bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinical Sensitivity, Specificity and Epidemiology of SARS-CoV-2 Serological Testing Using the Biozek COVID-19 Test
Authors: Souverein Dennis , Mank Theo G. , Euser Sjoerd M. , Herpers Bjorn L.
Published: December 2021

Background: Clinical validation using the Biozek COVID-19 test including sensitivity and specificity and associated patient-reported symptoms with SARS-CoV-2 seropositivity. Methods: 316 sera were analyzed including 47 hospitalized cases, 50 mild cases and 219 negative controls. Results were read visually by two technicians and in case of discrepancy by a third. Models were created between independent variables and IgG seropositivity using multivariable logistic regression analysis. Results: Sensitivity of both IgM and IgG together for hospitalized patients at all time periods was 68.1% (32/47) and 90.0% (27/30) after 10 days or more. From mild/asymptomatic cases the combined IgM and IgG sensitivity was 92.0% (46/50) and 91.8% (45/49) after 10 days or more. In the group of non-COVID-19 cases, the overall specificity was 99.1% (217/219). For IgG alone, the specificity was 99.5% (218/219). In the multivariable analysis loss of smell remained the strongest associated variable with an odds ratio (95%CI): 6.82 (5.61–8.31), p-value < 0.001. Our final prediction model yielded a ROC-AUC of 0.77 (0.74–0.81) showing acceptable discrimination. Conclusions: The Biozek COVID-19 test showed high specificity and good sensitivity 10 days after the first sickness day. Solely IgM positive tests must be interpreted with caution and preferably excluded. In order to capture most symptomatic COVID-19 cases, loss of smell should be included within symptomatic screening policies.
Association between Carotid Intima-Media Thickness and the Use of Biological or Small Molecule Therapies in Patients with Rheumatoid Arthritis
Authors: Rojas-Giménez Marta , López-Medina Clementina , Calvo-Gutiérrez Jerusalem , Puche-Larrubia María Ángeles , Gómez-García Ignacio , Seguí-Azpilcueta Pedro , Ábalos-Aguilera María del Carmen , Ruíz Desirée , Collantes-Estévez Eduardo , Escudero-Contreras Alejandro
Published: December 2021

Objective: The objective of this study was to assess the association of carotid intima-media thickness (CIMT), and also the presence of atheromatous plaque, with biological and targeted synthetic disease-modifying antirheumatic drugs, in an established cohort of patients with rheumatoid arthritis (RA). Patients and Methods: We conducted a cross-sectional observational study based on a cohort of patients with RA and a registry of healthy controls, in whom the CIMT and presence of atheromatous plaque were assessed by ultrasound. Data were collected on disease activity, lab results and treatments. Descriptive and bivariate analyses were performed and two multivariate linear regression models (with CIMT as the dependent variable) were constructed to identify variables independently associated with CIMT in our sample of patients with RA. Results: A total of 176 individuals (146 patients with RA and 30 controls) were included. A higher percentage of patients than controls had atheromatous plaque (33.8% vs. 12.5%, p = 0.036), but no differences were found in terms of CIMT (0.64 vs. 0.61, p = 0.444). Compared to values in patients on other therapies, the CIMT was smaller among patients on tumour necrosis factor alpha (TNFα) inhibitors (mean [SD]: 0.58 [0.10] vs. 0.65 [0.19]; p = 0.013) and among those on Janus kinase inhibitors (mean [SD]: 0.52 [0.02] vs. 0.64 [0.18]; p < 0.001), while no differences were found as a function of the use of the other therapies considered. The multivariate linear regression analysis to identify factors associated with CIMT in our patients, adjusting for traditional cardiovascular risk factors such as hypertension, high levels of low-density lipoproteins, diabetes mellitus and smoking, showed that male sex, older age and having a greater cumulative erythrocyte sedimentation rate were independently associated with a larger CIMT, while patients on TNFα inhibitors had a CIMT 0.075 mm smaller than those on other treatments. Conclusions: The use of TNFα inhibitors may protect against subclinical atherosclerosis in patients with RA, patients on this biologic having smaller CIMTs than patients on other disease-modifying antirheumatic drugs. Nonetheless, these results should be confirmed in prospective studies with larger sample sizes.
Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound
Authors: Hsieh Yung-Hsien , Hsu Fang-Rong , Dai Seng-Tong , Huang Hsin-Ya , Chen Dar-Ren , Shia Wei-Chung
Published: December 2021

