bluuebunny/crossref_metadata_2025 huggingface dataset:
Augmented Reality Applications in Education and Examining Key Factors Affecting the Users’ Behaviors
Authors: Ghobadi Mohsen , Shirowzhan Sara , Ghiai Mohammad Mehdi , Mohammad Ebrahimzadeh Fatemeh , Tahmasebinia Faham
Published: December 2022

Augmented Reality (AR) is increasingly influential in education. AR technology allows users to learn and practice in a simulated environment that enables repetition, correction, and failure without risk. The present study evaluated users’ attitudes towards using AR for learning complex tasks. The users are asked to interact with an AR Piling (ARP) application that shows various steps of a construction process. A set of selected practitioners and students used the application, and the evaluation involved various participants of different genders and backgrounds. A questionnaire was designed and data was collected through an online survey based on the Technology Acceptance Model (TAM). The model is modified considering education practices and adjusted to an AR app for learning purposes. The novelty of the model lies in various constructs such as technical quality, social influence, perceived immersion, learning, and perceived enjoyment. 200 responses were obtained and used for evaluating the proposed model. The attitude toward using AR and the perceived usefulness of AR were the two factors that determined the participants’ behavioral intention to use ARP. Respondents showed a high level of acceptance for AR. In education and higher learning contexts, the findings of this study contribute to a deeper understanding of how AR is accepted in complex learning environments. The study allows us to extend the TAM by examining how AR technology can be applied to teaching in universities and unpack the ways in which gender influences learning through AR application.
Measuring and Activating iSTEM Key Principles among Student Teachers in STEM
Authors: Spikic Sascha , Van Passel Wouter , Deprez Hanne , De Meester Jolien
Published: December 2022

Graduates with a STEM profile are in great demand, yet the outflow from these fields of study is highly insufficient. This is partly due to the fragmented way STEM learning content is taught in secondary education. Although the problem can be mitigated with the use of integrated STEM education (i.e., iSTEM), teachers are often unfamiliar with this type of education. To support teachers in implementing high-quality iSTEM education, a digital collaborative learning environment called “CODEM for iSTEM” was created. This study examined to what extent student teachers were immersed in six key principles of iSTEM education through cooperative design of iSTEM learning tools in multidisciplinary teams, namely “problem-centered learning”, “integration of different STEM disciplines”, “modeling”, “inquiry-based learning”, “design-based learning”, and “cooperative learning”.
Applying the Innovative Approach of Employing a Business Simulation Game and Prototype Developing Platform in an Online Flipped Classroom of an Entrepreneurial Summer Course: A Case Study of UAEU
Authors: Alkaabi Khaula
Published: December 2022

As opposed to only having subject knowledge, the necessity of skills has repeatedly been emphasized as essential to succeed in the practical world. Being able to adjust to new circumstances is crucial, especially in today’s world, where uncertainty, volatility, ambiguity, and complexity are ubiquitous. Experiential Learning Theory can be implemented during innovative pedagogical approaches to better prepare students for entering the market as successful entrepreneurs in such a tumultuous time—for instance, an entrepreneurial course might incorporate using business simulation games, such as MonsoonSim and prototype developing software, including Justinmind to experience enhanced experiential learning with thematic goals. In order to strengthen the quality of teaching and learning and foster self-directed learners better equipped to cope with existing challenges, this study investigates the processes of implementing a business simulation game along with prototype development software and distinguishes it from a typical classroom experience. The sample consists of students enrolled in a short summer entrepreneurial course designed by the United Arab Emirates University at the request of the Ministry of Education, United Arab Emirates (U.A.E.), for secondary and higher secondary school graduates. The students from private and government schools across all seven emirates of the United Arab Emirates participated in this course. The online flipped classroom was carefully designed by blending several approaches during the pre-class, in-class, and post-class activities. In order to provide an advanced learning experience, a web-based business simulation game (MosoonSim) and online prototype development software, Justinmind, were introduced during the course. The qualitative and quantitative results show that incorporating business simulation games had a significantly positive impact on students’ learning outcomes, such as critical and higher-order thinking, idea creation, and problem-solving capabilities, and improved engagement through an easy, enjoyable, and interactive environment. Furthermore, findings show that the learning outcomes are significantly influenced by the attributes of the group members and their consistency during group activities in a flipped classroom, and assigning a dedicated instructor or a senior group member having solid background knowledge in a particular subject can assist in significantly enhancing the learning outcomes of a whole group.
Review on Deep Learning Approaches for Anomaly Event Detection in Video Surveillance
Authors: Jebur Sabah Abdulazeez , Hussein Khalid A. , Hoomod Haider Kadhim , Alzubaidi Laith , Santamaría José
Published: December 2022

