bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of Vertical Phase Center Offset and Phase Center Variations for BDS-3 B1CB2a Signals
Authors: Xie Shichao , Huang Guanwen , Wang Le , Yan Xingyuan , Qin Zhiwei
Published: December 2022

The BeiDou Global Satellite Navigation System (BDS-3) broadcast newly developed B1C and B2a signals. To provide a better service for global users, the vertical phase center offset (PCO) and phase center variation (PCV) are estimated for the B1C/B2a ionospheric-free linear combination of the BDS-3 inclined geostationary orbit (IGSO) and medium earth orbit (MEO) satellites in this study. And considering the traditional PCC estimation method needs two Precise orbit determination (POD) processing, based on the correlation between PCO z-offset and PCV, the theoretical analysis and experimental comparison have been made to discuss whether the POD procedure for the PCO estimation can be omitted. The estimated z-offset time series revealed the inadequacy of the solar radiation pressure (SRP) model for the IGSO satellites and the MEO satellites with Pseudo Random Noise code (PRN) C45 and C46. The PCVraws estimated by the traditional method and the PCO estimation omitted method have the same characteristic. The final PCO z-offsets and PCVs calculated by the two schemes agreed very well with differences can be harmlessly ignored, which confirmed that the PCO estimation can be safely omitted to save computation time. The PCC model proposed in this study has been compared with the Test and Assessment Research Center of China Satellite Navigation Office (TARC/CSNO) released model, the qualities of the orbits and BDS-only precise point positioning (PPP) solutions of the new model both show improvements, except for the IGSO orbits. The analysis of the IGSO orbits further verifies the SRP model is not suitable for the IGSO satellites.
Quality Assessment of FY-3D/MERSI-II Thermal Infrared Brightness Temperature Data from the Arctic Region: Application to Ice Surface Temperature Inversion
Authors: Chen Haihua , Meng Xin , Li Lele , Ni Kun
Published: December 2022

The Arctic region plays an important role in the global climate system. To promote the application of Medium Resolution Spectral Imager-II (MERSI-II) data in the ice surface temperature (IST) inversion, we used the thermal infrared channels (channels 24 and 25) of the MERSI-II onboard Chinese FY-3D satellite and the thermal infrared channels (channels 31 and 32) of the Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautical and Space Administration (NASA) Aqua satellite for data analysis. Using the Observation–Observation cross-calibration algorithm to cross-calibrate the MERSI and MODIS thermal infrared brightness temperature (Tb) data in the Arctic, channel 24 and 25 data from the FY-3D/MERSI-II on Arctic ice were evaluated. The thermal infrared Tb data of the MERSI-II were used to retrieve the IST via the split-window algorithm. In this study, the correlation coefficients of the thermal infrared channel Tb data between the MERSI and MODIS were >0.95, the mean bias was −0.5501–0.1262 K, and the standard deviation (Std) was <1.3582 K. After linear fitting, the MERSI-II thermal infrared Tb data were closer to the MODIS data, and the bias range of the 11 μm and 12 μm channels was −0.0214–0.0119 K and the Std was <1.2987 K. These results indicate that the quality of the MERSI-II data is comparable to that of the MODIS data, so that can be used for application to IST inversion. When using the MERSI thermal infrared Tb data after calibration to retrieve the IST, the results of the MERSI and MODIS IST were more consistent. By comparing the IST retrieved from the MERSI thermal infrared calibrated Tb data with MODIS MYD29 product, the mean bias was −0.0612–0.0423 °C and the Std was <1.3988 °C. Using the MERSI thermal infrared Tb data after calibration is better than that before calibration for retrieving the IST. When comparing the Arctic ocean sea and ice surface temperature reprocessed data (L4 SST/IST) with the IST data retrieved from MERSI, the bias was 0.9891–2.7510 °C, and the Std was <3.5774 °C.
An In-Depth Assessment of the Drivers Changing China’s Crop Production Using an LMDI Decomposition Approach
Authors: Long Yuqiao , Wu Wenbin , Wellens Joost , Colinet Gilles , Meersmans Jeroen
Published: December 2022

