Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring and explaining large‐scale distribution of functional and phylogenetic diversity in birds: separating ecological drivers from methodological choices

Aim Investigations into how different facets of biodiversity are related has become a central research agenda in ecology. Here, we use a large-scale and high-resolution data set on bird distribution to examine the robustness of the relationships between species diversity and functional or phylogenetic diversity. Location France Methods We measured the functional and phylogenetic diversity of 1914 bird assemblages monitored over 10 years with a standardized protocol. We investigated the consequences of incorporating abundance versus presence–absence data, changing the number and identity of traits considered, or varying the spatial scale used to estimate functional and phylogenetic diversity. We further examined the outcomes of different null model procedures that aim to reveal ecological processes influencing the distribution of each facet of diversity. Results We found that the shape and strength of the relationship between species diversity and functional diversity are structured by several methodological choices. We show that increasing the quantity of information yielded by the indices (in particular, including abundances or increasing the number of traits considered) decreases the amount of functional redundancy estimated. Reducing the number of functional traits used to estimate functional diversity can change and even reverse the relationship of interest. Moreover, using alternative null models, we highlighted the specific role of environmental filtering and the link between species abundances and their functional originality (defined for a given species as the average of the functional distances with other species). The same tests applied to phylogenetic diversity revealed that its relationship with species diversity varies differently suggesting that this latter index cannot be used as a proxy for functional diversity. Main conclusions Our results show that the identification of patterns and processes linking species diversity to functional or phylogenetic diversity vary with methodological choices. We further show that the study of these sources of variation via robustness tests allows ecologically meaningful information to be separated from pure artefacts.
Intelligent Intersection Management with Non-Connected and Non-Autonomous Motorcycles

Intelligent intersection management is a very representative application of connected and autonomous vehicles, and there have been many related works from different perspectives. However, motorcycles have not yet been considered in intelligent intersection management, and this is a crucial problem for certain places, especially those with high population densities. In this paper, we consider motorcycles and identify the fundamental differences from existing intelligent intersection management. Considering the features of motorcycles, we propose to use grouping and two-phase left turns with waiting zones for motorcycles to improve the traffic efficiency of an intersection where vehicles are connected and autonomous and motorcycles are non-connected and non-autonomous. To the best of our knowledge, this is the first work in the literature to model and study the intelligent intersection management with motorcycles. Our case study demonstrates essential trade-offs and insights for designing intelligent intersection management with motorcycles.
A Literature Review: Empirical Research on Tax Havens, years 2008 to 2018

The exposure of several financial scandals and the crisis in 2008 revealed the true dimension of Tax Havens, and consequently brought them to the spotlight. Aiming to analyze the empirical research produced about Tax Havens, a sample of a time period of 10 years was collected from Scopus. To achieve this goal, an analysis was performed on a set of variables in order to characterize the investigators and the content of their articles in the selected sample. The results show that all continents are present in the sample, with Europe being the most active continent in discussing this subject. The Tax Havens theme is strongly correlated to tax avoidance and tax evasion and the year 2015 was the one with the most published articles.
Introducing a forensics data type taxonomy of acquirable artefacts from programmable logic controllers

The understanding of available data artefacts is fundamental to performing digital forensics. There is good understanding of what data artefacts are acquirable from common information technology (IT) systems such as a Windows operating system and what their potential forensic value could be. As a result, IT forensic investigators can make clear predictions about what information the acquired data would yield. The same level of understanding for programmable logic controllers (PLCs) found within industrial control systems (ICS) is limited. Previous research has restricted the discussion of PLC data to generic and common data formats. This makes it challenging to prepare for incidents proactively, develop new forensic capabilities and prioritise the collection of data should an incident occur. Examples of previous cyber incidents such as Stuxnet and Triton have employed ad-hoc methods for forensic the investigation, highlighting the lack of a systematic approach. This paper examines the specific data types stored on a PLC and describes a forensic artefact taxonomy based on the acquirable data. Data acquisition tests were performed primarily using third-party communication libraries that utilise the PLC’s proprietary industrial communications protocol to leverage data stored within memory structures of each of the tested PLCs. Three PLCs, from two different manufacturers were examined. Potential PLC attack scenarios, identified from the literature, were used to guide the evaluation of the acquirable data, categorised into high-level data types, to highlight the potential benefits of acquiring each form of data.
A Machine Learning Approach for Real Time Android Malware Detection

