bluuebunny/crossref_metadata_2025 huggingface dataset:
Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data
Authors: Shen Jiaqi , Shuai Yanmin , Li Peixian , Cao Yuxi , Ma Xianwei
Published: September 2021

It is necessary to understand the relationship between the impervious surface area (ISA) distribution, variation trends and potential driving forces over Dongying, Shandong Province. We extracted ISA information from Landsat images with 3–5 year intervals during 1995 to 2018 using Minimum Noise Fraction (MNF) transform, Pixel Purity Index (PPI), and Linear Spectral Mixture Analysis (LSMA), followed by the analysis on three driving forces of ISA expansion (physical geography, socioeconomic factors, and urban cultural features). Our results show the retrieved ISA thematic map fit the limited requirement of root mean square error (RMSE). The correct classification accuracy of ISA is greater than 83.08%. Further, the cross–comparison exhibits the general consistent with the ISA distribution of the land use classification map published by the National Basic Geographic Information Center. The gradual increasing trend can be captured on the expansion of ISA from 1995 to 2018. Despite of the central region always shown as the high ISA density, it still keeps increasing annually and radiating the surrounding region, especially in the southward which has formed into a new large–scale and high intensity of ISA in 2015–2018. Though the ISA patches scattered in the west region or along the northern and eastern part of the ocean coastline are still small, the expansion trend of ISA can be detected. The expansion intensity index (EII) of ISA measuring the situation of its expansion changes from the lowest value 0.12% between 1995 and 2000 up to the highest 0.73% between 2000 and 2005. Richly endowed by nature, the city’s natural geographical environment provides an elevated chance of further urbanization. The rapid increase of regional economy provides a fundamental driving force for expanding ISAs. The development of urban culture promotes the sustainable development of ISAs. Our results provide a scientific basis for future urban land use management, construction planning, and environmental protection in Dongying.
PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information
Authors: Li Wangbin , Sun Kaimin , Du Zhuotong , Hu Xiuqing , Li Wenzhuo , Wei Jinjiang , Gao Song
Published: September 2021

Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activities, such as product retrieval, land-use/cover classification, object detection, and especially for change detection. However, the complexity of clouds themselves make it difficult to detect thin clouds and small isolated clouds. To accurately detect clouds in satellite imagery, we propose a novel neural network named the Pyramid Contextual Network (PCNet). Considering the limited applicability of a regular convolution kernel, we employed a Dilated Residual Block (DRB) to extend the receptive field of the network, which contains a dilated convolution and residual connection. To improve the detection ability for thin clouds, the proposed new model, pyramid contextual block (PCB), was used to generate global information at different scales. FengYun-3D MERSI-II remote-sensing images covering China with 14,165 × 24,659 pixels, acquired on 17 July 2019, are processed to conduct cloud-detection experiments. Experimental results show that the overall precision rates of the trained network reach 97.1% and the overall recall rates reach 93.2%, which performs better both in quantity and quality than U-Net, UNet++, UNet3+, PSPNet and DeepLabV3+.
Guaranteed Robust Tensor Completion via ∗L-SVD with Applications to Remote Sensing Data
Authors: Wang Andong , Zhou Guoxu , Zhao Qibin
Published: September 2021

This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to various reasons such as sensor dead pixels, communication loss, electromagnetic interferences, cloud shadows, etc. To estimate the underlying tensor, a new penalized least squares estimator is first formulated by exploiting the low rankness of the signal tensor within the framework of tensor ∗L-Singular Value Decomposition (∗L-SVD) and leveraging the sparse structure of the outlier tensor. Then, an algorithm based on the Alternating Direction Method of Multipliers (ADMM) is designed to compute the estimator in an efficient way. Statistically, the non-asymptotic upper bound on the estimation error is established and further proved to be optimal (up to a log factor) in a minimax sense. Simulation studies on synthetic data demonstrate that the proposed error bound can predict the scaling behavior of the estimation error with problem parameters (i.e., tubal rank of the underlying tensor, sparsity of the outliers, and the number of uncorrupted observations). Both the effectiveness and efficiency of the proposed algorithm are evaluated through experiments for robust completion on seven different types of remote sensing data.
Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents
Authors: Uhl Johannes H. , Leyk Stefan , Li Zekun , Duan Weiwei , Shbita Basel , Chiang Yao-Yi , Knoblock Craig A.
Published: September 2021

