Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast frequency response analysis of large‐scale structures with non‐proportional damping

Modal frequency response analysis is an economical approach for large and complex structural systems since there is an enormous reduction in dimension from the finite element frequency response problem. However, when non‐proportional damping exists, the modal frequency response problem is expensive to solve at many frequencies because modal damping matrices are fully populated. This paper presents a new algorithm to solve the modal frequency response problem for large and complex structural systems with structural and viscous damping. The newly developed algorithm, fast frequency response analysis (FFRA) algorithm, solves the damped modal frequency response problem with O(m2) operations at each frequency. Then the FFRA algorithm is extended for solving a system of equations in optimization application with the modal correction approach, in which the mass, stiffness and damping matrices of a modified configuration differ from the original configuration. Numerical results show that the FFRA algorithm dramatically improves the performance of the modal frequency response analysis compared to conventional methods in industry while obtaining the same accuracy. Copyright © 2006 John Wiley & Sons, Ltd.
Current Trends in Electric Vehicle Charging Infrastructure; Opportunities and Challenges in Wireless Charging Integration

Nowadays, the imperative need for the reduction of Greenhouse Gas (GHG) emissions leads to the wider adoption of environmentally friendly transportation means. As a result, various policies underpinning the Electric Vehicle (EV) deployment are legislated globally, and several technical advances contributing to the electrification of the transportation sector are pursued. In this paper, a comprehensive overview of the current status of the infrastructure utilized for the realization of both conductive and contactless (wireless) charging of an EV battery is conducted. Furthermore, the issue of EV integration in conventional distribution networks, as well as in future power system architectures, is discussed in detail. Particular focus is given to wireless (i.e., inductive) charging. A detailed presentation of the respective standards and charging levels, as well as the magnetic couplers and the compensation network configurations, is carried out. Moreover, innovative concepts such as dynamic and quasi-dynamic wireless charging, as well as future challenges and opportunities, are presented and discussed. Finally, smart control and communication techniques applicable to EV charging are presented in the context of the future Internet of Energy (IoE) concept.
Relationships among strategically aligned performance indicators, controls, and performance

ABSTRACT This paper investigates if planning and cost controls and strategically aligned performance indicators (SAPI) are necessary and sufficient conditions to achieve a high level of organizational performance (OP). This article fills a gap in research by investigating elements of the management control system as necessary and sufficient conditions to achieve high levels of OP. Our findings show the reduced importance of planning controls and the great importance of aligning priorities and indicators to achieve high levels of performance. The paper is helpful for the practitioners that have to choose what kind of management controls are priorities to achieve high levels of performance. Management control frameworks are helpful for the literature and the practice. Still, the practitioners cannot implement the whole set of these components, considering the restriction of time and contingency aspects. The companies must choose what kind of management controls they have to implement, considering the goal of achieving performance. We used a quantitative methodology based on contingency theory in a survey of 89 Brazilian firms. The relationships were tested using partial least squares structural equations modeling (PLS-SEM), and necessary condition analysis (NCA) was applied to identify the management controls that are sufficient and necessary conditions for superior performance. The results of our study suggest that a high level of strategically aligned indicators is necessary to achieve a high level of performance. Results also suggest the importance of aligning strategic priorities with appropriated performance indicators, primarily defended in the normative (balanced scorecard) and empirical literature.
Potential association among posterior fossa bony volume and crowdedness, tonsillar hernia, syringomyelia, and CSF dynamics at the craniocervical junction in Chiari malformation type I

Objective The characteristic morphological parameters (bony posterior fossa volume (bony-PFV), posterior fossa crowdness, cerebellar tonsillar hernia, and syringomyelia) and CSF dynamics parameters at the craniocervical junction (CVJ) in Chiari malformation type I (CMI) were measured. The potential association between these characteristic morphologies and CSF dynamics at CVJ was analyzed. Methods A total of 46 cases of control subjects and 48 patients with CMI underwent computed tomography and phase-contrast magnetic resonance imaging. Seven morphovolumetric measures and four CSF dynamics at CVJ measures were performed. The CMI cohort was further divided into “syringomyelia” and “non-syringomyelia” subgroups. All the measured parameters were analyzed by the Pearson correlation. Results Compared with the control, the posterior cranial fossa (PCF) area, bony-PFV, and CSF net flow were significantly smaller (P < 0.001) in the CMI group. Otherwise, the PCF crowdedness index (PCF CI, P < 0.001) and the peak velocity of CSF (P < 0.05) were significantly larger in the CMI cohort. The mean velocity (MV) was faster in patients with CMI with syringomyelia (P < 0.05). In the correlation analysis, the degree of cerebellar tonsillar hernia was correlated with PCF CI (R = 0.319, P < 0.05), MV (R = −0.303, P < 0.05), and the net flow of CSF (R = −0.300, P < 0.05). The Vaquero index was well correlated with the bony-PFV (R= −0.384, P < 0.05), MV (R = 0.326, P < 0.05), and the net flow of CSF (R = 0.505, P < 0.05). Conclusion The bony-PFV in patients with CMI was smaller, and the MV was faster in CMI with syringomyelia. Cerebellar subtonsillar hernia and syringomyelia are independent indicators for evaluating CMI. Subcerebellar tonsillar hernia was associated with PCF crowdedness, MV, and the net flow of CSF at CVJ, while syringomyelia was associated with bony-PFV, MV, and the net flow of CSF at the CVJ. Thus, the bony-PFV, PCF crowdedness, and the degree of CSF patency should also be one of the indicators of CMI evaluation.
Assessing Area under the Curve as an Alternative to Latent Growth Curve Modeling for Repeated Measures Zero-Inflated Poisson Data: A Simulation Study

