bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Status of PSMA-Radiotracers for Prostate Cancer: Data Analysis of Prospective Trials Listed on ClinicalTrials.gov
Authors: Zippel Claus , Ronski Sarah C. , Bohnet-Joschko Sabine , Giesel Frederik L. , Kopka Klaus
Published: January 2020

The recent development of dedicated prostate-specific membrane antigen (PSMA) targeted radioligands shows the potential to change and improve the diagnosis and therapy of prostate cancer. There is an increasing number of prospective trials to further establish these tracers in the clinical setting. We analyzed data from the ClinicalTrials.gov registry including all listed prospective trials with PSMA-ligands for prostate cancer as of October 2019 concerning the different tracers and study characteristics. We found n = 104 eligible studies with a total of n = 25 different tracers in use: most frequently [68Ga]Ga-PSMA-11 (32%), followed by [18F]DCFPyL (24%) and [177Lu]Lu-PSMA-617 (10%). 85% are single-center, 15% multi-center studies. 95% national and 5% international studies. 34% are phase-II, 24% phase-I, 13% phase-I/-II, 12% phase-II/-III and phase-III and 7% early-phase-I. The primary purpose was classified as diagnostic in 72% of cases and therapeutic in 23% of cases. Most studies were executed in the USA (70%), followed by Canada (13%) and France (6%). This quantitative descriptive registry analysis indicates the rapid and global clinical developments and current status of PSMA-radioligands with emphasis on radiopharmaceutical and organizational aspects. It will be very interesting to see which tracers will prevail in the clinical setting.
Antibodies for the Treatment of Brain Metastases, a Dream or a Reality?
Authors: Cavaco Marco , Gaspar Diana , ARB Castanho Miguel , Neves Vera
Published: January 2020

The incidence of brain metastases (BM) in cancer patients is increasing. After diagnosis, overall survival (OS) is poor, elicited by the lack of an effective treatment. Monoclonal antibody (mAb)-based therapy has achieved remarkable success in treating both hematologic and non-central-nervous system (CNS) tumors due to their inherent targeting specificity. However, the use of mAbs in the treatment of CNS tumors is restricted by the blood–brain barrier (BBB) that hinders the delivery of either small-molecules drugs (sMDs) or therapeutic proteins (TPs). To overcome this limitation, active research is focused on the development of strategies to deliver TPs and increase their concentration in the brain. Yet, their molecular weight and hydrophilic nature turn this task into a challenge. The use of BBB peptide shuttles is an elegant strategy. They explore either receptor-mediated transcytosis (RMT) or adsorptive-mediated transcytosis (AMT) to cross the BBB. The latter is preferable since it avoids enzymatic degradation, receptor saturation, and competition with natural receptor substrates, which reduces adverse events. Therefore, the combination of mAbs properties (e.g., selectivity and long half-life) with BBB peptide shuttles (e.g., BBB translocation and delivery into the brain) turns the therapeutic conjugate in a valid approach to safely overcome the BBB and efficiently eliminate metastatic brain cells.
Influence of Regulated Deficit Irrigation and Environmental Conditions on Reproductive Response of Sweet Cherry Trees
Authors: Blanco Victor , Blaya-Ros Pedro José , Torres-Sánchez Roque , Domingo Rafael
Published: January 2020

The reproductive response of fifteen year old sweet cherry trees (Prunus avium L.) combination ‘Prime Giant’/SL64 under Mediterranean climate to deficit irrigation was studied in a commercial orchard in south-eastern Spain for four seasons. Three irrigation treatments were assayed: (i) control treatment, irrigated without restrictions at 110% of seasonal crop evapotranspiration; (ii) sustained deficit irrigation treatment, irrigated at 85% ETc during pre-harvest and post-harvest periods, and at 100% ETc during floral differentiation, and (iii) regulated deficit irrigation treatment, irrigated at 100% ETc during pre-harvest and floral differentiation and at 55% ETc during post-harvest. The duration and intensity of the phenological phases of sweet cherry trees, including cold accumulation, flowering, fruit set or fruit and vegetative growth, were assessed to ascertain whether the different irrigation strategies imposed affect the trees’ reproductive response (fruit yield, fruit size, leaf area, fruit physiological disturbances, and starch and soluble carbohydrates stock) in the same season or have a negative effect in the next season. Deficit irrigation did not advance, enhance or penalize flowering, fruit set or fruit growth. Neither did it diminish carbohydrate concentration in roots or cause an increase in the number of double fruits, which was more linked to high temperatures after harvest. However, deficit irrigation decreased vegetative growth and consequently the leaf area/fruit ratio, which, when it fell below 180 cm2 fruit−1, affected cherry size.
Adsorption of a Helical Filament Subject to Thermal Fluctuations
Authors: Chae M.-K. , Kim Y. , Johner A. , Lee N.-K.
Published: January 2020

