bluuebunny/crossref_metadata_2025 huggingface dataset:
N-Acetyl-Cysteine: Modulating the Cysteine Redox Proteome in Neurodegenerative Diseases
Authors: Martinez-Banaclocha Marcos
Published: February 2022

In the last twenty years, significant progress in understanding the pathophysiology of age-associated neurodegenerative diseases has been made. However, the prevention and treatment of these diseases remain without clinically significant therapeutic advancement. While we still hope for some potential genetic therapeutic approaches, the current reality is far from substantial progress. With this state of the issue, emphasis should be placed on early diagnosis and prompt intervention in patients with increased risk of neurodegenerative diseases to slow down their progression, poor prognosis, and decreasing quality of life. Accordingly, it is urgent to implement interventions addressing the psychosocial and biochemical disturbances we know are central in managing the evolution of these disorders. Genomic and proteomic studies have shown the high molecular intricacy in neurodegenerative diseases, involving a broad spectrum of cellular pathways underlying disease progression. Recent investigations indicate that the dysregulation of the sensitive-cysteine proteome may be a concurrent pathogenic mechanism contributing to the pathophysiology of major neurodegenerative diseases, opening new therapeutic opportunities. Considering the incidence and prevalence of these disorders and their already significant burden in Western societies, they will become a real pandemic in the following decades. Therefore, we propose large-scale investigations, in selected groups of people over 40 years of age with decreased blood glutathione levels, comorbidities, and/or mild cognitive impairment, to evaluate supplementation of the diet with low doses of N-acetyl-cysteine, a promising and well-tolerated therapeutic agent suitable for long-term use.
Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network
Authors: Yang Dan , Xiong Mengzhou , Wang Tao , Lu Guangtao
Published: February 2022

Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases. A non-destructive method to detect pipeline ponding using percussion acoustic signals and a convolution neural network (CNN) is proposed in this paper. During the process of detection, a constant energy spring impact hammer is used to apply an impact on the pipeline, and the percussive acoustic signals are collected. A Mel spectrogram is used to extract the acoustic feature of the percussive acoustic signal with different ponding volumes in the pipeline. The Mel spectrogram is transferred to the input layer of the CNN and the convolutional kernel matrix of the CNN realizes the recognition of pipeline ponding volume. The recognition results show that the CNN can identify the amount of pipeline ponding with the percussive acoustic signals, which use the Mel spectrogram as the acoustic feature. Compared with the support vector machine (SVM) model and the decision tree model, the CNN model has better recognition performance. Therefore, the percussion-based pipeline ponding detection using the convolutional neural network method proposed in this paper has high application potential.
Gaming in Pre-Roman Italy: Characterization of Early Ligurian and Etruscan Small Pieces, Including Dice
Authors: Angelini Ivana , Bettineschi Cinzia , Venturino Marica , Artioli Gilberto
Published: February 2022

An interesting assemblage of ancient ceramic materials connected or potentially connected with gaming activities has been characterized from the archaeometric point of view. The materials (washer-like pieces, small spheres, and cubic dice, with and without inscriptions) were found in the Villa del Foro excavation (Alessandria, Italy). They are related to the early Ligurian population of the site and their frequent contacts with Etruscan both in Etruria and in the Po Valley, in a period spanning the early VI century BC till the first half of the V century BC. Starting from the materials evidence, hypotheses are proposed concerning their possible use and cultural meaning. The studied cubic dice are discussed in the wider context of the pre-Roman diffusion of these objects.
Presence of Known and Emerging Honey Bee Pathogens in Apiaries of Veneto Region (Northeast of Italy) during Spring 2020 and 2021
Authors: Bordin Fulvio , Zulian Laura , Granato Anna , Caldon Mauro , Colamonico Rosa , Toson Marica , Trevisan Laura , Biasion Laura , Mutinelli Franco
Published: February 2022

A progressive honey bee population decline has been reported worldwide during the last decades, and it could be attributed to several causes, in particular to the presence of pathogens and parasites that can act individually or in synergy. The health status of nine apiaries located in different areas of the Veneto region (northeast of Italy) was assessed for two consecutive years (2020 and 2021) in spring, during the resumption of honey bee activity, for determining the presence of known (Nosema spp., Varroa mite and viruses) and less known or emerging pathogens (Lotmaria passim and Crithidia mellificae) in honey bees. After honey bees sampling from each of the nine apiaries, Nosema apis, Nosema ceranae, L. passim, C. mellificae, ABPV, CBPV, IAPV, KBV, BQCV, SBV, DWV-A, DWV-B and V. destructor were investigated either by microscopic observation or PCR protocols. The viruses BQCV, SBV, CBPV followed by N. ceranae and L. passim were the most prevalent pathogens, and many of the investigated hives, despite asymptomatic, had different degrees of co-infection. This study aimed to highlight, during the resumption of honey bee activity in spring, the prevalence and spreading in the regional territory of different honey bee pathogens, which could alone or synergistically alter the homeostasis of bees colonies. The information gathered would increase our knowledge about the presence of these microorganisms and parasites in the territory and could contribute to improve beekeepers practice.
The Effect of Sample Preparation and Measurement Techniques on Heavy Metals Concentrations in Soil: Case Study from Kraków, Poland, Europe
Authors: Operacz Agnieszka , Bigaj Adrianna , Hap Karolina , Kotowski Tomasz
Published: February 2022

