bluuebunny/crossref_metadata_2025 huggingface dataset:
Nitrogen and Sulfur Co-Doped Graphene Quantum Dots Anchored TiO2 Nanocomposites for Enhanced Photocatalytic Activity
Authors: Rawal Jishu , Kamran Urooj , Park Mira , Pant Bishweshwar , Park Soo-Jin
Published: May 2022

Herein, nitrogen (N) and sulfur (S) co-doped graphene quantum dots (GQDs) using different one-dimensional (1-D) carbon nanomaterials as precursors were synthesized, followed by heterojunction formation with TiO2. GQDs exhibit unlike physiochemical properties due to the disproportionate ratio of N and S heteroatoms and dissimilar reaction parameters. Tailored type-II band gap (Eg) alignment was formed with narrowed Eg value that improves photogenerated electron transfer due to π-conjugation. GQDs-TiO2 nanocomposites exhibit remarkably high methylene blue (MB) degradation up to 99.78% with 2.3–3 times elevated rate constants as compared with TiO2. CNF-GQDs-TiO2 demonstrates the fastest MB degradation (60 min) due to the synergistic effect of nitrogen and sulfur doping, and is considered the most stable photocatalyst among prepared nanocomposites as tested up to three cyclic runs. Whereas, C–O–Ti bonds were not only responsible for nanocomposites strengthening but also provide a charge transfer pathway. Moreover, charge transport behavior, generation of active species, and reaction mechanism were scrutinized via free-radical scavenger analysis.
Photocatalytic Degradation of Recalcitrant Pollutants of Greywater
Authors: Aslam Mohammad , Fazal Dawood Bin , Ahmad Faizan , Fazal Abdullah Bin , Abdullah Ahmad Zuhairi , Ahmed Mukhtar , Qamar Mohammad , Rafatullah Mohd
Published: May 2022

These days, many countries have a water shortage and have limited access to clean water. To overcome this, a new treatment is emerging, namely, the photocatalytic processing of greywater. Photocatalytic processes to remove the organic matter from different greywater sources are critically reviewed. Their efficiency in degrading the organic matter in greywater is scrutinized along with factors that can affect the activity of photocatalysts. Modified TiO2, ZnO and TiO2 catalysts show great potential in degrading organic materials that are present in greywater. There are several methods that can be used to modify TiO2 by using sol-gel, microwave and ultrasonication. Overall, the photocatalytic approach alone is not efficient in mineralizing the organic compounds, but it works well when the photocatalysis is combined with oxidants and Fe3+. However, factors such as pH, concentration and catalyst-loading of organic compounds can significantly affect photocatalytic efficiency.
Designing, Implementing, and Evaluating a Home-Based, Multidisciplinary, Family-Centered Pediatric Obesity Intervention: The ProxOb Program
Authors: Miolanne Magalie , Lambert Céline , Masurier Julie , Cardenoux Charlotte , Fillion Alicia , Beraud Sarah , Desblés Chloé , Rigal Amélie , Védrine Elodie , Dalmais Carla , Da Silva Bernadette , De L’Eprevier Elisabeth , Hazart Juliette , Chaput Jean-Philippe , Drapeau Vicky , Pereira Bruno , O’Malley Grace , Thivel David , Boirie Yves
Published: May 2022

Although family-based interventions have been suggested as promising approaches for preventing and treating pediatric obesity, available studies failed to include the whole family in its own natural environment and routine. This paper aims to detail the development, implementation, and evaluation phases of the ProxOb home-based, family-centered program and present its feasibility and early results. ProxOb provides families with a 6-month multidisciplinary, home-based, and family-centered intervention followed by an 18-month maintenance phase. A global psychosocial, clinical, and behavior evaluation was conducted at baseline (T0) at the end of the 6-month intervention (T1) and after the 18-month maintenance phase (T2). A total of 130 families with at least one child with obesity completed the ProxOb program so far, and more than 90% of them also presented at least one parent with overweight or obesity. Being part of a single-parent family seemed to increase the chance of completing the intervention (63.0% vs. 33.3% in the drop-outers subgroup, p = 0.03). The BMI z-score for children with obesity (T0 = 4.38 ± 1.05; T1 = 4.06 ± 1.07; T2 = 4.29 ± 1.12) significantly decreased between T0 and T1, followed by weight regain at T2. ProxOb proposes a feasible and replicable real-life approach to address childhood obesity while involving the children’s family.
Comparison of Trunk Motion between Moderate AIS and Healthy Children
Authors: Struber Lucas , Nougier Vincent , Griffet Jacques , Daniel Olivier , Moreau-Gaudry Alexandre , Cinquin Philippe , Courvoisier Aurélien
Published: May 2022

