Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Practice and Knowledge of Pediatric Surgeons about Eosinophilic Esophagitis in Children with Esophageal Atresia

Objective: The study examined the clinical practice and knowledge of pediatric surgeons regarding diagnosis and treatment of eosinophilic esophagitis (EoE) in children with esophageal atresia (EA). Methods: Pediatric surgeons who were involved in diagnosis and treatment of EA from Turkey were included. An online survey, which included 26 questions related to clinical practice and knowledge of pediatric surgeons was administered via Google forms. The survey included questions regarding descriptive information of participants, diagnosis, treatment, and knowledge about EoE in children with EA. Results: Fifty seven pediatric surgeons responded to the study. Reflux symptoms resistant to treatment and recurrent anastomotic strictures were most commonly reported as symptoms of EoE. Endoscopy biopsies was the most commonly selected diagnostic method for EoE with nearly half of the surgeons obtaining appropriate biopsies. Diet elimination, proton pump inhibitors and systemic steroids were most commonly reported to be used in the of EoE. pediatric surgeons low-level knowledge, occupational experience, the number of children with EA in annual follow-up and the number of children with EA who had EoE. In the diagnosis section, there were questions related to consultations in the long-term follow-up, the endoscopy applications and the diagnosis of EoE. There were two questions related to the treatment of GERD and two questions regarding the treatment of EoE. In the knowledge section, participants were asked to state “yes” or “no” for the statements. Only one practice and knowledge regarding diagnosis and treatment of EoE in children with EA. Therefore, improvement in the care of these children could be achieved by increasing awareness and knowledge of pediatric surgeons, decreasing heterogeneous nature of practice patterns and creating a consensus in clinical settings.
Teaching Project Management through an Information Technology-Based Method

New information technology-based teaching methods are essential to bring the issues faced by a project manager to the classrooms electronically. This paper describes implementation of a method to achieve this objective and compares its learning effectiveness with lectures and written case study methods. The major findings were that the information technology-based method was more effective compared to written case studies and lectures in developing higher-level cognitive skills and triggering interest in learning about project management. This research shows that information technology-based methods need to be deployed in order to impart higher-level cognitive skills to potential project managers.
New findings from conflict archives

As they pursue information and deploy violence during conflict, combatants compose, catalog, and preserve a wide variety of records, such as memos, investigative reports, and communiqués. In an increasing number of post-conflict scenarios, these records are being archived and released publicly, quickly becoming a critical new source of data for studies of peace and conflict. The objective of this special issue is to advance a new research agenda focused on the systematic analysis of conflict archives. The contributors each spent significant time collecting original data from often-dusty archives and, in many cases, developed new methodologies for sampling, cataloging, and analyzing historical documents. Their findings reveal how violence simultaneously shapes and is shaped by factors that remain largely unobservable using more conventional sources of conflict data, including clandestine mobilization, bureaucratic accountability, and political identities. By considering these studies in relation to one another, this introduction aims to provide readers with a comprehensive understanding of field research strategies and analytical techniques for studying original data from conflict archives. We conclude that while archival data are subject to their own biases that must be considered, this research agenda addresses significant limitations associated with traditional data sources and, in turn, pushes scholars to rethink many of the mechanisms underlying the causes and dynamics of peace and conflict.
Prediction of variable forecast horizons with artificial neural networks by embedding the temporal resolution warping

Artificial neural networks (ANNs) have achieved many successes in time series forecasting. The shortcomings of them are a fixed forecast horizon and an increasing inaccuracy for multi-step forecast techniques to extend the forecast horizon. We embed temporal resolution warping into an ANN to provide a dynamic forecast horizon, excluding multi-step forecasts. The ANN is improved to recognize different representations of patterns by mapping spacial frequencies to new frequencies according to their relevance in time. We demonstrate the drastically improvement in forecast accuracy on different datasets. In comparison to the multi-step approach, we achieve a constant accuracy for extending the forecast horizon.
Investigation into Phishing Risk Behaviour among Healthcare Staff

