Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

Modeling and allocation of the Electric vehicles Charging Stations (EVCS) within the distribution network, as per the growing use of Electric vehicles (EVs) is a challenging task. In this paper, to manage the EVCS, first, with the aim of peak shaving, valley filling, and flattening the load curve of the network, the optimal planning of EV’s charging/discharging is devised. In this regard, after modeling involving random variables, a novel hybrid method, based on the Multi Objective Particle Swarm Optimization (MOPSO) optimization algorithm and sequential Monte Carlo simulation is presented. The purpose of the presented optimal charge/discharge schedule is to control the rate and time of charging/discharging of EVs. In the proposed model, various battery operation strategies, including Uncontrolled Charging Mode (UCM), Controlled Charging Mode (CCM), and Smart Charge/Discharge Mode (SCDM) are also considered. In the next step, in order to implement the charge/discharge schedule of the EVCS profitably, a new formulation is presented for the allocation of two EVCSs (administrative and residential EVCS). In the proposed formulation, various objective functions such as power loss reduction, reducing power purchases from the upstream network, reducing voltage deviation in buses, improving reliability is addressed. Moreover, to incentivize the owner to construct the EVCS with adopted charging/discharging schedule, the optimal profits sharing between Distribution System Operator (DSO) and EVCS owner is also performed. The proposed formulation is applied to a standard network (IEEE 69 buses) and encouraging results are achieved.
Overview and Applicability of Land Use-mixed Indices in the Smart City

A high level of land use mix is one of a strategy of smart growth, new urbanism, smart city and transit orient development theories used to achieve a balance between urban land use form and active travel trips in the cities with a high level of urbanization. This study aims to assess the effectiveness and limitation of land use indices by literature review, and land use status of the central business district (CBD) area applied to indices to show the features. Following the idea of raising the land use mixture in our cities, the quantify steps and indices shows different features and thresholds. After the interpretation of land use-mixed indices, the land use pattern in the Melbourne CBD area was applied to four indices to discuss the limitation of land use-mixed indices. The CBD of Melbourne was divided into 77 grids, and the land use-mixed degrees were established by Entropy index, Dissimilarity index, Herfindahl-Hirschman index, and Gini index. The ten testing groups were also applied to the indices as the reference groups to state the extreme balance of land use mix. The features of four indices were all present after the review and calculation of land use mixed degree.
A Preliminary Study on the Factors Affecting the Adoption of E-Government Services by Malaysians

E-government is no longer stranger to everyone in this technological era. In Malaysia, e-government was used to enhance governmental processes and services and to shorten the time and increase efficiency. It is also known to enhance communication between government, citizens, businesses, agencies. However, it is reported that the government’s objective is not aligned with citizens’ needs in implementing e-government. After the adoption of e-government services by the Malaysian government, it is crucial to study the adoption of these services by Malaysian citizens and the factors which affect this adoption. Through Technology Acceptance Model, the present study has proposed a theoretical framework for the impact of social influence, awareness, perceived usefulness, perceived ease of use, and behavioral intention on the actual usage of online services by Malaysian citizens. The results show that social influence and awareness of the online services are significant predictors of perceived usefulness as well as perceived ease of use. In the future, the proposed framework will be statistically validated by first collecting the data from Malaysian citizens and then analyzing the data with the use of statistical software tools. This study will provide significant value towards the Knowledge Body which pertains to e-services, especially in Malaysia.
Z-MABAC Method for the Selection of Third-Party Logistics Suppliers in Fuzzy Environment

