Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk factors for diabetic kidney disease in adults with longstanding type 1 diabetes: results from the Canadian Study of Longevity in Diabetes

Abstract Objectives: Diabetic kidney disease (DKD) is an independent predictor of cardiovascular morbidity and mortality in type 1 diabetes (T1D). We aimed to explore clinical and biochemical factors, including the achievement of American Diabetes Association (ADA) recommended targets associated with DKD in people living with T1D for ≥50 years. Methods: This was a post hoc analysis of a cross-sectional study of 75 participants enrolled in the Canadian Study of Longevity in T1D. We explored diabetes-related complications, including neuropathy, retinopathy, cardiovascular disease, and DKD. Study participants were dichotomized based on the achievement of ADA recommended targets as the low-target group (achieving ≤4 targets, n = 31) and high-target group (achieving >4 targets, n = 44). The outcome of interest was DKD defined by estimated glomerular filtration rate (eGFR) values <60/mL/min/1.73 m2 and/or 24-h albumin excretion >30 mg. Multivariable logistic regression models were employed to estimate odds ratios (ORs) for DKD with 95% confidence intervals (CIs). Results: Of the 75 participants with prolonged T1D duration (45% male, mean age 66 years), 25 participants had DKD and 50 did not. There was no statistical difference between the high- and low-target groups in terms of age and body mass index. eGFR was significantly higher and the prevalence of diabetic retinopathy was significantly lower in the high-target group. Older age at diagnosis of T1D and lower frequency component to high-frequency component ratio increased the odds of having DKD. Conclusions: In adults with prolonged T1D duration, older age at diagnosis and lower heart rate variability may be associated with DKD.
Diffusion tensor imaging of the normal-appearing deep gray matter in primary and secondary progressive multiple sclerosis

Background Despite strongly overlapping patterns of clinical and histopathologic findings in primary and secondary progressive multiple sclerosis, differences concerning motor symptoms, central nervous system inflammation, atrophy, and demyelination that cannot be accounted for by lesion load alone remain to be elucidated. Purpose To evaluate the normal-appearing deep gray matter in patients with primary and secondary progressive multiple sclerosis, diffusion tensor imaging was used in this study. Material and Methods In 14 multiple sclerosis patients with primary and secondary progressive multiple sclerosis, axial echo-planar single-shot diffusion tensor imaging sequences with 32 diffusion-encoding directions and axial FLAIR sequences were acquired on a 3T system using an eight-channel SENSE head coil. FLAIR hyperintense multiple sclerosis lesions were outlined semi-automatically and normal-appearing deep gray matter was outlined manually (caudate nucleus, globus pallidus, putamen, thalamus, substantia nigra, and red nucleus). Fractional anisotropy and mean diffusivity values within the normal-appearing deep gray matter for the two groups were compared. Results Interhemispheric differences in mean diffusivity values (but not in fractional anisotropy), were significantly higher in primary progressive multiple sclerosis than in secondary progressive multiple sclerosis for the substantia nigra (P = 0.04) and the putamen (P = 0.021). Volumes, mean diffusivity, or fractional anisotropy of the remaining normal-appearing deep gray matter did not differ significantly. Conclusion This study showed a higher interhemispheric difference in the mean diffusivity in the substantia nigra and putamen in patients with primary progressive multiple sclerosis than in those with secondary progressive multiple sclerosis. These changes may represent edema, as well as axonal and myelin loss that can affect the normal-appearing deep gray matter of the two hemispheres differently and may point to differences in the laterality of motor symptoms.
Effects of exercise dose and type during breast cancer chemotherapy on longer‐term patient‐reported outcomes and health‐related fitness: A randomized controlled trial

The Combined Aerobic and Resistance Exercise (CARE) Trial compared different types and doses of exercise performed during breast cancer chemotherapy. Here, we report the longer‐term follow‐up of patient‐reported outcomes, health‐related fitness and exercise behavior at 6, 12 and 24 months postintervention. A multicenter trial in Canada randomized 301 breast cancer patients initiating chemotherapy to thrice weekly, supervised exercise consisting of a standard dose of 25–30 min of aerobic exercise (STAN; n = 96), a higher dose of 50–60 min of aerobic exercise (HIGH; n = 101) or a combined dose of 50–60 min of aerobic and resistance exercise (COMB; n = 104) performed for the duration of chemotherapy (median of 17 weeks). Primary outcomes were patient‐reported outcomes including quality of life, cancer‐related symptoms and psychosocial outcomes. Secondary outcomes were objective health‐related fitness (assessed at 12 months only) and self‐reported exercise behavior. A total of 269 (89.4%) participants completed patient‐reported outcomes at all three follow‐up time points and 263 (87.4%) completed the health‐related fitness assessment at 12‐month follow‐up. COMB was significantly superior to (i) STAN for sleep quality at 6‐month follow‐up (p = 0.027); (ii) HIGH for upper body muscular endurance at 12‐month follow‐up (p = 0.020); and (iii) HIGH for meeting the resistance exercise guideline at 6‐month follow‐up (p = 0.006). Moreover, self‐reported meeting of the combined exercise guideline during follow‐up was significantly associated with better patient‐reported outcomes and health‐related fitness. Performing combined exercise during and after breast cancer chemotherapy may result in better longer‐term patient‐reported outcomes and health‐related fitness compared to performing aerobic exercise alone.
Theoretical Analysis of the Peak-to-Average Power Ratio and Optimal Pulse Shaping Filter Design for GFDM Systems

