Miracle-dz/UniFAR-FTUs huggingface dataset:
THE OPTIMAL MAGNETIC FORCE FOR A NOVEL ACTUATOR COUPLED TO THE TYMPANIC MEMBRANE: A FINITE ELEMENT ANALYSIS

A new type of electromagnetic vibration transducer designed to be placed onto the tympanic membrane will be developed. Such an electromagnetic transducer should have the following characteristics: small in size, high-energy efficiency and suitable frequency bandwidth. In order to find out the optimal electromagnetic force and to predict the frequency-amplitude characteristics, a finite element middle ear biomechanical model was used to derive the optimal magnetic force of the actuator in this study. First, the electromagnetic transducer coupled to the ear drum was created by using a computer-aided design (CAD). Then the new coupled tympanic membrane-transducer complex was loaded to a 3-dimensional biomechanical model in the middle. The air gap between magnet and coil, input current and vibration force were calculated by finite element analysis simulation. In addition, gain and frequency response curves of the actuator were also calculated. Predicted displacement of tip of the malleus induced by the sound pressure of 80 dB SPL with different input currents are computed from the finite element model over the auditory frequency range of 100–8000 Hz as the force input into the ANSYS software. Simulated results show displacements of vibration are about 100 nm in the range from 100–1000 Hz and reduced when the frequencies are higher than 1000 Hz. Functional gains were about 20–25 dB across the 100 to 8,000 Hz frequency range.
Emphasised Research on Heart Disease Divination Applying Tree Based Algorithms and Feature Selection

Heart disease has evolved to become the most deadly ailment on the earth, and it has been the top reason for mortality worldwide. As a result, a dependable, efficient, and practical method for diagnosing and treating such disorders promptly is required. This study examines and compares several Machine Learning (ML) algorithms and approaches. Six ML classifiers are tested to see which one's the most successful at diagnosing heart disease. Tree-based techniques are among the most basic and extensively used ensemble learning approaches. According to the analysis, tree-based models such as Decision Tree (DT) and Random Forest (RF) deliver actionable insights with high efficacy, uniformity, and applicability. Relevant features are identified by using the Feature Selection (FS) process, and the output of classifiers is calculated based on these features. FS removes irrelevant features without impacting learning output. Our research intends to improve the system's efficiency. The goal of this research is to combine FS with tree-based algorithms to improve the accuracy of heart disease prediction.
Improved Ultrasonic Satellite System for the Localization of Mobile Robots

The localization of mobile robot in environment is a major concern in mobile robot navigation. So, many kinds of localization techniques have been researched for several years. Among them, the positioning system using ultrasound has received attention. Most of these ultrasonic positioning systems to synchronize the transmitters and receivers are used for RF (Radio Frequencies). However, due to the use of RF, the interference problems can not be avoided and the performance of radio frequencies directly affects the positioning performance. So we proposed the ultrasonic positioning system without synchronizing RF. The proposed system is based on existing USAT (Ultrasonic Satellite System) adopted infrastructure transmitting type, and consists of transmitter and receiver synchronizing modules instead of the radio frequency transmitters and receiver. The ultrasonic transmitters and receivers are synchronized individually by the transmitter and receiver synchronizing modules. In order to calculate the bias between the transmitter and receiver synchronizing modules, new positioning algorithm similar to GPS was proposed. The positioning performance of the improved USAT without synchronizing RF and the validity of the proposed positioning algorithm are verified and evaluated by experiments.
Resolvin D1‐loaded nanoliposomes promote M2 macrophage polarization and are effective in the treatment of osteoarthritis