We consider semiflexible chains governed by preferred curvature and twist and their flexural and twist moduli. These filaments possess a helical rather than straight three-dimensional (3D) ground state and we call them helical filaments (H-filament). Depending on the moduli, the helical shape may be smeared by thermal fluctuations. Secondary superhelical structures are expected to form on top of the specific local structure of biofilaments, as is documented for vimentin. We study confinement and adsorption of helical filaments utilizing both a combination of numerical simulations and analytical theory. We investigate overall chain shapes, transverse chain fluctuations, loop and tail distributions, and energy distributions along the chain together with the mean square average height of the monomers ⟨ z 2 ⟩ . The number fraction of adsorbed monomers serves as an order parameter for adsorption. Signatures of adsorbed helical polymers are the occurrence of 3D helical loops/tails and spiral or wavy quasi-flat shapes. None of these arise for the Worm-Like-Chain, whose straight ground state can be embedded in a plane.
The Effect of Extender Particle Size on the Glass Transition Temperature of Model Epoxy Coatings
Authors: Bashir Muhammad Ahsan , Jakobsen Martin Gjerde , Farstad Vivian Beate
Published: January 2020

Inorganic extenders are important constituents of 2K thermosetting epoxy-amine coatings and their physical properties play an important role in the final properties of the organic coatings. The effects of extender particle size and loading (i.e., the amount of extender in component A or in the total formulation) on the glass transition temperature (Tg) of model epoxy-amine coatings were studied in this work with differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). The obtained results show that the particle size and loading of feldspar particles (from 25 wt% to 70 wt%) do not influence the glass transition temperature of the model epoxy-amine coating significantly. In general, the smaller the particle size the lower the glass transition temperature of the coating but this depression in Tg seems negligible when seen relative to the change in extender particle size. Similar observations are reported for two model coatings having the same lamda (Λ) value but with silica of very different particle sizes (i.e., nanosilica and micron sized silica).
Dependence of the Physical Properties and Molecular Dynamics of Thermotropic Liquid Crystalline Copolyesters on p-Hydroxybenzoic Acid Content
Authors: Park Gi Tae , Lee Won Jun , Chang Jin-Hae , Lim Ae Ran
Published: January 2020

Two series of thermotropic liquid crystal copolymers (TLCPs) with different monomer structures and compositions were synthesized. The copolymers in the first series consisted of 2,5-diethoxyterephthalic acid (ETA), hydroquinone (HQ), and p-hydroxybenzoic acid (HBA), whereas those in the second series contained ETA, 2,7-dihydroxynaphthalene (DHN), and HBA. In both series, the molar ratio of HBA to the other monomers varied from 0 to 5. The thermal properties, degree of crystallinity, and stability of the liquid crystalline mesophase of the copolymers obtained at each HBA ratio were evaluated and compared. Overall, at each HBA content, the DHN-containing copolymer had better thermal properties, but the HQ-containing copolymer exhibited a higher degree of crystallinity and a more stable liquid crystalline mesophase. Furthermore, similar thermal stabilities were observed in both series. The dependence of the molecular dynamics of the TLCPs on the monomer structure was explained using 13C magic-angle spinning/cross-polarization nuclear magnetic resonance spectroscopy. An in-depth investigation of the relaxation time of each carbon revealed that the molecular motions of the TLCPs were greatly influenced by the structures of the monomers present in the main chain. The molecular dynamics of the HQ and DHN monomers in the two series were evaluated and compared.
Assessment of the Usefulness of the Twin-Screw Press in Terms of the Pressing Efficiency and Antioxidant Properties of Apple Juice
Authors: Wilczyński Kamil , Kobus Zbigniew , Nadulski Rafał , Szmigielski Marek
Published: January 2020

