Miracle-dz/UniFAR-FTUs huggingface dataset:
Genotyping of dermatophyte fungal species by AP-PCR technique

Dermatophytes fungi are causative agents of dermatophytosis. Surface skin fungi are called dermatophytes after the anatomical localization of the lesions. A total of 164 specimens were collected from patients with dermatophytosis (ring worm) distributed into 89/164(54.26%) specimens from male and 75/164(45.73%) specimens from female. The DTM culture medium was prepared by adding 0.2g of phenol red dye to 1 litter of PDA culture medium, utilized the short oligonucleotide (GACA)4 as a primer for identification of the tested dermatophyte isolates by AP-PCR. AP-PCR products for T. verrucosum isolates consisted of three bands at 150 and 300 bp and faint band in 600 bp. For the T. mentagrophytes isolates consists of four bright bands approximately at 200, 400,500 and 1500 bp. All T. rubrum isolates produced nearly similar band pattern, which consisted of five bright bands (approximately 100, 250, 320,500 bp) and one faint band at 2500 bp. M. canis strains revealed the most complex profiles, with up to 7 bands, ranging from 150 bp to 800 bp in size. These results indicated of the current that the AP-PCR technique is simple, reliable, accurate and easy to performed method for the identification dermatophytes fungi to the species level.
Real Time Robot Navigation in Virtually Created Environments

Normal 0 false false false EN-GB X-NONE AR-SA Research in autonomous mobile robots is gaining much more attention in recent years, particularly in coordinating rescue missions and inspections of affected structures within disaster zones. It is the aim of this paper to contribute towards such advancements by introducing mobile robots navigation in virtually generated rescue mission environments. The randomly generated missions are mapped to real environments hosting mobile robots, which can unrestrictedly move in any open surroundings, without the need for the physical obstacles presence. To achieve this, a GUI was developed to randomly create missions of different sizes and complexities. The GUI offers the developer the choice of automatically generating such missions, edit them and/or create them. The robots may be programmed by various solving algorithms to complete the course and find a solution. The advantage of this approach is that it offers environment and robot real-time merging, robot performance tracking and rapid (on-the-fly) algorithms development. In this paper, the rescuing robot will follow an embedded Wall-Following algorithm. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:. 0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;}
Energy Management of Microgrid Considering Demand Response Using Honey Badger Optimizer

Recently, Microgrids (MGs) have received great attention for solving power system problems, due to their low environmental effects and their economic benefits. This paper proposes a new application of an effective metaheuristic optimization method, namely, Honey Badger Algorithm (HBA), to solve energy management for optimal dispatch of the gridconnected MG incorporating Demand Response programs (DRP). Honey badger algorithm is used to solve an incentive DRP, with the aim of minimizing the total cost, which includes conventional generators fuel cost and the cost of power transaction with the main grid considering the load demand. In this paper, two case studies are conducted using HBA and simulation results are compared with those obtained by other algorithms (particle swarm optimization and JAYA algorithm). First case consists of three diesel generators, a PV generator and a wind generator. To prove the scalability of the HBA, the second case, which is much larger, is tested. Simulation results for both case studies obtained by PSO, JAYA, and HBA are deeply discussed. The results show the HBA's effectiveness in solving the energy management with DR problem for MG compared with other well-known optimization techniques.
From knowledge to belief

When acting in the real world, an intelligent agent must make decisions under uncertainty. For example, a doctor may need to decide upon the treatment for a particular patient. The standard solution to this problem is based on decision theory. It requires the agent to assign degrees of belief or subjective probabilities to the relevant assertions. The degrees of belief assigned should be based on the information available to the agent. A doctor, for example, may have information about particular patients, statistical correlations between symptoms and diseases, physical laws, default rules, and more. This thesis describes one approach, called the random-worlds method, for inducing degrees of belief from very rich knowledge bases. The random-worlds method is based on the principle of indifference: it treats as equally likely all the worlds that the agent considers possible. It deals with knowledge bases expressed in a language that augments first-order logic with statistical statements. By interpreting default rules as qualitative statistics, the approach integrates qualitative default reasoning with quantitative probabilistic reasoning. The thesis shows that a large number of desiderata that arise in direct inference (reasoning from statistical information to conclusions about individuals) and in default reasoning follow provably from the semantics of random worlds. Thus, random worlds naturally derives important patterns of reasoning such as specificity, inheritance, indifference to irrelevant information, and a default assumption of independence. Furthermore, the expressive power of random worlds and its intuitive semantics allow it to deal well with examples that are too complex for most other inductive reasoning systems. The thesis also analyzes the problem of computing degrees of belief according to random worlds. In general, the problem of computing degrees of belief is undecidable, even for knowledge bases with no statistical information. On the other hand, for knowledge bases that involve only unary predicates, there is a tight connection between the random-worlds method and the principle of maximum entropy. In fact, maximum entropy can be used as a computational tool for computing degrees of belief in many practical cases.
On the thickness dependence of the polarization switching kinetics in HfO2-based ferroelectric

