Miracle-dz/UniFAR-FTUs huggingface dataset:
High Temperature Accelerated Life Tests for GaInP/GaAs/Ge Solar Cells: Forward Versus Forward-Reverse Bias

This paper presents the comparison of two temperature Accelerated Life Tests (ALTs) for space commercial solar cells. The tests are carried out in dark conditions to circumvent the inherent problems of illumination ALTs. In the first ALT the solar cell working conditions were emulated by means of forward bias (that emulates the electric performance under illumination) while in the second ALT, forward bias was used together with reverse bias (that emulates the shadowing at eclipse periods or any other shadowing event such as for example the shadows cast by antennas) in order to reproduce more adequately the real operation conditions of solar cells. Forward to reverse time ratio is 4:1. Furthermore, the high temperatures used in the second ALT (190, 210 and 230 °C) allow a considerable time reduction of the test with respect to the first ALT (150, 165 and 180°C). The evaluation of the Arrhenius activation energy for an illumination power degradation of 10% was 0.88 eV in the forward bias test while 1.06 eV in the forward-reverse test. Reverse bias promotes similar degradation mode than forward one, namely parallel resistance reduction, but probably with a different degradation mechanism that degrades solar cell more intensely, thus reducing reliability which is more than 7 times shorter in the forward-reverse test than in forward one. Despite this reliability reduction, the time for a 90% reliability in the forward-reverse ALT is 32 years of continuous operation assuming a nominal temperature of 80°C which is typical of many GEO missions. The solar cells used in both ALTs were commercial GaInP/Ga(In)As/Ge triple junction and perform very robustly and exhibiting a high reliability for many space applications. It should be noted that these reliability figures are related to the degradation caused exclusively by high temperatures and other stressors like radiation are not considered here.
Towards Adaptive User Interfaces: A Model-Driven Approach for mHealth Applications Targeting Chronic Disease

The reclassification of previously fatal diseases and the ageing of the population have contributed to the prevalence of chronic diseases [1]. Self-management is crucial in managing chronic diseases [1], [2]. mHealth interventions have the potential to promote self-management by improving medication adherence and facilitating self-tracking capabilities [3]. However, researches show that those who could benefit the most from mHealth solutions tend to use them the least [4]. In order to scale up the deployment of mHealth applications, especially for patients with chronic diseases, it is necessary to design user-friendly systems that cater to the diverse needs of users [4]. However, several challenges must be considered in achieving this goal. First, chronic disease is a highly heterogeneous disease affecting patients in different ways (i.e., triggers, symptoms, severity varied) [5]. Therefore, patients may have diverse needs regarding self-management. Second, the user interface (UI) design should take into account the fact that the phases of chronic disease change over time [6]. Numerous chronic diseases either deteriorate over time if left untreated, while others may improve with proper care and management [7]. Furthermore, chronic diseases are often comorbid with other medical and/or psychopathological disorders, adding complexity to their management [6]. Third, chronic disease is typically long-lasting, often spanning a person's lifetime [8]. Therefore, it is crucial for mHealth technologies to maintain user engagement and motivation in the long run.
Secrecy Energy Efficiency Maximization for UAV-Enabled FDMA systems

In this paper, we consider an unmanned aerial vehicle (UAV), which serves multiple legitimate ground users with frequency division multiple access (FDMA) approach in the presence of a potential eavesdropper (EV) with uncertain location information. We also assume that UAV must also avoid flying over the no-fly-zone (NFZ). We aim to maximize the secrecy energy efficiency (SEE) by the robust joint design of the rotary-wing UAV three dimensional (3D) trajectory, bandwidth allocation, and transmit power. However, the optimization problem becomes challenging to solve optimally due to the non-convexity of the problem and the imperfect location information of the EV. We develop an efficient iterative algorithm to obtain its high-quality suboptimal solution. The numerical results indicate that our proposed joint optimization algorithm outperforms the benchmark schemes, resulting in significant enhancement of the energy efficiency of the system.
AIEPU at ALQAC 2023: Deep Learning Methods for Legal Information Retrieval and Question Answering

Given the widespread integration of Artificial Intelligence across various domains, the exploration of its applications in the legal field remains understudied, emphasizing the critical need for the development of effective deep learning approaches. This paper describes on our approaches to legal information retrieval (Task 1) and legal question answering (Task 2) in the Automated Legal Question Answering Competition (ALQAC 2023). Specifically, we employed the Paraformer model for information retrieval and leveraged large language models for answering questions. In the competition, we achieved the 1st place for Task 2. In this task, our findings demonstrate that fine-tuning appropriate prompts helps the large language models to achieve better performance. Our prompts are available at https://github.com/DucLong06/Legal-Prompts
Realizing Micro-patterned Laser-reduced Graphene for Antiviral Surfaces

