bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera
Authors: Fitwi Alem , Chen Yu , Sun Han , Harrod Robert
Published: November 2021

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.
Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study
Authors: Arntfield Robert , Wu Derek , Tschirhart Jared , VanBerlo Blake , Ford Alex , Ho Jordan , McCauley Joseph , Wu Benjamin , Deglint Jason , Chaudhary Rushil , Dave Chintan , VanBerlo Bennett , Basmaji John , Millington Scott
Published: November 2021

Lung ultrasound (LUS) is an accurate thoracic imaging technique distinguished by its handheld size, low-cost, and lack of radiation. User dependence and poor access to training have limited the impact and dissemination of LUS outside of acute care hospital environments. Automated interpretation of LUS using deep learning can overcome these barriers by increasing accuracy while allowing point-of-care use by non-experts. In this multicenter study, we seek to automate the clinically vital distinction between A line (normal parenchyma) and B line (abnormal parenchyma) on LUS by training a customized neural network using 272,891 labelled LUS images. After external validation on 23,393 frames, pragmatic clinical application at the clip level was performed on 1162 videos. The trained classifier demonstrated an area under the receiver operating curve (AUC) of 0.96 (±0.02) through 10-fold cross-validation on local frames and an AUC of 0.93 on the external validation dataset. Clip-level inference yielded sensitivities and specificities of 90% and 92% (local) and 83% and 82% (external), respectively, for detecting the B line pattern. This study demonstrates accurate deep-learning-enabled LUS interpretation between normal and abnormal lung parenchyma on ultrasound frames while rendering diagnostically important sensitivity and specificity at the video clip level.
AI-Enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-Lead Electrocardiogram
Authors: Urtnasan Erdenebayar , Joo Eun Yeon , Lee Kyu Hee
Published: November 2021

Healthy sleep is an essential physiological process for every individual to live a healthy life. Many sleep disorders both destroy the quality and decrease the duration of sleep. Thus, a convenient and accurate detection or classification method is important for screening and identifying sleep disorders. In this study, we proposed an AI-enabled algorithm for the automatic classification of sleep disorders based on a single-lead electrocardiogram (ECG). An AI-enabled algorithm—named a sleep disorder network (SDN)—was designed for automatic classification of four major sleep disorders, namely insomnia (INS), periodic leg movement (PLM), REM sleep behavior disorder (RBD), and nocturnal frontal-lobe epilepsy (NFE). The SDN was constructed using deep convolutional neural networks that can extract and analyze the complex and cyclic rhythm of sleep disorders that affect ECG patterns. The SDN consists of five layers, a 1D convolutional layer, and is optimized via dropout and batch normalization. The single-lead ECG signal was extracted from the 35 subjects with the control (CNT) and the four sleep disorder groups (seven subjects of each group) in the CAP Sleep Database. The ECG signal was pre-processed, segmented at 30 s intervals, and divided into the training, validation, and test sets consisting of 74,135, 18,534, and 23,168 segments, respectively. The constructed SDN was trained and evaluated using the CAP Sleep Database, which contains not only data on sleep disorders, but also data of the control group. The proposed SDN algorithm for the automatic classification of sleep disorders based on a single-lead ECG showed very high performances. We achieved F1 scores of 99.0%, 97.0%, 97.0%, 95.0%, and 98.0% for the CNT, INS, PLM, RBD, and NFE groups, respectively. We proposed an AI-enabled method for the automatic classification of sleep disorders based on a single-lead ECG signal. In addition, it represents the possibility of the sleep disorder classification using ECG only. The SDN can be a useful tool or an alternative screening method based on single-lead ECGs for sleep monitoring and screening.
Longitudinal Monitoring of Plasma Circulating Tumour DNA Enables the Prediction of Early Relapse in Patients with Non-Hodgkin Lymphoma: A Case Series
Authors: Ji Hongyan , Long Xiaolu , Gu Jia , Jin Jin , Mao Xia , Wang Zhiqiong , Ma Heng , Chen Liting
Published: November 2021

Growing evidence now suggests that circulating tumour DNA (ctDNA) has great potential as a non-invasive biomarker for disease monitoring, since ctDNA carries tumour-specific modifications. In particular, monitoring ctDNA has important implications for identifying patients with haematological malignancies at clinical risk of disease progression. We hereby describe three patients with B-cell non-Hodgkin lymphoma and investigate the clinical value of sequential ctDNA profiling for the early detection of tumour relapse. Somatic mutations in diagnostic tumour biopsy samples of these three patients were identified by applying high-throughput next-generation sequencing. Droplet digital PCR probes and primers were designed and tested for each hotspot mutation. Serial ctDNA analysis was subsequently conducted among these three patients. We found that the longitudinal monitoring of plasma ctDNA could predict for at least one month in advance compared with flow cytometry, cytology and conventional imaging modalities. Therefore, our results support liquid biopsy based on ctDNA as a non-invasive complementary modality to other detection methods for detecting early relapse and contribute to more precise management for non-Hodgkin lymphoma patients.
Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia
Authors: Boulila Wadii , Shah Syed Aziz , Ahmad Jawad , Driss Maha , Ghandorh Hamza , Alsaeedi Abdullah , Al-Sarem Mohammed , Saeed Faisal
Published: November 2021

