bluuebunny/crossref_metadata_2025 huggingface dataset:
Covalent Adaptable Network of Semicrystalline Polyolefin Blend with Triple-Shape Memory Effect
Authors: Lee Hann , Jang Yujin , Chang Young-Wook , Lim Changgyu
Published: September 2024

A covalent adaptable network (CAN) of semicrystalline polyolefin blends with triple-shape memory effects was fabricated by the reactive melt blending of maleated polypropylene (mPP) and maleated polyolefin elastomer (mPOE) (50 wt/50 wt) in the presence of a small amount of a tetrafunctional thiol (PETMP) and 1,5,7-triazabicyclo [4,4,0]dec-5-ene (TBD). The polymer blend formed a chemically crosslinked network via the reaction between the thiol group of PETMP and maleic anhydride of both polymers in the blend, which was confirmed by FTIR, the variation of torque during the melt mixing process, a solubility test, and DMA. DSC analysis revealed that the crosslinked polyolefin blends show two distinct crystalline melting transitions corresponding to each component polymer. Improved tensile strength as well as elongation at break were observed in the crosslinked blend as compared to the simple blend, and the mechanical properties were maintained after repeated melt processing. These results suggest that thermoplastic polyolefin blends can be transformed into a high-performance and value-added material with good recyclability and reprocessability.
Preparation and Utilization of a Highly Discriminative Absorbent Imprinted with Fetal Hemoglobin
Authors: Zhang Ka , Zhou Tongchang , Dicko Cedric , Ye Lei , Bülow Leif
Published: September 2024

Development in hemoglobin-based oxygen carriers (HBOCs) that may be used as alternatives to donated blood requires an extensive supply of highly pure hemoglobin (Hb) preparations. Therefore, it is essential to fabricate inexpensive, stable and highly selective absorbents for Hb purification. Molecular imprinting is an attractive technology for preparing such materials for targeted molecular recognition and rapid separations. In this case study, we developed human fetal hemoglobin (HbF)-imprinted polymer beads through the fusion of surface imprinting and Pickering emulsion polymerization. HbF was firstly covalently coupled to silica nanoparticles through its surface-exposed amino groups. The particle-supported HbF molecules were subsequently employed as templates for the synthesis of molecularly imprinted polymers (MIPs) with high selectivity for Hb. After removing the silica support and HbF, the resulting MIPs underwent equilibrium and kinetic binding experiments with both adult Hb (HbA) and HbF. These surface-imprinted MIPs exhibited excellent selectivity for both HbA and HbF, facilitating the one-step isolation of recombinant Hb from crude biological samples. The saturation capacities of HbA and HbF were found to be 15.4 and 17.1 mg/g polymer, respectively. The present study opens new possibilities for designed resins for tailored protein purification, separation and analysis.
Optimizing Surface Characteristics of Stainless Steel (SUS) for Enhanced Adhesion in Heterojunction Bilayer SUS/Polyamide 66 Composites
Authors: Yun Sang-Seok , Yoon Wanjun , Jang Keon-Soo
Published: September 2024

The increasing environmental concerns and stringent regulations targeting emissions and energy efficiency necessitate innovative material solutions that not only comply with these standards but also enhance performance and sustainability. This study investigates the potential of heterojunction bilayer composites comprising stainless steel (SUS) and polyamide 66 (PA66), aiming to improve fuel efficiency and reduce harmful emissions by achieving lightweight materials. Joining a polymer to SUS is challenging due to the differing physical and chemical properties of each material. To address this, various surface treatment techniques such as blasting, plasma, annealing, and etching were systematically studied to determine their effects on the microstructural, chemical, and mechanical properties of the SUS surface, thereby identifying mechanisms that improve adhesion. Chemical etching using HNO3/HCl and CuSO4/HCl increased surface roughness and mechanical properties, but these properties decreased after annealing. In contrast, K3Fe(CN)6/NaOH treatment increased the lap shear strength after annealing. Blasting increased surface roughness and toughness with increasing spray pressure and further enhanced these properties after annealing. Contact angle measurements indicated that the hydrophilicity of the SUS surface improved with surface treatment and further improved due to microstructure formation after annealing. This study demonstrates that customized surface treatments can significantly enhance the interfacial adhesion and mechanical properties of SUS/polymer heterojunction bilayer composites, and further research is recommended to explore the long-term stability and durability of these treatments under various environmental conditions.
Extrapolation Framework and Characteristic Analysis of Load Spectrum for Agriculture General Power Machinery
Authors: Song Dongdong , Wang Tieqing , Zhu Shuai , Liu Zhijie
Published: September 2024