In this work, graphene oxide (GO) was reduced by laser direct-writing (LDW) technology, a reduced graphene oxide (RGO) surface with micro-nano structures was fabricated by LDW. During the whole process, we can not only get pollution-free large areas RGO, but also control the RGO surface with micro-patterned grating structures. The antiviral performance against bovine viral diarrhea virus (BVDV) of micro-patterned RGO surface compared to GO surface and petri dish were evaluated. The micro-patterned RGO exhibited excellent antiviral property which can be used in hospitals and public to prevent the spread of virus infections.
Enhancing Resilience in Interconnected Cyber-Physical Power Networks

The article explores the growing dependence of the society on electricity and its pursuit of a low-carbon economy, which predetermines the need to explore and reveal opportunities to improve the security of expanding and changing electricity networks. The information and communication technologies (ICT) entering the electricity sector and the increasing generation capacity from RES requires a new approach to the security and reliability of the energy system in order to minimize risk factors. Particularly relevant, saturated with uncertainty now is the issue of the forthcoming integration into the energy system of information and telecommunications technologies. In this regard, some of the most significant factors are considered which, by using the available data for making adequate decisions, allow combining a proactive with a retroactive approach. To provide a reliable architecture for sharing data related to better observability and controllability of the network are considered some key, for the new energy systems, technological components.
Comparison of Pulse Compression Algorithm Implementations on Various Hardware Platforms

This paper presents the preparation and results of the comparison of two implementation methods for matched filtering (pulse compression) algorithm for air defense radar. The first method uses the General-Purpose computing on Graphics Processing Unit (GPGPU) technique in the form of the NVIDIA CUDA platform. The second method is based on computing with Intel's Central Processing Unit (CPU) and Advanced Vector Extension (AVX) instructions. Both ways have their own advantages and disadvantages and the purpose is to determine the possibilities of two implementation approaches.
AI-Enhanced Biomedical Antennas for 2mm Brain Tumor Detection Using Scattering, Admittance and Impedance Parameters: A Comparative Analysis

Automated detection and classification of brain abnormalities, such as tumors, is essential for understanding and tracking patient progress. In this study, advanced machine learning methods were applied to enhance the efficiency of a step-constant tapered slot antenna (STSA) for the detection and diagnosis of gliomas in the brain. The dataset used for our simulations, which were performed using the CST Microwave Studio software, consisted of tiny astrocytoma (with a tumor radius of 2 mm and a skin-tumor distance of 27 mm) and corresponding healthy controls. To simulate a human head, we employed a phantom model containing four antennas positioned orthogonally. Various machine learning algorithms were employed to identify and diagnose brain tumors. Throughout the extensive research, comprehensive analysis was conducted for the combined multimodal data, consisting of scattering (S), admittance (Y), and impedance (Z) parameters. The research findings revealed that the XGBoost algorithm, in combination with the combined S, Y, and Z parameters, achieved an impressive accuracy of 98.25%.
Automated Detection of Trajectory Groups Based on SNN-Clustering and Relevant Frequent Itemsets

Classification has been proposed for the automated detection of similarity groups in spatio-temporal data. However, recent approaches have introduced clustering based solutions to avoid the huge overhead for the manual classification of training and test data. This paper presents a combination of shared nearest-neighbor clustering and an adapted search for frequent itemsets to not only find similarity groups in sets of trajectories called team moves but also clusters of similar individual trajectories.Dynamic Time Warping is introduced as the underlying notion of trajectory distance on which the notion of trajectory similarity will be defined. Since the search for frequent itemsets is used to find similarity groups of team moves, an explicit distance criterion for team moves can be avoided. However, a notion of relevance is introduced that allows to distinguish trajectories with an impact on team moves from others. In addition, the paper will introduce enhanced quality indexes for shared nearest neighbor based trajectory clustering that allow to compare parameter settings in order to find the optimal clustering solution for a given problem.
Green AI Quotient: Assessing Greenness of AI-based software and the way forward

As the world takes cognizance of AI's growing role in greenhouse gas(GHG) and carbon emissions, the focus of AI research & development is shifting towards inclusion of energy efficiency as another core metric. Sustainability, a core agenda for most organizations, is also being viewed as a core non-functional requirement in software engineering. A similar effort is being undertaken to extend sustainability principles to AI-based systems with focus on energy efficient training and inference techniques. But an important question arises, does there even exist any metrics or methods which can quantify adoption of “green” practices in the life cycle of AI-based systems? There is a huge gap which exists between the growing research corpus related to sustainable practices in AI research and its adoption at an industry scale. The goal of this work is to introduce a methodology and novel metric for assessing “greenness” of any AI-based system and its development process, based on energy efficient AI research and practices. The novel metric, termed as Green AI Quotient, would be a key step towards AI practitioner's Green AI journey. Empirical validation of our approach suggest that Green AI Quotient is able to encourage adoption and raise awareness regarding sustainable practices in AI lifecycle.
Uplink and Downlink Communications Fusion for Enhanced Radar Sensing

