bluuebunny/crossref_metadata_2025 huggingface dataset:
Hydrothermal Modification of Wood: A Review
Authors: Ali Md. Rowson , Abdullah Ummi Hani , Ashaari Zaidon , Hamid Norul Hisham , Hua Lee Seng
Published: August 2021

Wood is a versatile material that is used for various purposes due to its good properties, such as its aesthetic properties, acoustic properties, mechanical properties, thermal properties, etc. Its poor dimensional stability and low natural durability are the main obstacles that limit its use in mechanical applications. Therefore, modification is needed to improve these properties. The hydrothermal modification of wood exposes wood samples to elevated temperatures and pressure levels by using steam, water, or a buffer solution as the treating medium, or by using superheated steam. Abundant studies regarding hydrothermally treated wood were carried out, but the negative effect on the wood’s strength is one of the limitations. This is a method that boosts the dimensional stability and improves the decay resistance of wood with minimal decrements of the strength properties. As an ecofriendly and cost-effective method, the hydrothermal modification of wood is also a promising alternative to conventional chemical techniques for treating wood. Researchers are attracted to the hydrothermal modification process because of its unique qualities in treating wood. There are many scientific articles on the hydrothermal modification of wood, and many aspects of hydrothermal modification are summarized in review papers in this field. This paper reviews the hydrothermally modified mechanical properties of wood and their potential applications. Furthermore, this article reviews the effects of hydrothermal modification on the various properties of wood, such as the dimensional stability, chemical properties, and durability against termites and fungi. The merits and demerits of hydrothermal wood modification, the effectiveness of using different media in hydrothermal modification, and its comparison with other treating techniques are discussed.
Multidirectional Overground Robotic Training Leads to Improvements in Balance in Older Adults
Authors: Thompson Lara A. , Badache Mehdi , Brusamolin Joao Augusto Renno , Savadkoohi Marzieh , Guise Jelani , Paiva Gabriel Velluto de , Suh Pius , Sanchez Guerrero Pablo , Shetty Devdas
Published: August 2021

For the rapidly growing aging demographic worldwide, robotic training methods could be impactful towards improving balance critical for everyday life. Here, we investigated the hypothesis that non-bodyweight supportive (nBWS) overground robotic balance training would lead to improvements in balance performance and balance confidence in older adults. Sixteen healthy older participants (69.7 ± 6.7 years old) were trained while donning a harness from a distinctive NaviGAITor robotic system. A control group of 11 healthy participants (68.7 ± 5.0 years old) underwent the same training but without the robotic system. Training included 6 weeks of standing and walking tasks while modifying: (1) sensory information (i.e., with and without vision (eyes-open/closed), with more and fewer support surface cues (hard or foam surfaces)) and (2) base-of-support (wide, tandem and single-leg standing exercises). Prior to and post-training, balance ability and balance confidence were assessed via the balance error scoring system (BESS) and the Activities specific Balance Confidence (ABC) scale, respectively. Encouragingly, results showed that balance ability improved (i.e., BESS errors significantly decreased), particularly in the nBWS group, across nearly all test conditions. This result serves as an indication that robotic training has an impact on improving balance for healthy aging individuals.
A New Method for Automated Measurement of Sand Dune Migration Based on Multi-Temporal LiDAR-Derived Digital Elevation Models
Authors: Dong Pinliang , Xia Jisheng , Zhong Ruofei , Zhao Zhifang , Tan Shucheng
Published: August 2021

While remote sensing methods have long been used for coastal and desert sand dune studies, few methods have been developed for the automated measurement of dune migration in large dune fields. To overcome a major limitation of an existing method named “pairs of source and target points (PSTP)”, this paper proposes a toe line tracking (TLT) method for the automated measurement of dune migration rate and direction using multi-temporal digital elevation models (DEM) derived from light detection and ranging (LiDAR) data. Based on a few simple parameters, the TLT method automatically extracts the base level of a dune field and toe lines of individual dunes. The toe line polygons derived from two DEMs are processed using logical operators and other spatial analysis methods implemented in the Python programming language in a geographic information system. By generating thousands of random sampling points along source toe lines, dune migration distances and directions are calculated and saved with the sampling point feature class. The application of the TLT method was demonstrated using multi-temporal LiDAR-derived DEMs for a 9 km by 2.4 km area in the White Sands Dune Field in New Mexico (USA). Dune migration distances and directions for three periods (24 January 2009–26 September 2009, 26 September 2009–6 June 2010, and 24 January 2009–6 January 2010) were calculated. Sensitivity analyses were carried out using different window sizes and toe heights. The results suggest that both PSTP and TLT produce similar sand dune migration rates and directions, but TLT is a more generic method that works for dunes with or without slipfaces that reach the angle of repose.
Mechanism of Land Subsidence Mutation in Beijing Plain under the Background of Urban Expansion
Authors: Guo Lin , Gong Huili , Ke Yinghai , Zhu Lin , Li Xiaojuan , Lyu Mingyuan , Zhang Ke
Published: August 2021