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements.
Data-Driven Feature Extraction-Transformer: A Hybrid Fault Diagnosis Scheme Utilizing Acoustic Emission Signals
Authors: Ma Chenggong , Gao Jiuyang , Wang Zhenggang , Liu Ming , Zou Jing , Zhao Zhipeng , Yan Jingchao , Guo Junyu
Published: September 2024

This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two techniques: the Wavelet Kernel Network (WKN) for noise reduction and the Convolutional Block Attention Module (CBAM) for feature enhancement. The wavelet function in the WKN reduces noise, while the attention mechanism in the CBAM enhances features. The Transformer module then processes the feature vectors and sends the results to the softmax layer for classification. To validate the proposed method’s efficacy, experiments were conducted using acoustic emission datasets from NASA Ames Research Center and the University of California, Berkeley. The results were compared using the four key metrics obtained through confusion matrix analysis. Experimental results show that the proposed method performs excellently in fault diagnosis using acoustic emission signals, achieving a high average accuracy of 99.84% and outperforming several baseline models, such as CNN, CNN-LSTM, CNN-GRU, VGG19, and ZFNet. The best-performing model, VGG19, only achieved an accuracy of 88.61%. Additionally, the findings suggest that integrating noise reduction and feature enhancement in a single framework significantly improves the network’s classification accuracy and robustness when analyzing acoustic emission signals.
The Indicative Role of Geochemical Characteristics of Fracturing Flowback Fluid in Shale Gas Wells on Production Performance
Authors: Yin Xingping , Fu Xiugen , Jiang Yuqiang , Fu Yonghong , Zhang Haijie , Jiang Lin , Wang Zhanlei , Li Miao
Published: September 2024

The geochemical properties of fracturing flowback fluids indirectly indicate the fracturing efficiency of the reservoir, the interaction between the reservoir and injected water, and the preservation of oil and gas, thereby offering robust data support for identifying fracturing flowback fluid sources, assessing fracturing effects, and proposing stimulation strategies. In this study, the ion characteristics, total salinity, and stable isotope ratio of fracturing flowback fluids of the Z202H1 and Z203 wells in Western Chongqing were measured. The findings suggest that with the extension of flowback time, the geochemical properties of fracturing flowback fluids evolve toward higher salinity and heavier stable isotope ratios, ultimately stabilizing. Upon comparing the water–rock reaction intensity and the rate of total salinity increase in the fracturing flowback fluids, it is concluded that fracturing flowback fluids contain a mixture of formation water. Because water–rock reactions elevate the total salinity of fracturing flowback fluids, we introduce the Water–Rock Reaction Intensity Coefficient (IR) to denote the intensity of these reactions. Based on the IR value, the binary mixture model for fracturing fluids in fracturing flowback fluids was adjusted. With the increase in flowback time, the content of fracturing fluids in fracturing flowback fluids of Z202H1 and Z203 stabilized at about 55% and 40% respectively. During the same flowback period, the fracturing flowback fluids of the Z203 well exhibit a higher total salinity, a heavier stable isotope ratio, a greater IR, and a lower fracturing fluid content in fracturing flowback fluids. This suggests that the fracturing effect of the Z203 well is superior to that of the Z202H1 well, leading to a higher production capacity of the Z203 well.
Editorial on the Special Issue “Progresses in Electrochemical Energy Conversion and Storage—Materials, Structures and Simulation”—Towards Better Electrochemical Energy Conversion and Storage Technologies
Authors: Xu Qian , Ma Qiang
Published: September 2024

Energy is at the heart of the sustainable development agenda, with an emphasis on efficient conversion and storage [...]
A Systematic Approach to Determining the Kinetics of the Combustion of Biomass Char in a Fluidised Bed Reactor
Authors: Newman S. G. , Kwong K. Y. , Marek E. J.
Published: September 2024

