bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Industrial Byproduct Gypsum on the Mechanical Properties and Stabilization of Hazardous Elements of Cementitious Materials: A Review
Authors: Wu Pengfei , Liu Xinyue , Liu Xiaoming , Zhang Zengqi , Wei Chao
Published: August 2024

Industrial byproduct gypsum (BPG) is a secondary product that is mainly composed of calcium sulfate discharged during industrial production. BPG primarily consists of desulfurized gypsum, phosphogypsum, and titanium gypsum, which account for 88% of the total BPG in China. The large-scale utilization of these three types of solid waste is crucial for the safe disposal of BPG. BPG contains various impurities and harmful elements, limiting its applications. The continuous accumulation of BPG poses a serious threat to the safety of the environment. Based on a literature review (2021–2023), it was found that 52% of BPG is used in the preparation of cementitious materials, and the addition of BPG results in an average improvement of 7–30% in the mechanical properties of cementitious materials. Moreover, BPG has a positive impact on the immobilization of hazardous elements in raw materials. Therefore, the utilization of BPG in cementitious materials is beneficial for its large-scale disposal. This study primarily reviews the effects and mechanisms of BPG on the mechanical properties of cementitious materials and the solidification of hazardous elements. Most importantly, the review reveals that BPG positively influences the hydration activity of silica–alumina-based solid waste (such as steel slag and blast furnace slag) and alkaline solid waste (such as carbide slag and red mud). This improves the proportion of solid waste in cement and reduces production costs and carbon emissions. Finally, this article summarizes and proposes the application of BPG in cementitious materials. The application of BPG + silica–alumina solid waste + alkaline solid-waste-based cementitious materials is expected to realize a new type of green ecological chain for the joint utilization of multiple industrial solid wastes and to promote the low-carbon sustainable development of industrial clusters.
Analysis of Resistance Influencing Factors of a Bench System Based on a Self-Developed Four-Wheel Drive Motor Vehicle Chassis Dynamometer
Authors: Huang Wanyou , Liu Dongying , Chu Ruixia , Qiu Fangyuan , Li Zhenyu , Jin Xiaoyue , Zhang Hongtao , Wang Yan , Ji Shaobo
Published: August 2024

In order to accurately simulate the actual road driving resistance of four-wheel drive motor vehicles based on the chassis dynamometer and efficiently test the vehicle performance, it is necessary to analyze the influencing factors of the additional loss resistance and the loading resistance of the chassis dynamometer bench system. In this paper, the effects of the drum speed, the sampling speed interval, and basic inertia on the test results of the additional loss resistance are tested and analyzed based on the self-developed chassis dynamometer of a four-wheel drive motor vehicle. The static and dynamic components of the additional loss resistance are defined by linear regression through ordinary least squares, and the additional loss resistance of the four-axis eight-drum chassis dynamometer and mainstream chassis dynamometer system for four-wheel drive motor vehicles are compared. In addition, the effects of the dynamometer type and the control strategy on the loading resistance are discussed, and the transient condition, steady-state condition, and overall operating condition deviation coefficient of loading force are defined, according to which the advantages and disadvantages of the control strategy of the chassis dynamometer system for four-wheel drive motor vehicles are evaluated. The analysis of the influencing factors and laws of the resistance of the four-wheel drive motor vehicle chassis dynamometer bench system can provide a reference basis for accurately simulating the resistance of vehicle road driving based on the bench testing.
Interdisciplinary Education Promotes Scientific Research Innovation: Take the Composite Control of the Permanent Magnet Synchronous Motor as an Example
Authors: Gao Peng , Fang Liandi , Pan Huihui
Published: August 2024

Intersecting disciplines, as an important trend in the development of modern academic research and education, have exerted a profound and positive influence on scientific research activities. Based on control theory and fractional-order theory, this paper presents a novel approach for the speed regulation of a permanent magnet synchronous motor (PMSM) in the presence of uncertainties and external disturbances. The proposed method is a composite control based on a model-free sliding mode and a fractional-order ultra-local model. The model-free sliding mode is a control strategy that utilizes the sliding mode control methodology without explicitly relying on a mathematical model of the system being controlled. The fractional-order ultra-local model is a mathematical representation of a dynamic system that incorporates the concept of fractional-order derivatives. The core of the controller is a new type of fractional-order fast nonsingular terminal sliding mode surface, which ensures high robustness, quick convergence, while preventing singularity. Moreover, a novel fractional-order nonlinear extended state observer is proposed to estimate both internal and external disturbances of the fractional-order ultra-local model. The stability of the system is analyzed using both the Lyapunov stability theory and the Mittag–Leffler stability theory. The analysis confirms the convergence stability of the closed-loop system under the proposed control scheme. The comparison results indicate that the proposed composite control based on the fractional-order ultra-local model is a promising solution for regulating the speed of PMSMs in the presence of uncertainties and disturbances.
EdgePose: An Edge Attention Network for 6D Pose Estimation
Authors: Feng Qi , Nong Jian , Liang Yanyan
Published: August 2024

