bluuebunny/crossref_metadata_2025 huggingface dataset:
Absence of Oral Opportunistic Infections in Patients with Inflammatory Bowel Disease Receiving Anti-TNF-α and Anti-Integrin-α4β7 Therapy
Authors: Saltović Ema , Mijandrušić-Sinčić Brankica , Braut Alen , Škrobonja Ivana , Sever Ella , Glažar Irena , Pezelj-Ribarić Sonja , Muhvić-Urek Miranda
Published: February 2022

Biological therapy of inflammatory bowel disease (IBD) carries an increased risk for the development of opportunistic infections due to immunomodulation. The aim of this study was to determine the prevalence and types of oral infections in IBD patients treated with biological (anti-TNF-α and anti-integrin-α4β7) and conventional medication protocols. The study included 20 IBD patients receiving anti-TNF-α therapy, 20 IBD patients receiving anti-integrin-α4β7 therapy and 20 IBD patients without immunomodulatory therapy. Participants completed questionnaires on medical information, oral lesions and symptoms. For each patient, clinical examination and a salivary flow rate test were performed, followed by a swab of the oral mucosa. The swab samples were cultured to identify Candida spp. and oral bacteria. No bacterial opportunistic infections were detected. Candidiasis was detected in four participants, with no significant difference between groups (p = 0.765). Hyposalivation was most common in the anti-TNF-α group, with a significant difference between groups (p = 0.036). There were no significant differences between groups in self-reported oral mucosal lesions and symptoms (p > 0.05), or in the distribution of oral mucosal lesions (p > 0.05). This study suggests that IBD patients receiving biological therapy are at no greater risk of developing oral opportunistic infections than IBD patients not receiving immunomodulatory therapy.
Attractor-Specific and Common Expression Values in Random Boolean Network Models (with a Preliminary Look at Single-Cell Data)
Authors: Villani Marco , D’Addese Gianluca , Kauffman Stuart A. , Serra Roberto
Published: February 2022

Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), which have also been widely studied as abstract models of complex systems and have been used to simulate different phenomena. We define the “common sea” (CS) as the set of nodes that take the same value in all the attractors of a given network realization, and the “specific part” (SP) as the set of all the other nodes, and we study their properties in different ensembles, generated with different parameter values. Both the CS and of the SP can be composed of one or more weakly connected components, which are emergent intermediate-level structures. We show that the study of these sets provides very important information about the behavior of the model. The distribution of distances between attractors is also examined. Moreover, we show how the notion of a “common sea” of genes can be used to analyze data from single-cell experiments.
An Edge Server Placement Method Based on Reinforcement Learning
Authors: Luo Fei , Zheng Shuai , Ding Weichao , Fuentes Joel , Li Yong
Published: February 2022

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.
Teaching Field Data Crowdsourcing Using a GPS-Enabled Cellphone Application: Soil Erosion by Water as a Case Study
Authors: Mikhailova Elena A. , Post Christopher J. , Zurqani Hamdi A. , Younts Grayson L.
Published: February 2022

Crowdsourcing is an important tool for collecting spatio-temporal data, which has various applications in education. The objectives of this study were to develop and test a laboratory exercise on soil erosion by water and field data crowdsourcing in an online introductory soil science course (FNR 2040: Soil Information Systems) at Clemson University. Students from different STEM disciplines (wildlife biology, forestry, and environmental and natural resources) participated in the study in the fall of 2021. They completed a sequence of self-contained digital teaching modules or reusable learning objects (RLOs), which are often used in online learning. The exercise included a field exercise and learning module to teach students about different types of water-based soil erosion as well as field data collection and crowdsourcing tools. As a result of this exercise, student familiarity with crowdsourcing was effectively increased, as shown by the post-assessment survey with a +31.2% increase in the “moderately familiar” category and a +28.3% increase in the “extremely familiar” category. The online quiz contained ten questions and was taken by 56 students with an average score of 9.5 (out of 10). A post-assessment survey found that most of the students indicated that the laboratory was an effective learning experience about field data crowdsourcing using a GPS-enabled cellphone application. Detailed students’ comments indicated enjoyment of learning (e.g., data collection, learning about different technologies), the value of multimedia (e.g., ArcGIS Survey123, cellphone), the flexibility of learning (e.g., field work), the content applicability (e.g., actual field examples of erosion by water), and criticism (e.g., technical issues). A word cloud derived from students’ comments about their laboratory exercise experience indicated the most frequent words used by students, such as “erosion”, “enjoyed”, and “different”, among others. Incorporating a learning module and field exercise using modern data collection technology into an undergraduate soil science education course enabled students to understand the value and methods for leveraging cellphone-based field collection methods to crowdsource data for environmental assessment. Practical recommendations for planning and executing future crowdsourcing exercises were developed using the current study as an example.
Comparison of In-Person and Virtual Labs/Tutorials for Engineering Students Using Blended Learning Principles
Authors: Schnieder Maren , Williams Sheryl , Ghosh Sourav
Published: February 2022

