Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Data-driven Control of Embedded Micro-grids in Developing Countries

Over a billion people worldwide have no access to electricity; and many that do, have sporadic access marred by frequent grid outages, particularly in Asia, Africa, and Latin America. The relatively affluent in these countries circumvent the problem by installing grid-connected backup systems, which provide uninterrupted supply. However, these backup systems increase losses and burden the local electricity grid. For instance, estimates show that in Pakistan, between 2% - 3% of annual energy generation is lost through battery charging. In addition, due to these elite backup systems, over 10% of all distribution transformers are overloaded. This overload causes needless expenditure that developing countries can not afford, as well as greenhouse gas emissions which accelerate climate change. This paper investigates various data-driven optimal control strategies to improve backup systems' operational efficiency. Preliminary results show that by employing a smart controller, household energy consumption can be reduced by 3% - 7%, while grid overload costs can be reduced by almost a quarter. Furthermore, model-assisted reinforcement learning controller is able to approach the performance of data-driven model predictive control at a fraction of the computational cost while far outperforming model-free reinforcement learning algorithm.
Competitive Feature Extraction for Activity Recognition based on Wavelet Transforms and Adaptive Pooling

Any application of machine learning requires feature extraction, whereupon the source data is processed to yield representations that are germane to obtaining the desired output. Traditional approaches to feature extraction include the estimation of statistical and structural properties of the data, the choice of which is mainly influenced by domain knowledge. However, Deep Learning has made it possible to learn the best features directly from the data itself, when sufficient data is available. For domains such as activity recognition where such plentiful data may be lacking, the application of deep learning may be limited. Therefore, methods which can yield deep learning-like performance without the need for massive amounts of data remain of interest.In this work we present a novel approach to feature extraction for sensor-generated activity recognition data. We first process the data using wavelet transforms, and subsequently use an adaptive pooling operator on the generated decomposition to obtain a compact, fixed-length representation of the data. Our experiments on seven different activity recognition datasets yield results comparable to those obtained from a deep neural network for all the considered datasets without the need for large amounts of data or any training overhead.
Chinese Event Detection Based on Multi-Feature Fusion and BiLSTM

With the rapid development of the Internet, the number of Internet users has grown rapidly, and the Internet has become more and more influential on people’s lives. As a result, the amount of network text is increasing rapidly, and it is difficult to extract interested event information from it only by manual reading. Therefore, event extraction technique automatically extracting useful information from a large amount of unstructured texts becomes increasingly important. Event detection is the first step of event extraction task and plays a vital role in it. However, current event detection research lacks comprehensive consideration of the context of the trigger words. A Chinese event detection method based on multi-feature fusion and BiLSTM is proposed in this paper. The contextual information of word is divided into sentence-level and document-level in the method. The contextual information is captured based on BiLSTM model. At the same time, a word representation method suitable for trigger word classification tasks is proposed in this paper. The word representation incorporates semantic information, grammar information, and document-level context information of word. The word vectors in the sentence are sequentially inputted into BiLSTM model to obtain output vectors containing sentence-level contextual information. Finally, output vectors of BiLSTM are inputted into the Softmax classifier to realize the identification of the trigger words. The experimental results show that Chinese Event Detection Based on Multi-feature Fusion and BiLSTM method proposed in this paper has high accuracy.
Joint Effects of Residual Hardware Impairments and Channel Estimation Errors on SWIPT Assisted Cooperative NOMA Networks

In this paper, we investigate the effects of residual hardware impairments (RHIs), channels estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC) on the cooperative non-orthogonal multiple access (NOMA) system over Nakagami- $m$ channels, where the amplify-and-forward (AF) relay can harvest energy from the source. The exact expressions for outage probability and ergodic sum rate are derived in closed-form. In addition, the asymptotic outage analyses in the high signal-to-noise (SNR) regime are carried out. The results show that the outage probability exists an error floor due to the existence of CEEs and compared with RHIs, CEEs have a more serious impact on the system outage performance. The close simulation results of Monte Carlo verify the accuracy of our theoretical derivation. Finally, the performance of energy efficiency is examined with RHIs, CEEs and ipSIC.
Backup Network Design Scheme for Multiple Link Failures to Avoid Overestimating Link Capacity

This paper shows how to design, within practical time constraints, a backup network that suppresses the required resources while providing a guaranteed level of recovery against multiple link failures. The conventional scheme based on robust optimization has the problem of overestimating the backup link capacity. The backup network design scheme proposed herein computes the probabilistic distribution function of required backup link capacity in polynomial time, and so addresses the optimization problem of minimizing the total backup network capacity. For large networks, we introduce the heuristic approach of simulated annealing that adopts our approach to computing backup link capacity. Numerical analyses show that the proposed scheme requires less total backup network capacity than the conventional scheme based on robust optimization.
DSmT-Based Fusion System for Fault Detection and Identification in Industrial Process

