bluuebunny/crossref_metadata_2025 huggingface dataset:
Sugar-Based Monoester Surfactants: Synthetic Methodologies, Properties, and Biological Activities
Authors: Verboni Michele , Perinelli Diego Romano , Buono Alessandro , Campana Raffaella , Sisti Maurizio , Duranti Andrea , Lucarini Simone
Published: October 2023

Glycolipids are biocompatible and biodegradable amphiphilic compounds characterized by a great scientific interest for their potential applications in various technological areas, including pharmaceuticals, cosmetics, agriculture, and food production. This report summarizes the available synthetic methodologies, physicochemical properties, and biological activity of sugar fatty acid ester surfactants, with a particular focus on 6-O-glucose, 6-O-mannose, 6-O-sucrose, and 6′-O-lactose ones. In detail, the synthetic approaches to this class of compounds, such as enzymatic lipase-catalyzed and traditional chemical (e.g., acyl chloride, Steglich, Mitsunobu) esterifications, are reported. Moreover, aspects related to the surface activity of these amphiphiles, such as their ability to decrease surface tension, critical micelle concentration, and emulsifying and foaming ability, are described. Biological applications with a focus on the permeability-enhancing effect across the skin or mucosa, antimicrobial and antifungal activities, as well as antibiofilm properties, are also presented. The information reported here on sugar-based ester surfactants is helpful to broaden the interest and the possible innovative applications of this class of amphiphiles in different technological fields in the future.
Healthcare-Associated Ventriculitis in Children during COVID-19 Pandemic: Clinical Characteristics and Outcome of a First Infection
Authors: Licona-Enríquez Jesús David , Labra-Zamora María Guadalupe , Ramírez-Reyes Alma Griselda , Miranda-Novales María Guadalupe
Published: October 2023

During the COVID-19 pandemic, patients in need of neurosurgical care suffered. Elective procedures were postponed, and emergency care visits decreased. Healthcare-associated ventriculitis (HAV) is a serious problem in children, with poor outcomes and frequent relapses. Our objective was to describe the clinical characteristics and the factors associated with a first HAV in children during two years of the pandemic. A retrospective cross-sectional study was performed from January 2021 to December 2022. The inclusion criteria were patients who developed a first HAV after a primary cerebrospinal fluid diversion procedure. The controls included patients without a first infection. Intraoperative and clinical data were extracted from medical records. A total of 199 CSF diversion surgeries were registered. A first infection occurred in 17 patients (8.5%), including 10 with external ventricular drain (EVD) and 6 with ventricular shunts. Gram-positive cocci were identified in 70.6%. Six patients recovered uneventfully, eight had relapse or superinfections, and three eventually died. Twenty patients were included as controls. Factors associated with a first infection were a younger age (median 9 vs. 102 months, p < 0.01), malnutrition (23.5% vs. 0%, p = 0.03), and an EVD placement (58.8% vs. 10%, p = 0.03). None of the intraoperative factors showed statistically significant differences. The rate of HAV was high. Most cases presented in children <1 year and with an EVD.
In Vitro Antioxidant and In Vivo Antigenotoxic Features of a Series of 61 Essential Oils and Quantitative Composition–Activity Relationships Modeled through Machine Learning Algorithms
Authors: Mladenović Milan , Astolfi Roberta , Tomašević Nevena , Matić Sanja , Božović Mijat , Sapienza Filippo , Ragno Rino
Published: October 2023

The antioxidant activity of essential oils (EOs) is an important and frequently studied property, yet it is not sufficiently understood in terms of the contribution of EOs mixtures’ constituents and biological properties. In this study, a series of 61 commercial EOs were first evaluated as antioxidants in vitro, following as closely as possible the cellular pathways of reactive oxygen species (ROS) generation. Hence, EOs were assessed for the ability either to chelate metal ions, thus interfering with ROS generation within the respiratory chain, or to neutralize 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and lipid peroxide radicals (LOO•), thereby halting lipid peroxidation, as well as to neutralize 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid cation radicals (ABTS•+) and hydroxyl radicals (OH•), thereby preventing the ROS species from damaging DNA nucleotides. Showing noteworthy potencies to neutralize all of the radicals at the ng/mL level, the active EOs were also characterized as protectors of DNA double strands from damage induced by peroxyl radicals (ROO•), emerging from 2,2′-azobis-2-methyl-propanimidamide (AAPH) as a source, and OH•, indicating some genome protectivity and antigenotoxicity effectiveness in vitro. The chemical compositions of the EOs associated with the obtained activities were then analyzed by means of machine learning (ML) classification algorithms to generate quantitative composition–activity relationships (QCARs) models (models published in the AI4EssOil database available online). The QCARs models enabled us to highlight the key features (EOSs’ chemical compounds) for exerting the redox potencies and to define the partial dependencies of the features, viz. percentages in the mixture required to exert a given potency. The ML-based models explained either the positive or negative contribution of the most important chemical components: limonene, linalool, carvacrol, eucalyptol, α-pinene, thymol, caryophyllene, p-cymene, eugenol, and chrysanthone. Finally, the most potent EOs in vitro, Ylang-ylang (Cananga odorata (Lam.)) and Ceylon cinnamon peel (Cinnamomum verum J. Presl), were promptly administered in vivo to evaluate the rescue ability against redox damage caused by CCl4, thereby verifying their antioxidant and antigenotoxic properties either in the liver or in the kidney.
αB-Crystallin Peptide Fused with Elastin-like Polypeptide: Intracellular Activity in Retinal Pigment Epithelial Cells Challenged with Oxidative Stress
Authors: Attia Sara Aly , Truong Anh Tan , Phan Alvin , Lee Shin-Jae , Abanmai Manal , Markanovic Marinella , Avila Hugo , Luo Haozhong , Ali Atham , Sreekumar Parameswaran G. , Kannan Ram , MacKay J. Andrew
Published: October 2023

