Miracle-dz/UniFAR-FTUs huggingface dataset:
Operational Planning of Distribution Networks Based on Utility Planning Concepts

The paper presents the operational planning of medium voltage (11 kV; 6.6 kV) distribution networks that is an integral part of the previously developed integrated planning approach based on utility planning concepts. The operational planning problem is decoupled from the investment planning stage and it is further divided into two phases, the quality of supply (QoS) planning and the minimization of operational costs. The QoS planning maximizes the benefits of the regulatory incentive regime by installing and automating switchgear, as well as finding optimal normally open points. It is solved in two ways, by applying genetic algorithm optimization and then the approach based on engineering rules. Operational cost minimization considers costs of O&M, switching, losses, and reliability. It includes security constraints for radially operated networks and allows that the locations of new switchgear from the QoS stage are changed. The simplified version of the model is also intended to be used as a real-time network reconfiguration tool. The entire methodology is tested on the 69-bus test system.
Non-canonical Notch Signaling Regulates Actin Remodeling in Cell Migration by Activating PI3K/AKT/Cdc42 Pathway

Tumor cell migration is a critical step in cancer metastasis. Over-activated Notch pathway can promote the migration of cancer cells, especially in the breast cancer. However, the underlying mechanism of non-canonical Notch signaling in modulating the migration has not yet been clearly characterized. Here we demonstrated that DAPT, a gamma secretase inhibitor, inhibited protrusion formation and cell motility, and then reduced the migration of triple-negative breast cancer cells, through increasing the activity of Cdc42 by non-canonical Notch pathway. Phosphorylation of AKT on S473 was surprisingly increased when Notch signaling was inhibited by DAPT. Inhibition of PI3K and AKT by LY294002 and MK2206, respectively, or knockdown of AKT expression by siRNA blocked DAPT-induced activation of Cdc42. Moreover, immunofluorescence staining further showed that DAPT treatment reduced the formation of lamellipodia and induced actin cytoskeleton remodeling. Taken together, these results indicated that DAPT inhibited Notch signaling and consequently activated PI3K/AKT/Cdc42 signaling by non-canonical pathway, facilitated the formation of filopodia and inhibited the assembly of lamellipodia, and finally resulted in the decrease of migration activity of breast cancer cells.
Evaluating the LoRaWAN Protocol Using a Permanent Outdoor Testbed

Low-power wide-area network (LPWAN) protocols, such as a long range wide area network (LoRaWAN), are key to ensuring scalable wireless communication for Internet of Things devices. In this paper, an analysis of this protocol through a performance evaluation of a permanent outdoor LoRaWAN tested is presented. To ensure accurate results, tests lasted at least 17 h and required 1000 packets per node. The evaluation focused on the impact that the adaptive data rate (ADR) scheme, payload length, link checks, and acknowledgements had on the packet delivery ratio (PDR) of the testbed. The collected data showed that enabling the ADR scheme reduced the PDR. The ADR scheme had six data rates, which consist of a spreading factor and bandwidth combination, to choose from. Analysis revealed that the scheme primarily assigning either the fastest data rate (SF7BW250) or the slowest (SF12BW125) to nodes, regardless of distance. Furthermore, the scheme’s assignments show the signs of oscillation, with nodes being instructed to abruptly change between SFs. The impact of payload length and link checks on the PDR was not pronounced, but enabling acknowledgements did show significant improvements.
Estimation of Flow Rate Through Analysis of Pipe Vibration

Abstract In this paper, implementation of soft sensing technique for measurement of fluid flow rate is reported. The objective of the paper is to design an estimator to physically measure the flow in pipe by analysing the vibration on the walls of the pipe. Commonly used head type flow meter causes obstruction to the flow and measurement would depend on the placement of these sensors. In the proposed technique vibration sensor is bonded on the pipe of liquid flow. It is observed that vibration in the pipe varies with the control action of stem. Single axis accelerometer is used to acquire vibration signal from pipe, signal is passed from the sensor to the system for processing. Basic techniques like filtering, amplification, and Fourier transform are used to process the signal. The obtained transform is trained using neural network algorithm to estimate the fluid flow rate. Artificial neural network is designed using back propagation with artificial bee colony algorithm. Designed estimator after being incorporated in practical setup is subjected to test and the result obtained shows successful estimation of flow rate with the root mean square percentage error of 0.667.
Bisphosphonate-loaded PLGA microspheres for bone regeneration in dental surgery: formulation, characterization, stability, and comprehensive release kinetic studies

