Applied Sciences

One of the most challenging problems during disasters involving crowds is pedestrian evacuation. It is very important in such situations to improve survival rates by getting all or most of the people out in the shortest possible time. Technological intervention through augmenting the evacuation process using an unmanned aerial vehicle (UAV) exhibits great potential in improving survival rates, but the exploration of this method is still in its infancy. Therefore, this study explores the potential of utilizing UAVs for crowd management during emergency evacuations. We conducted a rigorous study, using a simulation model featuring four UAVs and differing numbers of pedestrians, with use of two UAV guidance approaches: partial guidance and full guidance. The experimental results suggest that exploiting UAVs in crowd evacuation and following the partial guidance approach can lead to a more efficient evacuation process. Full article

Human and organizational factors (HOFs) play an important role in electric misoperation accidents (EMAs), but research into the reliability of human factors is still in its infancy in the field of EMAs, and further investment in research is urgently required. To analyze the HOFs in EMAs, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and fuzzy fault tree analysis (FFTA) was applied to EMAs for the first time in the paper. HFACS is used to identify and classify the HOFs with 135 accidents, reorganized as basic events (BEs), intermediate events (IEs), and top event (TE), and develop the architecture of fault tree (FT). Fuzzy aggregation is employed to address experts’ expressions and obtain the failure probabilities of the BEs and the minimal cut sets (MCSs) of the FT. The approach generates BEs failure probabilities without reliance on quantitative historical failure statistics of EMAs via qualitative records processing. The FFTA–HFACS model is applied for quantitative analysis of the probability of failure of electrical mishaps and the interaction between accident risk factors. It can assist professionals in deciding whether and where to take preventive or corrective actions and assist in knowledgeable decision-making around the electric operation and maintenance process. Finally, applying this hybrid method to EMAs, the results show that the probability of an EMAs is 1.0410E-2, which is a risk level that is likely to occur and must be controlled. Two of the most important risk factors are habitual violations and supervisory violation; a combination of risk factors of inadequate work preparation and paralysis, and irresponsibility on the part of employees are also frequent errors. Full article

Through human development and technological expansion, it has become apparent that the potential lies within each individual to have an essential part in the transcendence of society and the community. People less privileged than others may need more strength and determination to surpass their current resources to overcome normal and natural obstacles in order to simulate an environment where productivity and creativity exist. This paper aims to study an approach that will assist the elderly and people of determination in one of the most essential activities practiced by individuals: shopping. The study focuses on facilitating the acquirement of items from shelves and skipping the cashier line. The proposed system is a service robot supported by a robotic arm and a linear actuator as a lifting mechanism, controlled by a remote joystick to help the elderly or disabled people reach items on high shelves. The scanning system is based on barcode detection, using transfer learning. The network was designed using YOLOv2 layers connected to TinyYOLO as feature extraction layers. This network has proven to be the most practical, with 86.4% accuracy and real-time operation with 27 FPS in comparison to using the YOLOv2 layers with DarkNet or VGG19 as feature extraction layers. An anti-theft system is integrated into the robot to improve the reliability of the self-checkout feature. The system uses computer vision GMM and Kalman filter for item detection inside the cart, and the item is validated to be the one that has been scanned, using SURF for structural features, HSV for color, and load-sensors mounted to the base of the cart to measure the item’s weight. Full article

Conventional fractional open-circuit voltage (FOCV) methods in maximum power point tracking (MPPT) are widely adopted for their simple structure and low computing power requirements. However, under mismatch and environmental changing conditions, the FOCV methods introduce a large amount of energy loss due to their maximum power point being fixed at the initial setup. To reduce energy loss, the intermittent FOCV MPPT proposed in this paper regularly refreshes all the parameters for each condition in time by using an I-V curve tracer. The proposed intermittent FOCV consists of two phases: the scan and set phases. In scan phase, the I-V curve of a photovoltaic (PV) cell is scanned and its power is calculated. In set phase, the global MPP of the PV cell is extracted and set by controlling the 8-bit capacitance array. Simulation and calculation based on experimental results with a single PV cell show that the energy loss of the proposed intermittent FOCV under daily temperature and illuminance distributions decreased by up to 99.9% compared to that of the conventional FOCV. Full article