We propose a 6D pose estimation method that introduces an edge attention mechanism into the bidirectional feature fusion network. Our method constructs an end-to-end network model by sharing weights between the edge detection encoder and the encoder of the RGB branch in the feature fusion network, effectively utilizing edge information and improving the accuracy and robustness of 6D pose estimation. Experimental results show that this method achieves an accuracy of nearly 100% on the LineMOD dataset, and it also achieves state-of-the-art performance on the YCB-V dataset, especially on objects with significant edge information.
Relations among the Queue-Length Probabilities in the Pre-Arrival, Random, and Post-Departure Epochs in the GI/Ma,b/c Queue
Authors: Gai Jing , Chaudhry Mohan
Published: August 2024

In this paper, we present research results that extend and supplement our article recently published by MDPI. We derive the closed-form relations among the queue-length probabilities observed in the pre-arrival, random, and post-departure epochs for a complex, bulk-service, multi-server queueing system GI/Ma,b/c.
Numerical Solution of the Cauchy Problem for the Helmholtz Equation Using Nesterov’s Accelerated Method
Authors: Kasenov Syrym E. , Tleulesova Aigerim M. , Sarsenbayeva Ainur E. , Temirbekov Almas N.
Published: August 2024

In this paper, the Cauchy problem for the Helmholtz equation, also known as the continuation problem, is considered. The continuation problem is reduced to a boundary inverse problem for a well-posed direct problem. A generalized solution to the direct problem is obtained and an estimate of its stability is given. The inverse problem is reduced to an optimization problem solved using the gradient method. The convergence of the Landweber method with respect to the functionals is compared with the convergence of the Nesterov method. The calculation of the gradient in discrete form, which is often used in the numerical solutions of the inverse problem, is described. The formulation of the conjugate problem in discrete form is presented. After calculating the gradient, an algorithm for solving the inverse problem using the Nesterov method is constructed. A computational experiment for the boundary inverse problem is carried out, and the results of the comparative analysis of the Landweber and Nesterov methods in a graphical form are presented.
Clinical Characteristics, Prognostic Factors, and Outcomes of COVID-19 in Autoimmune Rheumatic Disease Patients: A Retrospective Case–Control Study from Astana, Kazakhstan
Authors: Rutskaya-Moroshan Kristina , Abisheva Saule , Abisheva Anilim , Amangeldiyeva Zhadra , Vinnik Tatyana , Batyrkhan Tansholpan
Published: August 2024

Background: Viral infections, including coronavirus disease 2019 (COVID-19), in patients with autoimmune rheumatic diseases (AIRDs) tend to present more severe disease. This study aims to investigate the clinical characteristics and risk factors for severe infection in rheumatologic patients. Methods: We included patients with a diagnosis of AIRD and COVID-19 infection between January 2022 and July 2023. Patients with AIRDs infected with SARS-CoV-2 were matched with control patients of the general population according to age (±5 years) and sex in a 1:1 ratio. Confirmed infection was defined if a patient had a positive polymerase chain reaction (PCR) test. The severity was divided into mild, moderate, severe, and critical according to the guidelines of the United States National Institutes of Health (NIH). Results: A total of 140 individuals (37 males, 103 females; mean age 56.1 ± 11.3 years) with rheumatic disease diagnosed with COVID-19 infection were enrolled in the study. AIRDs included rheumatoid arthritis (RA) (n = 63, 45%), ankylosing spondylitis (AS) (n = 35, 25%), systemic lupus erythematosus (SLE) (n = 26, 8.6%), and systemic sclerosis (SSc) (n = 16, 11.4%). The AIRDs group had more SARS-CoV-2-related dyspnea (38.6%), arthralgia (45.7%), and depression (27.1%) than the control group (p = 0.004). The rate of lung infiltration on radiographic examination was higher in 58 (41.4%, p = 0.005) patients with rheumatic diseases than in those without them. Severe SARS-CoV-2 infection was more common in the AIRDs group than in the control group (22% vs. 12%; p = 0.043). Conclusions: Patients with AIRDs experienced more symptoms of arthralgia, depression, and dyspnea. There was a trend towards an increased severity of the disease in patients with AIRDs. Patients with arterial hypertension, diabetes, chronic lung, and kidney disease, treated with corticosteroids, had a longer duration, and high activity of autoimmune disease had an increased risk of severe COVID-19.
The Effects of Maternal Nutrient Restriction during Mid to Late Gestation with Realimentation on Fetal Metabolic Profiles in the Liver, Skeletal Muscle, and Blood in Sheep
Authors: Smith Brandon I. , Vásquez-Hidalgo Manuel A. , Li Xiaomeng , Vonnahme Kimberly A. , Grazul-Bilska Anna T. , Swanson Kendall C. , Moore Timothy E. , Reed Sarah A. , Govoni Kristen E.
Published: August 2024

