bluuebunny/crossref_metadata_2025 huggingface dataset:
BMI and the Food Retail Environment in Melbourne, Australia: Associations and Temporal Trends
Authors: Needham Cindy , Strugnell Claudia , Allender Steven , Alston Laura , Orellana Liliana
Published: October 2023

Research into the link between food environments and health is scarce. Research in this field has progressed, and new comprehensive methods (i.e., incorporating all food retail outlets) for classifying food retail environments have been developed and are yet to be examined alongside measures of obesity. In this study, we examine the association and temporal trends between the food environment and BMI of a repeated cross-sectional sample of the adult population between 2008 and 2016. Methods: Food retail data for 264 postal areas of Greater Melbourne was collected for the years 2008, 2012, 2014, and 2016, and a container-based approach was used to estimate accessibility to supermarkets, healthy and unhealthy outlets. Data on BMI for postal areas was obtained from the Victorian Population Health Survey (n = 47,245). We estimated the association between the food environment and BMI using linear mixed models. Results indicated that BMI increased as accessibility to healthy outlets decreased by up to −0.69 kg/m2 (95%CI: −0.95, −0.44). BMI was lower with high and moderate access to supermarkets compared to low access by −0.33 kg/m2 (−0.63, −0.04) and −0.32 kg/m2 (−0.56, −0.07), and with high access to unhealthy outlets compared to low access (−0.38 kg/m2: −0.64, −0.12) and moderate access (−0.54 kg/m2: −0.78, −0.30). Conclusion: Our results show that increasing access and availability to a diverse range of food outlets, particularly healthy food outlets, should be an important consideration for efforts to support good health. This research provides evidence that Australia needs to follow suit with other countries that have adopted policies giving local governments the power to encourage healthier food environments.
Yeast Beta-Glucan Supplementation with Multivitamins Attenuates Cognitive Impairments in Individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Randomized, Double-Blind, Placebo-Controlled Trial
Authors: Lacasa Marcos , Alegre-Martin Jose , Sentañes Ramon Sanmartin , Varela-Sende Luisa , Jurek Joanna , Castro-Marrero Jesus
Published: October 2023

This research aimed to examine the potential alleviative effects of beta-glucan administration on fatigue, unrefreshing sleep, anxiety/depression symptoms and health-related quality of life in ME/CFS. A 36-week unicenter, randomized, double-blind, placebo-controlled trial was conducted in 65 ME/CFS patients, who were randomly allocated to one of two arms to receive four capsules each one of 250 mg beta-glucan, 3.75 µg vitamin D3, 1.05 mg vitamin B6, and 7.5 mg zinc (n = 35), or matching placebo including only microcrystalline cellulose as an excipient (n = 30) once daily. The findings showed that the beta-glucan supplementation significantly improved cognitive fatigue (assessed with FIS-40 scores) after the 36-week treatment compared to the baseline (p = 0.0338). Taken together, this study presents the novel finding that yeast-derived beta-glucan may alleviate cognitive fatigue symptoms in ME/CFS. Thus, it offers valuable scientific insights into the potential use of yeast beta-glucan as a nutritional supplement and/or functional food to prevent or reduce cognitive dysfunction in patients with ME/CFS. Further interventions are warranted to validate these findings and also to delve deeper into the possible immunometabolic pathomechanisms of beta-glucans in ME/CFS.
Association between Mineral Intake and Cognition Evaluated by Montreal Cognitive Assessment (MoCA): A Cross-Sectional Study
Authors: Lorenzo-Mora Ana M. , López-Sobaler Ana M. , Bermejo Laura M. , González-Rodríguez Liliana G. , Cuadrado-Soto Esther , Peral-Suárez África , Salas-González María Dolores , Delgado-Losada María Luisa , Rodríguez-Rojo Inmaculada C. , Barabash Ana , Maestú-Unturbe Fernando , Aparicio Aránzazu
Published: October 2023