Accurate and reliable laboratory results are an extremely important and integral part of conducting scientific research. Many factors influence the results obtained, including the type of determination method, accuracy and precision of measurements, and laboratory equipment used for the tests. This paper presents the results of measurements of heavy metal concentration in soil using two methods for adding soil components into solution and different laboratory methods and types of measuring equipment. The first method used was hot digestion of soil samples with a mixture of concentrated HNO3 and HClO4, after prior ashing of organic matter (IUNG method). The second method was a two-stage decomposition, where soil samples were hot digested, initially with oxidizing acid (HNO3) and subsequently with non-oxidizing acid (HF) (two-stage decomposition). The concentrations of selected heavy metals (Cr, Cu, Fe, Mn, Ni, Pb and Zn) were determined in solutions obtained by both digestion methods. The solutions obtained from soil decomposition were determined twice using atomic absorption spectrometry (AAS) and inductively coupled plasma mass spectrometry (ICP MS) methods in different laboratories using different types of spectrometers. In most cases, the measured concentrations of heavy metals are the highest for the two-stage solution samples and the measurements of their concentrations carried out using ICP. The exceptions are the measurements of Cu and Ni concentrations. In the case of Cu, lack of significant differentiation in concentrations of this metal may result from different forms of occurrence of Cu in soil than in the case of other metals. For Ni, however, a reversal of the trends seen for the other metals is observed and the type of spectrometer used for measurements is important. There may be an interference of the spectrometric spectrum of Ni with the spectra of the other determined metals. However, this is not clear at the present stage of the study. In conclusion, the results of this study indicate that the choice of soil sample preparation and the type of spectrometer used for measurements can, in many cases, determine the value of laboratory results, even if it is in an expected range of standard material. Research of published papers proved that most of them show only results based on one selected course of methodology without comparison with others. The novelty of the paper is the comparison of the measurements of heavy metal concentration in soil using two methods for adding soil components into solution and different laboratory methods and types of measuring equipment. Additionally, the article includes a discussion of the importance of methodology. We believe that the conclusions may help to better understand how sample preparation and measurement methods applied may influence the results obtained.
Vision-Guided Six-Legged Walking of Little Crabster Using a Kinect Sensor
Authors: Kim Jung-Yup , Park Min-Jong , Kim Sungjun , Shin Dongjun
Published: February 2022

A conventional blind walking algorithm has low walking stability on uneven terrain because a robot cannot rapidly respond to height changes of the ground due to limited information from foot force sensors. In order to cope with rough terrain, it is essential to obtain 3D ground information. Therefore, this paper proposes a vision-guided six-legged walking algorithm for stable walking on uneven terrain. We obtained noise-filtered 3D ground information by using a Kinect sensor and experimentally derived coordinate transformation information between the Kinect sensor and robot body. While generating landing positions of the six feet from the predefined walking parameters, the proposed algorithm modifies the landing positions in terms of reliability and safety using the obtained 3D ground information. For continuous walking, we also propose a ground merging algorithm and successfully validate the performance of the proposed algorithms through walking experiments on a treadmill with obstacles.
Encapsulation of Lacticaseibacillus rhamnosus GG: Probiotic Survival, In Vitro Digestion and Viability in Apple Juice and Yogurt
Authors: Romero-Chapol Oscar O. , Varela-Pérez Abigail , Castillo-Olmos Ana G. , García Hugo S. , Singh Jaspreet , García-Ramírez Pedro J. , Viveros-Contreras Rubí , Figueroa-Hernández Claudia Y. , Cano-Sarmiento Cynthia
Published: February 2022

