bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and Performance Evaluation of Nano-Calcium Carbonate-Modified Geopolymers Incorporating Fly Ash and Manganese Slag: A Comprehensive Investigative Study
Authors: Fu Yuhao , Xu Zifang , Zhang Xiang , Lu Qiqi , Sun Zihao , Wang Xuekai
Published: December 2023

Grounded in the auspicious horizons of geological polymers as alternative replacements for Portland cement and aligned with the national endeavor of constructing an ecological civilization and harnessing solid waste as a resource, this study delves into the integration of nanostructured calcium carbonate (CaCO3) into geological polymers derived from fly ash and manganese slag. Employing a comprehensive methodology involving modalities, such as X-ray diffraction, scanning electron microscopy, and attenuated total reflectance Fourier-transform infrared spectroscopy, the influence of nano-CaCO3 on the compressive strength, pore architecture, and polymerization degree of geological polymers is meticulously unveiled. The outcomes reveal that nano-CaCO3 adeptly infiltrates the intricate microporous architecture of geological polymers, thereby providing a compact and intrinsically reinforcing matrix, ultimately endowing a marked increase in compressive strength. The assimilation of nano-CaCO3 correlates conspicuously with an increase in monomeric calcium concentrations, thereby catalyzing and expediting the formation of polymeric assemblages within the system, which in turn accelerates the progression of geological polymerization. This catalytic effect augments the intricate three-dimensional lattice-like gel structures, consequently orchestrating a substantial amelioration in mechanical attributes. When the dosage of nano-CaCO3 was 3.5%, sodium silicate was 10%, and NaOH was 12%, the integrated performance of fly ash–Mn slag geopolymer was optimal. Specifically, the 28-day compressive strength reached 25.6 MPa, and the compressive strength of the weathering performance test increased by 8.31%. The polymer achieved 96.77% curing of Mn, and it was non-radioactive. Thus, the prepared geopolymers are safe and reliable and support the subsequent development of nanomaterial activators.
Advances in Process Safety and Protection of Cyber-Physical Systems
Authors: Yang Shuang-Hua , Cao Yi , Ding Yulong
Published: December 2023

Safety has remained the foremost concern in process systems engineering for decades [...]
Residence Time Distribution: Literature Survey, Functions, Mathematical Modeling, and Case Study—Diagnosis for a Photochemical Reactor
Authors: Nechita Mircea Teodor , Suditu Gabriel Dan , Puițel Adrian Cătălin , Drăgoi Elena Niculina
Published: December 2023

This paper aims to provide an overview of the fundamentals, development, and evolution of residence time distribution (RTD) methodology and its applications to the flow and mixing of fluids (and solid particles) modeling in different systems. A concise literature analysis is followed by a succinct presentation of RTD methodology’s experimental and theoretical foundations and RTD-based mathematical modeling, highlighting its importance. An experimental demonstration of RTD diagnostics on a photochemical reactor is performed to identify the most practical locations for the inlet/outlet pipes (axial or radial) and the photochemical reactor’s ideal working posture (horizontal, vertical, or inclined) and to understand the level of mixing and to determine the fluid flow defects. Using the relevant RTD functions and the corresponding central moments, it was possible to show that short circuits and dead zones occurred in each of the six considered reactor configurations. Following these investigations, design solutions were proposed to achieve a convenient exposure time, proper mixing, and uniform irradiation inside the reactor.
The Influence of Hydraulic Characteristics on Structural Performance in a Pump-Turbine under No-Load Conditions
Authors: Ren Shenming , Zheng Yuan , Yuan Cong , Liu Bin , Fernandez-Rodriguez Emmanuel , Zhang Yuquan
Published: December 2023

