Miracle-dz/UniFAR-FTUs huggingface dataset:
A six-month periodic fasting reduces microalbuminuria and improves metabolic control in patients with type 2 diabetes and diabetic nephropathy: a randomized controlled study

Aim: Novel dietary interventions focused on fasting, have gained scientific and public attention. Periodic fasting has emerged as a dietary modification promoting beneficial effects on metabolic syndrome. This study aimed to assess whether periodic fasting reduces albuminuria in patients with type 2 diabetes and diabetic nephropathy and determine whether a reduction in albuminuria relates to activation of nephropathy-driven pathways. Methods: Forty patients with type 2 diabetes (HbA1c 7.8{+/-}0.2% [62.1{+/-}2.3 mmol/mol]) and increased albumin-to-creatinine ratio (ACR) were randomized to fasting-mimicking diet (FMD) (n=21) or Mediterranean diet (n=19) for six months with three-month follow-up. Primary endpoint was the difference of the change in ACR from baseline to after six months between study groups. Subgroup analysis for patients with micro- versus macroalbuminuria at baseline was performed. Secondary endpoints comprised HOMA-IR, circulating markers of dicarbonyl detoxification (MG-H1, glyoxalase-1 and hydroxyacetone), lipid oxidation (acylcarnitines), DNA-damage/repair, (yH2Ax) and senescence (suPAR). Comparison was done by ANCOVA adjusted for age, sex, weight loss and baseline values of the respective outcome. Results: Difference of change in ACR between FMD and control group after six months was 110.3mg/g (95% CI 99.2, 121.5mg/g; P=0.45) in all patients, -30.3mg/g (95% CI -35.7, -24.9mg/g; P[≤]0.05] in patients with microalbuminuria, and 434.0mg/g (95% CI 404.7, 463.4mg/g; P=0.23) in those with macroalbuminuria at baseline. FMD led to change in HOMA-IR of -3,8 (95% CI -5,6, -2.0; P[≤]0.05) and in suPAR of -156.6pg/ml (95% CI -172.9, -140.4pg/ml; P[≤]0.05) after six months, while no change was observed in markers of dicarbonyl detoxification or DNA-damage/repair. Change in AC profile was related to patient responsiveness to ACR improvement. At follow-up only HOMA-IR reduction (-1.9 [95% CI -3.7, -0.1], P[≤]0.05) was sustained. Conclusions: When accompanied by intensive diabetes care, FMD improves microalbuminuria, HOMA-IR and suPAR levels. Lack of changes in markers of dicarbonyl detoxification and DNA-damage/repair might explain the relapse of albuminuria at follow-up.
EpiLPS: a fast and flexible Bayesian tool for near real-time estimation of the time-varying reproduction number

In infectious disease epidemiology, the instantaneous reproduction number R(t) is a time-varying metric defined as the average number of secondary infections generated by individuals who are infectious at time t. It is therefore a crucial epidemiological parameter that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible envelopes of R(t) by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of R(t) in only a few seconds; and (2) an approach based on a MCMC scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a Negative Binomial distribution to account for potential excess variability in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a ''plug-in'' estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of R(t) as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and current SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France.
Disruptions in Care: Consequences of the COVID-19 Pandemic in a Children's Hospital

Background Public health restrictions are an essential strategy to prevent the spread of COVID-19; however, unintended consequences of these interventions may have led to significant delays, deferrals and disruptions in medical care. This study explores clinical cases where the care of children was perceived to have been negatively impacted as a result of public health measures and changes in healthcare delivery and access due to the COVID-19 pandemic. Methods This study used a qualitative multiple case study design with descriptive thematic analysis of clinician-reported consequences of the COVID-19 pandemic on care provided at a childrens hospital. A quantitative analysis of overall hospital activity data during the study period was performed. Results The COVID-19 pandemic has resulted in significant change to hospital activity at our tertiary care hospital, including an initial reduction in Emergency Department attendance by 38% and an increase in ambulatory virtual care from 4% before COVID-19, to 67% in August, 2020. Two hundred and twelve clinicians reported a total of 116 unique cases. Themes including (1) timeliness of care, (2) disruption of patient-centered care, (3) new pressures in the provision of safe and efficient care and (4) inequity in the experience of the COVID-19 pandemic emerged, each impacting patients, their families and healthcare providers. Conclusion Being aware of the breadth of the impact of the COVID-19 pandemic across all of the identified themes is important to enable the delivery of timely, safe, high-quality, family-centred pediatric care moving forward.
How to Derive Non-Logophoric Backward Binding for Stative Location Verbs