The Kingdom of Saudi Arabia has suffered from COVID-19 disease as part of the global pandemic due to severe acute respiratory syndrome coronavirus 2. The economy of Saudi Arabia also suffered a heavy impact. Several measures were taken to help mitigate its impact and stimulate the economy. In this context, we present a safe and secure WiFi-sensing-based COVID-19 monitoring system exploiting commercially available low-cost wireless devices that can be deployed in different indoor settings within Saudi Arabia. We extracted different activities of daily living and respiratory rates from ubiquitous WiFi signals in terms of channel state information (CSI) and secured them from unauthorized access through permutation and diffusion with multiple substitution boxes using chaos theory. The experiments were performed on healthy participants. We used the variances of the amplitude information of the CSI data and evaluated their security using several security parameters such as the correlation coefficient, mean-squared error (MSE), peak-signal-to-noise ratio (PSNR), entropy, number of pixel change rate (NPCR), and unified average change intensity (UACI). These security metrics, for example, lower correlation and higher entropy, indicate stronger security of the proposed encryption method. Moreover, the NPCR and UACI values were higher than 99% and 30, respectively, which also confirmed the security strength of the encrypted information.
On the Quality of Street Lighting in Pedestrian Crossings
Authors: Zalesinska Malgorzata , Wandachowicz Krzysztof
Published: November 2021

The pedestrian is the least protected road user. A large number of accidents involving pedestrians occur at pedestrian crossings. Bad lighting or a complete lack of it is one of the causes of accidents in these places. Currently, there are no uniform requirements in Europe for the lighting of pedestrian crossings. Each country is trying to create its system of requirements and assessment of lighting of pedestrian crossings. Measurement procedures and required lighting parameter values often vary significantly across countries. This paper discusses the results of measurements carried out at selected pedestrian crossings using two different measurement grids. The lighting quality at the tested crossings has been assessed based on the lighting requirements in force in two EU countries. In addition, we present the results for the illuminance contrast of a measurement board, representing the silhouette of a pedestrian on a crossing, with the background. Based on the determined contrast, we attempted to verify the lighting conditions using an evaluation system described in the literature, which employs fuzzy logic algorithms.
Balancing Energy Trilemma Using Hybrid Distributed Rooftop Solar PV (DRSP)/Battery/Diesel Microgrid: A Case Study in Gilutongan Island, Cordova, Cebu, Philippines
Authors: Lacea Jaybee , Querikiol Edward , Taboada Evelyn
Published: November 2021

Design strategies for achieving reliable, affordable, and clean electricity are crucial for energy sustainability. Attaining it requires managing the three core factors (TCF) of the energy trilemma (ET) to increase reliability (energy equity), minimize the levelized cost of electricity (LCOE) (energy equity), and avoid potential CO2 emission (environmental sustainability) simultaneously. This paper aims to present a design strategy for the hybrid energy system microgrid (HESM) model, consisting of a distributed rooftop solar PV (DRSP), battery, and diesel-generator to meet the increasing demand while balancing the TCF of the ET. The design strategy was applied in a cluster of 11 households in Gilutongan Island, Cebu, Philippines, where there is no open land space for a solar PV microgrid system. This study used PVSyst and HOMER Pro software to perform the techno-enviro-economic (TEE) analysis to select all feasible system configurations (FSCs). To identify the optimal FSC, a scoring mechanism that considers the LCOE based on the 5% household electricity expense limit, the 5% unmet load fraction, and the renewable penetration fraction was used. Results show that the optimal system requires an average of 32.2% excess energy from DRSP to balance the TCF of the ET based on the energy demand considered. Thus, planning when energy demand increases is vital to map the next appropriate steps toward sustainable energy transition. Overall, the obtained results can support project developers and policymakers to make informed decisions in balancing the ET from various trade-offs of energy systems.
Optimization and Performance Assessment of a Logic Selectivity Solution Based on LoRa Communication
Authors: Liccardo Annalisa , Bonavolontà Francesco , Romano Ignazio , Schiano Lo Moriello Rosario
Published: November 2021