The paper presents the possibility of applying a twin-screw press for juice extraction from apples with different textural properties. The research was carried out with six different varieties; namely, Granny Smith, Modi, Ligol, Lobo, Boscop and Szampion. During the experiment, the following properties were measured: texture properties, pressing yield and polyphenolic content; and ascorbic acid content and antioxidant activity both in raw apples and apple juice. Based on the analysis, three hardness levels of apples can be distinguished, impacting the course of juice pressing in a twin-screw press (low hardness below 30 N, medium hardness 30–50 N and high hardness 50 N). The study showed that only high hardness apples are suitable for pressing on a twin-screw press. The mechanism by which texture properties influence the juice pressing process in the studied press was explained. It was further demonstrated that the hardness of apples has a positive impact on the degree of extraction of polyphenols and ascorbic acid into apple juice. This is caused by the different fracture patterns of tissues with different mechanical properties. High hardness apples (Granny Smith and Modi) were characterized by a considerably greater degree of bioactive substance extraction into juice than medium hardness apples.
‘Go and Prophesy in Your Own Land’: Foreign Prophets and Popularism in South Africa. Evoking the Need of Jonathanic Theology for Peaceful Resolution of Difference
Authors: Dube Bekithemba
Published: January 2020

Informed by a decoloniality lens and referencing motifs such as coloniality of power, knowledge, and being, this theoretical article analyses and problematises conflict, and reconstructs the experience of foreign and local prophets in South Africa. There is growing tension between foreign pastors and local pastors, with the former seemingly being popular because of performing ‘miracles,’ huge followings, and, in some cases, through mafia tendencies, which ignite the notion that expelling them from South Africa can be a counter-hegemony strategy to deal with popularism and criminality. The articles respond to two questions in this article: What factors influence conflict between migrant and local prophets? and, how can the story of David and Jonathan be used as a starting point for collective engagement in a process to achieve peace and healing? The article ends with arguing that the Jonathanic theology of peace, if pursued by migrant and local prophetic movements in South Africa, can reconstruct the prophetic terrain and assist in facilitating a rehumanising process, in addition to enacting the ontological density that has been lost. The article ends by arguing that Jonathanic theology is doable and desirable as a sustainable solution for religious conflict in South Africa.
Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network
Authors: Park Sung-Hwan , Jung Hyung-Sup , Lee Moung-Jin
Published: January 2020

Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, and this severely impedes the ability to detect the oil spill area. The objective of this study was to detect oil spill areas from high-resolution optic images using the artificial neural network (ANN) through effective suppression of severe sun glint effects. To enable this, a directional median filter (DMF) was adapted, and its use was compared with that of a traditional low-pass filter. A performance test was conducted using a KOMPSAT-2 image acquired during oil spill accidents that occurred in the Gulf of Mexico in 2010. The proposed method involved two main steps: (i) The sun glint effects caused by the ocean waves were corrected using the DMF; and (ii) the ANN approach was used to detect the oil spill area. The results show the following: (i) The designed DMF, which considers the size and angle of ocean waves, was proficient in correcting the sun glint effect in a high-resolution optical image; and (ii) oil spill areas were efficiently detected using the ANN approach with the proposed filtering method. The oil spill area was classified with accuracies of approximately 98.12% and 89.56% using the receiver operating characteristic (ROC) curve and probability of detection (POD) measurements, respectively. These results show that the accuracy of the proposed method is improved by about 9% compared to the traditional detecting algorithm.
Bridging the Semantic Gap between Land Tenure and EO Data: Conceptual and Methodological Underpinnings for a Geospatially Informed Analysis
Authors: Lee Cheonjae , de Vries Walter Timo
Published: January 2020