HfO2-based ferroelectric (FE–HfO2) is a promising material for low-power and high-capacity memory technology. Since thinner ferroelectric films are required for low voltage operation, the impact of film thickness on the switching kinetics of FE–HfO2 needs to be studied in detail. In this paper, metal/ferroelectric/metal capacitors are fabricated with several thicknesses of HfZrO2 (HZO) films and characterized to study the switching kinetics based on the nucleation limited switching (NLS) model. Thinner HZO capacitors show slower polarization switching and asymmetry in program and erase operation, although low-frequency polarization charge density is nearly the same for all thicknesses. Slow switching is due to the large variability of the activation field of FE domains and grain boundaries among small FE grains. The asymmetry is caused by the asymmetric interface property at the top and bottom interfaces. High-resolution TEM and electron diffraction mapping methods provide physical evidence for the above discussions.
Charm meson molecules and the X(3872)

The recently discovered resonance X(3872) is interpreted as a loosely-bound Swave charm meson molecule whose constituents are a superposition of the charm mesons D0D̄∗0 and D∗0D̄0. The unnaturally small binding energy of the molecule implies that it has some universal properties that depend only on its binding energy and its width. The existence of such a small energy scale motivates the separation of scales that leads to factorization formulas for production rates and decay rates of the X(3872). Factorization formulas are applied to predict that the line shape of the X(3872) differs significantly from that of a Breit-Wigner resonance and that there should be a peak in the invariant mass distribution for B → D0D̄∗0K near the D0D̄∗0 threshold. An analysis of data by the Babar collaboration on B → D(∗)D̄(∗)K is used to predict that the decay B → XK should be suppressed compared to B → XK. The differential decay rates of the X(3872) into J/ψ and light hadrons are also calculated up to multiplicative constants. If the X(3872) is indeed an S-wave charm meson molecule, it will provide a beautiful example of the predictive power of universality.
Hopfield neural network based on clustering algorithms for solving green vehicle routing problem

As a result of the rapidly increasing distribution network, the toxic gases emitted by the vehicles to the environment have also increased, thus posing a threat to health. This study deals with the problem of determining green vehicle routes aiming to minimize CO2 emissions to meet customers' demand in a supermarket chain that distributes fresh and dried products. A new method based on clustering algorithms and Hopfield Neural Network is proposed to solve the problem. We first divide the large-size green vehicle routing problem into clusters using the K-Means and K-Medoids algorithms, and then the routing problem for each cluster is found using the Hopfield Neural Network, which minimizes CO2 emissions. A real-life example is carried out to illustrate the performance and applicability of the proposed method. The research concludes that the proposed approach produces very encroaching results.
Research on building energy consumption prediction based on multi-level attention mechanism

According to the multi-dimensional and multi feature characteristics of building energy consumption data, considering the fluctuation of long-time series prediction, partial attention mechanism, full convolution neural network and full-time attention mechanism are introduced to capture the potential correlation between features and targets and the weight between features, and an improved GeoMAN (Geo-sensory Multi-level Attention Networks) building energy consumption prediction model is proposed, Realize the prediction of building energy consumption data. The improved GeoMAN model is used to compare the prediction indexes with four groups of common models, and other data sets are input to verify the generalization ability of the model. The experimental results show that the model performs better in RMSE, Mae and MRE, reflecting better predictability and robustness.
Linear Matrix Inequality-Based Multivariable Controller Design for Boost Cascaded Charge-Pump-Based Double-Input DC–DC Converter

An analysis and controller design of double-input dc–dc converter (DIDDC) is introduced in this article. This converter is integrated with buck and boost converters at the front-end and a charge pump on the back-end. Salient features of this topology are as follows: (i) ripple content is low in source currents, (ii) both the dc sources are connected through an inductor, which gives rise to a smooth source current waveform, (iii) both the sources supply power to load either in standalone or simultaneously together, and (iv) two control–loops (one for source current regulation, while the second one for load voltage regulation) are sufficient enough to ensure power transfer/management from dc sources to load. To ensure power management on the input dc sources while keeping a stable closed-loop converter system, a multivariable diagonal controller is designed. The first diagonal controller performs the load voltage regulation task, while the second controller regulates input of source-2 dc current. From the control point of view, the double-input single-output converter becomes a two-input two-output control problem and exhibits control–loop interactions due to the power flow from two different sources to load through common elements. The impact of off-diagonal elements’ interactions is accounted by using decouplers. The H∞ robust control theory and linear matrix inequality (LMI) principles are adopted to design reliable controllers. The structured controller of fixed order (PID) is designed by enforcing the necessary type LMIs. To demonstrate the feasibility and effectiveness of multivariable controllers of the DIDDC, a 60/24 V to 48 V, 150 W, 50 kHz topology prototype is constructed, and the power management aspects are investigated experimentally. Load voltage regulation, source current regulation, load sharing on the input dc sources, and power management aspects of LMI-based controllers were tested extensively for all possible disturbances. In all these test cases, the closed-loop double-input converter system is stable and robust in rejecting a variety of disturbances created.
Carrier Wi-Fi Calling Deployment Considerations

