Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization by Signals of Opportunity in the Absence of Transmitter Position

Localization of an object by signals of opportunity may not have the transmitter position and the synchronization with the transmitter. Additionally, a receiver may not be able to resolve the separate time delays of the line of sight signal from the transmitter and the back-scattered signal by the object, and it is only able to obtain the difference between the two. This paper investigates the use of such differential time delays (DTDs), one from each receiver by itself without the need of synchronization with the others, for positioning the object; and shows that localization is possible in absence of transmitter position. The performance is not necessarily worse than, and indeed can be better than, the TDOA approach that can operate without the transmitter position but dictates receiver synchronization. We then propose the use of both DTDs and TDOAs for improving the positioning accuracy and analyze the expected performance. Algebraic solutions are next derived for localization by DTDs, and by DTDs together with TDOAs. Theoretical study illustrates that the solutions attain the Cramer-Rao Lower Bound accuracy under the Gaussian noise model. A refinement step is also developed to extend the noise level for matching with the CRLB performance.
During and After Study Abroad Period: Perception and Attitudes on English Development and Maintenance

This study examines the development and maintenance of English skills from the perspective of higher education alumni during and after studying abroad. The researcher consider attitudes, more specifically how, what, and to what extent attitudes could encourage development and maintenance in the context of study abroad from the 120 respondents’ perspective. This study deploys quantitative data collection in form of a survey with both multiple choice items and open-ended questions. The numeric items were analyzed with tabulation and the open-ended questions responses were analyzed with the stages of Thematic Analysis. Foremost, the findings reveal that the respondents perceive oral production and fluency as the major skills that improved during the study abroad period, followed by an increase in vocabulary knowledge and listening skills. Most respondents regarded the improvement as an influence of high exposure to spoken English. Secondly, the majority of respondents conceive that their English keeps improving after the study abroad period. The most frequent reasons for using English are social life and entertainment purposes, which are mostly individual preferences, followed by academic purposes such as joining a course or working on journal articles. Work purpose, surprisingly, is not the main reason of using English after study abroad period. The conclusion is that study abroad benefits the respondents in developing their English and that attitude on affective domain (how we perceive a language) and conative (behaviour toward a language) help to maintain the cognitive domain (knowledge on a language) and its maintenance.
Mn(II) Quinoline Complex (4QMn) Restores Proteostasis and Reduces Toxicity in Experimental Models of Huntington’s Disease

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disorder, of the so-called minority diseases, due to its low prevalence. It is caused by an abnormally long track of glutamines (polyQs) in mutant huntingtin (mHtt), which makes the protein toxic and prone to aggregation. Many pathways of clearance of badly-folded proteins are disrupted in neurons of patients with HD. In this work, we show that one Mn(II) quinone complex (4QMn), designed to work as an artificial superoxide dismutase, is able to activate both the ubiquitin-proteasome system and the autophagy pathway in vitro and in vivo models of HD. Activation of these pathways degrades mHtt and other protein-containing polyQs, which restores proteostasis in these models. Hence, we propose 4QMn as a potential drug to develop a therapy to treat HD.
Fast Sequence Repetition Node-Based Successive Cancellation List Decoding for Polar Codes

Compared with the bit-wise successive cancellation list (SCL) decoding of polar codes, the node-based Fast SCL decoding significantly reduces the decoding latency by identifying special constituent codes and decoding these in parallel. To further reduce the latency of current Fast SCL decoders, we first propose a fast sequence repetition (SR) node-based SCL (Fast SR-SCL) decoding algorithm, which only involves one type of node in the SCL decoding tree. Furthermore, we employ the adaptive path splitting (APS) strategy to terminate the path splitting in the SR node early, without degrading the error-correcting performance. Numerical results show that for 5G uplink codes with a length of 1024 and rates of 1/4, 1/2, and 3/4, our decoder can deliver the same decoding performance while reducing the average latency by 34.5%, 38.0%, and 39.6% compared with the state-of-the-art Fast SCL decoder for a list size L = 8.
A new mathematical model for analytical risk assessment and prediction in IT systems

Abstract: In this paper, we propose a new formal model to describe risk analysis and measurement process for IT systems. Our model complies with international standards and recommendations for non-profit organisations. The model accounts for solutions used in widely known and recommended risk analysis methods and provides for evaluation of efficacy of these solutions. A simple example illustrates the application of the proposed model for effective risk analysis of any IT system.
Effectiveness of High Intensity Hilthera 4.0 Laser Treatment on Patients with Plantar Fasciitis