Researchers interested in the assessment of substance use trajectories, and predictors of change, have several data analysis options. These include, among others, generalized estimating equations and latent growth curve modeling. One difficulty in the assessment of substance use, however, is the nature of the variables studied. Although counting instances of use (e.g., the number of cigarettes smoked per day) would seem to be the best option, such data present difficulties in that the distribution of these variables is not likely normal. Count variables often follow a Poisson distribution, and when dealing with substance use in the general population, there is a preponderance of zeros (representing not using). As such, substance use counts may approximate a zero-inflated Poisson distribution. Unfortunately, analyses with zero-inflated Poisson random variables are not easily accommodated in many types of software and may be beyond access to most researchers. As such, an easier method would benefit researchers interested in assessing substance use change. The purpose of this study is to assess the area under the curve as an option when dealing with repeated measures data and contrast it to one popular method of longitudinal data analysis, latent growth curve modeling. Using a Monte Carlo simulation study with varying sample sizes, we found that the area under the curve performed well with different sample sizes and compared favorably to the performance of latent growth curve modeling, particularly when dealing with smaller sample sizes. The area under the curve may be a simpler alternative for researchers, especially when dealing with smaller sample sizes.
A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet

The aim of this study is to develop a computer-assisted solution for the efficient and effective detection of diabetic retinopathy (DR), a complication of diabetes that can damage the retina and cause vision loss if not treated in a timely manner. Manually diagnosing DR through color fundus images requires a skilled clinician to spot lesions, but this can be challenging, especially in areas with a shortage of trained experts. As a result, there is a push to create computer-aided diagnosis systems for DR to help reduce the time it takes to diagnose the condition. The detection of diabetic retinopathy through automation is challenging, but convolutional neural networks (CNNs) play a vital role in achieving success. CNNs have been proven to be more effective in image classification than methods based on handcrafted features. This study proposes a CNN-based approach for the automated detection of DR using Efficientnet-B0 as the backbone network. The authors of this study take a unique approach by viewing the detection of diabetic retinopathy as a regression problem rather than a traditional multi-class classification problem. This is because the severity of DR is often rated on a continuous scale, such as the international clinical diabetic retinopathy (ICDR) scale. This continuous representation provides a more nuanced understanding of the condition, making regression a more suitable approach for DR detection compared to multi-class classification. This approach has several benefits. Firstly, it allows for more fine-grained predictions as the model can assign a value that falls between the traditional discrete labels. Secondly, it allows for better generalization. The model was tested on the APTOS and DDR datasets. The proposed model demonstrated improved efficiency and accuracy in detecting DR compared to traditional methods. This method has the potential to enhance the efficiency and accuracy of DR diagnosis, making it a valuable tool for healthcare professionals. The model has the potential to aid in the rapid and accurate diagnosis of DR, leading to the improved early detection, and management, of the disease.
Performance Comparison of Conventional and AI based MPPT in Solar PV System

Obtaining Maximum power possible is a crucial task in PV systems. Various MPPT strategies based on classical and artificial intelligence methodologies have been proposed in the literature so far. Those approaches, on the other hand, have been tried on a variety of PV systems and in a variety of environments. This paper aims to provide a meticulous relative analysis of widely used AI based MPPT methods. The MPPT approaches that will be deliberated are: Artificial Neural Network (ANN), Perturb and Observe (P&O), Adaptive Neuro Fuzzy Inference System (ANFIS), Incremental Conductance (INC). In the same photovoltaic system, the developed MPPT controllers are assessed in the same weather conditions. The system is first modelled and then simulated in the MATLAB Simulink. Under the identical variable temperature and sun irradiance settings, simulations are run to confirm the accuracy of the suggested controllers. Finally, a comparative analysis is shown to evaluate the created methodologies based on performance. The concluding part contains recommendations on the validity of each MPPT approach that are believed to be valuable to researchers in the MPPT field.
Serum transthyretin and aminotransferases are associated with lean mass in people with coronary heart disease: Further insights from the CARE-CR study

