Miracle-dz/UniFAR-FTUs huggingface dataset:
Recombinant adenovirus expressing wild-type p53 is antiangiogenic: a proposed mechanism for bystander effect.

Angiogenesis is required for the growth and progression of malignancies. Recent studies have demonstrated that genetic alterations may accompany acquisition of the angiogenic phenotype. The tumor suppressor gene p53 is most frequently mutated in human cancers and is also known to be a transcriptional regulator of a variety of genes. Here, we investigated the antiangiogenic effect of the wild-type p53 (wt-p53) gene transfer on a human non-small cell lung cancer cell line. Mutant p53-expressing H226Br non-small cell lung cancer cells were transduced with the wt-p53 gene using a recombinant adenoviral vector (Ad5CMVp53) and applied to semiquantitative reverse transcription-PCRs for the detection of altered mRNA expression of angiogenic and/or antiangiogenic factors. In vivo neovascularization assay of Ad5CMVp53-infected cells was then performed using a membrane-diffusion chamber system s.c. transplanted in nu/nu mice. We also evaluated the effect of Ad5CMVp53-infected H226Br cells on nontransduced tumor cells in vivo by s.c. inoculating mixture of cells into nu/nu mice. Ad5CMVp53 infection markedly inhibited the expression of an angiogenic factor, vascular endothelial growth factor, and increased the expression of a novel antiangiogenic factor, brain-specific angiogenesis inhibitor 1, resulting in reduced neovascularization in vivo. Mixing experiments showed that tumor cells transduced with the wt-p53 gene inhibited the in vivo tumor growth of adjacent nontransduced cells. Our data suggest that a recombinant adenovirus expressing the wt-p53 gene is antiangiogenic, which may explain, in part, the mechanism of the bystander effect induced by the wt-p53 gene transfer on adjacent tumor cells.
A Novel Approach Towards Detecting Malaria Parasite in Thin Blood Smears Using a Sequential Convolutional Neural Network (CNN) Model

Inaccurate or delayed detection of malaria leads to increased casualties. In our proposed work, a novel approach was designed based on a Convolutional Neural Network (CNN) model using a sequential model. A collection of thin blood smears, both infected and uninfected with the malarial parasite, was used as the input, training, and testing of our proposed model. Our proposed approach resulted in an accuracy of 94.6% and a precision of 97.5%, which are significantly higher than those of the existing approaches. This accuracy can be further fine-tuned and increased by increasing the testing and training pools. The goal is to automate the entire malaria detection mechanism to ensure the highest number of true positive results, while minimizing the possibility of human errors or false results. The goal of this study is to achieve optimum accuracy while reducing the amount of time spent on testing.
FSS Integrated Hybrid Nanocomposite Microwave Absorber Structure for Stealth Applications

In this work, a hybrid microwave absorber (MA) comprising the frequency selective surfaces (FSS) and a thin lossy nanocomposite sheet, for stealth technology in the X-band (8.2-12.4 GHz) is proposed. The first step here requires the synthesis of exfoliated graphite (EG) and the barium strontium titanate (BST) fillers using the microwave-assisted intercalation and sol-gel techniques, respectively. The EG (4 wt%) and BST (10 wt%) fillers are then embedded into the epoxy resin to obtain the EGBST lossy nanocomposite sheet. The reflection coefficient (S11) of up to 13.7 dB covering a 10-dB absorption bandwidth of 1.94 GHz from 7.92-9.86 GHz is attained for this nanocomposite sheet having 2.1 mm thickness. Afterwards, the EGBST nanocomposite sheet is integrated with an optimized FSS layer to realize a hybrid MA (HMA) structure to enhance the overall absorption bandwidth. The optimized HMA structure enhances the 10-dB absorption bandwidth of the resultant sample to 3.67 GHz (8.12-11.79 GHz) covering 90% of X-band for a thickness of just 2 mm $(\sim \lambda / 15)$, attaining a minimum reflection coefficient of $-28 \mathrm{~dB}$. The proposed nanocomposite-based hybrid microwave absorber can potentially be used for the radar applications conforming to the stealth technology.
Proactive Millimeter-Wave Link Quality Prediction Utilizing Out-of-Band CSI Fingerprinting and Supervised Learning: An Experimental Study