The paper compares the effectiveness of in-person and virtual engineering laboratory sessions. The in-person and virtual laboratory sessions reported here comprise six experiments combined with short tutorials. The virtual lab combined enquiry-based learning and gamification principles. The integration of the virtual labs with in-person teaching created a blended learning environment. The effectiveness of this approach was assessed based on (i) the student feedback (i.e., a questionnaire with open-ended questions and Likert scale feedback), (ii) the students’ engagement with the virtual lab, and (iii) the impact on the academic performance (i.e., class test results). The students reported greater confidence in the understanding of theory in the virtual lab than the in-person lab. This is interesting given that the instruction for the virtual lab and the in-person lab of one experiment is identical (i.e., same instructor, same enquiry-based learning techniques, and same explanations). The students also appreciated the ability to complete the virtual lab anytime, anywhere, for as long as they needed, and highlighted the benefits of the interactivity. The median class test scores of the students who completed some or all the virtual lab experiments was higher than those who did not (83–89% vs. 67%).
AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge
Authors: Hsu Jiin-Chyr , Wu Fu-Hsing , Lin Hsuan-Hung , Lee Dah-Jye , Chen Yung-Fu , Lin Chih-Sheng
Published: February 2022

A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.
Encrypted Malicious Traffic Detection Based on Word2Vec
Authors: Ferriyan Andrey , Thamrin Achmad Husni , Takeda Keiji , Murai Jun
Published: February 2022

Network-based intrusion detections become more difficult as Internet traffic is mostly encrypted. This paper introduces a method to detect encrypted malicious traffic based on the Transport Layer Security handshake and payload features without waiting for the traffic session to finish while preserving privacy. Our method, called TLS2Vec, creates words from the extracted features and uses Long Short-Term Memory (LSTM) for inference. We evaluated our method using traffic from three malicious applications and a benign application that we obtained from two publicly available datasets. Our results showed that TLS2Vec is promising as a tool to detect such malicious traffic.
Smart Processing for Systems under Uncertainty or Perturbation
Authors: Lee Sanghyuk , Popescu Mihail , Osaba Eneko
Published: February 2022

Recently, systems have become more flexible and smarter in their implementation and functions [...]
An Optimal Procedure for the Design of Discrete Constrained Lens Antennas with Minimized Optical Aberrations. Part III: Three-Dimensional Architectures with an Extended Field of View
Authors: Toso Giovanni , Angeletti Piero
Published: February 2022

In this paper, an innovative procedure is proposed for the design of three-dimensional discrete lens antennas characterized by an extended field of view. While in a companion paper the design procedure was based on the definition of multifocal constrained lenses and on their evolution in rotationally symmetric ones, in this paper, lenses are assumed from the beginning to be rotationally symmetric and are derived by enforcing minimized optical aberrations specifically for the largest scanning directions. It is shown that, for discrete lenses exhibiting a feeding array with a cross section, projected in a plane perpendicular to the main lens axis, larger as compared to the back lens cross section, there are significant improvements (15–20%) in terms of maximum aberrations and, at the same time, similar or slightly improved accommodation in terms of volume can be obtained as compared to the architectures considered in the companion paper. Because of this property, the proposed lens antennas may be particularly useful in emerging applications requiring an extended field of view.
A Predicted-Risk-Based Protection Approach for Turbine Generator Shafts against Fatigue Damage due to Islanding
Authors: Babi Bombay , Bokoro Pitshou N.
Published: February 2022

A distributed generation steam turbine generator (hereafter referred to as turbine generator) improves the supply reliability of the local load when operated as a backup supply during islanding. Interconnection standards recommend removing the utility load from the island. Transient torques induced at the moments of islanding and removing the utility load from the island may cause shaft fatigue life loss and lead to fatigue damage. Therefore, a protection method is proposed in this work. The method is based on predicting the risk of shaft fatigue damage. Induced transient torque is first modeled. Fatigue study determines the local load size required to mitigate shaft torsional vibrations and avoid fatigue life loss during islanding. This is substituted in the torque function to obtain the critical torque. The risk of shaft fatigue damage is predicted by comparing the actual shaft torque with the critical torque. The turbine generator is shut down when the actual shaft torque during islanding exceeds the critical torque. Islanding on the local load is allowed when the actual shaft torque is smaller than the critical torque. The proposed method yields 0.011% shaft fatigue life loss under the most adverse islanding condition against 100% obtained with an online monitoring and protection system (OMPS).
Benchmarking Optimization-Based Energy Disaggregation Algorithms
Authors: Ajani Oladayo S. , Kumar Abhishek , Mallipeddi Rammohan , Das Swagatam , Suganthan Ponnuthurai Nagaratnam
Published: February 2022

Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED.
Experimental Investigation of a Mechanically Stable and Temperature/Salinity Tolerant Biopolymer toward Enhanced Oil Recovery Application in Harsh Condition Reservoirs
Authors: Xiong Chunming , Wei Falin , Zhang Song , Cai Cheng , Lv Jing , Shao Liming , Wang Dianlin
Published: February 2022

In search of robust polymers for enhanced oil recovery (EOR) application in reservoirs with harsh conditions, a water-soluble biopolymer was thoroughly investigated in this work to evaluate its applicability in such reservoirs. The experimental data revealed that compared to the commonly used EOR polymer, HPAM, the biopolymer was more efficient in thickening a brine solution as a result of its peculiar conformation. The presence of an electrolyte has almost no effect on the rheology of the biopolymer solution, even at an extremely high salt concentration (20 wt% NaCl). The relation between viscosity and the concentration curve was well fitted to the power-law model. Moreover, the rigid polymer chains rendered the polymer solution superior tolerance to elevated temperatures and salinity, but also led to considerable retention within tight porous media. The adsorption behavior was characterized by the average thickness of the hydrodynamic adsorbed layer on sand grains. The mechanical degradation was assessed by forcing the polymer solutions to flow through an abrupt geometry at ultra-high shear rates. The slight viscosity loss compared to HPAM proved the high mechanical stability of this polymer. These properties made it a promising alternative to HPAM in polymer flooding in the near future for high permeability oil reservoirs with harsh conditions.
Effects of Unconventional Additives in Gasoline on the Performance of a Vehicle
Authors: Lin Mao , Zhang Xiaoteng , Wen Mingsheng , Zhang Chuanqi , Kong Xiangen , Jin Zhiyang , Zheng Zunqing , Liu Haifeng
Published: February 2022

In order to meet stricter emissions regulations and fuel consumption regulations, the upgrading of fuel quality has become one of the most important trends in the development of internal combustion engines. In this article, 89 # gasoline (G89) that is available on the Chinese market was selected as the base fuel, and five unconventional additives, ethyl tert-butyl ether (ETBE), N-Methylaniline, sec-butyl acetate, p-methylphenol and isobutanol, were added to the base fuel and named as G89-1, G89-2, G89-3, G89-4 and G89-5, respectively. The effects of these unconventional additives on a PFI vehicle were investigated. The test was carried out on a chassis dynamometer and the NEDC cycle was adopted to simulate driving conditions. The results show that, in terms of fuel consumption, G89-3 showed the best performance for decreasing fuel consumption. In terms of gaseous emissions, G89-4 decreased all four gaseous emissions, CO2, CO, THC and NOx, to a greater extent, which indicates that blending p-methylphenol into gasoline has a better potential for the vehicle to achieve cleaner emissions. In terms of acceleration performance, the five additives all shortened the acceleration time. The effects of the different additives on shortening acceleration time are basically consistent with the RON of the fuel.
Low-Grade Flow Energy Harvesting by Low-Mass-Ratio Oscillating Bent Plate
Authors: Jiang Wei , Wu Fan , Mei Ziyue , Shi Rui , Xie Danmei
Published: February 2022

Low-grade renewable energy possesses large reserves and a wide distribution in the environment, but it is far from fully exploited due to the high cost–income ratio when using traditional convertors. A fluid-induced-vibration-based flow energy convertor with a low-cost bent plate as an oscillator is proposed to achieve better energy converting performance for low-grade flow energy conversion. The energy extraction performance and dynamic response of the bent plate are assessed numerically. The results demonstrate that the prescribed single-DOF (degree of freedom) bent plate can reach the maximum efficiency of 29.6% and power coefficient of 2.36 at the relative plunging amplitude of 3.5, while the double-DOF bent plate achieves a maximum efficiency of 37.3% and power coefficient of 1.42 at a smaller amplitude of 1.4. It is discovered that the adoption of pitching motion can help to control the variation pattern of the effective AOA (angle of attack), while the camber of the bent plate also regulates the effective AOA from the geometrical respect. The FIV-based single-DOF convertor can achieve an energy converting efficiency of 29.3% and approach the ideal sinusoidal motion trajectory closely, indicating that the optimal active motion mode can be realized by the passive motion mode with the appropriate choice of the dynamic parameters.
Influence of a Central Jet on Isothermal and Reacting Swirling Flow in a Model Combustion Chamber
Authors: Palkin Egor V. , Hrebtov Mikhail Yu. , Slastnaya Darya A. , Mullyadzhanov Rustam I. , Vervisch Luc , Sharaborin Dmitriy K. , Lobasov Aleksei S. , Dulin Vladimir M.
Published: February 2022