Background Low muscle mass disproportionately affects people with coronary heart disease compared to healthy controls but is under-researched and insufficiently treated. Inflammation, poor nutrition, and neural decline might contribute to low muscle mass. This study aimed to assess circulatory biomarkers related to these mechanisms [albumin, transthyretin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and C-terminal agrin fragment] and their relationship with muscle mass in people with coronary heart disease. Our findings could be beneficial to indicate mechanisms of sarcopenia, detect sarcopenia, and evaluate treatment. Methods Serum blood samples from people with coronary heart disease were analysed for biomarker concentrations using enzyme-linked immunosorbent assays. Skeletal muscle mass was estimated using dual X-ray absorptiometry derived appendicular lean mass and reported as skeletal muscle index (SMI; kg m−2), and as a proportion of total body mass [appendicular skeletal mass (ASM%)]. Low muscle mass was defined as a SMI <7.0 and <6.0 kg m−2, or ASM% <25.72 and <19.43% for men and women, respectively. Associations between biomarkers and lean mass were adjusted for age and inflammation. Results Sixty-four people were assessed; 14 (21.9%) had low muscle mass. People with low muscle mass had lower transthyretin (effect size 0.34, p = 0.007), ALT (effect size 0.34, p = 0.008), and AST (effect size 0.26, p = 0.037) concentrations, compared to those with normal muscle mass. SMI was associated with inflammation-corrected ALT (r = 0.261, p = 0.039) and with inflammation- and age-adjusted AST/ALT ratio (r = −0.257, p = 0.044). Albumin and C-terminal agrin fragment were not associated with muscle mass indices. Conclusion Circulatory transthyretin, ALT and AST were associated with low muscle mass in people with coronary heart disease. Low concentrations of these biomarkers might indicate that low muscle mass is partially explained by poor nutrition and high inflammation in this cohort. Targeted treatments to address these factors could be considered for people with coronary heart disease.
HIV and COVID-19 Co-Infection: Epidemiology, Clinical Characteristics, and Treatment

The COVID-19 pandemic has been a global medical emergency with a significant socio-economic impact. People with HIV (PWH), due to the underlying immunosuppression and the particularities of HIV stigma, are considered a vulnerable population at high risk. In this review, we report what is currently known in the available literature with regards to the clinical implications of the overlap of the two epidemics. PWH share the same risk factors for severe COVID-19 as the general population (age, comorbidities), but virological and immunological status also plays an important role. Clinical presentation does not differ significantly, but there are some opportunistic infections that can mimic or co-exist with COVID-19. PWH should be prime candidates for preventative COVID-19 treatments when they are available, but in the setting of resistant strains, this might be not easy. When considering small-molecule medications, physicians need to always remember to address potential interactions with ART, and when considering immunosuppressants, they need to be aware of potential risks for opportunistic infections. COVID-19 shares similarities with HIV in how the public perceives patients—with fear of the unknown and prejudice. There are opportunities for HIV treatment hidden in COVID-19 research with the leaps gained in both monoclonal antibody and vaccine development.
Another Look at Projection Properties of Hadamard Matrices

Abstract Suppose a large number of factors (q) is examined in an experimental situation. It is often anticipated that only a few (k) of these will be important. Usually, it is not known which of the q factors will be the important ones, that is, it is not known which k columns of the experimental design will be of further interest. Screening designs are useful for such situations. It is of practical interest for a given k to know all the classes of inequivalent projections of the design into the k dimensions that have certain statistical properties, since it helps experimenters in selecting a screening design with favorable properties. In this paper we study all the classes of inequivalent projections of certain two-level orthogonal arrays that arise from Hadamard matrices, using well known statistical criteria, such as generalized resolution and generalized minimum aberration. We also pay attention to each projection's distinct runs. Results are given for orthogonal arrays with n = 16, 20 and 24 runs, when they are projected into small dimensions. Useful remarks on design selection are made based on the frequency of appearance of each projection design in every Hadamard matrix.
Apomictic Malus plants exhibit abnormal pollen development

