bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Printed Pediatric Medicines in Hospital Pharmacies: Comparison of 2D and 3D-Printed Orodispersible Warfarin Films with Conventional Oral Powders in Unit Dose Sachets
Authors: Öblom Heidi , Sjöholm Erica , Rautamo Maria , Sandler Niklas
Published: July 2019

To date, the lack of age-appropriate medicines for many indications results in dose manipulation of commercially available dosage forms, commonly resulting in inaccurate doses. Various printing technologies have recently been explored in the pharmaceutical field due to the flexible and precise nature of the techniques. The aim of this study was, therefore, to compare the currently used method to produce patient-tailored warfarin doses at HUS Pharmacy in Finland with two innovative printing techniques. Dosage forms of various strengths (0.1, 0.5, 1, and 2 mg) were prepared utilizing semisolid extrusion 3D printing, inkjet printing and the established compounding procedure for oral powders in unit dose sachets (OPSs). Orodispersible films (ODFs) drug-loaded with warfarin were prepared by means of printing using hydroxypropylcellulose as a film-forming agent. The OPSs consisted of commercially available warfarin tablets and lactose monohydrate as a filler. The ODFs resulted in thin and flexible films showing acceptable ODF properties. Moreover, the printed ODFs displayed improved drug content compared to the established OPSs. All dosage forms were found to be stable over the one-month stability study and suitable for administration through a naso-gastric tube, thus, enabling administration to all possible patient groups in a hospital ward. This work demonstrates the potential of utilizing printing technologies for the production of on-demand patient-specific doses and further discusses the advantages and limitations of each method.
17-α Hydroxyprogesterone Nanoemulsifying Preconcentrate-Loaded Vaginal Tablet: A Novel Non-Invasive Approach for the Prevention of Preterm Birth
Authors: Patki Manali , Giusto Kiersten , Gorasiya Samir , Reznik Sandra E. , Patel Ketan
Published: July 2019

Preterm birth (PTB) is a major cause of infant mortality in the United States and around the globe. Makena®—once-a-week intramuscular injection of 17-α Hydroxyprogesterone caproate (17P)—is the only FDA approved treatment for the prevention of PTB. Invasive delivery of 17P requires hospitalization and expert personnel for injection. Vaginal delivery of 17P would be preferable, because of high patient compliance, reduced systemic exposure, fewer side effects, and no need for hospitalization. The objective of the present study was to prepare and evaluate a self-nanoemulsifying vaginal tablet of 17P. A solid self-nanoemulsifying preconcentrate (S-SNEDDS) of 17P and dimethylacetamide (DMA) was developed using medium chain triglycerides, a non- immunogenic surfactant, and co-processed excipient (PVA-F100). The tablet prepared was characterized for emulsification time, particle size, solid state properties, and drug release. The formulation showed >50% inhibition of TNF-α release from LPS-stimulated RAW 264.7 cells. Importantly, there were significant differences in rates of PTB and average time to delivery between control and vaginal 17P-treated groups in LPS-stimulated timed pregnant E15.5 mice. Considering the lacuna of therapeutic approaches in this area, vaginal delivery of 17P for the prevention of preterm birth has significant clinical relevance.
Chemical Composition of the Oleogum Resin Essential Oils of Boswellia dalzielii from Burkina Faso
Authors: DeCarlo Anjanette , Johnson Stephen , Ouédraogo Amadé , Dosoky Noura S. , Setzer William N.
Published: July 2019

Frankincense, the oleogum resin from members of Boswellia, has been used as medicine and incense for thousands of years, and essential oils derived from frankincense are important articles of commerce today. A new source of frankincense resin, Boswellia dalzielii from West Africa has been presented as a new, alternative source of frankincense. In this work, the oleogum resins from 20 different Boswellia dalzielii trees growing in Burkina Faso, West Africa were collected. Hydrodistillation of the resins gave essential oils that were analyzed by GC-MS and GC-FID. The essential oils were dominated by α-pinene (21.0%–56.0%), followed by carvone (2.1%–5.4%) and α-copaene (1.8%–5.0%). Interestingly, there was one individual tree that, although rich in α-pinene (21.0%), also had a substantial concentration of myrcene (19.2%) and α-thujene (9.8%). In conclusion, the oleogum resin essential oil compositions of B. dalzielii, rich in α-pinene, are comparable in composition to other frankincense essential oils, including B. sacra, B. carteri, and B. frereana. Additionally, the differences in composition between samples from Burkina Faso and those from Nigeria are very slight. There is, however, a rare chemotype of B. dalzielii that is dominated by myrcene, found both in Burkina Faso as well as Nigeria.
Elaboration and Characterization of Conductive Polymer Nanocomposites with Potential Use as Electrically Driven Membranes
Authors: Sangroniz Leire , Sangroniz Ainara , Fernández Mercedes , Etxeberria Agustin , Müller Alejandro J. , Santamaria Antxon
Published: July 2019

