bluuebunny/crossref_metadata_2025 huggingface dataset:
Catchment Soil Properties Affect Metal(loid) Enrichment in Reservoir Sediments of German Low Mountain Regions
Authors: Hahn Jens , Bui Thanh , Kessler Mathias , Weber Collin J. , Beier Thomas , Mildenberger Antje , Traub Martina , Opp Christian
Published: February 2022

Sediment management is a fundamental part of reservoir operation, but it is often complicated by metal(loid) enrichment in sediments. Knowledge concerning the sources of potential contaminants is therefore of important significance. To address this issue, the concentrations and the mobile fractions of metal(loid)s were determined in the sediments and the respective catchment areas of six reservoirs. The results indicate that reservoirs generally have a high potential for contaminated sediment accumulation due to preferential deposition of fine particles. The median values of the element-specific enrichment factor (EF) demonstrates slight enrichments of arsenic (EF: 3.4), chromium (EF: 2.8), and vanadium (EF: 2.9) for reservoir sediments. The enrichments of cadmium (EF: 8.2), manganese (EF: 3.9), nickel (EF: 4.8), and zinc (EF: 5.0) are significantly higher. This is enabled by a diffuse element release from the soils into the impounded streams, which is particularly favored by soil acidity. Leaching from the catchment soils partially enriches elements in stream sediments before their fine-grained portions in particular are deposited as reservoir sediment. We assume that this effect is of high relevance especially for reservoirs impounding small streams with forested catchments and weakly acid buffering parent material of soil formation.
The Effect of Dynamic Lighting for Working Shift People on Clinical Heart Rate Variability and Human Slow Wave Sleep
Authors: Chen Chien-Yu , Chen Hung-Wei
Published: February 2022

The quality of sleep during lunch breaks will affect the work efficiency, concentration, and mood of workers, and then increase the performance of workers. In this study, Dynamic CCT lighting is proposed as a method to control sleep quality, and a novel hypnotic lighting system is developed according to the experimental design. Pulse width modulation(PWM) is used for controlling sleep lights and adjusting and controlling the spectrum intensity of polychromatic LED to realize the color mixing, which conforms to CCT, illumination, uniformity, and other parameters in the experimental design. The control group of this study is in a dark room, and the experimental group is given dim light and Dynamic CCT lighting. Through volunteer psychological questionnaire evaluation, objective Sleep Wrist Actigraphy and Heart Rate Variability (HRV) are used analyzing sleep quality and Autonomic Nervous System (ANS). The result is found that the sleep environment with Dynamic CCT lighting is better than that with Dim light in three kinds of sleep lighting environments, while the sleep environment of Dynamic CCT lighting is very similar to that of a dark room. In terms of work efficiency after sleep, Dynamic CCT used in the sleep environment of lighting and Dim light is significantly better than that in a dark room.
Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator
Authors: Rosso Marco Martino , Cucuzza Raffaele , Aloisio Angelo , Marano Giuseppe Carlo
Published: February 2022

Nowadays, optimization problems are solved through meta-heuristic algorithms based on stochastic search approaches borrowed from mimicking natural phenomena. Notwithstanding their successful capability to handle complex problems, the No-Free Lunch Theorem by Wolpert and Macready (1997) states that there is no ideal algorithm to deal with any kind of problem. This issue arises because of the nature of these algorithms that are not properly mathematics-based, and the convergence is not ensured. In the present study, a variant of the well-known swarm-based algorithm, the Particle Swarm Optimization (PSO), is developed to solve constrained problems with a different approach to the classical penalty function technique. State-of-art improvements and suggestions are also adopted in the current implementation (inertia weight, neighbourhood). Furthermore, a new local search operator has been implemented to help localize the feasible region in challenging optimization problems. This operator is based on hybridization with another milestone meta-heuristic algorithm, the Evolutionary Strategy (ES). The self-adaptive variant has been adopted because of its advantage of not requiring any other arbitrary parameter to be tuned. This approach automatically determines the parameters’ values that govern the Evolutionary Strategy simultaneously during the optimization process. This enhanced multi-strategy PSO is eventually tested on some benchmark constrained numerical problems from the literature. The obtained results are compared in terms of the optimal solutions with two other PSO implementations, which rely on a classic penalty function approach as a constraint-handling method.
Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System
Authors: Ma Yayu , Mao Zhaoyong , Zhang Kehan
Published: February 2022

