Miracle-dz/UniFAR-FTUs huggingface dataset:
A cooperative framework for Universal Basic Mobility System: Mobility credits approach

Development of integrated mobility and traffic management strategies is an important aspect of the ongoing transition of urban mobility systems. Extending from existing credit schemes, this research presents a system design and evaluation of a framework based on the principle of Universal Basic Mobility. In particular, using premises of long-term cooperation and hierarchical self-organization, the system design includes user-based Mobility Credits interrelated with Priority Levels. To complement the cooperation framework, system architecture is formulated in line with the distributed ledger technology. The proposed framework is tested using web-based interaction in the form of stated-preference experiment. Results are analyzed through statistical distributions and a discrete-choice model of user decision-making within the proposed framework. This research concludes that this framework could nudge uses towards reciprocity and altruism in their travelling behavior. In addition, experiment participants have provided a range of comments related to positive features, potential for failure, and further development. Finally, the paper ends by raising several implications for wider citizen participation in the integrated mobility system design and evaluation.
Measurements on a 12.5/17.5 kV Inductive Adder with Extremely High Stability for the Damping Ring Kickers of the Compact Linear Collider at CERN

The specifications for the kicker systems for the Compact Linear Collider call for 900 ns duration pulses of ±12.5/±17.5 kV, of up to 433 A, with combined ripple and droop of not more than ±0.02% at 12.5 kV. Measured pulse waveforms of 12.5/17.5 kV prototypes have demonstrated ±0.02%/±0.2% relative waveform stability, respectively. In addition, measurements show that it is feasible to operate the inductive adder in two modes, at 12.5 kV for normal operation and 17.5 kV for dumping the beam: this inductive adder can still achieve the stringent stability specification of ±0.02% at 12.5 kV.
A Proposed Capacitor Voltage-Balancing Strategy for Double-Y STATCOM Operated Under Unbalanced Conditions

This paper proposes a novel capacitor voltage-balancing strategy for the double-Y MMC STATCOM operated under unbalanced conditions. The proposed method, based on zero-sequence voltage injection, manipulates the current distribution between the converter arms to minimize the converter ratings. The proposed balancing strategy is compared with the classical approach based on controlling the circulating dc currents. For the same unbalanced conditions, the proposed strategy leads to lower energy rating for the converter as compared with the classical one, while the semiconductor ratings remain the same in both strategies. Theoretical findings are verified through simulation results.
Classification of FACS-Action Units with CNN Trained from Emotion Labelled Data Sets

This paper explores the adaptation of a convolutional neural network (CNN) designed for emotion classification to the extraction of human expressions action units. An adaptation of the network structure transforms a single label into a multi-label classifier which supports the simultaneous recognition of multiple action units that compose human expressions, according to Ekman’s FACS. The dataset used for this work was FER-2013 that includes exemplars of seven basic expressions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral). In order to enhance the quality of the results, the dataset was augmented with random perturbations from a wide set including: translation, scale and horizontal flip. The results obtained demonstrate that it is possible to train a CNN for multi-label expression classification from an emotion-labelled dataset.
A Matrix-Decomposition-Based Context Tensor Approach for Personalized Travel Time Estimation

In this paper, we use the matrix-decomposition-based context-aware tensor approach to estimate the user's personalized travel time. The model mainly has the following steps: firstly, by dealing with the GPS trajectories received in current time period to filter the appropriate data and the filtered data is expanded into a third-order tensor, corresponding to the driver, the road segment and the period of time. Sorting the GPS data of the past period into a third-order tensor of the same form. Each data in the tensor represents the travel time of a driver on a certain road during a certain period of time. Secondly, based on the definition of the third-order tensor, the third-order tensor can be considered as a combination of multiple second-order tensors (matrices). According to the slicing operation, the matrix of the invisible position is taken from the tensor, and the pre-processing is performed by using matrix decomposition to fill the vacancy. Thirdly, the feature matrix of different time space and geographic location, i.e. the context information, is extracted by the known GPS trajectory dataset to decompose the travel-time tensor, and then the time cost of a certain driver on a certain road in a certain period is obtained. Personalized travel time forecasts provide users with more choices of travel options and travel routes.
Synchronization and Current Sharing for Nonlinear-oscillator-based Inverters in Islanded Three-phase Microgrid