A phishing attack is one of the less complicated ways to circumvent sophisticated technical security measures. It is often used to exploit psychological (as as well as other) factors of human users to succeed in social engineering attacks including ransomware. Guided by the state-of-the-arts in a phishing simulation study in healthcare and after deeply assessing the ethical dilemmas, an SMSbased phishing simulation was conducted among healthcare workers in Ghana. The study adopted an in-the-wild study approach alongside quantitative and qualitative surveys. From the state-of-the art studies, the in-the-wild study approach was the most commonly used method as compared to laboratory-based experiments and statistical surveys because its findings are generally reliable and effective. The attack results also showed that 61% of the targeted healthcare staff were susceptible, and some of the healthcare staff were not victims of the attack because they prioritized patient care and were not susceptible to the simulated phishing attack. Through structural equation modelling, the workload was estimated to have a significant effect on self-efficacy risk (r = 0.5, p-value = 0.05) and work emergency predicted a perceived barrier in the reverse direction at a substantial level of r = −0.46, p-value = 0.00. Additionally, Pearson’s correlation showed that the perceived barrier was a predictor of self-reported security behaviour in phishing attacks among healthcare staff. As a result, various suggestions including an extra workload balancing layer of security controls in emergency departments and better security training were suggested to enhance staff’s conscious care behaviour.
Self-Supervised Fine-Grained Cycle-Separation Network (FSCN) for Visual-Audio Separation

Audio mixture separation is still challenging due to heavy overlaps and interactions. To correctly separate audio mixtures, we propose a novel self-supervised Fine-grained Cycle-Separation Network (FCSN) for vision-guided audio mixture separation. In the proposed approach, we design a two-stage procedure to perform self-supervised separation on audio mixtures. Using visual information as guidance, a primary-stage separation is realized via a U-net network, then the residual spectrogram is calculated by removing separated spectrograms from the original audio mixture. At the second-stage separation, a cycle-separation module is proposed to refine separation using separated results and the residual spectrogram. Self-supervision learning between vision and audio modalities is presented to push the cycle separation until the residual spectrogram becomes empty. Extensive experiments are evaluated on three large-scale datasets, MUSIC (MUSIC-21), AudioSet, and VGGSound. Experiment results certify that our approach outperforms the state-of-the-art approaches, and demonstrate the effectiveness for separating audio mixtures with overlap and interaction.
In situ passivation effect of fertilizer passivation solutions with various pH on Pb-F contaminated soil

Abstract The Maoniuping rare earth mine in Mianning, Liangshan Prefecture, is the largest rare earth deposit in China and the second largest rare earth deposit in the world. During the mining of rare earths, F and Pb, both types of heavy metal waste can directly or indirectly enter the soil, causing local soil contamination, which in turn poses a threat to the health of local residents. In this study, soil samples were incubated with fertilizer passivation solutions with pH values of 4, 5, 6, 7, 8, and 9, with the soil water content adjusted to 50% of the maximum water holding capacity in the field. The Pb-F contamination of the soil samples was analyzed to determine the existing states of the soil F and Pb and to study the remediation effect of fertilizer passivation solutions on the Pb-F contaminated soil. The results showed that under different passivation conditions, the pH, Pb, and F of the soil substantially changed and the passivation effect was enhanced over time. The pH of the soil significantly increased. The analysis of the effects of F and Pb treatment revealed that when the passivation fertilizer solution of pH 5 was used, the available Pb and F in the soil decreased the most, and the residual state of Pb and F increased to 77.86% and 57.24%, respectively.
A Wireless Strain Sensor based on Piezoresistive Fabrics

Stretchable materials are frequently studied for the realization of strain sensors. The variation of the sheet impedance when an external mechanical stimulus is applied is employed to develop strain sensors. Several strain sensors have been proposed that use these materials but most of these require a hardwired system for sensor reading. Here a simple fully wireless strain sensor is proposed. The structure comprises a periodic surface whose elementary cell provides variable absorption as a function of the stretchable material sheet impedance.
Effects of fiber nonlinearity on error vector magnitude and bit error ratio for advanced modulation formats

Abstract. Fiber nonlinear (NLI) noise, as well as amplified spontaneous emission (ASE) noise, degrades the performance of coherent optical fiber communications. We find that the temporal distribution of NLI noise, which is unlike that of ASE noise and depends on the waveform of signal pulse, has an impact on the estimation of error vector magnitude (EVM) and bit error ratio (BER). The influences of NLI noise, as well as ASE noise, on EVM and BER are analyzed for quadrature phase-shift keying and M-ary quadrature amplitude modulation systems. Modified theoretical formulas are proposed to estimate EVM and BER for the system under the effect of NLI noise, ASE noise, or mixing of these two noises. For system with mixing of ASE and NLI noises, according to our model, the optimal channel power for the best EVM is not corresponding to the best optical-signal-to-noise ratio. Our model is validated by numerical simulations.
Successful conservative management of a large acute epidural hematoma in a patient with arrested hydrocephalus: A case report