As third-party logistics providers occupy an increasingly important position in the operation strategies of various enterprises, choosing a partner that suits them and can bring benefits to them has become an important decision-making issue for each enterprise. A lot of studies have been done with classical fuzzy methods, but there are relatively few studies that consider information reliability. The emergence of Z-numbers makes up for this deficiency, including the restriction on the evaluation object and the corresponding degree of confidence. In this article, making decisions based on Z-numbers can better transform language values into fuzzy numbers. By integrating the existing Multi-Attributive Border Approximation area Comparison (MABAC) method and Z-numbers, a new method for the selection of third-party logistics providers is provided. Finally, the feasibility and effectiveness of the approach are verified by comparing with the classical Multi Criteria Decision Making methods. The Z-number is a relatively new concept that can flexibly represent the confidence of information, which will be a relatively important research direction in the future.
Camera-LiDAR Data Fusion for Autonomous Mooring Operation

The use of camera and LiDAR sensors to sense the environment has gained increasing popularity in robotics. Individual sensors, such as cameras and LiDARs, fail to meet the growing challenges in complex autonomous systems. One such scenario is autonomous mooring, where the ship has to be tied to a fixed rigid structure (bollard) to keep it stationary safely. The detection and pose estimation of the bollard based on data fusion from the camera and LiDAR are presented here. Firstly, a single shot extrinsic calibration of LiDAR with the camera is presented. Secondly, the camera-LiDAR data fusion method using camera intrinsic parameters and camera to LiDAR extrinsic parameters is proposed. Finally, the use of an image-based segmentation method to segment the corresponding point cloud from the fused camera-LiDAR data is developed and tailored for its application in autonomous mooring operation.
Duration of labor induction in nulliparous women with hypertensive disorders of pregnancy and maternal and neonatal outcomes

Abstract Objective The objective of this study was to quantify the association between duration of labor induction in nulliparous women with hypertensive disorders of pregnancy and maternal and neonatal morbidity. Methods This was a secondary analysis of a multicenter cohort study of 228,438 deliveries in 19 U.S. hospitals. The analysis included nulliparous women ≥18 years old with singleton gestation diagnosed with hypertensive disorders of pregnancy and undergoing induction of labor for that indication. Duration of labor induction, defined as time from admission to delivery, was examined by 4 h intervals from <12 h to ≥24 h in relation to maternal and neonatal composite outcomes. Maternal composite outcome included operative vaginal delivery, chorioamnionitis, blood transfusion, intensive care unit admission, placental abruption, 3rd or 4th degree perineal laceration, endometritis, postpartum hemorrhage, or venous thromboembolism. Neonatal composite outcome included neonatal intensive care unit (NICU) admission, respiratory distress syndrome, 5-minute Apgar score ≤7, seizure, infection, intrapartum meconium aspiration, intracranial hemorrhage, shoulder dystocia, and neonatal death. The trends in proportions of outcomes that occurred at different intervals were examined by Cochran-Armitage trend test. Relative risks were calculated with <12 h as the reference category and potential confounders adjusted by log-binomial or Poisson regression. Possible correlations within centers were taken into account using generalized estimating equations. Results A total of 3,990 women met inclusion criteria. The median labor duration was 19.8 h (interquartile range 12.9 h-27.9h), with 849 (21.3%) lasting <12 h and 1,426 (35.7%) >24 h. The frequency of composite maternal outcome was not associated with labor duration; however, the rates of chorioamnionitis (p < .001) and postpartum hemorrhage (p < .001) increased as labor duration increased. The frequency of composite neonatal outcome was greater with increasing labor duration (p < .001). After multivariable adjustment, duration of labor induction was associated with increased risks of maternal composite outcome after 24 h (aRR 1.39, 95% CI 1.20–1.62) and neonatal composite outcome after 24 h (aRR 1.32, 95% CI 1.11–1.56). Conclusions In nulliparous women with hypertensive disorders of pregnancy, duration of labor induction was associated with increased risks for maternal and neonatal morbidity after 24 h.
Surface-Enhanced Raman Scattering-Based Lateral-Flow Immunoassay

