bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Polymer-Based Mechanical Metamaterial with Tailorable Large Negative Poisson’s Ratios
Authors: Gao Shanshi , Liu Weidong , Zhang Liangchi , Gain Asit Kumar
Published: July 2020

Mechanical metamaterials have attracted significant attention due to their programmable internal structure and extraordinary mechanical properties. However, most of them are still in their prototype stage without direct applications. This research developed an easy-to-use mechanical metamaterial with tailorable large negative Poisson’s ratios. This metamaterial was microstructural, with cylindrical-shell-based units and was manufactured by the 3D-printing technique. It was found numerically that the present metamaterial could achieve large negative Poisson’s ratios up to −1.618 under uniaxial tension and −1.657 under uniaxial compression, and the results of the following verification tests agreed with simulation findings. Moreover, stress concentration in this new metamaterial is much smaller than that in most of existing re-entrance metamaterials.
Nanostructured Chitosan/Maghemite Composites Thin Film for Potential Optical Detection of Mercury Ion by Surface Plasmon Resonance Investigation
Authors: Fauzi Nurul Illya Muhamad , Fen Yap Wing , Omar Nur Alia Sheh , Saleviter Silvan , Daniyal Wan Mohd Ebtisyam Mustaqim Mohd , Hashim Hazwani Suhaila , Nasrullah Mohd
Published: July 2020

In this study, synthesis and characterization of chitosan/maghemite (Cs/Fe2O3) composites thin film has been described. Its properties were characterized using Fourier transform infrared spectroscopy (FTIR), atomic force microscopy (AFM) and ultraviolet-visible spectroscopy (UV-Vis). FTIR confirmed the existence of Fe–O bond, C–N bond, C–C bond, C–O bond, O=C=O bond and O–H bond in Cs/Fe2O3 thin film. The surface morphology of the thin film indicated the relatively smooth and homogenous thin film, and also confirmed the interaction of Fe2O3 with the chitosan. Next, the UV-Vis result showed high absorbance value with an optical band gap of 4.013 eV. The incorporation of this Cs/Fe2O3 thin film with an optical-based method, i.e., surface plasmon resonance spectroscopy showed positive response where mercury ion (Hg2+) can be detected down to 0.01 ppm (49.9 nM). These results validate the potential of Cs/Fe2O3 thin film for optical sensing applications in Hg2+ detection.
Injection-Molded Parts of Partially Biobased Polyamide 610 and Biobased Halloysite Nanotubes
Authors: Marset David , Dolza Celia , Boronat Teodomiro , Montanes Nestor , Balart Rafael , Sanchez-Nacher Lourdes , Quiles-Carrillo Luis
Published: July 2020

This works focuses on the development of environmentally friendly composites with a partially biobased polyamide 610 (PA610), containing 63% biobased content, and a natural inorganic filler at the nanoscale, namely, halloysite nanotubes (HNTs). PA610 composites containing 10, 20, and 30 wt% HNTs were obtained by melt extrusion in a twin screw co-rotating extruder. The resulting composites were injection-molded for further characterization. The obtained materials were characterized to obtain reliable data about their mechanical, thermal, and morphological properties. The effect of the HNTs wt% on these properties was evaluated. From a mechanical standpoint, the addition of 30 wt% HNTs gave an increase in tensile modulus of twice the initial value, thus verifying how this type of natural load provides increased stiffness on injection molded parts. The materials prepared with HNTs slightly improved the thermal stability, while a noticeable improvement on thermomechanical resistance over a wide temperature range was observed with increasing HNTs content. The obtained results indicate that high biobased content composites can be obtained with an engineering thermoplastic, i.e., PA610, and a natural inorganic nanotube-shaped filler, i.e., HNTs, with balanced mechanical properties and attractive behavior against high temperature.
Capacitated Lot-Sizing Problem with Sequence-Dependent Setup, Setup Carryover and Setup Crossover
Authors: Kang Jangha
Published: July 2020