Over the last decades, growing crop production across China has had far-reaching consequences for both the environment and human welfare. One of the emerging questions is “how to meet the growing food demand in China?” In essence, the consensus is that the best way forward would be to increase crop yield rather than further extend the current cropland area. However, assessing progress in crop production is challenging as it is driven by multiple factors. To date, there are no studies to determine how multiple factors affect the crop production increase, considering both intensive farming (using yield and multiple cropping index) and large-scale farming (using mean parcel size and number of parcels). Using the Logarithmic-Mean-Divisia-Index (LMDI) decomposition method combined with statistical data and land cover data (GlobeLand30), we assess the contribution of intensive farming and large-scale farming changes to crop production dynamics at the national and county scale. Despite a negative contribution from MPS (mean parcel size, ), national crop production increased due to positive contributions from yield (), MCI (multiple cropping index, ), as well as NP (number of parcels, ). This allowed China to meet the growing national crop demand. We further find that large differences across regions persist over time. For most counties, the increase in crop production is a consequence of improved yields. However, in the North China Plain, NP is another important factor leading to crop production improvement. On the other hand, regions witnessing a decrease in crop production (e.g., the southeast coastal area of China) were characterized by a remarkable decrease in yield and MCI. Our detailed analyses of crop production provide accurate estimates and therefore can guide policymakers in addressing food security issues. Specifically, besides stabilizing yield and maintaining the total NP, it would be advantageous for crop production to increase the mean parcel size and MCI through land consolidation and financial assistance for land transfer and advanced agricultural infrastructure.
Automated Identification of Overheated Belt Conveyor Idlers in Thermal Images with Complex Backgrounds Using Binary Classification with CNN
Authors: Siami Mohammad , Barszcz Tomasz , Wodecki Jacek , Zimroz Radoslaw
Published: December 2022

Mechanical industrial infrastructures in mining sites must be monitored regularly. Conveyor systems are mechanical systems that are commonly used for safe and efficient transportation of bulk goods in mines. Regular inspection of conveyor systems is a challenging task for mining enterprises, as conveyor systems’ lengths can reach tens of kilometers, where several thousand idlers need to be monitored. Considering the harsh environmental conditions that can affect human health, manual inspection of conveyor systems can be extremely difficult. Hence, the authors proposed an automatic robotics-based inspection for condition monitoring of belt conveyor idlers using infrared images, instead of vibrations and acoustic signals that are commonly used for condition monitoring applications. The first step in the whole process is to segment the overheated idlers from the complex background. However, classical image segmentation techniques do not always deliver accurate results in the detection of target in infrared images with complex backgrounds. For improving the quality of captured infrared images, preprocessing stages are introduced. Afterward, an anomaly detection method based on an outlier detection technique is applied to the preprocessed image for the segmentation of hotspots. Due to the presence of different thermal sources in mining sites that can be captured and wrongly identified as overheated idlers, in this research, we address the overheated idler detection process as an image binary classification task. For this reason, a Convolutional Neural Network (CNN) was used for the binary classification of the segmented thermal images. The accuracy of the proposed condition monitoring technique was compared with our previous research. The metrics for the previous methodology reach a precision of 0.4590 and an F1 score of 0.6292. The metrics for the proposed method reach a precision of 0.9740 and an F1 score of 0.9782. The proposed classification method considerably improved our previous results in terms of the true identification of overheated idlers in the presence of complex backgrounds.
Multi-Scale Strengthened Directional Difference Algorithm Based on the Human Vision System
Authors: Zhang Yuye , Zheng Ying , Li Xiuhong
Published: December 2022

The human visual system (HVS) mechanism has been successfully introduced into the field of infrared small target detection. However, most of the current detection algorithms based on the mechanism of the human visual system ignore the continuous direction information and are easily disturbed by highlight noise and object edges. In this paper, a multi-scale strengthened directional difference (MSDD) algorithm is proposed. It is mainly divided into two parts: local directional intensity measure (LDIM) and local directional fluctuation measure (LDFM). In LDIM, an improved window is used to suppress most edge clutter, highlights, and holes and enhance true targets. In LDFM, the characteristics of the target area, the background area, and the connection between the target and the background are considered, which further highlights the true target signal and suppresses the corner clutter. Then, the MSDD saliency map is obtained by fusing the LDIM map and the LDFM map. Finally, an adaptive threshold segmentation method is employed to capture true targets. The experiments show that the proposed method achieves better detection performance in complex backgrounds than several classical and widely used methods.
Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems
Authors: Yousaf Muhammad Zain , Tahir Muhammad Faizan , Raza Ali , Khan Muhammad Ahmad , Badshah Fazal
Published: December 2022

