Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Polarization-adjusted Convolutional Codes (PAC): A Source-Channel Coding Method

Polarization-adjusted convolutional (PAC) code improves the error-correction ability of polar codes by concatenating a convolutional transform with polar transform. In this paper, we establish a source-channel coding framework to develop the theory of the optimal rate assignment for PAC codes. In the source-channel coding model, each column of the convolutional matrix corresponds to a source encoder, in which multiple bits are compressed into one polarized channel. According to the source coding theory, the distortion function of each source encoder is derived. With the Shannon coding theorem, the achievable source coding rates are characterized by the polarized channel capacity and the rate distortion function. With the achievable source coding rates, the rate assignment after convolutional transform is obtained. Simulation results show that the PAC codes achieve better rate assignment than polar codes, leading to a better exploitation of the capacity on the insufficiently polarized channels and an improved error-correction performance.
Impedance-Based Stability Analysis for a Dead - Beat-Controlled Grid-Forming Inverter Using Cross-Correlation Methods

A voltage-controlled voltage-source inverter is re-sponsible for providing a high-quality voltage to a set of loads re-gardless of their nature. Even in scenarios with a high penetration of power electronics devices and nonlinear, distorting loads, the voltage quality must be ensured via an efficient control strategy. Furthermore, stability assessment of such a system is quite important inasmuch as the overall stability depends both on the inverter impedance and on the time-variant equivalent impedance of the load. This paper describes a dead-beat controller design procedure for voltage-source inverters with LC output filter. A harmonic-stability analysis via the impedance-based criteria is presented taking into account the existence of unstable poles in the impedance ratio. The pseudo-random binary sequence technique and the cross-correlation method are employed to identify the impedances associated with the system stability.
Multi-Classification Prediction of Alzheimer’s Disease based on Fusing Multi-modal Features

Alzheimer’s Disease (AD) is an irreversible neurodegenerative disease that commonly occurs in the elderly. With the current accelerated aging process, the accurate diagnosis of early AD is essential for patient care and disease delay. In recent years, Magnetic Resonance Imaging (MRI) has become increasingly important in diagnosing AD due to advances in deep learning and neuroimaging technology. This paper proposes a model framework for multi-classification prediction of Alzheimer’s disease based on fusing multi-modal features. Firstly, the sMRI data are pre-processed based on ROI templates with different segmentation accuracy levels to extract morphological features including gray matter volume, surface area and cortical thickness, and then these features are combined with the corresponding Clinical Data to produce the Indicators dataset. Secondly, a 3DCNN-SE module is proposed to extract the primary features from the 3D MRI data. In order to reduce the dimensionality of the Indicators, an Indicator Selection Strategy is designed to select the most relevant features from the Indicators. Finally, a Multi-Attention-Fusion Module (MAFM) is developed to perform multi-modal data fusion on the results of feature extraction and selection, followed by a SoftMax classifier for AD disease diagnosis. We evaluated 596 patients from the Alzheimer’s Disease Neuroimaging Initiative(ADNI), including 198 patients with Alzheimer’s disease (AD), 200 patients with mild cognitive impairment (MCI), and 198 patients cognitively normal(CN). As a result, 88% accuracy is achieved on the three classifications, which is better than the related methods mentioned in literature.
Android Malware Detection: Looking beyond Dalvik Bytecode

Machine learning has been widely employed in the literature of malware detection because it is adapted to the need for scalability in vetting large scale samples of Android. Feature engineering has therefore been the key focus for research advances. Recently, a new research direction that builds on the momentum of Deep Learning for computer vision has produced promising results with image representations of Android byte-code. In this work, we postulate that other artifacts such as the binary (native) code and metadata/configuration files could be looked at to build more exhaustive representations of Android apps. We show that binary code and metadata files can also provide relevant information for Android malware detection, i.e., that they can allow to detect Malware that are not detected by models built only on bytecode. Furthermore, we investigate the potential benefits of combining all these artifacts into a unique representation with a strong signal for reasoning about maliciousness.
Fasting Intervention for Children With Unilateral Renal Tumors to Reduce Toxicity

