bluuebunny/crossref_metadata_2025 huggingface dataset:
Recycled PLA for 3D Printing: A Comparison of Recycled PLA Filaments from Waste of Different Origins after Repeated Cycles of Extrusion
Authors: Hidalgo-Carvajal David , Muñoz Álvaro Hortal , Garrido-González José J. , Carrasco-Gallego Ruth , Alcázar Montero Victoria
Published: September 2023

The objective of this work is to evaluate the reprocessing of PLA 3D printing waste from different origins, into filaments and films, and without the addition of any additive. Two types of waste were considered: a blend of different printing wastes (masks, visors, other components) of personal protective equipment coming from an association of Spanish coronamakers, and PLA waste from a single known commercial source. Both types of materials were subjected to repeated extrusion cycles and processed into films by compression molding. Samples were characterized after each cycle and their mechanical and viscosity properties evaluated. Diffusion-ordered NMR spectroscopy (DOSY) experiments were also carried out to estimate molecular weights. The results show a better performance for the PLA waste from the known origin, capable of withstanding up to three re-extrusion cycles per two for the waste blending, without significant degradation. Additionally, a model to address collection and mechanical recycling cycles under two different scenarios (full traceability and not full traceability) was proposed.
Exploring Bayesian Optimization for Photocatalytic Reduction of CO2
Authors: Zhang Yutao , Yang Xilin , Zhang Chengwei , Zhang Zhihui , Su An , She Yuan-Bin
Published: September 2023

The optimization of photocatalysis is complex, as heterogenous catalysis makes its kinetic modeling or design of experiment (DOE) significantly more difficult than homogeneous reactions. On the other hand, Bayesian optimization (BO) has been found to be efficient in the optimization of many complex chemical problems but has rarely been studied in photocatalysis. In this paper, we developed a BO platform and applied it to the optimization of three photocatalytic CO2 reduction systems that have been kinetically modeled in previous studies. Three decision variables, namely, partial pressure of CO2, partial pressure of H2O, and reaction time, were used to optimize the reaction rate. We first compared BO with the traditional DOE methods in the Khalilzadeh and Tan systems and found that the optimized reaction rates predicted by BO were 0.7% and 11.0% higher, respectively, than the best results of optimization by DOE, and were significantly better than the original experimental data, which were 1.9% and 13.6% higher, respectively. In both systems, we also explored the best combination of the surrogate model and acquisition function for BO, and the results showed that the combination of Gaussian processes (GP) and upper confidence bound (UCB) had the most stable search performance. Furthermore, the Thompson system with time dependence was optimized with BO according to the selectivity of CH4. The results showed that the optimized reaction time of BO agreed with the actual experimental data with an error of less than 5%. These results suggest that BO is a more promising alternative to kinetic modeling or traditional DOE in the efficient optimization of photocatalytic reduction.
A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects
Authors: Tang Shancheng , Jin Zicheng , Zhang Ying , Lu Jianhui , Li Heng , Yang Jiqing
Published: September 2023

Defect detection is crucial in quality control for fabric production. Deep-learning-based unsupervised reconstruction methods have been recognized universally to address the scarcity of fabric defect samples, high costs of labeling, and insufficient prior knowledge. However, these methods are subject to several weaknesses in reconstructing defect images into defect-free images with high quality, like image blurring, defect residue, and texture inconsistency, resulting in false detection and missed detection. Therefore, this article proposes an unsupervised detection method for fabric surface defects oriented to the timestep adaptive diffusion model. Firstly, the Simplex Noise–Denoising Diffusion Probabilistic Model (SN-DDPM) is constructed to recursively optimize the distribution of the posterior latent vector, thus gradually approaching the probability distribution of surface features of the defect-free samples through multiple iterative diffusions. Meanwhile, the timestep adaptive module is utilized to dynamically adjust the optimal timestep, enabling the model to flexibly adapt to different data distributions. During the detection, the SN-DDPM is employed to reconstruct the defect images into defect-free images, and image differentiation, frequency-tuned salient detection (FTSD), and threshold binarization are utilized to segment the defects. The results reveal that compared with the other seven unsupervised detection methods, the proposed method exhibits higher F1 and IoU values, which are increased by at least 5.42% and 7.61%, respectively, demonstrating that the proposed method is effective and accurate.
The Improved Cytotoxic Capacity of Functionalized Nanodiamonds with Metformin in Breast and Ovarian Cancer Cell Lines
Authors: Acuña-Aguilar Lucero Evelia , Conejo-Dávila Alain Salvador , Miki-Yoshida Mario , Hernández-de la Cruz Olga N. , Sánchez-Sánchez Gricelda , López-Camarillo César , Salas-Leiva Joan Sebastian , Zaragoza-Contreras Erasto Armando , Reyes-Martínez Reyna , Orrantia-Borunda Erasmo
Published: September 2023