This study was aimed to prepare and characterize capsules loaded with Lacticaseibacillus rhamnosus GG (LGG), evaluating cell viability under gastrointestinal in vitro conditions and during storage in yogurt and apple juice, an alternative to traditional probiotic foods for people who are lactose intolerant. The capsules were prepared by ionic gelation, with an emulsification process as pretreatment. Cell viability of encapsulated LGG was evaluated after two different homogenization processes: magnetic stirring (AM) and Ultraturrax® homogenizer (UT). The system with the best relationship between viability and morphology was UT, which produced a viability of 85.80%. During in vitro evaluation, the capsules provided higher protection than free cells, up to 100% of cell viability. The morphology of capsules of both systems displayed a continuous and homogeneous surface. The cell viability of the encapsulated probiotics added in apple juice stored for 22 days at 4 °C was 86.16% for AM and 100% for UT, while the viability of free cells was 80.50%. In natural yogurt, the cell viability of the probiotics encapsulated stored 30 days at 4 °C was 100% for AM, 100% for UT, and 92.68% for free cells. This study suggests an alternative to preserve probiotic bacteria in a potential functional food.
Energy Use in the EU Livestock Sector: A Review Recommending Energy Efficiency Measures and Renewable Energy Sources Adoption
Authors: Paris Bas , Vandorou Foteini , Tyris Dimitrios , Balafoutis Athanasios T. , Vaiopoulos Konstantinos , Kyriakarakos George , Manolakos Dimitris , Papadakis George
Published: February 2022

This study conducts a review bringing together data from a large number of studies investigating energy use in EU livestock systems. Such a study has not been conducted previously, and improvements in our understanding of energy use concentrations in livestock systems will aid in developing interventions to achieve the EU’s 2030 and 2050 sustainability targets. The results from the Life Cycle Assessments included in this review indicate that energy use is concentrated in feed, housing, and manure management. In most systems, animal feed is the dominant energy use category. Regarding specific livestock categories, the studies covered indicate that energy use requirements range from 2.1 to 5.3 MJ/kg per ECM for cow milk, 59.2 MJ/kg for a suckler cow–calf, and 43.73 MJ/kg for a dairy bull, 15.9 MJ/kg to 22.7 MJ/kg for pork production, 9.6 MJ/kg to 19.1 MJ/kg for broiler production, 20.5–23.5 MJ/kg for chicken egg production. Our review indicates dominance of and dependence on fossil fuel and discusses the situation and research around transitioning towards renewable energy sources and improving energy efficiency. Our analysis indicates that existing energy use data in livestock systems are fragmented and characterized by multiple methodologies and considerable data gaps. In our view, there is a need for the development of a standardized methodology for measuring energy use in livestock systems, which we consider a necessary step to develop interventions that reduce fossil energy use in livestock systems and its contribution to climatic change.
Assessment of PCBs in Surface Waters at Ultratrace Levels: Traditional Approaches and Biomonitoring (Lake Baikal, Russia)
Authors: Gorshkov Alexander G. , Kustova Olga V. , Bukin Yurij S.
Published: February 2022

This article presents the results of the assessment of PCB concentrations in surface waters at ultratrace level of concentrations. The assessment of PCB concentrations is based on data from monitoring PCBs in Baikal water within the conventional approach as well as from biomonitoring of PCBs using Baikal omul, Coregonus migratorius, Georgi, 1775 (C. migratorius), as a bioindicator. The time cycle of the monitoring covered the period from 2014 to 2021. The concentrations of PCBs in the water were estimated from the concentrations of seven indicator congeners: 28, 52, 101, 118, 138, 153, and 180, and from congeners of dioxin-like (dl) PCBs in the tissues of C. migratorius. The average value and the statistically significant range of the detected total concentrations (Ʃ7PCBs) in Baikal water were 0.30 and 0.26–0.34 ng/L, respectively. In the tissues of C. migratorius, the average value and the range of Ʃ7PCB concentrations were 5.6 and 4.9–6.3 ng/g (ww), respectively, and for dl-PCBs, 1.5 and 1.3–1.7 ng/g (ww), respectively. The total toxicity equivalent of the detected dl-PCBs was in the WHO-TEQ (2005) range from 0.03 to 0.06 pg/g (ww). The concentrations of Ʃ7PCBs in Baikal water and dl-PCBs in the tissues of C. migratorius corresponded to the concentration levels in the European alpine lakes and the tissues of S. trutta fish inhabiting these lakes.
Progressive Collapse of the Base-Isolated Frame Structures Supported by Stepped Foundation in Mountainous City
Authors: Yang Youfa , Chen Anxu , Yang Tianhang
Published: February 2022

Base-isolated frames supported by stepped foundation in mountainous areas possess their own particularity, so its progressive collapse dynamic response performance and dynamic effect propagation path are very different from those of ordinary flat ground-isolated structural systems. In order to study the progressive collapse performance of the base-isolated frames supported by stepped foundation in mountainous areas under two-directional coupled dynamic excitation, a four-span three-story plane frame demolition column test was simulated to verify the reliability of the computing platform. The common ground motion and three types of long-period ground motions were selected, and the two-dimensional dynamic coupling of the ordinary flat ground isolation structure and the base-isolated frames supported by stepped foundation in mountainous areas was obtained based on the demolition method. First, the seismic isolation structure was subjected to the collapse dynamic response under the vertical unbalanced load, then the collapse dynamic response under the vertical unbalanced load and the horizontal seismic coupling excitation was made; the two were compared and analyzed. It can be used as a reference for the design of progressive collapse of the frame structure of the base-isolated frames supported by stepped foundation in mountainous areas.
Signal-Based Acoustic Emission Clustering for Differentiation of Damage Sources in Corroding Reinforced Concrete Beams
Authors: Van Steen Charlotte , Verstrynge Els
Published: February 2022

