bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Climate Variability and Human Activities on Vegetation Dynamics across the Qinghai–Tibet Plateau from 1982 to 2020
Authors: Liu Yiyang , Xie Yaowen , Guo Zecheng , Xi Guilin
Published: October 2023

In recent years, vegetation on the Qinghai–Tibet Plateau (QTP) has undergone significant greening. However, the causal factors underpinning this phenomenon, whether attributable to temperature fluctuations, precipitation patterns, or anthropogenic interventions, remain a subject of extensive scholarly debate. This study conducted a comprehensive analysis of the evolving vegetation across the QTP. The National Oceanic and Atmospheric Administration Climate Data Record Advanced Very High Resolution Radiometer Normalized Vegetation Difference Index (NOAA CDR AVHRR NDVI) dataset was employed to elucidate the intricate relationship between climatic variables and human activities driving vegetative transformations. The findings were as follows: The NDVI on the QTP has exhibited a significant greening trend at a rate of 0.0013/a (per year). A minor decline, accounting for only 17.6% of grasslands, was observed, which was primarily concentrated in the northwestern and northern regions. Through residual analysis, climate change was found to be the predominant driver, explaining 70.6% of the vegetation variability across the plateau. Concurrently, noticeable trends in temperature and precipitation increases were observed on the QTP, with the southern region demonstrating improved sensitivity to precipitation alterations. In summary, these results substantiate that a confluence of climatic warming, enhanced moisture availability, and a reduction in livestock population collectively creates an environment conducive to enhanced vegetation vigor on the QTP. This study highlights the significance of acknowledging the dual influence of climate and human agency in shaping vegetative dynamics, which is a critical consideration for informed land management strategies and sustainable development initiatives on this ecologically pivotal plateau.
Multiscale Evaluation of Gridded Precipitation Datasets across Varied Elevation Zones in Central Asia’s Hilly Region
Authors: Gulakhmadov Manuchekhr , Chen Xi , Gulakhmadov Aminjon , Umar Nadeem Muhammad , Gulahmadov Nekruz , Liu Tie
Published: October 2023

The lack of observed data makes research on the cryosphere and ecology extremely difficult, especially in Central Asia’s hilly regions. Before their direct hydroclimatic uses, the performance study of gridded precipitation datasets (GPDS) is of utmost importance. This study assessed the multiscale ground evaluation of three reanalysis datasets (ERA5, MEERA2, and APHRO) and five satellite datasets (PERSIANN-PDIR, CHIRPS, GPM-SM2Rain, SM2Rain-ASCAT, and SM2Rain-CCI). Several temporal scales (daily, monthly, seasonal (winter, spring, summer, autumn), and annual) of all the GPDS were analyzed across the complete spatial domain and point-to-pixel scale from January 2000 to December 2013. The validation of GPDS was evaluated using evaluation indices (Root Mean Square Error, correlation coefficient, bias, and relative bias) and categorical indices (False Alarm Ratio, Probability of Detection, success ratio, and Critical Success Index). The performance of all GPDS was also analyzed based on different elevation zones (≤1500, ≤2500, >2500 m). According to the results, the daily estimations of the spatiotemporal tracking abilities of CHIRPS, APHRO, and GPM-SM2Rain are superior to those of the other datasets. All GPDS performed better on a monthly scale than they performed on a daily scale when the ranges were adequate (CC > 0.7 and r-BIAS (10)). Apart from the winter season, the CHIRPS beat all the other GPDS in standings of POD on a daily and seasonal scale. In the summer, all GPDS showed underestimations, but GPM showed the biggest underestimation (−70). Additionally, the CHIRPS indicated the best overall performance across all seasons. As shown by the probability density function (PDF %), all GPDS demonstrated more adequate performance in catching the light precipitation (>2 mm/day) events. APHRO and SM2Rain-CCI typically function moderately at low elevations, whereas all GPDS showed underestimation across the highest elevation >2500 m. As an outcome, we strongly suggest employing the CHIRPS precipitation product’s daily, and monthly estimates for hydroclimatic applications over the hilly region of Tajikistan.
Remotely Sensing the Invisible—Thermal and Magnetic Survey Data Integration for Landscape Archaeology
Authors: Blochin Jegor K. , Pavlovskaia Elena A. , Sadykov Timur R. , Caspari Gino
Published: October 2023

Archaeological landscapes can be obscured by environmental factors, rendering conventional visual interpretation of optical data problematic. The absence of evidence can lead to seemingly empty locations and isolated monuments. This, in turn, influences the cultural–historical interpretation of archaeological sites. Here, we assess the potential of integrating thermal and magnetic remote sensing methods in the detection and mapping of buried archaeological structures. The area of interest in an alluvial plain in Tuva Republic makes the application of standard methods like optical remote sensing and field walking impractical, as natural vegetation features effectively hide anthropogenic structures. We combined drone-based aerial thermography and airborne and ground-based magnetometry to establish an approach to reliably identifying stone structures concealed within alluvial soils. The data integration led to the discovery of nine buried archaeological structures in proximity to an Early Iron Age royal tomb, shedding light on ritual land use continuity patterns.
Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development
Authors: Pasternak Marta , Pawłuszek-Filipiak Kamila
Published: October 2023