Multi-Classifier fusion strategies have been widely applied in Fault Detection and Identification (FDI) problems. However, the outputs obtained by multi-classifier systems are often uncertain, imprecision or even conflicting. In this paper, we use the Dezert-Smarandache Theory (DSmT) to propose a novel fusion framework for FDI in industrial process, which can effectively improve the accuracy of detection and identification. Specifically, the training data of each feature are used to build a Kernel Density Estimation (KDE)-based model for each class. Then, the most discriminative model is selected and regarded as the corresponding template for each class. After that, a structure of Basic Belief Assignment (BBA) can be constructed, using the relationship between the test data and the selected KDE models of all considered classes. In order to eliminate the conflict between the obtained BBAs, Proportional Conflict Redistribution rule-5 (PCR5) is applied to fuse these acquired BBAs. Performances of our proposed method in this paper are evaluated through the Tennessee Eastman Process (TEP).
Evidence for and Against the Validity of Efficient Market Hypothesis

Abstract The concept of an efficient financial market, in literature known as efficient market hypothesis (EMH), has had a long and difficult development path from the idea itself to its final conception, as one of the central paradigms in modern finance. It has been tested and critically reviewed for decades, and the two basic types of problems it has encountered are theoretical paradoxes and market anomalies. The aim of the paper is to examine the validity of EMH through various financial market efficiency tests and the results of previous research. The intention is to answer the question of whether, despite theoretical paradoxes and market anomalies, the notion of validity can be attributed to the concept of an efficient financial market. In this regard, the paper presents plenty of evidence for and against the validity of weak, semi-strong, and strong form of EMH, to conclude that, even after more than half a century of research, financial literature has not reached a consensus on the presence or absence of the validity of this hypothesis.
Insulation Panels Made from Thermally Modified Bark

Abstract Thermally treated and ground poplar bark was used as the raw material for pressed bark insulation panels. Bark chips were treated for one, two, and three hours at 180 °C after a slow warming, drying process. The physical and mechanical properties of the pressed panels were investigated and compared to each other and to the control panel made of untreated bark chips. Thermal conductivity showed slight deviations and ranged from 0.064 – 0.067 W·m−1·K−1. The MOR and MOE showed a significant increase of 100%. The internal bond increased by 27% while the water absorption and thickness swelling decreased by 53.8% and 69.1% respectively. Panel density did not change significantly because the target density was the same for every panel type. The mechanical and physical properties of thermal insulation panels made of heat-treated chips increased significantly.
A Study of the Flow Behavior of Prevulcanised Natural Rubber Latex/singlewalled Carbon Nanotubes (SWCNT) Blends Using Rotational Viscometry and Power Law Model

This work describes the flow behavior of prevulcanised natural rubber latex (PvNRL) and PvNRL nanoblends containing 0.02, 0.04, 0.06, and 0.08 wt. % of aqueous dispersion of single-walled carbon nanotubes (SWCNT). The assay was performed under varying shear rates (between 0.1 – 100 1/s) at three separate isothermal temperatures (25, 30, and 35 °C) on a Modular Compact Rheometer (MCR) fitted with a concentric cylinder measuring system. A steady decrease in viscosity upon every single shear rate increment was observed for all the samples analysed. Thus, each measured viscosity was considered an apparent-viscosity; which confirms a typical non-Newtonian flow behavior. PvNRL blends containing highest wt. % SWCNT exhibited higher apparent viscosity at low shear rates, whereas the lowest wt. % SWCNT displayed a lower apparent viscosity, thus signifying a dilution effect. The power law model showed good fitting and successfully predicted the flow behavior within the modelled shear rate
Hypolipidemic, hepatoprotective, nephroprotective and anti-lipid peroxidation properties of a methanol extract of Paullinia pinnata root-bark, in alloxan-induced hyperglycemic rats

Abstract This study evaluated the hypolipidemic, hepatoprotective, nephroprotective and anti-lipid peroxidation properties of a methanol extract of Paullinia pinnata root-bark, in alloxan-induced hyperglycemic rats. The extract of P. pinnata root-bark was prepared using a cold maceration method with 80% methanol and concentrated at 40°C in hot air oven. The extract was administered once daily per os at 50, 100 and 200 mg/kg for 21 consecutive days. Distilled water (5 mL/kg) and glibenclamide (2 mg/kg) were used as the vehicle and reference standard, respectively. The serum lipid profile, markers of liver and kidney functions, antioxidant status (malondialdehyde level, superoxide dismutase and catalase activities), histopathological changes in liver and kidney were examined 24h after the last treatment on day 21. The extract reduced serum lipid profile, markers of liver and kidney functions of treated rats relative to vehicle-treated rats. The superoxide dismutase and catalase activities of the extract treated rats were also elevated relative to the vehicle-treated rats. The extract reversed liver and kidney injuries induced by alloxan in the treated rats. This study provides some basic information which suggest that P. pinnata could be effective in managing diabetic complications.
Reactivity of fly ashes milled in different milling devices