Abstract Current treatments for osteoarthritis (OA) offer symptomatic relief but do not prevent or halt the disease progression. Chronic low‐grade inflammation is considered a significant driver of OA. Specialized proresolution mediators are powerful agents of resolution but have a short in vivo half‐life. In this study, we have engineered a Resolvin D1 (RvD1)‐loaded nanoliposomal formulation (Lipo‐RvD1) that targets and resolves the OA‐associated inflammation. This formulation creates a depot of the RvD1 molecules that allows the controlled release of the molecule for up to 11 days in vitro. In surgically induced mice model of OA, only controlled‐release formulation of Lipo‐RvD1 was able to treat the progressing cartilage damage when administered a month after the surgery, while the free drug was unable to prevent cartilage damage. We found that Lipo‐RvD1 functions by damping the proinflammatory activity of synovial macrophages and recruiting a higher number of M2 macrophages at the site of inflammation. Our Lipo‐RvD1 formulation was able to target and suppress the formation of the osteophytes and showed analgesic effect, thus emphasizing its ability to treat clinical symptoms of OA. Such controlled‐release formulation of RvD1 could represent a patient‐compliant treatment for OA.
Effect of stitching capacitor distance for critical traces crossing split reference planes

This paper examines the EMC effect of reference plane splits in modern-day printed circuit board (PCBs). Initially, the EMC impact of the reference plane split is quantified by measuring the gap voltage which exists under a high-speed signal line which crosses a reference plane split. This is accomplished through a variety of full-wave analyses. The effect on the digital signal integrity of the transmission line is also shown. Subsequently, a novel new circuit based treatment is introduced which significantly reduces the ill effects of the discontinuity. It is also demonstrated that circuit based tools, as opposed to full-wave tools, can provide accurate analyses of these effects if the recommended discontinuity treatment is employed.
Gradient Conventional Recursive Neural Classifier Algorithm to Analyze the Malicious Software Detection Using Machine Learning

Malware manual analysis still requires formula rules to verify that malicious samples are considered suspicious. Find the source of their software and malware as part of the code anatomy. To solve the security problem of the malware caused by the Android operating system, an efficient hybrid detection scheme is proposed for Android malware as the previous methods have not been efficient enough to detect advanced malware to limit/prevent damage. Machine learning technology provides the main novelty with high efficiency and low overhead. To verify that, this proposed gradient Conventional Recursive Neural Classifier (GCRNC) algorithm is feasible and many extensive malware data sets have been tested to prove its efficacy. The method has been classified into three stages: preprocessing, feature selection, and classification. The first preprocessing stage is based on Count Vectordistributionused to remove and extract the file types from the specified data set. Before classification, the feature is selected using the Adaboost Random Decision Tree Selection (ARDTS) method. The dataset uses are established to train first, and it is used with the expert weight assigned to each attribute by the domain expert. The rules are established based on the absolute rights assigned to this organization. The value of each selected feature is extracted and stored with the corresponding category label. The values are established based on the absolute rights assigned to this organization. A classification algorithm based on Gradient Conventional Recursive Neural Classifier (GCRNC) has been proposed to improve the achieved functional classification performance by only contributing to the effective classification process useful to classifying android malicious software datasets.
Association of tracks from over the horizon radar

In Over-The-Horizon Radar (OTHR), multiple ionospheric layers cause several tracks per target to be observed. This effect causes ambiguities in target identification and track coordinate registration. A pattern classification approach is proposed for associating tracks. Neural networks and statistical methods are applied to combine track affinities and associate pairs of tracks. The proposed approach provides a practical and efficient way of dealing with multiple track association when the number of targets is large and optimal. Bayesian hypothesis testing is not practical.
Hyperparameters optimization of neural network using improved particle swarm optimization for modeling of electromagnetic inverse problems

Abstract Optimization of hyperparameters of artificial neural network (ANN) usually involves a trial and error approach which is not only computationally expensive but also fails to predict a near-optimal solution most of the time. To design a better optimized ANN model, evolutionary algorithms are widely utilized to determine hyperparameters. This work proposes hyperparameters optimization of the ANN model using an improved particle swarm optimization (IPSO) algorithm. The different ANN hyperparameters considered are a number of hidden layers, neurons in each hidden layer, activation function, and training function. The proposed technique is validated using inverse modeling of two meander line electromagnetic bandgap unit cells and a slotted ultra-wideband antenna loaded with EBG structures. Three other evolutionary algorithms viz. hybrid PSO, conventional PSO, and genetic algorithm are also adopted for the hyperparameter optimization of the ANN models for comparative analysis. Performances of all the models are evaluated using quantitative assessment parameters viz. mean square error, mean absolute percentage deviation, and coefficient of determination (R2). The comparative investigation establishes the accurate and efficient prediction capability of the ANN models tuned using IPSO compared to other evolutionary algorithms.
Modeling and Experimental Investigation on Vehicle Active Suspension Electromagnetic Actuator

