Miracle-dz/UniFAR-FTUs huggingface dataset:
Android App Testing: A Model for Generating Automated Lifecycle Tests

Android is currently the dominating OS in the market. An immense number of Android apps is deployed to the Google Play store every year. Android apps are no longer merely focusing on entertainment or socialization. In fact, the literature shows that apps specializing in critical domains such as health, education and even the military are growing in numbers. This puts more pressure on app developers to produce quality apps. Research shows that current Android app testing approaches rely heavily on manual testing. Research in automatic test generation for Android apps focuses mostly on automated GUI testing, with some approaches introducing model-based testing for test case inputs. However, no studies focus on generating lifecycle tests automatically, especially for testing lifecycle method conformance. In this research, we present a model-based solution approach as a tool to conduct assertion lifecycle tests automatically for Android activity lifecycle callback methods. Our objective is to build a framework to generate such a model. Finally, we evaluated our proposed framework in two ways. First using a group case study. Then we evaluate our work using 10 real world open source Android applications. The results of our evaluation are promising and show that our proposed framework is useful for detecting errors.
Ultrasonic detection of crack defects in pipe samples with a 132-channel test scanner in gas

Stress corrosion cracking (SCC) is a serious threat to gas pipelines that is challenging to detect with current in-line inspection technologies. Recently, the potential to detect SCC with Guided Ultrasonic Waves (GUW) using Halfwave's ART Scan® tool was successfully demonstrated in pressurized gas on real pipe samples. In this work, the ultrasonic detection of SCC is demonstrated by 3-meter-long pull-through experiments with a modified ART Scan® tool consisting of 132 broad-band transducer inside a pressure tank with pressurized gas at 60 bar as acoustic coupling medium. Mechanical translation and rotation from inside 4.6 meter long test spools allowed the acquisition of C-scans with resolutions down to $1.6\ \mathrm{mm}\times 0.2^{\circ}$. Acoustic signals were received in pitch-catch mode and processed to calculate different parameters like spectral power. Processed 2D scan results demonstrated the detection of crack regions and artificial notches. Resulting plots also demonstrated the sensitivity to undesired low-energy areas, of which some highly correlated with misalignment between the tool and the inner pipe wall. The crack sizing potential was successfully demonstrated by initial sizing algorithms applied to artificial notches that were machined one of the test pipes.
TOSNet: A Topic-Based Optimal Subnetwork Identification in Academic Networks

Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic-aware subnetwork identification is essential to discover potential researchers on particular research topics and provide quality work. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic.
Effects of Daily Stress in Mental State Classification

An external stimulus, event, or environment that stresses an individual is called a stressor. Many mental stress detection studies have been focused on the discrimination of the mental state with and without the experimental stressor. However, the mental state in the absence of experimental stressors may not represent accurately the nonstress (baseline) state because people inherently experience considerable stress in their daily lives. Therefore, we assumed that stress detection could be improved more accurately by considering the daily stress. In this study, functional near-infrared spectroscopy (fNIRS) was measured in 41 healthy participants to quantify their prefrontal cortical oxygenation during performing a cognitive task as an experimental stressor, considering individual daily stress level based on the self-report. We then extracted six signal features, including the slope, mean, standard deviation, peak, skewness, and kurtosis of the oxygenated hemoglobin concentration. Using various feature combinations and time windows, we successfully managed to classify daily stress (high/low) and the mental state (task/rest) with support vector machine classifiers. Specifically, individual daily stress level can be easily discriminated with signal features from fNIRS. Moreover, mental state classification performance improved significantly when the daily stress level was handled separately. The findings of this study show the feasibility of the fNIRS-based daily stress classification and can be used in the future to design a robust mental stress management system for the assessment of daily stress in individuals.
p53, ER, and Ki67 Expression in Canine Mammary Carcinomas and Correlation With Pathological Variables and Prognosis

