bluuebunny/crossref_metadata_2025 huggingface dataset:
Spiritual Addiction: Searching for Love in a Coldly Indifferent World
Authors: Wyner Garret B.
Published: March 2022

I describe “spiritual” addiction as a felt compulsion to seek surrogates in the absence of that spirit of unconditional love underlying core personality change. We awaken to a “real” world akin to a prison in which all sides seem morally compromised, so any choice seems to necessitate sacrificing our conscientious relationship to the truth. Thus, spiritual addiction runs deeper than physical and psychological addictions to include socially accepted “addictions” to all we associate with “success”—including our morality and religion. All that we seek may be grounded in a collectively imbibed prejudice toward truth itself. If so, such a prejudice, underlying spiritual addiction, compromises our will, reason, feelings, actions, and character—including all of our relationships. It underlies the reality of a collective moral crisis which, we show, is more deeply a religious crisis tempting us to doubt the reality and attainability of that unconditional love that provides a foundation for hope. To overcome the prejudice underlying spiritual addiction, we show how unconditional love can be realized by placing conscientiousness in the foreground of concern as we are guided by the most reliable moral and spiritual witnesses in our history distinct from any religious group claiming to speak in their name.
The Change: Yoga, Theology and the Menopause
Authors: Pavey Emma L.
Published: April 2022

In this article, I explore the interplay of yoga, theology and the time of perimenopause and menopause. Through an approach centered physically, theologically and philosophically on becoming, I find an integrated web of thinking, feeling and moving that weaves new ways of perceiving and living this time of change; an example, I suggest, of what Keller calls creatio ex profundis, new creation from the depths of a life. I bring aspects of process theology (along with feminist and queer theology), phenomenological materialism, embodiment and somatic psychology/physiology into conversation with personal narrative. I examine ideas of severance, threshold and emergence, and images such as release, holding and breath that resonate helpfully with the holistic embodiment of yoga, theologies of change and (peri)menopause.
Determinants of Life Insurance Demand: Empirical Evidence from BRICS Countries
Authors: Segodi Mmakgabo Pinkie , Sibindi Athenia Bongani
Published: April 2022

The life insurance industry has experienced phenomenal growth over the years. The broad aim of this study was to establish the variables that influence the demand for life insurance in the BRICS countries (Brazil, Russia, India, China and South Africa). Although many studies have investigated the determinants of life insurance demand, little research has considered the supply-side factors such as financial regulation. Therefore, this study also contemplated the effect of the financial regulation variable on life insurance demand. The inquiry employed a panel of the BRICS bloc of countries as a unit of analysis for 1999–2020 and applied panel data econometric techniques. The study found that the life insurance demand variable (proxied by life insurance density and alternatively by life insurance penetration) was negatively affected by income, unemployment, interest rates and inflation variables. Furthermore, the study documented a positive relationship between life insurance demand and the economic growth and financial freedom variables. This study implies that regulatory authorities should deregulate the life insurance sector to foster financial freedom.
Monitoring Complex Integrated Crop–Livestock Systems at Regional Scale in Brazil: A Big Earth Observation Data Approach
Authors: Kuchler Patrick Calvano , Simões Margareth , Ferraz Rodrigo , Arvor Damien , de Almeida Machado Pedro Luiz Oliveira , Rosa Marcos , Gaetano Raffaele , Bégué Agnès
Published: March 2022

Due to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop–livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop–Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil’s Agriculture Low Carbon Plan (ABC PLAN).
Multifunctional Signal Design for Measurement, Navigation and Communication Based on BOC and BPSK Modulation
Authors: Xue Linshan , Li Xue , Wu Weiren , Dong Jialin
Published: March 2022

In a multi-node spacecraft or unmanned aerial vehicle (UAV) group, a multifunctional signal containing measurement, navigation, and communication functions is required to simplify the receiver structure, and to increase spectrum utilization. The paper proposes a multifunctional signal based on a binary offset carrier (BOC) and binary phase shift keying (BPSK) modulation. The signals which carry different functions are orthogonal in the frequency domain, which causes the data transmission signal to not be affected by BOC signal. In addition, in the high carrier to noise ratio (C/N0), the spreading gain of the BOC signal ensures that the communication signal has a slight influence on navigation signals. Moreover, the multifunctional signal can achieve communication and measurement functions at the same frequency point and fast switching in multinodes. These advantages can simplify the receiver design and improve the frequency band utilization. Finally, in this study we completed the design and verification of the hardware system.
How High to Fly? Mapping Evapotranspiration from Remotely Piloted Aircrafts at Different Elevations
Authors: Ebert Logan A. , Talib Ammara , Zipper Samuel C. , Desai Ankur R. , Paw U Kyaw Tha , Chisholm Alex J. , Prater Jacob , Nocco Mallika A.
Published: March 2022

Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying imagery collection more economical and feasible than ever before. The goal of this work was to create, compare, and quantify uncertainty associated with evapotranspiration (ET) maps generated from different conditions and image capture elevations. We collected optical and thermal data from a commercially irrigated potato (Solanum tuberosum) field in the Wisconsin Central Sands using a quadcopter RPA system and combined multispectral/thermal camera. We conducted eight mission sets (24 total missions) during the 2019 growing season. Each mission set included flights at 90, 60, and 30 m above ground level. Ground reference measurements of surface temperature and soil moisture were collected throughout the domain within 15 min of each RPA mission set. Evapotranspiration values were modeled from the flight data using the High-Resolution Mapping of Evapotranspiration (HRMET) model. We compared HRMET-derived ET estimates to an Eddy Covariance system within the flight domain. Additionally, we assessed uncertainty for each flight using a Monte Carlo approach. Results indicate that the primary source of uncertainty in ET estimates was the optical and thermal data. Despite some additional detectable features at low elevation, we conclude that the tradeoff in resources and computation does not currently justify low elevation flights for annual vegetable crop management in the Midwest USA.
Surface Deformation Associated with the 22 August 1902 Mw 7.7 Atushi Earthquake in the Southwestern Tian Shan, Revealed from Multiple Remote Sensing Data
Authors: Chen Qingyu , Fu Bihong , Shi Pilong , Li Zhao
Published: March 2022

The 22 August 1902 Mw 7.7 Atushi earthquake is the most disastrous seismic event in the southwestern Tian Shan. However, the spatial distribution of surface rupture zones as well as the geometric feature of surface deformation remain unclear, and the seismogenic fault is still controversial. Based on geologic and geomorphic interpretations of multiple remote sensing imaging data, high-resolution DEM data derived from UAV imaging complemented by field investigations, we mapped two sub-parallel NEE-trending surface rupture zones with a total length of 108 km. In addition, ~60 km and ~48 km surface rupture zones are distributed along the pre-existing Atushi fault (ATF) and the Keketamu fault (KTF), respectively. The surface deformations are mainly characterized as bedrock scarp, hanging wall collapse scarp, pressure ridge, and thrust-related fold scarps along the two south-dipping thrust faults, which are defined as the seismogenic structure of the 1902 Mw 7.7 Atushi earthquake. Thus, we proposed the cascading-rupture model to explain the multiple rupture zones generated by the 1902 Mw 7.7 Atushi earthquake. Moreover, the multiple advanced remote sensing mapping techniques can provide a promising approach to recover the geometric and geomorphic features of the surface deformation caused by large seismic events in the arid and semi-arid regions.
Registration and Combined Adjustment for the Laser Altimetry Data and High-Resolution Optical Stereo Images of the GF-7 Satellite
Authors: Chen Jiyi , Tang Xinming , Xue Yucai , Li Guoyuan , Zhou Xiaoqing , Hu Liuru , Zhang Shuaitai
Published: March 2022

The GF-7 satellite is China’s first civil sub-meter resolution stereo mapping satellite, aiming at 1:10,000-scale mapping. To achieve this goal, apart from the stereo optical cameras that reach sub-meter resolution, the GF-7 satellite is equipped with a laser altimetry system capable of obtaining three-dimensional laser points (LPs) with high elevation accuracy. However, the combination of laser altimetry data and optical stereo images has not been thoroughly studied. In this paper, we exploit the images recorded by the highly integrated laser footprint cameras and propose a hierarchical phase correlation method based on a geographic pyramid for the registration of laser altimetry data and high-resolution optical stereo images, which lays a solid foundation for the following combined adjustment. Experiments show that the proposed registration method can automatically locate the LPs on high-resolution stereo images and meet the requirements of bundle adjustment. A series of bundle adjustment experiments were carried out, showing that laser altimetry data can significantly enhance the vertical accuracy of optical image stereo mapping and that elevation accuracy can reach roughly 1.0 m (RSME) without ground control points. Therefore, this study could be a good guide for global high-precision DSM acquisition with the GF-7 satellite.
A Confidence-Aware Cascade Network for Multi-Scale Stereo Matching of Very-High-Resolution Remote Sensing Images
Authors: Tao Rongshu , Xiang Yuming , You Hongjian
Published: March 2022

