bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Temporal Analysis of Changes of the Southern Part of the Baltic Sea Coast Using Aerial Remote Sensing Data
Authors: Michałowska Krystyna , Głowienka Ewa
Published: March 2022

Understanding processes that affect changes in the coastal zone and the ability to predict these processes in the future depends on the period for which detailed monitoring is carried out and on the type of coast. This paper analyzes a southern fragment of the Baltic coast (30 km), where there has been no anthropogenic impact (Slowinski National Park). The study was carried out covering a time interval of 65 years. Historic and current aerial photographs (orthophotomaps) from the following years: 1951, 1964, 1975, 1984, 1995, 2004, and 2016 were used. Changes in the position of the foredune toe line (FTL) in each years’ images were used. For each time interval (1951–1964, 1964–1975, 1975–1984, 1984–1995, 1995–2004, 2004–2016), the coastal area over which morphodynamic processes (erosion and accumulation) took place was calculated. The calculated RL (reference line)—FTL positions allowed us to determine differences in the shoreline course in subsequent years and to determine the extent of shifts/changes of the coastline in each time period. The study results showed an equilibrium between the processes of accumulation and erosion, proving that the development of the studied natural coastline is balanced. There was only a change in the trend of the characteristics of changes from erosive into accumulative ones and vice versa. Moreover, along the studied coast section, a certain periodicity in the coastline changes can be observed. The intervals where predominant erosion occurs alternate with those when accumulation prevails, and then the cycle repeats. The analysis of historic/current aerial images and orthophotomaps from 1951–2016 indicated that strong storms have a significant impact on the magnitude of change (accumulation/erosion) and the formation of the studied coastline.
On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition
Authors: Lee Gin Chong , Loo Chu Kiong
Published: March 2022

This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo State Network (ESN) input and reservoir weights, in the context of human action recognition (HAR). To ensure stability and echo state property in the reservoir, Recurrent Plots (RPs) and Recurrence Quantification Analysis (RQA) techniques are exploited for explainability and characterization of the reservoir dynamics and hence tuning ESN hyperparameters. The optimized self-organizing reservoirs are cascaded with a Convolutional Neural Network (CNN) to ensure that the activation of internal echo state representations (ESRs) echoes similar topological qualities and temporal features of the input time-series, and the CNN efficiently learns the dynamics and multiscale temporal features from the ESRs for action recognition. The hyperparameter optimization (HPO) algorithms are additionally adopted to optimize the CNN stage in SO-ConvESN. Experimental results on the HAR problem using several publicly available 3D-skeleton-based action datasets demonstrate the showcasing of the RPs and RQA technique in examining the explainability of reservoir dynamics for designing stable self-organizing reservoirs and the usefulness of implementing HPOs in SO-ConvESN for the HAR task. The proposed SO-ConvESN exhibits competitive recognition accuracy.
Precipitation and Soil Moisture Spatio-Temporal Variability and Extremes over Vietnam (1981–2019): Understanding Their Links to Rice Yield
Authors: Bui Luyen K. , Awange Joseph , Vu Dinh Toan
Published: March 2022

Vietnam, one of the three leading rice producers globally, has recently seen an increased threat to its rice production emanating from climate extremes (floods and droughts). Understanding spatio-temporal variability in precipitation and soil moisture is essential for policy formulations to adapt and cope with the impacts of climate extremes on rice production in Vietnam. Adopting a higher-order statistical method of independent component analysis (ICA), this study explores the spatio-temporal variability in the Climate Hazards Group InfraRed Precipitation Station’s (CHIRPS) precipitation and the Global Land Data Assimilation System’s (GLDAS) soil moisture products. The results indicate an agreement between monthly CHIRPS precipitation and monthly GLDAS soil moisture with the wetter period over the southern and South Central Coast areas that is latter than that over the northern and North Central Coast areas. However, the spatial patterns of annual mean precipitation and soil moisture disagree, likely due to factors other than precipitation affecting the amount of moisture in the soil layers, e.g., temperature, irrigation, and drainage systems, which are inconsistent between areas. The CHIRPS Standardized Precipitation Index (SPI) is useful in capturing climate extremes, and the GLDAS Standardized Soil Moisture Index (SSI) is useful in identifying the influences of climate extremes on rice production in Vietnam. During the 2016–2018 period, there existed a reduction in the residual rice yield that was consistent with a decrease in soil moisture during the same time period.
A Novel Prototype for Safe Driving Using Embedded Smart Box System
Authors: Irsan Muhamad , Hassan Rosilah , Hasan Mohammad Khatim , Lam Meng Chun , Hussain Wan Mohd Hirwani Wan , Ibrahim Anwar Hassan , Ahmed Amjed Sid Ahmed Mohamed Sid
Published: March 2022