Carrier Wi-Fi Calling is a solution that allows mobile operators to seamlessly offload mobile voice signaling and bearer traffic onto Wi- Fi access networks, which may or may not be managed by the mobile operators. Mobile data offload onto Wi-Fi access networks has already become very common, as Wi-Fi access has become more ubiquitous. However, the offload of mobile voice traffic onto Wi-Fi networks has become prevalent only in recent years. This was primarily driven by the native Wi-Fi Calling client support introduced by device vendors. The objective of this document is to provide a high level deployment reference to Mobile Operators and Wi- Fi Operators on Carrier Wi-Fi Calling.
Prevalence and antibiotic resistance of Staphylococcus aureus and Escherichia coli isolated from raw milk in East Java, Indonesia

Background and Aim: Raw milk can be a source of food-borne disease transmission and a medium for spreading antibiotic-resistant bacteria. Staphylococcus aureus and Escherichia coli are bacteria that have the pathogenic ability to attack host cells and are capable of harboring antibiotic-resistant genes. This study estimated the prevalence and antibiotic resistance of S. aureus and E. coli isolated from raw milk in East Java, Indonesia. Materials and Methods: Two hundred and fifty raw milk samples were collected from five dairy farms in East Java. S. aureus and E. coli were isolated using their respective selective media, whereas antibiotic susceptibility testing was performed using the Kirby–Bauer disk diffusion method. The methicillin-resistant S. aureus (MRSA) was confirmed using the oxacillin resistance screen agar test, and extended-spectrum beta-lactamase (ESBL)-producing E. coli was determined using the double-disk synergy test. The presence of mecA and blaTEM genes were screened by the polymerase chain reaction method. Results: Results indicated that the prevalence of S. aureus was 138 (55.2%) and that E. coli was 176 (70.4%). Of the 138 S. aureus isolated, 27 (19.6%) were MRSA, and among the 176 E. coli isolates identified, 3 (1.7%) were ESBL producers. The mecA gene was observed in 2 (7.4%) MRSA and all 3 (100%) ESBL-producing E. coli isolated harbored blaTEM genes. Conclusion: The presence of MRSA and ESBL-producing E. coli in raw milk is a serious public health threat, and public awareness should be raised about the dangers posed by these pathogenic organisms.
The Comparison of the Parameters Antioxidant Activity of Ginger (Zingiber officinale Roscoe) and Commercial Antioxidants

The study was aimed at evaluating the antioxidant activity of Zingiber officinale Roscoe (ginger) extract compared to antioxidant compounds such as; Butyl Hydroxy toluene (BHT), Diethyl-dithiocarbamate (DDC) and Ascorbic Acid (Vit. C). The antioxidant activity (DPPH), total phenolic content (TPC) and reducing power (RP) were studied. DPPH inhibitory values demonstrated through IC50 from ginger extract were significantly lower than those of other antioxidants at the same concentrations: ginger (122.2 ± 16.78%), Vit. C (158.21 ± 4.14%), BHT (195.84 ± 5.94%) and DDC (240.39 ± 43.75%) respectively. This proves that ginger extract is more active inhibiting DPPH free radicals. At a concentration of 100 μg / ml, the TPC level indicates the order: Vit. C > DDC > BHT > ginger respectively. As for RP test at concentration 100 μg / ml also Vit. C > ginger > DDC > BHT respectively. The results obtained in present study indicate that, ginger extract is a potential source of natural antioxidant capable of inhibiting and breaking the free radical chain and it’s expected to suppress the development of degenerative cells diseases.
Energy-Efficient Information Placement and Delivery Using UAVs

This article focuses on minimizing the energy consumption of a fleet of unmanned aerial vehicles (UAVs) disseminating information to a set of Internet of Things devices. In the considered scenario, each device wants to download a subset of files from a library of files. Considering the storage capacity of the UAVs, a framework is provided that minimizes energy consumption by optimally selecting the contributing UAVs, placing files, and planning the trajectory of each contributing UAV. In this framework, a combinatorial optimization problem is formulated, which is hard to solve directly for a practical number of devices, files, and/or UAVs. In order to tackle this challenge, we develop three solution approaches, namely, a multichromosome genetic algorithm (GA), a hybrid genetic-ant colony algorithm, and a GA with heuristic file placement. Results show that the proposed solution approaches minimize the total energy consumption and provide near-optimal solutions. Results also illustrate that the proposed framework optimizes the number of UAVs participating in the information delivery mission.
Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis

Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as prognostic biomarkers in PC. Gene expression profiles of datasets GSE101448, GSE15471, and GSE62452 were extracted from the GEO database. The “limma” package was performed to select differentially expressed genes (DEGs) between PC and normal tissue samples in each dataset. Robust rank aggregation (RRA) algorithm was conducted to integrate multiple expression profiles and identify robust DEGs. GO analysis and KEGG analysis were conducted to identify the functional correlation of the DEGs. The CIBERSORT algorithm was conducted to estimate the immune cell composition of each tissue sample. STRING and Cytoscape were used to establish the protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape was performed to identify hub genes. Survival analysis based on hub gene expression was performed with clinical information from TCGA database. 566 robust DEGs (338 upregulated genes and 226 downregulated genes) were identified. Tumor tissue had a higher infiltration of resting dendritic cells and tumor-associated macrophages (TAM), including M0, M1, and M2 macrophages, while infiltration levels of B memory cells, plasma cells, T cells CD8, T follicular helper cells, and NK cells in normal tissue were relatively higher. GO terms and KEGG pathway analysis results revealed enrichment in tumor-associated pathways, including the extracellular matrix organization, cell−substrate adhesion cytokine−cytokine receptor interaction, calcium signaling pathway, and glycine, serine, and threonine metabolism, to name a few. Finally, FN1, MSLN, PLAU, and VCAN were selected as hub genes. High expression of FN1, MSLN, PLAU, and VCAN in PC significantly correlated with poor prognosis. Integrated transcriptomic analysis was used to provide new insights into PC pathogenesis. FN1, MSLN, PLAU, and VCAN may be considered as novel biomarkers of PC.
Is hormonal analysis a predictive tool for grafting success in tomato?

: Anatomical, physiological, and biochemical analyses have been performed to predict graft compatibility. We analyzed if the concentrations of auxins, jasmonic acid, gibberellins, 1-aminocyclopropane-1-carboxylic acid, zeatin (cytokinin), salicylic acid, and abscisic acid could be used as predictors of compatibility between the rootstocks FOX1 and FOX4 (resistant to Fusarium wilt) and the scion of cherry tomatoes Sweet Heaven (SH). Self-grafted (SH/SH) and ungrafted SH plants (SH) were used as controls. Hormonal analyses were performed on leaves, at 20 and 70 days after grafting (DAG), and roots, at 20 DAG. No expressive concentrations of auxins, gibberellins, or jasmonic acid were detected. The concentrations of 1-aminocyclopropane-1-carboxylic acid, the immediate precursor of ethylene, and zeatin were altered at 20 DAG, but they stabilized at 70 DAG. Salicylic acid levels were reduced in the leaves of grafted plants at 70 DAG. The concentration of abscisic acid (ABA) in the leaves of SH grafted onto FOX1 was higher than in ungrafted and self-grafted plants at 70 DAG, suggesting some degree of incompatibility between these genotypes. The concentration of ABA in the combination FOX4/SH was similar to that in the self-grafted plants. Abscisic acid might be used as a reference phytohormone to predict graft compatibility among tomato genotypes.
Direct and indirect effects of universities on European regional productivity

Universities are fundamental in driving economic performance as they generate human capital, research and knowledge diffusion. We propose a novel analysis by investigating their effects on European regions' total factor productivity (TFP) over the period 2000 – 2016. We distinguish between direct effects, due to universities societal role ( “ third mission ” ), and indirect ones originating from human and technological capital creation. Our contribution is threefold. First and foremost, we provide evidence that the presence of universities has a sizeable impact on regional TFP. Second, this impact spreads across regional boundaries. Third, universities effectively drive human and technological capital growth, thus indirectly fostering productivity enhancements.
Statistical Strategies to Reproduce the Propagation Delay Time Variability Using Compact Models

In this brief, we propose three different strategies to estimate the propagation delay time of a nano-CMOS inverter subject to statistical variability. Based on statistical distribution of efficient parameters in the selected compact model, we have reproduced these parameters taking their statistical behavior into account. Since mean and standard deviation (SD) of each parameter and correlation coefficient between parameter pairs are important figures of merit, three strategies are proposed and the accuracies of obtained results using them are evaluated. The first strategy replicates the mean and SD of original parameters but ignores the correlations between them. The second strategy takes mean, SD and the highest correlation coefficient into account while the third strategy considers all of correlations as well as mean and SD of parameters. While the introduced error in the mean of propagation delay time remains negligible between these strategies, the error in the SD can be reached to 16%, 6%, and 3% for no correlation, partial correlation, and full correlation strategies, respectively.