bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Lower Body for AGV Based on SSD Algorithm with ResNet
Authors: Gao Xinbiao , Xu Junhua , Luo Chuan , Zhou Jun , Huang Panling , Deng Jianxin
Published: March 2022

Detection of human lower body provides an implementation idea for the automatic tracking and accurate relocation of automatic vehicles. Based on traditional SSD and ResNet, this paper proposes an improved detection algorithm R-SSD for human lower body detection, which utilizes ResNet50 instead of VGG16 to improve the feature extraction level of the model. According to the application of acquisition equipment, the model input resolution is increased to 448 × 448 and the model detection range is expanded. Six feature maps of the updated resolution network are selected for detection and the lower body image dataset is clustered into five categories for aspect ratio, which are evenly distributed to each feature detection map. The experimental results show that the model R-SSD detection accuracy after training reaches 85.1% mAP. Compared with the original SSD, the detection accuracy is improved by 7% mAP. The detection confidence in practical application reaches more than 99%, which lays the foundation for subsequent tracking and relocation for automatic vehicles.
Methodological Considerations in Assessing Interlimb Coordination on Poststroke Gait: A Scoping Review of Biomechanical Approaches and Outcomes
Authors: Couto Ana G. B. , Vaz Mário A. P. , Pinho Liliana , Félix José , Silva Sandra , Silva Augusta , Sousa Andreia S. P.
Published: March 2022

Objective: To identify and summarize biomechanical assessment approaches in interlimb coordination on poststroke gait. Introduction: Interlimb coordination involves complex neurophysiological mechanisms that can be expressed through the biomechanical output. The deepening of this concept would have a significant contribution in gait rehabilitation in patients with an asymmetric neurological impairment as poststroke adults. Inclusion criteria: Poststroke adults (>19 years old), with assessment of interlimb coordination during gait, in an open context, according to the Population, Concept, Context framework. Methods: A literature search was performed in PubMed, Web of Science™, Scopus, and gray literature in Google Scholar™, according to the PRISMA-ScR recommendations. Studies written in Portuguese or English language and published between database inception and 14 November 2021 were included. Qualitative studies, conference proceedings, letters, and editorials were excluded. The main conceptual categories were “author/year”, “study design”, “participant’s characteristics”, “walking conditions”, “instruments” and “outcomes”. Results: The search identified 827 potentially relevant studies, with a remaining seven fulfilling the established criteria. Interlimb coordination was assessed during walking in treadmill (n = 3), overground (n = 3) and both (n = 1). The instruments used monitored electromyography (n = 2), kinetics (n = 2), and kinematics (n = 4) to assess spatiotemporal parameters (n = 4), joint kinematics (n = 2), anteroposterior ground reaction forces (n = 2), and electromyography root mean square (n = 2) outcomes. These outcomes were mostly used to analyze symmetry indices or ratios, to calculate propulsive impulse and external mechanical power produced on the CoM, as well as antagonist coactivation. Conclusions: Assessment of interlimb coordination during gait is important for consideration of natural auto-selected overground walking, using kinematic, kinetic, and EMG instruments. These allow for the collection of the main biomechanical outcomes that could contribute to improve better knowledge of interlimb coordination assessment in poststroke patients.
Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study
Authors: Moro Matteo , Marchesi Giorgia , Hesse Filip , Odone Francesca , Casadio Maura
Published: March 2022

The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.
OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN
Authors: Viedma Ignacio A. , Alonso-Caneiro David , Read Scott A. , Collins Michael J.
Published: March 2022

Optical coherence tomography (OCT) of the posterior segment of the eye provides high-resolution cross-sectional images that allow visualization of individual layers of the posterior eye tissue (the retina and choroid), facilitating the diagnosis and monitoring of ocular diseases and abnormalities. The manual analysis of retinal OCT images is a time-consuming task; therefore, the development of automatic image analysis methods is important for both research and clinical applications. In recent years, deep learning methods have emerged as an alternative method to perform this segmentation task. A large number of the proposed segmentation methods in the literature focus on the use of encoder–decoder architectures, such as U-Net, while other architectural modalities have not received as much attention. In this study, the application of an instance segmentation method based on region proposal architecture, called the Mask R-CNN, is explored in depth in the context of retinal OCT image segmentation. The importance of adequate hyper-parameter selection is examined, and the performance is compared with commonly used techniques. The Mask R-CNN provides a suitable method for the segmentation of OCT images with low segmentation boundary errors and high Dice coefficients, with segmentation performance comparable with the commonly used U-Net method. The Mask R-CNN has the advantage of a simpler extraction of the boundary positions, especially avoiding the need for a time-consuming graph search method to extract boundaries, which reduces the inference time by 2.5 times compared to U-Net, while segmenting seven retinal layers.
A Pruning Method for Deep Convolutional Network Based on Heat Map Generation Metrics
Authors: Zhang Wenli , Wang Ning , Chen Kaizhen , Liu Yuxin , Zhao Tingsong
Published: March 2022