The aim of this work was to investigate the combustion of biochar in a fluidised bed and determine the intrinsic kinetic parameters for combustion: pre-exponential constant Ai and activation energy Ei. When analysing the rates of reaction, Regimes I, II and III were demonstrated, with values for the activation energy of 155, 57 and 9 kJ/mol, respectively, when combustion was limited by different factors: intrinsic kinetics, intraparticle and external mass transport phenomena. These mass transport phenomena were decoupled from a set of ‘apparent’ kinetics incorporating effectiveness factors, which we used as a starting point in the determination of the intrinsic kinetic parameters. We also investigated a simple approach to model the evolution of the char structure over the course of oxidation using an empirical function, fX, fitted with an O(7) polynomial. We then reassessed the division into three combustion regimes by exploring the changes in fX and the intraparticle effectiveness factor that occurred upon increasing the combustion temperature. Overall, we demonstrate that experiments in a fluidised bed can be used to determine biochar kinetics in a simplified but trustworthy way.
Drying Hot Red Chilies: A Comparative Study of Solar-Gas-Fired, Tunnel, and Conventional Dryers
Authors: Tang Lingdi , Chandio Farman Ali , Shaikh Sher Ali , Junejo Abdul Rahim , Mari Irshad Ali , Khoso Hyder Bakhsh , Hao Li , Syed Tabinda Naz , Ahmed Fiaz
Published: September 2024

Drying extends the shelf life of crops; thus, dryers with good designs will help them dry to an optimum level. The present research work was carried out to assess and compare the performance of conventional (CD), solar tunnel (STD), and solar-cum gas-fired dryers (SGD) for drying hot chilies. The Sanam variety of hot chilies was used in this study. Samples were dried using CD, STD, and SGD methods. The drying process was conducted over three days, from 9:00 to 17:00 daily. Results showed significant differences among the drying methods in temperature, relative humidity, and moisture content reduction (p < 0.0001). The SGD consistently outperformed the other methods, achieving the highest temperature (55 °C) and lowest relative humidity (17%), compared to the STD (44 °C, 23%) and CD (34 °C, 31%). The SGD demonstrated superior efficiency, reducing moisture content from 70% to 9.36% in just 36 h, while the STD required 50 h (to 11.37%) and CD took 84 h (to 9.63%). ANOVA and post hoc analyses revealed that the SGD significantly outperformed both the STD (p = 0.0412) and CD (p = 0.0018) in moisture content reduction. Additionally, the SGD and STD better preserved the color of hot chili samples compared to CD, as determined by the Essential Oil Association (EOA) method. It is concluded that the SGD is the most technically suitable method for drying hot chilies, offering improved efficiency and quality retention. It is recommended to use an SGD for optimal results in hot pepper drying.
Analysis of Scientific Production in Family Medicine in Mexico
Authors: Mendiola-Pastrana Indira R. , López-Ortiz Eduardo , Hernández-López Rubí G. , Romero-Henríquez Luisa F. , Dávila-Mendoza Rocío , López-Ortiz Geovani
Published: September 2024

Objective: This paper aims to evaluate the quality of scientific production in family medicine in Mexico and identify gaps in the development of research in this medical discipline. Methods: This cross-sectional study analyzed original articles, reviews, case reports, and editorials published from Mexico, from the year 2014 to 2023, in the three family medicine journals edited in the country. Several bibliometric indicators were evaluated. Attributes that confer validity in original articles were analyzed, and through random sampling, 10% were selected to determine their quality using checklists. Results: A total of 627 articles were analyzed; among these, 57.89% were original, 19.61% reviews, 17.06% editorials, and 5.42% case reports. Our analysis revealed significant disparities in research activity across regions in Mexico. Productivity, transience, and isolation indices were 2.79, 78.58%, and 54.05%, respectively, while the Price index was 42.74%. A small percentage of articles received funding and followed guidelines for medical research reporting (0.47% and 0.63%, respectively). The analysis of validity attributes in original articles revealed that 92.83% were observational, 88.98% were unicentric, in 47.38%, no sample size calculation was performed or specified, while in 12.12%, sampling was probabilistic. In the evaluation of the original articles, more than 60% showed limitations that compromised their quality. Conclusions: The number of published articles, along with their bibliometric, validity, and quality attributes, reflect significant gaps in the generation and dissemination of knowledge in family medicine in Mexico. This shows a transgenerational problem, identified in many countries, where the advancement of family medicine as a specialty is limited by low research productivity and methodological weaknesses in reporting.
The British Broadsheet Press and the Representation of “The Mosque” in the Aftermath of Post-7/7 Britain
Authors: Raja Irfan
Published: September 2024