Childhood renal tumors account for around 6% of all childhood cancers and 90% of these cases are Wilms tumor. In Europe, the SIOP-RTSG approach is considered standard of care and has resulted in five-year survival rates of over 90%. Efforts to decrease toxicity are now being pursued. Short-term fasting (STF), a short but strong reduction in calorie-intake, is associated with improved fitness, enhanced coping with acute physical stress and a lower risk of age-associated diseases. STF temporarily reduces growth to boost resilience, maintenance, and defense-mechanisms, by which toxic side-effects of (oxidative) damage and inflammation are largely prevented. Renal surgery for Wilms tumor carries a risk of acute kidney injury (AKI) and pediatric patients that had an episode of AKI are at increased risk for developing chronic renal disease. STF could mitigate surgery-induced stress and could further improve outcomes. We aim to investigate the effect of STF on renal function recovery after renal tumor surgery by conducting a single-center, prospective, randomized, non-blinded, intervention study. Children diagnosed with a unilateral renal tumor and opting for curative treatment are eligible for inclusion. The main study objective is to investigate the potential decrease in occurrence of AKI due to STF. Secondary objectives include renal function recovery, child's wellbeing, physical functioning, and feasibility of and adherence to STF in children with cancer.
The Super Thermal Conductivity Model for High-Temperature Heat Pipe Applied to Heat Pipe Cooled Reactor

Differing from the traditional Pressurized Water Reactor (PWR), heat pipe cooled reactor adopts the high-temperature heat pipes to transport fission heat generated in the reactor core. Therefore, the basis of coupling calculation on this type of reactor system is to have a suitable model for high-temperature heat pipe simulation. Not only is it required that this model can well describe the transient characteristics of heat pipe, but it should be simple enough to reduce the computational resource. In this paper, the super thermal conductivity model (STCM) for high-temperature heat pipe is proposed for this purpose. In this model, heat transport of vapor flow is simplified as high efficiency heat conductance. Combined with the network method, the evaporation rate, the condensation rate, and the flow rate in the wick region can be preliminarily obtained. Using recommended equations to calculate the heat transfer limitation, this model can realize the safety judgment of heat pipe. The heating system for high-temperature heat pipe is established to validate this model. The heating experiments with different heating powers is tested. Then, this model is applied on the numerical calculation of Kilowatt Reactor Using Stirling TechnologY (KRUSTY) reactor. In the steady state calculation, the results show that the temperature distribution on contact surface between fuel and heat pipe is nonuniform, which will lead to higher peak temperature and temperature difference for the reactor core. In the transient calculation, the load-following accident is chosen. Comparing with the experimental results, the applicability of the proposed model on heat pipe cooled reactor is verified. This model can be used for heat pipe cooled reactor simulation.
Helping People Through Space and Time: Assistance as a Perspective on Human-Robot Interaction

As assistive robotics has expanded to many task domains, comparing assistive strategies among the varieties of research becomes increasingly difficult. To begin to unify the disparate domains into a more general theory of assistance, we present a definition of assistance, a survey of existing work, and three key design axes that occur in many domains and benefit from the examination of assistance as a whole. We first define an assistance perspective that focuses on understanding a robot that is in control of its actions but subordinate to a user’s goals. Next, we use this perspective to explore design axes that arise from the problem of assistance more generally and explore how these axes have comparable trade-offs across many domains. We investigate how the assistive robot handles other people in the interaction, how the robot design can operate in a variety of action spaces to enact similar goals, and how assistive robots can vary the timing of their actions relative to the user’s behavior. While these axes are by no means comprehensive, we propose them as useful tools for unifying assistance research across domains and as examples of how taking a broader perspective on assistance enables more cross-domain theorizing about assistance.
Neuromuscular Fatigue Affects Calf Muscle Activation Strategies, but Not Dynamic Postural Balance Control in Healthy Young Adults

Neuromuscular fatigue could negatively affect postural balance, but its effects on dynamic postural regulation are still debated. This study aimed to investigate whether a fatigue protocol on calf muscle could affect muscle activation strategies and dynamic balance performance. Seventeen male adults (age 24.1 ± 4.6 years; height 183.9 ± 7.2 cm; weight 80.2 ± 7.2 kg) volunteered in the study. They performed a dynamic test on an instrumented platform, which provided anterior-posterior oscillations on the sagittal plane, before and after a localized fatigue protocol. High-density surface electromyographical (EMG) signals were recorded bilaterally from the soleus and the medial gastrocnemius muscles. The fatigue protocol, consisting of two quasi-isometric tiptoe standing exercise to failure with a fixed load, did not affect the global dynamic balance performance. Conversely, the frequency value corresponding to 95% of the total power spectrum density of the angular displacement signal increased after fatigue (from 1.03 ± 0.42 to 1.31 ± 0.42 Hz; p < 0.05). The EMG analysis showed a significant difference in the PRE/POST fatigue ratio of the root-mean-square (RMS) between the soleus and the gastrocnemius medialis muscles. No differences were detected for the coefficient of variation and the barycenter coordinates of the RMS EMG values between muscles and sides. The variations in the frequency content of the angular displacement and EMG activity across muscles may be related to an increase in the calf muscles stiffness after fatigue. The role of neuromechanical calf muscle properties seems to be relevant in maintaining the dynamic postural performance after a quasi-isometric fatigue protocol until failure.
Sediments as a Source of Iron, Manganese, Cobalt and Nickel to Continental Shelf Waters (Louisiana, Gulf of Mexico)