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations (σVV0, σVH0), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, σVH0 of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola σVH0 of Sentinel-1 offered higher performance (r = 0.88). Generally, σVV0, σVH0 performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture.
Novel Hybrid Model to Estimate Leaf Carotenoids Using Multilayer Perceptron and PROSPECT Simulations
Authors: Hao Weilin , Sun Jia , Zhang Zichao , Zhang Kan , Qiu Feng , Xu Jin
Published: October 2023

Leaf carotenoids (Cxc) play a crucial role in vegetation as essential pigments responsible for capturing sunlight and protecting leaf tissues. They provide vital insights into a plant physiological status and serve as sensitive indicators of plant stress. However, remote sensing of Cxc at the leaf level has been challenging due to the low Cxc content and weaker absorption features compared to those of chlorophylls in the visible domain. Existing vegetation indices have been widely applied but often lack a solid physical foundation, which limits their applicability and robustness in characterizing Cxc. Yet, physical models can confront this ill-posed problem, though with high operational costs. To address this issue, this study presents a novel hybrid inversion method that combines the multilayer perceptron (MLP) algorithm with PROSPECT model simulations to accurately retrieve Cxc. The effectiveness of the MLP method was investigated through comparisons with the classical PROSPECT model inversion (look-up table [LUT] method), the convolutional neural network (CNN) hybrid model, and the Transformer hybrid model. In the pooled results of six experimental datasets, the MLP method exhibited its robustness and generalization capabilities for leaf Cxc content estimation, with RMSE of 3.12 μg/cm2 and R2 of 0.52. The Transformer (RMSE = 3.14 μg/cm2, R2 = 0.46), CNN (RMSE = 3.42 μg/cm2, R2 = 0.28), and LUT (RMSE = 3.82 μg/cm2, R2 = 0.24) methods followed in descending order of accuracy. A comparison with previous studies using the same public datasets (ANGERS and LOPEX) also demonstrated the performance of the MLP method from another perspective. These findings underscore the potential of the proposed MLP hybrid method as a powerful tool for accurate Cxc retrieval applications, providing valuable insights into vegetation health and stress response.
Research on Motion Control and Wafer-Centering Algorithm of Wafer-Handling Robot in Semiconductor Manufacturing
Authors: Han Bing-Yuan , Zhao Bin , Sun Ruo-Huai
Published: October 2023

This paper studies the AWC (Active Wafer Centering) algorithm for the movement control and wafer calibration of the handling robot in semiconductor manufacturing to prevent wafer surface contact and contamination during the transfer process. The mechanical and software architecture of the wafer-handling robot is analyzed first, which is followed by a description of the experimental platform for semiconductor manufacturing methods. Secondly, the article utilizes the geometric method to analyze the kinematics of the semiconductor robot, and it decouples the motion control of the robot body from the polar coordinates and joint space. The wafer center position is calibrated using the generalized least-square inverse method for AWC correction. The AWC algorithm is divided into calibration, deviation correction, and retraction detection. These are determined by analyzing the robot’s wafer calibration process. In conclusion, the semiconductor robot’s motion control and AWC algorithm are verified through experiments for correctness, feasibility, and effectiveness. After the wafer correction, the precision of AWC is <± 0.15 mm, which meets the requirements for transferring robot wafers.
The Accuracy of CT Scanning in the Assessment of the Internal and External Qualitative Features of Wood Logs
Authors: Gejdoš Miloš , Gergeľ Tomáš , Michajlová Katarína , Bucha Tomáš , Gracovský Radovan
Published: October 2023