In the last few years, due to the continuous advancement of technology, human behavior detection and recognition have become important scientific research in the field of computer vision (CV). However, one of the most challenging problems in CV is anomaly detection (AD) because of the complex environment and the difficulty in extracting a particular feature that correlates with a particular event. As the number of cameras monitoring a given area increases, it will become vital to have systems capable of learning from the vast amounts of available data to identify any potential suspicious behavior. Then, the introduction of deep learning (DL) has brought new development directions for AD. In particular, DL models such as convolution neural networks (CNNs) and recurrent neural networks (RNNs) have achieved excellent performance dealing with AD tasks, as well as other challenging domains like image classification, object detection, and speech processing. In this review, we aim to present a comprehensive overview of those research methods using DL to address the AD problem. Firstly, different classifications of anomalies are introduced, and then the DL methods and architectures used for video AD are discussed and analyzed, respectively. The revised contributions have been categorized by the network type, architecture model, datasets, and performance metrics that are used to evaluate these methodologies. Moreover, several applications of video AD have been discussed. Finally, we outlined the challenges and future directions for further research in the field.
EiCSNet: Efficient Iterative Neural Network for Compressed Sensing Reconstruction
Authors: Zhou Ziqun , Wang Zeyu , Liu Fengyin , Shen Haibin
Published: December 2022

The rapid growth of sensing data demands compressed sensing (CS) in order to achieve high-density storage and fast data transmission. Deep neural networks (DNNs) have been under intensive development for the reconstruction of high-quality images from compressed data. However, the complicated auxiliary structures of DNN models in pursuit of better recovery performance lead to low computational efficiency and long reconstruction times. Furthermore, it is difficult for conventional neural network designs to reconstruct extra-high-frequency information at a very low sampling rate. In this work, we propose an efficient iterative neural network for CS reconstruction (EiCSNet). An efficient gradient extraction module is designed to replace the complex auxiliary structures in order to train the DNNs more efficiently. An iterative enhancement network is applied to make full use of the limited information available in CS for better iterative recovery. In addition, a frequency-aware weighted loss is further proposed for better image restoration quality. Our proposed compact model, EiCSNet2*1, improved the performance slightly and was nearly seven times faster than its counterparts, which shows that it has a highly efficient network design. Additionally, our complete model, EiCSNet6*1, achieved the best effect at this stage, where the average PSNR was improved by 0.37 dB for all testing sets and sampling rates.
Spatiotemporal Activity Mapping for Enhanced Multi-Object Detection with Reduced Resource Utilization
Authors: Shashank  , Sreedevi Indu
Published: December 2022

The accuracy of data captured by sensors highly impacts the performance of a computer vision system. To derive highly accurate data, the computer vision system must be capable of identifying critical objects and activities in the field of sensors and reconfiguring the configuration space of the sensors in real time. The majority of modern reconfiguration systems rely on complex computations and thus consume lots of resources. This may not be a problem for systems with a continuous power supply, but it can be a major set-back for computer vision systems employing sensors with limited resources. Further, to develop an appropriate understanding of the scene, the computer vision system must correlate past and present events of the scene captured in the sensor’s field of view (FOV). To address the abovementioned problems, this article provides a simple yet efficient framework for a sensor’s reconfiguration. The framework performs a spatiotemporal evaluation of the scene to generate adaptive activity maps, based on which the sensors are reconfigured. The activity maps contain normalized values assigned to each pixel in the sensor’s FOV, called normalized pixel sensitivity, which represents the impact of activities or events on each pixel in the sensor’s FOV. The temporal relationship between the past and present events is developed by utilizing standard half-width Gaussian distribution. The framework further proposes a federated optical-flow-based filter to determine critical activities in the FOV. Based on the activity maps, the sensors are re-configured to align the center of the sensors to the most sensitive area (i.e., region of importance) of the field. The proposed framework is tested on multiple surveillance and sports datasets and outperforms the contemporary reconfiguration systems in terms of multi-object tracking accuracy (MOTA).
Development of a Model for Trust Management in the Social Internet of Things
Authors: Rizwanullah Mohammed , Singh Sunil , Kumar Rajeev , Alrayes Fatma S. , Alharbi Abdullah , Alnfiai Mrim M. , Chaurasia Pawan Kumar , Agrawal Alka
Published: December 2022