When spatial land tenure relations are not available, the only effective alternative data method is to rely on the agricultural census at the regional or national scale, based on household surveys and a participatory mapping at the local scale. However, what if even these are not available, which is typical for conflict-affected countries, administrations suffering from a lack of data and resources, or agencies that produce a sub-standard quality. Would it, under such circumstances, be possible to rely on remotely sensed Earth Observation (EO) data? We hypothesize that it is possible to qualify and quantify certain types of unknown land tenure relations based on EO data. Therefore, this study aims to standardize the identification and categorization of certain objects, environments, and semantics visible in EO data that can (re-)interpret land tenure relations. The context of this study is the opportunity to mine data on North Korean land tenure, which would be needed in case of a Korean (re-)unification. Synthesizing land tenure data in conjunction with EO data would align land administration practices in the respective parts and could also derive reliable land tenure and governance variables. There are still many unanswered questions about workable EO data proxies, which can derive information about land tenure relations. However, this first exploration provides a relevant contribution to bridging the semantic gap between land tenure and EO data.
How Response Designs and Class Proportions Affect the Accuracy of Validation Data
Authors: Radoux Julien , Waldner François , Bogaert Patrick
Published: January 2020

Reference data collected to validate land-cover maps are generally considered free of errors. In practice, however, they contain errors despite best efforts to minimize them. These errors propagate during accuracy assessment and tweak the validation results. For photo-interpreted reference data, the two most widely studied sources of error are systematic incorrect labeling and vigilance drops. How estimation errors, i.e., errors intrinsic to the response design, affect the accuracy of reference data is far less understood. In this paper, we analyzed the impact of estimation errors for two types of classification systems (binary and multiclass) as well as for two common response designs (point-based and partition-based) with a range of sub-sample sizes. Our quantitative results indicate that labeling errors due to proportion estimations should not be neglected. They further confirm that the accuracy of response designs depends on the class proportions within the sampling units, with complex landscapes being more prone to errors. As a result, response designs where the number of sub-samples is predefined and fixed are inefficient. To guarantee high accuracy standards of validation data with minimum data collection effort, we propose a new method to adapt the number of sub-samples for each sample during the validation process. In practice, sub-samples are incrementally selected and labeled until the estimated class proportions reach the desired level of confidence. As a result, less effort is spent on labeling univocal cases and the spared effort can be allocated to more ambiguous cases. This increases the reliability of reference data and of subsequent accuracy assessment. Across our study site, we demonstrated that such an approach could reduce the labeling effort by 50% to 75%, with greater gains in homogeneous landscapes. We contend that adopting this optimization approach will not only increase the efficiency of reference data collection, but will also help deliver more reliable accuracy estimates to the user community.
A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America
Authors: Qiu Ruonan , Han Ge , Ma Xin , Xu Hao , Shi Tianqi , Zhang Miao
Published: January 2020

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.
An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas
Authors: Sztubecka Małgorzata , Skiba Marta , Mrówczyńska Maria , Bazan-Krzywoszańska Anna
Published: January 2020

Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.
A Hybrid Energy Harvesting Design for On-Body Internet-of-Things (IoT) Networks
Authors: Saraereh Omar A. , Alsaraira Amer , Khan Imran , Choi Bong Jun
Published: January 2020

The Internet-of-things (IoT) has been gradually paving the way for the pervasive connectivity of wireless networks. Due to the ability to connect a number of devices to the Internet, many applications of IoT networks have recently been proposed. Though these applications range from industrial automation to smart homes, healthcare applications are the most critical. Providing reliable connectivity among wearables and other monitoring devices is one of the major tasks of such healthcare networks. The main source of power for such low-powered IoT devices is the batteries, which have a limited lifetime and need to be replaced or recharged periodically. In order to improve their lifecycle, one of the most promising proposals is to harvest energy from the ambient resources in the environment. For this purpose, we designed an energy harvesting protocol that harvests energy from two ambient energy sources, namely radio frequency (RF) at 2.4 GHz and thermal energy. A rectenna is used to harvest RF energy, while the thermoelectric generator (TEG) is employed to harvest human thermal energy. To verify the proposed design, extensive simulations are performed in Green Castalia, which is a framework that is used with the Castalia simulator in OMNeT++. The results show significant improvements in terms of the harvested energy and lifecycle improvement of IoT devices.
Studies of NO2 Gas-Sensing Characteristics of a Novel Room-Temperature Surface-Photovoltage Gas Sensor Device
Authors: Kwoka Monika , Szuber Jacek
Published: January 2020