Analysis of kinematic and postural data of adolescent idiopathic scoliosis (AIS) patients seems relevant for a better understanding of biomechanical aspects involved in AIS and its etiopathogenesis. The present project aimed at investigating kinematic differences and asymmetries in early AIS in a static task and in uniplanar trunk movements (rotations, lateral bending, and forward bending). Trunk kinematics and posture were assessed using a 3D motion analysis system and a force plate. A total of fifteen healthy girls, fifteen AIS girls with a left lumbar main curve, and seventeen AIS girls with a right thoracic main curve were compared. Statistical analyses were performed to investigate presumed differences between the three groups. This study showed kinematic and postural differences between mild AIS patients and controls such as static imbalance, a reduced range of motion in the frontal plane, and a different kinematic strategy in lateral bending. These differences mainly occurred in the same direction, whatever the type of scoliosis, and suggested that AIS patients behave similarly from a dynamic point of view.
Child Health and Surgery: A Challenge for Future Clinical Research
Authors: Pelizzo Gloria , Calcaterra Valeria
Published: May 2022

Pediatric surgical conditions cross a broad range of disease categories and includes injuries, infections, tumors, rare disease and congenital anomalies [...]
Wake Propagation and Characteristics of a Multi-Rotor Unmanned Vehicle in Forward Flight
Authors: Throneberry Glen , Takeshita Adam , Hocut Christopher , Shu Fangjun , Abdelkefi Abdessattar
Published: May 2022

In this study, experimental investigations are used to explore the wake propagation and characteristics of a multi-rotor unmanned air vehicle (UAV) in a forward flight mission. Qualitative smoke visualization is used first to gain a qualitative understanding of wake characteristics above and below the body of the multi-rotor UAV which is used as guidance for quantitative particle image velocimetry (PIV) experiments which better resolve the region in the vicinity of the multi-rotor UAV body. The experimental results over a wide range of advance ratios show that as the advance ratio increases, achieved by either lower rotor speeds or higher flight speeds, the distance by which the wake propagates below the UAV is reduced. While above the UAV, the flow returns to the freestream flow closer to the body as the advance ratio increases. Therefore, this study concludes that proximity effects are reduced as the advance ratio increases. Findings from this study can be used to inform in situ sensor placement so that sensor readings are minimally affected by the wake from the multi-rotor UAV. Velocity measurement corrections are provided for sensors mounted above the UAV which can be used to improve sensor data reliability in forward flight. These results can advance autonomous sensing and increase the utility of multi-rotor UAV observations while providing designers and users further guidance to avoid proximity effects.
Interpolating Strange Attractors via Fractional Brownian Bridges
Authors: Raubitzek Sebastian , Neubauer Thomas , Friedrich Jan , Rauber Andreas
Published: May 2022

We present a novel method for interpolating univariate time series data. The proposed method combines multi-point fractional Brownian bridges, a genetic algorithm, and Takens’ theorem for reconstructing a phase space from univariate time series data. The basic idea is to first generate a population of different stochastically-interpolated time series data, and secondly, to use a genetic algorithm to find the pieces in the population which generate the smoothest reconstructed phase space trajectory. A smooth trajectory curve is hereby found to have a low variance of second derivatives along the curve. For simplicity, we refer to the developed method as PhaSpaSto-interpolation, which is an abbreviation for phase-space-trajectory-smoothing stochastic interpolation. The proposed approach is tested and validated with a univariate time series of the Lorenz system, five non-model data sets and compared to a cubic spline interpolation and a linear interpolation. We find that the criterion for smoothness guarantees low errors on known model and non-model data. Finally, we interpolate the discussed non-model data sets, and show the corresponding improved phase space portraits. The proposed method is useful for interpolating low-sampled time series data sets for, e.g., machine learning, regression analysis, or time series prediction approaches. Further, the results suggest that the variance of second derivatives along a given phase space trajectory is a valuable tool for phase space analysis of non-model time series data, and we expect it to be useful for future research.
Bridging Theory and Practice Using Facebook: A Case Study
Authors: Bastida-Escamilla Eduardo , Elias-Espinosa Milton Carlos , Franco-Herrera Froylan , Covarrubias-Rodríguez Mario
Published: May 2022

In the past few years, Facebook has been increasingly studied for academic purposes due to its potential benefits to undergraduate education. Problems commonly found in university education include the lack of course engagement and the gap between theory and practice. This research contributes to the literature by investigating the impact of incorporating a non-mandatory Facebook group on learning outcomes. The analysis was done using a Taguchi method design, conducted with three two-level controlled factors (term, Facebook, and teacher). Results indicated that the students who participated in Facebook groups were more engaged with the course and applied theoretical knowledge to real-life problems better than students who learned under traditional instructional designs. Moreover, the use of Facebook groups led to better evaluation of teachers by the students. Furthermore, the student academic impact (knowledge and competence) was higher even though this complementary activity was not included in the course grade. We concluded that Facebook groups are excellent support tools that boost student engagement and their understanding of theoretical concepts and applying them in practice.
Evaluating the Dimensionality of the Sociocultural Adaptation Scale in a Sample of International Students Sojourning in Los Angeles: Which Difference between Eastern and Western Culture?
Authors: Valenti Giusy Danila , Magnano Paola , Faraci Palmira
Published: May 2022