Smart mobile devices are increasingly popular and common in modern life. The vast majority of these use Android - an open operating system developed by Android Inc., which Google bought in 2005. Along with its popularity, Android has become the target of rapidly developing malicious applications. Moreover, the fact that the users can install applications both from Google Play as well as through third-party application markets also facilitates the development and spread of applications as well as malwares on Android. In this paper, we propose a machine learning method to detect malicious applications on Android devices. The features used for machine learning are based on the common behavior of malicious applications, the required permissions, and other features taken from the applications files. With a data set of nearly 30000 samples including malicious and safe applications, we achieved results with 98.66% accuracy. In addition, using and changing libraries in the C++ language has helped us achieve a good processing speed and decision making. Thereby creating a framework that can run real time on normal devices such as a usual laptop.
Development of antibiotic resistance and options to replace antimicrobials in animal diets

As there is a risk of developing antibiotic resistance, a number of commonly-used antimicrobial growth promoters have been banned in the EU member states. This decision has put new emphasis on using the diet to control enteric bacterial infections of pigs. Dietary carbohydrates constitute a major proportion of diets for pigs, and the carbohydrate fraction has a diverse composition, with different properties in the gastrointestinal tract, some of which are of importance to gut health. Findings from different studies indicate that dietary carbohydrate composition influences the expression of swine dysentery and infection with nematode worms after experimental challenge with Brachyspira hyodesenteriae and Oesophagostumum dentatum respectively. In both cases the type, amount and physico-chemical properties of the carbohydrates entering the large intestine played an important role in the infection, and emerging data suggest a synergism between different porcine pathogens. There is also increasing evidence that the feed structure, which relates to the type of plant material in the diet and the way it is processed, can be used to reduce Salmonella prevalence at the herd level. However, it should be stressed that using the diet to manage gut health is not straightforward, since the expression of a pathogen in many cases requires the presence of other components of the commensal biota.
An Efficient and Automated P2P Overlay System for Network Monitoring

Precise and efficient monitoring is vital to ensure that a network works according to the intended behaviour, as well as quickly acting in response to possible problems. The task of monitoring a network becomes complex with increasing network size and its heterogeneity. This work explores the development of a Peer-to-Peer (P2P) system to monitor network communication infrastructures. All the processes of creation of the system and the existing entities will be described as well as their communication capabilities. Then, the definition of a metalanguage will be exposed, allowing network administrators to conFigure and pre-program the network in an effective and versatile manner. The optimization of the monitoring system will be also described, reducing the traffic generated by the P2P network. It will be also addressed how the system became fault-tolerant, recovering its state even if any entity has a problem.
Epilepsili Hastalarda Psikososyal Destek

Epilepsi, hastalarin yasam kalitesini olumsuz olarak etkileyen norolojik bir hastaliktir. Hem tibbi hem de psikolojik faktorler epilepsi hastalarinin psikososyal islevleri uzerinde onemli bir etkiye sahiptir. Epilepsi, tekrarlayan nobetlerin varligi ile tanimlanmakla birlikte bireyin bilissel, psikolojik, entellektuel ve sosyal fonksiyonlarinda onemli sorunlara neden olmaktadir. Epilepsi hastalarinda psikososyal sorunlar, genel populasyona gore daha fazla gorulmektedir. Epilepsili bireylerde anksiyete, depresyon gibi psikolojik sorunlar yaninda benlik saygisinda azalma, sosyal izolasyon, okul basarisinda dusme, issizlik ve yasam kalitesinde azalma gibi sorunlar gorulmektedir. Psikososyal faktorler ayni zamanda davranissal ve bilissel bozukluklara da neden olmaktadir. Stigma, epilepsi hastalarinin karsi karsiya kaldigi en buyuk sorunlardan biridir ve hastalarin en sik hastaligini gizleme sebebidir. Stigma, ayirimcilik ve toplumun epilepsili hastalara yonelik onyargili tutumlari hastaligin kendisinden daha cok epilepsi hastalarina zarar verebilmektedir. Bu nedenle, epilepside psikososyal sorunlarin etiyolojisi, tedavisi ve onlenmesi buyuk onem tasimaktadir. Hastaligin yonetiminde, antiepileptik tedavinin etkin kullaniminin yani sira danismanlik, egitim, bilissel davranisci terapi, hasta ve ailesine sosyal destek sunumu onemlidir. Bu derlemenin amaci epilepsili hastalarda en cok yasanan psikososyal sorunlar ve bu sorunlara yonelik birey, aile ve toplumda izlenmesi gereken stratejiler ve alinmasi gereken onlemler hakkinda bilgi vermektir.
Li-Ion Batteries Releasable Capacity Estimation with Neural Networks on Intelligent IoT Microcontrollers

