bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
Authors: Yang Yuqian , Wang Bo , Zhao Cong
Published: March 2023

For rapidly developing smart manufacturing, Industrial ICT Systems (IICTSs) have become critical to safe and reliable production, and effective monitoring of complex IICTSs in practice is necessary but challenging. Since such monitoring data are organized generally as semi-structural logs, log parsing, the fundamental premise of advanced log analysis, has to be comprehensively addressed. Because of unrealistic assumptions, high maintenance costs, and the incapability of distinguishing homologous logs, existing log parsing methods cannot simultaneously fulfill the requirements of complex IICTSs simultaneously. Focusing on these issues, we present LogParser, a deep learning-based framework for both online and offline parsing of IICTS logs. For performance evaluation, we conduct extensive experiments based on monitoring log sets from 18 different real-world systems. The results demonstrate that LogParser achieves at least a 14.5% higher parsing accuracy than the state-of-the-art methods.
Rapid Identification of Chinese Hamster Ovary Cell Apoptosis and Its Potential Role in Process Robustness Assessment
Authors: Xiao Shang , Li Qiang , Jiang Jinlong , Huo Chengxiao , Chen Hao , Guo Meijin
Published: March 2023

Currently, the assessment of process robustness is often time-consuming, labor-intensive, and material-intensive using process characterization studies. Therefore, a simple and time-saving method is highly needed for the biopharmaceutical industry. Apoptosis is responsible for 80% of Chinese hamster ovary (CHO) cell deaths and affects the robustness of the cell culture process. This study’s results showed that a more robust process can support cells to tolerate apoptosis for a longer time, suggesting that the robustness of the process could be judged by the ability of cells to resist apoptosis. Therefore, it is necessary to establish a rapid method to detect the apoptosis of CHO cells. In trying to establish a new method for detecting apoptosis in large-scale cell cultures, glucose withdrawal was studied, and the results showed that CHO cells began to apoptose after glucose was consumed. Then, the concentration of extracellular potassium increased, and a prolongation of apoptosis time was observed. Further study results showed that the process with poor robustness was associated with a higher proportion of apoptosis and extracellular potassium concentration, so potassium could be used as a biochemical index of apoptosis. The strategy we present may be used to expedite the assessment of process robustness to obtain a robust cell culture process for other biologics.
Synthesizing Complex-Valued Multicoil MRI Data from Magnitude-Only Images
Authors: Deveshwar Nikhil , Rajagopal Abhejit , Sahin Sule , Shimron Efrat , Larson Peder E. Z.
Published: March 2023

Despite the proliferation of deep learning techniques for accelerated MRI acquisition and enhanced image reconstruction, the construction of large and diverse MRI datasets continues to pose a barrier to effective clinical translation of these technologies. One major challenge is in collecting the MRI raw data (required for image reconstruction) from clinical scanning, as only magnitude images are typically saved and used for clinical assessment and diagnosis. The image phase and multi-channel RF coil information are not retained when magnitude-only images are saved in clinical imaging archives. Additionally, preprocessing used for data in clinical imaging can lead to biased results. While several groups have begun concerted efforts to collect large amounts of MRI raw data, current databases are limited in the diversity of anatomy, pathology, annotations, and acquisition types they contain. To address this, we present a method for synthesizing realistic MR data from magnitude-only data, allowing for the use of diverse data from clinical imaging archives in advanced MRI reconstruction development. Our method uses a conditional GAN-based framework to generate synthetic phase images from input magnitude images. We then applied ESPIRiT to derive RF coil sensitivity maps from fully sampled real data to generate multi-coil data. The synthetic data generation method was evaluated by comparing image reconstruction results from training Variational Networks either with real data or synthetic data. We demonstrate that the Variational Network trained on synthetic MRI data from our method, consisting of GAN-derived synthetic phase and multi-coil information, outperformed Variational Networks trained on data with synthetic phase generated using current state-of-the-art methods. Additionally, we demonstrate that the Variational Networks trained with synthetic k-space data from our method perform comparably to image reconstruction networks trained on undersampled real k-space data.
Stimbiotics Supplementation Promotes Growth Performance by Improving Plasma Immunoglobulin and IGF-1 Levels and Regulating Gut Microbiota Composition in Weaned Piglets
Authors: Chen Wenning , Yin Chenggang , Li Jing , Sun Wenjuan , Li Yanpin , Wang Chengwei , Pi Yu , Cordero Gustavo , Li Xilong , Jiang Xianren
Published: March 2023