In this work, a general, facile, and relatively low-cost method to produce electrically driven non-porous membranes by revalorization of recycled polyolefins is proposed. The polymer matrices are poly(propylene) (PP) and poly(ethylene) (PE) and their corresponding recycled samples, which are respectively mixed with carbon nanotubes (CNT). The performances of the elaborated nanocomposites are studied by morphological, rheological, and electrical conductivity tests. The Joule heating effect is evaluated by applying an electric field and recording the corresponding temperature rise. An increase of 90 °C is obtained in certain cases, which represents the highest temperature enhancement reached so far by the Joule effect in thermoplastics, to our knowledge. The work shows a route to develop stimulus (voltage)-response (temperature) materials with low cost and with potential applications in many fields. As an example, the increase of the permeability with temperature of membranes made of the indicated nanocomposites, is analyzed.
Formation Features of Hybrid Nanocomposites Based on Polydiphenylamine-2-Carboxylic Acid and Single-Walled Carbon Nanotubes
Authors: Ozkan Sveta Zhiraslanovna , Karpacheva Galina Petrovna , Kostev Aleksandr Ivanovich , Bondarenko Galina Nikolaevna
Published: July 2019

Hybrid nanocomposites based on electroactive polydiphenylamine-2-carboxylic acid (PDPAC) and single-walled carbon nanotubes (SWCNTs) were obtained for the first time. Polymer-carbon nanomaterials were synthesized via in situ oxidative polymerization of diphenylamine-2-carboxylic acid (DPAC) in the presence of SWCNTs by two different ways. Hybrid SWCNT/PDPAC nanocomposites were prepared both in an acidic medium and in the heterophase system in an alkaline medium. In the heterophase system, the monomer and the SWCNTs are in the organic phase (chloroform) and the oxidant (ammonium persulfate) is in an aqueous solution of ammonium hydroxide. The chemical structure, as well as the electrical and thermal properties of the developed SWCNT/PDPAC nanocomposite materials were investigated.
Method of Desulfurization Process Selection Based on Improved Fuzzy Comprehensive Evaluation: A Case Study of Papermaking Desulfurization in China
Authors: Wang Zhiguo , Wang Fei , Zhu Yali , Gong Bengang
Published: July 2019

With the increasingly prominent global energy and environmental problems, more and more enterprises have been required to desulfurize the exhausted gases. Different enterprises have different demands for the desulfurization process, thus the choice of desulfurization process methods has become a focus of attention. Since the evaluation of the desulfurization process involves many factors, this paper proposes an improved fuzzy comprehensive evaluation method to evaluate the selection of desulfurization process when the traditional evaluation method is not applicable. Firstly, an evaluation system with two rating indicators was constructed, which considers the subjective and objective weights comprehensively. Secondly using the two hierarchical indicators, an effective desulfurization process method was obtained according to the principle of maximum membership degree. Finally, we took a real papermaking factory as an example to illustrate the detailed implementation processes of this method. The result shows that the model could be used as a comprehensive evaluation tool to select desulfurization scheme or optimize the desulfurization process.
To Be at One with the Land: Māori Spirituality Predicts Greater Environmental Regard
Authors: Lockhart Christopher , Houkamau Carla A. , Sibley Chris G. , Osborne Danny
Published: July 2019

