Miracle-dz/UniFAR-FTUs huggingface dataset:
Viability of Clostridium difficile in mortadella with added essential oils and reduced sodium nitrite content

The effects of additioning essential oil mixtures and reducing the sodium nitrite content (75 ppm) on Clostridium difficile inoculated in mortadellas and on the product’s technological characteristics were evaluated. 15 essential oils were tested preliminarily, but only four were selected. The minimum bactericidal concentrations found were 1.2 % for Ocimum basilicum, 0.3 % for Origanum vulgare and for Thymus vulgaris and 0.15 % for Litsea cubeba. There wasn`t significant difference in viable cell growth between treatments and the control; however, there was an increase in viable cells of approximately 2.5 log10 MPN/g after the fifth day. Combinations of oils didn`t prevent sporulation, however a synergistic effect was observed in vitro. There was a reduction in pH during storage and a greater amount of residual nitrite for the F1 treatment (Origanum vulgare (0.2 %); Thymus vulgaris (0.05 %) and Litsea cubeba (0.025 %), with 10.23 ppm, and reduction of residual nitrite during the storage period from 18.75 ppm on the first day of storage to 5.11 ppm on the last. No significant changes were observed in the technological characteristics of the product with the analyzed treatments.
Optimizing multi-agent based urban traffic signal control system

Abstract Agent-based approach is a popular tool for modelling and developing large-scale distributed systems such as urban traffic control system with dynamic traffic flows. This study proposes a multi-agent-based approach to optimize urban traffic network signal control, which utilizes a mathematical programming method to optimize the signal timing plans at intersections. To improve the overall network efficiency, we develop an online agent-based signal coordination scheme, underpinned by the communication among different intersection control agents. In addition, the initial coordination scheme that pre-adjusts the offsets between the intersections is developed based on the historical demand information. Comparison and sensitivity analysis are conducted to evaluate the performance of the proposed method on a customized traffic simulation platform using MATLAB and VISSIM. Simulation results indicate that the proposed method can effectively avoid network oversaturation and thus reduces average travel delay and improves average vehicle speed, as compared to rule-based multi-agent signal control methods.
Zero-Sequence Current Suppression Strategy With Reduced Switching Frequency for Open-End Winding PMSM Drives With Common DC BUS

Zero-sequence current (ZSC) is inevitable in the structure of an open-end winding permanent-magnet synchronous machine (OW-PMSM) system with a common dc bus due to the simultaneous existence of the ZSC loop and the zero-sequence voltage (ZSV) caused by the inverters and third electromotive force. To suppress the ZSC, a novel strategy with a reduced switching frequency is proposed in this paper. The main contribution of the proposed method is that the ZSC is suppressed totally with a reduced switching frequency. The duration time of the active voltage vectors is redistributed to generate the reference voltage in a zero-sequence loop, which leads to the suppression of the ZSC. Different from the conventional method, only the active voltage vectors instead of the zero voltage vectors are employed to suppress the ZSC. Consequently, the switching frequency in the modulation process can be reduced. In addition, the effective modulation range of the ZSV is analyzed, which further reveals the relationship among the effective modulation of the ZSV, the modulation index, and the ratio of the third flux linkage component to the fundamental flux linkage component. Finally, the effectiveness of the proposed method is validated by an OW-PMSM experimental setup with a common dc bus. Moreover, the proposed method is experimentally compared with the existing methods.
Orthophenylphenol in healthcare environments : a trial related to a new administration method and a review of the literature *

Alicem TEKİN**, Tuba DAL, Caferi Tayyar SELÇUK, Özcan DEVECİ, Recep TEKİN, Mahmut METE, Saim DAYAN, Salih HOŞOĞLU Department of Medical Microbiology, Faculty of Medicine, Dicle University, Diyarbakır, Turkey Department of Plastic and Reconstructive Surgery, Faculty of Medicine, Dicle University, Diyarbakır, Turkey Department of Infectious Diseases, Faculty of Medicine, Dicle University, Diyarbakır, Turkey
Bidirectional Sampling-Based Motion Planning Without Two-Point Boundary Value Solution