Ensuring service continuity has become a fundamental issue for companies involved in electricity distribution; in particular, isolating the smallest possible portion of the network as a result of faults has long been a primary objective. To this aim, solutions based on logic selectivity have been defined and implemented for an efficient search for the network branch affected by the fault and its subsequent isolation. The authors have recently presented a proposal for the implementation of logic selectivity that exploits the LoRa transmission protocol, an ideal solution in the case of areas not reachable by the currently exploited communication technologies. The present paper, instead, deals with the optimization of some LoRa parameters, which made it possible to exploit network configurations in terms of coverage range, sensitivity and signal-to-noise ratio. The performance of the new configuration has been assessed through a number of tests conducted in the laboratory and on-field, highlighting promising results in terms of both intervention times and reliability. In particular, tests conducted in both rural and urban areas have assured fault isolation times as low as 33 ms (fully compliant with the current regulations) in the presence of the most challenging fault condition.
Aerial Thermographic Image-Based Assessment of Thermal Bridges Using Representative Classifications and Calculations
Authors: Mayer Zoe , Heuer Julia , Volk Rebekka , Schultmann Frank
Published: November 2021

Since the middle of the 20th century many any buildings were built without any energy standards and still have a comparably poor energy quality. To obtain an overview of the current thermal quality of buildings in a whole city district, it may be promising to work with thermographic images obtained by unmanned aerial vehicles (UAV). Aerial thermography represents a fast and cost-efficient approach compared to traditional terrestrial thermography. In this paper, we describe an approach to finding thermal bridges on aerial thermographic images and characterizing them in terms of their risk of mold formation, energy losses, retrofit costs, and retrofit benefits. To identify thermal bridge types that can be detected reliably on aerial thermographic images, we use a dataset collected with a UAV in an urban district of the German city of Karlsruhe. We classify and characterize 14 relevant thermal bridge types for the German building cohorts of the 1950s and 1960s. Concerning the criterion of mold formation, thermal bridges of window components, basement ceiling slabs, balcony slabs, floor slabs, and attics are found to be particularly relevant to retrofit projects. Regarding energy savings, the retrofit of thermal bridges of window sills, window lintels, and attics shows high potential. The retrofit of attics seems to be less attractive, when also taking into account the necessary retrofit costs.
An Experimental Study of a Thermally Activated Ceiling Containing Phase Change Material for Different Cooling Load Profiles
Authors: Sinacka Joanna , Szczechowiak Edward
Published: November 2021

Increasing peak power demand implies the increasing significance of energy storage. Technologies that efficiently store heat and cold are also important for increasing the share of renewables and improving the efficiency of heating, ventilation, and air conditioning (HVAC) systems. The present experimental study investigated the dynamic behavior of a room with suspended thermally activated ceiling panels filled with a material containing 60% paraffin. The purpose of the study was to determine the specific cooling power and the total energy supplied to the phase change material (PCM) during regeneration. Convective heat flux density, radiant heat flux density, and the heat transfer coefficient (convective, radiant) at the ceiling surface were calculated. Analysis shows that shifting system activation to use lower temperatures during the night maintains thermal comfort.
Deployment and Optimisation of a Pilot-Scale IASBR System for Treatment of Dairy Processing Wastewater
Authors: Leonard Peter , Clifford Eoghan , Finnegan William , Siggins Alma , Zhan Xinmin
Published: November 2021

Increased pressure is being applied to industrial wastewater treatment facilities to adhere to more stringent regulations for the discharge of treated wastewater and to improve energy efficiency of the process. Nitrogen and phosphorous removal can be challenging to achieve efficiently, and in the case of phosphorous removal, can often necessitate the use of chemicals. There is a major drive globally to improve wastewater treatment infrastructure, whilst simultaneously reducing the carbon footprint of the process. The intermittently aerated sequencing batch reactor offers a modification of the well-known sequencing batch reactor process that can enable lower energy requirements than conventional sequencing batch reactor processes and can facilitate enhanced nutrient removal capacities. However, to date much of the previous literature has focused on relatively short laboratory-scale trials (often with synthetic wastewater) which may not be representative of larger scale system performance. This study explored the intermittently aerated sequencing batch reactor technology via a case-study deployment at a dairy production facility, in terms of treatment efficiency and energy efficiency with a focus on optimisation between phases. High treatment capacity and operational flexibility was achieved with NH4-N removals averaging >89%, PO4-P removal averaging >90% and total suspended solids removal averaging >97%. This research demonstrates the characteristics of intermittently aerated sequencing batch reactor technology at scale to effectively achieve biological nutrient removal. In addition, this study demonstrated that when effectively managed, energy savings and reductions in carbon emissions in the region of 36–68% are achievable through optimisation of reactor operation.
An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction
Authors: Chaibi Mohamed , Benghoulam EL Mahjoub , Tarik Lhoussaine , Berrada Mohamed , Hmaidi Abdellah El
Published: November 2021

Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation techniques to explain and improve the predictions of ML models. We compared first the predictive performance of Light Gradient Boosting (LightGBM) with three benchmark models, including multilayer perceptron (MLP), multiple linear regression (MLR), and support-vector regression (SVR), for estimating the global solar radiation (H) in the city of Fez, Morocco. Then, the predictions of the most accurate model were explained by two model-agnostic explanation techniques: permutation feature importance (PFI) and Shapley additive explanations (SHAP). The results indicated that LightGBM (R2 = 0.9377, RMSE = 0.4827 kWh/m2, MAE = 0.3614 kWh/m2) provides similar predictive accuracy as SVR, and outperformed MLP and MLR in the testing stage. Both PFI and SHAP methods showed that extraterrestrial solar radiation (H0) and sunshine duration fraction (SF) are the two most important parameters that affect H estimation. Moreover, the SHAP method established how each feature influences the LightGBM estimations. The predictive accuracy of the LightGBM model was further improved slightly after re-examination of features, where the model combining H0, SF, and RH was better than the model with all features.
Real Time Simulation of Power Electronics Medium Voltage DC-Grid Simulator
Authors: Sobanski Piotr , Miskiewicz Milosz , Bujak Grzegorz , Szlosek Marcin , Oikonomou Nikolaos , Pietilaeinen Kai
Published: November 2021

Power electronics medium-voltage (MV) systems must comply with the requirements defined in grid codes. These systems’ compatibility with the standards can be validated by specialized testing equipment: grid simulators. This paper presents a hardware in the loop (HiL) implementation and the simulation results of a MV multiphase DC/DC converter designed for MV DC grid emulation. By using ABB’s reliable, patented power converter hardware topology (US 10978948 B2) and by applying advanced control algorithms, the presented system can be used for special purposes, such as the emulation of fault events in a DC-grid used for the certification of other devices, or for other research goals. The presented concept of a power electronics DC-grid simulator (PEGS-DC) is characterized by high power capability and high voltage quality. In this paper, the general idea of a power electronics grid simulator applied for the testing of MV electrical systems is discussed. Then, details related to the PEGS-DC, such as its hardware topology and the applied modulation method are presented. Subsequently, the HiL setup is described. The main scope of this article focuses on model the description and presenting recorded HiL simulations.
Fuzzy Chaos Control of Fractional Order D-PMSG for Wind Turbine with Uncertain Parameters by State Feedback Design
Authors: Yang Li , Yang Fuzhao , Sheng Weitao , Zhou Kun , Huang Tianmin
Published: November 2021

To research the chaotic motion problem of the direct-drive permanent magnet synchronous generator (D-PMSG) for a wind turbine with uncertain parameters and fractional order characteristics, a control strategy established upon fuzzy state feedback is proposed. Firstly, according to the working mechanism of D-PMSG, the Lorenz nonlinear mathematical model is established by affine transformation and time transformation. Secondly, fractional order nonlinear systems (FONSs) are transformed into linear sub-model by Takagi–Sugeno (T-S) fuzzy model. Then, the fuzzy state feedback controller is designed through Parallel Distributed Compensation (PDC) control principle to suppress the chaotic motion. By applying the fractional Lyapunov stability theory (FLST), the sufficient conditions for Mittag–Leffler stability are formulated in the format of linear matrix inequalities (LMIs). Finally, the control performance and effectiveness of the proposed controller are demonstrated through numerical simulations, and the chaotic motions in D-PMSG can be eliminated quickly.
Effects of Diffusion, Adsorption, and Hysteresis on Huff-n-Puff Performance in Ultratight Reservoirs with Different Fluid Types and Injection Gases
Authors: Enab Khaled , Emami-Meybodi Hamid
Published: November 2021

Cyclic solvent injection, known as solvent huff-n-puff, is one of the promising techniques for enhancing oil recovery from shale reservoirs. This study investigates the huff-n-puff performance in ultratight shale reservoirs by conducting large-scale numerical simulations for a wide range of reservoir fluid types (retrograde condensate, volatile oil, and black oil) and different injection gases (CO2, C2H6, and C3H8). A dual-porosity compositional model is utilized to comprehensively evaluate the impact of multicomponent diffusion, adsorption, and hysteresis on the production performance of each reservoir fluid and the retention capacity of the injection gases. The results show that the huff-n-puff process improves oil recovery by 4–6% when injected with 10% PV of gas. Huff-n-puff efficiency increases with decreasing gas-oil ratio (GOR). C2H6 provides the highest recovery for the black oil and volatile oil systems, and CO2 provides the highest recovery for retrograde condensate fluid type. Diffusion and adsorption are essential mechanisms to be considered when modeling gas injection in shale reservoirs. However, the relative permeability hysteresis effect is not significant. Diffusion impact increases with GOR, while adsorption impact decreases with increasing GOR. Oil density reduction caused by diffusion is observed more during the soaking period considering that the diffusion of the injected gas caused a low prediction error, while adsorption for the injected gas showed a noticeable error.
Electrification of Online Ride-Hailing Vehicles in China: Intention Modelling and Market Prediction
Authors: Zhou Suyang , Chen Jinyi , Wu Zhi , Qiu Yue
Published: November 2021