Since setup operations have significant impacts on production environments, the capacitated lot-sizing problem considering arbitrary length of setup times helps to develop flexible and efficient production plans. This study discusses a capacitated lot-sizing problem with sequence-dependent setup, setup carryover and setup crossover. A new mixed integer programming formulation is proposed. The formulation is based on three building blocks: the facility location extended formulation; the setup variables with indices for the starting and the completion time periods; and exponential number of generalized subtour elimination constraints (GSECs). A separation routine is adopted to generate the violated GSECs. Computational experiments show that the proposed formulation outperforms models from the literature.
Psych: An International, Open Access, Peer Reviewed Journal on Fields Ranging from Experimental Psychology to Clinical Psychiatry
Authors: Schläpfer Thomas E.
Published: July 2020

It is with great honour and pleasure that I introduce, on behalf of the four Section Editors-in-Chief, the 53 members of the editorial board and the publisher MDPI, this journal [...]
Media Representations of Religion, Spirituality and Non-Religion in Australia
Authors: Weng Enqi , Halafoff Anna
Published: July 2020

Despite predictions of decline, religion has featured prominently in the public sphere and the media since the events of 11 September 2001. Previous research on media and religion in Australia post-September 11 has focused largely on its negative impacts, particularly on Muslim communities. This article, in contrast, examines media representations of religion, spirituality and non-religion on an ‘ordinary day’, of 17 September, over a three-year period in the city of Melbourne. Its findings reveal that religion, in its myriad forms, permeates many aspects of Australian public life, but in ways which do not always reflect the actual religious composition and lived experiences of worldview diversity in Australia.
Estimating the Volatility of Non-Life Premium Risk Under Solvency II: Discussion of Danish Fire Insurance Data
Authors: Cerchiara Rocco Roberto , Acri Francesco
Published: July 2020

We studied the volatility assumption of non-life premium risk under the Solvency II Standard Formula and developed an empirical model on real data, the Danish fire insurance data. Our empirical model accomplishes two things. Primarily, compared to the present literature, this paper innovates the fitting of Danish fire insurance data using a composite model with a random threshold. Secondly we prove, by fitting the Danish fire insurance data, that for large insurance companies the volatility of the standard formula is higher than the volatility estimated with internal models such as composite models, also taking into account the dependence between attritional and large claims.
Monitoring Residual Soil Moisture and Its Association to the Long-Term Variability of Rainfall over the Upper Blue Nile Basin in Ethiopia
Authors: Ayehu Getachew , Tadesse Tsegaye , Gessesse Berhan
Published: July 2020

Monitoring soil moisture and its association with rainfall variability is important to comprehend the hydrological processes and to set proper agricultural water use management to maximize crop growth and productivity. In this study, the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture product was applied to assess the dynamics of residual soil moisture in autumn (September to November) and its response to the long-term variability of rainfall in the Upper Blue Nile Basin (UBNB) of Ethiopia from 1992 to 2017. The basin was found to have autumn soil moisture (ASM) ranging from 0.09–0.38 m3/m3, with an average of 0.26 m3/m3. The ASM time series resulted in the coefficient of variation (CV) ranging from 2.8%–28% and classified as low-to-medium variability. In general, the monotonic trend analysis for ASM revealed that the UBNB had experienced a wetting trend for the past 26 years (1992–2017) at a rate of 0.00024 m3/m3 per year. A significant wetting trend ranging from 0.001 to 0.006 m3/m3 per year for the autumn season was found. This trend was mainly showed across the northwest region of the basin and covers about 18% of the total basin area. The spatial patterns and variability of rainfall and ASM were also found to be similar, which implies the strong relationship between rainfall and soil moisture in autumn. The spring and autumn season rainfall explained a considerable portion of ASM in the basin. The analyses also signified that the rainfall amount and distribution impacted by the topography and land cover classes of the basin showed a significant influence on the characteristics of the ASM. Further, the result verified that the behavior of ASM could be controlled by the loss of soil moisture through evapotranspiration and the gain from rainfall, although changes in rainfall were found to be the primary driver of ASM variability over the UBNB.
Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels
Authors: Shang Ronghua , Peng Pei , Shang Fanhua , Jiao Licheng , Shen Yifei , Stolkin Rustam
Published: July 2020

In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. Texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texture complexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texture complexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texture complexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images.
Combining and Comparing an Unmanned Aerial Vehicle and Multiple Remote Sensing Satellites to Calculate Long-Term River Discharge in an Ungauged Water Source Region on the Tibetan Plateau
Authors: Lou Hezhen , Wang Pengfei , Yang Shengtian , Hao Fanghua , Ren Xiaoyu , Wang Yue , Shi Liuhua , Wang Juan , Gong Tongliang
Published: July 2020