This paper presents a method which mimics coupled nonlinear Poincaré–Bendixson oscillators synchronization to realize current sharing in islanded three-phase Microgrid. We propose that each inverter be controlled to imitate the dynamics of coupled oscillators. When they are controlled to interact strongly enough, they will all start synchronization. We also introduce the comparison between Van der Pol oscillator corresponding to their physical characteristics. The dual-loop based on the oscillator is designed in αβ frame, and the phase dynamics are derived to observe system initial state performance. The behavior and performance of the proposed method were verified experimentally regarding load connection, inverter removal and current ratios changes.
Implementing violence and incident reporting measures on a forensic mental health unit

The assessment of risk and prediction of violence in mental health units can play a large role in creating a safer environment for both the staff and the patients. Nurses in forensic units are in a unique position in regards to assessment of violence as they spend a great deal of time with the patients. Nurses on a forensic mental health unit scored the Brøset Violence Checklist (BVC) twice daily for 12 weeks for all patients either resident on or admitted to the unit (N = 46). The Staff Observation Aggression Scale-Revised (SOAS-R) was used to report any adverse incidents (N = 51). Data were examined at the both the item and scale level. Main results showed the area under the curve values of the BVC score, slide rule, and the sum of BVC and slide rule score in turn demonstrated strong predictive accuracy for inpatient aggression (0.68–0.73). Through logistic regression analyses the BVC uniquely predicted inpatient aggression but adding the slide rule did not improve prediction. Predictive accuracy was found across three diagnostic groups – dementia, psychosis and substance use disorders. These results provide further support on the predictive accuracy of the BVC for short-term violence in forensic mental health settings.
Pedestrians’ Acceptance of Personal Mobility Devices on the Shared Path: A Structural Equation Modelling Approach

In order to promote active mobility, a pleasant and safe walking experience should be provided. However, the increasing use of personal mobility devices (PMDs) on footpaths has created new challenges for urban designers in Singapore. In this study, we conducted an online questionnaire to understand pedestrians’ attitudes towards the use of PMDs on a shared path. The main hypothesis was that, in accordance with the technology acceptance model (TAM), pedestrians who have experienced PMDs will tend to be accommodating of them. Based on 303 valid responses, this study analysed pedestrians’ levels of acceptance of PMDs. Structural equation modelling (SEM) was used to evaluate five constructs: the intention to use a PMD, ease of use, usefulness, perceived risk from PMD riders, and the environment. The results indicate that prior PMD riding experience does not affect the subject’s degree of acceptance of PMDs on a shared path. A high correlation was found between the environment and intention to use, as well as the perceived risk from PMD riders and the speed of the PMD. The findings of this study will provide insights for planners in making walking on a shared path a more pleasant experience for pedestrians while introducing PMDs as a new mobility service.
Hospital waste analysis in Kashan in 1380-81

Background : Waste materials are a matter of concern in most societies. In our country there is no documented analysis of these waste materials, thus, the resent study was designed to analyze the hospital wastes physically. Materials and methods : It was a descriptive study. Samples were gathered from hospital wastes of Shahid Beheshti, Shabih Khani, Matini, Naqavi, and Akhavan. Totally, 60 samples were gathered during the study. Then, a 50-kg sample was prepared, for which, waste analysis was performed according to different particles. Results : Totally, 15012 kg wastes were gathered. Shahid Beheshti had the highest and Matini the lowest wastes. Different particles were as follow: decayed materials 15.7%, plastic 13%, papers 5%, cloth 4.5%, glass 3.7%, metals 2.1%, and nylon 1.9%. Average monthly amount of waste for each Shahid Beheshti hospital bed was 2.9kg, for Naqavi, Shabih Khani, Matini and Akhavan hospitals were 3.6, 5.9, 4.5, and 3.7 kg, respectively. Conclusion : The monthly average amount of wastes is high in Kashan hospitals. It is worth surveying.
Multiple UAVs Formation for Emergency Equipment and Medicines Delivery Based on Optimal Fractional Order Controllers