Under the background of over-exploitation of groundwater and urban expansion, the land subsidence in the Beijing Plain has dramatically increased recently, and has demonstrated obvious mutation characteristics. Firstly, this paper used the land-use transfer matrix (LUTM) to quantify the urban expansion of Beijing, from 1990 to 2015. Secondly, the gravity center migration model (GCM) and standard deviation ellipse (SDE) methods were employed in order to quantitatively reveal the response relationship between urban expansion and land subsidence in the study area. Finally, the research innovatively combines multi-disciplinary (remote sensing, geophysical prospecting, spatial analysis, and hydrogeology), to analyze the mechanism of land subsidence mutation in the Beijing Plain, at multiple scales. The results showed the following: 1. The development direction of the urban expansion and the subsidence bowl (subsidence rate > 50 mm/year) were highly consistent, with values of 116.8° and 113.3°, respectively. 2. At the regional scale, the overall spatial distribution of subsidence mutations is controlled by the geological conditions, and the subsidence mutation time was mainly in 2005 and 2015. The area where mutation occurred in 2005 was basically located in the subsidence bowls, and the correlation between the confined water level and the subsidence rate was relatively high (r > 0.62). The area where the settlement mutation occurred in 2015, was mainly located outside the subsidence bowls, and the correlation between the confined water level and the subsidence rate was relatively low (r < 0.71). 3. In the typical subsidence area, the subsidence mutation occurred mostly in the places where the stratigraphic density is reduced, due to human activities (such as groundwater exploitation). Human activities caused the reduction in stratigraphic density, at 20 m and 90 m vertical depth in urban and rural areas, respectively. 4. At the local scale, clusters of subsidence mutation were located in the fault buffer zone, with a lateral influence range of nearly 1 km in Tongzhou. The scattered settlement mutation is distributed as a spot pattern, and the affected area is relatively small, which basically includes high-rise buildings.
Use of Multiplatform SAR Imagery in Mining Deformation Monitoring with Dense Vegetation Coverage: A Case Study in the Fengfeng Mining Area, China
Authors: Zhang Bochen , Wu Songbo , Ding Xiaoli , Wang Chisheng , Zhu Jiasong , Li Qingquan
Published: August 2021

Ground deformation related to mining activities may occur immediately or many years later, leading to a series of mine geological disasters, such as ground fissures, collapses, and even mining earthquakes. Deformation monitoring has been carried out with techniques, such as multitemporal interferometric synthetic aperture radar (MTInSAR). Over the past decade, MTInSAR has been widely used in monitoring mining deformation, and it is still difficult to retrieve mining deformation over dense vegetation areas. In this study, we use multiple-platform SAR images to retrieve mining deformation over dense vegetation areas. The high-quality interferograms are selected by the coherence map, and the mining deformation is retrieved by the MSBAS-InSAR technique. SAR images from TerraSAR-X, Sentinel-1A, Radarsat-2, and PALSAR-2 over the Fengfeng mining area, Heibei, China, are used to retrieve the deformation of mining activities covered with dense vegetation. The results show that the subsidence in the Fengfeng mining area reaches up to 90 cm over the period from July 2015 to April 2016. The root-mean-square error (RMSE) between the results from InSAR and leveling is 83.5 mm/yr at two mining sites, i.e., Wannian and Jiulong Mines.
Automatic Generation of Seamless Mosaics Using Invariant Features
Authors: Manandhar Prajowal , Jalil Ahmad , AlHashmi Khaled , Marpu Prashanth
Published: August 2021

