bluuebunny/crossref_metadata_2025 huggingface dataset:
Overview of Synthetic Cannabinoids ADB-FUBINACA and AMB-FUBINACA: Clinical, Analytical, and Forensic Implications
Authors: Lobato-Freitas Carolina , Brito-da-Costa Andreia Machado , Dinis-Oliveira Ricardo Jorge , Carmo Helena , Carvalho Félix , Silva João Pedro , Dias-da-Silva Diana
Published: February 2021

ADB-FUBINACA and AMB-FUBINACA are two synthetic indazole-derived cannabinoid receptor agonists, up to 140- and 85-fold more potent, respectively, than trans-∆9-tetrahydrocannabinol (∆9-THC), the main psychoactive compound of cannabis. Synthesised in 2009 as a pharmaceutical drug candidate, the recreational use of ADB-FUBINACA was first reported in 2013 in Japan, with fatal cases being described in 2015. ADB-FUBINACA is one of the most apprehended and consumed synthetic cannabinoid (SC), following AMB-FUBINACA, which emerged in 2014 as a drug of abuse and has since been responsible for several intoxication and death outbreaks. Here, we critically review the physicochemical properties, detection methods, prevalence, biological effects, pharmacodynamics and pharmacokinetics of both drugs. When smoked, these SCs produce almost immediate effects (about 10 to 15 s after use) that last up to 60 min. They are rapidly and extensively metabolised, being the O-demethylated metabolite of AMB-FUBINACA, 2-(1-(4-fluorobenzyl)-1H-indazole-3-carboxamide)-3-methylbutanoic acid, the main excreted in urine, while for ADB-FUBINACA the main biomarkers are the hydroxdimethylpropyl ADB-FUBINACA, hydroxydehydrodimethylpropyl ADB-FUBINACA and hydroxylindazole ADB-FUBINACA. ADB-FUBINACA and AMB-FUBINACA display full agonism of the CB1 receptor, this being responsible for their cardiovascular and neurological effects (e.g., altered perception, agitation, anxiety, paranoia, hallucinations, loss of consciousness and memory, chest pain, hypertension, tachycardia, seizures). This review highlights the urgent requirement for additional studies on the toxicokinetic properties of AMB-FUBINACA and ADB-FUBINACA, as this is imperative to improve the methods for detecting and quantifying these drugs and to determine the best exposure markers in the various biological matrices. Furthermore, it stresses the need for clinicians and pathologists involved in the management of these intoxications to describe their findings in the scientific literature, thus assisting in the risk assessment and treatment of the harmful effects of these drugs in future medical and forensic investigations.
Development and Validation of a GMP-Compliant High-Pressure Liquid Chromatography Method for the Determination of the Chemical and Radiochemical Purity of [18F]PSMA-1007, a PET Tracer for the Imaging of Prostate Cancer
Authors: Katzschmann Ines , Marx Heike , Kopka Klaus , Hennrich Ute
Published: February 2021

For the PET imaging of prostate cancer, radiotracers targeting the prostate-specific membrane antigen (PSMA) are nowadays used in clinical practice. [18F]PSMA-1007, a radiopharmaceutical labeled with fluorine-18, has excellent properties for the detection of prostate cancer. Essential for the human use of a radiotracer is its production and quality control under GMP-compliance. For this purpose, all analytical methods have to be validated. [18F]PSMA-1007 is easily radiosynthesized in a one-step procedure and isolated using solid phase extraction (SPE) cartridges followed by formulation of a buffered injection solution and for the determination of its chemical and radiochemical purity a robust, fast and reliable quality control method using radio-HPLC is necessary. After development and optimizations overcoming problems in reproducibility, the here described radio-HPLC method fulfills all acceptance criteria—for e.g., specificity, linearity, and accuracy—and is therefore well suited for the routine quality control of [18F]PSMA-1007 before release of the radiopharmaceutical. Recently a European Pharmacopeia monograph for [18F]PSMA-1007 was published suggesting a different radio-HPLC method for the determination of its chemical and radiochemical purity. Since the here described method has certain advantages, not least of all easier technical implementation, it can be an attractive alternative to the monograph method. The here described method was successfully validated on several radio-HPLC systems in our lab and used for the analysis of more than 60 batches of [18F]PSMA-1007. Using this method, the chemical and radiochemical purity of [18F]PSMA-1007 can routinely be evaluated assuring patient safety.
Type and Extent of Information on (Potentially Critical) Quality Attributes Described in European Public Assessment Reports for Adalimumab Biosimilars
Authors: Alsamil Ali M. , Giezen Thijs J. , Egberts Toine C. , Leufkens Hubert G. , Gardarsdottir Helga
Published: February 2021

