bluuebunny/crossref_metadata_2025 huggingface dataset:
Nonlinear Relationship Between the Yield of Solar-Induced Chlorophyll Fluorescence and Photosynthetic Efficiency in Senescent Crops
Authors: Liu Leizhen , Zhao Wenhui , Shen Qiu , Wu Jianjun , Teng Yanguo , Yang Jianhua , Han Xinyi , Tian Feng
Published: May 2020

It has been demonstrated that solar-induced chlorophyll fluorescence (SIF) is linearly related to the primary production of photosynthesis (GPP) in various ecosystems. However, it is unknown whether such linear relationships have been established in senescent crops. SIF and GPP can be expressed as the products of absorbed photosynthetically active radiation (APAR) with the SIF yield and photosystem II (PSII) operating efficiency, respectively. Thus, the relationship between SIF and GPP can be represented by the relationship between the SIF yield and PSII operating efficiency when the APAR has the same value. Therefore, we analyzed the relationship between the SIF yield and the PSII operating efficiency to address the abovementioned question. Here, diurnal measurements of the canopy SIF (760 nm, F760) of soybean and sweet potato were manually measured and used to calculate the SIF yield. The PSII operating efficiency was calculated from measurements of the chlorophyll fluorescence at the leaf level using the FluorImager chlorophyll fluorescence imaging system. Meanwhile, field measurements of the gas exchange and other physiological parameters were also performed using commercial-grade devices. The results showed that the SIF yield was not linearly related to the PSII operating efficiency at the diurnal scale, reflecting the nonlinear relationship between SIF and GPP. This nonlinear relationship mainly resulted from the heterogeneity and diurnal dynamics of the PSII operating efficiency and from the intrinsic diurnal changes in the maximum efficiency of the PSII photochemistry and the proportion of opened PSII centers. Intensifying respiration was another factor that complicated the response of photosynthesis to the variation in environmental conditions and negatively impacted the relationship between the SIF yield and the PSII operating efficiency. The nonlinear relationship between the SIF yield and PSII efficiency might yield errors in the estimation of GPP using the SIF measurements of senescent crops.
Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations
Authors: Ryu Han-Sol , Hong Sungwook
Published: May 2020

Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.
Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning
Authors: Chaves Pablo , Zuquim Gabriela , Ruokolainen Kalle , Van doninck Jasper , Kalliola Risto , Gómez Rivero Elvira , Tuomisto Hanna
Published: May 2020

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (<2000 m above sea level) using Landsat satellite imagery and climatic, edaphic, and elevation data as predictor variables. Genus-level floristic patterns have earlier been found to be indicative of species-level patterns. In correlation tests, the floristic variation of tree genera was most strongly related to Landsat variables and secondly to climatic variables. We used random forest regression, under varying criteria of feature selection and cross-validation, to predict the floristic composition on the basis of Landsat and environmental data. The best model explained >60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation.
Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method
Authors: Zhao Yifei , Su Fenzhen , Yan Fengqin
Published: May 2020

Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data. However, it is a non-trivial task to classify HSI accurately and rapidly due to its characteristics of having a large amount of data and massive noise points. To address this problem, in this work, a novel, semi-supervised, superpixel-level classification method for an HSI was proposed based on a graph and discrete potential (SSC-GDP). The key idea of the proposed scheme is the construction of the weighted connectivity graph and the division of the weighted graph. Based on the superpixel segmentation, a weighted connectivity graph is constructed usingthe weighted connection between a superpixel and its spatial neighbors. The generated graph is then divided into different communities/sub-graphs by using a discrete potential and the improved semi-supervised Wu–Huberman (ISWH) algorithm. Each community in the weighted connectivity graph represents a class in the HSI. The local connection strategy, together with the linear complexity of the ISWH algorithm, ensures the fast implementation of the suggested SSC-GDP method. To prove the effectiveness of the proposed spectral–spatial method, two public benchmarks, Indian Pines and Salinas, were utilized to test the performance of our proposal. The comparative test results confirmed that the proposed method was superior to several other state-of-the-art methods.
An Integrated Solution for 3D Heritage Modeling Based on Videogrammetry and V-SLAM Technology
Authors: Ortiz-Coder Pedro , Sánchez-Ríos Alonso
Published: May 2020