This paper demonstrates the feasibility of millimeter-wave (mmWave) link quality prediction based on out-of-band channel state information (CSI) fingerprinting. To overcome the pedestrian blockage problem of mmWave communications, a large number of computer vision-aided mmWave link quality prediction methods have been investigated. However, the use of cameras and LiDAR to acquire computer vision information entails privacy risks. In this paper, we employ 5 GHz band CSI fingerprinting - an aggregation of CSI measured at multiple locations, for mmWave link quality prediction. CSI reflects the propagation environment of the wireless communication channel and thus includes information on pedestrians that block mmWave communications. CSI fingerprinting, aggregated from various measurement locations, enables future mmWave link quality prediction owing to its sufficient spatial information. We conducted a real-world wireless communication experiment with commercial devices compliant with IEEE 802.11ad for the mmWave, and nine IEEE 802.11ac CSI measurement devices for 5 GHz, to experimentally evaluate our method. The experimental result revealed that our proposed method can deterministically and numerically predict the mmWave link quality deterioration caused by pedestrian blockage 500 ms in advance.
Stable, in-situ Developed Silver-Laser Induced Graphene (Ag-LIG) composite for SERS-based Uric Acid Detection

This study presents a method for easy and direct detection of uric acid using Surface-enhanced Raman spectroscopy (SERS) by in-situ synthesized Ag-LIG. Herein, Ag was uniformly distributed all over the Laser-Induced Graphene (LIG) matrix. This enhances the electromagnetic field leading to the production of the enhanced Raman spectra. Various characterization techniques were employed to observe crystalline, morphological, and lattice vibrations. The C-N stretching, and C-H bending were observed corresponding to varying concentrations. This confirms the detection of uric acid on the surface of Ag-LIG. The as-prepared electrode exhibited a detection limit of approximately 60 µM for uric acid, manifesting the simple and direct detection of uric acid.
An Innovative Solar Flare Metadata Collection for Space Weather Analytics

Space weather events can have a significant impact on electric systems and health, with solar flares being one of the central events in space weather forecasting. However, existing solar flare prediction tools heavily rely on the Geostationary Operational Environmental Satellites (GOES) classification system, using maximum X-ray flux measurements as proxies to label instances. This approach becomes problematic during solar minimum, where background X-ray flux fluctuations lead to false alarms and inaccurate predictions. To address this issue, we propose a new collection of solar flare intensity labels computed from GOES X-ray flux, introducing innovative labeling regimes that incorporate relative increases and cumulative measurements over prediction windows. Our goal is to improve the accuracy of flare prediction methods by reducing false positives and enhancing overall prediction performance. Throughout this paper, we introduce the concept of relative X-ray flux increase and explain how to derive relative X-ray flux increase metadata for generating new labels. Additionally, we present new cumulative indices and data-driven categorical labels designed for active regionbased and full-disk flare prediction models. We then evaluate the effectiveness of our new labels when applied to established solar flare prediction models, demonstrating that they significantly enhance prediction capabilities and complement existing efforts. With our innovative data-driven labels, we aim to enhance flare forecasting capabilities and provide more accurate and reliable predictions for space weather phenomena.
Intelligent Fault Diagnosis for EHA Based on Muti-Source Fusion Hypergraph Convolutional Neural Networks Under Small Sample

Electro-Hydrostatic Actuator (EHA) is widely used in the aerospace industry due to its ability of high-precision control and high load-carrying capacity. During their extensive service life, aviation EHA's hydraulic systems inevitably experience failures, leading to a degradation of control performance and potential safety incidents. Moreover, the diagnosis of hydraulic systems is challenged by their intricate structural, elusive failure mechanisms, and the difficulties in obtaining representative fault samples. To address this, a novel approach based on Muti-source fusion hypergraph convolutional neural networks (MF-HGCN) is proposed. In this study, a fusion approach is employed to integrate multiple fault features extracted from the EHA's hydraulic system. By calculating the Euclidean distance between various signal sources, a hypergraph structure is constructed. Each node corresponds to a specific signal channel. Subsequently, this hypergraph data is fed into the designed hypergraph convolutional neural network, enabling classification of the entire graph. Finally, the trained network model is utilized for intelligent fault diagnosis of the hydraulic system. The experimental results of EHA hydraulic system test rig from Nanjing University of Science and Technology demonstrate that the proposed intelligent fault diagnosis method has better diagnostic performance under limited fault samples.
Torque Ripple Suppression of Brushless DC Motor Under an Improved Segmented PWM