We studied flow dynamics in a model combustion chamber using Large-eddy simulations (LES) and Particle image velocimetry (PIV) at the Reynolds number Re of 15,000. The swirl is produced using a Turbomeca swirler and air flow, while combustion is supported by a central methane/air jet. We compared four flow regimes, assessing the effect of the central jet for isothermal and lean reacting conditions. A detailed comparison for isothermal and reactive cases without the central jet is described, validating the LES results against PIV. We observe that unsteady dynamics are governed by global instability in the form of a well-known precessing vortex core (PVC). The central jet slightly changes the dynamics of PVC in the isothermal case where a strong recirculation zone is still formed. However, for the reacting case, the bubble is completely destroyed with no signs of strong vortical structures in the inner shear layer. These observations are confirmed using spectral analysis and proper orthogonal decomposition, describing the contribution of different flow modes in terms of azimuthal harmonics.
3D Quantitative Characterization of Fractures and Cavities in Digital Outcrop Texture Model Based on Lidar
Authors: Liang Bo , Liu Yuangang , Shao Yanlin , Wang Qing , Zhang Naidan , Li Shaohua
Published: February 2022

The combination of lidar and digital photography provides a new technology for creating a high-resolution 3D digital outcrop model. The digital outcrop model can accurately and conveniently depict the surface 3D properties of an outcrop profile, making up for the shortcomings of traditional outcrop research techniques. However, the advent of digital outcrop poses additional challenges to the 3D spatial analysis of virtual outcrop models, particularly in the interpretation of geological characteristics. In this study, the detailed workflow of automated interpretation of geological characteristics of fractures and cavities on a 3D digital outcrop texture model is described. Firstly, advanced automatic image analysis technology is used to detect the 2D contour of the fractures and cavities in the picture. Then, to obtain an accurate representation of the 3D structure of the fractures and cavities on the digital outcrop model, a projection method for converting 2D coordinates to 3D space based on geometric transformations such as affine transformation and linear interpolation is proposed. Quantitative data on the size, shape, and distribution of geological features are calculated using this information. Finally, a novel and comprehensive automated 3D quantitative characterization technique for fractures and cavities on the 3D digital outcrop texture model is developed. The proposed technology has been applied to the 3D mapping and quantitative characterization of fractures and cavities on the outcrop profile for the Dengying Formation (second member), providing a foundation for profile reservoir appraisal in the research region. Furthermore, this approach may be extended to the 3D characterization and analysis of any point, line, and surface objects derived from outcrop photos, hence increasing the application value of the 3D digital outcrop model.
Overview of Demand-Response Services: A Review
Authors: Stanelyte Daiva , Radziukyniene Neringa , Radziukynas Virginijus
Published: February 2022

It is essential for the electricity sector to analyze and determine the distribution capacity throughput and apply new methods aimed at increasing the capacity of the transmission system. Consequently, the transition to modern electricity networks is two-sided, i.e., involving technological and social modifications. The demand response (DR) redistributes consumption away from peak times when grid load and costs are the highest. It incentivizes customers to use electricity when supply is high and inexpensive due to various market mechanisms. The present DR policy proposals stress the importance of fostering behavioral change through competitive pricing and customer participation in reducing carbon emissions and implementing smart energy solutions (including monitoring tools, such as smart meters and applications). The internet of things (IoT) has been applied to ensure adaptive monitoring of energy consumption and cost-effective and adequate demand-side management (DSM). The article is based on the research of the most recent sources of DR implementation methods applied at the power distribution level. It explains the main concepts, classifications, and entities implementing DSM programs, and suggests new visions and prospects for DSM and DR. Moreover, it discusses the application of blockchain technology potential for the internet of energy.
Mechanism Design for Efficient Offline and Online Allocation of Electric Vehicles to Charging Stations
Authors: Rigas Emmanouil S. , Gerding Enrico H. , Stein Sebastian , Ramchurn Sarvapali D. , Bassiliades Nick
Published: February 2022

The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total CO2 emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey–Clark–Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs’ aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs’ preferences. In contrast, the fixed-price one was found to be vulnerable to agents’ strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.
Buddhism in Addiction Recovery
Authors: Wang Vanessa , Stone Bryant M.
Published: February 2022