The role of the mosque has become increasingly vital, particularly in contemporary societies such as Britain, where both the place and status of religion in public life are constantly discussed and debated. Indeed, in the contemporary period, the role of the mosque has several dimensions, ranging from a social space, educational and cultural exchange, and community cohesion centre to a knowledge hub. In this context, this paper suggests that a mosque should be seen as an independent religious institution, although these are influenced by and responsive to governments, elites, pressure groups, public bodies, etc. Using a thematic analysis of news items in The Guardian and The Daily Telegraph over a period of two years (8 July 2005–7 July 2007), it finds that in the aftermath of 7/7, the mosque as a religious place and an institution for British Muslims is largely seen as incompatible and a threat to secular British society and that it is linked with radicalisation and terrorism. According to Quranic texts and revelations, it is indeed a fact that mosques are open to all. This is logical since God’s mercy, love, and forgiveness are for all of mankind without any distinction, which is perhaps why the mosque has been the sacred house of God Himself. This study aims to reveal the development of visible hostility in some sections of the British media and political campaigns. Moreover, it intends to trace the determination and idea of the mosque as a religious place and an institution for British Muslims rather than only a place for worship. Finally, this study will argue the role of the mosque in promoting community cohesion and mutual understanding within the Muslim and other faith communities located in Britain.
Historical Landscape: A Methodological Proposal to Analyse the Settlements of Monasteries in the Birth of Portugal
Authors: Vaz de Freitas Isabel , Lopes Hélder Silva , Albuquerque Helena
Published: September 2024

This study aims to understand and characterise the landscape of monasteries in early medieval Portugal using a methodology to better comprehend the factors influencing monastery construction. The research focuses on variables such as altitude, slope, aspect, hydrology, geomorphology, and topographic prominence. Using Geographic Information Systems (GIS) for detailed spatial analysis, the study reveals that monasteries were typically located in areas with slight elevations, gentle slopes, and proximity to watercourses, reflecting considerations about resource exploitations, access, and population development. The analysis shows no significant differences in construction preferences among different religious orders, indicating a general adaptability to the local environment rather than distinct criteria for each order. Despite the broad trends, individual orders exhibited some variability in their specific site selections, such as altitude and slope preferences. The findings highlight the importance of integrating historical and environmental data to understand settlement patterns, providing valuable insights into the strategic considerations behind monastery locations. Future research could expand on these findings by incorporating socio-economic impacts, enhancing our understanding of medieval monastic landscapes.
Authority from the Back of Beyond: Cosmic Travel as a Rhetorical Strategy across the Myth of Er, the Book of the Watchers, and the Dream of Scipio
Authors: Glass R. Gillian
Published: September 2024

Ancient Near Eastern and Mediterranean cosmologies shared general assumptions about the interconnectivity of heaven and earth. Plato’s Myth of Er, the Book of the Watchers in 1 Enoch, and Cicero’s Dream of Scipio, narrate the travels of Er, Enoch, and Scipio, respectively, into the Beyond, where they each learn astonishing things about the cosmos, and are tasked with imparting a message to humanity. This comparative study argues that cosmic travel is an integral means of constructing a rhetoric of authority designed to recruit its audiences to its socio-political vision. By analysing literary conventions like pseudepigraphy and epiphany in the features that make up cosmic travel, we better understand how each story bridges the gap between the narrated (story) world and the external (real) world. The ability to blend the realities of a story and its audiences stems from the ways in which tropes of legitimacy render spatio-temporal reality malleable, but is also imperative to the very authority these tropes offer. Without arguing for deliberate intertextuality between all these sources, this study compares the use of heavenly voyages as a literary device for legitimising worldview across cultures, times, and places.
Nietzsche and Spiritual Matters: A Reading of The Anti-Christ
Authors: Thiessen Mitch
Published: September 2024

This essay primarily consists of an interpretation of The Anti-Christ, arguably Nietzsche’s most abrasive and overall least appreciated book. By treating this late work as what it in fact is, on the other hand, namely, the culmination of Nietzsche’s lifelong polemic against Christianity and the source of many of his most explicit and revealing pronouncements on what a “revaluation of all values” ultimately means, this essay argues that this work presents us not only with the philosopher’s definitive position on Christianity, but with the real meaning of his thought as such.
‘It Never Ends’: Disability Advocacy and the Practice of Resilient Hope
Authors: Gould James B.
Published: September 2024

Political advocacy is an important religious practice. But social activism can be discouraging. This paper integrates moral theology, virtue ethics, positive psychology and spiritual formation to highlight the importance of resilient hope for social justice advocates. Part 1 describes an important justice issue—public services for disabled people. Part 2 defines transformational advocacy and outlines an advocacy theology. Part 3 analyzes despair and hope. Part 4 summarizes spiritual practices for building resilient hope.
Early Mission Calibration Performance of NOAA-21 VIIRS Reflective Solar Bands
Authors: Lei Ning , Xiong Xiaoxiong , Twedt Kevin , Li Sherry , Chang Tiejun , Mu Qiaozhen , Angal Amit
Published: September 2024