With the development of deep learning, researchers design deep network structures in order to extract rich high-level semantic information. Nowadays, most popular algorithms are designed based on the complexity of visible image features. However, compared with visible image features, infrared image features are more homogeneous, and the application of deep networks is prone to extracting redundant features. Therefore, it is important to prune the network layers where redundant features are extracted. Therefore, this paper proposes a pruning method for deep convolutional network based on heat map generation metrics. The ‘network layer performance evaluation metrics’ are obtained from the number of pixel activations in the heat map. The network layer with the lowest ‘network layer performance evaluation metrics’ is pruned. To address the problem that the simultaneous deletion of multiple structures may result in incorrect pruning, the Alternating training and self-pruning strategy is proposed. Using a cyclic process of pruning each model once and retraining the pruned model to reduce the incorrect pruning of network layers. The experimental results show that proposed method in this paper improved the performance of CSPDarknet, Darknet and Resnet.
Effective Transfer Learning with Label-Based Discriminative Feature Learning
Authors: Kim Gyunyeop , Kang Sangwoo
Published: March 2022

The performance of natural language processing with a transfer learning methodology has improved by applying pre-training language models to downstream tasks with a large number of general data. However, because the data used in pre-training are irrelevant to the downstream tasks, a problem occurs in that it learns general features rather than those features specific to the downstream tasks. In this paper, a novel learning method is proposed for embedding pre-trained models to learn specific features of such tasks. The proposed method learns the label features of downstream tasks through contrast learning using label embedding and sampled data pairs. To demonstrate the performance of the proposed method, we conducted experiments on sentence classification datasets and evaluated whether the features of the downstream tasks have been learned through a PCA and a clustering of the embeddings.
Fear of Falling Does Not Influence Dual-Task Gait Costs in People with Parkinson’s Disease: A Cross-Sectional Study
Authors: Prell Tino , Uhlig Manuela , Derlien Steffen , Maetzler Walter , Zipprich Hannah M.
Published: March 2022

Cognitive deficits and fear of falling (FOF) can both influence gait patterns in Parkinson’s disease (PD). While cognitive deficits contribute to gait changes under dual-task (DT) conditions, it is unclear if FOF also influences changes to gait while performing a cognitive task. Here, we aimed to explore the association between FOF and DT costs in PD, we additionally describe associations between FOF, cognition, and gait parameters under single-task and DT. In 40 PD patients, motor symptoms (MDS-revised version of the Unified Parkinson’s Disease Rating Scale, Hoehn and Yahr), FOF (Falls Efficacy Scale International), and Montreal Cognitive Assessment (MoCA) were assessed. Spatiotemporal gait parameters were recorded with a validated mobile gait analysis system with inertial measurement units at each foot while patients walked in a 50 m hallway at their preferred speed under single-task and DT conditions. Under single-task conditions, stride length (β = 0.798) and spatial variability (β = 0.202) were associated with FOF (adjusted R2 = 0.19, p < 0.001) while the MoCA was only weakly associated with temporal variability (adjusted R2 = 0.05, p < 0.001). Under DT conditions, speed, stride length, and cadence decreased, while spatial variability, temporal variability, and stride duration increased with the largest effect size for speed. DT costs of stride length (β = 0.42) and age (β = 0.58) explained 18% of the MoCA variance. However, FOF was not associated with the DT costs of gait parameters. Gait difficulties in PD may exacerbate when cognitive tasks are added during walking. However, FOF does not appear to have a relevant effect on dual-task costs of gait.
Delayed Improvement of Left Ventricular Function in Newly Diagnosed Heart Failure Depends on Etiology—A PROLONG-II Substudy
Authors: Mueller-Leisse Johanna , Brunn Johanna , Zormpas Christos , Hohmann Stephan , Hillmann Henrike Aenne Katrin , Eiringhaus Jörg , Bauersachs Johann , Veltmann Christian , Duncker David
Published: March 2022