The acquisition of satellite images over a wide area is often carried out across seasons because of satellite orbits and atmospheric conditions (e.g., cloud cover, dust, etc.). This results in spectral mismatch between adjacent scenes as the sun angle and the atmospheric conditions will be different for different acquisitions. In this work, we developed an approach to generate seamless mosaics using Scale-Invariant Features Transformation (SIFT). In this process, we make use of the overlapping areas between two adjacent scenes and then map spectral values of one imagery scene to another based on the filtered points detected by SIFT features to create a seamless mosaic. We make use of the Random Sample Consensus (RANSAC) method successively to filter out obtained SIFT points across adjacent tiles and to remove spectral outliers across each band of an image. Several high resolution satellite images acquired with WorldView-2 and Dubaisat-2 satellites, and medium resolution Sentinel-2 satellite imagery are used for experimentation. The experimental results show that the proposed approach can generate good seamless mosaics. Furthermore, Sentinel-2’s level 2A (L2A) product surface reflectance data is used to adjust the spectral values for color consistency.
Outdoor Mobile Mapping and AI-Based 3D Object Detection with Low-Cost RGB-D Cameras: The Use Case of On-Street Parking Statistics
Authors: Nebiker Stephan , Meyer Jonas , Blaser Stefan , Ammann Manuela , Rhyner Severin
Published: August 2021

A successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking.
An Attention-Guided Multilayer Feature Aggregation Network for Remote Sensing Image Scene Classification
Authors: Li Ming , Lei Lin , Tang Yuqi , Sun Yuli , Kuang Gangyao
Published: August 2021

Remote sensing image scene classification (RSISC) has broad application prospects, but related challenges still exist and urgently need to be addressed. One of the most important challenges is how to learn a strong discriminative scene representation. Recently, convolutional neural networks (CNNs) have shown great potential in RSISC due to their powerful feature learning ability; however, their performance may be restricted by the complexity of remote sensing images, such as spatial layout, varying scales, complex backgrounds, category diversity, etc. In this paper, we propose an attention-guided multilayer feature aggregation network (AGMFA-Net) that attempts to improve the scene classification performance by effectively aggregating features from different layers. Specifically, to reduce the discrepancies between different layers, we employed the channel–spatial attention on multiple high-level convolutional feature maps to capture more accurately semantic regions that correspond to the content of the given scene. Then, we utilized the learned semantic regions as guidance to aggregate the valuable information from multilayer convolutional features, so as to achieve stronger scene features for classification. Experimental results on three remote sensing scene datasets indicated that our approach achieved competitive classification performance in comparison to the baselines and other state-of-the-art methods.
Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques
Authors: Yue Yaru , Chen Chengdong , Liu Pengkun , Xing Ying , Zhou Xiaoguang
Published: August 2021

Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.
Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network
Authors: Ren Rui , Zhang Shujuan , Sun Haixia , Gao Tingyao
Published: August 2021

A pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation, luminance switch, and contrast ratio switch. To improve training speed and precision, a network model was optimized with a fine-tuned VGG 16 model in this research, transfer learning was applied after parameter optimization, and comparative analysis was performed by combining ResNet50, MobileNet V2, and GoogLeNet models. It turned out that the VGG 16 model output anticipation precision was 98.14%, and the prediction loss rate was 0.0669 when the dropout was settled as 0.3, learning rate settled as 0.000001, batch normalization added, and ReLU as activation function. Comparing with other finetune models and network models, this model was of better anticipation performance, as well as faster and more stable convergence rate, which embodied the best performance. Considering the basis of transfer learning and integration with strong generalization and fitting capacity of the VGG 16 finetune model, it is feasible to apply this model to the external quality classification of pepper, thus offering technical reference for further realizing the automatic classification of pepper quality.
Nd3+-Doped TiO2 Nanoparticles as Nanothermometer: High Sensitivity in Temperature Evaluation inside Biological Windows
Authors: Acosta Selene , Borrero-González Luis J. , Umek Polona , Nunes Luiz A. O. , Guttmann Peter , Bittencourt Carla
Published: August 2021