Nanodiamonds (ND-COOH) are used as drug delivery systems because of their attractive properties, as they allow for optimized transport of therapeutic agents in cellular models. Metformin (MET) is a drug used in diabetes mellitus therapy and exhibits anti-cancer properties. In this study, dispersed nanodiamonds were functionalized with metformin by directly binding them to 1,6-hexanediol (ND-MET), and their effects on the cytotoxicity of breast and ovarian cancer cells were evaluated in vitro. A simple synthesis of ND-MET was performed and characterized using FT-IR, XPS, Boehm titration, RAMAN, XDR, TEM, and dynamic light scattering (DLS). Data showed an increased intensity of the C-N bond band, indicating the presence of metformin in ND-MET. We detected signals at 1427 cm−1 and 1288 cm−1 corresponding to the C-N and C-H bonds, and adsorptions at 1061 cm−1 and 3208 cm−1 corresponding to the N-O and N-H bonds, respectively. The deconvolution of the C1s binding energy was also found at 286.24 eV. The biological effects of ND-MET were tested in both SKOV3 ovarian cancer and Hs-578T and MDA-MB-231 triple-negative breast cancer cell lines. In SKOV3 cells, the IC50 for the ND-MET complex was 35 ± 14 µg/mL, while for Hs-578T and MDA-MB-231 breast cancer cells, the IC50 for ND-MET was 759 ± 44 µg/mL and 454 ± 49 µg/mL, respectively. Our data showed that ND-MET could be used as an intracellular delivery system for metformin in cancer cells. Cell viability assays evidenced a reduced viability of all cell lines in a time and dose-dependent manner, with a significant sensitivity observed in SKOV3 ovarian cancer cells treated with ND-MET.
An Electro-Hydraulic-Load-Sensitive System on the Basis of Torque Open-Loop Control
Authors: Li YanWen , Yu Cong , Chen Gexin , Yang Mingkun , Zhang Yuhang , Wang Fei
Published: September 2023

Facing the development trend of electrification of construction machinery, in view of the drawbacks of the existing electro-hydraulic-load-sensitive system in terms of dynamic characteristics and usage of energy, based on the drive source of a servo motor-driven quantitative pump, an electro-hydraulic-load-sensitive system on the basis of torque open-loop control was proposed. Firstly, the working principle of the system was introduced and the system’s operating characteristics and energy consumption characteristics were theoretically analyzed. Secondly, in order to balance the system’s energy usage and maneuverability, a control strategy with a variable pressure margin was designed. Meanwhile, in order to solve the problem that the hydraulic pump’s mechanical efficiency causes system pressure control deviation, a torque compensation method based on offline data and speed prediction was proposed. Finally, simulation and testing were used to confirm the viability of the control strategy. The test results show that: the system could realize stable pressure margin control, and the response rise time was within 0.7 s under a variety of flow circumstances; the system could follow the control instruction to change linearly and the flow rate changed smoothly in the adjustable pressure gap control; after using the compensation method, the deviation of the pressure gap control was within 2%.
Risk Assessment of Coal Mine Gas Explosion Based on Fault Tree Analysis and Fuzzy Polymorphic Bayesian Network: A Case Study of Wangzhuang Coal Mine
Authors: Yang Jinhui , Zhao Jin , Shao Liangshan
Published: September 2023

The prevention and control of gas explosion accidents are important means to improving the level of coal mine safety, and risk assessment has a positive effect on eliminating the risk of gas explosions. Aiming at the shortcomings of current risk assessment methods in dynamic control, state expression and handling uncertainty, this study proposes a method combining fault tree analysis and fuzzy polymorphic Bayesian networks. The risk factors are divided into multiple states, the concept of accuracy is proposed to correct the subjectivity of fuzzy theory and Bayesian networks are relied on to calculate the risk probability and risk distribution in real time and to propose targeted prevention and control measures. The results show that the current risk probability of a gas explosion accident in Wangzhuang coal mine is as high as 35%, and among the risk factors, excessive ventilation resistance and spontaneous combustion of coal are sources of induced risk, and the sensitivity value of electric sparks is the largest, and the prevention and control of the key factors can significantly reduce the risk. This study can provide technical support to coal mine gas explosion risk management.
Axial Force Calculation Model for Completion String with Multiple Point Resistances in Horizontal Well
Authors: Nie Zhen , Shi Shuzhe , Wu Bohong , Huang Xueqin
Published: September 2023

Frequent accidents may happen during the string run-down and pull process due to the lack of accuracy in the prediction of string force analysis. In order to precisely predict the completion string axial force in horizontal wells, a new model is established, and an in-house software has been developed. The model aims to predict the multiple local resistances that occur at different points on the completion string, which makes up for the technical defects of the commonly used software. It can calculate resistance at different points of the string, which will lead to varying hook load amplification. This method can also predict the axial force of the completion string. By changing the hook load, location, and direction, the resistance can be determined more accurately. Based on the calculation and analysis, the relationship between local resistance, the blocking point, and the amplification factor is also obtained. Furthermore, this model is used to analyze the local resistance of a horizontal well with multiple external packers in the low-permeability Sadi Reservoir of Halfaya Oilfield, Iraq. The recorded data from in-site operations are compared with the predicted results from this model. The results show that the relative errors between the recorded data and model calculation are within the range of 10%, which indicates that the calculated values are reliable. Meanwhile, the results indicate the success of the subsequent completion design and the construction of the oilfield.
Study of the Optimization of Rail Pressure Characteristics in the High-Pressure Common Rail Injection System for Diesel Engines Based on the Response Surface Methodology
Authors: Li Ruichuan , Yuan Wentao , Xu Jikang , Wang Lin , Chi Feng , Wang Yong , Liu Shuqiang , Lin Jianghai , Zhang Qingguang , Chen Lanzheng
Published: September 2023

