bluuebunny/crossref_metadata_2025 huggingface dataset:
Defining a Digital Twin: A Data Science-Based Unification
Authors: Emmert-Streib Frank
Published: August 2023

The concept of a digital twin (DT) has gained significant attention in academia and industry because of its perceived potential to address critical global challenges, such as climate change, healthcare, and economic crises. Originally introduced in manufacturing, many attempts have been made to present proper definitions of this concept. Unfortunately, there remains a great deal of confusion surrounding the underlying concept, with many scientists still uncertain about the distinction between a simulation, a mathematical model and a DT. The aim of this paper is to propose a formal definition of a digital twin. To achieve this goal, we utilize a data science framework that facilitates a functional representation of a DT and other components that can be combined together to form a larger entity we refer to as a digital twin system (DTS). In our framework, a DT is an open dynamical system with an updating mechanism, also referred to as complex adaptive system (CAS). Its primary function is to generate data via simulations, ideally, indistinguishable from its physical counterpart. On the other hand, a DTS provides techniques for analyzing data and decision-making based on the generated data. Interestingly, we find that a DTS shares similarities to the principles of general systems theory. This multi-faceted view of a DTS explains its versatility in adapting to a wide range of problems in various application domains such as engineering, manufacturing, urban planning, and personalized medicine.
Tabular Machine Learning Methods for Predicting Gas Turbine Emissions
Authors: Potts Rebecca , Hackney Rick , Leontidis Georgios
Published: August 2023

Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learning models for predicting emissions for gas turbines. We compared an existing predictive emissions model, a first-principles-based Chemical Kinetics model, against two machine learning models we developed based on the Self-Attention and Intersample Attention Transformer (SAINT) and eXtreme Gradient Boosting (XGBoost), with the aim to demonstrate the improved predictive performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine learning techniques and determine whether XGBoost or a deep learning model performs the best on a specific real-life gas turbine dataset. Our analysis utilises a Siemens Energy gas turbine test bed tabular dataset to train and validate the machine learning models. Additionally, we explore the trade-off between incorporating more features to enhance the model complexity, and the resulting presence of increased missing values in the dataset.
New Stability Results for Abstract Fractional Differential Equations with Delay and Non-Instantaneous Impulses
Authors: Benchaib Abdellatif , Salim Abdelkrim , Abbas Saïd , Benchohra Mouffak
Published: August 2023

This research delves into the field of fractional differential equations with both non-instantaneous impulses and delay within the framework of Banach spaces. Our objective is to establish adequate conditions that ensure the existence, uniqueness, and Ulam–Hyers–Rassias stability results for our problems. The studied problems encompass abstract impulsive fractional differential problems with finite delay, infinite delay, state-dependent finite delay, and state-dependent infinite delay. To provide clarity and depth, we augment our theoretical results with illustrative examples, illustrating the practical implications of our work.
A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression
Authors: Ren Yumei , Tang Guoqiang , Li Xin , Chen Xuchang
Published: August 2023

In the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so it is important to select the optimal set of factors. Elastic Net, which combines the benefits of the L1 and L2 penalty terms, performs better at filtering features due to the complexity of the features in high-dimensional datasets than Lasso and Ridge regression. At the same time, the false discovery rate (FDR), which is important for making reliable investment decisions, is not taken into account by the current factor-selection methodologies. Therefore, this paper constructs the Knockoff Logistic regression Elastic Net (KF-LR-Elastic Net): combining Logistic regression with Elastic Net and using Knockoff to control the FDR of variable selection to achieve factor selection. Based on the selected factors, stock returns are predicted under Logistic regression. The overall model is denoted as Knockoff Logistic regression Elastic Net-Logistic regression (KL-LREN-LR). The empirical study is conducted with data on the CSI 300 index constituents in the Chinese market from 2016–2022. KF-LREN-LR is used for factor selection and stock-return forecasting to select the top 10 stocks and establish an investment strategy for daily position changing. According to empirical evidence, KF-LR-Elastic Net can select useful factors and control the FDR, which is helpful for increasing the accuracy of factor selection. The KF-LREN-LR forecast portfolio has the advantages of high return and controlled risk, so it is informative for optimizing asset allocation.
Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop
Authors: Zhao Nan , Feng Chun
Published: August 2023

In the context of the continuous development and maturity of intelligent manufacturing and intelligent logistics, it has been observed that the majority of vehicle maintenance in EMU trains still relies on traditional methods, which are characterized by excessive manual intervention and low efficiency. To address these deficiencies, the present study proposes the integration of Automatic Guided Vehicles (AGVs) to improve the traditional maintenance processes, thereby enhancing the efficiency and quality of vehicle maintenance. Specifically, this research focuses on the scenario of the maintenance workshop in EMU trains and investigates the task allocation problem for multiple AGVs. Taking into consideration factors such as the maximum load capacity of AGVs, remaining battery power, and task execution time, a mathematical model is formulated with the objective of minimizing the total distance and time required to complete all tasks. A multi-population genetic algorithm is designed to solve the model. The effectiveness of the proposed model and algorithm is validated through simulation experiments, considering both small-scale and large-scale scenarios. The results indicate that the multi-population genetic algorithm outperforms the particle swarm algorithm and the genetic algorithm in terms of stability, optimization performance, and convergence. This research provides scientific guidance and practical insights for enterprises adopting task allocation strategies using multiple AGVs.
Robot-Assisted Parathyroidectomy Using Indocyanine Green (ICG) Fluorescence in Primary Hyperparathyroidism
Authors: Park Shin-Young , Choi Yun Suk , Hwang Young Mi , Yi Jin Wook
Published: August 2023

