Miracle-dz/UniFAR-FTUs huggingface dataset:
Dinoprostone Versus Misoprostol for Cervical Ripening before Diagnostic Hysteroscopy in Nulliparous Women: A Randomized Controlled Trial

Background: Hysteroscopy has revolutionized the field of Gynecology and the management of many gynecological conditions. It has now become a standard part in the diagnosis of postmenopausal bleeding by the gynecological surgeons. Cost, convenience, accuracy, and patient acceptability of these procedures are clearly superior to those of traditional surgeries. As gynecologists have grown better acquainted with the benefits and techniques of operative hysteroscopy, it has become the method of choice for treatment of intrauterine pathology. Cervical ripening is a complicated process, being mediated by cytokines, growth factors, hormones and other biochemical compounds. Both dinoglandin and Misoprostol can be used for cervical ripening before introduction of hysteroscopy and hence reduce the incidence of complications. Objects: This study aims to assess the efficacy of dinoprostone compared to misoprostol in cervical ripening in nulliparous women undergoing diagnostic hysteroscopy. Methodology: a randomized controlled clinical trial comparing dinoprostone versus misoprostol for cervical ripening before diagnostic hysteroscopy in nulliparous women, it included 2 groups, 33 patients each. In the first group named (group D) dinoprostone 3 mg was applied vaginally 6 hours before diagnostic hysteroscopic procedure while in the second group named (group M) 400 mcg misoprostol was applied vaginally at the same timing. Results: There was no statistically significant difference between the groups that received misoprostol or dinoprostone with regard to age, duration of marriage, medical disorder, history of gynecological operations and type of gynecological operations. However, the use of misoprostol caused slightly less pain compared to dinoprostone but more side effects occurred with the use of misoprostol. Conclusion: There was no significant difference between dinoprostone and misoprostol in priming of cervix before diagnostic hysteroscopy in nulliparous women regarding ease of hysteroscope entry, pain or side effects.
Differentially Expressed MicroRNAs in Conservatively Treated Nontraumatic Osteonecrosis Compared with Healthy Controls

Deregulation of microRNAs (miRNAs) contributes to nontraumatic osteonecrosis of the femoral head (ONFH-N), but the differentially expressed circulating miRNAs in patients with ONFH-N receiving nonsurgical therapy are unknown. This study aimed to determine the miRNAs expression profile of patients with ONFH-N receiving conservative treatments. This was a case-control prospective study of 43 patients with ONFH-N and 43 participants without ONFH-N, enrolled from 10/2014 to 10/2016 at the Department of Orthopedics of the Linyi People's Hospital (China). The two groups were matched for age, gender, and living area. Microarray analysis and quantitative RT-PCR were used to examine the differentially expressed miRNAs. Bioinformatics was used to predict miRNA target genes and signaling pathways. Microarray and quantitative RT-PCR revealed that nine miRNAs were downregulated and five miRNAs were upregulated in ONFH-N (n = 3) compared with controls (n = 3). Bioinformatics showed that calcium-mediated signaling pathway, regulation of calcium ion transmembrane transporter activity, cytoskeletal protein binding, and caveolae macromolecular signaling complex were probably regulated by the identified differentially expressed miRNAs. In the remaining 80 subjects (n = 40/group), miR-335-5p was downregulated (P = 0.01) and miR-100-5p was upregulated (P = 0.02) in ONFH-N compared with controls. In conclusion, some miRNAs are differentially expressed in conservatively treated ONFH-N compared with controls. Those miRNAs could contribute to the pathogenesis of ONFH-N.
Guava: phytochemical composition of a potential source of antioxidants for cosmetic and/or dermatological applications