Regulatory approval of biosimilars predominantly relies on biosimilarity assessments of quality attributes (QAs), particularly the potentially critical QAs (pCQAs) that may affect the clinical profile. However, a limited understanding exists concerning how EU regulators reflect the biosimilarity assessments of (pC)QAs in European public assessment reports (EPARs) by different stakeholders. The type and extent of information on QAs and pCQAs in EPARs were evaluated for seven adalimumab biosimilars. Seventy-seven QAs, including 31 pCQAs, were classified and assessed for type (structural and functional attributes) and extent (biosimilarity interpretation and/or test results) of information in EPARs. Reporting on the QAs (35–75%) varied between EPARs, where the most emphasis was placed on pCQAs (65–87%). Functional attributes (54% QAs and 92% pCQAs) were reported more frequently than structural attributes (8% QAs and 22% pCQAs). About 50% (4 structural and 12 functional attributes) of pCQAs were consistently reported in all EPARs. Regulators often provided biosimilarity interpretation (QAs: 83% structural and 80% functional; pCQAs: 81% structural and 78% functional) but rarely include test results (QAs: 1% structural and 9% functional and pCQAs: 3% structural and 9% functional). Minor differences in structural attributes, commonly in glycoforms and charge variants, were often observed in adalimumab biosimilars but did not affect the functions and clinical profile. Despite the variability in reporting QAs in EPARs, the minor observed differences were largely quantitative and not essentially meaningful for the overall conclusion of biosimilarity of the seven adalimumab biosimilars.
Dual-Targeted Hyaluronic Acid/Albumin Micelle-Like Nanoparticles for the Vectorization of Doxorubicin
Authors: Curcio Manuela , Diaz-Gomez Luis , Cirillo Giuseppe , Nicoletta Fiore Pasquale , Leggio Antonella , Iemma Francesca
Published: February 2021

Drug targeting of tumor cells is one of the great challenges in cancer therapy; nanoparticles based on natural polymers represent valuable tools to achieve this aim. The ability to respond to environmental signals from the pathological site (e.g., altered redox potential), together with the specific interaction with membrane receptors overexpressed on cancer cells membrane (e.g., CD44 receptors), represent the main features of actively targeted nanoparticles. In this work, redox-responsive micelle-like nanoparticles were prepared by self-assembling of a hyaluronic acid–human serum albumin conjugate containing cystamine moieties acting as a functional spacer. The conjugation procedure consisted of a reductive amination step of hyaluronic acid followed by condensation with albumin. After self-assembling, nanoparticles with a mean size of 70 nm and able to be destabilized in reducing media were obtained. Doxorubicin-loaded nanoparticles modulated drug release rate in response to different redox conditions. Finally, the viability and uptake experiments on healthy (BALB-3T3) and metastatic cancer (MDA-MB-231) cells proved the potential applicability of the proposed system as a drug vector in cancer therapy.
Repurposing of Sitagliptin- Melittin Optimized Nanoformula against SARS-CoV-2; Antiviral Screening and Molecular Docking Studies
Authors: Al-Rabia Mohammed , Alhakamy Nabil , Ahmed Osama , Eljaaly Khalid , Alaofi Ahmed , Mostafa Ahmed , Asfour Hani , Aldarmahi Ahmed , Darwish Khaled , Ibrahim Tarek , Fahmy Usama
Published: February 2021