Background and Objectives: Surgical treatment for primary hyperparathyroidism (PHPT) has evolved from bilateral exploration through a long transcervical incision to focused parathyroidectomy with a minimal incision above the pathologic gland. Recently, endoscopic or robot-assisted parathyroid surgery without direct neck incision has been introduced. The aim of this study was to investigate the effectiveness of indocyanine green (ICG) fluorescence as a new method for the visual identification of abnormal hyperfunctioning parathyroid glands in robot-assisted parathyroidectomy using FireflyTM technology. We also aimed to conduct a comparative analysis between robot-assisted parathyroidectomy and conventional focused parathyroidectomy in order to identify clinical differences between the two surgical approaches. Materials and Methods: A total of 37 patients with PHPT underwent parathyroidectomy at a single university hospital between September 2018 and December 2022. Thirty-one patients underwent open focused parathyroidectomy (open group), and six patients underwent robot-assisted parathyroidectomy (robot group). Pre-operative localization via parathyroid SPECT-CT and an intraoperative parathyroid hormone (IOPTH) assay were used to successfully remove the pathologic parathyroid in both groups. ICG was administered only in the robot group. Results: Pathologic parathyroid showed a persistent fluorescence pattern under near-infrared vision. After the removal of the fluorescent parathyroid gland, IOPTH was normalized in all six patients in the robot group. However, the open group showed shorter hospital stays (1.8 ± 1.2 vs. 3.0 ± 0.0 days, p < 0.001) and shorter operation times (91.1 ± 69.1 vs. 152.5 ± 23.6 min, p = 0.001) than the robot group. After 6 months of surgery, PTH, calcium, and ionized calcium levels were all normalized without significant differences between the groups. Conclusions: Robot-assisted parathyroidectomy using ICG is helpful for the visual identification of the pathologic parathyroid gland. The advantage of robot parathyroidectomy is a better cosmetic outcome. However, it still does not show better clinical outcomes than conventional open focused parathyroidectomy.
Non-Invasive Ventilation Reduces Postoperative Respiratory Failure in Patients Undergoing Bariatric Surgery: A Retrospective Analysis
Authors: Imperatore Francesco , Gritti Fabrizio , Esposito Rossella , Giudice Claudia del , Cafora Chiara , Pennacchio Francesco , Maglione Francesco , Catauro Antonio , Pace Maria Caterina , Docimo Ludovico , Gambardella Claudio
Published: August 2023

Background and Objectives: Postoperative non-invasive ventilation (NIV) has been proposed as an attractive strategy to reduce morbidity in obese subjects undergoing general anaesthesia. The increased body mass index (BMI) correlates with loss of perioperative functional residual capacity, expiratory reserve volume, and total lung capacity. The aim of the current study is to evaluate the efficacy of NIV in a post-anaesthesia care unit (PACU) in reducing post-extubation acute respiratory failure (ARF) after biliointestinal bypass (BIBP) in obese patients. Materials and Methods: A retrospective analysis was conducted from January 2019 to December 2020 to compare acute respiratory failure within the first 72 postoperative hours and oximetry values of obese patients who underwent BIBP after postoperative NIV adoption or conventional Venturi mask. Results: In total, 50 patients who received NIV postoperative protocol and 57 patients who received conventional Venturi mask ventilation were included in the study. After 120 min in PACU pH, pCO2, pO2, and SpO2 were better in the NIV group vs. control group (p < 0.001). Seventy-two hours postoperatively, one patient (2%) in the NIV group vs. seven patients (12.2%) in the control group developed acute respiratory failure. Therefore, conventional Venturi mask ventilation resulted in being significantly associated (p < 0.05) with postoperative ARF with an RR of 0.51 (IC 0.27–0.96). Conclusions: After bariatric surgery, short-term NIV during PACU observation promotes a more rapid recovery of postoperative lung function and oxygenation in obese patients, reducing the necessity for critical care in the days following surgery. Therefore, as day-case surgery becomes more advocated even for morbid obesity, it might be considered a necessary procedure.
Geometrical Accuracy Analysis of Ti-6Al-4V Trusses Manufactured by Electron Beam Melting Process
Authors: Bellini Costanzo , Borrelli Rosario , Di Caprio Francesco , Di Cocco Vittorio , Franchitti Stefania , Iacoviello Francesco , Pirozzi Carmine , Sorrentino Luca
Published: August 2023