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.
Performance of Platelet Counting in Thrombocytopenic Samples: Comparison between Mindray BC-6800Plus and Sysmex XN-9000
Authors: Kim Hanah , Hur Mina , Lee Gun-Hyuk , Kim Seung-Wan , Moon Hee-Won , Yun Yeo-Min
Published: December 2021

The performance of platelet (PLT) counting in thrombocytopenic samples is crucial for transfusion decisions. We compared PLT counting and its reproducibility between Mindray BC-6800Plus (BC-6800P, Mindray, Shenzhen, China) and Sysmex XN-9000 (XN, Sysmex, Kobe, Japan), especially focused on thrombocytopenic samples. We analyzed the correlation and agreement of PLT-I channels in both analyzers and BC-6800P PLT-O mode and XN PLT-F channel in 516 samples regarding PLT counts. Ten thrombocytopenic samples (≤2.0 × 109/L by XN PLT-F) were measured 10 times to investigate the reproducibility with the desirable precision criterion, 7.6%. The correlation of BC-6800P PLT-I and XN PLT-I was arranged moderate to very high; but the correlation of BC-6800P PLT-O and XN PLT-F was arranged high to very high. Both BC-6800P PLT-I vs. XN PLT-I and BC-6800P PLT-O vs. XN PLT-F showed very good agreement (κ = 0.93 and κ = 0.94). In 41 discordant samples between BC-6800P PLT-O and XN PLT-F at transfusion thresholds, BC-6800P PLT-O showed higher PLT counts than XN-PLT-F, except the one case. BC-6800P PLT-O exceeded the precision criterion in one of 10 samples; but XN PLT-F exceeded it in six of 10 samples. BC-6800P would be a reliable option for PLT counting in thrombocytopenic samples with good reproducibility.
Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer
Authors: Andreeva Victoriya , Aksamentova Evgeniia , Muhachev Andrey , Solovey Alexey , Litvinov Igor , Gusarov Alexey , Shevtsova Natalia N. , Kushkin Dmitry , Litvinova Karina
Published: December 2021

The diagnosis and treatment of non-melanoma skin cancer remain urgent problems. Histological examination of biopsy material—the gold standard of diagnosis—is an invasive procedure that requires a certain amount of time to perform. The ability to detect abnormal cells using fluorescence spectroscopy (FS) has been shown in many studies. This technique is rapidly expanding due to its safety, relative cost-effectiveness, and efficiency. However, skin lesion FS-based diagnosis is challenging due to a number of single overlapping spectra emitted by fluorescent molecules, making it difficult to distinguish changes in the overall spectrum and the molecular basis for it. We applied deep learning (DL) algorithms to quantitatively assess the ability of FS to differentiate between pathologies and normal skin. A total of 137 patients with various forms of primary and recurrent basal cell carcinoma (BCC) were observed by a multispectral laser-based device with a built-in neural network (NN) “DSL-1”. We measured the fluorescence spectra of suspected non-melanoma skin cancers and compared them with “normal” skin spectra. These spectra were input into DL algorithms to determine whether the skin is normal, pigmented normal, benign, or BCC. The preoperative differential AI-driven fluorescence diagnosis method correctly predicted the BCC lesions. We obtained an average sensitivity of 62% and average specificity of 83% in our experiments. Thus, the presented “DSL-1” diagnostic device can be a viable tool for the real-time diagnosis and guidance of non-melanoma skin cancer resection.
Decentralized Triangular Guidance Algorithms for Formations of UAVs
Authors: Bassolillo Salvatore Rosario , Blasi Luciano , D’Amato Egidio , Mattei Massimiliano , Notaro Immacolata
Published: December 2021

This paper deals with the design of a guidance control system for a swarm of unmanned aerial systems flying at a given altitude, addressing flight formation requirements that can be formulated constraining the swarm to be on the nodes of a triangular mesh. Three decentralized guidance algorithms are presented. A classical fixed leader–follower scheme is compared with two alternative schemes: the former is based on the self-identification of one or more time-varying leaders; the latter is an algorithm without leaders. Several operational scenarios have been simulated involving swarms with obstacles and an increasing number of aircraft in order to prove the effectiveness of the proposed guidance schemes.
An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications
Authors: Singh Aman , Ogunfunmi Tokunbo
Published: December 2021

Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.
Integration of Phase Change Material in the Design of Solar Concentrator-Based Water Heating System
Authors: Rizal Teuku Azuar , Amin Muhammad , Widodo Syamsul Bahri , Abdul Rachman Nazaruddin , Amir Fazri , Pane Nurhalim , Mahlia Teuku Meurah Indra
Published: December 2021

Indonesia has been blessed with excellent solar heat distribution, which can be used as renewable energy to heat water. Various technologies have been developed to utilize these inexhaustible thermodynamic resources, in the form of photons arrays, converted into concentrated heat for daily use, i.e., solar water heater. This renewable-based water heating system can provide significant energy efficiency, benefit the environment, and reduce energy use costs. This experimental study attempts to harvest the energy from the sun using a cylindrical through collector (CTC) type solar concentrator. The CTC was made of the solar reflective film (SRF) affixed to concentrator collector surfaces which was then mounted on an adjustable angle frame of the concentrator collector support. The heat generated from the concentrator was stored in water, and phase change material is embedded in the system to retain the heat longer. The research was carried out in Langsa City, Aceh, Indonesia. The results showed that water heaters using CTC systems could produce 16 L of hot water retained at 40–60 °C for four hours. With the addition of beeswax, the water temperature of the same capacity can be maintained at 40–60 °C for around 5 h. This technology demonstrated an excellent result that produces as much as 60 L of water per day, increasing solar thermal energy efficiency. This technology presented a great potential for replication or even for further development on an industrial scale.
Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud
Authors: Balicki Jerzy
Published: December 2021

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.
Novel Low Complexity BP Decoding Algorithms for Polar Codes: Simplifying on Non-Linear Operations
Authors: Wang Yuhuan , Li Jianguo , Ye Neng , Bu Xiangyuan
Published: December 2021

The parallel nature of the belief propagation (BP) decoding algorithm for polar codes opens up a real possibility of high throughput and low decoding latency during hardware implementation. To address the problem that the BP decoding algorithm introduces high-complexity non-linear operations in the iterative messages update process, this paper proposes to simplify these operations and develops two novel low complexity BP decoding algorithms, namely, exponential BP (Exp-BP) decoding algorithm and quantization function BP (QF-BP) decoding algorithm. The proposed algorithms simplify the compound hyperbolic tangent function by using probability distribution fitting techniques. Specifically, the Exp-BP algorithm simplifies two types of non-linear operations into single non-linear operation using the piece-wise exponential model function, which can approximate the hyperbolic tangent function in the updating formula. The QF-BP algorithm eliminates non-linear operations using the non-uniform quantization in the updating formula, which is effective in reducing computational complexity. According to the simulation results, the proposed algorithms can reduce the computational complexity up to 50% in each iteration with a loss of less than 0.1 dB compared with the BP decoding algorithm, which can facilitate the hardware implementation.
A Hybrid Machine Learning Model for Predicting USA NBA All-Stars
Authors: Albert Alberto Arteta , de Mingo López Luis Fernando , Allbright Kristopher , Gómez Blas Nuria
Published: December 2021

Throughout the modern age, sports have been a very important part of human existence. As our documentation of sports has become more advanced, so have the prediction capabilities. Presently, analysts keep track of a massive amount of information about each team, player, coach, and matchup. This collection has led to the development of unparalleled prediction systems with high levels of accuracy. The issue with these prediction systems is that they are proprietary and very costly to maintain. In other words, they are unusable by the average person. Sports, being one of the most heavily analyzed activities on the planet, should be accessible to everyone. In this paper, a preliminary system for using publicly available statistics and open-source methods for predicting NBA All-Stars is introduced and modified to improve the accuracy of the predictions, which reaches values close to 0.9 in raw accuracy, and higher than 0.9 in specificity.
Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method
Authors: Németh Balázs , Fényes Dániel , Bede Zsuzsanna , Gáspár Péter
Published: December 2021

