Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Design and Modeling of MEMS-based Single Axis Capacitive Accelerometer

Structural design and modeling of single axis capacitive type accelerometer based on microelectromechanical system (MEMS) have studied in this article. The purpose in doing this article is to consider the structural design of the silicon sensitive element (SE), results of the deformation and the mechanical stress on the silicon SE with comb fingers at the linear accelerations ranging from 50g to 300g by using Multiphysics software ANSYS based on finite element analysis (FEA). Furthermore, the next deformation results of the silicon SE at the temperature ranges from −40°C to 80°C along the working axes X and Y have presented. Finally, to enhance the values of capacitance changes between sensitive comb fingers and increase the number of these comb fingers in 50, 60 and 70 (Model 1, Model 2 and Model 3) by using the program Ansys Maxwell were analyzed. According to the results of the whole structure, the optimized sensitivity of the capacitive accelerometer device at the single axis is proposed. The sensitivity of accelerometer can be improved through optimal selection of design parameters viz. length and width of the silicon membrane, overlap area of electrodes, number of comb fingers. Therefore, this structural design is more corresponding among the different types of accelerometer designs to optimize the sensitivity of the proposed accelerometer based on MEMS devices.
IL-10 Inhibits miR-155 Induction by Toll-like Receptors*

IL-10 is a potent anti-inflammatory cytokine that is crucial for down-regulating pro-inflammatory genes, which are induced by Toll-like receptor (TLR) signaling. In this study, we have examined whether modulation of microRNAs plays a role in the inhibitory effect of IL-10 on TLR4 signaling. Analyzing microRNAs known to be induced by TLR4, we found that IL-10 could inhibit the expression of miR-155 in response to lipopolysaccharide but had no effect on miR-21 or miR-146a. IL-10 inhibited miR-155 transcription from the BIC gene in a STAT3-dependent manner. This inhibitory effect of IL-10 on miR-155 led to an increase in the expression of the miR-155 target, SHIP1. This is the first example of IL-10 playing a role in microRNA function and suggests that through its inhibitory effect on miR-155, IL-10 has the ability to promote anti-inflammatory gene expression.
A Mission-Aware Battery Charging Algorithm for Unmanned Aerial Vehicles

In recent years, researchers have explored various charging technologies to extend the battery life of unmanned aerial vehicles (UAVs) during flight missions. However, challenges arise with the reliance on weather-dependent renewable energy and congestion at battery swap and charging stations during peak hours. This poses difficulties for time-sensitive missions where conventional methods may not meet immediate demands. While wireless charging methods have been proposed, they can lead to queues and disruptions as the UAV population grows. An integrated solution is lacking, considering mission characteristics and cost considerations. This paper introduces adaptive charging options: regular missions utilize battery swap, wired charging, or wireless charging, while time-critical missions employ in-flight UAV-to-UAV wireless charging. Fixed-wing UAVs can leverage solar energy during clear weather. Simulations demonstrate reduced power grid load, fulfilling individual UAV charging needs, and cutting costs. An advantageous outcome is achieved for all stakeholders involved.
IoT Driven Air Quality Surveillance and Forecasting

In order to lessen dependency on expensive sensors, this study introduces a low-cost model that makes use of computing power at the edge and the Internet of Things in an effort to develop affordable air quality monitoring and predictive platforms. The Raspberry Pi, also known as a device that provides edges with computing power, applies the Kalman Filter (KF) method to improve low-cost measurements by 27% at the boundary of the device. By combining data with related mistakes, the methodology uses the KF algorithm to predict the level of six pollutants in the air, including SO2, NO2, and PM2.5, with lightning speed. The KF procedure's higher predictive ability is shown by comparisons with three traditional prediction techniques: Autonomous Integrated Moving Standard, Increasingly Weighted Move Average, and Simple Moving Average, among others. The root-mean-square error measurement showed a significant decrease of 68.3% on averaged. These results highlight the system's potential to promote intelligent farming and are consistent with its potential for implementation in several domains where precise, immediate time quality of air monitoring is essential.
Shift work and chronic disease: the epidemiological evidence