Cloud computing is currently becoming a well-known buzzword in which business titans, such as Microsoft, Amazon, and Google, among others, are at the forefront in developing and providing sophisticated cloud computing systems to their users in a cost-effective manner. Security is the biggest concern for cloud computing and is a major obstacle to users adopting cloud computing systems. Maintaining the security of cloud computing is important, especially for the infrastructure. Several research works have been conducted in the cloud infrastructure security area; however, some gaps have not been completely addressed, while new challenges continue to arise. This paper presents a comprehensive survey of the security issues at different cloud infrastructure levels (e.g., application, network, host, and data). It investigates the most prominent issues that may affect the cloud computing business model with regard to infrastructure. It further discusses the current solutions proposed in the literature to mitigate the different security issues at each level. To assist in solving the issues, the challenges that are still unsolved are summarized. Based on the exploration of the current challenges, some cloud features such as flexibility, elasticity and the multi-tenancy are found to pose new challenges at each infrastructure level. More specifically, the multi-tenancy is found to have the most impact at all infrastructure levels, as it can lead to several security problems such as unavailability, abuse, data loss and privacy breach. This survey concludes by giving some recommendations for future research. Full article

Glyphosate (N-(phosphonomethyl)glycine) was developed in the early 1970s and at present is used as a herbicide to kill broadleaf weeds and grass. The widely occurring degradation product aminomethylphosphonic acid (AMPA) is a result of glyphosate and amino-polyphosphonate degradation. The massive use of the parent compound leads to the ubiquity of AMPA in the environment, and particularly in water. Considering this, it can be assumed that glyphosate and its major metabolites could pose a potential risk to aquatic organisms. This review summarizes current knowledge about residual glyphosate and its major metabolite AMPA in the aquatic environment, including its status and toxic effects in aquatic organisms, mainly fish. Based on the above, we identify major gaps in the current knowledge and some directions for future research knowledge about the effects of worldwide use of herbicide glyphosate and its major metabolite AMPA. The toxic effect of glyphosate and its major metabolite AMPA has mainly influenced growth, early development, oxidative stress biomarkers, antioxidant enzymes, haematological, and biochemical plasma indices and also caused histopathological changes in aquatic organisms. Full article

The rapid development of automated driving technology has brought many emerging technologies. The collision avoidance (CA) function by braking and/or steering maneuver of advanced driver assistance systems (ADAS), which contributes to the improvement of the safety of automated vehicles, has been playing an important role in recent modern passenger cars and commercial vehicles. When an automated vehicle needs to avoid multiple obstacles at the same time, consuming travel time and safety assurance of CA need to be carefully considered especially in the case related to unpredictable motion of obstacles. This paper proposes a feasible solution to this situation by controlling speed and the steering wheel angle. The proposed motion re-planning based on post-encroachment time (PET) provides a judgment of a function which calculates the possibility of unavoidable road accidents. Then the path re-planning layer of a novel two-layer model predictive control (TL-MPC) will re-plan a local trajectory and give a reference acceleration. Finally, the path tracking layer outputs steering wheel angle to follow the trajectory under the premise of ensuring safety constraints. The proposed control system is evaluated by co-simulations of MATLAB/Simulink and CarSim software. The results show that for various conditions of post-encroachment time, the ego vehicle adopting the proposed strategy will conduct reasonable behavior re-planning and consequently successfully avoid obstacles. Full article

Kinematic accuracy is a crucial indicator for evaluating the performance of mechanisms. Low-mobility parallel mechanisms are examples of parallel robots that have been successfully employed in many industrial fields. Previous studies analyzing the kinematic accuracy analysis of parallel mechanisms typically ignore the randomness of each component of input error, leading to imprecise conclusions. In this paper, we use homogeneous transforms to develop the inverse kinematics models of an improved Delta parallel mechanism. Based on the inverse kinematics and the first-order Taylor approximation, a model is presented considering errors from the kinematic parameters describing the mechanism’s geometry, clearance errors associated with revolute joints and driving errors associated with actuators. The response surface method is employed to build an explicit limit state function for describing position errors of the end-effector in the combined direction. As a result, a mathematical model of kinematic reliability of the improved Delta mechanism is derived considering the randomness of every input error component. And then, reliability sensitivity of the improved Delta parallel mechanism is analyzed, and the influences of the randomness of each input error component on the kinematic reliability of the mechanism are quantitatively calculated. The kinematic reliability and proposed sensitivity analysis provide a theoretical reference for the synthesis and optimum design of parallel mechanisms for kinematic accuracy. Full article

