bluuebunny/crossref_metadata_2025 huggingface dataset:
Unlocking Large-Scale Crop Field Delineation in Smallholder Farming Systems with Transfer Learning and Weak Supervision
Authors: Wang Sherrie , Waldner François , Lobell David B.
Published: November 2022

Crop field boundaries aid in mapping crop types, predicting yields, and delivering field-scale analytics to farmers. Recent years have seen the successful application of deep learning to delineating field boundaries in industrial agricultural systems, but field boundary datasets remain missing in smallholder systems due to (1) small fields that require high resolution satellite imagery to delineate and (2) a lack of ground labels for model training and validation. In this work, we use newly-accessible high-resolution satellite imagery and combine transfer learning with weak supervision to address these challenges in India. Our best model uses 1.5 m resolution Airbus SPOT imagery as input, pre-trains a state-of-the-art neural network on France field boundaries, and fine-tunes on India labels to achieve a median Intersection over Union (mIoU) of 0.85 in India. When we decouple field delineation from cropland classification, a model trained in France and applied as-is to India Airbus SPOT imagery delineates fields with a mIoU of 0.74. If using 4.8 m resolution PlanetScope imagery instead, high average performance (mIoU > 0.8) is only achievable for fields larger than 1 hectare. Experiments also show that pre-training in France reduces the number of India field labels needed to achieve a given performance level by as much as 10× when datasets are small. These findings suggest our method is a scalable approach for delineating crop fields in regions of the world that currently lack field boundary datasets. We publicly release 10,000 Indian field boundary labels and our delineation model to facilitate the creation of field boundary maps and new methods by the community.
Assimilation of Backscatter Observations into a Hydrological Model: A Case Study in Belgium Using ASCAT Data
Authors: Baguis Pierre , Carrassi Alberto , Roulin Emmanuel , Vannitsem Stéphane , Modanesi Sara , Lievens Hans , Bechtold Michel , De Lannoy Gabrielle
Published: November 2022

We investigated the possibilities of improving hydrological simulations by assimilating radar backscatter observations from the advanced scatterometer (ASCAT) in the hydrological model SCHEME using a calibrated water cloud model (WCM) as an observation operator. The WCM simulates backscatter based on soil moisture and vegetation data and can therefore be used to generate observation predictions for data assimilation. The study was conducted over two Belgian catchments with different hydrological regimes: the Demer and the Ourthe catchment. The main differences between the two catchments can be summarized in precipitation and streamflow levels, which are higher in the Ourthe. The data assimilation method adopted here was the ensemble Kalman filter (EnKF), whereby the uncertainty of the state estimate was described via the ensemble statistics. The focus was on the optimization of the EnKF, and possible solutions to address biases introduced by ensemble perturbations were investigated. The latter issue contributes to the fact that backscatter data assimilation only marginally improves the overall scores of the discharge simulations over the deterministic reference run, and only for the Ourthe catchment. These performances, however, considerably depend on the period considered within the 5 years of analysis. Future lines of research on bias correction, the data assimilation of soil moisture and backscatter data are also outlined.
Individual Tree Segmentation from Side-View LiDAR Point Clouds of Street Trees Using Shadow-Cut
Authors: Hua Zhouyang , Xu Sheng , Liu Yingan
Published: November 2022

Segmentation of vegetation LiDAR point clouds is an important method for obtaining individual tree structure parameters. The current individual tree segmentation methods are mainly for airborne LiDAR point clouds, which use elevation information to form a grid map for segmentation, or use canopy vertices as seed points for clustering. Side-view LiDAR (vehicle LiDAR and hand-held LiDAR) can acquire more information about the lower layer of trees, but it is a challenge to perform the individual tree segmentation because the structure of side-view LiDAR point clouds is more complex. This paper proposes an individual tree segmentation method called Shadow-cut to extract the contours of the street tree point cloud. Firstly, we separated the region of the trees using the binary classifier (e.g., support vector machine) based on point cloud geometric features. Then, the optimal projection of the 3D point clouds to the 2D image is calculated and the optimal projection is the case where the pixels of the individual tree image overlap the least. Finally, after using the image segmentation algorithm to extract the tree edges in the 2D image, the corresponding 3D individual tree point cloud contours are matched with the pixels of individual tree edges in the 2D image. We conducted experiments with the proposed method on LiDAR data of urban street trees, and the correctness, completeness, and quality of the proposed individual tree segmentation method reached 91.67%, 85.33%, and 79.19%, which were superior to the CHM-based method by 2.70%, 6.19%, and 7.12%, respectively. The results show that this method is a practical and effective solution for individual tree segmentation in the LiDAR point clouds of street trees.
Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment
Authors: Bu Lingxin , Lai Quan , Qing Song , Bao Yuhai , Liu Xinyi , Na Qin , Li Yuan
Published: November 2022

Xilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes particularly important. Based on multi-source data, a random forest regression algorithm was used to construct a grassland biomass estimation model, which was then used to analyze the spatiotemporal variation characteristics of grassland biomass. A quantitative assessment of drought risk (DR) in different grassland types was applied based on the theory of risk formation, and a structural equation model (SEM) was used to analyze the drivers of drought risk in different grassland types. The results show that among the eight selected variables that affect grassland biomass, the model had the highest accuracy (R = 0.90) when the normalized difference vegetation index (NDVI), precipitation (Prcp), soil moisture (SM) and longitude (Lon) were combined as input variables. The grassland biomass showed a spatial distribution that was high in the east and low in the west, gradually decreasing from northeast to southwest. Among the grasslands, desert grassland (DRS) had the highest drought risk (DR = 0.30), while meadow grassland (MEG) had the lowest risk (DR = 0.02). The analysis of the drivers of drought risk in grassland biomass shows that meteorological elements mainly drive typical grasslands (TYG) and other grasslands (OTH). SM greatly impacted MEG, and ET had a relatively high contribution to DRS. This study provides a basis for managing different grassland types in large areas and developing corresponding drought adaptation programs.
Grain Size Distribution Analysis of Different Activator Doped Gd2O2S Powder Phosphors for Use in Medical Image Sensors
Authors: Liaparinos Panagiotis , Michail Christos , Valais Ioannis , Fountos George , Karabotsos Athanasios , Kandarakis Ioannis
Published: November 2022

The structural properties of phosphor materials, such as their grain size distribution (GSD), affect their overall optical emission performance. In the widely used gadolinium oxysulfide (Gd2O2S) host material, the type of activator is one significant parameter that also changes the GSD of the powder phosphor. For this reason, in this study, different phosphors samples of Gd2O2S:Tb, Gd2O2S:Eu, and Gd2O2S:Pr,Ce,F, were analyzed, their GSDs were experimentally determined using the scanning electron microscopy (SEM) technique, and thereafter, their optical emission profiles were investigated using the LIGHTAWE Monte Carlo simulation package. Two sets of GSDs were examined corresponding to approximately equal mean particle size, such as: (i) 1.232 μm, 1.769 μm and 1.784 μm, and (ii) 2.377 μm, 3.644 μm and 3.677 μm, for Tb, Eu and Pr,Ce,F, respectively. The results showed that light absorption was almost similar, for instance, 25.45% and 8.17% for both cases of Eu dopant utilizing a thin layer (100 μm), however, given a thicker layer (200 μm), the difference was more obvious, 22.82%. On the other hand, a high amount of light loss within the phosphor affects the laterally directed light quanta, which lead to sharper distributions and therefore to higher resolution properties of the samples.
Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images
Authors: Poobalasubramanian Mangalraj , Park Eun-Sung , Faqeerzada Mohammad Akbar , Kim Taehyun , Kim Moon Sung , Baek Insuck , Cho Byoung-Kwan
Published: November 2022

Strawberry (Fragaria × ananassa Duch) plants are vulnerable to climatic change. The strawberry plants suffer from heat and water stress eventually, and the effects are reflected in the development and yields. In this investigation, potential chlorophyll-fluorescence-based indices were selected to detect the early heat and water stress in strawberry plants. The hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm–900 nm. From the hyperspectral cube, the region of interest (leaves) was identified, followed by the extraction of eight chlorophyll-fluorescence indices from the region of interest (leaves). These eight chlorophyll-fluorescence indices were analyzed deeply to identify the best indicators for our objective. The indices were used to develop machine-learning models to assess the performance of the indicators by accuracy assessment. The overall procedure is proposed as a new workflow for determining strawberry plants’ early heat and water stress. The proposed workflow suggests that by including all eight indices, the random-forest classifier performs well, with an accuracy of 94%. With this combination of the potential indices, namely the red-edge vegetation stress index (RVSI), chlorophyll B (Chl-b), pigment-specific simple ratio for chlorophyll B (PSSRb), and the red-edge chlorophyll index (CIREDEDGE), the gradient-boosting classifier performs well, with an accuracy of 91%. The proposed workflow works well with a limited number of training samples which is an added advantage.
A New Decentralized Robust Secondary Control for Smart Islanded Microgrids
Authors: Jasim Ali M. , Jasim Basil H. , Bureš Vladimír , Mikulecký Peter
Published: November 2022