Background Shift work, including night work, has been hypothesized to increase the risk of chronic diseases, including cancer, cardiovascular disease (CVD), metabolic syndrome and diabetes. Recent reviews of evidence relating to these hypotheses have focussed on specific diseases or potential mechanisms, but no general summary of the current data on shift work and chronic disease has been published. Methods Systematic and critical reviews and recent original studies indexed in PubMed prior to 31 December 2009 were retrieved, aided by manual searches of reference lists. The main conclusions from reviews and principle results from recent studies are presented in text and tables. Results Published evidence is suggestive but not conclusive for an adverse association between night work and breast cancer but limited and inconsistent for cancers at other sites and all cancers combined. Findings on shift work, in relation to risks of CVD, metabolic syndrome and diabetes are also suggestive but not conclusive for an adverse relationship. Conclusions Heterogeneity of study exposures and outcomes and emphasis on positive but non-significant results make it difficult to draw general conclusions. Further data are needed for additional disease endpoints and study populations.
Bi-functional Metasurface Based Wideband Cross-Polarizer and Narrowband Absorber

A thin, compact bi-functional metasurface for wideband cross-polarization conversion (CPC) and narrowband absorber has been reported in this paper. The proposed metal-dielectric-metal layer meta-atom contains circular split rings and four L-shaped metal strips on the top layer, FR-4 substrate in the middle layer, and the copper ground plane on the bottom layer. A wide band cross-polarization conversion from 12.8 to 24.7 GHz and narrow band absorptance from 30.5 to 31.2 GHz has been observed with the designed metasurface. Both functions of the metasurface are angularly stable for the incident plane wave up to 20°.
MLSD-GAN - Generating Strong High Quality Face Morphing Attacks Using Latent Semantic Disentanglement

Face-morphing attacks are a growing concern for biometric researchers, as they can be used to fool face recognition systems (FRS). These attacks can be generated at the image level (supervised) or representation level (unsupervised). Previous unsupervised morphing attacks have relied on generative adver-sarial networks (GANs). More recently, researchers have used linear interpolation of StyleGAN-encoded images to generate morphing attacks. In this paper, we propose a new method for generating high-quality morphing attacks using StyleGAN disentanglement. Our approach, called MLSD-GAN, spherically interpolates the disentangled latents to produce realistic and diverse morphing attacks. We evaluate the vulnerability of MLSD-GAN on two deep-learning-based FRS techniques. The results show that MLSD-GAN poses a significant threat to FRS, as it can generate morphing attacks that are highly effective at fooling these systems.
Realtime Hybrid Task-Based Control for Robots and Machine Tools

This paper presents work in the field of hard realtime robotics and machine control. We analyse the requirements of a hybrid realtime control task specification allowing the integration of discrete and continuous control tasks. We propose an application independent task structure providing data flow consistency under simulataneous access by different control layers. We provide an execution flow mechanism to guarantee execution time determinism, yet allowing flexibility to react to a changing environment. We use state machines for process monitoring and a thread-safe realtime event system to communicate changes. The tasks can be distributed over a network and communicate using interfaces or manipulate streams of data in the loop. The presented task structure is applied to a real world example.
High Resolution Remote Sensing Image Captioning Using Group Convolution and Attentional Mechanism

This paper proposes a remote sensing image description generation method based on group convolutional neural network and attention mechanism to address the problem of a single global feature extraction scale in the model for remote sensing image scene description generation. This method mainly applies grouping convolution modules to fuse the features of different network layers and global features at different scales in convolutional neural networks. To overcome the shortcomings of traditional convolutional neural networks in feature extraction, this paper proposes a remote sensing image description generation method based on the inverse bottleneck layer convolutional neural network and attention mechanism. This method significantly increases the global receptive field of convolutional neural networks by introducing inverse convolutional layers, grouped convolutions, and large-scale convolutional kernels. This method utilizes deep separable convolutional layers to further reduce network model parameters and model operations. The experimental results show that our method can generate more accurate text statements for describing remote sensing images.
Hyper-active gap filling

Much work has demonstrated that speakers of verb-final languages are able to construct rich syntactic representations in advance of verb information. This may reflect general architectural properties of the language processor, or it may only reflect a language-specific adaptation to the demands of verb-finality. The present study addresses this issue by examining whether speakers of a verb-medial language (English) wait to consult verb transitivity information before constructing filler-gap dependencies, where internal arguments are fronted and hence precede the verb. This configuration makes it possible to investigate whether the parser actively makes representational commitments on the gap position before verb transitivity information becomes available. A key prediction of the view that rich pre-verbal structure building is a general architectural property is that speakers of verb-medial languages should predictively construct dependencies in advance of verb transitivity information, and therefore that disruption should be observed when the verb has intransitive subcategorization frames that are incompatible with the predicted structure. In three reading experiments (self-paced and eye-tracking) that manipulated verb transitivity, we found evidence for reading disruption when the verb was intransitive, although no such reading difficulty was observed when the critical verb was embedded inside a syntactic island structure, which blocks filler-gap dependency completion. These results are consistent with the hypothesis that in English, as in verb-final languages, information from preverbal noun phrases is sufficient to trigger active dependency completion without having access to verb transitivity information.
Distributed Event-Triggered Control for DC Microgrids with Quantization Information