TiO2 nanoparticles doped with different amounts of Nd3+ (0.5, 1, and 3 wt.%) were synthetized by the sol–gel method, and evaluated as potential temperature nanoprobes using the fluorescence intensity ratio between thermal-sensitive radiative transitions of the Nd3+. XRD characterization identified the anatase phase in all the doped samples. The morphology of the nanoparticles was observed with SEM, TEM and HRTEM microscopies. The relative amount of Nd3+ in TiO2 was obtained by EDXS, and the oxidation state of titanium and neodymium was investigated via XPS and NEXAFS, respectively. Nd3+ was present in all the samples, unlike titanium, where besides Ti4+, a significantly amount of Ti3+ was observed; the relative concentration of Ti3+ increased as the amount of Nd3+ in the TiO2 nanoparticles increased. The photoluminescence of the synthetized nanoparticles was investigated, with excitation wavelengths of 350, 514 and 600 nm. The emission intensity of the broad band that was associated with the presence of defects in the TiO2, increased when the concentration of Nd3+ was increased. Using 600 nm for excitation, the 4F7/2→4I9/2, 4F5/2→4I9/2 and 4F3/2→4I9/2 transitions of Nd3+ ions, centered at 760 nm, 821 nm, and 880 nm, respectively, were observed. Finally, the effect of temperature in the photoluminescence intensity of the synthetized nanoparticles was investigated, with an excitation wavelength of 600 nm. The spectra were collected in the 288–348 K range. For increasing temperatures, the emission intensity of the 4F7/2→4I9/2 and 4F5/2→4I9/2 transitions increased significantly, in contrast to the 4F3/2→4I9/2 transition, in which the intensity emission decreased. The fluorescence intensity ratio between the transitions I821I880=F5/24I49/2F43/2I49/2 and I760I880=F47/2I49/2F43/2I49/2 were used to calculate the relative sensitivity of the sensors. The relative sensitivity was near 3% K−1 for I760I880 and near 1% K−1 for I821I880.
Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information at the Edge
Authors: Attallah Nour A. , Horsburgh Jeffery S. , Beckwith Arle S. , Tracy Robb J.
Published: August 2021

We present a new, open source, computationally capable datalogger for collecting and analyzing high temporal resolution residential water use data. Using this device, execution of water end use disaggregation algorithms or other data analytics can be performed directly on existing, analog residential water meters without disrupting their operation, effectively transforming existing water meters into smart, edge computing devices. Computation of water use summaries and classified water end use events directly on the meter minimizes data transmission requirements, reduces requirements for centralized data storage and processing, and reduces latency between data collection and generation of decision-relevant information. The datalogger couples an Arduino microcontroller board for data acquisition with a Raspberry Pi computer that serves as a computational resource. The computational node was developed and calibrated at the Utah Water Research Laboratory (UWRL) and was deployed for testing on the water meter for a single-family residential home in Providence City, UT, USA. Results from field deployments are presented to demonstrate the data collection accuracy, computational functionality, power requirements, communication capabilities, and applicability of the system. The computational node’s hardware design and software are open source, available for potential reuse, and can be adapted to specific research needs.
A Lightweight Fusion Distillation Network for Image Deblurring and Deraining
Authors: Zhang Yanni , Liu Yiming , Li Qiang , Wang Jianzhong , Qi Miao , Sun Hui , Xu Hui , Kong Jun
Published: August 2021

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.
IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering
Authors: Metia Santanu , Nguyen Huynh A. D. , Ha Quang Phuc
Published: August 2021

This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019–2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.
Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection
Authors: Lin Feng-Cheng , Ngo Huu-Huy , Dow Chyi-Ren , Lam Ka-Hou , Le Hung Linh
Published: August 2021

Human action recognition has attracted considerable research attention in the field of computer vision, especially for classroom environments. However, most relevant studies have focused on one specific behavior of students. Therefore, this paper proposes a student behavior recognition system based on skeleton pose estimation and person detection. First, consecutive frames captured with a classroom camera were used as the input images of the proposed system. Then, skeleton data were collected using the OpenPose framework. An error correction scheme was proposed based on the pose estimation and person detection techniques to decrease incorrect connections in the skeleton data. The preprocessed skeleton data were subsequently used to eliminate several joints that had a weak effect on behavior classification. Second, feature extraction was performed to generate feature vectors that represent human postures. The adopted features included normalized joint locations, joint distances, and bone angles. Finally, behavior classification was conducted to recognize student behaviors. A deep neural network was constructed to classify actions, and the proposed system was able to identify the number of students in a classroom. Moreover, a system prototype was implemented to verify the feasibility of the proposed system. The experimental results indicated that the proposed scheme outperformed the skeleton-based scheme in complex situations. The proposed system had a 15.15% higher average precision and 12.15% higher average recall than the skeleton-based scheme did.
Incorporating Interpersonal Synchronization Features for Automatic Emotion Recognition from Visual and Audio Data during Communication
Authors: Quan Jingyu , Miyake Yoshihiro , Nozawa Takayuki
Published: August 2021