The operating state of a pump-turbine unit under no-load conditions is directly related to its safe and stable operation. In order to probe into the influence of hydraulic characteristics on structural performance, a pump-turbine assembled in China is selected for research by using CFD (computational fluid dynamics) and unidirectional FSI (fluid–structure interaction) methods. The vortex distribution and the law of pressure pulsation propagation are analyzed to capture the peculiar flow phenomena. The results show that the vortex distribution in the runner channel appears initially at the suction side of the blades but then propagates toward the pressure side with GVO. This produces rotating stall frequencies (0.7fn) and a combination of the RSI, asymmetry of the water ring in vaneless space, and high-amplitude pressure pulsations in the downstream channel close to the runner inlet and elbow section of the draft tube. This, in turn, is associated with the structural stress of the runner and guide vane. The stress level of the guide vane becomes alleviated under no-load conditions with large GVO, but the stress distribution of the runner is no longer symmetrical, which aligns with the vortex evolution in the runner passage. The stress concentration that develops further along the blade root increases the structural failure, which is also captured and verified as a crack in the prototype runner. The phenomena suggest that the RPT should avoid operating under no-load conditions with large GVO as far as possible. Therefore, in the design or optimization of the pump-turbine unit, the structures of the guide vanes and runner could be treated as a whole to investigate the resulting internal flow and structure characteristics.
Prediction of Clean Coal Ash Content in Coal Flotation through a Convergent Model Unifying Deep Learning and Likelihood Function, Incorporating Froth Velocity and Reagent Dosage Parameters
Authors: Lu Fucheng , Liu Haizeng , Lv Wenbao
Published: December 2023

This study successfully achieved high-precision detection of the clean coal ash content in the coal froth flotation domain by integrating deep learning with the likelihood function. Methodologically, a novel data processing and prediction framework was established by combining a deep learning Keras neural network with the likelihood function from probability statistics. The SIFT algorithm was utilized to extract key feature points and descriptors from the images, and keypoint matching and mean-shift clustering algorithms were employed to accurately obtain information on foam motion trajectories and velocities. For parameter optimization, the maximum likelihood estimation was applied to find the optimal parameter estimates of the likelihood function, ensuring enhanced model accuracy. By incorporating the optimized likelihood function parameters into the Keras deep neural network, an efficient prediction model was constructed for the dosage of flotation reagents, froth velocity, and clean coal ash content. The model’s evaluation involved six performance metrics. The experimental results were highly significant, with R2 at 0.99997%, RMSE at 0.04458%, MAE at 0.00170%, MAPE at 0.02329%, RRSE at 0.00994%, and MAAPE at 0.00067%.
Dynamic Analysis of a Long Run-Out Rockslide Considering Dynamic Fragmentation Behavior in Jichang Town: Insights from the Three-Dimensional Coupled Finite-Discrete Element Method
Authors: Zhu Chun , Li Zhipeng , Bao Yiding , Ning Po , Zhou Xin , Wang Meng , Wang Hong , Shi Wenbing , Chen Bingbing
Published: December 2023

To clearly realize the dynamic process as well as the dynamic fragmentation behavior of a long run-out rockslide, a novel numerical method for landslide simulation of the coupled finite-discrete element method (FDEM) was applied and the Jichang rockslide was used as a case. The calibrated simulation result of the FDEM in a rockslide deposit corresponds well with the real rockslide deposit. The main run-out process of the rockslide lasts for 75 s and can be divided into acceleration and deceleration stages, which last for 33 s and 42 s, respectively. The maximum overall rockslide movement speed is 35 m/s while the partial sliding mass reaches 45 m/s. The fracturing, fragmentation, and disintegration processes of the sliding mass can be clearly observed from the dynamic scenarios. Fracture energy generated by rock fracturing constantly increases with time in a non-linear form. Of the total fracture energy, 54% is released in the initial 5 s because of fracturing, and 39% of the total fracture energy is released because of fragmentation and disintegration in the last 35 s. The accumulated friction energy increases in the whole run-out process, and its magnitude is much greater than the kinetic energy and fracture energy of the sliding mass.
City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona
Authors: Sheehan Annalisa , Beddows Andrew , Green David C. , Beevers Sean
Published: December 2023

Accurate traffic data is crucial for a range of different applications such as quantifying vehicle emissions, and transportation planning and management. However, the availability of traffic data is geographically fragmented and is rarely held in an accessible form. Therefore, there is an urgent need for a common approach to developing large urban traffic data sets. Utilising satellite data to estimate traffic data offers a cost-effective and standardized alternative to ground-based traffic monitoring. This study used high-resolution satellite imagery (WorldView-2 and 3) and Deep Learning (DL) to identify vehicles, road by road, in Barcelona (2017–2019). The You Only Look Once (YOLOv3) object detection model was trained and model accuracy was investigated via parameters such as training data set specific anchor boxes, network resolution, image colour band composition and input image size. The best performing vehicle detection model configuration had a precision (proportion of positive detections that were correct) of 0.69 and a recall (proportion of objects in the image correctly identified) of 0.79. We demonstrated that high-resolution satellite imagery and object detection models can be utilised to identify vehicles at a city scale. However, the approach highlights challenges relating to identifying vehicles on narrow roads, in shadow, under vegetation, and obstructed by buildings. This is the first time that DL has been used to identify vehicles at a city scale and demonstrates the possibility of applying these methods to cities globally where data are often unavailable.
Evaluating the Performance of Satellite Derived Temperature and Precipitation Datasets in Ecuador
Authors: Huber Magoffin Rachel , Hales Riley C. , Erazo Bolívar , Nelson E. James , Larco Karina , Miskin Taylor James
Published: December 2023