Generalized frequency division multiplexing (GFDM) has been regarded as a candidate for new multicarrier modulation schemes in future wireless communication systems (e.g., 5G systems). However, GFDM systems still suffer from the problem of high peak-to-average power ratio (PAPR). In this paper, theoretical analysis of the PAPR distribution for GFDM signals is performed. Exact closed-form expressions for the complementary cumulative distribution function (CCDF) of PAPR are firstly derived for critically sampled and oversampled GFDM signals, respectively, which tend to be sufficiently accurate as the number of subcarriers K is large enough (e.g., K ≥ 64). With aid of theoretical analysis results, an optimization criterion for the design of pulse shaping filters is obtained, which can nearly achieve the globally minimum CCDF results and is appropriate for practical applications. This criterion can also achieve the globally optimal result for minimizing the variance of instantaneous power. Based on this criterion, a measurement for the overall deviation of filter coefficients is proposed, which can help to roughly and efficiently evaluate the ability of PAPR reduction for pulse shaping filters. Although the PAPR reduction effect is limited with pulse shaping filters, it is found that large deviation may also lead to higher PAPR. Additionally, with the aid of closed-form CCDF expressions, PAPR characteristics of GFDM and other multicarrier systems (e.g., OFDM and FBMC/OQAM) are compared thoroughly. Simulation results verify the validity and accuracy of derived theoretical results, and demonstrate the effectiveness of the proposed optimization design criterion.
Diagnostics of Current Technical State of Transformer Equipment Using the Analytic Hierarchy Process

The new method of diagnostics of current technical state for power equipment at substations of electrical networks and industrial plants is proposed. It is based on the analytic hierarchy process and fuzzy logic. It was shown that it is possible to make a plausible prediction on the failures and fault reasons for power equipment and to minimize the time for decision making and fault reason elimination using pairwise comparisons of expert evaluations. To perform such analysis, it is necessary to have high-qualified experts and symptoms of defective state of power equipment. The comparison of these symptoms is realized using nine intensities of importance of the fundamental scale of Saaty. Fuzzy evaluations are based on the attempts to digitize verbal information (i.e. to represent information as linguistic variables by words or phrases in the form of numbers). Symptom ranking for possible faults is performed according to expert preferences that allows deriving the most considerable symptoms for each suggested defect and making a decision on a further operation of power equipment. The reliability of the proposed method is validated by calculations that demonstrate the adequacy of the model applied to transformers with high temperature superconducting windings (HTS transformers). It was shown that a conclusion on the further operation of a superconducting transformer or its removal for repair could be made if some fuzzy symptoms are observed with their evaluation using the scale of intensities of importance in the analytic hierarchy process. The new mathematical model developed with the use of fuzzy relations of transformer faults includes the elements of prediction for possible failures of power equipment in power supply systems and can be implemented into the systems of short-term forecasting and on-line diagnostics.
Thought disorder and family relations of schizophrenics. II. A classification of forms thinking.

A. Introduction We have summarized in a companion paper the conceptual and clinical basis for the hypothesis that specific links exist between family structure or patterns and individual schizophrenic thought disorder. 1 As part of an effort to evaluate this possibility systematically, we have evolved a new conceptualization and classification of forms of thinking. This formulation has been successfully used in studies in which the presence and variety of schizophrenic illness, defined especially in terms of form or style of thinking, were deduced ("predicted") using projective test data from the rest of the patient's family 2-4 and excerpts of parental interaction. 5 A methodological prerequisite for these predictive studies was that the judges who blindly evaluated the family data and the clinicians who independently diagnosed the patients from these families must understand in the same way the diagnostic categories or continua which were used.
Disambiguation and Result Expansion in Keyword Search Over Relational Databases

Recently, there has been a significant growth in keyword search due to its wide range of use-cases in people's everyday life. While keyword search has been applied to different kinds of data, ambiguity always exists no matter what data the query is being asked from. Generally, when users submit a query they need an exact answer that perfectly meets their needs, rather than wondering about different possible answers that are retrieved by the system. To achieve this, search systems need a Disambiguation functionality that can efficiently filter out and rank all possible answers to a query, before showing them to user. In this paper, we are going to describe how we are improving state of the art in various stages of a keyword-search pipeline in order to retrieve the answers that best match the user's intent.
Non-Interference through Annotated Multisemantics