During social interaction, humans recognize others’ emotions via individual features and interpersonal features. However, most previous automatic emotion recognition techniques only used individual features—they have not tested the importance of interpersonal features. In the present study, we asked whether interpersonal features, especially time-lagged synchronization features, are beneficial to the performance of automatic emotion recognition techniques. We explored this question in the main experiment (speaker-dependent emotion recognition) and supplementary experiment (speaker-independent emotion recognition) by building an individual framework and interpersonal framework in visual, audio, and cross-modality, respectively. Our main experiment results showed that the interpersonal framework outperformed the individual framework in every modality. Our supplementary experiment showed—even for unknown communication pairs—that the interpersonal framework led to a better performance. Therefore, we concluded that interpersonal features are useful to boost the performance of automatic emotion recognition tasks. We hope to raise attention to interpersonal features in this study.
Optimal Deployment of Charging Stations for Aerial Surveillance by UAVs with the Assistance of Public Transportation Vehicles
Authors: Huang Hailong , Savkin Andrey V.
Published: August 2021

To overcome the limitation in flight time and enable unmanned aerial vehicles (UAVs) to survey remote sites of interest, this paper investigates an approach involving the collaboration with public transportation vehicles (PTVs) and the deployment of charging stations. In particular, the focus of this paper is on the deployment of charging stations. In this approach, a UAV first travels with some PTVs, and then flies through some charging stations to reach remote sites. While the travel time with PTVs can be estimated by the Monte Carlo method to accommodate various uncertainties, we propose a new coverage model to compute the travel time taken for UAVs to reach the sites. With this model, we formulate the optimal deployment problem with the goal of minimising the average travel time of UAVs from the depot to the sites, which can be regarded as a reflection of the quality of surveillance (QoS) (the shorter the better). We then propose an iterative algorithm to place the charging stations. We show that this algorithm ensures that any movement of a charging station leads to a decrease in the average travel time of UAVs. To demonstrate the effectiveness of the proposed method, we make a comparison with a baseline method. The results show that the proposed model can more accurately estimate the travel time than the most commonly used model, and the proposed algorithm can relocate the charging stations to achieve a lower flight distance than the baseline method.
AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment
Authors: Ramalingam Balakrishnan , Tun Thein , Mohan Rajesh Elara , Gómez Braulio Félix , Cheng Ruoxi , Balakrishnan Selvasundari , Mohan Rayaguru Madan , Hayat Abdullah Aamir
Published: August 2021

Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called “Falcon”. The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level.
Co-Created Values in Crowdfunding for Sustainable Development of Enterprises
Authors: Kukurba Maria , Waszkiewicz Aneta Ewa , Salwin Mariusz , Kraslawski Andrzej
Published: August 2021

Crowdfunding (CF) is considered to be an innovative source of funding, and research into its effects on CF participants is being conducted from many different angles. In our article, we propose a qualitative analysis of CF’s impact on creating added value for small and medium enterprises (SMEs). This paper is a conceptual study based on the theory of value co-creation, the Norton–Kaplan value model, and the concept of economic value added (EVA) to identify the areas (perspectives) of business activity where crowdfunding may stimulate the growth of company value. Based on the map of co-relations in crowdfunding that we have created, we propose a new model of crowdfunding value analysis from the entrepreneur’s point of view. Our research demonstrates that crowdfunding not only presents an alternative financing option, but also affects all perspectives relevant to value creation. As compared to the existing literature, this analysis is the most comprehensive take on the importance of crowdfunding for increasing the value of small and medium enterprises to date, thus offering a material contribution to the fuller understanding of crowdfunding from the financial standpoint, as well as pointing to the importance of crowdfunding as a financing method influencing sustainable decision-making by small and medium enterprises (SMEs). The added value of the study is the proposed method of analysing the impact of CF on the value of enterprises by using the Norton–Kaplan model and EVA.
Effects of Acute-Partial Sleep Deprivation on High-Intensity Exercise Performance and Cardiac Autonomic Activity in Healthy Adolescents
Authors: Zhang Yue , Liang Andi , Song Jing , Zhang Yan , Niu Xiaodan , Xiao Tao , Chi Aiping
Published: August 2021

