Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Secondary Frequency Control Algorithm Considering Storage Efficiency

Storage technologies, such as batteries, have higher ramping capability than conventional generators, and so they could lead to better dynamic tracking performance when following a frequency control signal. However, storage units are energy constrained, so the energy content of the signal they follow should preferably be low. Our first contribution is a method for decomposing the target signal into a different signal for each generator and each storage unit, depending on their characteristics and on the storage energy levels. Faster units receive signals with higher ramping rates. Storage units with low energy-to-power ratios (<inline-formula> <tex-math notation="LaTeX">${\tau _{i}}$ </tex-math></inline-formula>) receive signals which are energy-neutral (efficiency-corrected) in shorter time frames than the signals for storage units with larger <inline-formula> <tex-math notation="LaTeX">${\tau _{i}}$ </tex-math></inline-formula>. Our method is particularly suitable when the <inline-formula> <tex-math notation="LaTeX">${\tau _{i}}$ </tex-math></inline-formula> vary by orders of magnitude over the storage population. It dispatches the units by solving a modified economic dispatch problem. The energy content of the storage unit signals is managed through a term penalizing energy level deviation in the cost function. The second contribution is a distributed implementation of our method, which approaches the dispatch of its centralized counterpart. Our distributed implementation innovates compared to other distributed algorithms in that it considers non-unity storage efficiency. Using a population of 1000 units we show that the distributed implementation is: 1) up to 700 times faster than the centralized implementation, therefore allowing for real-time implementation with a large number of units; and 2) robust to loss of communication links.
Vehicle Localization using 76GHz Omnidirectional Millimeter-Wave Radar for Winter Automated Driving*

This paper presents the 76GHz MWR (Millimeter-Wave Radar)-based self-localization method for automated driving during snowfall. Previously, there were many LIDAR (Light Detection and Ranging)-based localization techniques proposed for high measurement accuracy and robustness to changes of day and night. However, they did not provide effective approaches for snow conditions because of sensing noise (i.e., environmental resistance) created by snowfall. Therefore, this paper developed a MWR-based map generation and a real-time localization method by modeling the uncertainties of error propagation. Quantitative evaluations are performed on driving data with and without snow conditions, using a LIDAR-based method as the baseline. Experimental results show that a lateral root mean square error of about 0. 25m can be obtained, regardless of the presence or absence of snowfall. Thus, it can be investigated that a potential performance of radar-based localization.
The Complexity of Trauma Exposure and Response: Profiling PTSD and CPTSD Among a Refugee Sample

Objective: This study investigated the latent dimensional and categorical structure of ICD-11 complex posttraumatic stress disorder (CPTSD) within a refugee sample. Method: A subsample that identified as refugee (n = 308) was selected from the National Epidemiological Survey on Alcohol and Related Conditions. Factor mixture modeling (FMM) was employed to establish the dimensional structure of CPTSD symptomology and the categorical distribution of these dimensions. It was then evaluated whether trauma history could differentiate between the distribution of trauma response profiles. Results: A correlated 6-factor model with 5 latent classes was the best fitting model. Two classes were characterized by symptom profiles that were consistent with ICD-11 CPTSD and PTSD formulations. The remaining classes were characterized by nonspecific variation across dimensions. CPTSD class membership was predicted by traumas that were predominantly interpersonal in nature (serious neglect, physical assault, and sexual assault), whereas PTSD class membership was predicted by situational traumatic experiences (unarmed civilian in a conflict environment and a serious accident). A distinct dose-response effect was evident between cumulative traumatic exposure and CPTSD class membership. Conclusion: FMM class profiles distinguished between PTSD and CPTSD symptom formulations. Moreover, class membership was determined by specific trauma-exposure histories.
Automatic Detection of the Main Vine and Branches of Tomato Plants Grown in Greenhouses