This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values.
Detection of Winding Axial Displacement of a Real Transformer by Frequency Response Analysis without Fingerprint Data
Authors: Miyazaki Satoru
Published: December 2021

Detection of the axial displacement of power-transformer winding is important to ensure its highly reliable operation. Frequency response analysis is a promising candidate in detecting the axial displacement. However, a method of detecting the axial displacement at an incipient stage without the need for fingerprint data has not been investigated yet. This paper focuses on resonances showing a bipolar signature in the transfer function of inductive interwinding measurement, which is sensitive to the axial displacement of the winding. Transfer functions in the inductive interwinding measurements of eight power transformers are measured before shipping to elucidate the features of resonances showing a bipolar signature. The measured resonances showing the bipolar signature can be divided into the “stair type” and the “crossing-curve type”. It is found that the grounding points in an inductive interwinding measurement determine the type of resonance showing the bipolar signature, irrespective of the type of winding, such as interleaved or multilayer winding, the winding arrangement, and the existence of stabilizing and tertiary windings. On the basis of this finding, a method of detecting the axial displacement of a transformer winding is proposed. In the proposed method, the amplitudes of the resonances among three phases are compared, or the three-phase pattern of the resonances is compared with normal patterns. Therefore, the proposed method is applicable to three-phase transformers without fingerprint data. The proposed method is applied to a real transformer that experienced a ground fault due to a lightning strike at a nearby transmission tower, and the effectiveness of the proposed method is confirmed.
Hopf Bifurcation and Parameter Sensitivity Analysis of a Doubly-Fed Variable-Speed Pumped Storage Unit
Authors: Zhu Zhiwei , Tan Xiaoqiang , Lu Xueding , Liu Dong , Li Chaoshun
Published: December 2021

The doubly-fed variable speed pumped storage unit is a storage system suitable for joint operation with renewable energy sources to smooth the imbalance between renewable energy supply and electricity demand. However, its working principle and operation control are more complex than those of constant speed pumped storage. In this study, a nonlinear model of doubly-fed variable speed pumped storage units (VSPSUs) considering nonlinear characteristics of the head loss is established. The study finds that a supercritical Hopf bifurcation occurs in the system, and the area enclosed by the lower side of the bifurcation line and the coordinate axis is the stability domain of the system. The active power step perturbation from −0.3 to 0.3 will gradually reduce the area of the stability domain and narrow the adjustable range of the control parameters. In addition, the sensitivity of the model full state variables and the primary and secondary relationships to the changes of subsystem parameters is analyzed systematically using the trajectory sensitivity. It is found that there is a large difference in the sensitivity of different state variables to the parameters. The state variables are much more sensitive to the transfer coefficient of hydraulic turbine torque to guide vane opening, the unit inertia time constant, and the controller proportional gain change than other parameters, which are defined as highly sensitive parameters. The receiver response time constant and the turbine flow-to-head transfer coefficient are the corresponding low-sensitivity parameters.
Experimental Study to Analyze Feasibility of a Novel Panelized Ground-Source Thermoelectric System for Building Space Heating and Cooling
Authors: Miao Rui , Hu Xiaoou , Yu Yao , Zhang Qifeng , Lin Zhibin , Banawi Abdulaziz , Megri Ahmed Cherif
Published: December 2021

A thermoelectric module is a device that converts electrical energy into thermal energy through a mechanism known as the Peltier effect. A Peltier device has hot and cold sides/substrates, and heat can be pumped from the cold side to the hot side under a given voltage. By applying it in buildings and attaching it to building envelope components, such as walls, as a heating and cooling device, the heating and cooling requirements can be met by reversing the voltage applied on these two sides/substrates. In this paper, we describe a novel, panelized, ground source, radiant system design for space heating and cooling in buildings by utilizing the Peltier effect. The system is equipped with water pipes that are attached to one side of the panel and connected with a ground loop to exchange heat between the cold/hot sides of the thermoelectric module and the underground region. The ground loop is inserted in boreholes, similar to those used for a vertical closed-loop Ground Source Heat Pump (GSHP) system, which could be more than a hundred meters deep. Experiments were conducted to evaluate the feasibility of the developed panel system applied in buildings. The results show that: (1) the average cooling Coefficients Of Performance (COP) of the system are low (0.6 or less) even though the ground is used as a heat sink, and thus additional studies are needed to improve it in the future, such as to arrange the thermoelectric modules in cascade and/or develop a new thermoelectric material that has a large Seebeck coefficient; and (2) the developed system using the underground region as the heat source has the potential of meeting heating loads of a building while maintaining at a higher system coefficient of performance (up to ~3.0) for space heating, compared to conventional heating devices, such as furnaces or boilers, especially in a region with mild winters and relatively warm ground.
Estimation of the Solid Circulation Rate in Circulating Fluidized Bed System Using Adaptive Neuro-Fuzzy Algorithm
Authors: Asghar Aamer Bilal , Farooq Saad , Khurram Muhammad Shahzad , Jaffery Mujtaba Hussain , Ejsmont Krzysztof
Published: December 2021