Continental shelf sediments are a key source of trace metals to the ocean. In this study, we investigate the impact of sedimentary processes on water column concentrations of iron (Fe), manganese (Mn), cobalt (Co), and nickel (Ni) at five stations on the Louisiana continental shelf and slope, Gulf of Mexico. The highest trace metal concentrations were observed close to the seafloor at the most nearshore shelf station (water depth of 16 m), with most of the metals present in particulate form. This enrichment in the bottom water is likely the combined effect of input of trace metals in suspended matter from the Mississippi/Atchafalaya Rivers and, for Mn, Co, and Ni, benthic release from the shelf sediments. While particulate matter was the dominant carrier of Fe and Mn in bottom waters in the shelf and slope regions, Co and Ni were nearly exclusively present in dissolved form. Hence, lateral transport of Co and Ni in shelf waters is decoupled from that of Fe and Mn. Concentrations of particulate and dissolved trace metals in the water column generally decreased from the shelf to the slope, while those in the sediment increased. This suggests an increased retention of metals deposited on the sediment with distance from the coast, linked to the decrease in organic matter input and associated reductive sediment processes. The offshore decline in sediment trace metal mobilization is likely typical for river-dominated continental margins where most organic matter is deposited close to the coast.
Mental Pain Surrounding Suicidal Behaviour: A Review of What Has Been Described and Clinical Recommendations for Help

Objective To conduct a comprehensive review of scientific publications related to mental pain and suicide risk in order to deepen relevant aspects to guide clinical interventions. Method Using a text analysis tool, we collected the terms most frequently linked with that situation in published results of research using various tools to evaluate mental pain or psychache. Discussion We propose clinical interventions for the clinical conditions most commonly associated with mental pain.
Noninvasive Predictor for Premalignant and Cancerous Lesions in Endometrial Polyps Diagnosed by Ultrasound

Background There was no consensus for management of asymptomatic endometrial polyps (EPs) up to date. Objective The aim of present study was to determine the risk factors of malignant lesions in EPs diagnosed by ultrasound and establish a noninvasive predictor to decrease unnecessary hysteroscopy for EPs. Study Design We reviewed the records of all consecutive patients who underwent hysteroscopy for EPs in the Women’s Hospital School of Medicine Zhejiang University between January 1, 2001 and December 31, 2018. The patients with histological diagnoses of atypical hyperplasia or cancer were defined as malignancy, while the patients with histological diagnoses of benign lesions were randomly selected as benign group according to the ratio of 1:4 (malignancy:benign), matching by age and year of hospitalization. Logistic regression analysis was used to analyze the clinical parameters for predicting malignancy of EPs. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed to find a noninvasive predictor. The sensitivity, specificity, and the receiver operating characteristic curve (ROC) were used for assess the efficacy of the noninvasive predictor. New diagnosed EPs patients received in 2019 were used for verifying the accuracy of the noninvasive predictor. Results The age in 15,790 cases of benign lesions was significantly younger than that in 230 malignancy cases (41.97 ± 11.53 year vs 53.31 ± 11.61 years, p <0.001). AUB (OR 7.306, 95%CI 4.927–10.835), large EPs (OR 2.595, 95%CI 1.662–4.052), and blood flow signal in EPs (OR 2.690, 95%CI 1.872–3.866) were independent predictive factors of malignancy in all enrolled patients. A noninvasive predictor for malignancy of EPs was established, through combining with AUB, large polyps and blood flow signal. This predictor presented excellent sensitivity and NPV (91.3 and 95.8%), with acceptable specificity and AUC (0.801). Further validation in new diagnosed EPs also suggested excellent sensitivity and reasonable specificity (100 and 58.5%) of the predictor. Factors such as thickened endometrial thickness, menopause shorter than 10 years, hypertension, obesity and nulliparous were also validated as independent predictors of malignancy in different subgroup analysis. Conclusions The noninvasive predictor combined with other risk factors from subgroup analysis would be reliable to distinguish the benign lesions from malignancy for EPs diagnosed by ultrasound.
Longitudinal Associations of Self-Reported Visual, Hearing, and Dual Sensory Difficulties With Symptoms of Depression Among Older Adults in the United States