The Sociocultural Adaptation Scale (SCAS) measures the degree of sociocultural competence in new cultural settings, and, despite its popularity, research aiming at evaluating its dimensionality is lacking and has incongruent results. Moreover, the dimensionality of the scale has been mainly tested on different samples adjusted to Eastern culture. We administered the SCAS to 266 international students sojourning in Los Angeles to test which underlying dimensionality emerges if the measure is used to assess sociocultural adaptation to Western culture, also verifying its measurement invariance across sex. Findings from EFA showed a three-factor solution: Diversity Approach, Social Functioning, and Distance and Life Changes, and the CFA indicated a plausible goodness-of-fit to the empirical data. The examination of MGCFA suggested that the questionnaire showed an invariant structure across sex. Our results suggest that the dimensionality of the SCAS may differ according to the sojourners’ country of settlement, emphasizing Western–Eastern differences.
A Review on Deep Learning Techniques for IoT Data
Authors: Lakshmanna Kuruva , Kaluri Rajesh , Gundluru Nagaraja , Alzamil Zamil S. , Rajput Dharmendra Singh , Khan Arfat Ahmad , Haq Mohd Anul , Alhussen Ahmed
Published: May 2022

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.
RETRACTED: Energy Efficiency Maximization for Hybrid-Powered 5G Networks with Energy Cooperation
Authors: Cao Yang , Zhong Ye , Peng Xiaofeng , Pan Song
Published: May 2022

The extensive deployment of 5G cellular networks causes increased energy consumption and interference in systems, and to address this problem, this paper investigates the optimization problem of joint energy harvesting and energy cooperation to maximize energy efficiency (EE). First, considering user equipment (UE) quality of service (QoS) constraints, cellular base station power constraints, and renewable energy harvesting constraints, we construct a mixed-integer nonlinear programming problem for joint resource allocation. This problem is difficult to solve directly, thus we combine the fixed-variable method to solve the complex original problem in three less difficult subproblems of user association, power allocation, and energy cooperation by solving them separately using Lagrangian method, improved particle swarm optimization algorithm, and matching theory, respectively. Finally, the final solution to the original problem is obtained by combining the above three algorithms through convergent iterative algorithms. The simulation results show that the joint algorithm proposed in this paper has a better performance in throughput and energy efficiency compared with the comparison algorithms.
Learning-Based Text Image Quality Assessment with Texture Feature and Embedding Robustness
Authors: Jia Zhiwei , Xu Shugong , Mu Shiyi , Tao Yue
Published: May 2022

The quality of the input text image has a clear impact on the output of a scene text recognition (STR) system; however, due to the fact that the main content of a text image is a sequence of characters containing semantic information, how to effectively assess text image quality remains a research challenge. Text image quality assessment (TIQA) can help in picking a hard sample, leading to a more robust STR system and recognition-oriented text image restoration. In this paper, by arguing that the text image quality comes from character-level texture feature and embedding robustness, we propose a learning-based fine-grained, sharp, and recognizable text image quality assessment method (FSR–TIQA), which is the first TIQA scheme to our knowledge. In order to overcome the difficulty of obtaining the character position in a text image, an attention-based recognizer is used to generate the character embedding and character image. We use the similarity distribution distance to evaluate the character embedding robustness between the intra-class and inter-class similarity distributions. The Haralick feature is used to reflect the clarity of the character region texture feature. Then, a quality score network is designed under a label–free training scheme to normalize the texture feature and output the quality score. Extensive experiments indicate that FSR-TIQA has significant discrimination for different quality text images on benchmarks and Textzoom datasets. Our method shows good potential to analyze dataset distribution and guide dataset collection.
An Improved Supervoxel Clustering Algorithm of 3D Point Clouds for the Localization of Industrial Robots
Authors: Xie Zhexin , Liang Peidong , Tao Jin , Zeng Liang , Zhao Ziyang , Cheng Xiang , Zhang Jianhuan , Zhang Chentao
Published: May 2022

Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time.
Numerically Coupled Thermo-Hydro-Mechanical Analyses of Ultra-Heavy Oil Reservoirs during the Micro-Fracturing Stage
Authors: Gao Yanfang , Wang Xiaoyang , Jiang Hailong , Ding Shuaiwei
Published: May 2022