The Internet of Things (IoT) has evolved at a revolutionary pace in the last two decades of computer science. It is becoming increasingly fashionable for the IoT to be rebranded as the “Social Internet of Things” (SIoT), and this is drawing the attention of the scientific community. Smart items in the Internet of Things (IoT) ecosystem can locate relevant services based on the social ties between neighbors. As a result, SIoT displays the interplay between various items as a problem in the context of the social IoT ecosystem. Navigating a network can be difficult because of the number of friends and the complexity of social ties. By identifying difficulties with standard SIoT devices’ interaction with social objects, truthful friend computing (TFC) is a new paradigm for tracing such difficulties by utilising a relationship management component to improve network navigability. The concept of trust management can be useful as a strategy during collaborations among social IoT nodes. As a result, the trustor can use a variety of measures to evaluate a smart object’s trustworthiness. Hence, this article demonstrates the need for the trustor to evaluate the extent to which a given metric has contributed to the overall trust score and illustrates profitability when engaging in a transaction with other nodes. With the help of the SIoT, this paper used a unified fuzzy-based computational technique and a multiple-criteria decision-making approach to evaluate the trust weights. The statistical findings show that the computing of “truthful friends” is the biggest challenge for successful SIoT implementation at the initial level.
Deep Cascade AdaBoost with Unsupervised Clustering in Autonomous Vehicles
Authors: Duan Jianghua , Ye Hongfei , Zhao Hongyu , Li  Zhiqiang
Published: December 2022

In recent years, deep learning has achieved excellent performance in a growing number of application fields. With the help of high computation and large-scale datasets, deep learning models with huge parameters constantly enhance the performance of traditional algorithms. Additionally, the AdaBoost algorithm, as one of the traditional machine learning algorithms, has a minimal model and performs well on small datasets. However, it is still challenging to select the optimal classification feature template from a large pool of features in any scene quickly and efficiently. Especially in the field of autonomous vehicles, images taken by onboard cameras contain all kinds of targets on the road, which means the images are full of multiple features. In this paper, we propose a novel Deep Cascade AdaBoost model, which effectively combines the unsupervised clustering algorithm based on deep learning and the traditional AdaBoost algorithm. First, we use the unsupervised clustering algorithm to classify the sample data automatically. We can obtain classification subsets with small intra-class and large inter-class errors by specifying positive and negative samples. Next, we design a training framework for Cascade-AdaBoost based on clustering and mathematically demonstrate that our framework has better detection performance than the traditional Cascade-AdaBoost framework. Finally, experiments on the KITTI dataset demonstrate that our model performs better than the traditional Cascade-AdaBoost algorithm in terms of accuracy and time. The detection time was shortened by 30%, and the false detection rate was reduced by 20%. Meanwhile, the training time of our model is significantly shorter than the traditional Cascade-AdaBoost algorithm.
Experimental Analysis of Oxide-Based RAM Analog Synaptic Behavior
Authors: Aziza Hassan , Postel-Pellerin Jeremy , Moreau Mathieu
Published: December 2022

One of the important features of Resistive RAM (RRAM) is its conductance modulation, which makes it suitable for neuromorphic computing systems. In this paper, the conductance modulation of Oxide-based RAM (OxRAM) devices is evaluated based on experimental data to reveal its inherent analog synaptic behavior. A test chip made of a classical 1T-1R elementary memory array is used to demonstrate the conductance modulation. Using an array of cells, as opposed to an isolated cell, allows to catch temporal as well as spatial variabilities. Thus, the multiple resistance levels capability of OxRAMs is assessed in a more realistic context. Two different programming techniques are used to program the OxRAM cells. The first approach leverages on RESET (RST) voltage control. The second approach relies on compliance current control during the SET operation. In both approaches, although multiple resistance levels can be easily obtained, it is demonstrated that a successful implementation of a reliable conductance modulation scheme mainly depends on the ability to precisely control the impact of variability on the different conductance levels obtained after the programming operation.
Design and Analysis of the Trajectory of an Overtaking Maneuver Performed by Autonomous Vehicles Operating with Advanced Driver-Assistance Systems (ADAS) and Driving on a Highway
Authors: Ortega Josue , Lengyel Henrietta , Ortega Jairo
Published: December 2022

Overtaking is a maneuver that consists of passing another vehicle traveling on the same trajectory, but at a slower speed. Overtaking is considered one of the most dangerous, delicate and complex maneuvers performed by a vehicle, as it requires a quick assessment of the distance and speed of the vehicle to be overtaken, and also the estimation of the available space for the maneuver. In particular, most drivers have difficulty overtaking a vehicle in the presence of vehicles coming on other trajectories. To solve these overtaking problems, this article proposes a method of performing safe, autonomous-vehicle maneuvers through the PreScan simulation program. In this environment, the overtaking-maneuver scenario (OMS) is composed of highway infrastructure, vehicles and sensors. The proposed OMS is based on the solution of minimizing the risks of collision in the presence of any oncoming vehicle during the overtaking maneuver. It is proven that the overtaking maneuver of an autonomous vehicle is safe to perform through the use of advanced driver-assistance systems (ADAS) such as adaptive cruise control (ACC) and technology-independent sensors (TIS) that detect the driving environment of the maneuver.
Optimal Location and Sizing of Distributed Generators and Energy Storage Systems in Microgrids: A Review
Authors: Grisales-Noreña Luis , Restrepo-Cuestas Bonie , Cortés-Caicedo Brandon , Montano Jhon , Rosales-Muñoz Andrés , Rivera Marco
Published: December 2022