Research into global water resources is challenged by the lack of ground-based hydrometric stations and limited data sharing. It is difficult to collect good quality, long-term information about river discharges in ungauged regions. Herein, an approach was developed to determine the river discharges of 24 rivers in ungauged regions on the Tibetan Plateau on a long-term scale. This method involved coupling the Manning–Strickler formula, and data from an unmanned aerial vehicle (UAV) and the Gaofen-2, SPOT-5, and Sentinel-2 satellites. We also compared the discharges calculated by using the three satellites’ data. Fundamental information about the rivers was extracted from the UAV data. Comparison of the discharges calculated from the in-situ measurements and the UAV data gave an R2 value of 0.84, an average NSE of 0.79, and an RMSE of 0.11 m3/s. The river discharges calculated with the GF-2 remote sensing data and the in-situ experiments for the same months were compared and the R2, RMSE, and the NSE were 0.80, 1.8 m3/s, and 0.78, respectively. Comparing the discharges calculated over the long term from the measured in-situ data and the SPOT-5 and Sentinel-2 data gave R2 values of 0.93 and 0.92, and RMSE values of 2.56 m3/s and 3.16 m3/s, respectively. The results showed that the GF-2 and UAV were useful for calculating the discharges for low-flow rivers, while the SPOT-5 or the Sentinel-2 satellite gave good results for high-flow river discharges in the long-term. Our results demonstrate that the discharges in ungauged tributaries can be reliably estimated in the long-term with this method. This method extended the previous research, which described river discharge only in one period and provided more support to the monitoring and management of the tributaries in ungauged regions.
An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time
Authors: Nolde Michael , Plank Simon , Riedlinger Torsten
Published: July 2020

In the case of ongoing wildfire events, timely information on current fire parameters is crucial for informed decision making. Satellite imagery can provide valuable information in this regard, since thermal sensors can detect the exact location and intensity of an active fire at the moment the satellite passes over. This information can be derived and distributed in near-real time, allowing for a picture of current fire activity. However, the derivation of the size and shape of an already affected area is more complex and therefore most often not available within a short time frame. For urgent decision making though, it would be desirable to have this information available in near-real time, and on a large scale. The approach presented here works fully automatic and provides perimeters of burnt areas within two hours after the satellite scene acquisition. It uses the red and near-infrared bands of mid-resolution imagery to facilitate continental-scale monitoring of recently occurred burnt areas. To allow for a high detection capacity independent of the affected vegetation type, segmentation thresholds are derived dynamically from contextual information. This is done by using a Morphological Active Contour approach for perimeter determination. The results are validated against semi-automatically derived burnt areas for five wildfire incidents in Europe. Furthermore, these results are compared with three widely used burnt area datasets on a country-wide scale. It is shown that a high detection quality can be reached in near real-time. The large-scale inter-comparison shows that the results coincide with 63% to 76% of the burnt area in the reference datasets. While these established datasets are only available with a time lag of several months or are created by using manual interaction, the presented approach produces results in near-real time fully automatically. This work is therefore supposed to represent a valuable improvement in wildfire related rapid damage assessment.
A Label-Free Impedimetric Genosensor for the Nucleic Acid Amplification-Free Detection of Extracted RNA of Dengue Virus
Authors: Wu Ching-Chou , Yen Hao-Yu , Lai Lu-Ting , Perng Guey-Chuen , Lee Cheng-Rei , Wu Shuenn-Jue
Published: July 2020

Developing rapid and sensitive diagnostic methods for dengue virus (DENV) infection is of prime priority because DENV infection is the most prevalent mosquito-borne viral disease. This work proposes an electrochemical impedance spectroscopy (EIS)-based genosensor for the label-free and nucleic acid amplification-free detection of extracted DENV RNA intended for a sensitive diagnosis of DENV infection. A concentration ratio of 0.04 mM 6-mercaptohexanoic acid (MHA) to 1 mM 6-mercapto-1-hexanol (MCH) was selected to modify thin-film gold electrodes as a link to control the coverage of self-designed probe DNA (pDNA) at a density of 4.5 ± 0.4 × 1011 pDNA/cm2. The pDNA/MHA/MCH-modified genosensors are proven to improve the hybridization efficiency of a synthetic 160-mer target DNA (160mtDNA) with a 140-mer electrode side overhang as compared to other MHA/MCH ratio-modified genosensors. The MHA(0.04 mM)/MCH(1 mM)-modified genosensors also present good hybridization efficiency with the extracted DENV serotype 1 (DENV1) RNA samples, having the same electrode side overhangs with the 160mtDNA, showing a low detection limit of 20 plaque forming units (PFU)/mL, a linear range of 102–105 PFU/mL and good selectivity for DENV1. The pDNA density-controlled method has great promise to construct sensitive genosensors based on the hybridization of extracted DENV nucleic acids.
Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques
Authors: Wu Weibin , Zhang Zhenbang , Zheng Lijun , Han Chongyang , Wang Xiaoming , Xu Jian , Wang Xinrong
Published: July 2020