Lattice structures made of metal materials are very interesting since their structural efficiency is elevated, thanks to their good mechanical properties and light weight. Additive manufacturing processes are appropriate to produce those structures. However, the peculiar geometry of lattice structures causes the manufacturing process to create rather significant unconformities, affecting the structures’ mechanical properties. In this article, small trusses with different diameters were produced through electron beam melting (EBM) by varying the process parameters, like the orientation and the position in the build chamber. Then, their diameter was evaluated and compared with the nominal one. It was found that the orientation in the chamber was very influential on the geometrical error, as well as the nominal diameter, while the position in the building chamber was uninfluential. In particular, the highest deviation was found for the specimens oriented at 0° and those with a diameter of 1 mm. Moreover, a similar result was detected for the ovalisation of the truss section too.
Effects of Installing Different Types of Cooling Fins on the Cold Side of a Thermoelectric Power Generation Device on the Thermal Efficiency and Exergy Efficiency of Power Cable Surface Waste Heat Recovery
Authors: Hu Zihao , de León Francisco , Wang Rizhou , Li Yanzhe
Published: August 2023

This study investigates the thermal efficiency and exergy efficiency of a thermoelectric power generation device for recovering power cable surface waste heat. Numerical simulations are conducted to analyze the impact of different types of cooling fins on the system’s performance. The results demonstrate that the installation of cooling fins improves heat transfer efficiency and enhances the thermoelectric power generation device’s output power. Among the various fin designs, the system equipped with cooling fins with 17 teeth exhibits the highest performance. These findings highlight the importance of fin design in optimizing the system’s thermal efficiency and exergy efficiency. This study provides valuable insights for the development and improvement of thermoelectric power generation systems for power cable surface waste heat recovery applications.
Vinylation of N-Heteroarenes through Addition/Elimination Reactions of Vinyl Selenones
Authors: Palomba Martina , Dias Italo Franco Coelho , Cocchioni Michelangelo , Marini Francesca , Santi Claudio , Bagnoli Luana
Published: August 2023

A new protocol for the synthesis of N-vinyl azoles using vinyl selenones and azoles in the presence of potassium hydroxide was developed. This reaction proceeded under mild and transition metal-free conditions through an addition/elimination cascade process. Both aromatic and aliphatic vinyl selenones and various mono-, bi- and tri-cyclic azoles can be tolerated and give terminal N-vinyl azoles in moderate to high yields. A plausible mechanism is also proposed.
Non-Covalent Assembly of Multiple Fluorophores in Edible Protein/Lipid Hydrogels for Applications in Multi-Step Light Harvesting and White-Light Emission
Authors: Ding Jingwen , Kumar Challa V.
Published: August 2023

The design and production of biodegradable and sustainable non-toxic materials for solar-energy harvesting and conversion is a significant challenge. Here, our goal was to report the preparation of novel protein/lipid hydrogels and demonstrate their utility in two orthogonal fundamental studies—light harvesting and white-light emission. Our hydrogels contained up to 90% water, while also being self-standing and injectable with a syringe. In one application, we loaded these hydrogels with suitable organic donor-acceptor dyes and demonstrated the energy-transfer cascade among four different dyes, with the most red-emitting dye as the energy destination. We hypothesized that the dyes were embedded in the protein/lipid phase away from the water pools as monomeric entities and that the excitation of any of the four dyes resulted in intense emission from the lowest-energy acceptor. In contrast to the energy-transfer cascade, we demonstrate the use of these gels to form a white-light-emitting hydrogel dye assembly, in which excitation migration is severely constrained. By restricting the dye-to-dye energy transfer, the blue, green, and red dyes emit at their respective wavelengths, thereby producing the composite white-light emission. The CIE color coordinates of the emission were 0.336 and 0.339—nearly pure white-light emission. Thus, two related studies with opposite requirements could be accommodated in the same hydrogel, which was made from edible ingredients by a simple method. These gels are biodegradable when released into the environment, sustainable, and may be of interest for energy applications.
Anion Complexation by an Azocalix[4]arene Derivative and the Scope of Its Fluoride Complex Salt to Capture CO2 from the Air
Authors: Danil de Namor Angela F. , Al Hakawati Nawal
Published: August 2023

A newly synthesized upper rim azocalix[4]arene, namely 5,11,17,23-tetra[(4-ethylacetoxyphenyl) (azo)]calix[4]arene, CA-AZ has been fully characterized, and its chromogenic and selective properties for anions are reported. Among univalent anions, the receptor is selective for the fluoride anion, and its mode of interaction in solution is discussed. The kinetics of the complexation process were found to be very fast as reflected in the immediate colour change observed with a naked eye resulting from the receptor–anion interaction. An emphasis is made about the relevance in selecting a solvent in which the formulation of the process is representative of the events taking place in the solution. The composition of the fluoride complex investigated using UV/VIS spectrophotometry, conductance measurements and titration calorimetry was 1:1, and the thermodynamics of complexation of anions and CA-AZ in DMSO were determined. The fluoride complex salt was isolated, and a detailed investigation was carried out to assess its ability to remove CO2 from the air. The recycling of the complex was easily achieved. Final conclusions are given.
The Homoleptic Curcumin–Copper Single Crystal (ML2): A Long Awaited Breakthrough in the Field of Curcumin Metal Complexes
Authors: Arenaza-Corona Antonino , Obregón-Mendoza Marco A. , Meza-Morales William , Ramírez-Apan María Teresa , Nieto-Camacho Antonio , Toscano Rubén A. , Pérez-González Leidys L. , Sánchez-Obregón Rubén , Enríquez Raúl G.
Published: August 2023

