Miracle-dz/UniFAR-FTUs huggingface dataset:
A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel

The paper illustrates an artificial neural network (ANN) based procedure for the identification of pattern-of-use of some main domestic electric appliances from daily profiles of energy recordings taken at household's meter panel at 15 minute steps. The paper describes the architecture used which is structured into multiple subsequent stages based on ANNs. The application of the procedure to some real daily load diagrams as recorded at few household meters demonstrates the efficiency of the proposed approach. The utility of ANNs for extracting further useful information concerning energy usage from data bases typically available for electric companies is finally discussed.
Architectural Ecologies: Code, Culture and Technology at the Convergence

This special issue of ‘Systema’, presents eight contributions to the track “Architectural Ecologies: Code, Culture and Technology at the Convergence’ at the European Meetings on Cybernetics and Systems research (EMCSR) 2014 in Vienna. It describes one starting point of conversations about habitat, design, decision-making and digital operations from a variety of perspectives developed by architects and researchers working in the digital and computational paradigm. The issue aims at tackling the obvious disappearance of discipline specific boundaries in favor of novel systemic understanding of architecture, her becoming into being and cultural transformation.
MALWARE DETECTION SYSTEM BASED ON DEEP LEARNING TECHNIQUE

In this paper, we propose a malware classification and detection framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets, we applied a deep Convolutional Neural Network (CNN) based on Xception model to perform malware image classification. The Xception model is a recently developed special CNN architecture that is more powerful with less overfitting problems than the current popular CNN models such as VGG16, The experimental results on a Malimg Dataset which is comprising 9,821 samples from 26 different families ,Malware samples are represented as byteplot grayscale images and a deep neural network is trained freezing the convolutional layers of Xception model adapting the last layer to malware family classification , The performance of our approach was compared with other methods including KNN, SVM, VGG16 etc. , the Xception model can effectively be used to classify and detect malware families and achieve the highest validation accuracy than all other approaches including VGG16 model which are using image-based malware, our approach does not require any features engineering, making it more effective to adapt to any future evolution in malware, and very much less time consuming than the champion’s solution.
Unfalsified control : data-driven control design for performance improvement

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Understanding and coping with failures in large-scale storage systems

Reliability for very large-scale storage systems has become more and more important as the need for storage has grown dramatically. New phenomena related to system reliability appear as systems scale up. In such a system, failures are a normality. In order to ensure high reliability for petabyte-scale storage systems in scientific applications, characterization of failures and techniques of coping with them are studied in this thesis. The thesis first describes the architecture of a petabyte-scale storage system and characterizes the challenges of achieving high reliability in such a system. The long disk recovery time and the large number of system components are identified as the main obstacles against high system reliability. The thesis then presents a fast recovery mechanism, FARM, which greatly reduces data loss in the occurrence of multiple disk failures. Reliability of a petabyte-scale system with and without FARM has been evaluated. Accordingly, various aspects of system reliability, such as failure detection latency, bandwidth utilization for recovery, disk space utilization, and system scale, have been examined by simulations. The overall system reliability is modeled and estimated by quantitative analysis based on Markov models and event-driven simulations. It is found that disk failure models which take infant mortality into consideration result in more precise reliability estimation than the traditional model which assumes a constant failure rate, since infant mortality has a pronounced impact on petabyte-scale systems. To safeguard data against failures from young disk drives, an adaptive data redundancy scheme is presented and evaluated. A petabyte-scale storage system is typically built up by thousands of components in a complicated interconnect structure. The impact of various failures on the interconnection networks is gauged and the performance and robustness under degraded modes are evaluated in a simulated petabyte-scale storage system with different configurations of network topology. This thesis is directed towards understanding and coping with failures in petabyte-scale storage systems. It addresses several emerging reliability challenges posed by the increasing scale of storage systems and study the methods to improving system reliability. The research is targeted to help system architects in the designs of reliable storage systems at petabyte-scale and beyond.
Method of Wavelet Denoising in Harmonic Detection

Harmonic detection is an important step in grid harmonic status evolution and suppression measures implementation. While it’s accuracy is interference to the noise in the power grid. The different characteristics of signal and noise in the wavelet transform are proposed firstly, and the method of soft threshold de-noising based on wavelet multiresolution is analyzed in the paper. Using the method, the noisy harmonic signals in the power grid are processed and the de-noisy signals are analyzed by Fourier Transformation (FT) to obtain the contents of each harmonics. The simulation results show that the harmonic containing rate is close to the original signals after de-noising by WT. That is the method reduces the interference of noise and the accuracy of harmonic detection is improved.
Equilibrium User Centred Lighting Design Towards the development of a lighting design that is individually psychologically, physiologically and visually supportive