We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg–Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 Ω. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 Ω and is more robust.
Improving Network Training on Resource-Constrained Devices via Habituation Normalization
Authors: Lai Huixia , Zhang Lulu , Zhang Shi
Published: December 2022

As a technique for accelerating and stabilizing training, the batch normalization (BN) is widely used in deep learning. However, BN cannot effectively estimate the mean and the variance of samples when training/fine-tuning with small batches of data on resource-constrained devices. It will lead to a decrease in the accuracy of the deep learning model. In the fruit fly olfactory system, the algorithm based on the “negative image” habituation model can filter redundant information and improve numerical stability. Inspired by the circuit mechanism, we propose a novel normalization method, the habituation normalization (HN). HN first eliminates the “negative image” obtained by habituation and then calculates the statistics for normalizing. It solves the problem of accuracy degradation of BN when the batch size is small. The experiment results show that HN can speed up neural network training and improve the model accuracy on vanilla LeNet-5, VGG16, and ResNet-50 in the Fashion MNIST and CIFAR10 datasets. Compared with four standard normalization methods, HN keeps stable and high accuracy in different batch sizes, which shows that HN has strong robustness. Finally, the applying HN to the deep learning-based EEG signal application system indicates that HN is suitable for the network fine-tuning and neural network applications under limited computing power and memory.
Sensor-Based Assessment of Social Isolation and Loneliness in Older Adults: A Survey
Authors: Prabhu Deepa , Kholghi Mahnoosh , Sandhu Moid , Lu Wei , Packer Katie , Higgins Liesel , Silvera-Tawil David
Published: December 2022

Social isolation (SI) and loneliness are ‘invisible enemies’. They affect older people’s health and quality of life and have significant impact on aged care resources. While in-person screening tools for SI and loneliness exist, staff shortages and psycho-social challenges fed by stereotypes are significant barriers to their implementation in routine care. Autonomous sensor-based approaches can be used to overcome these challenges by enabling unobtrusive and privacy-preserving assessments of SI and loneliness. This paper presents a comprehensive overview of sensor-based tools to assess social isolation and loneliness through a structured critical review of the relevant literature. The aim of this survey is to identify, categorise, and synthesise studies in which sensing technologies have been used to measure activity and behavioural markers of SI and loneliness in older adults. This survey identified a number of feasibility studies using ambient sensors for measuring SI and loneliness activity markers. Time spent out of home and time spent in different parts of the home were found to show strong associations with SI and loneliness scores derived from standard instruments. This survey found a lack of long-term, in-depth studies in this area with older populations. Specifically, research gaps on the use of wearable and smart phone sensors in this population were identified, including the need for co-design that is important for effective adoption and practical implementation of sensor-based SI and loneliness assessment in older adults.
Use of Robot-Assisted Gait Training in Pediatric Patients with Cerebral Palsy in an Inpatient Setting—A Randomized Controlled Trial
Authors: Moll Fabian , Kessel Axel , Bonetto Anna , Stresow Johanna , Herten Monika , Dudda Marcel , Adermann Jens
Published: December 2022

Robot-assisted gait training (RAGT) provides a task-based support of walking using exoskeletons. Evidence shows moderate, but positive effects in the therapy of patients with cerebral palsy (CP). This study investigates the impact of RAGT on walking speed and gait parameters in pediatric CP patients. Thirty subjects (male = 23; female = 7), with a mean age of 13.0 ± 2.5 (9–17) years, and with spastic CP, were recruited. The intervention group (n = 15) underwent six 20-minute RAGT sessions with the Hybrid Assistive Limb (HAL) during an 11-day hospital stay. Additionally, a therapy concept including physiotherapy, physician-performed manual medicine, massage and exercise therapy was provided. The control group (n = 15) was treated with the therapy concept only. The outcome was based on a 10-Metre Walking Test (10MWT), 6-Minute Walking Test (6MWT), Gross Motor Function Measure (GMFM-88) and lower extremities passive range of motion. The intervention group achieved a mean increase in walking speed in the 10MWT (self-selected walking speed SSW) of 5.5 s (p = 0.378). There were no significant differences between the groups in the 10MWT (max) (p = 0.123) and the 6MWT (p = 0.8). Changes in the GMFM (total) and in the dimension standing and walking, running and jumping (D + E) showed clinically relevant significant results (p = 0.002 and p = 0.046). RAGT as a supplement to an inpatient therapy stay appears to have a positive, yet not significant impact on the gait parameters of pediatric CP patients as well as motivating them to practice walking. Further studies with adapted study designs are needed to evaluate different influencing factors.
A Differential Inertial Wearable Device for Breathing Parameter Detection: Hardware and Firmware Development, Experimental Characterization
Authors: De Fazio Roberto , Greco Maria Rosaria , De Vittorio Massimo , Visconti Paolo
Published: December 2022