The first single crystal structure of the homoleptic copper (II) ML2 complex (M=Cu (II), L = curcumin) was obtained and its structure was elucidated by X-ray diffraction showing a square planar geometry, also confirmed by EPR. The supramolecular arrangement is supported by C-H···O interactions and the solvent (MeOH) plays an important role in stabilizing the crystal packing Crystallinity was additionally assessed by XRD patterns. The log P value of the complex (2.3 ± 0.15) was determined showing the improvement in water solubility. The cytotoxic activity of the complex against six cancer cell lines substantially surpasses that of curcumin itself, and it is particularly selective against leukemia (K562) and human glioblastoma (U251) cell lines, with similar antioxidant activity to BHT. This constitutes the first crystal structure of pristine curcumin complexed with a metal ion.
Effects of Tea Polyphenols and Theaflavins on Three Oral Cariogenic Bacteria
Authors: Cui Xia , Xu Lei , Qi Kezhen , Lan Hai
Published: August 2023

In order to investigate the antibacterial mechanism of tea polyphenols and theaflavins against oral cariogenic bacteria, the pH value of the culture medium, the number of bacteria adhering to the smooth glass tube wall, and the electrical conductivity value within 10 h were measured, respectively. The effects of four concentrations of tea polyphenols and theaflavins below the MIC value were studied on acid production, adhesion, and electrical conductivity of oral cariogenic bacteria. The live/dead staining method was used to observe the effects of four concentrations of tea polyphenols and theaflavins below the MIC value on the biofilm formation of oral cariogenic bacteria under a laser scanning confocal microscope. With the increase in concentrations of tea polyphenols and theaflavins, the acid production and adhesion of the cariogenic bacteria gradually decreased, and the conductivity gradually increased. However, the conductivity increase was not significant (p < 0.05). Compared with the control group, the 1/2MIC and 1/4MIC tea polyphenols and theaflavins treatments significantly reduced the biomass of the cariogenic biofilm (p < 0.05). The confocal laser scanning microscope showed that the integrated optical density of green fluorescence of the cariogenic biofilm gradually decreased with the increase in agent concentration after the action of tea polyphenols and theaflavins.
Recent Progress of Preparation Strategies in Organic Nanoparticles for Cancer Phototherapeutics
Authors: Xie Quanquan , Tang Jiayi , Guo Shengze , Zhao Qi , Li Shengliang
Published: August 2023

Phototherapy has the advantages of being a highly targeted, less toxic, less invasive, and repeatable treatment, compared with conventional treatment methods such as surgery, chemotherapy, and radiotherapy. The preparation strategies are significant in order to determine the physical and chemical properties of nanoparticles. However, choosing appropriate preparation strategies to meet applications is still challenging. This review summarizes the recent progress of preparation strategies in organic nanoparticles, mainly focusing on the principles, methods, and advantages of nanopreparation strategies. In addition, typical examples of cancer phototherapeutics are introduced in detail to inform the choice of appropriate preparation strategies. The relative future trend and outlook are preliminarily proposed.
Improved Process for the Synthesis of 3-(3-Trifluoromethylphenyl)propanal for More Sustainable Production of Cinacalcet HCl
Authors: Rathod Vikas Damu , Paganelli Stefano , Kočevar Marijan , Krivec Marko , Piccolo Oreste
Published: August 2023

Cinacalcet (I), sold as hydrochloride salt, is a calcimimetic drug which has been approved for the treatment of secondary hyperparathyroidism in patients with chronic renal disease and for the treatment of hypercalcemia in patients with parathyroid carcinoma. Here, an improved method for the synthesis of 3-(3-trifluoromethylphenyl)propanal (II), a key intermediate for the preparation of I, is described. The protocol required a Mizoroki–Heck cross-coupling reaction between 1-bromo-3-(trifluoromethyl)benzene and acroleine diethyl acetal, catalyzed by Pd(OAc)2 in the presence of nBu4NOAc (tetrabutylammonium acetate), followed by the hydrogenation reaction of the crude mixture of products in a cascade process. Palladium species, at the end of the reaction, were efficiently recovered as Pd/Al2O3. The procedure was developed under conventional heating conditions as well as under microwave-assisted conditions. The obtained mixture of 1-(3,3-diethoxypropyl)-3-(trifluoromethyl)benzene (III), impure for ethyl 3-(3-trifluoromethylphenyl) propanoate (IV), was finally treated, under mild conditions, with potassium diisobutyl-tert-butoxyaluminum hydride (PDBBA) to obtain after hydrolysis 3-(3-trifluoromethylphenyl)propanal (II), in an excellent overall yield and very high purity. Microwave conditions permitted a reduction in reaction times without affecting selectivity and yield. The final API was obtained through reductive amination of (II) with (R)-(+)-1-(1-naphthyl)ethylamine (V) using a catalyst prepared by us with a very low content of precious metal.
Stability of Phenolic Compounds, Antioxidant Activity and Color Parameters in Colored-Flesh Potato Chips
Authors: Bravo Catalina , Peña Fabiola , Nahuelcura Javiera , Vidal Catalina , González Felipe , Jiménez-Aspee Felipe , Bustamante Luis , Contreras Boris , Ruiz Antonieta
Published: August 2023