Pine wilt disease (PWD) caused by pine wood nematode (PWN, Bursaphelenchus xylophilus) originated in North America and has since spread to Asia and Europe. PWN is currently a quarantine object in 52 countries. In recent years, pine wilt disease has caused considerable economic losses to the pine forest production industry in China, as it is difficult to control. Thus, one of the key strategies for controlling pine wilt disease is to identify epidemic points as early as possible. The use of hyperspectral cameras mounted on drones is expected to enable PWD monitoring over large areas of forest, and hyperspectral images can reflect different stages of PWD. The trend of applying hyperspectral techniques to the monitoring of pine wilt disease is analyzed, and the corresponding strategies to address the existing technical problems are proposed, such as data collection of early warning stages, needs of using unmanned aerial vehicles (UAVs), and establishment of models after preprocessing.
Piezoelectric Sensing Techniques in Structural Health Monitoring: A State-of-the-Art Review
Authors: Jiao Pengcheng , Egbe King-James I. , Xie Yiwei , Matin Nazar Ali , Alavi Amir H.
Published: July 2020

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.
Simultaneous Clamping and Cutting Force Measurements with Built-In Sensors
Authors: Rezvani Sina , Kim Chang-Ju , Park Simon S. , Lee Jihyun
Published: July 2020

The intensity of the clamping force during milling operations is very important, because an excessive clamping force can distort the workpiece, while inadequate clamping causes slippage of the workpiece. Since the overall clamping force can be affected by the cutting forces throughout machining, it is necessary to monitor the change of clamping and the cutting forces during the process. This paper proposes a hybrid system in the form of a vise with built-in strain gauges and in-house-developed piezoelectric sensors for simultaneous measurement of clamping and cutting forces. Lead zirconate titanate (PZT) sensors are fabricated and embedded in a layered jaw to measure the dynamic forces of the machine tool. A cross-shaped groove within the jaw is designed to embed strain gauges, which predominantly measure the static clamping forces. Sensor fusion technology combining the signals of the strain gauges and PZT piezoelectric sensors is used to investigate the interactions between cutting forces and clamping forces. The results show average errors of 11%, 17%, and 6% for milling forces in X, Y, and Z directions, respectively; and 19% error for clamping forces, confirming the capability of the setup to monitor the forces in milling.
Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks
Authors: Awan Faraz Malik , Minerva Roberto , Crespi Noel
Published: July 2020

Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.
Sensor Signal and Information Processing II
Authors: Woo Wai Lok , Gao Bin
Published: July 2020

This Special Issue compiles a set of innovative developments on the use of sensor signals and information processing. In particular, these contributions report original studies on a wide variety of sensor signals including wireless communication, machinery, ultrasound, imaging, and internet data, and information processing methodologies such as deep learning, machine learning, compressive sensing, and variational Bayesian. All these devices have one point in common: These algorithms have incorporated some form of computational intelligence as part of their core framework in problem solving. They have the capacity to generalize and discover knowledge for themselves, learning to learn new information whenever unseen data are captured.
Piezoelectric Energy Harvesting from Suspension Structures with Piezoelectric Layers
Authors: Wang Min , Xia Yiming , Pu Huayan , Sun Yi , Ding Jiheng , Luo Jun , Xie Shaorong , Peng Yan , Zhang Quan , Li Zhongjie
Published: July 2020