The outbreak of the COVID-19 pandemic in China has become an urgent health and economic challenge. The objective of the current work was to evaluate the efficacy of the combined complex of Sitagliptin (SIT) with melittin (MEL) against SARS-CoV-2 virus. SIT-MEL nano-conjugates were optimized by a full three-factor bi-level (23) factorial design. In addition, SIT concentration (mM, X1), MEL concentration (mM, X2), and pH (X3) were selected as the critical factors. Particle size (nm, Y1) and zeta potential (mV, Y2) were assessed as responses. Characterization of the optimized formula for Fourier-transformed infrared (FTIR) was carried out. The optimized formula showed particle size and zeta potential values of 77.42 nm and 27.67 mV, respectively. When compared with SIT and MEL, the combination of SIT-MEL complex has shown anti-viral potential against isolate of SARS-CoV-2 with IC50 values of 8.439 μM with significant improvement (p < 0.001). In addition, the complex showed IC50 in vitro 3CL-protease inhibition with IC50 7.216 µM. Molecular docking has revealed that formula components have good predicted pocket accommodation of the SARS-CoV-2 3-CL protease. An optimized formulation of SIT-MEL could guarantee both enhanced delivery to the target cells and the enhanced cellular uptake with promising activities against SARS-CoV-2.
Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values
Authors: Shuangao Wang , Padmanaban Rajchandar , Mbanze Aires A. , Silva João M. N. , Shamsudeen Mohamed , Cabral Pedro , Campos Felipe S.
Published: February 2021

Accelerated land use change is a current challenge for environmental management worldwide. Given the urgent need to incorporate economic and ecological goals in landscape planning, cost-effective conservation strategies are required. In this study, we validated the benefit of fusing imagery from multiple sensors to assess the impact of landscape changes on ecosystem services (ES) and their economic values in the Long County, Shaanxi Province, China. We applied several landscape metrics to assess the local spatial configuration over 15 years (2004–2019) from fused imageries. Using Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Landsat-8 Operational Land Imager (OLI) and Indian Remote Sensing Satellite System Linear Imaging Self Scanning Sensor 3 (IRS LISS 3) imageries fused for 2004, 2009, 2014 and 2019, we reclassified land use/land cover (LULC) changes, through the rotation forest (RF) machine-learning algorithm. We proposed an equivalent monetary metric for estimating the ES values, which also could be used in the whole China. Results showed that agriculture farmland and unused land decreased their spatial distribution over time, with an observed increase on woodland, grassland, water bodies and built-up area. Our findings suggested that the patterns of landscape uniformity and connectivity improved, while the distribution of landscape types stabilized, while the landscape diversity had a slight improvement. The overall ES values increased (4.34%) under a benefit transfer approach, mainly concerning woodland and grassland. A sensitivity analysis showed the selected economic value (EV) was relevant and suitable for the study area associated with our ES for LULC changes. We suggested that changes in landscape patterns affected the ESV trends, while the increases on some LULC classes slightly improved the landscape diversity. Using an interdisciplinary approach, we recommend that local authorities and environmental practitioners should balance the economic benefits and ecological gains in different landscapes to achieve a sustainable development from local to regional scales.
Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network
Authors: Zhuo Zheng , Zhou Zhong
Published: February 2021

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.
The Potential of GRASP/GARRLiC Retrievals for Dust Aerosol Model Evaluation: Case Study during the PreTECT Campaign
Authors: Konsta Dimitra , Tsekeri Alexandra , Solomos Stavros , Siomos Nikolaos , Gialitaki Anna , Tetoni Eleni , Lopatin Anton , Goloub Philippe , Dubovik Oleg , Amiridis Vassilis , Nastos Panagiotis
Published: February 2021

We use the Generalized Retrieval of Aerosol Surface Properties algorithm (GRASP) to compare with dust concentration profiles derived from the NMME-DREAM model for a specific dust episode. The GRASP algorithm provides the possibility of deriving columnar and vertically-resolved aerosol properties from a combination of lidar and sun-photometer observations. Herein, we apply GRASP for analysis of a Saharan dust outburst observed during the “PREparatory: does dust TriboElectrification affect our ClimaTe” campaign (PreTECT) that took place at the North coast of Crete, at the Finokalia ACTRIS station. GRASP provides column-averaged and vertically resolved microphysical and optical properties of the particles. The retrieved dust concentration profiles are compared with modeled concentration profiles derived from the NMME-DREAM dust model. To strengthen the results, we use dust concentration profiles from the POlarization-LIdar PHOtometer Networking method (POLIPHON). A strong underestimation of the maximum dust concentration is observed from the NMME-DREAM model. The reported differences between the retrievals and the model indicate a high potential of the GRASP algorithm for future studies of dust model evaluation.
Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine
Authors: Magidi James , Nhamo Luxon , Mpandeli Sylvester , Mabhaudhi Tafadzwanashe
Published: February 2021

Improvements in irrigated areas’ classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%.
Near-Infrared Spectroscopy (NIRS) in Traumatic Brain Injury (TBI)
Authors: Roldán María , Kyriacou Panayiotis A.
Published: February 2021