An algorithm for real-time detection of plants main vine and branches using the Distance Regularized Level Set Evolution (DRLSE) algorithm is presented in this paper. The algorithm tracks the target plant in the foreground from a set of seeding points automatically located on the main vine. The location of the seeding pints is determined in two different ways using the Hough transform and template searching. Test images are captured inside a greenhouse and include a huge amount of clutter from adjacent plants. The clutter creates a complex environment for traditional segmentation techniques to detect branches of the plant in the foreground. Images are normalized to speed the execution time of the DRLSE and protect the algorithm from local minima traps. Results demonstrate the capability of the algorithm in detecting the main vine and major branches. This algorithm is good for real-time maintenance (i.e., deleafing) using robots inside greenhouses.
Neuron Network Learning Algorithms Based on Small Samples

Learning algorithms for two-layered neuron networks are considered, subject to a small number of sample data. Traditionally, problems arise due to description redundancy in case the number of neurons is big, and insufficient capabilities to ensure the network's generalization properties in case their number is small. In order to ensure the model's efficiency, a special first approximation election algorithm has been developed, focused on ensuring the uniform approximation in the data areas, using the initial redundant number of neurons of which each is linked to a certain fixed point in the data area. Such fixation allows ensuring the required approximation quality in subsequent learning throughout the data area of interest. At all learning stages, the regularization is used to suppress the redundant neurons and intra-neuron links. The learning is a multi-stage process, with regularization coefficient growing from stage to stage, and with insignificant network components being deleted. When learning a neural network, the “best” model is extracted, according to the specified model selection criterion. Test examples of approximation and pattern identification solutions are provided, an example of an application problem solution is shown as well.
Comparison of Anorectal Manometry Testing in Left Lateral and Lithotomy Positions

Anorectal manometry in lithotomy and left lateral positions can be used interchangeably for anal resting pressure and anal squeeze pressure, but not for anorectal sensation. Objectives Anorectal manometry (ARM) is typically performed in left lateral position, but many practitioners are more familiar with the lithotomy position. We aimed to evaluate agreement between ARM performed in left lateral and lithotomy positions and patient preference for testing position. Methods We performed a prospective comparison study of left lateral versus lithotomy position for women undergoing ARM for the evaluation of fecal incontinence. Women were randomly assigned to undergo testing in either left lateral position first followed by lithotomy position, or vice versa. Women then completed a survey assessing preference of position. We performed Bland-Altman analysis to measure the level of agreement between anorectal measurements obtained in the 2 positions. Results Twenty-one women were enrolled (mean age, 65 ± 2.2 years). We noted an acceptable level of agreement between anal pressure values obtained in left lateral versus lithotomy positions: anal resting pressure (mean difference, 0.9 mm Hg; 95% limits of agreement, 30.2 and −28.5) and anal squeeze pressure (mean difference, 1.8 mm Hg; 95% limits of agreement, 54.3 and −50.7). The level of agreement for sensory values was outside the predetermined clinical acceptability range. Most women (17/21 [81%]) reported a “good” or “very good” experience in both positions. Conclusions Anorectal manometry testing in the 2 positions can be used interchangeably for anal resting and squeeze pressures, but not for anorectal sensation. This modification can be introduced into clinical practice to accommodate the preference of women and practitioners who favor lithotomy position.
A Controlled Load Estimator-Based Energy Management System for Water Pumping Systems

This paper presents the development of a controlled load estimator for a water pumping system (WPS) using the data generated from a PSCAD simulation model. A neural network (NN) is trained, using the generated data, to estimate the power demand of the WPS as a function of the control variables. This NN-based load model is then incorporated into the WPS energy management system to determine the optimal operational schedules of the pumps with the objective of minimizing the energy consumption costs and charges associated with peak power demand. Modeling related uncertainties are captured through a novel recursive mechanism for NN retraining, while operational uncertainties are accounted for by applying a receding horizon model predictive control technique. Simulation results indicate notable savings in total energy costs for the WPS facility after applying the proposed strategy as a result of operational schedules optimization and uncertainties mitigation.
Unified Interference Engineering for Wireless Information Secrecy