Bidirectional path and motion planning approaches decrease planning time, on average, compared to their unidirectional counterparts. In single-query feasible motion planning, using bidirectional search to find a continuous motion plan requires an edge connection between the forward search tree and the reverse search tree. Such a tree–tree connection requires solving a two-point boundary value problem (BVP). However, obtaining a closed-form two-point BVP solution can be difficult or impossible for many systems. While numerical methods can provide a reasonable solution in many cases, they are often computationally expensive, numerically unstable, or sensitive (to an initial guess) for the purposes of single-query sampling-based motion planning. To overcome this challenge, we present a novel bidirectional search strategy that does not require solving the two-point BVP. Instead of connecting the forward and reverse trees directly, the reverse tree’s cost information is used as a guiding heuristic for forward search. This enables the forward search to quickly grow down the reverse tree—converging to a fully feasible solution without a direct tree–tree connection and without the solution to a two-point BVP. In this article, we propose two algorithms that use this strategy for single-query feasible motion planning for various dynamical systems, performing experiments in both simulation and hardware testbeds. We find that these algorithms perform better than or comparable to the existing state-of-the-art methods with respect to quickly finding an initial feasible solution.
Event‐triggered adaptive tracking control for uncertain fractional‐order nonstrict‐feedback nonlinear systems via command filtering

This article puts forward an event‐triggered adaptive neural tracking control strategy for a class of uncertain fractional‐order nonstrict‐feedback nonlinear systems with full‐state constraints and dead‐zone inputs. The proposed control strategy is capable of surmounting an inherent shortcoming in most traditional backstepping control approaches which results from the repeated differentiation of virtual control functions, and enhancing the transmission efficiency of control signals by introducing an event‐triggered mechanism with a dynamic threshold condition. Furthermore, by constructing a novel barrier Lyapunov function in each step of the backstepping procedure, the design difficulty arising from the system state constraints issue under fractional‐order cases is resolved. It is strictly proved that the tracking error can converge into a desired small neighborhood of the origin and all signals in the closed‐loop system are bounded. Finally, a simulation example is given to verify the effectiveness of our proposed control strategy.
Multi-Objective Stochastic Optimal Operation of a Grid-Connected Microgrid Considering an Energy Storage System

This paper investigates the stochastic optimal operation of microgrid considering the influence of energy storage system (ESS). The uncertain factors related to renewable energies are also fully considered. Monte Carlo simulation (MCS) and scenario reduction technique are used to capture the randomness of renewable energy output. The objective is to minimize the operation cost and pollutant treatment cost of the microgrid system. Then, the multi-objective optimization problem is transformed to a single-objective problem by a line weighted sum method. A nonlinear programming tool—General Algebraic Modeling System (GAMS)—is adopted to solve the single-objective problem. Simulation results indicate that the total cost of the microgrid system can be reduced effectively when considering the effect of ESS and verify the correctness of the proposed strategy.
Influence of Soil Pore System Properties on the Degradation Rates of Organic Substances during Soil Aquifer Treatment (SAT)

Soil aquifer treatment (SAT) is a nature-inspired solution for improving the water quality through soil percolation. The biodegradation of organic matter typically occurs in the shallowest soil layer and it depends on the contaminant’s characteristics (water solubility, molecular structure) and specific soil properties (pore size distribution). The present study aims at identifying which grain size fraction of typically used sandy soils in the shallowest layer of SAT systems can provide the optimal conditions for microbiological growth that can be reached by a trade-off between soil moisture as well as nutrients and oxygen supply. For this, soil columns were used at a laboratory scale to determine the relationship between the pore size distribution of four different grain size fractions and biodegradation rates of organic matter from synthetic wastewater. The results obtained from this experimental setup indicate that bacterial colonies reached optimum growth when about 60% of the available pore space was filled with water. For the selected soil, this was achieved by the fraction with grain sizes in the range of 630 µm to 1000 µm, having pore diameters between 87 µm and 320 µm and a mean pore diameter of 230 µm.
Towards digital transformation: Lessons learned from traditional organizations

Digitally enabled organisations are supported by new information and communication technologies (ICT), referred to as digital technologies, which increasingly promise enormous opportunities for growth. This paper seeks to explore how traditional organisations (non-digitally native organisations) are implementing digital transformation initiatives. The study reviews ten case studies from the literature and analysis the approaches these organisations have taken to successfully implement digital technologies. The findings reveal a conceptual framework that seeks to support management in understanding the actions required to implement digital transformation. This study has implications for both researchers and practitioners in providing an exploratory review of digital transformation.
Investigando o uso de Características na Detecção de URLs Maliciosas

URLs maliciosas tornaram-se um canal poderoso para atividades criminosas na Internet. Embora as atuais soluções para verificação de URLs apresentem altas taxas de precisão, com resultados bem ajustados, um questionamento pode e deve ser feito: será que realmente é possível ou factível se obter percentuais beirando 100% de precisão nessas soluções? Neste sentido, este artigo conduz uma investigação de características, bases e formatos de URLs, visando mostrar que os resultados de validação e verificação de URLs são bastante dependentes de certos aspectos e fatores. Através da extração de características (léxicas, DNS e outras), diretamente da URL, algoritmos de aprendizagem foram empregados para questionar a influência dessas características no processo de validação e verificação de URLs. Assim, quatro (4) hipóteses foram elaboradas e a avaliação mostra que é possível discordar dos resultados de vários trabalhos já existentes na literatura.
A bibliometric review of research on employability: dataset from Scopus between 1972 and 2019

