bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementation of Wearable Sensing Technology for Movement: Pushing Forward into the Routine Physical Rehabilitation Care Field
Authors: Lang Catherine E. , Barth Jessica , Holleran Carey L. , Konrad Jeff D. , Bland Marghuretta D.
Published: October 2020

While the promise of wearable sensor technology to transform physical rehabilitation has been around for a number of years, the reality is that wearable sensor technology for the measurement of human movement has remained largely confined to rehabilitation research labs with limited ventures into clinical practice. The purposes of this paper are to: (1) discuss the major barriers in clinical practice and available wearable sensing technology; (2) propose benchmarks for wearable device systems that would make it feasible to implement them in clinical practice across the world and (3) evaluate a current wearable device system against the benchmarks as an example. If we can overcome the barriers and achieve the benchmarks collectively, the field of rehabilitation will move forward towards better movement interventions that produce improved function not just in the clinic or lab, but out in peoples’ homes and communities.
Coaxiality Evaluation of Coaxial Imaging System with Concentric Silicon–Glass Hybrid Lens for Thermal and Color Imaging
Authors: Takahata Tomoyuki
Published: October 2020

Thermal imaging is useful for tasks such as detecting the presence of humans and recognizing surrounding objects in the operation of several types of robots, including service robots and personal mobility robots, which assist humans. Because the number of pixels on a thermal imager is generally smaller than that on a color imager, thermal images are more useful when combined with color images, assuming that the correspondence between points in the images captured by the two sensors is known. In the literature, several types of coaxial imaging systems have been reported that can capture thermal and color images, simultaneously, from the same point of view with the same optical axis. Among them, a coaxial imaging system using a concentric silicon–glass hybrid lens was devised. Long-wavelength infrared and visible light was focused using the hybrid lens. The focused light was subsequently split using a silicon plate. Separate thermal and color images were then captured using thermal and color imagers, respectively. However, a coaxiality evaluation of the hybrid lens has not been shown. This report proposes an implementation and coaxiality evaluation for a compact coaxial imaging system incorporating the hybrid lens. The coaxiality of the system was experimentally demonstrated by estimating the intrinsic and extrinsic parameters of the thermal and color imagers and performing 2D mapping between the thermal images and color images.
A Modified TurboEdit Cycle-Slip Detection and Correction Method for Dual-Frequency Smartphone GNSS Observation
Authors: Xu Xiaofei , Nie Zhixi , Wang Zhenjie , Zhang Yuanfan
Published: October 2020

Recently, some smartphone manufacturers have subsequently released dual-frequency GNSS smartphones. With dual-frequency observations, the positioning performance is expected to be significantly improved. Cycle-slip detection and correction play an important role in high-precision GNSS positioning, such as precise point positioning (PPP) and real-time kinematic (RTK) positioning. The TurboEdit method utilizes Melbourne–Wübbena (MW) and phase ionospheric residual (PIR) combinations to detect cycle-slips, and it is widely used in the data processing applications for geodetic GNSS receivers. The smartphone pseudorange observations are proved to be much noisier than those collected with geodetic GNSS receivers. Due to the poor pseudorange observation, the MW combination would be difficult to detect small cycle-slips. In addition, some specific cycle-slip combinations, where the ratio of cycle-slip values at different carrier frequencies is close to the frequency ratio, are also difficult to be detected by PIR combination. As a consequence, the traditional TurboEdit method may fail to detect specific small cycle-slip combinations. In this contribution, we develop a modified TurboEdit cycle-slip detection and correction method for dual-frequency smartphone GNSS observations. At first, MW and PIR combinations are adopted to detect cycle-slips by comparing these two combinations with moving-window average values. Then, the epoch-differenced wide-lane combinations are used to estimate the changes of smartphone position and clock bias, and the cycle-slip is identified by checking the largest normalized residual whether it exceeds a predefined threshold value. The process of estimation and cycle-slip identification is implemented in an iterative way until there is no over-limit residual or there is no redundant measurement. At last, the cycle-slip values at each frequency are estimated with the epoch-differenced wide-lane and ionosphere-free combinations, and the least-square ambiguity decorrelation adjustment (LAMBDA) method is adopted to further obtain an integer solution. The proposed method has been verified with 1 Hz dual-frequency smartphone GNSS data. The results show that the modified TurboEdit method can effectively detect and correct even for specific small cycle-slip combinations, e.g., (4, 3), which is difficult to be detected with the traditional TurboEdit method.
Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration
Authors: Band Shahab S. , Janizadeh Saeid , Pal Subodh Chandra , Chowdhuri Indrajit , Siabi Zhaleh , Norouzi Akbar , Melesse Assefa M. , Shokri Manouchehr , Mosavi Amirhosein
Published: October 2020

