bluuebunny/crossref_metadata_2025 huggingface dataset:
Gold Recovery from Smelting Copper Sulfide Concentrate
Authors: Moosavi-Khoonsari Elmira , Tripathi Nagendra
Published: December 2024

Gold is a significant revenue source for custom copper smelters facing profitability challenges due to low treatment and refining charges, stricter regulations, and rising costs. Gold is also often blended with copper concentrates, but precise recovery rates from smelting processes are poorly documented despite gold critical economic importance. This paper aims to provide the first comprehensive estimates of gold first-pass recovery across various operational units within the copper sulfide concentrate processing flowsheet. It evaluates the effectiveness of different copper smelting and converting technologies in recovering gold. Optimizing gold first-pass recovery is especially important to enhance immediate financial returns and responsiveness to market dynamics, allowing companies to capitalize on favorable gold prices without delays. Given the absence of direct measurements for gold recovery rates, this research develops an estimation method based on understanding gold loss mechanisms during smelting. This study identifies and analyzes key input and output parameters by examining data from various copper producers. By correlating these parameters with gold loss, the research estimates gold first-pass recovery rates within the copper smelting process. Among integrated smelting-converting routes, the flash smelting to Peirce–Smith converting route achieves the highest gold first-pass recovery (98.8–99.5%), followed by the Mitsubishi continuous smelting and converting process (94.3–99.8%), bottom-blowing smelting to bottom-blowing converting (95.8%), flash smelting to flash converting (95.5%), Teniente smelting to Peirce–Smith converting (95.2%), and the Noranda continuous smelting and converting process (94.8%). The final recovery rates are expected to be higher considering the by-products’ internal recirculation and post-processing within the copper flow sheet. Additionally, superior gold recoveries are attributed to advanced metallurgical practices and control systems, which vary even among companies with similar technologies. This research demonstrates that copper smelting can effectively recover over 99% of gold from sulfide concentrates. Gold accumulates up to 1000 times its original concentration in anode slime during electrolytic refining, generating 5–10 kg of slime per ton of copper, which is further processed to recover gold and other by-products. Major smelters operate precious metal plants where recovering gold from highly concentrated anode slime is both cost-effective and efficient.
The Characterization of the Alcoholic Fermentation Process in Wine Production Based on Acoustic Emission Analysis
Authors: Sanchez-Roca Angel , Latorre-Biel Juan-Ignacio , Jiménez-Macías Emilio , Saenz-Díez Juan Carlos , Blanco-Fernández Julio
Published: December 2024

The present experimental study assessed the viability of utilizing an acoustic emission signal as a monitoring instrument to predict the chemical characteristics of wine throughout the alcoholic fermentation process. The purpose of this study is to acquire the acoustic emission signals generated by CO₂ bubbles to calculate the must density and monitor the kinetics of the alcoholic fermentation process. The kinetics of the process were evaluated in real time using a hydrophone immersed in the liquid within the fermentation tank. The measurements were conducted in multiple fermentation tanks at a winery engaged in the production of wines bearing the Rioja Denomination of Origin (D.O.) designation. Acoustic signals were acquired throughout the entirety of the fermentation process, via a sampling period of five minutes, and stored for subsequent processing. To validate the results, the measurements obtained manually in the laboratory by the winemaker were collected during this stage. Signal processing was conducted to extract descriptors from the acoustic signal and evaluate their correlation with the experimental data acquired during the process. The results of the analyses confirm that there is a high linear correlation between the density data obtained from the acoustic analysis and the density data obtained at the laboratory level, with determination coefficients exceeding 95%. The acoustic emission signal is a valuable decision-making tool for technicians and winemakers due to its sensitivity when describing variations in kinetics and density during the alcoholic fermentation process.
A New Use Strategy of Artificial Intelligence Algorithms for Energy Optimization in Plastic Injection Molding
Authors: Pascoschi Giovanni , De Filippis Luigi Alberto Ciro , Decataldo Antonio , Dassisti Michele
Published: December 2024

Plastic injection molding is a widespread industrial process in manufacturing. This article investigates the energy consumption in the injection molding process of fruit containers, proposing a new use strategy for the application of artificial intelligence algorithms. The aim is to optimize the process parameters, such as the mold temperatures, the injector temperatures, and the cycle time, to minimize energy consumption. This new use strategy, a hybrid use strategy, combines an unsupervised autoencoder with the K-Means algorithm to analyze production data and identify factors influencing energy consumption. The results show the capability of discovering different operating modes at different levels of energy requirements. An analysis of the process parameters reveals that the number of parts left to complete production, the current cycle counter, the number of shots left to complete the production, the material needed to complete the production, and the total time dedicated to production, so far, are the most relevant features for the optimization of the energy consumption per single piece. The study demonstrates the potential of common artificial intelligence algorithms if appropriately used to improve the sustainability of the plastic injection molding process.
Numerical Study of Biomass Combustion Using a Transient State Approach
Authors: Neves Filipe , Soares Armando A. , Rouboa Abel
Published: December 2024