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.
Assessment of Empirical and Semi-Analytical Algorithms Using MODIS-Aqua for Representing In-Situ Chromophoric Dissolved Organic Matter (CDOM) in the Bering, Chukchi, and Western Beaufort Seas of the Pacific Arctic Region
Authors: Santiago Melishia I. , Frey Karen E.
Published: September 2021

We analyzed a variety of satellite-based ocean color products derived using MODIS-Aqua to investigate the most accurate empirical and semi-analytical algorithms for representing in-situ chromophoric dissolved organic matter (CDOM) across a large latitudinal transect in the Bering, Chukchi, and western Beaufort Seas of the Pacific Arctic region. In particular, we compared the performance of empirical (CDOM index) and several semi-analytical algorithms (quasi-analytical algorithm (QAA), Carder, Garver-Siegel-Maritorena (GSM), and GSM-A) with field measurements of CDOM absorption (aCDOM) at 412 nanometers (nm) and 443 nm. These algorithms were compared with in-situ CDOM measurements collected on cruises during July 2011, 2013, 2014, 2015, 2016, and 2017. Our findings show that the QAA a443 and GSM-A a443 algorithms are the most accurate and robust representation of in-situ conditions, and that the GSM-A a443 algorithm is the most accurate algorithm when considering all statistical metrics utilized here. Our further assessments indicate that geographic variables (distance to coast, latitude, and sampling transects) did not obviously relate to algorithm accuracy. In general, none of the algorithms investigated showed a statistically significant agreement with field measurements beyond an approximately ± 60 h offset, likely owing to the highly variable environmental conditions found across the Pacific Arctic region. As such, we suggest that satellite observations of CDOM in these Arctic regions should not be used to represent in-situ conditions beyond a ± 60 h timeframe.
Intercalibration of ASCAT Scatterometer Winds from MetOp-A, -B, and -C, for a Stable Climate Data Record
Authors: Ricciardulli Lucrezia , Manaster Andrew
Published: September 2021

Scatterometers provide very stable ocean vector wind data records. This is because they measure the ratio of backscattered to incident microwave signal over the ocean surface as opposed to an absolute quantity (e.g., emitted microwave signal). They provide an optimal source of observations for building a long ocean vector wind Climate Data Record (CDR). With this objective in mind, observations from different satellite platforms need to be assessed for high absolute accuracy versus a common ground truth and for fine cross-calibration during overlapping periods. Here we describe the methodology for developing a CDR of ocean surface winds from the C-band ASCAT scatterometers onboard MetOp-A, -B, and -C. This methodology is based on the following principles: a common Geophysical Model Function (GMF) and wind algorithm developed at Remote Sensing Systems (RSS) and the use of in situ and satellite winds to cross-calibrate the three scatterometers within the accuracy required for CDRs, about 0.1 m/s at the global monthly scale. Using multiple scatterometers and radiometers for comparison allows for the opportunity to isolate sensors that are drifting or experiencing step-changes as small as 0.05 m/s. We detected and corrected a couple of such changes in the ASCAT-A wind record. The ASCAT winds are now very stable over time and well cross-calibrated with each other. The full C-band wind CDR now covers 2007-present and can be easily extended in the next decade with the launch of the MetOp Second Generation scatterometers.
Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning
Authors: Davis Dylan S. , Lundin Julius
Published: September 2021

Over the past several centuries, the iron industry played a central role in the economy of Sweden and much of northern Europe. A crucial component of iron manufacturing was the production of charcoal, which was often created in charcoal piles. These features are visible in LiDAR (light detection and ranging) datasets. These charcoal piles vary in their morphology by region, and training data for some feature types are severely lacking. Here, we investigate the potential for machine automation to aid archaeologists in recording charcoal piles with limited training data availability in a forested region of Jönköping County, Sweden. We first use hydrological depression algorithms to conduct a preliminary assessment of the study region and compile suitable training data for charcoal production sites. Then, we use these datasets to train a series of RetinaNet deep learning models, which are less computationally expensive than many popular deep learning architectures (e.g., R-CNNs), allowing for greater usability. Together, our results demonstrate how charcoal piles can be automatically extracted from LiDAR datasets, which has great implications for improving our understanding of the long-term environmental impact of the iron industry across Northern Europe. Furthermore, our workflow for developing and implementing deep learning models for archaeological research can expand the use of such methods to regions that lack suitable training data.
Evaluating the Spectral Response and Yield of Soybean Following Exposure to Sublethal Rates of 2,4-D and Dicamba at Vegetative and Reproductive Growth Stages
Authors: Oseland Eric , Shannon Kent , Zhou Jianfeng , Fritschi Felix , Bish Mandy D. , Bradley Kevin W.
Published: September 2021

