bluuebunny/crossref_metadata_2025 huggingface dataset:
A Privacy-Preserving Key Management Scheme with Support for Sybil Attack Detection in VANETs
Authors: Funderburg L. Ellen , Lee Im-Yeong
Published: February 2021

Vehicular ad hoc networks (VANETs) face two important and conflicting challenges with regards to security: preserve the privacy of vehicles in order to prevent malicious entities from tracking users and detect and remove bad actors that attempt to game the system for their own advantage. In particular, detecting Sybil attacks, in which one node attempts to appear as many, seemingly conflicts with the goal of privacy preservation, and existing schemes fail on either one or both accounts. To fill this gap, we present a hierarchical key management system which uses short group signatures to preserve member privacy at lower levels while allowing mid-level nodes to detect Sybil attacks and highly trusted nodes at the top of the hierarchy to completely reveal the real identities of malicious nodes in order to prevent them from rejoining the system and for use by legal authorities. In addition, we present an argument for relaxing the requirement of backward secrecy in VANET groups in the case when no malicious activity has been detected.
Surface Reconstruction from Structured Light Images Using Differentiable Rendering
Authors: Jensen Janus Nørtoft , Hannemose Morten , Bærentzen J. Andreas , Wilm Jakob , Frisvad Jeppe Revall , Dahl Anders Bjorholm
Published: February 2021

When 3D scanning objects, the objective is usually to obtain a continuous surface. However, most surface scanning methods, such as structured light scanning, yield a point cloud. Obtaining a continuous surface from a point cloud requires a subsequent surface reconstruction step, which is directly affected by any error from the computation of the point cloud. In this work, we propose a one-step approach in which we compute the surface directly from structured light images. Our method minimizes the least-squares error between photographs and renderings of a triangle mesh, where the vertex positions of the mesh are the parameters of the minimization problem. To ensure fast iterations during optimization, we use differentiable rendering, which computes images and gradients in a single pass. We present simulation experiments demonstrating that our method for computing a triangle mesh has several advantages over approaches that rely on an intermediate point cloud. Our method can produce accurate reconstructions when initializing the optimization from a sphere. We also show that our method is good at reconstructing sharp edges and that it is robust with respect to image noise. In addition, our method can improve the output from other reconstruction algorithms if we use these for initialization.
Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches
Authors: Chen Song-Lu , Liu Qi , Ma Jia-Wei , Yang Chun
Published: February 2021

As the license plate is multiscale and multidirectional in the natural scene image, its detection is challenging in many applications. In this work, a novel network that combines indirect and direct branches is proposed for license plate detection in the wild. The indirect detection branch performs small-sized vehicle plate detection with high precision in a coarse-to-fine scheme using vehicle–plate relationships. The direct detection branch detects the license plate directly in the input image, reducing false negatives in the indirect detection branch due to the miss of vehicles’ detection. We propose a universal multidirectional license plate refinement method by localizing the four corners of the license plate. Finally, we construct an end-to-end trainable network for license plate detection by combining these two branches via post-processing operations. The network can effectively detect the small-sized license plate and localize the multidirectional license plate in real applications. To our knowledge, the proposed method is the first one that combines indirect and direct methods into an end-to-end network for license plate detection. Extensive experiments verify that our method outperforms the indirect methods and direct methods significantly.
Enhancing SDN WISE with Slicing Over TSCH
Authors: Orozco-Santos Federico , Sempere-Payá Víctor , Albero-Albero Teresa , Silvestre-Blanes Javier
Published: February 2021