Traumatic brain injury (TBI) occurs when a sudden trauma causes damage to the brain. TBI can result when the head suddenly and violently impacts an object or when an object pierces the skull and enters brain tissue. Secondary injuries after traumatic brain injury (TBI) can lead to impairments on cerebral oxygenation and autoregulation. Considering that secondary brain injuries often take place within the first hours after the trauma, noninvasive monitoring might be helpful in providing early information on the brain’s condition. Near-infrared spectroscopy (NIRS) is an emerging noninvasive monitoring modality based on chromophore absorption of infrared light with the capability of monitoring perfusion of the brain. This review investigates the main applications of NIRS in TBI monitoring and presents a thorough revision of those applications on oxygenation and autoregulation monitoring. Databases such as PubMed, EMBASE, Web of Science, Scopus, and Cochrane library were utilized in identifying 72 publications spanning between 1977 and 2020 which were directly relevant to this review. The majority of the evidence found used NIRS for diagnosis applications, especially in oxygenation and autoregulation monitoring (59%). It was not surprising that nearly all the patients were male adults with severe trauma who were monitored mostly with continue wave NIRS or spatially resolved spectroscopy NIRS and an invasive monitoring device. In general, a high proportion of the assessed papers have concluded that NIRS could be a potential noninvasive technique for assessing TBI, despite the various methodological and technological limitations of NIRS.
Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing
Authors: Wang Biwei , Mao Yuan , Ashry Islam , Al-Fehaid Yousef , Al-Shawaf Abdulmoneim , Ng Tien Khee , Yu Changyuan , Ooi Boon S.
Published: February 2021

Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time- and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees.
Bio-Impedance Measurement Optimization for High-Resolution Carotid Pulse Sensing
Authors: Wang Ting-Wei , Chu Hsiao-Wei , Chou Lin , Sung Yen-Ling , Shih Yuan-Ta , Hsu Po-Chun , Cheng Hao-Min , Lin Shien-Fong
Published: February 2021

Continuous hemodynamic monitoring is important for long-term cardiovascular healthcare, especially in hypertension. The impedance plethysmography (IPG) based carotid pulse sensing is a non-invasive diagnosis technique for measuring pulse signals and further evaluating the arterial conditions of the patient such as continuous blood pressure (BP) monitoring. To reach the high-resolution IPG-based carotid pulse detection for cardiovascular applications, this study provides an optimized measurement parameter in response to obvious pulsation from the carotid artery. The influence of the frequency of excitation current, electrode cross-sectional area, electrode arrangements, and physiological site of carotid arteries on IPG measurement resolution was thoroughly investigated for optimized parameters. In this study, the IPG system was implemented and installed on the subject’s neck above the carotid artery to evaluate the measurement parameters. The measurement results within 6 subjects obtained the arterial impedance variation of 2137 mΩ using the optimized measurement conditions, including excitation frequency of 50 kHz, a smaller area of 2 cm2, electrode spacing of 4 cm and 1.7 cm for excitation and sensing functions, and location on the left side of the neck. The significance of this study demonstrates an optimized measurement methodology of IPG-based carotid pulse sensing that greatly improves the measurement quality in cardiovascular monitoring.
Full-Field Operational Modal Analysis of an Aircraft Composite Panel from the Dynamic Response in Multi-Impact Test
Authors: Molina-Viedma Ángel , López-Alba Elías , Felipe-Sesé Luis , Díaz Francisco
Published: February 2021

Experimental characterization and validation of skin components in aircraft entails multiple evaluations (structural, aerodynamic, acoustic, etc.) and expensive campaigns. They require different rigs and equipment to perform the necessary tests. Two of the main dynamic characterizations include the energy absorption under impact forcing and the identification of modal parameters through the vibration response under any broadband excitation, which also includes impacts. This work exploits the response of a stiffened aircraft composite panel submitted to a multi-impact excitation, which is intended for impact and energy absorption analysis. Based on the high stiffness of composite materials, the study worked under the assumption that the global response to the multi-impact excitation is linear with small strains, neglecting the nonlinear behavior produced by local damage generation. Then, modal identification could be performed. The vibration after the impact was measured by high-speed 3D digital image correlation and employed for full-field operational modal analysis. Multiple modes were characterized in a wide spectrum, exploiting the advantages of the full-field noninvasive techniques. These results described a consistent modal behavior of the panel along with good indicators of mode separation given by the auto modal assurance criterion (Auto-MAC). Hence, it illustrates the possibility of performing these dynamic characterizations in a single test, offering additional information while reducing time and investment during the validation of these structures.
Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application
Authors: Pant Shashank , Nooralishahi Parham , Avdelidis Nicolas P. , Ibarra-Castanedo Clemente , Genest Marc , Deane Shakeb , Valdes Julio J. , Zolotas Argyrios , Maldague Xavier P. V.
Published: February 2021