Water injection in SAGD well pairs has rapidly developed to improve the circulation preheating efficiency and the steam chamber propagation speed. However, a key problem is to evaluate the coupled thermo-geomechanical effects of water injection under complex operations. In this paper, a coupled thermo-hydro-mechanical model considering skeleton shear dilation and phase change of bitumen was established. Major conclusions were drawn that there were large temperature changes only in two separate areas around wells. The pore pressures in the reservoir around wells and the base rock right below the production well increased significantly. The void ratio was improved in the reservoir around wells, especially in the inter-well region. The reservoir was lifted, and the largest uplift was right above the wells. This information can guide engineers in properly evaluating the field operations.
Prediction of Node Importance of Power System Based on ConvLSTM
Authors: Wu Xu , Geng Junqi , Liu Meng , Song Zongxun , Song Huihui
Published: May 2022

In power systems, the destruction of some important nodes may cause cascading faults. If the most important node in the power system can be found, the important node can be protected in advance, thereby avoiding a blackout accident. At present, the evaluation algorithm of node importance is calculated based on the power flow of the power grid, so the calculation results must be lagging behind, and it does not have the ability to provide early warning for the power grid to provide protection signals. Therefore, it is necessary to predict the importance of nodes in the power system. After using a reasonable prediction model to predict the importance of nodes, we can simulate the future state of power system operation and avoid accidents for the dispatching agency of the power grid company according to the prediction results. This paper proposes a prediction model based on convolutional long short-term memory (ConvLSTM) to predict the importance of nodes. This method has obvious advantages over the long short-term memory (LSTM) network. The convolution operation is used to replace the original full connection operation of the LSTM network, which not only utilizes the advantages of convolution to extract spatial features but also retains the ability of LSTM to process time-series features. The simulation results show that the prediction of node importance using the ConvLSTM network is much more accurate than LSTM. Using statistical indicators to compare and analyze the prediction results, it can be seen that ConvLSTM has higher prediction accuracy. Therefore, using the ConvLSTM model to predict node importance has certain significance for grid dispatching agencies to accurately simulate the future state of the power system and avoid risks in advance.
Mechanical and Thermal Properties of 3D Printed Polycarbonate
Authors: Bahar Anis , Belhabib Sofiane , Guessasma Sofiane , Benmahiddine Ferhat , Hamami Ameur El Amine , Belarbi Rafik
Published: May 2022

This study aims at showing the potential of additive manufacturing as a new processing route for designing future insulators in the building sector. Polycarbonate (PC) is studied as a possible candidate for designing these new insulators. This polymer offers several advantages, among them fire resistance and stability of its physical properties at high temperatures. The 3D printing of PC is attempted using fused deposition modelling technology. The printing temperature and infill rate are varied to achieve optimal mechanical and thermal characteristics. The results show that an optimal printing temperature of 280 °C is needed to achieve high tensile performance. In addition, thermal properties including thermal conductivity and effusivity increase with the increase of the infill rate in opposition to the thermal diffusivity decrease.
Dynamic Simulation and Performance Enhancement Analysis of a Renewable Driven Trigeneration System
Authors: Rotas Renos , Iliadis Petros , Nikolopoulos Nikos , Tomboulides Ananias , Kosmatopoulos Elias
Published: May 2022

Research activity in the field of combined cooling heating power (or trigeneration) systems with high renewable energy source (RES) contributions has increased rapidly over the last few years, in line with the European Union legislation about energy communities. However, technical challenges arise regarding the synergetic, sustainable and optimal integration of RES in local energy systems. In the present study, the operation of a trigeneration system located in the student residences of Democritus University of Thrace in Greece is examined. The system involves a combination of highly promising renewable and storage technologies, including solar thermal energy and biomass for heat generation, hot water tanks for thermal energy storage, absorption refrigeration for cooling, along with Organic Rankine Cycle and photovoltaic systems for electricity generation. System modeling and simulation have been implemented in Dymola environment with the use of Modelica equation-based modeling language. The accuracy of the model response has been validated against available measurements. Specific design and control measures have been proposed, simulated in a transient fashion and evaluated in terms of (i) RES generation, (ii) solar fraction and (iii) temporal flexibility. The measures examined, including the placement of a Li-ion battery, resulted in an increase of 24.6% in the heating demand solar contribution and of 7.9% in the renewable energy generated for the electricity demand, over the examined periods.
Optical Wireless Power Transmission Using a GaInP Power Converter Cell under High-Power 635 nm Laser Irradiation of 53.5 W/cm2
Authors: Wong Yiu Leung , Shibui Shunsuke , Koga Masahiro , Hayashi Shunki , Uchida Shiro
Published: May 2022