This paper presents an approach for 3D reconstruction of heritage scenes using a videogrammetric-based device. The system, based on two video cameras with different characteristics, uses a combination of visual simultaneous localization and mapping (SLAM) and photogrammetry technologies. VSLAM, together with a series of filtering algorithms, is used for the optimal selection of images and to guarantee that the user does not lose tracking during data acquisition in real time. The different photogrammetrically adapted tools in this device and for this type of handheld capture are explained. An evaluation of the device is carried out, including comparisons with the Faro Focus X 330 laser scanner, through three case studies in which multiple aspects are analyzed. We demonstrate that the proposed videogrammetric system is 17 times faster in capturing data than the laser scanner and that the post-processing of the system is fully automatic, but takes more time than the laser scanner in post-processing. It can also be seen that the accuracies of both systems and the generated textures are very similar. Our evaluation demonstrates the possibilities of considering the proposed system as a new professional-quality measurement instrument.
Toward a Standardized Encoding of Remote Sensing Geo-Positioning Sensor Models
Authors: Jin Meng , Bai Yuqi , Devys Emmanuel , Di Liping
Published: May 2020

Geolocation information is an important feature of remote sensing image data that is captured through a variety of passive or active observation sensors, such as push-broom electro-optical sensor, synthetic aperture radar (SAR), light detection and ranging (LIDAR) and sound navigation and ranging (SONAR). As a fundamental processing step to locate an image, geo-positioning is used to determine the ground coordinates of an object from image coordinates. A variety of sensor models have been created to describe geo-positioning process. In particular, Open Geospatial Consortium (OGC) has defined the Sensor Model Language (SensorML) specification in its Sensor Web Enablement (SWE) initiative to describe sensors including the geo-positioning process. It has been realized using syntax from the extensible markup language (XML). Besides, two standards defined by the International Organization for Standardization (ISO), ISO 19130-1 and ISO 19130-2, introduced a physical sensor model, a true replacement model, and a correspondence model for the geo-positioning process. However, a standardized encoding for geo-positioning sensor models is still missing for the remote sensing community. Thus, the interoperability of remote sensing data between application systems cannot be ensured. In this paper, a standardized encoding of remote sensing geo-positioning sensor models is introduced. It is semantically based on ISO 19130-1 and ISO 19130-2, and syntactically based on OGC SensorML. It defines a cross mapping of the sensor models defined in ISO 19130-1 and ISO 19130-2 to the SensorML, and then proposes a detailed encoding method to finalize the XML schema (an XML schema here is the structure to define an XML document), which will become a profile of OGC SensorML. It seamlessly unifies the sensor models defined in ISO 19130-1, ISO 19130-2, and OGC SensorML. By enabling a standardized description of sensor models used to produce remote sensing data, this standard is very promising in promoting data interoperability, mobility, and integration in the remote sensing domain.
Observation of Turbulent Mixing Characteristics in the Typical Daytime Cloud-Topped Boundary Layer over Hong Kong in 2019
Authors: Huang Tao , Yim Steve Hung-lam , Yang Yuanjian , Lee Olivia Shuk-ming , Lam David Hok-yin , Cheng Jack Chin-ho , Guo Jianping
Published: May 2020

Turbulent mixing is critical in affecting urban climate and air pollution. Nevertheless, our understanding of it, especially in a cloud-topped boundary layer (CTBL), remains limited. High-temporal resolution observations provide sufficient information of vertical velocity profiles, which is essential for turbulence studies in the atmospheric boundary layer (ABL). We conducted Doppler Light Detection and Ranging (LiDAR) measurements in 2019 using the 3-Dimensional Real-time Atmospheric Monitoring System (3DREAMS) to reveal the characteristics of typical daytime turbulent mixing processes in CTBL over Hong Kong. We assessed the contribution of cloud radiative cooling on turbulent mixing and determined the altitudinal dependence of the contribution of surface heating and vertical wind shear to turbulent mixing. Our results show that more downdrafts and updrafts in spring and autumn were observed and positively associated with seasonal cloud fraction. These results reveal that cloud radiative cooling was the main source of downdraft, which was also confirmed by our detailed case study of vertical velocity. Compared to winter and autumn, cloud base heights were lower in spring and summer. Cloud radiative cooling contributed ~32% to turbulent mixing even near the surface, although the contribution was relatively weaker compared to surface heating and vertical wind shear. Surface heating and vertical wind shear together contributed to ~45% of turbulent mixing near the surface, but wind shear can affect up to ~1100 m while surface heating can only reach ~450 m. Despite the fact that more research is still needed to further understand the processes, our findings provide useful references for local weather forecast and air quality studies.
Accurate and Robust Floor Positioning in Complex Indoor Environments
Authors: Huang Jingyu , Luo Haiyong , Shao Wenhua , Zhao Fang , Yan Shuo
Published: May 2020