Every day, vehicle accidents occur and many of them might be avoided if the drivers demonstrated excellent driving without mistakes. This paper presents a novel prototype applied in a real transportation system, particularly for buses, to avoid accidents, which may involve numerous victims, and even occasionally cause death. This system consists of a wearable device and embedded system with several sensors connected via Bluetooth, similar to the Internet of Things (IoT). Wearable devices are made to monitor the driver’s heart rate and alert the driver if they are in a state of sleep deprivation to prevent any potential accidents. The embedded system includes a Global Positioning System (GPS), accelerometers, and gyroscopes attached to a Smart Box mounted on the bus. The embedded system alert function will be triggered if an accident occurs and automatically sends the geolocation of the accident to the registered phone number through a message using a mobile phone. The results for all scenarios were significant when measured by an automatic accident trigger via the smart box if the value of measured values in each axis exceeded 583. In conclusion, the implementation of this innovative solution at the system-level was shown to be satisfactory in terms of the safety mechanism used by the nominated drivers.
Identifying the Strength Level of Objects’ Tactile Attributes Using a Multi-Scale Convolutional Neural Network
Authors: Zhang Peng , Yu Guoqi , Shan Dongri , Chen Zhenxue , Wang Xiaofang
Published: March 2022

In order to solve the problem in which most currently existing research focuses on the binary tactile attributes of objects and ignores identifying the strength level of tactile attributes, this paper establishes a tactile data set of the strength level of objects’ elasticity and hardness attributes to make up for the lack of relevant data, and proposes a multi-scale convolutional neural network to identify the strength level of object attributes. The network recognizes the different attributes and identifies differences in the strength level of the same object attributes by fusing the original features, i.e., the single-channel features and multi-channel features of the data. A variety of evaluation methods were used for comparison with multiple models in terms of strength levels of elasticity and hardness. The results show that our network has a more significant effect in accuracy. In the prediction results of the positive examples in the predicted value, the true value has a higher proportion of positive examples, that is, the precision is better. The prediction effect for the positive examples in the true value is better, that is, the recall is better. Finally, the recognition rate for all classes is higher in terms of f1_score. For the overall sample, the prediction of the multi-scale convolutional neural network has a higher recognition rate and the network’s ability to recognize each strength level is more stable.
MetaMembranes for the Sensitivity Enhancement of Wearable Piezoelectric MetaSensors
Authors: Farhangdoust Saman , Georgeson Gary , Ihn Jeong-Beom
Published: March 2022

The low stretchability of plain membranes restricts the sensitivity of conventional diaphragm-based pressure and inflatable piezoelectric sensors. Using theoretical and computational tools, we characterized current limitations and explored metamaterial-inspired membranes (MetaMems) to resolve these issues. This paper develops two MetaMem pressure sensors (MPSs) to enrich the sensitivity and stretchability of the conventional sensors. Two auxetic hexagonal and kirigami honeycombs are proposed to create a negative Poisson’s ratio (NPR) in the MetaMems which enables them to expand the piezo-element of sensors in both longitudinal and transverse directions much better, and consequently provides the MPSs’ diaphragm a higher capability for flexural deformation. Polyvinylidene fluoride (PVDF) and polycarbonate (PC) are considered as the preferable materials for the piezo-element and MetaMem, respectively. A finite element analysis was conducted to investigate the stretchability behavior of the MetaMems and study its effect on the PVDF’s polarization and sensor sensitivity. The results obtained from theoretical analysis and numerical simulations demonstrate that the proposed MetaMems enhance the sensitivity of pressure sensors up to 3.8 times more than an equivalent conventional sensor with a plain membrane. This paper introduces a new class of flexible MetaMems to advance wearable piezoelectric metasensor technologies.
Wearable Sensor-Based Human Activity Recognition with Transformer Model
Authors: Dirgová Luptáková Iveta , Kubovčík Martin , Pospíchal Jiří
Published: March 2022

Computing devices that can recognize various human activities or movements can be used to assist people in healthcare, sports, or human–robot interaction. Readily available data for this purpose can be obtained from the accelerometer and the gyroscope built into everyday smartphones. Effective classification of real-time activity data is, therefore, actively pursued using various machine learning methods. In this study, the transformer model, a deep learning neural network model developed primarily for the natural language processing and vision tasks, was adapted for a time-series analysis of motion signals. The self-attention mechanism inherent in the transformer, which expresses individual dependencies between signal values within a time series, can match the performance of state-of-the-art convolutional neural networks with long short-term memory. The performance of the proposed adapted transformer method was tested on the largest available public dataset of smartphone motion sensor data covering a wide range of activities, and obtained an average identification accuracy of 99.2% as compared with 89.67% achieved on the same data by a conventional machine learning method. The results suggest the expected future relevance of the transformer model for human activity recognition.
Detection of β-Lactoglobulin by a Porous Silicon Microcavity Biosensor Based on the Angle Spectrum
Authors: Bai Lanlan , Gao Yun , Wang Jiajia , Aili Tuerxunnayi , Jia Zhenhong , Lv Xiaoyi , Huang Xiaohui , Yang Jie
Published: March 2022