Lateral flow immunoassays (LFIAs) have been developed and used in a wide range of applications, in point-of-care disease diagnoses, environmental safety, and food control. However, in its classical version, it has low sensitivity and can only perform semiquantitative detection, based on colorimetric signals. Over the past decade, surface-enhanced Raman scattering (SERS) tags have been developed in order to decrease the detection limit and enable the quantitative analysis of analytes. Of note, these tags needed new readout systems and signal processing algorithms, while the LFIA design remained unchanged. This review highlights SERS strategies of signal enhancement for LFIAs. The types of labels used, the possible gain in sensitivity from their use, methods of reading and processing the signal, and the prospects for use are discussed.
Work of Breathing Estimation during Spontaneous Breathing Test using Machine Learning Techniques

Prolonged support or premature weaning of mechanical ventilation leads to several complications of cardiopulmonary physiology. Recently, work of breathing is proposed as an alternative that provides objective information about the weaning process. However, the availability and ease of use of techniques for its estimation in a clinical context are limited. Thus, the application of computerized methods for work of breathing estimation becomes necessary to assist professionals. In this article, we compare the performance of different machine learning techniques in the work of breathing estimation tasks. The problem is divided into two classes: high and low work of breathing, based on information extracted from the pressure, volume, and flow signals recorded by the mechanical ventilator. The classification algorithms used were: support vector machines, neural networks, k nearest neighbors, which were trained and tested on ventilatory signals of subjects with high and low work of breathing. The results show that the classification system can recognize the work of breathing levels with an accuracy of up to 80%.
Open-Circuit Fault Diagnosis in Shunt Active Power Filters with No Additional Sensors

A real-time fault diagnosis method for neutral-point-clamped (NPC) active power filters (APF) is proposed. According to the different relationships between the APF’s harmonic current and reference voltage under normal condition and fault condition, the fault diagnosis algorithm can detect and locate the open-circuit faults (OCF) with no additional hardware or sensors. The selection basis of the two thresholds required by the fault diagnosis algorithm is also analyzed in the article. Fault detection can be achieved in just a few sample periods (in favorable conditions). All the signals used in this algorithm have already been employed in the APF’s controller and only addition and comparison are required in the algorithm, which is very simple. The efficiency of the proposed algorithm is verified by the simulation results.
Artificial Intelligence-Based Optimal Grasping Control

A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at the finger from data from the three air pressure sensors, a force estimation was developed based upon the learning of a deep neural network. The data from the three air pressure sensors were utilized as inputs to estimate the contact force at the finger. In the tactile module, the arrival time of the air pressure sensor data has been utilized to recognize the contact point of the robot finger against an object. Using the three air pressure sensors and arrival time, the finger location can be divided into 3 × 3 block locations. The resolution of the contact point recognition was improved to 6 × 4 block locations on the finger using an artificial neural network. The accuracy and effectiveness of the tactile module were verified using real grasping experiments. With this stable grasping, an optimal grasping force was estimated empirically with fuzzy rules for a given object.
Statistical analysis of smartphone MDT signaling power measurements for Radio Maritime LTE propagation study

A recent 3GPP standard feature called Minimization of Drive Test (MDT) enables huge collection of anonymous User Equipment (UE) radio measurements combined with GPS coordinates. This amount of georeferenced data empowers relevant radio coverage and quality analysis in live 4G LTE cellular Networks. In this article a new Radio Maritime propagation analysis is presented, leveraging Reference Signal Received Power smartphones measures produced during one of most crowded regattas in the world, held in Trieste gulf the 13th of October 2019. The opportunity offered by current use of smartphones everywhere, even in boats, and the possibility offered by RSRP data analysis perspective shows how analytics and modelling, applied to massive anonymous MDT radio data, can bring new type of knowledge and innovative ways to monitor sea events. In this work the MDT measurement of actual Received Power is compared with the free-space theoretical one. MDT data catch on the sea area are mainly considered in this study. Discussion about multipath effects on Received Power levels are reported too. The proposed methodology is also useful to predict and to reduce intra-system and inter-system interference issues.
Challenges in Resource-Constrained IoT Devices: Energy and Communication as Critical Success Factors for Future IoT Deployment