The commercialization of synthetic auxin-resistant crops and the commensurate increase in post-emergent auxin-mimic herbicide applications has resulted in millions of hectares of injury to sensitive soybeans in the United States since 2016. Visual yield loss estimations following auxin injury can be difficult. The goal of this research was to determine if spectral variations following auxin injury to soybean allow for more precise yield loss estimations. Identical field experiments were performed in 2018, 2019, and 2020 in Columbia, Missouri to compare the ability of established vegetative indices to differentiate between exposure levels of 2,4-D and dicamba in soybean and predict yield loss. Soybeans were planted at three timings for growth stage separation and were exposed to sublethal rates of 2,4-D and dicamba at the R2, R1, and V3 growth stages. A UAV-mounted multispectral sensor was flown over the trial 14 days after the herbicide treatments. The results of this research found that vegetative indices incorporating the red-edge wavelength were more consistent in estimating yield loss than indices comprised of only visible or NIR wavelengths. Yield loss estimations became difficult when soybean injury occurred during later reproductive stages when soybean biomass was increased. This research also determined that when injury occurs to soybean in vegetative growth stages late in the growing season there is a greater likelihood for yield loss to occur due to decreased time for recovery. The results of this research could provide direction for more objective and accurate evaluations of yield loss following synthetic auxin injury than what is currently available.
Artificial Intelligence Based Structural Assessment for Regional Short- and Medium-Span Concrete Beam Bridges with Inspection Information
Authors: Xia Ye , Lei Xiaoming , Wang Peng , Sun Limin
Published: September 2021

The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based methodology to solve the condition assessment of regional bridges and optimize their maintenance schemes. It includes data integration, condition assessment, and maintenance optimization. Data from bridge inspection reports is the main source of this data-driven approach, which could provide a substantial amount og condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified. Given the need to manage limited resources and ensure safety and functionality, adequate maintenance schemes for regional bridges are optimized with genetic algorithms. The proposed data-driven methodology is applied to real regional highway bridges. The regional inspection information is obtained with the help of emerging technologies. The established structural deterioration models achieve up to 85% prediction accuracy. The obtained optimal maintenance schemes could be chosen according to actual structural conditions, maintenance requirements, and total budget. Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges.
Deep Learning-Based Method for Detection of External Air Conditioner Units from Street View Images
Authors: Yang Fei , Wang Meng
Published: September 2021

Heat waves may negatively impact the economy and human life under global warming. The use of air conditioners can reduce the vulnerability of humans to heat wave disasters. However, air conditioner usage has been not clear until now. Traditional registration investigation methods are cumbersome and require expensive labor and time. This study used a Labelme image tagging tool and an available street view images database to firstly establish a monographic dataset to detect external air conditioner unit features and proposed two deep learning algorithms of Mask-RCNN and YOLOv5 to automatically retrieve air conditioners. The training dataset used street view images in the 2nd Ring Road area of downtown Beijing. The model evaluation mAP of Mask-RCNN and YOLOv5 reached 0.99 and 0.9428. In comparison, the performance of YOLOv5 was superior, which is attributed to the YOLOv5 model being better at detecting smaller target entities equipped with a lighter network structure and an enhanced feature extraction network. We demonstrated the feasibility of using street view images to retrieve air conditioners and showed their great potential to detect air conditioners in the future.
Time Varying Spatial Downscaling of Satellite-Based Drought Index
Authors: Chu Hone-Jay , Wijayanti Regita Faridatunisa , Jaelani Lalu Muhamad , Tsai Hui-Ping
Published: September 2021

Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.
Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors
Authors: Cho Hyuntae , Baek Yunju
Published: September 2021

