Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-stage Clustering for Profiling Residential Customer Demand

Since the power system operation and planning depend on generation and consumption behavior, load and generation patterns are fundamental inputs for power system analyses. TSOs and DSOs use typical daily profiles for representing the consumption of the end-users. The evolution of power systems, due to the increased integration of renewables, and the changed end-users practices are stressing the operation and planning processes. The updating of the typical load profiles to the behavior of current customers is often disregarded, and currently the used profiles refer to out-of-date and incomplete measurement campaigns. This paper proposes an innovative two-stage clustering methodology, able to find typical load profiles of residential customers. The focus to the residential customers is due to their extremely variable behavior in their consumption. The results add to the current practice useful improvements for planning and operation studies.
Where's Pikachu: Route Optimization in Location-Based Games

Along with the sudden rise in popularity of location-based games, the demand for tools to assist players in performance optimization increased. One major aspect lies within the analysis and planning of routes that contain a high amount of desirable game locations for the given player. However, personalized routes cannot be created by hand due to the amount of available in-game locations and the associated time constraints for real-world travel. This paper presents a system to dynamically create personalized route for players, based upon previous game data. These routes can be fully customized with regards to their time and location as well as the player's desired in-game goal, allowing them to e.g. specifically target their favourite species or maximize the amount of visited locations. The system is evaluated using a dataset of Berlin containing over 30.000 distinct locations with different associated in-game behaviour. Regarding the system's performance it is designed to work as an assistance system on a mobile device to assure its applicability in the context of location-based games.
Developing IoT applications: challenges and frameworks

Internet of things (IoT) is creating new opportunities for developing innovative applications by leveraging on existing and new technologies. In recent years, a variety of consumer and industrial IoT applications have been developed and deployed. Despite much progress, developing IoT applications is still a complex, time-consuming and challenging activity. This is because IoT systems involve a wide range of hardware and software components, depending on a variety of communication and distributed system technologies. Many IoT application frameworks of varying approaches have been developed to manage the complexities of developing IoT applications. However, there remains a paucity of surveys on these IoT application development frameworks. This study presents a comprehensive review and a comparative analysis of existing IoT application development frameworks and toolkits, illustrating their strengths and weaknesses. This study will assist in finding the most appropriate IoT application development paradigm for the desired IoT application. Finally, future research directions are highlighted to improve existing and future frameworks and toolkits for IoT applications.
Framework to Support Grid-Connected Microgrid Optimal Planning

Microgrids (MGs) are receiving increasing attention from distributed system operators (DSOs), since they can aid the electrical network when the main grid faces faults or extreme conditions. So, the planning process is an essential step in order to optimal estimate the daily network operation, admitting that the microgrid implementation depends on wide spectrum of players, interests and constraints. The present work is a first effort in establishing guidelines that standardize the problem of microgrid planning/scheduling process, defining a methodology that is capable of dealing with such heterogeneous problem. Besides, this strategy contributes with a broader view of microgrid planning process, suggesting an architecture that joins the optimization problem with the reliability and contingency issues. In order to have some results, a case of study of a hypothetical microgrid is performed.
A General Approach for Retrofit of Existing Buildings Towards NZEB: The Windows Retrofit Effects on Indoor Air Quality and the Use of Low Temperature District Heating

The European Union, through the Energy Performance of Building Directive (EPBD), has recently set the minimum requirements in order to reduce the energy consumption of existing buildings and achieving nearly zero energy buildings (NZEBs). In order to comply with these requirements different aspects of the building sector have to be dealt with. In the present paper two different aspects related to the energy and environmental performances of the existing building towards NZEB are analyzed: a) the use of the Smart Energy Systems with particular regard to the heat sharing systems, and b) the effect of the window retrofit on indoor air quality.
Up-regulation of the kinase gene SGK1 by progesterone activates the AP-1–NDRG1 axis in both PR-positive and -negative breast cancer cells

Preoperative progesterone intervention has been shown to confer a survival benefit to breast cancer patients independently of their progesterone receptor (PR) status. This observation raises the question how progesterone affects the outcome of PR-negative cancer. Here, using microarray and RNA-Seq–based gene expression profiling and ChIP-Seq analyses of breast cancer cells, we observed that the serum- and glucocorticoid-regulated kinase gene (SGK1) and the tumor metastasis–suppressor gene N-Myc downstream regulated gene 1 (NDRG1) are up-regulated and that the microRNAs miR-29a and miR-101-1 targeting the 3′-UTR of SGK1 are down-regulated in response to progesterone. We further demonstrate a dual-phase transcriptional and post-transcriptional regulation of SGK1 in response to progesterone, leading to an up-regulation of NDRG1 that is mediated by a set of genes regulated by the transcription factor AP-1. We found that NDRG1, in turn, inactivates a set of kinases, impeding the invasion and migration of breast cancer cells. In summary, we propose a model for the mode of action of progesterone in breast cancer. This model helps decipher the molecular basis of observations in a randomized clinical trial of the effect of progesterone on breast cancer and has therefore the potential to improve the prognosis of breast cancer patients receiving preoperative progesterone treatment.
Modeling and Analysis of Low Voltage, High Isolation Capacitive Type RF-MEMS Switches