Stereomatching plays an essential role in 3D reconstruction using very-high-resolution (VHR) remote sensing images. However, it still faces unignorable challenges due to the multi-scale objects in large scenes and the multi-modality probability distribution in challenging regions, especially the occluded and textureless areas. Accurate disparity estimation in stereo matching for multi-scale objects has become a hard but crucial task. In this paper, to tackle these problems, we design a novel confidence-aware unimodal cascade and fusion pyramid network for stereo matching. The fused cost volume from the coarsest scale is used to generate the initial disparity map, and then the learnable confidence maps are generated to construct the unimodal cost distributions, which are used to narrow down the next-stage disparity search range. Moreover, we design a cross-scale interaction aggregation module to leverage multi-scale information. Both smooth-L1 loss and stereo focal loss are applied to regularize the disparity map and unimodal cost distribution, respectively. Compared to two state-of-the-art stereo matching networks, extensive experimental results show that our proposed network outperforms them in terms of average endpoint error (EPE) and the fraction of erroneous pixels (D1).
Precision Oliviculture: Research Topics, Challenges, and Opportunities—A Review
Authors: Roma Eliseo , Catania Pietro
Published: March 2022

Since the beginning of the 21st century, there has been an increase in the agricultural area devoted to olive growing and in the consumption of extra virgin olive oil (EVOO). The continuous change in cultivation techniques implemented poses new challenges to ensure environmental and economic sustainability. In this context, precision oliviculture (PO) is having an increasing scientific interest and impact on the sector. Its implementation depends on various technological developments: sensors for local and remote crop monitoring, global navigation satellite system (GNSS), equipment and machinery to perform site-specific management through variable rate application (VRA), implementation of geographic information systems (GIS), and systems for analysis, interpretation, and decision support (DSS). This review provides an overview of the state of the art of technologies that can be employed and current applications and their potential. It also discusses the challenges and possible solutions and implementations of future technologies such as IoT, unmanned ground vehicles (UGV), and machine learning (ML).
Quantitative Measurement of Radio Frequency Interference for SMOS Mission
Authors: Xu Ming , Li Hongping , Chen Haihua , Yin Xiaobin
Published: March 2022

The Earth Exploration Satellite Service (EESS) for passive sensing has a primary frequency allocation in the 1400–1427 MHz band. All emissions unauthorized in this band are called RFI (Radio Frequency Interference). The SMOS (Soil Moisture and Ocean Salinity) mission is greatly perturbed by RFI impeding ocean salinity retrieval, especially in coastal areas such as the SCS (South China Sea), where the observed data has been discarded massively. At present, there is no way to eliminate the RFI impact on the retrieved salinity, other than by detecting and shutting down the emissions from the sources. However, it may be effective in a scientific sense if RFI can be quantified and applied to the salinity retrieval process. Therefore, this study proposes an RFI measuring method that can investigate contamination in both prominent and moderate respects, aroused either by on-site emissions or nearby continents. Based on the proposed method, two levels of hierarchical RFI maps of the SCS region, including the separated one and the merged one, are presented and discussed, indicating more severe contamination in northern and western SCS. Moreover, to verify the generalization of the method on open oceans far from continents, an area in the middle central Pacific is selected and tested. The result shows few or no RFI in this unattended region, which is consistent with the authors’ knowledge. This study presents the concept of the “RFI map” to describe the contamination, which will hopefully help researchers comprehend the RFI state intuitively and assist in ocean salinity retrieval statistically.
Multi-Target Localization of MIMO Radar with Widely Separated Antennas on Moving Platforms Based on Expectation Maximization Algorithm
Authors: Lu Jiaxin , Liu Feifeng , Sun Jingyi , Miao Yingjie , Liu Quanhua
Published: March 2022

This paper focuses on multi-target parameter estimation of multiple-input multiple-output (MIMO) radar with widely separated antennas on moving platforms. Aiming at the superimposed signals caused by multi-targets, the well-known expectation maximization (EM) is used in this paper. Target’s radar cross-section (RCS) spatial variations, different path losses and spatially-non-white noise appear because of the widely separated antennas. These variables are collectively referred to as signal-to-noise ratio (SNR) fluctuations. To estimate the echo delay/Doppler shift and SNR, the Q function of EM algorithm is extended. In addition, to reduce the computational complexity of EM algorithm, the gradient descent is used in M-step of EM algorithm. The modified EM algorithm is called generalized adaptive EM (GAEM) algorithm. Then, a weighted iterative least squares (WILS) algorithm is used to jointly estimate the target positions and velocities based on the results of GAEM algorithm. This paper also derives the Cramér-Rao bound (CRB) in such a non-ideal environment. Finally, extensive numerical simulations are carried out to validate the effectiveness of the proposed algorithm.
A Method for Automatic Inversion of Oblique Ionograms
Authors: Jiang Chunhua , Zhao Cong , Zhang Xuhui , Liu Tongxin , Chen Ziwei , Yang Guobin , Zhao Zhengyu
Published: March 2022