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments on the recently launched NOAA-21 (previously known as JPSS-2) satellite. The VIIRS, like its predecessors on the SNPP and NOAA-20 satellites, provides daily global coverage in 22 spectral bands from 412 nm to 12 μm. The geometrically and radiometrically calibrated observations are the basis for many operational applications and scientific research studies. A total of 14 of the 22 bands are reflective solar bands (RSBs), covering photon wavelengths from 412 nm to 2.25 μm. The RSBs were radiometrically calibrated prelaunch and have been regularly calibrated on orbit through the onboard solar diffuser (SD) and scheduled lunar observations. The on-orbit SD’s reflectance change is determined by the onboard solar diffuser stability monitor (SDSM). We review the calibration algorithms and present the early mission performance of the NASA N21 VIIRS RSBs. Using the calibration data collected at both the yaw maneuver and regular times, we derive the screen transmittance functions. The visible and near-infrared bands’ radiometric gains have been stable, nearly independent of time, and so were the radiometric gains of the shortwave-infrared bands after the second mid-mission outgassing. Further, we assess the Earth-view striping observed in the immediate prior collection (Collection 2.0) and apply a previously developed algorithm to mitigate the striping. The N21 VIIRS RSB detector signal-to-noise ratios are all above the design values with large margins. Finally, the uncertainties of the retrieved Earth-view top-of-the-atmosphere spectral reflectance factors at the respective typical spectral radiance levels are estimated to be less than 1.5% for all the RSBs, except band M11 whose reflectance factor uncertainty is 2.2%.
Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet
Authors: Yang Min , Yang Jie , Mao Hongxia , Zheng Chong
Published: September 2024

Change detection based on optical image processing plays a crucial role in the field of damage assessment. Although existing damage scene change detection methods have achieved some good results, they are faced with challenges, such as low accuracy and slow speed in optical image change detection. To solve these problems, an image change detection approach that combines infrared polarization imaging with a fast principal component analysis network (Fast-PCANet) is proposed. Firstly, the acquired infrared polarization images are analyzed, and pixel image blocks are extracted and filtered to obtain the candidate change points. Then, the Fast-PCANet network framework is established, and the candidate pixel image blocks are sent to the network to detect the change pixel points. Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. The experimental results show that the proposed Fast-PCANet method improves the PCC and the Kappa coefficient of infrared polarization images over infrared intensity images by 6.77% and 13.67%, respectively. Meanwhile, the inference speed can be more than seven times faster. The results verify that the proposed approach is effective and efficient for the change detection task with infrared polarization imaging. The study can be applied to the damage assessment field and has great potential for object recognition, material classification, and polarization remote sensing.
Assessing the Response of the Net Primary Productivity to Snow Phenology Changes in the Tibetan Plateau: Trends and Environmental Drivers
Authors: Liu Jiming , Shen Lu , Chen Zhaoming , Ni Jingwen , Huang Yan
Published: September 2024

Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology (SP) across the TP from 2011 to 2020. Interannual trends were assessed using the Theil–Sen non-parametric regression approach combined with the Mann–Kendall test. Additionally, the pathways through which snow cover affects the NPP, considering various environmental factors, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Approximately 10.72% of the TP showed a significant decrease in the NPP, accompanied by advancing trends in the Snow Onset Date (SOD) and Snow End Date (SED), as well as a gradual decrease in the Snow Cover Duration (SCD). The PLS-SEM results reveal that precipitation and soil temperature significantly influenced the NPP, with total effects of 0.309 and 0.206 in the SCD structural equation. Temperature had a relatively strong indirect effect on the NPP through its influence on the SOD and SCD, contributing 16% and 10% to the total effect, respectively. Neglecting the mediating effect of SP underestimates the environmental impact on the NPP. This study highlights how environmental factors influence the NPP through snow cover changes as the biomass increases, thereby enhancing our understanding of SP’s impact on the TP.
Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
Authors: Kim Sang-Il , Jin Donghyun , Kim Jiyeon , Ahn Do-Seob , Han Kyung-Soo
Published: September 2024