Circulating Fluidized Bed gasifiers are widely used in industry to convert solid fuel into liquid fuel. The Artificial Neural Network and neuro-fuzzy algorithm have immense potential to improve the efficiency of the gasifier. The main focus of this article is to implement the Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System modeling approach to estimate solid circulation rate at high pressure in the Circulating Fluidized Bed gasifier. The experimental data is obtained on a laboratory scale prototype in the Chemical Engineering laboratory at COMSATS University Islamabad. The Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System use four input features—pressure, single mean diameter, total valve opening and riser dp—and one output feature mass flow rate with multiple neurons in the hidden layers to estimate the flow of solid particles in the riser. Both Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System model worked on 217 data samples and output results are compared based on their Mean Square Error, Regression analysis, Mean Absolute Error and Mean Absolute Percentage Error. The experimental results show the effectiveness of Adaptive Neuro-Fuzzy Inference System (Mean Square Error is 0.0519 and Regression analysis R2=1.0000), as it outperformed Artificial Neural Network in terms of accuracy (Mean Square Error is 1.0677 and Regression analysis R2=0.9806).
Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges
Authors: Velusamy Parthasarathy , Rajendran Santhosh , Mahendran Rakesh Kumar , Naseer Salman , Shafiq Muhammad , Choi Jin-Ghoo
Published: December 2021

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).
Bacterial and Fungal Microbiota of Guinea Grass Silage Shows Various Levels of Acetic Acid Fermentation
Authors: Hou Jianjian , Nishino Naoki
Published: December 2021

This study aimed to gain insights into the bacterial and fungal microbiota associated with the acetic acid fermentation of tropical grass silage. Direct-cut (DC, 170 g dry matter [DM]/kg) and wilted (WT, 323 g DM/kg) guinea grass were stored in a laboratory silo at moderate (25 °C) and high (40 °C) temperatures. Bacterial and fungal microbiota were assessed at 3 days, 1 month, and 2 months after ensiling. Lactic acid was the primary fermentation product during the initial ensiling period, and a high Lactococcus abundance (19.7–39.7%) was found in DC silage. After two months, the lactic acid content was reduced to a negligible level, and large amounts of acetic acid, butyric acid, and ethanol were found in the DC silage stored at 25 °C. The lactic acid reduction and acetic acid increase were suppressed in the DC silage stored at 40 °C. Increased abundances of Lactobacillus, Clostridium, and Wallemia, as well as decreased abundances of Saitozyma, Papiliotrema, and Sporobolomyces were observed in DC silages from day three to the end of the 2 month period. Wilting suppressed acid production, and lactic and acetic acids were found at similar levels in WT silages, regardless of the temperature and storage period. The abundance of Lactobacillus (1.72–8.64%) was lower in WT than in DC silages. The unclassified Enterobacteriaceae were the most prevalent bacteria in DC (38.1–64.9%) and WT (50.9–76.3%) silages, and their abundance was negatively related to the acetic acid content. Network analysis indicated that Lactobacillus was involved in enhanced acetic acid fermentation in guinea grass silage.
Machine Learning Augmented Two-Fluid Model for Segregated Flow
Authors: Rastogi Ayush , Fan Yilin
Published: December 2021