Background: Oxidative stress-induced retinal degeneration is among the main contributing factors of serious ocular pathologies that can lead to irreversible blindness. αB-crystallin (cry) is an abundant component of the visual pathway in the vitreous humor, which modulates protein and cellular homeostasis. Within this protein exists a 20 amino acid fragment (mini-cry) with both chaperone and antiapoptotic activity. This study fuses this mini-cry peptide to two temperature-sensitive elastin-like polypeptides (ELP) with the goal of prolonging its activity in the retina. Methods: The biophysical properties and chaperone activity of cry-ELPs were confirmed by mass spectrometry, cloud-point determination, and dynamic light scattering ’DLS’. For the first time, this work compares a simpler ELP architecture, cry-V96, with a previously reported ELP diblock copolymer, cry-SI. Their relative mechanisms of cellular uptake and antiapoptotic potential were tested using retinal pigment epithelial cells (ARPE-19). Oxidative stress was induced with H2O2 and comparative internalization of both cry-ELPs was made using 2D and 3D culture models. We also explored the role of lysosomal membrane permeabilization by confocal microscopy. Results: The results indicated successful ELP fusion, cellular association with both 2D and 3D cultures, which were enhanced by oxidative stress. Both constructs suppressed apoptotic signaling (cleaved caspase-3); however, cry-V96 exhibited greater lysosomal escape. Conclusions: ELP architecture is a critical factor to optimize delivery of therapeutic peptides, such as the anti-apoptotic mini-cry peptide; furthermore, the protection of mini-cry via ELPs is enhanced by lysosomal membrane permeabilization.
Hermetia illucens Fermented with Lactobacillus plantarum KCCM12757P Alleviates Dextran Sodium Sulfate-Induced Colitis in Mice
Authors: Son Seok Jun , Han Ah-Ram , Sung Mi Jeong , Hong Sun Mee , Lee Sang-Hee
Published: October 2023

Inflammatory bowel disease (IBD) can severely affect humans and animals and is difficult to treat. Black soldier fly (Hermetia illucens; Hi) larvae (BSFL) are a sustainable source of protein. However, no studies exist on the antioxidant and anti-inflammatory functions of BSFL or fermented BSFL with respect to IBD. In this study, riboflavin-producing Lactobacillus plantarum KCCM12757P was isolated from a fish farm tank, and in conjunction with hot water-extracted Hi (HeHi) (termed HeHi_Lp), was used to determine optimal fermentation conditions to increase vitamin B2 concentration. This in vivo study investigated the therapeutic effects and mechanistic role of HeHi_Lp in chronic colitis-induced murine models. Histological changes, inflammatory cytokine levels, and intestinal barrier function were explored. Gut microbial communities and gene expression in the nuclear factor (NF)-κB signaling pathway were also studied. HeHi_Lp remarkably reduced the disease activity index, inflammatory cytokine (inducible nitric oxide synthase, cyclooxygenase 2, tumor necrosis factor α, interleukin (IL-6 and IL-1β) levels, and increased body weight and colon length. HeHi_Lp administration significantly raised zonula occludens 1, occludin and claudin 1 and improved the composition of the gut microbiota and beneficial intestinal bacteria. These results suggest that HeHi_Lp can be used as a dietary supplement in pet food to alleviate colitis.
Design Criteria for High-Gradient Radio-Frequency Linacs
Authors: Dolgashev Valery A.
Published: October 2023

This article will review methods used at the SLAC National Accelerator Laboratory and other world accelerator laboratories to design high-gradient normal conducting accelerating structures. A quest for compact radio-frequency linacs fueled decades of studies toward a higher accelerating gradient. A major phenomena limiting the increase of the gradient is vacuum radio-frequency breakdown; therefore, this paper will address the breakdown physics and discuss approaches that reduce the breakdown probability. This discussion will cover both the electrical design and fabrication technology of the accelerating structures to achieve practical operating accelerating gradients in excess of 100 MV/m. Most of the data described here were obtained during the development of 11 GHz linacs for electron–positron linear colliders, so extrapolation of the results to other frequencies should be performed cautiously.
Twenty Years of Progress in Microstructure Modelling for Ultrasonic Testing, from Shielded Metal Arc Welding to Gas Tungsten Arc Welding: An Analysis for Future Developments
Authors: Moysan Joseph , Gueudré Cécile , Ploix Marie-Aude , Corneloup Gilles
Published: October 2023