Performing high-intensity exercise (HIE) in the morning under sleep deprivation may harm the health benefits related to sufficient sleep and HIE. Therefore, the aim of this study was to explore the effects of acute-partial sleep deprivation on HIE performance and cardiac autonomic activity by monitoring heart rate variability (HRV) indices. Twenty-nine healthy male adolescents in college were recruited to perform a one-time HIE session on the treadmill (Bruce protocol) after ≥7 h of normal control sleep (control) and after ≤4 h of acute-partial sleep deprivation (SD). At the beginning of control and SD periods and after exercising under the two sleep conditions, heart rate (HR), standard deviation of normal to normal (SDNN), square root of the mean squared differences of successive NN intervals (RMSSD), normalized low frequency power (LFn), normalized high frequency power (HFn), number of pairs adjacent NN intervals differing by ≥50 ms in the entire recording count divided by the total number of all NN intervals (pNN50), and short axis and long axis value in Poincaré plot (SD1 and SD2) were measured at rest in an upright sitting position. The participants slept 7.63 ± 0.52 and 3.78 ± 0.69 h during control and SD periods, respectively (p < 0.001). Compared with the control participants, those suffering sleep deprivation experienced a significant decrease in exercise duration, RMSSD, HFn, SD1, and pNN50 as well as a significant increase in maximum heart rate during exercise (p < 0.05). SDNN, RMSSD, HFn, SD1, and pNN50 decreased significantly after exercise (p < 0.05 and 0.01 and 0.001, respectively). In summary, acute-partial sleep deprivation affected aerobic exercise performance the next morning and led to decreased cardiac vagus activity and cardiac autonomic dysfunction.
Intercomparison of Indoor and Outdoor Pollen Concentrations in Rural and Suburban Research Workplaces
Authors: Pelliccioni Armando , Ciardini Virginia , Lancia Andrea , Di Renzi Simona , Brighetti Maria Antonia , Travaglini Alessandro , Capone Pasquale , D’Ovidio Maria Concetta
Published: August 2021

Pollen exposure in occupational settings involves different categories of workers. In this paper the effects of diurnal pollen variations have been evaluated in two sites characterized by different vegetation and urbanization: the suburban site of Tor Vergata (TV) and the rural site of Monte Porzio Catone (MPC). Aerobiological and meteorological monitoring was performed in the two sites during the winter of 2017. The data analysis focuses on the comparison between pollen concentrations observed in relation to meteorological variables. In general, it can be stated that the indoor and outdoor dynamics for MPC and TV are different, with the outdoor concentration of pollen for MPC always higher than for TV, in accordance with significant presence of vegetation. The high nocturnal peaks detected in MPC and completely absent in TV could be caused by the presence of particular conditions of stagnation combined with greater emissions from the pollen sources. Furthermore the higher I/O ratio observed during the working hours in TV compared to MPC could be ascribed to the workers’ behavior. Exposure to pollen can be responsible for several health effects and the knowledge of its level can be useful to improve the evaluation and management of this biological risk.
Are Political Factors More Relevant Than Economic Factors in Firm-Level Renewable Energy Technology Export? Evidence from Path Analysis
Authors: Sung Bongsuk , Song Woo-Yong
Published: August 2021

Renewable energy technology (RET) firms are key economic entities in the export of RET-related products and components, in which RET firms’ exports are affected largely by policy and market. Nonetheless, the effects of policy and market factors on RET firm-level export have never received attention from researchers. This study aims to fill the gap by taking a political economy approach to establish a structural equation model to analyze the path of political-economic factor-firms’ market orientation-based export. This study reveals that RET firms’ market-orientation-based export enhancement depends entirely on political forces. Particularly, two government intervention instruments, environmental policy and export promotion policy, were highlighted. However, the effects of renewable energy policies on the exports of RET firms through market orientation are negative and statistically significant. This study proves that the effects of inter-firm competition and market attractiveness on RET firms’ exports through their market orientation are negative and statistically significant and insignificant, respectively. Further, this study demonstrates that RET firms’ market orientation has a significant positive effect on their export performance. We conclude that in order to improve RET-related policy effectiveness and efficiency, it will be useful to consider firms’ heterogeneity in response to external factors. Additionally, a full mediation model in the academic investigation of the effects of various external factors, including public policies and market factors, on firm-level export, and the implementation of firm-level export-induced policy, taking into account firms’ managerial interpretations to external factors, should be considered.
Effect of Climate Changes on Renewable Production in the Mediterranean Climate: Case Study of the Energy Retrofit for a Detached House
Authors: De Masi Rosa Francesca , Festa Valentino , Gigante Antonio , Mastellone Margherita , Ruggiero Silvia , Vanoli Giuseppe Peter
Published: August 2021