The key to modeling the interlocking of geogrid-reinforced ballast is considering both the continuous deformation characteristics of the geogrid and the discontinuity of the ballast particles. For this purpose, pullout tests using biaxial and triaxial geogrids were simulated using the coupled discrete element method (DEM) and finite difference method (FDM). In this coupled model, two real-shaped geogrid models with square and triangular apertures were established using the solid element in FLAC3D. Meanwhile, simplified shaped clumps were used to represent the ballast using PFC3D. The calibration test simulation showed that the accurately formed geogrid model can reproduce the deformation and strength characteristics of a geogrid. The pullout simulation results show that the DEM-FDM method can well predict the relationship between pullout force and displacement, which is more accurate than the DEM method. For ballast particles of 40 mm in size, both the experiment and simulation results showed that the triaxial geogrid of 75 mm is better than the 65-mm biaxial geogrid . In addition, the DEM-FDM method can study the interaction mechanism between the particles and the geogrid from a microscopic view, and also reveal the similar deformation behavior of the geogrid in the pullout process. Therefore, the DEM-FDM coupled method can not only investigate the interlocking mechanism between the ballast and particles but can also provide a great method for evaluating the performance of different types of geogrids. Full article

The design of sustainable and efficient materials for efficient energy storage and degradation of environmental pollutants (specifically organic dyes) is a matter of major interest these days. For this purpose, cerium- and ytterbium-based GO/g-C₃N₄/Fe₂O₃ composites have been synthesized to explore their properties, especially in charge storage devices such as supercapacitors, and also as photocatalysts for the degradation of carcinogenic dyes from the environment. Physicochemical studies have been carried out using XRD, FTIR, SEM, and BET techniques. Electrochemical techniques (cyclic voltammetry, galvanic charge discharge, and electrochemical impedance spectroscopy) have been employed to measure super-capacitance and EDLC properties. Results show that the gravimetric capacitance calculated from GCD results is 219 Fg⁻¹ for ytterbium- and 169 Fg⁻¹ for cerium-based nanocomposites at the current density of 1 A/g and scan rate of 2 mV/sec. The specific capacitance calculated for the ytterbium-based nanocomposite is 189 Fg⁻¹ as compared to 125 Fg⁻¹ for the cerium-based material. EIS results pointed to an enhanced resistance offered by cerium-based nanocomposites as compared to that of ytterbium, which can be assumed with the difference in particle size, as confirmed from structural studies including XRD. From obtained results, ytterbium oxide-based GO/g-C₃N₄/Fe₂O₃ is proven to be a better electro-catalyst as compared to cerium-based nanocomposites. Photocatalytic results are also in agreement with electrochemical results, as the degradation efficiency of ytterbium oxide-based GO/g-C₃N₄/Fe₂O₃ (67.11 and 83.50% for rhodamine B and methylene blue dyes) surpasses values observed for cerium-based GO/g-C₃N₄/Fe₂O₃ (63.08 and 70.61%). Full article

In this study, a new concept for a Ka-band 5G communication tunable reflector metasurface (MS) for beam steering at 28 GHz is proposed. Varactor diodes are used as the tunability component of each unit cell of this MS. Significant improvements in beam steering and bandwidth performance were achieved using this new concept referred to as the stripes configuration. Several different geometries of unit cells arranged in stripes were designed to achieve better performance in directionality, gain, sidelobe level (SLL), and bandwidth in the stripes configuration. Simulation results for a three-stripe MS with different unit cells in each stripe showed better performance in the phase dynamic range and reduced reflectance losses compared to a typical one-stripe MS. The simulation results showed a significant improvement of 3 dB, depending on the steering angle in reflectance gain, compared to a uniform MS (one stripe). Furthermore, a significant improvement of approximately 50% in the accuracy of the steering angle for different operating frequencies was demonstrated. Manufacturing considerations are discussed in this study. Full article