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.
Dynamic Response of PVDF Cantilever Due to Droplet Impact Using an Electromechanical Model
Authors: Hao Guannan , Dong Xiangwei , Li Zengliang , Liu Xiaoxiao
Published: October 2020

The dynamic response of a polyvinylidene fluoride (PVDF) cantilever beam under excitation of water droplet impact is investigated by developing an electromechanical model. In the model, the governing equations of beam motion and output voltage are derived in the theoretical way, such that the voltage across the PVDF layer and the cantilever deflection can be predicted. The motion of the beam is described by the multi-mode vibration model through which more accurate results can be obtained. The predicted results of the model are validated by the experiment. Combined with the experiment and the model, the effect of surface wettability on droplet-substrate interaction mechanisms is investigated, which provides an insight into the improvement of mechanical-to-electrical energy conversion efficiency in raindrop energy harvesting (REH) applications. Results show: (1) the droplet splash on a super-hydrophobic beam surface has a positive effect on voltage generation. The splash limit that affects the reaction force of the impacting droplet is experimentally determined and greatly dominant by the Weber number. (2) Small-scaled droplets in splash regime allow generating higher voltage output from a super-hydrophobic beam surface than from an untreated hydrophilic beam surface. (3) Tests of successive droplet impacts also show that a super-hydrophobic surface performs better over a hydrophilic surface by producing constant peak voltage and higher electrical energy harvested. In this case, the voltage measured from the hydrophilic surface decreases gradually as the water layer is accumulated. Overall, the electromechanical behaviors of a super-hydrophobic PVDF cantilever sensor can be well predicted by the model which shows a great potential in energy harvesting by maximizing the inelastic collision upon droplet-substrate interactions.
A Low-Power WSN Protocol with ADR and TP Hybrid Control
Authors: Hung Chung-Wen , Zhang Hao-Jun , Hsu Wen-Ting , Zhuang Yi-Da
Published: October 2020

Most Internet of Things (IoT) systems are based on the wireless sensor network (WSN) due to the reduction of the cable layout cost. However, the battery life of nodes is a key issue when the node is powered by a battery. A Low-Power WSN Protocol with ADR and TP Hybrid Control is proposed in this paper to improve battery life significantly. Besides, techniques including the Sub-1GHz star topology network with Time Division Multiple Access (TDMA), adaptive data rate (ADR), and transmission power control (TPC) are also used. The long-term testing results show that the nodes with the proposed algorithm can balance the communication quality and low power consumption simultaneously. The experimental results also show that the power consumption of the node with the algorithm was reduced by 38.46-54.44% compared with the control group. If using AAA battery with 1200 mAh, the node could run approximately 4.2 years with the proposed hybrid control algorithm with an acquisition period of under 5 s.
A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition
Authors: Thapa Keshav , Abdullah Al Zubaer Md. , Lamichhane Barsha , Yang Sung-Hyun
Published: October 2020

Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are typical for now, but recognizing complex activities such as concurrent and interleaving activity is still a major challenging issue. In this paper, we propose a two-phase hybrid deep machine learning approach using bi-directional Long-Short Term Memory (BiLSTM) and Skip-Chain Conditional random field (SCCRF) to recognize the complex activity. BiLSTM is a sequential generative deep learning inherited from Recurrent Neural Network (RNN). SCCRFs is a distinctive feature of conditional random field (CRF) that can represent long term dependencies. In the first phase of the proposed approach, we recognized the concurrent activities using the BiLSTM technique, and in the second phase, SCCRF identifies the interleaved activity. Accuracy of the proposed framework against the counterpart state-of-art methods using the publicly available datasets in a smart home environment is analyzed. Our experiment’s result surpasses the previously proposed approaches with an average accuracy of more than 93%.
Planar Interdigitated Aptasensor for Flow-Through Detection of Listeria spp. in Hydroponic Lettuce Growth Media
Authors: Sidhu Raminderdeep K. , Cavallaro Nicholas D. , Pola Cicero C. , Danyluk Michelle D. , McLamore Eric S. , Gomes Carmen L.
Published: October 2020