Power Transfer Efficiency (PTE) and Transferred Power (TP) are two crucial indicators of the wireless power transfer (WPT) system. However, in most compensatory topologies, the ideal coils design parameters of PTE and TP are inconsistent, implying that optimizing for one indicator would make another indicator less effective. As a result, in this article, the Thompson sampling efficient multi-objective optimization (TSEMO) algorithm is used to simultaneously optimize PTE and TP in the S-S compensation topology. Through the co-calculation of MATLAB and COMSOL, the coils can be optimized in the environment of magnetic material (ferrite) and conductive material (aluminum). Coils larger than the underwater vehicle’s size are deleted during the data interaction between MATLAB and COMSOL to guarantee that the optimal PTE and TP can be attained within the constrained area. Due to the open standard external software packages between MATLAB and COMSOL, the entire optimization process may be automated. The results show that for WPT systems with restricted input voltage, such as the battery, this design strategy can achieve better TP at the expense of lower PTE, which is highly beneficial in improving the system’s overall performance.
Manipulating Stress Responses during Spaceflight Training with Virtual Stressors
Authors: Finseth Tor , Dorneich Michael C. , Keren Nir , Franke Warren D. , Vardeman Stephen B.
Published: February 2022

Virtual reality (VR) provides the ability to simulate stressors to replicated real-world situations. It allows for the creation and validation of training, therapy, and stress countermeasures in a safe and controlled setting. However, there is still much unknown about the cognitive appraisal of stressors and underlying elements. More research is needed on the creation of stressors and to verify that stress levels can be effectively manipulated by the virtual environment. The objective of this study was to investigate and validate different VR stressor levels from existing emergency spaceflight procedures. Experts in spaceflight procedures and the human stress response helped design a VR spaceflight environment and emergency fire task procedure. A within-subject experiment evaluated three stressor levels. Forty healthy participants each completed three trials (low, medium, high stressor levels) in VR to locate and extinguish a fire on the International Space Station (VR-ISS). Since stress is a complex construct, physiological data (heart rate, heart rate variability, blood pressure, electrodermal activity) and self-assessment (workload, stress, anxiety) were collected for each stressor level. The results suggest that the environmental-based stressors can induce significantly different, distinguishable levels of stress in individuals.
Improving Mudstone Materials in Badland in Southwestern Taiwan by Increasing Density and Low-Cement Amount
Authors: Hsiao Darnhorng , Hsieh Chiasheng
Published: February 2022

The southwestern foothills in Taiwan contain abundant mudstone, which softens or slakes easily when contacting water. Thereafter, this condition causes high rates of erosion during heavy rain, eventually leading to the formation of badlands. To increase land use value in Taiwan, this study aimed to offer a new solution with the use of compaction techniques and also adding a small amount of cement as soil amendment. We examined the feasibility of this concept by performing a series of tests, including the basic physical property test, compaction test, unconfined compression test, static triaxial CU and UU tests, consolidation test, California bearing ratio (CBR) test, and triaxial permeability test. The specimens were created based on the controlled moisture at the wet side along the compaction curve. Three clods, three preparation methods, and two types of cement content were used for specimen preparation. The test results revealed that clod will affect the results of unconfined compression strength and soil permeability. For mudstone with added cement, its unconfined compression strength increased 7- to 10-fold and its shear strength increased 2- to 3-fold. The results of consolidation and the CBR test showed that improvement is possible by using low amounts of cement. Overall, the present method not only adheres to low-carbon and environmental protection requirements, but also verifies the feasibility of using compacted mudstone as an engineering material.
Uncovering Vegetation Changes in the Urban–Rural Interface through Semi-Automatic Methods
Authors: Barbosa Bruno , Rocha Jorge , Costa Hugo , Caetano Mário
Published: February 2022

Forest fires are considered by Portuguese civil protection as one of the most serious natural disasters due to their frequency and extent. To address the problem, the Fire Forest Defense System establishes the implementation of fuel management bands to aid firefighting. The aim of this study was to develop a model capable of identifying vegetation removal in the urban–rural interface defined by law for fuel management actions. The model uses normalised difference vegetation index (NDVI) of Sentinel-2 images time series and is based on the Welch t-test to find statistically significant differences between (i) the value of the NDVI in the pixel; (ii) the mean of the NDVI in the pixels of the same land cover type in a radius of 500 m; and (iii) their difference. The model identifies a change when the t-test points for a significant difference of the NDVI value in the ‘pixel’ as comparted to the ‘difference’ but not the ‘mean’. We use a moving window limited to 60 days before and after the analysed date to reduce the phenological variations of vegetation. The model was applied in five municipalities of Portugal and the results are promising to identify the places where the management of fuel bands was not carried out. This indicates which model could be used to assist in the verification of the annual management of the fuel bands defined in the law.
Continuous Co-Digestion of Agro-Industrial Mixtures in Laboratory Scale Expanded Granular Sludge Bed Reactors
Authors: Hernández Regalado Roberto Eloy , Häner Jurek , Baumkötter Daniel , Wettwer Lukas , Brügging Elmar , Tränckner Jens
Published: February 2022