Optical wireless power transmission (OWPT) system is a technology that supplies energy from remote locations, having some features such as long-distance transmission, high directivity, and no electromagnetic noise interference. This study investigated the optical transmission efficiency and photoelectric conversion efficiency with a transmission distance of 10 m using GaInP power converter cells with a small area of 2.40 × 2.40 mm2 and a 635 nm high-power laser over 50 W/cm2. As a result, we achieved a photoelectric conversion efficiency of 44.7% under 6.7 W/cm2 (0.14 W) and 37.2% under 53.5 W/cm2 (1.1 W) irradiation. These results suggested that W-class optical wireless power transmission could be realized by expanding the converter cell area. Additionally, it was found that the reductions of the divergence angle of the laser and the heat generation of the power converter cell were critical issues for further lengthening the distance and increasing the power.
Study of the Microstructural Characteristics of Low-Rank Coal under Different Degassing Pressures
Authors: Ma Yuanzhen , Wang Meng , Zhao Xinzhuo , Dai Xuguang , He Ye
Published: May 2022

Low-rank coal samples from the Xishanyao Formation in the southern Junggar basin of Xinjiang were processed under different negative pressures in order to examine the microstructural characteristics of low-rank coal reservoirs. The pore structures of low-rank coal under different negative pressures were tested using scanning electron microscopy, low-temperature nitrogen adsorption–desorption, and water saturation and centrifugal low-field NMR experiments. The results showed that for the low-rank coal samples from the study area, a high portion of the porosity and surface area came from micropores and small pores; the fractal dimension of the adsorption pores of the low-rank coal samples was divided into surface fractal dimension D1 and structural fractal dimension D2, which showed that the microstructure of the low-rank coal from the study area was complex. The transverse relaxation times T2 of the low-rank coal samples in the test were approximately 0.1~2.5, approximately 10, and greater than 100 ms; the T2 spectrum had basically three peak types. By combining scanning electron microscopy and nuclear magnetic resonance tests, it was concluded that the pore connectivity of the low-rank coal reservoirs in the study area was poor and the effective porosity was relatively low, which may be unfavorable for the exploration and development of coalbed methane.
Synthesis of High-Quality Two-Dimensional V2C MXene for Supercapacitor Application
Authors: Ai Wangsheng , Zhang Chunfei , Xia Lan , Miao He , Yuan Jinliang
Published: May 2022

Two-dimensional (2D) V2C MXene has fascinating potential for use as electrodes in high-energy-density supercapacitors because of its excellent electrical conductivity and large specific surface area. However, it is not feasible to synthesize V2C by etching vanadium carbon aluminide (V2AlC) with hydrofluoric acid, which is commonly used for preparing other MXenes. In this work, a modified method is developed for synthesizing high-quality 2D V2C. A mixture of sodium fluoride (NaF) and hydrochloric acid (HCl) was used as the etching agent, where V2AlC can be gently etched by a hydrothermal reactor-assisted method. As electrode materials for supercapacitors, V2C shows the characteristics of electric double layer capacitance. The electrochemical results show high specific capacitance (223.5 F/g in 1 M Na2SO4 at a current density of 100 mA/g) and good cycling stability (the capacitance retention rate can be maintained at 94.7% after 5000 cycles). This work provides a new method for the synthesis of high-quality V2C for application in related fields.
Multilevel Dynamic Master-Slave Control Strategy for Resilience Enhancement of Networked Microgrids
Authors: Huang Rui , Xiao Yu , Liu Mouhai , Shen Xia , Huang Wen , Peng Yelun , Shen John
Published: May 2022

Conventional power management methods of networked microgrids (NMGs) are limited to the failure of pinned communication terminals and heavy communication burdens. This paper proposes a multilevel dynamic master-slave control strategy via two-level dynamic leaders to realize the resilience enhanced power management of NMGs. The first level dynamic leader with considerations of distributed energy resources (DERs) feature is selected to guide the output of DERs and achieve the power management within individual microgrid (MG). Subsequently, the secondary level leader considering each MG feature is selected among the bidirectional interlinking converters (BICs), whose signals would be shared with other BICs by communication to achieve power management among MGs. Moreover, the local weight selecting method (LWSM) is proposed to automatically select the two-level dynamic leaders according to the real-time system operation state. Compared with conventional methods, the communication among MGs is essentially realized through the dynamic DER leaders instead of pinned ones. Therefore, unreliability issues in the event of pinned terminal outage and converters’ communication failure can be fully addressed and the communication bus within each only needs to transmit one DER’s signals. The proposed strategy can be also extended to NMGs with various topologies and provide the “plug and play” capabilities of DERs or MGs. Finally, the effectiveness and feasibility of the proposed strategy are verified through the PSCAD/EMTDC platform.
Experimental and Numerical Investigations of Bird Models for Bird Strike Analysis
Authors: Ćwiklak Janusz , Kobiałka Ewelina , Goś Artur
Published: May 2022