This paper proposes reusing uplink (UL) and downlink (DL) communication signals for target sensing. In this scenario, the dual functional radar and communication (DFRC) base station (BS) listens to the DL backscattered signal on a dedicated frequency band, as well as the UL signals transmitted by communication users via a frequency-division duplex protocol. The DL backscattered signal is sensed through a mono-static radar scheme, whereas the UL backscattered signal naturally forms a bi-static one. We derive the maximum likelihood criterion of the sensing problem, and propose an efficient iterative method to solve it. Simulations show the promising potential of the hybrid radar model and the capability of the proposed method to reach the Cramér-Rao bound.
CoMSA: A Modeling-Driven Sampling Approach for Configuration Performance Testing

Highly configurable systems enable customers to flexibly configure the systems in diverse deployment environments. The flexibility of configurations also poses challenges for performance testing. On one hand, there exist a massive number of possible configurations; while on the other hand, the time and resources are limited for performance testing, which is already a costly process during software development. Modeling the performance of configurations is one of the solutions to reduce the cost of configuration performance testing. Although prior research proposes various modeling and sampling techniques to build configuration performance models, the sampling approaches used in the model typically do not consider the accuracy of the performance models, leading to potential sub-optimal performance modeling results in practice. In this paper, we present a modeling-driven sampling approach (CoMSA) to improve the performance modeling of highly configurable systems. The intuition of CoMSA is to select samples based on their uncertainties to the performance models. In other words, the configurations that have the more uncertain performance prediction results by the performance models are more likely to be selected as further training samples to improve the model. CoMSA is designed by considering both scenarios where 1) the software projects do not have historical performance testing results (cold start) and 2) there exist historical performance testing results (warm start). We evaluate the performance of our approach in four subjects, namely LRZIP, LLVM, x264, and SQLite. Through the evaluation result, we can conclude that our sampling approaches could highly enhance the accuracy of the prediction models and the efficiency of configuration performance testing compared to other baseline sampling approaches.
Multimodal Cyberbullying Detection Using Deep Learning Techniques: A Review

The rise of social networks and online communication, facilitated by internet accessibility and modern technology, has brought numerous benefits. However, it has also given rise to cyberbullying, a harmful phenomenon characterized by disclosing private information and posting hostile content to shame individuals. The repercussions of cyberbullying are severe, impacting victims' mental health, social lives, and personalities. Given the vast daily data uploads on social media, there is a pressing need for automated cyberbullying detection tools. This paper conducts a review of research in cyberbullying detection, encompassing both traditional machine learning and deep learning studies, spanning unimodal and multimodal approaches. The search involved major academic digital libraries like ACM Digital Library, IEEE Xplore Digital Library, and Springer Link, yielding 250 research articles. A selection process and redundancy checks followed, narrowing down the articles to 45 based on specific criteria: publication between 2019 and 2023, a focus on cyberbullying detection and related online risks like hate speech, use of English language data, and the development or introduction of cyberbullying detection algorithms. The sig-nificant contributions of the retained articles were identified, alongside future research directions. The paper also provides summaries of the datasets and algorithms employed. It concludes by highlighting ongoing challenges in the field to be addressed in the future.
An Empirical Study of Regression Testing for Android Apps in Continuous Integration Environment

Continuous integration (CI) has become a popular method for automating code changes, testing, and software project delivery. However, sufficient testing prior to code submission is crucial to prevent build breaks. Additionally, testing must provide developers with quick feedback on code changes, which requires fast testing times. While regression test selection (RTS) has been studied to improve the cost-effectiveness of regression testing for lower-level tests (i.e., unit tests), it has not been applied to the testing of user interfaces (UI) in application domains such as mobile apps. UI testing at the UI level requires different techniques such as impact analysis and automated test execution. In this paper, we examine the use of RTS in CI settings for UI testing across various open-source mobile apps. Our analysis focuses on using Frequency Analysis to understand the need for RTS, Cost Analysis to evaluate the cost of impact analysis and test case selection algorithms, and Test Reuse Analysis to determine the reusability of UI test sequences for automation. The insights from this study will guide practitioners and researchers in developing advanced RTS techniques that can be adapted to CI environments for mobile apps.
Development of a Software Application for Hearing Assessment