Anaerobic co-digestion often improves the yields and stability of single anaerobic digestion. However, finding the right substrate proportions within mixtures and corresponding optimal operating conditions using a particular reactor technology often presents a challenge. This research investigated the anaerobic digestion of three mixtures from the liquid fractions of piglet manure (PM), cow manure (CWM), starch wastewater (SWW), and sugar beet (SBT) using three 30 L expanded granular sludge bed (EGSB) reactors. The synergistic effects of two three-substrate mixtures (i.e., PM+CWM+SWW and PM+CWM+SBT) were studied using the PM+CWM mixture as a benchmark. These were used to detect the predicted synergistic interactions found in previous batch tests. The methane productivity of both three-substrate mixtures (~1.20 LCH4/Lreact/d) was 2× the productivity of the benchmark mixture (0.64 LCH4/Lreact/d). Furthermore, strong indications of the predicted synergistic effects were found in the three-substrate mixtures, which were also stable due to their appropriate carbon-to-nitrogen ratio values. Moreover, the lowest averaged solid to hydraulic retention times ratio calculated for samples obtained from the top of the reactors was > 1. This confirmed the superior biomass retention capacity of the studied EGSB reactors over typical reactors that have been used in agricultural biogas plants with a continuous stirred tank reactor.
Local Temperature Development in the Fracture Zone during Uniaxial Tensile Testing at High Strain Rate: Experimental and Numerical Investigations
Authors: Galiev Elmar , Winter Sven , Reuther Franz , Psyk Verena , Tulke Marc , Brosius Alexander , Kräusel Verena
Published: February 2022

The quality of simulation results significantly depends on the accuracy of the material model and parameters. In high strain rate forming processes such as, e.g., electromagnetic forming or adiabatic blanking, two superposing and opposing effects influence the flow stress of the material: strain rate hardening and thermal softening due to adiabatic heating. The presented work contributes to understanding these influences better by quantifying the adiabatic heating of the workpiece during deformation and failure under high-speed loading. For this purpose, uniaxial tensile tests at different high strain rates are analyzed experimentally and numerically. A special focus of the analysis of the tensile test was put on identifying a characteristic time- and position-dependent strain rate. In the experiments, in addition to the measurement of the force and elongation, the temperature in the fracture region is recorded using a thermal camera and a pyrometer for higher strain rates. Simulations are carried out in LS-Dyna using the GISSMO model as a damage and failure model. Both experimental and simulated results showed good agreement regarding the time-dependent force-displacement curve and the maximum occurring temperature.
Multi-Factor Authentication with Randomly Selected Authentication Methods with DID on a Random Terminal
Authors: Kim Semin , Mun Hyung-Jin , Hong Sunghyuck
Published: February 2022

Background: Information and communication technology development provides services to various fields. In particular, the development of mobile technology has made ubiquitous services possible. However, as technology advances, security is being emphasized more and more. The existing method of user authentication by entering an ID and password is likely to leak sensitive information if the server is attacked or keylogged. Therefore, multi-level authentication is needed to prevent server attacks or keylogging. Methods: Biometric authentication technology has been utilized by smartphones, but it is challenging to apply in the case of a lost device because the central server does not manage biometric data. Encrypting transactions through blockchain technology makes data management more secure because blockchain technology is distributed, and there is no primary target for hackers. Results: The method proposed in this study can become a basis for an authentication method that guarantees both security and integrity due to the synergetic use of biometric and blockchain technologies. Conclusions: The information stored in the service provider does not include sensitive information such as the user’s biometric data but is only a means of verifying the user’s information in the blockchain transaction with guaranteed anonymity. Therefore, users can receive services with confidence and safety.
An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery
Authors: Chen Jianlong , Lu Chenlei , Chen Cong , Cheng Hangyu , Xuan Dongji
Published: February 2022

State-of-charge (SOC) estimation of lithium-ion battery is a key parameter of the battery management system (BMS). However, SOC cannot be obtained directly. In order to predict SOC accurately, we proposed a recurrent neural network called gated recurrent unit network that is based on genetic algorithm (GA-GRU) in this paper. GA was introduced to optimize the key parameters of the model, which can improve the performance of the proposed network. Furthermore, batteries were tested under four dynamic driving conditions at five temperatures to establish training and testing datasets. Finally, the proposed method was validated on dynamic driving conditions and compared with other deep learning methods. The results show that the proposed method can achieve high accuracy and robustness.
A Survey on Recent Advances in AI and Vision-Based Methods for Helping and Guiding Visually Impaired People
Authors: Walle Hélène , De Runz Cyril , Serres Barthélemy , Venturini Gilles
Published: February 2022

We present in this paper the state of the art and an analysis of recent research work and achievements performed in the domain of AI-based and vision-based systems for helping blind and visually impaired people (BVIP). We start by highlighting the recent and tremendous importance that AI has acquired following the use of convolutional neural networks (CNN) and their ability to solve image classification tasks efficiently. After that, we also note that VIP have high expectations about AI-based systems as a possible way to ease the perception of their environment and to improve their everyday life. Then, we set the scope of our survey: we concentrate our investigations on the use of CNN or related methods in a vision-based system for helping BVIP. We analyze the existing surveys, and we study the current work (a selection of 30 case studies) using several dimensions such as acquired data, learned models, and human–computer interfaces. We compare the different approaches, and conclude by analyzing future trends in this domain.
Phosphate Adsorption onto an Al-Ti Bimetal Oxide Composite in Neutral Aqueous Solution: Performance and Thermodynamics
Authors: Wei Xuefeng , Miao Juan , Lv Zhen , Wan Xiaoyang , Zhang Ning , Zhang Ruichang , Peng Shuge
Published: February 2022