Internet of Things (IoT) has been developing to become a free exchange of useful information between multiple real-world devices. Already spread all over the world in the most varied forms and applications, IoT devices need to overcome a series of challenges to respond to the new requirements and demands. The main focus of this manuscript is to establish good practices for the design of IoT devices (i.e., smart devices) with a focus on two main design challenges: power and connectivity. It groups IoT devices in passive, semi-passive, and active, giving details on multiple research topics. Backscatter communication, Wireless Power Transfer (WPT), Energy Harvesting (EH), chipless devices, Simultaneous Wireless Information and Power Transfer (SWIPT), and Wake-Up Radio (WUR) are some examples of the technologies that will be explored in this work.
Prediction of Mental Illness in Heart Disease Patients: Association of Comorbidities, Dietary Supplements, and Antibiotics as Risk Factors

Comorbidities, dietary supplement use, and prescription drug use may negatively (or positively) affect mental health in cardiovascular patients. Although the significance of mental illnesses, such as depression, anxiety, and schizophrenia, on cardiovascular disease is well documented, mental illnesses resulting from heart disease are not well studied. In this paper, we introduce the risk factors of mental illnesses as an exploratory study and develop a prediction framework for mental illness that uses comorbidities, dietary supplements, and drug usage in heart disease patients. Particularly, the data used in this study consist of the records of 68,647 patients with heart disease, including the patient’s mental illness information and the patient’s intake of dietary supplements, antibiotics, and comorbidities. Patients in age groups <61, gender differences, and drug intakes, such as Azithromycin, Clarithromycin, Vitamin B6, and Coenzyme Q10, were associated with mental illness. For predictive modeling, we consider applying various state-of-the-art machine learning techniques with tuned parameters and finally obtain the following: Depression: 78.01% accuracy, 79.13% sensitivity, 72.65% specificity, and 86.26% Area Under the Curve (AUC). Anxiety: 82.93% accuracy, 82.86% sensitivity, 83.35% specificity, and 88.45% AUC. Schizophrenia: 87.59% accuracy, 87.70% sensitivity, 85.14% specificity, and 92.73% AUC. Disease: 86.63% accuracy, 95.50% sensitivity, 77.76% specificity, and 91.59% AUC. From the results, we conclude that using heart disease information, comorbidities, dietary supplement use, and antibiotics enables us to accurately predict the mental health outcome.
Fluoroquinolones are associated with increased risk of aortic aneurysm or dissection: systematic review and meta-analysis.

Fluoroquinolone use has been associated with collagen disease events, raising safety concerns. We hypothesized that the use of fluoroquinolones is associated with aortic aneurysm (AA) and aortic dissection or rupture (AD/AR). We performed a systematic review with meta-analyses on studies published until March 2019. Seven observational studies were included, comprising 2 851 646 participants. The studies were evaluated regarding their risk of bias. Results on fluoroquinolone use risk comparing with non-treatment and with beta-lactam antibiotic use were extracted. The estimates were pooled through a random-effects model meta-analysis and heterogeneity assessed through the I2 statistic. Sensitivity analysis were performed, grouping studies per design and with exclusion of studies with critical risk of bias. Fluoroquinolone use was associated with a higher risk of AA/AD/AR, comparing with a non-treatment intervention (OR=2.26; 95% CI 1.93 to 2.65; I2=30%) and comparing with a beta-lactam intervention (OR=1.56; 95% CI 1.37 to 1.79; I2=0%). This harm effect remained significant when pooling the results for the AD/AR outcome only and across various study designs. Studies comparing with beta-lactam intervention were considered to have a moderate risk of bias, while the remaining ones were classified as having at least a serious risk of bias. All evaluated outcomes had very low Grading of Recommendation, Assessment, Development and Evaluation (GRADE) evidence. Fluoroquinolone use was associated with a significant risk of AA/AD/AR.
Versatility-preserving Multi-omics Data Analysis by Ranking the Nodes in Multilayer Network