Buddhism was established by Guatama Buddha as a practice to liberate sentient beings from suffering. Mindfulness-Based interventions (MBIs) are Western psychologists’ adaptation of mindfulness/Vipassana to treat mental illnesses. In addition to mindfulness, Buddhist recovery peer-support programs also adopt the Four Noble Truths, the Noble Eightfold Path, and the Five Precepts, which are the Buddha’s prescription to cease suffering and to discipline one’s ethical conduct.
Treatment of Respiratory Viral Coinfections
Authors: Alexander Paul , Dobrovolny Hana M.
Published: February 2022

With the advent of rapid multiplex PCR, physicians have been able to test for multiple viral pathogens when a patient presents with influenza-like illness. This has led to the discovery that many respiratory infections are caused by more than one virus. Antiviral treatment of viral coinfections can be complex because treatment of one virus will affect the time course of the other virus. Since effective antivirals are only available for some respiratory viruses, careful consideration needs to be given on the effect treating one virus will have on the dynamics of the other virus, which might not have available antiviral treatment. In this study, we use mathematical models of viral coinfections to assess the effect of antiviral treatment on coinfections. We examine the effect of the mechanism of action, relative growth rates of the viruses, and the assumptions underlying the interaction of the viruses. We find that high antiviral efficacy is needed to suppress both infections. If high doses of both antivirals are not achieved, then we run the risk of lengthening the duration of coinfection or even of allowing a suppressed virus to replicate to higher viral titers.
Single Cell Protein Production through Multi Food-Waste Substrate Fermentation
Authors: Tropea Alessia , Ferracane Antonio , Albergamo Ambrogina , Potortì Angela Giorgia , Lo Turco Vincenzo , Di Bella Giuseppa
Published: February 2022

Today, food valorization represents an important challenge to environmental sustainability. Food waste can be used as a substrate for single cell protein production suitable for animal feed. In this study, animal and agricultural food waste, represented by fish, pineapple, banana, apple, and citrus peels, have been used simultaneously as a fermentation substrate for single cell protein production by Saccharomyces cerevisiae, to evaluate the possibility of using a multi complex substrate for a simultaneous biovalorization of different food waste. The fermentation process was implemented by the supplementation of a hydrolytic enzyme and nutrient to allow the best yeast growing conditions. At the end of the process, the final substrate was enriched in protein, reaching up to 40.19% of protein, making the multisubstrate useful for animal feed. The substrate was also investigated for crude lipid, ash, lignin, soluble and insoluble sugar. The substrate composition at the end of the fermentation process was represented by 14.46% of crude lipid, 1.08% ash, 6.29% lignin. Conversely, the soluble and insoluble sugars dropped down from 20.5% to 6.10% and 19.15% to 2.14%, respectively, at the end of the process.
Nano Filling Effect of Nonmeat Protein Emulsion on the Rheological Property of Myofibrillar Protein Gel
Authors: Cai Ruying , Yang Zongyun , Li Zhen , Wang Peng , Han Minyi , Xu Xinglian
Published: February 2022

Incorporation of vegetable oils through pre-emulsification has received notable attention for delivering polyunsaturated fatty acids to emulsified-type meat products. The two important influencing factors of the rheological property of composite myofibrillar protein (MP) gel are emulsion droplet size and active or inactive interaction between interface and meat proteins. Incorporation of nonmeat protein emulsion (2% protein (w/w), egg-white protein isolate (EPI), porcine plasma protein (PPP), or sodium caseinate (SC)) with different droplet sizes (nano or macro) to a model of 2% MP gel was investigated in this research. The results of drop size measurement showed that 15,000 psi homogenizing could decrease the diameter of emulsion drop from macro- to nanoscale in the range of 324.4–734.5 nm. Active fillers (PPP and EPI emulsions) with nanodroplet size did not influence the viscosity of emulsion-filled composite cold sols but caused positive filling effects on the MP gel matrix after heating, as evidenced by the density microstructure. PPP and EPI nano-emulsion-filled composite MP had a significant high storage modulus enforcement effect, which reached nearly eight times those of other treatments (p < 0.05). Similarly, the results of thermal scanning rheology and a large-deformation mechanical test showed that PPP and EPI emulsions with nanoscale droplets, other than macroscale, had the highest gel strength of heat-induced emulsion-filled composite MP gel (p < 0.05). Overall, these findings will be helpful for selecting the correct pre-emulsified protein and designing the textural properties of foods.
Consumers’ Perception of Food and Agriculture Education in Farmers’ Markets in Taiwan
Authors: Ma Chun-Chieh , Chang Hsiao-Ping
Published: February 2022