With the widespread development of location-based services, the demand for accurate indoor positioning is getting more and more urgent. Floor positioning, as a prerequisite for indoor positioning in multi-story buildings, is particularly important. Though lots of work has been done on floor positioning, the existing studies on floor positioning in complex multi-story buildings with large hollow areas through multiple floors still cannot meet the application requirements because of low accuracy and robustness. To obtain accurate and robust floor estimation in complex multi-story buildings, we propose a novel floor positioning method, which combines the Wi-Fi based floor positioning (BWFP), the barometric pressure-based floor positioning (BPFP) with HMM and the XGBoost based user motion detection. Extensive experiments show that using our proposed method can achieve 99.2% accuracy, which outperforms other state-of-the-art floor estimation methods.
Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
Authors: Ariza Colpas Paola , Vicario Enrico , De-La-Hoz-Franco Emiro , Pineres-Melo Marlon , Oviedo-Carrascal Ana , Patara Fulvio
Published: May 2020

Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.
Resource Management in Cloud Radio Access Network: Conventional and New Approaches
Authors: Rodoshi Rehenuma Tasnim , Kim Taewoon , Choi Wooyeol
Published: May 2020

Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.
Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting
Authors: Khaki Saeed , Pham Hieu , Han Ye , Kuhl Andy , Kent Wade , Wang Lizhi
Published: May 2020

Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detects and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the ( x , y ) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.
A Distributed Oracle Using Intel SGX for Blockchain-Based IoT Applications
Authors: Woo Sangyeon , Song Jeho , Park Sungyong
Published: May 2020

A blockchain oracle problem is a problem that defines a mechanism for how to safely bring external data to the blockchain. Although there have been various research efforts to solve this problem, existing solutions are limited in that they do not support either data availability or data integrity. Furthermore, no solution has been proposed to minimize the response time when an oracle server is malicious or overloaded. This paper proposes a distributed oracle using Intel Software Guard Extensions (SGX). The proposed approach uses multiple oracle servers to support data availability. It also supports data integrity using Intel SGX and Transport Layer Security (TLS) communication. The reputation system, which favors oracle servers with short response times, minimizes the average response time even if some of the oracle servers are malicious. The benchmarking results show that the response time of the proposed approach with 3 oracle servers is only 14% slower than a centralized oracle called Town-crier and scales well even if the number of oracle servers is increased up to 9. The reputation system is also evaluated, and its feasibility is analyzed using various experiments.
Street-Scale Analysis of Population Exposure to Light Pollution Based on Remote Sensing and Mobile Big Data—Shenzhen City as a Case
Authors: Sun Bo , Zhang Yang , Zhou Qiming , Gao Duo
Published: May 2020

Most studies on light pollution are based on light intensity retrieved from nighttime light (NTL) remote sensing with less consideration of the population factors. Furthermore, the coarse spatial resolution of traditional NTL remote sensing data limits the refined applications in current smart city studies. In order to analyze the influence of light pollution on populated areas, this study proposes an index named population exposure to light pollution (PELP) and conducts a street-scale analysis to illustrate spatial variation of PELP among residential areas in cites. By taking Shenzhen city as a case, multi-source data were combined including high resolution NTL remote sensing data from the Luojia 1-01 satellite sensor, high-precision mobile big data for visualizing human activities and population distribution as well as point of interest (POI) data. Results show that the main influenced areas of light pollution are concentrated in the downtown and core areas of newly expanded areas with obvious deviation corrected like traditional serious light polluted regions (e.g., ports). In comparison, commercial–residential mixed areas and village-in-city show a high level of PELP. The proposed method better presents the extent of population exposure to light pollution at a fine-grid scale and the regional difference between different types of residential areas in a city.
An Efficient Data-Hiding Scheme Based on Multidimensional Mini-SuDoKu
Authors: Horng Ji-Hwei , Xu Shuying , Chang Ching-Chun , Chang Chin-Chen
Published: May 2020