PurposeEmployability has long been a concern of scholars because of its complicated nature and changes in the requirements in labor markets to meet the new requirements of the Industrial Revolution 4.0 and globalization. However, there has not yet been much systematic investigation of the existing literature tracing the trends, changes and developments in employability research. This paper aims to fill this void by analyzing data obtained from the Scopus dataset from 1972 to 2019.Design/methodology/approachA total of 1,703 Scopus-indexed articles were filtered from 7,885 documents. The results reflect a rapidly emerging literature on employability, given the worldwide interest in this topic. Using descriptive statistics and bibliometric analyses, the review identified trends in employability research, whose impact has been sustained for several decades.FindingsWith science mapping, the conceptual structure of scientific disciplines was visualized, highlighting three main schools of thought including employers’ requirements and higher education institutions’ preparation; the antecedents of employability; and the role of work-integrated learning in enhancing employability.Originality/valueThe paper calls for more integrative research focusing on personal agency with the development of career paths inside organizations as well as for a more focus on a multilevel perspective exploring both individuals' and organizations’ perceptions.
Optimal configuration of battery energy storage systems using for rooftop residential photovoltaic to improve voltage profile of distributed network

Due to the power mismatches between residential load and rooftop residential photovoltaic (PV) generation, voltage profile of low-voltage distribution system (LVDN) may exceed the allowable limit during the daytime and is well below the lower limit at night. To solve this problem, an optimal configuration of battery energy storage (BES) systems is used for rooftop residential PV to improve the voltage profile of LVDN. Firstly, typical curves of residential electric load and PV generation are analysed to demonstrate the principle of integrated voltage regulation method. Secondly, two-stage voltage regulation strategy is proposed to minimise the installed capacity of BES. More specifically, to limit the voltage with reasonable level, the reactive power capability of PV inverter can be utilised in inner stage, and BES is used in outer stage in term of storage charging at off-peak time and discharging at the peak time. Finally, optimal configuration model is achieved by combining the two-stage voltage control method and the constraint condition of total reduced PV generation. Simulation results indicate that the voltage level in LVDN could be controlled in a proper range by the proposed optimal configuration scheme and the total installed capacity of BES is the minimum.
Analysis and Suppression of Slotting and Cross-Coupling Effects on Current Control in PM Synchronous Motor Drives

This paper proposes a d–q cross-coupling current controller for permanent magnet synchronous motor (PMSM) drives. Slotting and cross-coupling effects in PMSMs cause the effective air-gap length to vary with the rotor angle and distort the distribution of the air-gap flux. Such distortions have a significant influence on the current control by changing the relationship between the stator currents and voltages. To investigate their effects, a d–q model is established in this paper, which is suitable for describing the voltage characteristics of the actual PMSMs. The model contains the angle-dependent parameters, the cross-coupling inductance between d–q axes, and the q-axis permanent magnet flux linkage, unlike the ideal d–q model. The transfer function matrix from the d–q currents to d–q voltages of PMSM is newly derived from the model. Based on that, the current controller with the feedback and feedforward terms of 2-by-2 matrix is proposed. It is designed so that the independent gains are in low-pass filter forms and the coupling control gains are zero with eliminating interference between d–q axes. The proposed controller contributes to providing the stable control performance by canceling the slotting and cross-coupling effects. The performance of the proposed current controller is verified by experimental results.
Analog Mobile Fronthaul Supporting 128 RF Chain With Nonlinear Compensator for B5G and 6G Ma-MIMO Beamforming

These paper details a fronthaul optical link using delay-division-multiplexing (DDM) scheme with Volterra based nonlinear compensator for beyond fifth generation (B5G) and 6G massive multiple-input multiple-output (Ma-MIMO) beamforming. Based on pre-allocated relative time delays, pre-processed signals can be deaggregated to different RF chain signals following spectral aliasing caused by the digital signal processing (DSP) at remote radio units. The Volterra based nonlinear compensator is used to compensate nonlinear distortion and IQ imbalance of the fronthaul link. By combined the DDM scheme and Volterra technique, we can easily deaggregate signals and mitigate the interference caused by nonlinear distortion and IQ imbalance at the same time. In experiments, the DDM scheme support 128 RF chain signals is demonstrated. With the Volterra compensator, the signal input power of Mach-Zehnder modulator (MZM) has 18 dB margin. IQ imbalance caused by phase misalignment can also be compensated by the IQ Volterra. It can keep the acceptable error vector magnitude (EVM) performance within a ±10° range. After 25-km optical link, the EVM of each RF Chain is underneath 8%. And the total received 128 RF chain signals will require CPRI data rate as high as 840 Gb/s.
A Layering Linear Discriminant Analysis-Based Fault Diagnosis Method for Grid-Connected Inverter