In patients with newly diagnosed heart failure with reduced ejection fraction (HFrEF), three months of optimal therapy are recommended before considering a primary preventive implantable cardioverter-defibrillator (ICD). It is unclear which patients benefit from a prolonged waiting period under protection of the wearable cardioverter-defibrillator (WCD) to avoid unnecessary ICD implantations. This study included all patients receiving a WCD for newly diagnosed HFrEF (n = 353) at our center between 2012 and 2017. Median follow-up was 2.7 years. From baseline until three months, LVEF improved in patients with all peripartum cardiomyopathy (PPCM), myocarditis, dilated cardiomyopathy (DCM), or ischemic cardiomyopathy (ICM). Beyond this time, LVEF improved in PPCM and DCM only (10 ± 8% and 10 ± 12%, respectively), whereas patients with ICM showed no further improvement. The patients with newly diagnosed HFrEF were compared to 29 patients with a distinct WCD indication, which is an explantation of an infected ICD. This latter group had a higher incidence of WCD shocks and poorer overall survival. All-cause mortality should be considered when deciding on WCD prescription. In patients with newly diagnosed HFrEF, the potential for delayed LVEF recovery should be considered when timing ICD implantation, especially in patients with PPCM and DCM.
Fluorometric Detection of Oil Traces in a Sea Water Column
Authors: Baszanowska Emilia , Otremba Zbigniew
Published: March 2022

This study focuses on broadening the knowledge of a fluorometric index to improve the detection of oil substances present in the marine environment. It is assumed that the value of this index will provide information about a possible oil discharge at some distance from the sensor. In this paper, the detection of oil present in seawater as a mixture of oils such as fuel, lubricate oil, or crude oil based on a fluorescence indicator-fluorometric index (FIo/w) is discussed. FIo/w was defined based on specific excitation and emission wavelengths coming from the obtained excitation–emission spectrum (EEM) of oil-free seawater and, in parallel, the same water but artificially polluted with oil. For this, measurements of a mixture of oils in seawater for an oil-to-water ratio in the range from 50 × 10−9 to 200 × 10−9 as well as oil-free seawater were performed. Laboratory measurements continued five times in months in the summer season with the coastal waters of the southern Baltic Sea (last spring, summer, and early autumn). The dependence of FIo/w on the presence of oil in seawater, the oil-in-water ratio, as well as months of the considered season has been demonstrated.
Insights into the Issue of Deploying a Private LoRaWAN
Authors: Fujdiak Radek , Mikhaylov Konstantin , Pospisil Jan , Povalac Ales , Misurec Jiri
Published: March 2022

The last decade has transformed wireless access technologies and crystallized a new direction for the internet of things (IoT). The modern low-power wide-area network (LPWAN) technologies have been introduced to deliver connectivity for billions of devices while keeping the costs and consumption low, and the range of communication high. While the 5G (fifth generation mobile network) LPWAN-like radio technologies, namely NB-IoT (narrowband internet of things) and LTE-M (long-term evolution machine type communication) are emerging, the long-range wide-area network (LoRaWAN) remains extremely popular. One unique feature of this technology, which distinguishes it from the competitors, is the possibility of supporting both public and private network deployments. In this paper we focus on this aspect and deliver original results comparing the performance of the private and public LoRAWAN deployment options; these results should help understand the LoRaWAN technology and give a clear overview of the advantages and disadvantages of the private versus public approaches. Notably, we carry the comparison along the three dimensions: the communication performance, the security, and the cost analysis. The presented results illustratively demonstrate the differences of the two deployment approaches, and thus can support selection of the most efficient deployment option for a target application.
An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship
Authors: Chen Jiawei , Luo Qizhang , Wu Guohua
Published: March 2022

Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.
Control Method of the Dual-Winding Motor for Online High-Frequency Resistance Measurement in Fuel Cell Vehicle
Authors: Chang Cheng , Zhou Yafu , Lian Jing , Liang Jicai
Published: March 2022

