Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship of illness perceptions with demographic features, pain severity, functional capacity, disability, depression, and quality of life in patients with chronic low back pain.

Objectives The aim of this study was to evaluate the relationship of illness perceptions (IPs) with demographic features, severity of pain, functional capacity, disability, depression, and quality of life in patients with chronic low back pain (CLBP). Patients and methods Between January 2015 and July 2015, a total of 114 patients with non-specific CLBP (86 females, 28 males; mean age 47.1±15.2 years; range, 18 to 85 years) were included. Non-specific CLBP was defined as low back pain not attributable to a recognizable, known specific pathology such as infection, tumor, inflammation for ≥12 weeks. The IPs using the revised Illness Perception Questionnaire (IPQ-R), pain severity using the visual analog scale (VAS), functional capacity using the Six-Minute Walk Test (6MWT), disability using the modified Oswestry Disability Index (m-ODI), depression using the Beck Depression Inventory (BDI), and quality of life using the Short Form-36 (SF-36) were assessed. Results There was a significant, positive correlation between the age, body mass index, duration of disease, pain scores, and IPQ-R- consequences, timeline (acute/chronic), and emotional responses subunits, whereas there was a significant, negative correlation between the IPQ-R-personal and treatment control subunits (p<0.001). The IPQ-R-timeline (acute/chronic), consequences, and emotional response subunits were positively and personal and treatment controls and illness coherence subunits were negatively correlated with the BDI and m-ODI (p<0.001). The IPQ-R-consequences and emotional responses subunits were negatively and timeline (acute/chronic), personal and treatment controls, and illness coherence subunits were positively correlated with the SF-36 subunits (p<0.05). Conclusion The IPs were negatively affected by advanced age, high body mass index, longer duration of disease, and increased severity of pain in CLBP patients. Based on these findings, positive IPs may be related with reduced disability and depression, and improved quality of life and functional capacity in this patient population. Developing new strategies for improving the negative IPs of patients with CLBP may be useful.
A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics

Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.
The Evolving Protein Engineering in the Design of Chimeric Antigen Receptor T Cells

The clinical success of chimeric antigen receptor (CAR) T cell immunotherapy in the treatment of haematological cancers has encouraged the extensive development of CAR design to improve their function and increase their applicability. Advancements in protein engineering have seen modifications to both the ecto- and endo-domains of the CAR, with recent designs targeting multiple antigens and including inducible elements. These developments are likely to play an important role in inducing effective CAR T cell responses in a solid tumour context, where clinical responses have not been effective to date. This review highlights the spectrum of novel strategies being employed in CAR design, including for example variations in targeting tumour antigens by utilising different ectodomain designs such as dual chain CARs, natural receptor or ligand-based CARs, and T cell receptor fusion constructs, and also reviews some of the innovative approaches to a “universal” CAR and various multi-antigen targeting CAR strategies. We also explore how choices in the endodomain impact CAR function and how these need to be considered in the overall CAR design.
A Distributed Multi-Tier Emergency Alerting System Exploiting Sensors-Based Event Detection to Support Smart City Applications

The development of efficient sensing technologies and the maturation of the Internet of Things (IoT) paradigm and related protocols have considerably fostered the expansion of sensor-based monitoring applications. A great number of those applications has been developed to monitor a set of information for better perception of the environment, with some of them being dedicated to identifying emergency situations. Current IoT-based emergency systems have limitations when considering the broader scope of smart cities, exploiting one or just a few monitoring variables or even allocating high computational burden to regular sensor nodes. In this context, we propose a distributed multi-tier emergency alerting system built around a number of sensor-based event detection units, providing real-time georeferenced information about the occurrence of critical events, while taking as input a configurable number of different scalar sensors and GPS data. The proposed system could then be used to detect and to deliver emergency alarms, which are computed based on the detected events, the previously known risk level of the affected areas and temporal information. Doing so, modularized and flexible perceptions of critical events are provided, according to the particularities of each considered smart city scenario. Besides implementing the proposed system in open-source electronic platforms, we also created a real-time visualization application to dynamically display emergency alarms on a map, demonstrating a feasible and useful application of the system as a supporting service. Therefore, this innovative approach and its corresponding physical implementation can bring valuable results for smart cities, potentially supporting the development of adaptive IoT-based emergency-aware applications.
A UNIVERSAL ECLECTIC GENETIC ALGORITHM FOR CONSTRAINED OPTIMIZATION