Why and in what way should daylight and artificial light be designed for the indoor environment in order to fulfil goals about visual comfort and light-related health? When the outdoor and indoor interactions of man, light, colour and space (MLCS) are compared to one other, the spectral component plays the decisive role (Hollwich 1979, p. 90). Through empirical studies and theoretical surveys the knowledge about the interaction of MLCS in the indoor and outdoor environment is investigated. Patterns in responses and preferences of room and light settings are studied. The process of lighting design is investigated and the process that gives a psychological, physiological and visual (PPV) support from light for the individual user is described. Lighting design for the indoor environment should give the same input to melanopsin and ipRGC as can be seen in daylight outdoors, to be physiologically supportive. The pattern that can be seen in the studies performed in the Thesis, in responses and preferences to light in the indoor environment, concerns differences between individuals and changes during the day. Laws, recommendations and methods for lighting design are developed for a general visual support from a static artificial light. The use of EN12464-1:2011 do not fulfill goals of visual comfort set out in the standard or light-related parts of goals of health in EC Treaty 137 or WHO target 9 and 13-17. It is argued in the Thesis that the findings of melanopsin and ipRGC demonstrate the need to develop laws, recommendations and methods for lighting design in a way that gives an individual support for visual comfort and light-related health. The computer calculated lighting design process (CCLDP) used in a majority of the lighting applications performed today is not done in contact with daylight, the user or the space, and by that it is not possible to support the individual user PPV by the use of the CCLDP. The user centred lighting design process (UCLDP), on the contrary, is done in contact with daylight, the user and the space and has by that the possibility to support the individual user PPV. Lighting quality (LQ) seen from the individual user’s perspective is a well functioning PPV support from daylight and a daylight mimicking artificial light. When LQ is defined seen from the contractors or the clients perspective it is a general and theoretical approach, not related to the individual user’s senses. Research in lighting design can be positioned around the lighting design process (LDP). This points out the cluster of topics that are related to lighting design, and their role is described in the research. The field of lighting science and design is theoretically delayed and a rapid development in the topic is valuable for the individual and for the society as well.
MosqGuide: A project to develop best practice guidance for the deployment of innovative genetic vector control strategies for malaria and dengue

The World Health Organisation Special Programme in Research and Training in Tropical Diseases (WHO/TDR) has funded a project, designated MosqGuide, to develop guidance on the potential deployment of different types of genetically modified (GM) mosquitoes to control vector borne diseases, specifically malaria and dengue. This guidance is intended to support disease endemic countries (DECs) and other stakeholders in considering the safety and legal/regulatory aspects, as well as ethical, cultural and social issues, of such deployment. Commissioned in 2008 as a three year project, MosqGuide will result in a series of best prac- tice documents developed by the project's international team of experts in regulation, vector control and management, arthropod molecular biology, social sciences and environmental risk assessment. Using fundamental principles of risk/benefit as a foundation, the MosqGuide project will prepare guidance as a series of modules aimed at different user groups, including researchers, regulators, public health officials, funding bodies and interested public. Each module will be tested with target audiences, primarily regulators and decision makers in the DECs, and will also feed into other WHO initiatives, such as the Regional Biosafety Training Centres for GM Vectors. The guidance will also include a module that demonstrates a prototype issues/response model to assist DECs in making an informed choice about whether and under what conditions to deploy specific genetic control methods for the control of mosquito vectors for malaria and dengue.
Do Epstein–Barr Virus Mutations and Natural Genome Sequence Variations Contribute to Disease?

