Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Residential Energy Consumption: A Case Study for Greece

Residential energy consumption forecasting has immense value in energy efficiency and sustainability. In the current work we tried to forecast energy consumption on residences in Athens, Greece. As a proof of concept, smart sensors were installed into two residences that recorded energy consumption, as well as indoors environmental variables (humidity and temperature). It should be noted that the data set was collected during the COVID-19 pandemic. Moreover, we integrated weather data from a public weather site. A dashboard was designed to facilitate monitoring of the sensors' data. We addressed various issues related to data quality and then we tried different models to forecast daily energy consumption. In particular, LSTM neural networks, ARIMA, SARIMA, SARIMAX and Facebook (FB) Prophet were tested. Overall SARIMA and FB Prophet had the best performance. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
Local angle domain elastic reverse time migration in anisotropic media

Elastic reverse time migration (ERTM) contributes to migrate the multi-component seismic data which have recorded the vector wavefields in the subsurface media. Besides to obtain structure image, it also provides more opportunity for reservoir prediction and characterization. Angle-domain prestack depth migration (PSDM) can generate common image gathers (CIGs) with fewer artifacts than conventional offset-domain PSDM, and thus it is helpful for estimating elastic parameters (such as velocities of Pand S-waves) and reservoir parameters. To obtain migrated results with reliable physical meaning, the coupled elastic wave modes should be separated before applying the imaging condition. However, this procedure in anisotropic media is significantly expensive and has come as a bottle-neck for ERTM. In this study, fast algorithms of mode separation and vector decomposition using low-rank approximate space-wavenumber-domain operators are applied to accurately separate the qP and qS modes and calculate the local phase angles of the incident wavefields at the moment of maximum energy. Accordingly, we develop an efficient algorithm of angle domain ERTM for TI media. A synthetic example proves that accurate elastic wave mode separation and vector decomposition greatly improve the angle-domain ERTM results.
Vermicompost Amendments Disrupt Feeding Behavior of Diaphorina citri Kuwayama and Boost Activities of Salicylic Acid and Jasmonic Acid Pathway-Related Enzymes in Citrus

Simple Summary Diaphorina citri Kuwayama is a phloem-feeding insect that uses piercing–sucking mouthparts to ingest phloem sap from plants. It is the most common citrus pest because it spreads the bacteria that causes huanglongbing during the probing and feeding process. Nowadays, eco-friendly methods such as soil amendment with vermicompost are gaining popularity in the integrated management of D. citri. Here, we investigated why vermicompost amendments cause negative effects to D. citri. We observed that vermicompost disrupted D. citri feeding behavior, particularly for the variables related to phloem sap ingestion, with a significant reduction in the duration of phloem sap ingestion as well as a longer time spent to reach and gain access to phloem sap. Additionally, we demonstrated that vermicompost amendments increased plant defense-associated enzyme activities. We speculate that vermicompost amendments enhance plant resistance against D. citri, which disrupts the insect feeding and eventually impairs the performance of D. citri. Furthermore, we showed how vermicompost amendments affect the feeding behavior of piercing–sucking insects for the first time, and the feeding behavior (decreased efficiency of phloem sap ingestion) suggests vermicompost may suppress the transmission of huanglongbing bacteria by D. citri, yet it still needs to be further studied. Abstract Plants grown with vermicompost amendments are known to be harmful to sap-sucking insects, but the underlying mechanism remains to be determined. Here we investigated the feeding behavior of Diaphorina citri Kuwayama on Citrus limon (L.) Burm. F using the electrical penetration graph technique. Plants were grown in soil with different vermicompost rates (0%, 20%, 40%, and 60% w/w). Additionally, plants were tested for the activity of salicylic acid (SA) and jasmonic acid (JA) pathway-related enzymes. When compared to the control, vermicompost treatments (40% and 60%) decreased duration of phloem sap feeding and increased duration of the pathway phase of D. citri, and the 60% vermicompost made it more difficult for D. citri to reach and gain access to phloem sap. Enzymatic assays indicated that the 40% amendment rate increased phenylalanine ammonia lyase (involved in the SA pathway) and polyphenol oxidase (involved in the JA pathway), while the 60% amendment rate increased -1,3-glucanases (involved in the SA pathway) and lipoxygenase (involved in the JA pathway). The 20% amendment rate had no effect on feeding or enzyme activities. This study revealed that vermicompost amendments can reduce the efficiency of D. citri feeding, which may result from increased plant resistance via the SA and JA pathway.
Multi-Antenna Coded Caching for Shared Caches With Arbitrary User-to-Cache Association