Dealing with the islanded operation of a microgrid (MG), the micro sources must cooperate autonomously to regulate the voltage and frequency of the local power grid. Droop controller-based primary control is a method typically used to self-regulate voltage and frequency. The first problem of the droop method is that in a steady state, the microgrid’s frequency and voltage deviate from their nominal values. The second concerns the power-sharing issue related to mismatched power line impedances between Distribution Generators (DGs) and MGs. A Secondary Control Unit (SCU) must be used as a high-level controller for droop-based primary control to address the first problem. This paper proposed a decentralized SCU scheme to deal with this issue using optimized PI controllers based on a Genetic Algorithm (GA) and Artificial Neural Networks (ANNs). The GA provides the appropriate adjustment parameters for all adopted PI controllers in the primary control-based voltage and current control loops and SCU-based voltage and frequency loops. ANNs are additionally activated in SCUs to provide precise online control parameter modification. In the proposed control structure, a virtual impedance method is adopted in the primary control scheme to address the power-sharing problem of parallel DGs. Further, in this paper, one of the main objectives includes electricity transmission over long distances using Low-Voltage DC Transmission (LVDCT) systems to reduce power losses and eradicate reactive power problems. Voltage Source Inverters (VSIs) are adopted to convert the DC electrical energy into AC near the consumer loads. The simulation results illustrated the feasibility of the proposed solutions in restoring voltage and frequency deviations, reducing line losses, as well as achieving active and reactive power sharing among the DGs connected to the MG.
Computation Offloading Game for Multi-Channel Wireless Sensor Networks
Authors: Hu Heng-Cheng , Wang Pi-Chung
Published: November 2022

Computation offloading for wireless sensor devices is critical to improve energy efficiency and maintain service delay requirements. However, simultaneous offloadings may cause high interferences to decrease the upload rate and cause additional transmission delay. It is thus intuitive to distribute wireless sensor devices in different channels, but the problem of multi-channel computation offloading is NP-hard. In order to solve this problem efficiently, we formulate the computation offloading decision problem as a decision-making game. Then, we apply the game theory to address the problem of allowing wireless sensor devices to make offloading decisions based on their own interests. In the game theory, not only are the data size of wireless sensor devices and their computation capability considered but the channel gain of each wireless sensor device is also included to improve the transmission rate. The consideration could evenly distribute wireless sensor devices to different channels. We prove that the proposed offloading game is a potential game, where the Nash equilibrium exists in each game after all device states converge. Finally, we extensively evaluate the performance of the proposed algorithm based on simulations. The simulation results demonstrate that our algorithm can reduce the number of iterations to achieve Nash equilibrium by 16%. Moreover, it improves the utilization of each channel to effectively increase the number of successful offloadings and lower the energy consumption of wireless sensor devices.
Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides
Authors: Anastasiev Alexey , Kadone Hideki , Marushima Aiki , Watanabe Hiroki , Zaboronok Alexander , Watanabe Shinya , Matsumura Akira , Suzuki Kenji , Matsumaru Yuji , Ishikawa Eiichi
Published: November 2022

In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface electromyography (sEMG). A total of 19 (65.9 ± 12.4 years old; 12 men, seven women) acute stroke survivors (12.4 ± 6.3 days since onset) with hand paresis (Brunnstrom stage 4 ± 1/4 ± 1, SIAS 3 ± 1/3 ± 2, FMA-UE 40 ± 20) performed 10 repetitive hand movements reflecting basic activities of daily living (ADLs): rest, fist, pinch, wrist flexion, wrist extension, finger spread, and thumb up. Signals were recorded using an eight-channel, portable sEMG device with electrode placement on the forearms and thenar areas of both limbs (four sensors on each extremity). Using data preprocessing, semi-automatic segmentation, and a set of extracted feature vectors, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) classifiers for statistical comparison and validity (paired t-tests, p-value < 0.05), we were able to discriminate myoelectrical patterns for each gesture on both paretic and non-paretic sides. Despite any post-stroke conditions, the evaluated total accuracy rate by the 10-fold cross-validation using SVM among four-, five-, six-, and seven-gesture models were 96.62%, 94.20%, 94.45%, and 95.57% for non-paretic and 90.37%, 88.48%, 88.60%, and 89.75% for paretic limbs, respectively. LDA had competitive results using PCA whereas k-NN was a less efficient classifier in gesture prediction. Thus, we demonstrate partial efficacy of the combination of sEMG and supervised learning for upper-limb rehabilitation procedures for early acute stroke motor recovery and various treatment applications.
Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey
Authors: Wang Zhengjie , Ma Mingjing , Feng Xiaoxue , Li Xue , Liu Fei , Guo Yinjing , Chen Da
Published: November 2022