Breathing monitoring is crucial for evaluating a patient’s health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient’s chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR—respiration rate; TI/TE—inhalation/exhalation time; IER—inhalation-to-exhalation time; V—flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland–Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation (r¯ = 0.92) and low mean difference (MD¯ = −0.27 BrPM (breaths per minute)), limits of agreement (LoA¯ = +1.16/−1.75 BrPM), and mean absolute error (MAE¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users.
Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation
Authors: Alkhoury Ludvik , Choi JiWon , Chandran Vishnu D. , De Carvalho Gabriela B. , Pal Saikat , Kam Moshe
Published: December 2022

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on “micro-motion” such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive “macro-motion”. Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive “macro-motion”, exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run.
A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning
Authors: Khalili Boshra , Ali Abbaspour Rahim , Chehreghan Alireza , Vesali Nahid
Published: December 2022

The rise in location-based service (LBS) applications has increased the need for indoor positioning. Various methods are available for indoor positioning, among which pedestrian dead reckoning (PDR) requires no infrastructure. However, with this method, cumulative error increases over time. Moreover, the robustness of the PDR positioning depends on different pedestrian activities, walking speeds and pedestrian characteristics. This paper proposes the adaptive PDR method to overcome these problems by recognizing various phone-carrying modes, including texting, calling and swinging, as well as different pedestrian activities, including ascending and descending stairs and walking. Different walking speeds are also distinguished. By detecting changes in speed during walking, PDR positioning remains accurate and robust despite speed variations. Each motion state is also studied separately based on gender. Using the proposed classification approach consisting of SVM and DTree algorithms, different motion states and walking speeds are identified with an overall accuracy of 97.03% for women and 97.67% for men. The step detection and step length estimation model parameters are also adjusted based on each walking speed, gender and motion state. The relative error values of distance estimation of the proposed method for texting, calling and swinging are 0.87%, 0.66% and 0.92% for women and 1.14%, 0.92% and 0.76% for men, respectively. Accelerometer, gyroscope and magnetometer data are integrated with a GDA filter for heading estimation. Furthermore, pressure sensor measurements are used to detect surface transmission between different floors of a building. Finally, for three phone-carrying modes, including texting, calling and swinging, the mean absolute positioning errors of the proposed method on a trajectory of 159.2 m in a multi-story building are, respectively, 1.28 m, 0.98 m and 1.29 m for women and 1.26 m, 1.17 m and 1.25 m for men.
Tribocorrosion Behavior of Micro/Nanoscale Surface Coatings
Authors: Hoque Md Ashraful , Yao Chun-Wei , Khanal Mukunda , Lian Ian
Published: December 2022

Wear and corrosion are common issues of material degradation and failure in industrial appliances. Wear is a damaging process that can impact surface contacts and, more specifically, can cause the loss and distortion of material from a surface because of the contacting object’s mechanical action via motion. More wear occurs during the process of corrosion, in which oxide particles or debris are released from the contacting material. These types of wear debris and accumulated oxide particles released during corrosion cause a combination of wear-corrosion processes. Bringing together the fields of tribology and corrosion research, tribocorrosion is a field of study which deals with mechanical and electrochemical interactions between bodies in motion. More specifically, it is the study of mechanisms caused by the combined effects of mechanical stress and chemical/electrochemical interactions with the environment. Tribocorrosion testing methods provide new opportunities for studying the electrochemical nature of corrosion combined with mechanical loading to establish a synergistic relationship between corrosion and wear. To improve tribological, mechanical, and anti-corrosion performances, several surface modification techniques are being applied to develop functional coatings with micro/nano features. This review of the literature explores recent and enlightening research into the tribocorrosive properties of micro/nano coatings. It also looks at recent discussions of the most common experimental methods and some newer, promising experimental methods in tribocorrosion to elucidate their applications in the field of micro/nano coatings.
CGUN-2A: Deep Graph Convolutional Network via Contrastive Learning for Large-Scale Zero-Shot Image Classification
Authors: Li Liangwei , Liu Lin , Du Xiaohui , Wang Xiangzhou , Zhang Ziruo , Zhang Jing , Zhang Ping , Liu Juanxiu
Published: December 2022

Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and to reason on unknown categories. Graph Convolutional Network (GCN) has recently been viewed as a potential approach to zero-shot learning. GCN enables knowledge transfer by sharing the statistical strength of nodes in the graph. More layers of graph convolution are stacked in order to aggregate the hierarchical information in the KG. However, the Laplacian over-smoothing problem will be severe as the number of GCN layers deepens, which leads the features between nodes toward a tendency to be similar and degrade the performance of zero-shot image classification tasks. We consider two parts to mitigate the Laplacian over-smoothing problem, namely reducing the invalid node aggregation and improving the discriminability among nodes in the deep graph network. We propose a top-k graph pooling method based on the self-attention mechanism to control specific node aggregation, and we introduce a dual structural symmetric knowledge graph additionally to enhance the representation of nodes in the latent space. Finally, we apply these new concepts to the recently widely used contrastive learning framework and propose a novel Contrastive Graph U-Net with two Attention-based graph pooling (Att-gPool) layers, CGUN-2A, which explicitly alleviates the Laplacian over-smoothing problem. To evaluate the performance of the method on complex real-world scenes, we test it on the large-scale zero-shot image classification dataset. Extensive experiments show the positive effect of allowing nodes to perform specific aggregation, as well as homogeneous graph comparison, in our deep graph network. We show how it significantly boosts zero-shot image classification performance. The Hit@1 accuracy is 17.5% relatively higher than the baseline model on the ImageNet21K dataset.
An Advanced Internet of Things System for Heatstroke Prevention with a Noninvasive Dual-Heat-Flux Thermometer
Authors: Tamura Toshiyo , Huang Ming , Yoshimura Takumi , Umezu Shinjiro , Ogata Toru
Published: December 2022

Heatstroke is a concern during sudden heat waves. We designed and prototyped an Internet of Things system for heatstroke prevention, which integrates physiological information, including deep body temperature (DBT), based on the dual-heat-flux method. A dual-heat-flux thermometer developed to monitor DBT in real-time was also evaluated. Real-time readings from the thermometer are stored on a cloud platform and processed by a decision rule, which can alert the user to heatstroke. Although the validation of the system is ongoing, its feasibility is demonstrated in a preliminary experiment.
Plasmonic Sensors beyond the Phase Matching Condition: A Simplified Approach
Authors: Tuniz Alessandro , Song Alex Y. , Della Valle Giuseppe , de Sterke C. Martijn
Published: December 2022

The conventional approach to optimising plasmonic sensors is typically based entirely on ensuring phase matching between the excitation wave and the surface plasmon supported by the metallic structure. However, this leads to suboptimal performance, even in the simplest sensor configuration based on the Otto geometry. We present a simplified coupled mode theory approach for evaluating and optimizing the sensing properties of plasmonic waveguide refractive index sensors. It only requires the calculation of propagation constants, without the need for calculating mode overlap integrals. We apply our method by evaluating the wavelength-, device length- and refractive index-dependent transmission spectra for an example silicon-on-insulator-based sensor of finite length. This reveals all salient spectral features which are consistent with full-field finite element calculations. This work provides a rapid and convenient framework for designing dielectric-plasmonic sensor prototypes—its applicability to the case of fibre plasmonic sensors is also discussed.
Individual Attitudes towards Immigration in Turkey: Evidence from the European Social Survey
Authors: Karapınar Kocağ Esra , Longhi Simonetta
Published: December 2022

One of the reasons why people hold anti-immigration attitudes is the fear that immigrants “rob jobs” of natives and decrease wages. However, academic literature finds that this is not the case. Nevertheless, in various countries, people still tend to oppose immigration. Opposition to immigration was particularly high in Turkey in the early 2000s, where almost half of the respondents to the Turkish part of the European Social Survey reported they would prefer to allow no immigrants into Turkey. This is although immigration to Turkey is very low. Turkey is becoming an important destination country as conflicts in neighboring countries force many people to flee. Therefore, understanding the opposition to immigration in Turkey is crucial for managing age immigration flows efficiently. For this purpose, we investigate the determinants of attitudes towards immigration in Turkey using the European Social Survey and Turkish population census data. The findings of the ordered probit model reveal that Turkish people tend to hold more negative attitudes towards immigration where the regional share of immigrants is higher. The little chance of contact with immigrants in Turkey through a lower share of immigrants compared to other European countries seems to influence natives’ pro-immigrant attitudes negatively.
How Do Hospitality Workers Perceive Their Work Skills before and after the Lockdown Imposed by the COVID-19 Pandemic?
Authors: Magalhães Carla , Araújo Arthur , Andrés-Marques Maria Isabel
Published: December 2022