To ensure and to demonstrate the mechanical integrity of a welded structure, precise ultrasonic testing (UT) is often mandatory. The importance of the link between nondestructive testing (NDT) and the assessment of structural integrity is recalled. However, it is difficult to achieve great efficiency as the welding of thick and heavy structural part produces heterogeneous material. Heterogeneity results from the welding process itself as well as from the material solidification laws. For thick components, several welding passes are deposited, and temperature gradients create material grain elongation and/or size variations. In many cases, the welded material is also anisotropic, this anisotropy being due to the metal used, for example, austenitic stainless steel. At the early stages of ultrasonic testing, this kind of welded material was considered too unpredictable, and thus too difficult to be tested by ultrasounds without possible diagnosis errors and misunderstandings. At the end of the 1990s, an algorithmic solution to predict the material organisation began to be developed using data included in the welding notebook. This algorithm or modelling solution was called MINA. This present work recalls, in a synthetic form, the path followed to create this algorithm combining the use of solidification laws and the knowledge of the order of passes in the case of shielded metal arc welding (SMAW). This work describes and questions the simplifications used to produce a robust algorithm able to give a digital description of the material for wave simulation code. Step by step, advances and demonstrations are described as well as the limitations, and ways to progress are sketched. Recent developments are then explained and discussed for modelling in the case of gas tungsten arc welding (GTAW), in addition to discussions about 3D modelling for the future. The discussion includes alternative ways to represent the welded material and challenges to continue to produce more and more convincing weld material model to qualify and to make use of UT methods.
Special Issue “GIS Applications in Green Development”
Authors: Maniatis Yannis
Published: October 2023

In the context of climate change, the role of Geographic Information Systems (GIS) in green developments cannot be overstated [...]
Anthropometric vs. Dental Variables of the Ageing Face: A Clinical Study
Authors: Yiallouridou Ioanna , Sarafidou Katia , Theocharidou Anna , Menexes George , Anastassiadou Vassiliki
Published: October 2023

Background: Biological ageing and pathological changes of teeth impact the appearance and may hinder the effectiveness of facial aesthetic treatments. The present cross-sectional study in Caucasian dentate individuals aimed to measure facial features of the lower and middle parts of the face across different ages and to identify any significant impacts of age, sex and dental variables on facial aesthetics. Methods: Fifty-eight adults (ranging from 26 to 79 years of age) participated in the study. Facial measurements were collected via digital two-dimensional photography, and intraoral variables were recorded. The data were digitally analysed using ImageJ software and statistically analysed using SPSS Software. Results: The lower and middle third of the face revealed statistically significant changes across the age span tested, with the middle third presenting higher values in females and the lower appearing more prominent in males. A negative correlation with age was revealed for the distance between the pupil and the orbital point. Intraoral dental variables were also significantly related to facial measures. Conclusions: The study enriches the anthropometric data available for the Greek population, with age and sex significantly impacting facial aesthetics and oral variables. Considering such information during preoperative planning for full-arch dental restorations or facial surgery is essential.
Digital Communication Forensics in 6G and beyond Networks
Authors: Alqabbani Albatool , Saleem Kashif , Almazyad Abdulaziz S.
Published: October 2023

6G networks can be truly intelligent wireless systems that have the flexibility for continuous technological improvements. However, security events affecting information systems have grown increasingly complicated and sophisticated. Intruders may be able to operate due to a lack of evidence authenticity. Using network forensics methods can solve many cybercrime instances. These methods can be used to locate the intruder and to determine the intrusion’s origin and how it can be avoided in the future. Existing network forensics techniques confront security threats and vulnerabilities in several networks, such as personal area networks, local area networks, wide area networks, and wireless wide area networks under telecom networks, in practice, but cannot operate in 6G and beyond telecom networks, which are critical at this moment. Thus, a comprehensive literature analysis of telecom networks is conducted to identify existing concerns and challenges in conducting digital communication forensics. Hence, a digital forensics system for 6G and beyond networks is proposed as an initial idea on the basis of the highlighted issues, thereby assessing security incidents and providing justified evidence that explains the footprints of the attackers.
TransRFT: A Knowledge Representation Learning Model Based on a Relational Neighborhood and Flexible Translation
Authors: Wan Boyu , Niu Yingtao , Chen Changxing , Zhou Zhanyang
Published: October 2023

The use of knowledge graphs has grown significantly in recent years. However, entities and relationships must be transformed into forms that can be processed by computers before the construction and application of a knowledge graph. Due to its simplicity, effectiveness, and great interpretability, the translation model lead by TransE has garnered the most attention among the many knowledge representation models that have been presented. However, the performance of this model is poor when dealing with complex relations such as one-to-many, many-to-one, and reflexive relations. Therefore, a knowledge representation learning model based on a relational neighborhood and flexible translation (TransRFT) is proposed in this paper. Firstly, the triples are mapped to the relational hyperplane using the idea of TransH. Then, flexible translation is applied to relax the strict restriction h + r = t in TransE. Finally, the relational neighborhood information is added to further improve the performance of the model. The experimental results show that the model has good performance in triplet classification and link prediction.
Planning and Operation of Electrical Energy Systems under Uncertainties
Authors: Tostado-Véliz Marcos , Kamel Salah , Kabeel Abd Elnaby
Published: October 2023

