bluuebunny/crossref_metadata_2025 huggingface dataset:
Pharmacists’ Perceptions and Drivers of Immunization Practices for COVID-19 Vaccines: Results of a Nationwide Survey Prior to COVID-19 Vaccine Emergency Use Authorization
Authors: Jacob Sonia Susan , Bridgeman Mary Barna , Kim Hyoeun , Toscani Michael , Kohler Racquel , Shiau Stephanie , Jimenez Humberto R. , Barone Joseph A. , Narayanan Navaneeth
Published: July 2021

Background: Pharmacists play a vital role in recommending and providing vaccines to improve public health and are on the front line of mass immunization efforts. Aim: The objective of this study is to evaluate pharmacists’ perceptions on COVID-19 vaccines prior to emergency use authorization (EUA) amid a global pandemic. Methods: A voluntary, anonymous, cross-sectional survey was conducted between September and November 2020. Survey respondents included a convenience sample of licensed pharmacists in the United States. The primary outcomes were pharmacists’ willingness to receive and recommend hypothetical COVID-19 vaccines. Covariates assessed in the survey included COVID-19 exposure or personal experience, primary pharmacy practice setting, background in training, geographic region, and prioritization of clinical data. The data were analyzed using descriptive and inferential statistics. Results: This study surveyed 763 pharmacists and results from 632 participants were included in final analysis. Overall, 67.1% of the pharmacists were willing to receive a COVID-19 vaccine and 63.4% of the pharmacists were willing to recommend a COVID-19 vaccine at ≤1 year from the time of vaccine approval. At >1 year after vaccine approval, 78% of the pharmacists were willing to receive a COVID-19 vaccine and 81.2% of the pharmacists were willing to recommend a COVID-19 vaccine. Conclusions: Survey findings suggest that, while a majority of pharmacists surveyed indicate acceptance of hypothetical COVID-19 vaccines, there remains to be hesitancy among pharmacists to receive or recommend vaccination.
Fuzzy Multi-Criteria Decision-Making Approach for Online Food Delivery (OFD) Companies Evaluation and Selection: A Case Study in Vietnam
Authors: Nguyen Ngoc Bao Tu , Lin Gu-Hong , Dang Thanh-Tuan
Published: July 2021

The COVID-19 pandemic has boosted the growth of the online food delivery (OFD) market in every corner of the world. In Vietnam, the food delivery service is rising rapidly and opening a large gateway of opportunities for numerous OFD platforms, also making it a competing business market in this country. Thus, to keep up with the ever-changing market dynamics, there are numerous measures and dimensions for the OFD entrepreneurs to take into consideration towards sustainable development. This paper’s objective is to evaluate major OFD companies in Vietnam based on a comprehensive set of criteria, which are social and environmental criteria (healthy and safety, information security, and environmental impact), economic criteria (delivery cost, operational capability, and risk management), service quality (order fulfillment, delivery speed, convenience of payment, online/offline service level, and customer feedback), and technology (web design, real-time tracking systems, and marketing techniques). To achieve this objective, this work proposes a multi-criteria decision-making (MCDM)-based framework combining the fuzzy analytic hierarchy process (FAHP) and the weighted aggregated sum product assessment (WASPAS). The FAHP is used to generate criteria weights in which fuzzy set theory is applied to translate the linguistic evaluation statements of experts. Then, WASPAS is used to rank the OFD companies against the selected criteria. The evaluation criteria that have obtained maximum weight priority in the FAHP analysis are “convenience of payment”, “delivery speed”, “online service level”, “order fulfillment”, and “delivery cost”. From the final ranking of WASPAS, Foody is today the best performing OFD player in Vietnam regarding the selected criteria, followed by GrabFood and Now. The proposed methodology can be an accurate and robust evaluation model for the industry, while the managerial implications of this study provide significant materials for decision-makers in the OFD market in improving their businesses towards sustainable development.
From Microbubbles to Nanobubbles: Effect on Flotation
Authors: Kyzas George Z. , Mitropoulos Athanasios C. , Matis Kostas A.
Published: July 2021

Attachment of particles and droplets to bubbles—the latter being of various fine sizes and created by different techniques (as described in detail)—forms the basis of flotation, a process which indeed was originated from mineral processing. Nevertheless, chemistry often plays a significant role in this area, in order for separation to be effective, as stressed. This (brief) review particularly discusses wastewater treatment applications and the effect of bubble size (from nano- to micro-) on the flotation process.
Comprehensive Management of Severe Dental Fluorosis with Adhesively Bonded All-Ceramic Restorations
Authors: Lee Jason D. , Inoue Natalie , Lee Christine , Park Sohyun , Lee Sang J.
Published: July 2021