With the increasing demand for human-computer interaction and health monitoring, human behavior recognition with device-free patterns has attracted extensive attention. The fluctuations of the Wi-Fi signal caused by human actions in a Wi-Fi coverage area can be used to precisely identify the human skeleton and pose, which effectively overcomes the problems of the traditional solution. Although many promising results have been achieved, no survey summarizes the research progress. This paper aims to comprehensively investigate and analyze the latest applications of human behavior recognition based on channel state information (CSI) and the human skeleton. First, we review the human profile perception and skeleton recognition progress based on wireless perception technologies. Second, we summarize the general framework of precise pose recognition, including signal preprocessing methods, neural network models, and performance results. Then, we classify skeleton model generation methods into three categories and emphasize the crucial difference among these typical applications. Furthermore, we discuss two aspects, such as experimental scenarios and recognition targets. Finally, we conclude the paper by summarizing the issues in typical systems and the main research directions for the future.
Selective Content Retrieval in Information-Centric Networking
Authors: Quevedo José , Corujo Daniel
Published: November 2022

Recently, novel networking architectures have emerged to cope with the fast-evolving and new Internet utilisation patterns. Information-Centric Networking (ICN) is a prominent example of this architecture. By perceiving content as the core element of the networking functionalities, ICN opens up a whole new avenue of information exchange optimisation possibilities. This paper presents an approach that progresses the base operation of ICN and leverages content identification right at the network layer, allowing to selectively retrieve partial pieces of information from content already present in ICN in-network caches. Additionally, this proposal enables information producers to seamlessly offload some content processing tasks into the network. The concept is discussed and demonstrated through a proof-of-concept prototype targeting an Internet of Things (IoT) scenario, where consumers retrieve specific pieces of the whole information generated by sensors. The obtained results showcase reduced traffic and storage consumption at the core of the network.
Unsupervised Domain Adaptation with Shape Constraint and Triple Attention for Joint Optic Disc and Cup Segmentation
Authors: Zhang Fengming , Li Shuiwang , Deng Jianzhi
Published: November 2022

Currently, glaucoma has become an important cause of blindness. At present, although glaucoma cannot be cured, early treatment can prevent it from getting worse. A reliable way to detect glaucoma is to segment the optic disc and cup and then measure the cup-to-disc ratio (CDR). Many deep neural network models have been developed to autonomously segment the optic disc and the optic cup to help in diagnosis. However, their performance degrades when subjected to domain shift. While many domain-adaptation methods have been exploited to address this problem, they are apt to produce malformed segmentation results. In this study, it is suggested that the segmentation network be adjusted using a constrained formulation that embeds prior knowledge about the shape of the segmentation areas that is domain-invariant. Based on IOSUDA (i.e., Input and Output Space Unsupervised Domain Adaptation), a novel unsupervised joint optic cup-to-disc segmentation framework with shape constraints is proposed, called SCUDA (short for Shape-Constrained Unsupervised Domain Adaptation). A shape constrained loss function is novelly proposed in this paper which utilizes domain-invariant prior knowledge concerning the segmentation region of the joint optic cup–optical disc of fundus images to constrain the segmentation result during network training. In addition, a convolutional triple attention module is designed to improve the segmentation network, which captures cross-dimensional interactions and provides a rich feature representation to improve the segmentation accuracy. Experiments on the RIM-ONE_r3 and Drishti-GS datasets demonstrate that the algorithm outperforms existing approaches for segmenting optic discs and cups.
Accessible Tutoring Platform Using Audio-Tactile Graphics Adapted for Visually Impaired People
Authors: Maćkowski Michał , Brzoza Piotr
Published: November 2022

One of the problems faced by people with blindness is access to materials presented in graphical form. There are many alternative forms of providing such information, but they are very often ineffective or have certain limitations. The development of mobile devices and touch sensors enabled the development of new tools to support such people. This study presents a solution called an accessible tutoring platform, using audio-tactile graphics for people with blindness. We aimed to research the influence of the developed platform for the alternative presentation of graphics information on better memorizing, recognizing, and learning. Another goal of the research was to verify the effectiveness of the proposed method for the alternative presentation of audio-tactile graphics. The effectiveness of the proposed solution was verified quantitatively and qualitatively on two groups of blind students from primary and secondary schools with the use of a developed platform and prepared materials for learning mathematics. The obtained research results show that the proposed method of verifying students’ knowledge and auto-selecting exercises with adapted audio description positively influences the improvement of learning effectiveness.
Digitizing a Therapeutic: Development of an Augmented Reality Dual-Task Training Platform for Parkinson’s Disease
Authors: Alberts Jay L. , Kaya Ryan D. , Scelina Kathryn , Scelina Logan , Zimmerman Eric M. , Walter Benjamin L. , Rosenfeldt Anson B.
Published: November 2022