Guava (Psidium guajava L.) is a native fruit of the American tropics with commercial applications for its taste, flavor and aroma. Numerous pharmacological uses have been described for it, such as the antiseptic effect of its leaves, the use of the fresh fruit and tea from its leaves for the treatment of diarrhea, dysentery, diabetes mellitus, and others. However, considering its rich composition, the guava also is a potential source of antioxidants to be used in the development of new formulations for cosmetic and/or dermatological applications, the main focus of this research. Herein, we describe the study of the phytochemical composition and the antioxidant activity of a guava extract prepared with non-toxic solvents aiming its use at biological applications. High performance liquid chromatography and mass spectrometry were employed to identify the major components, while thermoanalytical measurements and hot stage microscopy were used to assess the chemical stability of guava fruit extract. The antioxidant activity was also evaluated assessing the SOD-like activity and ABTS free radical scavenger. The results show that the extract is a rich source of phenolic compounds, such as quercetin, kaempferol, schottenol, among many others. All of the components found in guava extract exhibit biological effects according to the literature data, mainly antioxidant properties.
Trauma accident detecting and reporting system

Injuries due to fall or vehicle accident, including bikes or bicycles are relatively common. One of the reason for a person severe health menace after an accident is the unavailability of first aid, due to the delay in informing about the accident. Thus, in the case of incidents involving vehicle accidents or falls, response time is crucial for the timely provision of emergency medical services to the victims of accidents. An effective approach intended to reduce the number of falls-related deaths is: the use of a system for detecting and reporting the occurred accidents, as well as reducing the time between the occurrences of an accident and sending the first emergency respondents to the scene of the accident. This paper presents a solution using mobile terminals (smartphones) for detecting and preventing accidents like falls.
Dysregulated microRNAs with prognostic significance in human hepatocellular carcinoma : a study based on high-throughput microarray

Hepatocellular carcinoma (HCC) is the fifth most widespread type of malignancies with only 18% of postoperative 5-year survival. However, the prognostic value of microRNAs (miRNAs) for HCC has not been fully studied. In this study, we aimed to identify the potential candidates of dysregulated miRNAs for HCC prognosis in the profiling studies based on high-throughput microarray. The literature and NCBI Gene Expression Omnibus (GEO) database were used to identify HCC-related miRNAs. Based on the ranking results of a vote-strategy method, the most constantly stated dysregulated miRNAs were chosen as the predictive biomarkers for HCC patients’ survival. For metaanalysis on prognosis, eligible studies were searched and sufficient data were collected adequately. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to estimate the effect size. The top six upregulated and six downregulated miRNAs were identified based on 43 published miRNA profiling studies. Additional 30 studies involving 2735 HCC patients with follow-up data were also included. Among the top 12 dysregulated miRNAs, miR-222, miR-25, miR-221, miR-21, miR-214, miR-199a-3p and miR-199a-5p were closely associated with HCC patients’ survival. Both univariate and multivariate results suggested that the low quality of life for HCC patients was significantly related with high miR-21, miR-221, miR-25 and low miR-199a-3p, miR-214. In conclusion, miR-222, miR-221, miR-21 and miR-25 were significantly related with dismal prognosis for HCC patients, and miR-199a-3p and miR-214 emerged as significant predictors of unfavorable prognosis for HCC patients. These miRNAs may serve as potential candidates for HCC prognosis and therapy.
Tracking Control of Robot Manipulators with Unknown Models: A Jacobian-Matrix-Adaption Method

Tracking control of robot manipulators is a fundamental and significant problem in robotic industry. As a conventional solution, the Jacobian-matrix-pseudo-inverse (JMPI) method suffers from two major limitations: one is the requirement on known information of the robot model such as parameter and structure; the other is the position error accumulation phenomenon caused by the open-loop nature. To overcome such two limitations, this paper proposes a novel Jacobian-matrix-adaption (JMA) method for the tracking control of robot manipulators via the zeroing dynamics. Unlike existing works requiring the information of the known robot model, the proposed JMA method uses only the input-output information to control the robot with unknown model. The solution based on the JMA method transforms the internal, implicit, and unmeasurable model information to the external, explicit, and measurable input-output information. Moreover, simulation studies including comparisons and tests substantiate the efficacy and superiority of the proposed JMA method for the tracking control of robot manipulators subject to unknown models.
Wireless Powered Cooperative Non-Orthogonal Multiple Access Transmission