In this paper, carbon quantum dot-labelled β-lactoglobulin antibodies were used for refractive index magnification, and β-lactoglobulin was detected by angle spectroscopy. In this method, the detection light is provided by a He-Ne laser whose central wavelength is the same as that of the porous silicon microcavity device, and the light source was changed to a parallel beam to illuminate the porous silicon microcavity’ surface by collimating beam expansion, and the reflected light was received on the porous silicon microcavity’ surface by a detector. The angle corresponding to the smallest luminous intensity before and after the onset of immune response was measured by a detector for different concentrations of β-lactoglobulin antigen and carbon quantum dot-labelled β-lactoglobulin antibodies, and the relationship between the variation in angle before and after the immune response was obtained for different concentrations of the β-lactoglobulin antigen. The results of the experiment present that the angle variations changed linearly with increasing β-lactoglobulin antigen concentration before and after the immune response. The limit of detection of β-lactoglobulin by this method was 0.73 μg/L, indicating that the method can be used to detect β-lactoglobulin quickly and conveniently at low cost.
Acquisition of High Spectral Resolution Diffuse Reflectance Image Cubes (350–2500 nm) from Archaeological Wall Paintings and Other Immovable Heritage Using a Field-Deployable Spatial Scanning Reflectance Spectrometry Hyperspectral System
Authors: Radpour Roxanne , Delaney John K. , Kakoulli Ioanna
Published: March 2022

There is growing interest in bringing non-invasive laboratory-based analytical imaging tools to field sites to study wall paintings in order to collect molecular information on the macroscale. Analytical imaging tools, such as reflectance imaging spectrometry, have provided a wealth of information about artist materials and working methods, as well as painting conditions. Currently, scientific analyses of wall paintings have been limited to point-measurement techniques such as reflectance spectroscopy (near-ultraviolet, visible, near-infrared, and mid-infrared), X-ray fluorescence, and Raman spectroscopy. Macroscale data collection methods have been limited to multispectral imaging in reflectance and luminescence modes, which lacks sufficient spectral bands to allow for the mapping and identification of artist materials of interest. The development of laboratory-based reflectance and elemental imaging spectrometers and scanning systems has sparked interest in developing truly portable versions, which can be brought to field sites to study wall paintings where there is insufficient space or electrical power for laboratory instruments. This paper presents the design and testing of a simple hyperspectral system consisting of a 2D spatial spot scanning spectrometer, which provides high spectral resolution diffuse reflectance spectra from 350 to 2500 nm with high signal to noise and moderate spatial resolution (few mm). This spectral range at high spectral resolution was found to provide robust chemical specificity sufficient to identify and map many artists’ materials, as well as the byproducts of weathering and conservation coatings across the surface of ancient and Byzantine Cypriot wall paintings. Here, we present a detailed description of the hyperspectral system, its performance, and examples of its use to study wall paintings from Roman tombs in Cyprus. The spectral/spatial image processing workflow to make maps of pigments and constituent painting materials is also discussed. This type of configurable hyperspectral system and the imaging processing workflow offer a new tool for the field study of wall paintings and other immovable heritage.
Current Only-Based Fault Diagnosis Method for Industrial Robot Control Cables
Authors: Kim Heonkook , Lee Hojin , Kim Sang Woo
Published: March 2022

With the growth of factory automation, deep learning-based methods have become popular diagnostic tools because they can extract features automatically and diagnose faults under various fault conditions. Among these methods, a novelty detection approach is useful if the fault dataset is imbalanced and impossible reproduce perfectly in a laboratory. This study proposes a novelty detection-based soft fault-diagnosis method for control cables using only currents flowing through the cables. The proposed algorithm uses three-phase currents to calculate the sum and ratios of currents, which are used as inputs to the diagnosis network to detect novelties caused by soft faults. Autoencoder architecture is adopted to detect novelties and calculate anomaly scores for the inputs. Applying a moving average filter to anomaly scores, a threshold is defined, by which soft faults can be properly diagnosed under environmental disturbances. The proposed method is evaluated in 11 fault scenarios. The datasets for each scenario are collected when an industrial robot is working. To induce soft fault conditions, the conductor and its insulator in the cable are damaged gradually according to the scenarios. Experiments demonstrate that the proposed method accurately diagnoses soft faults under various operating conditions and degrees of fault severity.
Energy-Efficient UAV Movement Control for Fair Communication Coverage: A Deep Reinforcement Learning Approach
Authors: Nemer Ibrahim A. , Sheltami Tarek R. , Belhaiza Slim , Mahmoud Ashraf S.
Published: March 2022

Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption.
Deep Convolutional Neural Network-Based Hemiplegic Gait Detection Using an Inertial Sensor Located Freely in a Pocket
Authors: Shin Hangsik
Published: March 2022

In most previous studies, the acceleration sensor is attached to a fixed position for gait analysis. However, if it is aimed at daily use, wearing it in a fixed position may cause discomfort. In addition, since an acceleration sensor can be built into the smartphones that people always carry, it is more efficient to use such a sensor rather than wear a separate acceleration sensor. We aimed to distinguish between hemiplegic and normal walking by using the inertial signal measured by means of an acceleration sensor and a gyroscope. We used a machine learning model based on a convolutional neural network to classify hemiplegic gaits and used the acceleration and angular velocity signals obtained from a system freely located in the pocket as inputs without any pre-processing. The classification model structure and hyperparameters were optimized using Bayesian optimization method. We evaluated the performance of the developed model through a clinical trial, which included a walking test of 42 subjects (57.8 ± 13.8 years old, 165.1 ± 9.3 cm tall, weighing 66.3 ± 12.3 kg) including 21 hemiplegic patients. The optimized convolutional neural network model has a convolutional layer, with number of fully connected nodes of 1033, batch size of 77, learning rate of 0.001, and dropout rate of 0.48. The developed model showed an accuracy of 0.78, a precision of 0.80, a recall of 0.80, an area under the receiver operating characteristic curve of 0.80, and an area under the precision–recall curve of 0.84. We confirmed the possibility of distinguishing a hemiplegic gait by applying the convolutional neural network to the signal measured by a six-axis inertial sensor freely located in the pocket without additional pre-processing or feature extraction.
An Overview on Bipolar Junction Transistor as a Sensor for X-ray Beams Used in Medical Diagnosis
Authors: Santos Luiz A. P.
Published: March 2022

Although not manufactured to be used under X-ray photons, the commercial bipolar junction transistor (BJT) is an electronic device that can be used as an ionizing radiation sensor. In this article an overview on the BJT and its principle of operation were made for the purpose of better understanding how such a semiconductor device behaves when under diagnostic X-ray beam. Therefore, it addresses some topics such as the structure of the device, the bias configuration when operating in active mode, and so on. Even knowing that the most complete theory to describe the “transistor effect” is based on quantum theory (the energy band theory of solids), here it is preferable to take a simpler experimental approach to clearly understand the operation of the BJT. In electronics, the BJT is used as a current amplifier, and depending on the bias and point of view it also becomes a voltage amplifier. In the analysis of BJT under an X-ray beam, in addition to its operation as a sensor to measure the dose or some diagnostic X-ray tube parameter, it has also led to technological innovation in the technique of digital data storage based on the effect of radiation.
A Novel Untethered Hand Wearable with Fine-Grained Cutaneous Haptic Feedback
Authors: Abad Alexander Co , Reid David , Ranasinghe Anuradha
Published: March 2022

During open surgery, a surgeon relies not only on the detailed view of the organ being operated upon and on being able to feel the fine details of this organ but also heavily relies on the combination of these two senses. In laparoscopic surgery, haptic feedback provides surgeons information on interaction forces between instrument and tissue. There have been many studies to mimic the haptic feedback in laparoscopic-related telerobotics studies to date. However, cutaneous feedback is mostly restricted or limited in haptic feedback-based minimally invasive studies. We argue that fine-grained information is needed in laparoscopic surgeries to study the details of the instrument’s end and can convey via cutaneous feedback. We propose an exoskeleton haptic hand wearable which consists of five 4 × 4 miniaturized fingertip actuators, 80 in total, to convey cutaneous feedback. The wearable is described as modular, lightweight, Bluetooth, and WiFi-enabled, and has a maximum power consumption of 830 mW. Software is developed to demonstrate rapid tactile actuation of edges; this allows the user to feel the contours in cutaneous feedback. Moreover, to demonstrate the idea as an object displayed on a flat monitor, initial tests were carried out in 2D. In the second phase, the wearable exoskeleton glove is then further developed to feel 3D virtual objects by using a virtual reality (VR) headset demonstrated by a VR environment. Two-dimensional and 3D objects were tested by our novel untethered haptic hand wearable. Our results show that untethered humans understand actuation in cutaneous feedback just in a single tapping with 92.22% accuracy. Our wearable has an average latency of 46.5 ms, which is much less than the 600 ms tolerable delay acceptable by a surgeon in teleoperation. Therefore, we suggest our untethered hand wearable to enhance multimodal perception in minimally invasive surgeries to naturally feel the immediate environments of the instruments.
Design and Development of Internet of Things-Driven Fault Detection of Indoor Thermal Comfort: HVAC System Problems Case Study
Authors: Sahoh Bukhoree , Kliangkhlao Mallika , Kittiphattanabawon Nichnan
Published: March 2022