The electricity sector is evolving dramatically [...]
Intra-Rater and Test–Retest Reliability of Barbell Force, Velocity, and Power during the Landmine Punch Throw Test Assessed by the GymAware Linear Transducer System
Authors: Oleksy Łukasz , Kuchciak Maciej , Bril Grzegorz , Mika Anna , Przydział Marta , Pazdan-Śliż Iwona , Kielnar Renata , Racheniuk Henryk , Adamska Olga , Deszczyński Michał
Published: October 2023

Background: Velocity-based training (VBT) requires measurement of the velocity at which the barbell is moved in the concentric phase with regard to different resistance exercises, which provides accurate, indirect estimations of 1 RM. However, for assessing punch performance, no study has been carried out to date. The purpose of this study was to analyse the reliability of the GymAware linear transducer for the measurement of barbell velocity during the landmine push throw (LPT) test using four loads. Methods: Twenty-five healthy, physically active male students, aged 24.13 ± 2.82 years, volunteered to take part in this study. The reliability of the LPT test was measured at two separate visits, with a 2-day interval between them. One series of the test protocol included four parts of the LPT test with progressively increasing loads (20, 25, 30, and 35 kg) and 5 min intervals for rests between loads. Results: For all four loads, excellent intra-rater and test–retest reliability was noted for the mean force variable (ICC = 0.97–0.99). Additionally, very strong and significant correlations were established between measurements (r = 0.96–0.99). Poor reliability was observed for barbell height and total work (ICC below 0.5). A trend of decreasing reliability was detected with increasing barbell load. Furthermore, measurements without the barbell throw were more reliable than those with it. Conclusions: These results support the use of the GymAware linear transducer to track barbell velocity during the LPT test. This device may have valuable practical applications for strength and conditioning coaches. Therefore, we suggest that the LPT assessed with the GymAware linear transducer may be a useful method for evaluating upper limb strength and power during boxing punches.
Assessing Weak Adhesion in Single Lap Joints Using Lamb Waves and Machine Learning Methods for Structural Health Monitoring
Authors: Ramalho Gabriel M. F. , Lopes António M. , da Silva Lucas F. M.
Published: October 2023

The use of adhesive joints has become increasingly popular in various industries due to their many benefits, such as low weight and good mechanical performance. However, adhesive joints can suffer from defects, one of them being weak adhesion. This defect poses a significant risk to structural integrity and can lead to premature failure, but is hard to detect using existing nondestructive testing methods. Therefore, there is a need for an effective technique that can detect weak adhesion in single-lap joints (SLJ) to prevent failure and assist in maintenance, namely in the framework of structural health monitoring. This paper presents a novel approach utilizing machine learning and Lamb Waves (LW) to determine the level of weak adhesion. Firstly, a numerical model of SLJs with different levels of weak adhesion is created and an original approach is proposed for its validation with data from real samples so that reliable LW data can further be easily generated to train and test any other data-driven algorithm for tackling damage. Secondly, a damage detection method is proposed, based on artificial neural networks and fed with simulated data, to determine the level of damage in SLJs, independent of their location. The results show that the simulation model can be validated with a small set of experimental data, being capable of replicating real damage in SLJs. Additionally, the use of simulated data in the training algorithm can increase the accuracy of the simulation model up to 26% when compared to only considering experimental data. The adopted artificial neural network for detecting weak adhesion emerges as a promising approach, yielding a precision of over 95%. Thus, machine learning and LW data can be used to improve the reliability and accuracy of adhesive bonding quality control, as well function as a technique for structural health monitoring, which can enhance the safety and durability of bonded structures.
A Multi-Agent System in Education Facility Design
Authors: Strug Barbara , Ślusarczyk Grażyna
Published: October 2023

This paper deals with a multi-agent system which supports the designer in solving complex design tasks. The behaviour of design agents is modelled by sets of grammar rules. Each agent uses a graph grammar or a shape grammar and a database of facts concerning the subtask it is responsible for. The course of the design process is determined by the interaction between specialised agents. Space layouts of designs are represented by attributed graphs encoding both topological structures and semantic properties of solutions. The agents work in parallel on the common graph, independently generating layouts of different design components while specified node labels evoke agents using shape grammars. The agents’ cooperation allows them to combine a form-oriented approach with a functional-structural one in the design process, where the agents generate the general 3D form of the object based on design requirements together with the space layout based on the functional aspects of the solution. Based on the given design criteria, the agents search for admissible solutions within the design space that constitutes their operating environment. The proposed approach is illustrated by the example of designing kindergarten facilities.
An Optimized Flexible Accelerator for Elliptic Curve Point Multiplication over NIST Binary Fields
Authors: Aljaedi Amer , Rashid Muhammad , Jamal Sajjad Shaukat , Alharbi Adel R. , Alotaibi Mohammed
Published: October 2023