Significant negative impact caused by climate changes, such as economy and life losses, has been experienced globally in recent years, which has called for imminent development and adoption of low carbon technologies in order to mitigate the impact. In 2020, the Chinese government outlined the ‘Dual Carbon’ Goal where its carbon emission will peak before 2030 whilst China will become ‘Carbon Neutral’ before 2060. In China, the amount of carbon emissions from the transportation industry stands in second place and it is predicted that the carbon emission of China’s automobile industry will reach between 21.5 and 30 billion tons in 2030. Actions should be taken as quickly as possible to facilitate the transition from traditional fossil fuel vehicles to low carbon vehicles such as electric vehicles in order to reduce carbon emissions effectively. Based on the questionnaire that is designed to survey the electrification of online ride-hailing vehicles, this paper first establishes a consumer purchase intention model according to the perceived value theory. By evaluating six aspects including functional value, emotional value, social value, functional risk, financial risk and physical and mental risk, the regression model of the consumer purchase intention for electric vehicles is built. Subsequently, the average operating models for petroleum fuel vehicles, hybrid vehicles and electric vehicles are established, and on top of this, a fossil fuel price model can be derived. This price model can identify from which price it will be advantageous to use electric vehicles to run an online ride-hailing service. Moreover, a multi-agent model is established to illustrate the spread of electric vehicles in the online ride-hailing sector and the private car sector, which is used to predict the trend of the EV market development in China from 2020 to 2040.
Centralized Microgrid Control System in Compliance with IEEE 2030.7 Standard Based on an Advanced Field Unit
Authors: Pouraltafi-kheljan Soheil , Ugur Mesut , Bozulu Efecan , Çalişkan Bahadir Can , Keysan Ozan , Gol Murat
Published: November 2021

The necessity for the utilization of microgrids emerges from the integration of distributed energy resources, electric vehicles, and battery storage systems into the conventional grid structure. In order to achieve a proper operation of the microgrid, the presence of a microgrid control system is crucial. The IEEE 2030.7 standard defines the microgrid control system as a key element of the microgrid that regulates every aspect of it at the point-of-interconnection with the distribution system, and autonomously manages operations such as the transitions of operating modes. In this paper, a microgrid control system is developed to achieve real-time monitoring and control through a centralized approach. The controller consists of a centralized server and advanced field units that are also developed during this work. The control functions of the centralized server ensure the proper operation during grid-connected and island modes, using the real-time data received via the advanced field unit. The developed server and the field unit constitute a complete system solution. The server is composed of control function and communication, database, and user interface modules. The microgrid control functions comprise dispatch and transition core-level functions. A rule-based core-level dispatch function guarantees the security of supply to critical loads during the islanded mode. The core-level transition function accomplishes a successful transition between the operation modes. Moreover, a communication framework and a graphical user interface are implemented. The presented system is tested through thecases based on the IEEE 2030.8 standard.
Network Cost Estimation for Mini-Grids in Large-Scale Rural Electrification Planning
Authors: Ciller Pedro , Lumbreras Sara , González-García Andrés
Published: November 2021

Universal access to electricity is a crucial challenge in many developing countries. Establishing the electrification agenda of an underserved region is a complicated task where computer models play a critical role in calculating geospatial plans that efficiently allocate resources. Such plans should include—among other things—reasonable estimations of the designs and economic costs of standalone systems, mini-grids, and grid extensions. This implies that computer models need to estimate the network cost for many potential mini-grids. To that end, most planning tools apply quick rules of thumb or geometric methods that ignore power flows and electric constraints, which play a significant role in network designs. This paper presents a methodology that rapidly estimates any low-voltage mini-grid network cost without neglecting the impact of electrical feasibility in such cost. We present a case study where we evaluate our method in terms of accuracy and computation time. We also compare our method with a quick estimation similar to the ones most regional planning tools apply, showing the effectiveness of our method.
Estimation of Prediction Error in Regression Air Quality Models
Authors: Hoffman Szymon
Published: November 2021

Combustion of energy fuels or organic waste is associated with the emission of harmful gases and aerosols into the atmosphere, which strongly affects air quality. Air quality monitoring devices are unreliable and measurement gaps appear quite often. Missing data modeling techniques can be used to complete the monitoring data. Concentrations of monitored pollutants can be approximated with regression modeling tools, such as artificial neural networks. In this study, a long-term set of data from the air monitoring station in Zabrze (Silesia, South Poland) was analyzed. Concentration prediction was tested for the main air pollutants, i.e., O3, NO, NO2, SO2, PM10, CO. Multilayer perceptrons were used to model the concentrations. The predicted concentrations were compared to the observed ones to evaluate the approximation accuracy. Prediction errors were calculated separately for the whole concentration range as well as for the specified concentration subranges. Some different measures of error were estimated. It was stated that the use of a single measure of the approximation accuracy may lead to incorrect interpretation. The application of one neural network to the entire concentration range results in different prediction accuracy in various concentration subranges. Replacing one neural network with several networks adjusted to specific concentration subranges should improve the modeling accuracy.
Flexible Active Power Control for PV-ESS Systems: A Review
Authors: Wu Xiangqiang , Kerekes Tamas
Published: November 2021