Background: Mineral intake may protect against cognitive impairment (CI) and all-cause dementia, which affects a large number of adults worldwide. The aim of this study was to investigate the association between mineral intake and Montreal Cognitive Assessment (MoCA), which is a sensitive and specific test. Methods: In total, 201 adults were included in a cross-sectional study. They completed a three-day dietary record to estimate their average daily intake of minerals. Contributions to dietary reference intakes (DRIs) were also calculated. The participants were divided into tertiles according to their mineral intake. CI classifications were determined via the MoCA (score < 26). Apolipoprotein E (APOE) genotyping was carried out, and the patients’ anthropometric measurements and physical activity, health and personal data were collected. Results: The prevalence of CI in this selective sample was 54.2% (34.3% females and 19.9% males). In women, being in the third tertiles of iron and manganese intake was associated with lower odds of having CI (OR [95% CI]: 0.32 [0.11 ± 0.93]; 0.33 [0.12 ± 0.93], p < 0.05). No significant differences were observed for any of the nutrients studied in men. Conclusions: These findings suggest that a low mineral intake, especially low iron and manganese intake in women, is associated with a worse cognition as assessed by MoCA.
Under-Fuelling for the Work Required? Assessment of Dietary Practices and Physical Loading of Adolescent Female Soccer Players during an Intensive International Training and Game Schedule
Authors: McHaffie Samuel J. , Langan-Evans Carl , Strauss Juliette A. , Areta José L. , Rosimus Christopher , Evans Martin , Waghorn Ruth , Morton James P.
Published: October 2023

Previous studies demonstrate that “under-fuelling” (i.e., reduced carbohydrate (CHO) and energy intake (EI) in relation to recommended guidelines) is prevalent within adult female soccer players, the consequence of which may have acute performance and chronic health implications. However, the dietary practices of adolescent female soccer players, a population who may be particularly at risk for the negative aspects of low energy availability (LEA), are not well documented. Accordingly, we aimed to quantify EI and CHO intake, physical loading and estimated energy availability (EA) in elite national team adolescent female soccer players (n = twenty-three; age, 17.9 ± 0.5 years) during a 10-day training and game schedule comprising two match days on day six (MDa) and nine (MDb). The players self-reported their EI via the remote food photography method, whilst the physical loading and associated exercise energy expenditure were assessed via GPS technology. The relative CHO intake was significantly greater (all p < 0.05) on the day before the first match (MD-1a) (4.1 ± 0.8 g·kg−1), on the day before the second match (MD-1b) (4.3 ± 1.1 g·kg−1), MDa (4.8 ± 1.2 g·kg−1) and MDb (4.8 ± 1.4 g·kg−1) in comparison to most other days (<4 g·kg−1). The mean daily measured EA over the 10-day period was 34 ± 12 kcal·kg FFM−1·day−1 (with six players, i.e., 34%, presenting LEA), though, when adjusting the energy intake for potential under-reporting, these values changed substantially (44 ± 14 kcal·kg FFM−1·day−1, only one player was classed as presenting LEA). Such data suggest that the prevalence of LEA amongst female team sport athletes may be over-estimated. Nonetheless, players are still likely under-fuelling for the work required in relation to the daily CHO recommendations (i.e., >6 g·kg−1) for intensive training and game schedules. These data provide further evidence for the requirement to create and deliver targeted player and stakeholder education and behaviour change interventions (especially for younger athletes) that aim to promote increased daily CHO intake in female soccer players.
Huntington’s Disease Drug Development: A Phase 3 Pipeline Analysis
Authors: Van de Roovaart Hannah J. , Nguyen Nguyen , Veenstra Timothy D.
Published: October 2023

Huntington’s Disease (HD) is a severely debilitating neurodegenerative disorder in which sufferers exhibit different combinations of movement disorders, dementia, and behavioral or psychiatric abnormalities. The disorder is a result of a trinucleotide repeat expansion mutation that is inherited in an autosomal dominant manner. While there is currently no treatment to alter the course of HD, there are medications that lessen abnormal movement and psychiatric symptoms. ClinicalTrials.gov was searched to identify drugs that are currently in or have completed phase III drug trials for the treatment of HD. The described phase III trials were further limited to interventional studies that were recruiting, active not recruiting, or completed. In addition, all studies must have posted an update within the past year. PubMed was used to gather further information on these interventional studies. Of the nine clinical trials that met these criteria, eight involved the following drugs: metformin, dextromethorphan/quinidine, deutetrabenazine, valbenazine, Cellavita HD, pridopidine, SAGE-718, and RO7234292 (RG6042). Of these drug treatments, four are already FDA approved. This systematic review provides a resource that summarizes the present therapies for treating this devastating condition that are currently in phase III clinical trials in the United States.
Analysis of Two-Color Infrared Polarization Imaging Characteristics for Target Detection and Recognition
Authors: Zhang Yue , Fu Qiang , Luo Kaiming , Yang Wei , Zhan Juntong , Zhang Su , Shi Haodong , Li Yingchao , Yu Haixin
Published: October 2023