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.
Gamma-Ray Sensor Using YAlO3(Ce) Single Crystal and CNT/PEEK with High Sensitivity and Stability under Harsh Underwater Conditions
Authors: Lee Chanki , Kim Hee Reyoung
Published: February 2021

A new gamma-ray sensor, which could be employed in harsh underwater conditions, was developed using YAlO3(Ce) single crystal and carbon nanotube reinforced polyetheretherketone (CNT/PEEK). The sensor is compact, highly sensitive and stable, by providing real-time gross counts and an accumulated spectrum for fresh, saline, or contaminated water conditions. The sensor was tested in a water tank for quantification of the limit of detections. The Φ51 × 51 mm2 YAlO3(Ce) crystal exhibits a nearly perfect proportionality with a correlation of over 0.999 in terms of light yield per energy and possesses a high energy resolution. The chemically stable CNT/PEEK window material further enhances the detection efficiency by minimizing the background counts from penetrating gamma-rays. Data timeliness was obtained for regulation-based minimum detectable activity targets within 300 s. For a source-detector distance of up to 300 mm in water, the gross counts demonstrate the existence of radionuclides (Cs-137 and Co-60), owing to their higher efficiency (max. ~15 times) than those of the photopeak counts. Such differences between efficiency values are more likely in water than in air because of the high density of water, resulting in an increased build-up of scattered photons. The proposed sensor is suitable for autonomous underwater systems.
Design and Implementation of a Novel Interferometric Microwave Radiometer for Human Body Temperature Measurement
Authors: Sun Guangmin , Ma Pan , Liu Jie , Shi Chong , Ma Jingyan , Peng Li
Published: February 2021

In this paper, the key technology of interferometric microwave thermometer is studied, the research can be applied to the temperature measurement of human body and subcutaneous tissue. This paper proposes a hardware architecture of interferometric microwave thermometer with 2 GHz bandwidth, in which the phase shifter is used to correct phase error and the quadrature demodulator is used to realize autocorrelation detection function. The results show that when input power is 7 dBm, the detection sensitivity can reach 176.54 mV/dBm and the temperature resolution of the microwave radiometer can reach 0.4 K. Correction algorithm is designed to improve the accuracy of temperature measurement. After correction, the phase error is reduced from 40° to 1.4° and when temperature changes 0.1 °C, the voltage value changes obviously. Step-by-step calibration and overall calibration are used to calibrate the device. Inversion algorithm can determine the relationship between physical temperature and output voltage. The mean square error of water temperature inversion by multiple linear regression algorithm is 0.607 and that of BP neural network algorithm is 0.334. The inversion accuracy can be improved by reducing the temperature range. Our work provides a promising realization of accurate, rapid and non-contact detection device of human body temperature.
Multi-Output Monitoring of High-Speed Laser Welding State Based on Deep Learning
Authors: Xue Boce , Chang Baohua , Du Dong
Published: February 2021

In order to ensure the production quality of high-speed laser welding, it is necessary to simultaneously monitor multiple state properties. Monitoring methods combining vision sensing and deep learning models are popular but most models used can only make predictions on single welding state property. In this contribution, we propose a multi-output model based on a lightweight convolutional neural network (CNN) architecture and introduce the particle swarm optimization (PSO) technique to optimize the loss function of the model, to simultaneously monitor multiple state properties of high-speed laser welding of AISI 304 austenitic stainless steel. High-speed imaging is performed to capture images of the melt pool and the dataset is built. Test results of different models show that the proposed model can achieve monitoring of multiple welding state properties accurately and efficiently. In addition, we make an interpretation and discussion on the prediction of the model through a visualization method, which can help to deepen our understanding of the relationship between the melt pool appearance and welding state. The proposed method can not only be applied to the monitoring of high-speed laser welding but also has the potential to be used in other procedures of welding state monitoring.
A Possible Smart Metering System Evolution for Rural and Remote Areas Employing Unmanned Aerial Vehicles and Internet of Things in Smart Grids
Authors: Gaggero Giovanni Battista , Marchese Mario , Moheddine Aya , Patrone Fabio
Published: February 2021