Dental fluorosis is a common disorder caused by excessive fluoride intake during tooth development. The esthetic consequences of dental fluorosis can negatively affect oral health-related quality of life and have lasting psychosocial effects. In severe cases, where the fluorosed enamel is prone to chipping, flaking, and developing caries, minimally invasive procedures are ineffectual and a more substantial restorative approach is required to restore optimal function and esthetics. However, no definitive guidelines exist for the management and treatment of severe dental fluorosis due to the limited evidence available in the literature. This case report describes the full-mouth rehabilitation of a patient with severe dental fluorosis utilizing adhesively bonded all-ceramic crowns, veneers, and overlays. The successful follow-up on this case indicates that adhesively bonded restorations may provide a viable option in the functional and esthetic management of severely fluorosed dentition.
An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation
Authors: Nasiri Rezvan , Shushtari Mohammad , Arami Arash
Published: July 2021

In this paper, we present a novel adaptation rule to optimize the exoskeleton assistance in rehabilitation tasks. The proposed method adapts the exoskeleton contribution to user impairment severity without any prior knowledge about the user motor capacity. The proposed controller is a combination of an adaptive feedforward controller and a low gain adaptive PD controller. The PD controller guarantees the stability of the human-exoskeleton system during feedforward torque adaptation by utilizing only the human-exoskeleton joint positions as the sensory feedback for assistive torque optimization. In addition to providing a convergence proof, in order to study the performance of our method we applied it to a simplified 2-DOF model of human-arm and a generic 9-DOF model of lower limb to perform walking. In each simulated task, we implemented the impaired human torque to be insufficient for the task completion. Moreover, the scenarios that violate our convergence proof assumptions are considered. The simulation results show a converging behavior for the proposed controller; the maximum convergence time of 20 s is observed. In addition, a stable control performance that optimally supplements the remaining user motor contribution is observed; the joint angle tracking error in steady condition and its improvement compared to the start of adaptation are as follows: shoulder 0.96±2.53° (76%); elbow −0.35±0.81° (33%); hip 0.10±0.86° (38%); knee −0.19±0.67° (25%); and ankle −0.05±0.20° (60%). The presented simulation results verify the robustness of proposed adaptive method in cases that differ from our mathematical assumptions and indicate its potentials to be used in practice.
Forest Resistance and Resilience to 2002 Drought in Northern China
Authors: Li Xiran , Yao Yitong , Yin Guodong , Peng Feifei , Liu Muxing
Published: July 2021

Drought can weaken forest activity and even lead to forest mortality, and the response of different forest types to drought can be diverse. Deciduous broadleaf forest (DBF) and deciduous needleleaf forest (DNF) are two of the majority forest types in northern China. In this region, a severe drought event happened in 2002. However, due to the lack of data, the spatio-temporal characteristics of the ecosystem stability of different forest types here remain unclear. In this study, we used a machine learning approach (model tree ensemble, MTE) to drive fluxsite gross primary productivity (GPP), combined with remote sensing-based GPP and a vegetation index data (EVI), to analyze the spatial and temporal characteristics of resistance and resilience of DNF and DBF in northern China during and after the 2002 drought. The results showed that the DBFs were more acclimatized to the drought, while the resistance and resilience of DNF and DBF were diverse under different consecutive drought events. These results could be suggestive for forest conservation and vegetation modeling.
Energy-Efficient Time Synchronization Based on Nonlinear Clock Skew Tracking for Underwater Acoustic Networks
Authors: Liu Di , Zhu Min , Li Dong , Fang Xiaofang , Wu Yanbo
Published: July 2021