Following the trend of green product consumption, farmers’ markets sell green products and have gradually developed the promotion of environmentally friendly knowledge of food and agricultural education. However, past research on farmers’ markets has rarely been combined with the function of food and agricultural education. Therefore, this study explores the relationship between product knowledge, green perceived value, and purchase intention from the perspective of farmers’ market food and agricultural education. Furthermore, research and investigation were conducted with trust and local attachment as multiplicative adjustment variables. This study adopted structural equation modeling (SEM) to examine the structural relationship among local attachment, product knowledge, green perceived value, purchase intention, and model fit. We distributed 396 surveys at three large farmers’ markets in Taiwan, of which 88.6% were used in this study. The research results showed that product knowledge and green perceived value had a significant positive impact on purchase intention; trust positively affected product knowledge and purchase intention; the multiplication of local attachment and trust also positively impacted product knowledge and purchase intention. Based on the research results of this study, it is suggested that while transferring knowledge, the farmers’ markets can incorporate trust and emotional relationships, which will serve as stimulating factors that can increase consumers’ purchase intentions. Although the findings are local to Taiwan, its characteristics are typical of food and agricultural education, as well as farmers’ markets worldwide, especially in Asia.
Quality Evaluation and Mathematical Modelling Approach to Estimate the Growth Parameters of Total Viable Count in Sausages with Different Casings
Authors: Feng Chao-Hui
Published: February 2022

The growth kinetics for the total viable count (TVC) in sausages with modified hog casings (treated by surfactant solutions and slush salt with lactic acid), natural hog casings and sheep casings as a function of the storage time (up to 50 days) were studied for the first time. The growth of TVC was fitted by the Baranyi model, and the maximum specific growth rate, lag time and initial and final cell populations were estimated via DMFit. The coefficient of determination of the Baranyi model reached 0.94, 0.77 and 0.86 for sausages stuffed in modified hog casings (MHC), control hog casings (CHC) and natural sheep casings (NSC), respectively. The experimental data for the initial populations were 4.69 ± 0.10 log cfu/g for MHC, 4.79 ± 0.10 log cfu/g for CHC and 3.74 ± 0.14 log cfu/g for NSC, whilst the predicted initial cell populations for MHC, CHC and NSC were 4.81 ± 0.20 log cfu/g, 5.19 ± 0.53 log cfu/g and 3.74 ± 0.54 log cfu/g, respectively. Their shelf lives can also be predicted. The results show that the average pH value of MHC samples (6.96 ± 0.01) was significantly lower than that of CHC (7.09 ± 0.01) and NSC (7.05 ± 0.02) samples at day 50 (p < 0.05). Sausages with CHC possessed a significant higher water holding capacity (99.48 ± 0.14%) at d 29 than those with MHC (97.40 ± 0.46%) and NSC (98.55 ± 0.17%) (p < 0.05). On the last day, the average moisture content for samples with NSC (38.30 ± 3.23%) was significantly higher than that for those with MHC (29.38 ± 2.52%) and CHC (29.15 ± 1.16%) (p < 0.05).
Tailoring the Textural Characteristics of Fat-Free Fermented Concentrated Milk-Protein Based Microgel Dispersions by Way of Upstream, Downstream and Post-Production Thermal Inputs
Authors: Heck Anisa , Nöbel Stefan , Hitzmann Bernd , Hinrichs Jörg
Published: February 2022

There is a growing demand for new strategies to tailor the texture of fat-free fermented concentrated milk products, also referred to as milk protein-based (MPb) microgel dispersions. Methods should be easy to incorporate into the production scheme, offer labelling without added components and be cost-efficient. Thermal treatments are traditionally used upstream (milk heating) and downstream (pre-concentration heating) in the production of these dispersions, though there is little knowledge as to the effects that combinations of different thermal input levels have on final texture. Therefore, this study investigated combinations of thermal input at different intensities and steps in the production scheme at the pilot scale and the relationships with texture. We demonstrated that increasing the intensity of upstream milk heat treatment, in combination with downstream pre-concentration heating, increases gel firmness and apparent viscosity. Downstream pre-concentration heating produces final fat-free fermented concentrated MPb microgel particles that are resistant to post-heating aggregation. On the other hand, omission of downstream pre-concentration heating results in smaller particles that are sensitive to post-heating aggregation. Furthermore, gel firmness and apparent viscosity increase with post-heating. Consequently, combining different levels of thermal inputs upstream, downstream (pre-concentration) and post-production, can produce fat-free fermented concentrated MPb microgel dispersions with a range of different textures.
Using Microbial Responses Viewer and a Regression Approach to Assess the Effect of pH, Activity of Water and Temperature on the Survival of Campylobacter spp.
Authors: Icen Hayrunisa , Corbo Maria Rosaria , Sinigaglia Milena , Korkmaz Burcu Irem Omurtag , Bevilacqua Antonio
Published: February 2022