The way of generating and distributing energy throughout the electrical grid to all users is evolving. The concept of Smart Grid (SG) took place to enhance the management of the electrical grid infrastructure and its functionalities from the traditional system to an improved one. To measure the energy consumption of the users is one of these functionalities that, in some countries, has already evolved from a periodical manual consumption reading to a more frequent and automatic one, leading to the concept of Smart Metering (SM). Technology improvement could be applied to the SM systems to allow, on one hand, a more efficient way to collect the energy consumption data of each user, and, on the other hand, a better distribution of the available energy through the infrastructure. Widespread communication solutions based on existing telecommunication infrastructures instead of using ad-hoc ones can be exploited for this purpose. In this paper, we recall the basic elements and the evolution of the SM network architecture focusing on how it could further improve in the near future. We report the main technologies and protocols which can be exploited for the data exchange throughout the infrastructure and the pros and cons of each solution. Finally, we propose an innovative solution as a possible evolution of the SM system. This solution is based on a set of Internet of Things (IoT) communication technologies called Low Power Wide Area Network (LPWAN) which could be employed to improve the performance of the currently used technologies and provide additional functionalities. We also propose the employment of Unmanned Aerial Vehicles (UAVs) to periodically collect energy consumption data, with evident advantages especially if employed in rural and remote areas. We show some preliminary performance results which allow assessing the feasibility of the proposed approach.
Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites
Authors: Kiam Jane Jean , Besada-Portas Eva , Schulte Axel
Published: February 2021

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.
A Novel Pix2Pix Enabled Traveling Wave-Based Fault Location Method
Authors: Zhang Jinxian , Gong Qingwu , Zhang Haojie , Wang Yubo , Wang Yilin
Published: February 2021

This paper proposes a new Image-to-Image Translation (Pix2Pix) enabled deep learning method for traveling wave-based fault location. Unlike the previous methods that require a high sampling frequency of the PMU, the proposed method can translate the scale 1 detail component image provided by the low frequency PMU data to higher frequency ones via the Pix2Pix. This allows us to significantly improve the fault location accuracy. Test results via the YOLO v3 object recognition algorithm show that the images generated by pix2pix can be accurately identified. This enables to improve the estimation accuracy of the arrival time of the traveling wave head, leading to better fault location outcomes.
In Vitro Comparison of the Anti-Proliferative Effects of Galenia africana on Human Skin Cell Lines
Authors: Ndlovu Banele , De Kock Maryna , Klaasen Jeremy , Rahiman Farzana
Published: February 2021

Malignant melanoma is the major cause of skin cancer-related deaths. Surgery in combination with radiotherapy, immunotherapy or chemotherapy is used to eradicate cancer cells, however, this treatment option is limited by the tolerance of the surrounding healthy tissue. The extracts from Galenia africana have been shown to possess anti-cancer flavonoid compounds and can be a safer and cost-effective alternative treatment. The study aimed to compare the anti-proliferative effects of G. africana on human skin cells (HaCaT) and human malignant melanoma cells (A375). The cells were exposed to various concentrations of the G. africana extract at different times. In vitro assays were employed to determine cell viability and cytotoxicity. Hoechst 33342 staining was performed to observe the nuclear changes, including apoptosis. G. africana significantly reduced the cell viability of the A375 cells in a dose and time-dependent manner, while having no effect on the HaCaT cells. The A375 cells displayed nuclear condensation, brightly stained nuclei and nuclear fragmentation indicative of apoptosis. This suggests a clinical rationale for the use of G. africana as a potential anti-melanoma agent offering efficacy and low toxicity. This study provides new insights for future work on investigating the utilization of G. africana in malignant melanoma treatment.
Towards Territorially Embedded, Equitable and Resilient Food Systems? Insights from Grassroots Responses to COVID-19 in Italy and the City Region of Rome
Authors: Zollet Simona , Colombo Luca , De Meo Paola , Marino Davide , McGreevy Steven R. , McKeon Nora , Tarra Simona
Published: February 2021