One of the strategies of the European Green Deal is the increment of renewable integration in the civil sector and the mitigation of the impact of climate change. With a statistical and critical approach, the paper analyzes these aspects by means of a case study simulated in a cooling dominated climate. It consists of a single-family house representative of the 1980s Italian building stock. Starting from data monitored between 2015 and 2020, a weather file was built with different methodologies. The first objective was the evaluation of how the method for selecting the solar radiation influences the prevision of photovoltaic productivity. Then, a sensitivity analysis was developed, by means of modified weather files according to representative pathways defined by the Intergovernmental Panel on Climate Change Fifth Assessment Report. The results indicate that the climate changes will bring an increment of photovoltaic productivity while the heating energy need will be reduced until 45% (e.g., in March) and the cooling energy need will be more than double compared with the current conditions. The traditional efficiency measures are not resilient because the increase of the cooling demand could be not balanced. The maximization of installed photovoltaic power is a solution for increasing the resilience. Indeed, going from 3.3 kWp to 6.9 kWp for the worst emission scenario, in a typical summer month (e.g., August), the self-consumption increases until 33% meanwhile the imported electricity passes from 28% to 17%.
Academic Tourism and Transport Services: Student Perceptions from a Social Responsibility Perspective
Authors: Veloso Cláudia Miranda , Walter Cícero Eduardo , Sousa Bruno , Au-Yong-Oliveira Manuel , Santos Vasco , Valeri Marco
Published: August 2021

Firms aim at achieving their economic interests without neglecting the needs of local and regional communities and the environment itself. The process involves defining and implementing Social Responsibility strategies that leverage organizational competitiveness and sustainability. In this context, the current study purposes to demonstrate how Corporate Social Responsibility (CSR) and a sustainable orientation influence the satisfaction and loyalty of academic tourists to the Metro of Porto (in Portugal). More specifically, our study aims to assess the satisfaction of academic tourists with the Metro of Porto and, additionally, to analyze the variables that determine the satisfaction and loyalty of these tourists to this specific transport company, which has never been achieved before. Our proposed research model seeks to determine how CSR and a sustainable orientation influence academic tourists’ loyalty to the Metro of Porto transport services as well as their link to Service Quality, Perceived Value, Corporate Image and Tourist Satisfaction. From a sample of 256 academic tourists, customers of the Metro of Porto, a quantitative analysis was carried out using the structural equation model (SEM-PLS). The results obtained allow us to conclude that CSR and Service Quality have an impact on academic tourists’ satisfaction and loyalty to the Metro of Porto. The development of a region is intrinsically linked to transport networks that promote the integration and growth of productive systems. Our results demonstrate the relationship between regional development and business sustainability strategies that integrate Social Responsibility practices. We show the importance that Social Responsibility policies have for companies as a precursor to customer satisfaction and loyalty to a company. Hence, as a guarantee of obtaining economic results, firms must simultaneously contribute to the sustainable development of territories.
SmartISM: Implementation and Assessment of Interpretive Structural Modeling
Authors: Ahmad Naim , Qahmash Ayman
Published: August 2021

Interpretive Structural Modeling (ISM) is a technique to establish the interrelationships between elements of interest in a specific domain through experts’ knowledge of the context of the elements. This technique has been applied in numerous domains and the list continues to grow due to its simplistic concept, while sustainability has taken the lead. The partially automated or manual application of this technique has been prone to errors as witnessed in the literature due to a series of mathematical steps of higher-order computing complexity. Therefore, this work proposes to develop an end-to-end graphical software, SmartISM, to implement ISM technique and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)), generally applied along with ISM to classify variables. Further, a scoping review has been conducted to study the applications of ISM in the previous studies using Denyer and Tranfield’s (2009) framework and newly developed SmartISM. For the development of SmartISM, Microsoft Excel software has been used, and relevant algorithms and VBA (Visual Basic for Applications) functions have been illustrated. For the transitivity calculation the Warshall algorithm has been used and a new algorithm reduced conical matrix has been introduced to remove edges while retaining the reachability of variables and structure of digraph in the final model. The scoping review results demonstrate 21 different domains such as sustainability, supply chain and logistics, information technology, energy, human resource, marketing, and operations among others; numerous types of constructs such as enablers, barriers, critical success factors, strategies, practices, among others, and their numbers varied from 5 to 32; number of decision makers ranged between 2 to 120 with a median value of 11, and belong to academia, industry, and/or government; and usage of multiple techniques of discourse and survey for decision making and data collection. Furthermore, the SmartISM reproduced results show that only 29 out of 77 studies selected have a correct application of ISM after discounting the generalized transitivity incorporation. The outcome of this work will help in more informed applications of this technique in newer domains and utilization of SmartISM to efficiently model the interrelationships among variables.
Lensless Reflection Imaging of Obliquely Illuminated Objects I: Choosing a Domain for Phase Retrieval and Ptychography
Authors: Artyukov Igor A. , Popov Nikolay L. , Vinogradov Alexander V.
Published: August 2021