Māori, New Zealand’s indigenous population, have a unique connection to the environment (Harris and Tipene 2006). In Māori tradition, Papatūānuku is the land—the earth mother who gives birth to all things, including Māori (Dell 2017). Māori also self-define as tāngata whenua (people of the land), a status formally recognised in New Zealand legislation. Māori have fought to regain tino rangatiratanga (authority and self-determination; see Gillespie 1998) over lands lost via colonisation. Accordingly, Cowie et al. (2016) found that socio-political consciousness—a dimension of Māori identity—correlated positively with Schwartz’s (1992) value of protecting the environment and preserving nature. Yet, Māori perceptions of land also derive from spiritual associations. Our work investigated the spiritual component of Māori environmental regard by delineating between protecting the environment (i.e., a value with socio-political implications) and desiring unity with nature (i.e., a value with spiritual overtones) amongst a large national sample of Māori (N = 6812). As hypothesized, socio-political consciousness correlated positively with valuing environmental protection, whilst spirituality correlated positively with valuing unity with nature. These results demonstrate that Māori connection with the land is simultaneously rooted in spirituality and socio-political concerns.
Thermal Airborne Optical Sectioning
Authors: Kurmi Indrajit , Schedl David C. , Bimber Oliver
Published: July 2019

We apply a multi-spectral (RGB and thermal) camera drone for synthetic aperture imaging to computationally remove occluding vegetation for revealing hidden objects, as required in archeology, search-and-rescue, animal inspection, and border control applications. The radiated heat signal of strongly occluded targets, such as a human bodies hidden in dense shrub, can be made visible by integrating multiple thermal recordings from slightly different perspectives, while being entirely invisible in RGB recordings or unidentifiable in single thermal images. We collect bits of heat radiation through the occluder volume over a wide synthetic aperture range and computationally combine them to a clear image. This requires precise estimation of the drone’s position and orientation for each capturing pose, which is supported by applying computer vision algorithms on the high resolution RGB images.
A Fast High-Resolution Imaging Algorithm for Helicopter-Borne Rotating Array SAR Based on 2-D Chirp-Z Transform
Authors: Qin Mengyi , Li Dong , Tang Xianhui , Zeng Cao , Li Wenjun , Xu Liying
Published: July 2019

In view of the azimuth resolution of the helicopter-borne rotating array synthetic aperture radar (RoASAR) depending on the azimuth reconstruction angle and the sector distortion caused by the azimuth Deramp processing, this paper proposes an efficient helicopter-borne RoASAR high-resolution imaging algorithm based on two-dimensional (2-D) chirp-z transform (CZT). First, the high-order Taylor series expansion is performed on the slant range, and the accurate 2-D spectral expression of the point target is obtained by using the method of series reversion (MSR). Based on that, the space-variant characteristics of the range cell migration (RCM) terms are analyzed. After that, the space-variant RCM and the sector distortion effect caused by the azimuth Deramp processing are removed by using efficient 2-D CZT, thereby increasing the azimuth reconstruction angle and improving the azimuth resolution. The proposed algorithm is efficient without the interpolation operation, and it is easy to implement in real-time. Finally, the simulations are provided to verify the effectiveness of the proposed algorithm.
Variability and Geographical Origin of Five Years Airborne Fungal Spore Concentrations Measured at Saclay, France from 2014 to 2018
Authors: Sarda-Estève Roland , Baisnée Dominique , Guinot Benjamin , Sodeau John , O’Connor David , Belmonte Jordina , Besancenot Jean-Pierre , Petit Jean-Eudes , Thibaudon Michel , Oliver Gilles , Sindt Charlotte , Gros Valérie
Published: July 2019

Airborne fungal spores (AFS) represent the major fraction of primary biological aerosol particles (PBAPs), and they are studied worldwide largely due to their important role within the Earth system. They have an impact on climate and human health, and they contribute to the propagation of diseases. As their presence in the air depends largely on studied ecosystems, a spore trap was used to monitor their atmospheric concentrations from 2014 to December 2018 in Saclay, a suburban area in the megacity of Paris. The main objective of this work was: (1) to understand the atmospheric variability of AFS in relation to different variables such as meteorological factors, agricultural practice, and (2) to identify their geographical origin by using a source receptor model. During our period of observation, 30 taxa have been identified under a light microscope. In order of importance, Ascospores, Cladosporium, Basidiospores, Tilletiopsis, Alternaria were found to be the most abundant types respectively (50.8%, 33.6%, 7.6%, 1.8%, and 1.3%) accounting for 95% of the atmospheric concentrations. We observed a general decrease associated with a strong interannual variability. A bimodal seasonal cycle was identified with a first maximum in July and a second in October. The main parameters driving the atmospheric concentration are temperature and precipitation. The daily variability is strongly activated by successive periods of hot weather and rainfall, multiplying the concentration by a factor of 1000 in less than 12 hours. Results from the source receptor model ZeFir point out unambiguous different origins of AFS due to specific sources impacting the observation site. Our study also indicated that a hydrological stress has a direct effect on the daily concentrations. This last point should be taken into account for every stressed ecosystem studied in a global warming context. This is particularly important for Mediterranean areas where water is a key control of the growth and dispersion of fungal spores.
Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018
Authors: Rose Stine , Andersen Ole , Passaro Marcello , Ludwigsen Carsten , Schwatke Christian
Published: July 2019