This article proposes a flexible hardware accelerator optimized from a throughput and area point of view for the computationally intensive part of elliptic curve cryptography. The target binary fields, defined by the National Institute of Standards and Technology, are GF(2163), GF(2233), GF(2283), GF(2409), and GF(2571). For the optimization of throughput, the proposed accelerator employs a digit-parallel multiplier. The size of the digit is 41 bits. The proposed accelerator has reused the multiplication and squaring circuit for area optimization to compute modular inversions. Flexibility is included using three additional buffers on top of the proposed accelerator architecture to load different input parameters. Finally, a dedicated controller is used to optimize control signal handling. The architecture is modeled using Verilog and implemented up to the post-place-and-route level on a Xilinx Virtex-7 field-programmable gate array. The area utilization of our accelerator in slices is 1479, 1998, 2573, 3271, and 4469 for m=163 to 571. The time needed to perform one-point multiplication is 7.15, 10.60, 13.26, 20.96, and 30.42 μs. Similarly, the throughput over area figures for the same key lengths are 94.56, 47.21, 29.30, 14.58, and 7.35. Consequently, achieved results and a comprehensive performance comparison show the suitability of the proposed design for constrained environments that demand throughput/area-efficient implementations.
A Comparative Assessment of Bridge Deck Wearing Surfaces: Performance, Deterioration, and Maintenance
Authors: Kale Akshay , Kassa Yonas , Ricks Brian , Gandhi Robin
Published: October 2023

Bridge decks deteriorate faster compared to other bridge components, primarily influenced by traffic volume, while previous studies have examined the effect of bridge-wearing surfaces on deterioration, further understanding of the relationship between bridge performance and maintenance is needed for policy-making and planning purposes. In this study, we focus on nine influential variables to unravel the intricate connections among performance, deterioration, and maintenance of six distinct bridge-wearing surfaces: Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Surface Concrete, and Other. Statistical analyses were employed to determine associations between variables and concepts, exploring similarities and differences across various wearing surface types. In particular, machine learning algorithms were utilized to model the maintenance considering the performance and deterioration of the six diverse wearing surfaces. This approach allowed for an examination of interactions between those variables and concepts. We further applied a well-performing prediction model (which achieved an accuracy of 0.86 and an AUC score of approximately 0.83) to obtain interpretable insights regarding bridge deck surfaces. Analysis with interpretable methods such as SHAP (Shapley additive explanation) and PDP (partial dependency plot) revealed that deterioration, deck age, deck area, and overall performance were the most influential variables among average daily traffic, average daily truck traffic, and the number of spans significantly influenced the maintenance of bridge deck condition with different wearing surfaces. Notably, a strong relationship between performance and maintenance was observed in specific wearing surface types, such as Monolithic Concrete and Wood or Timber, while Other surface types exhibited different patterns. These findings highlight the need for tailored approaches when assessing bridge health, considering the distinct characteristics of different bridge deck types.
Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
Authors: Agathos Leonidas , Avgoustis Andreas , Avgoustis Nikolaos , Vlachos Ioannis , Karydis Ioannis , Avlonitis Markos
Published: October 2023

The importance of monitoring earthquakes for disaster management, public safety, and scientific research can hardly be overstated. The emergence of low-cost seismic sensors offers potential for widespread deployment due to their affordability. Nevertheless, vehicular noise in low-cost seismic sensors presents as a significant challenge in urban environments where such sensors are often deployed. In order to address these challenges, this work proposes the use of an amalgamated deep neural network constituent of a DNN trained on earthquake signals from professional sensory equipment as well as a DNN trained on vehicular signals from low-cost sensors for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise. To this end, we present low-cost seismic sensory equipment and three discrete datasets that—when the proposed methodology is applied—are shown to significantly outperform a generic stochastic differential model in terms of effectiveness and efficiency.
Influence of Ozone Treatment during Storage on Odour-Active Compounds, Total Titratable Acidity, and Ascorbic Acid in Oranges and Bananas
Authors: Ullrich Lisa , Gillich Elodie , André Amandine , Panarese Sandra , Imhaus Anne Flore , Fieseler Lars , Chetschik Irene
Published: October 2023

Ozone has the potential to improve the shelf life of fruits and vegetables by its antimicrobial properties, but the influence on the aroma properties has not been studied comprehensively at the molecular level. Therefore, the impact of ozone on odour-active compounds, total titratable acidity, ascorbic acid, weight loss, and fungal growth during storage is investigated in oranges and bananas. The fruits were stored at room temperature for 11 days either with or without ozone treatment. Interestingly, no significant impact of the ozone treatment on the contents of the selected odour-active compounds in bananas and oranges was observable. Furthermore, no impact on titratable acidity, weight loss, and ascorbic acid content were observable as a consequence of the ozone treatment at the respective ozone concentrations. In addition, the inhibitive effects of the growth and sporulation of moulds were demonstrated in this study. Finally, the data from this research show that ozonation has the potential of improving the shelf life of fruits without compromising their quality.
Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia
Authors: Chuan Lee Loo , Roslee Mardeni , Sudhamani Chilakala , Waseem Athar , Osman Anwar Faizd , Jusoh Mohamad Huzaimy
Published: October 2023