The massive Internet of Things (IoT) connecting various types of intelligent sensors for goods tracking in logistics, environmental monitoring and smart grid management is a crucial future ICT. High-end security and low power consumption are major requirements in scaling up the IoT. In this research, we propose an efficient data-hiding scheme to deal with the security problems and power saving issues of multimedia communication among IoT devises. Data hiding is the practice of hiding secret data into cover images in order to conceal and prevent secret data from being intercepted by malicious attackers. One of the established research streams of data-hiding methods is based on reference matrices (RM). In this study, we propose an efficient data-hiding scheme based on multidimensional mini-SuDoKu RM. The proposed RM possesses high complexity and can effectively improve the security of data hiding. In addition, this study also defines a range locator function which can significantly improve the embedding efficiency of multidimensional RM. Experimental results show that our data-hiding scheme can not only obtain better image quality, but also achieve higher embedding capacity than other related schemes.
Photoacoustic-Based Gas Sensing: A Review
Authors: Palzer Stefan
Published: May 2020

The use of the photoacoustic effect to gauge the concentration of gases is an attractive alternative in the realm of optical detection methods. Even though the effect has been applied for gas sensing for almost a century, its potential for ultra-sensitive and miniaturized devices is still not fully explored. This review article revisits two fundamentally different setups commonly used to build photoacoustic-based gas sensors and presents some distinguished results in terms of sensitivity, ultra-low detection limits, and miniaturization. The review contrasts the two setups in terms of the respective possibilities to tune the selectivity, sensitivity, and potential for miniaturization.
Sports Injuries in the Australian Regular Army
Authors: Orr Robin , Schram Ben , Pope Rodney
Published: May 2020

Sports participation in the military is important for physical fitness and building morale and camaraderie. However, injuries caused by sports are detrimental to military capability. The purpose of this study was to investigate patterns of injury from sports participation in Australian Regular Army personnel. Injury data spanning a two-year period were obtained from the Department of Defence Workplace Health, Safety, Compensation, and Reporting (WHSCAR) database. Data were extracted for the top five sporting activities causing injuries. The most common body sites, natures, and mechanisms of injuries across these five sports were then determined. Sports participation accounted for 11% (n = 1092) of reported injuries (n = 9828). Soccer presented with the greatest number of injuries (23.3%), followed by rugby union/league (22.9%), touch football (18.6%), Australian rules football (12.0%), and basketball/netball (11.9%). The ankle, knee, and shoulder were the most injured body sites (21.9%; 17.2%; 11.6% respectively) across these five sports, with soft tissue injury, dislocation, and fractures being the most common natures of injury (55.1%; 12.7%; 11.9% respectively). The most common mechanisms of injuries were contact with objects (35.1%) and falls (27.4%). The current injury rates, locations, and mechanisms are similar to historical rates suggesting little impact by injury mitigation strategies.
Effect of Road Markings and Traffic Signs Presence on Young Driver Stress Level, Eye Movement and Behaviour in Night-Time Conditions: A Driving Simulator Study
Authors: Babić Darko , Babić Dario , Cajner Hrvoje , Sruk Ana , Fiolić Mario
Published: May 2020

The study investigates how the presence of traffic signalling elements (road markings and traffic signs) affects the behaviour of young drivers in night-time conditions. Statistics show that young drivers (≤30 years old) are often involved in road accidents, especially those that occur in night-time conditions. Among other factors, this is due to lack of experience, overestimation of their ability or the desire to prove themselves. A driving simulator scenario was developed for the purpose of the research and 32 young drivers took two runs using it: (a) one containing no road markings and traffic signs and (b) one containing road markings and traffic signs. In addition to the driving simulator, eye tracking glasses were used to track eye movement and an electrocardiograph was used to monitor the heart rate and to determine the level of stress during the runs. The results show statistically significant differences (dependent samples t-test) between the two runs concerning driving speed, lateral position of the vehicle, and visual scanning of the environment. The results prove that road markings and traffic signs provide the drivers with timely and relevant information related to the upcoming situation, thus enabling them to adjust their driving accordingly. The results are valuable to road authorities and provide an explicit confirmation of the importance of traffic signalling for the behaviour of young drivers in night-time conditions, and thus for the overall traffic safety.
Michelia Essential Oil Inhalation Increases Fast Alpha Wave Activity
Authors: Koomhin Phanit , Sattayakhom Apsorn , Chandharakool Supaya , Sinlapasorn Jennarong , Suanjan Sarunnat , Palipoch Sarawoot , Na-ek Prasit , Punsawad Chuchard , Matan Narumol
Published: May 2020