Background: Arrested hydrocephalus is a condition wherein congenital hydrocephalus spontaneously ceases to progress due to a balance between production and absorption of cerebrospinal fluid. These patients rarely present with pressure symptoms so conservative treatment may be instituted. There are, however, little data on the long-term outcomes of these patients and how they present in the presence of other intracranial pathologies as they transition into adulthood. We aim to add to the growing knowledge about the management of patients with arrested hydrocephalus who have sustained traumatic hematomas. Case Description: To the best of our knowledge, we present the only reported case of a 34-year-old female with arrested hydrocephalus who sustained an acute epidural hematoma secondary to a fall and underwent a conservative management. She was asymptomatic except for mild headache that started on the 3rd day postinjury and was thus treated conservatively with favorable outcomes. A review of literature showed that adults with arrested hydrocephalus may develop intracranial hematomas after head injuries despite them manifesting with little or no symptoms. The hydrocephalus may have provided them with a form of internal decompression thus delaying symptomatology. Conclusion: Clinicians should be vigilant as these patients will present with either delayed or completely without neurologic symptomology. Tailored and individualized management of other intracranial pathologies should be adapted in this subset of patients.
Impact of the Upper Limb Physiotherapy on Behavioral and Brain Adaptations in Post-Stroke Patients

Many stroke patients suffer from motor impairments due to paralysis, and consequently, motor paralysis of upper limbs seems to be particularly prone to residual impairment compared to that of lower limbs. Although ‘learned non-use’ that by managing reasonably well using only the unaffected upper limb in their actions, the patients can achieve their desired behavior, and these success experiences strengthen this pattern of behavior can be interpreted as a post-stroke adaptation, physiotherapy may lead to poor recovery of motor impairment. This review article discusses the impact of upper limb physiotherapy after stroke on behavioral/brain adaptations. Our previous studies demonstrated that patients with severe post-stroke sensorimotor impairments in a chronic phase might have abnormal functional connectivity. To prevent such adaptation after stroke, upper limb physiotherapy is important. In rehabilitation practices, hyper-adaptation has been often observed in not only behavioral but also brain changes. Although several studies are reporting clinical efficacy in patients with moderate to mild paralysis, there might be no effective treatment for patients with severe motor paralysis. To overcome these serious problems, we have developed a novel approach, kinesthetic illusion induced by visual stimulation (KINVIS) therapy. We showed that the effects of KINVIS therapy with therapeutic exercise on upper limb motor functions were mediated by spasticity, and functional connectivity in the brain was also changed with the improvement of motor function after KINVIS therapy. Brain changes underlying behavioral changes need to be more examined, and the adaptation of stroke patients needs to be clarified in detail.
Crop Root Responses to Drought Stress: Molecular Mechanisms, Nutrient Regulations, and Interactions with Microorganisms in the Rhizosphere

Roots play important roles in determining crop development under drought. Under such conditions, the molecular mechanisms underlying key responses and interactions with the rhizosphere in crop roots remain limited compared with model species such as Arabidopsis. This article reviews the molecular mechanisms of the morphological, physiological, and metabolic responses to drought stress in typical crop roots, along with the regulation of soil nutrients and microorganisms to these responses. Firstly, we summarize how root growth and architecture are regulated by essential genes and metabolic processes under water-deficit conditions. Secondly, the functions of the fundamental plant hormone, abscisic acid, on regulating crop root growth under drought are highlighted. Moreover, we discuss how the responses of crop roots to altered water status are impacted by nutrients, and vice versa. Finally, this article explores current knowledge of the feedback between plant and soil microbial responses to drought and the manipulation of rhizosphere microbes for improving the resilience of crop production to water stress. Through these insights, we conclude that to gain a more comprehensive understanding of drought adaption mechanisms in crop roots, future studies should have a network view, linking key responses of roots with environmental factors.
Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information.
Intelligent Computing of Levenberg-Marquard Technique Backpropagation Neural Networks for Numerical Treatment of Squeezing Nanofluid Flow between Two Circular Plates