In recent years, there has been a large focus on the Arctic due to the rapid changes of the region. Arctic sea level determination is challenging due to the seasonal to permanent sea-ice cover, lack of regional coverage of satellites, satellite instruments ability to measure ice, insufficient geophysical models, residual orbit errors, challenging retracking of satellite altimeter data. We present the European Space Agency (ESA) Climate Change Initiative (CCI) Technical University of Denmark (DTU)/Technischen Universität München (TUM) sea level anomaly (SLA) record based on radar satellite altimetry data in the Arctic Ocean from the European Remote Sensing satellite number 1 (ERS-1) (1991) to CryoSat-2 (2018). We use updated geophysical corrections and a combination of altimeter data: Reprocessing of Altimeter Product for ERS (REAPER) (ERS-1), ALES+ retracker (ERS-2, Envisat), combination of Radar Altimetry Database System (RADS) and DTUs in-house retracker LARS (CryoSat-2). Furthermore, this study focuses on the transition between conventional and Synthetic Aperture Radar (SAR) altimeter data to make a smooth time series regarding the measurement method. We find a sea level rise of 1.54 mm/year from September 1991 to September 2018 with a 95% confidence interval from 1.16 to 1.81 mm/year. ERS-1 data is troublesome and when ignoring this satellite the SLA trend becomes 2.22 mm/year with a 95% confidence interval within 1.67–2.54 mm/year. Evaluating the SLA trends in 5 year intervals show a clear steepening of the SLA trend around 2004. The sea level anomaly record is validated against tide gauges and show good results. Additionally, the time series is split and evaluated in space and time.
Semi-Automatic Identification and Pre-Screening of Geological–Geotechnical Deformational Processes Using Persistent Scatterer Interferometry Datasets
Authors: Tomás Roberto , Pagán José Ignacio , Navarro José A. , Cano Miguel , Pastor José Luis , Riquelme Adrián , Cuevas-González María , Crosetto Michele , Barra Anna , Monserrat Oriol , Lopez-Sanchez Juan M. , Ramón Alfredo , Ivorra Salvador , Del Soldato Matteo , Solari Lorenzo , Bianchini Silvia , Raspini Federico , Novali Fabrizio , Ferretti Alessandro , Costantini Mario , Trillo Francesco , Herrera Gerardo , Casagli Nicola
Published: July 2019

This work describes a new procedure aimed to semi-automatically identify clusters of active persistent scatterers and preliminarily associate them with different potential types of deformational processes over wide areas. This procedure consists of three main modules: (i) ADAfinder, aimed at the detection of Active Deformation Areas (ADA) using Persistent Scatterer Interferometry (PSI) data; (ii) LOS2HV, focused on the decomposition of Line Of Sight (LOS) displacements from ascending and descending PSI datasets into vertical and east-west components; iii) ADAclassifier, that semi-automatically categorizes each ADA into potential deformational processes using the outputs derived from (i) and (ii), as well as ancillary external information. The proposed procedure enables infrastructures management authorities to identify, classify, monitor and categorize the most critical deformations measured by PSI techniques in order to provide the capacity for implementing prevention and mitigation actions over wide areas against geological threats. Zeri, Campiglia Marittima–Suvereto and Abbadia San Salvatore (Tuscany, central Italy) are used as case studies for illustrating the developed methodology. Three PSI datasets derived from the Sentinel-1 constellation have been used, jointly with the geological map of Italy (scale 1:50,000), the updated Italian landslide and land subsidence maps (scale 1:25,000), a 25 m grid Digital Elevation Model, and a cadastral vector map (scale 1:5000). The application to these cases of the proposed workflow demonstrates its capability to quickly process wide areas in very short times and a high compatibility with Geographical Information System (GIS) environments for data visualization and representation. The derived products are of key interest for infrastructures and land management as well as decision-making at a regional scale.
Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles
Authors: Xin Junfeng , Li Shixin , Sheng Jinlu , Zhang Yongbo , Cui Ying
Published: July 2019

