Miracle-dz/UniFAR-FTUs huggingface dataset:
Benchmarking outcomes for psychological treatments of chronic pain

In an attempt to bridge the widely acknowledged gap between research and clinical practice, this thesis examined the feasibility of benchmarking outcomes for published psychological treatments of chronic pain for application within routine clinical settings. Benchmarking outcomes is relatively common for psychological treatments in the mental health field, but in spite of the prevalence of chronic pain and its known impact on many areas of functioning, the chronic pain literature has previously only considered the generic application of benchmarks for developing services and considering standards for waiting times. Four studies of mixed methodological approaches were conducted. The first aimed to ascertain the extent of similarities between published psychological treatments of chronic pain and treatments delivered in routine clinical settings. This was to ensure that the application of benchmarks from the published literature to routine clinical settings would be meaningful. The second study examined whether the published literature was likely to facilitate the development of benchmarks, and the third sought clarification in terms of outcome domains within which useful benchmarks could be generated. The final study was a meta-analysis of data extracted from the published literature within specified outcome domains. The results suggested that it would be meaningful to apply benchmarks produced from the published literature to routine clinical settings, and that the literature would facilitate the development of benchmarks within several outcome domains. The meta-analysis led to the generation of four benchmarks. These were in the outcome domains of pain experience and physical functioning when compared with waiting list controls, and coping and cognitive appraisal and emotional functioning when compared with active controls. The impact of the design of each study and properties inherent within the literature on the benchmarks generated and their application within routine clinical settings was then considered, prior to suggestions for future research and clinical applications.
Sizing Approach of Distributed Energy Storage System in the Presence of Aggregators

with the development of storage technology, the participation of aggregators in the distribution network investment model has become an emerging business model. Therefore, this paper presented a bi-level optimal configuration model of the distributed energy storage system (DESS) for the distribution network. Firstly, the comprehensive investment benefit of DESS is constructed and network loss sensitivity indicators are introduced to evaluate the planning effect. In the upper objective function, energy storage aggregator maximizes the comprehensive income of DESS investment. And the lower level distribution network operator minimizes the network loss sensitivity of the distributed energy storage system after grid connection and adopts adaptive particles. The adaptive particle swarm algorithm is used to solve the upper and lower layers. Finally, the results of the example analysis verify the correctness and effectiveness of the proposed model and method. Through the decision to couple energy storage aggregator and the distribution network operator, the benefits can be achieved from those subjects.
Clinical patterns of orofacial infections

All patients with oro-facial infections who attended the Oral and Maxillofacial Department, Faculty of Dentistry, University of Malaya from January 1984 to December 1998 were included in this retrospective study. The list of patients were taken from record of patients attendance. The patients’ case notes were retrieved and reviewed using specially designed proforma. The data on patients’ demography, clinical presentation, source and site of infection and treatment were collected. Patients with inadequate information and unavailable case notes were excluded from this study.
Clustering algorithm based battery energy storage performance analysis method

Battery energy storage systems have been widely used in modern power systems. However, for a complex system with huge amount of batteries, the healthy, reliability and safety is still a big challenge. During the work of these systems, while one or several individual cells deteriorate or even broken, the entire battery pack will be affected. Fortunately, with the development of IOT technologies, an increasingly large amount of data can be collected from energy storage stations. Such data may be useful to understand battery status with certain big data tools. In this paper, k-means and DBSCAN clustering algorithm are introduced to identify and deteriorated batteries. Three parameters are proposed from the battery data, as an input model of clustering algorithms. The number of clusters and weight assignment are also adjusted considering battery’s special properties. The research used a lead-carbon energy storage system in establishing the method in the proof of concept work. As the result of clustering, an analysis of battery pack’s consistency was revealed and aged batteries were located. At the end, we verified the results by comparing the charging voltage curves of the batteries. This method may help the researcher to find deteriorated batteries in an easy way.
Automatic Annotation and Segmentation of Object Instances With Deep Active Curve Network

Object instance segmentation is a challenging task in computer vision research, and instance annotation is its key sub-task and a basic component of segmentation models. In this paper, we propose the Deep Active Curve Network (DACN) which combines powerful ResNet models with GCN-based active curves. This new method regards instance segmentation as a points/vertices, edges and object masks prediction task instead of only a pixel-labeling problem. The multi-scale encoder firstly predicts a coarse result through a combination of the edge and segmentation features in a multi-task learning framework, which is effective dealing with objects with rough boundaries. In order to generate an accurate object annotation, an iterative Graph Convolutional Network (GCN) is used to correct the encoder’s feature map and move all vertices of the predicted coarse result to the edge of the corresponding ground truth. A novel weighted loss function further estimates the location of points, edges, and segmented areas, and optimizes the annotation results. Finally, we use the improved 5-interpolation Catmull-Rom spline (CRS) algorithm which exploits key points to control the active curves around objects. In the experimental analysis, we demonstrate the effectiveness of our proposed method on three datasets in automatic mode, including Cityscapes, ADE20K and Rooftop. We further show the generalization ability of our approach on two novel cross-domain datasets.
Modeling of a Tree-Phase 12/8 Pole Switched Reluctance Motor in MATLAB