Lithium-Ion (Li-Ion) batteries are gaining remarkable popularity, due to their chemical ability to maximize battery life while increasing power energy density. These rechargeable batteries are widely used in mobile computing devices, such as smartphones and smartwatches, and automotive systems, such as hybrid and electric vehicles. The estimation of the releasable capacity allows the computation of the State of Health (SoH) of a battery, e.g. a measure of its functionality in energy storage and delivery, which is a fundamental parameter for the battery health monitoring. Several prognostic analysis approaches use machine learning algorithms, such as Support Vector Machines (SVMs), Random Forest regression and Artificial Neural Networks (ANNs). In this paper, we compare different machine learning algorithms for predicting maximal releasable capacity of Li-Ion batteries by analysing accuracy versus complexity, with special focus on implementing ANN on resource constrained microcontrollers (MCUs). In particular, Forward Neural Networks (FNNs) and Recurrent Neural Networks (RNNs) are compared. These approaches are applied on the Litium-Ion battery prognostic datasets made available by the National Aeronautics and Space Administration (NASA). Complexity is profiled on STM32 microcontrollers (MCUs) by using the toolset X-CUBE-AI, which automatically converts pre-trained ANNs and generates optimized and validated ANSI C code for STM32.
Dealing with inconsistent weather warnings: effects on warning quality and intended actions

In the past four decades, the private weather forecast sector has been developing next to National Meteorological and Hydrological Services, resulting in additional weather providers. This plurality has led to a critical duplication of public weather warnings. For a specific event, different providers disseminate warnings that are more or less severe, or that are visualized differently, leading to inconsistent information that could impact perceived warning quality and response. So far, past research has not studied the influence of inconsistent information from multiple providers. This knowledge gap is addressed here. An inconsistency matrix was developed and employed to categorize the level of inconsistency across multiple warnings. The matrix provides warning pairs inconsistent in visualization, text or both. A survey experiment was conducted in Switzerland (N = 1,335). The results show that half of the people who received warnings from different providers for the same event indicated that these were inconsistent. The evaluation of warning quality and intended actions in a decision scenario characterized by two severe rainfall warnings shows the negative impacts of inconsistency. For example, consistent warnings are least confusing and inconsistent visual and textual warnings are most confusing. However, there are no significant differences in the effects of inconsistent textual information compared to inconsistent visual information on warning quality and intended actions. These findings offer empirical justification to enhance co‐operation between public and private weather providers. To improve warnings, the providers should find an agreement to be consistent either in the text or in the visualization.
Interaction between the introduced eastern mosquitofish and two autochthonous Spanish toothcarps

In aquaria, adults and young-of-the-year (YOY) of both Spanish toothcarp Aphanius iberus and Valencia toothcarp Valencia hispanica received more aggression in the presence of eastern mosquitofish Gambusia holbrooki. YOY of both species also showed decreased feeding rates, as did V. hispanica adults. Adult V. hispanica also were more aggressive themselves and had their reproductive behaviour disrupted. Cases of eastern mosquitofish predation on small A. iberus were also observed. The occurrence and intensity of these potentially harmful effects, however, seemed to be modulated by water temperature, reproductive condition, relative size of the interacting fishes and species-specific habitat preferences. Thus, aggression almost disappeared at low water temperatures. Valencia hispanica and eastern mosquitofish used similar vertical positions in the water column and this apparently increased the frequency of their interactions. In contrast, A. iberus preferred positions closer to the bottom than the other two species and this vertical segregation appeared to lead to less direct interaction with eastern mosquitofish. In an outdoor mesocosm experiment, under semi-natural conditions, the presence of eastern mosquitofish resulted in an almost 70% reduction in the number of o.spring produced by A. iberus. The addition of extra, non-breeding males of A. iberus had no significant effect. YOY A. iberus from cages with eastern mosquitofish or extra males were significantly smaller (c. 1 mm, or 8% shorter) than those from cages without additional fish. Results for V. hispanica were not conclusive, as very few YOY were recovered. In contrast, eastern mosquitofish did not affect the survivorship of breeding adults of both native species.
Heart left ventricle segmentation in ultrasound images using deep learning

Automatic segmentation of the heart left ventricle (LV) is an important step in setting an adequate diagnostic in echocardiography. Some of the state-of-the-art methods for 2D segmentation include traditional methods like active shape models, active contours, level sets, Kalman filter etc., but also deep modern learning methods (i.e. convolutional neural networks), where accuracy usually surpasses the accuracy of traditional methods. Due to the promising results of convolutional neural network called U-net in different segmentation problems, we propose it for the extraction of the left heart ventricle. The results show that the network has been able to segment the left ventricle with the accuracy of around 83.5% on unseen data which surpasses the reported state-of-the-art results, even with a smaller database. Larger database will enable better learning that we are confident will contribute to even higher accuracy. Future work will include testing on larger databases in order to meet the needs for Big Data analysis, but pertain the accuracy and reduce the time necessary for manual analysis of images.
A Review of Off-Line Mode Dataset Shifts