Irrigation water is a primary source of fresh produce contamination by bacteria during the preharvest, particularly in hydroponic systems where the control of pests and pathogens is a major challenge. In this work, we demonstrate the development of a Listeria biosensor using platinum interdigitated microelectrodes (Pt-IME). The sensor is incorporated into a particle/sediment trap for the real-time analysis of irrigation water in a hydroponic lettuce system. We demonstrate the application of this system using a smartphone-based potentiostat for rapid on-site analysis of water quality. A detailed characterization of the electrochemical behavior was conducted in the presence/absence of DNA and Listeria spp., which was followed by calibration in various solutions with and without flow. In flow conditions (100 mL samples), the aptasensor had a sensitivity of 3.37 ± 0.21 kΩ log-CFU−1 mL, and the LOD was 48 ± 12 CFU mL−1 with a linear range of 102 to 104 CFU mL−1. In stagnant solution with no flow, the aptasensor performance was significantly improved in buffer, vegetable broth, and hydroponic media. Sensor hysteresis ranged from 2 to 16% after rinsing in a strong basic solution (direct reuse) and was insignificant after removing the aptamer via washing in Piranha solution (reuse after adsorption with fresh aptamer). This is the first demonstration of an aptasensor used to monitor microbial water quality for hydroponic lettuce in real time using a smartphone-based acquisition system for volumes that conform with the regulatory standards. The aptasensor demonstrated a recovery of 90% and may be reused a limited number of times with minor washing steps.
Study of Three Interface Pressure Measurement Systems Used in the Treatment of Venous Disease
Authors: Nandasiri Gayani K. , Shahidi Arash M. , Dias Tilak
Published: October 2020

The aim of the publication is to report the accuracy, repeatability and the linearity of three commercially available interface pressure measurement systems employed in the treatment of venous disease. The advances in the treatment and management of chronic venous disease by compression therapy have led to considerable research interest in interface pressure measurement systems capable of measuring low-pressure ranges (10–60 mmHg). The application of a graduated pressure profile is key for the treatment of chronic venous disease which is achieved by using compression bandages or stockings; the required pressure profiles are defined in standards (BSI, RAL-GZ, or AFNOR) for different conditions. However, achieving the recommended pressure levels and its accuracy is still deemed to be a challenge. Thus, it is vital to choose a suitable pressure measurement system with high accuracy of interface pressure. The authors investigated the sensing performance of three commercially available different pressure sensors: two pneumatic based (AMI and PicoPress®) and one piezoresistive (FlexiForce®) pressure sensors, with extensive experimental work on their performance in terms of linearity, repeatability, and accuracy. Both pneumatic based pressure measurement systems have shown higher accuracy in comparison to the flexible piezoresistive pressure sensors.
Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging
Authors: Awais Muhammad , Ghayvat Hemant , Krishnan Pandarathodiyil Anitha , Nabillah Ghani Wan Maria , Ramanathan Anand , Pandya Sharnil , Walter Nicolas , Saad Mohamad Naufal , Zain Rosnah Binti , Faye Ibrahima
Published: October 2020

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
Authors: Tanjung Dion , Byeon Seunggyu , Huh Junhwan , Kim Dong Hyun , Kim Jong Deok
Published: October 2020

The single-hop star-of-stars topology in low-power and wide-area networks (LPWAN) exhibits reliability and substandard coverage issues, especially in urban areas where line-of-sight (LoS) communication is difficult to achieve. Moreover, LPWAN trade-off the data rate to achieve longer coverage, preventing other end-devices from using the time resource. Locating other gateways is uneconomical as it requires infrastructure, such as the internet and a power connection. In this study, we propose a forwarding scheme with a relay to increase LPWAN coverage and reliability while not degrading the network’s capacity. A relay tends to incur unnecessary forwarding that degrades the network capacity unless proper countermeasure is prepared. Our works, namely opportunistic and on-demand network coding (OODC), minimize unnecessary forwarding and make good use of multiple-receiving relays. Network coding is also applied in the relay for better transmission efficiency and reliability simultaneously. Because network coding occurs header overhead, we perform a header compression technique to counter it. According to our simulation result, our method shows better reliability than fixed path forwarding. In an adaptive data rate settings, the OODC achieves a 92% packet delivery ratio (PDR), whereas a fixed-path forwarding only achieves an 84% PDR.
Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal
Authors: Tateno Shigeyuki , Liu Hongbin , Ou Junhong
Published: October 2020