This paper mainly describes a segmented PWM control method to minimize commutation torque ripple to achieve smooth operation of the motor at high and low speeds. The reasons for commutation torque ripple generated by brushless DC motors in three-phase six-state and two-phase conduction mode were analyzed. and a commutation torque ripple suppression strategy under ON-PWM modulation is proposed. This strategy considers the influence of the change of back EMF on the torque ripple during the commutation process, and further improves the compensation effect of commutation torque ripple, so as to realize the low torque ripple operation of the motor at low speed and high speed. By establishing a system simulation model in Matlab/Simulink environment, it is confirmed that the new control strategy can effectively reduce the commutation torque ripple, and verify the effectiveness and theoretical correctness of the control method.
Robust 3D Hand Tracking with Multi-View Videos

In this paper, we introduce a robust 3D hand tracking framework using multi-view video, designed for the construction of a database. For hand pose estimation, real-world videos present frequent challenging scenarios, where current methods could fail, such as complex finger poses, two-hand interactions, and self-occluded situations. To address these challenges, our focus lies in enhancing the robustness and stability of 3D hand pose tracking. Firstly, we leverage multiple pre-trained 2D hand pose estimation algorithms, and combine them based on confidence and two-hand interactions. Additionally, to consider both cross-frame and cross-view consistency, our framework optimizes the entire joints with three objectives: data, guidance, and feature constraints. Throughout evaluation on real-world data, we show that the proposed method is robust to various challenging cases.
Endothelin‐3 Regulates Endothelin‐1 Production in Cultured Human Endothelial Cells

Effects of endothelin-3 on the secretion of endothelin-1 and other endothelium-derived substances were investigated in cultured human umbilical vein endothelial cells. The present binding study showed two distinct subpopulations of binding sites for endothelin-3 with higher and lower affinities in cultured human endothelial cells. Endothelin-3 caused an increase in intracellular Ca2+ and inositol 1,4,5-trisphosphate levels and activated protein kinase C in a dose-dependent manner. Endothelin-3 also caused an increase in [3H] thymidine incorporation into cellular DNA and stimulated the production of cyclic guanosine 3',5'-monophosphate, 6-ketoprostaglandin Fla, and immunoreactive endothelin-1 in cultured human endothelial cells. NG-Monomethyl L-arginine (3xlO−4 mol/l) and indomethacin (10−5 mol/l) enhanced endothelin-3-induced endothelin-1 production. These results suggest that endothelin-3 bound to its specific receptors and then caused phosphoinositide breakdown, subsequently mobilizing intracellular Ca2+ and leading to protein kinase C activation and the initiation of DNA synthesis, resulting in the stimulation of endothelin-1 production by human endothelial cells. Furthermore, this endothelin-1 production may be suppressed by endothelium-derived relaxing factor and prostacyclin produced in response to endothelin-3 in cultured human endothelial cells.
1 Functional Impact of Diaphragm Muscle Sarcopenia in both Male and Female Mice 2 3 4

Functional Impact of Diaphragm Muscle Sarcopenia in both Male and Female Mice 2 3 4 Sarah M. Greising, Carlos B. Mantilla, Juan S. Medina-Martínez, Jessica M. Stowe, 5 Gary C. Sieck 6 7 Departments of Physiology and Biomedical Engineering and Anesthesiology, Mayo 8 Clinic, College of Medicine, 200 First Street SW, Rochester, Minnesota 55905, USA 9 10 For reprints contact: Gary C. Sieck, Ph.D. 11 200 First Street SW 12 SMH Jo 4-184 13 Rochester, MN 55905 14 sieck.gary@mayo.edu 15 Phone: (507) 284-6850 16 FAX: (507) 255-7300 17 18 19 Running Head: Diaphragm Aging & Sex Differences 20 Articles in PresS. Am J Physiol Lung Cell Mol Physiol (May 1, 2015). doi:10.1152/ajplung.00064. 2015
On the acceleration of simulated annealing

