Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Management System for Isolated Microgrids: A Coordinated UC-OPF-Based Stochastic Programming

This paper presents a novel mathematical approach for modelling microgrids’ Energy Management System (EMS), administrating distributed energy resources, such as diesel gensets, wind turbines, photovoltaic units, and energy storage systems, optimally and reliably. In the proposed approach, the EMS is designed based on a coordinated Unit Commitment-Optimal Power Flow (UC-OPF) framework, yielding optimal power dispatch decisions for gensets and storage units. In this approach, the UC operational constraints, such as ramp characteristics and minimum up and down times of gensets are considered while the power flow equations are taken into account, simultaneously. Being a severe source of uncertainty, the wind power generation deviation is handled using scenario-based stochastic programming technique. In order to evaluate the proposed system, a complex microgrid based on a CIGRE benchmark is used. The results indicate that the EMS optimal dispatch decisions are highly influenced by the uncertainty of wind generation compared to the deterministic assumptions made for this source of generation.
Analysis and Design of 4-to-1 Capacitor-Stacking Balancer for Stacked Voltage Domain

This paper presents an alternative method for achieving more efficient and reliable DC-DC conversion and balancing operations for low-power applications in a stacked voltage domain. This work comprehensively analyzes the operating principles and power conversion loss of a proposed capacitor-stacking balancing circuit at the system level. The analysis and design of the capacitor-stacking balancing circuit in the stacked voltage domain, including the time-domain operation, voltage equation, and dead-time effect, are explored and implemented. This study provides an opportunity to achieve a highly optimized system with high efficiency. A comprehensive analysis of efficiency at the system level shows the advantages and limitations according to each stacking method under a given system condition. Considering the redundancy issues of the previous method at system-level analysis, the capacitor-stacking balancing method is a preferable choice for low-power, high-reliability, and high-efficiency applications under light load conditions. This study also provides an analytical efficiency model under current imbalance, which is a notable difference from previous research and case studies concerning power converters. Prototype board with lithium-ion battery power and a core voltage of 0.825 V—a low-power application—was built to verify the proposed model and analysis. The experimental efficiency reached 94.9% at 20% of the maximum workload.
REGULATION OF KALLIKREIN 6 GENE EXPRESSION AND PROTEIN SECRETION IN COLON CANCER MODEL SYSTEMS

Colon cancer occurs in over 150,000 men and women in the United States each year and is fatal about one third of the time. There are many well characterized genetic transformations which commonly occur during colon carcinogenesis, including mutations in the Adenometous Polyposis Coli (APC), Kirsten RAS (K-RAS) and p53 genes among many others. There are also many alterations in gene and protein expression which are not yet completely elucidated. We have identified kallikrein 6 (KLK6) as a gene whose expression is dependent on cancer associated proteins such as K-RAS, SRC and caveolin1 (CAV-1). KLK6 is a member of the kallikrein protein family which consists of 15 secreted serine proteases. Like other types of proteases, kallikreins have been demonstrated to play a role in cancer progression and they hold promise as potential cancer biomarkers. The up-regulation of KLK6 was first identified by performing a microarray analysis on a mutant K-RAS transfected Caco2 cells. The activated K-RAS transfected cells expressed significantly more KLK6 than the mock transfected controls. Pathways downstream of K-RAS were found to induce KLK6 gene expression, including the p42/44 MAPK and the PI3-K/AKT pathways. Caveolae, plasma membrane associated structures, and their principle protein component, CAV-1, positively influence both KLK6 gene expression and KLK6 protein secretion in HCT116 colon cancer derived cells. Finally, it is shown that KLK6 increases the invasive potential of cells through laminin and Matrigel. Because KLK6 plays a role in cancer progression it may serves as a novel therapeutic target. Additionally, KLK6 holds potential for use as a serum
Molecular Methods in Epidemiology of Methicillin Resistant Staphylococcus aureus (MRSA): Advantages, Disadvantages of Different Techniques