Wireless communications are highly susceptible to eavesdropping due to their inherent broadcast nature. Recent works have proposed the use of interference for impeding the capabilities of the eavesdropping receivers in wireless networks. To fully unleash the potential of interference for wireless information secrecy, it is necessary to engineer network interference such that it impedes the capability of eavesdropping receivers while having mild effect on legitimate receivers. The task of generating network interference desirable for wireless secrecy is particularly challenging in heterogeneous networks due to their ample set of configurations. This paper proposes a unified interference engineering strategy (IES) for wireless information secrecy in heterogeneous networks. The unified IES combines zero forcing beamforming, artificial noise generation, cooperative jamming, and interference alignment to fully exploit the network’s capability in wireless secrecy. The proposed strategy enables multiple nodes with heterogeneous capabilities to achieve mutually beneficial coordination, thereby leading to a new level of wireless information secrecy.
Regulatory focus and self-determination motives interact to predict students' nutrition-habit intentions.

Regulatory focus (Higgins, 1997) and self-determination (Deci & Ryan, 2000) are two motivation theories widely applied in health-intervention research but never conjointly, despite sharing a number of theoretical similarities. In three studies (N = 578), we investigated how university students' self-determination motives to act upon their nutrition interact with induced regulatory focus and regulatory-message framing to predict their behaviors or intentions to improve their eating habits. Results revealed a fit between extrinsic motives and prevention focus: Intention increased with extrinsic motives in a prevention but not a promotion focus. The effect of intrinsic motives, however, was independent of the focus, which suggests that these motives drive intention and behavior independently of external cues. Hence, the results advocate for simultaneously taking into consideration self-determination and regulatory focus when planning a health intervention relying on motivation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
3-D Sequential Stacked Planar Devices Featuring Low-Temperature Replacement Metal Gate Junctionless Top Devices With Improved Reliability

3-D sequential integration requires top MOSFETs processed at a low thermal budget, which can impair the device reliability. In this paper, top junctionless (JL) devices are fabricated with a maximum processing temperature of 525 °C. The devices feature high k/metal replacement gate and low-temperature Si:P and SiGe:B 60% raised source and drain for nMOS and pMOS fabrication, respectively. Device matching, analog, and RF performance of the top tier devices are in-line with the state-of-the-art Si technology processed at high temperature (>1000 °C). JL devices operate at reduced electric field and can meet in specification reliability (10-year reliable operation at ${V}_{\textsf {G}}= {V}_{\textsf {th}}+ 0.6$ V, 125 °C), even without the use of “reliability” anneal. The top Si layer is transferred on CMOS planar bulk wafers with W metal-1 interconnects, using a SiCN to SiCN direct wafer bonding. Comparison with silicon-on-insulator devices fabricated with the same low-temperature flow shows no impact on device electrical performance from the Si layer transfer.
Our Results of Laparoscopic Partial Nephrectomies Without Pedicle Dissection: Possible Advantages and Disadvantages.

OBJECTIVES This study aimed to document the surgical and oncologic results of nephron sparing of non-ischemic laparoscopic partial nephrectomy without the step of hilus controlling and even without dissecting to expose the main renal vascularity and directly focusing on mass removal. MATERIALS & METHODS The records of the patients who underwent our modified laparoscopic partial nephrectomy technique were evaluated retrospectively. The patients' medical records, including tumor complexity calculated via R.E. N.A. L nephrometry scores, operation time, estimated blood loss, blood transfusions, hospital stay, pre- and postoperative serum creatinine levels, complications via the Clavien classification system, pathological status of surgical margin, and follow-up times, were documented. RESULTS The data of 55 patients with 58 renal units were evaluated. Almost all tumors were in the low complex group (91%), with a mean size of 31.74 ± 7.38 mm (range: 12-46 mm). Mean operation time, estimated blood loss, and transfusion rates were 138.62 ± 38.45 minutes (range: 90-240 min), 242.24 ± 107.12 mL (range: 100-500 mL), and 19%, respectively. The hemoglobin level decreased by a mean of 2.05 ± 0.87 g/dL. Whereas the perioperative complications were Clavien grades I, II, and III (74%, 23%, and 3%, respectively), mean hospital stay and follow-up time were 4.05 ± 1.97 and 19.67 ± 13.57 (ranges: 2-10 days and 1-44 months), respectively. CONCLUSION Present un-controlled results pointed that tumor-focusing nephron-sparing non-ischemic partial laparoscopic nephrectomy may be preferable for small-sized, low-complex renal masses.
Inactive immune pathways in triple negative breast cancers that showed resistance to neoadjuvant chemotherapy as inferred from kinase activity profiles