Sign languages are developed around the world for hearing-impaired people to communicate with others who understand them. Different grammar and alphabets limit the usage of sign languages between different sign language users. Furthermore, training is required for hearing-intact people to communicate with them. Therefore, in this paper, a real-time motion recognition system based on an electromyography signal is proposed for recognizing actual American Sign Language (ASL) hand motions for helping hearing-impaired people communicate with others and training normal people to understand the sign languages. A bilinear model is applied to deal with the on electromyography (EMG) data for decreasing the individual difference among different people. A long short-term memory neural network is used in this paper as the classifier. Twenty sign language motions in the ASL library are selected for recognition in order to increase the practicability of the system. The results indicate that this system can recognize these twenty motions with high accuracy among twenty participants. Therefore, this system has the potential to be widely applied to help hearing-impaired people for daily communication and normal people to understand the sign languages.
LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion
Authors: Podevijn Nico , Trogh Jens , Aernouts Michiel , Berkvens Rafael , Martens Luc , Weyn Maarten , Joseph Wout , Plets David
Published: October 2020

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution.
Development and Application of Resistance Strain Force Sensors
Authors: Zhao Yinming , Liu Yang , Li Yongqian , Hao Qun
Published: October 2020

Resistance strain force sensors have been applied to monitor the strains in various parts and structures for industrial use. Here, we review the working principles, structural forms, and fabrication processes for resistance strain gauges. In particular, we focus on recent developments in resistance stress transfer for resistance strain force sensors and the creep effect due to sustained loads and/or temperature variations. Various error compensation methods to reduce the creep effect are analyzed to develop a metrology standard for resistance strain force sensors. Additionally, the current status of carbon nanotubes (CNTs), silicon carbide (SiC), gallium nitride (GaN), and other wide band gap semiconductors for a wide range of strain sensors are reviewed. The technical requirements and key issues of resistance strain force sensors for future applications are presented.
Analytical and Experimental Study of Fatigue-Crack-Growth AE Signals in Thin Sheet Metals
Authors: Joseph Roshan , Giurgiutiu Victor
Published: October 2020

The acoustic emission (AE) method is a very popular and well-developed method for passive structural health monitoring of metallic and composite structures. AE method has been efficiently used for damage source detection and damage characterization in a large variety of structures over the years, such as thin sheet metals. Piezoelectric wafer active sensors (PWASs) are lightweight and inexpensive transducers, which recently drew the attention of the AE research community for AE sensing. The focus of this paper is on understanding the fatigue crack growth AE signals in thin sheet metals recorded using PWAS sensors on the basis of the Lamb wave theory and using this understanding for predictive modeling of AE signals. After a brief introduction, the paper discusses the principles of sensing acoustic signals by using PWAS. The derivation of a closed-form expression for PWAS response due to a stress wave is presented. The transformations happening to the AE signal according to the instrumentations we used for the fatigue crack AE experiment is also discussed. It is followed by a summary of the in situ AE experiments performed for recording fatigue crack growth AE and the results. Then, we present an analytical model of fatigue crack growth AE and a comparison with experimental results. The fatigue crack growth AE source was modeled analytically using the dipole moment concept. By using the source modeling concept, the analytical predictive modeling and simulation of the AE were performed using normal mode expansion (NME). The simulation results showed good agreement with experimental results. A strong presence of nondispersive S0 Lamb wave mode due to the fatigue crack growth event was observed in the simulation and experiment. Finally, the analytical method was verified using the finite element method. The paper ends with a summary and conclusions; suggestions for further work are also presented.
Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images
Authors: Yi Jinhui , Krusenbaum Lukas , Unger Paula , Hüging Hubert , Seidel Sabine J. , Schaaf Gabriel , Gall Juergen
Published: October 2020

