Miracle-dz/UniFAR-FTUs huggingface dataset:
Stretchable Pressure Sensor Using Thermoplastic Polyurethane and Conductive Inks

The development of wearable health devices is an emerging technology, and pressure sensors have been widely used in several of these applications. Plenty of research within pressure sensors is focused on tactile sensing and artificial skin. In this paper, a highly flexible and stretchable pressure sensor is presented. The sensor comprises stretchable thermoplastic polyurethane (TPU) film as substrate and stretchable conductive inks as electrodes and sensing material. Screen printing is used to fabricate electrodes and pressure sensing components on TPU sheets. Electrical and mechanical properties of the fabricated sensors indicate good mechanical and electrical stability while retaining pressure sensing properties and marginal deterioration even after 100 elongation cycles. The findings show that the presented stretchable pressure sensor has a great potential for usage on surfaces where bending and stretching will occur while retaining nearly all of its electrical and mechanical capabilities. The proposed sensor may be employed as a wearable device to detect human movements.
The Adaptability Problem Analysis on Structural Response Calculation with Wideband Signals Excitation

With the complex and diversified of engineering machinery’s load, the structural response calculation with the incentive of the broadband signal is becoming increasingly important. This paper analyses the adaptability problem on common structural response calculation with wideband signals excitation, and then used specific examples to illustrate the calculation methods problems that may arise in the calculation of broadband stimulus-response. Finally, the way is given to solve it.
3D Bio-Printability of Hybrid Pre-Crosslinked Hydrogels

Maintaining shape fidelity of 3D bio-printed scaffolds with soft biomaterials is an ongoing challenge. Here, a rheological investigation focusing on identifying useful physical and mechanical properties directly related to the geometric fidelity of 3D bio-printed scaffolds is presented. To ensure during- and post-printing shape fidelity of the scaffolds, various percentages of Carboxymethyl Cellulose (CMC) (viscosity enhancer) and different calcium salts (CaCl2 and CaSO4, physical cross-linkers) were mixed into alginate before extrusion to realize shape fidelity. The overall solid content of Alginate-Carboxymethyl Cellulose (CMC) was limited to 6%. A set of rheological tests, e.g., flow curves, amplitude tests, and three interval thixotropic tests, were performed to identify and compare the shear-thinning capacity, gelation points, and recovery rate of various compositions. The geometrical fidelity of the fabricated scaffolds was defined by printability and collapse tests. The effect of using multiple cross-linkers simultaneously was assessed. Various large-scale scaffolds were fabricated (up to 5.0 cm) using a pre-crosslinked hybrid. Scaffolds were assessed for the ability to support the growth of Escherichia coli using the Most Probable Number technique to quantify bacteria immediately after inoculation and 24 h later. This pre-crosslinking-based rheological property controlling technique can open a new avenue for 3D bio-fabrication of scaffolds, ensuring proper geometry.
Modeling Inertial Sensor Errors Using Autoregressive (AR) Models

The accelerometer and gyro sensor errors of a Strapdown Inertial Navigation System (SINS) consist of two parts: a deterministic part and a random part. The deterministic part includes biases and scale factors, which are determined by calibration and then removed from the raw measurements. The random part is correlated over time and is basically due to the variations in the SINS sensor bias terms. Therefore, these errors are modeled stochastically so that they can be included in the SINS error model. For most of the navigation-grade SINS systems, a first-order Gauss-Markov model with a fairly large correlation time is usually used to describe the random errors associated with inertial sensors. By studying the autocorrelation sequences of the noise components at the outputs of inertial sensors, we have determined that a first-order Gauss-Markov is not adequate to model such noise behavior. This paper offers a new method to model the inertial sensor noise as a higher order autoregressive (AR) process and adaptively estimates the AR model parameters. Three different methods of determining the AR model parameters are investigated, namely: the Yule-Walker (autocorrelation) method, the covariance method and the Burg’s method. The three algorithms were tested with different AR model orders with real SINS static data. The results showed that the Burg’s method gives the minimum prediction mean square estimation error and hence this method was used for generating the applied AR models. In this paper, the current algorithms for modeling inertial sensor errors are introduced. The different algorithms for AR modeling are discussed and their results are presented. The results after applying the Burg’s AR models with different orders are also analyzed.
Resonance-Aware Design Approach for Reducing Noise Coupling between Power Buses: Using Fan-Shaped Open Stub on Printed Circuit Boards

Power islands are often employed in printed circuit board designs to reduce the problem of power bus noise coupling between circuits. However, the resonances between two neighboring power buses, which could be supplying different voltages, can increase the ground bounce noise at high frequencies. Therefore, we propose a new resonance-aware design-a fan-shaped open stub structure embedded in the power bus to reduce noise. Based on our simulation and experimental results, |S21| of about -10 dB in the primitive structure could be reduced to -30 dB and -42 dB, respectively, demonstrating that this method is effective in controlling ground bounce noise.
Modeling dynamic behavior of leaf temperature at three-dimensional positions to step variations in air temperature and light.