The paper presents a MATLAB model of a 3 phase 12/8 SRM using equations, which define the dynamic behavior of the linear inductance profile of the motor. The model is simulated and validated through the voltage control strategy and the hysteresis current control method. The accuracy of the model is proved with simulation results.
Full Wave Computation of the Electromagnetic Field in Chaotic Reverberation Chambers

The geometry deformation of a rectangular shaped reverberation chamber (RC) improves the field mixing and the chaoticity within the RC, in addition to traditional stirring techniques. We noted how the position and number of hemispherical diffractors affect the RC, evaluating their typical key performance parameter such as the number of uncorrelated stirrer positions. Full wave finite-difference time-domain simulations were performed for the analysis.
Study of Superconducting Fault Current Limiters for MMC-MTDC with HVDC CBs

To minimize the impact of dc faults in modular multilevel converter based multi-terminal high voltage transmission system (MMC-MT-HVDC, MMC-MTDC), HVDC circuit breakers (HVDC CBs) combined with superconducting fault current limiters (SFCLs) was a preferred choice. For MMC-MTDC with HVDC CBs and SFCLs, this paper firstly analyzed the dc fault characteristics based on the equivalent electrical circuit for different operation stages. Then, this paper introduced the basic principle of resistive SFCLs and inductive SFCLs, and their effect in limiting the dc fault current. Based on the main purposes for installing SFCLs, this paper proposed a dc side main circuit parameters selection method for determining the parameters of the smoothing reactor and the SFCLs. The case studies are performed on a ±500 kV 3-ternimal MMC-MTDC system. The time-domain simulation results showed that both the resistive SFCLs and the inductive SFCLs could limit the dc fault current to a reasonable extend by selecting the proper parameters of the smoothing reactor and the SFCLs, and the energy stress on the voltage limiting element of the HVDC CBs is more severe when using the inductive SFCLs.
The Effect of Demand Variability on Supply Chain Performance

This paper studies the impact of demand variability on supply chain performance which is measured in terms of operational costs, customer satisfaction, and environmental footprint. The operations within the supply chain have been simulated using a supply chain game simulator. The simulation results show that demand variability has a negative impact on all three supply chain performance metrics but mostly on the operational cost. The results also show that there is a significant increase in performance measurements variability with the increase of demand variability. The results provide managers with insights for planning and improving supply chain performance.
Improving CFI of LCC-HVDC Systems Through Voltage Compensation Using UPFC

This paper proposes a voltage compensation scheme (VCS) to improve the commutation failure immunity (CFI) of line-commutated converter-based high voltage direct-current transmission (LCC-HVDC) systems with an unified power flow controller (UPFC). A voltage compensation control system was designed for the UPFC, which enables the UPFC to inject three-phase voltages in series with the receiving-end ac source to compensate the distortion and magnitude drop of the inverter-side ac voltage in the cases where external disturbances occur. In this way, satisfactory commutation voltage is provided for the inverter station of a LCC-HVDC system and CFI of the LCC-HVDC system is improved. Simulation studies were undertaken on the CIGRE LCC-HVDC benchmark system in the cases where single-phase-to-ground faults occurred in the receiving-end ac grid. CFI of the test system having the proposed VCS, a commutation failure prevention method (CFPREV), and a direct-current predictive control strategy (DCPC) implemented respectively was investigated.
Low Complexity Non-Intrusive Load Disaggregation of Air Conditioning Unit and Electric Vehicle Charging

Energy monitoring is inevitable towards achieving energy efficiency and conservation. Load disaggregation is one of the techniques towards effective energy monitoring. In the said domain, Non-Intrusive Appliance Load Monitoring (NIALM) is an attractive method where aggregated load data are acquired from a single metering point and segregated appliance level load is estimated using effective software techniques. This paper presents a low complexity event-based NIALM technique based on supervised machine learning. In this paper, the emphasis is on the disaggregation of Air Conditioning (AC) unit and Electric Vehicle (EV) charging loads due to their high significance for the overall power grid stability improvement. A comprehensive digital simulation has been carried out to validate the performance of the proposed approach and intended appliances are aptly classified having an outcome of 97% for same Data ID and 95% for different Data ID in terms of precision, recall, and f-score performance metrics.
Modeling of Distribution Transformer for Analysis of Core Losses of Different Core Materials Using FEM