This article studies a leader-follower formation algorithm based on fractional order proportional-derivative (FOPD) controller for multiple unmanned aerial vehicles (UAVs) when tackling an emergency health case. The controller parameters are tuned by using a particle swarm optimization (PSO) algorithm. Its performance is compared with an integer order proportional-derivative (IOPD) controller. Finally, the global path planning for the UAVs swarm is found by using a Dijkstra’s algorithm with quintic polynomial trajectory. This provides an optimal global path by considering the physical system dimension and constraints of acceleration and velocity of the UAV. The simulation tests using the virtual environment demonstrate that the proposed approach outperforms the IOPD controller.
Spatiotemporal Structures of Time Lags in the Brain as Revealed by Magnetoencephalography

Functional magnetic resonance imaging (fMRI) studies in humans have recently provided evidence of intrinsic activity that propagates throughout the brain with non-zero time lags. Nevertheless, the limited temporal resolution of fMRI does not allow to characterize this lagged propagation on time scales relevant to behavior, i.e., in the 10-100 millisecond range. Thus, a millisecond resolution technique such as magnetoencephalography (MEG) is needed. Here we aim at investigating the spatiotemporal structure of intrinsic propagated activity on 61 resting state MEG subjects taken from the Human Connectome Project (HCP) database. For this purpose, we relied on a multivariate connectivity approach based on the cross-correlation between the amplitude envelopes of the alpha frequency band signals associated with cortical areas. Our findings clearly revealed that patterns of time lags between regions exist on temporal scales of tens of milliseconds, thus supporting the notion that communication by relaying signals is a central mechanism implemented by the brain. Finally, we discriminated between cortical regions that initiate the intrinsic activity propagation (sources) and cortical regions that act as destinations of the activity propagation (sinks). We found significant sources in the bilateral parietal areas and in the precuneus, and significant sinks in the left inferior temporal cortex.
Optimal DC-Link RC Snubber Design for SiC MOSFET Applications

This paper presents a design methodology for an optimal DC-link RC snubber that can effectively suppress the parasitic ringing in high-speed, hard-switched converters with modern SiC MOSFETs. Unlike the traditional RC snubber across the switch, a snubber across the DC-link dampens only the high-frequency ringing and doesn’t increase the switching loss. By analyzing a small-signal impedance network relating the time-domain ringing to the frequency-domain Bode plot, this work derives the R-C combination that achieves the best damping effect. Closed-form expressions are developed so that this method can be easily implemented in actual designs.
Predictive Current Control of Mutually Coupled Switched Reluctance Motors Using Net Flux Method

This work presents a fixed switching frequency predictive current controller for Mutually Coupled Switched Reluctance Machines (MCSRMs). For high performance applications requiring high precision torque control, it is necessary to have high bandwidth current regulators. This can be satisfied by using a digital deadbeat control strategy. Since the controller is model based, it is essential to estimate the mutual flux accurately. This can be challenging in the case of reluctance machines because of the magnetic non-linearity, especially in the saturation region. In order to have an accurate reference current tracking, the controller is developed on the machine model which is based on the Net Flux Method (NFM). Dynamic simulations are carried out in Matlab/Simulink to control a 12/8, 1 HP MCSRM. The results are presented and compared with the performance of proportional integral (PI) controller.
Novel Dual-Sided Permanent Magnet Machines with Different Stator Magnet Arrangements