Temperature and precipitation data are crucial for hydrology and meteorology. In 2014, Ecuador started an automatic gauge network which monitors these variables. The measurements are not publicly available. Global gridded datasets from numerical models and remote sensors were previously the only way to obtain measurements for temperature and precipitation. Now that in situ measurements are beginning to be available in significant quantities, we assessed the performance of IMERG, CHIRPS, GLDAS and ERA5 for both temperature and precipitation using the in situ data. We used the Pearson R correlation coefficient, ME (Mean Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). We found that global gridded data were more suited for determining averages over time rather than for giving exact values at specific times for in situ gauges. The Pearson R values increased for all datasets when we used monthly aggregations in place of daily aggregations, suggesting that the monthly values are more correlated than the daily. The Pearson R value for temperature increased from 0.158 to 0.719 for the ERA5 dataset. Additionally, we show the statistical values for each of the three regions in Ecuador. We found that the IMERG and CHIRPS datasets, which contain station data, performed significantly better for both RMSE and MAE. Both IMERG and CHIRPS have a RMSE value a little over 260, whereas ERA5 and GLDAS had values over 300. We discuss the short comings of these datasets as being related to their relatively coarse resolution, lack of in situ data in Ecuador to calibrate against, and the rapidly varying terrain of Ecuador. We recommend using higher temporal and spatial resolution datasets for immediate applications. We recommend repeating this analysis in the future when more automatic gauges and longer time periods are available to facilitate a more detailed analysis which is presently not possible.
Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage
Authors: Li Weilong , Wang Jianjun , Zhang Yuting , Yin Quan , Wang Weiling , Zhou Guisheng , Huo Zhongyang
Published: December 2023

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R2 = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R2 = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R2 = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy.
MSSFF: Advancing Hyperspectral Classification through Higher-Accuracy Multistage Spectral–Spatial Feature Fusion
Authors: Chen Yuhan , Yan Qingyun , Huang Weimin
Published: December 2023

This paper presents the MSSFF (multistage spectral–spatial feature fusion) framework, which introduces a novel approach for semantic segmentation from hyperspectral imagery (HSI). The framework aims to simplify the modeling of spectral relationships in HSI sequences and unify the architecture for semantic segmentation of HSIs. It incorporates a spectral–spatial feature fusion module and a multi-attention mechanism to efficiently extract hyperspectral features. The MSSFF framework reevaluates the potential impact of spectral and spatial features on segmentation models and leverages the spectral–spatial fusion module (SSFM) in the encoder component to effectively extract and enhance these features. Additionally, an efficient Transformer (ET) is introduced in the skip connection part of deep features to capture long-term dependent features and extract global spectral–spatial information from the entire feature map. This highlights the significant potential of Transformers in modeling spectral–spatial feature maps within the context of hyperspectral remote sensing. Moreover, a spatial attention mechanism is adopted in the shallow skip connection part to extract local features. The framework demonstrates promising capabilities in hyperspectral remote sensing applications. The conducted experiments provide valuable insights for optimizing the model depth and the order of feature fusion, thereby contributing to the advancement of hyperspectral semantic segmentation research.
Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling
Authors: Erkkilä Anttoni , Tenkanen Maria , Tsuruta Aki , Rautiainen Kimmo , Aalto Tuula
Published: December 2023