This study presents the development of algorithms that allow the automatic detection of the presence or absence of central auditory disorders (CAD), the degree and type of hearing loss, the effectiveness of the hearing aid, and the type of audiogram of pure tone audiometry (PTA) results in patients. A software application has been implemented in Python, integrating all the algorithms. This software will streamline and simplify the tasks of medical personnel, reducing the likelihood of errors and typos in the calculations.
A remaining useful life prediction method for gyroscope based on LSTM

Remaining useful life (RUL) prediction plays a key role in assuring the safety and reliability of a practical system, and it can further reduce the maintenance cost caused by needless downtime or failure. Gyroscope is the key component in the missile, satellite carrier or space exploration rock, hence the performance of the gyroscope directly determines the accuracy of navigation and guidance. This paper presents a RUL prediction method for gyroscope on the basis of the Long Short-Term Memory (LSTM) method. The time-series drift rate data are first preprocessed to be the input-output form, and then the processed data are utilized to train a LSTM model. Once the LSTM model is trained, it can be used to predict the drift rate of gyroscope, and the according failure time and the RUL will be determined. The feasibility of the proposed drift rate together with the RUL prediction method is verified by the simulated gyroscope drift rate data.
Spatial Clustering, Inequality and Income Convergence

This paper examines the relationship between spatial clustering and inequality at the county scale with overall state per capita income in the U.S. over the period 1969-2000. For each of the 48 coterminous states we examine measures of inequality and spatial clustering and explore how a state's overall income level may be influenced by, or influence, these measures. Our exploratory analysis utilizes the open- source package Space-Time Analysis of Regional Systems (STARS) to illustrate some new techniques for analyzing regional income dynamics. The results provide insight into the possible relationships between inequality, clustering and relative income levels, and generates a number of interesting avenues for future research.
ScholarWorks@UMass Amherst ScholarWorks@UMass Amherst

Peer-to-peer (P2P) accommodation rentals continue to grow at a phenomenal rate. Examining how this business model affects the competitive landscape of accommodation services is of strategic importance to hotels and tourism destinations. This study explores the competitive edge of P2P accommodation in comparison to hotels by extracting key content and themes from online reviews to explain the key service attributes sought by guests. The results from text analytics using terminology extraction and word co-occurrence networks indicate that even though guests expect similar core services such as clean rooms and comfortable beds, different attributes support the competitive advantage of hotels and P2P rentals. While conveniences offered by hotels are unparalleled by P2P accommodation, the latter appeal to consumers driven by experiential and social motivations. Managerial implications for hotels and P2P accommodation are provided.
A Miniaturized Transducer for Sonothrombolysis with Vortex Phase Modulation

Vortex ultrasound based on phase modulation techniques has become one of the most fascinating frontiers of therapy including sonothrombolysis. But the most frequently used acoustic vortex generating method, multi-elements transducer array with independent and distinct components, can only be utilized in a limited quantity, have expensive constructions and circuitry, and require rigorous calibration and tuning, which restricts their potential uses. Based on Spiral Phase Plate (SPP) principle and Digital Light Processing (DLP) bottom-up printing method, we propose a miniaturized transducer combined with an SPP lens to generate vortex ultrasound for the sake of low complexity, cheap fabrication, and tunable flexibility. In this work, a 5.1 MHz transducer of a 2 mm × 2 mm aperture was developed generating an acoustic vortex with a distinguished angular phase change gradient and acceptable acoustic pressure maximum of 0.7 MPa in 4.4 mm distance away at 100Vpp input.
Bessel Function-Apodized Beams Improve Axial Range for Pointwise Shear Elasticity Estimation with Double-Profile Intersection (DoPIo) Elastography

Double-profile intersection (DoPIo) ultrasound can quantitatively estimate the shear elasticity of tissue at positions on-axis to regions of focused acoustic radiation force impulse (ARFI) excitations. However, previous implementations of DoPIo suffered from poor precision in elasticity estimates away from the focal depth, limiting the spatial range where the technique may be deployed. We hypothesized that a planar ARFI excitation with a Bessel function apodization, a technique initially employed for nondiffractive shear wave generation, would improve the axial extent where DoPIo-based elasticity estimates may hold. We demonstrated a DoPIo acquisition based on a Bessel excitation in silico and compared its performance against an equivalent simulation based on a focused excitation. The use of Bessel push and track beams led to more accurate elasticity estimates over a larger axial range, with the greatest improvements in stiffness accuracy by Bessel versus focused beams occurring 5 mm below the linear transducer array’s elevational focus. Thus, DoPIo estimation of shear elasticity benefits from the use of Bessel-apodized push and track beams.