Air pollution is a social problem, because the harmful suspended materials can cause diseases and deaths to humans. Specifically, particulate matters (PM), a form of air pollution, can contribute to cardiovascular morbidity and lung diseases. Nowadays, humans are exposed to PM pollution everywhere because it occurs in both indoor and outdoor environments. To purify or ventilate polluted air, one need to accurately monitor the ambient air quality. Therefore, this study proposed a practical particulate matter sensing and accurate calibration system using low-cost commercial sensors. The proposed system basically uses noisy and inaccurate PM sensors to measure the ambient air pollution. This paper mainly deals with three types of error caused in the light scattering method: short-term noise, part-to-part variation, and temperature and humidity interferences. We propose a simple short-term noise reduction method to correct measurement errors, an auto-fitting calibration for part-to-part repeatability to pinpoint the baseline of the signal that affects the performance of the system, and a temperature and humidity compensation method. This paper also contains the experiment setup and performance evaluation to prove the superiority of the proposed methods. Based on the evaluation of the performance of the proposed system, part-to-part repeatability was less than 2 μg/m3 and the standard deviation was approximately 1.1 μg/m3 in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.
Biometrics for Internet-of-Things Security: A Review
Authors: Yang Wencheng , Wang Song , Sahri Nor Masri , Karie Nickson M. , Ahmed Mohiuddin , Valli Craig
Published: September 2021

The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.
Identification of Multiple Cracks in Composite Laminated Beams Using Perturbation to Dynamic Equilibrium
Authors: Deng Aimin , Cao Maosen , Lu Qitian , Xu Wei
Published: September 2021

Identification of cracks in beam-type components is significant to ensure the safety of structures. Among the approaches relying on mode shapes, the concept of transverse pseudo-force (TPF) has been well proved for single and multiple crack identification in beams made of isotropic materials; however, there is a noticeable gap between the concept of TPF and its applications in composite laminated beams. To fill this gap, an enhanced TPF approach that relies on perturbation to dynamic equilibrium is proposed for the identification of multiple cracks in composite laminated beams. Starting from the transverse equation of motion, this study formulates the TPF in a composite laminated beam for the identification of multiple cracks. The capability of the approach is numerically verified using the FE method. The applicability of the approach is experimentally validated on a carbon fiber-reinforced polymer laminated beam with three cracks, the mode shapes of which are acquired through non-contact vibration measurement using a scanning laser vibrometer. In particular, a statistic manner is utilized to enable the approach to be feasible to real scenarios in the absence of material and structural information; besides, an integrating scheme is utilized to enable the approach to be capable of identifying cracks even in the vicinity of nodes of mode shapes.
Flexible Recruitments of Fundamental Muscle Synergies in the Trunk and Lower Limbs for Highly Variable Movements and Postures
Authors: Saito Hiroki , Yokoyama Hikaru , Sasaki Atsushi , Kato Tatsuya , Nakazawa Kimitaka
Published: September 2021

The extent to which muscle synergies represent the neural control of human behavior remains unknown. Here, we tested whether certain sets of muscle synergies that are fundamentally necessary across behaviors exist. We measured the electromyographic activities of 26 muscles, including bilateral trunk and lower limb muscles, during 24 locomotion, dynamic and static stability tasks, and we extracted the muscle synergies using non-negative matrix factorization. Our results show that 13 muscle synergies that may have unique functional roles accounted for almost all 24 tasks by combinations of single and/or merging of synergies. Therefore, our results may support the notion of the low dimensionality in motor outputs, in which the central nervous system flexibly recruits fundamental muscle synergies to execute diverse human behaviors. Further studies are required to validate the neural representation of the fundamental components of muscle synergies.
Teenage Time Use and Educational Attainment in Adulthood in Finland
Authors: Ojala Satu , Kan Man-Yee , Oinas Tomi
Published: September 2021