A new type of electromagnetic linear actuator for vehicle active suspension was designed, with the characteristics of large stroke and force and fast response. Combined with the finite element analysis software, the finite element model of the electromagnetic actuator was built up. Based on the simulation results, a prototype of electromagnetic linear actuator with appropriate parameters was developed. And the electromagnetic force test of the actuator was done. By comparison of the finite element simulation results and the experimental data, it shows the corresponding results are consistent, indicating that the design and analysis of the electromagnetic actuator is correct.
Vehicle State Detection in Highway Intelligent Space Based on Information Fusion

Intelligent space was established in the highway area in order to detect vehicles and their state information using multi-sensor information fusion. A vehicle location and speed detection method with fixed cameras was proposed by applying Pin-hole imaging model and the principle of Coordinate Transformation; vehicle state information of multi-sensors was fused by the decision method of basic belief function, on the basis of D-S evidence theory in feature-level fusion. Finally, a data acquisition experiment of vehicle state information was carried out by using multi-sensor combination of fixed cameras and ground magnetic induction loops. Then, the collected vehicle state information of these two kinds of sensors was fused and analyzed. Results showed that the accuracy of this fusion method is 5% higher than the fixed cameras. Therefore, the information fusion can reduce the uncertainty of each sensor effectively and improve the detection accuracy of the vehicle state information in highway intelligent space.
Study on Weld Seam Tracking System Based on Laser Vision Sensing

Seam tracking is a basic condition to ensure a fine welding shape. In order to meet this requirement, the researchers have put forward a variety of sensors and some of them have been applied to practice successfully, such as contact sensor, arc sensor and vision sensor. Laser vision sensor has been proven to be one of the most successfully technology for its many advantages. In this paper, based on laser visual sensing technology, a seam tracking system is established with an embedded microcontroller LPC1768. With this system, a real-time tracking algorithm is proposed and the seam tracing process is realized under a smaller error. Finally, the tracking errors resources and their influence have been simulated and analyzed.
The Introduction and Application of Cohesive Zone Model on Asphalt Concrete Fracture Behavior

The cohesive zone model (CZM) is being increasingly used to simulate fracture and fragmentation processes in metallic, polymeric, and ceramic materials and their composites. The CZM regards fracture as a gradual phenomenon in which separation takes place across an extended crack tip. This paper introduces the concept of CZM, the constitutive relations of CZM, the influence of the shape of the interface law and up-to-date applications of CZM to bituminous mixtures and pavement structures. Furthermore, some current challenges and the future directions to the modeling of fracture in bituminous materials and pavements are briefly discussed.
Chronobiological activity of cysteinyl leukotriene receptor 1 during basal and induced autophagy in the ARPE-19 retinal pigment epithelial cell line