About 5% of Triple negative breast cancer patients (TNBCs) who receive neoadjuvant chemotherapy (NAC) experience progressive disease (PD). Few reports are published on TNBCs with PD during NAC, whereas TNBCs that respond to NAC have been well-studied. We investigated kinase activity profiles of TNBCs to explore the biological differences underlying the lack of response to NAC. Among 740 TNBCs, 20 non-responders were identified. Seven non-responders and 10 TNBCs that did not receive NAC (control group) were evaluated. No correlation was observed between NAC response and age, menopausal status, tumor size and axillary lymph node status. Tyrosine kinase activity profiles of TNBC primary tissues from NAC non-responders and the controls were determined with a peptide microarray system. Kinase activity measurements showed that 35 peptides had significantly (p < 0.05) lower phosphorylation in non-responders. ZAP70, LCK, SYK and JAK2 were identified as differentially active upstream kinases. Pathway analysis suggested lower activity in immune-related pathways in non-responders. The number of tumor infiltrating lymphocytes (TILs) was significantly lower (p = 0.0053) in non-responders. Kinases related to the immune system are less activated in non-responders. TILs evaluation suggested that the immune system is hardly active in non-responders and is not activated by NAC treatment.
Elucidation of the O-antigen structure of Escherichia coli O63

The structure of the O-antigen polysaccharide (PS) from the Shiga-toxin producing Escherichia coli O63 has been elucidated using a combination of bioinformatics, component analyses and NMR spectroscopy. The O-antigen is comprised of tetrasaccharide repeating units with the following structure: →2)-β-d-Quip3N(d-allo-ThrAc)-(1→2)-β-d-Ribf-(1→4)-β-d-Galp-(1→3)-α-d-GlcpNAc-(1→ in which the N-acetylated d-allo-threonine is amide-linked to position 3 of the 3-amino-3-deoxy-d-Quip sugar residue. The presence of a predicted flippase and polymerase encoded in the O63 gene cluster is consistent with the Wzx/Wzy biosynthetic pathway and consequently the biological repeating unit has likely an N-acetyl-d-glucosamine residue at its reducing end. A bioinformatics approach based on predictive glycosyltransferase function present in ECODAB (E. coli O-antigen database) suggested the structural element β-d-Galp-(1→3)-d-GlcpNAc in the O-antigen. Notably, multiple gene sequence alignment of fdtA and qdtA from E. coli to that in E. coli O63 resulted in discrimination between the two, confirmation of the latter in E. coli O63, and consequently, together with qdtB, biosynthesis of dTDP-d-Quip3N. The E. coli O63 O-antigen polysaccharide differs in two aspects from that of E. coli O114 where the latter carries instead an l-serine residue, and the glycosidic linkage positions to and from the Quip3N residue are both changed. The structural characterization of the O63 antigen repeat supports the predicted functional assignment of the O-antigen cluster genes.
Effect of Treatment Modality on Long Term Renal Functions in Patients With Muscle Invasive Bladder Cancer.