Time synchronization plays an important role in the scheduling and position technologies of sensor nodes in underwater acoustic networks (UANs). The time synchronization (TS) algorithms face challenges such as high requirements of energy efficiency, the estimation accuracy of the time-varying clock skew and the suppression of the impulsive noise. To achieve accurate time synchronization for UANs, an energy-efficient TS method based on nonlinear clock skew tracking (NCST) is proposed. First, based on the sea trial temperature data and the crystal oscillators’ temperature–frequency characteristics, a nonlinear model is established to characterize the dynamic of clock skews. Second, a single-way communication scheme based on a receiver-only (RO) paradigm is used in the NCST-TS to save limited energy. Meanwhile, impulsive noises are considered during the communication process and the Gaussian mixture model (GMM) is employed to fit receiving timestamp errors caused by non-Gaussian noise. To combat the nonlinear and non-Gaussian problem, the particle filter (PF)-based algorithm is used to track the time-varying clock state and an accurate posterior probability density function under the GMM error model is also given in PF. The simulation results show that under the GMM error model, the accumulative Root Mean Square Errors (RMSE) of NCST-TS can be reduced from 10−4 s to 10−5 s compared with existing protocols. It also outperforms the other TS algorithms in the aspect of energy efficiency.
Cardiorespiratory Responses to 10 Weeks of Exoskeleton-Assisted Overground Walking Training in Chronic Nonambulatory Patients with Spinal Cord Injury
Authors: Park Jae Hyeon , Kim Hyeon Seong , Jang Seong Ho , Hyun Dong Jin , Park Sang In , Yoon JuYoung , Lim Hyunseop , Kim Mi Jung
Published: July 2021

Exercise intensity of exoskeleton-assisted walking in patients with spinal cord injury (SCI) has been reported as moderate. However, the cardiorespiratory responses to long-term exoskeleton-assisted walking have not been sufficiently investigated. We investigated the cardiorespiratory responses to 10 weeks of exoskeleton-assisted walking training in patients with SCI. Chronic nonambulatory patients with SCI were recruited from an outpatient clinic. Walking training with an exoskeleton was conducted three times per week for 10 weeks. Oxygen consumption and heart rate (HR) were measured during a 6-min walking test at pre-, mid-, and post-training. Exercise intensity was determined according to the metabolic equivalent of tasks (METs) for SCI and HR relative to the HR reserve (%HRR). Walking efficiency was calculated as oxygen consumption divided by walking speed. The exercise intensity according to the METs (both peak and average) corresponded to moderate physical activity and did not change after training. The %HRR demonstrated a moderate (peak %HRR) and light (average %HRR) exercise intensity level, and the average %HRR significantly decreased at post-training compared with mid-training (31.6 ± 8.9% to 24.3 ± 7.3%, p = 0.013). Walking efficiency progressively improved after training. Walking with an exoskeleton for 10 weeks may affect the cardiorespiratory system in chronic patients with SCI.
Improving the Screen Exploration of Smartphones Using Haptic Icons for Visually Impaired Users
Authors: González-Cañete Francisco Javier , López-Rodríguez José Luís , Galdón Pedro María , Diaz-Estrella Antonio
Published: July 2021

We report the results of a study on the learnability of the locations of haptic icons on smartphones. The aim was to study the influence of the use of complex and different vibration patterns associated with haptic icons compared to the use of simple and equal vibrations on commercial location-assistance applications. We studied the performance of users with different visual capacities (visually impaired vs. sighted) in terms of the time taken to learn the icons’ locations and the icon recognition rate. We also took into consideration the users’ satisfaction with the application developed to perform the study. The experiments concluded that the use of complex and different instead of simple and equal vibration patterns obtains better recognition rates. This improvement is even more noticeable for visually impaired users, who obtain results comparable to those achieved by sighted users.
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application
Authors: Alam Mahbub Ul , Rahmani Rahim
Published: July 2021

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.
FORESAM—FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds
Authors: Pereira Rickson , Boukerche Azzedine , da Silva Marco A. C. , Nakamura Luis H. V. , Freitas Heitor , Rocha Filho Geraldo P. , Meneguette Rodolfo I.
Published: July 2021

The Intelligent Transport Systems (ITS) has the objective quality of transportation improvement through transportation system monitoring and management and makes the trip more comfortable and safer for drivers and passengers. The mobile clouds can assist the ITS in handling the resource management problem. However, resource allocation management in an ITS is challenging due to vehicular network characteristics, such as high mobility and dynamic topology. With that in mind, we propose the FORESAM, a mechanism for resources management and allocation based on a set of FOGs which control vehicular cloud resources in the urban environment. The mechanism is based on a more accurate mathematical model (Multiple Attribute Decision), which aims to assist the allocation decision of resources set that meets the period requested service. The simulation results have shown that the proposed solution allows a higher number of services, reducing the number of locks of services with its accuracy. Furthermore, its resource allocation is more balanced the provided a smaller amount of discarded services.
Estimating the Characteristic Curve of a Directional Control Valve in a Combined Multibody and Hydraulic System Using an Augmented Discrete Extended Kalman Filter
Authors: Khadim Qasim , Kiani-Oshtorjani Mehran , Jaiswal Suraj , Matikainen Marko K. , Mikkola Aki
Published: July 2021