Poor maternal nutrition during gestation negatively affects offspring growth and metabolism. To evaluate the impact of maternal nutrient restriction and realimentation on metabolism in the fetal liver, skeletal muscle, and circulation, on day 50 of gestation, ewes (n = 48) pregnant with singletons were fed 100% (CON) or 60% (RES) of requirements until day 90 of gestation, when a subset of ewes (n = 7/treatment) were euthanized, and fetal samples were collected. The remaining ewes were maintained on a current diet (CON-CON, n = 6; RES-RES, n = 7) or switched to an alternative diet (CON-RES, RES-CON; n = 7/treatment). On day 130 of gestation, the remaining ewes were euthanized, and fetal samples were collected. Fetal liver, longissimus dorsi (LD), and blood metabolites were analyzed using LC-MS/MS, and pathway enrichment analysis was conducted using MetaboAnalyst. Then, 600, 518, and 524 metabolites were identified in the liver, LD, and blood, respectively, including 345 metabolites that were present in all three. Nutrient restriction was associated with changes in amino acid, carbohydrate, lipid, and transulfuration/methionine metabolic pathways, some of which were alleviated by realimentation. Fetal age also affected metabolite abundance. The differential abundance of metabolites involved in amino acid, methionine, betaine, and bile acid metabolism could impact fetal epigenetic regulation, protein synthesis, lipid metabolism, and signaling associated with glucose and lipid metabolism.
Development of the Intestinal Microbiota of Dairy Calves and Changes Associated with Cryptosporidium spp. Infection in Brazil
Authors: Bessegatto José Antônio , Lisbôa Júlio Augusto Naylor , Martins Felippe Danyel Cardoso , Freire Roberta Lemos , Facury Filho Elias Jorge , Alfieri Amauri Alcindo , Costa Marcio C.
Published: August 2024

Cryptosporidium spp. is one of the most important pathogens infecting nursing calves worldwide. This study aimed to investigate the intestinal microbiota of dairy calves during the first month of life and the impact of diarrhea caused by Cryptosporidium on a Brazilian farm. Fecal samples from 30 calves were collected during the first month of life, and fecal scores were recorded. Samples from the second, third, and fourth days of life were analyzed by DNA sequencing of the 16S rRNA gene. In addition, samples of sixteen calves positive for Cryptosporidium spp. were retrospectively chosen according to the development of diarrhea: four and two days before diarrhea, at the onset of diarrhea, after four days of diarrhea, at the end of diarrhea, and after six days of diarrhea resolution. Diarrhea was observed in all calves (100%), starting at day 5 of life, and all calves tested positive for Cryptosporidium in at least one sample. The microbiota richness increased with age but was retarded by diarrhea. Compositional changes associated with Cryptosporidium infection included increases in Fusobacterium, Prevotella, and Peptostreptococcus, as well as decreases in Collinsella and Lachnospiraceae. In conclusion, Cryptosporidium infection has the potential to decrease richness and change the composition of the intestinal microbiota of dairy calves.
Retrospective Analysis of Omicron in Minas Gerais, Brazil: Emergence, Dissemination, and Diversification
Authors: Fonseca Paula Luize Camargos , Braga-Paz Isabela , de Araújo e Santos Luiza Campos Guerra , Dias Rillery Calixto , de Souza Carolina Senra Alves , Carvalho Nara Oliveira , Queiroz Daniel Costa , Alves Hugo José , de Araújo João Locke Ferreira , Moreira Filipe Romero Rebello , Menezes Mariane Talon , Menezes Diego , Silva Aryel Beatriz Paz e , Ferreira Jorge Gomes Goulart , Adelino Talita Emile Ribeiro , Bernardes André Felipe Leal , Carobin Natália Virtude , Carvalho Renée Silva , Ferrari Carolina Zaniboni , Guimarães Natália Rocha , Lamounier Ludmila Oliveira , Souza Fernanda Gil , Vargas Luisa Aimeé , Ribeiro Marisa de Oliveira , Arruda Monica Barcellos , Alvarez Patricia , Moreira Rennan Garcias , de Oliveira Eneida Santos , Sabino Adriano de Paula , de Oliveira Jaqueline Silva , Januário José Nélio , Iani Felipe Campos de Melo , Souza Renan Pedra de , Aguiar Renato Santana
Published: August 2024