The behavior of the temperature and velocity, as well as the mass fraction of water (H2O) and wood inside a combustion chamber under specific operating conditions, were numerically investigated using a transient state model. For the computational fluid dynamics (CFD) simulation, the software Ansys Fluent 2024 R2 was used to analyze a turbulent flow in two dimensions. The species transport with volumetric responses and eddy dissipation were included in the combustion simulation. The study was conducted for the first 150 s. The findings show that in the lower and upper zones of the combustion chamber close to the walls, the temperature rises over time. The velocity between the outlets rises over time. Due to the fact that the wood goes through a process that reduces the amount of wood and produces H2O, it exhibits the opposite behavior for the mass fractions of wood and H2O.
Field Study on Washing of 4-Methoxy-2-Nitroaniline from Contaminated Site by Dye Intermediates
Authors: Wang Zhili , Lao Kangwen , Chen Chen , Zhu Hong , Yang Yanfei , Chen Honghan , Pang Hao
Published: December 2024

Dye intermediates are important industrial chemicals; there is a lack of systematic field experiments and relevant validation data regarding the remediation of groundwater contamination by dye intermediates. This study examines the eluting effects of alcohol eluting agents, non-ionic surfactants, and deionized water on 4-methoxy-2-nitroaniline (4M2N) in a contaminated aquifer medium from a historically polluted dye intermediate production site in northwest China. The findings indicate that alcohol eluting agents exhibit superior eluting effects compared to non-ionic surfactants. Under optimized conditions, including 60% n-propanol concentration, a liquid-to-solid ratio of 15:1, two eluting cycles, an elution pH of 3, and a 2 h eluting duration, the eluting concentration of 4-methoxy-2-nitroaniline reached 75.49 mg/kg, exceeding that of the composite eluting agent by two times more and deionized water by three times further. Analysis revealed that the liquid-to-solid ratio and number of eluting cycles are the primary factors influencing eluting efficiency. Field trials conducted using treated groundwater involved injecting 31,560 m3 of treated groundwater over 152 days, resulting in the extraction of 38,550 m3 and the removal of about 1887 kg of 4-methoxy-2-nitroaniline. The concentrations of contaminants in both pumping wells and monitoring wells exhibited a certain degree of increase at various times. Field applications of treated groundwater washing facilitated the release of 4-methoxy-2-nitroaniline from the aquifer medium, which significantly enhances remediation efficiency. This provides theoretical support for data analysis and the promotion of similar remediation efforts.
Evaluation Study of the Passivation Effect of Arsenic-Contaminated Farmland Soil
Authors: Liu Shuyue , Su Jikai , Zhang Zhiyu , Huang Yajing , Wang Xiaoyao , Yi Xiaoyun
Published: December 2024

In situ passivation technology, by reducing the bioavailability of arsenic in soil, thereby reducing its uptake by crops, is currently the main remediation method for arsenic-contaminated farmland soil. However, applying stabilizing materials may also affect the other properties of soil, ultimately influencing the growth of crops. The long growth cycles of crops and their susceptibility to factors such as agronomic measures make plant-based indicators less practical as evaluation indicators. In this study, five kinds of passivation materials, including iron-based biochar (T1), coffee residue (T2), red mud (T3), chitosan-modified iron filings (T4), and modified minerals (T5) were applied in pot experiments. The study analyzed the effects of the passivation materials on soil properties and the growth and safety traits of plants. Key soil indicators influencing biomass were identified, the passivation remediation effects were evaluated, and a method using soil property indicators instead of plant indicators for passivation remediation evaluation was developed. The results showed that key indicators influencing the biomass change of water spinach due to passivation treatment included total nitrogen, total phosphorus, and catalase activity. The improved comprehensive evaluation indicators for passivation effects include available arsenic in soil, total nitrogen, total phosphorus, and catalase activity. I-SI can be expressed as I-SI=0.6∆Assoil+0.4−2.152∆TN+0.422∆TP+0.334∆CAT−0.261. I-SI is highly feasible, where a higher value indicates better remediation efficacy. After evaluation, iron-based biochar was the best passivation effect. An evaluation method for the passivation effect was constructed based on these findings, aiming to simplify the process of comprehensive evaluation of the passivation effect and shorten the evaluation time, providing a new idea for assessing the passivation effect.
Exploring Hyaluronidase and Alpha-Glucosidase Inhibition Activities of the Hydrothermal Extract of Coffee Silverskin Obtained from a Central Composite Design
Authors: Kit Thavy , Ginting Agita Rachmala , Sumpavapol Punnanee , Chheang Lita , Thanasupsin Sudtida Pliankarom
Published: December 2024