In this work the characteristics of a novel type of room temperature NO2 gas sensor device based on the surface photovoltage effect are described. It was shown that for our SPV gas sensor device, using porous sputtered ZnO nanostructured thin films as the active gas sensing electrode material, the basic gas sensor characteristics in a toxic NO2 gas atmosphere are strongly dependent on the target NO2 gas flow rate. Moreover, it was also confirmed that our SPV gas sensor device is able to detect the lowest NO2 relative concentration at the level of 125 ppb, with respect to the commonly assumed signal-to-noise (S/N) ratio, as for the commercial devices.
Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles
Authors: Bridgelall Raj , Tolliver Denver
Published: January 2020

Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lower-cost and more-scalable solution spurred the idea of using sensors on board vehicles for a continuous and network-wide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with low-cost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve sub-meter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates.
Displacement Study of a Large-Scale Freeform Timber Plate Structure Using a Total Station and a Terrestrial Laser Scanner
Authors: Nguyen Anh Chi , Weinand Yves
Published: January 2020

Recent advances in timber construction have led to the realization of complex timber plate structures assembled with wood-wood connections. Although advanced numerical modelling tools have been developed to perform their structural analysis, limited experimental tests have been carried out on large-scale structures. However, experimental investigations remain necessary to better understand their mechanical behaviour and assess the numerical models developed. In this paper, static loading tests performed on timber plate shells of about 25 m span are reported. Displacements were measured at 16 target positions on the structure using a total station and on its entire bottom surface using a terrestrial laser scanner. Both methods were compared to each other and to a finite element model in which the semi-rigidity of the connections was represented by springs. Total station measurements provided more consistent results than point clouds, which nonetheless allowed the visualization of displacement fields. Results predicted by the model were found to be in good agreement with the measurements compared to a rigid model. The semi-rigid behaviour of the connections was therefore proven to be crucial to precisely predict the behaviour of the structure. Furthermore, large variations were observed between as-built and designed geometries due to the accumulation of fabrication and construction tolerances.
An Ultrasonic Object Detection Applying the ID Based on Spread Spectrum Technique for a Vehicle
Authors: Yi Donghee , Jin Heetae , Kim Moon Chan , Kim Suk Chan
Published: January 2020

When an ultrasonic sensor generates an ultrasonic wave and detects an obstacle from a reflected wave, a signal transmitted by other ultrasonic sensors would be interference. In this paper, to overcome the interference, a transducer transmits a signal with a unique ID modulated. The interference is ignored by verifying that the reflected signal includes its ID. The ID verification process uses a correlation between the received signal and the ID. Therefore, the ID is selected from orthogonal codes with good cross-correlation. Long code has the advantage of being more robust to interference. However, the reflected wave from nearby obstacles might return before the transmission ends. Therefore, the 7-bit Barker code is applied for near obstacle detection and a 31-bit Gold code is used for distant obstacle detection. The modulation technique is DQPSK, which is available in a narrow bandwidth and has a simple receiver structure. In ID recognition based on correlation, a near–far problem occurs due to a large amplitude difference between the received wave and interference. The addition of a zero-crossing detector solves this problem. The hardware is implemented based on the algorithm proposed in this paper. The simulation showed a detection rate of at least 90% and the the result of the real measurement represented a detection rate of 97.3% at 0.5 m and 94.5% at 2 m.
Halochromic Polystyrene Nanofibers Obtained by Solution Blow Spinning for Wine pH Sensing
Authors: Miranda Kelvi W.E. , Natarelli Caio V. L. , Thomazi Adriana C. , Ferreira Guilherme M. D. , Frota Maryana M. , Bastos Maria do Socorro R. , Mattoso Luiz H. C. , Oliveira Juliano E.
Published: January 2020

Colorimetric sensors developed by the solution blow spinning (SBS) technique have a rapid response to a variation in different physicochemical properties. In this study, polystyrene nanofibrous (PSNF) mats containing the bromothymol blue (BTB) indicator were obtained by SBS for the pH sensing of wine sample. The incorporation of the indicator did not promote changes in fiber diameter but led to the appearance of beads, allowing for the encapsulation of BTB. The halochromic property of BTB was retained in the PSNF material, and the migration tests showed that the indicator mats presented values below the maximum acceptable limit (10 mg dm−2) established by EU Commission Regulation No. 10/2011 for foods with an alcohol content up to 20%. The present study opens the possibility of applying nanostructured materials to innovative food packaging which, through nanosensory zones, change color as a function of the food pH.
Optimization of Weighting Window Functions for SAR Imaging via QCQP Approach
Authors: Liu Jin , Wang Wei , Song Hongjun
Published: January 2020