The dual-winding motor drive has recently been proposed in the field of fuel cell vehicles due to its performance and high robust advantages. Efforts for this new topology have been made by many researchers. However, the high-frequency resistance measurement of a proton exchange membrane fuel cell based on dual-winding motor drive architecture, which is important for water management to optimize the lifespan of fuel cells, was not employed in earlier works. In this paper, a new control method of the dual-winding motor is proposed by introducing a dc input current control to realize high-frequency resistance measurement and normal drive control simultaneously, without using extra dc-dc converter. On the basis of the revealed energy exchange principles among electrical ports and mechanical port of the dual-winding motor, the load ripple caused by high-frequency current perturbation is optimized based on the q-axis current distribution between two winding sets. The decoupling control algorithm for the coupling effect within and across windings is also discussed to improve the dynamic response during high-frequency resistance measurement. Finally, simulation results verify the effectiveness and improvement of the proposed method. Fast Fourier transform results indicated that the total harmonic distortion of the dc input current was reduced from 22.53% to 4.47% of the fundamental, and the torque ripple was suppressed from about ±4.5 Nm to ±0.5 Nm at the given operation points.
Incentive Schemes Increase Risky Behavior in a Safety-Critical Working Task: An Experimental Comparison in a Simulated High-Reliability Organization
Authors: Brandhorst Sebastian , Kluge Annette
Published: March 2022

As financial incentive schemes have the tendency to increase risky behavior, we analyzed their effect on rule-related behavior in a safety-critical task. We compared risky behavior (in terms of the amount of rule violations) between three payment condition: continuous, up-front, and bonus pay. Fifty-nine participants were trained as production personnel to conduct a start-up procedure of a simulated wastewater treatment plant, representing a high reliability organization. During the 5-h experimental study, risky behavior could have been applied 48 times while building a simulated production year. The results show that the conditions with an incentive scheme (up-front and bonus pay) led to significantly more rule violations than the condition with continuous payment without an incentive scheme. Our study highlights the general increased risk effect of incentive schemes and provides a starting point for industries to assess their implicit and explicit incentive schemes.
Nanocarrier Systems in Taste Masking
Authors: Nasr Nasr Eldin Hussein , ElMeshad Aliaa Nabil , Fares Ahmed Roshdy
Published: March 2022

Taste is the most crucial organoleptic parameter affecting patient compliance in the case of drugs with poor palatability. Taste masking is a major challenge for the development of orally ingested active pharmaceutical constituents in the pharmaceutical industry. Numerous conventional taste-masking techniques have been extensively studied. In parallel, affecting the drug solubility or release is a major concern of conventional taste-masking techniques. Recently, many nanocarrier systems have been introduced, claiming the advantage of effective taste masking without affecting either the drug solubility or its release. In this review, we will present new techniques for taste masking, including taste-masking techniques utilizing nanocarrier systems such as liposomes, polymeric and solid lipid nanoparticles, polymeric micelles, submicron lipid emulsions, and nanogels. We will chiefly highlight the composition of these systems and their applications in designing oral therapeutic delivery systems successful in masking the taste of bitter molecules.
Differential Seed Germination Responses of Tomato Landraces to Temperature under Climate Change Scenarios
Authors: Sousaraei Naeimeh , Torabi Benjamin , Soltani Elias , Mashayekhi Kambiz , Medina Joaquín
Published: March 2022

This study aims to quantify the seed germination response of six tomato landraces to temperature; predict future climate changes relative to the baseline period (1980–2009) for studied locations in the courses of near-term (2010–2039) and mid-term (2040–2069) under two representative concentration pathways (RCP 4.5 and 8.5); assess the impact of future climate change on the final germination percentage (FGP) and time to reach 50% germination (D50) in the studied landraces. The results show that FGP is zero at 10 °C, reaches the highest value at 15–35 °C, and ceases at 36–40 °C for all landraces. The results also demonstrate that the temperature increment is 0.8–1.3 °C and 1.5–2.7 °C in the near- and mid-term, respectively, under RCP 4.5; further, this increment is 0.9–1.9 °C for the near-term and 2.3–3.4 °C for the mid-term under RCP 8.5. It estimates that the D50 takes 2.5 to 3.8 days among the locations in the baseline course. In the near term, the D50 would be 2.2 to 3.4 days under RCP 4.5 and 2.1 to 3.3 days under RCP 8.5. For the mid-term, the D50 would be projected between 1.9 and 2.9 days under RCP 4.5 and 1.8 to 2.7 days under RCP 8.5. The FGP increases up to 19.5% for Gorgan and 21.3% for Varamin under climatic scenarios relative to baseline, and it will not change in the future climate for other landraces. In conclusion, global warming can result in rapid, uniform, and complete germination in different tomato landraces.
Spatial Analysis of Economic Activities as a Tool for Effective Urban Policies
Authors: Manika Stella , Karalidis Konstantinos , Gospodini Aspa
Published: March 2022