In this study, a 4D trajectory weather (4DT-Wx) prototype system was developed and evaluated for effective weather information integration in trajectory-based operation (TBO) environments. The system has two key distinguishing features: multi-model-based trajectory services and buffer zone information provision. We constructed a distributed processing system using Apache Spark, enabling the efficient processing of large-scale weather data. The performance evaluation demonstrated excellent scalability and efficiency in processing large-scale data. An analysis of the buffer configurations highlighted that buffer zone information is valuable in decision-making processes and has the potential to enhance the system performance. The system’s practical applicability is presented through visualizations of the extracted weather information. This system is expected to enhance aviation safety and operational efficiency, providing a foundation for addressing increasingly complex weather conditions and flight scenarios in the future. The approach presented in this study marks a significant step toward effective TBO implementation and the advancement of future air traffic management. The evaluation of the 4DT-Wx system analyzed the accuracy of weather data processing and the performance of distributed processing, finding that the temperature (T) estimation had the highest accuracy, and that the parallel processing using Apache Spark was most effectively modeled by Ahmed et al.’s model. The findings suggest the potential for further optimization in integrating various weather models and developing algorithms to enhance their utilization.
Genetic Algorithm Empowering Unsupervised Learning for Optimizing Building Segmentation from Light Detection and Ranging Point Clouds
Authors: Sulaiman Muhammad , Farmanbar Mina , Belbachir Ahmed Nabil , Rong Chunming
Published: September 2024

This study investigates the application of LiDAR point cloud datasets for building segmentation through a combined approach that integrates unsupervised segmentation with evolutionary optimization. The research evaluates the extent of improvement achievable through genetic algorithm (GA) optimization for LiDAR point cloud segmentation. The unsupervised methodology encompasses preprocessing, adaptive thresholding, morphological operations, contour filtering, and terrain ruggedness analysis. A genetic algorithm was employed to fine-tune the parameters for these techniques. Critical tunable parameters, such as the interpolation method for DSM and DTM generation, scale factor for contrast enhancement, adaptive constant and block size for adaptive thresholding, kernel size for morphological operations, squareness threshold to maintain the shape of predicted objects, and terrain ruggedness index (TRI) were systematically optimized. The study presents the top ten chromosomes with optimal parameter values, demonstrating substantial improvements of 29% in the average intersection over union (IoU) score (0.775) on test datasets. These findings offer valuable insights into LiDAR-based building segmentation, highlighting the potential for increased precision and effectiveness in future applications.
Mapping Earth Hummocks in Daisetsuzan National Park in Japan Using UAV-SfM Framework
Authors: Meng Yu , Watanabe Teiji , Hayakawa Yuichi S. , Sawada Yuki , Wang Ting
Published: September 2024

Earth hummocks are periglacial landforms that are widely distributed in arctic and alpine regions. This study employed an uncrewed aerial vehicle (UAV) and a structure from motion (SfM) framework to map and analyze the spatial distribution and morphological characteristics of earth hummocks across an extensive area in Daisetsuzan National Park, Japan. The UAV-captured images were processed using SfM photogrammetry to create orthomosaic images and high-resolution DEMs. We identified the distribution and morphological characteristics of earth hummocks using orthoimages, hillshade maps, and DEMs and analyzed how their morphological parameters relate to topographical conditions. A total of 18,838 individual earth hummocks in an area of approximately 82,599 m² were mapped and analyzed across the two study areas, surpassing the scale of existing studies. The average length, width, and height of these earth hummocks are 1.22 m, 1.03 m, and 0.15 m, respectively, and topographical features such as slope, aspect, and landforms are demonstrated to have an essential influence on the morphology of the earth hummocks. These findings enhance our understanding of topographical features. Furthermore, this study demonstrates the efficacy of utilizing the UAV-SfM framework with multi-directional hillshade mapping as an alternative to manual field measurements in studying periglacial landforms in mountainous regions.
Neural Network-Based Estimation of Near-Surface Air Temperature in All-Weather Conditions Using FY-4A AGRI Data over China
Authors: Liu Hai-Lei , Duan Min-Zheng , Zhou Xiao-Qing , Zhang Sheng-Lan , Deng Xiao-Bo , Zhang Mao-Lin
Published: September 2024

Near-surface air temperature (Ta) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather Ta estimation method utilizing FY-4A Advanced Geostationary Radiation Imager (AGRI) and the Global Forecast System (GFS), along with additional auxiliary data. The method includes two neural-network-based Ta estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the Ta (Ta,clear), whereas cloud top temperature and cloud top height were employed to estimate the Ta for cloudy skies (Ta,cloudy). The estimated Ta was validated using the 2020 data from 1211 stations in China, and the RMSE values of the Ta,clear and Ta,cloudy were 1.80 °C and 1.72 °C, while the correlation coefficients were 0.99 and 0.986, respectively. The performance of the all-weather Ta estimation model showed clear temporal and spatial variation characteristics, with higher accuracy in summer (RMSE = 1.53 °C) and lower accuracy in winter (RMSE = 1.88 °C). The accuracy in southeastern China was substantially better than in western and northern China. In addition, the dependence of the accuracy of the Ta estimation model for LST, CTT, CTH, elevation, and air temperature were analyzed. The global sensitivity analysis shows that AGRI and GFS data are the most important factors for accurate Ta estimation. The AGRI-estimated Ta showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for Ta estimation under all-weather conditions and are adaptable to other geostationary satellites.
A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture
Authors: Afif Oumaima , Franceschelli Leonardo , Iaccheri Eleonora , Trovarello Simone , Di Florio Di Renzo Alessandra , Ragni Luigi , Costanzo Alessandra , Tartagni Marco
Published: September 2024