In this study, a method is proposed to carry out automatic inversion of oblique ionograms to extract the parameters and electron density profile of the ionosphere. The proposed method adopts the quasi-parabolic segments (QPS) model to represent the ionosphere. Firstly, numerous candidate electron density profiles and corresponding vertical traces were, respectively, calculated and synthesized by adjusting the parameters of the QPS model. Then, the candidate vertical traces were transformed to oblique traces by the secant theorem and Martyn’s equivalent path theorem. On the other hand, image processing technology and characteristics of oblique echoes were adopted to automatically scale the key parameters (the maximum observable frequency and minimum group path, etc.) from oblique ionograms. The synthesized oblique traces, whose parameters were close to autoscaled parameters, were selected as the candidate traces to produce a correlation with measured oblique ionograms. Lastly, the proposed algorithm searched the best-fit synthesized oblique trace by comparing the synthesized traces with oblique ionograms. To test its feasibility, oblique ionograms were automatically scaled by the proposed method and these autoscaled parameters were compared with manual scaling results. The preliminary results show that the accuracy of autoscaled maximum observable frequency and minimum group path of the ordinary trace of the F2 layer is, respectively, about 91.98% and 86.41%, which might be accurate enough for space weather specifications. It inspires us to improve the proposed method in future studies.
Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers
Authors: Barzegari Yousof , Zarei Jafar , Razavi-Far Roozbeh , Saif Mehrdad , Palade Vasile
Published: March 2022

This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it protects the consensus deviation against FDI attacks.
Advances in Optical Sensors for Persistent Organic Pollutant Environmental Monitoring
Authors: Caroleo Fabrizio , Magna Gabriele , Naitana Mario Luigi , Di Zazzo Lorena , Martini Roberto , Pizzoli Francesco , Muduganti Mounika , Lvova Larisa , Mandoj Federica , Nardis Sara , Stefanelli Manuela , Di Natale Corrado , Paolesse Roberto
Published: March 2022

Optical chemical sensors are widely applied in many fields of modern analytical practice, due to their simplicity in preparation and signal acquisition, low costs, and fast response time. Moreover, the construction of most modern optical sensors requires neither wire connections with the detector nor sophisticated and energy-consuming hardware, enabling wireless sensor development for a fast, in-field and online analysis. In this review, the last five years of progress (from 2017 to 2021) in the field of optical chemical sensors development for persistent organic pollutants (POPs) is provided. The operating mechanisms, the transduction principles and the types of sensing materials employed in single selective optical sensors and in multisensory systems are reviewed. The selected examples of optical sensors applications are reported to demonstrate the benefits and drawbacks of optical chemical sensor use for POPs assessment.
Recent Advances in Vision-Based On-Road Behaviors Understanding: A Critical Survey
Authors: Trabelsi Rim , Khemmar Redouane , Decoux Benoit , Ertaud Jean-Yves , Butteau Rémi
Published: March 2022

On-road behavior analysis is a crucial and challenging problem in the autonomous driving vision-based area. Several endeavors have been proposed to deal with different related tasks and it has gained wide attention recently. Much of the excitement about on-road behavior understanding has been the labor of advancement witnessed in the fields of computer vision, machine, and deep learning. Remarkable achievements have been made in the Road Behavior Understanding area over the last years. This paper reviews 100+ papers of on-road behavior analysis related work in the light of the milestones achieved, spanning over the last 2 decades. This review paper provides the first attempt to draw smart mobility researchers’ attention to the road behavior understanding field and its potential impact on road safety to the whole road agents such as: drivers, pedestrians, stuffs, etc. To push for an holistic understanding, we investigate the complementary relationships between different elementary tasks that we define as the main components of road behavior understanding to achieve a comprehensive understanding of approaches and techniques. For this, five related topics have been covered in this review, including situational awareness, driver-road interaction, road scene understanding, trajectories forecast, driving activities, and status analysis. This paper also reviews the contribution of deep learning approaches and makes an in-depth analysis of recent benchmarks as well, with a specific taxonomy that can help stakeholders in selecting their best-fit architecture. We also finally provide a comprehensive discussion leading us to identify novel research directions some of which have been implemented and validated in our current smart mobility research work. This paper presents the first survey of road behavior understanding-related work without overlap with existing reviews.
A Highly Efficient RF-DC Converter for Energy Harvesting Applications Using a Threshold Voltage Cancellation Scheme
Authors: Basim Muhammad , Khan Danial , Ain Qurat Ul , Shehzad Khuram , Shah Syed Adil Ali , Jang Byeong-Gi , Pu Young-Gun , Yoo Joon-Mo , Kim Joon-Tae , Lee Kang-Yoon
Published: March 2022