Brazil is one of the countries most affected by COVID-19, with the highest number of deaths recorded. Brazilian Health Institutions have reported four main peaks of positive COVID-19 cases. The last two waves were characterized by the emergence of the VOC Omicron and its sublineages. This study aimed to conduct a retrospective surveillance study illustrating the emergence, dissemination, and diversification of the VOC Omicron in 15 regional health units (RHUs) in MG, the second most populous state in Brazil, by combining epidemiological and genomic data. A total of 5643 confirmed positive COVID-19 samples were genotyped using the panels TaqMan SARS-CoV-2 Mutation and 4Plex SC2/VOC Bio-Manguinhos to define mutations classifying the BA.1, BA.2, BA.4, and BA.5 sublineages. While sublineages BA.1 and BA.2 were more prevalent during the third wave, BA.4 and BA.5 dominated the fourth wave in the state. Epidemiological and viral genome data suggest that age and vaccination with booster doses were the main factors related to clinical outcomes, reducing the number of deaths, irrespective of the Omicron sublineages. Complete genome sequencing of 253 positive samples confirmed the circulation of the BA.1, BA.2, BA.4, and BA.5 subvariants, and phylogenomic analysis demonstrated that the VOC Omicron was introduced through multiple international events, followed by transmission within the state of MG. In addition to the four subvariants, other lineages have been identified at low frequency, including BQ.1.1 and XAG. This integrative study reinforces that the evolution of Omicron sublineages was the most significant factor driving the highest peaks of positive COVID-19 cases without an increase in more severe cases, prevented by vaccination boosters.
Impact of Novel Foods on the Human Gut Microbiome: Current Status
Authors: Martínez Ailín , Velázquez Lidiana , Díaz Rommy , Huaiquipán Rodrigo , Pérez Isabela , Muñoz Alex , Valdés Marcos , Sepúlveda Néstor , Paz Erwin , Quiñones John
Published: August 2024

The microbiome is a complex ecosystem of microorganisms that inhabit a specific environment. It plays a significant role in human health, from food digestion to immune system strengthening. The “Novel Foods” refer to foods or ingredients that have not been consumed by humans in the European Union before 1997. Currently, there is growing interest in understanding how “Novel Foods” affect the microbiome and human health. The aim of this review was to assess the effects of “Novel Foods” on the human gut microbiome. Research was conducted using scientific databases, focusing on the literature published since 2000, with an emphasis on the past decade. In general, the benefits derived from this type of diet are due to the interaction between polyphenols, oligosaccharides, prebiotics, probiotics, fibre content, and the gut microbiome, which selectively promotes specific microbial species and increases microbial diversity. More research is being conducted on the consumption of novel foods to demonstrate how they affect the microbiome and, thus, human health. Consumption of novel foods with health-promoting properties should be further explored to maintain the diversity and functionality of the gut microbiome as a potential tool to prevent the onset and progression of chronic diseases.
Variation in Bacterial and Fungal Communities in Soils from Three Major Apple Pear (Pyrus bretschneideri Rehd.) Orchards
Authors: Lyu Guangze , Hu Jiayang , Ma Jincai
Published: August 2024

Microbial communities are closely related to the overall health and quality of soil, but studies on microbial ecology in apple pear orchard soils are limited. In the current study, 28 soil samples were collected from three apple pear orchards, and the composition and structure of fungal and bacterial communities were investigated by high-throughput sequencing. The molecular ecological network showed that the keystone taxa of bacterial communities were Actinobacteria, Proteobacteria, Gemmatimonadetes, Acidobacteria, Nitrospirae, and Chloroflexi, and the keystone taxon of fungal communities was Ascomycota. Mantel tests showed that soil texture and pH were important factors shaping soil bacterial and fungal communities, and soil water soluble organic carbon (WSOC) and nitrate nitrogen (NO3−-N) were also closely related to soil bacterial communities. Canonical correspondence analysis (CCA) and variation partition analysis (VPA) revealed that geographic distance, soil texture, pH, and other soil properties could explain 10.55%, 13.5%, and 19.03% of the overall variation in bacterial communities, and 11.61%, 13.03%, and 20.26% of the overall variation in fungal communities, respectively. The keystone taxa of bacterial and fungal communities in apple pear orchard soils and their strong correlation with soil properties could provide useful clues toward sustainable management of orchards.
Emergence of NDM-1-Producing Pseudomonas aeruginosa Nosocomial Isolates in Attica Region of Greece
Authors: Pappa Olga , Louka Christina , Karadimas Kleon , Maikousi Evangelia , Tzoukmani Angeliki , Polemis Michalis , Panopoulou Anna-Danai , Daniil Ioannis , Chryssou Stella , Mellou Kassiani , Kjeldgaard Jette S. , Zarkotou Olympia , Papagiannitsis Costas , Tryfinopoulou Kyriaki
Published: August 2024