The core losses of the transformer are mainly dependent on the material of the transformer core. The material of the transformer core just not minimize the cost of the core losses but also increase the lifetime of the transformer. M5 material cold rolled grain oriented silicon steel is generally used for the manufacturing of the core of the transformer. In this study, 2- dimensional finite element models are used to determine the core losses of the transformer. M5 and M4 cold rolled grain oriented silicon steel materials were used for the manufacturing of the transformer core. In this study, a core loss of the three phase distribution transformer with a power of 400 kVA and a voltage level of 30/0.4 kV was investigated and the results presented in this paper are based on the finite element analysis. The results show that the core losses in the M4 cold rolled grain oriented silicon steel material are lesser than the M5 core material. For validation, the results of the finite element results are compared with the experimental results.
Gerstmann–Sträussler–Scheinker Disease

Gerstmann–Straussler–Scheinker (GSS) disease is a rare human neurodegenerative disease characterized clinically by early cerebellar ataxia and later progressive dementia, and neuropathologically by abundant multicentric amyloid plaques composed of the prion protein (PrP). It belongs to the genetic prion diseases such as genetic or familial Creutzfeldt–Jakob disease (gCJD/fCJD) and fatal familial insomnia (FFI), and is associated with specific missense mutations in the PrP gene (PRNP).
Pricing European Spread Call Options Under the Constant Elasticity of Variance with Time-Dependent Volatility Model

This paper focuses on the pricing of European spread call options. In particular, we consider univariate modelling by proposing constant elasticity of variance with time dependent volatility model and derive new analytic asymptotic approximation formula. In order to measure the accuracy of the derived formula, numerical examples are provided to compare the performance of the analytic asymptotic approximation formula with the exact solution. Results from the numerical examples demonstrate that the new analytic approximation provides an excellent approximation to the exact solution.
Ultra Wideband positioning technology in tracking moving objects in indoor spaces – a pilot study

The article presents a pilot study in which we have undertaken the task of evaluating the performance of Ultra Wideband system in tracking objects moving in indoor spaces. First trials were conducted for different path shapes. For the moving object, average positioning errors of 13.5 cm were noted with the maximum error not exceeding 35 cm. Such positioning error values are sufficient, e.g. to navigate a blind person in unfamiliar indoor spaces.
A 1.6-to-3.0-GHz Fractional- ${N}$ MDLL With a Digital-to-Time Converter Range-Reduction Technique Achieving 397-fs Jitter at 2.5-mW Power

This article analyzes the jitter-power tradeoff in multiplying delay-locked loops (MDLLs), which differs from the more typical phase-locked loop one, and identifies a design optimization criterion. The methodology is applied to a fractional- ${N}$ MDLL with a sub-sampling bang-bang phase detector and a novel digital-to-time converter (DTC) range-reduction technique, which limits the jitter added to the reference signal, at no additional power penalty. The prototype has been implemented in 65-nm CMOS and covers a 1.6-to-3.0-GHz tuning range, achieving an absolute rms jitter (integrated from 30 kHz to 30 MHz) of 397 fs at 2.5-mW power, with a corresponding jitter-power figure of merit of −244 dB. In-band fractional spurs are as low as −51.5 dB and the occupied core area is 0.0275 mm2.
Isolation Forest based Detection for False Data Attacks in Power Systems

Power systems become a primary target of cyber attacks because of the vulnerability of the integrated communication networks. An attacker is able to manipulate the integrity of real-time data by maliciously modifying the readings of meters transmitted to the control center. Moreover, it is demonstrated that such attack can escape the bad data detection in state estimation if the topology and network information of the entire power grid is known to the attacker. In this paper, we propose an isolation forest (IF) based detection algorithm as a countermeasure against false data attack (FDA). This method requires no tedious pre-training procedure to obtain the labels of outliers. In addition, comparing with other algorithms, the IF based detection method can find the outliers quickly. The performance of the proposed detection method is verified using the simulation results on the IEEE 118-bus system.
Low-Cost Precise Vehicle Localization Using Lane Endpoints and Road Signs for Highway Situations

In highways, lane markings are undoubtedly the most widely used landmarks for vehicle localization. However, they have a drawback in that they lack information on longitudinal position estimation and ego-lane identification. To alleviate this drawback, this paper presents a practical vehicle localization system for highways. The proposed system utilizes lane endpoints to enhance longitudinal position accuracy and road signs to improve the ego-lane identification accuracy. This system efficiently fuses the lane markings, lane endpoints and road signs along with a digital map and other low-cost sensors in a particle filter framework. Since it only uses low-cost sensors such as a monocular front-view camera, in-vehicle wheel speed and yaw rate sensors, as well as a low-end global positioning system (GPS), it is ready to mount on mass-produced vehicles. In the experiment, the proposed system was quantitatively evaluated using a dataset obtained while driving on 40 km stretch of highway, and outperformed previous approaches by showing a lateral position error of less than 0.12 m and a longitudinal position error of less than 0.25 m in terms of root mean square error (RMSE).
Functional-Link Neural Network for Nonlinear Equalizer in Coherent Optical Fiber Communications