Drivers of natural high-latitude biogenic methane fluxes were studied by combining atmospheric inversion modelling results of methane fluxes (CTE-CH4 model) with datasets on permafrost (ESA Permafrost CCI), climate (Köppen–Geiger classes) and wetland classes (BAWLD) and seasonality of soil freezing (ESA SMOS F/T) for the years 2011–2019. The highest emissions were found in the southern parts of the study region, while areas with continuous permafrost, tundra climate, and tundra wetlands had the lowest emissions. The magnitude of the methane flux per wetland area followed the order of permafrost zones excluding non-permafrost, continuous permafrost having the smallest flux and sporadic the largest. Fens had higher fluxes than bogs in the thaw period, but bogs had higher fluxes in the colder seasons. The freezing period when the soil status is between complete thaw and frozen contributed to annual emissions more in the warmest regions studied than in other regions. In the coldest areas, freezing period fluxes were lower and closer to wintertime values than elsewhere. Emissions during freezing periods were smaller than those during winter periods, but were of comparable magnitude in warm regions. The contribution of the thaw period to the total annual emission varied from 86% in warmest areas to 97% in the coldest areas, suggesting that the longest winter periods did not contribute significantly to the annual budget.
Remotely Sensed Agroclimatic Classification and Zoning in Water-Limited Mediterranean Areas towards Sustainable Agriculture
Authors: Faraslis Ioannis , Dalezios Nicolas R. , Alpanakis Nicolas , Tziatzios Georgios A. , Spiliotopoulos Marios , Sakellariou Stavros , Sidiropoulos Pantelis , Dercas Nicholas , Domínguez Alfonso , Martínez-López José Antonio , López-Urrea Ramón , Karam Fadi , Amami Hacib , Nciri Radhouan
Published: December 2023

Agroclimatic classification identifies zones for efficient use of natural resources leading to optimal and non-optimal crop production. The aim of this paper is the development of a methodology to determine sustainable agricultural zones in three Mediterranean study areas, namely, “La Mancha Oriental” in Spain, “Sidi Bouzid” in Tunisia, and “Bekaa” valley in Lebanon. To achieve this, time series analysis with advanced geoinformatic techniques is applied. The agroclimatic classification methodology is based on three-stages: first, the microclimate features of the region are considered using aridity and vegetation health indices leading to water-limited growth environment (WLGE) zones based on water availability; second, landform features and soil types are associated with WLGE zones to identify non-crop-specific agroclimatic zones (NCSAZ); finally, specific restricted crop parameters are combined with NCSAZ to create the suitability zones. The results are promising as compared with the current crop production systems of the three areas under investigation. Due to climate change, the results indicate that these arid or semi-arid regions are also faced with insufficient amounts of precipitation for supporting rainfed annual crops. Finally, the proposed methodology reveals that the employment and use of remote sensing data and methods could be a significant tool for quickly creating detailed, and up to date agroclimatic zones.
Estrus Detection and Dairy Cow Identification with Cascade Deep Learning for Augmented Reality-Ready Livestock Farming
Authors: Arıkan İbrahim , Ayav Tolga , Seçkin Ahmet Çağdaş , Soygazi Fatih
Published: December 2023

Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming.
Sensitivity Analysis of Intensity-Modulated Plastic Optical Fiber Sensors for Effective Aging Detection in Rapeseed Transformer Oil
Authors: Elele Ugochukwu , Nekahi Azam , Arshad Arshad , McAulay Kate , Fofana Issouf
Published: December 2023

As the focus tilts toward online detection methodologies for transformer oil aging, bypassing challenges associated with traditional offline methods, such as sample contamination and misinterpretation, fiber optic sensors are gaining traction due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances that are typical in high-voltage environments. This study delves into the sensitivity analysis of intensity-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Our findings highlight that elongating sensing length detrimentally affects both linearity response and repeatability, largely attributed to a diminished resistance to noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity.
Novel Calibration Approach for Monitoring Aerosol Hydrogen Isotopes Using Laser-Induced Breakdown Spectroscopy for Molten Salt Reactor Off-Gas Streams
Authors: Andrews Hunter B. , McFarlane Joanna
Published: December 2023