Ptychography is a lensless imaging technology that is validated from hard X-rays to terahertz spectral range. It is most attractive for extreme ultraviolet (EUV) and X-rays as optical elements are expensive and often not available. Typically, the set up involves coherently illuminated object that directs the scattered radiation normally to detector which is parallel to the object plane. Computer processing of diffraction patterns obtained when scanning the object gives the image, more precisely, the distribution of intensity and phase on its surface. However, this scheme is inefficient for EUV and X-rays due to poor reflectivity and low penetration in all materials. Reflection mode ptychography solves the problem if illumination angles do not exceed the critical angle of object material. Changing the geometry of experiment changes physical and mathematical model of image formation. Including: diffraction integral describing beam propagation from object to detector, inverse problem, optimization of object illumination angle, position and orientation of detector, choosing size and grid of coordinate and frequency computer domains. This paper considers the wavefield scattered to detector by obliquely illuminated object and determines a domain for processing of obtained scans. Solution of inverse problem with phase retrieval and resulting numerical images will be presented in the next paper.
Structure and Interdigitation of Chain-Asymmetric Phosphatidylcholines and Milk Sphingomyelin in the Fluid Phase
Authors: Frewein Moritz P. K. , Doktorova Milka , Heberle Frederick A. , Scott Haden L. , Semeraro Enrico F. , Porcar Lionel , Pabst Georg
Published: August 2021

We addressed the frequent occurrence of mixed-chain lipids in biological membranes and their impact on membrane structure by studying several chain-asymmetric phosphatidylcholines and the highly asymmetric milk sphingomyelin. Specifically, we report trans-membrane structures of the corresponding fluid lamellar phases using small-angle X-ray and neutron scattering, which were jointly analyzed in terms of a membrane composition-specific model, including a headgroup hydration shell. Focusing on terminal methyl groups at the bilayer center, we found a linear relation between hydrocarbon chain length mismatch and the methyl-overlap for phosphatidylcholines, and a non-negligible impact of the glycerol backbone-tilting, letting the sn1-chain penetrate deeper into the opposing leaflet by half a CH2 group. That is, penetration-depth differences due to the ester-linked hydrocarbons at the glycerol backbone, previously reported for gel phase structures, also extend to the more relevant physiological fluid phase, but are significantly reduced. Moreover, milk sphingomyelin was found to follow the same linear relationship suggesting a similar tilt of the sphingosine backbone. Complementarily performed molecular dynamics simulations revealed that there is always a part of the lipid tails bending back, even if there is a high interdigitation with the opposing chains. The extent of this back-bending was similar to that in chain symmetric bilayers. For both cases of adaptation to chain length mismatch, chain-asymmetry has a large impact on hydrocarbon chain ordering, inducing disorder in the longer of the two hydrocarbons.
Coagulopathy in Acute Puumala Hantavirus Infection
Authors: Koskela Sirpa , Mäkelä Satu , Strandin Tomas , Vaheri Antti , Outinen Tuula , Joutsi-Korhonen Lotta , Pörsti Ilkka , Mustonen Jukka , Laine Outi
Published: August 2021

Puumala hantavirus (PUUV) causes a hemorrhagic fever with renal syndrome (HFRS), also called nephropathia epidemica (NE), which is mainly endemic in Europe and Russia. The clinical features include a low platelet count, altered coagulation, endothelial activation, and acute kidney injury (AKI). Multiple connections between coagulation pathways and inflammatory mediators, as well as complement and kallikrein–kinin systems, have been reported. The bleeding symptoms are usually mild. PUUV-infected patients also have an increased risk for disseminated intravascular coagulation (DIC) and thrombosis.