IWSNs (Industrial Wireless Sensor Networks) have become the next step in the evolution of WSN (Wireless Sensor Networks) due to the nature and demands of modern industry. With this type of network, flexible and scalable architectures can be created that simultaneously support traffic sources with different characteristics. Due to the great diversity of application scenarios, there is a need to implement additional capabilities that can guarantee an adequate level of reliability and that can adapt to the dynamic behavior of the applications in use. The use of SDNs (Software Defined Networks) extends the possibilities of control over the network and enables its deployment at an industrial level. The signaling traffic exchanged between nodes and controller is heavy and must occupy the same channel as the data traffic. This difficulty can be overcome with the segmentation of the traffic into flows, and correct scheduling at the MAC (Medium Access Control) level, known as slices. This article proposes the integration in the SDN controller of a traffic manager, a routing process in charge of assigning different routes according to the different flows, as well as the introduction of the Time Slotted Channel Hopping (TSCH) Scheduler. In addition, the TSCH (Time Slotted Channel Hopping) is incorporated in the SDN-WISE framework (Software Defined Networking solution for Wireless Sensor Networks), and this protocol has been modified to send the TSCH schedule. These elements are jointly responsible for scheduling and segmenting the traffic that will be sent to the nodes through a single packet from the controller and its performance has been evaluated through simulation and a testbed. The results obtained show how flexibility, adaptability, and determinism increase thanks to the joint use of the routing process and the TSCH Scheduler, which makes it possible to create a slicing by flows, which have different quality of service requirements. This in turn helps guarantee their QoS characteristics, increase the PDR (Packet Delivery Ratio) for the flow with the highest priority, maintain the DMR (Deadline Miss Ratio), and increase the network lifetime.
3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts
Authors: Merino Ibon , Azpiazu Jon , Remazeilles Anthony , Sierra Basilio
Published: February 2021

Deep learning methods have been successfully applied to image processing, mainly using 2D vision sensors. Recently, the rise of depth cameras and other similar 3D sensors has opened the field for new perception techniques. Nevertheless, 3D convolutional neural networks perform slightly worse than other 3D deep learning methods, and even worse than their 2D version. In this paper, we propose to improve 3D deep learning results by transferring the pretrained weights learned in 2D networks to their corresponding 3D version. Using an industrial object recognition context, we have analyzed different combinations of 3D convolutional networks (VGG16, ResNet, Inception ResNet, and EfficientNet), comparing the recognition accuracy. The highest accuracy is obtained with EfficientNetB0 using extrusion with an accuracy of 0.9217, which gives comparable results to state-of-the art methods. We also observed that the transfer approach enabled to improve the accuracy of the Inception ResNet 3D version up to 18% with respect to the score of the 3D approach alone.
Identification of Multi-Class Drugs Based on Near Infrared Spectroscopy and Bidirectional Generative Adversarial Networks
Authors: Zheng Anbing , Yang Huihua , Pan Xipeng , Yin Lihui , Feng Yanchun
Published: February 2021

Drug detection and identification technology are of great significance in drug supervision and management. To determine the exact source of drugs, it is often necessary to directly identify multiple varieties of drugs produced by multiple manufacturers. Near-infrared spectroscopy (NIR) combined with chemometrics is generally used in these cases. However, existing NIR classification modeling methods have great limitations in dealing with a large number of categories and spectra, especially under the premise of insufficient samples, unbalanced samples, and sensitive identification error cost. Therefore, this paper proposes a NIR multi-classification modeling method based on a modified Bidirectional Generative Adversarial Networks (Bi-GAN). It makes full utilization of the powerful feature extraction ability and good sample generation quality of Bi-GAN and uses the generated samples with obvious features, an equal number between classes, and a sufficient number within classes to replace the unbalanced and insufficient real samples in the courses of spectral classification. 1721 samples of four kinds of drugs produced by 29 manufacturers were used as experimental materials, and the results demonstrate that this method is superior to other comparative methods in drug NIR classification scenarios, and the optimal accuracy rate is even more than 99% under ideal conditions.
Switchable Coupled Relays Aid Massive Non-Orthogonal Multiple Access Networks with Transmit Antenna Selection and Energy Harvesting
Authors: Tran Thanh-Nam , Voznak Miroslav
Published: February 2021