In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental consequences, early, non-invasive, and on-site detection of nutrient deficiency is required. Current non-invasive methods for assessing the nutrient status of crops deal in most cases with nitrogen (N) deficiency only and optical sensors to diagnose N deficiency, such as chlorophyll meters or canopy reflectance sensors, do not monitor N, but instead measure changes in leaf spectral properties that may or may not be caused by N deficiency. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images of sugar beets. To this end, we collected the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets growing on a long-term fertilizer experiment with nutrient deficiency plots comprising N, phosphorous (P), and potassium (K) deficiency, as well as the omission of liming (Ca), full fertilization, and no fertilization at all. We use the dataset to analyse the performance of five convolutional neural networks for recognizing nutrient deficiency symptoms and discuss their limitations.
A Microfluidic System for Stable and Continuous EEG Monitoring from Multiple Larval Zebrafish
Authors: Lee Yuhyun , Seo Hee Won , Lee Kyeong Jae , Jang Jae-Won , Kim Sohee
Published: October 2020

Along with the increasing popularity of larval zebrafish as an experimental animal in the fields of drug screening, neuroscience, genetics, and developmental biology, the need for tools to deal with multiple larvae has emerged. Microfluidic channels have been employed to handle multiple larvae simultaneously, even for sensing electroencephalogram (EEG). In this study, we developed a microfluidic chip capable of uniform and continuous drug infusion across all microfluidic channels during EEG recording. Owing to the modular design of the microfluidic channels, the number of animals under investigation can be easily increased. Using the optimized design of the microfluidic chip, liquids could be exchanged uniformly across all channels without physically affecting the larvae contained in the channels, which assured a stable environment maintained all the time during EEG recording, by eliminating environmental artifacts and leaving only biological effects to be seen. To demonstrate the usefulness of the developed system in drug screening, we continuously measured EEG from four larvae without and with pentylenetetrazole application, up to 60 min. In addition, we recorded EEG from valproic acid (VPA)-treated zebrafish and demonstrated the suppression of seizure by VPA. The developed microfluidic system could contribute to the mass screening of EEG for drug development to treat neurological disorders such as epilepsy in a short time, owing to its handy size, cheap fabrication cost, and the guaranteed uniform drug infusion across all channels with no environmentally induced artifacts.
Identification of the Regional and Economic Contexts of Sustainable Urban Logistics Policies
Authors: Reda Abel Kebede , Gebresenbet Girma , Tavasszy Lori , Ljungberg David
Published: October 2020

Urban logistics policies have become instrumental in achieving sustainable transport systems. Developing and emerging countries still lag far behind in the implementation of such policies when compared with developed countries. This exposure gap provides an opportunity for policy transfer, but this is a complex process requiring knowledge of many contextual factors and involving multiple steps. A good understanding of those contextual factors of measures by cities may be critical for a successful transfer. Our study aimed to identify the different contexts of urban logistics measures or policies worldwide and to assess their significance for policy transferability. In this study, urban logistics measures discussed in the literature were retrieved with a systematic literature review method and then the contexts were recorded, distinguishing between economic development levels and geographical regions. The analysis revealed that the economic level and geographical location of cities both have a strong association with the type of measure implemented. Barriers and drivers were identified by assessing policy transfer between developed and developing countries. Institutional and physical barriers appeared to be highly pertinent for a range of measures, while drivers or facilitators were identified from specific problems in developing countries and the respective measures in developed countries. Thus, the analysis of contextual factors can provide a first response to the key challenges and opportunities of sustainable urban logistics policies transfer to developing countries.
Economic Assessment and Community Management of Prosopis juliflora Invasion in Sweimeh Village, Jordan
Authors: Al-Assaf Amani , Tadros Maher J. , Al-Shishany Salsabeel , Stewart Stephanie , Majdalawi Mohammad , Tabieh Mohammad , Othman Yahia A.
Published: October 2020

Invasions by invasive alien species (IAS) are recognized as one of the largest threats to earth’s ecosystem services and represent rapidly growing economic costs as they damage local ecosystems and force surrounding communities to divert resources towards IAS management and control. The study objectives were to assess the economic impacts of a Prosopis juliflora invasion in Jordan and gauge community preferences for management plans. The study was conducted in Sweimeh, Jordan Valley using a combination of focus groups and randomized interviews with 203 local households. Direct-use values for products derived from P. juliflora and direct costs owing to P. juliflora’s presence have been calculated. A binary logistic regression model was then developed to predict the households’ preferences between two policy responses: P. juliflora management or complete eradication. The results revealed the dualistic role of P. juliflora in household livelihoods: it was widely used for firewood, fodder, and charcoal offering benefits valued at JOD 2165 per household/year (JOD 1 = USD 0.71). At the same time, the invasion reduced household welfare by taking over arable lands and injuring humans and animals. Consequently, the income lost as a result of the invasion was estimated by JOD 1410.5 per household/year. As a result, two-thirds of respondents choose the option of P.juliflora invasion management. Only respondents’ monthly income and perception of P. juliflora’s impact on the prevention of soil erosion were significant predictors of respondents’ preferences for the management plans. In conclusion, more coordinated responses between policy makers, institutions, and local communities are required to mitigate the negative impact of P. juliflora invasion.
Unraveling the Effects of Ethical Leadership on Knowledge Sharing: The Mediating Roles of Subjective Well-Being and Social Media in the Hotel Industry
Authors: Hayat Bhatti Misbah , Akram Umair , Hasnat Bhatti Muhammad , Rasool Hassan , Su Xin
Published: October 2020