Weighting window functions are commonly used in Synthetic Aperture Radar (SAR) imaging to suppress the high Peak SideLobe Ratio (PSLR) at the price of probable Signal-to-Noise Ratio (SNR) loss and mainlobe widening. In this paper, based on the method of designing a mismatched filter, we have proposed a Quadratically Constrained Quadratic Program (QCQP) approach, which is a convex that can be solved efficiently, to optimize the weighting window function with both amplitude and phase, expecting to offer better imaging performance, especially on PSLR, SNR loss, and mainlobe width. According to this approach and its modified form, we are able to design window functions to optimize the PSLR or the SNR loss under different kinds of flexible and practical constraints. Compared to the ordinary real-valued and symmetric window functions, like the Taylor window, the designed window functions are complex-valued and can be asymmetric. By using Synthetic Aperture Radar (SAR) point target imaging simulation, we show that the optimized weighting window function can clearly show the weak target hidden in the sidelobes of the strong target.
A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine
Authors: Liu Xiaoyang , Huang Haizhou , Xiang Jiawei
Published: January 2020

Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.
Investigation of 3 dB Optical Intensity Spot Radius of Laser Beam under Scattering Underwater Channel
Authors: Wang Wei , Li Xiaoji , Rajbhandari Sujan , Li Yanlong
Published: January 2020

An important step in the design of receiver aperture and optimal spacing of the diversity scheme for an underwater laser communication system is to accurately characterize the two-dimensional (2D) spatial distribution of laser beam intensity. In this paper, the 2D optical intensity distribution and 3 dB optical intensity spot radius (OISR) are investigated due to the dominating optical intensity of laser beam being within the 3 dB OISR. By utilizing the Henyey–Greenstein function to compute the scattering angles of photons, the effects of the scattering underwater optical channel and optical system parameters on 3 dB OISR are examined based on the Monte Carlo simulation method. We have shown for the first time that in the channel with a high density of scattering particles, the divergence angle of the laser source plays a negligible role in 3 dB OISR. This is an interesting phenomenon and important for optical communication as this clearly shows that the geometric loss is no longer important for the design of receiver aperture and optimal spacing of the diversity scheme for the underwater laser communication system in the highly scattering channel.
Enzymatic Platforms for Sensitive Neurotransmitter Detection
Authors: Baluta Sylwia , Zając Dorota , Szyszka Adam , Malecha Karol , Cabaj Joanna
Published: January 2020

A convenient electrochemical sensing pathway was investigated for neurotransmitter detection based on newly synthesized silole derivatives and laccase/horseradish-peroxidase-modified platinum (Pt)/gold (Au) electrodes. The miniature neurotransmitter’s biosensors were designed and constructed via the immobilization of laccase in an electroactive layer of the Pt electrode coated with poly(2,6-bis(3,4-ethylenedioxythiophene)-4-methyl-4-octyl-dithienosilole) and laccase for serotonin (5-HT) detection, and a Au electrode modified with the electroconducting polymer poly(2,6-bis(selenophen-2-yl)-4-methyl-4-octyl-dithienosilole), along with horseradish peroxidase (HRP), for dopamine (DA) monitoring. These sensing arrangements utilized the catalytic oxidation of neurotransmitters to reactive quinone derivatives (the oxidation process was provided in the enzymes’ presence). Under the optimized conditions, the analytical performance demonstrated a convenient degree of sensitivity: 0.0369 and 0.0256 μA mM−1 cm−2, selectivity in a broad linear range (0.1–200) × 10−6 M) with detection limits of ≈48 and ≈73 nM (for the serotonin and dopamine biosensors, respectively). Moreover, the method was successfully applied for neurotransmitter determination in the presence of interfering compounds (ascorbic acid, L-cysteine, and uric acid).
An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning
Authors: Guo Siyu , Zhang Xiuguo , Zheng Yisong , Du Yiquan
Published: January 2020