(1) a. Sally wanted Johni to kick himselfi. b. ★ Sally wanted himselfi to kick Johni. Backward binding refers to cases when this requirement appears to be flouted, and an anaphoric expression is bound by a DP below it. Such configurations have been noted previously for stative psych-verbs, as well as periphrastic psych predicates and more complex psych constructions (Belletti & Rizzi 1988; Landau 2010; Pesetsky 1987, 1995).
THE EFFECT OF CORPORATE SOCIAL RESPONSIBILITY AND BOARD GENDER DIVERSITY ON BANKRUPTCY: EVIDENCE FROM KOREA

This study investigates whether corporate social responsibility (CSR) activities and board gender diversity affect bankruptcy. The core issue focuses on the moderating effect between CSR activities and board gender diversity on bankruptcy. Using 4,654 firmyear observations from a sample of 581 non-financial firms listed on the Korean Stock Exchange over the period 2009–2017, we employ the fixed effect estimation and two-way fixed effect estimation of panel analysis to control endogenous. We find firms engaging more in CSR activities reduce the level of bankruptcy, but board gender diversity does not reduce the level of bankruptcy due to tiny portion of female director in the boardroom. The moderating effect on the relationship between CSR activities and board gender diversity reduce the level of bankruptcy. This result indicates that the influence of female directors on the boards of Korean listed firms is not yet strong but board gender diversity with good CSR activities positively operate to reduce the level of bankruptcy.
Machine Learning Driven Feature Sensitive Progressive Sampling Model for BigData Analytics

BigData requires processing a huge data volume, which is an undeniable challenge for academia-industries. The classical sampling techniques are limited when addressing dataimbalance, large data-heterogeneity, multi-dimensionality etc. To alleviate it, in this paper a novel machine learning driven feature sensitive progressive sampling (ML-FSPS) that in conjunction with an improved feature selection and classification environment achieves more than 95.7% of accuracy, even with 10-14% of the original data size. The proposed ML-FSPS model was applied for IoT-device classification problem that possesses exceedingly high data-imbalance, multi-dimensionality and heterogeneity issues. Functionally, the FSPS-driven analytics model at first performed active period segmentation followed by multi-dimensional (descriptive) statistical feature extraction and Wilcoxon Rank Sum Test based feature selection. Subsequently, it executed K-Means clustering over a gigantically huge feature instances ( network traces) Here, K-means algorithm clustered each feature samples into five distinct clusters. With initial sample size of 10%, FSPS model selected same amount of data elements (0.5-5% iteratively) from each cluster for each feature to perform multi-class classification using homogenous ensemble learning (HEL) model. Here HEL encompassed AdaBoost, Random Forest and Extended Tree ensemble algorithms as base classifiers. The simulation results affirmed that the proposed model achieves accuracy of almost 99% even with 10-16% of sample size. Keywords—Feature sensitive progressive sampling; BigData analytics; machine learning; ensemble learning; rank sum test; IoT-device classification
Towards Automated Checking of Input Data Usage with Facebook Infer

In modern, data-intensive applications the use and modiﬁ-cation of input data is very frequent. During the various transformations that the data suﬀers, parts can remain unused due to programming errors. These errors can be hard to detect and locate due to the high amount of data transformations, and can have real-life consequences. In this paper we propose the implementation of a recent analysis proposed by Urban and M¨uller as an analysis for Facebook Infer, a popular static program analyser for Java, C, C++ and Objective C. We show that our prototype can eﬀectively identify input data usage errors in the same benchmark used by related work.
Appendix to “Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks”

In the experiment part, we find obstacle encoding is helpful to the GNN explorer, which can optimize the explored path further with the smoother. We elaborate on the formulation of the obstacle encoding. In this work, we consider an obstacle as a 2D or 3D box depending on the workspace, denoted as o = (p1, · · · , pn, l1, · · · , ln) ∈ R, n ∈ [2, 3], where pi and li are the center and length of the box along the i-th dimension. The environment configuration is written as O ∈ R|{o}|×2n, where |{o}| is the number of the obstacles. Note that |{o}| is a variable number, since the number of obstacles can be different for each problem.
Smart UAV Dual LiDAR Environmental Collision Avoidance Systems

Research in unmanned aerial vehicles (UAVs) involves implementing sensors to perform collision avoidance algorithms in different types of environment for varying missions. One of these sensors that became prominent in research is a laser-based sensor called a LiDAR (Light Detection and Ranging). A LiDAR works by measuring the distance from certain objects through measuring the distance a laser is reflected off the target object. A LiDAR can be used to create a 3-D point cloud map that can be used to perform successful collision detection and avoidance algorithms to autonomously navigate a UAV through environmental obstacles. However, blind spots can sometimes occur with a single LiDAR. The purpose of this research involves developing a low-cost solution of a successful UAV collision avoidance system in different environments by implementing a dual LiDAR system. One 360° LiDAR is mounted on the bottom of the UAV to create a 3-D point cloud that is used for collision avoidance. A second face-level LiDAR is placed on the nose of the UAV to detect all immediate obstacles that are directly in front of the UAV. These two LiDARs can work in conjunction together for successful collision avoidance navigation through the implementation of 3-D point clouds to correctly identify objects. A successful collision avoidance algorithm is generated by combining the concepts of the angle displacement method, edge detection, and artificial potential field algorithms. This dual LiDAR system for collision avoidance will be tested through implementation in both an indoor and outdoor environment with obstacles in the UAVs route to its target destination.
nTopic Organic Trafﬁc Study