The estimation of the parameters of a simulation model such that the model’s behaviour matches closely with reality can be a cumbersome task. This is due to the fact that a number of model parameters cannot be directly measured, and such parameters might change during the course of operation in a real system. Friction between different machine components is one example of these parameters. This can be due to a number of reasons, such as wear. Nevertheless, if one is able to accurately define all necessary parameters, essential information about the performance of the system machinery can be acquired. This information can be, in turn, utilised for product-specific tuning or predictive maintenance. To estimate parameters, the augmented discrete extended Kalman filter with a curve fitting method can be used, as demonstrated in this paper. In this study, the proposed estimation algorithm is applied to estimate the characteristic curves of a directional control valve in a four-bar mechanism actuated by a fluid power system. The mechanism is modelled by using the double-step semi-recursive multibody formulation, whereas the fluid power system under study is modelled by employing the lumped fluid theory. In practise, the characteristic curves of a directional control valve is described by three to six data control points of a third-order B-spline curve in the augmented discrete extended Kalman filter. The results demonstrate that the highly non-linear unknown characteristic curves can be estimated by using the proposed parameter estimation algorithm. It is also demonstrated that the root mean square error associated with the estimation of the characteristic curve is 0.08% with respect to the real model. In addition, all the errors in the estimated states and parameters of the system are within the 95% confidence interval. The estimation of the characteristic curve in a hydraulic valve can provide essential information for performance monitoring and maintenance applications.
Tunable In Situ 3D-Printed PVDF-TrFE Piezoelectric Arrays
Authors: Ikei Alec , Wissman James , Sampath Kaushik , Yesner Gregory , Qadri Syed N.
Published: July 2021

In the functional 3D-printing field, poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) has been shown to be a more promising choice of material over polyvinylidene fluoride (PVDF), due to its ability to be poled to a high level of piezoelectric performance without a large mechanical strain ratio. In this work, a novel presentation of in situ 3D printing and poling of PVDF-TrFE is shown with a d33 performance of up to 18 pC N−1, more than an order of magnitude larger than previously reported in situ poled polymer piezoelectrics. This finding paves the way forward for pressure sensors with much higher sensitivity and accuracy. In addition, the ability of in situ pole sensors to demonstrate different performance levels is shown in a fully 3D-printed five-element sensor array, accelerating and increasing the design space for complex sensing arrays. The in situ poled sample performance was compared to the performance of samples prepared through an ex situ corona poling process.
BlockPres: A Novel Blockchain-Based Incentive Mechanism to Mitigate Inequalities for Prescription Management System
Authors: Litchfield Alan , Khan Arshad
Published: July 2021

The study presents a blockchain-based incentive mechanism intended to encourage those in underserved communities to engage with healthcare services. The smart healthcare system, which is the result of the amalgamation of advanced technologies, has emerged recently and is increasingly seen as essential to meet the needs of modern society. An important part of the healthcare system is the prescription management system, but studies show that prescription affordability and accessibility play a part in creating unequal access for underserved communities. This is a form of unequal access that results in those living in underserved communities to become disengaged from accessing healthcare services. In New Zealand, the prescription management system plays a crucial role and this study seeks to address the issue by presenting the BlockPres framework, which uses a novel incentive mechanism to encourage patients to participate and engage with services in order to be rewarded. The blockchain attribute of immutability in BlockPres enhances equality and participation by providing sophisticated authorisation and authentication capabilities for healthcare providers and patients. BlockPres empowers the patient by assigning ownership or control of some patient information to the patient. A simulation is carried out using the Ethereum blockchain and the evaluation of successful transaction completion and superficial performance assessment demonstrates that the blockchain would be sufficient to cope with the needs of a prescription management system. Furthermore, for the simulation, a BlockPres Smart Contract is developed using solidity and implemented in Remix. The Ropsten network is used as the simulation environment and the initial results show that the proposed incentive mechanism mitigates unequal access.
Sensorless Speed Estimation for the Diagnosis of Induction Motors via MCSA. Review and Commercial Devices Analysis
Authors: Bonet-Jara Jorge , Quijano-Lopez Alfredo , Morinigo-Sotelo Daniel , Pons-Llinares Joan
Published: July 2021