In this paper, we propose a generator for piezoelectric energy harvesting from suspension structures. This device consists of a leaf spring and eight pairs of piezoelectric layers attached to inner and outer surfaces. We present a special type of leaf spring, which can magnify the force from the workload to allow the piezoelectric layers to achieve larger deformation. The generator is to solve the problem of vibration energy reutilization in a low-frequency vibration system. To verify the efficiency of the proposed configuration, a series of experiments are operated. The results indicate that the resonance frequency (25.2 Hz) obtained from the sweep experiment is close to the simulation result (26.1 Hz). Impedance-matching experiments show that the sum of the output power attains 1.7 mW, and the maximum single layer reaches 0.6 mW with an impedance matching of 610 KΩ, and the instantaneous peak-peak power density is 3.82 mW/cm3. The capacitor-charging performance of the generator is also excellent under the series condition. For a 4.7 μF capacitor, the voltage is charged to 25 V in 30 s and limited at 32 V in 80 s. These results demonstrate the exploitable potential of piezoelectric energy harvesting from suspension structures.
An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram
Authors: Vo Khuong , Le Tai , Rahmani Amir M. , Dutt Nikil , Cao Hung
Published: July 2020

The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.
RETRACTED: Paricio, A.; Lopez-Carmona, M.A. MuTraff: A Smart-City Multi-Map Traffic Routing Framework. Sensors 2019, 19, 5342
Authors:   Sensors Editorial Office
Published: July 2020

It has been brought to our attention that the methods and results presented in [...]
Sensitivity Enhancement of Curvature Fiber Sensor Based on Polymer-Coated Capillary Hollow-Core Fiber
Authors: Herrera-Piad Luis A. , Hernández-Romano Iván , May-Arrioja Daniel A. , Minkovich Vladimir P. , Torres-Cisneros Miguel
Published: July 2020

In this paper, we propose and experimentally demonstrate a simple technique to enhance the curvature sensitivity of a bending fiber optic sensor based on anti-resonant reflecting optical waveguide (ARROW) guidance. The sensing structure is assembled by splicing a segment of capillary hollow-core fiber (CHCF) between two single-mode fibers (SMF), and the device is set on a steel sheet for measuring different curvatures. Without any surface treatment, the ARROW sensor exhibits a curvature sensitivity of 1.6 dB/m−1 in a curvature range from 0 to 2.14 m−1. By carefully coating half of the CHCF length with polydimethylsiloxane (PDMS), the curvature sensitivity of the ARROW sensor is enhanced to −5.62 dB/m−1, as well as an increment in the curvature range (from 0 to 2.68 m−1). Moreover, the covered device exhibits a low-temperature sensitivity (0.038 dB/°C), meaning that temperature fluctuations do not compromise the bending fiber optic sensor operation. The ARROW sensor fabricated with this technique has high sensitivity and a wide range for curvature measurements, with the advantage that the technique is cost-effective and easy to implement. All these features make this technique appealing for real sensing applications, such as structural health monitoring.
ACE: ARIA-CTR Encryption for Low-End Embedded Processors
Authors: Seo Hwajeong , Kwon Hyeokdong , Kim Hyunji , Park Jaehoon
Published: July 2020

In this paper, we present the first optimized implementation of ARIA block cipher on low-end 8-bit Alf and Vegard’s RISC processor (AVR) microcontrollers. To achieve high-speed implementation, primitive operations, including rotation operation, a substitute layer, and a diffusion layer, are carefully optimized for the target low-end embedded processor. The proposed ARIA implementation supports the electronic codebook (ECB) and the counter (CTR) modes of operation. In particular, the CTR mode of operation is further optimized with the pre-computed table of two add-round-key, one substitute layer, and one diffusion layer operations. Finally, the proposed ARIA-CTR implementations on 8-bit AVR microcontrollers achieved 187.1, 216.8, and 246.6 clock cycles per byte for 128-bit, 192-bit, and 256-bit security levels, respectively. Compared with previous reference implementations, the execution timing is improved by 69.8%, 69.6%, and 69.5% for 128-bit, 192-bit, and 256-bit security levels, respectively.
Optimizing Irradiation Geometry in LED-Based Photoacoustic Imaging with 3D Printed Flexible and Modular Light Delivery System
Authors: Kuriakose Maju , Nguyen Christopher D. , Kuniyil Ajith Singh Mithun , Mallidi Srivalleesha
Published: July 2020