Online monitoring is a key challenge for the continued development of molten salt reactor (MSR) technology. Laser-induced breakdown spectroscopy (LIBS) has previously been demonstrated to be a viable tool for monitoring aerosolized species and noble gases in real time, but the ability to discern varying isotopes in these streams has not yet been investigated for MSR applications. Tritium will form in MSRs from ternary fission and from (n,α)-reactions occurring in lithium-containing salts. This study compares three spectrometers of varying resolutions and types for measuring hydrogen isotope shifts in LIBS spectra of wetted filter paper. For each spectrometer, multivariate models were built (i.e., principal component regression, partial least squares regression, and multivariate curve resolution) to quantify the isotope ratio. The top models were then modified and corrected to apply the models to aerosol samples with varying isotope ratios. This novel calibration strategy offers an 82% reduction in volume of the calibration samples needed and is a more viable pathway for calibrating deployable LIBS systems. Lastly, this calibration model was compared with an all-aerosol trained model for monitoring hydrogen isotopes during a real-time test where the protium/deuterium ratio, along with representative salt species (i.e., lithium, sodium, and potassium) were adjusted dynamically. Results of this test validated the predictive capabilities of the transferred model and highlighted the capabilities of LIBS for real-time monitoring of MSR effluent streams.
Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems
Authors: Chen Ying , Tang Yue , Jiang Bin , Zhao Yinan , Bao Jianrong , Tang Xianghong
Published: December 2023

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.
Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera
Authors: Ino Takumi , Samukawa Mina , Ishida Tomoya , Wada Naofumi , Koshino Yuta , Kasahara Satoshi , Tohyama Harukazu
Published: December 2023

Accuracy validation of gait analysis using pose estimation with artificial intelligence (AI) remains inadequate, particularly in objective assessments of absolute error and similarity of waveform patterns. This study aimed to clarify objective measures for absolute error and waveform pattern similarity in gait analysis using pose estimation AI (OpenPose). Additionally, we investigated the feasibility of simultaneous measuring both lower limbs using a single camera from one side. We compared motion analysis data from pose estimation AI using video footage that was synchronized with a three-dimensional motion analysis device. The comparisons involved mean absolute error (MAE) and the coefficient of multiple correlation (CMC) to compare the waveform pattern similarity. The MAE ranged from 2.3 to 3.1° on the camera side and from 3.1 to 4.1° on the opposite side, with slightly higher accuracy on the camera side. Moreover, the CMC ranged from 0.936 to 0.994 on the camera side and from 0.890 to 0.988 on the opposite side, indicating a “very good to excellent” waveform similarity. Gait analysis using a single camera revealed that the precision on both sides was sufficiently robust for clinical evaluation, while measurement accuracy was slightly superior on the camera side.
Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection
Authors: Xu Da , Liu Mengfei , Yao Xinpeng , Lyu Nengchao
Published: December 2023

The detection of abnormal lane-changing behavior in road vehicles has applications in traffic management and law enforcement. The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors can lead to unsafe interactions with surrounding vehicles, thereby increasing traffic risks. Therefore, solely focusing on individual vehicle perspectives and neglecting the influence of surrounding vehicles in abnormal lane-changing behavior detection has limitations. To address this, this study proposes a framework for abnormal lane-changing behavior detection. Initially, the study introduces a novel approach for representing vehicle trajectories that integrates information from surrounding vehicles. This facilitates the extraction of feature parameters considering the interactions between vehicles and distinguishing between different phases of lane-changing. The Light Gradient Boosting Machine (LGBM) algorithm is then employed to construct an abnormal lane-changing behavior detection model. The results indicate that this framework exhibits high detection accuracy, with the integration of surrounding vehicle information making a significant contribution to the detection outcomes.
Improving Balance and Movement Control in Fencing Using IoT and Real-Time Sensorial Feedback
Authors: Niță Valentin-Adrian , Magyar Petra
Published: December 2023

Fencing, a sport emphasizing the equilibrium and movement control of participants, forms the focal point of inquiry in the current study. The research endeavors to assess the efficacy of a novel system designed for real-time monitoring of fencers’ balance and movement control, augmented by modules incorporating visual feedback and haptic feedback, to ascertain its potential for performance enhancement. Over a span of five weeks, three distinct groups, each comprising ten fencers, underwent specific training: a control group, a cohort utilizing the system with a visual real-time feedback module, and a cohort using the system with a haptic real-time feedback module. Positive outcomes were observed across all three groups, a typical occurrence following a 5-week training regimen. However, noteworthy advancements were particularly discerned in the second group, reaching approximately 15%. In contrast, the improvements in the remaining two groups were below 5%. Statistical analyses employing the Wilcoxon signed-rank test for repeated measures were applied to assess the significance of the results. Significance was solely ascertained for the second group, underscoring the efficacy of the system integrated with visual real-time feedback in yielding statistically noteworthy performance enhancements.
Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing
Authors: Kim Hanjin , Kim Young-Jin , Kim Won-Tae
Published: December 2023