Capacitive type radio frequency micro electro mechanical switches (RF-MEMS) to reduce actuation voltages and to obtain low insertion losses with high isolation is discussed. In this study, we report design, modeling and simulation for three structural configurations using ANSYS to obtain the voltage with and without residual stresses effect, further high frequency simulations are performed using HFSS to obtain low insertion losses and high isolation. The designed switches require 4.3 to 6.5 V as pull-in voltage for actuation. Two of the switch designs have insertion loss of less than 0.45 to 0.6 dB at 22 GHz, and isolation greater than 50 dB for all three designs.
Photochemical crosslinking of caries-affected dentin combined with total- or self-etch systems.

PURPOSE To evaluate the effects of collagen crosslinking with riboflavin 0.1% and ultraviolet-A (UVA) 5.4 J on bond strength of total-etch or self-etch adhesives on caries-affected dentin. METHODS Sixty human caries-affected molars were randomly divided into three groups: control (C), riboflavin (R), and riboflavin + 3 minutes of UVA (R+UVA). After each treatment, either total-etch or self-etch adhesives were applied following the manufacturer's instructions, and composite stubs were built up on the treated surfaces. They were de-bonded in tension to measure bond strength. Twelve extra molars were used for scanning electron microscope (SEM) analysis. RESULTS We observed that R+UVA-treated group yielded significantly higher bond strengths for carious dentin when the total-etch adhesive was applied. For the self-etch adhesive, no statistical differences were observed between the three pretreated-groups. CONCLUSION Our results, for the first time, are suggesting that etching with phosphoric acid potentialized the benefits of R+UVA crosslinking on carious dentin. R+UVA dentinal collagen crosslinking improves bond strength for caries-affected dentin when using a total-etch adhesive, but did not affect it when using a self-etch adhesive.
Obesity Is Associated with Early Onset of Gastrointestinal Cancers in California

Background Although it is well known that obesity is a risk factor for gastrointestinal (GI) cancer, it is not well established if obesity can cause earlier GI cancer onset. Methods A cross-sectional study examining the linked 2004–2008 California Cancer Registry Patient Discharge Database was performed to evaluate the association between obesity and onset age among four gastrointestinal cancers, including esophageal, gastric, pancreatic, and colorectal cancers. Regression models were constructed to adjust for other carcinogenic factors. Results The diagnosis of obesity (BMI > 30) was associated with a reduction in diagnosis age across all four cancer types: 3.25 ± 0.53 years for gastric cancer, 4.56 ± 0.18 years for colorectal cancer, 4.73 ± 0.73 years for esophageal cancer, and 5.35 ± 0.72 for pancreatic cancer. The diagnosis of morbid obesity (BMI > 40) was associated with a more pronounced reduction in the age of diagnosis: 5.48 ± 0.96 years for gastric cancer, 7.75 ± 0.30 years for colorectal cancer, 7.67 ± 1.26 years for esophageal cancer, and 8.19 ± 1.25 years for pancreatic cancer. Both morbid obesity and obesity remained strongly associated with earlier cancer diagnosis for all four cancer types even after adjusting for other available cancer risk factors. Conclusions The diagnosis of obesity, especially morbid obesity, was associated with a significantly earlier gastrointestinal cancer onset in California. Further research with prospective cohort data may be required to establish the causal relationship between obesity and cancer onset age.
Impact of Cell Microcracks Size and Spatial Distribution on Output Power of PV Modules

In this paper, we analyze the shape, orientation and spatial distribution of microcracks in 41 different PV-modules with the aim to further investigate the impact of microcracks to the reduction of power output. A first very important result is that there is a good correlation between the percentage of inactive areas (black areas given by electroluminescence test) and the power variation with respect to the nominal power value measured in PV modules. More information has been provided by the position and shape of the microcracks in the PV module.
Aberrant Structural Brain Connectivity in Adolescents with Attentional Problems Who Were Born Prematurely