Essential oils are volatile fragrance liquids extracted from plants, and their compound annual growth rate is expected to expand to 8.6% from 2019 to 2025, according to Grand View Research. Essential oils have several domains of application, such as in the food and beverage industry, in cosmetics, as well as for medicinal use. In this study, Michelia alba essential oil was extracted from leaves and was rich in linalool components as found in lavender and jasmine oil. The effects of inhaling michelia oil on human brainwaves still need to be elucidated. Ten male and ten female subjects were recruited. Thirty-two-channel electroencephalography was recorded. The raw data were spectrally analyzed for slow alpha, fast alpha, low beta, mid beta, and high beta activities. The results demonstrate that michelia leaf oil could reduce the alertness level observed by beta wave decrease and fast alpha wave activity increase. The inhalation of pure linalool showed virtually identical responses as the michelia oil inhalation. In conclusion, the sedative effects of michelia oil inhalation might originate from its major linalool component.
South Korean Consumers’ Attitudes toward Small Business Owners Participating in the 2019 Anti-Japan Boycott
Authors: Song Eugene
Published: May 2020

This study investigated consumer attitudes and the motivation of small business owners who participated in the 2019 anti-Japan boycott in South Korea (hereafter, Korea). The main areas of inquiry involved self-expression, the realization of justice, and consumer attitudes as elements of patriotic consumption. A seven-day survey was conducted among 500 adult consumers aged 18 years and older in Korea. The retrieved data were subjected to frequency analyses, reliability analyses, factor analyses, paired t-tests, and regressions. Declining Japanese product sales indicated high levels of a consumer boycott. The analyses demonstrated that consumers held positive attitudes toward small business owners who shared their boycotting beliefs and goals. Second, self-expression and the realization of justice motivation were relatively high, as were attitudes toward the participation message and small business owners who were participating in the boycott. Korean consumers had relatively high intentions to visit the stores of small business owners who were participating in the boycott. Lastly, self-expression motivations, motivations to realize justice, consumer attitudes toward the boycott participation messages of small business owners, and consumer attitudes toward the small business owners themselves had statistically significant positive effects. Small business owners holding general consumer beliefs about boycott participation should actively spread their support messages, and this would provide an excellent opportunity to create positive long-term awareness. This study provided a unique insight into Korean consumer behaviors when patriotism was considered. The findings have significant implications for small business owners looking to sustain themselves during product boycotts.
Comparing Reports of Child Sexual and Physical Abuse Using Child Welfare Agency Data in Two Jurisdictions with Different Mandatory Reporting Laws
Authors: Mathews Ben , Bromfield Leah , Walsh Kerryann
Published: May 2020

Empirical analysis has found that mandatory reporting legislation has positive effects on case identification of child sexual abuse both initially and over the long term. However, there is little analysis of the initial and ongoing impact on child protection systems of the rate of reports that are made if a reporting duty for child sexual abuse is introduced, especially when compared with rates of reports for other kinds of child maltreatment. This research analysed government administrative data at the unique child level over a seven-year period to examine trends in reports of child sexual abuse, compared with child physical abuse, in two Australian states having different socio-legal dimensions. Data mining generated descriptive statistics and rates per 100,000 children involved in reports per annum, and time trend sequences in the seven-year period. The first state, Western Australia, introduced the legislative reporting duty in the middle of the seven-year period, and only for sexual abuse. The second state, Victoria, had possessed mandatory reporting duties for both sexual and physical abuse for over a decade. Our analysis identified substantial intra-state increases in the reporting of child sexual abuse attributable to the introduction of a new legislative reporting duty, and heightened public awareness resulting from major social events. Victoria experienced nearly three times as many reports of physical abuse as Western Australia. The relative burden on the child protection system was most clearly different in Victoria, where reports of physical abuse were relatively stable and two and a half times higher than for sexual abuse. Rates of children in reports, even at their single year peak, indicate sustainable levels of reporting for child welfare agencies. Substantial proportions of reports were made by both legislatively mandated reporters, and non-mandated community members, suggesting that government agencies would benefit from engaging with communities and professions to enhance a desirable reporting practice.
Why Do Czech Customers Come to Upper Palatinate? Motives, Sales Volume, and the Importance of Distance: A Case Study of Shopping in Bavaria
Authors: Segerer Matthias , Hommerová Dita , Šrédl Karel
Published: May 2020

