Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling electromagnetic surface waves with scalar and tensor impedance surfaces

The propagation characteristics of electromagnetic waves on various isotropic (scalar) impedance surfaces have been studied for some time, in order to control surface waves and leaky-wave radiation. One and two dimensional periodic impedance surfaces as well as tensor impedance surfaces, have been explored for enhanced control of wave guidance. The desire to integrate antennas and electromagnetic devices onto the surfaces of vehicles and other platforms has driven recent interest in both scalar and tensor impedance surfaces. Great strides have been made in the design of devices based on impedance surfaces. To date, these devices are designed by first determining the surface impedance variation necessary to yield the desired performance. The surfaces are then implemented as printed-circuit board (PCB) structures consisting of a patterned metallic cladding over a grounded dielectric substrate. In other words, these two-layer structures are modeled with an idealized tensor impedance boundary condition. In this work, we aim to capture the propagation characteristics of PCB-based tensor impedance surfaces more accurately by modeling them as an impedance sheet over a grounded dielectric substrate. The impedance sheet represents the patterned metallic cladding of the PCB tensor impedance surface. Dispersion equations for scalar and tensor PCB versions are found. In addition, an extraction method is presented that allows the impedance sheet to be found using only two full-wave simulations. Using the dispersion equation and the extraction method together, the dispersion properties of the printed-circuit impedance surface can be predicted. A method for designing transformation electromagnetics devices using tensor impedance surfaces is also presented. Further, a printed leaky-wave antenna based on a sinusoidally modulated scalar impedance surface is presented.
Optimal basis set for ab-initio calculations of energy levels in tunneling structures, using the covariance matrix of the wave functions

The paper proposes a method to obtain the optimal basis set for solving the self consistent field (SCF) equations for large atomic systems in order to calculate the energy barriers in tunneling structures, with higher accuracy and speed. Taking into account the stochastic-like nature of the samples of all the involved wave functions for many body problems, a statistical optimization is made by considering the covariance matrix of these samples. An eigenvalues system is obtained and solved for the optimal basis set and by inspecting the rapidly decreasing eigenvalues one may seriously reduce the necessary number of vectors that insures an imposed precision. This leads to a potentially significant improvement in the speed of the SCF calculations and accuracy, as the statistical properties of a large number of wave functions in an large spatial domain may be considered. The eigenvalue problem has to be solved only few times, so that the amount of time added may be much smaller that the overall iterating SCF calculations. A simple implementation of the method is presented for a situation where the analytical solution is known, and the results are encouraging.
Takeover Protections and Asset Prices∗

We study the effects of takeover feasibility on asset prices and returns in a unified framework. We show theoretically that takeover protections increase equity risk, stock returns, and bond yields by removing a valuable put option to sell the firm when approaching financial distress. We investigate these claims empirically and find that distressed firms experience a significant decrease in value and increase in returns and market betas after the passage of anti-takeover laws, in line with our predictions. At issue bond yields are also higher when an anti-takeover law is in effect. Consistent with the model, the effects of anti-takeover laws on bond yields are greater when bondholders have more bargaining power.
PRBM_A_362849 1411..1421

Background/Purpose: In the context of COVID-19 lockdowns, extant research suggests that secondary coping (a strategy aimed at adjusting oneself self to the stressor) is more robustly associated with better mental health than primary coping (a strategy aimed at adjusting the stressor to oneself). We investigated whether these findings are generalizable to Spain—one of the most severely affected countries at that time. We also tested whether the link between secondary coping and mental health (as measured by anxiety) can be accounted for by how individuals perceive the COVID-19 impact (ie, perceived life changes and personal global impact) and how frequently they use traditional and social media to check COVID-19-related information. Methods: A diverse community sample (N = 408), collected during the first lockdown in Spain (early April 2020), completed a multimeasure online survey including the targeted variables. Results: Secondary coping outperformed primary coping in predicting reduced anxiety during the lockdown in Spain. Moreover, lower perceived life changes from COVID-19 and reduced personal global impact from COVID-19 both mediated the negative secondary coping-anxiety relationship. No indirect effects emerged for either conventional or social media exposure. Conclusion: These results (a) strengthen the cross-cultural validity of the link between secondary coping and anxiety and (b) advance our understanding of the psychological mechanisms underlying this association.
CEV_5992_Tocilizumab: The evidence for its place in the treat