In this paper, DC micro grids with digital communication networks are taken into consideration, and a distributed event-triggered control strategy based on quantization information is proposed for accurate load current sharing in DC micro grids. According to the designed control strategy, each distributed generator (DG) sends the quantization information to its neighboring DGs only when a specific condition is triggered. As a result, the control signal is updated by discrete-time quantization information. It is analyzed by the Lyapunov stability method that accurate current sharing is achieved. An example of DC micro grids is used to verify the proposed control strategy.
Customer choice between electronic and traditional markets: an economic analysis

Switching between electronic and traditional markets, consumers nowadays impact profoundly on market makers. To successful conduct business, firms have to carefully making channel decisions to meet the needs of customers. The purpose of this article is to offer normative models for analyzing consumer channel selection behaviors between traditional and electronic markets. Referred to transaction cost economics and consumer decision processes, the models assumed that customers would like to purchase on the market that brings them the highest utility values in terms of effort, waiting time, and cost savings. Equilibrium solutions at which customers are indifferent in choosing between electronic and traditional channels are also derived for both risk-neutral and risk-averse consumers.
Posttranslational association of immunoglobulin heavy chain binding protein with nascent heavy chains in nonsecreting and secreting hybridomas

A rat monoclonal antibody specific for immunoglobulin (Ig) heavy chain binding protein (BiP) has allowed the examination of the association of BiP with assembling Ig precursors in mouse B lymphocyte-derived cell lines. The anti-BiP monoclonal antibody immunoprecipitates BiP along with noncovalently associated Ig heavy chains. BiP is a component of the endoplasmic reticulum and binds free intracellular heavy chains in nonsecreting pre-B (mu+, L-) cell lines or incompletely assembled Ig precursors in (H+, L+) secreting hybridomas and myelomas. In the absence of light chain synthesis, heavy chains remain associated with BiP and are not secreted. The association of BiP with assembling Ig molecules in secreting hybridomas is transient and is restricted to the incompletely assembled molecules which are found in the endoplasmic reticulum. BiP loses affinity and disassociates with Ig molecules when polymerization with light chain is complete. We propose that the association of BiP with Ig heavy chain precursors is a novel posttranslational processing event occurring in the endoplasmic reticulum. The Ig heavy chains associated with BiP are not efficiently transported from the endoplasmic reticulum to the Golgi apparatus. Therefore, BiP may prevent the premature escape and eventual secretion of incompletely assembled Ig molecules.
Leveraging Gram Matrix Representation in Shallow DNN for GI Tract MRI Image Segmentation

GI tract MRI image segmentation is important for the diagnosis and treatment of many diseases. In case of GI tract cancer or tumor treatment, radio oncologists must apply X-ray beams pointing towards the tumor cell while avoiding the other organs. The traditional segmentation process for the MRI scan is time consuming and labor intensive. A computer aided fast and accurate method is required. UNet, UNet++, Attention UNet are some of the deep learning architectures used in this purpose having remarkable performance. Most of these architectures inherit significant computational complexity. In this paper, a gram matrix oriented shallow DNN is proposed which will bypass the traditional attention mechanism. Due to the simplified computation o f t he proposed model, i t yields similar or superior segmentation performance as compared to existing models and it has much lower number of trainable parameters in the range of 10 to 75 percent less the other models.
An Improved Extended Kalman Filter for UAV Localization in UWB Navigation System

In this paper, we propose a new approach to estimate yaw angle of UAV operating in UWB navigation system. Absolute orientation algorithm which is commonly used to match images is used to align optical-flow-inertial coordinate to UWB navigation coordinate. An improved extended kalman filter fusing optical-flow data and initializing with absolute orientation estimation is designed. A simulation and experiment are conducted to verify the feasibility of the approach of yaw estimation along with accuracy of localization and reach a satisfied result.
Overview of Computerized Adaptive Testing

Computerized Adaptive Testing (CAT), which provides a flexible and effective means of measuring an individual's knowledge, skills, and talents, has completely transformed the field of educational and psychological assessment. The computer adaptive testing (CAT) has both adaptive properties and fast individualized feedback. It creates algorithms to obtain more accurate results while sparing test takers' time. Early research suggested that CAT can be less stressful and more motivating than a traditional paper-pencil test. The article presents an overview of the fundamental ideas of CAT, such as item response theory (IRT) and item banks' function. The CAT components covered here include item bank development, item selection processes, trait estimate, and stopping rule.
Learnable Frequency Filters and Low-Frequency Domain Randomization Based Domain Generalization for Medical Image Segmentation