Organisms such as plants grow as a result of the influence of their genetics and microenvironment, consisting of physical, chemical, and biological factors. The microenvironment or microclimate is the environment closely surrounding these organisms and varies in time and space. To control processes more optimally, it is necessary to understand how living organisms respond dynamically to their physical microenvironment. Most models being developed to explain this relationship through steady-state models are descriptive (deterministic) and too complex to be used for control purposes. Therefore, it is attempted in this work to develop a dynamic black box model. For this study only air temperature was considered in 3-D (three dimensions). The objective of this research was to model the dynamic response of leaf temperature to time variations in air temperature closely surrounding that leaf, and to light-dark alterations. This biosystem is modeled using an ARX model structure (black box). Three-dimensional gradients in air temperature around the plant were shown and analyzed. It is demonstrated that a complex process such as the response of leaf temperature to changes in 3-D ambient air temperature and light-dark alterations can be modeled with a mean r 2 between 92.7% and 99.9%.
Reducing Maintenance and Rehabilitation Costs through the Use of AMIR Compaction

Highway engineers and administrators, and Construction industry made necessary improvements to asphalt pavements in the field of asphalt binders to bond asphalt layers, material selection, testing procedure, mix design method, material handling and through significant technological advancement in laying process. These actions and efforts created an assumption that newly constructed asphalt pavements which meet design objectives are structurally sound. However no significant improvements to the current compaction equipment and techniques were made to date. Pavement surface cracks which are induced by current compaction method ruins the above combined effort made by authorities and industry to achieve long lasting pavement. Improving compaction practice by introducing a new compactor; AMIR, can provide the quality of highway network with respect to authorities and industry expectation. This paper as well as earlier studies shows that AMIR roller applies low compaction pressure over a long contact duration which helps to provide efficient particle contact, expulsion of entrained air and keeps the initial stiffness response of the asphalt at low. Large contact area of the roller minimizes horizontal forces applied to the asphalt mat and provides a high degree of confinement during compaction. Elimination of roller induced cracking reduces surface permeability, increases density with less passes, improves resistance to fatigue damage and permits the full compaction energy to be applied to the pavement layer. This will enhance the existing necessary improvements made by transportation authorities in highway construction. Also, the end product is expected to reduce the required maintenance and rehabilitation costs which have been performed more frequent than expected.
Cost-Efficient Server Configuration and Placement for Mobile Edge Computing

Computing resource configuration and site selection of edge servers (ESs) are two critical steps to build up a mobile edge computing (MEC) platform. In this paper, the joint optimization problem of configuration and placement for ES in the MEC environment is investigated. First, we treat each ES as an M/G/m queueing model, and establish mathematical models to characterize the MEC environment, such that the performance and operational expenditures (OPEX) of the system can be calculated analytically. Then, we design a two-stage method and develop a series of algorithms based on bisection algorithm and genetic algorithm (GA) to obtain the optimal configuration scheme and sub-optimal placement scheme (including the deployment quantity) of ESs, with the goal of minimizing OPEX while maintaining system performance at a predetermined level. Finally, we conduct experiments based on a real base station dataset provided by Shanghai Telecom to show the effectiveness of the proposed algorithms. To the best of our knowledge, this work is the first research of the joint optimization problem of configuration and placement for ES in the MEC environment, where the main objective is to increase the cost efficiency.
A Framework for Organizing Lean Product Development

Abstract: While in the last 20 years a large number of frameworks have been presented in literature, currently there is no consensus on how to define Lean Product Development (PD). We used content analysis to investigate existing approaches and integrated them into a single, coherent framework consisting of 11 Lean PD components. To better understand the nature of the novel definition of Lean PD, we conducted a theoretical investigation of the component interdependencies. We hypothesize that Lean PD needs to be understood as a system of highly interwoven components that only in their concurrency lead to high performance in PD.
Adaptation at Full Speed - Regulatory Changes in Fast Moving Industries

Prior research provides rich insights into the mechanisms to enhance an organization? s ability to adapt. Even though human resources have been found to play a key role to enable organizations to reply to environmental opportunities and threats, evidence at the individual level is scarce. A comprehensive understanding of the determinants of adaptation is particularly important in fast moving industries, where organizations typically do not get a second chance to make up for a failure to adapt. This paper aims at filling this research gap. It uses a unique data set on Formula One motorsport constructors. A pre-post experimental design with exogenous regulatory change allows for the operationalization of adaptation as well as a causal interpretation of the results. The findings indicate that whereas domain-specific experience at the level of the individual decreases the adaptive ability of an organization, experience from moderately distant technological fields increases it. To exploit experience from more distant fields, absorptive capacity is needed. Additionally, results provide first evidence that learning matters with respect to the determinants of adaptation.
Laplace Transform and Semi-Hyers–Ulam–Rassias Stability of Some Delay Differential Equations