Correspondence: Troels Herlin Professor of Pediatric rheumatology, Department of Pediatrics, Aarhus University Hospital, Skejby, DK-8200 Aarhus N Denmark Tel +45-8949 6704 Fax +45-8949 6023 Email troeherl@rm.dk Introduction: Juvenile idiopathic arthritis (JIA) is one of the most common chronic diseases with childhood onset. It comprises different subtypes of which the systemic onset subtype is often resistant to treatment. With the advent of biological treatment with tumor necrosis factor-α (TNFα)-inhibitors, the clinical outcome of JIA has improved considerably, but only for subtypes other than systemic JIA. Substantial evidence shows that the proinflammatory cytokine interleukin-6 (IL-6) plays a pivotal role in systemic JIA. The blockage of IL-6 action by tocilizumab, a humanized anti-IL-6-receptor monoclonal antibody, could therefore be an effective treatment of systemic JIA. Aims: The purpose of this article was to review the clinical trials of tocilizumab and to discuss its place in the treatment of JIA with the focus on the systemic onset of disease. Evidence review: Two phase II studies and one phase III clinical trial of tocilizumab demonstrating the clinical efficacy and safety in systemic onset JIA have been published. Within those studies, sustained and high response rates of clinical improvement have been achieved with American College of Rheumatology Pediatric criteria (ACRPed) 30, 50, and 70 observed in 98%, 94%, and 90% of patients, respectively, after 48 weeks. One study regarding the clinical efficacy of tocilizumab for the treatment of oligoand polyarticular JIA has been presented only as a conference abstract. Place in therapy: The very promising results seen so far in patients with severe systemic JIA and acceptable tolerability gives tocilizumab a central role in the future therapy in controlling this disease. No other biological therapy has achieved similar high response rates when treating with tocilizumab 8 mg/kg every two weeks to patients with systemic onset JIA, but direct comparison of the efficacy of different biological agents are not yet available.
Digital Resilience Formation During Covid and Before Covid-A Digital Resilience Formation During Covid and Before Covid-A Thematic Analysis in Higher Education Context Thematic Analysis in Higher Education Context

These days, the indispensability of digital resilience copes up the challenging covid situation. This study investigates a digital resilience formation process in the context of higher education since it is one of the most disrupted sectors caused by the Covid pandemic. In this study, we explore what are the dimensions of the digital resilience formation process, whether and how do these dimensions differ in importance between before the pandemic and during the pandemic? The purpose of this research is thus twofold. First, to build a model of the multidimensional digital resilience formation process in the education domain. Second, collecting articles and using two different datasets, seeks to compare the relative importance of the dimensions between two different timelines (past covid era and during the Covid pandemic era), using semantic content and quantitative content analysis.
IDENTIFICATION OF GMS FRICTION MODEL WITHOUT FRICTION FORCE MEASUREMENT

This paper deals with an online identification of the Generalized Maxwell Slip (GMS) friction model for both presliding and sliding regime at the same time. This identification is based on robust adaptive observer without friction force measurement. To apply the observer, a new approach of calculating the filtered friction force from the measurable signals is introduced. Moreover, two approximations are proposed to get the friction model linear over the unknown parameters and an approach of suitable filtering is introduced to guarantee the continuity of the model. Simulation results are presented to prove the efficiency of the approach of identification.
ANALYSIS OF AN EFFECTIVE, SCALABLE AND SECURED DATA SHARING SERVICE IN CLOUD COMPUTING

-Scalable and flexible privacy-preserving data sharing scheme in the cloud ensures both semantic security and effective availability of user data. To preserve privacy and guarantee data confidentiality against the cloud, the scheme employs a cryptographic primitive, named cipher-text policy attribute-based encryption (CP-ABE) and combines it with an identity-based encryption (IBE) technique; each data file is described by a set of meaningful attributes, allowing each user to be assigned an access structure that defines the scope of data files they can have access to. To enforce these access structures, this scheme defines a public-private key pair for each attribute. For each user’ secret key, there is a combination of user’s ID and the attribute’s secret key, thereby ensuring that each attribute presents a different key to each user. Data files are encrypted by public key components and access matrices converted from the access structure; user secret keys are defined to reflect their access privileges so that a user can only decrypt a ciphertext if they have the matched attributes to satisfy the ciphertext. To resolve the challenging issues of collusion resistance, this scheme provides users with a public key fitted to their secret keys; This paper use user’s ID to “tie” together the attributes belonging to this user so that they cannot be successfully combined with another’s user’s attributes.
A Human Movement Stream Processing System for Estimating Worker Locations in Shipyards

Estimating the locations of workers in a shipyard is beneficial for a variety of applications such as selecting potential forwarders for transferring data in IoT services and quickly rescuing workers in the event of industrial disasters or accidents. In this work, we propose a human movement stream processing system for estimating worker locations in shipyards based on Apache Spark and TensorFlow serving. First, we use Apache Spark to process location data streams. Then, we design a worker location prediction model to estimate the locations of workers. TensorFlow serving manages and executes the worker location prediction model. When there are requirements from clients, Apache Spark extracts input data from the processed data for the prediction model and then sends it to TensorFlow serving for estimating workers’ locations. The worker movement data is needed to evaluate the proposed system but there are no available worker movement traces in shipyards. Therefore, we also develop a mobility model for generating the workers' movements in shipyards. Based on synthetic data, the proposed system is evaluated. It obtains a high performance and could be used for a variety of tasks in shipyards.
Optimization of Aedes albopictus (Diptera: Culicidae) Mass Rearing through Cost-Effective Larval Feeding