Phosphorus (P) pollution and phosphorus recovery are important issues in the field of environmental science. In this work, a novel Al-Ti bimetal composite sorbent was developed via a cost-effective co-precipitation approach for P removal from water. The adsorptive performance and characteristics of P onto Al-Ti sorbent were evaluated by batch adsorption experiments. The effects of Al:Ti molar ratio, initial P concentration and reaction temperature were investigated. The microstructural characteristics of the Al-Ti sorbent were confirmed by scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, Fourier transform infrared (FTIR) spectroscopy, and nitrogen adsorption-desorption measurements. Kinetic studies showed that the adsorption of P on Al-Ti oxide proceeds according to pseudo-second-order kinetics. The maximum adsorption capacity of phosphate on the Al-Ti oxide calculated from linear Langmuir models was 68.2 mg-P/g at pH 6.8. The Al-Ti oxide composite sorbent showed good potential for P recovery, owing to its large adsorption capacity and ease of regeneration.
Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagasse Ash: A Mini-Review
Authors: Seroka Ntalane Sello , Taziwa Raymond T. , Khotseng Lindiwe
Published: February 2022

This current study reviews the utilization of the traditional extraction methods and latest findings in extraction of silica from agricultural wastes, in particular, sugarcane bagasse, using inorganic acids to produce nano-silicon. The three key processes discussed in detail include electrochemical, ball milling, and sol–gel processes. The sugarcane bagasse has been identified as the cheapest source of producing silica from the potential raw material for the preparation of nano-silicon. The acid-base extraction and precipitation methodology involves the use of bases like sodium hydroxide (NaOH) and potassium hydroxide (KOH), and acids such as hydrofluoric acid (HF), sulphuric acid (H2SO4), nitric acid (HNO3), and hydrochloric acid (HCl) for the treatment of the ash. Sugarcane bagasse has notably emerged as an excellent and sustainable source of both tailored silica particles and bioenergy production. The ability to manipulate the engineered silica particles at the nano-level from sugarcane bagasse-based silica is explained in detail. Silica is a significant raw material with various industrial applications, with much research underway to extract it efficiently from industrial agro-waste, such as sugarcane bagasse. The production of highly pure silicon nanoparticles from sugarcane bagasse ash will serve as an important synthetic route in lowering the manufacturing costs and providing a low-cost polycrystalline silicon semiconductor for niche application in thin film solar technology.
Artificial Intelligence-Based Methods for Business Processes: A Systematic Literature Review
Authors: Gomes Poliana , Verçosa Luiz , Melo Fagner , Silva Vinícius , Filho Carmelo Bastos , Bezerra Byron
Published: February 2022

Companies are usually overloaded with data that they may not know how to take advantage of. On the other hand, artificial intelligence (AI) techniques are known to “keep learning” as the data increase. In this context, our research question emerges: what AI-based methods, in the literature, could be used to automatize business processes and support the decision-making processes of companies? To fill this gap, in this paper, we performed a review of the literature to identify these techniques. We ensured the usage of methods since they allowed reproducibility and extensions. We applied our search string in the Scopus and Web of Science databases and discovered 21 relevant papers pertaining to our question. In these papers, we identified methods that automated tasks and helped analysts make assertive decisions when designing, extending, or reengineering business processes. The authors applied diverse AI techniques, such as K-means, Bayesian networks, and swarm intelligence. Our analysis provides statistics about the techniques and problems being tackled and point to possible future directions.
Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
Authors: Mitro Nikos , Krommyda Maria , Amditis Angelos
Published: February 2022

The building materials of Cultural Heritage monuments are subjected to continuous degradation throughout the years, mainly due to their exposure to harsh and unexpected weather phenomena related to Climate Change. The specific climatic conditions at their vicinity, especially when there are local peculiarities such as onshore breeze, are of crucial importance for studying the deterioration rate and the identification of proper mitigation actions. Generalized models that are based on climate data can provide an insight on the deterioration but fail to offer a deeper understanding of this phenomenon. To this end, in the context of the EU-funded HYPERION project a distributed smart sensor network will be deployed at the Cultural Heritage monuments in four study areas as the solution to this problem. The developed system, which is demonstrated in this paper, includes smart IoT devices, called Smart Tags, designed to provide environmental measurements close to monuments, a middle-ware to facilitate the communication and a visualization platform where the collected information is presented. Last but not least, special focus is given to the device’s NB-IoT connectivity and its power efficiency by conducting various tests and extract useful conclusions.
Secondary Metabolites and Their Applications
Authors: Gallardo Eugenia , Seca Ana M. L.
Published: February 2022