Here, we report on the emergence and spread of multidrug-resistant NDM-1-producing P. aeruginosa isolates from patients hospitalized in the Attica region, Greece, in 2022 to provide data on their resistome, their virulome, the genetic environment of blaNDM-1, and their molecular epidemiology. A total of 17 carbapenem-resistant P. aeruginosa isolates identified as NDM-producers by immunochromatography at the hospital level were sent to the Central Public Health Laboratory, in the frame of the laboratory surveillance of carbapenem-resistant pathogens, for further characterization. The initial screening for genetic AMR determinants was carried out by PCR and the MDR Direct Flow Chip assay. Typing was performed by MLST and DLST, the latter in a subset of isolates. Further analysis was performed by whole-genome sequencing (WGS) of six isolates from both hospitals to analyze their entire genomes and elucidate their genetic relatedness. All isolates were allocated to international high-risk clones, sixteen to ST773 and one to ST308. Five ST773 and the sole ST308 isolate were found to harbor the blaNDM-1 gene, along with various other ARGs integrated into their chromosomes, as well as with a wide variety of virulence genes. The blaNDM-1 gene was located in the integrative and conjugative elements ICE6600-like and ICETn43716385 in ST773 and ST308 isolates, respectively. Single-nucleotide polymorphism analysis of the five ST773 isolates indicated their clonal spread in both hospitals. These results suggested that two different molecular events contributed to the emergence of NDM-1-producing P. aeruginosa isolates in Athenian hospitals, highlighting the need for ongoing surveillance.
Quality Assessment of Reconstructed Cow, Camel and Mare Milk Powders by Near-Infrared Spectroscopy and Chemometrics
Authors: Majadi Mariem , Barkó Annamária , Varga-Tóth Adrienn , Maukenovna Zhulduz Suleimenova , Batirkhanovna Dossimova Zhanna , Dilora Senkebayeva , Lukacs Matyas , Kaszab Timea , Mednyánszky Zsuzsanna , Kovacs Zoltan
Published: August 2024

Milk powders are becoming a major attraction for many industrial applications due to their nutritional and functional properties. Different types of powdered milk, each with their own distinct chemical compositions, can have different functionalities. Consequently, the development of rapid monitoring methods is becoming an urgent task to explore and expand their applicability. Lately, there is growing emphasis on the potential of near-infrared spectroscopy (NIRS) as a rapid technique for the quality assessment of dairy products. In the present work, we explored the potential of NIRS coupled with chemometrics for the prediction of the main functional and chemical properties of three types of milk powders, as well as their important processing parameters. Mare, camel and cow milk powders were prepared at different concentrations (5%, 10% and 12%) and temperatures (25 °C, 40 °C and 65 °C), and then their main physicochemical attributes and NIRS spectra were analyzed. Overall, high accuracy in both recognition and prediction based on type, concentration and temperature was achieved by NIRS-based models, and the quantification of quality attributes (pH, viscosity, dry matter content, fat content, conductivity and individual amino acid content) also resulted in high accuracy in the models. R2CV and R2pr values ranging from 0.8 to 0.99 and 0.7 to 0.98, respectively, were obtained by using PLSR models. However, SVR models achieved higher R2CV and R2pr values, ranging from 0.91 to 0.99 and 0.80 to 0.99, respectively.
Simulation of Novel Nano Low-Dimensional FETs at the Scaling Limit
Authors: Guo Pengwen , Zhou Yuxue , Yang Haolin , Pan Jiong , Yin Jiaju , Zhao Bingchen , Liu Shangjian , Peng Jiali , Jia Xinyuan , Jia Mengmeng , Yang Yi , Ren Tianling
Published: August 2024

The scaling of bulk Si-based transistors has reached its limits, while novel architectures such as FinFETs and GAAFETs face challenges in sub-10 nm nodes due to complex fabrication processes and severe drain-induced barrier lowering (DIBL) effects. An effective strategy to avoid short-channel effects (SCEs) is the integration of low-dimensional materials into novel device architectures, leveraging the coupling between multiple gates to achieve efficient electrostatic control of the channel. We employed TCAD simulations to model multi-gate FETs based on various dimensional systems and comprehensively investigated electric fields, potentials, current densities, and electron densities within the devices. Through continuous parameter scaling and extracting the sub-threshold swing (SS) and DIBL from the electrical outputs, we offered optimal MoS2 layer numbers and single-walled carbon nanotube (SWCNT) diameters, as well as designed structures for multi-gate FETs based on monolayer MoS2, identifying dual-gate transistors as suitable for high-speed switching applications. Comparing the switching performance of two device types at the same node revealed CNT’s advantages as a channel material in mitigating SCEs at sub-3 nm nodes. We validated the performance enhancement of 2D materials in the novel device architecture and reduced the complexity of the related experimental processes. Consequently, our research provides crucial insights for designing next-generation high-performance transistors based on low-dimensional materials at the scaling limit.
Cotton Pectate Lyase GhPEL48_Dt Promotes Fiber Initiation Mediated by Histone Acetylation
Authors: Zhong Anlin , Zou Xianyan , Wei Zhenzhen , Gan Lei , Peng Jun , Li Yonghui , Wang Zhi , Liu Yuanyuan
Published: August 2024