We investigated the role of teenage everyday social ties in educational outcomes by examining the association between teenage time use and educational attainment in adulthood. The sample consisted of young people aged 10–18 from the 1979 Finnish Time Use Survey, and the same respondents’ educational attainment later in life recorded from population register data at the year of 40th birthday (n = 366 men and 393 women). We assessed the associations of time spent with the parents, on studying, leisure activities, as well as social connectedness with friends and participation in extracurricular activities, with educational outcomes. Our findings indicated that time spent with the father is positively associated with the likelihood of completing tertiary education for both daughters and sons. In particular, time spent with lower-education fathers was associated with teenagers’ future tertiary education. However, intense friendships and participation in extracurricular activities were not associated with academic achievement later in life. The findings suggest that educational attainment is partly explained by teenage time spent with the family. Less-educated fathers can enhance the attainment of higher education of their children by spending more time with their teenage children.
Influencing Factors of Residents’ Perception of Responsibilities for Heritage Conservation in World Heritage Buffer Zone: A Case Study of Libo Karst
Authors: Fang Ruonan , Zhang Juan , Xiong Kangning , Woo Kyung-Sik , Zhang Ning
Published: September 2021

Local residents of buffer zones, as a key factor in the World Heritage conservation and sustainable development, have not received sufficient attention in most developing countries, especially in the mountainous areas where poor and backward ethnic minorities live. To fill this research gap, this paper takes the Karst World Heritage buffer zone in Libo, Guizhou Province, southwest mountainous area of China, as the research area, and explores the factors that influence the perception of residents’ responsibility for the World Heritage conservation by taking local residents who are involved in tourism management as the research subjects. Data were collected in the buffer zone of the Libo World Heritage site and 186 valid data were generated. SPSS 25.0 and AMOS 27.0 software were used to analyze the questionnaire data and construct a structural equation model. The results showed that environmental protection behavior had the greatest impact on residents’ perception of responsibility for heritage conservation (0.93), followed by the recognition of heritage value (0.55), tourism positive impact (0.39), and place identification (0.34), among which the positive impact of tourism had a greater impact on the perception of heritage value (0.52). The results of the study emphasize the importance of the recognition of heritage value and positive tourism influence on the formation of residents’ perception of responsibility for heritage conservation, and provide an empirical basis for the conservation of the World Natural Heritage.
Dementia and COVID-19 in Chile, New Zealand and Germany: A Research Agenda for Cross-Country Learning for Resilience in Health Care Systems
Authors: Laporte Uribe Franziska , Arteaga Oscar , Bruchhausen Walter , Cheung Gary , Cullum Sarah , Fuentes-García Alejandra , Miranda Castillo Claudia , Kerse Ngaire , Kirk Ray , Muru-Lanning Marama , Salinas Ríos Rodrigo Alejandro , Schrott Lothar , Slachevsky Andrea , Roes Martina
Published: September 2021

The COVID-19 pandemic has revealed existing gaps in policies, systems and services, stressing the need for concerted global action on healthy aging. Similar to the COVID-19 pandemic, dementia is a challenge for health systems on a global scale. Our hypothesis is that translational potential lies in cross-country learning by involving three high-income countries with distinct geo-political-cultural-social systems in Latin America (Chile), the South Pacific (New Zealand) and Europe (Germany). Our vision is that such cross-country learning will lead to providing adequate, equitable and sustainable care and support for families living with dementia during a pandemic and beyond. We are proposing a vision for research that takes a multi-disciplinary, strength-based approach at the intersection of health care research, disaster research, global health research and dementia research. We present some insights in support of our hypothesis and proposed research agenda. We anticipate that this research has the potential to contribute towards strengthening and transforming health care systems in times of crises and beyond.
Measuring How Decision Support Systems Improve Newsvendors’ Performance: The Subjects’ Version
Authors: D’Urso Diego , Chiacchio Ferdinando , Demerouti Evangelia
Published: September 2021

Despite the emerging contribution of machine automation, artificial intelligence and information systems, humans remain yet the most fragile ring of any organization. Decision support systems are widespread, supporting us to decide among uncertainties, such as weather conditions, suppliers’ performances and financial opportunities, but how humans take into account this information and, most of all, how they trust their own management knowledge is a controversial issue. This paper assesses, by means of a controlled experiment and ex post interviews, how individuals consider and use decision support systems in the context of the Newsvendor Problem. In accordance with prior research, the results show that individuals’ order quantities are pull-to-center biased. Moreover, ex post direct interviews suggest that (i) the individuals’ trust in decision support systems is not blind; (ii) individuals do not play the business game as a real task, (iii) they are biased by the type of incentive promised and (iv) they seem not skilled or trained enough. Ex post interviews shed a new light on controlled human experiments: they should be better analyzed and re-engineered.
The Relationship between Inclusion Climate and Voice Behaviors beyond Social Exchange Obligation: The Role of Psychological Needs Satisfaction
Authors: Paolillo Anna , Sinval Jorge , Silva Sílvia A. , Scuderi Vittorio E.
Published: September 2021