Abstract Bone regeneration is critical process in maxillofacial surgery for preventing complications of osteoclastic activity. Alendronate sodium is widely used bisphosphonate for bone regeneration. Its poor-bioavailability and side effects are still challenging. In this study, poly(lactic-co-glycolic acid) (PLGA) microspheres showed sustained release of alendronate sodium to provide potential prolonged residence at the operation site. Characterization, morphology, stability, and release kinetics were analyzed. Release kinetics found to fit multiple models, release mechanism was shown to be combined in relation to Fickian diffusion and polymer erosion. Consequently, alendronate-loaded PLGA microspheres were elucidated in detail and presented as a locally applicable drug delivery system in maxillofacial surgery. Graphical Abstract
Phosphorus recovery from wastewater and bio-based waste: an overview

ABSTRACT Phosphorus is one of the most important macronutrients needed for the growth of plants. The fertilizer production market uses 80% of natural, non-renewable phosphorus resources in the form of phosphate rock. The depletion of those deposits forces a search for other alternatives, including biological waste. This review aims to indicate the most important ways to recover phosphorus from biowaste, with particular emphasis on wastewater, sewage sludge, manure, slaughter or food waste. A comparison of utilized methods and directions for future research based on the latest research is presented. Combining biological, chemical, and physical methods with thermal treatment appears to be the most effective way for the treatment of wastewater sludge in terms of phosphorus recovery. Hydrothermal, thermochemical, and adsorption on thermally treated adsorbents are characterized by a high phosphorus recovery rate (over 95%). For animal by-products and other biological waste, chemical methods seems to be the most optimal solution with a recovery rate over 96%. Due to its large volume and relatively low phosphorus content, wastewater is a resource that requires additional treatment to recover the highest possible amount of phosphorus. Pretreatment of wastewater with combined methods seems to be a possible way to improve phosphorus recovery. A compressive evaluation of combined methods is crucial for future research in this area. GRAPHICAL ABSTRACT
T$k$QS: A Top-$k$ Keyword Query Suggestion System

Providing semantically related keyword queries is useful for users who are unfamiliar with the searching domain or have imprecise ideas about their query intentions. This paper describes a keyword query semantic relationship measuring approach and presents a corresponding demo system TkQS for top- $k$ keyword query suggestion. The demo system consists of two components, coupling relationship measuring component and top@ $k$ query suggestion component. The first component computes the keyword coupling relationships and query semantic relationships while the second component provides top- $k$ semantic relevant queries for the given query. Demo system and experimental results identified the efficiency of our approach.
Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection

Induction motor fault identification is essential to improve efficiency in industrial processes improving costs, production line, and maintenance time. This paper presents a novel motor fault detection methodology based on quaternion signal analysis. The proposed method establishes the quaternion coefficients as the value of motor current measurement, and the variables x, y, and z are the measurements from a triaxial accelerometer mounted on the induction motor chassis. The method obtains the rotation of quaternions and applies quaternion rotation statistics such as mean, cluster shades, and cluster prominence in order to get their features, and these are used to classify the motor state using the proposed tree classification algorithm. This methodology is validated experimentally and compared to other methods to determine the efficiency of this method for feature detection and motor fault identification and classification.
Research on Floating-Point Encryption Based on Extended CryptDB

With the development of cloud storage, the security problems are getting more and more concern. For now, the security of database is ensured mainly by data encryption. While encrypting data, CryptDB empowers encrypted database more computing power. This thesis extends the query computing capacity of CryptDB for floating-point numbers, and realizes many functions, such as the query rewriting, encrypted storage, and UDF function of SUM for floating-point numbers. And a performance test has been done on improved CryptDB by adopting TPCW, which makes CryptDB more practical.
Resilience Interventions Conducted in Western and Eastern Countries—A Systematic Review

Previous research has demonstrated the efficacy of psychological interventions to foster resilience. However, little is known about whether the cultural context in which resilience interventions are implemented affects their efficacy on mental health. Studies performed in Western (k = 175) and Eastern countries (k = 46) regarding different aspects of interventions (setting, mode of delivery, target population, underlying theoretical approach, duration, control group design) and their efficacy on resilience, anxiety, depressive symptoms, quality of life, perceived stress, and social support were compared. Interventions in Eastern countries were longer in duration and tended to be more often conducted in group settings with a focus on family caregivers. We found evidence for larger effect sizes of resilience interventions in Eastern countries for improving resilience (standardized mean difference [SMD] = 0.48, 95% confidence interval [CI] 0.28 to 0.67; p < 0.0001; 43 studies; 6248 participants; I2 = 97.4%). Intercultural differences should receive more attention in resilience intervention research. Future studies could directly compare interventions in different cultural contexts to explain possible underlying causes for differences in their efficacy on mental health outcomes.
Phosphatase GhDsPTP3a interacts with annexin protein GhANN8b to reversely regulate salt tolerance in cotton (Gossypium spp.).