This study was conducted to investigate the effects of dietary supplementation with stimbiotics (STB) on growth performance, diarrhoea incidence, plasma antioxidant capacity, immunoglobulin concentration and hormone levels, and faecal microorganisms in weaned piglets. Compared with the control (CT) group, the addition of STB improved the body weight (BW) of piglets on days 28 and 42 (p < 0.05) and increased daily weight gain and daily feed intake from days 14–28 and throughout the trial period (p < 0.05). Correspondingly, the plasma insulin-like growth factor 1 (IGF-1) level on day 42 was significantly improved by STB (p < 0.05). VistaPros (VP) group levels of immunoglobulin (Ig) A and G were significantly higher on days 14 and 42 (p < 0.05) than the CT group levels. In addition, the activity of plasma catalase tended to be increased on day 14 (p = 0.053) in the VP group, as for superoxide dismutase, glutathione peroxidase, and malondialdehyde, STB did not significantly affect their levels (p > 0.05). Moreover, dietary STB increased the relative abundance of beneficial bacteria, including norank_f_Muribaculaceae, Rikenellaceae_RC9_gut_group, Parabacteroides, and unclassified_f__Oscillospiraceae. In summary, STB improved the immunity and IGF-1 levels in the plasma of weaned piglets and consequently promoted the growth performance of weaned piglets.
Weibull Regression and Machine Learning Survival Models: Methodology, Comparison, and Application to Biomedical Data Related to Cardiac Surgery
Authors: Cavalcante Thalytta , Ospina Raydonal , Leiva Víctor , Cabezas Xavier , Martin-Barreiro Carlos
Published: March 2023

In this article, we propose a comparative study between two models that can be used by researchers for the analysis of survival data: (i) the Weibull regression model and (ii) the random survival forest (RSF) model. The models are compared considering the error rate, the performance of the model through the Harrell C-index, and the identification of the relevant variables for survival prediction. A statistical analysis of a data set from the Heart Institute of the University of São Paulo, Brazil, has been carried out. In the study, the length of stay of patients undergoing cardiac surgery, within the operating room, was used as the response variable. The obtained results show that the RSF model has less error rate for the training and testing data sets, at 23.55% and 20.31%, respectively, than the Weibull model, which has an error rate of 23.82%. Regarding the Harrell C-index, we obtain the values 0.76, 0.79, and 0.76, for the RSF and Weibull models, respectively. After the selection procedure, the Weibull model contains variables associated with the type of protocol and type of patient being statistically significant at 5%. The RSF model chooses age, type of patient, and type of protocol as relevant variables for prediction. We employ the randomForestSRC package of the R software to perform our data analysis and computational experiments. The proposal that we present has many applications in biology and medicine, which are discussed in the conclusions of this work.
Inflammation, Microcalcification, and Increased Expression of Osteopontin Are Histological Hallmarks of Plaque Vulnerability in Patients with Advanced Carotid Artery Stenosis
Authors: Balmos Ioan Alexandru , Horváth Emőke , Brinzaniuc Klara , Muresan Adrian Vasile , Olah Peter , Molnár Gyopár Beáta , Nagy Előd Ernő
Published: March 2023

Background: severe carotid artery stenosis is a major cause of ischemic stroke and consequent neurological deficits. The most important steps of atherosclerotic plaque development, leading to carotid stenosis, are well-known; however, their exact timeline and intricate causal relationships need to be more characterized. Methods: in a cohort of 119 patients, who underwent carotid endarterectomy, we studied the histological correlations between arterial calcification patterns and localization, the presence of the inflammatory infiltrate and osteopontin expression, with ulceration, thrombosis, and intra-plaque hemorrhage, as direct signs of vulnerability. Results: in patients with an inflammatory infiltrate, aphasia was more prevalent, and microcalcification, superficial calcification, and high-grade osteopontin expression were characteristic. Higher osteopontin expression was also correlated with the presence of a lipid core. Inflammation and microcalcification were significantly associated with plaque ulceration in logistic regression models; furthermore, ulceration and the inflammatory infiltrate were significant determinants of atherothrombosis. Conclusion: our results bring histological evidence for the critically important role of microcalcification and inflammatory cell invasion in the formation and destabilization of advanced carotid plaques. In addition, as a calcification organizer, high-grade osteopontin expression is associated with ulceration, the presence of a large lipid core, and may also have an intrinsic role in plaque progression.
Photodynamic Activation of Cholecystokinin 1 Receptor Is Conserved in Mammalian and Avian Pancreatic Acini
Authors: Wang Jie , Cui Zong Jie
Published: March 2023