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.
PRISER: Managing Notification in Multiples Devices with Data Privacy Support
Authors: Silva Luis Augusto , Leithardt Valderi Reis Quietinho , Rolim Carlos O. , González Gabriel Villarrubia , Geyer Cláudio F. R. , Silva Jorge Sá
Published: July 2019

With the growing number of mobile devices receiving daily notifications, it is necessary to manage the variety of information produced. New smart devices are developed every day with the ability to generate, send, and display messages about their status, data, and information about other devices. Consequently, the number of notifications received by a user is increasing and their tolerance may decrease in a short time. With this, it is necessary to develop a management system and notification controls. In this context, this work proposes a notification and alert management system called PRISER. Its focus is on user profiles and environments, applying data privacy criteria.
Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson’s Disease
Authors: Haji Ghassemi Nooshin , Hannink Julius , Roth Nils , Gaßner Heiko , Marxreiter Franz , Klucken Jochen , Eskofier Björn M.
Published: July 2019

Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson’s disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians’ gait assessment and to monitor patients in their daily environment.
Measurement and Characterization of Electromagnetic Noise in Edge Computing Networks for the Industrial Internet of Things
Authors: Li Huiting , Liu Liu , Li Yiqian , Yuan Ze , Zhang Kun
Published: July 2019

Edge computing and the Internet of Things (IOT) provide the technological basis for the development of intelligent manufacturing nowadays. In order to support the intelligent interconnection and application of all kinds of equipment in the industrial field, edge computing should be equipped close to or embedded in all kinds of equipment nodes in the industrial wireless network. Therefore, it is meaningful to investigate the wireless network design of the Industrial Internet of Things. Low power wireless sensor devices are widely used in the Industrial Internet of Things (IIoT), which are sensitive to electromagnetic noise. The electromagnetic noises in industrial scenarios are significantly different from the conventional assumed white noise. In this paper, the measurement results of electromagnetic noises at three different test positions are given in an automobile factory. The spectrum occupancy of the factory wireless environment in the 300 MHz–3 GHz band was obtained by frequency domain measurement. In the time domain measurement, four statistical parameters of the three bands of 315 MHz, 433 MHz, and 916 MHz were measured, and the electromagnetic noise distributions in different plant areas and different frequency bands were analyzed. According to the measurement results, the time-varying characteristics of electromagnetic noise can be characterized by continuous hidden Markov models (CHMM). These results are informative to the design and optimization for the edge computing networks for IIoT.
An Efficient RSS Localization for Underwater Wireless Sensor Networks
Authors: L. N. Nguyen Thu , Shin Yoan
Published: July 2019

Localization is a key-enabling technology for many applications in underwater wireless sensor networks. Traditional approaches for received signal strength (RSS)-based localization often require uniform distribution for anchor nodes and suffer from poor estimates according to unpredictable and uncontrollable noise conditions. In this paper, we establish an RSS-based localization scheme to determine the location of an unknown normal sensor from a certain measurement set of potential anchor nodes. First, we present a practical path loss model for wireless communication in underwater acoustic environments, where anchor nodes are deployed in a random circumstance. For a given area of interest, the RSS data collection is performed dynamically, where the measurement noises and the correlation among them are taken into account. For a pair of transmitter and receiver, we approximate the geometry distance between them according to a linear regression model. Thus, we can obtain a quick access for the range information, while keeping the error, the communication head and the response time low. We also present a method to correct noises in the distance estimate. Simulation results demonstrate that our localization scheme achieves a better performance for certain scenario settings. The successful localization probability can be up to 90%, where the anchor rate is fixed at 10%.
Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform
Authors: Zhou Chengquan , Ye Hongbao , Hu Jun , Shi Xiaoyan , Hua Shan , Yue Jibo , Xu Zhifu , Yang Guijun
Published: July 2019

The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.
Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing
Authors: Sanchez-Iborra Ramon , Santa José , Gallego-Madrid Jorge , Covaci Stefan , Skarmeta Antonio
Published: July 2019

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.
Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection
Authors: Pan Jing , Sun Hanqing , Song Zhanjie , Han Jungong
Published: July 2019