Most of the world’s population is infected by the Epstein–Barr virus (EBV), but the incidence of the diseases associated with EBV infection differs greatly in different parts of the world. Many factors may determine those differences, but variation in the virus genome is likely to be a contributing factor for some of the diseases. Here, we describe the main forms of EBV genome sequence variation, and the mechanisms by which variations in the virus genome are likely to contribute to disease. EBV genome deletions or polymorphisms can also provide useful markers for monitoring disease. If some EBV strains prove to be more pathogenic than others, this suggests the possible value of immunising people against infection by those pathogenic strains.
The influence of harshness and unpredictability on female sexual development: Addressing gene–environment interplay using a polygenic score

Abstract Recent developments in the application life history theory to human development indicate two fundamental dimension of the early environment – harshness and unpredictability – are key regulators life history strategies. Few studies have examined the manner with which these dimensions influence development, though age at menarche (AAM) and age at first sexual intercourse have been proposed as possible mechanisms among women. Data from the Avon Longitudinal Study of Parents and Children (N = 3,645) were used to examine direct and indirect effects of harshness (financial difficulties) and unpredictability (paternal transitions) on lifetime and past year sexual partners during adolescence and young adulthood. Genetic confounding was addressed using an AAM polygenic score (PGS) and potential gene-by-environment interactions were also evaluated using the PGS. Path model results showed only harshness was directly related to AAM. Harshness, unpredictability, and AAM were indirectly related to lifetime and past year sexual partner number via age at first sexual intercourse. The PGS did not account for any of the associations and no significant interactions were detected. Implications of these results for developmental models derived from life history theory are discussed as well as the role of PGSs in gene–environment interplay research.
Design and Development of Inverted U-Slot Rectangular Ring Coupled Monopole Microstrip Antenna for Quad Band Operation

Abstract: This paper present a novel design and development of inverted U-slot rectangular ring coupled monopole microstrip antenna (IURCMMA) for quad band operation. The monopole microstrip antennas are commonly designed for wide band operation. However, by placing the optimum ring slots in the form of slits on the radiating patch, the antenna can be made to operate at different frequency bands. The proposed antenna operates in the frequency range of 1.5 to 10 GHz with a peak gain of 8.69 dB and gives omni directional radiation pattern in both E and H planes. The measured and simulated results of return loss are in good agreement with each other. With these features the proposed antenna may find many applications at microwave frequency range. Keywords: Monopole, Rectangular, Bandwidth, Quad band , Gain.
Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model

Since the beginning of the new century, risk events such as the world economic crisis have occurred, which have greatly impacted the real economy of our country. A wireless network is a network implemented using wireless communication technology. It includes both global voice and data networks that allow users to establish long-distance wireless connections, as well as infrared technology and radio frequency technology optimized for short-distance wireless connections. These events have a great impact on many small- and medium-sized listed companies, resulting to many small- and medium-sized listed companies going bankrupt. Indeed, one of the important reasons for the frequent bankruptcy of small- and medium-sized listed companies is the lack of awareness of risk prevention and effective financial risk early warning mechanism. The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization. This method shows many unique advantages when dealing with classification problems and has been widely used in many fields. The purpose of this article is to realize the financial risk analysis of listed companies through wireless network communication and the optimal fuzzy SVM artificial intelligence model, which help small- and medium-sized listed companies find abnormalities in their business management activities in advance and deal with market risks in a timely manner. Taking 81 small- and medium-sized listed companies as the research object, this paper chooses the small- and medium-sized listed companies in every quarter of 2018 as the research sample. By using the financial and nonfinancial data of small- and medium-sized listed companies and introducing the support vector machine (SVM) with the fuzzy method, the model of the fuzzy support vector machine (FSVM) is constructed. And the performance of the FSVM under four different kernel functions is compared and studied. At the same time, the performance of the FSVM is compared with other artificial intelligence models. The empirical results show that different kernel functions have different effects on the prediction performance of the FCM-SVM model. Under the Gauss radial basis function, the prediction accuracy of the FCM-SVM is over 86%. It can be seen that in predicting the financial crisis of small- and medium-sized listed companies, the FCM-SVM model with Gauss radial basis function has the best predictive performance. The FSVM model based on Gauss radial basis function not only has the advantages of linearity, being polynomial, and nonlinearity of neurons but also is significantly superior to the traditional artificial intelligence model.
Traffic and Energy Consumption Modelling of Electric Vehicles: Parameter Updating from Floating and Probe Vehicle Data