Several studies have identified a work environment that promotes inclusiveness as a significant predictor of affiliative organizational citizenship behavior or OCB (such as helping), whereas not much research has focused on inclusion and challenging OCB (i.e., voice). Moreover, no previous studies have explored the above-mentioned relationship in the light of self-determination theory (SDT), given that social exchange theory has traditionally been used as the main explanatory mechanism. Therefore, the aim of the present research was to test the mediating role of basic psychological needs satisfaction in the relationship between inclusion climate, promotive voice and prohibitive voice. Data were collected through self-report questionnaires administered to 246 employees of an international company operating in the service industry. Structural equation modelling was used to analyze the data utilizing R software. Results showed that satisfaction of the needs for autonomy, competence and relatedness partially mediated the relationship between inclusion climate and promotive and prohibitive voice, therefore supporting the idea that social exchange might not be the only determinant for employees to engage in voice behavior. Most importantly, those findings underline how a truly inclusive workplace needs to fulfil its employees’ basic needs of behaving volitionally, feeling effective and connecting meaningfully; this would motivate the workers to voice their suggestions and concerns.
High-Resolution Hydrological-Hydraulic Modeling of Urban Floods Using InfoWorks ICM
Authors: Sidek Lariyah Mohd , Jaafar Aminah Shakirah , Majid Wan Hazdy Azad Wan Abdul , Basri Hidayah , Marufuzzaman Mohammad , Fared Muzad Mohd , Moon Wei Chek
Published: September 2021

Malaysia, being a tropical country located near the equatorial doldrums, experiences the annual occurrence of flood hazards due to monsoon rainfalls and urban development. In recent years, environmental policies in the country have shifted towards sustainable flood risk management. As part of the development of flood forecasting and warning systems, this study presented the urban flood simulation using InfoWorks ICM hydrological−hydraulic modeling of the Damansara catchment as a case study. The response of catchments to the rainfall was modeled using the Probability Distributed Moisture (PDM) model due to its capability for large catchments with long-term runoff prediction. The interferometric synthetic aperture radar (IFSAR) technique was used to obtain high-resolution digital terrain model (DTM) data. The calibrated and validated model was first applied to investigate the effectiveness of the existing regional ponds on flood mitigation. For a 100-year flood, the extent of flooded areas decreased from 12.41 km2 to 3.61 km2 as a result of 64-ha ponds in the catchment, which is equivalent to a 71% reduction. The flood hazard maps were then generated based on several average recurrence intervals (ARIs) and uniform rainfall depths, and the results showed that both parameters had significant influences on the magnitude of flooding in terms of flood depth and extent. These findings are important for understanding urban flood vulnerability and resilience, which could help in sustainable management planning to deal with urban flooding issues.
Entrepreneurial Income and Wellbeing: Women’s Informal Entrepreneurship in a Developing Context
Authors: Muhammad Said , Kong Ximei , Saqib Shahab E. , Beutell Nicholas J.
Published: September 2021

This study examines the impact of women’s entrepreneurial income on wellbeing. Women entrepreneurs (N = 504) from district Mardan, Khyber Pakhtunkhwa, Pakistan participated in the study. ANOVAs and multiple regressions were used to analyze the impact of predictors on the dependent variables (i.e., per capita income, education, health, entertainment, social, household, and other miscellaneous expenditures, investment, savings, and charity). The findings indicated that women’s informal entrepreneurship has a significant role in family, economic, and societal wellbeing. The results contribute to the understanding of women’s entrepreneurial income on individual and family wellbeing. Women’s informal entrepreneurship plays an important role, particularly in the developing world, consistent with the multiplier effect of women entrepreneurs’’ wellbeing. Measures were suggested to empower such women informal, home-based entrepreneurs in view of the United Nations’ Sustainable Development Goals.
Local Place-Identities, Outgoing Tourism Guidebooks, and Israeli-Jewish Global Tourists
Authors: Mazor Tregerman Maya
Published: September 2021