The article presents experimental and numerical studies of bird models during impacts with rigid and deformable targets. The main aim of the studies is the validation of bird models in order to prepare them for the numerical simulation of bird impact against aircraft windshields and other parts of aircraft, thus improving the air transportation safety by providing cost-effective solutions for designing bird strike-resistant aircraft. The experimental investigations were conducted with a special set-up of a gas gun equipped with high-speed cameras, tensiometers and force sensors. The simulations were developed on the basis of LS-DYNA software by means of the SPH method for the bird model shape of the cylinder with hemispherical endings at the speed of 116 m/s. The results of studies into such things as the impact force, pressure and bird model deformation were compared. Moreover, the authors’ and other researchers’ results were assessed. It can be noted that the curves of the impact force obtained as a result of the numerical analysis correlated well with the experimental ones.
A Real-Time Load Prediction Control for Fuel Cell Hybrid Vehicle
Authors: Fu Jun , Zeng Linghong , Lei Jingzhi , Deng Zhonghua , Fu Xiaowei , Li Xi , Wang Yan
Published: May 2022

The development of hydrogen energy is an effective solution to the energy and environmental crisis. Hydrogen fuel cells and energy storage cells as hybrid power have broad application prospects in the field of vehicle power. Energy management strategies are key technologies for fuel cell hybrid systems. The traditional optimization strategy is generally based on optimization under the global operating conditions. The purpose of this project is to develop a power allocation optimization method based on real-time load forecasting for fuel cell/lithium battery hybrid electric vehicles, which does not depend on specific working conditions or causal control methods. This paper presents an energy-management algorithm based on real-time load forecasting using GRU neural networks to predict load requirements in the short time domain, and then the local optimization problem for each predictive domain is solved using a method based on Pontryagin’s minimum principle (PMP). The algorithm adopts the idea of model prediction control (MPC) to transform the global optimization problem into a series of local optimization problems. The simulation results show that the proposed strategy can achieve a good fuel-saving control effect. Compared with the rule-based strategy and equivalent hydrogen consumption strategy (ECMS), the fuel consumption is lower under two typical urban conditions. In the 1800 s driving cycle, under WTCL conditions, the fuel consumption under the MPC-PMP strategy is 22.4% lower than that based on the ECMS strategy, and 10.3% lower than the rules-based strategy. Under CTLT conditions, the fuel consumption of the MPC-PMP strategy is 13.12% lower than that of the rule-based strategy, and 3.01% lower than the ECMS strategy.
Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon
Authors: Abbas Asad , Qaisar Saeed Mian , Waqar Asad , Ullah Nasim , Al Ahmadi Ahmad Aziz
Published: May 2022

Load variations in any power system result in loss escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in existing methods that, ideally, the allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and the allocation of DGs must be done in accordance with the variations of the connected load. Therefore, the current paper addresses the aforementioned problem by the distinctive optimal allocation of DGs for each variability of 24 h load horizon. However, a single exclusive solution is considered among all allocations of 24 h. The min-max regret concept has been utilized in order to deal with such a methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royale optimization (BRO), accelerated particle swarm optimization (APSO), and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises active and reactive power losses and voltage deviation. Hence, in order to find the robustness of the proposed technique, Conseil international des grands reseaux electriques’ (CIGRE) MV benchmark model incorporating 14 buses has been used considerably as a test network. In the end, the results of three proposed algorithms have been compared.
Developing an Automated Tool for Quantitative Analysis of the Deconvoluted Electrochemical Impedance Response of a Solid Oxide Fuel Cell
Authors: Alboghobeish Mohammad , Monforti Ferrario Andrea , Pumiglia Davide , Della Pietra Massimiliano , McPhail Stephen J. , Pylypko Sergii , Borello Domenico
Published: May 2022

Despite being commercially available, solid oxide fuel cell (SOFC) technology requires further study to understand its physicochemical processes for diagnostics, prognostics, and quality assurance purposes. Electrochemical impedance spectroscopy (EIS), a widely used characterization technique for SOFCs, is often accompanied by the distribution of relaxation times (DRT) as a method for deconvoluting the contribution of each physicochemical process from the aggregated impedance response spectra. While EIS yields valuable information for the operation of SOFCs, the quantitative analysis of the DRT and its shifts remains cumbersome. To address this issue, and to create a replicable benchmark for the assessment of DRT results, a custom tool was developed in MATLAB to numerically analyze the DRT spectra, identify the DRT peaks, and assess their deviation in terms of peak frequency and DRT amplitude from nominal operating conditions. The preliminary validation of the tool was carried out by applying the tool to an extensive experimental campaign on 23 SOFC button-sized samples from three production batches in which EIS measurements were performed in parametric operating conditions. It was concluded that the results of the automated analysis via the developed tool were in accordance with the qualitative analysis of previous studies. It is capable of providing adequate additional quantitative results in terms of DRT shifts for further analysis and provides the basis for better interoperability of DRT analyses between laboratories.
Study on the Discharge Characteristics along the Surface and Charge Movement Characteristics of Insulating Media in an Airflow Environment
Authors: Zhang Guangquan , Zhang Xueqin , Wang Bo , Guo Yujun , Gao Guoqiang , Wu Guangning
Published: May 2022