This paper proposes two novel dual-sided permanent magnets (DSPM) machines with different stator magnet arrangements, i.e., consequent pole (CP) and U-shaped interior magnets. Due to the dual PM field modulation effect, abundant air-gap field harmonics are produced to further improve torque capability. The machine topologies and working mechanism are introduced from the perspective of a simple PM magneto-motive force (MMF)-permeance based model. Then, global optimization is performed to optimize the design parameters in order to obtain the best torque quality. The electromagnetic characteristics of the proposed DSPM machines having selected stator/rotor pole combinations are comparatively investigated by finite element (FE) method, which can provide guidance for the stator magnet design for the proposed DSPM machines.
A Bayesian Spatiotemporal Approach for Bus Speed Modeling*

Bus speed modeling is essential for effective operation and management of public transit systems. Space-time interaction patterns are being ignored when modeling bus speed, and this would lead to biased statistical inferences. This paper proposed a spatiotemporal Bayesian model to characterize space-time interaction patterns among road segments using large-scale bus GPS data and to further develop the bus speed prediction model based on that. Results showed that a type II interaction pattern existed in the data, and the mean absolute percentage errors (MAPEs) of the test sets were 11.3% for the AM peak and 22.5% for the PM peak. Results were further compared with existing work. It was found that the proposed model presented a superior predictive performance while keeping the interpretability of contributing factors and space-time interaction patterns.
TAXONOMIC AND PHARMACOGNOSTIC AUTHENTICATION OF PROBLEMATIC MEDICINAL PLANTS USED AS HERBAL MEDICINE

The present research project pertains to detailed taxonomic and pharmacognositc studies of seven herbal medicines namely Neem (Azadirachta indica) the officinal part of this plant that is leaves and aerial parts used in traditional medicines for diabetes, skin diseases, malaria, hepatitis etc.; Green Tea (Camellia sinensis) the leaves of this plant are used for digestion, anaxiety, cholesterol and pain; Sana Makki (Cassia angustifolia) the aerial parts of this plant are used for constipation, piles, migraine, heart diseases etc.; Brahmi (Centella asiatica) the aerial parts are used for wounds, digestion, mental depression etc. ; Lemon Grass (Cymbopogon citratus) the aerial parts are used for cough, cold, fever, flu and allergy; Henna (Lawsonia inermis) the leaves and powdered leaves are used for hair, jaundice and skin diseases and Tobacco (Nicotiana tabaccum) the leaves are used for wounds, baldness, pneumonia etc.The present study was aimed to investigate indigenous medicinal uses, marketing status, macro and microscopical characters (LM & SEM) of pollen, foliar epidermal anatomy, behavior of powdered drug on treatment with different chemical reagents, fluorescence analysis (under visible & UV light) and preliminary phytochmeical tests to differentiate the genuine source from its adulterant.Such investigation may provide basis for authentication, standardization and characterization of genuine drug.The details of information regarding the nomenclature, distribution, organoleptic analysis, macro and microscopic characters, solubility tests, fluorescence analysis, preliminary phytochmeical screening and pictorial guide established in this study facilitate in authentication of genuine drug Azadirachta indica (Neem) from its substitute Melia azedarach, Camellia sinensis (Green Tea) from Chenopodium ambrosoides, Cassia angustifolia (Sana Makki) from Cassia obtusifolia, Centella asiatica (Brahmi) from Bacopa monnieri, Cymbopogon citratus (Lemon Grass) from Cymbopogon jwarancusa, Lawsonia inermis (Henna) from Mirabilus jalapa and Nicotiana tabaccum (Tobbaco) from Helianthus annuus.It is concluded form this study that such type of findings will be useful in preparation of monographs on these plants particularly and establishing taxo-pharmacognostic standardization on identification, quality, purity and classification of the medicinal plants as per WHO guidelines for drug discovery development at global perspectives.
Power quality enhancement by SiC Active Power Filters in Oil and Gas Platforms