The article proposes a new switchable coupled relay model for massive MIMO-NOMA networks. The model equips a much greater number of antennas on the coupled relays to dramatically improve capacity and Energy Efficiency (EE). Each relay in a coupled relay is selected and delivered into a single transmission block to serve multiple devices. This paper also plots a new diagram of two transmission blocks which illustrates energy harvesting and signal processing. To optimize the system performance of a massive MIMO-NOMA network, i.e., Outage Probability (OP) and system throughput, this paper deploys a Transmit Antenna Selection (TAS) protocol to select the best received signals from the pre-coding channel matrices. In addition, to achieve better EE, Simultaneously Wireless Information Power Transmit (SWIPT) is implemented. Specifically, this paper derives the novel theoretical analysis in closed-form expressions, i.e., OP, system throughput and EE from a massive MIMO-NOMA network aided by switchable coupled relays. The theoretical results obtained from the closed-form expressions show that a massive MIMO-NOMA network achieves better OP and greater capacity and expends less energy than the MIMO technique. Finally, independent Monte Carlo simulations verified the theoretical results.
Traceable Reference Full Metrology Chain for Innovative Aspheric and Freeform Optical Surfaces Accurate at the Nanometer Level
Authors: Arezki Yassir , Su Rong , Heikkinen Ville , Leprete François , Posta Pavel , Bitou Youichi , Schober Christian , Mehdi-Souzani Charyar , Alzahrani Bandar Abdulrahman Mohammed , Zhang Xiangchao , Kondo Yohan , Pruss Christof , Ledl Vit , Anwer Nabil , Bouazizi Mohamed Lamjed , Leach Richard , Nouira Hichem
Published: February 2021

The design of innovative reference aspheric and freeform optical elements was investigated with the aim of calibration and verification of ultra-high accurate measurement systems. The verification is dedicated to form error analysis of aspherical and freeform optical surfaces based on minimum zone fitting. Two thermo-invariant material measures were designed, manufactured using a magnetorheological finishing process and selected for the evaluation of a number of ultra-high-precision measurement machines. All collected data sets were analysed using the implemented robust reference minimum zone (Hybrid Trust Region) fitting algorithm to extract the values of form error. Agreement among the results of several partners was observed, which demonstrates the establishment of a traceable reference full metrology chain for aspherical and freeform optical surfaces with small amplitudes.
A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors
Authors: Chiou Shin-Yan , Lin Kun-Ju , Dong Ya-Xin
Published: February 2021

Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families.
Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs
Authors: Melo Aurelio G. , Pinto Milena F. , Marcato Andre L. M. , Honório Leonardo M. , Coelho Fabrício O.
Published: February 2021

Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments.
A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors
Authors: Naresh Varnakavi. , Lee Nohyun
Published: February 2021

A biosensor is an integrated receptor-transducer device, which can convert a biological response into an electrical signal. The design and development of biosensors have taken a center stage for researchers or scientists in the recent decade owing to the wide range of biosensor applications, such as health care and disease diagnosis, environmental monitoring, water and food quality monitoring, and drug delivery. The main challenges involved in the biosensor progress are (i) the efficient capturing of biorecognition signals and the transformation of these signals into electrochemical, electrical, optical, gravimetric, or acoustic signals (transduction process), (ii) enhancing transducer performance i.e., increasing sensitivity, shorter response time, reproducibility, and low detection limits even to detect individual molecules, and (iii) miniaturization of the biosensing devices using micro-and nano-fabrication technologies. Those challenges can be met through the integration of sensing technology with nanomaterials, which range from zero- to three-dimensional, possessing a high surface-to-volume ratio, good conductivities, shock-bearing abilities, and color tunability. Nanomaterials (NMs) employed in the fabrication and nanobiosensors include nanoparticles (NPs) (high stability and high carrier capacity), nanowires (NWs) and nanorods (NRs) (capable of high detection sensitivity), carbon nanotubes (CNTs) (large surface area, high electrical and thermal conductivity), and quantum dots (QDs) (color tunability). Furthermore, these nanomaterials can themselves act as transduction elements. This review summarizes the evolution of biosensors, the types of biosensors based on their receptors, transducers, and modern approaches employed in biosensors using nanomaterials such as NPs (e.g., noble metal NPs and metal oxide NPs), NWs, NRs, CNTs, QDs, and dendrimers and their recent advancement in biosensing technology with the expansion of nanotechnology.
Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera
Authors: Bai Jie , Li Sen , Zhang Han , Huang Libo , Wang Ping
Published: February 2021

Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.
Securing Schools against Terrorist Attacks
Authors: Hošková-Mayerová Šárka , Bekesiene Svajone , Beňová Petra
Published: February 2021

The population is nowadays increasingly threatened by events that have an immediate impact on their health and lives. One of the most endangered targets are the so-called soft targets. These are such targets that are characterized by a high population concentration, and low or even no level of security against violent or even terrorist attacks. The research carried out by the authors clearly showed that one of the important and easily vulnerable soft targets are schools. This article focuses on the safety of schools and their facilities. The authors focused on finding out the safety of schools as soft targets in the Czech Republic. The security level of schools was measured at selected nursery, elementary, and secondary schools in the city of Brno. As well as technical elements, other factors contributing to the overall safety of schools were also verified. It was found that although a large number of schools have at least basic elements of security available, systemic and organizational measures are not sufficient for technical measures to be important.
Temporary Use as a Participatory Placemaking Tool to Support Cultural Initiatives and Its Connection to City Marketing Strategies—The Case of Athens
Authors: Karachalis Nicholas
Published: February 2021

For many cities, abandoned or underused spaces pose a major challenge, but temporary use is being manifested as a tool that can offer solutions. So-called “meanwhile spaces” provide opportunities for city officials to create the conditions for the cultural economy to be supported while simultaneously meeting urban regeneration goals and supporting city marketing purposes. In European cities, such as Berlin, Ghent, Nantes, and Bremen, there are examples where “temporary urbanism” is being incorporated into mainstream policy and is forming part of the city’s identity and tourism promotion. Temporary use is discussed in the context of current challenges facing European cities (austerity crisis, unemployment, refugee crisis, lack of affordable housing or office space). In this paper, the focus is on the creative sector. More recently, the COVID-19 pandemic has sparked discussion on how temporary use can cover a community’s needs. In this contextual framework, the scope was to explore the conditions under which temporary use becomes an effective policy tool and discuss management issues and difficulties that may arise. A description and analysis of the particularities of initiatives that were implemented in Athens compared to the ones in other European cities are presented. This paper is partly based on the works of the Athens URBACT Refill project, which included an extended consultation process with local stakeholders. Further field research was conducted regarding how temporary use initiatives with a cultural focus have been managed and the way the relations between owners, managers, and users were formed. Feedback from users and stakeholders’ representatives is presented. The key findings refer to the evaluation of the approaches used in temporary use initiatives with a cultural focus, the future perspectives, and the difficulty with drawing early conclusions on the effectiveness of these initiatives and the impact on the reputation of the city. Finally, the discussion is linked with promoting Athens as a city that allows experimentation by citizens’ groups and cultural initiatives in the context of temporary interventions.
A Future Outlook of Narratives for the Built Environment in Japan
Authors: Kamei Miho , Mastrucci Alessio , van Ruijven Bas J.
Published: February 2021

The evolution of long-term sustainable societies is closely connected to the transformation of the physical built environment in which those societies operate. In this paper, we present a comprehensive set of narratives for the built environment in Japan, consistent with the shared socio-economic pathways (SSPs) framework, to assess the future evolution of the adaptation and mitigation challenges. We focus on the linkage between sustainability factors and human living environments including urban form, buildings, and basic infrastructures. We introduce a new, sixth narrative to the SSPs, an alternative interpretation of SSP1. Whereas the original SSP1 assumes high societal and environmental sustainability combined with relatively high economic growth, the SSP1 variant does not highly rely on economic growth and is oriented towards a lower and more locally oriented consumption lifestyle. Nature-based solutions are integrated and examined in the new SSP1 narrative, which is aligned with the adaptation to the digital era with freedom of location. Recent global crises such as climate change and the COVID-19 pandemic may accelerate the transformation of societies. Therefore, this study attempts to imply the benefits and trade-offs of alternative pathways for the built environment.
Nudging Consumers toward Healthier Food Choices: A Field Study on the Effect of Social Norms
Authors: Gonçalves Diogo , Coelho Pedro , Martinez Luis F. , Monteiro Paulo
Published: February 2021