This article reviews the main methodologies employed for the optimal location, sizing, and operation of Distributed Generators (DGs) and Energy Storage Systems (ESSs) in electrical networks. For such purpose, we first analyzed the devices that comprise a microgrid (MG) in an environment with Distributed Energy Resources (DERs) and their modes of operation. Following that, we examined the planning and operation of each DER considered in this study (DGs and ESSs). Finally, we addressed the joint integration of DGs and ESSs into MGs. From this literature review, we were able to identify both the objective functions and constraints that are most commonly used to formulate the problem of the optimal integration and operation of DGs and ESSs in MGs. Moreover, this review allowed us to identify the methodologies that have been employed for such integration, as well as the current needs in the field. With this information, the purpose is to develop new mathematical formulations and approaches for the optimal integration and operation of DERs into MGs that provide financial and operational benefits.
Review of Strategies to Mitigate Dust Deposition on Solar Photovoltaic Systems
Authors: Vedulla Gowtham , Geetha Anbazhagan , Senthil Ramalingam
Published: December 2022

In recent years, there has been an increased focus on developing and utilizing renewable energy resources due to several factors, including environmental concerns, rising fuel costs, and the limited supply of conventional fossil fuels. The most appealing green energy conversion technology is solar energy, and its efficient application can help the world achieve Sustainable Development Goal 7: Access to affordable, clean energy. Irradiance, latitude, longitude, tilt angle, and orientation are a few variables that affect the functioning of a solar photovoltaic (PV) system. Additionally, environmental factors like dust accumulation and soiling of panel surfaces impact the cost of maintaining and producing electricity from a PV system. Dust characteristics (kind, size, shape, and meteorological elements), one of the largest factors affecting PV panel performance, need to be investigated to devise specific solutions for efficiently harnessing solar energy. The essential findings of ongoing investigations on dust deposition on the surface of PV structures and various mitigating measures to tackle soiling issues are presented in this review study. This comprehensive assessment critically evaluates the current research on the soiling effect and PV system performance improvement techniques to determine the academic community’s future research priorities.
Enhancing Thermal Performance, Exergy and Thermodynamics Efficiency of Premixed Methane/Air Micro-Planar Combustor in Micro-Thermophotovoltaic Systems
Authors: Tong Jinshen , Cai Tao
Published: December 2022

The present work numerically investigates the effect of a cavity implemented in a premixed methane/air micro-combustor on enhancing its thermal performances and thermodynamic efficiencies for micro-thermophotovoltaic applications. The 3D time-domain numerical model is first validated by comparing its predictions with the experimental data available in the literature. Then it is applied to examine the effects of the cavity dimensionless axial location (xc/L), cavity volume (Vc), the equivalence ratio ϕ and hydrogen blended ratio (α) on the temperature uniformity and enhancement of the combustor outer wall and exergy efficiency. It is found that implementing a cavity in the combustion chamber increases the outer wall mean temperature (OWMT) and the exergy efficiency up to approximately 65 K and 10%, respectively. The optimal cavity dimensionless axial location (xc/L) is set to 1/9, and the height (Hc_dims) is 1/5, respectively. However, the cavity length Lc and angle θc are found to play negligible roles on improving thermal performance. Additionally, increasing the inlet velocity leads to a higher OWMT but a low exergy efficiency, regardless of the equivalence ratio. In general, this work confirms the feasibility of applying a cavity structure to enhance energy efficiency for micro-power generation systems.
Transient Multi-Physics Modeling and Performance Degradation Evaluation of Direct Internal Reforming Solid Oxide Fuel Cell Focusing on Carbon Deposition Effect
Authors: Li Zheng , Yang Guogang , Shen Qiuwan , Li Shian , Wang Hao , Liao Jiadong , Jiang Ziheng , Zhang Guoling
Published: December 2022