Corrosion in reinforced concrete (RC) structures is a major durability issue that requires attention in terms of monitoring, in order to assess the degraded condition and reduce financial costs for maintenance and repair. The acoustic emission (AE) technique has been found to be useful to monitor damage due to steel corrosion in RC. However, further development of monitoring protocols is still necessary towards on-site application. In this paper, a hierarchical clustering algorithm based on cross-correlation is developed and applied to automatically distinguish damage sources during the corrosion process. The algorithm is verified on dummy samples and corroding RC prisms. It is able to distinguish two clusters of which the first one containing AE signals due to corrosion, absorption, hydration, and micro-cracking, and the second one AE signals due to macro-cracking. Electromagnetic interference can be distinguished as a third cluster and filtered subsequently. Due to overlapping characteristics, further differentiation of the first cluster is not possible. Afterwards, the algorithm is scaled up to two sets of RC beams, one set with a uniform corrosion zone, and the other set with a local corrosion zone. In addition, on this sample scale, the algorithm is able to successfully differentiate macro-cracking from corrosion and micro-cracking. It can therefore serve as an additional tool to assess the extent of corrosion-induced damage.
The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media
Authors: Alrehili Marwa M. , Yafooz Wael M. S. , Alsaeedi Abdullah , Emara Abdel-Hamid M. , Saad Aldosary , Al Aqrabi Hussain
Published: February 2022

The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ implicit interests. This paper is aims to propose a novel user interest mining approach and model based on demographic data, big five personality traits and similarity between the topics based on clusters. To demonstrate the leverage of combining user personality traits and demographic data into interest investigation, various experiments were conducted on the collected data. The experimental results showed that looking at personality and demographic data gives more accurate results in mining systems, increases utility, and can help address cold start problems for new users. Moreover, the results also showed that interesting topics were the dominant factor. On the other hand, the results showed that the current users’ implicit interests can be predicted through the cluster based on similar topics. Moreover, the hybrid model based on graphs facilitates the study of the patterns of interaction between users and topics. This model can be beneficial for researchers, people on social media, and for certain research in related fields.
Design and Evaluation of Vacuum Central Drum Seed Metering Device
Authors: Wang Baolong , Na Yi , Liu Jian , Wang Zaiman
Published: February 2022

For the purpose of reducing the number of conventional seed-metering devices and high demand for vacuums, this study designed a vacuum central drum seed-metering device, that is intended to replace multiple seed-metering devices with one, which is comprised of the inner and outer drum. This can be replaced with different diameters of suction holes, ventilation housing, seeding tube, agitating devices, seed box, and seed-cleaning devices, etc. A hybrid rice seed Jingliangyou 1212 was applied as an experimental material, and a JPS-12 computer vision metering device test bench was used to test singular-factor and multi-factor seeding performance of the seed-metering device. The singular-factor performance test of the metering device was conducted under negative pressure of seed suction in the range of 1∼1.5 kPa, at the metering device rotation speed of 10∼60 rpm, with diameters of inlet holes being 2 mm (chamfer: 45°), 2, and 1.5 mm, respectively. The number of seeds was counted by a sucking hole under different factor combinations. The multi-factor test was carried out by rotation rate, negative pressure, and types of sucking holes. A rate of 2 ± 1 rice seed per sucking hole is regarded as the qualified standard. It shows that the qualification rate (2 ± 1 rice seed per sucking hole) of seed suction can reach 97.4% under a combination of metering device rotation speed of 30 r/min, negative pressure of 1.0 kPa, and suction hole diameters of 1.5 mm. High-speed photography was used to study the trajectory of seed-metering at different rotation rates, a locomotive axis was applied to fit the motion curve, and 3D-printing was used to make the seed-metering tube so that the seed collisions could be reduced. This study provides evidence for further optimizing the performance of the vacuum central drum direct-seeding machine for hybrid rice.
The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery—Pilot Study
Authors: Pawuś Dawid , Paszkiel Szczepan
Published: February 2022

This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot.
Screening of Scaffolds for the Design of G-Quadruplex Ligands
Authors: Figueiredo Joana , Peitinho David , Campello Maria Paula Cabral , Oliveira Maria Cristina , Paulo António , Mergny Jean-Louis , Cruz Carla
Published: February 2022