Coffee silverskin (CS), the main by-product of coffee roasting production, contains various valuable bioactive compounds in its chemical compositions. Hydrothermal water extraction (HDTE) is one of the promising techniques for valorizing the organic fraction of CS into functional bioactive ingredients, which can be further exploited in various applications. This study aimed to evaluate the hyaluronidase and α-glucosidase inhibition activities of the CS extracts obtained under optimized water extraction conditions. Process optimization was performed using central composite design response surface methodology (CCD-RSM) with a broader range of extraction temperatures (25, 137.5, and 250 °C), reaction times (5, 38.5, and 72 min), and solid-to-liquid ratios (1:10, 1:80, and 1:150). The highest yield of 39.62% was obtained at 137.5 °C, with a reaction time of 72 min and an S/L ratio of 1:80. The total caffeoylquinic acid contents (T-CQA) were quantified based on the sum of three major isomers, including 3-CQA, 4-CQA, and 5-CQA. The results revealed that the highest T-CQA (2.76 ± 0.20 mg/g CS) was significantly obtained (p < 0.05) by subcritical water extraction (SWE) at 143.2 °C with an S/L ratio of 1:10 and an extraction time of 10.41 min. At such conditions, the total phenolic content (TPC), antioxidant properties (AP), and caffeine were 96.13 mg gallic acid equivalence per gram (GAE/g) CS, 20.85 ± 0.17 mg Trolox equivalence per gram (TE/g) CS, and 10.84 ± 1.25 mg/g CS, respectively. The 50% inhibition capacity (IC50) of hyaluronidase and α-glucosidase inhibition of the CS extracted were 5.00 mg/mL and 9.00 mg/mL, respectively. Our results supported the potential direct or indirect applications of CS, such as hydrothermal CS extract (HDT-CSE), in functional food or drinks. Repurposing CS residue to manufacture new products can efficiently reduce the amount of organic waste in landfills, thus conserving resources and energy and contributing to a lower overall carbon footprint in coffee production.
Self-Tuning Oxygen Excess Ratio Control for Proton Exchange Membrane Fuel Cells Under Dynamic Conditions
Authors: Li Heran , Sun Chuanyu , Li Jing , Mei Jian , Jiang Jinhai , Fan Fulin , Yang Weihong , Zhuo Ran , Song Kai
Published: December 2024

Reasonable and effective control of a cathode air supply system is conducive to improving the dynamic response, operating efficiency, and reliability of fuel cell systems. This paper proposes a novel data-driven adaptive oxygen excess ratio (OER) control strategy based on online parameter identification for fuel cell systems. The proposed control scheme employs a second-order active disturbance rejection controller (ADRC) derived from the proportional-integral-derivative tuning rule to effectively deal with model uncertainties and external disturbances. Online parameter identification continuously translates the cathode air supply system into the second-order model, enabling the real-time adaptation of controller parameters to varying operating conditions. Simulation results demonstrate that the OER control strategy proposed significantly improves voltage stability and system efficiency under dynamic conditions compared to traditional methods. The innovation of this paper is that, based on consideration of the nonlinear slow time-varying characteristics of a PEMFC and the frequent disturbance of load current, adaptive control under system dynamic conditions can be considered. Combining the parameter identification scheme, an adaptive online self-tuning scheme is designed for the identified system model, which avoids the tediousness of a complex modeling process and has promotion value in practical applications.
Study on the Influence of Geological Parameters of CO2 Plume Geothermal Systems
Authors: Song Jifeng , Zheng Huaan , Zhou Yuxia , Liang Yukai , Zeng Qianyi , Fu Meilong
Published: December 2024

At present, there are relatively few studies on the exploitation of geothermal resources in depleted high-temperature gas reservoirs with carbon dioxide (CO2) as the heat-carrying medium. Taking the high-temperature gas reservoir of the Huangliu Formation in the Yingqiong Basin as the research object, we construct an ideal thermal and storage coupling model of a CO2 plume geothermal system using COMSOL6.2 software to conduct a sensitivity analysis of geological parameters in the operation of a CO2 plume geothermal system. The simulation results show that, compared with medium- and low-temperature reservoirs, a high-temperature reservoir exhibits higher fluid temperature in the production well and a higher heat extraction rate owing to a higher initial reservoir temperature but has a shorter system operational lifetime; the influence of the thermal conductivity of a thermal reservoir on the CO2 plume geothermal system is relatively minor, being basically negligible; and the thinner the thermal reservoir is, the faster the fluid temperature in the production well decreases and the shorter the thermal breakthrough time. These findings form a basis for selecting heat extraction areas for CO2 plume geothermal systems and also provide a theoretical reference for future practical CO2 plume geothermal system projects.
Rheological, Thermal, and Textural Characteristics of White, Milk, Dark, and Ruby Chocolate
Authors: Zarić Danica B. , Rakin Marica B. , Bulatović Maja Lj. , Dimitrijević Ivan D. , Ostojin Vanja D. , Lončarević Ivana S. , Stožinić Milica V.
Published: December 2024