The negative impacts of the COVID-19 pandemic have further exposed and exacerbated the structural weaknesses and inequalities embedded in the global industrial agri-food system. While the mainstream narrative continues to emphasise the importance of ensuring the uninterrupted functioning of global supply chains to counter COVID-related disruptions, the pandemic has also highlighted the resilience of small-scale, sustainable family farming and of spatially and socially embedded food systems. Based on a quantitative and qualitative analysis of three surveys, this study examines organic and agroecological farmers’ responses to the first COVID-related lockdown (March–May 2020) in Italy, as well as the responses of grassroots alternative food networks (AFN) in the city region of Rome. The results show how local grassroots action played a significant role in ensuring food access, provisioning, and distribution, often in the face of delayed or insufficient action of mainstream food system actors and institutions. These grassroots responses identify opportunities and barriers for agri-food system transformation away from neoliberal, market-based interventions and towards policies that support food sovereignty and democracy in the context of localised, agroecology-based and more resilient agri-food systems.
Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings
Authors: Bienvenido-Huertas David , Pulido-Arcas Jesús A. , Rubio-Bellido Carlos , Pérez-Fargallo Alexis
Published: February 2021

In recent times, studies about the accuracy of algorithms to predict different aspects of energy use in the building sector have flourished, being energy poverty one of the issues that has received considerable critical attention. Previous studies in this field have characterized it using different indicators, but they have failed to develop instruments to predict the risk of low-income households falling into energy poverty. This research explores the way in which six regression algorithms can accurately forecast the risk of energy poverty by means of the fuel poverty potential risk index. Using data from the national survey of socioeconomic conditions of Chilean households and generating data for different typologies of social dwellings (e.g., form ratio or roof surface area), this study simulated 38,880 cases and compared the accuracy of six algorithms. Multilayer perceptron, M5P and support vector regression delivered the best accuracy, with correlation coefficients over 99.5%. In terms of computing time, M5P outperforms the rest. Although these results suggest that energy poverty can be accurately predicted using simulated data, it remains necessary to test the algorithms against real data. These results can be useful in devising policies to tackle energy poverty in advance.
Towards the Anchovy Biorefinery: Biogas Production from Anchovy Processing Waste after Fish Oil Extraction with Biobased Limonene
Authors: Paone Emilia , Fazzino Filippo , Pizzone Daniela Maria , Scurria Antonino , Pagliaro Mario , Ciriminna Rosaria , Calabrò Paolo Salvatore
Published: February 2021

Anchovies are among the largest fish catch worldwide. The anchovy fillet industry generates a huge amount of biowaste (e.g., fish heads, bones, tails) that can be used for the extraction of several potentially valuable bioproducts including omega-3 lipids. Following the extraction of valued fish oil rich in omega-3, vitamin D3 and zeaxanthin from anchovy fillet leftovers using biobased limonene in a fully circular process, the solid residue (anchovy sludge) was used as starting substrate for the production of biogas by anaerobic digestion. In spite of the unbalanced carbon to nitrogen (C/N) ratio, typical of marine biowaste, the anchovy sludge showed a good methane yield (about 280 mLCH4·gVS−1), proving to be an ideal substrate for co-digestion along with other carbon rich wastes and residues. Furthermore, the presence of residual limonene, used as a renewable, not-toxic and edible extraction solvent, does not affect the microbial methanogenesis. The results reported in this study demonstrate that anchovy leftovers after the fish oil extraction process can be efficiently used as a starting co-substrate for the production of biogas in a modern biorefinery.
Forecasting Agroforestry Ecosystem Services Provision in Urban Regeneration Projects: Experiences and Perspectives from Milan
Authors: Zanzi Ambrogio , Andreotti Federico , Vaglia Valentina , Alali Sumer , Orlando Francesca , Bocchi Stefano
Published: February 2021

The expansion of urban agglomerates is causing significant environmental changes, while the demand and need for sustainability keep on growing. In this context, urban and peri-urban agriculture can play a crucial role, mainly if associated with an agroecological approach. Indeed, the extensive use of living fences and tree rows can improve the environmental quality, assuring ecosystem services (ES), developing a sustainable urban food system and increasing local productions and the related socio-economic improvements. This study aims to assess the benefits of an agroecological requalification of a dismissed peri-urban area in the South Milan Agricultural Regional Park (Italy), by evaluating two possible scenarios, both involving planting trees and shrubs in that area. The software I-Tree Eco simulates the ecosystem services provision of planting new hedgerows, evaluating the benefits over 30 years. The study underlines the difference between the two scenarios and how the planted area becomes an essential supplier of regulating ecosystem services for the neighbourhoods, increasing carbon storage and air pollution removal. Results were then analysed with a treemap, to better investigate and understand the relationship between the different ecosystem services, showing a notable increase in carbon sequestration at the end of the simulation (at year 30). The study shows a replicable example of a methodology and techniques that can be used to assess the ES in urban and peri-urban environments.
Indigenous Resilience to Disasters in Taiwan and Beyond
Authors: Berkes Fikret , Tsai Huei-Min , Bayrak Mucahid Mustafa , Lin Yih-Ren
Published: February 2021