This case study investigates the cross-border shopping behavior and potential of Czech customers in Upper Palatinate (Bavaria). Based on a point of sale survey (POS) and two household surveys with more than 500 participants, the expenditures of Czech customers in the retail market in Upper Palatinate are estimated using linear potential methods. Using a logit model, the study also attempts to identify the main drivers of cross-border shopping, aiming at increasing its intensity and thus furthering the development of the cross-border region. The distance from the place of residence to the border is the strongest influencing variable, but demographic characteristics also impact the decision to go shopping in Bavaria. Finally, specific activities within the categories of “welcome culture” and “marketing and communication” aimed at promoting the cross-border shopping of Czech customers in Upper Palatinate are proposed. Local retailers should especially benefit from the frequency function of grocery stores as well as develop combination offers, e.g., with tourist facilities, following sustainable development trends.
Linkage Analysis of the Resources, Population, and Economy in China’s Key State-Owned Forest Areas
Authors: Zhang Xiaoxiao , Ke Shuifa
Published: May 2020

With the evolution of forestry policy, society, and the economy, the relationship between the resources, population, and the economy in key state-owned forest areas has continuously changed, and this has had an important impact on China’s forestry development and protection of resources. Based on the China Forestry Statistical Yearbook and forest resource inventory data, this paper conducts a macroanalysis of the linkages between forest resources, the number of employees, and economic development in key state-owned forest areas from 1975 to 2017. The research results show that in these areas, forestry policies and the external macroenvironment has a strong impact on the relationship between resources, population, and the economy; there is a decoupling relationship between resources and the economy; the population has a restrictive relationship with the economy; and a sustainable development relationship has not yet formed between resources, population, and the economy. Based on these findings, policy suggestions are put forward, including scientific felling and moderate management, introducing and training of high-quality talents, strengthening of industrial reform, and introduction of a market-oriented mechanism.
Regional Differences and Firms’ Innovation Self-Choice Behavior: Insights from China
Authors: Zhao Jianfeng , Wang Jiguang
Published: May 2020

Although many studies examine the influence of external factors (e.g., financial development, institutional condition, government intervention, and degree of marketization) on firms’ innovation behavior, they are rarely related to the core issue of heterogeneity in entrepreneurship. The different levels of entrepreneurs’ characteristics usually mean huge differences in the skill level or efficiency of firms. Thus, the differences that exist in innovation ability and innovation behavior also reflect the difference of susceptibility to external factors. The core issue of heterogeneity determines not only the self-choice mode of a firm’s innovation but also the degree and pattern of an internal condition imposed by external factors, and it then influences the firm’s innovation behavior. Based on the perspective of entrepreneurship, this paper integrates heterogeneous trade theory into firms’ R&D analysis frameworks by using the data of listed companies on the Growth Enterprise Market to explore the heterogeneous influence mechanism of financial development and government intervention on firms’ R&D input. First, by constructing a theoretical model, this study finds that the innovation self-choice phenomenon exists in heterogeneous firms. A higher financial development and a lower government intervention lead to an increase in firms’ R&D input benefits. Second, the empirical research finds that financial development reduces the innovation‒cash flow sensitivity. Moreover, the reduction of government intervention alleviates the degree of capital misallocation of financial development and promotes R&D input. Third, as a moderator variable, entrepreneurs’ risk-taking propensity strengthens the promotion effect of financial development and government intervention on firms’ R&D investment. Financial development would strengthen the effect of government intervention on innovation self-choice behavior.
Energy Related CO2 Emissions before and after the Financial Crisis
Authors: Sadorsky Perry
Published: May 2020