The qualitative evaluation of harvested raw logs and sawlogs is mainly based on the quantitative and qualitative evaluation of the visible macroscopic features of the wood. Modern methods allow for the analysis of whole logs by means of computed tomography. These devices can analyze the internal qualitative features of wood that are not visible on the external structures of the logs. The aim of this work was to evaluate the detection accuracy of a CT-scanning device intended for scanning logs on the internal qualitative features of wood using model trunks. Two logs of beech and oak with a length of 4 m were selected for the analysis, based on availability. Qualitative features were identified through computed tomography scanning, visually identified on cut sections, and then manually measured in accordance with applicable legislation. Relatively good agreement was demonstrated for the detected features in terms of identifying their location (dimension in millimeters from the end of the log). For this parameter, the average differences were 0.90% on the beech log and only 1.21% on the oak log. Relatively high accuracy was shown via CT detection of qualitative features in the beech section (with average differences in dimensions of only 3.5%). In the case of the oak log, the dimensions of the quality features were significantly overestimated. These results indicate that CT scanning technology may have a problem with some hardwood species. It was primarily developed for coniferous tree species, and software algorithms are, therefore, not yet fully adapted to the precise detection of the dimensions of individual quality features. Despite the detected differences, it was confirmed that the CT technology of scanning harvested wood can have a fundamental impact on optimization procedures in the recovery and processing of wood. Renting a scanning line for a certain capacity of wood volume appears to be a deployment option for forestry operations and smaller wood processing operations. Thus, this technology can become an important factor in improving the economic evaluation of the final production of wood.
Preparation and Performance Analysis of 3D Thermoformed Fluidic Polymer Temperature Sensors for Aquatic and Terrestrial Applications
Authors: Gul Jahan Zeb , Khan Maryam , Rehman Muhammad Muqeet , Mohy Ud Din Zia , Kim Woo Young
Published: October 2023

Employing a combination of Polyethylene terephthalate (PET) thermoforming and 3D-printed cylindrical patterns, we carefully engineer a linear resistive temperature sensor. This intricate process involves initial PET thermoforming, yielding a hollow cylindrical chamber. This chamber is then precisely infused with a composite fluid of graphite and water glue. Ensuring electrical connectivity, both ends are affixed with metal wires and securely sealed using a hot gun. This cost-effective, versatile sensor adeptly gauges temperature shifts by assessing composite fluid resistance alterations. Its PET outer surface grants immunity to water and solubility concerns, enabling application in aquatic and aerial settings without extra encapsulation. Rigorous testing reveals the sensor’s linearity and stability within a 10 °C to 60 °C range, whether submerged or airborne. Beyond 65 °C, plastic deformation arises. To mitigate hysteresis, a 58 °C operational limit is recommended. Examining fluidic composite width and length effects, we ascertain a 12 Ω/°C sensitivity for these linear sensors, a hallmark of their precision. Impressive response and recovery times of 4 and 8 s, respectively, highlight their efficiency. These findings endorse thermoforming’s potential for fabricating advanced temperature sensors. This cost-effective approach’s adaptability underscores its viability for diverse applications.
A Symmetrized Dot Pattern Extraction Method Based on Frobenius and Nuclear Hybrid Norm Penalized Robust Principal Component Analysis and Decomposition and Reconstruction
Authors: Wang Lijing , Wei Shichun , Xi Tao , Li Hongjiang
Published: October 2023

Due to their symmetrized dot pattern, rolling bearings are more susceptible to noise than time–frequency characteristics. Therefore, this article proposes a symmetrized dot pattern extraction method based on the Frobenius and nuclear hybrid norm penalized robust principal component analysis (FNHN-RPCA) as well as decomposition and reconstruction. This method focuses on denoising the vibration signal before calculating the symmetric dot pattern. Firstly, the FNHN-RPCA is used to remove the non-correlation between variables to realize the separation of feature information and interference noise. After, the residual interference noise, irrelevant information, and fault features in the separated signal are clearly located in different frequency bands. Then, the ensemble empirical mode decomposition is applied to decompose this information into different intrinsic mode function components, and the improved DPR/KLdiv criterion is used to select components containing fault features for reconstruction. In addition, the symmetrized dot pattern is used to visualize the reconstructed signal. Finally, method validation and comparative analysis are conducted on the CWRU datasets and experimental bench data, respectively. The results show that the improved criteria can accurately complete the screening task, and the proposed method can effectively reduce the impact of strong noise interference on SDPs.
Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms
Authors: Apacionado Bryan Vivas , Ahamed Tofael
Published: October 2023

Sooty mold is a common disease found in citrus plants and is characterized by black fungi growth on fruits, leaves, and branches. This mold reduces the plant’s ability to carry out photosynthesis. In small leaves, it is very difficult to detect sooty mold at the early stages. Deep learning-based image recognition techniques have the potential to identify and diagnose pest damage and diseases such as sooty mold. Recent studies used advanced and expensive hyperspectral or multispectral cameras attached to UAVs to examine the canopy of the plants and mid-range cameras to capture close-up infected leaf images. To bridge the gap on capturing canopy level images using affordable camera sensors, this study used a low-cost home surveillance camera to monitor and detect sooty mold infection on citrus canopy combined with deep learning algorithms. To overcome the challenges posed by varying light conditions, the main reason for using specialized cameras, images were collected at night, utilizing the camera’s built-in night vision feature. A total of 4200 sliced night-captured images were used for training, 200 for validation, and 100 for testing, employed on the YOLOv5m, YOLOv7, and CenterNet models for comparison. The results showed that YOLOv7 was the most accurate in detecting sooty molds at night, with 74.4% mAP compared to YOLOv5m (72%) and CenterNet (70.3%). The models were also tested using preprocessed (unsliced) night images and day-captured sliced and unsliced images. The testing on preprocessed (unsliced) night images demonstrated the same trend as the training results, with YOLOv7 performing best compared to YOLOv5m and CenterNet. In contrast, testing on the day-captured images had underwhelming outcomes for both sliced and unsliced images. In general, YOLOv7 performed best in detecting sooty mold infections at night on citrus canopy and showed promising potential in real-time orchard disease monitoring and detection. Moreover, this study demonstrated that utilizing a cost-effective surveillance camera and deep learning algorithms can accurately detect sooty molds at night, enabling growers to effectively monitor and identify occurrences of the disease at the canopy level.
SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism
Authors: Sun Cuimin , Zhou Xingzhi , Zhang Menghua , Qin An
Published: October 2023