This paper considers a three-stage multiple-input single-output (MISO) downlink system with wireless powered cooperative non-orthogonal multiple access (NOMA) transmission. In the first stage, a transmitter and a user with the best-of-the-effort quality-of-service (QoS) requirement activate a passive user by wireless power transfer (WPT) via radio frequency (RF) signal. In the second stage, the information is sent to both users with NOMA scheme. Finally, the best- of-the-effort user forwards the information to the passive user with decode-and-forward (DF) protocol in the third stage. By jointly optimizing the time fraction of each stage, the transmitter's beamforming vectors and the user's forwarding power, the data rate of the best- of-the-effort user is maximized while satisfying the minimum signal-to-interference-plus- noise ratio (SINR) and data transmission requirement of the passive user. Analytic solutions of the time fraction and forwarding power are derived, while the beamforming vectors can be obtained either in closed-form, or by solving a semidefinite programming (SDP) problem. The numerical results validate the proposed algorithm, and the significant impact over the system performance due to the nonlinear energy harvesting model is also verified.
Dual-circularly polarized planar cavity-backed antenna for broadband applications

Dual-circularly polarized (CP) antenna planar cavity- backed antenna is proposed for broadband applications. The antenna is realized by exploiting a stacked structure of two-layer dielectric substrates, where the top layer comprises substrate integrated waveguide (SIW) planar cavity and radiating square shaped patch. To realize dual-CP, the patch is excited by pins, which are symmetrically placed at an offset to the diagonal axis of the patch. Furthermore, these pins are excited individually by two 50 ñ printed microstrip line feeds at the back side of the second layer of the substrate. Thus, right-handed circular polarization/left-handed circular polarization (RHCP/LHCP) can be realized by a one feeding line at a time. Moreover, this feeding topology is found useful for bandwidth enhancement. These pins introduce strong loading effect in the cavity and generate additional resonance in the vicinity of the patch resonant that help to achieve a broadband frequency response. The simulated results show that an antenna is operating for an impedance bandwidth of nearly 8.59 to 11.43 GHz and an axial-ratio bandwidth of early 500 MHz under the mandatory criterion of less than 3-dB for CP.
Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas

Rapid, reliable, and continuous information is an essential component in disaster monitoring and management. Remote sensing data could be a solution, but often cannot provide continuous data due to an absence of global coverage and weather and daylight dependency. To overcome these challenges, this study makes use of weather and day/light independent Sentinel-1 data with a global coverage to monitor localized effects of different types of disasters using the Coherence Change-Detection (CCD) technique. Coherence maps were generated from Synthetic Aperture Radar (SAR) images and used to classify areas of change and no change in six study areas. These sites are located in Syria, Puerto Rico, California, and Iran. The study areas were divided into street blocks, and the standard deviation was calculated for the coherence images for each street block over entire image stacks. The study areas were classified by land-use type to reveal the spatial variation in coherence loss after a disaster. While temporal decorrelation exhibits a general loss in coherence over time, disaster occurrence, however, indicates a significant loss in coherence after an event. The variations of each street block from the average coherence for the entire image stack, as measured by a high standard deviation after a particular disaster, is an indication of disaster induced building damage.
Region Growing Curriculum Generation for Reinforcement Learning