Using immunohistochemistry, 170 canine mammary carcinomas were evaluated for p53, ER (estrogen receptor), and Ki67. Of the 170 tumors, 89 were grade I (52.3%), 36 were grade II (21.2%), and 45 were grade III (26.4%). Eight cases (0.5%) were positive for p53 and 69/170 cases (40.5%) were positive for ER. Ki67 values were 24 ± 18% (mean ± SD). Using a cutoff value of 33.3% Ki67-positive neoplastic nuclei, 38/159 (23.8%) were classified as high proliferative and 121/159 (76.2%) as low proliferative. p53-positive cases had significantly higher Ki67 expression and higher histological grade. ER expression was not correlated with p53 expression but was significantly related to low Ki67 values and low histological grade. Moreover, ER-positive cases had significantly longer survival compared to ER-negative tumors, and ER expression had better correlation with tumor-related survival than histological grade. In summary, p53 accumulated in a small subset of canine mammary tumors and was associated with higher proliferative activity and higher histological grade. ER expression was confirmed as a differentiation marker associated with more favorable prognosis and biological behavior. The combined use of these 3 markers could be used in addition to histological grade to predict the biological behavior of canine mammary carcinomas.
Chlamydia pecorum–Induced Arthritis in Experimentally and Naturally Infected Sheep

Chlamydia pecorum is an obligate intracellular pathogen with a wide host range including livestock such as sheep, cattle, goats, and pigs as well as wildlife species such as koalas. Chlamydial polyarthritis is an economically important disease resulting in swollen joints, lameness, stiffness, and weight loss in young sheep. In the present study, tissues from sheep experimentally or naturally infected with Chlamydia pecorum were assessed by histopathology and immunohistochemistry. Carpal, hock, and stifle joints as well as spleen, liver, kidney, lymph nodes, lung, and brain of 35 sheep from different inoculation groups were available. Two different C. pecorum strains (IPA and E58), different routes of administration (intraarticular or intravenous), UVA-irradiated IPA strain, and corresponding noninfected control groups were investigated. Similar investigations on tissues from 5 naturally infected sheep were performed. The most obvious inflammatory lesions were observed in synovial tissues and, notably, in the renal pelvis from the experimentally infected group and naturally infected animals. This resulted in chronic or chronic-active arthritis and pyelitis. Intralesional chlamydial inclusions could be demonstrated by immunohistochemistry in both tissues. Immunohistochemical evaluation of the presence and distribution of macrophages, T and B cells in synovial tissues revealed macrophages as the most prevalent inflammatory cell population. Previous observations indicated that C. pecorum isolates can infect circulating monocytes. Together with the finding of the histological lesions in synovial tissues and internal organs alongside the presence of C. pecorum DNA, these observations suggest chlamydial arthritis in lambs is the result of hematogeneous spread of C. pecorum.
Identification of novel step‐up regimen of intralesional triamcinolone acetonide in scalp alopecia areata based on a double‐blind randomized controlled trial

Although intralesional triamcinolone acetonide (TA) is the most commonly prescribed treatment for localized alopecia areata (AA), the literature regarding the optimal concentration for attaining better efficacy with the most acceptable side effects is scarce. To compare hair regrowth and local side effects of various concentrations of intralesional TA in scalp AA using clinical and dermoscopic parameters. A double‐blind randomized control trial with four treatment groups (10, 5, 2.5 mg/ml TA and normal saline [NS]) was conducted between March 2018 and August 2019. After recruitment, each AA patch was divided into quadrants and randomized before first injection. Injections were given and outcome parameters were analyzed every 4‐weekly till 12‐weeks. Statistical analysis was done by the R software employing generalized estimation equation. P‐value <. 05 was considered significant. Out of 105‐patients (168‐AA patches), 75‐patients (121‐patches) completed the study. Hair regrowth scale of all TA concentrations was better than NS group (P < .001). Other parameters such as quadrants with poor clinical response and dermoscopic disease activity signs were also favorable in TA groups in comparison to NS. However the evidence of atrophy and telangiectasia was maximum in 10 mg/mL group. 10 mg/mL TA showed a comparatively better response at the cost of increased adverse effects. Based on the clinical benefit and adverse risk assessment from our study, it may be better to start with 2.5 mg/mL intralesional TA in limited scalp AA patients. It can be implied that the concentration of TA can be increased as a step‐up regimen based on the serial clinical and dermoscopic response.
Online and On-Board Battery Impedance Estimation of Battery Cells, Modules or Packs in a Reconfigurable Battery System or Multilevel Inverter