Several non-traditional methods of solving nonlinear constrained optimization problems have been introduced in the past: evolutionary programming, genetic algorithms and simulated annealing, among others. However these methods present some limitations. In this paper we describe a GA which departs from canonical GAs (CGA) and achieves most of the characteristics of what, in the literature, has been called an Idealized GA (IGA). This GA is designed to approach the IGA and we have it called an Eclectic Genetic Algorithm (EGA). We describe the application of an EGA to the solution of a set of six constrained nonlinear problems which have been recently discussed in the literature. Also the performance of an EGA is compared those of the CGA and eliTist GA (TGA).
Integrating Scholarly Data with Knowledge Graphs

Scholarly data platforms have been extensively established internationally. However, although in the Brazilian academic community there is large academic research platforms, e.g., Lattes, containing incredible amounts of information, it does not offer a structured way to query and analyze this data. Such information remains in an isolated way without connections with other sources, which prevents interesting data analyses. In this work, we design and develop the Knowlattes Graph framework for generating Knowledge graphs from scholarly data sources. We explore the current Lattes platform by extracting information from it to create a RDF triple store. In addition, we provide techniques to create links to external triple datasets, building a knowledge graph for scholarly data containing Brazilians researchers and publications.
Challenges and Opportunities in Utilizing IoT-Based Stress Maps as a Community Mood Detector

Stress has been known to cause physical and mental issues like depression, anxiety, insomnia, lower immunity, stroke, as well as leading to suicidal thoughts or violence towards others. Stress is not just a state of mind, but it is measurable. With the ubiquity of Internet of Things (IoT), and the integration with highly sensitive biosensors, it may be feasible to use these devices for detecting stress in public places. Moreover, correlating such stress data with social media streams can lead to insights into the psychological well-being of the community as a whole. We present a framework of such a community stress map based on social media and explore techniques for gathering data for measuring stress levels as well as detecting abnormal levels. This stress map can then be leveraged by emergency and crisis response teams for public safety and help them be proactive in allocating resources to the stressed areas indicated in the map.
Component-Based Interactive Framework for Intelligent Transportation Cyber-Physical Systems

While emerging technology for self-driving automation in vehicles progresses rapidly, the transition to an era of roads full of fully connected and automated vehicles (CAVs) may take longer than expected. Until then, it is inevitable that CAVs should coexist and interact with drivers of non-autonomous vehicles (NAVs) in urban roads. During this period of transition, it is critical to provide road safety with the mixed vehicular traffic and uncertainty caused by human drivers. To investigate the issues caused by the coexistence and interaction with humans, we propose to build a component-based and interactive intelligent transportation cyber-physical systems (ITCPS) framework. Our design of the interactive ITCPS framework aims to provide a standardized structure for users by defining core components. The framework is specified by behavior models and interfaces for the desired ITCPS components and is implemented as a form of human and hardware-in-the-loop system. We developed an intersection crossing assistance service and an automatic emergency braking service as an example of practical applications using the framework. To evaluate the framework, we tested its performance to show how effectively it operates while supporting real-time processing. The results indicate that it satisfies the timing requirements of vehicle-to-vehicle (V2V) communication and the limited processing time required for performing the functions of behavior models, even though the traffic volume reaches the road capacity. A case study using statistical analysis is conducted to assess the practical value of the developed experimental environment. The results of the case study validate the reliability among the specified variables for the experiments involving human drivers. It has shown that V2V communication support has positive effects on road safety, including intersection safety, braking events, and perception-reaction time (PRT) of the drivers. Furthermore, V2V communication support and PRT are identified as the important indicators affecting road safety at an un-signalized intersection. The proposed interactive framework is expected to contribute in constructing a comprehensive environment for the urban ITCPS and providing experimental support for the analysis of human behavior in the coexistence environment.
Towards Intelligible Robust Anomaly Detection by Learning Interpretable Behavioural Models