Infrared polarization imaging has demonstrated significant advantages in target detection and recognition in natural environments. While there have been numerous research achievements on medium-wave/long-wave infrared polarization in a single band, there is a lack of related conclusions on the characteristics of wide spectral two-color infrared polarization imaging. To address this gap, this article employs a theoretical model in infrared polarization imaging to investigate the influence of temperature, incident angle, and refractive index on polarization degree and polarization angle. These findings have been substantiated through discussions on experimental results. We conducted a comparative analysis between intensity imaging and polarization imaging within the framework of traditional single-band infrared imaging. We then delineated the benefits of dual-color infrared polarization imaging. Finally, we harnessed image registration fusion algorithms to amalgamate multiple-band images. Our findings demonstrate that this fusion technique not only enables penetration through water mist but also enhances target visibility, providing theoretical support for all-weather target detection and identification.
An Automatic Welding Robot for the Roof of Spiral Steel Silo
Authors: Zhang Yuying , Yuan Hao , Cao Wenwu , Zhang Dong , Peng Xudong , Yang Zhixian
Published: October 2023

A spiral steel silo has become the preferred choice for building steel silos because of its short construction period, low cost, and high strength. Owing to the harsh outdoor welding conditions of the spiral steel silo and the high risk of manual welding, in this paper, an automatic welding robot is proposed, and its key technology is studied. The welding process parameters consisted of welding voltage, current, and the distance between the conductive nozzle and the base material, which were determined through experiments. The key technology of seam tracking on the roof of a spiral steel silo was studied. Finally, a weld bead tracking experiment and prototype welding experiment were carried out. The results suggest that the prototype can track the weld bead stably and accurately, and the welding effect can meet welding process requirements when the welding current is 200 A, the voltage is 20 V, and the distance between the welding nozzle and the base material is 12 mm.
Effect of Tip Clearance on the Cavitation Performance of High-Speed Pump-Jet Propeller
Authors: Gan Gongchang , Duan Yuchen , Yi Jinbao , Fu Qiang , Zhu Rongsheng , Shi Wenhao
Published: October 2023

To investigate the impact of tip clearance variation on the cavitation performance of a high-speed pump-jet propeller, Fluent software was employed for simulation calculations. The study utilized the RNG k-ε turbulence model and ZGB cavitation, conducting three-dimensional numerical simulations under both steady and transient conditions. A pump-jet propeller with a rated speed of 20,000 r/min was used to set three kinds of clearance for simulation, and the simulation proved to be reliable by comparison with the experiment. Initially, the analysis examined the effect of tip clearance on cavitation characteristics and cavitation volume under steady-state conditions, while also studying the distribution patterns of cavitation. Subsequently, the radial force and pressure pulsation of the jet propeller are analyzed by FFT processing, aiming at the influence of tip clearance on the structural strength of the pump-jet propeller. Under the design conditions, the smaller tip clearance shows better performance, while the larger tip clearance shows stronger anti-cavitation ability than the smaller clearance. In addition, the greater the tip clearance, the greater the radial force and pressure pulsation, which will adversely affect the pump-jet structure. The research results provide some references for further research on the effect of high-speed pump-jet propeller structures on performance.
Research on the Performance of a Centrifugal Aviation Fuel Pump Based on Response Surface Methodology
Authors: Zhou Jianbo , He Dongjing , Zhang Rui , Zhao Weidong
Published: October 2023

The performance of centrifugal aviation fuel pumps can be crucial in determining aircraft efficiency and safety. Here, we delve into the intricate interplay between flight altitude and flow rate on pump performance. Employing the central composite design method, we characterized the relationship between these parameters and the pump’s head and efficiency. Our analysis, underpinned by response surface methodology, revealed distinct optimal operating conditions at 10,000 m and 12,000 m flight altitudes, pinpointed at flow rates of 1.1016 Q and 1.1222 Q, respectively. Moreover, the emergence and growth of cavitation regions with increasing flow rates underscore the challenges in pump operation. Significantly, we identify rotor–stator interactions as a dominant influence on pressure fluctuations, especially near the impeller exit. These findings not only elucidate the operational dynamics of aviation fuel pumps but also pave the way for advanced pump designs and optimizations.
Development of an Energy Harvesting System for the Conversion of Mechanical Energy of Human Movement into Electrical Energy and Its Integration into High Performance Wearable Clothing
Authors: Kukle Silvija , Blūms Juris , Kataševs Aleksejs , Baķe Ieva , Jurķāns Vilnis , Gorņevs Ilgvars , Terļecka Gaļina , Zelča Zane , Okss Aleksandrs
Published: October 2023