Sugarcane is an important raw material for sugar and chemical production. However, in recent years, various sugarcane diseases have emerged, severely impacting the national economy. To address the issue of identifying diseases in sugarcane leaf sections, this paper proposes the SE-VIT hybrid network. Unlike traditional methods that directly use models for classification, this paper compares threshold, K-means, and support vector machine (SVM) algorithms for extracting leaf lesions from images. Due to SVM’s ability to accurately segment these lesions, it is ultimately selected for the task. The paper introduces the SE attention module into ResNet-18 (CNN), enhancing the learning of inter-channel weights. After the pooling layer, multi-head self-attention (MHSA) is incorporated. Finally, with the inclusion of 2D relative positional encoding, the accuracy is improved by 5.1%, precision by 3.23%, and recall by 5.17%. The SE-VIT hybrid network model achieves an accuracy of 97.26% on the PlantVillage dataset. Additionally, when compared to four existing classical neural network models, SE-VIT demonstrates significantly higher accuracy and precision, reaching 89.57% accuracy. Therefore, the method proposed in this paper can provide technical support for intelligent management of sugarcane plantations and offer insights for addressing plant diseases with limited datasets.
Comparing Three Methods of Selecting Training Samples in Supervised Classification of Multispectral Remote Sensing Images
Authors: Zhang Hongying , He Jinxin , Chen Shengbo , Zhan Ye , Bai Yanyan , Qin Yujia
Published: October 2023

Selecting training samples is crucial in remote sensing image classification. In this paper, we selected three images—Sentinel-2, GF-1, and Landsat 8—and employed three methods for selecting training samples: grouping selection, entropy-based selection, and direct selection. We then used the selected training samples to train three supervised classification models—random forest (RF), support-vector machine (SVM), and k-nearest neighbor (KNN)—and evaluated the classification results of the three images. According to the experimental results, the three classification models performed similarly. Compared with the entropy-based method, the grouping selection method achieved higher classification accuracy using fewer samples. In addition, the grouping selection method outperformed the direct selection method with the same number of samples. Therefore, the grouping selection method performed the best. When using the grouping selection method, the image classification accuracy increased with the increase in the number of samples within a certain sample size range.
Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches
Authors: De Silva Malithi , Brown Dane
Published: October 2023

Plant diseases pose a critical threat to global agricultural productivity, demanding timely detection for effective crop yield management. Traditional methods for disease identification are laborious and require specialised expertise. Leveraging cutting-edge deep learning algorithms, this study explores innovative approaches to plant disease identification, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance accuracy. A multispectral dataset was meticulously collected to facilitate this research using six 50 mm filter filters, covering both the visible and several near-infrared (NIR) wavelengths. Among the models employed, ViT-B16 notably achieved the highest test accuracy, precision, recall, and F1 score across all filters, with averages of 83.3%, 90.1%, 90.75%, and 89.5%, respectively. Furthermore, a comparative analysis highlights the pivotal role of balanced datasets in selecting the appropriate wavelength and deep learning model for robust disease identification. These findings promise to advance crop disease management in real-world agricultural applications and contribute to global food security. The study underscores the significance of machine learning in transforming plant disease diagnostics and encourages further research in this field.
Borderline Personality Features and Mate Retention Behaviors: The Mediating Roles of Suspicious and Reactive Jealousy
Authors: Zeigler-Hill Virgil , Vonk Jennifer
Published: October 2023