In this paper, a self-threshold voltage (Vth) compensated Radio Frequency to Direct Current (RF-DC) converter operating at 900 MHz and 2.4 GHz is proposed for RF energy harvesting applications. The threshold voltage of the rectifying devices is compensated by the bias voltage generated by the auxiliary transistors and output DC voltage. The auxiliary transistors compensate the threshold voltage (Vth) of the PMOS rectifying device while the threshold voltage (Vth) of the NMOS rectifying device is compensated by the output DC voltage. The proposed RF-DC converter was implemented in 180 nm Complementary Metal-Oxide Semiconductor (CMOS) technology. The experimental results show that the proposed design achieves better performance at both 900 MHz and 2.4 GHz frequencies in terms of PCE, output voltage, sensitivity, and effective area. The peak power conversion efficiency (PCE) of 38.5% at −12 dBm across a 1 MΩ load for 900 MHz frequency was achieved. Similarly, for 2.4 GHz frequency, the proposed circuit achieves a peak PCE of 26.5% at −6 dBm across a 1 MΩ load. The proposed RF-DC converter circuit shows a sensitivity of −20 dBm across a 1 MΩ load and produces a 1 V output DC voltage.
Design and Application of Partial Immersion Focused Ultrasonic Transducers for Austenitic Weld Inspection
Authors: Zhang Yuan , Qin Zixing , Luo Shizhou , Hyunjo Jeong , Zhang Shuzeng
Published: March 2022

Austenitic stainless steel is a widely used material in the industry, and the welding technique enables stainless steel components to have different shapes for different applications. Any flaws in the weld will degrade the performance of the austenitic component; thus, it is essential to ultrasonically and nondestructively test flaws in welds to ensure service safety. Recently, weld inspection has been performed using contact transducers, but missed detections or false positives for flaws in welds usually occur due to a poor coupling condition in the detection, a low signal-to-noise ratio, and instantaneous noises. In this study, a partial immersion focused (PIF) ultrasonic transducer is designed and used for austenitic weld inspection to address the above issues. The detailed design and manufacture of the PIF transducer are described, and the advantages of the transducer are shown by comparing the results detected using different kinds of transducers. In addition, in order to suppress false positives, a B-image method optimized using a time-dependent threshold is proposed. Experiments are performed to detect flaws in a welded specimen. All the artificial flaws are evaluated using the developed transducer and the proposed method, but minor flaws are mis-detected when planar transducers are used, verifying the method proposed in this paper.
Accuracy of Estimated Bioimpedance Parameters with Octapolar Segmental Bioimpedance Analysis
Authors: Jiang Fanglin , Tang Saizhao , Eom Jin-Jong , Song Keon-Hyoung , Kim Hyeoijin , Chung Sochung , Kim Chul-Hyun
Published: April 2022

The validity of the impedance parameters of the five body segments estimated using octapolar segmental bioelectrical impedance analysis (OS-BIA) has not been confirmed. This study aimed to verify the accuracy of the resistance (R), reactance (Xc), and phase angle of each five-body segment. The accuracy of the OS-BIA at 50 kHz was measured based on the direct tetrapolar segmental BIA. The differences in the estimated impedance parameters of the five body segments were compared to those measured from the OS-BIA in elderly men (N = 73) and women (N = 63). The estimated 50 kHz-R (Ω) was significantly higher than the measured 50 kHz-R in the right and left arms, and lower than the measured 50 kHz-R of the trunk, right leg, and left leg (all, p < 0.05). The estimated 50 kHz-phase angles in all the five body segments were significantly lower than the measured ones (all, p < 0.05). The findings suggest that the estimated impedance parameters, R, Xc, and phase angle of the trunk, were remarkedly underestimated, limiting the assessment of the physiological state of the organs in the body. Therefore, further intensive research is needed in the field of estimated segmental BIA in the future.
Effective Safety Message Dissemination with Vehicle Trajectory Predictions in V2X Networks
Authors: Li Hantao , Liu Feng , Zhao Zhongliang , Karimzadeh Mostafa
Published: April 2022

Exploring data connection information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications using advanced machine learning approaches, an intelligent transportation system (ITS) can provide better safety services to mitigate the risk of road accidents and improve traffic efficiency. In this work, we propose an end-edge-cloud architecture to deploy machine learning-driven approaches at network edges to predict vehicles’ future trajectories, which is further utilized to provide an effective safety message dissemination scheme. With our approach, the traffic safety message will only be disseminated to relevant vehicles that are predicted to pass by accident areas, which can significantly reduce the network data transmission overhead and avoid unnecessary interference. Depending on the vehicle connectivity, our system adaptively chooses vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications to disseminate safety messages. We evaluate the system by using a real-world VANET mobility dataset, and experimental results show that our system outperforms other mechanisms without considering any predicted vehicle trajectory density information.
Detection and Recognition of Pollen Grains in Multilabel Microscopic Images
Authors: Kubera Elżbieta , Kubik-Komar Agnieszka , Kurasiński Paweł , Piotrowska-Weryszko Krystyna , Skrzypiec Magdalena
Published: April 2022