The aim of this review article is to compare molecular typing methods of methicillin resistant Staphylococcus aureus (MRSA) strains. Resistance to methicillin and other beta-lactam antibiotics is caused by the mecA gene, which is situated on a mobile genetic element, the Staphylococcal Cassette Chromosome mec (SCCmec). While hospital acquired (HA)-MRSA strains are typically multi-resistant, community associated (CA)-MRSA strains are by large more susceptible to many antibiotics. More resently multi-resistant livestock associated (LA)-MRSA strains have been recovered from bovine mastitis, but these strains are only prevalent in certain high-risk groups of workers in direct contact with live animals. The epidemiology of MRSA might be investigated by pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST), spa typing, DNA microarray hybridisation and SCCmec typing. Until recently the most useful technique was the PFGE, which has high discriminatory index, but technically difficult and slow, the portability is limited, and multiple nomenclature and misclassification of some lineages also can occur. The mec (SCCmec) typing has standard nomenclature wich able to distinguish between the HA-MRSA and CA-MRSA strains. The spa typing might be a useful tool for epidemiological studies of MRSA, due to its rapidity, accessibility, high throughput, and standardized nomenclature. The MLST has high discriminatory power, defines core genetic population and also has portability and standard nomenclature, but the weaknessess of this method are the low throughput and high cost. Though, the DNA miroarray hybridization test is the most modern technique, which able to detect of presence of a huge number of genomic loci, but due to the high cost and complexity not preferred in routine practice. Considering all data of these molecular techniques, the most recommended methods are the spa and staphylococcal cassette chromosome mec (SCCmec) typing. Both are informative enough to define particular strain characteristics and utilise standardised nomenclatures, making them applicable globally. Continuous efforts to follow up the changing epidemiology of MRSA infection in humans and animals are necessary, not only for effective infection control and appropriate antibiotic treatment, but also monitoring the evolution of the MRSA clones.
Introducing a New Linear Regression Based Method for Early DDoS Attack Detection in SDN

The software Defined Networks, by separating the data plane and control plane of the network, have made a drastic change to the scope of computer networks. Although this separation has accelerated and simplified the management, configuration and error detection, it has also caused some new security problems. One of these problems is the Vulnerability of the software defined networks' architecture to distributed denial of service attacks on the network's controllers. One of the most recent distributed denial of service attacks which entropy-based methods are incapable of detecting, is to send fake packets with different source to random addresses in a software defined network. In this paper, given the SDN structure and tra □ ic analysis, a statistical trapezoid model is introduced to estimate number of table misses for each switch. Then, using the linear regression method and EWMA estimation, the threshold of the table misses in specified time intervals, is estimated. The evaluation results imply that using this method, one can detect DDoS attacks in early stage in software defined networks, regardless of the sort of DDoS attack.
Emotions Understanding Model from Spoken Language using Deep Neural Networks and Mel-Frequency Cepstral Coefficients

The ability to understand people through spoken language is a skill that many human beings take for granted. On the contrary, the same task is not as easy for machines, as consequences of a large number of variables which vary the speaking sound wave while people are talking to each other. A sub-task of speeches understanding is about the detection of the emotions elicited by the speaker while talking, and this is the main focus of our contribution. In particular, we are presenting a classification model of emotions elicited by speeches based on deep neural networks (CNNs). For the purpose, we focused on the audio recordings available in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. The model has been trained to classify eight different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise) which correspond to the ones proposed by Ekman plus the neutral and calm ones. We considered as evaluation metric the F1 score, obtaining a weighted average of 0.91 on the test set and the best performances on the "Angry" class with a score of 0.95. Our worst results have been observed for the sad class with a score of 0.87 that is nevertheless better than the state-of-the-art. In order to support future development and the replicability of results, the source code of the proposed model is available on the following GitHub repository: https://github.com/marcogdepinto/Emotion-Classification-Ravdess
An AMC Inspired Wearable UWB Antenna for Skin Cancer Detection