Given the pivotal function of ethical leadership (EL) in the hospitality industry, this study explains how moral guidance can help to reduce mental stress. The modern complex and hectic working style of organizations demands ethical conduct, in order to sustain the positive behavior of employees for knowledge sharing (KS). For this reason, in this study, we restrict our awareness to the usage of social media (SM) for social identity and aspects of subjective well-being (SWB) for happiness intensity. The time-lag method is applied for data collection from 406 supervisors and subordinates of the hotel industry in Pakistan. Partial least squares structural equation modeling (PLS-SEM) and bootstrapping are utilized to scan the data. The results of the current study demonstrate that ethical leadership provides motivational strength for knowledge sharing amongst employees. Moreover, the serial mediation effects of subjective well-being and social media boost knowledge sharing by the induction of ethical values. Our findings indicate that knowledge sharing is an important product of subjective well-being and social media. Therefore, we recommend that managers focus on ethical leadership values and employee well-being (e.g., life satisfaction), as well as highlight the individuality of employees to promote knowledge sharing. The presented research adds to the literature by establishing a new connection between ethical leadership and knowledge sharing by opening the black box of contextual (i.e., ethical leadership) and developmental (i.e., subjective well-being and social media) factors.
Methods for Downscaling National Material Consumption Data to the Regional and Municipal Levels
Authors: Lavers Westin Alexandra , Rosado Leonardo , Kalmykova Yuliya , Patrício João
Published: October 2020

Decision makers are looking to reach consumption-based environmental impact targets like Sustainable Development Goal 12, Responsible Consumption and Production. These goals require multifaceted action at municipal, regional, and national levels; however, there are limitations to calculating consumption at the regional and municipal levels. Consumption is dependent on the socioeconomic metabolism of the area, with its unique composition of industries, consumers, and public facilities, which may affect the most appropriate measures to meet goals effectively. In this study, we evaluated several methods to extrapolate municipality- and region-specific consumption from national-level data so that consumption estimates can be used to aid decision makers or make further analyses like environmental impact evaluation. We compared four approaches and validated our findings using reported consumption values as well as results from another model in use. We found that using per capita values for consumption is satisfactory for counties, but consumption in municipalities and metropolitan areas with populations smaller than 500,000 inhabitants was better described by one of the suggested methods on average.
Charcoal and Wood Biomass Utilization in Uganda: The Socioeconomic and Environmental Dynamics and Implications
Authors: Bamwesigye Dastan , Kupec Petr , Chekuimo Georges , Pavlis Jindrich , Asamoah Obed , Darkwah Samuel Antwi , Hlaváčková Petra
Published: October 2020

Charcoal and firewood fuel biomass utilization is thought to be the main cause of deforestation in Uganda. Moreover, the practice of utilizing charcoal and wood fuel in Uganda is said to impact the health of many women and children in the region. The goal of this study was to comprehensively analyze charcoal and wood fuel utilization processes in Uganda and sub-Saharan Africa and the environmental and socioeconomic dynamics and implications. The study equally intended to model out some possible improvements to wood fuel use while conserving natural forests. Both qualitative and qualitative approaches were used to study the charcoal and wood fuel energy situation in Uganda. The study collected field data (sample size: 199) which was subjected to descriptive analysis. The findings show that over 90% of households in Uganda and the sub- Saharan region use firewood and charcoal wood fuel, and that this fuel use creates social and environmental hazards. Our findings are also in agreement with numerous empirical studies showing that firewood and charcoal biomass are among the major causes of deforestation in Uganda and the sub-Saharan region. Ceteris paribus, we propose the adoption of Improved Eco-Stoves (ICE), which not only enable comprehensive combustion but also lessen the quantity of firewood used by more than 60%, together with policy decisions on the government of Uganda, given peoples willingness to take on alternative energy sources such as gas and electricity.
Calibrating the Impacts of Regional Trade Integration and Renewable Energy Transition on the Sustainability of International Inbound Tourism Demand in South Asia
Authors: Murshed Muntasir , Mahmood Haider , Alkhateeb Tarek Tawfik Yousef , Banerjee Suvajit
Published: October 2020

