Miracle-dz/UniFAR-FTUs huggingface dataset:
Māori and Pasifika language, identity, and wellbeing in Aotearoa New Zealand

ABSTRACT For Māori and Pasifika living in Aotearoa New Zealand, language, identity, and wellbeing are inter-linked and multifaceted. However, as English is the most widely spoken language in Aotearoa New Zealand, some Māori and Pasifika peoples will not be able to speak their community language. Ratings of ethnic identity centrality, self-esteem, life satisfaction and the Personal Wellbeing Index of monolingual (English only) and bilingual Māori (N = 4691) and Pasifika peoples (N = 868) were compared using New Zealand Attitudes and Values Study data collected during 2018/2019. Bilingual Māori and Pasifika peoples reported higher levels of ethnic identity centrality than English only speaking Māori and Pasifika. Bilingualism also moderated the relationship between ethnic identity centrality and self-esteem. Pasifika who only speak English have relatively positive and similar levels of wellbeing to Pasifika who are bilingual whereas bilingualism seems to strengthen the relationship between ethnic identity and wellbeing for Māori.
Mittag-Leffler Function and Its Application to Viscoelastic Stress Relaxation

Functions of Mittag-Leffler(M-L) type play a special role in the fractional order viscoelastic theory. However,this function converges very slowly and it will likely result in divergence in numerical evaluation. The convergence criterion and the evaluation of M-L functions is discussed. When applying fractional Maxwell model to the stress relaxation,the parameters determined by using the asymptotic solutions of M-L functions are not correct to describe the relaxation process. A method combining genetic algorithm and conjugated gradient was proposed to optimize the model parameters. And the fractional Maxwell model based on the optimized parameters can be used to simulate viscoelastic relaxation process very well.
Validation of models: statistical techniques and data availability

This paper shows which statistical techniques can be used to validate simulation models, depending on which real-life data are available. Concerning this availability, three situations are distinguished: (i) no data; (ii) only output data; and (iii) both input and output data. In case (i)-no real data-the analysts can still experiment with the simulation model to obtain simulated data; such an experiment should be guided by the statistical theory on the design of experiments. In case (ii) only output data-real and simulated output data can be compared through the well-known two-sample Student t statistic or certain other statistics. In case (iii)-input and output data-trace-driven simulation becomes possible, but validation should not proceed in the popular way (make a scatter plot with real and simulated outputs, fit a line, and test whether that line has unit slope and passes through the origin); alternative regression and bootstrap procedures are presented. Several case studies are summarized, to illustrate the three types of situations.
Spine Surgeons Burnout and Quality of Life

Our research evaluates the prevalence of burnout using Maslach Burnout Inventory and EuroQol 5-dimensions to assess the quality of life. Our results showed that burnout is a common condition among spine surgeons worldwide and there is a significant association between burnout scores and decreased general quality of life. Study Design. Cross-sectional survey study. Objective. To evaluate the prevalence of burnout, assess the personal and professional characteristics associated with burnout in spine surgeons and determine their quality of life. Summary of Background Data. Burnout is a syndrome characterized by emotional exhaustion, depersonalization, and decreased sense of accomplishment that leads to decreased effectiveness at work. To date, there has been a lack of information on the prevalence of burnout among spine surgeons worldwide and the risk factors associated with this condition. Methods. An electronic survey with members of AO Spine was performed in May 2018. The survey evaluated demographic variables, practice characteristics, burnout, and quality of life. Maslach Burnout Inventory (MBI) and EuroQol 5-dimensions (EQ5D) were used to evaluate burnout and quality of life, respectively. Results. A total of 818 surgeons from 86 countries completed the survey. The prevalence of burnout was 30.6%. In the multiple linear model, emotional fatigue was independently associated with younger age (B = –0.17, CI95% = –0.26 to –0.07, P < 0.0001), and longer working hours per week (B = –2.71, CI95% = –4.34 to –1.07, P = 0.001); depersonalization was independently associated with younger age (B = –0.13, CI95% = –0.19 to –0.07, P < 0.0001), practicing outside Latin America (LA) (B = 0.71, CI95% = 0.41–1.01, P < 0.0001) and currently being a fellow (B = 0.54, CI95% = 0.06–1.02, P = 0.02); and higher scores of personal fulfilment was associated with practicing in LA (B = –1.27, CI95% = –1.69 to –0.85, P < 0.0001). Conclusion. Burnout is a common condition among spine surgeons worldwide. There is a significant association between burnout scores and decreased general quality of life. These results highlight the need to develop interventional programs to better identify, prevent, and manage this condition among practicing spine surgeons. Level of Evidence: 4
Passive Resonant Sensors: Trends and Future Prospects