In this paper we present a Wearable UWB antenna for Skin Cancer detection integrated with Artificial magnetic Conductor. Wideband impedance bandwidth of this antenna realized between the frequency range of 8–12 GHz ($\mathrm{S}11 \leq-10\text{dB}$) for X-band applications. This range of frequency provides the lesser penetration of the signal. So due to this advantage, this proposed antenna is suitable for skin cancer detection. The wideband is designed to mimic the electrical characteristics of human skin tissues because of the correlation of frequency with the electrical property of human skin tissues. An artificial magnetic conductor (AMC) is used in terms of a reflector to reduce the backward radiation form antenna so that it protects the human body from harmful radiations. The proposed antenna provides better performance in terms of bandwidth and realized again. Scattering parameter used in this paper to evaluate the difference between cancerous and normal skin tissues.
An Efficient Traffic Classification Scheme Using Embedded Feature Selection and LightGBM

Machine Learning (ML) techniques have been widely used in anomaly-based Intrusion Detection System (IDS) in the big data era. Although many advanced approaches are proposed recently, there are still several key limitations that should not be ignored. Firstly, data pre-processing methods have not gained sufficient attention, and they are vital to efficiency of model training especially with massive volume of collected samples. Secondly, in spite that deep learning is able to acquire more hidden interrelations from input data, it usually suffers from high complexity with numerous parameters tuned and much training time consumed. Thirdly, KDD99 data sets and their variants are leveraged by most of the literature for traffic classification, but they are proved to be outdated and inadequate for IDS evaluation. Therefore, to cope with these challenges, in this paper, we firstly propose a data pre-processing approach with under-sampling and embedded feature selection, in order to relieve the imbalance of traffic samples and extract dominant features of incoming flows. Then, we utilize LightGBM to build an traffic classification approach for IDS with better accuracy and efficiency. Finally, we evaluate our proposed approaches based on CIC-IDS2018, the data set issued in 2018 that contains comprehensive real network traffic. Extensive experiments are performed, and related results have confirmed the advantages of our proposed approaches over several other comparisons.
Faecal density counts for monitoring changes in red fox numbers in rural Britain

Summary 1. Quantifying animal density is a fundamental requirement for the successful management of canid species. Faecal transects along linear features represent a costeffective means of quantifying relative density. However, it is unclear whether such counts can be utilized to estimate absolute density. 2. In Britain, due to proposed legislative changes in permitted culling practices, and as part of a wider monitoring programme, the current and future density of red foxes Vulpes vulpes is of applied interest. In this study, we counted fox faeces in 444 1-km squares throughout mainland Britain to construct a baseline index of fox density against which future changes could be measured. By incorporating estimates of the defecation rate and the proportion of scats associated with linear features, we estimated absolute density in seven landscapes. These estimates were compared with existing data on fox density. 3. On average, captive foxes consumed 0·85 kg food day −1 . Defecation rate (8 scats fox −1 day −1 ) was not affected by the type of food consumed. 4. The proportion of scats associated with linear features was determined by feeding individual free-living foxes a known amount of food containing an indigestible marker. On average, 5·7% of scats were deposited along linear features. 5. Mean fox density in landscapes ranged from 0·21 to 2·23 foxes km −2 . These estimates agreed closely with the limited data available on fox density in Britain. The total rural fox population was estimated to be 225 000 foxes (95% confidence interval 179 000‐ 271 000). Including foxes in urban areas, the total national population is estimated to be approximately 258 000 individuals. 6. Synthesis and applications. The results suggest that faecal density counts have the potential to be used to estimate fox density over large spatial scales, although further work on estimating the proportion of scats associated with linear features and the use of faeces in territorial marking by canids is required.
Segmentation of Multivariate Industrial Time Series Data Based on Dynamic Latent Variable Predictability