The objective of this study is to determine whether content optimization solely driven by nTopic recommendations impacts organic search trafﬁc from Google. The study used 3 randomly applied treatments of nTopic modiﬁed content, random keyword modiﬁed content, and unmodiﬁed content. nTopic modiﬁed content saw an organic trafﬁc lift of 17.5%, random keyword modiﬁed content saw a 10% drop and unmodiﬁed content saw drop in trafﬁc of 15%. This indicates that nTopic optimized content can create increases in organic search trafﬁc that cannot be explained solely by content freshness measurements or random ﬂuctuations.
Characterization and compensation for nonlinearities of high-power amplifiers used on the FLASH and XFEL accelerators

This paper presents an approach for characterization and linearization of high-power amplifiers used on an experimental high-energy accelerator. These devices may be considered as a part of the RF-field control system for the Free-electron LASer in Hamburg (FLASH, webpage at http://flash.desy.de) (and for the future XFEL (European X-ray Free Electron Laser: http://xfel.desy.de) and ILC (International Linear Collider: http://www.linearcollider.org)). For reasons of economy, the devices are utilized to their limits, so a high degree of linearity is to be desired. This makes the precise control of the accelerating field very challenging. In order to alleviate this problem one can linearize the whole chain of amplifiers using a software pre-distorter. To implement it, one needs first to characterize the pre-amplifiers thoroughly. This paper describes the characterization method. The linearizer implementation is then presented and the results obtained are discussed.
Socially Reactive Navigation Models for Mobile Robots

This work considers socially acceptable behaviors in traditional reactive navigation systems, allowing a robot to approach a group of humans in a socially acceptable manner by considering the personal space and the group space. In contrast to the fixed parameters of social distancing, this work presents an adaptive model; that is, the parameters of the personal and group space’s cost functions adapt according to the arrangement of the group and space constraints, avoiding the choice of initial parameters. A socially aware navigation system capable of approaching groups is implemented for a general-purpose mobile robot. The adaptive personal and group space algorithm is integrated with the standard navigation system of ROS, representing their information in a costmap layer. The adaptation of spaces is tested using fixed and adaptive parameters for different groups provided by three datasets. The navigation system is evaluated through simulation experiments, demonstrating that the robot is capable of approaching groups and, at the same time, provides a more realistic space modeling adapted to the context.
Pros and Cons of Fault Injection Approaches for the Reliability Assessment of Deep Neural Networks

In the last years, the adoption of Artificial Neural Networks (ANNs) in safety-critical applications has required an in-depth study of their reliability. For this reason, the research community has shown a growing interest in understanding the robustness of artificial computing models to hardware faults. Indeed, several recent studies have demonstrated that hardware faults induced by an external perturbation or due to silicon wear out and aging effects can significantly impact the ANN inference leading to wrong predictions. This work classifies and analyses the principal reliability assessment methodologies based on Fault Injection at different abstraction levels and with different procedures. Some of the most representative academic and industrial works proposed in the literature are described and the principal advantages, and drawbacks are highlighted.
Near-ground Measurement and Modeling for Archaeological Park of Pisac in Cusco for LoRa Technology

This research work presents the details of a nearground received power measurement and modeling based on it, inside the archaeological park of Pisac in the city of Cusco. The measurements were performed at a working frequency of 920 MHz in the industrial, scientific, and medical (ISM) band, using transceiver devices with LoRa technology. The power of the received signal is obtained while the transmitter moves at a constant speed of 0.4 m/s, to characterize the fading that occurred within this archaeological park, making appropriate use of the moving average filter separates the fading to large and small scale, then on the filtered signal is used the algorithm of linear regression, to obtain a model that characterizes the exponent of propagation loss and the shading factor. In addition, the small-scale fading is characterized according to its probability distribution, the statistical parameters of the distributions are obtained based on the small-scale measurements. We also measured variations of receiving antenna height, ranging from 20cm, 50cm, 80cm, and 120cm above the ground, finding that the height has a strong influence on the propagation loss exponent, while the shadow variation shows a smaller variation. The model obtained is validated by the coefficient of determination and the root mean square error (RMSE) value. Keywords—Archaeological park; LoRa; propagation model; statistics; near-ground
STUDY ON MAGNET SORTING OF THE CSNS/RCS DIPOLES