In many smart textile development studies, sensors and electro-conductive yarns have been widely investigated and used as essential components, especially in the fields of medicine, sport, work wear, and special applications. Wearable sensors provide a means to monitor the wearer’s health through physiological measurements in a natural setting or are used to detect potential hazards and alert users and/or caretakers. The aim of the research is to develop a prototype of wearable electronics that consists of high-performance clothing with an integrated energy harvesting system for converting the mechanical energy of human movements into electrical energy. Within the framework of the research, a system for determining human physiological and/or environmental parameters and transmitting data was developed and integrated into clothing modified with sol–gel technology for indoor and outdoor use. Although the created flat inductive elements of the energy harvesting system retain their performance during the hydrothermal treatment process, at the same time, the other elements of the smart clothing system (especially electro-conductive yarns) rapidly lose their electrical conductivity. The modified knitwear provided a longer time between washing cycles to protect the embedded wearable electronics, and the impact of surface modification with sol–gel on wearing comfort was evaluated.
Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm
Authors: Fan Yongsheng , Zhan Changshu , Aljuaid Mohammed
Published: October 2023

Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes.
Alleviation of Selected Environmental Waste through Biodegradation by Black Soldier Fly (Hermetia illucens) Larvae: A Meta-Analysis
Authors: Zulkifli Sunarto , Jayanegara Anuraga , Pramudya Bambang , Fahmi Melta Rini , Rahmadani Mardiah
Published: October 2023

Alleviation of environmental waste is a significant challenge, contributing to greenhouse gas emissions and wasting valuable resources. To address this issue sustainably, valorization techniques are being explored to convert environmental waste into valuable bio-based products. Additionally, the use of black soldier fly (Hermetia Illucens) larvae has emerged as a potential solution to degrade environmental waste and produce biomass. This study aimed to quantify the waste reduction index (WRI) of environmental waste through biodegradation by black soldier fly (BSF) larvae. A meta-analysis method was employed, involving a comprehensive search in the Scopus database for analysis. A total of 45 articles were analyzed and the results indicate that kitchen waste and fruit and vegetable wastes have a positive effect on WRI and other variables. The WRI of kitchen waste and fruit and vegetable wastes is 4.77 ± 2.98 g/day and 2.72 ± 2.14 g/day, respectively. Fecal waste results in a lower WRI than those of other waste categories, i.e., 2.22 ± 1.29 g/day. Overall, the BSF larvae effectively reduce organic environmental wastes and convert them into their body mass, which is rich in protein. This study contributes to a deeper understanding of the potential of BSF in waste management, offering insights into sustainable waste reduction strategies.
Assessing Spectral Band, Elevation, and Collection Date Combinations for Classifying Salt Marsh Vegetation with Unoccupied Aerial Vehicle (UAV)-Acquired Imagery
Authors: Routhier Michael , Moore Gregg , Rock Barrett
Published: October 2023

New England salt marshes provide many services to humans and the environment, but these landscapes are threatened by drivers such as sea level rise. Mapping the distribution of salt marsh plant species can help resource managers better monitor these ecosystems. Because salt marsh species often have spatial distributions that change over horizontal distances of less than a meter, accurately mapping this type of vegetation requires the use of high-spatial-resolution data. Previous work has proven that unoccupied aerial vehicle (UAV)-acquired imagery can provide this level of spatial resolution. However, despite many advances in remote sensing mapping methods over the last few decades, limited research focuses on which spectral band, elevation layer, and acquisition date combinations produce the most accurate species classification mappings from UAV imagery within salt marsh landscapes. Thus, our work classified and assessed various combinations of these characteristics of UAV imagery for mapping the distribution of plant species within these ecosystems. The results revealed that red, green, and near-infrared camera image band composites produced more accurate image classifications than true-color camera-band composites. The addition of an elevation layer within image composites further improved classification accuracies, particularly between species with similar spectral characteristics, such as two forms of dominant salt marsh cord grasses (Spartina alterniflora) that live at different elevations from each other. Finer assessments of misclassifications between other plant species pairs provided us with additional insights into the dynamics of why classification total accuracies differed between assessed image composites. The results also suggest that seasonality can significantly affect classification accuracies. The methods and findings utilized in this study may provide resource managers with increased precision in detecting otherwise subtle changes in vegetation patterns over time that can inform future management strategies.
The Big Picture: An Improved Method for Mapping Shipping Activities
Authors: Troupiotis-Kapeliaris Alexandros , Zissis Dimitris , Bereta Konstantina , Vodas Marios , Spiliopoulos Giannis , Karantaidis Giannis
Published: October 2023

Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.
Examining the Potential of Sentinel Imagery and Ensemble Algorithms for Estimating Aboveground Biomass in a Tropical Dry Forest
Authors: Salazar Villegas Mike H. , Qasim Mohammad , Csaplovics Elmar , González-Martinez Roy , Rodriguez-Buritica Susana , Ramos Abril Lisette N. , Salazar Villegas Billy
Published: October 2023

Accurate estimations of aboveground biomass (AGB) in tropical forests are crucial for maintaining carbon stocks and ensuring effective forest management. By combining remote sensing (RS) data with ensemble algorithms, reliable AGB estimates in forests can be obtained. In this context, the freely available Sentinel-1 (S-1 SAR) and Sentinel 2 multispectral imagery (S-2 MSI) data have a significant role in enhancing accurate AGB estimations at a lower cost, which is relevant for the tropical dry forest (TDF) regions where AGB estimation is uncertain or there is a lack of comprehensive exploration. This study aims to address this gap by presenting a cost-effective and reliable AGB estimation approach in the TDF region of Colombia. For this purpose, we modeled and compared the performance of two ensemble algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to estimate AGB using three predictor categories (polarizations/textures, spectral bands/vegetation indices, and a combination of both). We then examined the modeling potential of S-1 SAR and S-2 MSI imagery in predicting forest AGB and subsequently identified the most suitable variables. To construct AGB models’ field data, we employed a clustered distributed sampling approach involving 100 subsample plots, each with an area of 400 m2. Stepwise multiple linear regression was applied to identify suitable predictors from the original satellite bands, vegetation indices, and texture metrics. To produce a map of AGB, predicted AGB values were calculated for every pixel within a specific satellite subscene using the most effective ensemble algorithm. Our study findings show that the RF model, which employed combined predictor sets, displayed superior performance when evaluated against the independent validation set. The RF model successfully estimated AGB with a high degree of accuracy, achieving an R2 value of 0.78 and an RMSE value of 42.25 Mg/ha−1. In contrast, the XGBoost model performed less accurately, obtaining an R2 value of only 0.60 and an RMSE value of 48.41 Mg/ha−1. The results also indicate that S-2 vegetation indices data were more appropriate for this purpose than S-1 texture data. Despite this, S-1 cross-polarized textures were necessary during the dry season for the combined datasets. The top predictive variables for S-2 images were cab and cw, as well as red-edge bands during the wet season. As for S-1 images, texture D_VH _Hom during the dry season was the most important variable for explaining performance. Overall, the proposed approach of using freely available Sentinel data seems to improve the accuracy of AGB estimation in heterogeneous forest cover and, as such, they should be recommended as a data source for forest AGB assessment.
Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape
Authors: Sullivan Franklin B. , Hunsaker Adam G. , Palace Michael W. , Jacobs Jennifer M.
Published: October 2023

Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s−1, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s−1 and 4 m s−1 and snow-on flight speeds of 6 m s−1 over open fields and between 2 and 4 m s−1 over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover.
Crop Classification and Growth Monitoring in Coal Mining Subsidence Water Areas Based on Sentinel Satellite
Authors: Cui Ruihao , Hu Zhenqi , Wang Peijun , Han Jiazheng , Zhang Xi , Jiang Xuyang , Cao Yingjia
Published: October 2023