Time series segmentation is an important vehicle of data mining and extensively applied in the areas of machine learning and anomaly detection. In real world tasks, dynamics widely exist in time series but have been little concerned. This paper proposes an algorithm which can partition a multivariate time series into subsequences at points where the dynamics change. In process industries, the change of dynamics often relates to operation regime change, working condition shifting or faults. Therefore, to segment time series according to change of dynamics can be useful in data preprocessing and getting deep insight of the process in various industrial process monitoring tasks. The proposed algorithm recursively merges neighborhood subsequences through a heuristic bottom-up procedure. The cost of merging is defined as the mutual predictability of the subsequence models which are constructed using the dynamic-inner principal component analysis algorithm. Then, the goal becomes finding the segmentation scheme which minimizes the averaged dynamic prediction errors of each subsequence model. The proposed algorithm is evaluated on both simulated data and the time series data collected from an industrial processing plant. The results show that it outperforms the static principal component analysis based methods.
Exploiting Categorization of Online News for Profiling City Areas

Profiling city areas, in terms of citizens’ behaviour and commercial and social activities, is an interesting issue in the context of smart cities, especially considering a real-time streaming context. Several methods have been proposed in the literature, exploiting different data sources. In this paper, we propose an approach to perform profiling of city areas based on articles of local online newspapers, by exploiting information regarding the text as well as metadata such as geo-localization and tags. In particular, we use tags associated with each article for identifying macro-categories through clustering analysis on tags embeddings. Further, we employ a text categorization model based on SVM to label online a new article, represented as Bag-of-Words, with one of such categories. The categorization approach has been integrated into a framework recently proposed by the authors for profiling city areas exploiting different web sources of data: the online newspapers are monitored continuously, thus producing a news stream to be analysed. We show experiments performed on the city of Rome, considering data from 2014 to 2018. We discuss the results obtained by adopting different classifiers and present that the best classifier, namely an SVM, can achieve an accuracy and an f1-score up to 93% and 79%, respectively.
Assessing the Habitat Structure for Common Snook (Centropomus undecimalis Bloch, 1792) and Tarpon (Megalops atlanticus Valenciennes, 1847) in Santa Teresa Lagoons, Puerto Rico

This paper assessed whether the habitat structure of the Santa Teresa lagoons, Humacao Natural Reserve (HNR), Puerto Rico was able to support long-term fisheries of tarpon (Megalops atlanticus) and common snook (Centropomus undecimalis). Relations between fish and invertebrate communities and the physicochemical and habitat structure of the lagoon system were identified. Santa Teresa 2 (ST2) was physicochemically and structurally different than Santa Teresa 1 (ST1). Reduced turbidity and increased habitat complexity created by submersed vegetation in ST2 were reflected in the composition of the fisheries resources. Tarpon predominated in ST2 and common snook predominated in ST1. Catch-perunit-effort (CPUE) of tilapia (Mozambique tilapia Oreochromis mossambicus and redbreast tilapia Tilapia rendalli) was the highest in ST2, and CPUE of grass shrimp (Palaemonetes sp.) was the highest in ST1. Tilapia represented 96% of the tarpon diet, whereas grass shrimp represented 23% of the snook diet. Links between fish habitat use and habitat structure indicated that the Santa Teresa lagoons have the habitat structure necessary to support tarpon and snook long-term fisheries.
Factors for Enhancement of Intracranial Atherosclerosis in High Resolution Vessel Wall MRI in Ischemic Stroke Patients