The 1.6 GeV Rapid Cycling Synchrotron (RCS) of the China Spallation Neutron Source (CSNS) is a high-power pulsed proton machine aiming for a 500 kW output beam power. Now, the routine output beam power has been increased to 100 kW. However, the horizontal bare orbit in the ring is large and the number of correctors is small, which brings great challenges to the ramp-up of beam power. It is found that the bare orbit in AC mode is 3 to 4 mm larger than that in DC mode. The reason is that the AC dipoles field error is larger than DC dipoles field error. Therefore, it is proposed to sort dipoles again according to the AC dipoles field error. In order to reduce the risk of beam commissioning, fewer magnets should to be moved to achieve smaller orbit. The best results of moving 2 to 6 magnets were calculated. After sorting, the orbit can be reduced by 3 to 4 mm, which reduces the difficulty of orbit correction.
Sustainability of an Integrated E-Payment System in Developing Countries

Over the recent years, there has been tremendous ease in monetary transactions; all thanks to a convenient payment platform. The Integrated e-payment system (IEPS) facilitates financial transactions electronically. This study aims to examine the factorial models necessitating the continuous use of the integrated e-payment system in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT) with two additional variables; processing time and processing charges proposed. The study data were collected from 285 valid respondents in Ghana through the random sampling approach. The hypothesis verification was conducted via the SmartPLS. The result of the structural equation modelling indicate that processing charges, processing time, and social influence are the critical influencers of IEPS continuous usage intention. In addition, it also implies that financial institutions and banks liaise with designers to, perhaps, consider consumers’ perspectives in their design; such that will bring about convenience, fast processing and minimal cost in order to foster continuous use. DOI: https://dx.doi. org/10.4314/jasem.v25i8. 6 Copyright: Copyright © 2021 Kissi and Idoga. This is an open access article distributed under the Creative Commons Attribution License (CCL), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Dates: Received: 10 May 2021; Revised: 28 June 2021; Accepted: 01 July 2021
A Third-Order 9-Bit 10-MHz CMOS Modulator With One Active Stage

We present a wideband architecture for modula- tors using a single active stage and two switched capacitor passive stages. The mixed active-passive implementation has performance advantages over traditional switched-capacitor (SC) or contin- uous-time implementations, particularly for high-resolution, wideband applications with high sampling rates and moderate oversampling ratios. Design insensitivity to clock jitter and process variations is achieved by the good choice of the modulator archi- tecture. The proposed modulator is designed in 0.13- m CMOS technology and meets all major requirements for application in IEEE 802.16 wireless MAN receivers. Circuit simulations show that the modulator with a single bit quantizer consumes 5.5 mW from a 1.2-V power supply and achieves a 9-bit resolution over a 10-MHz bandwidth at an OSR of . Good performance is also achieved for lower bandwidth applications. Index Terms—Analog-to-digital (A/D) converter (ADC), delta-sigma, passive, receiver, switched capacitor (SC), wideband.
Explanation-based Data Augmentation for Image Classification

Existing works have generated explanations for deep neural network decisions to provide insights into model behavior. We observe that these explanations can also be used to identify concepts that caused misclassiﬁcations. This allows us to understand the possible limitations of the dataset used to train the model, particularly the under-represented regions in the dataset. This work proposes a framework that utilizes concept-based explanations to automatically augment the dataset with new images that can cover these under-represented regions to improve the model performance. The framework is able to use the explanations generated by both interpretable classiﬁers and post-hoc explanations from black-box classiﬁers. Experiment results demonstrate that the proposed approach improves the accuracy of classiﬁers compared to state-of-the-art augmentation strategies.
Heuristics and Think-aloud Method for Evaluating the Usability of Game-based Language Learning

—Digital learning environments are increasingly becoming popular in recent years. The rising usage of cell phones has invited researchers to design and develop learning applications and games for mobile phones. Specifically, game-based language learning is being promoted by researchers in many parts of the world. “Language Learning Serious Game (LLSG)” is based on a theoretical model constructed by the researcher that supports children learning English as a second language in a cultural context. The usability of such games is evaluated based on well-defined heuristics and other standard methods. This research aims to appraise the usability of LLSG through heuristics and think-aloud approaches while involving all essential stakeholders, including language experts, students, teachers, and game developers. The researcher proposed the heuristics in a cultural context, whereas the think-aloud review is compiled from the rigorous discussion session involving these stakeholders to evaluate the LLSG. The findings obtained from the heuristics evaluation reveal that the usability of LLSG is acceptable. On the other hand, various interesting suggestions and reviews were gathered from the discussion between experts and students. This evaluation will further improve the future versions of the game.