The performance degradation issue caused by carbon deposition has limited the commercial application of natural-gas-fueled solid oxide fuel cells. Most previous corresponding studies are based on thermodynamic equilibrium analyses, while long-term transient evaluation work is lacking. Therefore, a transient multi-physics numerical model is developed in present work. The corresponding long-term performance degradation evaluation is then conducted. The results show that, for a direct internal reforming solid oxide fuel cell, the increase in carbon deposition and deterioration of performance degradation were concentrated in the first 180 days of steady−state operation and slowed down at the later stage. The electrode inlet rapidly developed a high concentration of carbon deposition after 180 days of steady−state operation. The deposited carbon deteriorated the gas transport and decayed reaction activity within the porous electrode, eventually inducing a deactivation zone with 0 current density at the inlet. Key measures to inhibit carbon deposition should be implemented within the first 180 days of operation, and the pre-reformed operation of natural gas is encouraged for natural-gas-fueled solid oxide fuel cells.
Application of Paraffin-Based Phase Change Materials for the Amelioration of Thermal Energy Storage in Hydronic Systems
Authors: Malkawi Dua’a S. , Rabady Rabi Ibrahim , Malkawi Mosa’b S. , Al Rabadi Said Jereis
Published: December 2022

This study aims at investigating the improvement in the thermal performance of energy storage for a hydronic system when it is equipped with evacuated tubes integrated within a hot water tank. The PCM shell in the bottom section is thicker than at the top to maintain a uniform, minimal water temperature difference of 5 °C between the top and bottom sections of the hot water tank. The thermal performance of the system was analyzed in diverse months when the ambient temperature fluctuated. The results have revealed that the thermal performance in December, March, and June was 80%, 81%, and 84%, respectively, meaning that the thermal performance is optimal in warm weather. The results confirmed that the system had boosted the presence of hot water throughout the whole day, including the time of the sun’s absence, due to the release of stored PCM latent heat. The designed system solves the overheating problem and expands the availability of hot water through the cold weather. The system is characterized by lower heat losses because the average water temperature has decreased.
Numerical Simulation of Geothermal Reservoir Reconstruction and Heat Extraction System Productivity Evaluation
Authors: Zhu Jinshou , Cui Zhenpeng , Feng Bo , Ren Hao , Liu Xin
Published: December 2022

The key to ensuring the economic feasibility of EGS mainly includes two points. On the one hand, it is necessary to ensure the connectivity of the artificial fracture network; on the other hand, it is necessary to determine the most efficient geothermal energy exploitation mode. Most previous studies have only focused on one of the points. To restitute the entire geothermal energy development process, the two parts should be combined to conduct research. In this study, a random fractured medium model was established based on the TOUGH2-BIOT simulation program and the whole process of reservoir stimulation was analyzed. According to the results of reservoir stimulation, different geothermal energy exploitation schemes are set up, and the heat transfer efficiency of the conventional double vertical wells, the horizontal wells, and the double-pipe heat exchange system are comparatively analyzed. The results show that reservoir reconstruction is mainly divided into three stages: In the first stage, the hydraulic aperture of the conducting fractures reaches the maximum value; in the second stage, the non-conductive fractures overcome the in situ stress and become conducting fractures; in the third stage, the rock in the reservoir undergoes shear failure, the fractures expand and connect, and finally, a fracture network is formed. After each stage, the volume of the enhanced permeability area is approximately 10,000, 21,000, and 33,000 m3, respectively. After 30 years of exploitation, the outlet temperature and thermal power output of conventional double vertical wells are the highest, while the horizontal wells have the highest heat extraction ratio. The temperature of a production well in the conventional double vertical wells model, horizontal wells, and double-pipe heat exchange system is 101 °C, 93.4 °C, and 91.6 °C, a decrease of 41.2%, 45.7%, and 46.7%, respectively. The thermal power output is 6.67 MW, 6.31 MW, and 6.1 MW, a decrease of 39.4%, 42.6%, and 44.5%, respectively. The heat extraction ratio of the horizontal wells is 2% higher than the double-pipe heat exchange system and 6.5% higher than the conventional double vertical wells.
Uncertainty and Sensitivity Analysis of Hydrogen Source Term under Severe Accident of Marine Reactor
Authors: Chen Yuqing , Wang Haifeng
Published: December 2022

In order to explore the hydrogen source term characteristics under severe accidents of marine pressurized water reactors (PWR) and effectively assess the hydrogen risk, the best estimation program SCDAP/RELAP5/MOD3.2 is used to establish the marine reactor severe accident analysis model. Based on the Latin Hypercube sampling (LHS) method and the Wilks sampling theory, a set of methods for the uncertainty analysis of severe accidents is developed. This method can be applied to the uncertainty and sensitivity analysis of different target parameters. The phenomenon identification and ranking table (PIRT) under the severe accident induced by the break are established, and 14 uncertain parameters are selected as input variables. The established PIRT fills the gap in the uncertainty and sensitivity analysis of severe accidents of marine reactors and provides a reference for subsequent research. The quantitative uncertainty analysis of the calculation results is carried out, and the uncertainty range of hydrogen production is defined. The Spearman correlation coefficient is used to evaluate the sensitivity of input parameters, and the sensitivity of each parameter to hydrogen production is obtained. The results show that under the severe accident caused by the medium equivalent diameter break, the uncertainty range of hydrogen production in the zirconium–water reaction in the pressure vessel is 20.14 kg~22.19 kg with 95% confidence, and the fuel cladding thickness has a significant positive correlation on the hydrogen production.
Allocation of Joint Costs and Price Setting for Electricity and Heat Generated in Cogeneration
Authors: Atănăsoae Pavel
Published: December 2022