Grid-connected inverters are the core equipment for connecting marine energy power generation systems to the public electric utility. The variation of current sensor fault severity will make fault samples multimodal. However, linear discriminant analysis assumes that the same fault is independent and identically distributed. To solve this problem, this paper proposes a layering linear discriminant analysis method based on traditional linear discriminant analysis. The proposed method divides the historical fault data based on the sensor fault severity layer-by-layer until the distribution of the same fault category in each subset is very close. Linear discriminant analysis is used to analyze historical fault data in each subgroup, and the kappa coefficient is applied as the basis for ending the training process. A BP neural network is employed to estimate the fault severity during the testing process, and the fault diagnosis sub-model is selected. The proposed method enables the accurate diagnosis of faults with different distributions in the same category and provides an accurate estimate of the sensor’s fault severity degree. The estimated value of the sensor’s fault degree can provide critical information for the maintenance of the equipment and can be used to correct the sensor’s output.
VIRTUAL HOME TEST BENCH FOR DOMESTIC ENERGY MANAGEMENT SOLUTIONS

One of society’s major goals within the next decades will be to make the energy supply fully renewable. Up to now energy management systems still need to be incorporated into industry processes and residential buildings. In this paper, a test bench that evaluates energy management systems using electronic loads and a calculation and controller engine to create characteristic consumption profiles for residential buildings is presented. Within this test bench different situations and installations can be tested and different consumption profiles can be used. The modular test bench can handle simple test cases with pure energy management relying on time-depending energy prices and also complex houses using, for example, an inverter heat pump, photovoltaics and/or a battery storage.
Metabotropic Glutamate Receptor 5 in the Dysgranular Zone of Primary Somatosensory Cortex Mediates Neuropathic Pain in Rats

The primary somatosensory cortex (S1) plays a key role in the discrimination of somatic sensations. Among subdivisions in S1, the dysgranular zone of rodent S1 (S1DZ) is homologous to Brodmann’s area 3a of primate S1, which is involved in the processing of noxious signals from the body. However, molecular changes in this region and their role in the pathological pain state have never been studied. In this study, we identified molecular alteration of the S1DZ in a rat model of neuropathic pain induced by right L5 spinal nerve ligation (SNL) surgery and investigated its functional role in pain symptoms. Brain images acquired from SNL group and control group in our previous study were analyzed, and behaviors were measured using the von Frey test, acetone test, and conditioned place preference test. We found that metabotropic glutamate receptor 5 (mGluR5) levels were significantly upregulated in the S1DZ contralateral to the nerve injury in the SNL group compared to the sham group. Pharmacological deactivation of mGluR5 in S1DZ ameliorated symptoms of neuropathic allodynia, which was shown by a significant increase in the mechanical paw withdrawal threshold and a decrease in the behavioral response to cold stimuli. We further confirmed that this treatment induced relief from the tonic-aversive state of chronic neuropathic pain, as a place preference memory associated with the treatment-paired chamber was formed in rats with neuropathic pain. Our data provide evidence that mGluR5 in the S1DZ is involved in the manifestation of abnormal pain sensations in the neuropathic pain state.
The Role of Intravitreal Corticosteroids in the Treatment of DME: Predictive OCT Biomarkers

This work aims to summarize predictive biomarkers to guide treatment choice in DME. Intravitreal anti-VEGF is considered the gold standard treatment for centers involving DME, while intravitreal steroid treatment has been established as a second-line treatment in DME. However, more than 1/3 of the patients do not adequately respond to anti-VEGF treatment despite up to 4-weekly injections. Not surprisingly, insufficient response to anti-VEGF therapy has been linked to low-normal VEGF levels in the serum and aqueous humor. These patients may well benefit from an early switch to intravitreal steroid treatment. In these patients, morphological biomarkers visible in OCT may predict treatment response and guide treatment decisions. Namely, the presence of a large amount of retinal and choroidal hyperreflective foci, disruption of the outer retinal layers and other signs of chronicity such as intraretinal cysts extending into the outer retina and a lower choroidal vascular index are all signs suggestive of a favorable treatment response of steroids compared to anti-VEGF. This paper summarizes predictive biomarkers in DME in order to assist individual treatment decisions in DME. These markers will help to identify DME patients who may benefit from primary dexamethasone treatment or an early switch.
Data Science and Big Data in Energy Forecasting

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.