Segregated flow, including stratified and annular flows, is commonly encountered in several practical applications such as chemical, nuclear, refrigeration, and oil and gas industries. Accurate prediction of liquid holdup and the pressure gradient is of great importance in terms of system design and optimization. The current most widely accepted model for segregated flow is a physics-based two-fluid model that treats gas and liquid phases separately by incorporating mass and momentum conservation equations. It requires empirically derived closure relationships that have the limitation of being applicable only under a narrow range of input parameters under which they were developed. In this paper, we proposed a more generalized machine learning augmented two-fluid model, using a database that spans the range of various flowing conditions and fluid properties. Machine learning algorithms such as random forest, neural networks, and gradient boosting were tested for the best performing data-driven predictive model. The new model proposed in this work successfully captures the complex, dynamic, and non-linear relationships between the friction factor and flowing conditions. A comprehensive model evaluation against nineteen existing correlations shows the best results from the proposed model.
Characterization and Evolution of Volatile Compounds of Cabernet Sauvignon Wines from Two Different Clones during Oak Barrel Aging
Authors: Qian Xu , Jia Fangyuan , Cai Jian , Shi Ying , Duan Changqing , Lan Yibin
Published: December 2021

Xinjiang is a major wine-making region in China, but its hot climate in summer and intense sun exposure negatively affect the aroma quality of Cabernet Sauvignon wine. The aim of this study was to characterize and differentiate the volatile composition of Cabernet Sauvignon wines from two clones (169 and 191) in Xinjiang, and to study their aromatic profile evolution during 12-month oak barrel aging period. Results showed that before aging, clone 169 wine contained higher concentrations of several alcohols and ethyl esters, while acetate esters and furanic compounds were higher in clone 191 wine. After aging, levels of many terpenes, norisoprenoids, volatile phenols and phenolic aldehydes were significantly higher in clone 169 wine than 191 wine. Aroma series analysis revealed that clone 169 wine exhibited higher floral and roasty aromas after aging, while clone 191 wine had stronger chemical aroma. Principal component analysis indicated that aging process played a primary role in the alteration of volatile profile in these wines. Clone played a secondary role and oak barrel had a tertiary contribution to the variation. The present work indicates that clone 169 is a better choice for producing high-quality aged Cabernet Sauvignon wine with intense and elegant aroma in Xinjiang.
Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder
Authors: Delatour Thierry , Becker Florian , Krause Julius , Romero Roman , Gruna Robin , Längle Thomas , Panchaud Alexandre
Published: December 2021

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5–10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.
Which Factors Drive Consumer Decisions during Milk Purchase? New Individuals’ Profiles Considering Fresh Pasteurized and UHT Treated Milk
Authors: Merlino Valentina Maria , Massaglia Stefano , Borra Danielle , Mimosi Antonio , Cornale Paolo
Published: December 2021

The cow’s milk market is going through a critical period characterised by a continuous contraction in consumption as a consequence of the lack of competitiveness on the market of the conventional product (commodity) versus numerous specialties. This paper aimed to define the profiles of milk consumers in terms of individual preferences (assessed using the best-worst scaling methodology) and socio-demographic features. A survey was conducted in several stores of large-scale retail, convenience stores, and open-air markets distributed in north-west Italy to collect data from 1216 respondents. For milk shopper purchasing habits, two consumer groups were defined and compared in terms of preferences: the fresh pasteurized milk consumer (FPc) (56% of the total sample) and the ultra-high temperature treated milk consumer (UHTc) (35%). A series of two-ways multivariate analysis of variance (MANOVA) were conducted to assess the effect of individuals’ socio-demographic characteristics and the type of milk chosen on the consumer preferences, simultaneously. Significant differences in milk purchasing habits and preferences emerged when comparing the two consumer groups (UHTc and FPc). Empirical evidence of the study supported the starting hypothesis, suggesting the significance or relevance of the consumer socio-demographic characteristic, as well as their interaction effect with the type of milk on the level of importance given to the considered milk quality attributes. On the contrary, the gender results were not significant for the milk preferences definition. The assessment of consumer preferences, associated with the individuals’ socio-demographic characteristics could have important implications for outlining more effective marketing strategies based on a more targeted communication (i.e., related to the sustainability dimension of the local product, nutritional value and brand), leading the consumer back to the commodity rediscovery concerning individuals’ features and habits.
Effect of Tamarillo Fortification and Fermentation Process on Physicochemical Properties and Nutrient and Volatiles Content of Yoghurt
Authors: Diep Tung Thanh , Yoo Michelle Ji Yeon , Rush Elaine
Published: December 2021