Sensors based on “resonance phenomenon” span a broad spectrum of important current applications including detection of biological and chemical agents, and measurements of physical quantities. Resonance phenomena occur with all types of waves: electromagnetic, optic, and acoustic. This review reports about the most recent advances in the design and applications of resonant “passive” sensors, i.e. resonant sensors able to operate with a distant power source and/or able to communicate with a distant transceiver. The sensors considered in this review include acoustic, magnetoelastic, and electromagnetic transducers. They are presented through their relevant technological aspects and through they major advantages which include their integrability within embedded systems and/or systems requiring energy autonomy. Furthermore, the use of these resonant sensors is illustrated in a large variety of applications, ranging from environmental monitoring, structural health monitoring, food packaging monitoring, wearable or implanted sensors for the monitoring of physiological parameters in healthcare related applications, to IoT and future industry monitoring applications.
“Walls within walls: examining the variegated purposes for walling in Ghanaian gated communities”

Abstract This paper examines the functions walls perform in gated communities from the standpoints of both gated community developers and their residents. It posits three types of walls and scrutinises the purpose for each. Drawing empirical data from face-to-face interviews with 11 developers and 20 residents drawn from two gated communities in the Greater Accra Metropolitan Area in Ghana, the paper finds that, contrary to received wisdom, internal cluster walls in gated communities are used to segregate residents into different economic and social classes, often under the pretext of offering them different housing choices. It further casts doubts on the widely touted view that gated communities offer a better sense of security as residents express anxieties over suspected criminals living among them. The paper concludes by calling for a re-examination of several features of gated communities, including the meaning of the concept itself and the typologies that exist to bring out more of such nuances.
Higher Order Sliding Mode Observer-Based Sensor Fault Detection in DC Microgrid’s Buck Converter

Fault detection in a Direct Current (DC) microgrid with multiple interconnections of distributed generation units (DGUs) is an interesting topic of research. The occurrence of any sensor fault in the DC microgrid should be detected immediately by the fault detection network to achieve an overall stable performance of the system. This work focuses on sensor fault diagnosis of voltage and current sensors in interconnected DGUs of the microgrid. Two separate higher order sliding mode observers (HOSM) based on model dynamics are designed to estimate the voltage and current and generate the residuals for detecting the faulty sensors in DGUs. Multiplicative single and multiple sensor faults are considered in voltage and current sensors. By appropriate selection of threshold, single and multiple sensor fault detection strategies are formulated. A hierarchical controller is designed to ensure equal sharing of current among the DGUs of the DC microgrid and stabilize the system. Simulations are performed to validate the proposed approach for various configurations of the DC microgrid under various load and off noise conditions.
A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting

The problem of electricity load forecasting has emerged as an essential topic for power systems and electricity markets seeking to minimize costs. However, this topic has a high level of complexity. Over the past few years, convolutional neural networks (CNNs) have been used to solve several complex deep learning challenges, making substantial progress in some fields and contributing to state of the art performances. Nevertheless, CNN architecture design remains a challenging problem. Moreover, designing an optimal architecture for CNNs leads to improve their performance in the prediction process. This article proposes an effective approach for the electricity load forecasting problem using a deep neuroevolution algorithm to automatically design the CNN structures using a novel modified evolutionary algorithm called enhanced grey wolf optimizer (EGWO). The architecture of CNNs and its hyperparameters are optimized by the novel discrete EGWO algorithm for enhancing its load forecasting accuracy. The proposed method is evaluated on real time data obtained from datasets of Australian Energy Market Operator in the year 2018. The simulation results demonstrated that the proposed method outperforms other compared forecasting algorithms based on different evaluation metrics.
Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.
Simulation of waves propagation into composites thin shells by FEM methodologies for training of deep neural networks aimed at damage reconstruction

Structural Health Monitoring (SHM) deals mainly with structures instrumented by secondary bonded or embedded sensors that, acting as both signal generators and receivers, are able to “interrogate” the structure about its “health status”. Sensorised structures appear promising for reducing the maintenance costs and the weight of aerospace composite structures, without any reduction of the safety level required. Much effort has been spent during last years on signal analysis techniques in order to extract from signals provided by the sensors networks many parameters, metrics, and images correlated to damages existence, location and extensions. As in many other technological fields, like medical image diagnostics, deep learning techniques in general and artificial neural networks in particular can be a very powerful instrument for damage patterns reconstruction and selection provided that a sufficient and consistent amount of data related to healthy and damaged configuration of the item under test are available. Within this work explicit finite element analysis has been employed to simulate waves propagation within composite plates with and without delaminations due to impacts. The numerical results have been previously validated with analytical solutions and experimental signals then have been used to populate the data sets necessary for deep learning. This paper will present the preliminary results achieved by the authors.
Evaluation of mental models of prospective science teachers on chemical reactions