Network anomaly detection for enterprise cyber security is challenging for a number of reasons. Network traffic is voluminous, noisy, and the notion of what traffic should be considered malicious changes over time as new malware appears. To be most useful, an anomaly detection algorithm should be robust in its performance as new types of malware appear: maintaining a low false positive rate but raising alarms at traffic patterns which correspond to malicious behaviour; and provide intelligible alarms that present their reasoning to support both the analysis of the alarms and necessary incident response. In this paper we investigate new methods for building anomaly detectors using interpretative behavioural models which, we argue, can capture “normal” behaviours at a suitable level of abstraction to provide robustness, in addition to being inherently intelligible as they are interpretable for the security analyst. We consider two such models: a simple Markov Chain model with minimal behavioural structure and a Finite State Automata (FSA) with more structure, and show how these can be learned from normal network traffic alone. Our results show that the FSA performs better than common classifier methods with comparable results to standard Botnet detection methods. The results also indicate that the additional structure in the FSA is important. The FSA shows promise for robustness, although further work (with more data) is needed to fully explore this.
Interferometry using Phase Slope Estimation

The terrain elevation can be estimated with active radar and sonar by observing the scene from vertically displaced sensors and estimating the difference in time of arrival of the signal returns. A high precision estimate is available from the phase difference between preregistered time series. This estimate has an inherent 2π wrap ambiguity, but the dynamics over the signal bandwidth can be used to estimate the correct wrap. Common absolute phase estimators based on either cross-correlation or the phase slope between sub-band images, depend on either spatial smoothing or down-sampling. We propose a generalization of the sub-band methods for phase slope estimation that has the potential of providing absolute phase estimates at full image resolution.
Controllable p-type doping of PbS colloidal quantum dots

Colloidal quantum dots (CQDs) are very promising for future use in optoelectronic devices because of the tunability of the band gap and low-cost production techniques. Doping CQDs, which is a very important requirement for application in semiconductor devices, is however still challenging. In this bachelor research project the possibility of doping CQD thin films by altering the stoichiometry of the QDs is investigated. Field-effect transistors (FETs) with lead sulfide (PbS) CQDs as active layer were fabricated and sulfide ions were added to shift the QD stoichiometry. The two important graphical products of FET measurements, namely output and transfer curves, were compared for FETs treated with different amounts of sulfide. The intrinsic PbS QDs were found to exhibit ambipolar behaviour, with electrons being the dominant charge carrier. It was found that changing the stoichiometry towards more S-rich character gives rise to p-type doping, and eventually shifts the behaviour from semiconducting to metallic-like. The conductance of the device at zero gate voltage was increased by almost three orders of magnitude. The transition to the metallic state is estimated to occur at a S/Pb ratio of 1.3. This result is in line with predictions based on density functional theory (DFT) calculations reported in literature.
Author ' s personal copy Factor-augmenting technology choice and monopolistic competition q

We put forward a tractable, interpretable, and easily generalizable framework for modeling endogeneous factor-augmenting technology choice by monopolistically competitive firms. The setup is framed within the standard Dixit and Stiglitz (1977) model of monopolistic competition. Optimal technology choice is made here either by final goods producers or the representative household which holds the shares of (differentiated) intermediate goods producers. These two cases have different implications for the distribution of output but they yield the same aggregate level of output, the same aggregate production function and equivalent macroeconomic dynamics. Thanks to this property, the proposed framework can be used as a building block in a variety of embedding structures, including those which require to be solved recursively (separately for the dynamics of aggregate variables and for the distribution in each time period). 2013 Elsevier Inc. All rights reserved.
Conversion of Pinus nigra Plantations with Natural Regeneration in the Slovenian Karst: The Importance of Intermediate, Gradually Formed Canopy Gaps

