Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of flood hazards on small and medium companies: strategies for property level protection and business continuity

Worldwide floods have become one of the costliest weather-related hazards, causing large-scale human, economic, and environmental damage during the recent past. Recent years have seen a large number of such flood events around the globe, with Europe and the United Kingdom being no exception. Currently, about one in six properties in England is at risk of flooding (EA, 2009), and the risk is expected to further increase in the future (Evans et al., 2004). Although public spending on community-level flood protection has increased and some properties are protected by such protection schemes, many properties at risk of flooding may still be left without adequate protection. As far as businesses are concerned, this has led to an increased need for implementing strategies for property-level flood protection and business continuity, in order to improve their capacity to survive a flood hazard. Small and medium-sized enterprises (SMEs) constitute a significant portion of the UK business community. In the United Kingdom, more than 99% of private sector enterprises fall within the category of SMEs (BERR, 2008). They account for more than half of employment creation (59%) and turnover generation (52%) (BERR, 2008), and are thus considered the backbone of the UK economy. However, they are often affected disproportionately by natural hazards when compared with their larger counterparts (Tierney and Dahlhamer, 1996; Webb, Tierney, and Dahlhamer, 2000; Alesch et al., 2001) due to their increased vulnerability. Previous research reveals that small businesses are not adequately prepared to cope with the risk of natural hazards and to recover following such events (Tierney and Dahlhamer, 1996; Alesch et al., 2001; Yoshida and Deyle, 2005; Crichton, 2006; Dlugolecki, 2008). For instance, 90% of small businesses do not have adequate insurance coverage for their property (AXA Insurance UK, 2008) and only about 30% have a business continuity plan (Woodman, 2008). Not being adequately protected by community-level flood protection measures as well as property- and business-level protection measures threatens the survival of SMEs, especially those located in flood risk areas. This chapter discusses the potential effects of flood hazards on SMEs and the coping strategies that the SMEs can undertake to ensure the continuity of their business activities amid flood events. It contextualizes this discussion within a survey conducted under the Engineering and Physical Sciences Research Council (EPSRC) funded research project entitled “Community Resilience to Extreme Weather — CREW”.
Analysis of photonic crystal double heterostructure resonant cavities

xiv Chapter 1: Overview and Motivation 1 1.1 Photonic integrated circuits . 1 1.2 Two-dimensional photonic crystals . 3 1.3 Photonic crystal integrated circuits . 7 1.4 Numerical analysis of photonic crystals . 10 1.5 Thesis overview . 19 Chapter 2: Electromagnetic resonant cavity analysis 21 2.
Mobile Measurement of a Dynamic Field via Compressed Sensing

In this paper, measuring dynamic signals at points of interest (POIs) using a mobile agent is considered, where the agent is required to repeatedly measure at and transit between the POIs. Dynamic field sensing is needed in areas ranging from nanomechanical mapping of live sample to crop monitoring. Existing work on mobile sensing, however, has been focused on cooperatively tracking one or few known or unknown POIs, whereas the dynamics of the signals is ignored. Challenges arise from capturing and recovering the dynamics at each POI by using the data intermittently measured by the mobile agent, resulting in temporal-spatial coupling in mobile sensing. Moreover, trade off between the sensing cost and the performance needs to be addressed. We propose a compressed-sensing based approach to tackle this problem. First, a check-and-removal process based on random permutation and partition of the measurement periods is developed to avoid the temporal-spatial coupling under the agent speed constraint. Then a shuffle-and-pair process based on the simulate-annealing is proposed to minimize the transition distance while preserving the performance. It is shown that the distribution of the measurement periods between the POIs converges. The proposed approach is illustrated through a simulation study of measuring the temperature-dependent nanomechanical variations of a polymer sample.
Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases

The discretionary damage of mental suffering in fatal car accident cases in Taiwan is subjective, uncertain, and unpredictable; thus, plaintiffs, defendants, and their lawyers find it difficult to judge whether spending much of their money and time on the lawsuit is worthwhile and which legal factors judges will consider important and dominant when they are assessing the mental suffering damages. To address these problems, we propose k-nearest neighbor, classification and regression trees, and random forests as learning algorithms for regression to build optimal predictive models. In addition, we reveal the importance ranking of legal factors by permutation feature importance. The experimental results show that the random forest model outperformed the other models and achieved good performance, and “the mental suffering damages that plaintiff claims” and “the age of the victim” play important roles in assessments of mental suffering damages in fatal car accident cases in Taiwan. Therefore, litigants and their lawyers can predict the discretionary damages of mental suffering in advance and wisely decide whether they should litigate or not, and then they can focus on the crucial legal factors and develop the best litigation strategy.
Teachers’ Social–Emotional Competence: History, Concept, Models, Instruments, and Recommendations for Educational Quality