In the last decade, progress has been made in G-quadruplex (G4) ligands development, but for most compounds, the ligand binding mode is speculative or based on low resolution methods, with its discovery based on structure-based approaches. Herein, we report the synthesis of small (MW < 400 Da) heterocycle compounds, containing different aromatic scaffolds, such as phenyl, quinoline, naphthalene, phenanthroline and acridine moieties, in order to explore their stabilization effect towards different DNA G4s, such as those found in c-MYC, KRAS21 and VEGF promoters, 21G human telomeric motif and pre-MIR150. The fluorescence resonance energy transfer (FRET) melting assay indicates that the acridine moiety is the most active scaffold, followed by phenanthroline. The different scaffolds are promising in terms of drug-like properties and, in general, the IC50 values of the respective heterocycle compounds are lower in a cancer cell line, when compared with a normal cell line. The acridine derivative C5NH2 has the most favorable cytotoxic profile in terms of cell selectivity.
Assistive Multimodal Robotic System (AMRSys): Security and Privacy Issues, Challenges, and Possible Solutions
Authors: Marchang Jims , Di Nuovo Alessandro
Published: February 2022

Assistive robotic systems could be a suitable solution to support a variety of health and care services, help independent living, and even simulate affection, to reduce loneliness. However, adoption is limited by several issues, as well as user concerns about ethics, data security, and privacy. Other than the common threats related to internet connectivity, personal robotic systems have advanced interaction possibilities, such as audio, video, touch, and gestures, which could be exploited to gain access to private data that are stored in the robot. Therefore, novel, safer methods of interaction should be designed to safeguard users’ privacy. To solicit further research on secure and private multimodal interaction, this article presents a thorough study of the state-of-the-art literature on data security and user privacy in interactive social robotic systems for health and care. In our study, we focus on social robotics to assist older people, which is a global challenge that is receiving a great deal of attention from the robotics and social care communities. This application will have a significant positive impact on the economy and society, but poses various security and privacy issues. This article analyses the key vulnerable areas where data leakage could occur during a multimodal interaction with a personal assistive robotic system. Thus, blockchain with a resource-aware framework, along with a continuous multifactor authentication mechanism, are envisaged as a potential solution for making such systems secure by design; therefore, increasing trust, acceptability, and adoption. Among the key cybersecurity research challenges, it is crucial to create an intelligent mechanism that autonomously determines the right trade-off between continuous user prompts and system usability, according to data types and personal preferences.
Numerical Modeling and Experimental Investigation of Effective Elastic Properties of the 3D Printed Gyroid Infill
Authors: Bean Philip , Lopez-Anido Roberto A. , Vel Senthil
Published: February 2022

A numerical homogenization approach is presented for the effective elastic moduli of 3D printed cellular infills. A representative volume element of the infill geometry is discretized using either shell or solid elements and analyzed using the finite element method. The elastic moduli of the bulk cellular material are obtained through longitudinal and shear deformations of a representative volume element under periodic boundary conditions. The method is used to analyze the elastic behavior of gyroid infills for varying infill densities. The approach is validated by comparing the Young’s modulus and Poisson’s ratio with those obtained from compression experiments. Results indicate that although the gyroid infill exhibits cubic symmetry, it is nearly isotropic with a low anisotropy index. The numerical predictions are used to develop semi-empirical equations of the effective elastic moduli of gyroid infills as a function of infill density in order to inform design and topology optimization workflows.
A Numerical Study of Chemical Compatibility of GCLs
Authors: Hou Juan , Sun Rui , Chu Chen-Xi , Karen Mpundu , Nasser Marem
Published: February 2022

A series of COMSOL numerical models were established to study the chemical compatibility of GCLs (geosynthetic clay liner). The effect of chemistry on the mesoscopic structure and the hydraulic conductivity of GCLs was investigated. The factors, including the initial mobile porosity, the swelling ratio, the pore size, and the ionic strength, were discussed as well. The mesoscopic mechanism of the physical and chemical processes of GCLs was explored by the COMSOL models. The hypothesis that the final mobile porosity and the final pore size are the key factors of the hydraulic conductivity of GCLs was proven by the simulation. Meanwhile, when the ionic strength increased from low to medium, the changes in pore size, mobile porosity, and hydraulic conductivity were obvious. However, when the ionic strength increased from medium to high, the changes of these parameters tended to be gentle, and the changes in hydraulic conductivity were not obvious. Moreover, a theoretical model considering the effect of the initial particle size, the initial mobile porosity, and the ionic strength was developed to predict the hydraulic conductivity of GCLs in a chemical solution. This theoretical model was verified by experimental data. A good agreement was obtained.
Structural Optimization of Vented Brake Rotors with a Fully Parameterized Model
Authors: Huang Yao-Tien , Liu Ying-Chieh , Chen Kun-Nan , Lai Yueh-Mei
Published: February 2022