This paper establishes a mathematical model of the high-pressure common rail injection system used in diesel engines according to the parameters of its key components, and AMESim 2020 software was used to establish a simulation model of the common rail injection system used in diesel engines. The simulation model mainly includes a high-pressure oil pump model, a common rail pipe model, and a model of four injectors. This paper also describes an experimental analysis of the accuracy of the established simulation model. Through a simulation analysis of the system rail’s pressure fluctuation and pressure characteristics, it was concluded that the length of the common rail pipe, the diameter of the common rail pipe, and the inner diameter of the high-pressure fuel pipes are important influencing parameters for the rail pressure characteristics of the system. In this study, according to the original common rail pipe and high-pressure fuel pipe model, a response surface methodology was used to optimize and analyze the parameters of the common rail pipe and high-pressure fuel pipes, and the optimal size parameters for the common rail pipe and high-pressure fuel pipes were obtained with the minimum rail pressure fluctuations and the average rail pressure setpoint. After the optimization, the pressure for the common rail pipe of the high-pressure common rail system was increased by 0.82%, the pressure fluctuation was reduced by 21.66%, the injection pressure was increased by 1.15%, the single injection volume was increased by 0.86%, and its fuel injection characteristics were significantly improved.
Judgment Basis and Mechanical Analysis of Current Collector Failure in the Winding Process of a Lithium-Ion Battery
Authors: Zhang Yuxin , Zhao Chunhui , Du Xiaozhong , Zhao Jianjun , Hu Yijian
Published: September 2023

The winding process is one of the essential processes in the manufacturing of lithium-ion batteries (LIBs). Current collector failure frequently occurs in the winding process, which severely increases the production cost and reduces production efficiency. In order to solve this problem, we first analyze the relationship between different process parameters and the failure of the current collector, and put forward the standard to determine the failure of the current collector. Moreover, we conducted tensile experiments to validate the differences in the mechanical performance of the current collector under different thicknesses. Finally, the circumferential stress and strain of the current collector winding were calculated using finite element analysis. The accuracy of the proposed criterion for determining current collector failure was verified through experimental measurements of stress and strain. The results demonstrate that the criterion proposed in this study can accurately calculate the maximum stress during the current collector winding process, providing a powerful tool for addressing the issue of current collector failure in the winding process.
Comprehensive Evaluation Index System and Application of Low-Carbon Resilience of Power Grid Containing Phase-Shifting Transformer under Ice Disaster
Authors: Zhang Jing , Cheng Huilin , Yang Peng , Zhang Bingyan , Zhang Shiqi , Lu Zhigang
Published: September 2023

In view of the high impact of extreme disasters, this paper comprehensively evaluates power grid performance from a new low-carbon toughness perspective. First, considering the increase in carbon emissions and the recovery time of carbon emissions, low-carbon resilience indicators are proposed. At the same time, considering the power-regulation effect of the phase-shifter transformer, the fault and response model of a power grid under an ice disaster is established, and then, a comprehensive evaluation index system of low-carbon toughness of the power grid is constructed. The weight determination is carried out using the fuzzy analytic hierarchy process-entropy-based weight method, while the fuzzy comprehensive evaluation center of gravity method is used to evaluate the power grid comprehensively. Finally, examples are presented to verify the feasibility of the proposed method, emphasizing its potential for evaluating the comprehensive performance of low-carbon and toughness of the power grid in the future.
An Improved Dual Second-Order Generalized Integrator Phased-Locked Loop Strategy for an Inverter of Flexible High-Voltage Direct Current Transmission Systems under Nonideal Grid Conditions
Authors: Peng Lai , Fu Zhichao , Xiao Tao , Qian Yang , Zhao Wei , Zhang Cheng
Published: September 2023

High-voltage flexible power systems, with their intrinsic characteristics, play an increasingly important role in electronic power systems. Synchronization between the inverter and the grid needs to be achieved by a phase-locked loop (PLL), the performance of which determines the quality of power transmission. This paper proposes a PLL adapted to extremely harsh grid conditions. Firstly, the traditional synchronous reference frame PLL and the dual second-order generalized integrator (DSOGI-PLL) are analyzed, and the errors in phase-locking and the shortcomings of these two methods in the presence of DC components in the grid are pointed out. Secondly, based on the harmonic grid voltage, a repetitive control internal model is introduced by DSOGI to realize the real-time tracking and regulation of the harmonic signals in order to suppress the harmonic voltage disturbance. In addition, a DC bias elimination and frequency adaptive method is proposed to solve the problems of DC bias and grid voltage frequency fluctuation in order to achieve adaptive tracking of the grid phase. Finally, the superiority of the proposed strategy is verified through simulations and experiments.
Synergistic Catalysis of Reservoir Minerals and Exogenous Catalysts on Aquathermolysis of Heavy Oil
Authors: Zang Yunlei , Liu Huaizhu , Chen Dong , Zhang Shu , Li Shanjian , Chen Gang
Published: September 2023