We investigated the roles that suspicious jealousy and reactive jealousy might play in the associations between borderline personality features (BPF) and mate retention behaviors. Study 1 (N = 406) found that BPF had positive indirect associations with benefit-provisioning behaviors and cost-inflicting behaviors through suspicious jealousy but not through reactive jealousy. Study 2 (N = 334 (a dyadic sample of 167 romantic couples)) revealed actor effects such that BPF had positive indirect associations with benefit-provisioning behaviors and cost-inflicting behaviors through suspicious jealousy for both men and women. In addition, the positive association between BPF and benefit-provisioning behaviors was mediated by reactive jealousy in women but not in men. The only partner effect that emerged from these analyses showed that BPF in women were negatively associated with the benefit-provisioning behaviors reported by their male partners. Discussion focuses on the implications of these results for the function that jealousy might serve in the strategies used by individuals with BPF to maintain their romantic relationships.
Symbiotic Evolution Mechanism of the Digital Innovation Ecosystem for the Smart Car Industry
Authors: Wu Bicong , Negassi Syoum
Published: October 2023

As an essential product in the automotive industry, the smart car industry has attracted widespread attention from scholars. However, there are few studies on the evolution of innovation ecosystems under different modes for the smart car industry. Thus, we construct evolution equations based on the symbiosis theory and Lotka–Volterra. Specifically, MATLAB simulations are utilized to comprehensively analyze the specific conditions for different symbiotic modes. The results indicate that (1) the smart car digital innovation ecosystem includes four symbiotic units. They are vehicle manufacturers, internet enterprises, supporting enterprises, and application groups. The symbiotic relationship among units depends on the symbiosis coefficient. (2) According to the symbiosis coefficient, the system is mainly divided into five symbiotic models: independent symbiosis, competitive symbiosis, parasitic symbiosis, partial symbiosis, and mutually beneficial symbiosis. Moreover, the mutually beneficial symbiosis is the optimal evolution model, and it is also a model that can enable the sustainable development of the smart car industry, which has been verified through case studies.
Unveiling the Railway Traffic Knowledge in Tibet: An Advanced Model for Relational Triple Extraction
Authors: Luo Weiqun , Wang Jiabao , Yan Xiangwei , Jiang Guiyuan
Published: October 2023

To address the deficiency of existing relation extraction models in effectively extracting relational triples pertaining to railway traffic knowledge in Tibet, this paper constructs a Tibet Railway Traffic text dataset and provides an enhanced relation extraction model. The proposed model incorporates subject feature enhancement and relational attention mechanisms. It leverages a pre-trained model as the embedding layer to obtain vector representations of text. Subsequently, the subject is extracted and its semantic information is augmented using an LSTM neural network. Furthermore, during object extraction, the multi-head attention mechanism enables the model to prioritize relations associated with the aforementioned features. Finally, objects are extracted based on the subjects and relations. The proposed method has been comprehensively evaluated on multiple datasets, including the Tibet Railway Traffic text dataset and two public datasets. The results on the Tibet dataset achieve an F1-score of 93.3%, surpassing the baseline model CasRel by 0.8%, indicating a superior applicability of the proposed model. On the other hand, the model achieves F1-scores of 91.1% and 92.6% on two public datasets, NYT and WebNLG, respectively, outperforming the baseline CasRel by 1.5% and 0.8%, which highlights the good generalization ability of the proposed model.
Socio-Economic Value and Availability of Plant-Based Non-Timber Forest Products (NTFPs) within the Charcoal Production Basin of the City of Lubumbashi (DR Congo)
Authors: Nghonda Dieu-donné N’tambwe , Muteya Héritier Khoji , Moyene Apollinaire Biloso , Malaisse François , Sikuzani Yannick Useni , Kalenga Wilfried Masengo , Bogaert Jan
Published: October 2023

The overexploitation of forest resources in the charcoal production basin of the city of Lubumbashi (DR Congo) is reducing the resilience of miombo woodlands and threatening the survival of the riparian as well as urban human populations that depend on it. We assessed the socio-economic value and availability of plant-based non-timber forest products NTFPs in the rural area of Lubumbashi through ethnobotanical (100 respondents) and socio-economic (90 respondents) interviews, supplemented with floristic inventories, in two village areas selected on the basis of the level of forest degradation. The results show that 60 woody species, including 46 in the degraded forest (Maksem) and 53 in the intact forest (Mwawa), belonging to 22 families are used as sources of NTFPs in both villages. Among these species, 25 are considered priority species. NTFPs are collected for various purposes, including handcrafting, hut building, and traditional medicine. Moreover, the ethnobotanical lists reveal a similarity of almost 75%, indicating that both local communities surveyed use the same species for collecting plant-based NTFPs despite differences in the level of degradation of the miombo woodlands in the two corresponding study areas. However, the plant-based NTFPs that are collected from miombo woodlands and traded in the urban markets have significant economic value, which ranges from USD 0.5 to USD 14.58 per kg depending on the species and uses. NTFPs used for handicraft purposes have a higher economic value than those used for other purposes. However, the sustainability of this activity is threatened due to unsustainable harvesting practices that include stem slashing, root digging, and bark peeling of woody species. Consequently, there is a low availability of plant-based NTFPs, particularly in the village area, where forest degradation is more advanced. It is imperative that policies for monitoring and regulating harvesting and promoting the sustainable management of communities’ plant-based NTFPs as a priority, be undertaken to maintain their resilience.
Stakeholder Analysis in the Context of Natural Disaster Mitigation: The Case of Flooding in Three U.S. Cities
Authors: Ter-Mkrtchyan Ani V. , Franklin Aimee L.
Published: October 2023