Abstract The effect of two different milling devices, namely attrition mill versus vibration mill, on the reactivity of fly ash was studied. High calcium fly ash from 4th Thermal power station of Ulaanbaatar (Mongolia) was used for the experiments. The raw and processed samples were characterized by XRD, SEM, Particle size distribution, BET, Blaine surface area and density measurements. The efficiency of 1 hour milling was evaluated with the Blaine surface area set to be more than 5000 cm2/g. The physical and chemical properties of the attrition milled fly ash changed not much compared to the vibration milled samples. For example the d50 particle size became reduced from 29 µm to 6 µm by attrition milling and in vibration milled fly ash it was reduced to 7 µm. The density increased from 2.44 g/cm3 of raw fly ash to 2.84 g/cm3 and 2.79 g/cm3 in attrition and vibration milled samples, respectively. Mechanical milling revealed not only a particle size reduction but also the formation of a denser microstructure. As a result the vibration milled fly ash showed a weaker interaction with the alkaline solution (8 M NaOH used here) compared to the attrition milled fly ash. Consequently, compressive strength of the binder prepared using the attrition milled fly ash was higher, 61 MPa, while for vibration milled fly ash it was 49 MPa. For comparison unmilled fly ash, it was 21 MPa.
Synergetic Control for HVAC System Control and VAV Box Fault Compensation

Abstract Synergetic control is proposed for heating, ventilating and air-conditioning (HVAC) system control. The synergetic controller is developed using the nonlinear model of the HVAC system. Occupancy information in each zone is required in the design of the controller which offers inherent comfort according to the occupancy in the zone. The stability of the building system using the proposed control is verified through the Lyapunov approach. It is also proved that the synergetic controller is robust to external disturbances. Then, synergetic theories are used to design a reconfigurable control for damper stuck failures in variable air volume (VAV) to recover the nominal performance. Simulations are provided to validate the effectiveness of the proposed controller for a three-zone building.
Event–Based Feedforward Control of Linear Systems with input Time–Delay

Abstract This paper proposes a new method for the analysis of continuous and periodic event-based state-feedback plus static feed-forward controllers that regulate linear time invariant systems with time delays. Measurable disturbances are used in both the control law and triggering condition to provide better disturbance attenuation. Asymptotic stability and L2-gain disturbance rejection problems are addressed by means of Lyapunov–Krasovskii functionals, leading to performance conditions that are expressed in terms of linear matrix inequalities. The proposed controller offers better disturbance rejection and a reduction in the number of transmissions with respect to other robust event-triggered controllers in the literature.
Estimates of the Laplacian Spectrum and Bounds of Topological Invariants for Riemannian Manifolds with Boundary

Abstract We set out to obtain estimates of the Laplacian Spectrum of Riemannian manifolds with non-empty boundary. This was achieved using standard doubled manifold techniques. In simple terms, we pasted two copies of the same manifold along their common boundary thereby obtaining a Riemannian manifold with empty boundary and with a C0−metric. This made it possible to adapt some estimates of the spectrum dependent on the volume or genus of the manifold as calculated in recent years by several authors. In order to extend further estimates that depend on the curvature, it is necessary to regularize the metric of the doubled manifold so that the new metric is isometric to that of each copy and such that the curvature has a finite lower bound. Controlling the curvature in this way also makes estimates of topological invariants available.
Occurrence of Enterocin Genes in Enterococci from Slovak Milk Product Žinčica

Abstract Žinčica is a popular Slovak dairy product made from ewes’ milk. It is a by-product resulting during ewes’ lump cheese processing. Microbiota in Žinčica have rarely been studied, especially enterococci; however, they can produce beneficial substances – bacteriocins. In this study, the presence of Enterocins (Ents) genes were analysed in enterococci from Žinčica and partially also the inhibition activity. Samples of Žinčica were collected from different agrofarms producing ewes’ lump cheese (34) in Central Slovakia. In the enterococci tested, Ent P gene was the most frequently detected (in 6 out of 7 enterococci), followed by Ent A and Ent L50B genes. Ent B gene was detected only in E. faecium 30E1. On the other hand, E. faecalis 31E2 did not contain Ent genes, although it showed inhibition activity against the indicator strains Enterococcus avium EA5, Staphylococcus aureus SA5, Listeria monocytogenes CCM4699 (inhibition zone sizing up to 20 mm). E. faecium 30E1 contained genes of four Ents; however, it showed no inhibition activity. Growth of the four indicators was inhibited due to the antimicrobial activity of E. faecium 32E1 with Ent P gene detection. This is the first study reporting on the occurrence of Ent genes in enterococci from Žinčica.
Foreword to the special issue on data fusion in remote sensing