Potato (Solanum tuberosum) chips are the most consumed snacks worldwide today. Colored potato chips prepared from potato cultivars with red and purple flesh are a novel alternative to traditional potato chips because of their higher phenolic compound content, such as anthocyanins and hydroxycinnamic acid derivatives (HCADs), which might make these chips healthier compared with traditional chips. There is little information on the stability of these compounds. In this study, the nutritional value of these chips was evaluated by determining phenolic profiles, antioxidant activity and color parameters with liquid chromatography diode array and mass spectrometry detection (HPLC-DAD-ESI-MS/MS) and spectrophotometric methods during storage for four months. Five anthocyanins and three HCADs were detected, with the latter compounds being the most abundant, with concentrations on average between the first (97.82 mg kg−1) and the last (31.44 mg kg−1) week of storage. Similar trends were observed in antioxidant activity and stability, with the CUPRAC method showing the highest response among all the methods employed. The color indices were stable throughout the storage time. Based on these results, colored-flesh potato chips are an optimal alternative for consumption because of their high retention of phenolic compounds and antioxidant activity during storage, providing potential benefits to human health.
Self-Healable and Reprocessable Silicon Elastomers Based on Imine–Boroxine Bonds for Flexible Strain Sensor
Authors: Wang Peng , Wang Zhuochao , Liu Lu , Ying Guobing , Cao Wenxin , Zhu Jiaqi
Published: August 2023

Silicon elastomers with excellent self-healing and reprocessing abilities are highly desirable for the advancement of next-generation energy, electronic, and robotic applications. In this study, a dual cross-linked self-healing polysiloxane elastomer was facilely fabricated by introducing an exchangeable imine bond and boroxine into polydimethylsiloxane (PDMS) networks. The PDMS elastomers exhibited excellent self-healing properties due to the synergistic effect of dynamic reversible imine bonds and boroxine. After healing for 2 h, the mechanical strength of the damaged elastomers completely and rapidly recovered at room temperature. Furthermore, the prepared PDMS elastomers could be repeatedly reprocessed multiple times under milder conditions without significant degradation in mechanical performance. In addition, a stretchable and self-healable electrical sensor was developed by integrating carbon nanotubes (CNTs) with the PDMS elastomer, which can be employed to monitor multifarious human motions in real time. Therefore, this work provides a new inspiration for preparing self-healable and reprocessable silicone elastomers for future flexible electronics.
Synthesis of a Series of Trimeric Branched Glycoconjugates and Their Applications for Supramolecular Gels and Catalysis
Authors: Bietsch Jonathan , Chen Anji , Wang Dan , Wang Guijun
Published: August 2023

Carbohydrate-derived molecular gelators have found many practical applications as soft materials. To better understand the structure and molecular gelation relationship and further explore the applications of sugar-based gelators, we designed and synthesized eight trimeric branched sugar triazole derivatives and studied their self-assembling properties. These included glucose, glucosamine, galactose, and maltose derivatives. Interestingly, the gelation properties of these compounds exhibited correlations with the peripheral sugar structures. The maltose derivative did not form gels in the tested solvents, but all other compounds exhibited gelation properties in at least one of the solvents. Glucose derivatives showed superior performance, followed by glucosamine derivatives. They typically formed gels in toluene and alcohols; some formed gels in ethanol-water mixtures or DMSO water mixtures. The glycoclusters 9 and 10 demonstrated rate acceleration for the copper-catalyzed azide-alkyne cycloaddition (CuAAC) reactions. These were further studied for their metallogels formation properties, and the copper metallogels from compound 9 were successfully utilized to catalyze click reactions. These metallogels were able to form a gel column, which was effective in converting the reactants into the triazole products in multiple cycles. Moreover, the same gel column was used to transform a second click reaction using different reactants. The synthesis and characterization of these compounds and their applications for catalytic reactions were discussed.
The Relationship between Whole-Milk Dairy Foods and Metabolic Health Highlights an Opportunity for Dietary Fat Recommendations to Evolve with the State of the Science
Authors: Torres-Gonzalez Moises
Published: August 2023