Simple Summary The Asian tiger mosquito (Aedes albopictus) is an important invasive species of medical concern, which could be successfully suppressed by including the sterile insect technique (SIT) in integrated mosquito management. This technique is based on the mass rearing of males, and their sterilization and release into the habitats to compete with wild males in the mating process. Our research compared the effectiveness of three larval diet recipes (IAEA-BY, BCWPRL, and MIX-14) in the rearing of Ae. albopictus males in order to evaluate the available economical feeding alternatives. The separation of male pupae was done by the sieving method, and reared adult males were tested for flight capacity and longevity. The application of BCWPRL resulted in a higher portion of sieved male pupae than females, but the obtained number of both pupae and adult males was lower and the development was slower than the other two diets. The adult mean survival time was the highest in males fed with MIX-14 and the lowest in males fed with IAEA-BY. Males fed by IAEA-BY also demonstrated higher initial mortality in the adult stage. The diets BCWPRL and MIX-14 are cheaper than IAEA-BY (2.28 and 5.30 times, respectively). The diet MIX-14 represents a candidate for replacing the effective but still expensive IAEA-BY diet. Abstract Aedes (Stegomyia) albopictus (Skuse, 1895) is an invasive important medical and veterinary pest species. The sterile insect technique (SIT) involves the mass rearing of males, and their sterilization and release into the habitat to compete with wild males. Our research objective was to compare the effectiveness of three larval diet recipes (IAEA-BY, BCWPRL, and MIX-14) in the laboratory rearing of Ae. albopictus males to evaluate the available economical feeding alternatives. The separation of sexes was done in the pupal stage by sieving. Reared males were tested for flight capacity and longevity. The application of the BCWPRL diet resulted in a higher portion of sieved male pupae than females, but the development of males was the slowest, and the number of obtained males (pupae and adults) was lower compared to the other two diets. The adult mean survival time was the highest in males fed with MIX-14 and the lowest in males fed with IAEA-BY. Males fed by IAEA-BY also demonstrated higher initial mortality in the adult stage. The diets BCWPRL and MIX-14 are economically more convenient than IAEA-BY (2.28 and 5.30 times cheaper, respectively). The cheapest diet, MIX-14, might represent a candidate for replacing the effective but still expensive IAEA-BY larval diet, providing lower costs of sterile male production.
The Robustness of Detecting Known and Unknown DDoS Saturation Attacks in SDN via the Integration of Supervised and Semi-Supervised Classifiers

: The design of existing machine-learning-based DoS detection systems in software-deﬁned networking (SDN) suffers from two major problems. First, the proper time window for conducting network trafﬁc analysis is unknown and has proven challenging to determine. Second, it is unable to detect unknown types of DoS saturation attacks. An unknown saturation attack is an attack that is not represented in the training data. In this paper, we evaluate three supervised classiﬁers for detecting a family of DDoS ﬂooding attacks (UDP, TCP-SYN, IP-Spooﬁng, TCP-SARFU, and ICMP) and their combinations using different time windows. This work represents an extension of the runner-up best-paper award entitled ‘Detecting Saturation Attacks in SDN via Machine Learning’ published in the 2019 4th International Conference on Computing, Communications and Security (ICCCS). The results in this paper show that the trained supervised models fail in detecting unknown saturation attacks, and their overall detection performance decreases when the time window of the network trafﬁc increases. Moreover, we investigate the performance of four semi-supervised classiﬁers in detecting unknown ﬂooding attacks. The results indicate that semi-supervised classiﬁers outperform the supervised classiﬁers in the detection of unknown ﬂooding attacks. Furthermore, to further increase the possibility of detecting the known and unknown ﬂooding attacks, we propose an enhanced hybrid approach that combines two supervised and semi-supervised classiﬁers. The results demonstrate that the hybrid approach has outperformed individually supervised or semi-supervised classiﬁers in detecting the known and unknown ﬂooding DoS attacks in SDN.
Expansion properties of finite simple groups

We prove that if G is SL_2(F) or PSL_2(F), where F is a finite field, and A is a set of generators of G, then either |AAA| > |A|^(1+epsilon), where epsilon is an absolute positive real number, or AAA=G. As a corollary we get that the diameter of any Cayley graph of G is Poly-Logarithmic in |G|.
PHOTO MANIPULATION, THE EASY WAY