This paper presents a study for active power filters’ (APFs) application in a typical offshore O&G platform, where reactive power compensation and current harmonic mitigation are often needed to meet minimum power quality requirements. As size and weight are critical constraints in offshore installations, possible benefits of using Silicon Carbide (SiC) switches for the APF implementation are also investigated. Different compensation strategies have been compared, varying the connection point of the APF between two different voltage levels and assigning the APFs different compensation targets. Improvements in Power Quality (PQ) indexes as well as APFs rating, efficiency and design complexity have been considered for both SiC and Silicon-based solutions to identify trade-offs suitable for the considered application.
A Novel Dual-Sided PM Machine with Stator Spoke-Type PM Structure

This paper proposes a novel dual-sided permanent magnet (DSPM) machine with stator spoke-type PMs, which employs a dual PM field modulation effect to enhance the effective harmonics and torque capability. The machine topology and torque production mechanism are described first. The main design parameters of the DSPM machines with different stator/rotor pole combinations are globally optimized. The electromagnetic characteristics of the selected cases are investigated by finite element (FE) method. The simulation results show that the DSPM machine with 6/14-pole combination exhibits the highest torque density of 34.5 Nm/L, which confirms the effectiveness of the proposed DSPM design for the torque improvement.
Improving Inertial Velocity Estimation Through Magnetic Field Gradient-based Extended Kalman Filter

In this paper we focus on the velocity estimation problem of a rigid body and how to improve it with magneto-inertial sensors-based theory. We provide a continuous-time model that describes the motion of the body and we augment it after by introducing a new magnetic field gradient equation instead of using its value directly as an input for the model, as done usually in the corresponding literature. We investigate the advantage of moving to higher order spatial derivatives of the magnetic field in the estimation of velocity. These derivatives are computed thanks to a determined arrangement of magnetometers array. Within this framework, a specific set configuration of Extended Kalman Filters (EKFs) is proposed to focus mainly on the estimation of velocity and attitude of the body, but includes also an estimation of the magnetic field and its gradient. Some simulations for a specific scenario are proposed to show the improvements that we bring to the velocity estimation.
Accuracy Assessment and Learned Error Mitigation of UWB ToF Ranging

Ultra-wideband (UWB) time of flight (ToF) ranging is nowadays one of the most attractive technologies to implement indoor localization solutions with reliable dm-level accuracy. UWB systems are generally resistant to multipath interference. However, non-line-of-sight (NLOS) components with small relative delays may introduce errors in the estimated distances well exceeding the typically achievable accuracy. We present an empirical accuracy assessment of UWB ranging using a commercial UWB system. A particular focus is on the magnitude and spatial patterns of multipath errors. A large dataset comprising distances between 0.2 and 100 m was collected in a geodetic metrology lab and outdoors for this purpose. We derived ground truth of the distances with superior accuracy using a laser tracker and a total station. The results show that multipath errors are highly repeatable if the geometrical configuration does not change, but can degrade the accuracy of the distance measurements locally by almost an order of magnitude. While typical biases were on the order of a few cm, peak values exceeding 60 cm were found, and the standard deviation, typically about 3 cm or less, increased up to almost 15 cm in certain locations. We have used a part of the large dataset to train an artificial neural network (ANN) for error prediction. The ANN uses the measured ranges and additional signal features, also provided by the ranging nodes, as inputs. For error mitigation, the ANN output was subtracted from the original range measurements. This improved the ranging accuracy over the whole test data by practically eliminating the bias on average and reducing the overall standard deviation by a factor of 3. The results indicate that the relation between the output of the UWB node and the range errors can be modeled to a practically useful degree even without in-depth knowledge of the specific UWB node’s measurement process and characteristics. The proposed approach can help to significantly improve the UWB ranging performance in presence of multipath and thus also the accuracy of a corresponding localization system.