Deep reinforcement learning (DRL) has excellent performance in continuous control problems and it is widely used in path planning and other fields. An autonomous path planning model based on DRL is proposed to realize the intelligent path planning of unmanned ships in the unknown environment. The model utilizes the deep deterministic policy gradient (DDPG) algorithm, through the continuous interaction with the environment and the use of historical experience data; the agent learns the optimal action strategy in a simulation environment. The navigation rules and the ship’s encounter situation are transformed into a navigation restricted area, so as to achieve the purpose of planned path safety in order to ensure the validity and accuracy of the model. Ship data provided by ship automatic identification system (AIS) are used to train this path planning model. Subsequently, the improved DRL is obtained by combining DDPG with the artificial potential field. Finally, the path planning model is integrated into the electronic chart platform for experiments. Through the establishment of comparative experiments, the results show that the improved model can achieve autonomous path planning, and it has good convergence speed and stability.
Crowdsensing Influences and Error Sources in Urban Outdoor Wi-Fi Fingerprinting Positioning
Authors: Leca Cristian-Liviu , Nicolaescu Ioan , Ciotirnae Petrica
Published: January 2020

Wi-Fi fingerprinting positioning systems have been deployed for a long time in location-based services for indoor environments. Combining mobile crowdsensing and Wi-Fi fingerprinting systems could reduce the high cost of collecting the necessary data, enabling the deployment of the resulting system for outdoor positioning in areas with dense Wi-Fi coverage. In this paper, we present the results attained in the design and evaluation of an urban fingerprinting positioning system based on crowdsensed Wi-Fi measurements. We first assess the quality of the collected measurements, highlighting the influence of received signal strength on data collection. We then evaluate the proposed system by comparing the influence of the crowdsensed fingerprints on the overall positioning accuracy for different scenarios. This evaluation helps gain valuable insight into the design and deployment of urban Wi-Fi positioning systems while also allowing the proposed system to match GPS-like accuracy in similar conditions.
RGB-D Image Processing Algorithm for Target Recognition and Pose Estimation of Visual Servo System
Authors: Li Shipeng , Li Di , Zhang Chunhua , Wan Jiafu , Xie Mingyou
Published: January 2020

This paper studies the control performance of visual servoing system under the planar camera and RGB-D cameras, the contribution of this paper is through rapid identification of target RGB-D images and precise measurement of depth direction to strengthen the performance indicators of visual servoing system such as real time and accuracy, etc. Firstly, color images acquired by the RGB-D camera are segmented based on optimized normalized cuts. Next, the gray scale is restored according to the histogram feature of the target image. Then, the obtained 2D graphics depth information and the enhanced gray image information are distort merged to complete the target pose estimation based on the Hausdorff distance, and the current image pose is matched with the target image pose. The end angle and the speed of the robot are calculated to complete a control cycle and the process is iterated until the servo task is completed. Finally, the performance index of this control system based on proposed algorithm is tested about accuracy, real-time under position-based visual servoing system. The results demonstrate and validate that the RGB-D image processing algorithm proposed in this paper has the performance in the above aspects of the visual servoing system.
SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems
Authors: Chang Chuan-Yu , Srinivasan Kathiravan , Chen Mao-Cheng , Chen Shao-Jer
Published: January 2020

In recent years, there are several cost-effective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body structures of the body. Further, such intelligent sensing systems such as ultrasound systems have been deployed by medical doctors around the globe for efficient detection of several diseases and disorders in the human body. Even though the ultrasound sensing system is a useful tool for obtaining the imagery of various body parts, there is always a possibility of inconsistencies in these images due to the variation in the settings of the system parameters. Therefore, in order to overcome such issues, this research devises an SVM-enabled intelligent genetic algorithmic model for choosing the universal features with four distinct settings of the parameters. Subsequently, the distinguishing characteristics of these features are assessed utilizing the Sorensen-Dice coefficient, t-test, and Pearson’s R measure. It is apparent from the results of the SVM-enabled intelligent genetic algorithmic model that this approach aids in the effectual selection of universal features for the breast cyst images. In addition, this approach also accomplishes superior accuracy in the classification of the ultrasound image for four distinct settings of the parameters.
Attitude Sensor from Ellipsoid Observations: A Numerical and Experimental Validation
Authors: Modenini Dario , Locarini Alfredo , Zannoni Marco
Published: January 2020