Photoacoustic (PA) imaging–a technique combining the ability of optical imaging to probe functional properties of the tissue and deep structural imaging ability of ultrasound–has gained significant popularity in the past two decades for its utility in several biomedical applications. More recently, light-emitting diodes (LED) are being explored as an alternative to bulky and expensive laser systems used in PA imaging for their portability and low-cost. Due to the large beam divergence of LEDs compared to traditional laser beams, it is imperative to quantify the angular dependence of LED-based illumination and optimize its performance for imaging superficial or deep-seated lesions. A custom-built modular 3-D printed hinge system and tissue-mimicking phantoms with various absorption and scattering properties were used in this study to quantify the angular dependence of LED-based illumination. We also experimentally calculated the source divergence of the pulsed-LED arrays to be 58° ± 8°. Our results from point sources (pencil lead phantom) in non-scattering medium obey the cotangential relationship between the angle of irradiation and maximum PA intensity obtained at various imaging depths, as expected. Strong dependence on the angle of illumination at superficial depths (−5°/mm at 10 mm) was observed that becomes weaker at intermediate depths (−2.5°/mm at 20 mm) and negligible at deeper locations (−1.1°/mm at 30 mm). The results from the tissue-mimicking phantom in scattering media indicate that angles between 30–75° could be used for imaging lesions at various depths (12 mm–28 mm) where lower LED illumination angles (closer to being parallel to the imaging plane) are preferable for deep tissue imaging and superficial lesion imaging is possible with higher LED illumination angles (closer to being perpendicular to the imaging plane). Our results can serve as a priori knowledge for the future LED-based PA system designs employed for both preclinical and clinical applications.
Space State Representation Product Evaluation in Satellite Position and Receiver Position Domain
Authors: Pelc-Mieczkowska Renata , Tomaszewski Dariusz
Published: July 2020

In Global Navigation Satellite Systems (GNSS) positioning, important terms in error budget are satellite orbits and satellite clocks correction errors. International services are developing and providing models and correction to minimize the influence of these errors both in post-processing and real-time applications. The International GNSS Service (IGS) Real-Time Service (RTS) provides real-time orbits and clock corrections for the broadcast ephemeris. Real-time products provided by IGS are generated by different analysis centres using different algorithms. In this paper, four RTS products—IGC01, CLK01, CLK50, and CLK90—were evaluated and analysed. To evaluate State Space Representation (SSR) products’ GPS satellites, the analyses were made in three variants. In the first approach, geocentric real-time Satellite Vehicle (SV) coordinates and clock corrections were calculated. The obtained results were compared with the final IGS, ESA, GFZ, and GRG ephemerides. The second approach was to use the corrected satellite positions and clock corrections to determine the Precise Point Position (PPP) of the receiver. In the third analysis, the impact of SSR corrections on receiver Single Point Position (SPP) was evaluated. The first part of the research showed that accuracy of the satellite position is better than 10 cm (average 3 to 5 cm), while in the case of clock corrections, mean residuals range from 2 cm to 17 cm. It should be noted that the errors of the satellites positions obtained from one stream differ depending on the reference data used. This shows the need for an evaluation of correction streams in the domain of the receiver position. In the case of PPP in a kinematic mode, the tests allowed to determine the impact that the use of different streams has on the final positioning results. These studies showed differences between specific streams, which could not be seen in the first study. The best results (3D RMS at 0.13 m level) were obtained for the CLK90 stream, while for IGC01, the results were three times worse. The SPP tests clearly indicate that regardless of the selected SSR stream, one can see a significant improvement in positioning accuracy as compared to positioning results using only broadcast ephemeris.
Estimating Multidimensional Poverty among Cassava Producers in Nigeria: Patterns and Socioeconomic Determinants
Authors: Olarinde Luke Oyesola , Abass Adebayo Busura , Abdoulaye Tahirou , Adepoju Adebusola Adenike , Fanifosi Emmanuel Gbenga , Adio Matthew Olufemi , Adeniyi Obadiah Adekunle , Wasiu Awoyale
Published: July 2020