The penetration of solar energy in the modern power system is still increasing with a fast growth rate after long development due to reduced environmental impact and ever-decreasing photovoltaic panel cost. Meanwhile, distribution networks have to deal with a huge amount and frequent fluctuations of power due to the intermittent nature of solar energy, which influences the grid stability and could cause a voltage rise in the low-voltage grid. In order to reduce these fluctuations and ensure a stable and reliable power supply, energy storage systems are introduced, as they can absorb or release energy on demand, which provides more control flexibility for PV systems. At present, storage technologies are still under development and integrated in renewable applications, especially in smart grids, where lowering the cost and enhancing the reliability are the main tasks. This study reviews and discusses several active power control strategies for hybrid PV and energy storage systems that deliver ancillary services for grid support. The technological advancements and developments of energy storage systems in grid-tied PV applications are also reviewed.
Fabrication of High-Performance Flexible Supercapacitor Electrodes with Poly(3,4-ethylenedioxythiophene) (PEDOT) Grown on Carbon-Deposited Polyurethane Sponge
Authors: Tong Linyue , Sonnenberg Laura A. , Wu Wei , Boyer Steven M. , Fox Maggie T. , Li Boxiao , Bernier William E. , Jones Wayne E.
Published: November 2021

Composite porous supercapacitor electrodes were prepared by growing poly(3,4-ethylenedioxythiophene) (PEDOT) on graphite nanoplatelet- or graphene nanoplatelet-deposited open-cell polyurethane (PU) sponges via a vapor phase polymerization (VPP) method. The resulting composite supercapacitor electrodes exhibited great capacitive performance, with PEDOT acting as both the conductive binder and the active material. The chemical composition was characterized by Raman spectroscopy and the surface morphology was characterized by scanning electron microscopy (SEM). Cyclic voltammetry (CV), charge-discharge (CD) tests and electrochemical impedance spectroscopy were utilized to study the electrical performance of the composite electrodes produced in symmetrically configured supercapacitor cells. The carbon material deposited on PU substrates and the polymerization temperature of PEDOT affected significantly the PEDOT morphology and the electrical properties of the resulting composite sponges. The highest areal specific capacitance 798.2 mF cm−2 was obtained with the composite sponge fabricated by VPP of PEDOT at 110 °C with graphene nanoplatelet-deposited PU sponge substrate. The capacitance retention of this composite electrode was 101.0% after 10,000 charging–discharging cycles. The high flexibility, high areal specific capacitance, excellent long-term cycling stability and low cost make these composite sponges promising electrode materials for supercapacitors.
Regulation Intensity, Freedom of Production Decision and the Poverty of Farmers: Evidence from the Panda Nature Reserves in China
Authors: Lin Chao , Gao Lan
Published: November 2021

The harmonious coexistence between human and nature is a topic of general concern. Existing studies generally agree that the establishment of nature reserves can effectively protect biodiversity, but less attention has been given to the effects of the regulation implied therein upon the multidimensional poverty of surrounding farmers. This paper verified the effects of regulation intensity upon the multidimensional poverty of farmers at the two levels of theory and empirical evidence from the perspective of freedom of production decisions based on the research data involving farmers around the Panda Nature Reserves (PNRs) in China. We have found that regulation intensity will significantly increase the multidimensional poverty of farmers. Heterogeneity analysis indicates that the regulation of agricultural production and the regulation of pollutants will produce a significant positive effect on the multidimensional poverty of farmers. The inherent mechanism is that regulation affects farmers’ freedom of production decisions, thereby worsening the state of their multidimensional poverty. The conclusion of this paper not only contributes to expanding the theoretical studies on regulations and the multidimensional poverty of farmers but also offers suggestions on how the Chinese government can strike a balance between ecological protection and the prosperity of farmers.
Composition of the Protein Ingredients from Insoluble Oat Byproducts Treated with Food-Grade Enzymes, Such as Amylase, Cellulose/Xylanase, and Protease
Authors: Aiello Gilda , Li Yuchen , Xu Ruoxian , Boschin Giovanna , Juodeikiene Grazina , Arnoldi Anna
Published: November 2021

The manufacture of plant-based drinks has the drawback of a huge production of underexploited press cakes. In particular, the oat press cake is mainly used in feed formulation, whereas added-value applications in human nutrition are scarce. Considering that enzymatic treatments may be useful to improve the nutritional quality of these insoluble byproducts, this study aimed to evaluate whether the treatment with some food-grade enzymes, such as amylase, cellulase/xylanase, protease, and their combination, may be useful to achieve this goal. Proteomic and peptidomic studies showed that the enzymatic treatments improved the protein extraction yields and induced a release of low molecular weight (LMW) peptides that were demonstrated to provide a useful antioxidant activity. In the treated oat press cake proteins, the concentration of the bound phenolic compounds was decreased, with the exception of caffeic acid, which was increased, and avenanthramides, which remained unchanged. Finally, the enzymatic treatment decreased the concentration of phytic acid. All these results indicate that the enzymatic treatments may be useful to ameliorate the nutritional profile of these protein ingredients, before their inclusion in different food products.
Quorum Quenching Enzyme (PF-1240) Capable to Degrade AHLs as a Candidate for Inhibiting Quorum Sensing in Food Spoilage Bacterium Hafnia alvei
Authors: Shen Yue , Cui Fangchao , Wang Dangfeng , Li Tingting , Li Jianrong
Published: November 2021