Cholecystokinin 1 receptor (CCK1R) is the only G protein coupled receptor that is activated in type II photodynamic action, but whether this is a property common to both mammalian and avian species is not known. In this work, pancreatic acini were isolated from the rat, mouse, and Peking duck, and photodynamic CCK1R activation was examined. Isolated pancreatic acini were exposed to photosensitizer sulphonated aluminum phthalocyanine (SALPC) and photodynamic action elicited by a brief light-emitting diode (LED 675 nm) pulse (1.5 min); photodynamic CCK1R activation was assessed by Fura-2 fluorescent calcium imaging. Photodynamic action was found to induce persistent calcium oscillations in rat, mouse, and Peking duck pancreatic acini, with the sensitivity order of mouse > rat > Peking duck. Photodynamically-activated CCK1R could be inhibited reversibly by CCK1R antagonist devazepide (1 μM); photodynamic CCK1R activation was blocked by pre-incubation with 1O2 quencher Trolox C (300 µM). The sensitivity of photodynamic CCK1R activation was correlated with the increasing size of the disordered region in intracellular loop 3. These data suggest that photodynamic CCK1R activation is conserved in both mammalian and avian species, as evidenced by the presence of the photodynamic activation motif “YFM” in transmembrane domain 3.
Free Fatty Acids from Cow Urine DMSO Fraction Induce Cell Death in Breast Cancer Cells without Affecting Normal GMSCs
Authors: Raj Ajay Kumar , Upadhyay Vidhi , Lokhande Kiran Bharat , Swamy K. Venkateswara , Bhonde Ramesh Ramchandra , Sarode Sachin C. , Sharma Nilesh Kumar
Published: March 2023

Objective: The objective of this study was to explore the biological relevance of free fatty acids derived from cow urine DMSO fraction (CUDF) by employing in vitro and in silico approaches. Background: Metabolic heterogeneity at the intra- and intercellular levels contributes to the metabolic plasticity of cancer cells during drug-induced response. Free fatty acid (FFA) availability at intra- and intercellular levels is related to tumor heterogeneity at interpatient and xeno-heterogeneity levels. Methods: We collected fresh urine from healthy cows and subjected it to fractionation in DMSO using drying, vortexing, and centrifugation. Finally, the sterile filtrate of cow urine DMSO fraction (CUDF) was evaluated for antiproliferative and proapoptotic effects in MCF-7 and ZR-75-1 breast cancer cells using routine cell-based assays. Intracellular metabolites were studied with the help of a novel in-house vertical tube gel electrophoresis (VTGE) method to reveal the nature of CUDF components in MCF-7 cells. Identified intracellular FFAs were studied for their molecular interactions with targeted receptor histone deacetylase (HDAC) using molecular docking and molecular dynamics (MD) simulations. Results: CUDF showed a significant reduction in cell viability and cell death in MCF-7 and ZR-75-1 breast cancer cells. Interestingly, FFAs tetracosanedioic acid, 13Z-docosenoic acid (erucic acid), nervonic acid, 3-hydroxy-tetradecanoic acid, and 3-hydroxcapric acid were found inside the treated MCF-7 cancer cells. These FFAs, including tetracosanedioic acid, indicated a specific affinity to HDAC at their inhibitory sites, similar to trichostatin A, a known inhibitor. Conclusions: This study reports on FFAs derived from CUDF as potential antiproliferative and pro-cell death agents against breast cancer cells. MD simulations hinted at tetracosanedioic acid and other FFAs as inhibitors of HDAC that could explain the observed effects of FFAs in cancer cells.
Naturally Occurring N-Terminal Fragments of Bovine Milk Osteopontin Are Transported across Models of the Intestinal Barrier
Authors: Christensen Brian , Nielsen Nanna R. , Sørensen Marie R. , Jacobsen Lotte N. , Ostenfeld Marie S. , Sørensen Esben S.
Published: March 2023

Osteopontin (OPN) is a bioactive integrin-binding protein found in high concentrations in milk, where it is present both as a full-length protein and as several N-terminally derived fragments. OPN resists gastric digestion, and via interaction with receptors in the gut or by crossing the intestinal barrier into circulation, ingested milk OPN may influence physiological processes. The aim of this study was to investigate OPN interaction with intestinal cells and its transport across models of the intestinal barrier. Immunodetection of OPN incubated with Caco-2 cells at 4 °C and 37 °C showed that OPN binds to the intestinal cells, but it is not internalised. Transepithelial transport was studied using mono- and co-cultures of Caco-2 cells and mucus-producing HT29-MTX cells in transwell membranes. OPN was shown to cross the barrier models in a time-, temperature-, and energy-dependent process inhibited by wortmannin, indicating that the transport takes place via the transcytosis pathway. Analyses of the naturally occurring milk mixture of full-length and N-terminal fragments showed that the N-terminal fragments of OPN bound intestinal cells most effectively and that the fragments were transported across the intestinal membrane models. This suggests that proteolytic processing of OPN increases its biological activity after ingestion.
Simultaneous Measurement of Changes in Mitochondrial and Endoplasmic Reticulum Free Calcium in Pancreatic Beta Cells
Authors: Jeyarajan Sivakumar , Zhang Irina X , Arvan Peter , Lentz Stephen I. , Satin Leslie S.
Published: March 2023