This study aimed at developing a model for evaluating the survival of various Campylobacter jejuni strains under different conditions in culture media and poultry data from ComBase. Campylobacter data of culture media (116) and poultry (19) were collected from Microbial Responses Viewer, an additional tool of ComBase. The Weibull equation was selected as a suitable model for the analysis of survival data because of the nonlinearity of survival curves. Then, the fitting parameters (first reduction time and shape parameter) were analysed through a Kruskall–Wallis test and box-whisker plots, thus pointing out the existence of two classes of temperature (0–12 °C and 15–25 °C) and pH (4–6.5 and 7–7.5) acting on the viability of C. jejuni. Finally, a general regression model was used to build a comprehensive function; all factors were significant, but temperature was the most significant variable, followed by pH and water activity. In addition, desirability and prediction profiles highlighted a negative correlation of the first reduction time with temperature and a positive correlation with pH and water activity.
Colorimetric Analysis and Determination of Histamine in Samples of Yellowfin Tuna (Thunnus albacares) Marketed in Sardinia (Italy) by a Combination of Rapid Screening Methods and LC-MS/MS
Authors: Pais Giovanni Luigi , Meloni Domenico , Mudadu Alessandro Graziano , Crobu Luigi , Pulina Alessandro , Chessa Giannina
Published: February 2022

The consumption of fishery products has been steadily increasing in recent decades. Among the quantitatively more important species, the yellowfin tuna (Thunnus albacares), is one of the main at-risk species as regards the possibility to present important levels of histamine and to be associated with the so-called “Scombroid Fish Poisoning”. The main aim of the present study was to evaluate the colorimetric parameters, the occurrence, and the quantification of histamine contamination in yellowfin tuna samples marketed in Sardinia (Italy) by a combination of rapid screening and official control methods. A total of 20 samples of yellowfin tuna loins collected from large retailers, fishmongers and local markets were analyzed for the qualitative and quantitative determination of histamine by the lateral flow test HistaSure™ Fish Rapid Test and LC-MS/MS, respectively. Moreover, all the samples were examined to assess the conformity with the EU rules on labelling and subjected to colorimetric analysis according to the CIE-L*a*b* standard. Visual inspection of yellowfin tuna labels highlighted a 30% of non-compliances. A significant (p < 0.05) difference was reported for brightness (L *), redness (a *), and yellowness (b *). The results of histamine occurrence agreed with the food safety criteria (<100 mg/kg) laid down in EC Regulation 2073/2005 in the 95% and in the 90% of the samples with the rapid screening methods and LC-MS/MS, respectively. A highly significant sessional variation (p < 0.00001) was pointed out. Moreover, the two methods showed an agreement rate of 85%. The results of the present study confirmed the utility of lateral flow tests for the fast qualitative determination of histamine in yellowfin tuna. Rapid screening test should be strengthened by comparison with the official method especially in case of uncertain or positive results.
Detection of Peanut Traces in Food by an Official Food Safety Laboratory
Authors: Tramuta Clara , Decastelli Lucia , Barcucci Elisa , Ingravalle Francesco , Fragassi Sandra , Lupi Samantha , Bianchi Daniela Manila
Published: February 2022

Food safety laboratories rely on validated methods that detect hidden allergens in food to ensure the safety and health of allergic consumers. Here we present test results for the validation and accreditation of a real-time PCR assay for the detection of peanut traces in food products. The method was tested on five classes of food matrices: bakery and pastry products, meats, ready-to-eat and dairy products, and grains and milling products. Blank samples were spiked starting with the peanut samples (Arachis hypogaea) at a concentration of 1000 ppm. Serial dilutions were then prepared with the DNA extracted from the blank samples to a final concentration of 0.5 ppm. The limit of detection in grains and milling products, ready-to-eat, meats, bakery and pastry products was 0.5 ppm (range, Ct 27–34) and 2.5 ppm in dairy products (range, Ct 25–34). In order to determine the exclusivity parameter of the method, the ragù matrix was contaminated with Prunus dulcis (almonds), Glycine max (soy), Sinapis alba (mustard), Apium graveolens (celery), Allium cepa (onion), Pisum sativum (peas), Daucus carota (carrots), and Theobroma cacao (cocoa); no cross-reactions were observed. The method was rated satisfactory for sensitivity (98%), specificity (100%), robustness, and repeatability and it was fully validated and accredited.
Food-Based Dietary Guidelines and Protein Quality Definitions—Time to Move Forward and Encompass Mycoprotein?
Authors: Derbyshire Emma
Published: February 2022