Food choices influence the health of individuals, and supermarkets are the place where part of the world population makes their food choices on a daily basis. Different methods to influence food purchasing habits are used, from promotions to food location. However, very few supermarket chains use social norms, the human need to conform to the perceived behavior of the group, to increase healthy food purchase habits. This research seeks to understand how a social norm nudge, a message conveying fruit and vegetable purchasing norms positioned in strategic places, can effectively change food choices. Using data from an intervention in a Portuguese supermarket, the fruit and vegetable purchase quantities of 1636 customers were measured over three months and compared with the corresponding period of the previous year. The results show that the nudge intervention positively affected those whose purchasing habits are categorized as less healthy, while those with healthy habits were slightly negatively affected. Moreover, a follow-up inferential statistical analysis allows us to conclude that applying this intervention at a larger scale would deliver significant financial results for the supermarket chain in which the study took place, by decreasing the costs related to produce perishability while simultaneously improving the health of the consumer and the sustainability of the planet.
What Lies beneath Sustainable Education? Predicting and Tackling Gender Differences in STEM Academic Success
Authors: Pilotti Maura A. E.
Published: February 2021

In many societies across the globe, females are still underrepresented in science, technology, engineering, and math (STEM fields), although they are reported to have higher grades in high school and college than males. The present study was guided by the assumption that the sustainability of higher education critically rests on the academic success of both male and female students under conditions of equitable educational options, practices, and contents. It first assessed the persistence of familiar patterns of gender bias (e.g., do competencies at enrollment, serving as academic precursors, and academic performance favor females?) in college students of a society in transition from a gender-segregated workforce with marked gender inequalities to one whose aims at integrating into the global economy demand that women pursue once forbidden careers thought to be the exclusive domain of men. It then examined how simple indices of academic readiness, as well as preferences for fields fitting traditional gender roles, could predict attainment of key competencies and motivation to graduate (as measured by the average number of credits completed per year) in college. As expected, females had a higher high school GPA. Once in college, they were underrepresented in a major that fitted traditional gender roles (interior design) and over-represented in one that did not fit (business). Female students’ performance and motivation to graduate did not differ between the male-suited major of business and the female-suited major of interior design. Male students’ performance and motivation to graduate were higher in engineering than in business, albeit both majors were gender-role consistent. Although high school GPA and English proficiency scores predicted performance and motivation for all, preference for engineering over business also predicted males’ performance and motivation. These findings offered a more complex picture of patterns of gender bias, thereby inspiring the implementation of targeted educational interventions to improve females’ motivation for and enrollment in STEM fields, nowadays increasingly available to them, as well as to enhance males’ academic success in non-STEM fields such as business.
Increased Likelihood of High Nitrous Oxide (N2O) Exchange in Soils at Reduced Microbial Diversity
Authors: Christensen Søren , Hol Wilhelmina H. Gera , Kurm Viola , Vestergård Mette
Published: February 2021

Rare soil organisms are normally considered of less importance for ecosystem functioning. We present results that oppose this view. In otherwise well-aerated soils, anaerobic/microaerophilic production or consumption of the trace gas N2O occurs in small soil volumes, when intense decomposition activity at the site leads to local oxygen depletion. At such patch scales, the control of microbial growth and oxygen consumption may depend on the specific organisms present. We assessed N2O turnover in an experiment, where soil dilution from 10−2 over 10−4 to 10−6 followed by microbial regrowth resulted in similar microbial biomass and respiration but reduced diversity. We found an increasing number of very high N2O turnover rates when soil dilution increased from 10−2 over 10−4 to 10−6, as revealed from a significantly increased skewness of the frequency distribution of N2O turnover levels. N2O turnover also tended to increase (p = 0.08) by 20–30% when soil was diluted from 10−2 to 10−6. This suggests that rare soil organisms regulate the local activity of fast-growing microorganisms and thus reduce the probability that anoxic/microaerophilic soil volumes develop. Future studies may reveal which less abundant organisms prevent development of anoxic/microaerophilic conditions in well-aerated soils.
Air Pollution Assessment in China: A Novel Group Multiple Criteria Decision Making Model under Uncertain Information
Authors: Hadi-Vencheh Abdollah , Tan Yong , Wanke Peter , Loghmanian Seyed Mohammadreza
Published: February 2021