Controlling thermal comfort in the indoor environment demands research because it is fundamental to indicating occupants’ health, wellbeing, and performance in working productivity. A suitable thermal comfort must monitor and balance complex factors from heating, ventilation, air-conditioning systems (HVAC Systems) and outdoor and indoor environments based on advanced technology. It needs engineers and technicians to observe relevant factors on a physical site and to detect problems using their experience to fix them early and prevent them from worsening. However, it is a labor-intensive and time-consuming task, while experts are short on diagnosing and producing proactive plans and actions. This research addresses the limitations by proposing a new Internet of Things (IoT)-driven fault detection system for indoor thermal comfort. We focus on the well-known problem caused by an HVAC system that cannot transfer heat from the indoor to outdoor and needs engineers to diagnose such concerns. The IoT device is developed to observe perceptual information from the physical site as a system input. The prior knowledge from existing research and experts is encoded to help systems detect problems in the manner of human-like intelligence. Three standard categories of machine learning (ML) based on geometry, probability, and logical expression are applied to the system for learning HVAC system problems. The results report that the MLs could improve overall performance based on prior knowledge around 10% compared to perceptual information. Well-designed IoT devices with prior knowledge reduced false positives and false negatives in the predictive process that aids the system to reach satisfactory performance.
ECG Data Analysis with Denoising Approach and Customized CNNs
Authors: Mishra Abhinav , Dharahas Ganapathiraju , Gite Shilpa , Kotecha Ketan , Koundal Deepika , Zaguia Atef , Kaur Manjit , Lee Heung-No
Published: March 2022

In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart’s rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics.
A Comprehensive Evaluation of Generating a Mobile Traffic Data Scheme without a Coarse-Grained Process Using CSR-GAN
Authors: Tokunaga Tomoki , Mizutani Kimihiro
Published: March 2022

Large-scale mobile traffic data analysis is important for efficiently planning mobile base station deployment plans and public transportation plans. However, the storage costs of preserving mobile traffic data are becoming much higher as traffic increases enormously population density of target areas. To solve this problem, schemes to generate a large amount of mobile traffic data have been proposed. In the state-of-the-art of the schemes, generative adversarial networks (GANs) are used to transform a large amount of traffic data into a coarse-grained representation and generate the original traffic data from the coarse-grained data. However, the scheme still involves a storage cost, since the coarse-grained data must be preserved in order to generate the original traffic data. In this paper, we propose a scheme to generate the mobile traffic data by using conditional-super-resolution GAN (CSR-GAN) without requiring a coarse-grained process. Through experiments using two real traffic data, we assessed the accuracy and the amount of storage data needed. The results show that the proposed scheme, CSR-GAN, can reduce the storage cost by up to 45% compared to the traditional scheme, and can generate the original mobile traffic data with 94% accuracy. We also conducted experiments by changing the architecture of CSR-GAN, and the results show an optimal relationship between the amount of traffic data and the model size.
Analytical Models for Multipath and Switch Leakage for the SWOT Interferometer
Authors: Ahmed Razi , Esteban-Fernández Daniel , Hensley Scott
Published: March 2022

The Ka-Band Radar Interferometer (KaRIn) instrument on the Surface Water and Ocean Topography (SWOT) mission is a single-pass synthetic aperture radar (SAR) interferometer tasked with, among others, measuring ocean topography to within a few centimeters over kilometer scale resolutions. A SAR interferometer relies on very precise phase difference measurements between two spatially distant antennas to estimate topography. Multipath phase caused by unintended scattering off the spacecraft structure is a known error source for radar interferometers and takes up a significant portion of the KaRIn error budget. This paper outlines some analytical multipath models that were used for instrument design, performance analysis and mitigation of the multipath signal.
Doing Well by Creating Economic Value through Social Values among Craft Beer Breweries: A Case Study in Responsible Innovation and Growth
Authors: Erhardt Niclas , Martin-Rios Carlos , Bolton Jason , Luth Matthew
Published: March 2022