Downsampling input images is a simple trick to speed up visual object-detection algorithms, especially on robotic vision and applied mobile vision systems. However, this trick comes with a significant decline in accuracy. In this paper, dual-resolution dual-path Convolutional Neural Networks (CNNs), named DualNets, are proposed to bump up the accuracy of those detection applications. In contrast to previous methods that simply downsample the input images, DualNets explicitly take dual inputs in different resolutions and extract complementary visual features from these using dual CNN paths. The two paths in a DualNet are a backbone path and an auxiliary path that accepts larger inputs and then rapidly downsamples them to relatively small feature maps. With the help of the carefully designed auxiliary CNN paths in DualNets, auxiliary features are extracted from the larger input with controllable computation. Auxiliary features are then fused with the backbone features using a proposed progressive residual fusion strategy to enrich feature representation.This architecture, as the feature extractor, is further integrated with the Single Shot Detector (SSD) to accomplish latency-sensitive visual object-detection tasks. We evaluate the resulting detection pipeline on Pascal VOC and MS COCO benchmarks. Results show that the proposed DualNets can raise the accuracy of those CNN detection applications that are sensitive to computation payloads.
Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People
Authors: Antón Miguel Ángel , Ordieres-Meré Joaquín , Saralegui Unai , Sun Shengjing
Published: July 2019

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.
Analytical Model of a Wireless Sensor Network (WSN) Node Operation with a Modified Threshold-Type Energy Saving Mechanism
Authors: Kempa Wojciech M.
Published: July 2019

In this article, a model of the operation of a wireless sensor network (WSN) node with an energy saving mechanism based on a threshold-controlled multiple vacation policy is considered. When the queue of packets directed to the node becomes empty, a multiple vacation period is started during which the receiving/transmitting of packets is blocked. In such a period, successive vacations of a fixed constant duration are taken until a predetermined number of N packets accumulated in the queue is detected. Then, at the completion epoch of this vacation, the processing restarts normally. The analytic approach is based on the conception of an embedded Markov chain; integral equations and renewal theory are applied to study the queue-size transient behaviour. The representations for the Laplace transforms of the queue-size distribution at an arbitrary fixed time t and on the idle and processing periods are obtained. The compact-form formulae for the distributions of the idle and processing period duration are derived. Numerical examples are attached as well.
Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm
Authors: Wei Yang , Wang Hao , Tsang Kim Fung , Liu Yucheng , Wu Chung Kit , Zhu Hongxu , Chow Yuk-Tak , Hung Faan Hei
Published: July 2019

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.
Sounds of Soil: A New World of Interactions under Our Feet?
Authors: Rillig Matthias C. , Bonneval Karine , Lehmann Johannes
Published: July 2019

Soils are biodiversity-dense and constantly carry chemical flows of information, with our mental image of soil being dark and quiet. But what if soil biota tap sound, or more generally, vibrations as a source of information? Vibrations are produced by soil biota, and there is accumulating evidence that such vibrations, including sound, may also be perceived. We here argue for potential advantages of sound/vibration detection, which likely revolve around detection of potential danger, e.g., predators. Substantial methodological retooling will be necessary to capture this form of information, since sound-related equipment is not standard in soils labs, and in fact this topic is very much at the fringes of the classical soil research at present. Sound, if firmly established as a mode of information exchange in soil, could be useful in an ‘acoustics-based’ precision agriculture as a means of assessing aspects of soil biodiversity, and the topic of sound pollution could move into focus for soil biota and processes.
The Social Production of Food Waste at the Retail-Consumption Interface
Authors: Audet René , Brisebois Éliane
Published: July 2019

A major problem throughout the world, food waste is an issue that must be addressed not only by all actors in the agri-food chain but also without a silo mentality. To examine this problem, this article considers the interfaces between the stages of the agri-food chain, by emphasizing the interconnectivity of the different links in the chain, and focusing on the interface between retail distribution and consumption. We show that food waste is socially produced through the interactions and practices of the different actors within food systems. The study presented in this article results from a collaboration research project with two organizations involved in the food waste debate. The data analyzed are derived from an online survey of 1026 Quebec consumers and from 14 semi-directed interviews with retail distribution merchants in the Montreal area, Canada. By identifying, describing and analyzing the consumption and commercial and logistical management practices that contribute to food waste, our analysis demonstrates the existence of four symbolic processes that generate food waste at the retail–consumption interface: the economization of waste, the construction of edibility, the construction of freshness, and the moralization of waste. We argue that these processes should be considered when designing solutions to food waste.
Maximizing Environmental Impact Savings Potential through Innovative Biorefinery Alternatives: An Application of the TM-LCA Framework for Regional Scale Impact Assessment
Authors: Croxatto Vega Giovanna Croxatto , Sohn Joshua , Bruun Sander , Olsen Stig Irving , Birkved Morten
Published: July 2019