Sensorless speed estimation has been extensively studied for its use in control schemes. Nevertheless, it is also a key step when applying Motor Current Signature Analysis to induction motor diagnosis: accurate speed estimation is vital to locate fault harmonics, and prevent false positives and false negatives, as shown at the beginning of the paper through a real industrial case. Unfortunately, existing sensorless speed estimation techniques either do not provide enough precision for this purpose or have limited applicability. Currently, this is preventing Industry 4.0 from having a precise and automatic system to monitor the motor condition. Despite its importance, there is no research published reviewing this topic. To fill this gap, this paper investigates, from both theoretical background and an industrial application perspective, the reasons behind these problems. Therefore, the families of sensorless speed estimation techniques, mainly conceived for sensorless control, are here reviewed and thoroughly analyzed from the perspective of their use for diagnosis. Moreover, the algorithms implemented in the two leading commercial diagnostic devices are analyzed using real examples from a database of industrial measurements belonging to 79 induction motors. The analysis and discussion through the paper are synthesized to summarize the lacks and weaknesses of the industry application of these methods, which helps to highlight the open problems, challenges and research prospects, showing the direction in which research efforts have to be made to solve this important problem.
Status Estimation and In-Process Connection of Kanbans Using BLE Beacons and LPWA Network to Implement Intra-Traceability for the Kanban System
Authors: Shima Kosuke , Yamaguchi Masahiro , Yoshida Takumi , Otsuka Takanobu
Published: July 2021

IoT-based measurement systems for manufacturing have been widely implemented. As components that can be implemented at low cost, BLE beacons have been used in several systems developed in previous research. In this work, we focus on the Kanban system, which is a measure used in manufacturing strategy. The Kanban system emphasizes inventory management and is used to produce only required amounts. In the Kanban system, the Kanban cards are rotated through the factory along with the products, and when the products change to a different process route, the Kanban card is removed from the products and the products are assigned to another Kanban. For this reason, a single Kanban cannot trace products from plan to completion. In this work, we propose a system that uses a Bluetooth low energy (BLE) beacon to connect Kanbans in different routes but assigned to the same products. The proposed method estimates the beacon status of whether the Kanban is inside or outside a postbox, which can then be computed by a micro controller at low computational cost. In addition, the system connects the Kanbans using the beacons as paired connection targets. In an experiment, we confirmed that the system connected 70% of the beacons accurately. We also confirmed that the system could connect the Kanbans at a small implementation cost.
A Review on Mental Stress Assessment Methods Using EEG Signals
Authors: Katmah Rateb , Al-Shargie Fares , Tariq Usman , Babiloni Fabio , Al-Mughairbi Fadwa , Al-Nashash Hasan
Published: July 2021

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.
LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms
Authors: Behjati Mehran , Mohd Noh Aishah Binti , Alobaidy Haider A. H. , Zulkifley Muhammad Aidiel , Nordin Rosdiadee , Abdullah Nor Fadzilah
Published: July 2021

Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).
Fast Sensing of Hydrogen Cyanide (HCN) Vapors Using a Hand-Held Ion Mobility Spectrometer with Nonradioactive Ionization Source
Authors: Bocos-Bintintan Victor , Ratiu Ileana Andreea
Published: July 2021

Sensitive real-time detection of vapors produced by toxic industrial chemicals (TICs) always represents a stringent priority. Hydrogen cyanide (HCN) is definitely a TIC, being widely used in various industries and as an insecticide; it is a reactive, very flammable, and highly toxic compound that affects the central nervous system, cardiovascular system, eyes, nose, throat, and also has systemic effects. Moreover, HCN is considered a blood chemical warfare agent. This study was focused toward quick detection and quantification of HCN in air using time-of-flight ion mobility spectrometry (ToF IMS). Results obtained clearly indicate that IMS can rapidly detect HCN at sub-ppmv levels in air. Ion mobility spectrometric response was obtained in the negative ion mode and presented one single distinct product ion, at reduced ion mobility K0 of 2.38 cm2 V−1 s−1. Our study demonstrated that by using a miniaturized commercial IMS system with nonradioactive ionization source model LCD-3.2E (Smiths Detection Ltd., London, UK), one can easily measure HCN at concentrations of 0.1 ppmv (0.11 mg m−3) in negative ion mode, which is far below the OSHA PEL-TWA value of 10 ppmv. Measurement range was from 0.1 to 10 ppmv and the estimated limit of detection LoD was ca. 20 ppbv (0.02 mg m−3).
Control System Design of an Underactuated Dynamic Body Weight Support System Using Its Stability
Authors: Gembalczyk Grzegorz , Gierlak Piotr , Duda Slawomir
Published: July 2021