PURPOSE To compare of changes in glomerular filtration rate (GFR) in patients who underwent radical cystectomy (RC) and multimodal treatment (MMT). MATERIALS AND METHODS We identified 472 consecutive patients who underwent RC or treated with MMT for muscle invasive bladder cancer (MIBC) at our institution, between January 1995 and December 2010. After ex-cluding the patients who died within 5 years or without 5 years of follow-up, 175 and 59 patients who were treated with RC and MMT, respectively were included to the study. GFR was measured before treatment and every 6 months after treatment till the end of 60th month. RESULTS The mean age and mean baseline GFR were 66.5±5.7 years and 85.1±18.2 mL/min/1.73m2, respectively for all patients. We detected statistically significant higher decrease rates for GFRs in MMT group compared to RC group at every follow up period till 42nd month. Renal function decreasing was found to be more prominent during first year of follow-up (79.1 to 65.9 mL/min/1.73m2) in MMT group. However, GFR decreased more reg-ularly in RC group (~4 mL/min/1.73m2 per year). MMT, lower baseline GFR, Diabetes Mellitus, hypertension, and ureteroenteric anastomotic stricture development were associated with low GFR under 60 and 45 mL/min at the end of five years. CONCLUSION Decreased renal function is noted in many MIBC patients after RC or MMT in the long-term fol-low-up. Renal function deterioration is more prominent within the first year after MMT.
Pyramid Histogram of Double Competitive Pattern for Finger Vein Recognition

Finger vein is a new and secure biometric for personal authentication due to its line-structure network with abundant local and orientation features. However, these features cannot be well represented by the existing local descriptors. To effectively utilize these rich orientation features in finger vein images, this paper proposes a new local descriptor, namely, pyramid histogram of double competitive pattern (PHDCP). For a finger vein image, the PHDCP first obtains a bank of filtered images using Gabor filters with large kernel size and rich orientations, by which the local line features are captured. Then, the orientation orders with the largest and smallest responses, which are the most robust features, are selected to generate an encoded map. Finally, a column-partition-based pyramid histogram extraction method is presented to capture the hierarchical features from the encoded image. Numerical experiments are conducted on two public finger vein data sets, MMCBNU_6000 and UTFVP. The experimental results demonstrate that the proposed PHDCP performs much better than the existing local descriptors.
Energy-Efficient Deployment of Edge Dataenters for Mobile Clouds in Sustainable IoT

Achieving quick responses with limited energy consumption in mobile cloud computing is an active area of research. The energy consumption increases when a user’s request (task) runs in the local mobile device instead of executing in the cloud. Whereas, latency become an issue when the task executes in the cloud environment instead of the mobile device. Therefore, a tradeoff between energy consumption and latency is required in building sustainable Internet of Things (IoT), and for that, we have introduced a middle layer named an edge computing layer to avoid latency in IoT. There are several real-time applications, such as smart city and smart health, where mobile users upload their tasks into the cloud or execute locally. We have intended to minimize the energy consumption of a mobile device as well as the energy consumption of the cloud system while meeting a task’s deadline, by offloading the task to the edge datacenter or cloud. This paper proposes an adaptive technique to optimize both parameters, i.e., energy consumption and latency by offloading the task and also by selecting the appropriate virtual machine for the execution of the task. In the proposed technique, if the specified edge datacenter is unable to provide resources, then the user’s request will be sent to the cloud system. Finally, the proposed technique is evaluated using a real-world scenario to measure its performance and efficiency. The simulation results show that the total energy consumption and execution time decrease after introducing an edge datacenters as a middle layer.
Real-Time Vehicle Detection from UAV Imagery

Fast and accurate vehicle detection in unmanned aerial vehicle (UAV) imagery is a meaningful but challenging task, playing an important role in a wide range of applications. Due to its tiny size, few features, variable scales and imbalance vehicle sample problems in UAV imagery, current deep learning methods used in this task cannot achieve a satisfactory performance both in accuracy and speed, which is obvious a classical trade-off problem. In this paper, we propose a single-shot vehicle detector, which focuses on accurate and real-time vehicle detection in UAV imagery. We make contributions in the following two aspects: 1) presenting a multi-scale feature fusion module to combine the high resolution but semantically weak features with the low resolution but semantically strong features, aiming to introduce context information to enhance the feature representation of the small vehicles; 2) proposing a dynamic training strategy (DTS) which constructs the network to learn more discriminative features of hard examples, via using cross entropy and focal loss function alternately. Experimental results show that our method can achieve 90.8% accuracy in UAV images and can run at 59 FPS on a single NVIDIA 1080Ti GPU for the small vehicle detection in UAV images.