In this study, based on existing heavy oil extraction technology, combined with the mineral composition in a reservoir, the synergistic catalytic effect of reservoir minerals and exogenous catalysts under the reaction system of a hydrogen-rich environment not only reduces the viscosity of thick oil but also reduces the extraction cost and further improves the recovery rate of heavy oil. In this study, the impacts of different reservoir minerals and exogenous catalysts on the aquathermolysis of heavy oil were investigated. The research results showed that the sodium montmorillonite within the reservoir minerals exhibited an optimal catalytic effect, and the synergistic catalytic effect of sodium montmorillonite and catalyst C-Fe (catechol iron) resulted in a viscosity reduction rate of 60.47%. Furthermore, the efficiency of different alcohols as hydrogen donors was screened, among which ethanol had the best catalytic effect. Under the optimal reaction conditions, the viscosity reduction rate after the addition of ethanol was 75.25%. Infrared spectroscopy, elemental analysis, thermogravimetry, and differential scanning calorimetry were used to study the changes in heavy oil before and after hydrothermal cracking. Element analysis showed that the synergistic catalytic effect of sodium-based montmorillonite and catalyst C-Fe increased the hydrocarbon ratio from 0.116 to 0.117, and the content of S and N elements decreased. This fully confirms the catalytic effect of sodium-based montmorillonite and C-Fe catalyst for he hydrogenation reaction of the unsaturated carbon in heavy oil.
Study on Brittleness Characteristics and Fracturing Crack Propagation Law of Deep Thin-Layer Tight Sandstone in Longdong, Changqing
Authors: Zhou Changjing , Sun Zhonghua , Xiao Yuanxiang , Huang Guopeng , Kuang Dan , Li Minghui
Published: September 2023

Tight-sandstone oil and gas resources are the key areas of unconventional oil and gas resources exploration and development. Because tight-sandstone reservoirs usually have the characteristics of a low porosity and ultralow permeability, large-scale hydraulic fracturing is often required to form artificial fractures with a high conductivity to achieve efficient development. The brittleness of rock is the key mechanical factor for whether fracturing can form a complex fracture network. Previous scholars have carried out a lot of research on the brittleness characteristics of conglomerate and shale reservoirs, but there are few studies on the brittleness characteristics of sandstone with different types and different coring angles in tight-sandstone reservoirs and the fracture propagation law of sandstone with different brittleness characteristics. Based on this, this paper carried out a systematic triaxial compression and hydraulic fracturing experiment on the tight sandstone of Shan 1 and He 8 in the Longdong area of the Changqing oilfield. Combined with CT scanning cracks, the brittleness characteristics and fracturing crack propagation law of different types and different coring angles of sandstone under formation-confining pressure were clarified. The results show that there are great differences between different types of sandstone in the yield stage and the failure stage. The sandstone with a quartz content of 100% has the highest peak strength and a strong brittleness. Sandstones with a high content of natural fractures and dolomite have a lower peak strength and a weaker brittleness. There are also differences in the peak strength and fracture morphology of sandstone with different coring angles due to geological heterogeneity. The sandstone with a comprehensive brittleness index of 70.30 produces a more complex fracture network during triaxial compression and hydraulic fracturing than the sandstone with a comprehensive brittleness index of 14.15. The research results have important guiding significance for on-site fracturing construction of tight-sandstone reservoirs.
Geological Features, Paleosedimentary Environment, and Organic Matter Accumulation Mechanisms of the Lacustrine Shale Oil System: A Case Study of the Jurassic Dongyuemiao Member in the Sichuan Basin
Authors: Wang Enze , Li Yang , Guo Tonglou , Xiong Liang , Dong Xiaoxia , Wang Tong , Shi Kaibo
Published: September 2023