Autophagy is an important cellular mechanism for maintaining cellular homeostasis, and its impairment correlates highly with age and age-related diseases. Retinal pigment epithelial (RPE) cells of the eye represent a crucial model for studying autophagy, as RPE functions and integrity are highly dependent on an efficient autophagic process. Cysteinyl leukotriene receptor 1 (CysLTR1) acts in immunoregulation and cellular stress responses and is a potential regulator of basal and adaptive autophagy. As basal autophagy is a dynamic process, the aim of this study was to define the role of CysLTR1 in autophagy regulation in a chronobiologic context using the ARPE-19 human RPE cell line. Effects of CysLTR1 inhibition on basal autophagic activity were analyzed at inactive/low and high lysosomal degradation activity with the antagonists zafirlukast (ZTK) and montelukast (MTK) at a dosage of 100 nM for 3 hours. Abundances of the autophagy markers LC3-II and SQSTM1 and LC3B particles were analyzed in the absence and presence of lysosomal inhibitors using western blot analysis and immunofluorescence microscopy. CysLTR1 antagonization revealed a biphasic effect of CysLTR1 on autophagosome formation and lysosomal degradation that depended on the autophagic activity of cells at treatment initiation. ZTK and MTK affected lysosomal degradation, but only ZTK regulated autophagosome formation. In addition, dexamethasone treatment and serum shock induced autophagy, which was repressed by CysLTR1 antagonization. As a newly identified autophagy modulator, CysLTR1 appears to be a key player in the chronobiological regulation of basal autophagy and adaptive autophagy in RPE cells.
THE EVOLUTION OF KNOWLEDGE IN FOREST PRODUCTS MANUFACTURING

The manuscript is conceptual; it assesses the use of real-time process data by forest products manufacturers as related to data discovery and the co-evolutionary existence of data mining inductive algorithms. Forest products companies have intellectual latency concerning process variation and such latency leads to higher than necessary costs of manufacturing, inferior product quality, and inaccurate prediction. Strength properties from the laboratory are unreliable for real-time prediction given their historic temporal orientation. Induction of real-time process data when aligned with destructive test data from the laboratory improves manufacturing latency and results in discovery of unknown sources of process variation. This discovery of unknown sources of process variation allows for quantification of variation and defines the relationship with product attributes which improves accuracy of predictions. The manuscript outlines the importance of the real-time relational database and data quality verification as essential steps in the data discovery journey. These essential steps are often overlooked and lead to antidotal analyses that limit process improvement and predictability. Statistical methods for inductive data mining are discussed, e.g., multiple linear regression analysis (MLR), regression trees (RT), principal component analysis (PCA), and quantile regression. The strengths and weaknesses of each method with appropriate citations are noted.
Revised design criteria for stormwater facilities to meet pollution reduction and flow control requirements, also considering predicted climate effects

There is a need to revise existing design methods for stormwater pollutant treatment, flow transport and detention facilities. The aim is to increase the accuracy in predicting the performance compared with design only based upon areal and volumetric functions and to optimize design by considering more site-specific data, receiving water quality criteria and forecasted climate effects. During the latest years, flow proportional concentration data from in- and outlets from wet ponds and constructed wetlands, have been compiled. Furthermore, other kind of data from the specific facilities have been compiled, such as areas, volumes, proportion of vegetation, outlet design details and length:width ratio. The parameters are used to revise design methods and are implemented in the operative stormwater and recipient software model StormTac. Design criteria and parameters for calculating design flow and sizing required detention volume are also presented. The climate effects on some of the studied parameters, e.g. design flow and inlet concentration, are discussed. The paper presents the climate factor based upon the hypothesis that it is a function of the design rain duration and reoccurrence time.
An accelerometer sensor finite element analysis and design based on fiber Bragg grating

A three-axis fiber Bragg grating for the measurement of acceleration is reported. Results obtained using intensity modulation-direct detection (IM-DD)method demonstrate a demodulated signal output range of 1.87Hz~80Hz.Modal analysis and harmonic analysis are performed utilizing finite element analysis method. The strain change is proportional to the acceleration. The sensitivity is 55. 3με/g. Low cross-axis sensitivity is obtained. Arrays of such devices can be utilized in a variety of applications such as structural monitoring.
Novel Mutation in the Acetohydroxyacid Synthase (AHAS), Gene Confers Imidazolinone Resistance in Chickpea Cicer arietinum L. Plants