Vented brake rotors used in an automobile behave similarly to centrifugal fans, drawing cool air from the inboard side, passing it through the disc vents, and exhausting it from the periphery. A vented brake rotor with a better heat dispersing ability is often superior to a solid rotor, in both thermal performance and brake efficiency. In this research, a fully parameterized model for a ventilated brake rotor is created using the ANSYS Parametric Design Language, to uniquely define the rotor’s geometry. With this parameterized model, two structural optimization cases are studied in this paper. The first one investigated is a modal frequency separation problem: The frequency differences in a tangential mode sandwiched between two nodal diameter modes of the brake rotor model are maximized. An automatic identification scheme for extracting correct mode orders is implemented in the program to track the correct modes during optimization. The second case is a thermal deformation problem: The distortion on the frictional surfaces of the rotor loaded with heat flux generated during the braking process is minimized. The optimization results show that a brake rotor design with a thinner outboard disc and a thicker inboard disc provides a great choice for rotor coning reduction.
Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
Authors: Lv Bo , Jin Li , Zhang Yanan , Wang Hao , Li Xiaoyu , Guo Zhi
Published: February 2022

Compared with the traditional few-shot task, the few-shot none-of-the-above (NOTA) relation classification focuses on the realistic scenario of few-shot learning, in which a test instance might not belong to any of the target categories. This undoubtedly increases the task’s difficulty because given only a few support samples, this cannot represent the distribution of NOTA categories in space. The model needs to make full use of the syntactic information and word meaning information learned in the pre-training stage to distinguish the NOTA category and the support sample category in the embedding space. However, previous fine-tuning methods mainly focus on optimizing the extra classifiers (on top of pre-trained language models (PLMs)) and neglect the connection between pre-training objectives and downstream tasks. In this paper, we propose the commonsense knowledge-aware prompt tuning (CKPT) method for a few-shot NOTA relation classification task. First, a simple and effective prompt-learning method is developed by constructing relation-oriented templates, which can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks. Second, external knowledge is incorporated into the model by a label-extension operation, which forms knowledgeable prompt tuning to improve and stabilize prompt tuning. Third, to distinguish the NOTA pairs and positive pairs in embedding space more accurately, a learned scoring strategy is proposed, which introduces a learned threshold classification function and improves the loss function by adding a new term focused on NOTA identification. Experiments on two widely used benchmarks (FewRel 2.0 and Few-shot TACRED) show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field.
Wind Microgeneration Strategy for Meeting California’s Carbon Neutral Grid Goal
Authors: Niswander Geoffrey , Xydis George
Published: February 2022

As California’s Renewables Portfolio Standard continues to phase power production from fossil fuels, carbon neutral sources will need to be implemented. This sets small-scale wind production and battery storage in a position to integrate into current grid infrastructure as means of production. This would be an “E Pluribus Unum” approach where many decentralized small production and storage units would act in combination to provide a stable grid. This is often referred to as distributed generation (DG). By distributing the grid’s production in this manner and designating predetermined regional hubs for control (in the event of a fractured grid due to natural disaster), the state and its residents will be able to maintain power for critical infrastructure and basic utilities. This work presents, in detail, a sustainable plan for achieving carbon neutral Californian grid by 2045.
Molecular Dynamics of Atomic Layer Deposition: Sticking Coefficient Investigation
Authors: Kunene Thokozane Justin , Tartibu Lagouge Kwanda , Karimzadeh Sina , Oviroh Peter Ozaveshe , Ukoba Kingsley , Jen Tien-Chien
Published: February 2022

This study focused on the atomic scale growth dynamics of amorphous Al2O3 films microscale structural relaxation. Classical Molecular Dynamics (MD) can not entirely model the challenging ALD dynamics due to the large timescales. The all-atom approach has rules based on deposition actions modelled MD relaxations that form as input to attain a single ALD cycle. MD relaxations are used to create a realistic equilibrium surface. This approach is fitting to this study as the investigation of the sticking coefficient is only at the first monolayer that includes the layering of a hydroxyl surface of alumina. The study provides insight between atomic-level numerical information and experimental measurements of the sticking coefficient related to the atomic layer deposition. The MD modeling was for the deposition of Al2O3, using trimethylaluminum (TMA) and water as precursors. The film thickness of 1.7 Å yields an initial sticking coefficient of TMA to be 4.257 × 10−3 determined from the slope of the leading front of the thickness profile at a substrate temperature of 573 K. This work adds to the knowledge of the kinetic nature of ALD at the atomic level. It provides quantitative information on the sticking coefficient during ALD.
Vegetarian “Sausages” with the Addition of Grape Flour
Authors: Tremlova Bohuslava , Havlova Lenka , Benes Patrik , Zemancova Johana , Buchtova Hana , Tesikova Karolina , Dordevic Simona , Dordevic Dani
Published: February 2022