The science of dietary fats has evolved, and a body of evidence indicates they are complex bioactive nutrients that have different effects on health depending on their food source, chain length, degree of saturation, and other factors that can be affected by food processing, handling, and storage. As such, it is becoming increasingly clear that the effects of foods on obesity and metabolic health cannot be predicted simply with their fat content. The aim of this opinion article is to provide a brief overview of select recent research on the effects of whole-milk dairy foods on body composition and indicators of metabolic health across the lifespan to show the gap between current knowledge and dietary guidance. As the state of the science on dietary fats and human health evolves to consider the complexity of food matrices, the total nutrient package they deliver, and the health impacts associated with dietary patterns, so too must guidelines for dietary fat.
Supersymmetric AdS Solitons, Ground States, and Phase Transitions in Maximal Gauged Supergravity
Authors: Gallerati Antonio
Published: August 2023

We review some recent soliton solutions in a class of four-dimensional supergravity theories. The latter can be obtained from black hole solutions by means of a double Wick rotation. For special values of the parameters, the new configurations can be embedded in the gauged maximal N=8 theory and uplifted in the higher-dimensional D=11 theory. We also consider BPS soliton solutions, preserving a certain fraction of supersymmetry.
Nanotechnology-Based Strategies to Combat Multidrug-Resistant Candida auris Infections
Authors: Hetta Helal F. , Ramadan Yasmin N. , Al-Kadmy Israa M. S. , Ellah Noura H. Abd , Shbibe Lama , Battah Basem
Published: August 2023

An emerging multidrug-resistant pathogenic yeast called Candida auris has a high potential to spread quickly among hospitalized patients and immunodeficient patients causing nosocomial outbreaks. It has the potential to cause pandemic outbreaks in about 45 nations with high mortality rates. Additionally, the fungus has become resistant to decontamination techniques and can survive for weeks in a hospital environment. Nanoparticles might be a good substitute to treat illnesses brought on by this newly discovered pathogen. Nanoparticles have become a trend and hot topic in recent years to combat this fatal fungus. This review gives a general insight into the epidemiology of C. auris and infection. It discusses the current conventional therapy and mechanism of resistance development. Furthermore, it focuses on nanoparticles, their different types, and up-to-date trials to evaluate the promising efficacy of nanoparticles with respect to C. auris.
Effect of Self-Recirculating Casing Treatment on the Aerodynamic Performance of Ultra-High-Pressure-Ratio Centrifugal Compressors
Authors: Fan Tengbo , Wang Baotong , Yan Chuanxiang , Zhang Wenchao , Song Zhaoyun , Zheng Xinqian
Published: August 2023

The motivation to design a more efficient and compact aircraft engine leads to a continuous increase in overall pressure ratio and decrease in the stage number in compressors. Compared to the traditional multi-stage compressor, a single-stage ultra-high-pressure-ratio centrifugal compressor with a pressure ratio higher than 10.0 can significantly improve the engine’s power-to-weight ratio and fuel economy with a reduced structure complexity. Thus, it has great potential to be adopted in the compression system of advanced aero engines, such as turboshaft engines, in the future. However, the highly narrow Stable Flow Range (SFR) of ultra-high-pressure-ratio centrifugal compressors is a severe restriction for engineering applications. This research focuses on the aerodynamic performance of a ultra-high-pressure-ratio centrifugal compressor, and three-dimensional simulation is employed to investigate the effect of Self-Recirculating Casing Treatment (SRCT) on the performance and stability of the centrifugal compressor. Firstly, the parametric model of SRCT is established to investigate the effect of geometry parameters (rear slot distance and rear slot width) on the aerodynamic performance of the centrifugal compressor. It is concluded that SRCT improves the compressor’s SFR but deteriorates its efficiency. Also, a non-linear and non-monotone relationship exists between the SFR and rear slot distance or width. Then, the flow mechanism behind the effect of SRCT is explored in detail. By introducing the SRCT, an additional flow path is provided across the blade along the circumferential direction, and the behavior of the shock wave and tip leakage flow is significantly changed, resulting in the obviously different loading distribution along the streamwise direction. As a result, the mixing and flow separation loss are enhanced in the impeller flow passage to deteriorate the efficiency. On the other hand, the blockage effect caused by the mixing of slot recirculation and mainstream flow near the impeller inlet increases the axial velocity and reduces the incidence angle below the 90% spanwise section, which is considered to effectively stabilize the impeller flow field and enhance the stability.
Nanofluids as a Waste Heat Recovery Medium: A Critical Review and Guidelines for Future Research and Use
Authors: Pereira José , Moita Ana , Moreira António
Published: August 2023

