as donors. Recently, De et al. (2023) proposed that the system SWIFT J0850.8-4219 contains a neutron star at the propeller stage. We study this possibility by applying various models
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At the moment, there are two neutron star X-ray binaries with massive red supergiants as donors. Recently, De et al. (2023) proposed that the system SWIFT J0850.8-4219 contains a neutron star at the propeller stage. We study this possibility by applying various models of propeller spin-down. We demonstrate that the duration of the propeller stage is very sensitive to the regime of rotational losses. Only in the case of a relatively slow propeller model proposed by Davies and Pringle in 1981, the duration of the propeller is long enough to provide a significant probability to observe the system at this stage. Future determination of the system parameters (orbital and spin periods, magnetic field of the compact object, etc.) will allow putting strong constraints on the propeller behavior.
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In mechanical equipment, rolling bearing components are constantly exposed to intricate and diverse environmental conditions, rendering them vulnerable to wear, performance degradation, and potential malfunctions. To precisely extract and discern rolling bearing vibration signals amidst intricate noise interference, this paper introduces a fault
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In mechanical equipment, rolling bearing components are constantly exposed to intricate and diverse environmental conditions, rendering them vulnerable to wear, performance degradation, and potential malfunctions. To precisely extract and discern rolling bearing vibration signals amidst intricate noise interference, this paper introduces a fault feature extraction and diagnosis methodology that seamlessly integrates an improved Fourier decomposition method (FDM), singular value decomposition (SVD), and maximum second-order cyclostationary blind convolution (CYCBD). Initially, the FDM is employed to meticulously decompose the bearing fault signals into numerous signal components. Subsequently, a comprehensive weighted screening criterion is formulated, aiming to strike a balance between multiple indicators, thereby enabling the selective screening and reconstruction of pertinent signal components. Furthermore, SVD and CYCBD techniques are introduced to carry out intricate processing and envelope demodulation analysis of the reconstructed signals. Through rigorous simulation experiments and practical rolling bearing fault diagnosis tests, the method’s noteworthy effectiveness in suppressing noise interference, enhancing fault feature information, and efficiently extracting fault features is unequivocally demonstrated. Furthermore, compared to traditional time–frequency analysis methods such as EMD, EEMD, ITD, and VMD, as well as traditional deconvolution methods like MED, OMEDA, and MCKD, this method exhibits significant advantages, providing an effective solution for diagnosing rolling bearing faults in environments with strong background noise.
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