Chickpea (Cicer arietinum L.) is an important crop in crop-rotation management in Israel. Imidazolinone herbicides have a wide spectrum of weed control, but chickpea plants are sensitive to acetohydroxyacid synthase (AHAS; also known as acetolactate synthase [ALS]) inhibitors. Using the chemical mutagen ethyl methanesulfonate (EMS), we developed a chickpea line (M2033) that is resistant to imidazolinone herbicides. A point mutation was detected in one of the two genes encoding the AHAS catalytic subunit of M2033. The transition of threonine to isoleucine at position 192 (203 according to Arabidopsis) conferred resistance of M2033 to imidazolinones, but not to other groups of AHAS inhibitors. The role of this substitution in the resistance of line M2033 was proven by genetic transformation of tobacco plants. This resistance showed a single-gene semidominant inheritance pattern. Conclusion: A novel mutation, T192I (T203I according to Arabidopsis), providing resistance to IMI herbicides but not to other groups of AHAS inhibitors, is described in the AHAS1 protein of EMS-mutagenized chickpea line M2033.
Association of Hypertriglyceridemic Waist Phenotype with Obstructive Sleep Apnea: A Cross-Sectional Study

Objective Hypertriglyceridemic waist (HTGW) phenotype is an independent risk factor for metabolic disorders. Although obstructive sleep apnea (OSA) is associated with metabolic disorders, it is unclear whether there is an association between HTGW phenotype and OSA. Methods We enrolled consecutive participants presenting to a sleep center in Shanghai, China. Full-night polysomnography was performed, and serum triglyceride (TG) levels and waist circumference (WC) were calculated. HTGW phenotype was defined as increased WC (men > 90 cm, women > 80 cm) and elevated TG levels (> 1.7 mmol/L). Participants were classified into four groups: normal TG with normal WC (NTNW); normal TG with increased WC (NTGW); elevated TG with normal WC (HTNW); and elevated TG with enlarged WC, namely HTGW. The relationships between HTGW phenotypes and OSA were assessed using binary (apnea hypopnea index, [AHI]≥5/hr) and multinomial logistic regression analyses (clinical AHI severity categories). Results We included 3190 participants in this cross-sectional study. Compared to the NTNW phenotype, participants with NTGW and HTGW phenotypes had the significantly higher risk of OSA (AHI ≥5/hr, odds ratio [OR] = 2.51, 95% confidence interval [CI] = 1.91–3.31; OR = 3.76, 95% CI = 2.67–5.31, respectively), after adjustment for confounders. In subgroup analyses categorised by age, sex, and BMI, the aforementioned associations remained significant. The association between the NTGW, HTGW phenotype and OSA risk remained significant across the OSA severity groups. Multinomial logistic regression also revealed that the mild (OR = 1.63, 95% CI = 1.07–2.50), moderate (OR = 1.875, 95% CI = 1.22–2.88), and severe OSA (OR = 3.18, 95% CI = 2.14–4.73) were associated with HTGW phenotype. Conclusion Both NTGW and HTGW phenotype were positively associated with OSA risk in all subgroups. Further longitudinal follow-up studies are needed to determine the causal link and prognostic role of these metabolic factors.
A time-series probabilistic preventive maintenance strategy based on multi-class equipment condition indicators

Abstract This paper focuses on the condition-based maintenance of a high gravity reactor, which is used to process the emissions from the production process in petrochemical industries. Three indicators, i.e. the current, temperature, and amplitude, are monitored together to indicate whether the equipment has failed. Based on these indicators, this paper focuses on the cumulative probability of equipment operating indicators corresponding to the empirical distribution function and calls it the p-value. To improve the prediction accuracy, a time series preventive maintenance strategy based on the appropriate p-value is designed to determine whether each indicator exceeds the threshold. Then, considering various indicators comprehensively, a method aiming to reduce and minimize the costs is proposed. To test the robustness of the method, the results of the prediction accuracy and the cost reduction level based on different threshold values are given. The empirical application shows that the method can judge whether each indicator exceeds the threshold under a threshold change. Moreover, as the threshold increases, the advantages of the probabilistic approach are more obvious. Compared with the conventional strategy, the probabilistic approach can greatly reduce the costs.