Lacustrine shale has garnered significant attention due to its significant resource potential. The Jurassic shale in the Sichuan Basin is an important target for lacustrine shale exploration in China. However, previous studies have predominantly focused on the Da’anzhai member of the Ziliujing Formation, and little attention has been paid to the shale of other strata. This paper aims to address this gap by investigating the Jurassic Dongyuemiao member in the Sichuan Basin. The study systematically characterizes the geological properties of the Dongyuemiao shale system, reconstructs the paleosedimentary environment, identifies the key factors influencing organic matter (OM) enrichment and physical properties, and assesses its resource potential through comparative analysis. Our results show that the Dongyuemiao shale was deposited in an oxic and semi-humid sedimentary environment characterized by intense weathering conditions. The enrichment of OM in the shale is primarily controlled by redox conditions and salinity, with redox conditions playing the most crucial role in OM accumulation. In terms of pore system characterization, clay mineral-associated pores dominate the pore types in the Dongyuemiao shale, while two types of organic matter-associated pores are also widely observed. The development of framework grain-associated pores is limited and only present in certain siliceous and carbonate minerals. The shales of the Dongyuemiao member and the Da’anzhai member exhibit slight differences in TOC content. However, the kerogen in the Dongyuemiao member displays higher hydrocarbon generation potential, and the Dongyuemiao shale exhibits more favorable pore structure parameters. Overall, the Dongyuemiao shale does not exhibit any significant disadvantages compared to the Da’anzhai shale. Therefore, it deserves greater attention in future exploration endeavors. The research findings presented in this paper provide a solid theoretical foundation for expanding the exploration scope of lacustrine shale in the Sichuan Basin.
Process Optimization of Sea Buckthorn Fruit Powder Effervescent Tablets by Random Centroid Methodology Combined with Fuzzy Mathematical Sensory Evaluation
Authors: Ma Yan , Lian Yali , Meng Xintao , Xu Mingqiang , Zhang Ting , Zou Hui , Yang Haiyan
Published: September 2023

Solid beverages of effervescent tablets have good taste and portable features and are favored by consumers, but product quality and nutrition cannot meet the need of increasing nutritional requirements. Sea buckthorn fruit has a special flavor and nutrient-rich characteristics, but the related products of effervescent tablets have not been developed. In this paper, different additive contents (sea buckthorn fruit powder, erythritol, disintegrant, maltodextrin, polyvinylpyrrolidone (PVP)) were optimized using the random centroid method; the obtained effervescent effect sensory evaluation characteristics (appearance, beverage, appearance, taste, solubility) were used to establish a fuzzy mathematic model for sensory evaluation method of process optimizing; and the nutritional components and characteristics of optimized sea buckthorn powder effervescent tablets were compared to the ones of the commercial product. The results show that the optimal process conditions (47.7% sea buckthorn fruit powder, 1.3% erythritol, 1:1 disintegrant ratio, 2% maltodextrin and 2.9% PVP) were obtained according to the highest fuzzy comprehensive sensory score (87.76). Moreover, the optimized one contains a higher content of vitamin C (50.36 mg/100 g), carotenoids (10.18 mg/100 g), total phenols (11.52 GAE/g), and total flavonoids (28.46 mg RE/100 g), as well as a shorter disintegration time (10 s). The results indicate the RCO, combined with fuzzy mathematical sensory evaluation, is preferably suitable for effervescent tablet process optimization, and the quality indicators met the requirements of the effervescent tablet.
Static and Fatigue Characterization of Adhesive T-Joints Involving Different Adherends
Authors: Serra Georgino C. G. , Ferreira José A. M. , Reis Paulo N. B.
Published: September 2023

It is very important to understand the damage mechanisms as well as the mechanical response of T-joints involving different materials on the base plate. For this purpose, two configurations were studied. In one, the joint is composed of a base plate and a T-element, both in Al 6063-T5, while in the other one, the aluminum base plate was replaced by a glass fiber composite. Finally, each configuration was divided into two batches, where in one, the elements were bonded with a stiff adhesive (Araldite® AV 4076-1/HY 4076) while in the other, a more ductile adhesive (Araldite® AW 106/HV 953 U) was used. The static and fatigue strength of all configurations was evaluated in bending. In all cases, the damage occurred at the end of the T-element, where a crack appeared and propagated toward the interior of the T-joint. The bending strength is highest for joints involving aluminum and the ductile adhesive, which is 2.8 times higher than the same configuration involving composite base plates and 1.7 times higher than that using the stiff adhesive. Finally, the highest fatigue lives were obtained for T-joints involving Al 6063-T5 base plates, and regardless of the base plate material, the ductile adhesive promoted the highest fatigue strength.
Effects of Layering Angle and Prestress on Dynamic Load Energy Conversion and Damage Mechanism of Sandstone
Authors: Fan Wenbing , Zhang Junwen , Yang Yang , Fang Kailun , Wu Shaokang , Xing Yulong
Published: September 2023

Previously conducted studies have established the conversion relationship between incident energy, reflected energy, transmitted energy and absorbed energy of rocks under dynamic load. In this paper, the combined dynamic and static loading tests of sandstone under different prestress and different bedding angles are carried out to explore the law of the influence of prestress and bedding angles on energy evolution and damage evolution. The purpose is to provide some reference for deep mining, rock engineering design and geological hazard assessment. The energy conversion and damage characteristics of sandstone in the whole process of deformation are studied, and the internal energy conversion mechanism of sandstone under dynamic load is proposed. It is found that the increase in prestress will lead to the increase in the initial energy value of sandstone and further affect the shape of the energy evolution curve. In addition, the relationship between strain and energy transformation is established, and it is found that the energy transformation in different stages is different. At the same time, the relationship between prestress and damage characteristics and bedding angle and damage characteristics is established, and it is found that bedding angle and prestress significantly affect the damage characteristics.
Investigation of the Adsorption Process of Triclosan from an Aqueous Solution, Using Nanoporous Carbon Adsorbents, Obtained after Treatment of Organic Household and Vegetable Waste
Authors: Stoycheva Ivanka , Petrova Bilyana , Tsyntsarski Boyko , Dolashka Pavlina , Kosateva Angelina , Petrov Nartzislav
Published: September 2023