Augmented reality (AR) may be a useful tool for the delivery of dual-task training. This manuscript details the development of the Dual-task Augmented Reality Treatment (DART) platform for individuals with Parkinson’s disease (PD) and reports initial feasibility, usability, and efficacy of the DART platform in provoking dual-task interference in individuals with PD. The DART platform utilizes the head-mounted Microsoft HoloLens2 AR device to deliver concurrent motor and cognitive tasks. Biomechanical metrics of gait and cognitive responses are automatically computed and provided to the supervising clinician. To assess feasibility, individuals with PD (N = 48) completed a bout of single-task and dual-task walking using the DART platform. Usability was assessed by the System Usability Scale (SUS). Dual-task interference was assessed by comparing single-task walking and walking during an obstacle course while performing a cognitive task. Average gait velocity decreased from 1.06 to 0.82 m/s from single- to dual-task conditions. Mean SUS scores were 81.3 (11.3), which placed the DART in the “good” to “excellent” category. To our knowledge, the DART platform is the first to use a head-mounted AR system to deliver a dual-task paradigm and simultaneously provide biomechanical data that characterize cognitive and motor performance. Individuals with PD were able to successfully use the DART platform with satisfaction, and dual-task interference was provoked. The DART platform should be investigated as a platform to treat dual-task declines associated with PD.
Older Adults’ Response to Color Visibility in Indoor Residential Environment Using Eye-Tracking Technology
Authors: Wang Ze-Yu , Cho Ji Young
Published: November 2022

Older adults spend relatively more time in indoor residential environments than young people. As their visual and cognitive abilities decline, they experience a high risk of accidents in indoor environments; thus, understanding their gaze behavior while considering cognitive capacity is essential in preventing potential accidents and planning for aging-friendly environments. The purpose of this study was to examine whether the use of color in living environments affects the visual attention of older adults. The following two experiments were conducted using eye-tracking technology: Experiment 1: Older adults’ gaze behaviors were measured while viewing four images consisting of nine sets of door–door frames with different color combinations of safety colors, black and white; and Experiment 2: Based on results from Experiment 1, images of simulated living environments were created, and older adults’ gaze behaviors were measured while viewing eight images in which two door colors were applied to verify spatial color visibility. Their cognitive state represented by the Mini-Mental State Examination (MMSE) was then compared with their gaze behavior. The results show that: (a) participants paid more attention to doors with color combinations including red (red–black and white–red) and remained longer on the red–black combination; (b) older adults looked at red–black doors faster than white doors in the same position; (c) the dwell time on a red–black door was longer than on that of the corresponding position of a white door; and (d) based on participants’ MMSE values, the gaze behaviors of the group with low cognition were less focused and observable than those of the group with normal cognition. The results of this study are significant in that they reveal that indoor color can improve the visibility of elements that can impact safety in the living environments of older adults and enhance their quality of life.
Earthquake Event Recognition on Smartphones Based on Neural Network Models
Authors: Chen Meirong , Peng Chaoyong , Cheng Zhenpeng
Published: November 2022

Using sensors embedded in smartphones to study earthquake early warning (EEW) technology can effectively reduce the high construction and maintenance costs of traditional EEW systems. However, due to the impact of human activities, it is very difficult to accurately detect seismic events recorded on mobile phones. In this paper, to improve the detection accuracy of earthquakes on mobile phones, we investigated the suitability of different types of neural network models in seismic event detection. Firstly, we collected three-component acceleration records corresponding to human activities in various scenarios such as walking, running, and cycling through our self-developed mobile application. Combined with traditional strong-motion seismic event records fusing typical mobile phone accelerometer self-noise, all records were used for establishing the training and testing dataset. Finally, two types of neural network models, fully connected and convolutional neural networks, were trained, validated, and tested. The results showed that the accuracy rates of the neural network models were all over 98%, and the precision rate for seismic events and the recall rate for non-earthquake events could both reach 99%, indicating that the introduction of neural networks into the earthquake recognition on smartphones can significantly enhance the accuracy of seismic event recognition. Therefore, we can exceedingly reduce the amount of data transmitted to the processing server, further lowering the load on the server processor and effectively increasing the lead time at each target site for an EEW system.
Progress towards a Miniaturised PIV System
Authors: Özer Özgün , Quinn Mark Kenneth
Published: November 2022