The scourge of poverty, including its correlates, has been witnessing an incremental sequence over the years in Nigeria despite the natural endowment of the country. Efforts by various stakeholders to address this problem have not yielded tangible results. Using cross-sectional data collected in 2015 on 775 cassava farmers spread across four geographical zones, this study estimates multidimensional poverty of cassava producers in Nigeria. This is to determine the factors responsible for poverty increase and contribution(s) of these factors to poverty. The study found that about 74% of the respondents were multidimensionally poor. Assets and public/housing utility were the main contributors to aggregate multidimensional poverty index (MPI), while education and health contributed most to povertyreduction. The results also showed major contributing indicators to MPI to be formal employment, school enrolment, years of schooling, frequency of hospital visits, and household assets’ ownership. The South-eastzone of Nigeria had the highest adjusted headcount of poverty among cassava producers. The estimated coefficient of age, farming experience, years of schooling, household size, and access to informal credit were significant determinants of poverty in the study area. In conclusion, the results suggest that although Nigeria is a federation of more than 30 states that continue to rely on nation-wide policy initiatives of the central government, policies on cassava aiming to lift millions of people out of poverty should instead vary according to the peculiar poverty dimensions of each federation unit. We suggest reform in the agriculture sector that will emphasize facilitation and access to incentives (credits, training, extension, cooperate system, etc.) by younger farmers to engage in modern cassava farming, thereby, enhancing the chances of rural cassava growers to move out of poverty.
Increasing the Proportion of Plant-Based Foods Available to Shift Social Consumption Norms and Food Choice among Non-Vegetarians
Authors: Raghoebar Sanne , Van Kleef Ellen , De Vet Emely
Published: July 2020

Increasing the relative availability of plant-based (versus animal source) foods seems promising in shifting consumption, but it remains unknown how and under what circumstances this happens. We performed two availability manipulations including different foods. The impact on food choice, social norm perceptions about what others do (descriptive) or approve of (injunctive), and salience was assessed. Non-vegetarian participants were visually (Study 1, n = 184) or physically (Study 2, n = 276) exposed to (a) four plant-based and two animal source foods or (b) vice versa. Participants chose one food item, either hypothetically (Study 1) or actually (Study 2), and reported the perceived social norms and salience of plant-based and animal source foods. The results showed no direct effects on food choice, injunctive norms, or salience. An increased proportion of plant-based (versus animal source) foods was interpreted in Study 1 as plant-based foods being less often chosen by others, whereas in Study 2, these foods were interpreted as being more often chosen (marginally significant), while animal source foods were interpreted as being less often chosen. The results suggest that a higher availability of plant-based foods influences descriptive norms, but future research should examine aspects potentially contributing to the contradictory normative interpretations (e.g., norm salience).
Selection of the Best Method for Underpinning Foundations Using the PROMETHEE II Method
Authors: Dachowski Ryszard , Gałek Katarzyna
Published: July 2020

This article proposes applying the preference-ranking organization method for enrichment of evaluations (PROMETHEE) II in the selection of the optimal solution and ranking of selected methods for underpinning foundations. Analysis of the selected foundation-underpinning methods was based on a complex decision-making problem that included aspects of the three pillars of sustainable development, and it was extended to include technological and constructional criteria. The study used the following criteria for assessing proposed variants: price, bearing capacity, noise and vibrations, necessary equipment, necessary excavations under final structure, failure rates, and necessary foundation shoring. Analysis using the PROMETHEE II method allowed for identifying strengths and weaknesses of selected methods for underpinning foundations. The PROMETHEE II method enabled to create a ranking of foundation-underpinning methods. Jet-grouting and root-pile methods were the highest scorers in this ranking and those that fulfilled the identified criteria to the greatest extent. Moreover, analysis showed that the PROMETHEE II method was useful in solving problems of choice in the construction industry.
Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction
Authors: Stajkowski Stephen , Kumar Deepak , Samui Pijush , Bonakdari Hossein , Gharabaghi Bahram
Published: July 2020

Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.
Inclusive Leadership and Education Quality: Adaptation and Validation of the Questionnaire “Inclusive Leadership in Schools” (LEI-Q) to the Italian Context
Authors: Crisol Moya Emilio , Molonia Tiziana , Caurcel Cara María Jesús
Published: July 2020