The thermal energy storage and conversion process possesses high energy losses in the form of waste heat. The losses associated with energy conversion achieve almost 90% of the worldwide energy supply, and approximately half of these losses are waste heat. Hence, waste heat recovery approaches intend to recuperate that large amount of wasted heat from chimneys, vehicles, and solar energy systems, among others. The novel class of thermal fluids designated by nanofluids has a high potential to be employed in waste heat recovery. It has already been demonstrated that nanofluids enhance energy recovery efficiency by more than 20%. Also, the use of nanofluids can improve the energy capacity of steelworks systems by around three times. In general, nanofluids can improve efficiency and reduce exergy destruction and carbon emissions in devices like heat exchangers. The current work summarizes the application of nanofluids in waste heat recovery and discusses the involved feasibility factors. Also, the critical survey of more than one hundred scientific papers enabled the overview of the environmental aspects of the nanofluid’s waste heat recovery. Finally, it discusses the main limitations and prospects of the use of nanofluids in waste heat recovery processes.
An Improved Discrete Jaya Algorithm for Shortest Path Problems in Transportation-Related Processes
Authors: Wang Ren , Zhou Mengchu , Wang Jinglin , Gao Kaizhou
Published: August 2023

Shortest path problems are encountered in many engineering applications, e.g., intelligent transportation, robot path planning, and smart logistics. The environmental changes as sensed and transmitted via the Internet of Things make the shortest path change frequently, thus posing ever-increasing difficulty for traditional methods to meet the real-time requirements of many applications. Therefore, developing more efficient solutions has become particularly important. This paper presents an improved discrete Jaya algorithm (IDJaya) to solve the shortest path problem. A local search operation is applied to expand the scope of solution exploration and improve solution quality. The time complexity of IDJaya is analyzed. Experiments are carried out on seven real road networks and dense graphs in transportation-related processes. IDJaya is compared with the Dijkstra and ant colony optimization (ACO) algorithms. The results verify the superiority of the IDJaya over its peers. It can thus be well utilized to meet real-time application requirements.
Semi-Analytical Modeling of Pyrolysis Front and Ignition Temperatures of Thermally Reactive Single Solid Particles
Authors: Haseli Yousef
Published: August 2023

A semi-analytical approach is developed for predicting pyrolysis front temperature in a charring solid undergoing thermal decomposition. The pre-reaction heating stage is described using an analytical formulation and invoking the concept of thermal penetration depth. The solution for the solid conversion stage accounts for decomposition enthalpy, the convective flow of volatiles, and a reaction front characterized by a uniform temperature that progresses toward the inner layers. This method incorporates empirical relations into the analytical model. Two scenarios are considered. First, the solution of the pyrolysis model combined with the data of conversion time versus external heat flux leads to an algebraic expression that reveals the existence of a maximum pyrolysis-front temperature. Explicit relations are derived for both the extremum pyrolysis temperature and optimum applied heat flux. In the second case, an expression is derived for the ignition temperature of a solid fuel (e.g., wood) by incorporating ignition delay time measurements into the heating stage model. The newly derived expression allows the ignition temperature to be described as a function of the Biot number and external heat flux. The relation obtained for the ignition temperature explains the experimental trends reported in some previous studies where two local extremums were observed for the ignition temperature in the absence of volatile reactions.
Development of Small-Diameter Artificial Vascular Grafts Using Transgenic Silk Fibroin
Authors: Tanaka Takashi , Hara Sakiko , Hendawy Hanan , El-Husseiny Hussein M. , Tanaka Ryo , Asakura Tetsuo
Published: August 2023

Silk fibroin (SF) is a suitable material for vascular prostheses for small arteries. SF is useful not only as a base material for artificial vascular grafts but also as a coating material. This study prepared three types of transgenic SF (vascular endothelial growth factor (VEGF), Arg-Glu-Asp-Val (REDV), and Tyr-Ile-Gly-Ser-Arg (YIGSR)) incorporating expression factors that are thought to be effective for endothelialization as coating materials. We compared the contribution of these materials to early endothelialization in vivo when using them as a porous transgenic SF coating. A porous coating of transgenic SF containing VEGF, REDV, and YIGSR was applied to a silk small-diameter artificial vascular graft base with a diameter of 1.5 mm and a length of 3 cm. Two and four weeks after implantation of these artificial grafts into the abdominal aorta of rats, they were removed and evaluated by histologic examination. Transgenic SF coating incorporating VEGF and REDV demonstrated higher tissue infiltration and continuous endothelialization in the center of the graft compared to YIGSR at 4 weeks after implantation. VEGF and REDV are capable of endothelialization as early as 4 weeks after implantation, confirming the usefulness of transgenic SF when used as a porous coating.
Obesity and Life History: The Hypothesis of Psychological Phenotypes
Authors: Rizzo Amelia , Sitibondo Aldo
Published: August 2023

The aim of the present study is to postulate the existence of psychological phenotypes associated with obesity, based on individual history. While metabolic phenotypes have been acknowledged in the field of medicine, the same cannot be affirmed in the realm of psychology. A longstanding tradition in obesity research has sought to identify shared characteristics among individuals affected by obesity, including personality traits. However, research found no adequate empirical evidence to support the existence of a specific psychological and psychopathological profile among individuals with obesity. Recent efforts in the literature have attempted to correlate these findings and ascertain which metabolic phenotype correlates with a diminished quality of life. We propose a novel differentiation between two categories: (1) individuals who affected by obesity since childhood, and (2) individuals who developed obesity following a life event. Further investigations are imperative to amass experimental data that substantiate this hypothesis. Proactively identifying psychological phenotypes is presumed to impact therapeutic outcomes.
Transformations in Islamic Pilgrimage Patterns and Meanings: Piety, Politics, Resistance, and Places of Memory in Islamic Pilgrimage Sites in Israel/Palestine
Authors: Luz Nimrod
Published: August 2023