In high groundwater level mining areas, subsidence resulting from mining can lead to waterlogging in farmland, causing damage to crops and affecting their growth and development, thereby affecting regional food security. Therefore, it is necessary to restore agricultural production in the coal mining subsidence water areas in the densely populated eastern plains. This study focuses on the Yongcheng coal mining subsidence water areas. It utilizes Sentinel-1 and Sentinel-2 data from May to October in the years 2019 to 2022 to monitor the growth and development of crops. The results demonstrated that (1) the accuracy of aquatic crops categorization was improved by adjusting the elevation of the study region with Mining Subsidence Prediction Software (MSPS 1.0). The order of accuracy for classifying aquatic crops using different machine learning techniques is Random Forest (RF) > Classification and Regression Trees (CART) ≥ Support Vector Machine (SVM). Using the RF method, the obtained classification results can be used for subsequent crop growth monitoring. (2) During the early stages of crop growth, when vegetation cover is low, the Radar Vegetation Index (RVI) is sensitive to the volume scattering of crops, making it suitable for tracking the early growth processes of crops. The peak RVI values for crops from May to July are ranked in the following order: rice (2.595), euryale (2.590), corn (2.535), and lotus (2.483). (3) The order of crops showing improved growth conditions during the mid-growth stage is as follows: rice (47.4%), euryale (43.4%), lotus (27.6%), and corn (4.01%). This study demonstrates that in the Yongcheng coal subsidence water areas, the agricultural reclamation results for the grain-focused model with rice as the main crop and the medicinal herb-focused model with euryale as the main crop are significant. This study can serve as a reference for agricultural management and land reclamation efforts in other coal subsidence water areas.
Data Mining and Fusion Framework for In-Home Monitoring Applications
Authors: Ekerete Idongesit , Garcia-Constantino Matias , Nugent Christopher , McCullagh Paul , McLaughlin James
Published: October 2023

Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.
Gas-Sensing Mechanisms and Performances of MXenes and MXene-Based Heterostructures
Authors: John Riya Alice B. , Vijayan Karthikeyan , Septiani Ni Luh Wulan , Hardiansyah Andri , Kumar A Ruban , Yuliarto Brian , Hermawan Angga
Published: October 2023

MXenes are a class of 2D transition-metal carbides, nitrides, and carbonitrides with exceptional properties, including substantial electrical and thermal conductivities, outstanding mechanical strength, and a considerable surface area, rendering them an appealing choice for gas sensors. This manuscript provides a comprehensive analysis of heterostructures based on MXenes employed in gas-sensing applications and focuses on addressing the limited understanding of the sensor mechanisms of MXene-based heterostructures while highlighting their potential to enhance gas-sensing performance. The manuscript begins with a broad overview of gas-sensing mechanisms in both pristine materials and MXene-based heterostructures. Subsequently, it explores various features of MXene-based heterostructures, including their composites with other materials and their prospects for gas-sensing applications. Additionally, the manuscript evaluates different engineering strategies for MXenes and compares their advantages to other materials while discussing the limitations of current state-of-the-art sensors. Ultimately, this review seeks to foster collaboration and knowledge exchange within the field, facilitating the development of high-performance gas sensors based on MXenes.
Three-Dimensional Reconstruction Based on Multiple Views of Structured Light Projectors and Point Cloud Registration Noise Removal for Fusion
Authors: Feng Yun , Wu Rongyu , Liu Xiaojun , Chen Liangzhou
Published: October 2023

Structured light technology is typical for capturing 3D point cloud data. This paper proposes a 3D reconstruction system to obtain point cloud data of complex objects based on nine-order Gray code and an eight-step structured light projection combined with a phase shift and phase unwrapping method. In this system, two projectors serve as bilateral projectors for structured light, along with a camera and rotating platforms. These components were used to obtain point cloud data from multiple perspectives, which helps avoid the shadow areas caused by a single projection angle and provides complementary point cloud data. The point clusters scanned under each perspective were transformed into the same coordinate system. Furthermore, a distance-based point cloud noise removal algorithm was proposed to optimize platform noise and facilitate point cloud data fusion. The experimental results proved that the system effectively captures 3D point cloud data for complex objects. The dimensional quantitative analysis of an aero engine blade was also performed.
Attention-Aware Patch-Based CNN for Blind 360-Degree Image Quality Assessment
Authors: Sendjasni Abderrezzaq , Larabi Mohamed-Chaker
Published: October 2023