Evidence conflicts on the association between sensory difficulty and depression. Few studies have examined this association using longitudinal or population-based data. We used data from Rounds 1–9 of the nationally representative National Health and Aging Trends Study to evaluate the longitudinal association between self-reported visual, hearing, and dual sensory difficulties and clinically significant depressive symptoms. Multivariable Cox regression models were used to evaluate the hazard of incident depressive symptoms. Group-based trajectory modeling identified depressive symptom trajectories (DSTs). Multinomial logistic regression was used to examine the association between sensory status and DSTs. A total of 7,593 participants were included: 56.5% were female, 53.0% were 65–74 years old, 19.0% (95% CI 17.9–20.2%) had hearing, 5.6% (4.9–6.4%) had visual, and 3.3% (2.9–3.8%) had dual sensory difficulties at baseline. Hazard ratios for depressive symptoms in those with visual, hearing, and dual sensory difficulties were 1.25 (95% CI 1.00–1.56, p = 0.047), 0.98 (95% CI 0.82–1.18, p = 0.82), and 1.67 (95% CI 1.29–2.16, p < 0.001), respectively, relative to those without sensory difficulty. A model with four trajectory groups best fit the data. Group 1 (35.8% of the sample, 95% CI: 34.1–37.4) had persistently low risk of depressive symptoms; Group 2 (44.8%, 43.4–46.3) had low but increasing risk; Group 3 (7.1%, 6.2–8.3) had moderate risk; and Group 4 (12.4%, 11.5–13.3) had moderate to high risk that increased. Compared to those without sensory difficulties, individuals with each difficulty were significantly more likely to belong to a group other than Group 1. This study reveals associations between sensory difficulties and mental health that can inform public health interventions.
A Simple Logistic Regression Model for Predicting the Likelihood of Recurrence of Atrial Fibrillation Within 1 Year After Initial Radio-Frequency Catheter Ablation Therapy

Background The clinical factors associated with the recurrence of atrial fibrillation (Af) in patients undergoing catheter ablation (CA) are still ambiguous to date. Purpose 1. To recognize preoperative serologic factors and clinical features associated with Af recurrence after the first ablation treatment. 2. To Develop a Logical Regression Model for Predicting the Likelihood of Recurrence Within 1 Year After the Initial Radio-Frequency Catheter Ablation (RFCA) Therapy. Methods Atrial fibrillation patients undergoing RFCA at our institution from January 2016 to June 2021 were included in the analysis (n = 246). A combined dataset of relevant parameters was collected from the participants (clinical characteristics, laboratory results, and time to recurrence) (n = 200). We performed the least absolute shrinkage and selection operator (Lasso) regression with 100 cycles, selecting variables present in all 100 cycles to identify factors associated with the first recurrence of atrial fibrillation. A logistic regression model for predicting whether Af would recur within a year was created using 70% of the data as a training set and the remaining data to validate the accuracy. The predictions were assessed using calibration plots, concordance index (C-index), and decision curve analysis. Results The left atrial diameter, albumin, type of Af, whether other arrhythmias were combined, and the duration of Af attack time were associated with Af recurrence in this sample. Some clinically meaningful variables were selected and combined with recognized factors associated with recurrence to construct a logistic regression prediction model for 1-year Af recurrence. The receiver operating characteristic (ROC) curve for this model was 0.8695, and the established prediction model had a C-index of 0.83. The performance was superior to the extreme curve in the decision curve analysis. Conclusion Our study demonstrates that several clinical features and serological markers can predict the recurrence of Af in patients undergoing RFCA. This simple model can play a crucial role in guiding physicians in preoperative evaluation and clinical decision-making.
The View Outside of the Box: Reporting Outcomes Following Radical Cystectomy Using Pentafecta From a Multicenter Retrospective Analysis

We aimed at characterization of the patients undergoing radical cystectomy (RC) using the prognostic model (a modified pentafecta). In the multicenter retrospective study, we enrolled 304 patients with bladder cancer (pTis-4N0-2M0) who underwent RC between 2015 and 2020 in experienced centers. The definition of the pentafecta was as follows: no Clavien–Dindo grade III–V complications at 90 days and no long-term complications related to urinary diversion <12 months, negative surgical margins, ≥10 lymph nodes (LNs) resected, and no recurrence ≤12 months. RC-pentafecta achievement rate was 22% (n = 67), varying from 47% to 88% attainment rate for different pentafecta components, and was the lowest for sufficient LN yield. Both 12-month recurrence-free survival (RFS) and cancer-specific mortality were compromised in pentafecta failers compared with achievers (57.8% vs. 100% and 33.8% vs. 1.5%, respectively). The following were identified as crucial predictors of RC pentafecta achievement: modality of the surgery, type of urinary diversion, histological type of bladder cancer, advanced staging, and elevated preoperative serum creatinine. In conclusion, we found that the pentafecta achievement rate was low even in high-volume centers in patients undergoing cystectomy. The complexity of the procedure directly influenced the attainment rate, which in turn led to an increase in cancer-specific mortality rate among the pentafecta failers.