In order to compare the maximum potential environmental impact savings that may result from the implementation of innovative biorefinery alternatives at a regional scale, the Territorial Metabolism-Life Cycle Assessment (TM-LCA) framework is implemented. With the goal of examining environmental impacts arising from technology-to-region (territory) compatibility, the framework is applied to two biorefinery alternatives, treating a mixture of cow manure and grape marc. The biorefineries produce either biogas alone or biogas and polyhydroxyalkanoates (PHA), a naturally occurring polymer. The production of PHA substitutes either polyethylene terephthalate (PET) or biosourced polylactide (PLA) production. The assessment is performed for two regions, one in Southern France and the other in Oregon, USA. Changing energy systems are taken into account via multiple dynamic energy provision scenarios. Territorial scale impacts are quantified using both LCA midpoint impact categories and single score indicators derived through multi-criteria decision assessment (MCDA). It is determined that in all probable future scenarios, a biorefinery with PHA-biogas co-production is preferable to a biorefinery only producing biogas. The TM-LCA framework facilitates the capture of technology and regionally specific impacts, such as impacts caused by local energy provision and potential impacts due to limitations in the availability of the defined feedstock leading to additional transport.
Determinants of Consumers’ Purchasing Behavior for Certified Aquaculture Products in South Korea
Authors: Yi Sangchoul
Published: July 2019

This study aims to investigate consumers’ decision-making process for purchasing certified aquaculture products using the theory of the planned behavior (TPB) model. Based on the TPB model, this study empirically examines the relationship among TPB constructs, namely; attitude, social norm, perceived behavioral control, and behavioral intention to purchase sustainably produced products (i.e., Aquaculture Stewardship Council-certified products). Data from a web-based survey conducted through an online survey company (960 usable response samples) are analyzed using structural equation modeling. The results indicate that all latent variables (i.e., attitude, social norm, and perceived behavioral control) positively affect consumers’ behavioral intention. More importantly, environmental awareness moderates the relationships between TPB constructs and behavioral intention. In the case of the high awareness group in this study, attitude influences behavioral intention more than social norm does. On the contrary, in the case of the low awareness group, social norm influences behavioral intention more than attitude does.
Thematic Tourist Trails: Sustainability Assessment Methodology. The Case of Land Flowing with Milk and Honey
Authors: Widawski Krzysztof , Oleśniewicz Piotr
Published: July 2019

The aim of the paper was to examine the sustainable character of a thematic tourist trail, using the example of the Land Flowing with Milk and Honey trail in south-western Poland, whose creators emphasize their commitment to the idea of sustainable development. The trail elements that determine its character were indicated. On the basis of the point grading method modified by the authors, tourist and sustainable attractiveness was analysed both for particular elements and the whole trail. For this purpose, several indices were applied: the product regionality index, thematic trail tourism index, and stay attractiveness index. The final effect was the determination of the thematic trail potential, expressed by the quality and uniqueness of products on the trail and by utilizing trail elements in creating a tourist product of trail character. The results clearly point at high sustainable value of the products that were the basis for the trail design. On the other hand, shortcomings are indicated in terms of translating these resources into an attractive tourist offer, which would keep tourists in the region of the trail for a longer time.
The Three Musketeers Relationships between Hong Kong, Shanghai and Shenzhen Before and After Shanghai–Hong Kong Stock Connect
Authors: Cheng Andy Wui-Wing , Chow Nikolai Sheung-Chi , Chui David Kam-Hung , Wong Wing-Keung
Published: July 2019