Bright-red Laird’s Large tamarillo is a unique and under-utilised fruit that is a dietary source of carotenoids, vitamins C and E, and dietary fibre. The effects of the addition of freeze-dried tamarillo powder (5–15%) to milk and yoghurt starter either before (PRE) or after (POS) fermentation on physicochemical properties were examined. Using LC-MS and GG-MS, nutrient and volatile contents of tamarillo yoghurt were also examined. The addition of tamarillo prior to fermentation was associated with a more yellow colour and higher concentrations of tocopherol compared to when tamarillo was added after fermentation. Higher elastic modulus, PUFAs, pro-vitamin A content, and vitamin C retention were observed for POS than PRE. All tamarillo yoghurts showed improvement in syneresis, lower lactose content, and higher concentrations of antioxidant vitamins than the commercial premium-assorted fruits yoghurt from New Zealand Food Composition Data. Yoghurt fortified with tamarillo powder offers the potential for the development of a high-value nutritional product that could be a good source of vitamin C and a source of vitamin E and β-carotene, and maintain the volatiles that give tamarillo its distinctive flavour.
Inhibitory Effect and Potential Mechanism of Lactobacillus plantarum YE4 against Dipeptidyl Peptidase-4
Authors: Sha Jia , Song Jiajia , Huang Yechuan , Zhang Yuhong , Wang Hongwei , Zhang Yu , Suo Huayi
Published: December 2021

This study investigated the inhibitory effect and mechanism of 12 LAB strains isolated from Chinese fermented foods on dipeptidyl peptidase-4 (DPP-4) using the Caco-2 cell model. The results showed that the inhibitory effect of cell-free extracts (CFEs) collected from each LAB strain on DPP-4 was higher than that of the cell-free excretory supernatants. The CFEs from Lactobacillus plantarum YE4 (YE4-CFE) exhibited the strongest DPP-4 inhibitory activity (24.33% inhibition). Furthermore, YE4-CFE altered the TNF and MAPK signaling pathways. Additionally, the YE4-CFE ultrafiltration fraction (<3 kDa) displayed a similar DPP-4 inhibitory activity to YE4-CFE. UHPLC-MS/MS identified 19 compounds with a relative proportion of more than 1% in the <3 kDa fraction, and adenine, acetylcholine, and L-phenylalanine were the top three substances in terms of proportion. Altogether, the inhibitory effect of YE4-CFE on DPP-4 was associated with the TNF and MAPK signaling pathways, and with the high proportion of adenine, acetylcholine, and L-phenylalanine.
Fractal–Fractional Michaelis–Menten Enzymatic Reaction Model via Different Kernels
Authors: Alqhtani Manal , Saad Khaled M.
Published: December 2021

In this paper, three new models of fractal–fractional Michaelis–Menten enzymatic reaction (FFMMER) are studied. We present these models based on three different kernels, namely, power law, exponential decay, and Mittag-Leffler kernels. We construct three schema of successive approximations according to the theory of fractional calculus and with the help of Lagrange polynomials. The approximate solutions are compared with the resulting numerical solutions using the finite difference method (FDM). Because the approximate solutions in the classical case of the three models are very close to each other and almost matches, it is sufficient to compare one model, and the results were good. We investigate the effects of the fractal order and fractional order for all models. All calculations were performed using Mathematica software.
The Pain at Work Toolkit for Employees with Chronic or Persistent Pain: A Collaborative-Participatory Study
Authors: Blake Holly , Somerset Sarah , Greaves Sarah
Published: December 2021