This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz–4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels.
A Survey on the Design of Virtual Reality Interaction Interfaces
Authors: Chen Meng-Xi , Hu Huicong , Yao Ruiqi , Qiu Longhu , Li Dongxu
Published: September 2024

Virtual reality (VR) technology has made remarkable progress in recent years and will be widely used in the future. As a bridge for information exchanges between users and VR systems, the interaction interface is pivotal for providing users with a good experience and has emerged as a key research focus. In this review, we conducted a comprehensive search of the Web of Science and CNKI databases from 2011 to 2023 to identify articles dedicated to VR interaction interface design. Through a meticulous analysis of 438 articles, this paper offers a substantial contribution to the emerging field of VR interactive interface research, providing an in-depth review of the principal research advancements. This review revealed that the majority of studies are centered on practical case analyses within specific application scenarios, employing empirical evaluation methods to assess objective or subjective metrics. We then concentrated on elucidating the foundational principles of interface design and their evaluation methodologies, providing a reference for future research endeavors. Additionally, the limitations, challenges, and future directions in VR interaction interface design research were discussed, highlighting the need for further research in design evaluation to continuously refine the development of standards and guidelines for VR interactive interface design. According to the findings of this review, there is a necessity to enhance research on information design for multi-channel interactive interfaces. Furthermore, it is essential to focus on the diverse characteristics of users to propose more inclusive design solutions. Adopting interdisciplinary approaches could lead to breakthroughs in the creation of personalized and adaptive VR interaction interfaces.
Θ-Net: A Deep Neural Network Architecture for the Resolution Enhancement of Phase-Modulated Optical Micrographs In Silico
Authors: Kaderuppan Shiraz S. , Sharma Anurag , Saifuddin Muhammad Ramadan , Wong Wai Leong Eugene , Woo Wai Lok
Published: September 2024

Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy, while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net (‘bead’) architectures (termed ‘Θ-Net’ in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico. The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology).
VQGNet: An Unsupervised Defect Detection Approach for Complex Textured Steel Surfaces
Authors: Yu Ronghao , Liu Yun , Yang Rui , Wu Yingna
Published: September 2024

Defect detection on steel surfaces with complex textures is a critical and challenging task in the industry. The limited number of defect samples and the complexity of the annotation process pose significant challenges. Moreover, performing defect segmentation based on accurate identification further increases the task’s difficulty. To address this issue, we propose VQGNet, an unsupervised algorithm that can precisely recognize and segment defects simultaneously. A feature fusion method based on aggregated attention and a classification-aided module is proposed to segment defects by integrating different features in the original images and the anomaly maps, which direct the attention to the anomalous information instead of the irregular complex texture. The anomaly maps are generated more confidently using strategies for multi-scale feature fusion and neighbor feature aggregation. Moreover, an anomaly generation method suitable for grayscale images is introduced to facilitate the model’s learning on the anomalous samples. The refined anomaly maps and fused features are both input into the classification-aided module for the final classification and segmentation. VQGNet achieves state-of-the-art (SOTA) performance on the industrial steel dataset, with an I-AUROC of 99.6%, I-F1 of 98.8%, P-AUROC of 97.0%, and P-F1 of 80.3%. Additionally, ViT-Query demonstrates robust generalization capabilities in generating anomaly maps based on the Kolektor Surface-Defect Dataset 2.
Reducing Measurement Costs of Thermal Power: An Advanced MISM (Mamba with Improved SSM Embedding in MLP) Regression Model for Accurate CO2 Emission Accounting
Authors: Wang Yinchu , Liu Zilong , Huang Hui , Xiong Xingchuang
Published: September 2024