Vegan sausages with the addition of grape flour represent a way to reduce the intake of processed meat and at the same time to increase the intake of a healthy substance of plant origin. Grape flour obtained from grape marc as a byproduct of wine production is a source of many bioactive substances, such as antioxidants and polyphenols. The study was conducted using vegetarian sausage production: six batches of sausages with different concentrations of grape flour (0%, 1%, 3%, 7%, 10%, and 20%) were produced. The following analyses were applied for the evaluation of these vegetarian sausages: ferric reducing antioxidant power assay (FRAP), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), total polyphenolic content, total protein content, and textural and sensory parameters. The results clearly indicated that the grape seed flour addition resulted in a higher antioxidant capacity of experimentally produced vegan sausages. Based on the sensory evaluation, vegan sausages with 1% (according to taste evaluation, these samples were the most acceptable by panelists) and 3% additions of grape flour were selected as the most suitable since they were statistically more acceptable than samples produced with 20% grape flour addition. The results of this study confirm that the addition of grape flour to vegan sausages is nutritionally beneficial for consumers because it increases the antioxidant capacity and polyphenol content; however, a slight decrease in protein content was recorded too. The sustainability of the product is also achieved using the grape flour since it is a waste material generated worldwide within grape processing.
Lithography Hotspot Detection Method Based on Transfer Learning Using Pre-Trained Deep Convolutional Neural Network
Authors: Liao Lufeng , Li Sikun , Che Yongqiang , Shi Weijie , Wang Xiangzhao
Published: February 2022

As the designed feature size of integrated circuits (ICs) continues to shrink, the lithographic printability of the design has become one of the important issues in IC design and manufacturing. There are patterns that cause lithography hotspots in the IC layout. Hotspot detection affects the turn-around time and the yield of IC manufacturing. The precision and F1 score of available machine-learning-based hotspot-detection methods are still insufficient. In this paper, a lithography hotspot detection method based on transfer learning using pre-trained deep convolutional neural network is proposed. The proposed method uses the VGG13 network trained with the ImageNet dataset as the pre-trained model. In order to obtain a model suitable for hotspot detection, the pre-trained model is trained with some down-sampled layout pattern data and takes cross entropy as the loss function. ICCAD 2012 benchmark suite is used for model training and model verification. The proposed method performs well in accuracy, recall, precision, and F1 score. There is significant improvement in the precision and F1 score. The results show that updating the weights of partial convolutional layers has little effect on the results of this method.
Analysis, Simulations, and Experiments for Far-Field Fourier Ptychography Imaging Using Active Coherent Synthetic-Aperture
Authors: Yang Mingyang , Fan Xuewu , Wang Yuming , Zhao Hui
Published: February 2022

Fourier ptychography (FP) is a powerful phase retrieval method that can be used to reconstruct missing high-frequency details and high-space-bandwidth products in microscopy. In this study, we further advanced the application of FP in microscopic imaging to the field of macroscopic far-field imaging, incorporating camera scanning for spatial resolution improvement. First, on the basis of the Fraunhofer diffraction mechanism and the transmission imaging model, we found the analysis of the associated theoretical fundamentals via simulations and experiments to be crucially relevant to the far-field of FP imaging. Second, we built an experimental device with long-distance imaging and experimentally demonstrated the relationship between the spectrum overlap ratio and the reconstructed high-resolution image. The simulation and experimental results showed that an overlap ratio higher than 50% had a good reconstruction effect. Third, camera scanning was used to obtain low-resolution intensity images in this study, for which the scanning range was wide and spherical wave illumination was satisfied, and therefore different positions corresponded to different aberrations of low-resolution intensity images, and even different positions of the same image had aberration differences, leading to inconsistencies in the aberrations of different images. Therefore, in the reconstruction process, we further overcame the effect of the inconsistency of aberrations of different images using the partition reconstruction method, which involves cutting the image into smaller parts for reconstruction. Finally, with the proposed partition reconstruction algorithm, we were able to resolve 40 μm line width of GBA1 resolution object and obtain a spatial resolution gain of 4× with a working distance of 2 m.
Novel Microfluidics Device for Rapid Antibiotics Susceptibility Screening
Authors: Grigorov Emil , Peykov Slavil , Kirov Boris
Published: February 2022