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device’s signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.
Green and Fast Extraction of Chitin from Waste Shrimp Shells: Characterization and Application in the Removal of Congo Red Dye
Authors: Gharbi Fatma Zohra , Bougdah Nabil , Belhocine Youghourta , Sbei Najoua , Rahali Seyfeddine , Damous Maamar , Seydou Mahamadou
Published: December 2023

Due to their detrimental and carcinogenic effects, synthetic organic dyes pose significant environmental and health risks. Consequently, addressing the bioremediation of industrial wastewater containing these organic dyes has become an urgent environmental concern. The adsorption using low-cost and green materials is one of the best alternative techniques for the removal of dyes. This study aims to investigate the use of chitin to eliminate Congo red (CR), an anionic dye, from wastewater. The chitin was produced from shrimp shell in a quick and environmentally friendly manner by utilizing a co-solvent (glycerol/citric acid (GLC)). The resulting adsorbent was characterized through various techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), and FT-IR spectroscopy. The effectiveness of CR removal with chitin was studied with respect to contact time, adsorbent dose, initial pH, equilibrium isotherms, and kinetic and thermodynamic parameters. It was observed that variations in the dye concentration and pH significantly influenced the removal of CR with chitin. Under optimal operating conditions (pH = 7, contact time = 130 min, temperature = 50 °C), the adsorption capacity reached 29.69 ± 0.2 mg/g. The experimental data revealed that CR adsorption onto a chitin adsorbent is better represented by a Langmuir isotherm.
Investigating On-Site Energy Consumption Patterns Using Vertical Electrical Sounding (VES) and Geographic Information System (GIS) Techniques
Authors: Abdullah Haris , Farid Hafiz Umar , Naeem Maira , Zubair Nadeem , Khan Zahid Mahmood , Shahzad Huzaifa , Sikandar Pervaiz , Abrar Muhammad , Shakoor Aamir , Mubeen Muhammad
Published: December 2023

In recent decades, the fortunes of energy economies have been closely linked in Pakistan. A major energy inefficiency issue was found in Pakistan due to the mismatch between horsepower (HP) requirements and bore depth. Keeping this in view, a total of 194 tubewells were chosen for an energy audit in the Multan region, Pakistan. The Terrameter SAS 4000 was used to measure the accurate demand of the head during the resistivity surveys at all of the selected locations. The results showed that the tubewell sets were installed arbitrarily at high power, irrespective of the provided flow and head, and these pumps used more energy for their flow. The results revealed that the efficiency of the tubewell sets increased from 35 to 54%, from 55 to 80%, from 49 to 80%, and from 48 to 75% for centrifugal pumps with electric motors and diesel engines and for turbines with electric motors and diesel engines, respectively. A weighted overlay analysis indicated that the efficiency of tubewells covering 838.12, 1131.8, and 2077.1 km2 for centrifugal pumps with electric motors, diesel engines, and turbines, respectively, was enhanced for the study area. Similarly, the energy saved for the study area covered 1423.8, 1161.1, and 1131.1 km2, as shown by the overlay analysis. The results revealed that the annual energy saving was found of 3486 kw for 194 tubewells, resulted in the saving of USD 0.204 million in operational costs over one year. The overall results indicate the strong need to adopt proper investigations of the head and power requirements before installing a system in the study area.
Empowering Clusters: How Dynamic Capabilities Drive Sustainable Supply Chain Clusters in Egypt
Authors: Barakat Mahmoud , Wu Jialin Snow , Tipi Nicoleta
Published: December 2023