This research identifies ways community dialogue can inform natural disaster mitigation planning. We use stakeholder analysis to explore indirect public engagement related to flooding in three U.S. cities (Tulsa, OK; Fayetteville, AR; and Waco, TX). Using publicly available data, we identify the types of stakeholders and potential motivating factors leading them to contribute to community discourse. We find a wide range of engaged stakeholders representing governments, organizations, groups, and individuals directly and indirectly impacted by a natural disaster. These results provide information valuable for tailoring direct engagement efforts to reach residents not participating in the discussion, especially those with elevated vulnerabilities or untapped resources who can co-produce flood mitigation strategies designed to make their property and public infrastructure more flood-resilient and improve community sustainability.
How Is Job Performance Affected by Ergonomics in the Tourism and Hospitality Industry? Mediating Roles of Work Engagement and Talent Retention
Authors: El-Sherbeeny Ahmed M. , Al-Romeedy Bassam Samir , Abd elhady Mohamed Hani , Sheikhelsouk Samar , Alsetoohy Omar , Liu Sijun , Khairy Hazem Ahmed
Published: October 2023

Optimizing the interaction between individuals and their work environment has become increasingly relevant in enhancing employee well-being and driving the overall success of businesses. The goal of this study is to provide information about how ergonomics affects job performance in the tourism and hospitality industry. The full-time staff employees of Egypt’s category (A) travel agencies and five-star hotels were the source of the study’s data. The partial least squares structural equation modeling (PLS-SEM) technique of analysis was utilized to explore how ergonomics influences job performance while taking into account the mediating roles of work engagement and talent retention. WarpPLS statistical software version 7.0 was used to analyze the 389 valid replies obtained. The findings revealed that there is a positive relationship between the employees’ perception of ergonomics on their job performance, in addition to the positive relationships between the perception of ergonomics and work engagement and talent retention. The work engagement and talent retention were also found to have a positive relationship with job performance. Furthermore, research revealed that work engagement and talent retention act as mediators between ergonomics and job performance. The results of this research significantly advance the Job Demands-Resources (JD-R) model theory. The study also offers evidence-based recommendations to organizations in the tourism and hospitality industry, empowering them to establish supportive work environments that enhance the job performance, work engagement, and talent retention. Businesses in this industry could create work environments that prioritize the well-being, comfort, and safety of their employees by embracing ergonomic concepts.
Wastewater Treatment in Central Asia: Treatment Alternatives for Safe Water Reuse
Authors: Kalmakhanova Marzhan S. , Diaz de Tuesta Jose L. , Malakar Arindam , Gomes Helder T. , Snow Daniel D.
Published: October 2023

Due to water scarcity and ready availability, treated wastewater in Central Asia is increasingly reused and seen as a valuable resource, requiring effective management with particular care for human health, environmental protection, and water security. Due to limited technical and economic support and poorly developed regulatory systems, many cities have inadequate wastewater treatment infrastructure. Improved wastewater effluent management is paramount due to its relationship with surface and groundwater quality used for drinking and agricultural irrigation. This paper presents a brief review of the published literature reporting on current wastewater treatment technologies and effluent composition, with particular attention paid to reuse needs. The impact of these practices on water quality is further assessed from information and reports gathered from various sources on the quantity and quality of surface waters and groundwaters. Finally, alternatives to current wastewater treatment practices in Central Asia will be explored with a particular emphasis on the removal of contaminants of emerging concern, including biological treatment systems, adsorption, advanced oxidation processes, and managed/unmanaged aquifer recharge techniques based on permeable reactive barriers, aiming to increase the availability and quality of surface waters and groundwaters for safe water reuse.
Analyzing the Drivers of Agricultural Irrigation Water Demand in Water-Scarce Areas: A Comparative Study of Two Regions with Different Levels of Irrigated Agricultural Development
Authors: Hua Mengya , Zhou Yuyan , Hao Cailian , Yan Qiang
Published: October 2023