Assessment of and controlling air pollution are urgent global issues where international cooperation is deemed necessary. Although a very relevant data source can be obtained through continuous monitoring of air quality, measuring air pollutant concentrations is quite difficult when compared to other environmental indicators. We mainly have three different aims for the current study: (1) we propose the computation of the interval weights of decision makers (DMs) based on a group multiple criteria decision making (GMCDM) model; (2) we aim to rank the overall preferences of DMs by the possibility concepts; (3) we aim to evaluate the air quality in China using the most recent data based on our proposed method. We consider three monitoring stations, namely Luhu Park, Wanqingsha, and Tianhu, and the data for SO2, NO2, and PM10 are collected for November 2017, 2018, and 2019. The results from our innovative model show that November 2019 had the best air quality. Finally, robustness analyses are also performed to confirm the discriminatory power of the proposed approach.
RETRACTED: De Corato, U. Soil Microbiome Manipulation Gives New Insights in Plant Disease-Suppressive Soils from the Perspective of a Circular Economy: A Critical Review. Sustainability 2021, 13, 10
Authors: De Corato Ugo
Published: February 2021

The journal retracts the article “Soil Microbiome Manipulation Gives New Insights in Plant Disease-Suppressive Soils from the Perspective of a Circular Economy: A Critical Review” by Ugo De Corato [...]
HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks
Authors: Javeed Madiha , Gochoo Munkhjargal , Jalal Ahmad , Kim Kibum
Published: February 2021

The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.
ICT Skills of University Students from the Faculty of Sport and Physical Education during the COVID-19 Pandemic
Authors: Batez Maja
Published: February 2021

Background: (1) The COVID-19 pandemic has generated significant changes in teaching methods around the world, and the ideal of online education has become a reality. (2) Methods: A questionnaire was modified for this study in order to determine the following levels of Information and Communication Technologies (ICT) skills: file creation, file management, the use of emails, the use of the internet, and online communication. In total, 360 students from the Faculty of Sports and Physical Education (FSPE), University of Novi Sad, participated in the study. (3) Results: The results show that there are differences between the estimated level of ICT skills and the ICT skills used in online education, such that students estimate their level of ICT skills as being higher than is necessary for online education (p < 0.05). There is also a correlation between the satisfaction with online education and ICT skills, showing that students with higher ICT knowledge are more satisfied with online education (p < 0.05). There is another correlation between the satisfaction with online education and the frequency of ICT use—the more ICTs are included, the more satisfied the students will be (p < 0.05). (4) Conclusions: The results of this study can serve as a recommendation for the implementation of FSPE students’ training in ICT skills, as well as an important basis for the systematic creation, improvement, and sustainability of online education in universities.
The Entrepreneurial Environment in Greek Rural Areas: The Entrepreneur’s Viewpoint
Authors: Vlachou Charisia , Iakovidou Olga , Sergaki Panagiota , Menexes Georgios
Published: February 2021

The business environment that prevails in a region has a significant influence on the businesses operating in that geographical area. A strong business environment can enhance not only the growth and success of businesses, but also their retention and sustainable development in the area. The literature review shows that a small number of empirical studies have examined the business environment in rural areas. This research attempts to highlight the factors (road access, technology infrastructure, etc.) that make up the business environent in rural areas, as well as to clarify the degree of satisfaction that the entrepreneurs themselves derive from them. For the purposes of the survey, a questionnaire was filled in by 240 entrepreneurs from rural areas in the Regional Unit (RU) of Serres, in the northern part of Greece, who assessed their satisfaction with the business environment using a five-point Likert-type scale, through personal interviews. The principal component analysis identified six factors, which showed higher satisfaction among the categories of “infrastructure” and “social capital”.
Application of an Improved Ultrasound Full-Waveform Inversion in Bone Quantitative Measurement
Authors: Suo Meng , Zhang Dong , Yang Yan
Published: February 2021