The purpose of this study was to identify the neural correlates of attentional problems in adolescents born prematurely and determine neonatal predictors of those neural correlates and attention problems. Of the 24 subjects, 12 had attention deficits. A set of axonal pathways connecting the frontal, parietal, temporal, and occipital lobes had significantly lower fractional anisotropy in subjects with attentional problems. The temporoparietal connection between the left precuneus and left middle temporal gyrus was the most significantly underconnected interlobar axonal pathway. Low birth weight and ventriculomegaly, but not white matter injury or intraventricular hemorrhage on neonatal MR imaging, predicted temporoparietal hypoconnectivity in adolescence. BACKGROUND AND PURPOSE: Differences in structural brain connectivity that underlie inattention have been previously investigated in adolescents with attention deficit/hyperactivity disorder, but not in the context of premature birth, which is often associated with attentional problems. The purpose of this study was to identify the neural correlates of attentional problems in adolescents born prematurely and determine neonatal predictors of those neural correlates and attention problems. MATERIALS AND METHODS: The study included 24 adolescents (12.5 ± 1.8 years of age; 12 girls, 12 boys) who were born prematurely and underwent MR imaging of the brain and cognitive assessment, both shortly after birth and as adolescents. Structural connectivity was assessed at adolescence using diffusion tensor imaging and tractography. RESULTS: Of the 24 subjects, 12 had attention deficits. A set of axonal pathways connecting the frontal, parietal, temporal, and occipital lobes had significantly lower fractional anisotropy in subjects with attentional problems. The temporoparietal connection between the left precuneus and left middle temporal gyrus was the most significantly underconnected interlobar axonal pathway. Low birth weight and ventriculomegaly, but not white matter injury or intraventricular hemorrhage on neonatal MR imaging, predicted temporoparietal hypoconnectivity in adolescence. However, neither birth weight nor other neonatal characteristics were associated with attention deficits directly. CONCLUSIONS: We identified an aberrant structural brain connectivity pattern, involving temporoparietal hypoconnectivity, in prematurely born adolescents with attentional problems. We also identified birth weight as a potential neonatal predictor of the temporoparietal hypoconnectivity. These findings add to our understanding of the neural basis and etiology of inattention in adolescents after premature birth.
Effective Frequency Hopping Pattern for ToA Estimation in NB-IoT Random Access

In narrow band-Internet of Things (NB-IoT) systems, a preamble of physical random access channel (NPRACH) is based on a single-tone waveform combined with frequency hopping, which is appropriate to achieve the goals of IoT, such as long battery lifetime and extended coverage. Since the hopping pattern of a preamble is predetermined, it is possible to estimate time-of-arrival (ToA) using phase difference. The accurate ToA is very important for device positioning and uplink signal decoding. In this paper, we design a new frequency hopping pattern of NPRACH preamble which fully utilizes all feasible hopping distances for a given number of subcarriers in NPRACH. The proposed hopping pattern maintains the compatibility with the standard system, by keeping the NPRACH structure in standard with minimal change. The simulation results show that the proposed hopping pattern greatly improves the accuracy of ToA estimation without additional system overhead.
Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing

Large-scale smart energy metering deployment worldwide and integration of smart meters within the smart grid will enable two-way communication between the consumer and energy network, thus ensuring improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data. However, NILM remains a challenging problem since NILM is susceptible to sensor noise, unknown load noise, transient spikes, and fluctuations. In this paper, we tackle this problem using novel graph signal processing (GSP) concepts, applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based method is generic and can be used to improve results of various event-based NILM approaches. We demonstrate significant improvement in performance using three state-of-the-art NILM methods, both supervised and unsupervised, and real-world active power consumption readings from the REDD and REFIT1 data sets, sampled at 1 and 8 s, respectively.1The REFIT dataset used to generate the results can be accessed via DOI 10.15129/31da3ece-f902-4e95-a093-e0a9536983c4.
Performance Analysis and Power Control of Cell-Free Massive MIMO Systems With Hardware Impairments

As an interesting network architecture for future wireless communication systems, cell-free (CF) massive multiple-input multiple-output (MIMO) distributes an excess number of access points (APs) with single or multiple antennas to cooperatively communicate with several user equipments (UEs). To realize CF massive MIMO in production, hardware impairments become a crucial problem since cheaper and low-quality antennas are needed to ensure economic and energy feasibility. In this paper, we propose a framework for performance analysis in the CF massive MIMO with classical hardware distortion models. For both uplink and downlink, closed-form spectral and energy efficiency expressions are derived, respectively. Based on these results, we provide significant insights into the practical impact of hardware impairments on CF massive MIMO. For example, the impact of hardware distortion at the APs asymptotically vanishes. Furthermore, in order to ensure uniformly good service to the users, we propose a max–min power control algorithm to maximize the minimum UE rate. Via analytical and numerical results, we prove that CF massive MIMO can tolerate hardware impairments without performance reduction.
Basic Study on Facial Oil Blotting Paper Counting Using a Webcam