We consider the multi-antenna shared cache setup where the server is equipped with multiple transmit antennas and is connected to a set of users that are assisted with a smaller number of helper nodes. The helper nodes serve as caches that are shared among the users. Each user gets connected to exactly one helper cache. For this setting, under uncoded cache placement, an optimal multi-antenna coded caching scheme supporting all types of user-to-cache association is known in the literature which is applicable only when the number of antennas is at most the number of users connected to the least occupied cache. The other known multi-antenna shared caching schemes considered only uniform association of users to helper caches. In this work, we propose a multi-antenna coded caching scheme for the shared cache setting with more diverse user-to-cache associations where the number of antennas is not less than the number of users connected to the least occupied cache. Under certain scenarios, our scheme achieves the same performance exhibited by some optimal schemes.
REGIONAL FRACTAL AND ARTIFICIAL OBJECT DETECTION

Artificial object detection and segmentation in natural background are key points of pattern recognition and computer vision.The effect of complex texture of natural background and other factors,make it difficult to segment artificial object from natural background.In this paper,an algorithm based on watershed transform and regional fractal texture analysis is proposed to detect and segment artificial object from natural background.
Banks’ Physical Footprint and Financial Technology Adoption

We investigate how the presence of physical bank branches moderates financial technology diffusion. Our identification strategy uses services suspensions caused by criminal groups that perform hit-and-run raids exploding branch facilities and rendering them inoperable for months. We show that the shock depletes the cash inventory of branches, but the stock of credit and deposits remain unaffected. We then document that customers increase their usage of noncash payments after the events. We investigate a new instant payment technology called Pix that was a remarkable success in terms of adoption. After robbery events, the number and value of Pix intra-municipality transactions increase, as well as the number of users. We also find Pix usage spillover effects that go beyond cash substitution. First, the number of Pix transactions and users also increases when either the payer or the payee is in an unaffected municipality. Second, we show that there are local spillovers to digital institutions, indicating that cash dependence can be an impediment to their expansion. Our results shed light on the determinants of technology adoption and the consequences of the recent transition in the banking industry from a physical branch-based model to an increasing reliance on digital services.
A Note on the Roots Distribution and Stability of the PnP Problem: A Note on the Roots Distribution and Stability of the PnP Problem

The PnP problem is a widely used method in pose determination since it is a single-view based approach and does not need any image point correspondences,a difficult task itselfin computer vision.The multiplicity and stability of solutions in the PnP problem are two important issues,they somehow directly determine the sucess or failure of a real application.Then questions arise:What is the distribution of the multiple solutions? Does the exsistence of multple solutions necessarily mean that the solutions are not stable? There are no related reports on such issues in the literature to our knowledge.In this work,these two issues are investigated in terms of P3P problem,and the obtained results could provide some insight for the problem of the multiplicity and stability of the PnP problem,and could be of reference to the people in the field.
Observation of Single Top Quark Production at DØ using Bayesian Neural Networks

We present observation of the electroweak production of single top quarks in 2.3 fb of data recorded by the DO experiment at the Fermilab Tevatron collider operating at 1.96 TeV center-of-mass energy. The cross section of single top quark production for the combined tb+tqb channels measured is 4.70 +1.18 −0.93 pb using Bayesian neural networks (BNN). The probability to measure a cross section at this value or higher in the absence of signal is 3.2 x 10, corresponding to a 5.4 standard deviation significance for the observation.
A heavy traffic result for the finite dam

The steady state content Z of a finite dam in discrete time is investigated for small absolute values of the expected net input and correspondingly large values of the dam capacity. It is shown that under general conditions Z has, asymptotically, a truncated exponential distribution, a result that supplements previous knowledge in queueing theory.
A Sub-mW Cortex-M4 Microcontroller Design for IoT Software-Defined Radios

We present an Internet-of-Things (IoT) software-defined radio platform based on an ultra low-power microcontroller. Whereas conventional wireless IoT radios often implement a single protocol, we demonstrate that general-purpose microcontrollers running software implementations of wireless physical layers are a promising solution to increase interoperability of IoT devices. Yet, since IoT devices are often energy-constrained, the underlying challenge is to implement the digital signal processing of the radio in software while maintaining an overall very low power consumption. To overcome this problem, we propose an ultra low-power microcontroller architecture with an ARM Cortex-M4 processor for the protocol-specific computations and a hardware digital front-end for the generic signal processing. The proposed architecture has been prototyped in 28nm FDSOI and the physical layers of the well-known LoRa and Sigfox protocols have been implemented in software. Thanks to the efficient hardware/software partitioning and an ultra-low power digital implementation, experimental evaluations of the microcontroller prototype show sub-mW power consumptions (32 – $332~\mu \text{W}$ ) for the digital signal processing of the software-defined radios.
Correction of Error of Airborne Anemometers Caused by Self-Excited Air Turbulence