The present research aims to compare hospitality workers’ confidence about the perceived need to improve their soft and hard skills during and after the lockdown period. With this purpose, a questionnaire was applied to a sample of workers from four- and five-star hotels located in the historic centre of Porto (Portugal). Data collection took place in two different time periods: April 2020 and April 2022. The findings evidence that workers are more confident of their skills but are also more aware of the need to develop them, particularly their soft skills. This is likely related to the context of remote work, which intensified the need to learn, and evidenced the necessity of skills such as teamwork and adaptation or flexibility, which showed the greatest increase in perceived need to improve. Despite such an increase, the competencies workers feel like they need to improve the most are still hard, i.e., linguistic and digital. Accordingly, those in which workers are the most confident are soft, i.e., teamwork, interpersonal relationships, and adaptation or flexibility, which is likely because those were the most developed during the pandemic. The findings provide useful insights for human resources management in the hospitality sector. The study points to good practices aiming to address the real development needs of hospitality workers.
Ecological Insight, Anatomical Features, and Fiber Characterization of Leptadenia pyrotechnica (Forrsk.) Decne. as a Promising Resource
Authors: Abd-ElGawad Ahmed M. , Assaeed Abdulaziz M. , Bonanomi Giuliano , El-Amier Yasser A.
Published: December 2022

Wild plants are considered promising natural eco-friendly resources for fibers. Leptadenia pyrotechnica is a xerophytic shrub that flourishes in a sandy desert habitat with high biomass production; therefore, it could be a potential resource for fibers. The present study aimed to investigate the vegetation composition of L. pyrotechnica communities and their correlation with soil variables. Additionally, this study aimed to evaluate the anatomical features of the stem as well as fiber characteristics, including chemical, biometry, morphological, and optical properties. The vegetation analysis showed the presence of 60 species belonging to 22 families, with a prevalence of therophytes. Four communities were determined, dominated by L. pyrotechnica and with co-dominance of the shrubs Haloxylon salicornicum, Ochradenus baccatus, and Retama raetam. The soil organic matter, salinity, texture, and cations were parameters that substantially affect the L. pyrotechnica community. The anatomical investigation showed the structural (anatomical) adaptation of L. pyrotechnica to arid habitats. Chemical analysis of the raw plant material revealed satisfactory levels of cellulose and hemicellulose (48.61% and 18.59%), while lignin and ash contents were relatively low, compared to hardwoods and softwoods. The fiber characterization revealed that fibesr length was 0.72 mm, while width and cell wall thickness were 20.46 and 6.48 μm, respectively. The optical properties revealed a birefringence of 0.028, indicating a good refractive index. These fiber characteristics showed that L. pyrotechnica could be used as raw material for the production of good-quality paper. A further feasibility study is recommended for the evaluation of L. pyrotechnica fibers, as a promising resource for papermaking at a large scale.
Bibliometric Analysis of the Scientific Research of Food Industry By-Products in the Period 1976–2021
Authors: Hasenay Sanda , Ačkar Đurđica
Published: December 2022

The combined necessity for sustainable production, a decrease in the human impact on the environment, the European Union striving to be climate neutral, and the pressure on the food industry to introduce sustainable technologies and products and reduce emissions into the environment to the minimum has led to perceiving food industry by-products (FIB) as valuable raw materials rather than waste. This has driven a constant increase in scientific research regarding the use of FIB. The aim of this research was to establish focal points and directions of the scientific research regarding FIB. Descriptive bibliography and visualization using software CiteSpace II were used to analyze research published in journals indexed in the Web of Science Core Collection and determine and explain development trends. The results showed a constant growth of the scientific interest for FIB, which intensified since 2015. Research is being conducted worldwide, with Spain (13%) and Italy (11%) as leading countries. The results are published in journals with high impact factors and quartiles, using references published in the same rank.
Energy Productivity and Environmental Degradation in Germany: Evidence from Novel Fourier Approaches
Authors: Addai Kwaku , Ozbay Rahmi Deniz , Castanho Rui Alexandre , Genc Sema Yilmaz , Couto Gualter , Kirikkaleli Dervis
Published: December 2022