Particle image velocimetry is an important optical flow diagnostic tool due to its capacity for investigating a whole flow field without introducing disturbances. However, a significant drawback of PIV methods is their requirement for optical access, making capturing data in closed cavities and confined spaces extremely challenging. A potential approach to overcome this difficulty is miniaturising the system and placing the optical components inside the model. Conventional cross-correlation PIV methods do not allow this due to the size of current PIV cameras. In this study, a miniaturised autocorrelation-based stereo PIV system, which is volumetrically 1.2% of the conventional PIV cameras, was developed and tested. The miniature system is compared with a conventional stereo PIV in wind tunnel experiments up to 16 m/s free stream velocity and a 1.6% velocity difference is observed in the boundary layer flow. Despite a comparatively slow measurement rate of 4.5 Hz, the miniature PIV system demonstrates the ability to measure inside confined spaces and cavities and the ability to be mounted on board models and vehicles. However, limitations remain around conducting measurements with large velocity ranges and with regions of reversed flow due to the challenge of resolving a velocity of 0 m/s.
How Hoteliers Act in the Form of Organized Crime in Human Trafficking: A Case Study from Turkey
Authors: Cengiz Mahmut , Demir Oguzhan Omer
Published: November 2022

Because of supply and demand factors, human trafficking for sexual exploitation has always been a profitable industry. Turkey, as a host country for immigrants from both former Soviet countries and the Middle East, combines supply and demand together, attracting illicit business. Few studies have been conducted in the previous two decades to investigate the organized criminal element of human trafficking in this region. This research is based on ethnographic research in which trafficking victims (N = 11) were interviewed, and on-site observations were made. Our findings revealed that the trafficking industry in our study area was carried out by persons who were only loosely related to one another. There was no sophisticated, long-lasting sex trafficking organization. Membership was not severely limited, and individuals did not identify themselves as members of a well-known criminal organization. We provided policy recommendations and proposals for future research to address female trafficking for sexual exploitation.
Online Hate Speech and Immigration Acceptance: A Study of Spanish Provinces
Authors: Sánchez-Holgado Patricia , Amores Javier J. , Blanco-Herrero David
Published: November 2022

Online hate speech against migrants and refugees poses a grave challenge for coexistence and democracy. However, it also offers an opportunity to measure social acceptance towards this group. Using the Intergroup Contact and the Mediated Intergroup Contact Theory, and an already validated methodology, this article seeks to validate whether the use of hate speech as a predictor of social acceptance is valid at a provincial level in Spain. Contrasting 97,710 tweets about migrants and refugees with secondary data from public Spanish institutions about acceptance of immigration and foreign population, no correlation was observed, rejecting the main hypotheses, and hinting that the application of this approach might not be recommended for smaller entities, such as provinces (NUTS 3). However, the study offers descriptive data about racist hate speech spread on Twitter in Spain, and also discusses the need for more studies using big data to increase knowledge about online hate speech against migrants and refugees.
Non-Intrusive Load Monitoring of Residential Loads via Laplacian Eigenmaps and Hybrid Deep Learning Procedures
Authors: Moradzadeh Arash , Zakeri Sahar , Oraibi Waleed A. , Mohammadi-Ivatloo Behnam , Abdul-Malek Zulkurnain , Ghorbani Reza
Published: November 2022

Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE), a convolutional neural network (CNN), and a recurrent neural network (RNN), called LE-CRNN. In the proposed model architecture, LE, with its high ability in dimensional reduction, transfers the salient features and specific values of power consumption curves (PCCs) of household electrical appliances (HEAs) to a low-dimensional space. Then, the combined model of CRNN significantly improves the structure of CNN in fully connected layers so that the process of identification and separation of the HEA type can be performed without overfitting problems and with very high accuracy. In order to implement the suggested model, two real-world databases have been used. In a separate scenario, a conventional CNN is applied to the data for comparing the performance of the suggested model with the CNN. The designed networks are trained and validated using the PCCs of HEAs. Then, the whole energy consumption of the building obtained from the smart meter is used for load disaggregation. The trained networks, which contain features extracted from PCCs of HEAs, prove that they can disaggregate the total power consumption for houses intended for the Reference Energy Disaggregation Data Set (REDD) and Almanac of Minutely Power Dataset (AMPds) with average accuracies (Acc) of 97.59% and 97.03%, respectively. Finally, in order to show the accuracy of the developed hybrid model, the obtained results in this study are compared with the results of similar works for the same datasets.
Do Rainfall Shocks Prompt Commercial Input Purchases Amongst Smallholder Farmers in Diverse Regions and Environments in Malawi?
Authors: Makate Clifton , Makate Marshall
Published: November 2022