Analysis of pollen material obtained from the Hirst-type apparatus, which is a tedious and labor-intensive process, is usually performed by hand under a microscope by specialists in palynology. This research evaluated the automatic analysis of pollen material performed based on digital microscopic photos. A deep neural network called YOLO was used to analyze microscopic images containing the reference grains of three taxa typical of Central and Eastern Europe. YOLO networks perform recognition and detection; hence, there is no need to segment the image before classification. The obtained results were compared to other deep learning object detection methods, i.e., Faster R-CNN and RetinaNet. YOLO outperformed the other methods, as it gave the mean average precision (mAP@.5:.95) between 86.8% and 92.4% for the test sets included in the study. Among the difficulties related to the correct classification of the research material, the following should be noted: significant similarities of the grains of the analyzed taxa, the possibility of their simultaneous occurrence in one image, and mutual overlapping of objects.
Upper and Lower Leaf Side Detection with Machine Learning Methods
Authors: Dawod Rodica Gabriela , Dobre Ciprian
Published: April 2022

Recent studies have approached the identification of foliar plant diseases using artificial intelligence, but in these works, classification is achieved using only one side of the leaf. Phytopathology specifies that there are diseases that show similar symptoms on the upper part of the leaf, but different ones on the lower side. An improvement in accuracy can be achieved if the symptoms of both sides of the leaf are considered when classifying plant diseases. In this context, it is necessary to establish whether the captured image represents the leaf on its upper or lower side. From the research conducted using botany books, we can conclude that a useful classification feature is color, because the sun-facing part is greener, while the opposite side is shaded. A second feature is the thickness of the primary and secondary veins. The veins of a leaf are more prominent on the lower side, compared to the upper side. A third feature corresponds to the concave shape of the leaf on its upper part and its convex shape on the lower part. In this study, we aim to achieve upper and lower leaf side classification using both deep learning methods and machine learning models.
Iris Image Compression Using Deep Convolutional Neural Networks
Authors: Jalilian Ehsaneddin , Hofbauer Heinz , Uhl Andreas
Published: April 2022

Compression is a way of encoding digital data so that it takes up less storage and requires less network bandwidth to be transmitted, which is currently an imperative need for iris recognition systems due to the large amounts of data involved, while deep neural networks trained as image auto-encoders have recently emerged a promising direction for advancing the state-of-the-art in image compression, yet the generalizability of these schemes to preserve the unique biometric traits has been questioned when utilized in the corresponding recognition systems. For the first time, we thoroughly investigate the compression effectiveness of DSSLIC, a deep-learning-based image compression model specifically well suited for iris data compression, along with an additional deep-learning based lossy image compression technique. In particular, we relate Full-Reference image quality as measured in terms of Multi-scale Structural Similarity Index (MS-SSIM) and Local Feature Based Visual Security (LFBVS), as well as No-Reference images quality as measured in terms of the Blind Reference-less Image Spatial Quality Evaluator (BRISQUE), to the recognition scores as obtained by a set of concrete recognition systems. We further compare the DSSLIC model performance against several state-of-the-art (non-learning-based) lossy image compression techniques including: the ISO standard JPEG2000, JPEG, H.265 derivate BPG, HEVC, VCC, and AV1 to figure out the most suited compression algorithm which can be used for this purpose. The experimental results show superior compression and promising recognition performance of the model over all other techniques on different iris databases.
Special Issue “Fighting Fake News: A Generational Approach”
Authors: Loos Eugène , Ivan Loredana
Published: March 2022

To reach a state of equal opportunity in our society, access to credible, accessible information [...]
Hotel Naming in Russian Cities: An Imprint of Foreign Cultures and Languages between Europe and Asia
Authors: Kormazina Olga P. , Ruban Dmitry A. , Yashalova Natalia N.
Published: March 2022

Hotel naming can contribute to cultural exchange, and big countries boasting lengthy peripheries and sharp “cores” are suitable for studying this contribution. Foreign cultural and linguistic imprints in hotel names is studied in four big cities of Russia, namely Rostov-on-Don, Nizhniy Novgorod, Krasnoyarsk, and Vladivostok. It is established that the hotels with names bearing foreign-culture elements constitute up to 20–25% of all hotels in each given city. These elements can be linked to many, chiefly West European countries. The English foreign-language elements are the most common, whereas the French and Italian elements occur in subordinate numbers. The linguistic-cultural types of the hotel names are commonly toponyms and landscape-related symbols. The imprint of foreign cultures and languages in the hotel names diminishes together with the increase in distance from the western state border.
A Critical Analysis of the Challenges of Collaborative Governance in Climate Change Adaptation Policies in Bandar Lampung City, Indonesia
Authors: Mukhlis Maulana , Perdana Ryzal
Published: March 2022