The current research is based on a socio-historical approach to the cultural role of tourism media in the reconstruction of cultural identities, specifically place-identity. It explores the role of Israeli outgoing tourism guidebooks in the reconstruction of local, Israeli place-identities. Stemming from a multidisciplinary methodological approach to the research of the book publishing industry, 17 titles written in Hebrew for Israeli outgoing tourists are chosen for their cultural stance and a manifest textual referencing of issues regarding Israeli identity. Critical discourse analysis of lingual content is used for exploring the texts’ social actions regarding the Israeli identity by following the inclusion and omission of tourist information and suggested itineraries. Results suggest justification of tourism abroad as the books’ main textual strategy. Six textual tactics are used for reconstructing Israeli tourists’ pre-trip motivations, on-trip tourist roles and behaviors, and post-trip reflections. Israeli outgoing tourism is reconstructed as creating a temporary, playful sphere for reiterating Israel’s predominance in the lives of Israelis even while touring abroad. The cultural significance of tourism media is discussed in conclusion by pointing at the books’ double role in both marketing and cultural construction of a consensual Israeli-Jewish pace-identity amidst global changes.
A Comparative Study for Forced Ventilation Systems in Industrial Buildings to Improve the Workers’ Thermal Comfort
Authors: Elhadary Mohamed I. , Alzahrani Abdullah Mossa Y. , Aly Reda M. H. , Elboshy Bahaa
Published: September 2021

The appropriate ventilation for factory spaces with regard to volume flow rate and air velocity inside the factory is one of the most important factors in the improvement of the thermal comfort of workers and in the reduction of the percentage of pollution they are exposed to, which in turn helps to improve the work environment and increase productivity. It also could improve the performance of machines. Hence, overheating can cause various problems and malfunctions. In this study, three types of mechanical ventilation systems are compared: the wall fan extract ventilation system, the roof fan extract ventilation system, and the spot cooling system. The Ansys software has been used to conduct the computational fluid dynamics (CFD) simulations for the different cases and the ventilation effectiveness factor (VEF) has been used to compare the performances of the three systems. The ventilation factor notably relies on the temperature distribution produced through the modeling and the results show that the most optimal system that can be used for similar factory spaces is the forced ventilation system. Finally, it is also the best in terms of energy consumption, despite the increase in the initial cost of its installation.
Low Cost Road Health Monitoring System: A Case of Flexible Pavements
Authors: Khahro Shabir Hussain , Javed Yasir , Memon Zubair Ahmed
Published: September 2021

A healthy road network plays a significant role in the socio-economic development of any country. Road management authorities struggle with pavement repair approaches and the finances to keep the existing road network to its best functionality. It has been observed that real-time road condition monitoring can drastically reduce road and vehicle maintenance expenses. There are various methods to analyze road health, but most are either expensive, costly, time-consuming, labor-intensive, or imprecise. This study aims to design a low-cost smart road health monitoring system to identify the road section for maintenance. An automized sensor-based system is developed to assist the road sections for repair and rehabilitation. The proposed system is mounted in a vehicle and the data have been collected for a more than 1000 km road network. The data have been processed using SPSS, and it shows that the proposed system is adequate for detecting the road quality. It is concluded that the proposed system can identify the vulnerable sections to add to the pavement maintenance plan. In the future, the created application can be launched as a smart citizen app where each car driver can install this application and can monitor the road quality automatically.
Cybernetics Approach Using Agent-Based Modeling in the Process of Evacuating Educational Institutions in Case of Disasters
Authors: Ionescu Ștefan , Nica Ionuț , Chiriță Nora
Published: September 2021

In the context of an emergency, evacuating people from a location in the shortest possible time is essential, as is the high degree of safety that people should expect when evacuating. Lately, in Romania there have been more and more fire events generated by different causes. This article will use agent-based modeling to simulate an emergency evacuation model in NetLogo. The model has been used to perform and analyze various scenarios. With the help of NetLogo, we managed to perform 400 simulations with the evacuation of 180 people (students, teachers, and non-teaching staff) based on which we developed several recommendations to streamline the evacuation process in order to reduce the possibility of death. The present research will help to identify the evacuation times from a school, but it will also highlight certain aspects that may occur during the evacuation. The model that was used in this research took into account the individual particularities of the people taking part in the evacuation, emphasizing the effects that form in a crowd of people when evacuating; effects such as the funnel effect, which is caused by the formation of bottlenecks around narrow areas. All these things are part of the analysis of the measurement of entropy of the exhaust system, a problem that has captured all of the specialists’ attention. Finally, solutions have been proposed to improve evacuation time in case of disasters.
Social Life Cycle Assessments: A Review on Past Development, Advances and Methodological Challenges
Authors: Pollok Louisa , Spierling Sebastian , Endres Hans-Josef , Grote Ulrike
Published: September 2021