Non-interference can be dened as a program property that give guaranties on the independence of specic (public) outputs of a program from specic (secret) inputs. The notion of non-interference does not depend on one particular execution of the program (unlike illegal memory access for example), but on its global behavior. To develop a certied system verifying information ows, such as non-interference, we propose to only rely on the execution of the program, and thus investigate such properties using directly the derivation tree of an execution. Considering a single execution is clearly not sucient to determine if a program has the non-interference property. Surprisingly, studying every execution independently is also not sucient. This is why we propose a formal approach that builds, from a given semantics, a multisemantics that allows to reason on several executions at once. Adding annotations in this multisemantics lets us capture the dependencies between inputs and outputs of a program. To motivate and demonstrate our approach, we provide a concrete example where it is clear that reasoning on all the executions at once is required, and we show that our approach works on this example. This is a work in progress, partially formalized in Coq. Ultimately, our goal is to automatically build the multisemantics from the semantics, and to prove that the method correctly approximate non-interference, i.e., if a pair of input and output are independent according to the annotations, then changing the input does not result in a dierent output.
Direct Power Control Method With Minimum Reactive Power Reference for Three-Phase AC-to-DC Matrix Rectifiers Using Space Vector Modulation

This paper proposes a direct power control (DPC) method with a minimum reactive power reference for ac-to-dc matrix rectifiers using space vector modulation. Based on the calculated active and reactive power reference values, the developed DPC algorithm directly controls both the active and reactive power components of the source by operating the three-phase matrix rectifiers, thereby obtaining the sinusoidal input currents and output dc voltage/current of the rectifier. The proposed DPC method instantaneously determines a source reactive power reference command to achieve a unity input power factor or maximum achievable power factor on given input and output conditions, because ac-to-dc matrix rectifiers cannot always attain a unity power factor due to the existence of the input capacitors. In addition, the developed algorithm calculates the minimum reactive power reference without using any circuit parameters. Thus, the proposed algorithm is free from any inevitable parameter uncertainty, resulting in a more robust and accurate power factor control for the rectifier. Based on the minimum reactive power reference, the proposed algorithm enables matrix rectifiers to operate at an input power factor that can be maximally obtained at all times. The effectiveness of the proposed method was verified by the simulations and experiments.
Improved Space Vector Modulation of Quasi Z-Source Inverter to Suppress DC-Link Voltage Sag

The three traditional strategies ZSVM1, ZSVM2, and ZSVM6 have been commonly used to control the quasi Z-source inverter (qZSI). However, there is a serious problem that the dc-link voltage of the qZSI drops under light load, leading to the distortion of the output voltage and current of the inverter. The peak dc-link voltage also becomes much higher than its reference value, endangering the safety of the inverter and load. This paper proposes an improved SVM, namely M-ZSVM1, to suppress the dc-link voltage sag under a light load. Through analyzing the reason, the harm, and the influence factors of the dc-link voltage sag, the diode current interruption is seen as the cause of the dc-link voltage sag. Therefore, an asymmetric shoot-through state distribution method is presented to achieve the minimum current ripple, which can increase the diode current. The qZSI can work properly without the dc-link voltage sag under wide load range, improving the safety and reliability of the qZSI. Moreover, other improved ZSI topologies can also be controlled by the proposed strategy to suppress the dc-link voltage sag.
On Use of Adaptive Schedules in Semantic Modeling of Real- Time Control Algorithms

The paper presents a semantic model for time-driven real-time control algorithms based on adaptive schedule. In contrast to known models of semantics of programs and reactive systems based on the notion of state, use of adaptive schedules allows avoiding surplus detalization which leads to a ‘state explosion’ problem. On the other hand, adaptive schedules correspond well to the nature of real-time control algorithms. The use of this model allows automated checking of the control algorithms' synchronization properties. The paper describes two case studies of application of a presented model for automation of design and verification of spacecraft's onboard flight control programs. The use of adaptive schedules semantics together with domain specific language, and automation toolset allow formal specification and verification of the important properties of realtime systems.
Parallel Many-Objective Search for Unit Tests