This paper discusses the stability of systems controlling patient body weight support systems which are used in gait re-education. These devices belong to the class of underactuated mechanical systems. This is due to the application of elastic shock-absorbing connections between the active part of the system and the passive part which impacts the patient. The model takes into account properties of the system, such as inertia, attenuation and susceptibility to the elements. Stability is an essential property of the system due to human–device interaction. In order to demonstrate stability, Lyapunov’s theory of stability, which is based on the model of system dynamics, was applied. The stability of the control system based on a model that requires knowledge of the structure and parameters of the equations of motion was demonstrated. Due to inaccuracies in the modeling of the rope (one of the basic elements of the device), an adaptive control system was introduced and its stability was also proved. The authors conducted simulation and experimental tests that illustrate the functionality of the analyzed control systems.
Adsorption Characteristics and Mechanism of Calcium Ions on Different Molybdenite Surfaces via Experiments and DFT Simulations
Authors: Sun Liqing , Cao Yijun , Li Lin , Zeng Qingliang
Published: July 2021

Calcium ions are common in flotation process water, and have a significant effect on the molybdenite floatability, making separation of molybdenite from other minerals more difficult. Therefore, to improve the separation selectivity, the research of how calcium ions affect the molybdenite surface properties is of great significance. In this study, various methods including flotation tests, contact angle measurements, batch adsorption tests and Density Functional Theory (DFT) simulations were carried out to understand the adsorption characteristics and mechanism. Results of the contact angle measurements showed that the inhibition effects of calcium ions on molybdenite flotation kinetics were mostly attributed to the decrease of the edge surface hydrophobicity, as the contact angle of the edge surface decreased more than the face surface after treatment with calcium ions. While fitting the results of batch adsorption tests with adsorption kinetics and isotherm models, it was found that the Lagergen pseudo-first-order equation and the Freundlich isotherm model nicely follow the experimental trend. Moreover, DFT calculation results indicated that both Ca2+ and CaOH+ preferentially adsorb on the molybdenite (100) surface, particularly the edge surface, which was consistent with the contact angle results. Ca2+ adsorbed on the Mo-top site on the S-(100) surface by forming Ca-S bonds, transferring electrons from Ca 3d orbitals to S 3p orbitals. CaOH+ adsorbed on the S-top site of Mo-(100) surface by forming a strong covalent Mo-O bond and S-Ca bond. The results provide a basis for understanding and improving the separation effect of molybdenite from other minerals in the presence of calcium ions.
Why Do People Work? An Empirical Test of Hybrid Work Orientations
Authors: Pitacho Liliana , da Palma Patrícia Jardim , Correia Pedro , Lopes Miguel Pereira
Published: July 2021

The present study analyzed whether one’s work orientation can be organized into work orientation profiles beyond the three pure orientations of job, career, and calling. We tested the existence of these hybrid work orientations in a sample of 959 adults aged from 18 to 71 years old (M = 40.61, SD = 9.54). A cluster analysis showed that the best result consisted of four profiles: “Career-Calling”, “Career-Job”, “Pure Job”, and “Indifference”. Theoretical and practical implications of profile approach to the study of work orientations are discussed at the end.
A Recommender System for Mobility-as-a-Service Plans Selection
Authors: Arnaoutaki Konstantina , Bothos Efthimios , Magoutas Babis , Aba Attila , Esztergár-Kiss Domokos , Mentzas Gregoris
Published: July 2021

Transportation and mobility in smart cities are undergoing a grave transformation as new ways of mobility are introduced to facilitate seamless traveling, addressing travelers’ needs in a personalized manner. A novel concept that has been recently introduced is Mobility-as-a-Service (MaaS), where mobility services are bundled in MaaS Plans and offered to end-users through a single digital platform. The present paper introduces a recommender system for MaaS Plans selection that supports travelers to select bundles of mobility services that fit their everyday transportation needs. The recommender filters out unsuitable plans and then ranks the remaining ones on the basis of their similarity to the users’ characteristics, habits and preferences. The recommendation approach is based on Constraint Satisfaction Problem (CSP) formalisms combined with cosine similarity techniques. The proposed method was evaluated in experimental settings and was further embedded in real-life pilot MaaS applications. The experimental results showed that the proposed approach provides lists of MaaS PlanMaaS Plans that users would choose in a real-life MaaS setting, in most of the cases. Moreover, the results of the real-life pilots showed that the majority of the participants chose an actual MaaS Plan from the top three places of the recommendation lists.
Environmental Risk Mitigation by Biodiesel Blending from Eichhornia crassipes: Performance and Emission Assessment
Authors: Abdul Wahhab Hasanain A. , Al-Kayiem Hussain H.
Published: July 2021