Self-management tools for people with chronic or persistent pain tend to focus on symptom reporting, treatment programmes or exercise and do not address barriers to work, facilitators of work ability, or workplace pain self-management strategies. We developed the Pain at Work (PAW) toolkit, an evidence-based digital toolkit to provide advice on how employees can self-manage their pain at work. In a collaborative-participatory design, 4-step Agile methodology (N = 452) was used to co-create the toolkit with healthcare professionals, employers and people with chronic or persistent pain. Step 1: stakeholder consultation event (n = 27) established content and format; Step 2: online survey with employees who have persistent pain (n = 274) showed employees fear disclosing their condition, and commonly report discrimination and lack of line manager support. Step 3: online employer survey (n = 107) showed employers rarely provide self-management materials or education around managing pain at work, occupational health recommendations for reasonable adjustments are not always actioned, and pain-related stigma is common. Step 4: Toolkit development integrated findings and recommendations from Steps 1–3, and iterative expert peer review was conducted (n = 40). The PAW toolkit provides (a) evidence-based guidelines and signposting around work-capacity advice and support; (b) self-management strategies around working with chronic or persistent pain, (c) promotion of healthy lifestyles, and quality of life at work; (d) advice on adjustments to working environments and workplace solutions to facilitate work participation.
Evaluating the Operational Efficiency and Quality of Tertiary Hospitals in Taiwan: The Application of the EBITDA Indicator to the DEA Method and TOBIT Regression
Authors: Lin Chung-Shun , Chiu Cheng-Ming , Huang Yi-Chia , Lang Hui-Chu , Chen Ming-Shu
Published: December 2021

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.
On-Site Identification of Pottery with pXRF: An Example of European and Chinese Red Stonewares
Authors: Simsek Franci Gulsu , Colomban Philippe
Published: December 2021

The invention of European hard porcelain, which aims at imitating kaolin-containing white paste of Chinese porcelain, had been started by the development of the technology of “red porcelain”, so-called “Jaspisporzellan” by Johann Friedrich Böttger in the early-eighteenth century at Meissen (Saxony). The visual features of the earlier Böttger red stoneware were rather similar to the one produced in Yixing, China. The prominence of Böttger productions allowed the manufacturing to be expanded across Europe to different countries (Holland, England, France, etc.). In this study, the chemical characteristics of nine European unglazed objects produced in England, France, Russia, and Holland from the 17th to 19th century and 10 Chinese (unglazed or enameled) red stoneware have been studied by using an on-site characterization technique pXRF. The results were compared with the previous studies carried out on 25 unglazed, polished, and non-polished Böttger artefacts. This non-invasive, speedy technique allows a methodology to be created for distinguishing the technological differences related to the provenance and authenticity of the artefacts. The elemental measurements explicitly show the significant discrepancy of Dutch objects from the main group, which involves other European and Chinese ones. Both a Lambertus van Eenhoorn (Delft) statue and an Ary de Milde (Delft) teapot are distinguishable from other European red stoneware by the high content of iron and calcium and high content of titanium and potassium, respectively, found in their body compositions. An overall comparison was made between the measurements made at different times in order to evaluate the error range arising from the measurement procedure (e.g., energy resolution of other series of the same instrument model).
Biocontrol of Large Patch Disease in Zoysiagrass (Zoysia japonica) by Bacillus subtilis SA-15: Identification of Active Compounds and Synergism with a Fungicide
Authors: Kim Young-Sun , Lee Kyo-Suk , Kim Hong-Gi , Lee Geung-Joo
Published: December 2021

Bacillus subtilis SA-15 is a plant growth-promoting bacterium isolated from non-farming soil. We aimed to identify lipopeptides produced by B. subtilis SA-15 and evaluate the control efficacy of B. subtilis SA-15 against large patch disease caused by Rhizoctonia solani AG 2-2 (IV) in zoysiagrass (Zoysia japonica). Bacillus subtilis SA-15 inhibited mycelial growth of R. solani AG 2-2 (IV) in vitro and produced fengycin A and dehydroxyfengycin A, which are antifungal compounds. Fengycin A and deghydroxyfengycin A inhibited R. solani mycelial growth by 30.4 and 63.2%, respectively. We formulated B. subtilis SA-15 into a wettable powder and determined its control efficiency against large patch in a field trial. The control efficacy was 51.2–92.0%. Moreover, when B. subtilis SA-15 powder was applied together with half the regular dose of the fungicide pecycuron, the control efficacy was 88.5–100.0%. These results indicate that B. subtilis SA-15 can be used to control soil-borne diseases, including large patch caused by R. solani, because of lipopeptide production. The use of this bacterium can also reduce the amount of fungicide needed, providing an eco-friendly management option for turfgrass.