The economic activities of each city greatly shape and predict their development as well as make them more competitive both locally and globally. In the last two decades, as part of the international economic crisis, Greece has been at the center of changes that have resulted in the closure of thousands of businesses. This significantly affected Greek cities by changing their economic profile and robustness via the spatial distribution of their economic activities. Economic geography, as a sector that examines the geographical distribution of economic activities, is an important methodological base for analysing business locations and urban spatial processes. This paper aims to analyse, through a combination of economic geography theories and spatial analysis methods, the spatial patterns of economic activities and to identify urban areas that are resilient in difficult times of crisis. Thus cities that have the ability, via the proposed analysis/methodological framework, to control and evaluate their economic profile and prospects, can be transformed into smart cities by adopting ad hoc urban renaissance and resilient policies.
Athlos: A Framework for Developing Scalable MMOG Backends on Commodity Clouds
Authors: Kasenides Nicos , Paspallis Nearchos
Published: March 2022

The development of resource-intensive, distributed, real-time applications like Massively Multiplayer Online Game (MMOG) backends entails a variety of challenges, some of which have been extensively studied. Despite some advancements, the development and deployment of MMOG backends on commodity clouds and high-level computing layers continues to face several obstacles, including a non-standardized development methodology, lack of provisions for scalability, and the need for abstractions and tools to support the development process. In this paper, we describe a set of models, methods, and tools for developing scalable MMOG backends and hosting them on commodity cloud platforms. We present Athlos, a framework that allows game developers to leverage our methodology to rapidly prototype MMOG backends that can run on any type of cloud environment. We evaluate this framework by conducting simulations based on several case-study MMOGs to benchmark its performance and scalability, and compare the development effort needed, and quality of the code produced with other approaches. We find that MMOGs developed using this framework: (a) can support a very high number of simultaneous players under a given latency threshold, (b) elastically scale both in terms of runtime and state, and (c) significantly reduce the amount of effort required to develop them. Coupled with the advantages of high-level computing layers such as Platform, Backend, and Function-as-a-Service, we argue that our framework accelerates the development of high-performance, scalable MMOGs, that leverage the resources of commodity cloud platforms.
The Stacy-G Class: A New Family of Distributions with Regression Modeling and Applications to Survival Real Data
Authors: Reis Lucas D. Ribeiro , Cordeiro Gauss M. , Lima Maria do Carmo S.
Published: March 2022

We study the Stacy-G family, which extends the gamma-G class and provides four of the most well-known forms of the hazard rate function: increasing, decreasing, bathtub, and inverted bathtub. We provide some of its structural properties. We estimate the parameters by maximum likelihood, and perform a simulation study to verify the asymptotic properties of the estimators for the Burr-XII baseline. We construct the log-Stacy-Burr XII regression for censored data. The usefulness of the new models is shown through applications to uncensored and censored real data.
Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion
Authors: Wei Yu-Chih , Ou Yan-Ling , Li Jianqiang , Wu Wei-Chen
Published: March 2022

As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.
Linking Entrepreneurial Activities and Community Prosperity/Poverty in United States Counties: Use of the Enterprise Dependency Index
Authors: Toerien Daan Francois
Published: March 2022

More than 3000 U.S. counties are used to examine a hypothesis that the enterprise dependency index (population numbers/enterprise numbers, EDI) can serve as a measure of community prosperity/poverty. The theoretical derivation of EDI is presented. Then, a slightly nonlinear relationship between the total and poor populations of the counties is recorded. Poverty is slightly more systematically concentrated in smaller counties. The foregoing indicates that poverty forms part of the demographic–socioeconomic–entrepreneurial nexus of human settlements. The EDIs and poverty rates of counties are statistically significantly and positively correlated. The nonlinear power law, however, explains only about 45% of the variation, suggesting that the two measures are not identical. Further analyses confirm the independence of the two measures. Poverty is only one part of the two-part prosperity/poverty continuum. Measurement of poverty rates seemingly ignores the economic impacts of prosperity in communities. The analyses suggest that EDI, based on the ability of communities to ‘carry’ enterprises, is a more sensitive measure of community prosperity/poverty than the poverty rate. The hypothesis that EDI is a useful measure of community prosperity/poverty is accepted. Further research is, however, needed to optimize the use of this measure.
Random Vibration Analysis of a Coupled Aircraft/Runway Modeled System for Runway Evaluation
Authors: Liu Shifu , Ling Jianming , Tian Yu , Hou Tianxin , Zhao Xindong
Published: March 2022