The 2008–2009 financial crisis, often referred to as the Great Recession, presented one of the greatest challenges to economies since the Great Depression of the 1930s. Before the financial crisis, and in response to the Kyoto Protocol, many countries were making great strides in increasing energy efficiency, reducing carbon dioxide (CO2) emission intensity and reducing their emissions of CO2. During the financial crisis, CO2 emissions declined in response to a decrease in economic activity. The focus of this research is to study how energy related CO2 emissions and their driving factors after the financial crisis compare to the period before the financial crisis. The logarithmic mean Divisia index (LMDI) method is used to decompose changes in country level CO2 emissions into contributing factors representing carbon intensity, energy intensity, economic activity, and population. The analysis is conducted for a group of 19 major countries (G19) which form the core of the G20. For the G19, as a group, the increase in CO2 emissions post-financial crisis was less than the increase in CO2 emissions pre-financial crisis. China is the only BRICS (Brazil, Russia, India, China, South Africa) country to record changes in CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period that were lower than their respective values in the pre-financial crisis period. Compared to the pre-financial crisis period, Germany, France, and Italy also recorded lower CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period. Germany and Great Britain are the only two countries to record negative changes in CO2 emissions over both periods. Continued improvements in reducing CO2 emissions, carbon intensity and energy intensity are hard to come by, as only four out of nineteen countries were able to achieve this. Most countries are experiencing weak decoupling between CO2 emissions and GDP. Germany and France are the two countries that stand out as leaders among the G19.
Association between Body Fat Percentage and Physical Performance in Male Korean Police Officers
Authors: Kim Jooyoung , So Wi-Young , Kim Sangwoon
Published: May 2020

Body composition can affect a police officer’s physical performance. We analyzed the correlation between body fat percentage (%fat) and physical performance in 326 male Korean police officers (age, 44.2 ± 9.2 years; height, 174.0 ± 4.5 cm; weight, 76.4 ± 8.8 kg) who were classified into two groups according to their %fat: high-fat (HFG, %fat ≥ 25%; n = 135) and low-fat (LFG, %fat < 25%; n = 191). Physical performance tests included a 100-m sprint, push-ups, sit-ups, and a hand-grip strength test. The results showed significant differences in performance between the groups in the 100-m sprint (p = 0.001) and in the sit-up test (p = 0.033), but not for push-ups (p = 0.130) or hand-grip strength (p = 0.255). A significant positive correlation was found between %fat and the 100-m sprint (r = 0.255, p < 0.000), and a negative correlation was found between %fat and push-ups (r = −0.117, p = 0.035) and sit-ups (r = −0.199, p < 0.000). However, there was no significant correlation between %fat and hand-grip strength (r = −0.093, p = 0.095). To improve physical performance, an intervention training program is recommended for reducing %fat, because high levels of %fat in male Korean police officers were associated with low levels of physical performance.
Effects of the COVID-19 Lockdown on Urban Mobility: Empirical Evidence from the City of Santander (Spain)
Authors: Aloi Alfredo , Alonso Borja , Benavente Juan , Cordera Rubén , Echániz Eneko , González Felipe , Ladisa Claudio , Lezama-Romanelli Raquel , López-Parra Álvaro , Mazzei Vittorio , Perrucci Lucía , Prieto-Quintana Darío , Rodríguez Andrés , Sañudo Roberto
Published: May 2020

This article analyses the impact that the confinement measures or quarantine imposed in Spain on 15 March 2020 had on urban mobility in the northern city of Santander. Data have been collected from traffic counters, public transport ITS, and recordings from traffic control cameras and environmental sensors to make comparisons between journey flows and times before and during the confinement. This data has been used to re-estimate Origin-Destination trip matrices to obtain an initial diagnostic of how daily mobility has been reduced and how the modal distribution and journey purposes have changed. The impact on externalities such as NO2 emissions and traffic accidents have also been quantified. The analysis revealed an overall mobility fall of 76%, being less important in the case of the private car. Public transport users dropped by up to 93%, NO2 emissions were reduced by up to 60%, and traffic accidents were reduced by up to 67% in relative terms.
The Role of Social Capital for Short-Video Platform Users’ Travel Intentions: SEM and Fsqca Findings
Authors: Liao Shan-Shan , Lin Ching-Yuan , Chuang Ying-Ji , Xie Xing-Zheng
Published: May 2020