This special issue comprises a series of selected papers about the current status and the latest advances in the area of data fusion for remote sensing applications. It contains six original contributions, tutorial-style, covering a wide range of the contemporary fast moving topics in the field. Data fusion has a long lasting tradition in remote sensing, since Earth observation is by definition dynamic (thus implying a temporal component), multiscale (multiple spatial resolutions) and related to different physical quantities (thus requiring multiview / multisensor capability). Data quality and continuity are also key questions because, to provide the desired monitoring products, data must be complete and usable. Nowadays, with the availability of images being less and less of an issue, the need for data fusion strategies becomes even more crucial. We are overwhelmed by the wealth of data (e.g., the Landsat and Sentinel archives are growing everyday, while drone-based imagery is widely accessible for institutional and private customers), and research must explore new ways of making sense of this huge (and still growing) amount of data. In this framework, this special issue provides six snapshots of recent advances in cutting-edge scientific directions. They have been identified through a two-step submission/review process including, first, a selection within white paper submissions and, then, a traditional blind peer review of full paper submissions: 1) Multitemporal fusion and analysis: In this paper, the authors present the state of the art in change detection and multitemporal image processing. It is a long-term topic for data fusion, as acknowledged by many past efforts from the whole data processing community (e.g., see the 2009-2010 IEEE GRSS Data Fusion Contest or numerous Bayesian, kernel, and Markov approaches developed in the past two decades), and is now active more than ever thanks to the aforementioned increased access to data and to long satellite image time series.
An unusual insertion of an accessory band of the semitendinosus tendon: case report and review of the literature.

The Pes Anserinus (PA) is characterized by high morphological variability. It consists of three tendons: the sartorius, gracilis and semitendinosus. The semitendinosus and gracilis tendons are routinely harvested for reconstruction anterior cruciate ligament, and the presence of accessory bands within them can handicap the harvesting process. This report presents a case of a rare insertion of accessory band of the semitendinosus tendon (to the fascia of the soleus muscle and tibia). The current classification should be extended to accommodate such "rare cases" to facilitate more successful hamstring grafts.
Breaking the web: life beyond the at-risk mental state for psychosis

Abstract Psychiatry's most recent foray into the area of risk and prevention has been spear-headed by work on at-risk mental states for psychotic disorders. Twenty-five years' research and clinical application have led us to reformulate the clinical evolution of these syndromes, blurred unhelpful conceptual boundaries between childhood and adult life by adopting a developmental view and has changed the shape of many mental health services as part of a global movement to increase quality. But there are problems: fragmentary psychotic experiences are common in young people but transition from risk-state to full syndrome is uncommon away from specialist clinics with rarefied referrals and can, anyway, be subtle; diagnostic over-shadowing by the prospect of schizophrenia and other psychotic disorders may divert clinical attention from the kaleidoscopic and disabling range of probably treatable psychopathology with which people with risk syndromes present. We use a 19th Century lyric poem, The Lady of Shallot, as an allegory for Psychiatry warning us against regarding these mental states only as pointers towards diagnoses that probably will not occur. Viewed from the fresh perspective of common mental disorders they tell us a great deal about the psychopathological crucible of the second and third decades, the nature of diagnosis, and point towards new treatment paradigms.
A Prospective Study of High-School Adolescent Sexting Behavior and Psychological Distress

Abstract Relying on conceptualization of sexting as a deviant behavior, which appear to be related with mental health problems, we examined the relationship between sexting and psychological distress. A one-year longitudinal study was performed among 216 girls and 143 boys, aged from 15 to 17 years (M = 16.32, SD = .64). Participants completed the Sexting Behavior Questionnaire and the Depression Anxiety and Stress Scale at both time points of the study. The results showed that the prevalence for receiving sexts between the two time points was around 30%, and for sending sexts around 60%. The prevalence rates of receiving and sending sexually explicit context were quite stable over time, though they varied significantly across gender. Participants usually had sexted with a current or (ex)partner or friends. The number of person with whom participants exchange sexts did not exceed five. Anxiety and stress symptoms were more prevalent among sexters who receive sexts at the baseline than follow-up study (p < .05). Stress has been found to be significant predictors of sending sext at the baseline study (p < .05), while depression of receiving and sending at the follow-up study (p < .001). This study confirms that sexting is common among youth, and appears to be associated with negative consequences.