This study compares the rheological, thermal, and textural characteristics of four types of chocolate—white, milk, ruby, and dark—produced by the same manufacturer. White, milk, and ruby chocolates contain 36% fat, while dark chocolate has 39%. Cocoa content varies from 28% in white, 33% in milk, 47% in ruby, to 70% in dark chocolate. Rheological properties were assessed with a rotational rheometer, while density was measured with a gas pycnometer. Particle size distribution (PSD) was evaluated using laser diffraction, and thermal properties were analyzed with differential scanning calorimetry (DSC). The DSC results indicated that enthalpy increased with cocoa content, whereby dark chocolate showed the highest value (55.04 J g−1) and white chocolate the lowest (35.3 J g−1). PSD followed a monomodal pattern; dark chocolate had the smallest particles, leading to the highest hardness. Density ranged from 1.2773 to 1.2067 g∙cm−3. The results from classical rotational rheological measurements were in accordance with oscillatory measurements. Rheological measurements confirmed that the Casson yield stress was the highest for milk chocolate (17.61 Pa). The viscosity values decreased with increasing shear rate for all chocolates. All chocolate samples showed strong shear-thinning behavior up to a 100 s−1 shear rate. Oscillatory measurements showed the paste-like nature of all samples, i.e., storage modulus G’ dominates loss modulus G’’ at small shear stress values, and the complex modulus G*, which represents the stiffness, varied as follows: milk > white > dark > ruby. This study offers valuable insights into the properties of chocolates during production and storage, helping manufacturers anticipate key characteristics for new confectionery products.
Optimal Strategies for Filament Orientation in Non-Planar 3D Printing
Authors: Atarihuana Sebastian , Fernández Felipe , Erazo José , Narváez Mateo , Hidalgo Víctor
Published: December 2024

The structural integrity and surface quality of parts produced using traditional fused deposition modeling depend on factors such as layer height, filament and build orientation, print speed, nozzle temperature, and, crucially for this study, both planar and non-planar slicing. Recent research on non-planar slicing techniques has shown significant improvements in surface smoothness and mechanical properties. Key approaches include non-planar slicing for 3-axis printers, adaptive slicing to optimize material placement in critical areas, and post-processing. However, current studies lack a comprehensive method for parameterizing filament direction across both planar and non-planar layers. This work presents an approach to generate optimal trajectories for planar and non-planar layers using contours derived from level set functions. The methodology demonstrates the advantages of non-planar printing, particularly with a filament orientation of 30° for inclined surfaces, ensuring better surface quality, uniformity, and structural integrity. This emphasizes the importance of trajectory planning and filament orientation in achieving high-quality prints on inclined geometries. This research highlights the necessity of a methodology that tailors filament paths based on the load-bearing requirements of each part, demonstrating its potential to enhance surface quality and structural performance, and further the advancement of the 3D printing industry.
A Study on the Influence of Natural Fractures in Tight Sandstone Reservoirs on Hydraulic Fracture Propagation Behavior and Post-Fracture Productivity
Authors: Gu Tuan , Yu Xiang , Zhang Linpeng , Su Xu , Wu Yibei , Jie Yenan , Wang Haiyang , Zhou Desheng
Published: December 2024

Tight sandstone gas reservoirs are rich in reserves and are an important part of unconventional oil and gas resources. However, natural fractures’ impact on hydraulic fracture propagation behavior and network formation mechanisms remain unclear. Exploring how to optimize fracturing parameters to maximize post-fracturing productivity requires further investigation. Therefore, this study focused on the characteristics of tight sandstone gas reservoirs and established a three-dimensional numerical simulation model for hydraulic fracture propagation and post-fracturing productivity using production history matching to validate the reliability of the model. Based on this model, this study investigated the influence mechanisms of natural fracture angles, density, and lengths on hydraulic fracture propagation behavior and network formation. The spatial distribution of hydraulic fracture widths in three dimensions is also explored. When natural fracture angles are lower, a greater number of natural fractures are activated, leading to more developed secondary hydraulic fractures and the formation of complex fracture networks. Hydraulic fractures tend to penetrate directly through high-angle natural fractures. Single-well cumulative gas production increases initially with increasing natural fracture angles, then decreases, but increases with higher natural fracture density and length. Optimal fracturing in areas with longer natural fractures, lower angles, and higher density distribution enhances single-well productivity effectively.
Performance of CaO-Promoted Ni Catalysts over Nanostructured CeO2 in Dry Reforming of Methane
Authors: Pérez-Madrigal Vicente , Santiago-Salazar Didier , Ortega-Avilés Mayahuel , Ríos-Valdovinos Edna , Albiter Elim , Valenzuela Miguel A. , Pola-Albores Francisco
Published: December 2024