Social values are key to the sustainability of organizations. Drawing on value-based research, stakeholder theory, and corporate social responsibility research, this paper builds a case study of the interplay between social values, innovation, and economic growth. The craft beer industry is a fast-growing industry with a potential emphasis on social values built on small-scale production and localism. We examine how craft breweries attempt to resolve tensions derived from pursuing economic and social values simultaneously. As breweries continue to grow, owners face decisions of scale and growth, which may undermine a value-driven industry with close ties to the local community. Findings from six craft breweries, operating in Northern New England, USA, suggest a complex managerial dilemma involving (a) small-batch innovation for niche and mass production for growth, (b) responsible innovation for balancing local authenticity and geographical expansion, and (c) independent and business partnering. We further unpacked the tensions that operated at local and non-local levels.
Light and Water Conditions Co-Regulated Stomata and Leaf Relative Uptake Rate (LRU) during Photosynthesis and COS Assimilation: A Meta-Analysis
Authors: Wang Pengyuan , Wang Shaoqiang , Chen Bin , Amir Muhammad , Wang Lei , Chen Jinghua , Ma Li , Wang Xiaobo , Liu Yuanyuan , Zhu Kai
Published: March 2022

As a trace gas involved in hydration during plant photosynthesis, carbonyl sulfide (COS) and its leaf relative uptake rate (LRU) is used to reduce the uncertainties in simulations of gross primary productivity (GPP). In this study, 101 independent observations were collected from 22 studies. We extracted the LRU, stomatal conductance (gs), canopy COS and carbon dioxide (CO2) fluxes, and relevant environmental conditions (i.e., light, temperature, and humidity), as well as the atmospheric COS and CO2 concentrations (Ca,COS and Ca,CO2). Although no evidence was found showing that gs regulates LRU, they responded in opposite ways to diurnal variations of environmental conditions in both mixed forests (LRU: Hedges’d = −0.901, LnRR = −0.189; gs: Hedges’d = 0.785, LnRR = 0.739) and croplands dominated by C3 plants (Hedges’d = −0.491, LnRR = −0.371; gs: Hedges’d = 1.066, LnRR = 0.322). In this process, the stomata play an important role in COS assimilation (R2 = 0.340, p = 0.020) and further influence the interrelationship of COS and CO2 fluxes (R2 = 0.650, p = 0.000). Slight increases in light intensity (R2 = 1, p = 0.002) and atmospheric drought (R2 = 0.885, p = 0.005) also decreased the LRU. The LRU saturation points of Ca,COS and Ca,CO2 were observed when ΔCa,COS ≈ 13 ppt (R2 = 0.580, p = 0.050) or ΔCa,CO2 ≈ −18 ppm (R2 = 0.970, p = 0.003). This study concluded that during plant photosynthesis and COS assimilation, light and water conditions co-regulated the stomata and LRU.
Measuring Access and Egress Distance and Catchment Area of Multiple Feeding Modes for Metro Transferring Using Survey Data
Authors: Li Xia , Liu Zhenyu , Ma Xinwei
Published: March 2022

Multiple feeding modes, including walking, bus, private bike, docked bike-sharing, private electric bike (e-bike), car, and taxi, are applied for better accessibility in a metro-based trip. It is crucial to understand their access/egress distances and corresponding catchment areas of metro stations. This paper determines these two distances and accessible areas of stations for different feeding modes based on Nanjing Population Survey data and GIS data by using a network-based approach in Nanjing, China. Considering the distribution of access/egress distance, regression models are established for the exploration of the threshold of distance to delineate catchment areas. What is more, the spatio-temporal characteristics of multiple feeding modes are analyzed. The results indicate that the average feeding distance of walking is the shortest, but docked bike-sharing has the shortest average feeding time, about 8 min. The average feeding time of private e-bikes is close to that of the private bike, but the feeding distance of private e-bikes is about 1.3 times as long as that of private bikes. Moreover, the origin of an over-10 km transfer for accessing metro stations is usually far away from metro lines and the transferring station is mostly the terminal station. Generally, longer access distance means larger catchment area but the result is also influenced by the condition of street network. Moreover, catchment areas for the same feeding modes are different between urban and suburban areas.
Investigating the Emission of Hazardous Chemical Substances from Mashrabiya Used for Indoor Air Quality in Hot Desert Climate
Authors: Jung Chuloh , Al Qassimi Nahla
Published: March 2022