The world is undergoing rapid environmental change [...]
Who Wants Chicken? Uncovering Consumer Preferences for Produce of Alternative Chicken Product Methods
Authors: Escobedo del Bosque Cynthia I. , Spiller Achim , Risius Antje
Published: February 2021

As ethical and environmental concerns regarding current poultry production systems arise, consumers look for alternatives. This study assesses consumers’ preferences for chicken meat of dual-purpose breeds (DPBs), regionally produced feedstuff, and specific breeds, along with attitudes and social norms that explain these preferences. We conducted an online survey (n = 934) including a discrete choice experiment and elements of the theory of planned behavior. Results show that after price, product and feedstuff origin are preferred by consumers, followed by breeding form and specific breed. Utilities for each attribute and level were calculated and consumer segments were created using latent class analysis. Three different consumer groups were identified: (1) price-sensitive consumers, (2) price-sensitive and origin-oriented consumers, and (3) origin-oriented consumers. We conclude that although consumers are interested in meat from DPBs, this attribute alone is not enough to influence the purchase decision, and geographical origin seems to be of crucial importance. However, by highlighting important attributes (i.e., animal welfare, regional/local production), DPB products could be introduced to the market. The consumption of these alternative products has economic implications, such as not relying on imports and promoting local production/consumption, along with social implications as refraining from killing day-old chicks.
Effects of Biostimulants on Primary and Secondary Substance Contents in Lettuce Plants
Authors: Jang Se Ji , Kuk Yong In
Published: February 2021

Lettuce (Lactuca sativa L.) is not only easy to grow, but it is also common in diets around the world. This study was conducted to determine the impact of Chinese chive (Allium tuberosum Rottler), soybean (Glycine max L.) leaf, and soybean stem extracts on lettuce plants’ secondary substances, minerals, amino acids, and free sugars. This study was carried out in order to better understand which types of plant extract applications are most effective in increasing lettuce plants’ nutritional value. While not all applications produced an increased nutritional value in the lettuce plants, we found that soybean leaf extract treatments increased the Mg and Ca contents and Chinese chive treatments increased the Fe contents in the lettuce plants. Additionally, glucose and maltose contents in lettuce plants were higher after treatments of Chinese chive and soybean leaf extracts. Finally, all selected extracts increased the total and free amino acid levels in our test plants.
Vulnerability Comparison between Karst and Non-Karst Nature Reserves—With a Special Reference to Guizhou Province, China
Authors: Chen Yue , Xiong Kangning , Ren Xiaodong , Cheng Cai
Published: February 2021

Studying vulnerability has become one of the hot issues of ecological research under global change and sustainable development scenarios. However, there are few studies focusing on the vulnerability of karst areas, especially on the comparison between karst and non-karst areas. This study integrated climate factors, soil and vegetation factors, and social factors within a vulnerability evaluation framework that combined the analytical hierarchy process (AHP) technique to evaluate and compare the ecological environments of 10 karst nature reserves and five non-karst nature reserves in Guizhou Province, China. The evaluation results were divided into five categories: potential, light, moderate, severe, and extremely severe vulnerability. The results indicated that the general vulnerability of national nature reserves in Guizhou Province showed moderate vulnerability. The vulnerability index of karst nature reserves was significantly higher than that of non-karst nature reserves. The general vulnerability of karst nature reserves showed moderate vulnerability, while that of non-karst nature reserves showed light vulnerability. Average soil thickness, arable area, and the length of roads may be the factors contributing to the significant difference in the vulnerability between karst and non-karst nature reserves. Overall, this study is conducive to a comprehensive understanding of the ecological environment status of national nature reserves in Guizhou Province, and is of great significance to maintaining ecological security and sustainable development in karst areas.