Ni (20 wt.%) catalysts supported on CeO2 were synthesized using the incipient wetness impregnation method and promoted with varying amounts of CaO (0, 5, 10, 15, 20 wt.%). The catalysts were evaluated in dry reforming of methane (DRM) at 650 °C for 24 h. Fresh catalysts were characterized by XRD, WD-XRF, H2-TPR, N2 physisorption, HDP, SEM, and FT-IR spectroscopy (DRIFT and ATR), while spent catalysts were characterized by XRD, Raman spectroscopy, and SEM. The incorporation of 5–15 wt.% CaO on CeO2 significantly improved its catalytic performance. FT-IR analysis confirmed the presence of CaCO3 bands, indicating carbonate formation. The Ni/CeO2 catalyst with 15 wt.% CaO exhibited the highest catalytic activity. The promoted catalysts demonstrated high stability, attributed to strong interactions between CeO2, CaO, and CaCO3. However, when CaO promotion reached 20 wt.%, catalytic activity decreased. Despite large carbon formations, the catalysts maintained their stability with no significant deactivation due to sintering or coke accumulation.
Data Digitization in Manufacturing Factory Using Palantir Foundry Solution
Authors: Krajný Peter , Janeková Jaroslava , Fabianová Jana
Published: December 2024

This research describes an online solution for the collection and processing of production data, which are gathered from manufacturing and assembly processes at automotive companies. The solution describes the process for live monitoring of the production health and then evaluation through reports, with the option to generate reports for up to six months. Since the data are located in multiple sources, it is challenging to monitor them live or generate reports on demand. The solution described in this research outlines applications that simplify users’ tasks and provide immediate insights into the processes and health of production lines. Research will be divided into three applications which are delivered in one package, which is called Cycle Time Deviation (CTD): (i) workshop application for live monitoring; (ii) for evaluating data older than 24 h, the shift report application; and (iii) for comparing and monitoring the impact of process changes on the analysis, the before and after application—the Plant Improvement Tracker (PIT)—will be presented. The aim of the research is to describe the proposed solution that was implemented in a multinational automotive corporation and to outline the benefits gained from the implementation.
Thermodynamic Modeling and Research for Processing Complex Concentrate Blends in Custom Copper Smelters for Maximum Revenue
Authors: Shishin Denis , Tripathi Nagendra , Sineva Svetlana , Jak Evgueni
Published: December 2024

Custom copper smelters, which are dependent on purchased concentrates, are facing increasing economic pressures amid falling treatment and refining fees. With the declining availability of high-grade, low-impurity concentrates, copper demand is expected to surge to support the transition to renewable energy. This study, which is based on recent observations of Chinese custom smelters, examines their strategies to address the challenge of purchasing concentrates at record-low treatment and refining charges. By investing in slag flotation technology, smelters can enhance copper, gold, and silver recovery. By blending high-grade and low-grade concentrates, they can capitalize on the gap between the recoverable and payable metals, which are often referred to as “free metals”, while also benefiting from byproducts, mainly sulfuric acid. While this approach offers economic opportunities, it introduces operational complexities. To mitigate these, laboratory testing, combined with advanced digital predictive tools based on thermodynamics, is crucial. This study demonstrates the use of thermodynamic models supported by experimental work for analyzing furnace operations. FactSage® software and a custom database are employed to define the operating window of two common flowsheets: (1) flash smelting + flash converting and (2) bottom-blown smelting + bottom-blowing converting.
New Insights into Red and White Quinoa Protein Isolates: Nutritional, Functional, Thermal Properties
Authors: Alsaleem Khalid A. , Moftah Rofida F. , El-Geddawy Mennatallah M. A.
Published: December 2024

Quinoa (Chenopodium quinoa Willd.) seeds, renowned for their nutritional richness and balanced amino acid profile, offer promising potential as food ingredients. This study focused on extracting and characterizing the protein isolates from red and white quinoa varieties to evaluate their physicochemical and functional properties. Protein isolation involved alkaline solubilization and isoelectric precipitation, followed by characterization through amino acid analysis, phenolic profiling, scanning electron microscopy (SEM), zeta potential measurement, particle size distribution analysis, Differential Scanning Calorimetry (DSC), and rheological studies. The results showed that both the red and white quinoa protein isolates exhibited high protein content and essential amino acids, with notable differences in their amino acid compositions. The phenolic and flavonoid content varied between the red and white quinoa seeds, highlighting their potential antioxidant properties. SEM revealed distinct microstructural differences between the red and white quinoa protein isolates. Zeta potential measurements indicated the negative surface charges, influencing the stability in the solution. A particle size distribution analysis showed the monomodal distributions with minor variations in the mean particle size. The DSC profiles demonstrated multiple denaturation peaks, reflecting the complex protein compositions. Rheological studies indicated diverse gelation behaviors and mechanical properties. Overall, this comprehensive characterization underscores the potential of quinoa protein isolates as functional food ingredients with diverse applications in the food industry.
MOLA: Enhancing Industrial Process Monitoring Using a Multi-Block Orthogonal Long Short-Term Memory Autoencoder
Authors: Ma Fangyuan , Ji Cheng , Wang Jingde , Sun Wei , Tang Xun , Jiang Zheyu
Published: December 2024