In the case of cogeneration, the allocation of operation costs has a particularity specific to the joint production of heat and electricity from the same primary energy source. The cost allocation is also influenced by the demand conditions faced by the joint products, and prices must be sufficient for both products to remain profitable and competitive with other power generation alternatives in the respective markets. Therefore, the choice of cost allocation methodology is influenced by market conditions. The paper presents an algorithm for determining the prices of energy and heat produced in cogeneration according to demand and market conditions. The economic market method was extended with an algorithm based on duality theory for verifying allocation methods and establishing forecasting strategies. An example is provided for a cogeneration plant (1.287 MWe; 5.386 MWth) based on ORC (Organic Rankine Cycle) technology and biomass fuel. The fuel cost has a major influence on the total costs of the cogeneration plant, between 68% and 77% in the analyzed scenarios. The fuel cost variation is considered in the application of the cost allocation methods for setting the prices of heat and electricity.
Regional Climate Change Effects on the Viticulture in Portugal
Authors: Wunderlich Rainer Ferdinand , Lin Yu-Pin , Ansari Andrianto
Published: December 2022

The grapevine (Vitis vinifera) is widely cultivated for the production of wine and other commodities. Wine is globally traded, with an annual market value of approximately USD 4 billion in Portugal alone. However, climate change is expected to profoundly alter regional temperature and precipitation regimes across the Iberian Peninsula and, thus, in continental Portugal, potentially threatening to impact viticulture. We used boosted regression trees and environmental variables describing the climate, soil, topography, and irrigation with a large number of presences (N = 7002) to estimate grapevine suitability for a baseline (1981–2010) and three future periods spanning from 2011 to 2100 using two climate trajectories (SSP3-7.0 and SSP5-8.5) and irrigation scenarios (continued and ceased). Under SSP3-7.0 with irrigation and SSP5-8.5 without irrigation, our results suggest a decline in suitable viticulture area across continental Portugal of ~20% and ~80% by 2041–2070 and 2011–2041, respectively. Following this decline, our data suggest a potential recovery by 2071–2100 of ~6% and ~186%, respectively. However, regional change is more complex: by 2071–2100, the Região Norte, the Douro wine region, and the Algarve, for example, each would experience future changes in suitable area in the range of approximately −92% to −48%, −86% to −24%, and −59% to 267%, respectively, depending mostly on the practicality of irrigation.
Food Waste to Bioethanol: Opportunities and Challenges
Authors: Bibra Mohit , Samanta Dipayan , Sharma Nilesh Kumar , Singh Gursharan , Johnson Glenn R. , Sani Rajesh K.
Published: December 2022

The increasing global population will require sustainable means to sustain life and growth. The continuous depletion and increasing wastage of the energy resources will pose a challenge for the survival of the increasing population in the coming years. The bioconversion of waste generated at different stages of the food value chain to ethanol can provide a sustainable solution to the depleting energy resources and a sustainable way to address the growing food waste issue globally. The high carbohydrate and nitrogen content in the food waste can make it an ideal alternative substrate for developing a decentralized bioprocess. Optimizing the process can address the bottleneck issues viz. substrate collection and transport, pretreatment, fermentative organism, and product separation, which is required to make the process economic. The current review focuses on the opportunities and challenges for using the food loss and waste at different stages of the food value chain, its pretreatment, the fermentation process to produce bioethanol, and potential ways to improve the process economics. The impact of substrate, fermentative organisms’ process development, downstream processing, and by-product stream to make the bioethanol production from the waste in the food value chain a commercial success are also discussed.
Ethereum-Based Information System for Digital Higher Education Registry and Verification of Student Achievement Documents
Authors: Kistaubayev Yerlan , Mutanov Galimkair , Mansurova Madina , Saxenbayeva Zhanna , Shakan Yassynzhan
Published: December 2022