Energy balance of the photovoltaic system is influenced by many factors. In this article the effect of tilt and azimuth angle changes of the photovoltaic system energy production is analyzed. These parameters have significant impact on the amount of solar radiation which hits on the photovoltaic panel surface and therefore also on the energy absorbed by the module surface. The main aim of research was identification of the optimal position of photovoltaic system installation in the southern Slovakia regions. The experimental apparatus had two setups consisting of polycrystalline photovoltaic modules. The first setup was used for identification of the tilt angle changes in the range (0–90°). The second one was focused on the detection of the azimuth angle effect to the energy production. The measurement results were statistically processed and mathematically analyzed. Obtained dependencies are presented as two-dimensional and three-dimensional graphical relations. Regression equations characterize time relations between the tilt or azimuth angle and the energy produced by the photovoltaic system in Southern Slovakia. Obtained simplified mathematical model was verified by analytical model. Presented models can be used for the dimensioning and optimization of the photovoltaic system energy production. Full article

In the automotive field, the requirements in terms of carbon emissions and improved efficiency are shifting the focus of designers towards reduced engine size. As a result, the dynamic balancing of an engine with strict limitations on the number of cylinders, the weight and the available space becomes a challenging task. The present contribution aims at providing the designer with a tool capable of selecting fundamental parameters needed to correctly balance an internal combustion engine, including the masses and geometry of the elements to be added directly onto the crankshaft and onto the balancing shafts. The relevant elements that distinguish the tool from others already proposed are two. The first is the comprehensive matrix formulation which makes the tool fit for a wide variety of engine configurations. The second is an optimisation procedure that selects not only the position of the mass and centre of gravity of the counterweight but also its complete geometric configuration, thus instantaneously identifying the overall dimensions and weight of the crankshaft. Full article

In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmentation network to be applied to point clouds of buildings. However, training such networks requires large amounts of fine-labeled buildings’ point-cloud data, presenting a major challenge in practice because they are difficult to obtain. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. In order to reduce the number of required annotated labels, we proposed a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings’ point clouds with limited supervision, named 3DLEB-Net. In general, it consists of two steps. The first step (Autoencoder, AE) is composed of a Dynamic Graph Convolutional Neural Network (DGCNN) encoder and a folding-based decoder. It is designed to extract discriminative global and local features from input point clouds by faithfully reconstructing them without any label. The second step is the semantic segmentation network. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluated our approach based on the Architectural Cultural Heritage (ArCH) dataset. Compared to the fully supervised DL methods, we found that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labeled training data from fully supervised methods as input. Moreover, we conducted a series of ablation studies to show the effectiveness of the design choices of our model. Full article

The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. Previous studies on container volume forecasting were based on traditional statistical methodologies, such as ARIMA, SARIMA, and regression. However, these methods do not explain the complexity and variability of data caused by changes in the external environment, such as the global financial crisis and economic fluctuations. Deep learning can explore the inherent patterns of data and analyze the characteristics (time series, external environmental variables, and outliers); hence, the accuracy of deep learning-based volume prediction models is better than that of traditional models. However, this does not include the study of overall trends (upward, steady, or downward). In this study, a novel deep learning prediction model is proposed that combines prediction and trend identification of container volume. The proposed model explores external variables that are related to container volume, combining port volume time-series decomposition with external variables and deep learning-based multivariate long short-term memory (LSTM) prediction. The results indicate that the proposed model performs better than the traditional LSTM model and follows the trend simultaneously. Full article