This paper shows two approaches to determine the battery impedance of battery cells or battery modules when used in a reconfigurable battery system (RBS) or in any type of modular multilevel converter (MMC) for electric drive applications. A generic battery model is used and the concepts of the recursive time and frequency-domain parameter extraction, using a current step and an electrochemical impedance spectroscopy, are explained. Thus, it is shown and demonstrated that the balancing current of neighboring cells/modules ,when in parallel operation, can be used, similar to the time-domain parameter extraction utilizing a current step, to determine the battery parameters. Furthermore, it is shown and demonstrated that a part of the inverter can be used as variable AC voltage source to control a sinusoidal current through the motor inductances of the drive train, which can be injected to the inserted battery cells/modules of an adjacent phase to perform an on-board impedance spectroscopy. Using either of the two presented approaches, the individual battery impedances can be easily determined, yielding the state of health (SOH) and the power capability of individual battery cells/modules. Nonetheless, the analyzed approaches were just considered to be applied at machine standstill, which is not suitable for grid-tied applications.
History of Childhood Trauma and Association With Borderline and Dissociative Features

Abstract Both borderline personality features and dissociative symptoms have been associated with different types of childhood trauma. The aim of this investigation was to analyze to what extent emotional, physical, and sexual child maltreatment predict borderline personality features and dissociative symptoms. For this purpose, we analyzed data from 86 consecutively admitted patients who completed the Borderline Symptom List, the Childhood Trauma Questionnaire, and the German version of the Dissociative Experiences Scale for differential diagnosis of a borderline personality disorder. Hierarchical regression analyses revealed that borderline features were mainly predicted by emotional abuse, whereas pathological dissociation was best predicted by sexual and physical abuse. This evidence supports the hypothesis that different kinds of maltreatment may lead to different psychopathological symptoms in adulthood and should be taken into account in the therapy.
Analysis of Quantum Radar Cross-Section by Canonical Quantization Method (Full Quantum Theory)

This article investigates the difference between two quantum-based theories to calculate the radar cross-section (RCS). Quantum radar cross-section (QRCS) has been commonly analyzed using the dipole approximation method, and the related results show that it can improve the sidelobe of the interference pattern in contrast to the classical methods. This study, on the other hand, utilizes the canonical quantization (or microscopic) method, which is a more comprehensive theory than the dipole approximation method to calculate the radar cross-section. It is shown that there are some similarities between two methods; nonetheless, there are some crucial quantities and factors that have been ignored in the dipole approximation methods. The main difference arises due to the interaction Hamiltonian that two methods relied on. The theoretical calculation shows some critical points suggesting that the dipole approximation method cannot cover all aspects of the radar cross-section calculation. To verify the mentioned point, we establish a new method in which the radar cross-section is calculated by merging the quantum approach with the method of moment (MoM), called quantum-method of moment (QMoM). The simulation results show that the newly established method is in harmony with the canonical quantization method.
Site-Specific Evolutionary Rate Shifts in HIV-1 and SIV

Site-specific evolutionary rate shifts are defined as protein sites, where the rate of substitution has changed dramatically across the phylogeny. With respect to a given clade, sites may either undergo a rate acceleration or a rate deceleration, reflecting a site that was conserved and became variable, or vice-versa, respectively. Sites displaying such a dramatic evolutionary change may point to a loss or gain of function at the protein site, reflecting adaptation, or they may indicate epistatic interactions among sites. Here, we analyzed full genomes of HIV and SIV-1 and identified 271 rate-shifting sites along the HIV-1/SIV phylogeny. The majority of rate shifts occurred at long branches, often corresponding to cross-species transmission branches. We noted that in most proteins, the number of rate accelerations and decelerations was equal, and we suggest that this reflects epistatic interactions among sites. However, several accessory proteins were enriched for either accelerations or decelerations, and we suggest that this may be a signature of adaptation to new hosts. Interestingly, the non-pandemic HIV-1 group O clade exhibited a substantially higher number of rate-shift events than the pandemic group M clade. We propose that this may be a reflection of the height of the species barrier between gorillas and humans versus chimpanzees and humans. Our results provide a genome-wide view of the constraints operating on proteins of HIV-1 and SIV.
Thermoelectric Properties of InA Nanowires from Full-Band Atomistic Simulations