In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by introducing an orthogonality-based loss function to constrain the latent space output. This helps eliminate the redundancy in the features identified, thereby improving the overall monitoring performance. On top of this, a multi-block monitoring structure is proposed, which categorizes the process variables into multiple blocks by leveraging expert process knowledge about their associations with the overall process. Each block is associated with its specific orthogonal long short-term memory autoencoder model, whose extracted dynamic orthogonal features are monitored by distance-based Hotelling’s T2 statistics and quantile-based cumulative sum (CUSUM) designed for multivariate data streams that are nonparametric and heterogeneous. Compared to having a single model accounting for all process variables, such a multi-block structure significantly improves overall process monitoring performance, especially for large-scale industrial processes. Finally, we propose an adaptive weight-based Bayesian fusion (W-BF) framework to aggregate all block-wise monitoring statistics into a global statistic that we monitor for faults. Fault detection speed and accuracy are improved by assigning and adjusting weights to blocks based on the sequential order in which alarms are raised. We demonstrate the efficiency and effectiveness of our MOLA framework by applying it to the Tennessee Eastman process and comparing the performance with various benchmark methods.
How Are Perfectionism Groups Determined Among Korean College Students? Applying the Best-Fitting Short Form of Hewitt and Flett’s Multidimensional Perfectionism Scale (HF-MPS) to a Korean Sample
Authors: Heo Jungyeong , Petscher Yaacov , Paek Insu
Published: December 2024

Background: Perfectionism is a prominent personality trait in modern society, warranting further research across diverse cultural contexts to develop culturally appropriate measures and identify culture-specific perfectionism profiles. This study aims to identify perfectionism profiles and evaluate the best-fitting short form of Hewitt and Flett’s Multidimensional Perfectionism Scale (HF-MPS) within a Korean context. Methods: Data were collected from 276 Korean college students to compare three short forms of the HF-MPS: Cox et al.’s, Hewitt et al.’s, and a newly proposed version. Reliability and validity were assessed for each short form, and latent profile analysis with distal outcomes was conducted using the best-fitting short form to identify perfectionism profiles. Results: All three short forms demonstrated good reliability and validity. However, the newly proposed short form showed the highest alignment with the original scale and the best model fit. Using this short form, three perfectionism profiles were identified: high perfectionists (HiP), average perfectionists (AvP), and non-perfectionists (NP). Non-perfectionists displayed the most adaptive emotional adjustment, while high and average perfectionists exhibited similar levels of emotional adjustment. Conclusion: This study identified three distinct perfectionism profiles within a Korean sample using the best-fitting short form of the HF-MPS. Findings suggest that perfectionism factors may be more closely interrelated in this cultural context, with self-oriented or other-oriented perfectionism potentially buffering against emotional maladjustment. These results underscore the importance of culturally tailored approaches to understanding and assessing perfectionism.
A Wavelet Decomposition Method for Estimating Soybean Seed Composition with Hyperspectral Data
Authors: Giri Aviskar , Sagan Vasit , Alifu Haireti , Maiwulanjiang Abuduwanli , Sarkar Supria , Roy Bishal , Fritschi Felix B.
Published: December 2024

Soybean seed composition, particularly protein and oil content, plays a critical role in agricultural practices, influencing crop value, nutritional quality, and marketability. Accurate and efficient methods for predicting seed composition are essential for optimizing crop management and breeding strategies. This study assesses the effectiveness of combining handheld spectroradiometers with the Mexican Hat wavelet transformation to predict soybean seed composition at both seed and canopy levels. Initial analyses using raw spectral data from these devices showed limited predictive accuracy. However, by using the Mexican Hat wavelet transformation, meaningful features were extracted from the spectral data, significantly enhancing prediction performance. Results showed improvements: for seed-level data, Partial Least Squares Regression (PLSR), a method used to reduce spectral data complexity while retaining critical information, showed R2 values increasing from 0.57 to 0.61 for protein content and from 0.58 to 0.74 for oil content post-transformation. Canopy-level data analyzed with Random Forest Regression (RFR), an ensemble method designed to capture non-linear relationships, also demonstrated substantial improvements, with R2 increasing from 0.07 to 0.44 for protein and from 0.02 to 0.39 for oil content post-transformation. These findings demonstrate that integrating handheld spectroradiometer data with wavelet transformation bridges the gap between high-end spectral imaging and practical, accessible solutions for field applications. This approach not only improves the accuracy of seed composition prediction at both seed and canopy levels but also supports more informed decision-making in crop management. This work represents a significant step towards making advanced crop assessment tools more accessible, potentially improving crop management strategies and yield optimization across various farming scales.
Coupling Coordination and Influencing Mechanism of Ecosystem Services Using Remote Sensing: A Case Study of Food Provision and Soil Conservation
Authors: Li Yu , Zhen Weina , Shi Donghui , Tang Yihang , Xia Bing
Published: December 2024