In this paper, we study semi-Hyers–Ulam–Rassias stability and generalized semi-Hyers–Ulam–Rassias stability of differential equations x′t+xt−1=ft and x″t+x′t−1=ft,xt=0ift≤0, using the Laplace transform. Our results complete those obtained by S. M. Jung and J. Brzdek for the equation x′t+xt−1=0.
The Evaluation of Public Service Inspection: A Theoretical Framework

Inspection is an important and growing element of the regulation of public services. We develop a theoretical framework for the evaluation of inspection and illustrate the practical relevance of this framework with reference to the new Best Value inspectorate for UK local government. The framework contains three elements that facilitate the effectiveness of an inspection system (a director, a detector and an effector), five potential problems that impede its effectiveness (resistance, ritual compliance, regulatory capture, performance ambiguity and information gaps) and a pivotal mediating variable which is the expertise of inspectors. We combine these variables into a matrix that can be used as a checklist for the evaluation of inspection and we identify hypotheses concerning the circumstances that lead to successful inspection outcomes.
Convolutional Neural Networks for Classification of Drones Using Radars

The ability to classify drones using radar signals is a problem of great interest. In this paper, we apply convolutional neural networks (CNNs) to the Short-Time Fourier Transform (STFT) spectrograms of the simulated radar signals reflected from the drones. The drones vary in many ways that impact the STFT spectrograms, including blade length and blade rotation rates. Some of these physical parameters are captured in the Martin and Mulgrew model which was used to produce the datasets. We examine the data under X-band and W-band radar simulation scenarios and show that a CNN approach leads to an F1 score of 0.816±0.011 when trained on data with a signal-to-noise ratio (SNR) of 10 dB. The neural network which was trained on data from an X-band radar with 2 kHz pulse repetition frequency was shown to perform better than the CNN trained on the aforementioned W-band radar. It remained robust to the drone blade pitch and its performance varied directly in a linear fashion with the SNR.
Coloniality, Epistemic Imbalance, and Africa’s Emigration Crisis

The paper has two complementary objectives. First, it sustains an analysis of the concept of ‘coloniality’ that accounts for the epistemic imbalance in the modern world, demonstrating precisely how Africa is adversely affected, having been caught up in the throes of coloniality and its epistemic implications. Second – and complementarily – the paper attempts to bring this very concept of ‘coloniality’ into the discourse on Africa’s emigration crisis, arguing that Africa’s emigration crisis is traceable, inter alia, to the epistemic imbalance in the very structure of modernity. This imbalance results from the stifling of Africa’s epistemic resources under Western epistemic hegemony. Epistemic coloniality, of course interacting with some material factors, creates a sufficient condition for emigration. It is further theorized that the apparent lack of epistemic will on the part of Africans to mobilize some surviving epistemic resources to address some problems on their own is also a function of coloniality.
Immunofluorescence Studies on the Expression of the SARS-CoV-2 Receptors in Human Term Placenta

Until September 2021, the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2; COVID-19) pandemic caused over 217 million infections and over 4.5 million deaths. In pregnant women, the risk factors for the need of intensive care treatment are generally the same as in the overall population. Of note, COVID-19-positive women deliver earlier than COVID-19-negative women, and the risk for severe neonatal and perinatal morbidity and mortality is significantly higher. The probability and pathways of vertical transmission of the virus from the pregnant woman to the fetus are highly controversial. Recent data have shown that 54 (13%) of 416 neonates born to COVID-19-positive women were infected. Here, we investigated term placentas collected before the SARS-CoV-2 pandemic and studied the main COVID-19 receptors angiotensin-converting enzyme 2 (ACE2), transmembrane protease serine subtype 2 (TMPRSS2), as well as neuropilin 1 (NRP1). We performed real-time PCR and immunofluorescence on cryosections in combination with markers for syncytiotrophoblast, endothelial cells, macrophages and stromal cells. The PCR studies showed expression of both the truncated delta form of ACE2, which does not bind the COVID-19 spike protein, and the long form. The ACE2 antibody used does not distinguish between the two forms. We did not observe expression of the canonical SARS-CoV-2 entry machinery on syncytio- and cytotrophoblast. ACE2 and TMPRSS2 are co-expressed in a subpopulation of stromal cells, which in part are CD68-positive macrophages. NRP1 is localized to endothelial cells. In sum, the term placenta is not an organ that directly favors vertical transmission of COVID-19; however, microtraumas and placentitis may weaken its barrier function.
Postoperative Complications and Curative Effect of Interventional Diagnosis and Treatment through Radial Artery and Femoral Artery