The simulated annealing algorithm has been very successful in the field of VLSI design automation. The main drawback to the algorithm is its sometimes prohibitive computational cost. This dissertation examines two methods for accelerating the algorithm. The first method is a uni-processor approach called two-stage simulated annealing. The second method is a multi-processor approach called population-oriented simulated annealing. In a traditional homogeneous two-stage simulated annealing system, the paradigm's early temperatures are replaced by a faster heuristic method. The main problem in the design of a two-stage simulated annealing system is the determination of the new starting temperature such that solution quality is maintained while acceleration is maximized. Most of the early work in this area avoided the problem of formalizing a general method of starting temperature determination by finding a reasonable ad hoc constant starting temperature for the low temperature simulated annealing phase. This dissertation instead presents a more formal method for approximating the starting temperature for traditional homogeneous two-stage simulated annealing systems that is both computationally inexpensive and accurate in practice. The proposed method is tested on three NP-hard combinatorial problems using two disparate homogeneous cooling schedules in an attempt to show applicability to different homogeneous simulated annealing formulations. Other robustness issues are also explored in addition to a new parameterized stop criterion for the classic cooling schedule. Parallelization is another approach to the problem of accelerating the simulated annealing algorithm. However, an efficient application-independent parallelization has yet to be presented. Instead, this dissertation examines an alternative thermodynamic/genetic hybrid approach to parallelization, inspired by the genetic algorithm. The proposed thermodynamic/genetic hybrid algorithm, called population-oriented simulated annealing, combines the advantages of the genetic algorithm's efficient parallelization with simulated annealing's superior convergence control. Both sequential and distributed results are presented for two NP-hard combinatorial problems. The sequential versions are empirically shown to compare favorably with other effective heuristic methods in terms of solution quality given equal amounts of computation time. Both distributed versions experimentally illustrate the algorithm's efficient parallelization as both are seen to exhibit continued speedup up to 10 processors, indicating the possibility of further scaling of the algorithm to larger numbers of processors.
Traffic Warning Sign Recognition System Using Computer Vision and Machine Learning

The paper is devoted to the development and testing of a traffic warning sign recognition system. The implementation using cascade classifier as detector and convolutional neural network as recognizer was proposed. Hardware part of the system was implemented on the basis of single board computers. The influence of applied single board computer architectures and usage of neural hardware accelerators on the system parameters was analyzed. The proposed system can be used to detect and recognize traffic warning signs in real time with high performance and accuracy. The result detection accuracy is 83% with recall of 84% and recognition accuracy is 98.85%. The result time for one frame is 250 milliseconds with a total distance up to 15 meters.
Identifying Occurrences of Abnormal and Drunk Driving Using Smartphones

Drunk driving is the act of operating a motor vehicle while under the influence of alcohol or other drugs that can impair a person's ability to drive. It is a serious offense that can lead to deadly consequences. Alcohol impairs judgment, coordination and reaction time. When someone is driving under the influence, they are unable to make quick decisions or react to sudden changes in the environment. This puts themselves and other drivers at risk. Drunk driving is a major cause of fatal car crashes, and it is illegal in every state. In initial phase of research, major focus have been given for reviewing existing methods. Overall drunk driving detection problem can be solved by three methods i.e, 1) Dynamic Time Warping, 2) Threshold-based, and 3) Machine Learning methods. Most works in the related architecture follows one or more of these methods. The proposed methodology in the study uses threshold-based and machine learning based methods to detect the drunk driving incidents in details. Based on extensive studies on drunk driving detection, research gaps and problem definition of this research work are highlighted. In the future, a system to overcome the limitations presented in the base paper will be developed.
A Survey on Multi-Modal Hate Speech Detection