Blockchain is a new and modern technology that is gradually being used in various fields due to its ability to decentralize and organize secure and reliable data exchange and storage. One of the related research areas generating increasing interest is the field of education, with particular focus on the digitization and automation of educational management processes and the ability to store and verify digital documents about student progress. The main goal of this study is to develop a platform that creates a unified digital register of students’ educational achievements, which is one of the most pressing issues in the field of education, based on the Ethereum blockchain architecture. Blockchain is expensive; therefore, there is a need to consider performance criteria when evaluating any decision made about the technology, especially the most important aspects such as predicting traffic behavior, estimating transaction costs and providing the necessary indicators of system quality and functionality. However, most research ignores the evaluation of performance indicators, such as throughput, the speed of transactions and the amount of data stored in the Ethereum blockchain database, which are the main evaluation criteria. This paper aims to eliminate this gap by evaluating the performance of the developed platform and by discussing the obtained experimental results. Thus, the main results of this work are the design and deployment of a blockchain platform and the analysis of its transaction costs. We conclude that the proposed blockchain solution is applicable as a system for the accounting and verification of loans and student academic achievements.
Pseudomonas putida: Sensitivity to Various Antibiotics, Genetic Diversity, Virulence, and Role of Formic Acid to Modulate the Immune-Antioxidant Status of the Challenged Nile tilapia Compared to Carvacrol Oil
Authors: Alzahrani Othman , Elumalai Preetham , Nada Hend , Ahmed Shaimaa , Zaglool Asmaa , Shawky Sherif , Alkafafy Mohamed , Mahboub Heba
Published: December 2022

The Pseudomonas putida strain was primarily identified and tested in vitro against antibiotic sensitivity for several antibiotics using the disc diffusion method. This isolate was also tested against sensitivity to carvacrol oil (c) and formic acid (f). The genotyping of Pseudomonas spp. and virulotyping for P. putida isolate was carried out and verified by 16S rDNA-PCR amplification. Furthermore, we assessed the efficacy of carvacrol oil and formic acid in vivo for treatment of P. Putida infection. For the in vivo challenge, 180 fish (Nile tilapia, Oreochromis niloticus) were divided into six groups: (G1: control (unchallenged), G2: carvacrol prophylaxis (3 g/kg), G3: formic acid prophylaxis (5 mL/kg), G4: control positive (challenged), G5: carvacrol treatment (3 g/kg), and G6: formic acid treatment (5 mL/kg); 30 fish per group) with three replicates. Following the challenge, nitric oxide and lysozyme activity were measured as essential indicators for fish immunity. The antioxidant parameters including SOD and catalase were computed to reflect the antioxidant status. Furthermore, relative percent survival (RPS) and mortality percent were evaluated to indicate functional immunity. The findings of the antibiotic sensitivity test showed that ciprofloxacin exhibited the largest inhibition zone. Additionally, formic acid (f) displayed the greatest inhibition zone compared to carvacrol oil (c) and was more effective in stimulating the immune-antioxidant response compared to carvacrol oil. The tested exotoxin A (tox A), exoenzyme S (exo S), and the nan1 associated-virulence genes were identified in the P. putida isolate. Overall, the current study verified the virulence of P. putida and highlighted the promising role of dietary addition of formic acid for enhancing the immune-antioxidant indicators and for mitigating P. putida infection. Future studies could be devoted to this field.
Navier–Stokes Equations and Bulk Viscosity for a Polyatomic Gas with Temperature-Dependent Specific Heats
Authors: Kosuge Shingo , Aoki Kazuo
Published: December 2022

A system of Navier–Stokes-type equations with two temperatures is derived, for a polyatomic gas with temperature-dependent specific heats (thermally perfect gas), from the ellipsoidal statistical (ES) model of the Boltzmann equation extended to such a gas. Subsequently, the system is applied to the problem of shock-wave structure for a gas with large bulk viscosity (or, equivalently, with slow relaxation of the internal modes), and the numerical results are compared with those based on the ordinary Navier–Stokes equations. It is shown that the latter equations fail to describe the double-layer structure of shock profiles for a gas with large bulk viscosity.
Oil-in Water Vegetable Emulsions with Oat Bran as Meat Raw Material Replacers: Compositional, Technological and Structural Approach
Authors: Herrero Ana M. , Merino Esther , Muñoz-González Irene , Ruiz-Capillas Claudia
Published: December 2022