This paper aims to evaluate the overarching relationships between International Inbound Tourism Demand (IITD), regional trade integration and Renewable Energy Transition (RET) in the context of selected South Asian economies between 1990 and 2016. The results from the panel data econometric analyses, accounting for cross-sectional dependency and slope heterogeneity issues, showed that higher degrees of intra-regional trade between the South Asian economies exert positive impacts on the IITD in South Asia. Similarly, undergoing a RET was found to stimulate the IITD further. Besides, the joint favorable impacts of regional trade integration and RET on South Asia’s IITD were also ascertained. Therefore, these findings impose key policy implications concerning the pertinence of strengthening regional trade cooperation between the South Asian neighbors and boosting renewable energy consumption for enhancing the IITD further.
The Influence of Reclaimed Asphalt Pavement on the Mechanical Performance of Bituminous Mixtures. An Analysis at the Mortar Scale
Authors: Hidalgo Ana E. , Moreno-Navarro Fernando , Tauste Raúl , Rubio-Gámez M. Carmen
Published: October 2020

The main characteristics of bituminous mixtures manufactured with a considerable amount of reclaimed asphalt pavement (RAP), compared to conventional mixtures, are a reduction in workability, an increase in stiffness, and a loss of ductility, due to the presence of the aged bitumen contained in the RAP particles. To minimize these impacts, softer binders or rejuvenators are commonly used in the design of these mixtures in order to restore part of the ductility lost and to reduce the stiffness. In spite of previous investigations demonstrating that the mortar plays an essential role in the workability, long-term performance, and durability of bituminous mixtures (where cracking, cohesion, and adhesion problems all start at this scale), not many studies have assessed the impacts caused by the presence of RAP. In response to this, the present paper analyzes the workability, fatigue performance, and water sensitivity of bituminous mortars containing different amounts of RAP (from 0% to 100%) and rejuvenators. Mortar specimens were compacted using a gyratory compactor and studied via dynamic mechanical analysis under three point bending configuration. The results demonstrated that the presence of RAP reduces the workability and ductility of asphalt mortars. However, it also causes an increase in their stiffness, which induces a more elastic response and causes an increase in their resistance to fatigue, which could compensate for the loss of ductility. This aspect, together with the low water sensitivity shown, when using Portland cement as an active filler, would make it possible to produce asphalt materials with high RAP contents with a similar long-term mechanical performance as traditional ones. In addition, the use of rejuvenators was demonstrated to effectively correct the negative workability and ductility impacts caused by using RAP, without affecting the fatigue resistance and material adhesion/cohesion.
Reassessing the Environmental Kuznets Curve in Relation to Energy Efficiency and Economic Growth
Authors: Zhang Jie , Alharthi Majed , Abbas Qaiser , Li Weiqing , Mohsin Muhammad , Jamal Khan , Taghizadeh-Hesary Farhad
Published: October 2020

Energy consumption and its efficiency are significant factors for economic growth and environmental stress. This study postulates the occurrence of the Environmental Kuznets Curve hypothesis (EKC) by using the Autoregressive-Distributed Lag (ARDL) model. Furthermore, a data envelopment analysis (DEA) model is used to measure energy efficiency, energy intensity, and environment to view the trajectory of EKC for the underline economies. For this purpose, a panel dataset from 1990–2013 of 15 developing countries is analyzed to verify the objectives mentioned above. The results of the panel ARDL support EKC’s theory for underline economies, as GDP positively impacts carbon emissions, while the square of GDP is negatively related. The DEA-based results found relatively low environmental conditions in these emerging economies due to high energy intensity and low energy efficiency. This outcome suggests that renewable energy sources must be treated as an essential factor for achieving sustainable economic goals without environmental degradation.
Evaluating Basic Income, Basic Service, and Basic Voucher for Social and Ecological Sustainability
Authors: Lee Sophia Seung-Yoon , Lee Ji-eun , Kim Kyo-seong
Published: October 2020