Apomixis is the asexual reproduction through seeds that leads to the production of genetically uniform progeny. It has become an important tool in plant breeding because it facilitates the retention of genotypes with desirable traits and allows seeds to be obtained directly from mother plants. Apomixis is rare in most economically important crops, but it occurs in some Malus species. Here, the apomictic characteristics of Malus were examined using four apomictic and two sexually reproducing Malus plants. Results from transcriptome analysis showed that plant hormone signal transduction was the main factor affecting apomictic reproductive development. Four of the apomictic Malus plants examined were triploid, and pollen was either absent or present in very low densities in the stamen. Variation in the presence of pollen was associated with variation in the apomictic percentage; specifically, pollen was absent in the stamens of tea crabapple plants with the highest apomictic percentage. Furthermore, pollen mother cells failed to progress normally into meiosis and pollen mitosis, a trait mostly observed in apomictic Malus plants. The expression levels of meiosis-related genes were upregulated in apomictic plants. Our findings indicate that our simple method of detecting pollen abortion could be used to identify apple plants that are capable of apomictic reproduction.
A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.
Pre-immunotherapy radiotherapy enhanced the efficacy of multi-line sintilimab in unresectable advanced esophageal squamous cell carcinoma

Background The use of immunotherapy for the treatment of esophageal squamous cell carcinoma (ESCC) is gradually increasing. In this retrospective study, we evaluated the efficacy and explored potential factors of prognosis in multi-line sintilimab for unresectable advanced ESCC. Methods All pathological specimens were available from our Department of Pathology. We performed PD-L1 immunohistochemical staining of surgical or puncture specimens from 133 patients. We evaluated the efficacy of multi-line sintilimab and found potential factors according to multivariate analysis. We assessed the relationship between radiotherapy and immunotherapy, and according to whether patients had received radiotherapy within 3 months prior to immunotherapy, we attempted to analyze differences in progression-free survival (PFS) and overall survival (OS). Results A total of 133 patients were enrolled in this retrospective study between January 2019 and December 2021. The median follow-up was 16.1 months. All patients were treated with at least two cycles of sintilimab. Of all patients, a total of 74 experienced disease progression, with a median progression-free survival of 9.0 months (95% CI 7.701–10.299). We found that pre-immunotherapy radiotherapy was a possible predictor that affected the prognosis of multi-line sintilimab and that 3 months was a significant cutoff. A total of 128 patients (96.2%) had received radiotherapy prior to immunotherapy. Of those patients, 89 (66.9%) had received radiation therapy within 3 months prior to immunotherapy. PFS was considerably longer in patients who were treated within 3 months of radiotherapy than in patients who did not receive radiation therapy within 3 months of radiation therapy prior to immunotherapy (median progression-free survival 10.0 months [95% CI 8.030–11.970] vs. 5.0 months [95% CI 2.755–7.245]). Among all patients, the median overall survival was 14.9 months (95% CI 12.558–17.242). Overall survival was significantly longer in patients who had previously received radiotherapy within 3 months prior to immunotherapy than in those who had not (median overall survival 15.3 months [95% CI 13.724–16.876] vs. 12.2 months [10.001–14.399]. Conclusion Based on this retrospective study, sintilimab is a significant option for patients with unresectable advanced ESCC who have been previously treated, and pre-immunotherapy radiotherapy within 3 months enhanced the efficacy.
Design and Verification of Lateral Flight Control System for Fixed-wing Drone

We study the design of the lateral flight control system for fixed-wing drones and implement it in the autopilot’s waypoints navigation and course tracking of the flight. The autopilot uses AHRS to measure the three-axis attitude for the attitude stabilization flight mode and atmospheric sensors are used as the altimeter and airspeed meter to complete the longitudinal flight control system. Autopilot uses the course angle of GPS and the aileron of the drone for lateral control. After accomplishing the attitude, longitudinal and lateral flight control systems, autopilot and drone have the autonomous flight ability to designate waypoints according to the mission. It is shown that Infineon’s TC275 multi-core processor is the main chip of autopilot and an electric glider with a wingspan of 2.6 m and a take-off weight of 3 kg is used for the autonomous flight test bed.
Survey of DDoS Attack Detection Technology for Traceability

Target attack identification and detection has always been a concern of network security in the current environment. However, the economic losses caused by DDoS attacks are also enormous. In recent years, DDoS attack detection has made great progress mainly in the user application layer of the network layer. In this paper, a review and discussion are carried out according to the different detection methods and platforms. This paper mainly includes three parts, which respectively review statistics-based machine learning detection, target attack detection on SDN platform and attack detection on cloud service platform. Finally, the research suggestions for DDoS attack detection are given.