The increased consumption of fossil fuels worldwide has resulted in unprecedented historic environmental degradation and global warming. According to the United Nations, this is both the defining crisis of our time and a race the world could win given the right policy attention. Researchers seek to find critical pathways to provide policy recommendations for reducing environmental degradation. This paper aims to investigate the effect of energy productivity on environmental degradation in Germany while controlling for economic growth, primary energy consumption, and globalization for the period between 1990Q1 and 2019Q4. The outcomes of the Fourier ARDL long-run estimates indicate that (i) both energy productivity and globalization have a negative effect on carbon emissions in Germany, and (ii) both economic growth and primary energy consumption have positive effects on carbon dioxide emissions. These outcomes provide significant policy insights to EU members with respect to reducing their reliance on Russian energy imports amidst the rising energy bills and ongoing geopolitical war with Ukraine while increasing investments to realize their energy turnaround policy objectives.
Linking Environmental Corporate Social Responsibility with Green Innovation Performance: The Mediating Role of Shared Vision Capability and the Moderating Role of Resource Slack
Authors: Ruan Rongbin , Chen Wan , Zhu Zuping
Published: December 2022

Environmental corporate social responsibility is important for firms to achieve both economic benefits and the sustainable development of firms and the environment, which are of great concern to theorists and practitioners. However, the relationship between environmental corporate social responsibility and green innovation performance is still unclear. To address the research gap, we propose a research model that incorporates the mediating effect of shared vision capability, and the moderating effect of resource slack, to investigate whether and when environmental corporate social responsibility affects green innovation performance. Data were obtained from 351 respondents of Chinese firms through a questionnaire. The results confirmed that environmental corporate social responsibility is positively associated with green innovation performance. The results also confirmed that shared vision capability mediated the environmental corporate social responsibility–green innovation performance link. Resource slack statistically significantly moderated the relationship between environmental corporate social responsibility and green innovation performance. These findings offer novel insight for managers when formulating management policies about environmental corporate social responsibility, shared vision capability, and green innovation performance, which can help enterprises to achieve the goal of sustainable development and promote environmental friendliness in society at large.
Identification and Assessment of Groundwater and Soil Contamination from an Informal Landfill Site
Authors: Liu Xinyang , Wang Yu
Published: December 2022

Landfills are a potential source of local environmental pollution of all kinds, and the gradual destruction of seepage-proof structures in informal landfills will lead to contamination of the surrounding soil and groundwater environment. In this study, an informal landfill site in eastern China is used as the research object. Using technologies such as unmanned vessels and monitoring well imaging to delineate the amount and distribution of polluting media, sampling of the surrounding soil, sediment, groundwater, and surface water for testing, analysis, and evaluation is carried out visually and finely for heavy metals, petroleum hydrocarbons, volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), and other indicators. The test results show that volatile phenols are the main contaminant species in the shallow groundwater, chlorinated hydrocarbons and benzene were prevalent in the deep groundwater, hexachlorobenzene and lead in the surface soil, and di(2-Ethylhexyl) phthalate in the deep soil (5.5 m), with a maximum exceedance of 1.24 times. Nearly 10 years have passed since the waste dumping incident at the landfill, but characteristic contaminants are still detected in the topsoil of the dumping area, which shows the long-term nature of the environmental impact of illegal dumping on the site. The study recommends that when developing a comprehensive remediation plan, the persistence of the environmental impact of the waste should be considered and appropriate remediation measures should be screened.
How Do Different Land Uses/Covers Contribute to Land Surface Temperature and Albedo?
Authors: Varamesh Saeid , Mohtaram Anbaran Sohrab , Shirmohammadi Bagher , Al-Ansari Nadir , Shabani Saeid , Jaafari Abolfazl
Published: December 2022