Current calculation methods for the carbon content as received (Car) of coal rely on multiple instruments, leading to high costs for enterprises. There is a need for a cost-effective model that maintains accuracy in CO2 emission accounting. This study introduces an MISM model using key parameters identified through correlation and ablation analyses. An Improved State-Space Model (ISSM) and an IS-Mamba module are integrated into a Multi-Layer Perceptron (MLP) framework, enhancing information flow and regression accuracy. The MISM model demonstrates superior performance over traditional methods, reducing the Root Mean Square Error (RMSE) by 22.36% compared to MLP, and by 9.65% compared to Mamba. Using only six selected parameters, the MISM model achieves a precision of 0.27% for the discrepancy between the calculated CO2 emissions and the actual measurements. An ablation analysis confirms the importance of certain parameters and the effectiveness of the IS-Mamba module at improving model performance. This paper offers an innovative solution for accurate and cost-effective carbon accounting in the thermal power sector, supporting China’s carbon peaking and carbon neutrality goals.
Polar Motion Ultra-Short-Term Prediction of Least-Squares+Multivariate Autoregressive Hybrid Method by Using the Kalman Filter
Authors: Tan Zhirong , Ye Fei , Hua Liangchun
Published: September 2024

The polar motion (PM, including two parameters PMx and PMy) ultra-short-term prediction (1–10 days) is demanded in the real-time navigation of satellites and spacecrafts. Improving the PMx and PMy ultra-short-term predictions accuracies are a key to optimize the performance of these related applications. Currently, the least squares (LS)+autoregressive (AR) hybrid method is regarded as one of the most capable approaches for ultra-short-term predictions of PMx and PMy. The Kalman filter has proven to be effective in improving the ultra-short-term prediction performance of the LS+AR hybrid method, but the PMx and PMy ultra-short-term predictions accuracies are still not able to satisfy some related applications. In order to improve the performance of PM ultra-short-term prediction, it is worth exploring the combinations of existing methods. Throughout the existing predicted methods, the LS+multivariate autoregressive (MAR) hybrid method by using the Kalman filter has the potential to improve the accuracy of PM ultra-short-term prediction. In addition, a PM prediction performance analysis of the LS+MAR hybrid method by using the Kalman filter, namely the LS+MAR+Kalman hybrid method, is still missing. In this contribution, we proposed the LS+MAR+Kalman hybrid method for PM ultra-short-term prediction. The data sets for PM predictions, which range from 1 to 10 days, have been tested based on the International Earth Rotation and Reference Systems Service Earth Orientation Parameter (IERS EOP) 14 C04 series to assess the performance of the LS+MAR+Kalman hybrid model. The experimental results illustrated that the LS+MAR+Kalman hybrid method can effectively execute PMy ultra-short-term predictions. The improvement of PMy prediction accuracy can rise up to 12.69% for 10-day predictions, and the improvement of ultra-short-term predictions is 7.64% on average.
Complementary Metal–Oxide–Semiconductor-Based Magnetic and Optical Sensors for Life Science Applications
Authors: Azadmousavi Tayebeh , Ghafar-Zadeh Ebrahim
Published: September 2024

Optical and magnetic sensing methods are integral to both research and clinical applications in biological laboratories. The ongoing miniaturization of these sensors has paved the way for the development of point-of-care (PoC) diagnostics and handheld sensing devices, which are crucial for timely and efficient healthcare delivery. Among the various competing sensing and circuit technologies, CMOS (complementary metal–oxide–semiconductor) stands out due to its distinct cost-effectiveness, scalability, and high precision. By leveraging the inherent advantages of CMOS technology, recent developments in optical and magnetic biosensors have significantly advanced their application in life sciences, offering improved sensitivity, integration capabilities, and reduced power consumption. This paper provides a comprehensive review of recent advancements, focusing on innovations in CMOS-based optical and magnetic sensors and their transformative impact on biomedical research and diagnostics.
Human-to-Robot Handover Based on Reinforcement Learning
Authors: Kim Myunghyun , Yang Sungwoo , Kim Beomjoon , Kim Jinyeob , Kim Donghan
Published: September 2024

This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots’ ability to safely exchange diverse objects with humans during human–robot interactions (HRIs). The study integrates an adaptive HRI hand for versatile grasping and incorporates image recognition for efficient object identification and precise coordinate estimation. A tailored reinforcement-learning environment enables the robot to dynamically adapt to diverse scenarios. The effectiveness of this approach is validated through simulations and real-world applications. The HRI hand’s adaptability ensures seamless interactions, while image recognition enhances cognitive capabilities. The reinforcement-learning framework enables the robot to learn and refine skills, demonstrated through successful navigation and manipulation in various scenarios. The transition from simulations to real-world applications affirms the practicality of the proposed system, showcasing its robustness and potential for integration into practical robotic platforms. This study contributes to advancing intelligent and adaptable robotic systems for safe and dynamic HRIs.