Plantar fasciitis is one of the most common causes of heel pain. Conservative lines of treatment include non-steroidal anti-inflammatory drugs, corticosteroid injections, Physiotherapy, stretching exercises and using heel pads or orthotics. The purpose of the study is to investigate the effects of high intensity Hilthera 4.0 laser treatment on pain and functional capacity in patients with acute plantar fasciitis. Total 30 participants were randomly recruited and were evaluated before the first treatment session and after 8 sessions of treatment with Hilthera 4.0 Laser. Treatment Parameters are frequency (Hz) 25, one shot emission (MJ) 500, and a total energy 3000W for 15 minutes each session. Outcome measures are VAS score for measuring Pain and Foot Function Index score for measuring Foot Function. IBM SPSS version 16.0 software was used for statistical analysis. Paired sample t-tests are used to measure the pre and post test results. Results shows that there is a significant difference in VAS Score and Foot Function Score pre and post treatment with ‘p’ value < 0.05 when Hilthera 4.0 laser is used for pain and Foot function in patients with Plantar Fasciitis. In conclusion Hilthera 4.0 Laser Therapy is effective in treating pain and Functional limitation in patients with Plantar Fasciitis. KEY WORDS: Plantar Fasciitis, Hilthera 4.0 Laser Therapy, VAS Score, Foot Function Index Score
Safety Management in Substation Project Construction Based on Advanced Internet of Things and Smart Sensors Technologies

The infrastructure construction of the power grid is a key part of the national power supply. Power construction is characterized by a complex environment, including many personnel and equipment, and cross-industry multi-unit collaboration. Therefore, it is difficult to achieve precise safety management in substation project construction (SPC). To improve the safety management and control of SPC, a security management and control system is proposed and realized based on the advanced Internet of Things (IoT) and smart sensors technologies. Based on the characteristics of the SPC, the analysis of the construction process of the safety management system is carried out. The operation parameters are sampled by smart sensors, and uploaded by industrial gateway devices and IoT communication devices. The monitoring data would be analyzed by artificial intelligence algorithms and the results would be shown in the building information modeling (BIM) map. Finally, a management and control system covering monitoring, early warning and alarming has been constructed. The application of smart construction site systems is also discussed in the safety management and control of SPC. Combined with the application of the actual field system, the rationality of the safety management and control system is verified.
Percutaneous ultrasound-guided drainage of pneumomediastinum through the retropharyngeal space: a case report

Percutaneous ultrasound-guided drainage of pneumomediastinum through the retropharyngeal space: a case report Si Qin, Xu-Tao Lin, Yi-Min Wang, Yao Chen, Rui Cui and Guang-Jian Liu * Department of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China, Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China and and Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
A Modification of the HP Filter Aiming at Reducing the End-Point Bias

It is well known that the Hodrick-Prescott (HP) filter suffers from an end-point bias. This is problematic when the filter is used recursively for economic policy (in this case the end-point is the point of interest). The usual way to deal with this problem is to extend the series with ARIMA forecasts. However, the usefulness of this approach is limited by the quality of the forecast. This is why the present paper explores an alternative way to deal with the end-point bias which does not use forecasts: the penalty function that the HP filter minimizes is modified in order to reduce (however, without eliminating) the difference in treatment of the end-point compared to other points; this yields a modified HP filter. It is shown that (compared to the usual HP filter) the modified HP filter (recursively applied) has the following properties. i) The end-point bias is reduced. ii) The business cycle component is bigger; the modified HP filter is in fact approximately equivalent to another modification of the HP filter: increasing the gap between trend and data by a constant (43% when the smoothing parameter is 100). iii) The amplitude response of the modified HP filter is closer to the one of the ideal filter, but there is a phase shift.
Energy-Efficient Reconfigurable 4|16|64|256-QAM Transmitter Based on PAM2|4-Driven Optical DACs

We propose, model and optimize an optical Digital to Analog Converter (oDAC) significantly improving the tradeoffs between spectral-, energy- and footprint- efficiencies, based on modifying our recently introduced multi-parallel oDAC, by adopting Multi-Level-Driven modulation for the individual Mach-Zehnder Modulators (MZM) in the parallelized paths. The most compact instance of this improved oDAC structure coherently combines two parallel optical paths, each comprising an MZM electrically driven by either PAM2 or PAM4. This oDAC is flexibly reprogrammable to generate ultra-high-speed, energy-efficient, optical bipolar PAM-<inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$C=2$ </tex-math></inline-formula>,4,8,16. Two such oDAC units are IQ-nested to generate 4|16|64|256-QAM constellations.
Distributed Approach to Adaptive SDN Controller Placement Problem