The gas–solid interface of high-voltage insulating equipment is a weaker part of insulating equipment insulation, and preventing the occurrence of discharge along the surface of insulating equipment is a critical problem for high-voltage insulation. This article investigates the discharge characteristics and charge movement characteristics of insulating media under an airflow environment. The surface discharge characteristics of the insulating medium in the airflow environment were obtained by using a high-velocity airflow test platform, and the surface discharge voltage characteristics, discharge path characteristics, and force conditions of the discharge process were analyzed. The surface charge motion characteristics of the insulating medium in the high-velocity airflow environment were also tested, and the distribution characteristics, dissipation characteristics and conduction mechanism of the surface charge of the insulating medium in the high-velocity airflow environment were revealed. The research results showed that: the discharge voltage along the insulating medium surface gradually increases with the increasing velocity of airflow; the discharge path along the surface of the insulating medium gradually shifts backward under the action of airflow; under the action of airflow, the charge on the insulating medium surface is blown away, thus reducing the charge concentration on the insulating medium surface; the trap level center of the insulating medium gradually decreases under the action of airflow, which provides the conditions for the charge blowing effect on the insulating medium surface. This investigation supplies the theory support for the protection of insulation equipment in an airflow environment and technical guidance for the insulation design of insulating equipment in an airflow environment to ensure the secure and steady running of insulating equipment in high-speed trains and high-voltage transmission lines.
Overview of Nutraceuticals and Cardiometabolic Diseases following Socio-Economic Analysis
Authors: Bruno Giacomo Matteo , Dovera Federico , Ciccarone Antonio , Colombo Giorgio Lorenzo
Published: May 2022

The importance of functional food and nutraceutical products to deal with cardiometabolic diseases (CMDs) and metabolic syndrome (MetS) has gained attention in the past few years. The aim of this narrative review is to highlight the potential and effectiveness of nutraceutical in the improvement of CMDs and MetS biomarkers, alongside their burden of disease and economic health expenditure. A science database search was conducted between May and June 2021. A total of 35 studies were included in this paper. We included male and female subjects, children, and adults, in good health or with cardiovascular or metabolic disease. CMDs and MetS have gradually become worldwide health problems, becoming two of the major causes of morbidity and mortality in western countries. The results indicate a positive link between daily consumption of nutraceutical products and an improvement in cardiometabolic and anthropometric biomarkers. In this paper we included a wide range of nutraceutical products. Most of them showed promising data, indicating that nutraceuticals could provide a new therapeutic treatment to reduce prevalence and pharmaceutical expenditures attributed to CMDs and MetS. Unfortunately, there is a huge vacuum of data on nutraceutical usage, savings, and burden reduction. Therefore, further clinical and pharmaco-economic research in the field is highly required.
Bacterial Composition and Interactions in Raw Milk and Teat Skin of Dairy Cows
Authors: Yan Hui , Du Wen , Ji Shoukun , Guo Chunyan , Zhang Yujing , Wang Yajing , Cao Zhijun , Li Shengli
Published: May 2022

The microbiota in raw milk plays an important role in the health of dairy cows and the safety of dairy products, which might be influenced by that in teat skin. However, the microbiota composition in raw milk and teat skin, as well as the bacterial interaction between the two adjacent spatial locations, remains elusive. Here, we investigated the composition, diversity, and co-occurrence network of the bacterial communities in raw milk and on teat skin, as well as the shift of bacterial communities during the teat bath using 469 samples from 156 individual cows. We observed that raw milk and teat skin harbored significantly different bacterial communities according to an assessment of the genera numbers (p < 0.05) and PCoA analysis (ANOSIM p < 0.05). The microbiota in raw milk was dominated by Proteobacteria (58.5% in relative abundance) at the phylum level and by Pseudomonas (51.2%) at the genus level, while that in teat skin was dominated by Firmicutes (46.9%) at the phylum level and by Pseudomonas (11.0%) at the genus level. We observed a massive difference between the bacterial subnetworks in raw milk and teat, and the bacterial abundance in these two adjacent spatial locations was positively correlated (p < 0.05). Using Bayesian algorithms, we identified that 92.1% of bacteria in raw milk were transferred from teat skin, while 63.6% of bacteria on teat skin were transferred from raw milk. Moreover, microbiota composition in teat skin could be affected by the teat bath with iodine disinfectant, which tended to be more similar to that in raw milk after the teat bath (p < 0.05), while the abundance of the dominant genus Pseudomonas significantly increased (p < 0.05). These findings expand our knowledge on the microbiota composition in teat skin and raw milk, as well as the interaction between these two adjacent spatial locations.
The Effect of Message Framing in Promoting the Mediterranean Diet: The Moderating Role of Eating Self-Efficacy
Authors: Carfora Valentina , Morandi Maria , Catellani Patrizia
Published: May 2022