Teachers’ social–emotional competencies are essential to educational quality. This study aimed to describe the theoretical background and relevance of teachers’ social–emotional competencies. We conducted a systematic review with a critical, theoretical review approach. The results showed that the concept has an increasingly complex history and followed a structured course from 1920 to present. Five main models have been identified: emotional regulation, prosocial classroom, Collaborative Association of Social Emotional Learning, Bar-On emotional intelligence, and emotional intelligence. There are measurement instruments consistent with four of the identified models; however, the model that does not have its own instrument uses different available scales. Specific recommendations are proposed to develop social and emotional competencies in educational public policies, which include school leadership, assessment, and teacher professional training. In conclusion, it is relevant to have clear guidelines that conceive and conceptualize social–emotional competence univocally. These guidelines would allow the design of instruments with a comprehensive and sufficient theoretical base that reflect the multidimensionality of the concept, provide a precise measure to assess the effectiveness of intervention programs, and enlist teachers who seek the development of the different skills that involve social–emotional competencies.
The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios

A variety of tools are available to collect, process and analyse learning data obtained from the clickstream generated by students watching learning resources in video format. There is also some literature on the uses of such data in order to better understand and improve the teaching-learning process. Most of the literature focuses on large scale learning scenarios, such as MOOCs, where videos are watched hundreds or thousands of times. We have developed a solution to collect clickstream analytics data applicable to smaller scenarios, much more common in primary, secondary and higher education, where videos are watched tens or hundreds of times, and to analyse whether the solution is useful to teachers to improve the learning process. We have deployed it in a real scenario and collected real data. Furthermore, we have processed and presented the data visually to teachers for those scenarios and have collected and analysed their perception of their usefulness. We conclude that the collected data are perceived as useful by teachers to improve the teaching and learning process.
Recent Advances in Understanding the Pathogenesis of Rheumatoid Arthritis: New Treatment Strategies

Rheumatoid arthritis (RA) is considered a chronic systemic, multi-factorial, inflammatory, and progressive autoimmune disease affecting many people worldwide. While patients show very individual courses of disease, with RA focusing on the musculoskeletal system, joints are often severely affected, leading to local inflammation, cartilage destruction, and bone erosion. To prevent joint damage and physical disability as one of many symptoms of RA, early diagnosis is critical. Auto-antibodies play a pivotal clinical role in patients with systemic RA. As biomarkers, they could help to make a more efficient diagnosis, prognosis, and treatment decision. Besides auto-antibodies, several other factors are involved in the progression of RA, such as epigenetic alterations, post-translational modifications, glycosylation, autophagy, and T-cells. Understanding the interplay between these factors would contribute to a deeper insight into the causes, mechanisms, progression, and treatment of the disease. In this review, the latest RA research findings are discussed to better understand the pathogenesis, and finally, treatment strategies for RA therapy are presented, including both conventional approaches and new methods that have been developed in recent years or are currently under investigation.
Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling

Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87; b = 0.82; M2: R = 0.98; b = 0.93; M3: R = 0.99; b = 0.99; M4: R = 0.99; b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV).
AEVITA : designing biomimetic vehicle-to-pedestrian communication protocols for autonomously operating & parking on-road electric vehicles

With research institutions from various private, government and academic sectors performing research into autonomous vehicle deployment strategies, the way we think about vehicles must adapt. But what happens when the driver, the main conduit of information transaction between the vehicle and its surroundings, is removed? The ÆVITA system aims to fill this communication void by giving the autonomous vehicle the means to sense others around it, and react to various stimuli in as intuitive ways as possible by taking design cues from the living world. The system is comprised of various types of sensors (computer vision, UWB beacon tracking, sonar) and actuators (light, sound, mechanical) in order to express recognition of others, announcement of intentions, and portraying the vehicle’s general state. All systems are built on the 2nd version of the 1/2 –scale CityCar concept vehicle, featuring advanced mixed-materials (CFRP + Aluminum) and a significantly more modularized architecture. Thesis Supervisor: Kent Larson Title: Principal Research Scientist, MIT Media Lab
Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems

Classical methods for inverse problems are mainly based on regularization theory, in particular those, that are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and a great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond to the likelihood and prior-probability models, respectively. The Bayesian approach gives more flexibility in choosing these terms and, in particular, the prior term via hierarchical models and hidden variables. However, the Bayesian computations can become very heavy computationally. The machine learning (ML) methods such as classification, clustering, segmentation, and regression, based on neural networks (NN) and particularly convolutional NN, deep NN, physics-informed neural networks, etc. can become helpful to obtain approximate practical solutions to inverse problems. In this tutorial article, particular examples of image denoising, image restoration, and computed-tomography (CT) image reconstruction will illustrate this cooperation between ML and inversion.
Assessment of the Hardening Behavior and Tensile Properties of a Cold-Rolled Bainitic–Ferritic Steel

The automotive field is continuously researching safer, high-strength, ductile materials. Nowadays, dual-phase (DP) steels are gaining importance, since they meet all these requirements. Dual-phase steel made of ferrite and bainite is the object of a complete microstructural and mechanical characterization, which includes tensile and bending tests. This specific steel contains ferrite and bainite in equal parts; ferrite is the soft phase while bainite acts as a dispersed reinforcing system. This peculiar microstructure, together with fine dispersed carbides, an extremely low carbon content (0.09 wt%), and a minimal degree of strain hardening (less than 10%) allow this steel to compete with traditional medium-carbon single-phase steels. In this work, a full pearlitic C67 steel containing 0.67% carbon was used as a benchmark to build a comparative study between the DP and SP steels. Moreover, the Crussard–Jaoul (C-J) and Voce analysis were adopted to describe the hardening behavior of the two materials. Using the C-J analysis, it is possible to separately analyze the ferrite and bainite strain hardening and understand which alterations occur to DP steel after being cold rolled. On the other hand, the Voce equation was used to evaluate the dislocation density evolution as a function of the material state.
Analisis Metoda Estimasi Peta Kedalaman Dari Citra RGB 2D Untuk Penerapan Teknik Persepsi Pada Kendaraan Otonom

Seiring berjalannya perkembangan teknologi kendaraan otonom, dilakukan berbagai usaha untuk dapat menciptakan metode pengolahan data sebagai masukkan pada sistem kontrolnya. Salah satunya adalah pengembangan metode pada sistem persepsi visual, yaitu estimasi peta kedalaman dari pengolahan citra RGB 2D. Metode ini dikembangkan untuk dapat memperkuat penggunaan kamera 2D yang tidak mampu memberikan informasi ruang 3D dari lingkungan layaknya LiDAR, Radar, atau teknik ultrasonik. Metode yang akan dipresentasikan pada makalah ini menerapkan teknik Jaringan Syaraf Tiruan untuk dapat mengestimasi jarak objek dari titik perekaman. Analisis yang tajam mengenai persepsi yang dihasilkan telah dilakukan dengan menggunakan tiga arsitektur, penggunaan jaringan penskalaan global dan lokal pada arsitektur pertama, penambahan jaringan prediksi menengah pada arsitektur kedua, serta penggunaan empat modul jaringan yang terdiri dari pengkode, dekoder, gabungan fitur multi skala, dan jaringan perbaikan pada arsitektur ketiga. Pelatihan dan pengujian dicoba dengan menggunakan beberapa data set yang merepresentasikan kondisi di dalam dan di luar ruangan. Hasil simulasi dan analisis yang diperoleh memperlihatkan bahwa arsitektur ketiga memberikan hasil yang paling baik dalam memberikan informasi ruang 3D dari lingkungannya.
Design and Manufacturing Optoelectronic Sensors for the Measurement of Refractive Index Changes under Unknown Polarization State

This article proposes a new method for detecting slight refractive index changes under conditions of unknown polarization state. It is argued that an insignificant modification of the tilted fiber Bragg grating (TFBG) structure and selecting the appropriate spectral region allows us to accurately track changes in the refractive index. It has also been proven that the method can be easily made insensitive to temperature and that the sensitivity to changes in the polarization plane of the input light can be significantly reduced, which is crucial in later practical applications. Analytes in the form of an aqueous glucose solution were used to calibrate the sensor. The proposed method, based on perpendicular tilted fiber Bragg grating (P-TFBG), has a wide range of universality because its development and slight modification will enable the detection of glucose, pathogens, and viruses.
Educational Needs among Women Admitted to High Secure Forensic Psychiatric Care