In software defined networking (SDN), a controller may manage several SDN switches to be cost-effective while a switch may demand management service from multiple controllers for fault tolerance. The controller placement problem (CPP) is to determine the locations of SDN controllers to minimize the total deployment cost subject to constraints such as controller-switch latency, inter-controller latency, and controller capacity. This problem is challenging especially in interconnected geo-distributed SDN networks. Existing centralized solutions do not well adapt to network dynamics. This paper proposes several distributed mechanisms based on the exact potential game. These mechanisms dynamically adapt to network faults such as link and controller failures. The simulation result shows that these mechanisms need fewer controllers than an existing approach in static networks. When links or controllers may fail, our mechanisms still perform better while only a part of the network nodes is affected. The latter is impossible in non-adaptive approaches.
An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change

In this article, the principles of the gray model and BP neural network model are analyzed, and the characteristics of land-use change and spatial and temporal distribution are studied in-depth, and at the same time, to explore the influence of land-use change on ESV, the relationship between the two is analyzed using gray correlation degree, and a mathematical model is constructed to maximize the benefits of the regional system, coupling economic and ecological benefits, combined with Geo SOS-FLUS model to achieve the optimization of land use. This article constructs a combined prediction model of a gray neural network. The gray differential equation parameters correspond to the weights and thresholds of the neural network, and the optimized parameters are determined by training the neural network to make it stable. Then the training results of the BP neural network are fitted with the results obtained from the gray GM (1.1) model. Finally, the prediction results of the three models, gray GM (1.1), BP God Meridian, and gray neural network model, are compared and analyzed. The global spatial autocorrelation and local spatial aggregation patterns of regional soil erosion and its erosion factors are analyzed using the Exploratory Spatial Data Analysis (ESDA) method in spatial measurement theory.
Effects of Casein Phosphopeptide-Selenium Complex on the Immune Functions in Beagle Dogs

Simple Summary Although the functions of casein phosphopeptide (CPP) and selenium (Se) as immunomodulators have been demonstrated, research on the regulation of pets’ immunity in the form of an organic combination of these two substances have yet to be reported, particularly in dogs. The purpose of this research was to look into the effects of the casein phosphopeptide-selenium complex (CPP-Se) on dog immunity. Our findings show that feeding a snack supplemented with CPP-Se for 30 days can increase the number of blood lymphocytes and improve the expression of cytokine-related genes in peripheral blood lymphocytes (PBL). In addition, serum levels of cytokine-related proteins and immunoglobulins were increased. Furthermore, cell proliferation and expression of cytokine-related mRNAs were also increased when PBL was exposed to CPP-Se in vitro. Collectively, feeding CPP-Se strengthens various immune indicators in dogs, thereby enhancing immunity. This research provides theoretical support for the development of related functional foods and formulations to improve the immunity and health of dogs and other pets in the future. Abstract The health of pets is becoming a growing concern for the pet industry and its owners. Immunity is one of the foundational supports for health, thus developing a functional bioactive substance that can boost pets’ immunity is essential. Many studies have shown that casein phosphopeptide (CPP) and selenium (Se) can individually regulate immunity in many species, but there has been no reported research on the immunomodulatory function of casein phosphopeptide–selenium complex (CPP-Se). The objective of this study was to investigate the function of CPP-Se on immunomodulation in dogs. Twenty Beagle dogs were equally divided into two groups and fed either a control snack or a test snack supplemented with 0.03% CPP-Se for 30 days. Anticoagulated blood, serum and peripheral blood lymphocytes (PBL) were collected from dogs at 0 d, 10 d, 20 d and 30 d to detect the change in the number of immune cells and the expression of cytokine-related mRNAs and proteins. PBL isolated from blood were exposed to CPP-Se in vitro to measure the proliferative responses and cytokine-related mRNAs expression. During the time the test snack was fed, the number of lymphocytes increased significantly, whereas neutrophils and monocytes remained unaltered. The expression of interleukin-4 (IL-4), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), CD4 molecule (CD4) and CD8α molecule (CD8α) was up-regulated, while interleukin-1β (IL-1β) was down-regulated, and interleukin-10 (IL-10) declined initially and subsequently increased. ELISA detection revealed a significant increment in serum IL-4, IL-6, Immunoglobulin M (IgM) and IFN-γ, except for IgG. Furthermore, CPP-Se treatment increased the proliferation and the expression of cytokine-related mRNAs in PBL cultured in vitro. This is the first study to demonstrate that CPP-Se can improve immunity in the dog.
A study of real-gas effect on SCO2 compressor performance using similitude method