We propose and experimentally demonstrate a simple nonlinear equalizer based on functional-link neural network (FLNN). The nonlinear stochastic mapping enables FLNN to serve as a nonlinear network, so we construct an FLNN with the signals from the two polarizations and the mapped features as input to combat the fiber nonlinearity in coherent optical transmission systems. The FLNN can use the Moore-Penrose generalized inverse or the ridge regression to solve the weights, which can speed up the training process, and avoid the iterative and time-consuming training process that exist universally in most of the deep neural networks. We also extend the FLNN to the multi-channel transmissions. All of the received signals from different channels are stretched as the input and then we use a joint FLNN to extract features and equalize the nonlinear distortions. We conduct simulations and experiments to verify the proposed scheme. In the simulation and experiment, we transmit a 128 Gb/s polarization division multiplexed 16-QAM (PDM-16-QAM) signal over 1000-km and 600-km standard single mode fiber (SSMF), respectively. Both the simulation and experimental results show that the FLNN has similar performance as deep neural network (DNN), which can improve the transmission performance in the nonlinear region. Moreover, the FLNN can avoid the gradient dissipation and local minimum problems in DNN, which simplify the training process. We also extend the proposed scheme in a five-channel ( $5\times160$ Gb/s) multiplexed transmission system. In simulation, we use joint FLNN and joint DNN to compensate the nonlinear distortions, respectively. We find that the BERs of the five channels can be below 7% HD-FEC with nonlinear equalizer.
Fractional Derivative Modeling of Free Convective Flow over a Vertical Plate with Stability Analysis

The problem fractional derivative of flow and heat transfer by natural convection from a heated semi-infinite wall immersed in a fluid is studied numerically. The time derivative terms in both the momentum and energy equations are assumed fractional. Fractional derivative is a generalization to the normal derivative and it would be interesting to study its possible effects on both velocity and temperature fields. For accurate implementation, it is important that the results obtained by fractional derivative formulations reduce to the results obtained by normal derivative when the order of differentiation becomes the same. In this work, it is found that both the velocity and temperature profiles using fractional derivatives around first order (i.e., smaller and larger than one) encompass those obtained using first order time derivative. Nusselt number variations as well as friction coefficient profiles follow a similar pattern.
Do PISA Scores Relate to Happiness?

The Program for International Student Assessment (PISA) tests the skills and knowledge of 15-year old students across the world in the key subjects of reading, writing, and math. Student achievement on PISA tests is believed to be a solid indicator of a nation’s economic health. However, it is now believed that economic prosperity is not the sole measure of the happiness and well-being of a nation. Therefore, since 2012, the UN Sustainable Development Solutions Network measures the well-being of people using seven key metrics. The objective of this paper is to investigate how PISA scores relate to these metrics of happiness. To examine this relationship, countries which administered PISA tests are first clustered into two groups, namely, High-Achieving and Under-Performing based on their average PISA scores. Subsequently, the average happiness metrics are compared across these two clusters for statistical significance using the t-test. It was found that metrics such as GDP and health differ significantly across the two clusters. However, human-centric, progressive metrics, especially freedom and generosity do not differ significantly across the two clusters. Therefore, although PISA scores do translate into economic prosperity and better health, they do not necessarily lead to more equitable and kinder societies.
KNN-Based Visitor Positioning For Museum Guide System

This study focuses on designing and implementing a Positioning System (PS) that is addressed as a component of the Location-aware Museum Guide System (GMS). The level of accuracy of the Positioning System (PS) is an important aspect in determining the suitability of the information received by the visitors. The design flow of the system begins by identifying the location of implementation. After that, choose the components to build the system. The principle used in this study is the use of existing infrastructure to reduce the cost of system development. A recent study was completed in the museum gathered information about the museum environment to assist with the design process. The system design is proposed by using WLAN technology with RSSI-based fingerprinting techniques. The algorithm used for this fingerprint technique is KNN. The addition of Access Point (AP) and AP filtering methods were also applied to improve the system performance. The test results showed that there were significant differences on accuracy level of PS among three times trial tested to the expectation of accuracy level at 1.2 meter. First trial was without additional support the existing infrastructure in the Museum is unable to provide an accurate estimating position. It was only 3.75 m. The second was by adding five APs from 6 to 11 APs, the accuracy level was 2.55 m. The last was to implement the AP filtering. It can provide improvement to 1.83 m.