To investigate the efficacy and safety of percutaneous coronary intervention in patients with coronary heart disease via radial and femoral artery pathways. Total 122 patients with coronary heart disease who underwent percutaneous coronary intervention in our hospital were selected and divided into observation group (n=61, percutaneous coronary intervention via radial artery) and control group (n=61, percutaneous coronary intervention via femoral artery). The perioperative conditions (operation time, puncture time, postoperative bed rest time and hospital stay) of the two groups were compared. Before and 3 mo after the operation, the heart function indexes [left ventricular ejection fraction, left ventricular end systolic diameter] operation success rate and related complications. There was no significant difference in the stenosis degree of left anterior descending, left circumflex artery, right coronary artery, calcification, angulation and target vessel between the two groups (p>0.05). The puncture time, operation time, postoperative bed time and hospitalization time of the observation group were lower than those of the control group (p<0.05). There was no significant difference in the success rate of percutaneous coronary intervention between the observation group and the control group (p>0.05). In addition, 3 mo after operation, left ventricular ejection fraction and left ventricular end systolic diameter in both groups were lower than those before operation (all p<0.05). Finally, the incidence of puncture site complications in the observation group was lower than that in the control group (p<0.05). The effect of transradial and femoral percutaneous coronary intervention in the treatment of coronary heart disease is similar, but the recovery is faster, the incidence of postoperative complications and serious cardiovascular adverse events is lower, and the safety is higher.
Impact of solar activity on atmospheric circulation and oceanic surface currents

It has been shown, on the basis of observational data, that under the change in the level of solar activity, well-formed changes are observed in the field of velocities in lower atmospheric layer and in the ocean. The changes depend on the season and the solar activity level. Increased solar activity is followed by amplification of circulation at 10-meter level and changing of wind stresses in most regions. In this case, oceanic surface currents change too. Specifically in September– October, the pre-equatorial current is significantly enhanced in the western part of Pacific and weakened in the eastern part of it.
Perspectives of Queer Hmong Youth

This article highlights some of the tensions that exist for Hmong people who identify as lesbian, gay, bisexual, transgender, and queer (LGBTQ). It uncovers differences and similarities found between the experiences of queer Hmong youth and the larger population of queer youth living in the United States. Despite the perception that a traditional Hmong culture holds no place for queer Hmong Americans, individuals are finding spaces for acceptance and slowly moving the larger Hmong community to a place of understanding and tolerance. A vital part of this movement was Shades of Yellow (SOY), an organization that supported queer Hmong from its inception in 2005 until the group disbanded in June 2017. The life stories of three of its members inform this study, offering a more nuanced look at the experiences of queer Hmong youth living in the Midwest.
Comparative study of BOF Steelmaking Process based on ANFIS and GRNN Model

BOF steelmaking process is most important process to obtain the desired quality of steel consideration of two parameter end point temperature and end point carbon percentage by two model ANFIS and GRNN. The main aim of present paper is steelmaking through Basic Oxygen Furnace (BOF) to achieve higher productivity and the lower production cost in the minimum time by desired endpoint carbon content and temperature of raw material (molten steel). It is a very complicated physical chemical process however most of the industries use it. To achieve this various modeling are used such as mechanistic model, statistical model, regression and neural network models. Neural Network usages Fuzzy Logic Controllers is adaptive in nature and gives superior and faster results, without using of accurate mathematical model and well response for complex non-linear multi dimensional system. The present paper about predict the end point of carbon content and temperature in the basic oxygen furnace process using ANFIS and GRNN model. After that we compared both result. ANFIS has provided a new method for solving the problem of prediction and control of end point carbon content and end point temperature of complex BOF process. ANFIS model have five layers but GRNN have only two that's the main reason behind the more accuracy of ANFIS model.
A Direct Method for Evaluating the Structural Needs of Flexible Pavements Based on FWD Deflections

A direct and simple method (YONAPAVE) for evaluating the structural needs of flexible pavements is presented. It is based on the interpretation of measured FWD deflection basins using mechanistic and practical approaches. YONAPAVE estimates the effective Structural Number (SN) and the equivalent subgrade modulus independently of the pavement or layer thicknesses. Thus, there is no need to perform boreholes which are expensive, time consuming and disruptive to traffic. Knowledge of the effective SN and the subgrade modulus, together with an estimate of the traffic demand, allows for the determination of the overlay required to accommodate future needs. YONAPAVE simple equations can be solved using a pocket calculator, making it suitable for rapid estimates in the field. The simplicity of the method, and its independency of major computer programs, makes YONAPAVE suitable for estimating the structural needs of a road network using FWD data collected on a routine or periodic basis along the network roads. With increasing experience and confidence, YONAPAVE can be used as the basis for NDT structural evaluation and overlay design at the project level.