An attention-aware patch-based deep-learning model for a blind 360-degree image quality assessment (360-IQA) is introduced in this paper. It employs spatial attention mechanisms to focus on spatially significant features, in addition to short skip connections to align them. A long skip connection is adopted to allow features from the earliest layers to be used at the final level. Patches are properly sampled on the sphere to correspond to the viewports displayed to the user using head-mounted displays. The sampling incorporates the relevance of patches by considering (i) the exploration behavior and (ii) a latitude-based selection. An adaptive strategy is applied to improve the pooling of local patch qualities to global image quality. This includes an outlier score rejection step relying on the standard deviation of the obtained scores to consider the agreement, as well as a saliency to weigh them based on their visual significance. Experiments on available 360-IQA databases show that our model outperforms the state of the art in terms of accuracy and generalization ability. This is valid for general deep-learning-based models, multichannel models, and natural scene statistic-based models. Furthermore, when compared to multichannel models, the computational complexity is significantly reduced. Finally, an extensive ablation study gives insights into the efficacy of each component of the proposed model.
WD-YOLO: A More Accurate YOLO for Defect Detection in Weld X-ray Images
Authors: Pan Kailai , Hu Haiyang , Gu Pan
Published: October 2023

X-ray images are an important industrial non-destructive testing method. However, the contrast of some weld seam images is low, and the shapes and sizes of defects vary greatly, which makes it very difficult to detect defects in weld seams. In this paper, we propose a gray value curve enhancement (GCE) module and a model specifically designed for weld defect detection, namely WD-YOLO. The GCE module can improve image contrast to make detection easier. WD-YOLO adopts feature pyramid and path aggregation designs. In particular, we propose the NeXt backbone for extraction and fusion of image features. In the YOLO head, we added a dual attention mechanism to enable the model to better distinguish between foreground and background areas. Experimental results show that our model achieves a satisfactory balance between performance and accuracy. Our model achieved 92.6% mAP@0.5 with 98 frames per second.
A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI)
Authors: Debnath Anjan , Hasan Md. Mahedi , Raihan M. , Samrat Nadim , Alsulami Mashael M. , Masud Mehedi , Bairagi Anupam Kumar
Published: October 2023

The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2’s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.
Exploring the Potential of Utilizing Aquatic Macrophytes for Enhanced Phytoremediation of Zinc in Artificial Wastewater: Characteristics and Parameter Studies
Authors: Tan Hui Wun , Pang Yean Ling , Lim Steven , Chong Woon Chan , Lai Chin Wei , Abdullah Ahmad Zuhairi
Published: October 2023

Heavy metal pollution due to industrialization can threaten the surrounding environment and living organisms. Phytoremediation is a green technique that uses hyperaccumulator plants to eliminate or decrease heavy metals in polluted water bodies. The aim of this study was to investigate the changes in morphology of Pistia stratiotes (water lettuce) and Eichhornia crassipes (water hyacinth) before and after phytoremediation of zinc (Zn) by using scanning electron microscopy (SEM), electron dispersive X-ray spectroscopy (EDX) and Fourier transform infrared spectroscopy (FTIR). The SEM images showed the formation of small granular aggregates on the surfaces of the leaf and root. EDX results confirmed the uptake of Zn metal, especially in the plant roots. The FTIR spectra showed the Zn metal binding with several characteristic functional groups (O-H, C-H and C=O bonds). Different parameters were also studied to optimize the Zn uptake rate. Water lettuce achieved 80.1% phytoremediation of Zn after 5 days at optimum conditions (10 ppm of Zn, 6 ppm of sodium chloride and natural solution pH). Meanwhile, water hyacinth reached up to 88% when increasing the sodium chloride up to 9 ppm. In conclusion, Zn phytoremediation using both plants can be a potential remediation method for improving the quality of water.
Mulch and Grass Cover Unevenly Halt Runoff Initiation and Sediment Detachment during the Growing Season of Hazelnut (Corylus avellana L.) in Croatia
Authors: Matisic Manuel , Reljic Marko , Dugan Ivan , Pereira Paulo , Filipovic Vilim , Filipovic Lana , Krevh Vedran , Bogunovic Igor
Published: October 2023