Learning a policy capable of moving an agent between any two states in the environment is important for many robotics problems involving navigation and manipulation. Due to the sparsity of rewards in such tasks, applying reinforcement learning in these scenarios can be challenging. Common approaches for tackling this problem include reward engineering with auxiliary rewards, requiring domain-specific knowledge or changing the objective. In this work, we introduce a method based on region-growing that allows learning in an environment with any pair of initial and goal states. Our algorithm first learns how to move between nearby states and then increases the difficulty of the start-goal transitions as the agent's performance improves. This approach creates an efficient curriculum for learning the objective behavior of reaching any goal from any initial state. In addition, we describe a method to adaptively adjust expansion of the growing region that allows automatic adjustment of the key exploration hyperparameter to environments with different requirements. We evaluate our approach on a set of simulated navigation and manipulation tasks, where we demonstrate that our algorithm can efficiently learn a policy in the presence of sparse rewards.
Heart Rate Extraction Based on Near-Infrared Camera: Towards Driver State Monitoring

In this paper, a remote sensing method for heart rate (HR) detection is proposed via near-infrared facial video data, which can be used for the online monitoring of the physiological parameters of the drivers and further analysis so as to enhance the driving safety. The proposed HR detection method is based on an automatic facial tracking algorithm, i.e., the Kanade-Lucas-Tomasi, to transform the facial images into time-series signals. Empirical mode decomposition, bandpass filtering and fast Fourier transformation are applied to the time-series signals for the HR extraction. Indoor experiments in different scenarios are conducted to discuss the involved impact factors, i.e., the distance between the near-infrared camera and the participant, the illumination intensity and the video duration, to acquire the optimal settings for HR measurement. It is more robust and convenient than those of the current commercial devices and webcam-based HR measurement approaches, especially in complex illumination environments, such as wearing hat, glasses or makeup, with measurement accuracy 95%. Field experiments are also performed for the prognosis of physical state of the driver.
Loss analysis of Vivaldi antenna for radar applications

The loss analysis of FR4 substrate in an L-band Vivaldi antenna for radar applications has been performed. The effect of both conductor loss and dielectric loss is studied by computing the gain of the antenna at different frequencies. The losses are computed using mathematical equations and described with the help of EM simulation models. It is observed that both dielectric loss and conductor loss are inversely proportional to substrate thickness. The analysis shows that dielectric loss is independent of antenna geometry.
On the Use of Naming Scheme for Controlling Flying Router in Information Centric Networking

Recently, Information-Centric Networking (ICN), is attracting attention as a network capable of flexible communication especially in IoT mobility environments. As a research on flexible controls in ICN, we have been involved in a movable router using Unmanned Aerial Vehicles (UAVs) or drones (called Flying Routers (FRs)) to realize communication in disjoint networks. In previous studies, control of Flying Routers was targeted reactively where FRs move according to the events on receiving Interest or Data. However, in order to realize a collaborative movement strategy such as content retrieval in consideration of cooperation by multiple FRs, more strategic ways which directly control FRs according to decision of path optimization. For this purpose, in this paper, we design a communication control system which strategically controls FRs by sending explicit Interests. We incorporates ICN technologies in the design of the system: naming scheme and strategy layer, which enable flexible control over FRs. Furthermore, we present a Proof-of-Concept (PoC) implementation of the system to confirm the feasibility of the design.
The "Prediflood" database of historical floods in Catalonia (NE Iberian Peninsula) AD 1035–2013, and its potential applications in flood analysis

"Prediflood" is a database of historical floods that occurred in Catalonia (NE Iberian Peninsula), between the 11th century and the 21st century. More than 2700 flood cases are catalogued, and more than 1100 flood events. This database contains information acquired under modern historiographical criteria and it is, therefore, suitable for use in multidisciplinary flood analysis techniques, such as meteorological or hydraulic reconstructions.
Self-Adaptive Neighbor Discovery in Mobile Ad Hoc Networks with Directional Antennas