Understanding the trade-offs and synergies between ecosystem services is essential for effective ecological management. We selected food provisioning and soil conservation services to explore their intrinsic link and trade-offs. We evaluated these services in Minnesota from 1998 to 2018 using multi-source remote sensing data. The coupling coordination degree model (CCDM) was employed to quantify the relationship between these services. The CCDM evaluates the degree of coordination between systems by measuring their interactions. In addition, we used the geographically weighted regression (GWR) model to identify factors influencing this relationship. Our findings reveal that, while Minnesota’s food provision services have shown a significant overall upward trajectory, distinct declines occurred in 2008 and 2018. In contrast, soil conservation services showed considerable variability from year to year, without a clear trend. Over time, the relationship between food provision and soil conservation services evolved from uncoordinated and transitional to more coordinated development. Our analysis indicates that climate–soil indicators (Z1) exert the most significant influence on the coupling coordination degree (CCD), followed by topography (Z3), vegetation quality (Z4), and socio-economic indicators (Z2). This suggests that natural environmental factors have a greater impact than socio-economic factors. Spatial analysis highlights that topography exhibits significant spatial heterogeneity and serves as the primary spatial driving factor. This study explores the trade-offs between food provision and soil conservation ecosystem services in Minnesota, enhancing the understanding of trade-offs among different ecosystem services and providing insights for global sustainable agricultural development.
Methods for the Construction and Editing of an Efficient Control Network for the Photogrammetric Processing of Massive Planetary Remote Sensing Images
Authors: Ma Xin , Liu Chun , Geng Xun , Wang Sifen , Li Tao , Wang Jin , Liu Pengying , Zhang Jiujiang , Wang Qiudong , Wang Yuying , Wang Yinhui , Peng Zhen
Published: December 2024

Planetary photogrammetry remains an important technical means of producing high-precision planetary maps. High-quality control networks are fundamental to successful bundle adjustment. However, current software tools used by the planetary mapping community to construct and edit control networks exhibit very low efficiency. Moreover, redundant and invalid control points in the control network can further increase the time required for the bundle adjustment process. Due to a lack of targeted algorithm optimization, existing software tools and methods are unable to meet the photogrammetric processing requirements of massive planetary remote sensing images. To address these issues, we first proposed an efficient control network construction framework based on approximate orthoimage matching and hash quick search. Next, to effectively reduce the redundant control points in the control network and decrease the computation time required for bundle adjustment, we then proposed a control network-thinning algorithm based on a K-D tree fast search. Finally, we developed an automatic detection method based on ray tracing for identifying invalid control points in the control network. To validate the proposed methods, we conducted photogrammetric processing experiments using both the Lunar Reconnaissance Orbiter (LRO) narrow-angle camera (NAC) images and the Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) PolyCam images; we then compared the results with those derived from the famous open-source planetary photogrammetric software, the United States Geological Survey (USGS) Integrated Software for Imagers and Spectrometers (ISIS) version 8.0.0. The experimental results demonstrate that the proposed methods significantly improve the efficiency and quality of constructing control networks for large-scale planetary images. For thousands of planetary images, we were able to speed up the generation and editing of the control network by more than two orders of magnitude.
Three-Dimensional Pulsed-Laser Imaging via Compressed Sensing Reconstruction Based on Proximal Momentum-Gradient Descent
Authors: Gao Han , Zhang Guifeng , Huang Min , Xu Yanbing , Zheng Yucheng , Yuan Shuai , Li Huan
Published: December 2024

Compressed sensing (CS) is a promising approach to enhancing the spatial resolution of images obtained from few-pixel array sensors in three-dimensional (3D) laser imaging scenarios. However, traditional CS-based methods suffer from insufficient range resolutions and poor reconstruction quality at low CS sampling ratios. To solve the CS reconstruction problem under the time-of-flight (TOF)-based pulsed-laser imaging framework, a CS algorithm based on proximal momentum-gradient descent (PMGD) is proposed in this paper. To improve the accuracy of the range and intensity reconstructed from overlapping samples, the PMGD framework is developed by introducing an extra fidelity term based on a pulse shaping method, in which the reconstructed echo signal obtained from each sensor pixel can be refined during the iterative reconstruction process. Additionally, noise level estimation with the fast Johnson–Lindenstrauss transform is adopted, enabling the integration of a denoising neural network into PMGD to further enhance reconstruction accuracy. The simulation results obtained on real datasets demonstrate that the proposed method can yield more accurate reconstructions and significant improvements over the recently developed CS-based approaches.
A Review of Animal-Based Welfare Indicators for Calves and Cattle
Authors: Harris Sierra , Shallcrass Michael , Cohen Shari
Published: December 2024