Drawing on systems theory, the extended resource-based view and dynamic capability theory, this research develops an integrated framework to offer a more nuanced understanding of the mediating role of dynamic capabilities between supply chain cluster design characteristics and economic, social and environmental sustainability. Data were collected from organisations in Egypt through a questionnaire survey and were analysed using structural equation modelling. Based on 811 responses, a multi-mediation model analysis using Smart-PLS recognises that geographical concentration, networked collaboration and supporting services systems are critical supply chain cluster design characteristics that offer a pool of resources and collective knowledge that help organisations build absorptive capacity and resilience, which, in turn, help in protecting, reconfiguring and developing the shared resources to achieve high levels of environmental, social and economic sustainability. The findings affirm that supply chain clusters can be used as a tool to maintain desired sustainability levels in a dynamic business environment as it will allow organisations to cope with sudden changes in the market quickly and efficiently. This will allow organisations to improve sustainability performance by developing their dynamic capabilities with enhanced supporting services and network collaboration, thereby adequately exploiting external knowledge and opportunities, as well as effectively mitigating business vulnerability. This will also help in extending the three theoretical lenses through contextualising their abstract ideas in a supply chain cluster context and analyse data from a developing market. This is particularly important as further investigation is needed regarding supply chain clusters and the relationship between dynamic capabilities and sustainability in different contexts, especially since previous studies focused on developed economies and did not use the combination of these variables.
An Exploration of the Relevance between Sustainable Craft and Service Design Based on a Literature Review Study
Authors: Zhang Li , de Bont Cees , Gurpinar Avsar , Tang Mingxi
Published: December 2023

Sustainable craft is a relatively new concept, and a growing body of literature has examined sustainable craft from a multidisciplinary perspective. However, these researchers found a dearth of research that examines service design as a prospective transformative tool in sustainability crafts. Therefore, this study identifies published articles and the most productive journals, institutions, and countries by conducting a bibliometric analysis of the Scopus database of research articles on sustainable crafts in the 21st century. Based on that, we conduct an open discussion about sustainable crafts and service design. Finally, we obtain some key findings about these two areas.
Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile
Authors: Parra Francisco , González Jaime , Chacón Max , Marín Mauricio
Published: December 2023

Landslides represent one of the main geological hazards, especially in Chile. The main purpose of this study is to evaluate the application of machine learning algorithms (SVM, RF, XGBoost and logistic regression) and compare the results for the modeling of landslide susceptibility in the province of Chañaral, III region, Chile. A total of 86 sites are identified using various sources, in addition to 86 non-landslide sites. This spatial data management and analysis are conducted using QGIS software. The sites are randomly divided, and then a cross-validation process is applied to calculate the accuracy of the models. After that, from 22 conditioning factors, 12 are chosen based on the information gain ratio (IGR). Subsequently, five factors are excluded by the correlation criterion. After this analysis, two indices not previously utilized in the literature, the NDGI (normalized difference glacier index) and EVI (enhanced vegetation index), are employed for the final model. The performance of the models is evaluated through the area under the ROC (receiver operating characteristic) curve (AUC). To study the statistical behavior of the model, the Friedman nonparametric test is performed to compare the performance with the other algorithms and the Nemenyi test for pairwise comparison. Of the algorithms used, RF (AUC = 0.957) and XGBoost (AUC = 0.955) have the highest accuracy values measured in AUC compared to the other models and can be used for the same purpose in other geographic areas with similar characteristics. The findings of this investigation have the potential to assist in land use planning, landslide risk reduction, and informed decision making in the surrounding zones.
Innovative Transformation and Valorisation of Red Mill Scale Waste into Ferroalloys: Carbothermic Reduction in the Presence of Alumina
Authors: Khanna Rita , Konyukhov Yuri , Li Kejiang , Jayasankar Kalidoss , Maslennikov Nikita , Zinoveev Dmitry , Kargin Jumat , Burmistrov Igor , Leybo Denis , Kravchenko Maksim , Mukherjee Partha Sarathy
Published: December 2023

Primary and secondary mill scales (MSs) are waste products produced by the surface oxidation of steel during the hot (800 to 1200 °C) rolling process in downstream steelmaking. While the primary MS is comprised of FeO, Fe3O4, and Fe2O3 in a range of proportions, the secondary MS primarily contain red ferric oxide (Fe2O3) (red MS). We report a novel route for extracting iron from red MS and transforming it into ferro-aluminium alloys using carbothermic reduction in the presence of alumina. The red MS powder was blended with high-purity alumina (Al2O3) and synthetic graphite (C) in a range of proportions. The carbothermic reduction of red MS-Al2O3-C blends was carried out at 1450 °C and 1550 °C under an argon atmosphere for 30 min and then furnace-cooled. The red MS was completely reduced to iron at these temperatures with reduced iron distributed around the matrix as small droplets. However, the addition of alumina unexpectedly resulted in a significant increase in the number and sizes of iron droplets generated, much higher reactivity, and the formation of ferrous alloys. A small amount of alumina reduction into metallic aluminium was also observed at 1450 °C. There is an urgent need to identify the true potential of industrial waste and the materials within it. This study showed that red MS is a valuable material source that could be transformed into ferro-aluminium alloys. These alloys find application in a range of industrial sectors such as construction, automotive, infrastructure, etc.
Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model
Authors: Aljebreen Mohammed , Alrayes Fatma S. , Aljameel Sumayh S. , Saeed Muhammad Kashif
Published: December 2023