In this work we theoretically explore the effect of dimensionality on the thermoelectric power factor of indium arsenide (InA) nanowires by coupling atomistic tight-binding calculations to the Linearized Boltzmann transport formalism. We consider nanowires with diameters from 40 nm (bulk-like) down to 3 nm close to one-dimensional (1D), which allows for the proper exploration of the power factor within a unified large-scale atomistic description across a large diameter range. We find that as the diameter of the nanowires is reduced below d < 10 nm, the Seebeck coefficient increases substantially, as a consequence of strong subband quantization. Under phonon-limited scattering conditions, a considerable improvement of ~6× in the power factor is observed around d = 10 nm. The introduction of surface roughness scattering in the calculation reduces this power factor improvement to ~2×. As the diameter is decreased to d = 3 nm, the power factor is diminished. Our results show that, although low effective mass materials such as InAs can reach low-dimensional behavior at larger diameters and demonstrate significant thermoelectric power factor improvements, surface roughness is also stronger at larger diameters, which takes most of the anticipated power factor advantages away. However, the power factor improvement that can be observed around d = 10 nm could prove to be beneficial as both the Lorenz number and the phonon thermal conductivity are reduced at that diameter. Thus, this work, by using large-scale full-band simulations that span the corresponding length scales, clarifies properly the reasons behind power factor improvements (or degradations) in low-dimensional materials. The elaborate computational method presented can serve as a platform to develop similar schemes for two-dimensional (2D) and three-dimensional (3D) material electronic structures.
Arterial thrombosis after REBOA use in placenta accreta spectrum: a case series

Abstract Background The use of resuscitative endovascular balloon of the aorta (REBOA) is a useful strategy for bleeding control in placenta accreta spectrum (PAS) management. The incidence of complications associated with this procedure is variable. We report three cases of arterial thrombosis associated with REBOA, and we also analyze the factors that facilitated its occurrence. Case report Three women with PAS, presented common femoral and external iliac arterial thrombosis after REBOA use. Among the contributing factors probably associated with thrombosis, we identified the absence of ultrasound guidance for vascular access and the not using of heparin during aortic occlusion. Conclusions REBOA use is not exempt from complications and must be performed by experienced groups applying strategies to reduce the risks of complications.
Design of an Iteration-Reduced LDPC-CC Decoder Based on Compact Decoding Architecture

In recent years, the demand for low latency and high reliability in industrial have been increasing rapidly. To meet these requirements, we propose a new decoding architecture of low-density parity-check convolutional codes (LDPC-CC). The proposed decoding architecture reduces the distance of the neighboring processors by using the rationale of compact pipeline decoder. At the same time, the stopping rule is employed in the process of iteration and the weighting factor is added to reduce the error propagation caused by the update of the parity-check node, thus accelerating the convergence of the decoding. The results show that the proposed algorithm has about 0.3 dB gain compared with the on-demand variable node activation (OVA) algorithm when the bit rate error (BER) is 10–4 and maximum number of iterations is 10. Meanwhile, the initial delay and storage requirement are reduced by about 50% and the average number of iterations per decoded bit is also reduced largely.
Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study

Background Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care. Objective This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows. Methods We conducted semistructured interviews with 15 frontline emergency department physicians and rapid response team nurses who participated in the Sepsis Watch quality improvement initiative. Interviews were audio recorded and transcribed. We used a modified grounded theory approach to identify key themes and analyze qualitative data. Results A total of 3 dominant themes emerged: perceived utility and trust, implementation of Sepsis Watch processes, and workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by the perceived accuracy and utility of the model from personal program experience. Implementation of Sepsis Watch was facilitated by the easy-to-use tablet application and communication strategies that were developed by nurses to share model outputs with physicians. Barriers included the flow of information among clinicians and gaps in knowledge about the model itself and broader workflow processes. Conclusions This study generated insights into how frontline clinicians perceived machine learning models and the barriers to integrating them into clinical workflows. These findings can inform future efforts to implement machine learning interventions in real-world settings and maximize the adoption of these interventions.
Terrain Referenced Integrity Monitor for an Unmanned Aircraft Systems Precision Approach

This paper discusses the use of an on-board radar altimeter and Digital Elevation Model (DEM) for terrain referenced integrity monitoring during an Unmanned Aircraft Systems precision approach and landing. For operation of commercial UAS in the National Airspace System (NAS), a Performance Based Navigation (PBN) system applicable to all phases of flight is needed for unrestricted airspace and airport access. Methods, such as Radar Altimeter (RALT) Aiding, are currently being researched to create a Global Navigation Satellite System (GNSS) based navigation system with sufficient positional accuracy for a CAT-IIIc like precision approach and landing. While these systems have successfully addressed the vertical accuracy, horizontal accuracy, and vertical integrity requirements, further development for horizontal integrity is needed. This paper builds on previous work by the authors on radar altimeter augmentation to GNSS and discusses additional measures to improve the horizontal integrity of the navigation system. Performance of the proposed system is assessed in nominal and off-nominal conditions using data collected with a DC-3 aircraft at the Ohio University airport.