The drastic property changes of supercritical CO2 (SCO2) make the performance of SCO2 compressors different from that of conventional compressors. This study deals with the real-gas effect on the SCO2 compressor performance by theoretical analysis and numerical validation. A set of similitude parameters are firstly deduced by dimensional analysis. Then keeping these parameters unchanged, the performance variation of a SCO2 compressor is analyzed when the inlet flow condition changes in a certain temperature and pressure range. Results show that the proposed similitude method could accurately reflect the variation trend of SCO2 compressor performance under different inflow conditions. The real-gas effect enables the compressor to obtain higher pressure ratio with lower temperature rise. It is recommended that the inlet temperature of SCO2 compressor should be as close as possible to the critical temperature and the pressure should be about 150 kPa higher than the corresponding pseudo-critical pressure at the same temperature.
WiFi Interference-Based Adversarial Attacks on NTC Using CSI Sensing

With the emergence of next generation networks, Network Traffic Classification (NTC) has seen greater importance in network management and security. Recently, Channel State Information (CSI) based WiFi sensing techniques have shown their potential for NTC applications [1], [2] as a privacy-preserving yet effective tool. As CSI could be prone to interference, this paper examines the performance of CSI-based NTC models under interference-induced adversarial attacks. Specifically, the impact of spectral allocation of the interference, underlying interfering network traffic type, and physical location of the interference are studied and quantified. We conducted experiments using off-the-shelf devices to test the NTC performance, with and without the adversarial interference attack of ping, buffered video streaming, and live video streaming network traffics. Subsequently, we found that spectral allocation of the attacking interference and the underlying traffic types of interference could be used to deceive the established NTC model, and different network traffic types show different robustness across the interference cases. Namely, ping suffers the most in the spectrally manipulated attack, with the classification accuracy down to as low as 23.2%, whereas Twitch might be completely misidentified as other traffic in underlying traffic type controlled attack.
Balanced Score Propagation for Botnet Detection

Botnets are a group of computers infected by malware, and have become a major threat to the security of the entire Internet, which enable attacks to cause serious damages to the world, such as DDoS and ransomware campaigns. To manage millions of infected computers, botnets have evolved to use layered and distributed infrastructure. To eradicate the botnet threat, we have developed a novel balanced score propagation technique on graphs to detect the entire structure of a botnet with high precision. Conventional score propagation techniques detect a botnet node close to known one based on the score that quantifies the level of the relevance between the two. Their detection coverage is limited to neighboring nodes and thus misses the macro structure. Our new technique, balanced score propagation, can reveal the entire botnet structures by enabling the detection of both neighboring and distant botnet nodes. The technique employs direct score propagation to distant nodes by adding virtual edges to change the score propagation path. We evaluate the detection precision of the proposed method by experiments using real-world network traffic. The result shows that the proposed method can also detect botnet nodes at intermediate distances effectively as well as nearby and distant nodes, and the precision is improved an average of 1.8 times more than existing methods including graph embedding whose primary focus is the detection of the overall botnet structure.
Switching From Corporate to Nonprofit Work: Career Transitions of Commercially Imprinted Managers

Despite an increasing number of executives who transition from for-profit to not-for-profit organizations, our understanding of how commercially imprinted managers navigate the new setting remains limited. We collected data in the form of biographical interviews and observations with managers who had previously held a leading position in a commercial company and moved to work for a not-for-profit organization. We offer a typology of three responses that commercially imprinted managers used and identify conditions related to individuals’ biographies that facilitate the use of such responses, including social sector engagement, hybridity in previous job positions, international exposure, volunteering, and a diverse network.
Automobile Rear Axle Health Monitoring Technology Based on Deep Learning

The rear axle is prone to fatigue fracture due to the complicated load impact throughout the vehicle’s service procedure. Real-time suspension health monitoring during operation is an excellent strategy for avoiding such situations. This paper takes the automobile rear axle as the research object and uses the deep learning model to study the rear axle health monitoring technology. A damage prediction model combining multi-Gaussian fitting and long-term and short-term memory neural network is proposed for automobile rear axle health monitoring. The feature extraction method based on multi-Gaussian fitting can obtain comprehensive power spectral density information with fewer feature parameters, which is conducive to improve the calculation efficiency. The LSTM model can accurately identify the fatigue damage value of the rear axle from the extracted characteristic information and ensure the high precision of the damage prediction results. Several deep learning-based comparison models are used to demonstrate that the GAPSD LSTM model has good prediction accuracy and computation efficiency at the same time. It has been proven that the GAPSD LSTM model can efficiently and accurately forecast the fatigue damage of the rear axle by comparing the damage prediction results of all deep learning models.