The free calcium (Ca2+) levels in pancreatic beta cell organelles have been the subject of many recent investigations. Under pathophysiological conditions, disturbances in these pools have been linked to altered intracellular communication and cellular dysfunction. To facilitate studies of subcellular Ca2+ signaling in beta cells and, particularly, signaling between the endoplasmic reticulum (ER) and mitochondria, we designed a novel dual Ca2+ sensor which we termed DS-1. DS-1 encodes two stoichiometrically fluorescent proteins within a single plasmid, G-CEPIA-er, targeted to the ER and R-CEPIA3-mt, targeted to mitochondria. Our goal was to simultaneously measure the ER and mitochondrial Ca2+ in cells in real time. The Kds of G-CEPIA-er and R-CEPIA3-mt for Ca2+ are 672 and 3.7 μM, respectively. Confocal imaging of insulin-secreting INS-1 832/13 expressing DS-1 confirmed that the green and red fluorophores correctly colocalized with organelle-specific fluorescent markers as predicted. Further, we tested whether DS-1 exhibited the functional properties expected by challenging an INS-1 cell to glucose concentrations or drugs having well-documented effects on the ER and mitochondrial Ca2+ handling. The data obtained were consistent with those seen using other single organelle targeted probes. These results taken together suggest that DS-1 is a promising new approach for investigating Ca2+ signaling within multiple organelles of the cell.
Applying Evolutionary Computation to Optimize the Design of Urban Blocks
Authors: Yang Ling , Chang Hsiao-Tung , Ma He , Wang Tao , Xu Jian , Chen Jingjing
Published: March 2023

This empirical study in this paper focuses on the application of evolutionary computation in parametric urban block design to solve the problem of how to effectively find the optimal design solution set among a large number of schemes obtained through parametric urban block design. Through the application of evolutionary computation, a set of parameterized intelligent generation methods of urban blocks under the guidance of multi-conflict objective optimization can be established. The empirical study presented is based on a typical Russian block redevelopment design along the Chinese Eastern Railway. The design aims to transform and redevelop the original block on the basis of protecting its historical buildings and street pattern, taking into account environmental and economic considerations. The final results show that under the premise of reasonable overall evolution, a large number of block design schemes with complex design objectives can be obtained. Furthermore, according to the index data analysis of Pareto’s overall optimal, mean value, median value, extreme value, etc., it provides designers with methods to select a series of optimal schemes quickly and efficiently based on different factors such as comprehensive balance, average and middle ranking, and individual best.
The Floor Plan Design Method of Exhibition Halls in CGAN-Assisted Museum Architecture
Authors: Min Xiao , Zheng Liang , Chen Yile
Published: March 2023

The floor plan designs of traditional museum exhibition halls are generally developed according to the position and streamlined accessibility of the exhibits. However, there are often many floors in the same building, and multi-story exhibition halls are similar, so architects often spend a large amount of time and energy designing floors individually. Thus, this paper proposes a conditional generative adversarial network (CGAN)-based method for designing the floor plans of museum exhibition halls, which can help architects to work more efficiently. In this study, the basic concepts and structures of CGAN are first introduced; then, the design and training process of the CGAN model used are described in detail, and the datasets and evaluation metrics adopted are briefly described. In the Results and Discussion sections, this paper presents an example of the generated floor plan design of a museum exhibition hall and evaluates and analyzes the floor plan design of a museum exhibition hall generated using the proposed method. Finally, this paper summarizes the advantages of the proposed method, but also notes its shortcomings. If the number of data sets is not sufficient, the scope of the application will be relatively small. For example, museums converted from certain historical buildings are not applicable. The research results show the following: (1) the method proposed in this paper takes advantage of the CGAN model and can generate a museum exhibition hall floor plan design with certain regularity according to the given conditions, rather than pure random generation. (2) This method can automatically generate a variety of plan designs for museum exhibition halls in different schemes, providing designers with more choices and flexibility. (3) This method can carry out design optimization through human–computer interaction, and iterative improvement can be carried out according to user needs and feedback, which improves the practicability of the design.
Detection of Levofloxacin Using a Simple and Green Electrochemically Polymerized Glycine Layered Carbon Paste Electrode
Authors: Bhimaraya Kanthappa , Manjunatha Jamballi G. , Moulya Karnayana P. , Tighezza Ammar M. , Albaqami Munirah D. , Sillanpää Mika
Published: March 2023