The aggressive growth of Eichhornia crassipes (Water Hyacinth) plants causes severe damage to the irrigation, environment, and waterway systems in Iraq. This study aims to produce, characterize, and test biofuel extracted from the Eichhornia crassipes plant in Iraq. The extracted biodiesel was mixed at 10%, 20%, and 40% with neat diesel to produce three biodiesel samples. The methodology consists of the physiochemical properties of the samples that were characterized. The performance of the IC engine fueled by neat and biodiesel samples was measured under various operational conditions. The exhaust gases were analyzed to estimate the compounds to assess the environmental impact. The results showed that the density and viscosity of mixtures increase and the calorific value decrease with biodiesel. The engine test showed that the diesel + 10BE, diesel + 20BE, and diesel + 40BE enhanced brake thermal efficiency using 2.6%, 4.2%, and 6.3%, respectively, compared to neat diesel. Exhaust tests show a slight reduction, of 0.85–3.69% and 2.48–6.93%, in CO and HC emission, respectively. NOx is higher by 1.87–7.83% compared with neat diesel. The results revealed that biodiesel blended from Eichhornia crassipes is a viable solution to mitigate the drastic impact on the environment and economy in Iraq. The blended biodiesel has good potential to be mixed with the locally produced diesel from oil refineries.
The nZEB Requirements for Residential Buildings: An Analysis of Thermal Comfort and Actual Energy Needs in Portuguese Climate
Authors: Resende Jaime , Corvacho Helena
Published: July 2021

As of now, in the EU, all new buildings will have to comply with the requirements for nearly zero energy buildings (nZEB). Portugal defines limits for the maximum useful energy needs for heating and for the maximum primary energy needs and establishes that 50% of primary energy needs must be covered by local renewable energy sources, based on the dwellings’ nominal condition of use. However, the actual use is different. Thus, a simulation tool is used to assess thermal comfort and energy needs for different conditions of use of a dwelling complying with nZEB requirements. Eight different locations are chosen, covering all Portuguese climate zones. The nZEB requirements lead to unusually high levels of thermal insulation, especially in the coldest regions, so special care must be taken by the designers to avoid overheating in summer. Without using HVAC system, some discomfort is found but comfort is possible with low energy consumption. Furthermore, the compliance with nZEB requirements proved to be enough to ensure that a significant percentage of useful energy needs for heating, cooling and domestic hot water is supplied by local renewable energy sources. This is key in order to overcome the high rate of energy poverty in Portugal.
Life Cycle Assessment on Electric Moped Scooter Sharing
Authors: Schelte Nora , Severengiz Semih , Schünemann Jaron , Finke Sebastian , Bauer Oskar , Metzen Matthias
Published: July 2021

Due to their small size and low energy demand, light electric vehicles (LEVs), such as electric moped scooters, are considered as a space efficient and eco-friendly alternative for mobility in cities. However, the growth of electric moped scooter sharing services raises the question of how environmentally friendly this business model is, considering the entire lifecycle. Due to the dynamic market and insufficient availability of public data on the business processes of sharing services only a few studies on the impact of shared electric mopeds are available. Especially there is a lack of research on the impacts of key operational logistic parameters of the sharing system. This paper aims to fill this gap by conducting a life cycle assessment using the example of an electric moped scooter manufactured and used in sharing services in Germany, based on different operating scenarios. The results show that e-moped sharing has a similar environmental impact on global warming potential, in terms of passenger kilometers, as public transport, especially if long product lifetimes as well as efficient operation logistics are realized.
Strategic Challenges for Sustainable Governance of the Bioeconomy: Preventing Conflict between SDGs
Authors: Maksymiv Yuliia , Yakubiv Valentyna , Pylypiv Nadia , Hryhoruk Iryna , Piatnychuk Iryna , Popadynets Nazariy
Published: July 2021