Inspired by the large number of applications for symmetric nonlinear equations, an improved full waveform inversion algorithm is proposed in this paper in order to quantitatively measure the bone density and realize the early diagnosis of osteoporosis. The isotropic elastic wave equation is used to simulate ultrasonic propagation between bone and soft tissue, and the Gauss–Newton algorithm based on symmetric nonlinear equations is applied to solve the optimal solution in the inversion. In addition, the authors use several strategies including the frequency-grid multiscale method, the envelope inversion and the new joint velocity–density inversion to improve the result of conventional full-waveform inversion method. The effects of various inversion settings are also tested to find a balanced way of keeping good accuracy and high computational efficiency. Numerical inversion experiments showed that the improved full waveform inversion (FWI) method proposed in this paper shows superior inversion results as it can detect small velocity–density changes in bones, and the relative error of the numerical model is within 10%. This method can also avoid interference from small amounts of noise and satisfy the high precision requirements for quantitative ultrasound measurements of bone.
On the 2PN Pericentre Precession in the General Theory of Relativity and the Recently Discovered Fast-Orbiting S-Stars in Sgr A*
Authors: Iorio Lorenzo
Published: February 2021

Recently, the secular pericentre precession was analytically computed to the second post-Newtonian (2PN) order by the present author with the Gauss equations in terms of the osculating Keplerian orbital elements in order to obtain closer contact with the observations in astronomical and astrophysical scenarios of potential interest. A discrepancy in previous results from other authors was found. Moreover, some of such findings by the same authors were deemed as mutually inconsistent. In this paper, it is demonstrated that, in fact, some calculation errors plagued the most recent calculations by the present author. They are explicitly disclosed and corrected. As a result, all of the examined approaches mutually agree, yielding the same analytical expression for the total 2PN pericentre precession once the appropriate conversions from the adopted parameterisations are made. It is also shown that, in the future, it may become measurable, at least in principle, for some of the recently discovered short-period S-stars in Sgr A*, such as S62 and S4714.
Role of Biochar in Improving Sandy Soil Water Retention and Resilience to Drought
Authors: Li Ling , Zhang Yong-Jiang , Novak Abigayl , Yang Yingchao , Wang Jinwu
Published: February 2021

In recent years, plants in sandy soils have been impacted by increased climate variability due to weak water holding and temperature buffering capacities of the parent material. The projected impact spreads all over the world, including New England, USA. Many regions of the world may experience an increase in frequency and severity of drought, which can be attributed to an increased variability in precipitation and enhanced water loss due to warming. The overall benefits of biochar in environmental management have been extensively investigated. This review aims to discuss the water holding capacity of biochar from the points of view of fluid mechanics and propose several prioritized future research topics. To understand the impacts of biochar on sandy soils in-depth, sandy soil properties (surface area, pore size, water properties, and characteristics) and how biochar could improve the soil quality as well as plant growth, development, and yield are reviewed. Incorporating biochar into sandy soils could result in a net increase in the surface area, a stronger hydrophobicity at a lower temperature, and an increase in the micropores to maximize gap spaces. The capability of biochar in reducing fertilizer drainage through increasing water retention can improve crop productivity and reduce the nutrient leaching rate in agricultural practices. To advance research in biochar products and address the impacts of increasing climate variability, future research may focus on the role of biochar in enhancing soil water retention, plant water use efficiency, crop resistance to drought, and crop productivity.
Electrochemical/Peroxymonosulfate/NrGO-MnFe2O4 for Advanced Treatment of Landfill Leachate Nanofiltration Concentrate
Authors: Wang Jiaqi , He Zhengguang , Wang Yuzhong , Lu Manjing
Published: February 2021