The absorption of triclosan on the surface of four different carbon adsorbents, obtained on the base of plant and household waste (RDF), provided by Sofia Waste Plant, was investigated. The obtained results indicate that the most important parameters, which determine the process of triclosan adsorption by adsorbent surface are pore texture, the size of the accessible surface for the molecules of triclosan, and the chemical nature of the adsorbent surface. It was found that the obtained adsorbents can be successfully applied for the extraction of triclosan from waste and drinking water. The possibilities for solid products, obtained by thermal treatment of household and vegetable waste, to be applied successfully in a water purification technique are discussed.
Optimization of Batch Crystallization of Magnetic Lysozyme Crystals and Study of the Continuous Crystallization Process
Authors: Yang Shanshan , Hou Lixia , Su Min
Published: September 2023

Protein crystallization is a widely employed technique for purifying protein drugs, offering notable benefits such as cost-effectiveness and high purity. However, the success of this method is influenced by factors such as the molecular weight and spatial structure of proteins. The challenges associated with achieving crystallization and the prolonged duration required for crystallization induction pose limitations on its widespread industrial implementation. In this study, we employed lysozyme derived from egg white as a representative protein to investigate the polymer-assisted self-assembly of magnetic lysozyme. Through the optimization of the initial interstitial crystallization process of magnetic lysozyme, we manipulated the supersaturation level of lysozyme and applied magnetic nanoparticle treatment. As a result, we successfully reduced the crystallization time from 24 h to 60 min. Subsequently, the findings derived from the analysis of data pertaining to the interstitial crystallization process of lysozyme were utilized to optimize the design and configuration of a push flow crystallizer (PFC) as well as a slug flow crystallizer (SFC). The analysis encompassed the examination of various factors, including the residence time of crystallization, the yield of the process, the shape of the crystals formed, and the distribution of crystal sizes. Ultimately, it was determined that the SFC demonstrated optimal suitability for the crystallization of magnetic lysozyme. The typical V-PFC crystal size is 16 m and the yield is 60%. V-SFC crystals have an average size of 13 m and a yield of 85%.
CLAP: Gas Saturation Prediction in Shale Gas Reservoir Using a Cascaded Convolutional Neural Network–Long Short-Term Memory Model with Attention Mechanism
Authors: Yang Xuefeng , Zhang Chenglin , Zhao Shengxian , Zhou Tianqi , Zhang Deliang , Shi Zhensheng , Liu Shaojun , Jiang Rui , Yin Meixuan , Wang Gaoxiang , Zhang Yan
Published: September 2023

Gas saturation prediction is a crucial area of research regarding shale gas reservoirs, as it plays a vital role in optimizing development strategies and improving the efficiency of exploration efforts. Despite the advancements in deep learning techniques, accurately modeling the complex nonlinear relationships involved in gas saturation prediction remains a challenge. To address this issue, we propose a novel cascaded model, CLAP, combining convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) with an attention mechanism. It effectively captures and visualizes the intricate nonlinear relationships, enabling accurate gas saturation prediction in shale gas reservoirs. In this study, nine logging curves from 27 shale gas wells in the Changning area of the Sichuan Basin were used to train the CLAP model for predicting the gas saturation of the Wufeng-Longmaxi Formation shale. Compared to the Archie and random forest models, the CLAP model exhibited enhanced accuracy in predicting shale gas saturation. Promisingly, the CLAP model demonstrates outstanding statistical performance in gas saturation prediction, achieving an impressive R2 score of 0.762 and a mean square error (MSE) score of 0.934. These positive results highlight the effectiveness and potential utility of our proposed CLAP model in accurately predicting gas saturation in shale gas reservoirs. The application of deep learning techniques, such as CNNs, LSTM, and attention mechanisms, presents a promising avenue for further advancements in this field.
Transitioning towards Net-Zero Emissions in Chemical and Process Industries: A Holistic Perspective
Authors: Glavič Peter , Pintarič Zorka Novak , Levičnik Helena , Dragojlović Vesna , Bogataj Miloš
Published: September 2023

Given the urgency to combat climate change and ensure environmental sustainability, this review examines the transition to net-zero emissions in chemical and process industries. It addresses the core areas of carbon emissions reduction, efficient energy use, and sustainable practices. What is new, however, is that it focuses on cutting-edge technologies such as biomass utilization, biotechnology applications, and waste management strategies that are key drivers of this transition. In particular, the study addresses the unique challenges faced by industries such as cement manufacturing and highlights the need for innovative solutions to effectively reduce their carbon footprint. In particular, the role of hydrogen as a clean fuel is at the heart of revolutionizing the chemical and process sectors, pointing the way to cleaner and greener operations. In addition, the manuscript explores the immense importance of the European Green Deal and the Sustainable Development Goals (SDGs) for the chemical industry. These initiatives provide a clear roadmap and framework for advancing sustainability, driving innovation, and reducing the industry’s environmental impact, and are a notable contribution to the existing body of knowledge. Ultimately, alignment with the European Green Deal and the SDGs can bring numerous benefits to the chemical industry, increasing its competitiveness, promoting societal well-being, and supporting cross-sector collaboration to achieve shared sustainability goals. By highlighting the novelty of integrating cutting-edge technologies, addressing unique industrial challenges, and positioning global initiatives, this report offers valuable insights to guide the chemical and process industries on their transformative path to a sustainable future.
Three-Dimensional Accuracy of Surgical Guides for Static Computer-Aided Implant Surgery: A Systematic Review
Authors: Lo Russo Lucio , Pierluigi Mariani , Zhurakivska Khrystyna , Digregorio Chiara , Lo Muzio Eleonora , Laino Luigi
Published: September 2023