ABSTRACT Forensic psychiatric patients’ low educational attainment is a strong predictor for recidivism. However, there is a lack of studies on the educational background among women admitted to forensic psychiatric care. The study aimed to investigate the educational background among women within a high secure forensic psychiatric setting. A mixed-method design was applied. Data were collected from registers, including medical records, forensic psychiatric investigations, and verdicts (n = 93), and by conducting interviews (n = 61) with women admitted to forensic psychiatric care in Sweden. The women’s education length varied between 5 and 18 years. According to interviews and registers, more than 70% of the women were found to have some school-related problems. In the interviews, some women associated school with conflicts, loneliness, and learning difficulties. Others explained how their disadvantageous home environment and many school changes affected their learning and problems developing and maintaining social relationships. Women with a neurodevelopmental disorder were to a higher degree documented with school-related problems than women without such disorders (97% vs. 63%). The educational attainment varied among the women, and consequently, their educational needs were heterogeneous. Therefore, they should be offered tailored education and vocational support to have an increased prerequisite for adjusting to and participating in society.
A Comparative Review of Building Information Modeling Frameworks

The building sector influences the Gross Domestic Product (GDP) of many countries. There is a vast amount of waste generated in the building process, with many projects suffering from delivery delays, running over budget, and resulting in buildings of minimal quality. Building Information Modeling (BIM) has shown great potential towards solving these problems. BIM sets frameworks for Architects, Engineers, and Contractors (AEC), enhancing management of the building process. This paper analyzes select framework methods, tools, and processes, identifying key guidelines required to create a BIM framework customized to local requirements; this will have a positive effect on the construction industry by lowering the cost of buildings and improving the communication among parties, and leading to the development of frameworks based on local indicators. A comprehensive literature review was conducted to determine components and factors from structure frameworks with the main findings then classified and categorized into the following sectors -- government support, maturity level measurement, standards, protocol, database, and education plan. This permits the development of a comparison highlighting the frameworks’ differences and summing up possible strategies for BIM implementation on several bases, such as region differentiation and the local industries’ Political, Economic, Socio-Cultural, Technological (PEST) aspects, allowing for greater understanding of BIM implementation on various scales. The selection of the BIM frameworks was dependent on specific criteria with a minimum score of 4 out of 5 required to merit inclusion. Three strategies for the creation of a BIM framework were discovered and were seen to vary across regions. The discovery of these strategies lays the groundwork for future research into the development of these frameworks to determine the potential advantages and downsides of each.
Control Design of a 99% Efficiency Transformerless EV Charger Providing Standardized Grid Services

A nonisolated DC charger is proposed for electric vehicle (EV) battery system. The leakage current can be reduced by the novel charging system without bulky transformer. A zero sequence voltage control method is developed to stabilize the common-mode voltage, thus, reduce the leakage current. The proposed fast charger includes two energy conversion stages: DC–DC converter for battery side control; DC–AC converter for grid interface and common-mode voltage control. The parameters of the system with the <italic>LCL</italic> filter are analyzed and optimized for a better performance of grid interface. Grid service functions are designed for the EV charger to provide grid-voltage/frequency compensations. High power (22 kW) and high efficiency (<inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>99%) are achieved with low-leakage current (<inline-formula><tex-math notation="LaTeX">$<$</tex-math></inline-formula> 20 mA). The experiments are implemented to verify the proposed EV charger system.
Challenges for Digitalisation in Building Renovation to Enhance the Efficiency of the Process: A Spanish Case Study

Although construction is one of the largest industries in the world, it is one of the least digitised and its productivity is still very low. Excesses of time and cost are common and are even more accentuated for building renovation. Recently, the building information modeling (BIM) methodology has strongly entered in the construction sector and appears to be an effective paradigm shift. Considering all of the previously mentioned aspects, this article addresses the identification and analysis of the critical barriers of renovation and the potential for digitalisation to overcome them using BIM. The methodology that was used is based on an open innovation approach called Living Labs, where consultations with the key stakeholders of the construction process aims for a higher digitalisation to focus on real needs and fitted to the user’s requirements. Starting from a worldwide survey, the analysis of the Spanish casuistry is deepened. From the analysis of barriers and opportunities, the necessary requirements for an optimal BIM application in renovation are highlighted. After identifying the key aspects that each stakeholder’ typology has considered as relevant, a set of key performance indicators have been selected, to monitor the improvements in the renovation process when BIM is adopted.