The easy and rapid analytical tool, electrochemically polymerized (EP) glycine (GN) layered carbon paste electrode (LCPE), was used for the analysis of levofloxacin (LN) using cyclic voltammogram (CV) and differential pulse voltammetry (DPV). The surface features and activities of the electrochemical sensors EPGNLCPE and bare carbon paste electrode (BCPE) were analyzed using electrochemical impedance spectroscopy (EIS), field emission scanning electron microscopy (FE-SEM), CV and DPV methods. The modified sensor (EPGNLCPE)offers a vibrant and sensitive electrochemical LN oxidation peak by controlling overpotential and the electrode material fouling effect unlike BCPE. Under improved experimental conditions, the DPV method was used to analyze LN on EPGNLCPE by varying its concentration in 0.2 M phosphate buffer solution from 30 to 90 µM, resulting in a good linear relationship(between peak current and concentration), lower limit of detection (LOD: 8.436 × 10−7 M) and lower limit of quantification (LOQ: 2.812 × 10−6 M). Finally, real-time application of the sensor was tested by analyzing LN in medicinal samples, and good LN recovery was observed.
Adaptive Volumetric-Modulated Arc Radiation Therapy for Head and Neck Cancer: Evaluation of Benefit on Target Coverage and Sparing of Organs at Risk
Authors: Franzese Ciro , Tomatis Stefano , Bianchi Sofia Paola , Pelizzoli Marco , Teriaca Maria Ausilia , Badalamenti Marco , Comito Tiziana , Clerici Elena , Franceschini Davide , Navarria Pierina , Di Cristina Luciana , Dei Damiano , Galdieri Carmela , Reggiori Giacomo , Mancosu Pietro , Scorsetti Marta
Published: March 2023

Background: Radiotherapy is essential in the management of head–neck cancer. During the course of radiotherapy, patients may develop significant anatomical changes. Re-planning with adaptive radiotherapy may ensure adequate dose coverage and sparing of organs at risk. We investigated the consequences of adaptive radiotherapy on head–neck cancer patients treated with volumetric-modulated arc radiation therapy compared to simulated non-adaptive plans: Materials and methods: We included in this retrospective dosimetric analysis 56 patients treated with adaptive radiotherapy. The primary aim of the study was to analyze the dosimetric differences with and without an adaptive approach for targets and organs at risk, particularly the spinal cord, parotid glands, oral cavity and larynx. The original plan (OPLAN) was compared to the adaptive plan (APLAN) and to a simulated non-adaptive dosimetric plan (DPLAN). Results: The non-adaptive DPLAN, when compared to OPLAN, showed an increased dose to all organs at risk. Spinal cord D2 increased from 27.91 (21.06–31.76) Gy to 31.39 (27.66–38.79) Gy (p = 0.00). V15, V30 and V45 of the DPLAN vs. the OPLAN increased by 20.6% (p = 0.00), 14.78% (p = 0.00) and 15.55% (p = 0.00) for right parotid; and 16.25% (p = 0.00), 18.7% (p = 0.00) and 20.19% (p = 0.00) for left parotid. A difference of 36.95% was observed in the oral cavity V40 (p = 0.00). Dose coverage was significantly reduced for both CTV (97.90% vs. 99.96%; p = 0.00) and PTV (94.70% vs. 98.72%; p = 0.00). The APLAN compared to the OPLAN had similar values for all organs at risk. Conclusions: The adaptive strategy with re-planning is able to avoid an increase in dose to organs at risk and better target coverage in head–neck cancer patients, with potential benefits in terms of side effects and disease control.
Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis
Authors: Özbilgin Ferdi , Kurnaz Çetin , Aydın Ertan
Published: March 2023

Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model’s performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.
End-to-End Deep Learning Method for Detection of Invasive Parkinson’s Disease
Authors: Mahmood Awais , Mehroz Khan Muhammad , Imran Muhammad , Alhajlah Omar , Dhahri Habib , Karamat Tehmina
Published: March 2023

Parkinson’s disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson’s disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson’s disease patients is the unavailability of reliable procedures for diagnosing Parkinson’s disease. In the literature, we observed different methods for diagnosing Parkinson’s disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson’s disease is a difficult task because the important features that can help in detecting Parkinson’s disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson’s disease and develop a reliable model which can diagnose Parkinson’s disease at its early stages. Early diagnostic systems for the detection of Parkinson’s disease are needed to diagnose Parkinson’s disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson’s disease rating scale, known as the Total Unified Parkinson’s Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson’s disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson’s disease in its early stages.
Mechanistic Modelling of Biomass Growth, Glucose Consumption and Ethanol Production by Kluyveromyces marxianus in Batch Fermentation
Authors: Salazar Yolocuauhtli , Valle Paul A. , Rodríguez Emmanuel , Soto-Cruz Nicolás O. , Páez-Lerma Jesús B. , Reyes-Sánchez Francisco J.
Published: March 2023