It is not uncommon that collaborative governance is now generating new attention in Indonesia as a method of governing. This is because of the terrible historical experience of governance during the New Order era, including the state’s dominant role, the government’s unwillingness to engage actors outside the state, and a centralized and top-down development pattern. Collaborative governance, specifically the recommendation to involve multiple stakeholders (government, private sector, and society) in government management and public policy, addresses these issues. Therefore, the purpose of this study was to provide a critical review of the ongoing collaborative governance process and to determine whether various challenges associated with collaborative governance are influencing the success of climate change adaptation policies in increasing adaptive capacity in Bandar Lampung City. This study was conducted in Bandar Lampung City on the Indonesian island of Sumatra using a qualitative approach and involved informants who were both apparatus and members of the Bandar Lampung City Climate Change Resilience Coordination Team. Interviews and document analysis were used to collect data, which were then analyzed using a content analysis procedure. The findings indicate that this city has achieved a number of milestones in its efforts to improve the adaptive capacity of government institutions and society. However, the collaborative governance approach, which is hailed as the optimal method for implementing long-term public policies, is not always smooth, demonstrating that collaborative governance remains vulnerable to failure due to cultural, institutional, and political factors. The article concludes with suggestions for future research.
Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
Authors: Qi Qianru , Cheng Rongjun , Ge Hongxia
Published: March 2022

In recent years, online ride-hailing has become an indispensable part of residents’ travel mode. Therefore, the prediction of online ride-hailing travel demand has become extremely important. In the era of big data, the application of big data in the field of transportation is becoming more extensive. Based on the open data of ride-hailing trips in Haikou City, Hainan Province, provided by the Didi platform and combined with the rainfall data of Haikou City, this paper proposes a gate recurrent unit (GRU) model considering rainfall factors and rest days factors for short-term trip demand prediction. The K-fold cross-validation method is adopted to adjust the parameters of the model to the optimal ones through the training set. The improved GRU model is compared with the original GRU model and other classic models, and the model is evaluated by root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 score indexes. Finally, it is proved that the GRU model proposed in this paper greatly improves the prediction accuracy of short-term online ride-hailing travel demand.
Toward a Conceptual Framework to Foster Green Entrepreneurship Growth in the Agriculture Industry
Authors: Savastano Marco , Samo Altaf Hussain , Channa Nisar Ahmed , Amendola Carlo
Published: March 2022

In recent times, the importance of green entrepreneurship in the socio-economic development, environmental management, and prosperity of underprivileged societies has widely been acknowledged by both academics and policymakers alike. Despite this importance, there is a lack of research on what the external and internal factors that support and foster the growth of green agricultural ventures are. This research aims to propose a framework suggesting factors that play a significant role in flourishment of green enterprises, with a focus on developing countries. Through an extensive review and in-depth analysis of the existing literature in the fields of green entrepreneurship and agriculture, we propose a conceptual framework highlighting the internal and external factors that, when strategically aligned, foster the growth of green agricultural enterprises.
Urban Spatial Development Based on Multisource Data Analysis: A Case Study of Xianyang City’s Integration into Xi’an International Metropolis
Authors: Hu Yiyi , He Yi , Li Yanlin
Published: March 2022

The study of urban spatial development focuses on the process of urbanization, which involves the urban economy, population, the scale of urban construction land and the construction land’s structure. All this influences the economic structure, social structure and functional structure of the city. Taking Xianyang City, a core part of Xi’an international metropolis, as an example, this study, based on night light remote sensing data from 1992 to 2013, land use data from 1980 to 2015 (6 periods), AutoNavi Map (AMAP) Points of Interest (POI) data, and the patch-generated land use simulation model (PLUS), simulates the spatial–temporal pattern change characteristics of land use in Xianyang City from 2025 to 2035. The results show that: (1) During 1985–2015, urban land use showed a significant upward trend (p < 0.05); (2) From 1992 to 2013, the change in night light in the Xianyang City Administrative Region showed an upward trend. The gravitational center of Xianyang City’s built-up area moves southeast first and then northeast. After the beginning of 2010, the gravitational center of Xianyang City’s built-up area moved faster; (3) The distribution of different types of urban centers in Xianyang City is basically the same; (4) From 2005 to 2035, the overall land use in Xianyang City showed a trend of “multi polar explosive growth in construction land, slow growth in forest land, and first a decrease then an increase in wetland water body”. The urban spatial structure has changed from a single-center development model to a point–axis development model. The study of urban space development can provide some reference for the layout of urban construction in the future.
Women Entrepreneurship for Sustainability: Investigations on Status, Challenges, Drivers, and Potentials in Qatar
Authors: Al-Qahtani Muneera , Fekih Zguir Mariem , Al-Fagih Luluwah , Koç Muammer
Published: March 2022