Although a Mediterranean diet (MeDiet) provides several psychophysical health benefits, research on how to effectively promote MeDiet adherence is still lacking. In the present study, we tested the effectiveness of a messaging intervention aimed at promoting the adherence to the Mediterranean diet. A total of 435 Italian participants responded to a questionnaire on their eating self-efficacy and adherence to the MeDiet at Time 1. Then, participants were randomly assigned to three different conditions: (a) gain messages focused on the positive outcomes of MeDiet adherence; (b) non-loss messages focused on the avoided negative outcomes associated with MeDiet adherence; (c) no messages (control). After the 2 week intervention, participants answered some questions regarding their perception of threat and distress, evaluation of the messages, and adherence to the MeDiet at Time 2. We also tested whether the messaging intervention influenced participants’ MeDiet adherence at Time 2. Results confirmed that the messaging intervention enhanced the MeDiet adherence (F(2,432) = 4.61; p = 0.01, ηp2 = 0.02), with no difference between exposure to gain or non-loss messages (95% LLCI = −0.32; 95% ULCI = 0.54). We then tested whether message framing effectiveness was influenced by eating self-efficacy, and results showed that gain messages were more persuasive for participants with low eating self-efficacy (effect size = 0.01; p for interaction = 0.03). Discussion suggests that tailoring messages according to receivers’ psychological characteristics seems to be pivotal to enhance the persuasiveness of messages aimed at promoting the MeDiet adherence.
Bioactivity and Chemical Profile of Rubus idaeus L. Leaves Steam-Distillation Extract
Authors: De Santis Diana , Carbone Katya , Garzoli Stefania , Laghezza Masci Valentina , Turchetti Giovanni
Published: May 2022

The leaves of Rubus idaeus L., a by-product of the fruit food industry, are a known source of bioactive molecules, although the chemical composition has only been partially investigated. The main objective of this study was to examine the biological activities and the chemical composition of the extract of leaves of R. idaeus (RH), obtained by steam distillation (SD). The antioxidant capacity; the total phenolic content (TPC); the cytotoxic activity against tumor cell lines; and the antibacterial activity, in addition to the study of the chemical fingerprinting, carried out by Gas/Chromatography-Mass-Spectrometry (GC/MS) and Headspace (HS)-GC/MS, were established. The extract showed a strong antioxidant capacity and a modest antibacterial activity against two bacterial strains, as well as significant cytotoxic activity against tumor cell lines (Caco-2 and HL60) and being proliferative on healthy cells. Many of the GC-identified volatile molecules (1,8-cineol, β-linalool, geraniol, caryophyllene, τ-muurolol, citral, α-terpineol, 3- carene, α-terpinen-7-al, etc.) can explain most of the biological properties exhibited by the extract of R. idaeus L. The high biological activity of the RH and the high compatibility with the various matrices suggest good prospects for this extract, both in the food and cosmetic fields or in dietary supplements for improving human health.
An Exploration of Listeria monocytogenes, Its Influence on the UK Food Industry and Future Public Health Strategies
Authors: Macleod Joshua , Beeton Michael L. , Blaxland James
Published: May 2022

Listeria monocytogenes is a Gram-positive intracellular pathogen that can cause listeriosis, an invasive disease affecting pregnant women, neonates, the elderly, and immunocompromised individuals. Principally foodborne, the pathogen is transmitted typically through contaminated foods. As a result, food manufacturers exert considerable efforts to eliminate L. monocytogenes from foodstuffs and the environment through food processing and disinfection. However, L. monocytogenes demonstrates a range of environmental stress tolerances, resulting in persistent colonies that act as reservoirs for the reintroduction of L. monocytogenes to food contact surfaces and food. Novel technologies for the rapid detection of L. monocytogenes and disinfection of food manufacturing industries have been developed to overcome these obstacles to minimise the risk of outbreaks and sporadic cases of listeriosis. This review is aimed at exploring L. monocytogenes in the UK, providing a summary of outbreaks, current routine microbiological testing and the increasing awareness of biocide tolerances. Recommendations for future research in the UK are made, pertaining to expanding the understanding of L. monocytogenes dissemination in the UK food industry and the continuation of novel technological developments for disinfection of food and the food manufacturing environment.