This paper discusses the restructuring of the social protection system in the changing labor market by comparing and critically reviewing policy ideas of Universal Basic Income (UBI), Universal Basic Voucher (UBV), and Universal Basic Service (UBS) with a focus on how the Social and Ecological Transition (SET) can be achieved. UBS is a concept often paired with UBI, and UBV is considered a middle way between UBI (cash) and UBS (in-kind). This study first analyzes Korea’s basic income, basic service, and basic voucher cases, according to Bohnenberger’s nine types of social benefits through Standing’s policy evaluation principles. Additionally, we evaluated how each of the benefits included in basic income, basic service, and basic voucher can contribute to social and ecological sustainability in the Korean context. Through this evaluation, to pursue SET in the future, what kind of policy efforts should be accompanied with basic income through a Korean case analysis was discussed. The paper focuses on Korea in particular, as all three policies have been initiated here.
Public Awareness: What Climate Change Scientists Should Consider
Authors: Rahimi Mohammad
Published: October 2020

In this Opinion, the importance of public awareness to design solutions to mitigate climate change issues is highlighted. A large-scale acknowledgment of the climate change consequences has great potential to build social momentum. Momentum, in turn, builds motivation and demand, which can be leveraged to develop a multi-scale strategy to tackle the issue. The pursuit of public awareness is a valuable addition to the scientific approach to addressing climate change issues. The Opinion is concluded by providing strategies on how to effectively raise public awareness on climate change-related topics through an integrated, well-connected network of mavens (e.g., scientists) and connectors (e.g., social media influencers).
Future Climate Change Renders Unsuitable Conditions for Paramo Ecosystems in Colombia
Authors: Cresso Matilda , Clerici Nicola , Sanchez Adriana , Jaramillo Fernando
Published: October 2020

Paramo ecosystems are tropical alpine grasslands, located above 3000 m.a.s.l. in the Andean mountain range. Their unique vegetation and soil characteristics, in combination with low temperature and abundant precipitation, create the most advantageous conditions for regulating and storing surface and groundwater. However, increasing temperatures and changing patterns of precipitation due to greenhouse-gas-emission climate change are threatening these fragile environments. In this study, we used regional observations and downscaled data for precipitation and minimum and maximum temperature during the reference period 1960–1990 and simulations for the future period 2041–2060 to study the present and future extents of paramo ecosystems in the Chingaza National Park (CNP), nearby Colombia’s capital city, Bogotá. The historical data were used for establishing upper and lower precipitation and temperature boundaries to determine the locations where paramo ecosystems currently thrive. Our results found that increasing mean monthly temperatures and changing precipitation will render 39 to 52% of the current paramo extent in CNP unsuitable for these ecosystems during the dry season, and 13 to 34% during the wet season. The greatest loss of paramo area will occur during the dry season and for the representative concentration pathway (RCP) scenario 8.5, when both temperature and precipitation boundaries are more prone to be exceeded. Although our initial estimates show the future impact on paramos and the water security of Bogotá due to climate change, complex internal and external interactions in paramo ecosystems make it essential to study other influencing climatic parameters (e.g., soil, topography, wind, etc.) apart from temperature and precipitation.
Radiation Protection Legislation and Sustainable Development of a Rural Green Tuff Village of Ischia Island
Authors: La Verde Giuseppe , D’Avino Vittoria , Sabbarese Carlo , Ambrosino Fabrizio , Roca Vincenzo , Raulo Adelaide , Pugliese Mariagabriella
Published: October 2020

Radiological risk affects the quality of the environment in buildings since population and workers can be potentially exposed to high levels of radiation. Radon gas emanating from both subsoil and building materials represents the most important source of radiation exposure for people. This study investigates the sustainability concept of a small rural village of Ischia Island, named Ciglio, in relation to radiation protection legislation concerning the radiological risk for workers. Radon activity concentration was measured in typical green-tuff dwellings and in water samples collected from a local waterfall E-Perm devices. Moreover, for green tuff as building material, the radon emanation coefficient was calculated by gamma spectroscopy. The results highlight the importance of performing environmental radon monitoring and investigating the radon content of building materials, especially in geographical areas characterized by traditional use of typical stones for constructions. In conclusion, the sustainable development of rural buildings is possible if the radiological risk for inhabitants and workers is assessed in line with the national radiation protection legislation.