Salinity is among the major factors limiting crop production worldwide. Despite having moderate salt-tolerance, cotton (Gossypium spp.) suffers severe yield losses to salinity stresses, largely due to being grown on saline-alkali and dry lands. To identify genetic determinants conferring salinity tolerance in cotton, we deployed a functional genomic screen using a cotton cDNA library in a virus-induced gene silencing (VIGS) vector. We have revealed that silencing of GhDsPTP3a, which encodes a protein phosphatase, increases cotton tolerance to salt stress. Yeast two-hybrid screens indicated that GhDsPTP3a interacts with GhANN8b, an annexin protein, which plays a positive role in regulating cotton response to salinity stress. Salt stress induces GhANN8b phosphorylation, which is subsequently dephosphorylated by GhDsPTP3a. Ectopic expression of GhDsPTP3a and GhANN8b oppositely regulates plant salt tolerance and calcium influx. In addition, we have revealed that silencing of GhDsPTP3a or GhANN8b exerts opposing roles in regulating GhSOS1 transcript levels, and ectopic expression of GhANN8b elevates Na+ efflux in Arabidopsis under salinity stress. Our study demonstrates that a cotton phosphatase GhDsPTP3a and an annexin protein GhANN8b interact and reversely modulate Ca2+ and Na+ fluxes in cotton salinity responses. This article is protected by copyright. All rights reserved.
Remembering the pain of surgery one year later: a longitudinal examination of anxiety in children's pain memory development.

Children who develop greater negatively biased recall of pain (i.e., recalled pain is higher than the initial pain report) following surgery are at risk for developing chronic pain; therefore, identifying risk factors for the development of biased pain memories is important. Higher anxiety has been implicated in the development of greater negatively biased recall of pain; however, studies have not examined anxiety at multiple time points before and after a surgery and its relationship to children's post-surgical pain memories after one year. This prospective study examined a cohort of 237 children and adolescents undergoing major surgery. Anxiety sensitivity, pain catastrophizing, and pain anxiety were assessed at baseline, 48-72 hours post-surgery, and at 6- and 12-month follow-ups. Pain intensity at rest, movement-evoked pain intensity, and pain unpleasantness were assessed daily in hospital. Memories for pain were elicited via telephone one-year post surgery. Findings revealed that children who had higher levels of anxiety at baseline and 48-72 hours post-surgery developed greater negatively biased recall of pain intensity 12 months after surgery. Specifically, higher anxiety sensitivity at baseline and greater tendencies to catastrophize about pain at baseline and in the immediate acute recovery phase were most strongly linked to greater negatively biased recall of pain. Greater negatively biased recall of pain was related to higher pain intensity at 6- and 12-months post-surgery. Findings support conceptual models of anxiety and pain memory biases and can inform intervention efforts to reduce anxiety in the pre- and post-op periods to minimize negative biases in pain memories.
Perspective: a review of lifecycle management research on complex products in smart-connected environments

Smart-connected technologies, such as the Internet of things and cloud computing, are transforming how industries and enterprises do business by improving the lifecycle management of their product. Therefore, to study the lifecycle management of complex products under smart-connected environment, many studies have been published outlining new methods for smart development, intelligent manufacturing, and data-driven services. This paper first proposes the concept and architecture of smart-connected products and then systematically reviews the current research about new paradigms of product development, manufacturing and services. The paper also summarises the conceptual framework, research issues and opportunities of each phase of the product lifecycle management.
Human Activity Recognition System Based on Channel State Information

Human activity recognition (HAR) based on Channel State Information (CSI) has been widely concerned in recent years due to its properties of low cost and privacy protection. Whats more, unlike vision-based and sensor-based HAR systems, it is not sensitive to illumination intensity and does not need any accessory equipment. The detection threshold in existing HAR system is set manually thus require researchers to reset the threshold when environment changes or a new activity needs to be recognized. The utilization of single classifier with poor robustness cannot achieve a high recognition accuracy. We propose a CSI-based Device-free HAR (CDHAR) system to recognize common human activities, such as ‘Walk’, ‘Run’, ‘Sit down’, ‘Squat’ and ‘Fall down’. First of all, CDHAR obtain an adaptive detection threshold to complete the extraction of activity durations. Second, it proposes a random subspace classifier ensemble method for classification. At last, we prototype CDHAR on commodity WiFi devices and evaluate its performance both in typical indoor and outdoor environments. Our results show even experimental site changes, the extraction accuracy rate can achieve 99.80% and 99.60% in outdoor and indoor environment, respectively. Based on the extracted data, the recognition accuracy rate can reach 91.2and 90.2% in outdoor and indoor environment, respectively. CDHAR solves the problem of setting the detection threshold manually and overcomes the low robustness of the single classifier at the same time. Furthermore, the results in above two scenarios demonstrate that CDHAR can also achieve slightly recognition accuracy improvement over existing recognition methods.
“My World Is Upside Down”: Transnational Iraqi Youth and Parent Perspectives on Resettlement in the United States