The ability of farmers to acquire inputs through purchase from available markets empowers them with the autonomy and capacity to diversify inputs, consequently enhancing the resilience of their cropping activities to various shocks. This paper investigates whether climate shocks, particularly rainfall shocks, influence commercial input purchase decisions by smallholder farmers in contrasting geographic regions in Malawi, with a particular emphasis on fertilizer, agrochemicals, seed, and labor. The empirical approach integrates historical weather information, climate shock perceptions with a longitudinal household survey data set to model commercial input purchasing decisions using appropriate latent variable models. The findings suggest that exposure to recent rainfall shocks, especially droughts, stimulates commercial input purchasing across regions, especially in drier central and southern regions of Malawi. This result holds true for general input purchase decisions and for specific inputs such as agrochemicals, fertilizer, seed, and labor. Although drought shocks considerably increase the probability of acquiring inputs through purchase, they occasionally diminish the intensity of purchases. Both objective and subjective measures of lagged rainfall shocks are revealed as significant determinants of commercial input purchases across regions in Malawi. In addition to regional heterogeneity findings, further analysis shows that the relatively wealthier, male-headed families and those with access to information are more likely to invest in purchased inputs in response to drought shocks. Scaling up policies that remove demand- and supply-side barriers to smallholder farmers’ access to commercial inputs from available markets is necessary for adaptation to rainfall shocks.
CNN-Based Inspection Module for Liquid Carton Recycling by the Reverse Vending Machine
Authors: Lee Chang Su , Lim Dong-Won
Published: November 2022

To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to a huge surge in plastic and paper waste which came from natural resources. To reduce the consumption of resources and protect the environment from pollution, such as that from landfills, waste should be recycled. One of precious recyclable materials from household waste is liquid cartons that are made of high-quality paper. To promote sustainable recycling, this paper proposes a vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) for collecting liquid packaging cartons in the reverse vending machine (RVM). The RVM is an unmanned automatic waste collector, and thus it needs the intelligence to inspect whether a deposited item is acceptable or not. The whole processing algorithm for collecting cartons, including the inspection step, is presented. When the waste is inserted into the RVM by a user after scanning the barcode on the waste, it is relocated to the inspection module, and the item is weighed. To develop the inspector, an experimental set-up with a video camera was built for image data generation and preparation. Using the image data, the inspection agent was trained. To make a good selection for the model, 17 pretrained CNN models were evaluated, and DenseNet121 was selected. To access the performance of the cameras, four different types were also evaluated. With the same CNN model, this paper found the effect of the number of training epochs being set to 10, 100, and 500. In the results, the most accurate agent was the 500-epoch model, as expected. By using the RVM process logic with this model, the results showed that the accuracy of detection was over 99% (overall probability from three inspections), and the time to inspect one item was less than 2 s. In conclusion, the proposed model was verified for whether it would be applicable to the RVM, as it could distinguish liquid cartons from other types of paper waste.
A Dynamic Social Network Matching Model for Virtual Power Plants and Distributed Energy Resources with Probabilistic Linguistic Information
Authors: Cai Mei , Hu Suqiong , Wang Ya , Xiao Jingmei
Published: November 2022

Virtual power plants (VPPs) offer an effective means to address the imbalance issue between electricity supply and demand to advance the world’s low-carbon development. To fully utilize the limited resources in the virtual power plant planning stage, a two-sided match between VPPs and distributed energy companies is needed to better implement resource aggregation management. Because of the vagueness in this matching environment, the probabilistic linguistic term set (PLTS) is necessary to apply to express the decision makers’ preference. Considering the complex social relationships and intense competition among companies, a dynamic social network two-sided matching model is proposed for solving the multi-attribute two-sided matching decision-making problem. Firstly, we present a matching satisfaction degree described by PLTS. A dynamic social trust degree based on the sliding time concept is proposed. Secondly, the social trust network relationships are built based on the direct and indirect dynamic trust degree among companies. This relationship is then combined with an improved trust rank algorithm to identify the most authoritative and the most trusted company to provide the target company with a recommendation for the next moment. Besides, given that companies compete for limited resources, we further define the competitive satisfaction degree and apply the two-sided matching model. Additionally, then a two-sided matching model is developed. Finally, our model is tested numerically to ensure its accuracy and reliability.
Spatio-Temporal Coupling Relationship between Rural Population and Cultivated Land Changes at the County Level in Guangdong Province
Authors: Ma Pei-Fang , Wu Da-Fang , Lin Tong , Wu Zhao-Jun , Li Zhao-Cheng , Liang Yi-Xuan , Zhang Yu-Cheng , Lai Huan-Ming
Published: November 2022