Dataset shifts are present in many real-world applications, since data generation is not always fully controlled and is subject to noise, degradation, and other natural variations. In machine learning, the lack of regularity in data can degrade performance by breaching error constraints. Different methods have been proposed to solve shifting problems; however, shifts in off-line learning mode are not as well examined. Off-line shifts consist of problems where drifts occur only with unlabeled data. Most methods aimed at dataset shifts consider that new labeled data can be received after training, which is not always the case. Here, a review on dataset shift characteristics and causes is presented as a tool for the analysis and implementation of machine learning methods targeting off-line mode dataset shift problems. In this context, a relationship between statistical learning risk functions and error degradation due to variation in data distribution was straightforwardly derived. Moreover, this paper provides a consistent survey of recent popular machine learning methods that address off-line mode dataset shift problems, focusing on the main characteristics of unlabeled data shifts.
Mapping of the Perception of Theft Crimes from Analysis of Newspaper Articles Online

The analysis of crimes represents a great challenge to law enforcement agencies and organizations that collect information to analyze accurately and effectively the distribution of the behavior of criminal events, considering that the sources to be used in the process of generating intelligence are diverse in content and/or structure. Tools that have contributed to the analysis of fields in which massive amounts of data are available is ʺdata miningʺ, a powerful tool that can be used effectively in the analysis of large amounts of data and in the subsequent derivation of important analytical results. This paper presents a methodology of analysis of crime facts from online newspapers, identifying the different communes where the greatest number of criminal events occur, which gives an idea of potentially more dangerous places, through the detection and geographical mapping of critical points, or the analysis of the nature of the crime through the extraction of entities. Statistics that measure the predictive capacity of the model indicate that the methodology is robust to recognize crime events within the body of the news.
Community relaxation in fragmented landscapes: the relation between species richness, area and age

Estimates of species loss due to habitat destruction are normally based on calculations employing the species–area relation, S = cAz. The validity of this approach is based on the assumption that the value of the exponent (z) defining the slope of the species–area relation in nonfragmented communities is at a steady state and that z is thus a constant. However, departure from such an assumption renders this approach unreliable. Here I report the results from a natural field experiment using “model” bryophyte-based microlandscapes designed to follow the species richness dynamics of microarthropod communities postfragmentation. Community isolation due to fragmentation initiated a delayed community relaxation process and resulted in substantial local extinction. Over the period of the experiment z declined in the control communities and yet remained fairly stable in the fragmented communities. I conclude that predictions of species loss due to habitat fragmentation that do not take into account the fact that often z may not be a constant may lead to error-prone predictions of future species loss.
Surgical management of congenital hairy nevus of face with Limberg flap, a case report

Introduction: Cosmetic deformity of the face is always a subject of concern for the patient. Besides the aesthetic considerations, the possibility for large congenital nevi to become malignant is significant and emerges as an important factor in the treatment and management of this entity. The case report is about a four years old female child who reported with a large black hairy pigmented lesion on the left side of the midface region. The child often faced discrimination at school. The child’s parents brought her to our institute for the correction of this major cosmetic defect. Procedure: Surgical management involved excision of the lesion and reconstruction of the defect using a Limberg flap. In the immediate post operative phase, regular topical antiseptic ointment application was done for 2 weeks. Suture removal was done after 2 weeks and regular application and massage with an Allantoin based cream was done for the next 6 months. Summary: Healing for the patient was uneventful and scars gradually decreased with massaging. There was no recurrence of the CNN. At the 2 year follow-up, the scar marks had become very faint. According to the testimony of the patient, the patient feels increasingly confident and more socially accepted as a result of the procedure. Conclusion: We would like to propose that with proper planning and analysis, Limberg flap can produce satisfactory functional and aesthetic reconstructive results for many full thickness defects of the facial region.
Enhancing Data Monitoring Scheme Based on Reinforcement Learning in IIoT Systems

The high-efficiency and security-guarantee of Industrial Internet of Things systems are critical in industry area. In this paper, the novel data collection procedure is established to monitor and classify the data flow in IIoT systems firstly. Then, a Q-learning algorithm based on reinforcement learning is introduced to detect the system fault and equipment error in IIoT systems. Simulation results show that the proposed data collection procedure and data analysis method can detect the equipment fault more efficiently than before.