This paper presents results concerning mechanistic modeling to describe the dynamics and interactions between biomass growth, glucose consumption and ethanol production in batch culture fermentation by Kluyveromyces marxianus (K. marxianus). The mathematical model was formulated based on the biological assumptions underlying each variable and is given by a set of three coupled nonlinear first-order Ordinary Differential Equations. The model has ten parameters, and their values were fitted from the experimental data of 17 K. marxianus strains by means of a computational algorithm design in Matlab. The latter allowed us to determine that seven of these parameters share the same value among all the strains, while three parameters concerning biomass maximum growth rate, and ethanol production due to biomass and glucose had specific values for each strain. These values are presented with their corresponding standard error and 95% confidence interval. The goodness of fit of our system was evaluated both qualitatively by in silico experimentation and quantitative by means of the coefficient of determination and the Akaike Information Criterion. Results regarding the fitting capabilities were compared with the classic model given by the logistic, Pirt, and Luedeking–Piret Equations. Further, nonlinear theories were applied to investigate local and global dynamics of the system, the Localization of Compact Invariant Sets Method was applied to determine the so-called localizing domain, i.e., lower and upper bounds for each variable; whilst Lyapunov’s stability theories allowed to establish sufficient conditions to ensure asymptotic stability in the nonnegative octant, i.e., R+,03. Finally, the predictive ability of our mechanistic model was explored through several numerical simulations with expected results according to microbiology literature on batch fermentation.
Thermoelectric Rectification and Amplification in Interacting Quantum-Dot Circuit-Quantum-Electrodynamics Systems
Authors: Lu Jincheng , Wang Rongqian , Wang Chen , Jiang Jian-Hua
Published: March 2023

Thermoelectric rectification and amplification were investigated in an interacting quantum-dot circuit-quantum-electrodynamics system. By applying the Keldysh nonequilibrium Green’s function approach, we studied the elastic (energy-conserving) and inelastic (energy-nonconserving) transport through a cavity-coupled quantum dot under the voltage biases in a wide spectrum of electron–electron and electron–photon interactions. While significant charge and Peltier rectification effects were found for strong light–matter interactions, the dependence on electron–electron interaction could be nonmonotonic and dramatic. Electron–electron interaction-enhanced transport was found under certain resonance conditions. These nontrivial interaction effects were found in both linear and nonlinear transport regimes, which manifested in charge and thermal currents, rectification effects, and the linear thermal transistor effect.
Productivity of Services in the Countries of Central and Eastern Europe: Analysis Using Malmquist Indices
Authors: Alcalá-Ordóñez Alejandro , Alcalá-Olid Francisco , Cárdenas-García Pablo
Published: March 2023

This research aims to study the growth of productivity in the service sector in the former Central and Eastern European Countries (CEECs) and their determinants. For this purpose, non-parametric frontier techniques were used to measure the variations in productivity and determine the explanatory factors of these changes in total factor productivity; the methodology of the Malmquist index with output orientation and its decomposition in technical change, pure technical efficiency and scale efficiency was used for the period 2000–2019. The results obtained indicate that the productivity of services in the most recently incorporated countries grew by 1.3 per 100 on average per year compared to 1.6 per 100 in manufacturing. The most important driver of such growth was found to be improvement in technical change (frontier shift) rather than improvement in efficiency.
Gamifying Physics Laboratory Work Increases Motivation and Enhances Acquisition of the Skills Required for Application of the Scientific Method
Authors: Okariz Ana , Huebra Marta , Sarasola Ane , Ibarretxe Julen , Bidegain Gorka , Zubimendi Jose Luis
Published: March 2023

Although increasing student motivation is widely accepted to enhance learning outcomes, this relationship has scarcely been studied quantitatively. Therefore, this study aimed to address this knowledge gap by exploring the effects of gamification on students’ motivation and consequently their learning performance, regarding the proper application of the scientific method. To motivate students and enhance their acquisition of new skills, we developed a gamification framework for the laboratory sessions of first-year physics in an engineering degree. Data regarding student motivation were collected through a Likert-scale-type satisfaction questionnaire. The inter-item correlations and Cronbach’s alpha confirmed the internal consistency of the questionnaire. In addition, the learning outcome was assessed based on the students’ laboratory reports. Students participating in gamified activities were more motivated than those participating in non-gamified activities and obtained better learning results overall. Our findings suggest that gamified laboratory sessions boost students´ extrinsic motivation, and consequently inspire their intrinsic motivation and increase their learning performance. Finally, we discuss our results, with a focus on specific skills and the short- and long-term effects of gamification.
High-Capacity Double-Sided Square-Mesh-Type Chipless RFID Tags
Authors: Noman Muhammad , Haider Usman A. , Ullah Hidayat , Ikram Muhammad , Rmili Hatem , Tahir Farooq A.
Published: March 2023