Runway roughness is one of the most critical performance factors for runway evaluation, which directly impacts airport operation safety and pavement preservation cost. Properly evaluated runway roughness could optimize the decision-making process for runway preservation and therefore reduce the life cycle cost of the runway pavement asset. In this paper, the excitation effect of runway roughness is analyzed using a coupled aircraft/runway system. The coupled system is composed of a two degrees-of-freedom (2-DOF) aircraft model and a typical asphalt runway structure model established under runway roughness random excitation in this work. The dynamic differential equations for the coupled system are derived based on D’Alembert’s principle. The system’s vibration responses are determined via the pseudo excitation method and three response laws, i.e., the center of gravity acceleration (CGA), the dynamic load coefficient (DLC) of the landing gear, and the runway structural displacement, which are investigated under different modes. The results show that the first-order mode of the runway structure, vertical deformation, is the most significant of the four modes. Moreover, uneven excitation has a significant effect on the distribution of the aircraft’s vibration response. Compared with a single aircraft system, the developed coupled aircraft/runway system has different dynamic responses, and the degree of difference depends on the taxiing speed. The coupled effect on the CGA increases significantly with an increase in speed, with up to a 7.3% percentage difference. The coupled effect on the DLC first increases and then decreases as the aircraft speed increases, reaching a maximum of about 6% percentage difference at 120 km/h.
Sustainable Development: Use of Agricultural Waste Materials for Vanillic Acid Recovery from Wastewater
Authors: Víctor-Ortega María Dolores , Fajardo Ana S. , Airado-Rodríguez Diego
Published: March 2022

The management of agricultural waste is an important issue related to environment protection, as the inappropriate disposal of this waste yields negative effects on the environment. Proper management of industrial effluents is totally aligned with sustainable development goal (SDG) number six “clean water and sanitation”, as well as partially related to other several SDG. In this work, two agricultural waste materials were used for vanillic acid recovery from wastewater. In this scenario, vanillic acid could be considered as both an organic pollutant present in several industrial effluents and a high added-value product when isolated. Therefore, its removal from wastewaters, as well as its recovery and isolation, are very interesting from environmental and economical points of view. Peanut and pistachio shells were studied as no-cost and readily accessible potential adsorbents for the removal and recovery of vanillic acid from aqueous solutions. The evolution of equilibrium isotherms of vanillic acid on both biosorbents was investigated. Three isotherm models (Langmuir, Freundlich, and Temkin) were tested to fit the experimental equilibrium data and compared. The Langmuir model provided the best correlation for vanillic acid biosorption onto both peanut and pistachio shells. Finally, the negative values of ΔG indicated that the biosorption process was spontaneous and thermodynamically favorable for both agricultural waste materials. Accordingly, peanut and pistachio shells were shown to be very efficient low-cost adsorbents, and a promising alternative for vanillic acid recovery from industrial wastewaters.
Nutrient Remediation Efficiency of the Sedge Plant (Cyperus alopecuroides Rottb.) to Restore Eutrophic Freshwater Ecosystems
Authors: Galal Tarek M. , Gharib Fatma A. , Al-Yasi Hatim M. , Al-Mutairi Khalid A. , Mansour Khalid H. , Eid Ebrahem M.
Published: March 2022

The current study investigated the nutrients removal efficiency of the sedge macrophyte Cyperus alopecuroides to treat water eutrophication, besides evaluating the recycling possibility of the harvested material. Samples of sediment, water, and plant tissues were taken seasonally from six polluted and three unpolluted locations for this investigation. The growth properties of C. alopecuroides showed remarkable seasonal differences in plant density and biomass, with the maximum values (7.1 individual/m2 and 889.6 g/m2, respectively) obtained during summer and the minimum (4.1 individual/m2 and 547.2 g/m2, respectively) in winter. In polluted locations, the above-ground tissues had an efficiency to remove more contents of N and P (11.9 and 3.8 g/m2, respectively) than in unpolluted ones (7.1 and 3.4 g/m2, respectively). The high-nutrient standing stock of C. alopecuroides supports its potential use for nutrient removal from eutrophic wetlands. The tissues of C. alopecuroides had the maximum nutrients removal efficiency to remediate great amounts of Na, K, and N in summer, and Ca, P, and Mg in spring. Above- and below-ground parts of C. alopecuroides from unpolluted locations can be considered as a rough forage for beef cattle, dairy cattle, goats, and sheep. The present study indicated the potential of C. alopecuroides in restoring eutrophic freshwater ecosystems, and, thus, it can be used in similar habitats worldwide.
A Novel Fuzzy-Based VIKOR–CRITIC Soft Computing Method for Evaluation of Sustainable Supply Chain Risk Management
Authors: Amin Fahim ul , Dong Qian-Li , Grzybowska Katarzyna , Ahmed Zahid , Yan Bo-Rui
Published: March 2022