This study presents new techniques based on the artificial intelligence neural network with Levenberg-Marquardt Scheme with backpropagation (ANN-LMS). The boundary value problem BVP is obtained from the governing equations of the flow model. Along with ANN-LMS, the semianalytical method namely the optimal homotopy analysis method (OHAM) is used for validating the results. ANN-LMS optimized the absolute error and increased the accuracy of the solution. The effect of physical parameters is discussed with the help of plots and tables.
On the Group-Fairness-Aware Influence Maximization in Social Networks

The goal of influence maximization (IM) is to find a set of $k$ nodes that maximize their influence spread over a social network. IM is a widely used model for information propagation in social networks. Although IM has been widely investigated in recent years, the fair propagation of information is still understudied. In this article, we consider the group-fairness-aware IM, where the objective is to ensure that information has been fairly distributed across different groups in the population. We extend the notion of group fairness in IM, introduced by Tsang et al., by considering the speed of information propagation in different groups of a social network and formulating it as a new group-fairness metric. We propose a multiobjective metaheuristic (SetMOGWO), based on the multiobjective gray wolf optimizer, to improve the fair propagation of information in IM concerning various fairness metrics. Experimental results show that the proposed algorithm outperforms the previous work with respect to all introduced fairness metrics. We also carry out detailed experimental analyses on real-world networks and report interesting relationships between fairness concepts and network characteristics, including the price of ensuring fairness in social networks, the effect of social groups’ structure on fairness, and the dependence among fairness metrics. Especially, we show that traditional IM techniques often neglect smaller groups. On the other hand, we show that, with a low cost, it can be ensured that these groups receive a fair portion of resources in fair order.
Artificial Neural Network Based Efficient Maximum Power Point Tracking for Photovoltaic Systems

In this paper, we propose a novel Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) technique to enhance the overall solar power generation and to regulate the voltage variations due to the varying solar irradiance levels by effectively controlling the duty cycle of the DC-DC Boost Converter. The proposed technique effectively regulates the voltage transients. Moreover, it also enhances the overall power production of the Photovoltaic (PV) plant. Simulation carried out in Matlab have proved effectiveness of the proposed ANN technique and to counter check its performance, these simulation results have been compared with the traditional perturb and observe maximum power point tracking technique. This results comparison proves that the proposed ANN-based MPPT technique exhibits optimal voltage regulation capability as compared to the traditional perturb and observe MPPT method, it provides better control under abrupt temperature and irradiance level variations.
Power-Management Topologies for PV-FC Hybrid Grid Connected System

This paper presents a method to operate a grid connected hybrid system. The hybrid system composed of a Photovoltaic (PV) array and a Proton exchange membrane fuel cell (PEMFC) is considered. The PV array normally uses a maximum power point tracking (MPPT) technique to continuously deliver the highest power to the load when variations in irradiation and temperature occur, which make it become an uncontrollable source. In coordination with PEMFC, the hybrid system output power becomes controllable. Two operation modes, the unit-power control (UPC) mode and the feeder-flow control (FFC) mode, can be applied to the hybrid system. The coordination of two control modes, the coordination of the PV array and the PEMFC in the hybrid system, and the determination of reference parameters are presented. The proposed operating strategy with a flexible operation mode change always operates the PV array at maximum output power and the PEMFC in its high efficiency performance band, thus improving the performance of system operation, enhancing system stability, and decreasing the number of operating mode changes.
Linear Method for Radial Distribution Systems including Voltage Control Devices

Linear methods for steady-state analysis of distri-bution systems are receiving more attention due to the wide spreading of distributed energy resources (DERs). A new con-strained Jacobian-based method has recently been presented. It provides the sensitivity matrices that linearly relate the variations of the electrical variables characterizing the supplying substation and each branch of the network to the variations of the powers injected by DERs. In this paper this method is extended to account for the presence of voltage control devices (VCDs). A new model of the supplying substation is proposed to account for changes on the operating conditions of the higher voltage network as well as to include the presence of HV/MV transformer with on-load tap-changer (OLTC). Furthermore, step-voltage regulators (SVRs) and magtech voltage boosters (MVBs) are modeled to take into account the presence of VRDs along the feeders. Numerical results are presented to give evidence of the effectiveness of the proposed approach.