Meta-heuristic search algorithms such as genetic algorithms have been applied successfully to generate unit tests, but typically take long to produce reasonable results, achieve sub-optimal code coverage, and have large variance due to their stochastic nature. Parallel genetic algorithms have been shown to be an effective improvement over sequential algorithms in many domains, but have seen little exploration in the context of unit test generation to date. In this paper, we describe a parallelised version of the many-objective sorting algorithm (MOSA) for test generation. Through the use of island models, where individuals can migrate between independently evolving populations, this algorithm not only reduces the necessary search time, but produces overall better results. Experiments with an implementation of parallel MOSA on the EvoSuite test generation tool using a large corpus of complex open source Java classes confirm that the parallelised MOSA algorithm achieves on average 84% code coverage, compared to 79% achieved by a standard sequential version.
Synthesis of Artificial Intelligence Based Adaptive Controllers for Dynamic Objects

The article suggests a neural network-based method for implementing adaptive control in automatic control systems for trajectorial displacements of technological objects. The analysis of the structures of automatic control systems with complementary correction allows to propose two schemes for the inclusion of a neural network controller in the automatic control loop: 1)synthesis of the autonomous additional component by the neural network controller, implementation of the component in the control signal and organization of its channel; 2)software control correction implemented in the control signal generating device. These structures presuppose the online learning of the neural network. As an error signal used to adjust the weighting coefficients of the neural network, we propose to use a difference signal between the output coordinate of the reference model and the control object. In this research the corrections to the control law, synthesized in the system correction loop, are assumed to be unknown, i.e. we con-sider the reference value for the neural network output signal to be absent. The work outlines approaches to choosing the number of layers, neurons in them, activation functions and the delay time of input signals of the network. We also offer a modification of the gradient training method for the neural network. The re-search results of the correction loop are presented on the example of the robotic device actuator.
Towards Longitudinal Analytics on Social Media Data

We are witnessing increasing interests in longitudinal analytics on social media data. Longitudinal analytics takes into account an interval and considers the temporal popularity of social media data in the interval, rather than only considering recently generated social media data in real-time search. We study a fundamental functionality in longitudinal analytics—the top-k temporal keyword (TkTK) querying. A TkTK query takes as input a set of query keywords and an interval, and returns the top-k most significant social items, e.g., tweets, where the significance of a social item is defined based on a combination of the textual relevance and temporal popularity. We model social media data as a forest of linkage trees along the time dimension, which well models the propagation processes, e.g., replies and forwards, among different social items. Based on the forest, we model the temporal popularity of a social item across time as a popularity time series. We design two indexing structures that index social items' popularity time series and textual content in a holistic manner—the temporal popularity inverted index (TPII) and the log-structured merge octree (LSMO). Empirical studies with two substantial social media data sets offer insight into the design properties of the indexes and confirm that LSMO enables both efficient query processing and indexing structure updates.
Blood Transfusion System Using Data Mining Techniques and GRA

We are suggesting a blood transfusion system based on various data mining algorithms. We have applied and compared various algorithms such as Decision tree, Random forest and logistic regression, before and after applying GRA. The algorithm with most efficiency will be used to develop a system for predicting the nature of a blood donor, based on past data of donation. The prediction will be useful at various blood donation camps for contacting the donors, in case of a requirement.
An Automatically Mode-Matched MEMS Gyroscope Based on Phase Characteristics

An automatic mode-matching method is proposed based on phase difference between a ratio scaled drive signal that is applied on sense electrode and a sense output to improve the scale factor and the bias instability of gyroscope. The method is simulated by Simulink tool and verified by a FPGA-based digital control circuit on a cobweb-like disk resonator gyroscope (DRG). The test result shows that the scale factor and zero-bias instability performance under mode-matching are improved by 68.8 times and 23.1 times, respectively, compared with the mode mismatch.
Effect of Users Height Distribution on the Coverage of mmWave Cellular Networks With 3D Beamforming

In this paper, we study the effect of users’ height distribution on the coverage probability of millimeter-wave (mmWave) cellular networks that utilize three-dimensional beamforming (3DBF). The users and base stations (BSs) are equipped with multiple antennas and both line-of-sight (LOS) and non-LOS links exist in the channel which are, respectively, modeled by the Nakagami- $m$ and Rayleigh distribution. In this setup, we investigate the tilt angle optimization of the BS antenna arrays for maximizing the coverage probability under two regimes of noise limited and interference limitedd. In both cases, by adopting a stochastic geometry approach, we analytically derive the coverage probability, and then, find the optimal tilt angle that maximizes this probability. In addition, in the noise limited regime, we show that the optimal tilt angle depends on the average distance between each user and its serving BS and also their effective height. In the interference-limited regime, we further consider different rules for associating users to the BSs. Meanwhile, since in this regime, the tilt angle optimization is very complex, we propose a low complexity approach to find the optimal tilt angle that has a performance close to the optimal solution. We further study the asymptotic behavior when the density of the BSs or signal-to-interference ratio tends to infinity or zero. Finally, through the numerical simulations, we show that using the 3DBF and also incorporating the users’ height distribution in the tilt angle optimization lead to a substantial improvement in the coverage probability of the mmWave cellular networks.