The determination of hub nodes in complex biological networks is important as they propagate major information and significantly interact with other nodes. The challenging problem is to identify those nodes in biological networked systems that are characterized by different types of genetic interactions, constructing a multilayer network. Here we describe an algorithm that allows us to calculate the degree centrality of each node in multiple networks and as a result, we rank the nodes. The main objective is to identify those nodes preserving the versatility in the multilayer network. These nodes play the most vital roles in the whole interconnected structure, connecting together various types of relations. To demonstrate the effectiveness of the proposed approach, gene (mRNA) expression and methylation data are integrated to identify the most influential nodes. In this regard, a novel method is proposed that ranks the nodes on the basis of their score in the multilayer network. The ranking method imports a weight for every node. This study focuses on feature selection by integrating multiple data in terms of complex networks.
Animal Models: A Useful Tool to Unveil Metabolic Changes in Hepatocellular Carcinoma

Simple Summary Hepatocellular carcinoma (HCC) represents an important health problem. At the moment, systemic therapies offered only modest clinical benefits. Thus, HCC represents a cancer extremely difficult to treat, and therapeutic breakthroughs are urgently needed. Metabolic reprogramming of neoplastic cells has been recognized as one of the core hallmarks of cancer. Experimental animal models represent an important tool that allows to investigate metabolic changes underlying HCC development and progression. In the present review, we characterize available rodent models of hepatocarcinogenesis. Moreover, we discuss the possibility that pharmacological targeting of Warburg metabolism may represent an additional tool to improve already available therapeutic approaches for HCC. Abstract Hepatocellular carcinoma (HCC) is one the most frequent and lethal human cancers. At present, no effective treatment for advanced HCC exist; therefore, the overall prognosis for HCC patients remains dismal. In recent years, a better knowledge of the signaling pathways involved in the regulation of HCC development and progression, has led to the identification of novel potential targets for therapeutic strategies. However, the obtained benefits from current therapeutic options are disappointing. Altered cancer metabolism has become a topic of renewed interest in the last decades, and it has been included among the core hallmarks of cancer. In the light of growing evidence for metabolic reprogramming in cancer, a wide number of experimental animal models have been exploited to study metabolic changes characterizing HCC development and progression and to further expand our knowledge of this tumor. In the present review, we discuss several rodent models of hepatocarcinogenesis, that contributed to elucidate the metabolic profile of HCC and the implications of these changes in modulating the aggressiveness of neoplastic cells. We also highlight the apparently contrasting results stemming from different animal models. Finally, we analyze whether these observations could be exploited to improve current therapeutic strategies for HCC.
Machine Learning based Resource Allocation Strategy for Network Slicing in Vehicular Networks

To deal with the lack of prediction and management for vehicular network slice in existing research, this paper designs a machine learning based resource allocation strategy for vehicular network slicing. Firstly, a traffic prediction mechanism based on Convolutional Long Short-Term Memory (ConvLSTM) is proposed, which will capture the spatial-temporal dependencies of the traffic to predict traffic of complex slice services in the vehicular networks. Secondly, considering the imbalance of wireless resource utilization caused by the space-time difference between application scenarios, a shared proportional fairness scheme is proposed to achieve efficient and differentiated utilization of wireless resources. Finally, on the basis of ensuring the demand of each slice, the resource allocation algorithm based on the primal-dual interior-point method is used to solve the optimal slice weight allocation to minimize the system delay. Simulation results show that the service traffic prediction mechanism can be used to predict service traffic in the future. The average error rates of SMS, phone, and web traffic will be reduced, so that the user load distribution can be obtained a priori. Based on the predicted load distribution, slice weight distribution is performed in advance so that arranging delay is saved. The resource allocation algorithm based on the primal-dual interior-point method can well calculate the optimal slice weight distribution at this time.