Food-Based Dietary Guidelines (FBDG) lack uniformity globally, with the integration of protein food sources being highly variable. Protein guidance tends to be dichotomous, e.g., animal versus plant with other categories such as fungal proteins being overlooked. In 2019 the EAT Lancet Food in the Anthropocene report was a chief driver questioning the need to supply healthy diets from sustainable food systems. Some countries are developing FBDG that integrate these aspects, but these are quite often protracted, too subtle or misaligned with other countries, diluting the effects of meaningful global change. Protein quality metrics also underpin the dissemination of dietary guidance. However, for protein, these remain based on a food’s essential amino acid profile and digestibility scores, thus are nutritionally and physiologically centric. It has been proposed that this definition is becoming increasingly myopic from a wider societal perspective. Updated indices should include contemporary issues such as protein diversity and environmental outcomes. Taken together, there is opportunity for renewed thinking about both FBDG and protein quality definitions, with scope to include both health and environmental outcomes and need to move towards the concept of protein diversification.
Towards the Use of Natural Compounds for Crop Protection and Food Safety
Authors: Pilkington Lisa I.
Published: February 2022

The six research articles/communications and one review that comprise this Special Issue, which concerns studies towards natural compound use for crop protection and food safety purposes, highlight the most recent research and investigations into this exciting area [...]
Ultrasonication Assisted Catalytic Transesterification of Ceiba Pentandra (Kapok) Oil Derived Biodiesel Using Immobilized Iron Nanoparticles
Authors: Pasawan Mithileth , Chen Shiao-Shing , Das Bhanupriya , Chang Hau-Ming , Chang Chang-Tang , Nguyen Thi Xuan Quynh , Ku Hong-Ming , Chen Yue-Fang
Published: February 2022

The embedded immobilized enzymes (Rhizopus-oryzae) on the magnetic nanoparticles (Fe3O4-NPs) is a new application for the sustainable production of high-quality biodiesel. In this study, biodiesel is derived from Kapok oil via ultrasonication (US)-assisted catalytic transesterification method. A novel attempt is made to prepare magnetic nanoparticles embedded by an immobilized enzyme to solve the problem of enzyme denaturation. This innovative method resulted in optimum biodiesel conversion of 89 ± 1.17% under reactant molar ratio (methanol: oil) of 6:1, catalyst loading 10 wt% with a reaction time of 4 h at 60 °C. The kinetic and thermal study reveals that conversion of Kapok oil to biodiesel follows a pseudo first-order reaction kinetic with a lower ΔE of 30.79 kJ mol−1. The ΔH was found to be 28.06 kJ mol−1 with a corresponding ΔS of −237.12 J mol−1 K−1 for Fatty Acid Methyl Ester formation. The ΔG was calculated to be from 102.28 to 109.40 kJ mol−1 for temperature from 313 K to 343 K. The positive value of ΔH and ΔG is an indication of endothermic and non-spontaneous reaction. A negative ΔS indicates the reactant in the transition state possesses a higher degree of ordered geometry than in its ground state. The immobilized catalysts provided great advantages towards product separation and efficient biodiesel production. Highlights: 1. Effective catalytic transesterification assisted by the ultrasonication method was used for bi-odiesel production. 2. Magnetite nanoparticles synthesized by the co-precipitation method were used as heteroge-neous catalysts. 3. An immobilized enzyme (Rhizopus-oryzae) was embedded in the heterogeneous catalyst, as it is reusable and cost-effective. 4. The maximum biodiesel yield obtained from Kapok oil was 93 ± 1.04% by catalytic trans-esterification reactions.
A Note on Time Inconsistency and Endogenous Exits from a Currency Union
Authors: Saito Yuta
Published: February 2022

This paper investigates the effects of members’ exits from a currency union on the credibility of the common currency. In our currency union model, the inflation rate of the common currency is determined by majority voting among N member countries that are heterogeneous with respect to their output shocks. Once an inflation rate of the common currency has been selected, each member decides whether to remain in the currency union or not. If a member decides to exit, it has to pay a fixed social cost and individually chooses the inflation rate of its currency. Unlike previous research on this topic, we focus on the possibility of achieving an optimal outcome, which generates no inflation bias, when more than one member is expected to leave the currency union. We show that the optimal outcome can only be achieved if no members leave the currency union.