The identification of secondary metabolites present in both terrestrial and marine species continues to be a fundamental and privileged path for the emergence of new and fundamental natural products available on the market with very different applications [...]
Beneficial Effects of Caffeic Acid Phenethyl Ester on Wound Healing in a Diabetic Mouse: Role of VEGF and NO
Authors: Park Sin-Hee , Song Soo-Young , Park Eun-Hye , Kim Eunmin , Oh Gyu Chul , Choo Eun Ho , Hwang Byung-Hee , Chang Kiyuk , Oak Min-Ho
Published: February 2022

Cutaneous wound healing is delayed in patients with diabetes. Caffeic acid phenethyl ester (CAPE) has been identified as an effective constituent of propolis with improved wound healing abilities via an oxidative stress decrease. However, its impact on wound healing in diabetic models and its underlying mechanisms remain unclear. Determining the vascular endothelial growth factor (VEGF) contents in a human vascular smooth muscle cell (VSMC)-conditioned medium was assessed using human VEGF immunoassay and vascular reactivity using porcine coronary artery rings. Later, C57BL/6 or db/db mice were anesthetized, after which a 6-mm biopsy punch was manipulated for perforation via the back skin. Subsequently, CAPE was applied to the wound and changed daily. Furthermore, the injury in each mouse was digitally photographed, and the wound area was quantified. We observed that CAPE increased VEGF levels in human VSMC-conditioned medium, improved endothelium-dependent nitric oxide (NO)-mediated vasorelaxation, inhibited U46619-induced vasoconstriction porcine coronary artery, and enhanced cutaneous wound healing in the diabetic mouse model. Hence, we propose that CAPE improves wound healing in diabetic mice, which is aided by increased VEGF and NO expression.
Application of the MiL and HiL Simulation Techniques in Stewart Platform Control Development
Authors: Walica Dominik , Noskievič Petr
Published: February 2022

During the integration phase of a system development, we are often concerned as to whether the designed control algorithm could be performed on the selected controller in real-time. One of the tools to test and validate the control scheme is the Hardware-in-the-Loop (HiL) simulation technique, which is a part of a model-based design methodology. This approach requires a simulation model of a controlled system running in a real-time loop with an intended controller and a control algorithm, which are objects of interest in this method. To perform the test, the control algorithm must be deployed to the controller such as a PLC. This paper presents a use case of the HiL technique in the design of the Stewart platform control, where the controller is PLCnext from Phoenix Contact. The control algorithm was first verified in the Model-in-the-Loop simulation (MiL) and then generated as a code from the Matlab/Simulink environment and deployed to the PLCnext, which resulted in a smoother transition from the design to the integration and testing phase. The presented method is also applicable to other controllers that support code generation.
A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process
Authors: Chaudhry Shubham , Soulaïmani Azzeddine
Published: February 2022

Selective laser melting (SLM) is a metal-based additive manufacturing (AM) technique. Many factors contribute to the output quality of SLM, particularly the machine and material parameters. Analysis of the parameters’ effects is critical, but using traditional experimental and numerical simulation can be expensive and time-consuming. This paper provides a framework to analyze the sensitivity and uncertainty in SLM input and output parameters, which can then be used to find the optimum parameters. The proposed data-driven approach combines machine learning algorithms with high-fidelity numerical simulations to study the SLM process more efficiently. We have considered laser speed, hatch spacing, layer thickness, Young modulus, and Poisson ratio as input variables, while the output variables are numerical predicted normal strains in the building part. A surrogate model was constructed with a deep neural network (DNN) or polynomial chaos expansion (PCE) to generate a response surface between the SLM output and the input variables. The surrogate model and the sensitivity analysis found that all five parameters were important in the process. The surrogate model was combined with non-intrusive optimization algorithms such as genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) to perform an inverse analysis and find the optimal parameters for the SLM process. Of the three algorithms, the PSO performed well, and the DNN model was found to be the most efficient surrogate model compared to the PCE.
The Indicator of GDI Engine Operating Mode and Its Influence on Eco-Driving
Authors: Pawlak Grzegorz Marek , Wołczyński Zbigniew
Published: February 2022

Elements of car construction, especially the information available on a dashboard, can stimulate the way of driving. The experiment described in the paper aimed to examine how the information provided by the indicator, which informs about the operational mode of a gasoline direct injection (GDI) engine, can contribute to eco-driving and the possible learning of acceleration pedal operation by a driver. The analysis of the fuel injection process affected by driver behaviour was an essential part of the experiment. The experiment was divided into two parts. The first one (nine tests) consisted of driving without access to the indicator information. In the second part, the information on the mode of the engine run was available for the driver. The results confirmed that the information about the type of fuel mixture used for the supply of the GDI engine facilitates an economical driving style (about 10% fuel savings) and motivates the driver to engage in eco-driving.
Local Path-Planning Simulation and Driving Test of Electric Unmanned Ground Vehicles for Cooperative Mission with Unmanned Aerial Vehicles
Authors: Kim Mingeuk , Yoo Seungjin , Lee Dongwook , Lee Geun-Ho
Published: February 2022