This article aims to evaluate sustainable supply chain risks using a novel fuzzy VIKOR–CRITIC technique. The research contributions of this study are twofold. First and foremost, this is the first attempt to integrate the fuzzy VIKOR approach with the CRITIC method in order to eradicate the inadequacies of the VIKOR method. Second, this is the first study to look at the sustainable supply chain risk management in Pakistan’s logistics industry. Four logistics companies were chosen for the study, and thirty criteria were established and divided into four categories using acquired data and literature studies. According to the findings, organizational risks are the most important to consider, whereas environmental hazards have the least influence. Supply delays, freight rate/oil price fluctuations, bankruptcy, and natural catastrophe are the four most important criteria in these categories. Limited suppliers, cargo tracking, IT system failure, and international politics are the four least significant criteria in the four risk categories. The findings are useful for the logistics industry operating in CPEC for risk mitigation and sustainable operation. The research may be used as a guideline for risk identification and management by practitioners and decision-makers in Pakistani logistics organizations.
Time–Cost Schedules and Project–Threats Indication
Authors: Kuda Frantisek , Dlask Petr , Teichmann Marek , Beran Vaclav
Published: March 2022

One of the most common disciplines in a business or economic project is timing and resource review. Despite the frequency of use, the level of sophistication is not high enough to maintain its level of importance. Exceeding deadlines and non-compliance with contractual costs is more than common. Moreover, there are projects where uncertainties are a naturally accompanying phenomenon. Research projects, implementation of solutions in a time-limited situation, or in an environment of limited knowledge creates risk. Any project proposal faces future realization risks when its planning management does not know with certainty where the current risks and uncertainties may come from. Decision-making, risk management dynamics, and simulations have developed in recent decades into an erudite and useful discipline. The aim is to indicate how much of the time–cost schedule proposal is stable, controllable, and economically feasible. The approach is based on the idea that modern resource scheduling requires nonlinear dynamic calculating models and simulations. The methodology presented is based on the dynamics of underlying physical and economic processes that form a spatial pattern of a time series. The article’s objective is devoted to the early indication of a dynamic project schedule’s instability and predisposition to bifurcation and chaos. In other words, the aim is to show not only what will happen but how diverse and damaging the project may become in the future.
Synergistic Effects of Acacia Prunings-Derived Biochar and Nitrogen Application on the Mineral Profile of Maize (Zea mays L.) Grains
Authors: Ullah Habib , Alam Sahib , Ahmad Waqar , Morrow Stuart , Sabir Muhammad , Hemar Yacine
Published: March 2022

Despite the proven impact of biochar (BC) on crop yield, little is known about its effect on cereal grain quality. We explored the effect of acacia pruning-derived biochar and nitrogen (N) applications on the mineral profile of maize grains in a field study. Biochar was applied at the rates of 5, 10, 15, and 20 t ha−1 and N at 100, 150, and 200 kg ha−1 along with the control (BC or N not applied) in a split-plot arrangement using a randomized complete block design. At crop maturity, the grains were analyzed for K, P, Ca, Mg, Zn, Fe, and Cu content. The results showed that BC application at the rate of 10 t ha−1 along with N at 200 kg ha−1 resulted in the highest concentrations of K, P, Ca, and Mg in grains. The Fe content was the maximum at the N application rate of 200 kg ha−1 while Zn and Cu had the highest concentration at 150 kg N ha−1 with no BC. It was concluded that the integrated use of BC and N could be a valuable strategy to improve the nutritional quality of maize grains. The enrichment of BC with micronutrients is recommended to achieve the desired concentration of micronutrients in maize grains to help cure malnutrition. However, further investigation is warranted to validate the impact of BC made of different feedstocks on soils of contrasting mineralogy as organo-mineral interactions might mask the true potential of BC.
Spatializing Social Networking Analysis to Capture Local Innovation Flows towards Inclusive Transition
Authors: Bevilacqua Carmelina , Sohrabi Poya , Hamdy Nourhan
Published: March 2022