A simple one-pot method was used to successfully embed manganese ferrite (MnFe2O4) nanoparticles on the nitrogen-doped reduced graphene oxide matrix (NrGO), which was used to activate peroxymonosulfate to treat the landfill leachate nanofiltration concentration (LLNC) with electrochemical enhancement. NrGO-MnFe2O4 and rGO-MnFe2O4 were characterized by various means. This indicates that nitrogen-doped could induce more graphene oxide (GO) spall and reduction to produce more active centers, and was favorable for uniformly loading MnFe2O4 particles. The comparison between electrochemical/peroxymonosulfate/NrGO-MnFe2O4 (EC/PMS/NrGO-MnFe2O4) system and different catalytic systems shows that electrochemical reaction, NrGO and MnFe2O4 can produce synergies, and the chemical oxygen demand (COD) removal rate of LLNC can reach 72.89% under the optimal conditions. The three-dimensional (3D-EEM) fluorescence spectrum shows that the system has a strong treatment effect on the macromolecules with intense fluorescence emission in LLNC, such as humic acid, and degrades into substances with weak or no fluorescence characteristics. Gas chromatography-mass spectrometry (GC-MS) indicates that the complex structure of refractory organic compounds can be simplified, while the simple small molecular organic compounds can be directly mineralized. The mechanism of catalytic degradation of the system was preliminarily discussed by the free radical quenching experiment. Therefore, the EC/PMS/NrGO-MnFe2O4 system has significant application potential in the treatment of refractory wastewater.
Assessing Stream-Aquifer Connectivity in a Coastal California Watershed
Authors: Malama Bwalya , Pritchard-Peterson Devin , Jasbinsek John J. , Surfleet Christopher
Published: February 2021

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.
Experimental Study on the Optimal Strategy for Power Regulation of Governing System of Hydropower Station
Authors: Wang Cong , Wang De-Kuan , Zhang Jian-Ming
Published: February 2021

Active power instability during the power regulation process is a problem that affects the operation security of hydropower stations and the power grid. This paper focuses on the dynamic response to power regulation of a hydro-turbine governor in the power control mode. Firstly, the mathematical model for the hydro-turbine governing system connected to the power grid is established. Then, considering the effect of water hammer and the guide vane operating speed on power oscillation and reverse power regulation, a novel control strategy based on the S-curve acceleration and deceleration control algorithm (S-curve control algorithm) is proposed to improve power regulation. Furthermore, we carried out field tests in a real hydropower station in order to compare the regulation quality of the novel control strategy based on the S-curve control algorithm with the traditional linear control strategy. Finally, the obtained results show that the proposed optimal control strategy for the hydro-turbine governor improves the stability of power regulation by effectively suppressing reverse power regulation and overshoot. This study provides a good solution for the instability of power and reverse power regulation during the regulation process of the hydro-turbine governor in the power control mode.
Una Especie Nueva de Inga (Fabaceae) de los Bosques de Yungas de La Paz, Bolivia
Authors: Aparicio Claudia , Fuentes Alfredo F. , Maldonado Carla
Published: February 2021

Inga yungasensis C. Aparicio & A. Fuentes (Caesalpinioideae, Fabaceae), is a new species from the Andean foothills found in the humid lower montane forests (Yungas region) of northern La Paz, Bolivia. Inga yungasensis belongs to section Complanatae T. D. Penn. and is similar to I. extra-nodis T. D. Penn., a species from Ecuador, by its leaves without winged rachis and with stipitate glands. It differs mainly by the smaller size of its calyx (2–2.5 vs. 6–14 mm) and corolla (6–9 vs. 20–23 mm). Additional differences between I. extra-nodis and other morphologically similar species are discussed as well as the conservation status assessment.
Detection of Pneumonia with a Novel CNN-based Approach
Authors: Erdem Ebru , Aydin Tolga
Published: February 2021

Pneumonia is a seasonal infectious lung tissue inflammatory disease. According to the World Health Organization (WHO), early diagnosis of the disease reduces the risk of its transmission and death. Various deep learning and machine learning algorithms were used for pneumonia detection. This study aims to analyze the lung images and diagnose pneumonia disease by employing deep learning approaches. We have suggested a novel deep learning framework for the detection of pneumonia in lung. A comparison was made between the proposed new deep learning model and pre-trained deep learning models. 88.62% accuracy rate has been obtained from the proposed deep learning structure. It was observed that by utilizing the new deep neural network developed, the accuracy results of VGG16 (88.78%) and VGG19 (88.30%), which are among the popular deep learning architectures, can be approximated. The test results show that our proposed model has a better recall value (97.43%) (VGG16 (93.33%) and VGG19 (96.92%)), and a better F1-Score (91.45%) (VGG16 (91.22%) and VGG19 (91.19%)).