Recently, various studies related to the development of unmanned vehicles have been conducted around the world. In particular, unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been developed and utilized for various purposes. In this study, we developed a method for the path generation of UGVs in a system in which one operator controls many different types of unmanned vehicles. In the driving control system (DCS), it is necessary to process sensor data such as GPS/INS and LiDAR when generating a path by receiving the target waypoint from the ground control station. In addition, the DCS must upload the current location, posture, state, etc., as well as save driving log. Therefore, in order to recognize obstacles in real time and generate a path, a safe path generation algorithm with a short computation time is required. Among the various path generation methods, the potential field algorithm was selected, and the algorithm was modified to reduce the computation time. The computation time before and after modification of the algorithm was obtained and compared through simulation, and the algorithm was verified through application to an actual system by performing an obstacle avoidance experiment and a simultaneous control experiment for two UGVs.
Enhancing the Spectroelectrochemical Performance of WO3 Films by Use of Structure-Directing Agents during Film Growth
Authors: Nguyen Thi Hai Quyen , Eberheim Florian , Göbel Sophie , Cop Pascal , Eckert Marius , Schneider Tim P. , Gümbel Lukas , Smarsly Bernd M. , Schlettwein Derck
Published: February 2022

Thin, porous films of WO3 were fabricated by solution-based synthesis via spin-coating using polyethylene glycol (PEG), a block copolymer (PIB50-b-PEO45), or a combination of PEG and PIB50-b-PEO45 as structure-directing agents. The influence of the polymers on the composition and porosity of WO3 was investigated by microwave plasma atomic emission spectroscopy, energy-dispersive X-ray spectroscopy, scanning electron microscopy, X-ray diffraction, and gas sorption analysis. The electrochromic performance of the WO3 thin films was characterized with LiClO4 in propylene carbonate as electrolyte. To analyze the intercalation of the Li+ ions, time-of-flight secondary ion mass spectrometry, and X-ray photoelectron spectroscopy were performed on films in a pristine or reduced state. The use of PEG led to networks of micropores allowing fast reversible electrochromic switching with a high modulation of the optical transmittance and a high coloration efficiency. The use of PIB50-b-PEO45 provided isolated spherical mesopores leading to an electrochromic performance similar to compact WO3, only. Optimum characteristics were obtained in films which had been prepared in the presence of both, PEG and PIB50-b-PEO45, since WO3 films with mesopores were obtained that were interconnected by a microporous network and showed a clear progress in electrochromic switching beyond compact or microporous WO3.
Evaluation of Hydrogen Cyanide in the Blood of Fire Victims Based on the Kinetics of the Reaction with Ninhydrin
Authors: Tabian Daniel , Bulgaru Iliescu Diana , Diac Madalina Maria , Badea Mihaela , Toma Sebastian Ionut , Drochioiu Gabi
Published: February 2022

An original kinetic spectrophotometric procedure was developed for the determination of hydrogen cyanide (HCN) in the whole blood of fire victims. Cyanide poisoning by smoke inhalation is common in forensic medicine, but the blood HCN of fire victims has not been studied in detail so far. In this research project, we developed a simple, fast, sensitive, and selective quantification method for both free and metabolized HCN based on the kinetics of cyanide reaction with ninhydrin. The method was linear in range, from 0.26 to 2.6 μg mL−1, with a coefficient of determination of r = 0.994. A high molar absorptivity of 4.95 × 105 L mol−1 cm−1 was calculated under the reaction conditions. The limit of quantification was 0.052 μg mL−1; the detection limit was 0.012 μg mL−1 and the standard error was ±2.7%. This micro method proved to be accurate, sensitive, and selective and has been successfully applied to the analysis of blood samples, allowing rapid monitoring of blood cyanide in several fire victims.
Ultrasound Protects Human Chondrocytes from Biochemical and Ultrastructural Changes Induced by Oxidative Stress
Authors: Ungur Rodica Ana , Muresan Adriana , Olteanu Diana Elena , Florea Adrian , Ciortea Viorela Mihaela , Irsay Laszlo , Borda Ileana Monica , Codea Răzvan Andrei , Ober Ciprian Andrei , Bâlici Ștefana , Căinap Simona , Dronca Eleonora , Martiș (Petruț) Georgiana Smaranda , Onac Ioana Anamaria , Suciu Şoimiţa Mihaela
Published: February 2022