The location of the local network of firms impacts, positively or negatively, their economic performance. The interactions between different sectors in a territory are still not easily observable. We test the complexity of the economic structure at a local level, given the availability of data at a very granular scale. This could greatly assist in observing sectors or/and locations that play a dominant role in the regional economy. Thus, in order to interpret the economic structure of a territory, we used cluster-based analysis. The analysis helps in evaluating the interconnections among sectors that constitute a cluster. A novel method of describing the territorial economic structure is presented by applying Social Network Analysis (SNA) within cluster-based analysis to characterize the importance of both location and economic interconnections. In this study, we focus on the industrial agglomerations in Calabria, Italy, to underpin the potential of the region’s industries by using social networking analysis metrics. This research put forward new interpretations of SNA metrics that describe regional economic compositions. Our findings reveal that territorial social networks are a potential instrument for understanding interactions in regional systems and economic clusters and might help in highlighting local industrial potentials. We believe that this study’s results could be considered as the initial steps for a pioneer data-driven place-based structural analysis model.
Impact of Environmental Concern on Ecological Purchasing Behavior: The Moderating Effect of Prosociality
Authors: Chen Libin , Wu Qi , Jiang Lin
Published: March 2022

Promoting ecological consumption is gradually becoming a social consensus. A crucial point of ecological consumption is consumers’ willingness to purchase eco-friendly products, which has become an important consideration for government policymakers. In order to achieve social responsibility, enterprises are also finding ways to encourage consumers to buy eco-friendly products. In this study, we explored the relationship between environmental concern and ecological purchasing behavior, and we tested the moderating effect of prosociality in order to explain why people act in a manner that benefits society. A self-administered questionnaire was developed to gather data, and statistical hypotheses were validated through a structural equation model. The results indicated that enhancing consumers’ environmental concerns can effectively influence their eco-purchasing behavior. Therefore, ecological attitude and ecological responsibility have a direct influence on ecological purchasing behavior. Environmental concern positively influences ecological attitude and ecological responsibility through ecological values and ecological affects. Furthermore, the prosociality moderating effect is also significant. Prosociality positively moderates the relationships among ecological responsibility, ecological attitude, and ecological purchasing behavior. The relationships among these factors are important to consider when developing ecological marketing campaigns and communication strategies to influence consumers’ ecological behavior. Therefore, fostering prosociality among citizens is also an effective way to enhance the level of eco-consumption.
Hybrid Differential Evolution-Based Regression Tree Model for Predicting Downstream Dam Hazard Potential
Authors: Abdelkader Eslam Mohammed , Al-Sakkaf Abobakr , Alfalah Ghasan , Elshaboury Nehal
Published: March 2022

There are a large number of dams throughout the United States, and a considerable portion of them are categorized as having high hazard potential. This state of affairs constitutes a challenge, especially when coupled with their rapid deterioration. As such, this research paper proposes an optimized data-driven model for the fast and efficient prediction of dam hazard potential. The proposed model is envisioned on two main components, namely model development and model assessment. In the first component, a hybridization of the differential evolution algorithm and regression tree to forecast downstream dam hazard potential is proposed. In this context, the differential evolution (DE) algorithm is deployed to: (1) automatically retrieve the optimal set of input features affecting dam hazard potential; and (2) amplify the search mechanism of regression tree (REGT) through optimizing its hyper parameters. As for the second component, the developed DE-REGT model is validated using four folds of comparative assessments to evaluate its prediction capabilities. In the first fold, the developed DE-REGT model is trialed against nine highly regarded machine learning and deep learning models. The second fold is designated to structure, an integrative ranking of the investigated data-driven models, counting on their scores in the performance evaluation metrics. The third fold is used to study the effectiveness of using differential evolution for the hyper parameter optimization of regression tree. The fourth fold aims at testing the usefulness of using differential evolution as a feature extractor algorithm. Performance comparative analysis demonstrated that the developed DE-REGT model outperformed the remainder of the data-driven models. It accomplished mean absolute percentage error, relative absolute error, mean absolute error, root squared error, root mean squared error and a Nash–Sutcliffe efficiency of 9.62%, 0.27, 0.17, 0.31, 0.41 and 0.74, respectively. Results also revealed that the developed model managed to perform better than other meta-heuristic-based regression tree models and classical feature extraction algorithms, exemplifying the appropriateness of using differential evolution for hyper parameter optimization and feature extraction. It can be argued that the developed model could assist policy makers in the prioritization of their maintenance management plans and reduce impairments caused by the failure or misoperation of dams.