GhPEL48_Dt, a Pectate lyase (PEL, EC4.2.2.2), is a crucial enzyme involved in cell-wall modification and pectin degradation. Studies have shown that the GhPEL48_Dt also plays a significant role in cotton-fiber development; however, the specific function and regulatory mechanism of GhPEL48_Dt in cotton-fiber development are still not fully understood. Here, we found that the histone deacetylase inhibitor-Trichostatin A significantly reduces the transcript levels of GhPEL48_Dt and its enzyme activity. Further, silencing of GhPEL48_Dt significantly inhibits the initiation and elongation of cotton fibers by promoting pectin degradation, and the heterologous expression of GhPEL48_Dt promotes the development of trichomes and root hairs in Arabidopsis, which suggests that GhPEL48_Dt plays a positive and conserved role in single cell i.e., fiber, root hair, and leaf trichome development. Collectively, this paper provides a comprehensive analysis of the fundamental characteristics and functions of GhPEL48_Dt in fiber development, including the regulatory role of histone acetylation on GhPEL48_Dt, which contributes to the understanding of pectin degradation pathways and establishes a theoretical foundation for elucidating its regulatory mechanism.
Reconstruction of the Municipal Wastewater-Treatment Plant According to the Principles of Aerobic Granular Sludge Cultivation
Authors: Hutňan Miroslav , Jankovičová Barbora , Jajcaiová Lenka , Sammarah Mikhael , Kratochvíl Karol , Šoltýsová Nikola
Published: August 2024

The work presents the concept of aerobic granular sludge (AGS) and its potential for wastewater treatment. The work also evaluates the condition of the SBR (Sequencing Batch Reactor) type of municipal wastewater-treatment plant (WWTP) after its reconstruction into a system with AGS. The WWTP parameters achieved before and after reconstruction were compared. Operational measurements of the process during the individual phases of the treatment process showed a balanced concentration profile of the monitored parameters in the span of the semicontinuous cycle. Laboratory tests showed that the sludge from the WWTP has nitrification and denitrification rates comparable to the rates achieved for flocculent sludge, and it is also comparable to the nitrification and denitrification rates of AGS with size of granules below 400 µm. Despite the fact that complete sludge granulation was not achieved, the results measured at the WWTP confirmed the advantages of the AGS concept. Neither anaerobic nor anoxic conditions were identified in the SBR during the individual phases of operation, yet high removal efficiencies of ammonia and nitrate nitrogen and orthophosphate phosphorus were achieved. The concentration of ammonia and nitrate nitrogen at the WWTP effluent was below 5 mg/L, and the concentration of phosphorus was below 0.5 mg/L.
Pour Point Prediction Method for Mixed Crude Oil Based on Ensemble Machine Learning Models
Authors: Duan Jimiao , Kou Zhi , Liu Huishu , Lin Keyu , He Sichen , Chen Shiming
Published: August 2024

Pipelines are the most common way to transport crude oil. The crude oil developed from different fields is mixed first and then transported. The pour point of mixed crude oil is very important for pipeline schemes and ensuring the safe, efficient, and flexible operation of the pipeline. An integrated machine learning model based on XGBoost is identified as optimal to predict the pour point of mixed crude oil by comprehensive comparison among six different types of machine learning models: multiple linear regression, random forest, support vector machine, LightGBM, backpropagation neural network, and XGBoost. A mixed crude oil pour point prediction model with strong engineering adaptability is proposed, focusing on enhancing the flexibility of machine learning model inputs (using density and viscosity instead of component crude oil pour points) and addressing challenges such as data volume and input missing in engineering scenarios. With the inputs of pour point Tg, density ρ, viscosity μ, and ratio Xi in component oils, the mean absolute error of the model prediction estimations after training with 8912 data is 1.12 °C, when the pour point Tg of the component crude oil is missing, the mean absolute error is 1.93 °C and the percentage of the predicted absolute error within 2 °C is 88.0%. This study can provide support for the intelligent control of flow properties of pipeline transport mixed oil.
Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks
Authors: Zhong Lihua , Ye Tong , Yang Yuyao , Pan Feng , Feng Lei , Qi Shuzhe , Huang Yuping
Published: August 2024

As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems.
PID Controller Design for an E. coli Fed-Batch Fermentation Process System Using Chaotic Electromagnetic Field Optimization
Authors: Roeva Olympia , Slavov Tsonyo , Kralev Jordan
Published: August 2024