Society’s interest in social impacts of products, services and organizational behaviors is rapidly growing. While life cycle assessments to evaluate environmental stressors have generally been well established in many industries, approaches to evaluate social impacts such as Social Life Cycle Assessment (S-LCA) lack methodological consistency and standardization. The aim of this paper is to identify past developments and methodological barriers of S-LCA and to summarize how the automotive industry contributed to the advancement or application of this method. Therefore, a qualitative content analysis of 111 studies published between 2015 and 2020 is used to gather information on past scientific and political milestones, methodological barriers impeding S-LCA and the participation of the automotive sector. The review shows that a broad range of sectors such as the automotive industry contributed to the testing and advancement of S-LCA in the past but that S-LCA remains a young and immature method. Large-scale application is impeded by major barriers such as the variety of impact categories and sub-categories, the lacking integration of the Sustainable Development Goals (SDGs), issues of linking LCA structures to social phenomena or the difficult tracking of social impact pathways. Further research on standardization possibilities, the connection to political social targets and the testing of methods is necessary to overcome current barriers and increase the applicability and interpretability results.
The Role of Citizen Science and Deep Learning in Camera Trapping
Authors: Adam Matyáš , Tomášek Pavel , Lehejček Jiří , Trojan Jakub , Jůnek Tomáš
Published: September 2021

Camera traps are increasingly one of the fundamental pillars of environmental monitoring and management. Even outside the scientific community, thousands of camera traps in the hands of citizens may offer valuable data on terrestrial vertebrate fauna, bycatch data in particular, when guided according to already employed standards. This provides a promising setting for Citizen Science initiatives. Here, we suggest a possible pathway for isolated observations to be aggregated into a single database that respects the existing standards (with a proposed extension). Our approach aims to show a new perspective and to update the recent progress in engaging the enthusiasm of citizen scientists and in including machine learning processes into image classification in camera trap research. This approach (combining machine learning and the input from citizen scientists) may significantly assist in streamlining the processing of camera trap data while simultaneously raising public environmental awareness. We have thus developed a conceptual framework and analytical concept for a web-based camera trap database, incorporating the above-mentioned aspects that respect a combination of the roles of experts’ and citizens’ evaluations, the way of training a neural network and adding a taxon complexity index. This initiative could well serve scientists and the general public, as well as assisting public authorities to efficiently set spatially and temporarily well-targeted conservation policies.
The Genesis and Performance of Gender Focal Person Structures in Rwanda and Uganda National Agricultural Organisations: A Critique
Authors: Mangheni Margaret Najjingo , Musiimenta Peace , Boonabaana Brenda , Tufan Hale Ann
Published: September 2021

Gender Focal Persons (GFPs) are commonly recruited by organisations as part of institutional efforts to mainstream gender. Despite their wide usage, these structures often struggle to achieve the intended goals. The underlying factors that explain their limited success are not well understood; yet, this would inform strategies for institutionalising gender in research institutions. This paper traces the genesis and operationalisation of the gender focal person structures in Rwanda and Uganda national agricultural research organisations, to unearth factors influencing their performance. Results presented are based on document reviews and qualitative interviews with scientists, managers, and GFPs in the two organisations. We found that the GFPs hinged on individuals and donors, and operated in an ad hoc manner without streamlined procedures. The structures were not embedded in institutional frameworks, hence their low visibility within the organisations. They were characterized by informality, voluntarism, unclear terms of reference, and accountability frameworks. We conclude that the ineffective performance of the GFPs in both organisations is explained by the informal approach used to establish, operationalise, and nurture them. Institutionalising the structure would require that the pioneering champions and donors successfully negotiate the embedding of GFPs into the mainstream; eventually, guaranteeing allocation of adequate human and financial resources from national budgets, as well as the establishment of accountability systems.