Dubai has the reputation of a continuously growing city, with skyscrapers and mega residential projects. Many new residential projects with poor choices of material and ventilation have led to a faster rise in sick building syndrome (SBS) in Dubai than in any other country, and the IAQ (indoor air quality) has become more critical. Volatile organic compounds (VOCs) and formaldehyde (HCHO) affect the health of residents, producing the phenomenon known as SBS (sick building syndrome). It has been reported that wood materials used for furniture and wooden windows and doors are a significant source of indoor air pollution in new houses. This paper aims to identify the factor elements emitting harmful chemical substances, such as VOCs and HCHO, from wooden mashrabiya (traditional Arabic window) by examining the characteristics of the raw and surface materials through test pieces. As a methodology, a small chamber system was used to test the amount of hazardous chemicals generated for each test piece. For Total volatile organic compounds (TVOC) and HCHO, the blank concentration before the injection and the generation after seven days were measured. The results showed that to reduce TVOC, it is necessary to secure six months or more as a retention period for raw materials and surface materials. The longer the retention period, the smaller the TVOC emission amount. In the case of mashrabiya, an HCHO low-emitting adhesive and maintenance for one month or more are essential influencing factors. It was proven that using raw materials with a three-month or more retention period and surface materials with a one-month or more retention period is safe for indoor mashrabiya. This study is the first study in the Middle East to identify factors and characteristics that affect the emission of hazardous chemicals from wood composite materials, such as wood mashrabiya, that affect indoor air quality in residential projects in Dubai. It analyzes the correlation between emission levels and the retention period of raw and surface materials, in order to provide a new standard for indoor air pollutants.
Regional Economic Resilience in the Central-Cities and Outer-Suburbs of Northeast China
Authors: Li Qiao , Guan Haoming , Feng Zhangxian , Long Wang
Published: March 2022

The global economic downturn and the new normal status of China’s economy have revealed that the Northeastern region of the country is facing challenges due to poor adaptability and weak regional economic resilience. In regard to the regional cities of China’s municipal-administered county system and from the perspective of the relationship between the economic resilience of the central-cities and the outer-suburbs, it is helpful to explore the characteristics of the regional economic resilience in Northeast China from 2007 to 2018. We find that: (1) The economic resilience of the central-cities and outer-suburbs in Northeast China has deteriorated. Compared with the central-cities, the economic resilience of outer-suburbs has weakened even more. (2) The types of regional economic resilience of the central-cities and outer-suburbs of Northeast China have diversified and weakened. The main types of economic resilience have changed from city-prospered-suburb-prospered and city-balanced-suburb-prospered to city-balanced-suburb-balanced and city-declined-suburb-declined. There are differences in the types of municipal economic resilience that are affected by changes in the economic resilience levels of central-cities and outer-suburbs. (3) It is further confirmed that the industrial structure has an important influence on the resilience of the regional economy. The economic resilience of the outer-suburbs in the Northeast China appear to be more vulnerable and more heavily influenced by changes in the proportion of the secondary industry than the central-cities.
House Prices and Marriage in Spain
Authors: González-Val Rafael
Published: March 2022

The aim of this study is to examine the link between house prices and marriage in Spain. We consider data from 50 Spanish provinces (NUTS III regions) and from local civil registries in 282 cities with populations greater than 25,000 inhabitants. The regional data cover the 1995–2018 period, whereas the local sample includes information from 2005 to 2018. The marriage rate is defined as the annual absolute number of marriages per thousand inhabitants in each region or city. We used data on Spain because the Spanish housing market experienced a strong rise in house prices until 2006, when the housing bubble ended and prices dramatically decreased. By using different econometric techniques (panel data models with fixed effects and dynamic panel data models), our results reveal that there is a significant negative relationship between house prices and the marriage rate at both the regional and local levels. Overall, this study highlights the important consequences of rising house prices on family formations. Therefore, public authorities should try to reduce fluctuations in house prices and to facilitate access to home ownership for young couples.
Towards the Development of Smart and Sustainable Transportation System for Foodservice Industry: Modelling Factors Influencing Customer’s Intention to Adopt Drone Food Delivery (DFD) Services
Authors: Jasim Noor Islam , Kasim Hairoladenan , Mahmoud Moamin A.
Published: March 2022

While the attempts to deploy drones in the foodservice industry focus on the technical aspects, research studies from behavioral perspectives that support service substantially are still in their infancy. When new technology-based services are introduced, it is crucial to examine and understand consumers’ perceptions by identifying a set of actions that influence acceptance and fulfilling their target. Therefore, service providers of drone food delivery (DFD) services need to identify significant factors that influence potential consumers to use drone delivery. Although a few existing models are significant, these models lack a comprehensive basic theory that addresses factors which influence consumers’ intention and behavior. To overcome this limitation and propose a more comprehensive model, relevant research studies from the domain of drone delivery services and other emerging technology such as IoT, Autonomous Vehicles, and Mobile Banking are identified, reviewed, and analyzed, and ten potential factors are subsequently extracted. This study’s data were collected from 209 participants who regularly order food online for delivery and were analyzed using SPSS. Descriptive statistics, reliability, Pearson correlation, regression analysis, r-squared, and standardized beta coefficient analyses are carried out to present the study’s findings. The results show that there is a significant relationship between behavioral intention and the user behavior of DFD. Although the participants in this study are yet to experience drone technology in the foodservice domain, the identified factors explain around 32.9% of the variation in the use behavior of DFD services. In the early stage of adoption, it is highly recommended for stakeholders to conduct marketing campaigns through media channels such as television and different social media platforms to bring awareness of this technology.
Digital Transformation as a Demographic and Economic Integrated Policy for Southeast Asian Developing Countries
Authors: Rhee Taehwan , Wood Jacob , Kim Jungsuk
Published: March 2022