Quorum sensing (QS) is widely present in microorganisms in marine aquatic products. Owing to the use of antibiotics, many spoilage bacteria in aquatic products are drug resistant. In order to slow down this evolutionary trend, the inhibition of spoilage phenotype of spoilage bacteria by interfering with QS has become a research hot spot in recent years. In this study, we found a new QS quenching enzyme, PF-1240; it was cloned and expressed in Pseudomonas fluorescens 08. Sequence alignment showed that its similarity with N-homoserine lactone (AHL) acylase QuiP protein of Pseudomonas fluorescens (Pf 0-1) was 78.4%. SDS-PAGE confirmed that the protein is a dimer composed of two subunits, which is similar to the structure of AHL acylases. The concentration of heterologous expression in Escherichia coli (DE3) was 26.64 μg/mL. Unlike most AHL acylases, PF-1240 can quench AHLs with different carbon chain lengths and inhibit the quorum sensing of the aquatic spoilage bacterium Hafnia alvei. It can significantly reduce the formation rate of biofilm of H. alvei to 44.4% and the yield of siderophores to 54%, inhibit the production of protease and lipase, and interfere with the motility of H. alvei. Through these corruption phenotypes, the specific application effect of PF-1240 can be further determined to provide a theoretical basis for its application in the preservation of practical aquatic products.
Nanotechnology as a Key to Enhance the Benefits and Improve the Bioavailability of Flavonoids in the Food Industry
Authors: Ayala-Fuentes Jocelyn C. , Chavez-Santoscoy Rocio Alejandra
Published: November 2021

Nanotechnology has impacted the food industry, mainly on developing healthier, safer, and high-quality functional food. Flavonoids are valuable compounds present in plants, fruits, grains, roots, stems, tea, and wine, among others; they possess many benefits for health due to their antioxidant properties toward reactive oxygen species, anti-inflammatory, and antiproliferative, among others. These characteristics make flavonoids attractive in various industrial areas such as medicine, nutraceutical, cosmetology, and pharmaceutical. Unfortunately, flavonoids lack long-term stability, are sensitive to light, long periods of darkness with low oxygen concentration, and often present a low water solubility and poor bioavailability. Nanoencapsulation is an alternative to improve bioavailability and sensitivity in the manufacturing process, based on encapsulating substances on a nanoscale. Nanocapsules are a promising strategy in significantly enhancing the delivery of compounds to various sites in the body. The development of biopolymers to encapsulate sensitive compounds is increasing, as well as the search for the non-toxic, biodegradable, natural and biocompatible characteristics of polymers, is fundamental. The present review describes the recent techniques and technologies for the nanoencapsulation of flavonoids. It discusses their potential advantages and possible limitations, compares natural and synthetic biopolymers, and finally, details nanoparticle regulation.
Recyclability and Redesign Challenges in Multilayer Flexible Food Packaging—A Review
Authors: Bauer Anna-Sophia , Tacker Manfred , Uysal-Unalan Ilke , Cruz Rui M. S. , Varzakas Theo , Krauter Victoria
Published: November 2021

Multilayer flexible food packaging is under pressure to redesign for recyclability. Most multilayer films are not sorted and recycled with the currently available infrastructure, which is based on mechanical recycling in most countries. Up to now, multilayer flexible food packaging was highly customizable. Diverse polymers and non-polymeric layers allowed a long product shelf-life and an optimized material efficiency. The need for more recyclable solutions asks for a reduction in the choice of material. Prospectively, there is a strong tendency that multilayer flexible barrier packaging should be based on polyolefins and a few recyclable barrier layers, such as aluminium oxide (AlOx) and silicon oxide (SiOx). The use of ethylene vinyl alcohol (EVOH) and metallization could be more restricted in the future, as popular Design for Recycling Guidelines have recently reduced the maximum tolerable content of barrier materials in polyolefin packaging. The substitution of non-recyclable flexible barrier packaging is challenging because only a limited number of barriers are available. In the worst case, the restriction on material choice could result in a higher environmental burden through a shortened food shelf-life and increased packaging weights.
The Effect of Prickly Ash (Zanthoxylum bungeanum Maxim) on the Taste Perception of Stewed Sheep Tail Fat by LC-QTOF-MS/MS and a Chemometrics Analysis
Authors: Huang Yan , Pu Dandan , Hao Zhilin , Yang Xiao , Zhang Yuyu
Published: November 2021