The United Nations Sustainable Development Programme for 2030 has established the principles of sustainable action for today’s society, one of which is to achieve quality and more inclusive education. Our society has reached the stage where quality education is no longer a privilege of a few, but a fundamental right. Therefore, not achieving quality education is equivalent to not having had the opportunity to grow and develop as fully as one is entitled to in a free and just society, which needs inclusive leadership. The objective in this study is the validation of the instrument “Inclusive Leadership in Schools” for the Italian context. The methods used are content validity through expert judgement; construct validity through exploratory and confirmatory factor analysis; and reliability through Cronbach’s alpha coefficient and the split-half method. The results reveal that both the version for school administrators and teachers and the version for families have optimal levels of validity and reliability for measuring the degree of inclusive leadership in Italian schools. The results suggest that inclusive leadership effects are expected to operate most closely via their influence on developing the improvement of teaching and learning quality and promoting a favourable school climate and culture that emphasize high expectations and quality education.
Food Secure Metropolitan Areas: The Transition Support System Approach
Authors: Dijkshoorn-Dekker Marijke , Linderhof Vincent , Mattijssen Thomas J.M. , Polman Nico
Published: July 2020

Metropolitan areas are highly complex systems where a transition towards food security encompasses a systematic change in the whole food chain. Existing decision support tools that have been developed to inform policy-makers are mostly data-driven but hardly consider the activities and preferences of different stakeholders in the food system. The aim of this article is to contribute to urban food security by combining stakeholder participation with model-driven decision support. For this purpose, we developed and tested the Transition Support System (TSS) approach. In this TSS approach, decision support tools and participatory processes are mutually employed to promote urban food security in an ongoing, stakeholder-inclusive and reflexive process of governance. Our application of the TSS approach in two contrasting case studies highlights how this can contribute towards the development of new perspectives on urban food security, building a joint agenda towards the future and better (mutual) understanding of the issues at stake. We conclude that promoting a transition in the urban food system demands joint learning and reflexive evaluation in order to adapt governance, requiring researchers to play a supporting role in a broader process of change.
Voice Assistant and Route Optimization System for Logistics Companies in Depopulated Rural Areas
Authors: Lozano Murciego Álvaro , Jiménez-Bravo Diego M. , Pato Martínez Denis , Valera Román Adrián , Luis Lazo Gabino
Published: July 2020

Nowadays, the depopulation of Europe’s rural areas and the ageing of the population in these areas has led to the disappearance of basic services such as supermarkets, fishmongers, household goods, etc. In response to this problem, there are European and local initiatives to mitigate these effects by investing in Internet and Communication Technologies (ICT) infrastructure and logistics services for these areas with the aim of moving services to these isolated areas. The problem with these services lies in the difficulty of the use of ICT by aged people due to digital divide and, at the same time, the high cost in human resources, time, vehicles, and fuel presented by delivery services in these areas. This work presents a system that combines the use of intelligent personal assistants (IPA) to facilitate access to information technology for aged people to place orders and the optimization of delivery routes in a rural environment by a socially-oriented logistics company. This work presents a case study based on the fixed delivery routes of a social logistics company and describes how the proposed system could help in the optimization of routes and the reception of orders by the elderly via ICTs thanks to the use of IPAs. Subsequent to the study, route cost savings have been observed as well as the homogeneity of the cost of the routes it can provide compared to a static and predefined fixed route planning. This study also shows how the IPAs make possible the reception of orders in real time placed by the older adults from their homes. This work is aimed at improving the sustainability of services in depopulated rural areas while saving costs for the logistics companies that perform these services.
Vegetable and Gardening Tower of Othmar Ruthner in the Voivodeship Park of Culture and Recreation in Chorzów—The First Example of Vertical Farming in Poland
Authors: Kleszcz Justyna , Kmiecik Piotr , Świerzawski Jakub
Published: July 2020

The purpose of this paper is to analyze the possibility of introducing urban vertical agriculture in Poland through the example of the only comprehensively implemented facility of this type so far. Through the example of Othmar Ruthner’s no longer existing vegetable and gardening tower in Chorzów, the benefits and damages resulting from an attempt to construct such facilities are presented. The paper also contains an analysis and an attempt to critically assess the phenomenon of the vertical greenhouse that was built in Poland, along with an attempt to assess the real causes of its failure. Based on source material analysis, examples of implementation, and available literature on the subject, an analysis of the possibilities of transforming the facility at the time of its demolition and now was made. The phenomenon of the vertical farm in Chorzów, as a trace of the contemporary architectural heritage of Upper Silesia, is a topic for a broader discussion about the architectural heritage created by buildings which are not so much industrial as technical, with a role so far only secondary to what we call the main architectural functions.