ABSTRACT The U.S. war with Iraq led to the displacement of millions of Iraqis, many of whom have resettled in the United States as refugees. We explore the challenges Iraqi families face after resettlement, with a particular focus on the agency of refugees and challenges/opportunities of familial social reproduction in a transnational context. We conducted 181 qualitative interviews with 38 Iraqis (11 youth, 27 adults) and 5 service providers. Our findings highlight the importance of exploring refugee agency and illuminate how the interplay between structure and agency in transnational contexts is a useful framework for understanding transformations around social roles.
Improve Traffic Prediction Using Accident Embedding on Ensemble Deep Neural Networks

Traffic Prediction in a large-scale network has become an important topic of an intelligent transportation system (ITS). The problem is challenging due to various nonlinear temporal and difficulty for longer-step ahead prediction. The recurring congestion on rush-hour and non-recurring congestion on accidents are the main patterns. While most of the previously proposed techniques focus on rush-hour, unexpected accidents that affect local traffic were not considered in these works. To improve the prediction, we propose an ensemble deep neural network to archive the recurring and non-recurring congestion together combining CNN and LSTM architectures. Furthermore, the accident information is utilized and embedded using stacked autoencoder. An experiment was conducted on two data sets: England's Highways and Thailand's Expressways. The results showed that a combination of CNN and LSTM can be improved by using an embedded vector of accident information.
Post-earthquake Search via an Autonomous UAV: Hybrid Algorithm and 3D Path Planning

In the earthquake rescue, the unmanned aerial vehicle (UAV)equipped with radar life detector can be used for trapped people searching. In order to shorten the time of UAV detection, we propose a coupling method of Dijkstra's algorithm and simulated annealing (SA)algorithm to optimize the search path. Concisely, the mathematical model is further abstracted as the Traveling Salesman Problem (TSP)and the shortest loop can be obtained by SA algorithm. We implement this algorithm to a real earthquake event in Jiuzhaigou (Sichuan Province, China). Moreover, the digital elevation model (DEM)data is extracted from Google Earth to demonstrate the real geo-environment. Setting six critical areas as our life detection targets, UAV 3D path simulation is conducted with MATLAB. The results indicate that our method can achieve the function of obstacle avoidance and speed up the search process obviously.
Classification and Interactions of LRR Receptors and Co-receptors Within the Arabidopsis Plasma Membrane – An Overview

Receptor kinases (RK) constitute the largest protein kinase family in plants. In particular, members of the leucine-rich repeat-receptor kinases (LRR-RKs) are involved in the perception of various signals at the plasma membrane. Experimental evidence over the past years revealed a conserved activation mechanism through ligand-inducible heterodimer formation: a ligand is recognized by a receptor kinase with a large extracellular domain (ECD). This ligand binding receptor directly interacts with a so-called co-receptor with a small ECD for ligand fixation and kinase activation. A large proportion of LRR-RKs is functionally still uncharacterized and the dynamic complexity of the plasma membrane makes it difficult to precisely define receptor kinase heterodimer pairs and their functions. In this review, we give an overview of the current knowledge of LRR receptor and co-receptor functions. We use ECD lengths to classify the LRR receptor kinase family and describe different interaction properties of ligand-binding receptors and their respective co-receptor from a network perspective.
Editing Operators for Cross-Sectional Data-Sets

We introduce a tool suite of editing operators for 3D models of free-form objects, which apply on cross-sectional data-sets with the form of generalized cylinders. The objective is to provide an automated framework that allows a model to be modi ed according to the user intentions. A new feature is extracted from the 3D model, called the curve of centroids, which is used as a basis for applying modi cations on the object. A series of editing operators are de ned, which use this curve for performing high level editing on the model. A set of userde ned parameters is used to control the editing process, making the new set of operators easy to use. We present some examples on how these editing operators can be applied on a series of data-sets in various situations, ranging from reverse engineering and digital reconstruction to medical simulations, or for creating works of art.
A Frequency Prediction Method for Frequency-Agile Radar Based on LSTM

The tuning parameters of the frequency-agile radar of rotary-tuned magnetron are time-varying. By detecting self-calibration error, the conventional prediction method determines that the parameters have been changed and then start to accumulate data all over again, during which the data is required, and the error is large. In this paper, the frequency variation of the rotary-tuned magnetron frequency-agile radar with various parameters changing is predicted in real time by using the long short-term memory network. Compared with the existing work, the proposed method not only improves the prediction accuracy, but also has strong adaptability to the time-varying frequency law of multiple segments of data. Therefore, it has better application value and stability. The performance of the proposed algorithm is proved by simulation data experiment.