The aim of this study is to examine prospective science teachers' (PSTs) mental models and meanings for the concept of chemical reaction. For this purpose, this study adopted a phenomenological research design including 48 PSTs. To determine the mental modeling about chemical reactions, PSTs were given an interview card showing the reaction between magnesium and oxygen gas, and were asked to draw and visualize this reaction and a semi-structured interview was held about the specified reaction. The results revealed that the majority of the PSTs used the particle atom model to model for the chemical reactions, and as their education level increased, the models they used shifted from the particle model to the atomic model. In addition, the explanations showed that all the PSTs explained the reaction of magnesium with oxygen gas at the macroscopic level in the first stage and when asked the evocative interview question, most of them explained the reaction at the microscopic level, but some PSTs continued to make explanations at the macroscopic level despite the hinted interview problem. It was also found that the transition from the macroscopic level to the microscopic level of the PSTs’ explanations with the evocative interview question was influenced by the education level and their mental models regarding the reaction. In chemistry teaching starting from the high school senior year, it is recommended to use visual materials related to reactions based on atomic models instead of particle-based materials.
How to Support Start-Ups in Developing a Sustainable Business Model: The Case of an European Social Impact Accelerator

Sustainability-oriented start-ups are fundamental to developing solutions for, and to fostering, a societal transition towards a low carbon society. In this context, social impact accelerators (SIAs) are organizations specializing in accelerating the progress of sustainability-oriented start-ups. In order to design their accelerator elements (e.g., training, coaching, and funding) effectively, SIAs must be aware of the knowledge needs of start-ups to support them in developing a sustainable business model (SBM). Using a case study approach, we present one of the largest cleantech accelerator programs in Europe, the EIT Climate-KIC RIS Accelerator. Based on the program’s curriculum and manual in 2019, we analyze from the perspective of the program how cleantech start-ups could be supported in the development of their SBMs by presenting accelerator elements that are intended to support start-ups in reducing their knowledge needs by (1) providing new knowledge to start-ups (e.g., trainings, workshops, and e-learning), (2) supporting start-ups’ assimilation of new knowledge (e.g., coaching), and (3) supporting start-ups’ application of new knowledge (e.g., documentation of planning and reporting as part of the program’s contract design). Further, we discuss the knowledge needs of 63 European start-ups before and their progress as a result of accelerator participation in developing a SBM based on qualitative and quantitative data. All 63 start-ups participated in the same batch of the accelerator in 2019. Regarding the development of a SBM, knowledge needs are described considering the triple bottom line including the economic, ecological, and social layer of a business model. Based on the start-ups’ evaluation, we reflect—with a focus on the environmental layer—about the most promising content and support elements of our SIA case to address the different layers, discuss their combination, and present improvement potentials to reduce start-ups’ knowledge needs. With our findings, we claim to contribute to theory development in the emerging literature on SIAs and give practitioners working with sustainability-oriented start-ups insights into the usefulness of start-up support programs and different accelerator elements for developing a SBM.
Validación de la Escala de Comportamientos de Sexting en Adolescentes: Prevalencia y Consecuencias Asociadas

The objective of this study was threefold: to validate a version of the Sexting Behavior Scale in adolescents, to assess the prevalence of sexting behaviors, and to determine their associated consequences. A total of 1,271 (50.8p girls) students participated, with a mean age of 15.6 (SD = 1.13). The results show through the confirmatory factor analysis a good fit for the two-dimensional model consisting of items that are grouped into sending photos and/or videos (α = .83) and sending text messages with erotic/sexual content (α = .83); 52.8p of adolescents have been identified to carry out sexting behaviors, with texts (91.7p) being more erotic/sexual than photos and/or videos (57.8p), and boys the ones that most content send. In addition, 11.6p of adolescents were forced to send sexting content and 5.5p of them were blackmailed.
Automatic Recognition of Geomagnetic Suitability Areas for Path Planning of Autonomous Underwater Vehicle