Both the demand for agricultural irrigation and the level of water-saving technology in water-scarce regions have met food demand with technological progress and economic growth. There are differences in irrigation water demand drivers in regions with different levels of irrigated agricultural development. However, the relationship between related drivers in response to regional irrigation water demand is not fully understood. This study quantified the driving influence of six indicators, including technological progress, planting structure, water conservation management, economic development, planting scale, and consumption intensity, on agricultural irrigation water demand in JC (Jinchang) and WW (Wuwei), two cities in the Shiyang River Basin, from 2011 to 2020. The results shows that economic development is the main driver of the increase in irrigation water demand, with 29% and 43% driving contributions in JC and WW, respectively. Consumption intensity contributes the most to the decrease in irrigation water demand, with 31% and 23% of driving contribution in JC and WW, respectively. Cropping size has a greater positive drive on irrigation water demand in non-agricultural areas relative to agricultural areas. Planting structure has a more pronounced negative drive on irrigation water demand in agricultural areas relative to non-agricultural areas. In particular, relative to irrigated areas, the proportion of water-saving irrigated areas to the sown areas has a greater impact on changes in irrigation water demand, with a significant rebound effect when it exceeds 80%, so that blindly expanding water-saving irrigated areas will drive an increase in irrigation water demand. The results of this study can provide useful suggestions for agricultural water management in water-scarce areas with different levels of water-saving irrigation development, and realize the sustainable development of agriculture in water-scarce areas.
Edible Insects: A Study of the Availability of Insect-Based Food in Poland
Authors: Szulc Karolina
Published: October 2023

In Poland, there has been a significant surge of interest in exploring insects as a novel food source in recent years. This increased interest is evident in various sectors of society, from researchers and food manufacturers to consumers and culinary experts. For example, research institutions have been conducting studies to assess the nutritional value and environmental impact of insect-based foods, while food companies are actively experimenting on how incorporating insect-based ingredients into their product lines affects consumer preferences. Although insect-based food products are highly valued in many parts of the world, their development has only gained attention in Poland over the last decade. Researchers and startups began conducting studies and developing insect-based food alternatives at the beginning of 2015. Therefore, the aim of this study was to examine the market availability of insect-based food products in Poland. Since the success of insect-based food products in the Polish market depends on the willingness of consumers to buy this type of food alternative, the results of a narrow recent literature review have been summarized in this study and point out consumer perceptions and barriers to choosing insect-based food products. The aim of this narrow literature review was to provide information on consumer perceptions and barriers to consuming insect-based food products so that producers and manufacturers in Poland can design marketing strategies. To achieve the aim of this study, the websites of several food stores were examined that sell insect-based food products in Poland and created a database. Then, to identify the perceptions and barriers towards insect-based food products, the results of a few studies conducted in Poland and other countries were summarized. Six online food stores operating in Poland that sell insect-based food products were found. In total, seventy-three products that were available on the market were analyzed. The results showed that Polish consumers have access to a large variety of insect-based food alternatives, such as whole insects, protein bars, and several types of powdered insect-based food products. However, their purchasing behavior is reluctant due to several barriers, such as food neophobia and disgust, lack of familiarity with the product, lack of information about the handling and preparation of insects, and ways to incorporate insects into the daily diets of consumers. Insect-based food producers and manufacturers in Poland should consider these results when tailoring their marketing strategies.
Finite Element Analysis-Based Blast Performance Evaluation for Reinforced Concrete Columns with Shear and Flexure Failure Modes
Authors: Kim Ye-Eun , Park Jaeeun , To Quoc Bao , Lee Kihak , Shin Jiuk
Published: October 2023

Blast loads have significantly damaged non-ductile reinforced concrete building structures with seismically deficient column details, which have lower shear capacity than the building structures with the current code-required details. This paper aims to evaluate the blast performance of non-ductile (shear-governed) and ductile (flexure-governed) RC columns using ductility- and residual capacity-based performance limits. To accomplish this research goal, the shear- and flexure-governed RC column models were developed using a finite element method and validated with previous experimental results. The efficient blast modeling method representing the reflected and incident waves was implemented in the validated column models, and these were simulated under various blast loading scenarios. The blast-induced responses for each column were investigated and utilized to determine the performance levels using the two different performance limits. The flexure-governed column model has higher performance levels than the shear-governed column model because of the lower ductility capacity of the shear-governed column model. The residual capacity-based performance level of the shear-governed column was underestimated compared to the ductility-based limits specified in a current design code.
Investigation of Lips-Guided-Flow Combustion Chamber and Miller Cycle to Improve the Thermal Efficiency of a Highly Intensified Diesel Engine
Authors: Wang Ziyu , Cao Rulou , Li Yanfang , Hao Caifeng , Liu Jinlong , An Yanzhao , Ma Renwei
Published: October 2023