The paper analyzes approaches to understanding the concept of the bioeconomy in highly cited journals, and bioeconomy strategies in associations of countries (the OECD and EU) and at the national level. Strategic challenges for sustainable bioeconomy governance are identified and an understanding of this concept is presented. The main aims, such as decoupling, the use of biotechnology, the use of sustainable biomass in the production process, and a high level of corporate social responsibility, are proposed for the identification of industries related to the bioeconomy. Specific goal achievement within the bioeconomy strategy and possible risks of conflict between SDGs are presented in the model. The bioeconomy should be considered a tool for achieving SDGs; the most relevant being 2, 3, 7, 9, 12, and 13–15. However, the achievement of bioeconomy goals can lead to a conflict between SDGs in practice. Therefore, this aspect must be taken into account at the stage of developing bioeconomy strategy and regulated in governance, to prevent conflicts between SDGs and to involve citizens and stakeholders in participating consciously in this process.
Multi-Objective Optimization of Forth Flotation Process: An Application in Gold Ore
Authors: Zhang Wen , Yuan Qinghe , Jia Shun , Li Zhaojun (Steven) , Yin Xianhui
Published: July 2021

In order to improve production control ability in the gold ore flotation process, the output index in this process was studied. Flotation is an effective gold recovery process. Gold concentrate grade and gold recovery rate are the key output indicators of the flotation process. However, in the existing studies exploring the impact of parameter changes on the output indicators, the control ability of the output indicators is insufficient, and the interaction between variables is inadequately considered. Therefore, a multi-objective optimization model based on response surface methodology and the non-dominated sorting genetic algorithm-II (NSGA-II) is proposed in this paper. Firstly, the experiment was designed based on the Box-Behnken principle. Based on the experimental results, the interaction between variables was analyzed and the response polynomial was fitted. Secondly, a multi-objective optimization model was constructed, and the NSGA-II was used to solve the model. Finally, an example of gold ore flotation was used to verify the effectiveness of the method. The optimal solution was a gold concentrate grade of 75.46 g/t and a gold recovery rate of 85.98%.
Formation of the Traffic Flow Rate under the Influence of Traffic Flow Concentration in Time at Controlled Intersections in Tyumen, Russian Federation
Authors: Morozov Viacheslav , Iarkov Sergei
Published: July 2021

Present experience shows that it is impossible to solve the problem of traffic congestion without intelligent transport systems. Traffic management in many cities uses the data of detectors installed at controlled intersections. Further, to assess the traffic situation, the data on the traffic flow rate and its concentration are compared. Latest scientific studies propose a transition from spatial to temporal concentration. Therefore, the purpose of this work is to establish the regularities of the influence of traffic flow concentration in time on traffic flow rate at controlled city intersections. The methodological basis of this study was a systemic approach. Theoretical and experimental studies were based on the existing provisions of system analysis, traffic flow theory, experiment planning, impulses, probabilities, and mathematical statistics. Experimental data were obtained and processed using modern equipment and software: Traficam video detectors, SPECTR traffic light controller, Traficam Data Tool, SPECTR 2.0, AutoCad 2017, and STATISTICA 10. In the course of this study, the authors analyzed the dynamics of changes in the level of motorization, the structure of the motor vehicle fleet, and the dynamics of changes in the number of controlled intersections. As a result of theoretical studies, a hypothesis was put forward that the investigated process is described by a two-factor quadratic multiplicative model. Experimental studies determined the parameters of the developed model depending on the directions of traffic flow, and confirmed its adequacy according to Fisher’s criterion with a probability of at least 0.9. The results obtained can be used to control traffic flows at controlled city intersections.
Conditions under Which Rural-to-Urban Migration Enhances Social and Economic Sustainability of Home Communities: A Case Study in Vietnam
Authors: Le Thi Huyen , Nakagawa Yoshinori , Kobayashi Yutaka
Published: July 2021

Rural-to-urban migration contributes to the economic and social sustainability of sending communities. The aim of this study was to obtain quantitative evidence supporting the theoretical argument that (i) rural-to-urban migrants contribute to the sustainability of their sending communities, and (ii) once they return, they are likely to behave prosocially as return migrants because they feel a responsibility to apply the knowledge and skills they acquired during migration for the sake of others in their sending communities. A cross-sectional survey was conducted in Hanoi, Vietnam, a typical destination city of domestic rural-to-urban migrants. Three hundred rural-to-urban migrants participated in this survey. The ultivariate regression analysis results indicate that rural-to-urban migrants contribute more to the social and economic sustainability of their rural home communities when they have spent longer in their migration destinations and have accumulated skills and knowledge because their experiences foster a sense of responsibility toward their home communities. This is the first quantitative investigation of the relationship between rural-to-urban migrants’ characteristics representing their accumulation of skills and knowledge in their destination cities and their supportive attitudes toward their home communities. This investigation seemed important because it was expected to clarify the conditions under which rural-to-urban migration stimulates migrants’ sense of responsibility and thus their contributions to the social and economic sustainability of their sending communities.