Based on the data regarding the rural population and cultivated land at the county level in Guangdong Province in 2000, 2010 and 2020, this paper analyzes the changes in rural population and cultivated land area at the county level and their spatio-temporal coupling characteristics based on GIS technology and model methods such as the elastic coefficient, and reveals the transformation process of the rural “man–land” relationship in Guangdong Province. The results show that the rural population of Guangdong Province shows a continuous growth trend, and the cultivated land area and per capita cultivated land area show a trend of continuous reduction. From 2000 to 2010, the rural population grew by 1.37% annually, while the cultivated land area and per capita cultivated land area decreased by 0.09% and 1.28% annually, respectively. From 2010 to 2020, the rural population grew by 0.07% annually, while the cultivated land area and per capita cultivated land area decreased by 1.09% and 1.15% annually, respectively. The overall coupling type of rural population and cultivated land change in Guangdong Province presents a trend of mainly maladjusted types and gradually changing to coordinated types. From 2000 to 2010, 70.64% of the counties displayed maladjusted development types. From 2010 to 2020, the number of counties with maladjusted development types decreased, while the proportion of coordinated types increased. The natural environment, economic development level and governmental decision-making behaviors were all driving factors of coupled development of rural population and cultivated land, and the driving mechanisms of different factors have clear spatial differences in different regions of Guangdong Province. The governments and relevant departments should stick to the red line of cultivated protection, make intensive use of cultivated land and improve the livelihoods of farmers. Moreover, the Pearl River Delta, eastern Guangdong, western Guangdong and northern Guangdong should improve the cooperation mechanism for rural revitalization. The study is conducive to providing a scientific basis for rural population and cultivated land utilization management, rural revitalization and new rural construction, and promoting urban–rural integration development under the new situation.
Direct and Spillover Effects of Urban Land Expansion on Habitat Quality in Chengdu-Chongqing Urban Agglomeration
Authors: Wang Sicheng , Lu Feng , Wei Guoen
Published: November 2022

Urban land expansion has dramatically changed the spatial distribution patterns and functional structure of habitats. Previous studies on the spatial externality effect of urban land expansion on the habitat quality of urban agglomerations are still insufficient. With the use of remote sensing and statistical data from 2000 to 2018, this study explored the evolutionary relationship between urban land expansion and habitat quality in the Chengdu-Chongqing urban agglomeration (CUA) using the bivariate local autocorrelation method and spatial Durbin model. Partial differential equation decomposition of the local and spatial spillover effects was implemented to investigate the marginal effects of the influencing factors. The highlights of the results are as follows: CUA’s urban land increased by 2890.42 km2 from 2000 to 2018, mainly caused by urban encroachment over farmland and grassland. New urban lands were situated primarily in the main urban districts of Chengdu and Chongqing; urban expansion intensity slowed to 7.64% in 2010–2018, declining by 53.95% from 2000 to 2010. The average habitat quality decreased to 0.905, and two “ring-shaped decline areas” were formed around the main urban areas of Chengdu and Chongqing. “Low-High” and “Low-Low” clusters were the main associations between urban land expansion and habitat quality changes. The impact of urban land expansion on local habitat quality changed from insignificant to negative, while its spatial spillover effects over adjacent areas have increased the negative environmental externalities to habitat quality in adjacent areas through spatial spillovers. Our findings provide evidence for urban agglomerations such as CUA that are still being cultivated to carry out cross-city joint protection strategies of habitat quality, also proving that habitat quality protection should be an integration of urban expansion regulation, natural adaptation and socioeconomic adjustment.
Integrating Woody Biochar, Women, and Youth in Maine’s Bioenergy Industry: Benefits and Challenges
Authors: Novak Abigayl , Glover Katherine , Li Ling
Published: November 2022

Over 30% of Maine’s forestry industry is made up of women, with women in the minority comprising a small part of this. Improving representation in this industry can serve as a way for more women to enter the industry, to the benefit of their own professional paths and addressing climate change. Here, we share an effective approach to increase the number of women in the bioenergy sector, with an example of programming that centers mentoring and targets women in underrepresented groups. We found that approximately 29% of women hold executive or leadership positions in Maine companies related to bioenergy. We also discuss current strategies for improving gender-diverse teams in the bioenergy industry and increasing women’s presence in this industry. Presented as a case study, our training program had two goals: (1) a focus on the benefits of biochar and its importance in boosting Maine’s economy and mitigating climate change; and (2) using gender-diverse teams to encourage the next generation of girls and young women. Research universities are well-positioned to offer similar programs, and thus play a critical role in increasing and retaining the number of young women in forestry, agricultural, renewable energy, sustainable materials, and technologies programs. Our experience illustrates that gender-diverse teams can lead to better collaboration, innovation, and teamwork in the bioenergy and agricultural industry. This is the dynamic environment that can foster the relationship-building and mentoring necessary to retain underrepresented groups in the industry.