Since the mid-19th century, Pinus nigra plantations have played a key role in the restoration of degraded European landscapes. Nowadays, these plantations are aging and prone to natural disturbances, insect infestations, and diseases. For their successful gradual conversion, knowledge of optimal gap spatiotemporal dynamics is crucial. We studied herb and natural regeneration patterns along with site factors on 477 subplots within 44 plots distributed over four stand types: closed stand (14% diffuse light), open stand (21%), gap edge (23%), and gap (57%). Despite the abundant Quercus petraea, Q. cerris, and Q. pubescens mast year, no one-year seedlings were recorded, which is likely due to the summer drought. Short seedlings (h < 20 cm) of Quercus sp. , Fraxinus ornus and Ostrya carpinifolia were more successful within closed stands. Short Quercus seedlings were positively associated with soil depth and negatively associated with soil nutrients, distance to seed trees, and Sesleria autumnalis coverage. Taller Quercus seedlings required more light than both of its strongest competitors and were positively related to humid soils and less rocky sites. Ungulate overbrowsing significantly impeded natural regeneration. The results indicate a satisfactory Quercus density for conversion and the importance of advanced regeneration, which should be gradually, but persistently, released by progressively widening gaps.
CFD Modeling of Flow over Ogee Spillway by Using Different Turbulence Models

Ogee spillway are the most important structures used to control flood water and built at the same time concrete or masonry dams are constructed. Due to such an importance, they shall be studied. The development of computer science and different types of computational fluid dynamics (CFD) software, the behavior of ogee spillways can be studied in a short time and without paying high expenses. In this study, FLUENT software has been used to simulate flow over ogee spillway and results were compared with experimental data. As flow over ogee spillway is turbulent and has a free surface, its characteristics are complex and often difficult to be predicted. This study assesses the performance of different turbulence models to predict the hydraulic condition of flow over ogee spillway. The volume of fluid (VOF) method is applied to obtain the free surface in each case. The results indicate that in the case the RNG k-ɛ turbulent model is used, the accuracy of the results obtained from the flow over ogee spillway is increased.
Gate Drive and Efficiency Analysis for a Silicon Carbide MOSFET Based Electric Motor Drive

With increasing interest in vehicle electrification and renewable energy systems, the demand for improved power density and operating efficiency in energy conversion systems is intensifying. The recent wide-spread availability of Silicon Carbide (SiC) semiconductor switching components have offered potential for significant improvements to in energy conversion systems used for motor drives and inverters, and DC to DC converters,. SiC switching devices such as SiC MOSFETs and Schottky diodes are particularly attractive to developers of applications requiring compact, high efficiency energy conversion because of their low conduction and switching losses, their ability to operate at very high temperatures, and their high frequency switching capability as compared to Silicon-based MOSFETS and IGBTs. Though SiC power electronic components offer great potential improvements in power conversion systems, fully realizing their benefits presents a challenge. SiC switches, due to their ultra fast switching speed are susceptible to transients introduced by rapid change in the drain-to-source voltage. Therefore, the gate drive requirements of SiC MOSFETS require a thorough analysis in order to prevent high dv/dt transients from causing erratic switching behavior or unnecessary switching loss. In addition, stray impedances can be the source of resonance during switching, which can lead to potentially destructive transient overshoots. In this paper, a SiC based inverter used in motor drives is analyzed. The gate drive for a half bridge is developed, and trade-offs involving the optimization of power conversion efficiency within the operating condition limitations of the switching devices are analyzed by simulation. Switching and conduction losses are computed for the system and efficiency is determined. Finally, the overall impact of PWM switching frequency on the conversion efficiency is assessed.
The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.