This paper presents a novel methodology for designing high-capacity frequency domain chipless RFID tags based on the backscattering principle. The tag consists of multiple square open-loop resonators loaded with a varying number of square metallic patches to form a mesh structure. Thus, in contrast to conventional designs, the overall physical size of each resonator remains fixed and does not change with respect to its operating resonant frequency. The RCS response of the proposed tag can be easily manipulated by varying the loading factor of each resonator. The tag size is further miniaturized by placing resonators on both sides of the substrate used. The tag configuration with resonators arranged in the form of a single row (4 × 1) printed on both sides of the substrate is finally chosen for maximum robustness and efficiency. The frequency shift coding (FSC) technique is used to encode more than one bit per resonator by using segments within sub-bands. The proposed tag encodes 16-bit data in a frequency band from 5.9 to 10.5 GHz and has a very high code density of 23.51 bits/cm2 and a spectral efficiency of 3.47 bits/GHz. The design methodology is novel and leads to a very efficient chipless RFID tag that can be used in a variety of high-data-capacity applications.
Improved Design and Application of Security Federation Algorithm
Authors: Yang Xiaolei , Liu Yongshan , Xie Jiabin , Hao Tianbao
Published: March 2023

(1) Background: To avoid affecting the accuracy and practicability of the security federation model due to the geographical and environmental factors involved in each local model and set the corresponding weights for each local model, the local model parameters and weights participated in the calculation at the same time. (2) Methods: Apply the improved model to the income evaluation of taxi drivers. Multiple linear regression was used to fit the local model parameters, and the loss function value was calculated. Then, according to the improved security federation algorithm, the model parameters and local model weights were encrypted by using the Paillier homomorphic encryption algorithm, and the encrypted model parameter information was uploaded to the aggregation server for aggregation average. (3) Results: The experimental results show that after 1000 iterations, the accuracy curve converges in the interval [0.93, 0.97]; the mean accuracy value was 94.27%, and the mean loss function value was 1.0886. It was the same understanding that the mean value of the loss function calculated by the traditional model was 1.9910. (4) From the model and data, the accuracy of the improved model has been improved. It can better reflect the income of taxi drivers.
Feasibility and Potential Assessment of Solar Resources: A Case Study in North Shewa Zone, Amhara, Ethiopia
Authors: Feleke Solomon , Anteneh Degarege , Pydi Balamurali , Satish Raavi , El-Shahat Adel , Abdelaziz Almoataz Y.
Published: March 2023

The feasibility and potential assessment (PA) of solar PV energy is one of the key factors in identifying the most promising areas for the installation of solar PV stations. It determines the useful energy generated in the given area. This paper assesses the solar energy distribution and PA in the North Shewa administration zone. Based on the data collected and analysis made, it is found that more than 80% of the North Shewa areas are suitable for the solar energy generation for off-grid and on-grid systems. Hence, the solar potential of the North Shewa zone completely fulfills the standards of sunshine, solar radiation, and temperature. That is, most of the areas have solar radiation of 5.2 kWh/m2, and daily sunshine is greater than 7 h. The maximum energy production is found in December in Shewa Robit, Mehal Meda, Eneware, Debre Berhan, Alem Ketema, and Sela Dengay with 175.35 kWh, 188.18 kWh, 180.78 kWh, 189.54 kWh, 175.78 kWh, and 189.63 kWh, respectively. This is a good opportunity for investors to invest in solar PV electricity generation. It will solve the issue of electricity supply to the community, which currently relies on wood and fossil fuels. It also highlights the positive opportunities for the future implementation of solar energy.
Environmental Efficiency of Enterprises: Trends, Strategy, Innovations
Authors: Asif Mohammad
Published: March 2023

This study examines the impact of green process innovation (GPI), green entrepreneurial orientation (GPO), and proactive sustainable strategy (PSS) on environmental performance (EP). Data were collected from 294 Indian agriculture technology firms. Structural equation modeling (SEM) was used to analyze the data using Lisrel 8.80. This study aimed to analyze how green entrepreneurial orientation, sustainability strategies, and green process innovation improve the environmental performance of agricultural technology firms. The results show that green process innovation, sustainability strategy, and entrepreneurial orientation play a significant role in enhancing agricultural technology firms’ environmental performance. Agricultural technology firms achieve high environmental performance primarily through strategy or sustainability. In every green process, innovation is crucial and essential. This research offers several practical implications that can be utilized by managers of agricultural technology firms to develop systems with cleaner production techniques in agribusiness. The novelty of the study lies in analyzing the direct relationships among green entrepreneurial orientation, strategy, and innovation in promoting the environmental performance of agricultural technology firms by drawing data from an agriculturally oriented developing country such as India.
Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review
Authors: Oubrahim Zakarya , Amirat Yassine , Benbouzid Mohamed , Ouassaid Mohammed
Published: March 2023

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes.
Progress and Prospects of Air Water Harvesting System for Remote Areas: A Comprehensive Review
Authors: Thavalengal Mohammed Sanjid , Jamil Muhammad Ahmad , Mehroz Muhammad , Xu Ben Bin , Yaqoob Haseeb , Sultan Muhammad , Imtiaz Nida , Shahzad Muhammad Wakil
Published: March 2023