Background: The purpose of this study was to evaluate the dimensional accuracy of surgical guides for static computer-aided implant placements processed using milling or 3D printing. Methods: A comprehensive literature search was performed on electronic databases inclusive of PUBMED, SCOPUS, Cochrane Database of Systematic Reviews, EBSCO host Research Databases, and Web of Knowledge were searched without restriction to date. Studies investigating the surgical guides fabricated by milling or 3D-printing, comparing them with their computer-aided design model, and reporting outcome measures about the accuracy of the internal/external surface, the angular deviation of the sleeves, and the vertical or horizontal deviations of the sleeves’ access were included. Results: From 1928 retrieved records, 33 studies were selected; 11 out of them fulfilled the eligibility criteria. All studies analyzed printed surgical guides, while only two studies analyzed both printed and milled templates. Studies were very heterogeneous in methodology and equipment; moreover, different parameters were used for accuracy measurements which made their results not comparable and quantitative synthesis not feasible. Conclusion: There is no clear evidence to address which manufacturing technology provides surgical guides with better accuracy, although milling might achieve better results, at least in terms of reduced variation. For additive technologies, several factors could influence accuracy. Since this issue has sensible clinical implications, future studies are encouraged.
Clinical Results of the Use of Low-Cost TKA Prosthesis in Low Budget Countries—A Narrative Review
Authors: Bori Edoardo , Deslypere Clara , Estaire Muñoz Laura , Innocenti Bernardo
Published: September 2023

Despite the orthopedics markets in the US and the EU reaching a plateau, the market size in countries such as Brazil, Russia, India, and China is steadily growing. As a result, major orthopedic companies are shifting their focus towards these markets and developing products tailored to their needs. However, a significant challenge associated with this new opportunity is the requirement for the development of more affordable prostheses compared to those sold in the US and Europe. With the introduction of these lower-cost models into the market, this article aims to assess their performance in comparison to traditional models. A literature review was conducted, analyzing four parameters—the Hospital for Special Surgery Score, Knee Society Score, Range of Motion, and Western Ontario and McMaster Universities Arthritis Index—to evaluate different models. The findings indicated that low-cost models perform either equally well or, in some cases, slightly worse than traditional ones. It is worth to mention that the existing literature on this topic is limited, resulting in a relatively small number of models and studies included in this specific study. Nevertheless, this latter serves as a valuable foundation for future in-depth analyses and investigations.
Effect of Macroeconomic Dynamics on Bank Asset Quality under Different Market Conditions: Evidence from Ghana
Authors: Apau Richard , Sibindi Athenia , Jeke Leward
Published: September 2023

This study assesses the dynamic relationship between macroeconomic factors and bank asset quality based on changes in the condition of stock market returns. A dynamic panel two-step system, the Generalized Method of Moments (system GMM) model, is employed using panel data from 18 universal banks spanning the period of 2007 to 2021. The analysis revealed that the real GDP growth rate, the average lending rate, and the real exchange rate represent a set of macroeconomic factors with a marked influence on banks’ asset quality, where a unit increase in these variables drive 0.02 percent, 0.98 percent, and 0.27 percent improvement in asset quality, respectively. In addition, a high-inflation rate was found to exert an adverse effect of −0.32 percent on asset quality, as it affects borrowers’ financial ability to meet loan repayment obligations. Furthermore, the study verified the existence of a positive relationship between market condition and asset quality, where a rise in the market return drives a 0.07 percent improvement in bank asset quality. This implies that bank performance adapts to changes in market conditions as posited under the Adaptive Market Hypothesis (AMH). Bank managers should consolidate banks’ asset bases during conditions of market stability to withstand periodic market fluctuations to boost trading momentum. Policy recommendations are suggested to foster a conducive business environment for bank stability.
Pump It: Twitter Sentiment Analysis for Cryptocurrency Price Prediction
Authors: Koltun Vladyslav , Yamshchikov Ivan P.
Published: September 2023