This work aims to explore the contribution of prickly ash (Zanthoxylum bungeanum Maxim) on the taste perception of stewed sheep tail fat. Liquid chromatography-tandem quadrupole time of flight mass spectrometry (LC-QTOF-MS) was applied to analyze the taste-related compounds. A total of 99 compounds in different sheep tail fat samples were identified. The semi-quantitative results showed that there were differences between the samples. The partial least squares discriminant analysis (PLS-DA) model without overfitting was used to investigate the effect of prickly ash. Eleven marker compounds were predicted with a variable importance for projection > 1, fold change > 2 and p < 0.05. An additional experiment showed that guanosine 5′-monophosphate, malic acid, inosine and adenosine 5′-monophosphate could improve the umami and saltiness taste of stewed sheep tail fat.
On the Design of Power Law Filters and Their Inverse Counterparts
Authors: Mahata Shibendu , Herencsar Norbert , Kubanek David
Published: November 2021

This paper presents the optimal modeling of Power Law Filters (PLFs) with the low-pass (LP), high-pass (HP), band-pass (BP), and band-stop (BS) responses by means of rational approximants. The optimization is performed for three different objective functions and second-order filter mother functions. The formulated design constraints help avoid placement of the zeros and poles on the right-half s-plane, thus, yielding stable PLF and inverse PLF (IPLF) models. The performances of the approximants exhibiting the fractional-step magnitude and phase responses are evaluated using various statistical indices. At the cost of higher computational complexity, the proposed approach achieved improved accuracy with guaranteed stability when compared to the published literature. The four types of optimal PLFs and IPLFs with an exponent α of 0.5 are implemented using the follow-the-leader feedback topology employing AD844AN current feedback operational amplifiers. The experimental results demonstrate that the Total Harmonic Distortion achieved for all the practical PLF and IPLF circuits was equal or lower than 0.21%, whereas the Spurious-Free Dynamic Range also exceeded 57.23 and 54.72 dBc, respectively.
Regulation of the Fanconi Anemia DNA Repair Pathway by Phosphorylation and Monoubiquitination
Authors: Ishiai Masamichi
Published: November 2021

The Fanconi anemia (FA) DNA repair pathway coordinates a faithful repair mechanism for stalled DNA replication forks caused by factors such as DNA interstrand crosslinks (ICLs) or replication stress. An important role of FA pathway activation is initiated by monoubiquitination of FANCD2 and its binding partner of FANCI, which is regulated by the ATM-related kinase, ATR. Therefore, regulation of the FA pathway is a good example of the contribution of ATR to genome stability. In this short review, we summarize the knowledge accumulated over the years regarding how the FA pathway is activated via phosphorylation and monoubiquitination.
The Effect of Travel Burden on Depression and Anxiety in African American Women Living with Systemic Lupus
Authors: White Ashley A. , Smalls Brittany L. , Ba Aissatou , Faith Trevor D. , Ramakrishnan Viswanathan , Johnson Hetlena , Rose Jillian , Dismuke-Greer Clara L. , Oates Jim C. , Egede Leonard E. , Williams Edith M.
Published: November 2021

The United States has a deficit of rheumatology specialists. This leads to an increased burden in accessing care for patients requiring specialized care. Given that most rheumatologists are located in urban centers at large hospitals, many lupus patients must travel long distances for routine appointments. The present work aims to determine whether travel burden is associated with increased levels of depression and anxiety among these patients. Data for this study were collected from baseline visits of patients participating in a lupus study at MUSC. A travel/economic burden survey was assessed as well as the 8-item Patient Health Questionnaire (PHQ-8) and the 7-item Generalized Anxiety Disorder (GAD-7) survey as measures of depression and anxiety, respectively. Linear regression models were used to assess the relationship between travel burden and depression and anxiety. Frequency of healthcare visits was significantly associated with increased depression (β = 1.3, p = 0.02). Significant relationships were identified between anxiety and requiring time off from work for healthcare appointments (β = 4, p = 0.02), and anxiety and perceived difficulty in traveling to primary care providers (β = 3.1, p = 0.04). Results from this study provide evidence that travel burden can have an effect on lupus patients’ anxiety and depression levels.
Health in Prison: Does Penitentiary Medicine in Italy Still Exist?
Authors: Bolcato Matteo , Fiore Vito , Casella Filomena , Babudieri Sergio , Lucania Luciano , Di Mizio Giulio
Published: November 2021

Despite the detailed legislative developments that have occurred within the context of prison medicine in Italy, problems of a management nature continue to affect prisoner health and management, which in turn impact the prison system’s ability to offer prisoners a real opportunity for rehabilitation. Certain behavioral aspects reported in prisons may alter and negatively impact the normal doctor-patient relationship, including elements that hinder the therapeutic alliance and impede proper clinical risk prevention and management. However, practical steps may be taken in connection with the analysis of flows and healthcare services that may enable prison administrations to bring about a true, modern restructure of the prison system.