The development of intelligent transport systems, mobile cellular networks, microwave links, and vehicle communications has accelerated with the use of wireless connections as a communication channel in 5G wireless technology. Weather, including rain, fog, snow, sand, and dust, impacts wireless communication channels in various ways. These effects are more pronounced at the high frequencies of millimeter-wave bands. Recently, the 5G network has made it possible to support a variety of applications with fast speeds and high-quality content. To facilitate the use of high-millimeter-wave frequencies, a recent study investigated how sand and dust affect the 5G communication channel. In this paper, we consider the impact of frequent and heavy rainfall on millimeter-wave propagation and cross-polarization of the wave at various points along the propagation path caused by rainfall in urban and highway scenarios in Malaysia. We estimate rainfall attenuation, path loss, and link margin at various millimeter-wave frequencies. From our simulation results, it is evident that rainfall attenuation, path loss, and link margin depend on the operating frequency, path difference, and rainfall rate. In this paper, we estimate and compare the optimal path difference values under urban and highway scenarios both with and without rainfall attenuation.
Tuning of Morphology and Surface Properties of Porous Silicones by Chemical Modification
Authors: Racles Carmen , Vasiliu Ana-Lavinia
Published: October 2023

The behavior of materials against water is a key element in many practical applications. Silicones are hydrophobic by nature and can be chemically modified to become hydrophilic or highly hydrophobic, while combining intrinsic surface properties with morphological details may lead to superhydrophobic materials. Chemically modified porous silicones and their surface properties have rarely been investigated. Our aim in this study was to tune the surface properties of porous silicone materials by a combination of chemical modification and emulsion templating The porous silicones were obtained by two cross-linking reactions in toluene–water emulsion, in mild conditions: dehydrocoupling of poly(methylhydrogen)siloxane (PMHS) and dimethyl-methylhydrogensiloxane copolymers and UV-initiated thiol-ene addition on a poly(dimethyl-methylvinyl)siloxane, respectively. Apart from the pores generated by water droplets, in the first process, additional large pores appeared due to hydrogen evolution. Their size and number diminished along with the cross-linking degree; thus, the porosity was tuned by adjusting the composition of the reaction mixture. Chemical modifications were performed in situ to introduce more hydrophobic groups (hexane and trimethylsilane) or hydrophilic groups (thioethanol), modifications that were followed by FT-IR spectroscopy. The inner morphology and powder wetting behavior of the crushed samples were investigated by SEM, tensiometry analyses, and contact angle measurements. The materials showed morphological particularities and surface properties that spanned from hydrophilic to superhydrophobic with lotus or petal effects.
Influence of Time-Series Length and Hyperparameters on Temporal Convolutional Neural Network Training in Low-Power Battery SOC Estimation
Authors: Wang Xiaoqiang , Lu Haogeng , Li Jianhua
Published: October 2023

In battery management systems, state of charge (SOC) estimation is important for ensuring the safety and reliability of batteries. Currently, there are various methods for estimating SOC, and the neural network method is the most popular. However, when the battery’s SOC is low (below 20%), the uncertainty in neural network parameters can lead to significant bias in SOC estimation. To address these problems, this study proposes a method based on genetic algorithm (GA) optimization of a time-serialization convolutional neural network (TSCNN) model. First, the population is initialized according to the optimized hyperparameters of the TSCNN model, whereby the experimental data are converted into time-series data. Subsequently, neural network models are built based on the population, thereby using the effect of the network as the fitness function for GA optimization. Finally, an optimized network structure is obtained for accurate SOC estimation. During the optimization process, the optimized data exhibited abnormal phenomena, usually manifested as exceeding the data limits or being zero. In the past, abnormal data were discarded and new data were regenerated; however, this reduces the correlation between data. Therefore, this study proposes a check function to enhance the correlation between the data, converting abnormal data into normal data by limiting the data range. To the best of our knowledge, it is the first time that a GA is being proposed to optimize the time-series length of a convolutional neural network (CNN) while the neural network parameters are optimized so that the time-series length and neural network parameters achieve the best match. In the experimental results, the maximum error was 4.55% for the dynamic stress test (DST) dataset and 2.58% for the urban dynamometer driving schedule (UDDS) dataset. When the battery SOC was below 20%, the estimation error did not incur a huge error. Therefore, the optimization method proposed for the TSCNN model in this study can effectively improve the accuracy and reliability of SOC estimation in the low-battery state.
Evaluating Drought Effects on Soil: Innovative Soil Salinity Monitoring via SAR Data, Sentinel-2 Imagery, and Machine Learning Algorithms in Kerkennah Archipelago
Authors: Hihi Sarra , Katlane Rim , Kilani Boubaker , Zekri Mohamed Waddah , Bensalah Rafik , Siewert Christian , Kallel Monem
Published: October 2023