In Mobile Ad hoc Networks (MANETs), directional antenna is a good solution if longer transmission distance is needed. When nodes are equipped with directional antennas, however, neighbor discovery becomes challenging especially for the decentralized networks. In most existing studies on neighbor discovery with directional antennas, nodes switch their directional antennas to different sectors either randomly or in a predefined sequences, and the steering time of each sector is the same. However, they cannot guarantee the performance when the nodes are not uniformly deployed and their mobility is not pure random. For practical considerations, we propose a novel neighbor discovery algorithm which makes nodes be able to sense the topology changes around them and arrange their directional antennas accordingly. We also do simulations to compare the performances of the proposed algorithm and the traditional random algorithm. The results show that the proposed algorithm has a better performance especially in the scenario where the node density is low.
Low-Complexity Coordinated Beamforming for Full Duplex MIMO Wireless Cellulars

In this paper, we consider a full duplex (FD) MIMO multi-cell system, where each FD base station (BS) with multiple antennas serves multiple single-antenna user equipments (UEs) with half-duplex radios. Advancement in interference management is in demand to handle an even more severe case compared to single-cell scenario, as FD in multi-cell incurs additional BS-BS and UE–UE interferences. To this end, we propose a coordinated beamfroming (BF) at the BSs that maximizes a weighted sum rate subject to per-BS power constraint and BS–BS interference power constraints, with UE–UE interference handled by a pre-scheduling process. By converting the original non-convex problem into an auxiliary convex optimization, we can obtain the optimal BF vectors in closed-form. By utilizing an important feature of the optimal solution that naturally leads to a distributed implementation, we develop a low-complexity distributed BF approach, which only requires local channel state information. Simulation results demonstrate the performance of the proposed coordinated BF scheme, and reveal that the overall FD gains can be achieved despite the additional interferences introduced in the FD enabled cellulars.
Action Extraction in Continuous Unconstrained Video for Cloud-Based Intelligent Service Robot

Transferring complex computing to the cloud server side leverages cloud-based intelligent service robots that are capable of highly complex computing tasks such as video analysis. In practical behavior surveillance applications, the captured videos from intelligent service robots are continuous. Action extraction from continuous unconstrained video is an important prerequisite for action analysis, such as action classification and recognition, abnormal event detection, and crowd emotion sensing. This paper proposes a novel approach for action extraction in continuous unconstrained video, which has three parts: spatial location estimation, temporal action path searching; and spatial-temporal action compensation. Spatial location estimation utilizes both human appearance and motion cues to obtain frame-level bounding boxes. Then, with the spatial action proposal results as a priori, the searching of temporal action paths is formulated as an optimal estimation problem by accounting for the missed detections and false alarms of the spatial location estimation. To solve the temporal action path searching problem, we propose the Markov Chain Monte Carlo algorithm and illustrate its convergence property. Extensive experiments on the challenging UCF-Sports and UCF-101 data sets show the effectiveness of our approach and obtain superior performance compared with the state-of-the-arts.
Constructing Network Slices with Locator/ID Split and Locator Division for 5G Core Network

The 5G core network will employ network slices, logical networks over physical infrastructure, each of which provides a specific service. There are two typical methods for network slicing: the hop- by-hop method using SDN and the edge-overlay method using Layer 2 over Layer 3 (L2 over L3) tunneling. In the hop-by-hop method, migration from the existing infrastructure is difficult and the SDN controller will become a single point of failure. In the edge-overlay method, tunneling causes header overhead and throughput degradation. This paper proposes a network slicing method without using SDN nor tunneling for the 5G core network using IPv6. In the proposed method, the lower 64-bit part of an IPv6 address (the node identifier) identifies a node and the upper 64-bit part (the locator) specifies the subnet to which the node is attached. This approach is suitable to support mobility management without tunneling such as MocLis. The slice identifier and the subnet number in the slice are embedded within the locator. Thus, the proposed method avoids the problems of system migration, a single point of failure, header overhead, and throughput degradation. In the proof of concept prototype, a Docker container is used as a virtual node in a network slice and a MPLS label-switched path is used as a virtual link between virtual nodes. TCP Throughput evaluation shows that the proposed method is superior to the edge-overlay method.