This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes.
The Story of the Watchers as a Counter Narrative: Enochic Responses to the Authority of Mesopotamian Sages
Authors: Annus Amar
Published: August 2024

The extant texts of Enochic Judaism present accounts about the early history of humankind, which use the motifs familiar from the ancient Mesopotamian historiography. The different versions of the Jewish story of the Watchers originate as counter narratives about the antediluvian sages, or apkallus, which are known from the cuneiform literature. The myth of Adapa in Akkadian offers a version of the narrative in which the sage was promoted in heaven to a god-like status, which concept is corroborated with an entry in the cuneiform compendium Tintir and the Chronicle of the Esagila. This tradition also associated Adapa with the immortalized flood survivor. In the form of a counter narrative, the Enochic texts of Judaism attributed the heavenly assumption to Enoch and presented the Watchers as the demonic entities subordinated to him. The fall of the Watchers, the promotion of Enoch, and the primordial battle against evil forces are depicted in the Animal Apocalypse of 1 Enoch 85–90. A very similar scenario is found in the Chronicle of Esagila, which describes the early history of humankind from the Babylonian perspective. It can be demonstrated that the Animal Apocalypse uses this or a similar cuneiform historiographic source as the basis of its counter narrative.
Eight-Bar Elbow Joint Exoskeleton Mechanism
Authors: Figliolini Giorgio , Lanni Chiara , Tomassi Luciano , Ortiz Jesús
Published: August 2024

This paper deals with the design and kinematic analysis of a novel mechanism for the elbow joint of an upper-limb exoskeleton, with the aim of helping operators, in terms of effort and physical resistance, in carrying out heavy operations. In particular, the proposed eight-bar elbow joint exoskeleton mechanism consists of a motorized Watt I six-bar linkage and a suitable RP dyad, which connects mechanically the external parts of the human arm with the corresponding forearm by hook and loop velcro, thus helping their closing relative motion for lifting objects during repetitive and heavy operations. This relative motion is not a pure rotation, and thus the upper part of the exoskeleton is fastened to the arm, while the lower part is not rigidly connected to the forearm but through a prismatic pair that allows both rotation and sliding along the forearm axis. Instead, the human arm is sketched by means of a crossed four-bar linkage, which coupler link is considered as attached to the glyph of the prismatic pair, which is fastened to the forearm. Therefore, the kinematic analysis of the whole ten-bar mechanism, which is obtained by joining the Watt I six-bar linkage and the RP dyad to the crossed four-bar linkage, is formulated to investigate the main kinematic performance and for design purposes. The proposed algorithm has given several numerical and graphical results. Finally, a double-parallelogram linkage, as in the particular case of the Watt I six-bar linkage, was considered in combination with the RP dyad and the crossed four-bar linkage by giving a first mechanical design and a 3D-printed prototype.
Closed-Form Continuous-Time Neural Networks for Sliding Mode Control with Neural Gravity Compensation
Authors: Urrea Claudio , Garcia-Garcia Yainet , Kern John
Published: August 2024

This study proposes the design of a robust controller based on a Sliding Mode Control (SMC) structure. The proposed controller, called Sliding Mode Control based on Closed-Form Continuous-Time Neural Networks with Gravity Compensation (SMC-CfC-G), includes the development of an inverse model of the UR5 industrial robot, which is widely used in various fields. It also includes the development of a gravity vector using neural networks, which outperforms the gravity vector obtained through traditional robot modeling. To develop a gravity compensator, a feedforward Multi-Layer Perceptron (MLP) neural network was implemented. The use of Closed-Form Continuous-Time (CfC) neural networks for the development of a robot’s inverse model was introduced, allowing efficient modeling of the robot. The behavior of the proposed controller was verified under load and torque disturbances at the end effector, demonstrating its robustness against disturbances and variations in operating conditions. The adaptability and ability of the proposed controller to maintain superior performance in dynamic industrial environments are highlighted, outperforming the classic SMC, Proportional-Integral-Derivative (PID), and Neural controllers. Consequently, a high-precision controller with a maximum error rate of approximately 1.57 mm was obtained, making it useful for applications requiring high accuracy.
Enhancing Wireless Network Efficiency with the Techniques of Dynamic Distributed Load Balancing: A Distance-Based Approach
Authors: Alkalsh Mustafa Mohammed Hasan , Kliks Adrian
Published: August 2024

The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests.
Research on High-Precision Measurement Method for Small-Size Gears with Small-Modulus
Authors: Niu Peng , Cheng Qiang , Zhang Xinlei , Liu Zhifeng , Zhao Yongsheng , Yang Congbin
Published: August 2024