An airborne anemometer, which monitors wind on the basis of Meteorological Multi-rotor UAVs (Unmanned Aerial Vehicles), is important for the prevention of catastrophe. However, its performance will be affected by the self-excited air turbulence generated by UAV rotors. In this paper, for the purpose of the correction of an error, we developed a method for the elimination of the influence of air turbulence on wind speed measurement. The corresponding correction model is obtained according to the CFD (Computational Fluid Dynamics) simulation of a six-rotor UAV which is carried out with the sliding grid method and the S-A turbulence model. Then, the model is applied to the developed prototype by adding the angle of attack compensation model of the airborne anemometer. It is shown by the actual application that the airborne anemometer can maintain the original measurement accuracy at different ascent speeds.
Targeting the Human Influenza a Virus: The Methods, Limitations, and Pitfalls of Virtual Screening for Drug-like Candidates Including Scaffold Hopping and Compound Profiling

In this study, we describe the input data and processing steps to find antiviral lead compounds by a virtual screen. Two-dimensional and three-dimensional filters were designed based on the X-ray crystallographic structures of viral neuraminidase co-crystallized with substrate sialic acid, substrate-like DANA, and four inhibitors (oseltamivir, zanamivir, laninamivir, and peramivir). As a result, ligand–receptor interactions were modeled, and those necessary for binding were utilized as screen filters. Prospective virtual screening (VS) was carried out in a virtual chemical library of over half a million small organic substances. Orderly filtered moieties were investigated based on 2D- and 3D-predicted binding fingerprints disregarding the “rule-of-five” for drug likeness, and followed by docking and ADMET profiling. Two-dimensional and three-dimensional screening were supervised after enriching the dataset with known reference drugs and decoys. All 2D, 3D, and 4D procedures were calibrated before execution, and were then validated. Presently, two top-ranked substances underwent successful patent filing. In addition, the study demonstrates how to work around reported VS pitfalls in detail.
Physicochemical and Rheological Characteristics of Commercial and Monovarietal Wheat Flours from Peru

In Peru, wheat (Triticum aestivum L.) is one of the main resources in the food industry; however, due to its low harvested area, it is the second most imported cereal. The quality of wheat flour was studied to verify that it has desirable characteristics for the preparation of bakery products. The quality of commercial and monovarietal wheat flours was assessed by measuring their physicochemical and rheological parameters, as well as the gluten content and wheat protein fractions. Eight commercial wheat flours and four monovarietal wheat flours (Barba negra, Candeal, Espelta, and Duro) from Peru were evaluated. Commercial wheat flours presented significantly higher levels of protein and gluten index compared to monovarietal wheat flours (p < 0.05). Between both groups, no significant differences were observed in the content of wet and dry gluten. Interestingly, monovarietal wheat flours presented a higher percentage of gliadins and albumins/globulins, as well as lower levels of glutenin, compared to commercial wheat flours (p < 0.05). According to the logistic regression models, the baking strength (W) was the most important parameter to evaluate the quality of commercial and monovarietal wheat flours. Our results show that monovarietal wheat flours show a lower quality compared to commercial wheat flours.
Mechanical Regulation of Oral Epithelial Barrier Function

Epithelial cell function is modulated by mechanical forces imparted by the extracellular environment. The transmission of forces onto the cytoskeleton by modalities such as mechanical stress and matrix stiffness is necessary to address by the development of new experimental models that permit finely tuned cell mechanical challenges. Herein, we developed an epithelial tissue culture model, named the 3D Oral Epi-mucosa platform, to investigate the role mechanical cues in the epithelial barrier. In this platform, low-level mechanical stress (0.1 kPa) is applied to oral keratinocytes, which lie on 3D fibrous collagen (Col) gels whose stiffness is modulated by different concentrations or the addition of other factors such as fibronectin (FN). Our results show that cells lying on intermediate Col (3 mg/mL; stiffness = 30 Pa) demonstrated lower epithelial leakiness compared with soft Col (1.5 mg/mL; stiffness = 10 Pa) and stiff Col (6 mg/mL; stiffness = 120 Pa) gels, indicating that stiffness modulates barrier function. In addition, the presence of FN reversed the barrier integrity by inhibiting the interepithelial interaction via E-cadherin and Zonula occludens-1. Overall, the 3D Oral Epi-mucosa platform, as a new in vitro system, will be utilized to identify new mechanisms and develop future targets involved in mucosal diseases.
Automated Input Data Management for Discrete Event Simulation Models of Emergency Departments

: Emergency Department (ED) overcrowding, that relates to congestion due to the high number of patients, has a negative effect on patient waiting time. The analysis of the flow of patients through Discrete Event Simulation (DES), that models the operation of a system through a sequence of events, is a relevant approach to find a solution to such problem. This technique relies on high-quality input data which needs to be previously managed in a complete process known as Input Data Management (IDM). The objective of this research is to propose a tool to automate the IDM process required for DES models of ED. To do so, we used a case with real data in order to contextualize the problem, evaluate required statistical methods, and gather specific requirements. Based on these results, a prototype was designed and developed by using web and cloud application development tools.
Elements Characterising Multicomponent Interventions Used to Improve Disease Management Models and Clinical Pathways in Acute and Chronic Heart Failure: A Scoping Review