Hazelnut orchards are popular for cropping on sloped sites, which are often highly erodible. This study aimed to assess the impact of soil management and season in a hazelnut orchard on soil properties and hydrological response. Three treatments (Tilled, Straw, and Grass) were established in Munije (Croatia) on Stagnosol. In Spring, Summer, and Fall, a rainfall simulation was performed (intensity of 58 mm h−1 for 30 min). Results reveal higher water stable aggregate values were observed for the Straw treatment in all seasons. Higher soil organic matter (SOM) content was noticed for the Grass treatment in all seasons, while lower values were recorded for the Tilled treatment. Sediment loss in Summer was up to 650% and 1300% higher for the Tilled treatment compared with the Straw and Grass treatments. This study strengthens the comprehension of utilizing a permanent ground cover in hazelnut orchards as a sustainable practice, contributing to the mitigation of soil erosion processes and the improvement of soil properties. The Straw treatment is a viable option since it increases soil stability and SOM, consequently preventing high soil erosion.
Comprehensive Investigation of Cooling, Heating, and Power Generation Performance in Adsorption Systems Using Compound Adsorbents: Experimental and Computational Analysis
Authors: Lu Zisheng
Published: October 2023

The extensive utilization of petrochemical energy sources has led to greenhouse gas emissions, the greenhouse effect, the frequent occurrence of extreme weather events, and the severe degradation of Earth’s ecosystems. The development of renewable energy technologies has become an inevitable trend. This paper investigates an adsorption-based cooling/heating/power generation technology driven by low-grade solar thermal energy. The research results demonstrate that the adsorption performance of vermiculite compound adsorbents impregnated with LiCl solution is superior to those impregnated with CaCl2 solution, with the former exhibiting adsorption at lower p/po partial pressure ratios. Furthermore, at an adsorption bed temperature of 25 °C and a p/po partial pressure of 0.8, the adsorption cooling performance of Comp. 2 compound adsorbent impregnated with LiCl solution reaches 5760.7 kJ/kg, with a coefficient of performance (COP) of 0.75, heating performance of 9920.8 kJ/kg, COPh of 1.51, and power generation capacity of 10.6 kJ/kg. This research contributes to the advancement of sustainable energy technologies and the mitigation of environmental impacts associated with petrochemical energy sources.
Fuzzy Analysis of the Strategic Actions of Travel Agencies in Boyacá, Colombia, in a Post-COVID-19 Era
Authors: Blanco-Mesa Fabio , Leon-Castro Ernesto
Published: October 2023

The economic impact of COVID-19 is undeniable, and one of the sectors most affected by this situation was tourism; when departures were canceled and what is known as “The Great Lockdown” began, the activity of this sector was paralyzed. In this regard, knowing which strategic actions must be implemented in order to recover economically is vital. This study aims to identify the importance of the strategic actions of travel agencies in Boyacá following the COVID-19 crisis using aggregation operators and fuzzy techniques. The methodology uses the experton method, Bonferroni’s OWAAC method, maximum similarity sub-relationships and Pichat’s algorithm, and the relative incidence analysis method to determine the importance of the actions taken. The findings show that most managers’ implemented strategic actions, including highlighting financial capacity and marketing (improvement actions and establishing alliances), which were the strategic actions with the highest incidence. These actions identify a focus for activities to reactivate the business and are related to the company’s routine operations.
Thermal Performance Analysis and Multi-Factor Optimization of Middle–Deep Coaxial Borehole Heat Exchanger System for Low-Carbon Building Heating
Authors: Liang Mingshan , Tu Jianhua , Zeng Lingwen , Zhang Zhaoqing , Cheng Nan , Luo Yongqiang
Published: October 2023

Ground-source heat pumps with deep borehole heat exchangers can fully utilize deep geothermal energy, effectively reducing the consumption of non-renewable energy for building air conditioning and achieving energy conservation and emissions reduction goals. However, the middle–deep coaxial borehole heat exchange (MDBHE) development is insufficient, and there is currently a lack of definitive guidelines for system optimal design and operation. This paper firstly establishes an effective and efficient system model and examines nine important parameters related to the design and operation of the MDBHE using a single-factor analysis. Thereafter, we compare and analyze the impact of different parameters through an orthogonal experimentation method. The findings reveal that the three most significant factors are borehole depth, inlet temperature, and mass flow rate, in descending order of importance. In addition, in terms of operation mode, this paper makes a comparative analysis of the operation of the MDBHE in variable flow mode and constant flow mode. The results showed that the average energy consumption of the pump in the variable flow mode decreased by 9.6%, and the surrounding ground temperature recovered at a faster rate.