This study examines the sustainability of financial integration between China (represented by Shenzhen and Shanghai) stock markets and Hong Kong stock market over the period of pre and post launch of the Stock Connect Scheme. This paper aims to fill the gap in the financial literature by providing empirical research on the dynamics of the financial integration process, and examining the sustainability of financial integration among the three Chinese stock markets. We apply cointegration and both linear and nonlinear causalities to investigate whether the Shanghai–Hong Kong Stock Connect has any impact on both market capitalizations and market indices of Hong Kong, Shanghai, and Shenzhen markets. Through cointegration tests and linear Granger causality techniques, it was found that the stock markets from mainland China are increasingly influencing the Hong Kong stock market after the introduction of the Stock Connect Scheme; however, when using nonlinear Granger causality analysis for confirming China market dominance, the result shows an reverse relationship whereby the Hong Kong stock market is still relevant to understand and predict China stock market after the introduction of the Stock Connect Scheme. Overall, our findings support the view that the Shanghai–Hong Kong Stock Connect has a significant impact on both market capitalizations and market indices of the Hong Kong, Shanghai, and Shenzhen markets, but Hong Kong stock market is still relevant to understand and predict China stock market after the introduction of the Stock Connect Scheme. The change in share premium difference between mainland China’s domestic A-share markets and Hong Kong’s H-share market could change investors’ appetites or sentiments. Further research includes examining whether there is any functional relationship including nonlinear relationship and studying the dynamic drivers of the relationships.
Review of Methodological Choices in LCA-Based Textile and Apparel Rating Tools: Key Issues and Recommendations Relating to Assessment of Fabrics Made From Natural Fibre Types
Authors: Watson K. J. , Wiedemann S. G.
Published: July 2019

Life cycle assessment (LCA) is a key tool for determining environmental impacts for textiles and apparel and is the basis for the publicly available Higg Material Sustainability Index (MSI) developed by the Sustainable Apparel Coalition (SAC). This paper reviews and evaluates the Higg MSI with respect to rating of fabrics made from natural fibre types, with the aim of providing a constructive analysis of methodological issues identified by comparison with the International Standards and LCA guidelines. The major issues identified by the review were: (1) lack of sufficient guidance for comparative analysis and public disclosure; (2) incomplete system boundaries and the choice of functional unit; (3) the choice of attributional LCA methods and variable methods applied for handling multi-functionality; (4) use of generalised data and small datasets, without reported sensitivity or uncertainty; (5) exclusion of important impact categories, choice of LCIA methods and lack of coverage of non-LCA assessed issues; and (6) the choice of the weighting and normalisation approach. This review found that the provision of, and adherence to the appropriate standards and best practice in LCA would rectify most of these issues. To achieve the laudable aims of the Higg MSI, further development and refinement is needed to ensure robust information is provided to improve the sustainability of textiles.
Paralytic Shellfish Toxins and Ocean Warming: Bioaccumulation and Ecotoxicological Responses in Juvenile Gilthead Seabream (Sparus aurata)
Authors: Barbosa Vera , Santos Marta , Anacleto Patrícia , Maulvault Ana Luísa , Pousão-Ferreira Pedro , Costa Pedro Reis , Marques António
Published: July 2019

Warmer seawater temperatures are expected to increase harmful algal blooms (HABs) occurrence, intensity, and distribution. Yet, the potential interactions between abiotic stressors and HABs are still poorly understood from ecological and seafood safety perspectives. The present study aimed to investigate, for the first time, the bioaccumulation/depuration mechanisms and ecotoxicological responses of juvenile gilthead seabream (Sparus aurata) exposed to paralytic shellfish toxins (PST) under different temperatures (18, 21, 24 °C). PST were detected in fish at the peak of the exposure period (day five, 0.22 µg g−1 N-sulfocarbamoylGonyautoxin-1-2 (C1 and C2), 0.08 µg g−1 Decarbamoylsaxitoxin (dcSTX) and 0.18 µg g−1 Gonyautoxin-5 (B1)), being rapidly eliminated (within the first 24 h of depuration), regardless of exposure temperature. Increased temperatures led to significantly higher PST contamination (275 µg STX eq. kg−1). During the trial, fish antioxidant enzyme activities (superoxide dismutase, SOD; catalase, CAT; glutathione S-transferase, GST) in both muscle and viscera were affected by temperature, whereas a significant induction of heat shock proteins (HSP70), Ubiquitin (Ub) activity (viscera), and lipid peroxidation (LPO; muscle) was observed under the combination of warming and PST exposure. The differential bioaccumulation and biomarker responses observed highlight the need to further understand the interactive effects between PST and abiotic stressors, to better estimate climate change impacts on HABs events, and to develop mitigation strategies to overcome the potential risks associated with seafood consumption.