This paper explores recent transformations in Islamic pilgrimage patterns in Israel/Palestine. The meanings assigned to traditional Muslim sites, and the conduct and practices of the dwindling number of pilgrims who visit them, are the struggling victims of strategic socio-political erasures caused by dramatic geo-political changes. Since 1948, the hegemony of the State of Israel has perversely politicized sacred Islamic sites beyond their traditional religious functions. Muslim pilgrims, for their part, engage in rituals that have become a counterweight to Israeli ethnocratic imperatives. The reconstruction of an Islamic pilgrimage map presents a shared imaginative landscape as lieux de mémoire that undergird political and social resistance. The dogged survival of Islamic pilgrimage comprises a counterweight to state power. Muslims fight to affirm Palestinian identity, reclaim heritage spaces as anchors for identity, and actively engage with land claiming.
Lipoma Arborescens Might Be an Unusual Cause of Knee Pain in Adolescents: A Case Report
Authors: Moretti Lorenzo , Bizzoca Davide , Abbaticchio Andrea Michele , Geronimo Alessandro , Solarino Giuseppe , Moretti Biagio
Published: August 2023

Lipoma arborescens (LA) is a rare benign soft tissue tumor characterised by a hyperproliferation of villi and fat cells in the joint synovium. It is most frequently localized in the knee as reported here. This is a case report of a 16-year-old adolescent, affected by type I diabetes mellitus, who reported left knee pain and functional limitation to medical attention. She performed a physical examination, MRI and biopsy using an arthroscopic approach, leading to the LA diagnosis and classification. The LA has been thus treated with an arthroscopic synovectomy, which is the treatment of choice for LA, characterized by a low recurrence rate.
Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City
Authors: Alotaibi Nouf Saeed , Ahmed Hassan Ibrahim , Kamel Samah Osama M.
Published: August 2023

The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two phases. The first phase relies on the intelligent process automation (IPA) technique and contains five modules named, specifically, dataset collection and pre-processing module, intelligent automation detection module, analysis module, detection rules and action module, and database module. In the first phase, each module composes an intelligent connecting module to give feedback reports about each module and send information to the next modules. Therefore, any change in each process can be easily detected and labeled as an intrusion. The intelligent connection module (ICM) may reduce the search time, increase the speed, and increase the security level. The second phase is the dynamic adaptation of the attack detection model based on reinforcement one-shot learning. The first phase is based on a multi-classification technique using Random Forest Trees (RFT), k-Nearest Neighbor (K-NN), J48, AdaBoost, and Bagging. The second phase can learn the new changed behaviors based on reinforced learning to detect zero-day attacks and malicious events in IoT-based smart cities. The experiments are implemented using a UNSW-NB 15 dataset. The proposed model achieves high accuracy rates using RFT, K-NN, and AdaBoost of approximately 98.8%. It is noted that the accuracy rate of the J48 classifier achieves 85.51%, which is lower than the others. Subsequently, the accuracy rates of AdaBoost and Bagging based on J48 are 98.9% and 91.41%, respectively. Additionally, the error rates of RFT, K-NN, and AdaBoost are very low. Similarly, the proposed model achieves high precision, recall, and F1-measure high rates using RFT, K-NN, AdaBoost, and Bagging. The second phase depends on creating an auto-adaptive model through the dynamic adaptation of the attack detection model based on reinforcement one-shot learning using a small number of instances to conserve the memory of any smart device in an IoT network. The proposed auto-adaptive model may reduce false rates of reporting by the intrusion detection system (IDS). It can detect any change in the behaviors of smart devices quickly and easily. The IADM can improve the performance rates for IDS by maintaining the memory consumption, time consumption, and speed of the detection process.
Continual Deep Learning for Time Series Modeling
Authors: Ao Sio-Iong , Fayek Haytham
Published: August 2023

The multi-layer structures of Deep Learning facilitate the processing of higher-level abstractions from data, thus leading to improved generalization and widespread applications in diverse domains with various types of data. Each domain and data type presents its own set of challenges. Real-world time series data may have a non-stationary data distribution that may lead to Deep Learning models facing the problem of catastrophic forgetting, with the abrupt loss of previously learned knowledge. Continual learning is a paradigm of machine learning to handle situations when the stationarity of the datasets may no longer be true or required. This paper presents a systematic review of the recent Deep Learning applications of sensor time series, the need for advanced preprocessing techniques for some sensor environments, as well as the summaries of how to deploy Deep Learning in time series modeling while alleviating catastrophic forgetting with continual learning methods. The selected case studies cover a wide collection of various sensor time series applications and can illustrate how to deploy tailor-made Deep Learning, advanced preprocessing techniques, and continual learning algorithms from practical, real-world application aspects.