Land surface temperature (LST) and land surface albedo (LSA) are the two key regional and global climate-controlling parameters; assessing their behavior would likely result in a better understanding of the appropriate adaptation strategies to mitigate the consequences of climate change. This study was conducted to explore the spatiotemporal variability in LST and LSA across different land use/cover (LULC) classes in northwest Iran. To do so, we first applied an object-oriented algorithm to the 10 m resolution Sentinel-2 images of summer 2019 to generate a LULC map of a 3284 km2 region in northwest Iran. Then, we computed the LST and LSA of each LULC class using the SEBAL algorithm, which was applied to the Landsat-8 images from the summer of 2019 and winter of 2020. The results showed that during the summer season, the maximum and minimum LSA values were associated with barren land (0.33) and water bodies (0.11), respectively; during the winter season, the maximum LSA value was observed for farmland and snow cover, and the minimum value was observed in forest areas (0.21). The maximum and minimum LST values in summer were acquired from rangeland (37 °C) and water bodies (24 °C), respectively; the maximum and minimum values of winter values were detected in forests (4.14 °C) and snow cover (−21.36 °C), respectively. Our results revealed that barren land and residential areas, having the maximum LSA in summer, were able to reduce the heating effects to some extent. Forest areas, due to their low LSA and high LST, particularly in winter, had a greater effect on regional warming compared with other LULC classes. Our study suggests that forests might not always mitigate the effects of global warming as much as we expect.
Quantitative Evaluation and Characteristic Analysis of Resource Allocation Efficiency of the Energy Industry in the Yangtze River Economic Belt
Authors: Dong Wanhao , Yao Jin , Zhang Bochao
Published: December 2022

The resource allocation efficiency of the energy industry in the Yangtze River economic belt is related to the green and high-quality development of the region. This study constructed a quantitative model which modified from the traditional HK model to evaluate the efficiency of energy industry resource allocation. This paper makes a quantitative evaluation of the efficiency of inter-industrial and inter-regional resource allocation of the energy industry in the Yangtze River economic belt from 2000 to 2019. The results show that: the average annual loss of total factor productivity of the energy industry in the Yangtze River economic belt is as high as 25.11% due to the misallocation of resources among the energy industries, capital misallocation, labor misallocation, and intermediate input misallocation were 12.32%, 7.08%, and 5.08%, respectively. The misallocation of resources among the energy industries of the provinces in upper, middle, and lower reaches is gradually increasing in turn. The average annual loss of total factor productivity (TFP) in the energy industry of the Yangtze River economic belt is 6.5% due to the misallocation of resources between regions. Capital misallocation, labor misallocation, and intermediate input misallocation were 2.48%, 3.40%, and 0.63%, respectively. In the upper, middle, and lower reaches of the Yangtze River, the labor force, capital, and intermediate input of each province’s energy industry were misallocated to a different extent. According to the trend of resource input in the energy industry of various provinces and cities, the middle and lower reaches of the Yangtze River also show different characteristics.
Coordinated Distribution or Client Introduce? Analysis of Energy Conservation and Emission Reduction in Canadian Logistics Enterprises
Authors: Bai Yuntao , Gao Yuan , Li Delong , Liu Dehai
Published: December 2022

Due to the large area and small population of Canada, the efficiency of logistics enterprises is low, and each logistics enterprise needs to cooperate to save energy and reduce emissions. Considering that each logistics enterprise can realize the maximization of its own benefit by controlling the distribution volume and the input of facilities. In this article, the differential game model of individual distribution, coordinated distribution and paid introduction of customers for each logistics enterprise is constructed, the balanced distribution volume, capital input and social welfare functions of each logistics enterprise under the three modes are obtained, and the applicable conditions of various distribution cooperation channels are compared. The research results show that if the organizational cost between logistics enterprises is greater than the communication cost, the benefits of large-scale logistics enterprises under the introduction customer mode are greater than those under the collaborative distribution mode. However, only the communication cost and organizational cost are relatively small, and the profit of small-scale logistics enterprises under the introduction of the customer mode is smaller than that under the collaborative distribution mode.
Four Perspectives on a Sustainable Future in Nosara, Costa Rica
Authors: Munno Greg , Salas Castro Álvaro , Nabatchi Tina , Freitag Christian
Published: December 2022

The town of Nosara on Costa Rica’s Nicoya peninsula is home to a vibrant community of diverse residents and is adjacent to an important turtle nesting site. However, tensions between lifelong residents, more recent transplants, visitors, and developers have increased as more of the world discovers this once-isolated haven. Climate change, income inequality, and alienation from a distant government apparatus have further complicated effective land-use planning and fractured social cohesion. Using a mixed-method approach of in-depth interviews (n = 67), Q methodology (n = 79), and public deliberation (n = 88), we explored residents’ priorities for the future of their town. The results indicate four different perspectives on Nosara’s future. Despite the tensions among those four perspectives, they show consensus on one overarching community issue: the need for a sustainable development plan. The case also shows how Q-methodology can assist scholars and practitioners who embrace participatory approaches to policy development and conflict resolution in the environmental arena.