The clothing industry is among the most polluting and waste-generating industries in the world, and it is responsible for the release of large amounts of greenhouse gases. The industry’s massive size and significant environmental footprint with regard to water and energy consumption and waste generation make it a valid improvement candidate. While in recent years, global clothing brands and retailers have taken steps to reduce their ecological footprint, there still is a lot of room for improvement. In this research, we view this sustainability issue from a lifecycle perspective and study the new business models (NBMs) that may arise from the utilization of additive manufacturing (AM) technology. AM is emerging as a method of production for final parts. Moreover, as the range of material and available production processes expands, it is increasingly important to study the potential impact of this promising production technology and potential NBMs enabled by it on the clothing industry. Additionally, the obstacles to AM utilization in the clothing industry are explored. We utilize secondary data related to relevant implementation cases to theoretically study the NBMs that AM can enable to improve sustainability. Three NBMs of “clothing as a service”, “collaborative consumption”, and “direct sale/distribution” were envisioned through the study of current AM applications in other industries, as well as current fashion trends. The results of this research have implications for the sustainability of the fashion industry while also providing directions for AM technology development. Full article

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods. Full article

This study aimed to evaluate the healing of furcation when repaired with Chitosan (CS) scaffold impregnated or not with Simvastatin (SIM) compared with CollaCote (CL) in goat premolar teeth. Root canal treatment was performed in 52 mandibular premolars followed by furcal perforation induction. The perforation was repaired with CL, CS, or CS with SIM after leaving it untreated for 4 weeks. White mineral trioxide aggregate was carried into the furcal site followed by a 2–3 mm resin-modified glass ionomer. The perforation was left untreated, and the access cavity was left open without coronal filling in the control group. The animals were sacrificed after one and three months. Block sections of the premolars were prepared and examined histologically to evaluate the inflammation and type of healing. Hard tissue formation was found in CL, CS, and CS/SIM groups in both periods. At one month, no significant differences were detected among the experimental groups, whereas at three months, CS without SIM showed significantly better performance compared to CL and CS/SIM groups (p = 0.040). Therefore, repairing furcal perforation with CS scaffolds shows desirable biological responses and healing characteristics in favor of bone regeneration at three months. Full article

The magnitude of pollution in Lake Hawassa has been exacerbated by population growth and economic development in the city of Hawassa, which is hydrologically closed and retains pollutants entering it. This study was therefore aimed at examining seasonal and spatial variations in the water quality of Lake Hawassa Watershed (LHW) and identifying possible sources of pollution using multivariate statistical techniques. Water and effluent samples from LHW were collected monthly for analysis of 19 physicochemical parameters during dry and wet seasons at 19 monitoring stations. Multivariate statistical techniques (MVST) were used to investigate the influences of an anthropogenic intervention on the physicochemical characteristics of water quality at monitoring stations. Through cluster analysis (CA), all 19 monitoring stations were spatially grouped into two statistically significant clusters for the dry and wet seasons based on pollution index, which were designated as moderately polluted (MP) and highly polluted (HP). According to the study results, rivers and Lake Hawassa were moderately polluted (MP), while point sources (industry, hospitals and hotels) were found to be highly polluted (HP). Discriminant analysis (DA) was used to identify the most critical parameters to study the spatial variations, and seven significant parameters were extracted (electrical conductivity (EC), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), sodium ion (Na⁺), and potassium ion (K⁺) with the spatial variance to distinguish the pollution condition of the groups obtained using CA. Principal component analysis (PCA) was used to qualitatively determine the potential sources contributing to LHW pollution. In addition, three factors determining pollution levels during the dry and wet season were identified to explain 70.5% and 72.5% of the total variance, respectively. Various sources of pollution are prevalent in the LHW, including urban runoff, industrial discharges, diffused sources from agricultural land use, and livestock. A correlation matrix with seasonal variations was prepared for both seasons using physicochemical parameters. In conclusion, effective management of point and non-point source pollution is imperative to improve domestic, industrial, livestock, and agricultural runoff to reduce pollutants entering the Lake. In this regard, proper municipal and industrial wastewater treatment should be complemented, especially, by stringent management that requires a comprehensive application of technologies such as fertilizer management, ecological ditches, constructed wetlands, and buffer strips. Furthermore, application of indigenous aeration practices such as the use of drop structures at critical locations would help improve water quality in the lake watershed. Full article