Currently, racial oil blotting papers are counted by hand. However, it takes a long time and this is hard work. Hence, an automatic counting system is necessary. In order to achieve this, we developed a cheap system using a webcam. This system firstly captures one image of arranged papers by shifting. Secondly, the paper boundaries are obtained as straight lines by utilizing the proposed image processing. By counting the acquired lines, the system outputs the number of papers. However, the captured image has noises such as the change of illumination and shadows. They make the extraction of the accurate boundaries difficult. For the accurate extraction, optimal parameters of the image processing must be set. Since the prediction of the optimal parameters in advance is difficult, the proposed method adopts genetic algorithm (GA). Also, Hough transformation for the straight line detection is improved. By applying the proposed method, the accurate paper counting is possible. For the experiment, 14 images were captured in various environments using a general webcam. Then, the proposed method was compared to a latest related work. As a result, F-measure of each method was 0.995 and 0.945.
Bounding DRAM Interference in COTS Heterogeneous MPSoCs for Mixed Criticality Systems

Commercial off-the-shelf (COTS) heterogeneous multiple processors systems-on-chip (MPSoCs) are appealing platforms for emerging mixed criticality systems (MCSs). To satisfy MCS requirements, the platform must guarantee predictable timing bounds for critical applications, without degrading average performance for noncritical applications. In particular, this paper studies the main memory subsystem, which in modern MPSoCs is typically based on double data rate synchronous dynamic access memory. While there exists previous work on worst-case DRAM latency analysis, such work only covers a small subset of possible COTS configurations, which are not targeted at MCS. Therefore, we derive a generalized interference delay analysis for DRAM main memory that accounts for a breadth of features deployed in COTS platforms. We then explore the design space by studying the effects of each feature on both the worst-case delay for critical applications, and the bandwidth for noncritical applications.
Overview of Artifacts Detection and Elimination Methods for BCI Using EEG

Electroencephalogram (EEG) is the most commonly utilized method of mapping brain wave patterns from the scalp. It is a process of extracting electrical activities of the brain in the form of EEG signals using non-invasive methods. In EEG based techniques, a challenge exists in the form of signal contamination caused by internal and external factors in the recorded EEG signals. , such as the artifacts, interferences among multiple EEG sources, and noise. Signal pre-processing is the most significant phase for EEG based applications, for instance, the brain computer interface (BCI) systems. This is because the quality of data acquired from the brain through EEG measurements is dependent on the effectiveness of artifact detection and removal methods. During EEG recording there are several factors that influence the quality of data. One of the factors of signal contamination occurs as the result of physiological and non-physiological artifacts. To detect and remove such contaminations from EEG signals, there are several efficient artifact detection and elimination techniques based on certain pre-processing methods. The main objective of this study is to demonstrate various types of artifacts in EEG recordings as results of external and internal factors. Furthermore, the study will also present an extensive review of existing artifacts identification and elimination methods applied on EEG signals.
Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

In recent years, there has been a huge trend to penetrate renewable energy sources into energy networks. However, these sources introduce uncertain power generation depending on environmental conditions. Therefore, finding ‘optimal’ and ‘feasible’ operation strategies is still a big challenge for network operators and thus, an appropriate optimization approach is of utmost importance. In this paper, we formulate the optimal power flow (OPF) with uncertainties as a chance constrained optimization problem. Since uncertainties in the network are usually ‘non-Gaussian’ distributed random variables, the chance constraints cannot be directly converted to deterministic constraints. Therefore, in this paper we use the recently-developed approach of inner-outer approximation to approximately solve the chance constrained OPF. The effectiveness of the approach is shown using DC OPF incorporating uncertain non-Gaussian distributed wind power.
Software Experience for an Ontology-Based Approach for the Definition of Alarms in Geographical Sensor Systems

This paper presents a system based on ontologies for the definition of alarms in sensor systems. Although we consider that the ontology and the system are interesting themselves, we have a special impact on their experience with the software tools used and the technology related to the Semantic Web. This approach is intended to illustrate the process for those readers not familiar with this field and who can consider the use of ontologies for their next developments. An ontology has been designed in the OWL language, complemented with Semantic Web Rule Language rules and search elements in the Semantic Query-enhanced Web Rule Language. The set has offered satisfactory results in simulation. Other interesting contributions of this paper are: a survey of the available literature in the field of the use of Semantic Web technologies and ontologies for the detection of events from data obtained from sensors and a study on the tools and vocabularies to be used to create a system that interfaces with ontologies and an educational method—reporting our experience—to help university students understand this topic.