The age of Southeast Asian developing countries’ populations is still younger than that of other regions around the world. However, recent statistics show that the tide is now turning in this regard, with many of these populations beginning to age at rates much faster than many other countries. Such developments require immediate policy action in order to create a sustainable path towards economic growth before demographic changes become less benign in the medium term. In this study, we discuss the economic consequences of population aging, increases in the economic support ratio, and a declining potential growth rate. We argue that it is essential for Southeast Asian developing countries to raise total factor productivity (TFP) growth rates so as to achieve more sustainable economic outcomes. By conducting panel regressions using data from 82 countries across the 1996–2019 study period, our study shows that increasing research and development (R&D) spending and the facilitation of structural changes that transform the digital economy landscape are key policy options that promote TFP growth.
Artificial Intelligent in Education
Authors: Hamal Oussama , El Faddouli Nour-Eddine , Harouni Moulay Hachem Alaoui , Lu Joan
Published: March 2022

The application of Artificial Intelligence or AI in education has been the subject of academic research for more than 30 years. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. Nowadays, there are several new challenges in the field of education technology in the era of smart phones, tablets, cloud computing, Big Data, etc., whose current research questions focus on concepts such as ICT-enabled personalized learning, mobile learning, educational games, collaborative learning on social media, MOOCs, augmented reality application in education and so on. Therefore, to meet these new challenges in education, several fields of research using AI have emerged over time to improve teaching and learning using digital technologies. Moreover, each field of research is distinguished by its own vision and methodologies. In this article, to the authors present a state of the art finding in the fields of research of Artificial Intelligence in Education or AIED, Educational Data Mining or EDM and Learning Analytics or LA. We discuss their historical elements, definition attempts, objectives, adopted methodologies, application examples and challenges.
Evaluation of Rural Tourism Landscape Resources in Terms of Carbon Neutrality and Rural Revitalization
Authors: Li Weiwen , Zhou Yijiang , Dai Xingan , Hu Fang
Published: March 2022

Rural tourism landscape resources are important ingredients of rural revitalization and modernization in developing countries and regions. Evaluation methods play a crucial role in the planning, design, transformation, development, and protection of these resources. However, there has been a lack of research on the evaluation of rural tourism landscape resources, especially from the perspective of rural revitalization and carbon neutrality. From the perspective of carbon neutrality and rural revitalization, this article establishes an indicator system to evaluate rural tourism landscape resources based on previous evaluation methods and expert consultations on landscape planning. An evaluation model based on the intuitionistic fuzzy VIKOR method structure matching is also suggested. Some practical suggestions are put forward to promote the values of rural tourism landscape resources through empirical analyses of three regions in Changsha, Hunan, China. Our study shows that the evaluation results could objectively reflect the values and existing problems of rural tourism landscape resources, which could provide practical tools for local government departments to make decisions, and landscape architects to plan and design. Based on this model, further suggestions are provided to improve rural tourism landscape resources.
How Can We Make a Sustainable Workplace? Workplace Ostracism, Employees’ Well-Being via Need Satisfaction and Moderated Mediation Role of Authentic Leadership
Authors: Jang Eunmi , Chen Xing
Published: March 2022

Ostracism in the workplace is a common phenomenon in modern society that impairs employees’ well-being. This study suggests that workplace ostracism reduces subjective well-being by examining the effect of workplace ostracism on subjective well-being. Based on self-determination theory and resource conservation theory, this study explores the underlying processes and their contingent factors in the relationship between workplace ostracism and employee well-being. Specifically, this study hypothesizes that workplace ostracism decreases employees’ well-being by enhancing employees’ need satisfaction. Furthermore, the perception of a direct supervisor’s authentic leadership positively moderates the relationship between workplace ostracism and employees’ need satisfaction. This study used moderated mediation analysis to evaluate our predictions using a two-time online survey of 485 Korean employees. The findings revealed that workplace ostracism has a detrimental impact on employee well-being via need satisfaction. However, perceptions of a direct supervisor’s authentic leadership positively moderate the association between workplace ostracism and need satisfaction. Our results have important practical and theoretical implications in the workplace ostracism literature.