Economic diversification is vital in achieving sustainable economic development. This diversification is chiefly important for countries relying on natural resources for their economy, such as Qatar. Private enterprises, mainly through small-to-medium enterprises (SMEs), have been recognized as crucial and effective means for healthy economic diversification, stimulating economic development, and ensuring equal sharing and distribution of wealth. Recognizing the vulnerabilities within the current economic model has motivated Qatar to relentlessly diversify away from its heavy reliance on natural gas exports. Such diversified and sustainable development cannot be achieved without the contribution of women, who make up a significant percentage of the educated and talented population in this country. However, despite the conspicuous support the state has been providing them with in an aim to promote for their active economic participation as part of Qatar National Vision 2030, women’s full potential to contribute both socially and economically is either hindered or not fully acknowledged due to several socio-economic factors. There is a dearth of studies reporting on the perceived status, challenges, and access to needed resources by women entrepreneurs in Qatar. To address such scarcity, this study builds, in a first step, on a systematic literature review (SLR) to unlock themes related to women and entrepreneurship by analyzing 23 peer-reviewed articles concentrating on the cases of Qatar and the MENA region. Subsequently, a qualitative approach is followed to investigate and examine current successes and obstacles by interviewing 25 actual and aspiring women entrepreneurs in Qatar as well as 5 incubators and government representatives. In a final stage, and based on a design thinking approach and applying the gender-aware framework (known as the 5Ms framework), the paper proposes a preliminary conceptual framework to address the pertaining barriers and pave the way for more progressive policies to cater to the needs of women entrepreneurs and to further promote for sustainable development.
Estimating Willingness to Pay for Alpine Pastures: A Discrete Choice Experiment Accounting for Attribute Non-Attendance
Authors: Notaro Sandra , De Salvo Maria , Raffaelli Roberta
Published: March 2022

Alpine pastures generate important ecosystem services, some closely related to the environment, others to historical and cultural aspects. The economic valuation of these services helps their recognition in public policies, thus encouraging their conservation and improvement. Discrete Choice Experiments are particularly useful in estimating ecosystem services as they allow the evaluation of each individual ecosystem service, allowing for policy modulation. However, preferences and willingness to pay may be influenced by some heuristics that respondents adopt when making their choices. The present study contributes to the Attribute-Non-Attendance (ANA) literature by analyzing the effect of serial ANA on WTP for the improvement of the ecosystem services of an Alpine pasture, the Entrelor pasture located in Val d’Aosta (North-West Italy). The novelty of this study is that we investigated ANA by asking a first group of respondents which attributes were ignored during choices, and a second group which attributes they considered. Our results show that considering ANA matters in DCE. In particular, framing the question positively (which attributes were attended) yields differences in marginal WTPs that are significantly and systematically higher for all the attributes. Conversely, with negative framing, differences in marginal WTP seem to be insignificant and unstable both in terms of magnitude and sign. Moreover, positively framing the ANA question can be more informative, as ANA appears more frequently. These results suggest that respondents probably do not feel judged for not having adopted the expected degree of attention with a positively framed ANA question.
Implementing Water-Energy-Land-Food-Climate Nexus Approach to Achieve the Sustainable Development Goals in Greece: Indicators and Policy Recommendations
Authors: Papadopoulou Chrysaida-Aliki , Papadopoulou Maria P. , Laspidou Chrysi
Published: March 2022

In 2015 the updated Agenda 2030, outlining the new priorities towards future sustainability, was published by the United Nations reflecting the general directions towards a fairer and more sustainable pattern of socio-economic development. The specialisation of the 17 Sustainable Development Goals (SDGs) and indicators into local contexts constitutes a basic prerequisite as particular characteristics, needs and peculiarities should be considered. This paper focuses on the exploration of local-oriented indicators and policy recommendations that have the potential to boost the successful implementation of SDGs in Greece. A nexus approach is adopted underlining the need to analyse complexities, motivate systemic thinking and develop integrated policies targeting at the sustainable and efficient use of resources. It also highlights the need for designing local-specific indicators and policy initiatives, encapsulating particular characteristics and conditions and boosting the advancement of SDGs. Such an approach has the potential to be implemented not only at national but also at an operational level in local scale (e.g., River Basin Scale). The outcomes indicate that emphasis should be given on smart water management and precision agriculture, extensive use of technologies capturing Greenhouse Gases (GHGs), dynamic penetration of Renewable Energy Sources (RES) in gross final energy production/consumption and protection of terrestrial ecosystems.