The aim of the study was to assess the effects of therapeutic ultrasound (US) on oxidative stress (OS)-induced changes in cultured human chondrocytes (HCH). For this, monolayer HCH were randomized in three groups: a control group (CG), a group exposed to OS (OS group), and a group exposed to US and OS (US-OS group). US exposure of the chondrocytes was performed prior to OS induction by hydrogen peroxide. Transmission electron microscopy (TEM) was used to assess the chondrocytes ultrastructure. OS and inflammatory markers were recorded. Malondialdehyde (MDA) and tumor necrosis factor (TNF)-α were significantly higher (p < 0.05) in the OS group than in CG. In the US-OS group MDA and TNF-α were significantly lower (p < 0.05) than in the OS group. Finally, in the US-OS group MDA and TNF-α were lower than in CG, but without statistical significance. TEM showed normal chondrocytes in CG. In the OS group TEM showed necrotic chondrocytes and chondrocytes with a high degree of vacuolation and cell organelles damages. In the US-OS group the chondrocytes ultrastructure was well preserved, and autophagosomes were generated. In conclusion, US could protect chondrocytes from biochemical (lipid peroxidation, inflammatory markers synthesis) and ultrastructural changes induced by OS and could stimulate autophagosomes development.
A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification
Authors: Jia Ruofan , Nahli Abdelwahed , Li Dan , Zhang Jianqiu
Published: February 2022

With the recent internet connectivity revolution, and the fast-growing prevalence of camera-enabled devices, images play a vital role in several fields of modern life. Photos, which often have been seen as evidence in courts, are nowadays subject to more sophisticated tricky forgery. To detect the image stitching between originally unassociated people/scenes and other combining forgery, an algorithm used to extract multiple specific image features, such as grayscale, complementary color wavelet (CCW) based chroma, sharpness, and natural scene statistics (NSS), is first presented in this paper. It is illustrated that a random forest model can be trained by these extracted features and then be employed to classify the stitching tampered/untampered images. The experimental results show that the proposed algorithm favorably outperforms the techniques reported in the literature, and achieves a state-of-the-art performance with higher accuracy values of 91%, 95.24%, and 88.02% on the Tampering ImageNet, Columbia, and CASIA ITDE V2.0 datasets, respectively. The precision, recall, and F1-score were also improved to a certain extent.
The 3D Product Model Research Evolution and Future Trends: A Systematic Literature Review
Authors: Kirpes Carl , Hu Guiping , Sly Dave
Published: February 2022

The 3D product model has long been a tool used by engineers to design and plan for the physical creation of a 3D object. The way in which the 3D product model has been applied to production and assembly processes has evolved over time, yet the current body of knowledge does not document that evolution. The purpose of this article is to collect and structure the evolution of 3D product model research, categorizing the ways in which the body of knowledge has evolved over time, while also providing a look into projected applications and research focuses of the 3D product model. The result of this article is the development of sixteen 3D product model research themes and the categorization of the body of knowledge within those themes, establishing a basis for 3D product model research ontology. Then, the paper explores where the evolution of the 3D product model is trending based on discussions with industry experts. The authors aim to provide a foundation for a comprehensive and interdisciplinary discussion amongst academia and industry about the current state and future trends of research on the 3D product model and its application in production and assembly processes.
Ionospheric Variability during the 2020–2021 SSW: COSMIC-2 Observations and WACCM-X Simulations
Authors: Pedatella Nicholas
Published: February 2022

Variability in the ionosphere during the 2020–2021 sudden stratospheric warming (SSW) is investigated using a combination of Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) observations and the Whole Atmosphere Community Climate Model with thermosphere–ionosphere eXtension (WACCM-X) simulations. The unprecedented spatial–temporal sampling of the low latitude ionosphere afforded by COSMIC-2 enables investigating the short-term (<5 days) variability in the ionosphere during the SSW event. The COSMIC-2 observations reveal a reduction in the diurnal and zonal mean ionosphere total electron content (ITEC) and reduced amplitude of the diurnal variation in the ionosphere during the SSW. Enhanced ITEC amplitudes of the semidiurnal solar and lunar migrating tides and the westward propagating semidiurnal tide with zonal wavenumber 3 are also observed. The WACCM-X simulations demonstrate that these variations are driven by variability in the stratosphere–mesosphere during the 2020–2021 SSW event. The results show the impact of the 2020–2021 SSW on the mean state, diurnal, and semidiurnal variations in the ionosphere, as well as the capabilities of the COSMIC-2 mission to observe short-term variability in the ionosphere that is driven by meteorological variability in the lower atmosphere.
Quick Predictions of Onset Times and Rain Amounts from Monsoon Showers over Urban Built Environments
Authors: Gumber Siddharth , Ghosh Satyajit
Published: February 2022