The preliminary design and validation of a novel, high accuracy horizon-sensor for small satellites is presented, which is based on the theory of attitude determination from ellipsoid observations. The concept consists of a multi-head infrared sensor capturing images of the Earth limb. By fitting an ellipse to the imaged limb arcs, and exploiting some analytical results available from projective geometry, a closed form solution for computing the attitude matrix is provided. The algorithm is developed in a dimensionless framework, requiring the knowledge of the shape of the imaged target, but not of its size. As a result, the solution is less sensitive to the limb shift caused by the atmospheric own radiance. To evaluate the performance of the proposed method, a numerical simulator is developed, which generates images captured in low Earth orbit, including also the presence of the atmosphere. In addition, experimental validation is provided due to a dedicated testbed, making use of a miniature infrared camera. Results show that our sensor concept returns rms errors of few hundredths of a degree or less in determining the local nadir direction.
Justice and Civil Liberties on Sex Work in Contemporary International Human Rights Law
Authors: Brooks-Gordon Belinda , Wijers Marjan , Jobe Alison
Published: January 2020

To fulfil obligations in international law State parties have to take the issue of human trafficking seriously. The United Nations Committee on the Elimination of Discrimination Against Women (CEDAW) provides General Recommendations (GR) to member states on the interpretation of the Women’s Convention. In 2018 the CEDAW Committee started to develop a GR on trafficking in women and girls in a process planned to conclude in 2020. The first stage towards this was through the publication of a Concept Note to serve as a basis for dialogue during the two-year international consultation period. The Concept Note is a vital link in a textual chain because it frames the policy problem and actively constructs its own ‘documentary reality’. This article provides a critical analysis of the CEDAW Concept Note on the grounds that such analysis provides an understanding of its discursive construction of trafficking, migrant labour and sex work, by an institution responsible for international jurisprudence on human rights. Analysis of the Concept Note explores the documentary constructions including narratives that merge adult women with girls, the symbolism of exploitation, the silencing of scientific research, the elision of sex worker voices, and sex work as work. The analysis leads us to conclude that the General Recommendation should define what counts as ‘exploitation’, and ‘forced labour’, and address the growing international recognition of best evidence on the wider impact of sex work laws, in order that legal framing and constructions of sex trafficking are not erroneously used to curtail rights of sex workers.
Carbon Balance of Grasslands on the Qinghai-Tibet Plateau under Future Climate Change: A Review
Authors: Leng Rong , Yuan Quanzhi , Wang Yushuang , Kuang Qian , Ren Ping
Published: January 2020

Climate change has brought significant impacts upon the natural ecological environment and human social development. The future carbon balance study has become an important part of research on the impacts of climate change. The Qinghai-Tibet Plateau (QTP) is a key area for studying climate change. Grassland, as a typical ecosystem of the QTP, embodies the sensitivity of the plateau to the climatic environment, so the carbon balance of grassland under future climate change conditions is important for studying global change. This paper reviewed the literature on carbon balance projection of grassland on the QTP under climate change. Two types of research methods were used to analyze and discuss the studies’ results, including experimental scenario projection and model projection. The experiment projected that appropriate temperature and moisture could enhance the carbon sink capacity of a grassland ecosystem, where moisture played a leading role. The model projection results showed that the carbon balance under different spatial and temporal scales were different. Although both can project the carbon balance of the study area, there are still some uncertainties. In addition, this research area should also consider the influence of human activity and plateau pikas to more accurately project the future carbon balance.
Evaluation Model and Strategy for Selecting Carbon Reduction Technology for Campus Buildings in Primary and Middle Schools in the Yangtze River Delta Region, China
Authors: Luo Xiaoyu , Ma Cong , Ge Jian
Published: January 2020

Cutting down global warming and reducing greenhouse gas emissions such as carbon dioxide are important global targets. Accounting for a third of global energy consumption, the building construction industry is an important target for carbon reduction. Campus buildings, of which there are a large number in China, differ from other building types, as they have noteworthy energy-use characteristics and technology selection requirements. This study identifies the carbon reduction technologies in Chinese primary and middle schools commonly used for energy and water conservation, and then evaluates their performance according to degrees of carbon reduction, maturity and economic suitability. Based on these three indicators, the study creates a three-dimensional evaluation model for the different technologies examined in order to obtain a selection ranking. The study offers guidance for project practice in the construction of primary and middle schools and helps to promote the development of the low-carbon campus.