The Kerkennah archipelago in Tunisia is one of the most vulnerable areas where the influence of climate change is undeniable. Soil salinization has emerged as a major consequence of climate variation on this island. In this study, remote sensing techniques were implemented to develop a model for predicting soil salinity from satellite images. Machine learning algorithms, Sentinel-1 and Sentinel-2 data, and ground truth measurements were used to estimate soil salinity. Several algorithms were considered to achieve accurate findings. These algorithms are categorized as polynomial regression, random forest regression, exponential regression, and linear regression. The results demonstrate that exponential regression is the pre-eminent algorithm for estimating soil salinity with high predictive accuracy of R2 = 0.75 and RMSE = 0.47 ds/m. However, spatiotemporal soil salinity maps reveal distinct and clear distribution patterns, highlighting salty areas (i.e., sebkhas) and agricultural parcels. Thus, through the model, we explore areas of moderately high salinity within agricultural lands that could be affected by irrigation practices. The present work demonstrates a reliable model for soil salinity monitoring in the Kerkennah archipelago and inspires more successful technologies such as remote sensing and machine learning to improve the estimation of soil salinity in climate-affected vulnerable areas.
Characteristic Variation of Particulate Matter-Bound Polycyclic Aromatic Hydrocarbons (PAHs) during Asian Dust Events, Based on Observations at a Japanese Background Site, Wajima, from 2010 to 2021
Authors: Bai Pengchu , Wang Yan , Zhang Hao , Zhang Xuan , Zhang Lulu , Matsuki Atsushi , Nagao Seiya , Chen Bin , Tang Ning
Published: October 2023

Asian dust (AD) events and total suspended particles (TSPs) were observed at the Kanazawa University Wajima Air Monitoring Station (KUWAMS), a Japanese background site, during the East Asian winter monsoon periods (from November to May of the following year) from 2010 to 2021. Nine kinds of polycyclic aromatic hydrocarbons (PAHs) were determined in each TSP sample. In this study, a total of 54 AD events were observed. According to the different pathways of long-range transportation, AD events were divided into AD-high events (transported at higher altitudes, approximately 4000 m) and AD-low events (transported at lower altitudes, approximately 2500 m). The TSP concentrations increased sharply in the AD events and were higher in the AD-high events (39.8 ± 19.5 μg/m3) than in the AD-low events (23.5 ± 10.5 μg/m3). AD did not have a significant effect on the ΣPAHs characteristic variation, as ΣPAHs (ΣPAHs = fluoranthene, pyrene, benz[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, benzo[ghi]perylene, and indeno [1,2,3-cd]pyrene) concentrations in non-AD periods, AD-high events, and AD-low events were 543 ± 374, 404 ± 221, and 436 ± 265 pg/m3, respectively. The PAH compositions were also consistent. As a result, the TSP concentration was affected by the input air mass transported at higher altitudes from the desert region while the PAH concentration was impacted by the air mass at lower altitudes, which carried the PAHs emitted from fossil fuels and biomass combustion in northeastern China. Moreover, the health risks of PAHs were calculated with the inhalation lifetime cancer risk, which ranged from 10−6 to 10−5 ng/m3, indicating a potential carcinogenic risk at the KUWAMS during the East Asian winter monsoon periods.
Study on Spatial and Temporal Distribution Characteristics of the Cooking Oil Fume Particulate and Carbon Dioxide Based on CFD and Experimental Analyses
Authors: Ding Minting , Zhang Shunyu , Wang Jiahua , Ye Feng , Chen Zhenlei
Published: October 2023

The cooking oil fume particulate (COFP) produced by indoor cooking can harm human health seriously, and therefore requires urgent monitoring and optimization. In this paper, the kitchen cooking simulation process was established by using computational fluid dynamics (CFD) based on the fluid dynamics theory. Combined with the user defined function (UDF), the spatial and temporal distributions of COFP and carbon dioxide (CO2) during the cooking process were simulated and analyzed, respectively. Both simulation results were verified using experimental data. Moreover, this paper introduces a COFP concentration correlation function that utilizes the spatiotemporal correlation between COFP and CO2 concentrations during the cooking process. The function is based on the spatiotemporal distribution of CO2 concentration. By comparing it with traditional calculations, the proposed function is shown to achieve a remarkable 70% improvement in efficiency and maintain an accuracy rate exceeding 90%. This enables the rapid analysis and control of COFP concentration through monitoring and analyzing CO2 levels in the kitchen.
Mean Flow from Phase Averages in the 2D Boussinesq Equations
Authors: Wingate Beth A. , Rosemeier Juliane , Haut Terry
Published: October 2023

The atmosphere and ocean are described by highly oscillatory PDEs that challenge both our understanding of their dynamics and their numerical approximation. This paper presents a preliminary numerical study of one type of phase averaging applied to mean flows in the 2D Boussinesq equations that also has application to numerical methods. The phase averaging technique, well-known in dynamical systems theory, relies on a mapping using the exponential operator, and then an averaging over the phase. The exponential operator has connections to the Craya–Herring basis pioneered by Jack Herring to study the fluid dynamics of oscillatory, nonlinear fluid dynamics. In this paper, we perform numerical experiments to study the effect of this averaging technique on the time evolution of the solution. We explore its potential as a definition for mean flows. We also show that, as expected from theory, the phase-averaging method can reduce the magnitude of the time rate of change in the PDEs, making them potentially suitable for time stepping methods.
Fabrication, Characterization, and In Vitro Cytotoxicity Assessment of Tri-Layered Multifunctional Scaffold for Effective Chronic Wound Healing
Authors: Ijaola Ahmed Olanrewaju , Subeshan Balakrishnan , Pham Anh , Uddin Md. Nizam , Yang Shang-You , Asmatulu Eylem
Published: October 2023