Introduction: High resolution vessel wall MRI (VW-MRI) has enabled to characterize intracranial atherosclerosis (ICAS). We studied to identify the factors for enhancement of ICAS in VW-MRI in patients with acute ischemic stroke. Methods: Consecutive patients with acute ischemic stroke or TIA who underwent VW-MRI between January 2017 and December 2017 were included. Enhancement on VW-MRI was defined as an increase in intensity on contrast-enhanced T1-weighted sequence. We compared the clinical and the radiologic findings between patients with wall enhancement and those without wall enhancement. Results: Of the 48 patients with ICAS, 28 patients revealed enhancement on VW-MRI. Patients with enhancement were more likely to have severe stenotic lesions and higher levels of total cholesterol, triglycerides, low-density cholesterol, Apo (b), and Apo (b)/Apo (a) lipoprotein ratio (p < 0.05). Multivariable analysis demonstrated that total cholesterol (OR: 5.378, 95% CI, 1.779–16.263), triglycerides (OR: 3.362, 95% CI, 1.008–11.209), low density lipoprotein cholesterol (OR: 4.226, 95% CI, 1.264–14.126), Apo (b) lipoprotein (OR: 3639.641, 95% CI, 17.854–741954.943) levels, and Apo (b)/Apo (a) lipoprotein ratio (OR, 65.514; 95% CI, 1.131–3680.239) were independently associated with enhancement of ICAS. High-density lipoprotein cholesterol and Apo (a) lipoprotein levels were not significantly different between the patients with wall enhancement and those without wall enhancement. Conclusions: The presence and severity of enhancement of ICAS was significantly associated with dyslipidemic conditions. These results suggest that strict lipid modification should be achieved for the management of ICAS.
Overexpression of ZePrx in Nicotiana tabacum Affects Lignin Biosynthesis Without Altering Redox Homeostasis

Class III plant peroxidases (Prxs) are involved in the oxidative polymerization of lignins. Zinnia elegans Jacq. Basic peroxidase (ZePrx) has been previously characterized as capable of catalyzing this reaction in vitro and the role in lignin biosynthesis of several of its Arabidopsis thaliana homologous has been previously confirmed. In the present work, ZePrx was overexpressed in Nicotiana tabacum to further characterize its function in planta with particular attention to its involvement in lignin biosynthesis. Since Prxs are known to alter ROS levels by using them as electron acceptor or producing them in their catalytic activity, the impact of this overexpression in redox homeostasis was studied by analyzing the metabolites and enzymes of the ascorbate-glutathione cycle. In relation to the modification induced by ZePrx overexpression in lignin composition and cellular metabolism, the carbohydrate composition of the cell wall as well as overall gene expression through RNA-Seq were analyzed. The obtained results indicate that the overexpression of ZePrx caused an increase in syringyl lignin in cell wall stems, suggesting that ZePrx is relevant for the oxidation of sinapyl alcohol during lignin biosynthesis, coherently with its S-peroxidase nature. The increase in the glucose content of the cell wall and the reduction of the expression of several genes involved in secondary cell wall biosynthesis suggests the occurrence of a possible compensatory response to maintain cell wall properties. The perturbation of cellular redox homeostasis occurring as a consequence of ZePrx overexpression was kept under control by an increase in APX activity and a reduction in ascorbate redox state. In conclusion, our results confirm the role of ZePrx in lignin biosynthesis and highlight that its activity alters cellular pathways putatively aimed at maintaining redox homeostasis.
Invasive Species and Amphibian Conservation

Abstract The recognition that invasive alien species (IAS) are among the greatest threats to biodiversity has stimulated a growing interest in their impacts on native amphibians. Here we describe the multifaceted consequences of biological invasions on native amphibians and identify potential mechanisms and strategies that could better enable the long-term persistence of native species. IAS can influence amphibian fitness, population size, and community structure via multiple pathways and can exert major, direct impacts through predation, competition, and hybridization. The consequences of indirect impacts, too, such as habitat alteration and the spread of emerging diseases, can be particularly severe in native populations. Native amphibians may respond to IAS by modulating aspects of their behavior, morphology, or life history. Nevertheless, it is still unclear the extent to which phenotypic plasticity and rapid evolution may help native species withstand the impacts of IAS in invaded communities. Practical management strategies focused on prevention, monitoring, and early control are the most effective approaches to allay the impacts of IAS and should be prioritized in proactive conservation plans. Eradications of IAS and mitigation approaches, should IAS become established, are feasible and can greatly improve the status of native populations.