An investigation into the lips-guided-flow combustion chamber (LGFC) and Miller cycle was conducted on a highly intensified diesel engine under rated power conditions to improve thermal efficiency. The radius and depth of the chamber bowl and lips were optimized to intensify the guided flow and fuel/air mixing. The experimental and simulated results show that the LGFC had a higher fuel/air mixture quality and quicker combustion rate, leading to a higher indicated power and higher thermal efficiency. A late intake valve closing (LIVC) Miller cycle with a higher expansion ratio of 11 and a lower compression ratio of 8.2 was used to control the energy distribution of the thermodynamic cycle and reduce the mechanical and thermal loads. The results show that the maximum combustion temperature was decreased by about 45 K and the thermal efficiency was improved by 2.1%. The research results are useful to guide the improvement in thermal efficiency through combustion chamber design and Miller cycle application for highly intensified diesel engines.
Planning and Promoting an Authentic Slow Food Tourism Corridor in a Suburban Town in the US
Authors: Baimoratova Saule , Chhabra Deepak , Timothy Dallen J.
Published: October 2023

Interest in slow tourism is surging as consumers seek immersive experiences and self-enrichment. Only a handful of studies have examined this phenomenon minutely from an authenticity standpoint. To fill this vacuum, by employing a case study technique, this study examines the manner in which authenticity is promoted and showcased in a broad range of iconic food venues in the suburban town of Gilbert (Arizona, USA). Content analysis of the signature websites of fourteen iconic food venues is conducted in addition to an online survey of the venue managers. Based on the results, this study presents a roadmap for developing and promoting a slow food tourism corridor for Gilbert.
Remote Sensing Classification of Temperate Grassland in Eurasia Based on Normalized Difference Vegetation Index (NDVI) Time-Series Data
Authors: Xu Xuefeng , Tang Jiakui , Zhang Na , Zhang Anan , Wang Wuhua , Sun Qiang
Published: October 2023

The Eurasian temperate grassland is the largest temperate grassland ecosystem and vegetation transition zone globally. The spatiotemporal distribution and changes of grassland types are vital for grassland monitoring and management. However, there is currently a lack of a unified classification method and standard distribution map of Eurasian temperate grassland types. The Normalized Difference Vegetation Index (NDVI) from remote sensing data is commonly used in grassland monitoring. In this paper, the Accumulated Rate of NDVI Change Index (ARNCI) was proposed to characterize the annual NDVI trend of different temperate grassland types, and four transitional categories were introduced to account for the overlap between them. Based on survey data on the distribution of Eurasian temperate grassland types in the 1980s, the study area was divided into three sub-regions: Northern China, Central Asia, and Mongolia. Regionally, pixel-based ARNCI maps in the 1980s and 1990s were successfully calculated from using NOAA’s AVHRR NDVI time-series products. The ARNCI classification thresholds for different sub-regions were determined, and classification experiments and validation were conducted for each sub-region. The overall accuracies of grasslands types classification for Northern China, Central Asia, and Mongolia in the 1980s were 75.3%, 64.2%, and 84.6%, respectively, which demonstrated that there were variations in classification accuracy in the three sub-regions, and the overall performance was favorable. Finally, distribution maps of Eurasian temperate grassland types in the 1980s and 1990s were obtained, and the spatiotemporal changes of grassland types were analyzed and discussed. The ARNCI method is simple to operate and easy to obtain data, and it can be conveniently used in grassland type classification. The maps firstly address the lack of remote sensing classification maps of Eurasian temperate grassland types, and provide a promising tool for monitoring grassland degradation, management, and utilization.
From Green Inclusive Leadership to Green Organizational Citizenship: Exploring the Mediating Role of Green Work Engagement and Green Organizational Identification in the Hotel Industry Context
Authors: Abdou Ahmed Hassan , Al Abdulathim Majed Abdulaziz , Hussni Hasan Nadia Rebhi , Salah Maha Hassan Ahmed , Ali Howayda Said Ahmed Mohamed , Kamel Nancy J.
Published: October 2023

This study aims to explore the pivotal role of green inclusive leadership (GIL) in driving sustainability efforts in the hotel industry, guided by the social exchange theory (SET). The research centers on three core objectives: firstly, evaluating how GIL directly impacts green work engagement (GWE), green organizational identification (GOI), and green organizational citizenship behavior (GOCB); secondly, examining how GWE and GOI influence GOCB; and thirdly, investigating whether GWE and GOI act as mediators in the relationship between GIL and GOCB. This study gathered data via an online questionnaire, completed by 446 front-line employees in eco-conscious five-star Saudi Arabian hotels. This study employed PLS-SEM with bootstrapping techniques to scrutinize seven hypotheses, encompassing both direct and indirect connections among study variables. The findings underscore the significant, positive influence of GIL on employees’ GWE and GOI, motivating their active participation in GOCBs. Furthermore, GWE and GOI play pivotal roles in enhancing employees’ engagement in GOCBs. Notably, GWE and GOI emerge as substantial mediators in the relationship between GIL and GOCB. These insights carry significant practical implications, poised to strengthen GOCB and drive sustainability initiatives in the hotel industry.