With the enhancement of the Internet of Things (IoT), smart cities have developed the idea of conventional urbanization. IoT networks permit distributed smart devices to collect and process data in smart city structures utilizing an open channel, the Internet. Accordingly, challenges like security, centralization, privacy (i.e., execution data poisoning and inference attacks), scalability, transparency, and verifiability restrict faster variations of smart cities. Detecting malicious URLs in an IoT environment is crucial to protect devices and the network from potential security threats. Malicious URL detection is an essential element of cybersecurity. It is established that malicious URL attacks mean large risks in smart cities, comprising financial damages, losses of personal identifications, online banking, losing data, and loss of user confidentiality in online businesses, namely e-commerce and employment of social media. Therefore, this paper concentrates on the proposal of a Political Optimization Algorithm by a Hybrid Deep Learning Assisted Malicious URL Detection and Classification for Cybersecurity (POAHDL-MDC) technique. The presented POAHDL-MDC technique identifies whether malicious URLs occur. To accomplish this, the POAHDL-MDC technique performs pre-processing to transform the data to a compatible format, and a Fast Text word embedding process is involved. For malicious URL recognition, a Hybrid Deep Learning (HDL) model integrates the features of stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM). Finally, POA is exploited for optimum hyperparameter tuning of the HDL technique. The simulation values of the POAHDL-MDC approach are tested on a Malicious URL database, and the outcome exhibits an improvement of the POAHDL-MDC technique with a maximal accuracy of 99.31%.
Optimization of Renewable Energy Supply Chain for Sustainable Hydrogen Energy Production from Plastic Waste
Authors: Doniavi Ehsan , Babazadeh Reza , Hasanzadeh Rezgar
Published: December 2023

Disposing of plastic waste through burial or burning leads to air pollution issues while also contributing to gas emissions and plastic waste spreading underground into seas via springs. Henceforth, this research aims at reducing plastic waste volume while simultaneously generating clean energy. Hydrogen energy is a promising fuel source that holds great value for humanity. However, achieving clean hydrogen energy poses challenges, including high costs and complex production processes, especially on a national scale. This research focuses on Iran as a country capable of producing this energy, examining the production process along with related challenges and the general supply chain. These challenges encompass selecting appropriate raw materials based on chosen technologies, factory capacities, storage methods, and transportation flow among different provinces of the country. To deal with these challenges, a mixed-integer linear programming model is developed to optimize the hydrogen supply chain and make optimal decisions about the mentioned problems. The supply chain model estimates an average cost—IRR 4 million (approximately USD 8)—per kilogram of hydrogen energy that is available in syngas during the initial period; however, subsequent periods may see costs decrease to IRR 1 million (approximately USD 2), factoring in return-on-investment rates.
An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters
Authors: Li Binglin , Xu Hao , Lian Yufeng , Li Pai , Shao Yong , Tan Chunyu
Published: December 2023

Prediction of water quality parameters is a significant aspect of contemporary green development and ecological restoration. However, the conventional water quality prediction models have limited accuracy and poor generalization capability. This study aims to develop a dependable prediction model for ammonia nitrogen concentration in water quality parameters. Based on the characteristics of the long-term dependence of water quality parameters, the unique memory ability of the Long Short-Term Memory (LSTM) neural network was utilized to predict water quality parameters. To improve the accuracy of the LSTM prediction model, the ammonia nitrogen data were decomposed using Empirical Modal Decomposition (EMD), and then the parameters of the LSTM model were optimized using the Improved Whale Optimization Algorithm (IWOA), and a combined prediction model based on EMD-IWOA-LSTM was proposed. The study outcomes demonstrate that EMD-IWOA-LSTM displays improved prediction accuracy with reduced RootMean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) in comparison to the LSTM and IWOA-LSTM approaches. These research findings better enable the monitoring and prediction of water quality parameters, offering a novel approach to preventing water pollution rather than merely treating it afterwards.