This study aimed to summarise different interventions used to improve clinical models and pathways in the management of chronic and acute heart failure (HF). A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. MEDLINE (via PubMed), Embase, The Cochrane Library, and CINAHL were searched for systematic reviews (SR) published in the period from 2014 to 2019 in the English language. Primary articles cited in SR that fulfil inclusion and exclusion criteria were extracted and examined using narrative synthesis. Interventions were classified based on five chosen elements of the Chronic Care Model (CCM) framework (self-management support, decision support, community resources and policies, delivery system, and clinical information system). Out of 155 SRs retrieved, 7 were considered for the extraction of 166 primary articles. The prevailing setting was the patient’s home. Only 46 studies specified the severity of HF by reporting the level of left ventricular ejection fraction (LVEF) impairment in a heterogeneous manner. However, most studies targeted the populations with LVEF ≤ 45% and LVEF < 40%. Self-management and delivery systems were the most evaluated CCM elements. Interventions related to community resources and policy and advising/reminding systems for providers were rarely evaluated. No studies addressed the implementation of a disease registry. A multidisciplinary team was available with similarly low frequency in each setting. Although HF care should be a multi-component model, most studies did not analyse the role of some important components, such as the decision support tools to disseminate guidelines and program planning that includes measurable targets.
Using Nanomaterials for SARS-CoV-2 Sensing via Electrochemical Techniques

Advancing low-cost and user-friendly innovations to benefit public health is an important task of scientific and engineering research. According to the World Health Organization (WHO), electrochemical sensors are being developed for low-cost SARS-CoV-2 diagnosis, particularly in resource-limited settings. Nanostructures with sizes ranging from 10 nm to a few micrometers could deliver optimum electrochemical behavior (e.g., quick response, compact size, sensitivity and selectivity, and portability), providing an excellent alternative to the existing techniques. Therefore, nanostructures, such as metal, 1D, and 2D materials, have been successfully applied in in vitro and in vivo detection of a wide range of infectious diseases, particularly SARS-CoV-2. Electrochemical detection methods reduce the cost of electrodes, provide analytical ability to detect targets with a wide variety of nanomaterials, and are an essential strategy in biomarker sensing as they can rapidly, sensitively, and selectively detect SARS-CoV-2. The current studies in this area provide fundamental knowledge of electrochemical techniques for future applications.
Large-Scale Insect Pest Image Classification

— One of the main issues with agricultural production is insect attack, which leads to poor crop quality. Farmers, however, have a complicated and time-consuming task in detecting and categorizing insects. Therefore, research on an effective system for image-based automated insect classification is crucial. The conventional “softmax” function is utilized to determine the category for new image occurrences and minimize “cross-entropy” loss in the bulk of current research, which focuses on employing deep convolutional neural networks to categorize insect images. This paper presents a novel method for large-scale insect pest image classification by combining fine-tuning EfficientNets and Power Mean Support Vector Machine (SVM). First, EfficientNet models are fine-tuned and re-trained on new insect pest image datasets. The retrieved features from EfficientNet models are then utilized to create a machine learning classifier. In the network’s classification stage, the traditional “softmax” function is substituted with a non-linear classifier, Power Mean SVM. As a result, rather than “cross-entropy loss,” the training process focuses on reducing “margin-based loss. ” Several benchmark insect image datasets were used to evaluate our proposed method. According to the numerical results, our method outperforms other cutting-edge methods for large-scale insect pest image categorization. With fine-tuning EfficientNets and Power Mean SVM, the classification accuracy of the proposed method for the Xie24, D0, and IP102 large insect pest datasets is 99%, 99%, and 72.31%, respectively. To our knowledge, these are the best performing image classification results for these datasets.
Towards a Better Evaluation of Disaster Management Solutions

: Worldwide, disaster management endeavours are confronted with a rising number of calamitous events triggered by climate change, pandemics and armed conflicts. The increasing rate and complexity of such occurrences has determined governments worldwide to attempt improving the disaster management effort by adopting various specialised artefacts, among which disaster management frameworks feature prominently. It appears however, that such artefacts display shortcomings such as lack of directly applicable guidance, ambiguity and a lack of agility in the face of constant change inherent to disaster events. This situation poses a conundrum to disaster management decision-makers who need to select such frameworks in the knowledge that they have the necessary qualities, employ a suitable architecture and contain the required elements to effectively guide the typically trans-disciplinary and cross-organisational disaster management effort. This paper seeks to assist in this regard by providing a novel, multi-pronged appraisal approach for candidate disaster management frameworks.