This paper focuses on the estimation of energy consumption of Electric Vehicles (EVs) by means of models derived from traffic flow theory and vehicle locomotion laws. In particular, it proposes a bi-level procedure with the aim to calibrate (or update) the whole parameters of traffic flow models and energy consumption laws by means of Floating Car Data (FCD) and probe vehicle data. The reported models may be part of a procedure for designing and planning transport and energy systems. This aim is to verify if, and in what amount, the existing parameters of the resistances/energy consumptions model calibrated in the literature for Internal Combustion Engines Vehicles (ICEVs) change for EVs, considering the above circular dependency between supply, demand, and supply–demand interaction. The final results concern updated parameters to be used for eco-driving and eco-routing applications for design and a planning transport system adopting a multidisciplinary approach. The focus of this manuscript is on the transport area. Experimental data concern vehicular data extracted from traffic (floating car data and probe vehicle data) and energy consumption data measured for equipped EVs performing trips inside a sub-regional area, located in the Città Metropolitana of Reggio Calabria (Italy). The results of the calibration process are encouraging, as they allow for updating parameters related to energy consumption and energy recovered in terms of EVs obtained from data observed in real conditions. The latter term is relevant in EVs, particularly on urban routes where drivers experience unstable traffic conditions.
OPEN AND DISTANCE LEARNING FOR HUMAN CAPITAL DEVELOPMENT:PROPELLING ASIA FORWARD

The mission to develop human capital capable of making a positive impact on national progress is firmly linked to higher education. Universally considered a focal element that is greatly influenced by globalisation, international competition and the rapid evolution of information and communication technology, higher education is especially crucial in Asia, where creating access for people to obtain university degrees is often mired by limited resources and facilities, especially in less developed settings. However, alternative approaches to higher education, such as that offered by open and distance learning (ODL), are now recognised as a means for a more accessible, affordable and flexible pathway towards better opportunities for university degrees and ultimately, for boosting human capital and the economy. Asia has proven true to its robust and dynamic reputation; a region that is home to some of the world’s oldest open universities, many of which were established to provide learning opportunities to working adults. Today, the Asian ODL environment continues to evolve and the ODL approach is well accepted across the region. By meeting workforce-related educational needs, these developments clearly indicate ODL’s potential to contribute to human capital development and national progress. This paper will briefly review the current ODL scene in Asia and discuss how open universities can further contribute to human capital development. This paper will also explore several key issues related to ODL and economic progress, such as lifelong learning and international collaboration, and how these will be an imperative for Asian countries to move forward in a competitive and borderless world.
Frequency‑Specific Regional Homogeneity Alterations and Cognitive Function in Obstructive Sleep Apnea Before and After Short-Term Continuous Positive Airway Pressure Treatment

Purpose Previous studies have demonstrated abnormal local spontaneous brain activity in the conventional frequency bands (0.01–0.08 Hz) in obstructive sleep apnea (OSA). However, it is not clear whether these abnormalities are associated with the specific frequency band of low-frequency oscillations or whether it can be improved with a continuous positive airway pressure (CPAP) treatment. This study aimed to investigate the regional homogeneity (ReHo) in specific frequency at baseline (pre-CPAP) and after one month of CPAP adherence treatment (post-CPAP) in OSA patients. Methods Twenty-one patients with moderate-to-severe OSA and 21 age- and sex-matched healthy controls (HCs) were included in the final analysis. ReHo was calculated in three different frequency bands (typical frequency band: 0.01–0.1 Hz; slow-5 band: 0.01–0.027 Hz; slow-4 band: 0.027–0.073 Hz), respectively. A partial correlational analysis was performed to assess the relationship between altered ReHo and clinical evaluation. Results OSA patients revealed increased ReHo in the brainstem, bilateral inferior temporal gyrus (ITG)/fusiform, and right-cerebellum posterior lobe (CPL), and decreased ReHo in the bilateral inferior parietal lobule (IPL), right superior temporal gyrus (STG), and left precentral gyrus (PG) compared to HC groups in different frequency bands. Significantly changed ReHo in the bilateral middle temporal gyrus (MTG), PG, medial frontal gyrus (MFG), supplementary motor area (SMA), CPL, IPL, left superior frontal gyrus (SFG), ITG, MTG, and right STG were observed between post-CPAP and pre-CPAP OSA patients, which was associated with specific frequency bands. The altered ReHo in specific frequency bands was correlated with Montreal cognitive assessment score, Epworth sleepiness scale, and apnea hypopnea index in pre-CPAP OSA patients. Conclusion These findings indicate that OSA has frequency-related abnormalities of spontaneous neural activity before and after short-term CPAP treatment, which might contribute to a better understanding of local neural psychopathology and may serve as potential biomarkers for clinical CPAP treatment.