As the human population continues to rise, so does the consumption of animal proteins and products. To meet this demand, animal agriculture has intensified. Simultaneously, there are increased public concerns related to improving calf and cattle welfare to ensure ethical and sustainable livestock production. To meet these expectations, it is essential to maintain high standards of cattle (Bos taurus and Bos indicus) welfare. The use of animal-based welfare indicators is critical when assessing and developing assessments for animal welfare. A review of calf and cattle animal-based individual and herd health indicators in the scientific and grey literature was conducted. Indicators were initially grouped into the categories of behavioral, physiological, or physical indicators and further analyzed to determine potential affective states, ease of training, cost, special equipment, time, and current use as herd health indicators. The indicators identified in this review have the potential to inform future research priorities, improve animal welfare assessment, and support uplift in animal welfare in cattle production and research to better meet societal expectations of animal care and use.
Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics
Authors: Liang Shuang , Bai Xue , Gu Yu
Published: December 2024

Cancer is the second leading cause of death, significantly threatening human health. Effective treatment options are often lacking in advanced stages, making early diagnosis crucial for reducing mortality rates. Circulating tumor cells (CTCs) are a promising biomarker for early detection; however, their automatic detection is challenging due to their heterogeneous size and shape, as well as their scarcity in blood. This study proposes a data generation method using the Segment Anything Model (SAM) combined with a copy–paste strategy. We develop a detection network based on the Swin Transformer, featuring a backbone network, scale adapter module, shape adapter module, and detection head, which enhances CTC localization and identification in images. To effectively utilize both generated and real data, we introduce an improved loss function that includes a regularization term to ensure consistency across different data distributions. Our model demonstrates exceptional performance across five evaluation metrics: accuracy (0.9960), recall (0.9961), precision (0.9804), specificity (0.9975), and mean average precision (mAP) of 0.9400 at an Intersection over Union (IoU) threshold of 0.5. These results are achieved on a dataset generated by mixing both public and local data, highlighting the robustness and generalizability of the proposed approach. This framework surpasses state-of-the-art models (ADCTC, DiffusionDet, CO-DETR, and DDQ), providing a vital tool for early cancer diagnosis, treatment planning, and prognostic assessment, ultimately enhancing human health and well-being.
Room-Temperature Ammonia Sensing Using Polyaniline-Coated Laser-Induced Graphene
Authors: Santos-Ceballos José Carlos , Salehnia Foad , Güell Frank , Romero Alfonso , Vilanova Xavier , Llobet Eduard
Published: December 2024

The reliable detection of ammonia at room temperature is crucial for not only maintaining environmental safety but also for reducing the risks of hazardous pollutants. In this study, the electrochemical modification of laser-induced graphene (LIG) with polyaniline (PANI) led to the development of a chemo-resistive nanocomposite (PANI@LIG) for detecting ammonia levels at room temperature. The composite is characterized by field emission scanning electron microscopy, Fourier transforms infrared, and Raman and X-ray photoelectron spectroscopy. This work marks the first utilization of PANI@LIG for gas sensing and introduces a simple but effective approach for fabricating low-cost wearable gas sensors with high sensitivity and flexibility.
Using UMAP for Partially Synthetic Healthcare Tabular Data Generation and Validation
Authors: Lázaro Carla , Angulo Cecilio
Published: December 2024

In healthcare, vast amounts of data are increasingly collected through sensors for smart health applications and patient monitoring or diagnosis. However, such medical data often comprise sensitive patient information, posing challenges regarding data privacy, and are resource-intensive to acquire for significant research purposes. In addition, the common case of lack of information due to technical issues, transcript errors, or differences between descriptors considered in different health centers leads to the need for data imputation and partial data generation techniques. This study introduces a novel methodology for partially synthetic tabular data generation, designed to reduce the reliance on sensor measurements and ensure secure data exchange. Using the UMAP (Uniform Manifold Approximation and Projection) visualization algorithm to transform the original, high-dimensional reference data set into a reduced-dimensional space, we generate and validate synthetic values for incomplete data sets. This approach mitigates the need for extensive sensor readings while addressing data privacy concerns by generating realistic synthetic samples. The proposed method is validated on prostate and breast cancer data sets, showing its effectiveness in completing and augmenting incomplete data sets using fully available references. Furthermore, our results demonstrate superior performance in comparison to state-of-the-art imputation techniques. This work makes a dual contribution by not only proposing an innovative method for synthetic data generation, but also studying and establishing a formal framework to understand and solve synthetic data generation and imputation problems in sensor-driven environments.
3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness
Authors: Zhuang Wei , Zhang Yunhong , Wang Yuan , He Kaiyang
Published: December 2024

Evaluating students’ learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students’ learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.