In recent years, the convolutional neural network represented by U-net has achieved great success in the field of medical image segmentation. However, due to different data acquisition protocols and differences in instrument parameters, the data in actual clinical applications will have a distribution shift problem with the training data of the model, also known as the domain shift problem. This can lead to a decrease in model performance, hindering the application of the model in real-world clinical settings. Traditional convolutional neural networks have local perception properties, and their inability to model long-range dependencies hinders the improvement of domain generalization performance. In addition, the traditional DG method of feature space domain randomization is not effective in medical image segmentation tasks due to the limited search space of feature styles and the inability to keep semantic information unchanged. To address the above issues, this paper proposes a medical image segmentation network named GFDR-U net for domain generalization. The network utilizes globally learnable filters to model long-range dependencies, and introduces a low-frequency domain randomization method to solve the problem of semantic invariance. Extensive experiments show that our method outperforms the state-of-the-art methods.
Causal Convolution-Transformer for Time Series Forecasting of Dry Weight in Hemodialysis Patients

Dry weight (DW) is a crucial parameter used to determine the ultrafiltration volume before a hemodialysis (HD) session. In clinical practice, it is defined as the lowest postdialysis body weight that does not lead to adverse symptoms during dialysis. However, the precise measurement of dry weight in the medical field remains a “missing piece in the puzzle”. Due to the complexity and instability of the existing methods for evaluating DW and significant inter-individual variations among patients, ensuring timely and accurate assessment of DW presents a significant challenge. In this study, we convert the assessment of dry weight into a task of time series forecasting and an advanced time series forecasting model, named the causal convolution-transformer, is proposed for the clinical forecasting of DW in HD patients. The proposed approach combines the exceptional performance of the transformer in time series data processing with the analytical capability of causal convolution for capturing local front and back information. We collect a total of 929,753 medical data samples from 3,276 HD patients in a hospital and extract 37 clinically relevant features associated with DW based on clinical expertise. After conducting experiments and performing a comparative analysis with clinical evaluation, the mean square error for the causal convolution-transformer model is 0.018 and the R-square is 0.963, which effectively accomplishes the task of forecasting DW.
Biomarkers for Early Detection of Malignant Mesothelioma: Diagnostic and Therapeutic Application

Malignant mesothelioma (MM) is a rare and aggressive tumour of the serosal cavities linked to asbestos exposure. Improved detection methods for diagnosing this type of neoplastic disease are essential for an early and reliable diagnosis and treatment. Thus, focus has been placed on finding tumour markers for the non-invasive detection of MM. Recently, some blood biomarkers have been described as potential indicators of early and advanced MM cancers. The identification of tumour biomarkers alone or in combination could greatly facilitate the surveillance procedure for cohorts of subjects exposed to asbestos, a common phenomenon in several areas of western countries.
Investigating Gender Convergence in Alcohol Consumption in Finland, Germany, The Netherlands, and Switzerland: A Repeated Survey Analysis

Since the 1970s there has been concern that women may have started to adopt the drinking behaviors of men, a process seen as the result of emancipation in which women increasingly move into professions and/or lifestyles similar to those of men. This is called the convergence hypothesis. The present analysis investigated a possible closing of the gender gap and includes four countries that had national survey data for at least two time points: Finland (1984, 1992), Germany (1984, 1990), The Netherlands (1981, 1989), and Switzerland (1987, 1992). Significant convergence could be found only in Finland, which consisted of a greater increase of Finnish women becoming current drinkers and of a greater relative increase in the women's mean consumption. The fact that gender convergence was found only for Finland could be due not only to the longer observation period, but also to the egalitarian position of women and the changing drinking culture of the country.
A Human-Machine Co-Driving Evaluation System Based on the Integration of Subjectivity and Objectivity *

The integration of human-machine co-driving marks an inevitable stage in the progression of autonomous driving technology. Situations where a driver's intentions differ from those of the automated driving system can pose risks to both the vehicle and other road users. Hence, accurately measuring these disparities becomes a crucial challenge in promoting a smooth and safe human-machine co-driving experience. This research delves into quantifying conflicts that arise during human-machine co-driving by combining subjective and objective approaches. Using a simulation platform and devices to capture physiological signals during collaborative driving scenarios, the study explores effective methods for evaluating these inherent conflicts. In this paper, a new human-machine co-driving system based on visual feedback is introduced and validated through experiments in a simulated driving environment. By correlating subjective driver perceptions with empirical data, the reliability of the conflict quantification metric is ultimately confirmed.