In recent years, excessive utilization of antibiotics has led to the emergence of antibiotic microbial resistance on a planetary scale. This recent phenomenon represents a serious threat to public health, as well as an enormous burden for healthcare systems’ budgets worldwide. Novel, rapid and cheap methods for antibiotic susceptibility screening are urgently needed for this obstacle to be overcome. In this paper, we present a microfluidic device for on-chip antibiotic resistance testing, which allows for antibiotic microbial resistance detection within 6 hours. The design, fabrication and experimental utilization of the device are thoroughly described and analyzed, as well as possibilities for future automation of the whole process. The accessibility of such a device for all people, regardless of economic status, was of utmost importance for us during the development of the project.
Advances, Applications, and Comparison of Thermal (Pasteurization, Sterilization, and Aseptic Packaging) against Non-Thermal (Ultrasounds, UV Radiation, Ozonation, High Hydrostatic Pressure) Technologies in Food Processing
Authors: Chiozzi Viola , Agriopoulou Sofia , Varzakas Theodoros
Published: February 2022

Nowadays, food treatment technologies are constantly evolving due to an increasing demand for healthier and tastier food with longer shelf lives. In this review, our aim is to highlight the advantages and disadvantages of some of the most exploited industrial techniques for food processing and microorganism deactivation, dividing them into those that exploit high temperatures (pasteurization, sterilization, aseptic packaging) and those that operate thanks to their inherent chemical–physical principles (ultrasound, ultraviolet radiation, ozonation, high hydrostatic pressure). The traditional thermal methods can reduce the number of pathogenic microorganisms to safe levels, but non-thermal technologies can also reduce or remove the adverse effects that occur using high temperatures. In the case of ultrasound, which inactivates pathogens, recent advances in food treatment are reported. Throughout the text, novel discoveries of the last decade are presented, and non-thermal methods have been demonstrated to be more attractive for processing a huge variety of foods. Preserving the quality and nutritional values of the product itself and at the same time reducing bacteria and extending shelf life are the primary targets of conscious producers, and with non-thermal technologies, they are increasingly possible.
Valley–Mountain Circulation Associated with the Diurnal Cycle of Precipitation in the Tropical Andes (Santa River Basin, Peru)
Authors: Rosales Alan G. , Junquas Clementine , da Rocha Rosmeri P. , Condom Thomas , Espinoza Jhan-Carlo
Published: February 2022

The Cordillera Blanca (central Andes of Peru) represents the largest concentration of tropical glaciers in the world. The atmospheric processes related to precipitations are still scarcely studied in this region. The main objective of this study is to understand the atmospheric processes of interaction between local and regional scales controlling the diurnal cycle of precipitation over the Santa River basin located between the Cordillera Blanca and the Cordillera Negra. The rainy season (December–March) of 2012–2013 is chosen to perform simulations with the WRF (Weather Research and Forecasting) model, with two domains at 6 km (WRF-6 km) and 2 km (WRF-2 km) horizontal resolutions, forced by ERA5. WRF-2 km precipitation shows a clear improvement over WRF-6 km in terms of the daily mean and diurnal cycle, compared to in situ observations. WRF-2 km shows that the moisture from the Pacific Ocean is a key process modulating the diurnal cycle of precipitation over the Santa River basin in interaction with moisture fluxes from the Amazon basin. In particular, a channeling thermally orographic flow is described as controlling the afternoon precipitation along the Santa valley. In addition, in the highest parts of the Santa River basin (in both cordilleras) and the southern part, maximum precipitation occurs earlier than the lowest parts and the bottom of the valley in the central part of the basin, associated with the intensification of the channeling flow by upslope cross-valley winds during mid-afternoon and its decrease during late afternoon/early night.
Ambient Particulate Air Pollution and Daily Hospital Admissions in 31 Cities in Poland
Authors: Adamkiewicz Łukasz , Maciejewska Katarzyna , Rabczenko Daniel , Drzeniecka-Osiadacz Anetta
Published: February 2022

A strong and consistent association has been observed between morbidity or mortality rates and PM concentration, and is well documented in many countries. In Poland, despite poor air quality, studies concerning the evaluation of acute health effects of ambient air pollution on morbidity from respiratory or cardiovascular diseases are rare. We examined the short-term impact of PMx concentration on hospital admission in 31 Polish cities based on the daily PM10, PM2.5 concentration, meteorological variables, and hospital data. The generalized additive model (GAM) and a random-effects meta-analysis were used to assess the impact of air pollution on morbidity within the exposed population. Almost 1.6 million cardiovascular admissions and 600 thousand respiratory disorders were analyzed. The RR values for PM10-related cardiovascular and respiratory hospital admissions in Poland are equal to 1.0077 (95% confidence interval, 1.0062 to 1.0092) and 1.0218 (95% confidence interval, 1.0182 to 1.0253), respectively, while for PM2.5 1.0088 (95% confidence interval, 1.0072 to 1.0103) and 1.0289 (95% confidence interval, 1.0244 to 1.0335), respectively. Moreover, a moderate heterogeneity of RR estimates was observed between the analyzed cities (I2 values from 27% to 45%). The presented analysis confirms the significant association between hospital admission and PMx concentration, especially during heating seasons.