Life is dependent on water. However, in terms of the potential effects, water scarcity is quickly emerging as one of the most critical problems in the world. To access more fresh water for drinking, sanitation, and irrigation, water can be harvested from different forms of water on earth. Atmospheric harvesting is the best alternative for producing fresh water for everyday life and reducing global water shortages. To date, many modern technologies have been introduced for this application, with several prototypes being demonstrated. Thus, this study explores the potential benefits of the current atmospheric water harvesting systems in terms of their modes, atmospheric conditions, and production rate and examines the key factors that affect the efficiency of atmospheric water harvesting, such as temperature and humidity. According to the studies, there has been a significant advancement in energy harvesting and conversion technology, along with atmospheric water harvesting, over the past few years, including new mechanisms and technical paths. However, there are still many obstacles; in particular, most of the technologies depend on outdoor conditions. In order to overcome this issue, new directions need to be investigated. Here, we discuss the principles, advantages, limitations, and potential applications of these technologies.
Electric Vehicles Charging Using Photovoltaic Energy Surplus: A Framework Based on Blockchain
Authors: Cavalcante Irvylle , Júnior Jamilson , Manzolli Jônatas Augusto , Almeida Luiz , Pungo Mauro , Guzman Cindy Paola , Morais Hugo
Published: March 2023

In the present day, it is crucial for individuals and companies to reduce their carbon footprints in a society more self-conscious about climate change and other environmental issues. In this sense, public and private institutions are investing in photovoltaic (PV) systems to produce clean energy for self-consumption. Nevertheless, an essential part of this energy is wasted due to lower consumption during non-business periods. This work proposes a novel framework that uses solar-generated energy surplus to charge external electric vehicles (EVs), creating new business opportunities. Furthermore, this paper introduces a novel marketplace platform based on blockchain technology to allow energy trading between institutions and EV owners. Since the energy provided to charge the EV comes from distributed PV generation, the energy’s selling price can be more attractive than the one offered by the retailers—meaning economic gains for the institutions and savings for the users. A case study was carried out to evaluate the feasibility of the proposed solution and its economic advantages. Given the assumptions considered in the study, 3213 EVs could be fully charged by one institution in one year, resulting in over EUR 45,000 in yearly profits. Further, the economic analysis depicts a payback of approximately two years, a net present value of EUR 33,485, and an internal rate of return of 61%. These results indicate that implementing the proposed framework could enable synergy between institutions and EV owners, providing clean and affordable energy to charge vehicles.
Available Kinetic Energy Sources on the Human Body during Sports Activities: A Numerical Approach Based on Accelerometers for Cantilevered Piezoelectric Harvesters
Authors: Hoareau Damien , Jodin Gurvan , Laaraibi Abdo-rahmane Anas , Prioux Jacques , Razan Florence
Published: March 2023

Physical activity involves movements, which can be considered sources of kinetic energy, that are expected to be important during sports activities. Several transducers can transform this energy into electrical energy. Piezoelectric generators are widely used, and several applications highlight their relevance. However, the generated output power is location dependent, and the analysis of the placement of this kind of generator can be challenging. In order to assess the availability of kinetic energy sources, an acceleration data analysis method is presented. Temporal and harvester model-based studies, using data from 17 inertial measurement units (IMUs) located across the whole human body, were conducted. The results show that piezoelectric cantilever-beam harvesters can be very sensitive to impacts. Extremity segments, such as the feet or hands, can be considered as good energy sources. The most relevant features are proposed as criteria to easily evaluate the harvestable energy sources.
Distribution of Rare-Earth Elements in Ashes Produced in the Coal Combustion Process from Power Boilers
Authors: Adamczyk Zdzisław , Komorek Joanna , Kokowska-Pawłowska Magdalena , Nowak Jacek
Published: March 2023

The growing demand for rare-earth elements and yttrium (REY) in modern technologies has resulted in the systematic depletion of primary ores. For this reason, research is being conducted around the world on alternative sources of rare-earth elements, e.g., on the possibilities of recovering REY from coal waste or coal combustion. The article presents the results of comprehensive tests of the fuel—hard coal, and high-temperature HTA ash, fly ash, and bottom ash. Examined samples were taken from a Polish power plant. In the tests, fuel quality parameters were determined in accordance with the standards; microscopic observations in reflected and transmitted light, as well as a scanning electron microscope (SEM/EDS), were used, and chemical and phase composition were determined using ICP-MS and XRD methods, respectively. The distribution of REY between these ashes was analyzed. Their suitability as alternative sources of REY was assessed. The obtained results showed that the process of hard coal combustion in pulverized coal boilers influenced the geochemical differentiation of REY elements in energy waste. This differentiation is manifested by higher concentrations of REY and critical elements in the fly ash than in the bottom ash. The obtained values of the Coutl prospective coefficient made it possible to classify the analyzed fly and bottom ashes as prospective REY raw materials.