Predicting the onset times of precipitation over densely populated cities for the purposes of timely evacuation is a challenge. This paper explored a flooding event over an urban built environment in a South Asian mega city, Chennai, where extant urban planning models rely on predicted rainwater amounts for early warning and impact assessment studies. However, the time duration of flooding events is related to the nature of the urban sprawl in the built environment. Any evacuation measure is invariably tied down to the time duration over which the precipitation event occurs, and therefore to the expected time of a precipitation event to begin. In this context, a crucial parameter useful to municipal authorities is the onset time of precipitation. This study used optimised analytical formulations to predict this time, and the derived analytical expressions for the case study yielded comparable times estimated from a computer-intensive full-scale large eddy model within an accuracy of 2%. It is suggested that municipal authorities (who are non-experts in fluid mechanics) use this early prediction for the purposes of quick alerts to a congested city’s most vulnerable citizens within urban sprawls. However, for the procedure to work at its best, it involves a two-stage procedure. The first step involves the use of a parcel model to obtain the expected cloud droplet spectral spreads based on the prevailing dynamical characterisations. The second step involves an optimisation procedure involving cloud spectral properties from the first step to quantify both the auto-conversion rates and the threshold. Thereafter, an onset time calculation based on cloud properties is estimated. These new results are cast in closed form for easy incorporation into meteorological applications over a variety of urban scales. Rain mass amounts were also predicted analytically and used to configure Aeronautical Reconnaissance Coverage Geographic Information System (ARCGIS) to compute low drainage flow rates over the vulnerable parts of Chennai city. It was found that heavy precipitation over the North Chennai region yielded discharge rates to the tune of ~250 m3s−1 during a 24 h period, causing intense flooding in the low-lying areas around the Cooum River basin with a large population density, with estimates sufficiently corroborating observations.
Global Monitoring of Ionospheric Weather by GIRO and GNSS Data Fusion
Authors: Galkin Ivan , Froń Adam , Reinisch Bodo , Hernández-Pajares Manuel , Krankowski Andrzej , Nava Bruno , Bilitza Dieter , Kotulak Kacper , Flisek Paweł , Li Zishen , Wang Ningbo , Dollase David Roma , García-Rigo Alberto , Batista Inez
Published: February 2022

Prompt and accurate imaging of the ionosphere is essential to space weather services, given a broad spectrum of applications that rely on ionospherically propagating radio signals. As the 3D spatial extent of the ionosphere is vast and covered only fragmentarily, data fusion is a strong candidate for solving imaging tasks. Data fusion has been used to blend models and observations for the integrated and consistent views of geosystems. In space weather scenarios, low latency of the sensor data availability is one of the strongest requirements that limits the selection of potential datasets for fusion. Since remote plasma sensing instrumentation for ionospheric weather is complex, scarce, and prone to unavoidable data noise, conventional 3D-var assimilative schemas are not optimal. We describe a novel substantially 4D data fusion service based on near-real-time data feeds from Global Ionosphere Radio Observatory (GIRO) and Global Navigation Satellite System (GNSS) called GAMBIT (Global Assimilative Model of the Bottomside Ionosphere with Topside estimate). GAMBIT operates with a few-minute latency, and it releases, among other data products, the anomaly maps of the effective slab thickness (EST) obtained by fusing GIRO and GNSS data. The anomaly EST mapping aids understanding of the vertical plasma restructuring during disturbed conditions.
A Review of Air Pollution Mitigation Approach Using Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API)
Authors: Enitan Ibironke Titilayo , Durowoju Olatunde Samod , Edokpayi Joshua Nosa , Odiyo John Ogony
Published: February 2022

Air pollution is a global environmental issue, and there is an urgent need for sustainable remediation techniques. Thus, phytoremediation has become a popular approach to air pollution remediation. This paper reviewed 28 eco-friendly indigenous plants based on both the air pollution tolerance index (APTI) and anticipated performance index (API), using tolerance level and performance indices to evaluate the potential of most indigenous plant species for air pollution control. The estimated APTI ranged from 4.79 (Syzygium malaccense) to 31.75 (Psidium guajava) among the studied indigenous plants. One of the selected plants is tolerant, and seven (7) are intermediate to air pollution with their APTI in the following order: Psidium guajava (31.75) > Swietenia mahogany (28.08) > Mangifera indica L. (27.97) > Ficus infectoria L. (23.93) > Ficus religiosa L. (21.62) > Zizyphus Oenoplia Mill (20.06) > Azadirachta indica A. Juss. (19.01) > Ficus benghalensis L. (18.65). Additionally, the API value indicated that Mangifera indica L. ranges from best to good performer; Ficus religiosa L. and Azadirachta indica A. Juss. from excellent to moderate performers; and Cassia fistula L. from poor to very poor performer for air pollution remediation. The Pearson correlation shows that there is a positive correlation between API and APTI (R2 = 0.63), and this implies that an increase in APTI increases the API and vice versa. This paper shows that Mangifera indica L., Ficus religiosa L., and Azadirachta indica A. Juss. have good potential for sustainable reduction in air pollution for long-term management and green ecomanagement development.
Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China
Authors: Zaib Shah , Lu Jianjiang , Bilal Muhammad
Published: February 2022

In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0–50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants’ (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O3. PM10 was the primary pollutant, followed by O3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O3. This study provides useful information and a valuable reference for future research on air quality in northwest China.