Abstract Currently, integrated navigation systems with the inertial navigation system (INS)/geomagnetic navigation system (GNS) have been widely used in underwater navigation of autonomous underwater vehicle (AUV). Restricting AUV to navigate in the geomagnetic suitability areas (GSA) as far as possible can effectively improve the accuracy of integrated navigation systems. In order to improve the classification accuracy of GSA, a new optimal classification method based on principal component analysis (PCA) and improved back propagation (BP) neural network is proposed. PCA is used to extract the independent characteristic parameters containing the main components. Then, considering similarity coefficient, the initial weights and thresholds of BP neural network is optimized by improved adaptive genetic algorithm (IAGA). Finally, the correspondence between the geomagnetic characteristic parameters and matching performance is established based on PCA and improved adaptive genetic algorithm and back propagation (IAGA-BP) neural network for the automatic recognition of GSA. Simulated experiments based on PCA and IAGA-BP neural network shows high classification accuracy and reliability in the GSA selection. The method could provide important support for AUV path planning, which is an effective guarantee for AUV high precision and long voyage autonomous navigation.
Trade‐off between threshold gain and directionality of emission for modes of two‐dimensional eccentric microring lasers analysed using lasing eigenvalue problem

Anna I. Repina, Kazan, st. Kremlin, 18, 420008, Russia. Email: airepinas@gmail.com Abstract This article discusses how a trade‐off between the high directionality of emissions and low threshold gain can be achieved in active eccentric microring cavities. Our findings are based on the lasing eigenvalue problem formalism, considered using the method of analytical regularisation, and an extremely fast and accurate dedicated Galerkin method, applied to a set of associated Muller boundary integral equations. This method allows us to investigate symmetric and antisymmetric modes separately, on the threshold of nonattenuation in time emission. Numerical results show that the directivities of emission of working modes in a given frequency range, together with their threshold values of gain, are controlled by the size and location of the air hole in the cavity. The high efficiency of the developed code allows us to make an elementary optimisation of the considered cavity; this code is a promising engineering tool in the design of microring lasers.
Estimation of Adaptation Parameters for Dynamic Video Adaptation in Wireless Network Using Experimental Method

A wireless network gives flexibility to the user in terms of mobility that attracts the user to use wireless communication more. The video communication in the wireless network experiences Quality of Services (QoS) and Quality of Experience (QoE) issues due to network dynamics. The parameters, such as node mobility, routing protocols, and distance between the nodes, play a major role in the quality of video communication. Scalable Video Coding (SVC) is an extension to H.264 Advanced Video Coding (AVC), allows partial removal of layers, and generates a valid adapted bit-stream. This adaptation feature enables the streaming of video data over a wireless network to meet the availability of the resources. The video adaptation is a dynamic process and requires prior knowledge to decide the adaptation parameter for extraction of the video levels. This research work aims at building the adaptation parameters that are required by the adaptation engines, such as Media Aware Network Elements (MANE), to perform adaptation on-the-fly. The prior knowledge improves the performances of the adaptation engines and gives the improved quality of the video communication. The unique feature of this work is that, here, we used an experimental evaluation method to identify the video levels that are suitable for a given network condition. In this paper, we estimated the adaptation parameters for streaming scalable video over the wireless network using the experimental method. The adaptation parameters are derived using node mobility, link bandwidth, and motion level of video sequences as deciding parameters. The experimentation is carried on the OMNeT++ tool, and Joint Scalable Video Module (JSVM) is used to encode and decode the scalable video data.
Air Quality in East Asia during the heavy haze event period of 10 to 15 January 2013

A prolonged heavy haze event that has caused for the Environmental Protection Bureau (EPB) in Beijing to take emergency measures for the protection of the public health and the reduction of air pollution damages in China has been analyzed with the use of the Aerosol modeling System (AMS) to identify causes of this event. It is found that the heavy haze event is associated with high aerosols and water droplets concentrations. These high aerosol concentrations are mainly composed of anthropogenic aerosols, especially secondary inorganic aerosols formed by gas-to-particle conversion of gaseous pollutants in the eastern part of China whereas those in the northeastern parts of China are composed of the mixture of the anthropogenic aerosols and the Asian dust aerosol originated from the dust source regions of northern China and Mongolia. These high aerosol concentrations are found to be subsequently transported to the downwind regions of the Korean Peninsula and Japan causing a prolonged haze event there. It is also found that the Asian dust aerosol originated from northern China and Mongolia and the anthropogenic aerosols produced by chemical reactions of pollutants in the high emissions region of eastern China can cause significantly adverse environmental impacts in the whole Asian region by increased atmospheric aerosol loadings that may cause respiration diseases and visibility reduction and by excess deposition of aerosols causing adverse impacts on terrestrial and marine eco-systems.