This study examined the antecedents of travel intentions in the context of Chinese short-video platforms. Based on a review of the literature on travel intentions, we proposed an integrated model containing determinants of social capital and an elaboration likelihood model. In total, the data from 496 valid questionnaires were analyzed through structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM findings revealed that the determinants of social capital influenced the characteristics of tourist-generated content (TGC), which in turn affected users’ travel intentions. Homophily and interpersonal influence were direct antecedents of travel intentions, whereas tie strength non-significantly affected travel intentions. The fsQCA results revealed four configurations of the determinants of social capital and TGC characteristics required to achieve a high level of travel intention. Source credibility was discovered to be a necessary but not sufficient condition for travel intention. These findings offer insights for both academics and tourism marketers.
Time-Dependent Effects of Bentazon Application on the Key Antioxidant Enzymes of Soybean and Common Ragweed
Authors: Czékus Zalán , Farkas Máté , Bakacsy László , Ördög Attila , Gallé Ágnes , Poór Péter
Published: May 2020

The presence or absence of light is one of the most significant environmental factors affecting plant growth and defence. Therefore, the selection of the most appropriate time of application may maximize the benefits of photosynthetic inhibitors. In this work, the concentration and daytime or night-time-dependent effects of bentazon were tested in soybean and common ragweed. The recommended dose (1440 g ha−1) and also half the recommended dose significantly reduced the maximum quantum yield (Fv/Fm) and increased H2O2 levels in common ragweed. Interestingly, bentazon did not change Fv/Fm in soybean. The activity of superoxide dismutase changed in a dose-dependent manner only in common ragweed. The activity of ascorbate peroxidase, catalase and glutathione S-transferase (GST), as well as the contents of ascorbate (AsA) and glutathione (GSH) did not change significantly in this plant species. In soybean, alterations in H2O2 levels were lower but GST and APX activity, as well as AsA and GSH levels were higher compared to common ragweed. At the same time, the rate of lipid peroxidation and ion leakage increased upon bentazon, and were higher in the light phase-treated leaves in the case of both plant species. These results can contribute to optimizing the effects and uses of herbicides in agriculture.
Mass Transfer Performance Study for CO2 Absorption into Non-Precipitated Potassium Carbonate Promoted with Glycine Using Packed Absorption Column
Authors: Mustafa Nur Farhana Ajua , Mohd Shariff Azmi , Tay Wee Horng , Abdul Halim Hairul Nazirah , Mhd Yusof Siti Munirah
Published: May 2020

The removal of carbon dioxide (CO2) at offshore operation requires an absorption system with an environmentally friendly solvent that can operate at elevated pressure. Potassium carbonate promoted with glycine, PCGLY, is a green solvent that has potential for offshore applications. For high solvent concentrations at elevated pressure, the by-product of CO2 absorption consists of precipitates that increase operational difficulty. Therefore, this study was done to assess the CO2 absorption performance of non-precipitated PCGLY with concentration 15wt%PC+3wt%GLY, which is known to have comparable solubility performance with MDEA. A packed absorption column was used to identify the CO2 removal efficiency, mass transfer coefficient in liquid film, k l a e , and overall volumetric mass transfer coefficient, K G a v . A simplified rate-based model was used to determine k l a e and K G a v based on the experimental data with a maximum MAE value, 0.057. The results showed that liquid flow rates and liquid temperature gives significant effects on the k l a e and K G a v profile, whereas gas flow rate and operating pressure had little effect. The CO2 removal efficiency of PCGLY was found to be 77%, which was only 2% lower than 1.2 kmol/m3 MDEA. K G a v of PCGLY is comparable with MDEA. The absorption process using PCGLY shows potential in the CO2 sweetening process at offshore.
Proactive Divestiture and Business Innovation: R&D Input and Output Performance
Authors: Lee Kyungsuk , Roh Taewoo
Published: May 2020

We investigate the impact of proactive divestitures on innovative activities at a firm-level. Research concerning the relationship between proactive divestiture and innovation performance remains unexplored and requires a close investigation. Furthermore, we argue that proactive-divestiture is an essential means to achieve corporate sustainability by fostering innovation outcomes. To explore such a relationship, this study integrates research on knowledge-based view and organizational inertia and encompasses the model of financial distress. We hypothesize that proactive divestiture increases both the firm’s R&D intensity and the number of patents and propose that prior divestiture experiences and divested-unit size would moderate this relationship. Results indicate that proactive post-divestiture firms have increased in R&D inputs but not significantly in output. We found mixed results for such a relationship as prior experiences increased, but interestingly, the relationship revealed to be more significant for both input and output as divested-unit size decreased. This study contributes to our understandings of how proactive divestiture can reinforce knowledge capacity, distant from a traditional resource-based view that mainly regarded divestiture as a mere responsive action vis-à-vis financial pressure.