This study demonstrates the significant impact of market sentiment, derived from social media, on the daily price prediction of cryptocurrencies in both bull and bear markets. Through the analysis of approximately 567 thousand tweets related to twelve specific cryptocurrencies, we incorporate the sentiment extracted from these tweets along with daily price data into our prediction models. We test various algorithms, including ordinary least squares regression, long short-term memory network and neural hierarchical interpolation for time series forecasting (NHITS). All models show better performance once the sentiment is incorporated into the training data. Beyond merely assessing prediction error, we scrutinise the model performances in a practical setting by applying them to a basic trading algorithm managing three distinct portfolios: established tokens, emerging tokens, and meme tokens. While NHITS emerged as the top-performing model in terms of prediction error, its ability to generate returns is not as compelling.
Generalizing Spacecraft Recognition via Diversifying Few-Shot Datasets in a Joint Trained Likelihood
Authors: Yang Xi , Kong Dechen , Lin Ren , Yang Dong
Published: September 2023

With the exploration of outer space, the number of space targets has increased dramatically, while the pressures of space situational awareness have also increased. Among them, spacecraft recognition is the foundation and a critical step in space situational awareness. However, unlike natural images that can be easily captured using low-cost devices, space targets can suffer from motion blurring, overexposure, and excessive dragging at the time of capture, which greatly affects the quality of the images and reduces the number of effective images. To this end, specialized or sufficiently versatile techniques are required, with dataset diversity playing a key role in enabling algorithms to categorize previously unseen spacecraft and perform multiple tasks. In this paper, we propose a joint dataset formulation to increase diversity. Our approach involves reformulating two local processes to condition the Conditional Neural Adaptive Processes, which results in global feature resampling schemes to adapt a pre-trained embedding function to be task-specific. Specifically, we employ variational resampling to category-wise auxiliary features, adding a generative constraint to amortize task-specific parameters. We also develop a neural process variational inference to encode representation, using grid density for conditioning. Our evaluation of the BUAA dataset shows promising results, with no-training performance close to a specifically designed learner and an accuracy rate of 98.2% on unseen categories during the joint training session. Further experiments on the Meta-dataset benchmark demonstrate at least a 4.6% out-of-distribution improvement compared to the baseline conditional models. Both dataset evaluations indicate the effectiveness of exploiting dataset diversity in few-shot feature adaptation. Our proposal offers a versatile solution for tasks across domains.
Monitoring of the Rehabilitation of the Historic World War II US Air Force Base in Greenland
Authors: Bouček Tomáš , Stará Lucie , Pavelka Karel , Pavelka Karel
Published: September 2023

After the end of World War II, many military air bases in Greenland were abandoned with all the material left in place. One of these sites was the Bluie East Two military air base. A specific feature of this area is that it contained thousands of old barrels formerly used for fuel storage. In 2019, a rehabilitation of this area began. A few months prior to the rehabilitation, our expedition visited the area and mapped it using an unmanned aerial vehicle (UAV). This made it probably the latest documentation before the start of the sanitation. The aim of such mapping was to estimate the number of barrels in a given location. The second objective was to monitor the progress of the rehabilitation over the years. For this purpose, satellite data were acquired for the years 2019 through 2022. A supervised classification was performed to automatically detect sites with barrel occurrences, which enabled subsequent change detection. We conclude that a total of 33,786 old barrels were located in the investigated area in 2019. However, we suggest this number is a lower estimate of the actual number of barrels due to the factors we mention in our paper. The results further indicate that between the years 2019 and 2022 the barrels were removed from more than half of the area.
Combined Retrievals of Turbidity from Sentinel-2A/B and Landsat-8/9 in the Taihu Lake through Machine Learning
Authors: Yang Zhe , Gong Cailan , Lu Zhihua , Wu Enuo , Huai Hongyan , Hu Yong , Li Lan , Dong Lei
Published: September 2023

Lakes play a crucial role in the earth’s ecosystems and human activities. While turbidity is not a direct biochemical indicator of lake water quality, it is relatively easy to measure and indicates trophic status and lake health. Although ocean color satellites have been widely used to monitor water color parameters, their coarse spatial resolution makes it hard to capture the fine spatial variability of turbidity in lakes. The combination of Sentinel-2 and Landsat provides an opportunity to monitor lake turbidity with high spatial and temporal resolution. This study aims to generate consistent turbidity products in Taihu Lake from 2018 to 2022 using the Multispectral Instrument (MSI) on board Sentinel-2A/B and the Operational Land Imager (OLI) on board Landsat-8/9. We first tested the performance of three atmospheric correction methods to retrieve consistent reflectance from MSI and OLI images. We found that the Rayleigh correction and a subtraction of the SWIR band from Rayleigh-corrected reflectance can generate the most consistent reflectance (the coefficient of determination (R2) > 0.84, the mean absolution percentage error (MAPE) < 7%, the median error (ME) < 0.0035, and slope > 0.92). Machine learning models outperformed an existing semi-analytical retrieval algorithm in retrieving turbidity (MSI: R2 = 0.92, MAPE = 18.78%, and OLI: R2 = 0.93, MAPE = 16.20%). The consistency of turbidity from the same-day MSI and OLI images was also satisfactory (N = 3110 and MAPE = 26.48%). The distribution of turbidity exhibited obvious spatial and seasonal variability in Taihu Lake from 2018 to 2022. The results show the potential of MSI and OLI when combined to monitor inland lake water quality.