Small-modulus gears, which are essential for motion transmission in precision instruments, present a measurement challenge due to their minuscule gear gaps. A high-precision measurement method under the influence of positioning errors is proposed, enabling precise evaluation of the machining quality of small-modulus gears. Firstly, a compound measurement platform for small-modulus gears is developed. Using a 3D model of the measurement system, the mathematical relationships governing motion transmission between various components are analyzed. Secondly, the formation mechanism of gear positioning error is revealed and its important influence on measurement accuracy is discussed. An optimization method for spatial coordinate transformation matrices under positioning errors of gears is proposed. Thirdly, the study focuses on small-sized gears with a modulus of 0.1 mm and a six-level accuracy. Based on the aforementioned measurement system, the tooth profile measurement points are collected in the actual workpiece coordinate system. Then, gear error parameters are extracted based on the established models for tooth profile deviation and pitch deviation. Finally, the accuracy and effectiveness of the proposed measurement method are verified by comparing the measurement results of the P26 gear measuring center.
Electrochemical Determination of Dopamine with a Carbon Paste–Lanthanum (III) Oxide Micro-Composite Electrode: Effect of Cetyl Trimethyl Ammonium Bromide Surfactanton Selectivity
Authors: Nagles Edgar , Riesco Fernando , Roldan-Tello Luz
Published: August 2024

This paper presents a new application of a lanthanum oxide (III)-modified carbon paste electrode (LaOX/CPE) for dopamine (DP) detection in the presence of ascorbic acid (AA). The presence of cetyl trimethyl ammonium bromide (CTAB) facilitated the LaOX/CPE electrode’s ability to detect DP amidst AA interference, resulting in a substantial 70.0% increase in the anodic peak current for DP when compared to the unmodified carbon paste electrode (CPE). CTAB enabled clear separation of the anodic peaks for DP and AA by nearly 0.2 V, despite their initially overlapping potential values, through the ion–dipole interaction of AA and CTAB. The electrode was characterized using cyclic voltammetry (CV) and energy-dispersive spectroscopy (EDS). The method demonstrated a detection limit of 0.06 µmol/L with a relative standard deviation (RSD) of 6.0% (n = 15). Accuracy was assessed through the relative error and recovery percent, using urine samples spiked with known quantities of DP.
Independent Vector Analysis for Feature Extraction in Motor Imagery Classification
Authors: Moraes Caroline Pires Alavez , dos Santos Lucas Heck , Fantinato Denis Gustavo , Neves Aline , Adali Tülay
Published: August 2024

Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain–computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.
An Introduction to Ventra: A Programmable Abdominal Phantom for Training, Educational, Research, and Development Purposes
Authors: Tayebi Salar , Wise Robert , Zarghami Ashkan , Dabrowski Wojciech , Malbrain Manu L. N. G. , Stiens Johan
Published: August 2024

Background: Intra-abdominal pressure (IAP) is a critical parameter in the care of critically ill patients, as elevated IAP can lead to reduced cardiac output and organ perfusion, potentially resulting in multiple organ dysfunction and failure. The current gold standard for measuring IAP is an indirect technique via the bladder. According to the Abdominal Compartment Society’s Guidelines, new measurement methods/devices for IAP must be validated against the gold standard. Objectives: This study introduces Ventra, an abdominal phantom designed to simulate different IAP levels, abdominal compliance, respiration-related IAP variations, and bladder dynamics. Ventra aims to facilitate the development and validation of new IAP measurement devices while reducing reliance on animal and cadaveric studies. Additionally, it offers potential applications in training and education for biomedical engineering students. This study provides a thorough explanation on the phantom’s design and fabrication, which provides a low-cost solution for advancing IAP measurement research and education. The design concept, technical aspects, and a series of validation experiments determining whether Ventra is a suitable tool for future research are presented in this study. Methods: Ventra’s performance was evaluated through a series of validation tests using a pressure gauge and two intra-gastric (Spiegelberg and CiMON) and two intra-bladder (Accuryn and TraumaGuard) pressure measurement devices. The mean and standard deviation of IAP recordings by each device were investigated. Bland–Altman analysis was used to evaluate bias, precision, limits of agreement, and percentage error for each system. Concordance analysis was performed to assess the ability of Ventra in tracking IAP changes. Results: The phantom demonstrated excellent agreement with reference pressure measurements, showing an average bias of 0.11 ± 0.49 mmHg. A concordance coefficient of 100% was observed for the phantom as well. Ventra accurately simulated different abdominal compliances, with higher IAP values resulting in lower compliance. Abdominal volume changes showed a bias of 0.08 ± 0.07 L/min, and bladder fill volume measurements showed an average difference of 0.90 ± 4.33 mL for volumes ranging from 50 to 500 mL. Conclusion: The validation results were in agreement with the research guidelines of the world abdominal society. Ventra is a reliable tool that will facilitate the development and validation of new IAP measurement devices. It is an effective educational tool for biomedical engineering students as well.