Chronic wounds have been a global health risk that demands intensive exploration. A tri-layered biomaterial scaffold has been developed for skin wounds. The top layer of the scaffold is superhydrophobic, and the bottom layer is hydrophilic, both of which were electrospun using recycled expanded polystyrene (EPS) and monofilament fishing line (MFL), respectively. The intermediate layer of the scaffold comprised hydrogel by cross-linking chitosan (CS) with polyethylene glycol. The surface morphology, surface chemistry, thermal degradation, and wettability characteristics of each layer of the scaffold were examined. Also, the antibacterial activity and in vitro cytotoxicity study on the combined tri-layered scaffold were assessed against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Data revealed exceptional water repellency of the heat-treated electrospun top superhydrophobic layer (TSL) with a high-water contact angle (WCA) of 172.44°. A TSL with 15 wt% of micro-/nano-inclusions had the best thermal stability above 400 °C. The bottom hydrophilic layer (BHL) displayed a WCA of 9.91°. Therapeutically, the synergistic effect of the combined tri-layered scaffold significantly inhibited bacteria growth by 70.5% for E. coli and 68.6% for S. aureus. Furthermore, cell viability is enhanced when PEG is included as part of the intermediate CS hydrogel layer (ICHL) composition.
Temperature Upshifts in Mammalian Cell Culture: A Suitable Strategy for Biosimilar Monoclonal Antibodies?
Authors: Marschall Lukas , Gottimukkala Chitti Babu , Kayal Biswajit , Veeraraghavan Veerabhadra Madurai , Mandal Samir Kumar , Bandyopadhyay Suman , Herwig Christoph
Published: October 2023

Temperature downshifts are the gold standard when setting up control strategies for mammalian cell culture processes. These shifts are performed to prolong production phases and attain heightened levels of productivity. For the development of biosimilars, however, the bottleneck is in achieving a prespecified product quality. In a late-stage development project, we investigated the impact of temperature shifts and other process parameters with the aim of optimizing the glycosylation profile of a monoclonal antibody (mAb). We applied a design of experiments approach on a 3 L scale. The optimal glycosylation profile was achieved when performing a temperature upshift from 35.8 °C to 37 °C. Total afucosylated glycan (TAF) decreased by 1.2%, and galactosylated glycan species (GAL) increased by up to 4.5%. The optimized control strategy was then successfully taken to the manufacturing scale (1000 L). By testing two sets of set points at the manufacturing scale, we demonstrated that the statistical models predicting TAF and GAL trained with small-scale data are representative of the manufacturing scale. We hope this study encourages researchers to widen the screening ranges in process development and investigate whether temperature upshifts are also beneficial for other mAbs.
Postoperative Nausea and Vomiting Prediction: Machine Learning Insights from a Comprehensive Analysis of Perioperative Data
Authors: Kim Jong-Ho , Cheon Bo-Reum , Kim Min-Guan , Hwang Sung-Mi , Lim So-Young , Lee Jae-Jun , Kwon Young-Suk
Published: October 2023

Postoperative nausea and vomiting (PONV) are common complications after surgery. This study aimed to present the utilization of machine learning for predicting PONV and provide insights based on a large amount of data. This retrospective study included data on perioperative features of patients, such as patient characteristics and perioperative factors, from two hospitals. Logistic regression algorithms, random forest, light-gradient boosting machines, and multilayer perceptrons were used as machine learning algorithms to develop the models. The dataset of this study included 106,860 adult patients, with an overall incidence rate of 14.4% for PONV. The area under the receiver operating characteristic curve (AUROC) of the models was 0.60–0.67. In the prediction models that included only the known risk and mitigating factors of PONV, the AUROC of the models was 0.54–0.69. Some features were found to be associated with patient-controlled analgesia, with opioids being the most important feature in almost all models. In conclusion, machine learning provides valuable insights into PONV prediction, the selection of significant features for prediction, and feature engineering.
Microstructural Adaptation for Prey Manipulation in the Millipede Assassin Bugs (Hemiptera: Reduviidae: Ectrichodiinae)
Authors: Zha Shiyu , Wang Zhiyao , Li Xinyu , Chen Zhaoyang , Wang Jianyun , Li Hu , Cai Wanzhi , Tian Li
Published: October 2023

Species in Ectrichodiinae are known for their prey specialization on millipedes. However, knowledge of the morphological adaptations to this unique feeding habit was limited. In the current study, we examined the microstructures of the antennae, mouthparts, and legs of four millipede feeding ectrichodiines, Ectrychotes andreae (Thunberg, 1888), Haematoloecha limbata Miller, 1953, Labidocoris pectoralis (Stål, 1863), and Neozirta eidmanni (Taueber, 1930), and compared them with those of three species of tribelocephalines, a group closely related to Ectrichodiinae. On the antennae, we found four types of antennal sensilla. On the mouthparts, we recognized four types of labial sensilla. Sampled ectrichodiines have distinctly more and denser slightly transverse ridges on the external side of mandibles than tribelocephalines. E. andreae and H. limbata possess numerous small papillae fringed with densely arranged finger-print-like grains on the trochanter and femur; these probably facilitate the immobilization of prey. Overall, our study illustrates, at a microstructural level, the remarkable morphological adaption of prey manipulation in ectrichodiine, and has enhanced our understanding about stenophagy in the family Reduviidae.