Hate speech is a speech that expresses an attack on a person based on their ethnicity, race, sexual identity, belief, age, or unfitness. The integral complexity of this activity makes it necessary to differentiate hate speech from other types of social molestation. Online content commonly considers two kinds of data, i.e., text and images. Memes can also be used to share views and ideas through social media. Primarily memes are generated only for amusement, while some memes could be hate-related, generally a collection of text and images. Identification of offensive memes can help to identify and mitigate harmful effects on social media. This task is challenging because the image and text in such memes might be irrelevant. To make this model, one should deeply understand the images and texts. Almost all the models are based on past neural networks, and someworks are also related to identifying deceptive content for image and text data. Evenson, present-day techniques are competitive but not reaching optimum performance due to a significant gap in connecting images and text data. With a few papers, a model that detects hate speech from text and images is considered a vision language task. Most authors are motivated by Visual Questions answering the problem that identifies answers with image and text inputs.
Management of irrigation with saline water in a Sicilian vineyard

Water demand is increasing worldwide. In regions affected by water scarcity such as those located in the Mediterranean basin, water supplies are already degraded, or subjected to degradation processes, which worsen the shortage of water. In such regions, competition for scarce water resources among users will inevitably reduce the supplies of freshwater available for crop irrigation. Detailed studies and experimental projects are needed to develop management scenarios aimed at preventing desertification and conflicts for use of the increasingly limited water resources and cultivable land. This paper illustrates soil-plant responses to irrigation with saline water in a Sicilian vineyard located in a wine production area (Mazara del Vallo, Trapani, TP). Two irrigation treatments of different salinities, R and L, having electrical conductivities of 0.6 and 1.6 dS m, respectively, were applied at several dates, using a drip system, in a silty-clay soil. Soil measurements were taken to monitor water content (θ) and EC of saturated extract (ECsat). Plant responses to water and salinity conditions were explored by measuring crop transpiration (T) and stomatal conductance (Gs) at several dates during the irrigated season. Results showed that significantly lower T and Gs values were measured in field sites with lowest and highest ECsat.
MEMS Thermo-Nanomechanical Membrane Flexure (T-NMF) Device for Temperature Sensing

A promising choice for mechanical sensing applications is the nanomechanical cantilever sensor (NMC), which converts physical change on the cantilever surface into nanomechanical motion that may be detected by suitable transduction techniques. Structural modification of this device into an optimized membrane-based sensor platform and the integration of highly sensitive transduction materials to the NMC sensors can lead to appreciable enhancement in the performance of the sensor, thereby creating an ultra-sensitive nanomechanical sensor platform. This paper reports the development of a MEMS Thermo-Nanomechanical Membrane Flexure (T-NMF) sensor for meeting the demand for a highly sensitive, miniaturized, realtime temperature sensor, based on the principle of deflection of the bimorph membrane with change in temperature, due to the difference in thermal coefficients of expansion of different layers of materials present on the membrane. Four inverse trapezoidal flexures are used to suspend the circular membrane of the T-NMF sensor. The two layers of the membrane are selected to be silicon and aluminum. A thin film of Indium Tin Oxide (ITO), which has a high gauge factor, is deposited as a piezoresistor on each of the flexures, for electromechanical transduction. The design and simulation of the T-NMF sensor is carried out using COMSOL Multiphysics 6.0 software, after optimizing the beam geometry. This optimized design showed an improved thermal sensitivity of around 2 times higher than that of the conventional cantilever bimorph sensor of comparable dimensions.
Performance Analysis of Non Linear Energy Harvesting in Relay assisted NOMA Cooperative Network

In this paper, we have analysed the performance of a relay assisted non orthogonal multiple access (NOMA) cooperative network considering non linear energy harvesting (NLEH) at the relay node. The signals from the two base station uplinked to the relay node through Nakagami-m fading channel. At the relay node, NLEH has been applied to harvest energy from the received signal. Using the harvested energy the data is downlinked to the two end users through Nakagami-m fading distribution. The performance of the system has been explored by deriving the outage probability at both the end users using numerical integration.