The typical smartphone user has many thousands of photos in their personal collection. Photo acquisition is effortless, and the next challenge is in devising methods to easily edit such large collections. Specifically, we need manipulation algorithms that are powerful enough for experts, yet simple for novices to master. We identified three key directions to empower novice users with expert-level editing capabilities while maintaining an overall simplicity in the process. Those directions are (1) better selection masks, (2) high-level goal-centric algorithms and (3) domain specific algorithms. In this thesis we give examples from each category. Given a photo, a novice user will typically either not edit it at all, or apply a simple global operation such as exposure correction. In contrast, a professional photo editor might perform local edits, specifying a selection mask to limit the operation to specific photo regions, or combining regions from several photos into a single composition. To ease selection mask creation we present a new patch embedding technique that allows for single-click selection masks. Novices often think in terms of goals (e.g. improve lighting, de-clutter photo) and less in technical terms such as color spaces and image layers. One example of a high-level goal is the removal of distracting elements from photos. The task is motivated by the way professional photographers operate. They carefully frame the scene and might move objects around in order to stage the perfect photo. We define “photo distractors” as the elements that, if removed, would improve the photo. Using a simple slider interaction we allow users to automatically remove such distractors from photos. It is at times useful to tailor solutions to specific photo types. As an example we show that, specifically for human heads, simple controls can induce sophisticated edits. Given a single portrait photo as input, we can change the pose of the head and the camera distance. This allows users to correct the “selfie effect”, i.e. big noses and small ears or to transform distant photos into selfies.
Water Segmentation with Deep Learning Models for Flood Detection and Monitoring

Flooding is a natural hazard that causes a lot of deaths every year and the number of ﬂood events is increasing worldwide because of climate change eﬀects. Detecting and monitoring ﬂoods is of paramount importance in order to reduce their impacts both in terms of aﬀected people and economic losses. Automated image analysis techniques capable to extract the amount of water from a picture can be used to create novel services aimed to detect ﬂoods from ﬁxed surveillance cameras, drones, crowdsourced in-ﬁeld observations, as well as to extract meaningful data from social media streams. In this work we compare the accuracy and the prediction performances of recent Deep Learning algorithms for the pixel-wise water segmentation task. Moreover, we release a new dataset that enhances well-know benchmark datasets used for multi-class segmentation with speciﬁc ﬂood-related images taken from drones, in-ﬁeld observations and social media.
Wave propagation on a random lattice

Motivated by phenomenological questions in quantum gravity, we consider the propagation of a scalar field on a random lattice. We describe a procedure to calculate the dispersion relation for the field by taking a limit of a periodic lattice. We use this to calculate the lowest order coefficients of the dispersion relation for a specific one-dimensional model.
Perceptions of Stakeholders on Health and Management Risks for the Recovery of Household Solid Waste in Adiopodoumé (Abidjan)

The Services Platform of Yopougon (PFS-Yop) has set up an organic waste recovery center in Adiopodoumé for training, the promotion of sustainable peri-urban agriculture and the development of entrepreneurship in the District of Abidjan. However, this recovery initiative is marred by structuralorganizational problems which explain the demotivation and reluctance of the stakeholders in households solid waste management. The general objective of this study is to determine the factors limiting the control of households solid waste recovery in Adiopodoumé. The qualitative approach included ten semistructured interviews with five workers from Groupe Bio, the technical manager of the town hall, four representatives of the village community, and eleven focus groups of seventy-eight household members. The survey results revealed that the success of the organic waste recovery system involves adapting actions and results to socio-economic, health, and environmental challenges, but above all, strengthening awareness and involvement of stakeholders nationals. In this sense, the lack of communication, the lack of shelter, and the dysfunction in the remuneration of actors are perceived as demotivating factors in the managing the recovery of household solid waste in Adiopodoumé.
Advantages and Challenges of Differential Immune Cell Count Determination in Blood and Milk for Monitoring the Health and Well-Being of Dairy Cows

A key challenge of the 21st century will be to provide the growing world population with a sustainable and secure supply of food. Consequently, the dairy farming’s primary task is to lower milk losses and other inefficiencies associated with diseased cows. Moreover, a shift from curative to preventive health management would be desirable for mastitis and a wide variety of other infectious and non-infectious cattle diseases, some of which are known to have profound negative effects on the performance and well-being of cows. Differential cell counting (DCC), a procedure that aims to determine the proportions of different somatic cell types in raw milk samples, has not only the potential to optimize mastitis diagnostics, but it could furthermore serve as a diagnostic tool for monitoring the general and overall health status of dairy cows. Based on a broad search of the literature, the practical utility of various types of DCC is summarized and discussed in this review. Since it might be of advantage to interpret DCC with the aid of data from studies in humans, differences between the immune systems of humans and dairy cattle, with a special focus on surface marker expression profiles and γδ (gamma delta) T-cell characteristics, are also described.