The unique composition and technological properties of some oat bran components (mainly protein and soluble fiber) and olive oil make them a good choice to form oil-in-water vegetable emulsions. The different concentrations of oat bran were studied to form olive oil-in water (O/W) emulsions to apply as a replacement for fat and meat. As a result, four O/W emulsions (OBE) were formulated with 10% (OBE10), 15% (OBE15), 20% (OEB20), and 30% (OBE30) oat bran concentrations and 40% olive oil, with the corresponding amount of water added for each O/W emulsion. Composition, technological properties (thermal stability, pH, texture), and lipid structural characteristics were evaluated. The results showed that low oat bran content (OEB10)—with a lower concentration of oat protein and β-glucans—resulted in an O/W emulsion with an aggregated droplet structure and lower thermal stability and hardness. These connections between composition, technology, and structural properties of olive O/W emulsions elaborated with oat bran could help in making the optimal choice for their potential application in the production of foods such as healthier meat products.
Probiotic Properties and Proteomic Analysis of Pediococcus pentosaceus 1101
Authors: Escobar-Sánchez Monserrat , Carrasco-Navarro Ulises , Juárez-Castelán Carmen , Lozano-Aguirre Beltrán Luis , Pérez-Chabela M. Lourdes , Ponce-Alquicira Edith
Published: December 2022

Pediococcus pentosaceus 1101 was identified by using 16S rRNA and MALDI-Biotyper. The strain was exposed to conditions that resemble the gastrointestinal tract (GT) to evaluate its probiotic properties. That included the growth kinetics, proteolytic and inhibitory activities within a pH range, survival at low pH and in the presence of bile salts, antagonistic activity, cell-adhesion properties, and antibiotic resistance. The evaluation was followed by a genomic and proteomic analysis that involved the identification of proteins obtained under control and gastrointestinal conditions. The strain showed antagonistic activity against Gram-negative and Gram-positive bacteria, high resistance to acidity (87% logarithmic survival rate, pH 2) and bile salts (99% logarithmic survival rate, 0.5% w/v), and hydrophobic binding, as well as sensitivity to penicillin, amoxicillin, and chloramphenicol. On the other hand, P. pentosaceus 1101 has a genome size of 1.76 Mbp, with 1754 coding sequences, 55 rRNAs, and 33 tRNAs. The proteomic analysis showed that 120 proteins were involved in mechanisms in which the strain senses the effects of acid and bile salts. Moreover, the strain produces at least one lytic enzyme (N-acetylmuramoyl-L-alanine amidase; 32 kDa) that may be related to the antimicrobial activity. Therefore, proteins identified might be a key factor when it comes to the adaptation of P. pentosaceus 1101 into the GT and associated with its technological and probiotic properties.
Effect of Brown Algae and Lichen Extracts on the SCOBY Microbiome and Kombucha Properties
Authors: Golovkina Darya A. , Zhurishkina Elena V. , Ayrapetyan Olga N. , Komissarov Artem E. , Krylova Anastasiya S. , Vinogradova Elizaveta N. , Toshchakov Stepan V. , Ermilov Filipp K. , Barsegyan Artak M. , Kulminskaya Anna A. , Lapina Irina M.
Published: December 2022

Kombucha tea was made by the fermentation of SCOBY culture of green tea broth with the addition of Fucus vesiculosus algae extract, Cetraria islandica lichen extract and their mixture. Kombucha was also made without the herbal supplements as a control. After 11 days of fermentation, in addition to the yeast Brettanomyces bruxellensis and the bacteria Komagataeibacter rhaeticus and Komagataeibacter hansenii contained in all of the samples, the yeast Zygosaccharomyces bailii and bacteria Komagataeibacter cocois were detected in the samples with the herbal extracts. In all of the kombucha with herbal additives, the total fraction of yeast was decreased as compared to the control. The total content of polyphenols and the antioxidant activity of the beverages with and without the addition of herbal extracts were comparable. The kombucha made with the algae extract showed an increased content of sucrose and organic acids, while the fructose and glucose content in the samples with algae and the mixture of extracts were lower than in the other samples. The samples with the algae extract had the highest organoleptic indicators “aroma”, “clarity” and “acidity”, while the control samples had slightly higher indicators of “taste” and “aftertaste”. The results of this study indicate the potential of algae and lichens as functional supplements for obtaining non-alcoholic fermented beverages with additional nutraceutical value.
Influence of Stirring Parameters on Creaminess of Spring Blossom Honey Measured by Crystal Size, Whiteness Index and Mouthfeel
Authors: Meixner Mario , Weber Mareike , Lella Sebastian , Rozhon Wilfried , Dasbach Margot
Published: December 2022

Spring blossom honey from regions with many rape fields tends to crystalize rapidly after harvesting. The crystallization process needs to be controlled by stirring in order to avoid the formation of coarse crystals and to ensure the creaminess of honey. The aim of this study was to investigate how various parameters of the stirring process influence the creaminess of spring blossom honey in order to give recommendations for beekeeping practices. The creaminess was quantified by measuring the crystal size by microscopic analysis, measuring the whiteness index by color analysis using CIE Lab and by sensory analysis. We investigated the influence of five stirring parameters, including the type of stirring device, honey pretreatment, stirring temperature (14 °C to room temperature), stirring interval (1 to 24 times) and stirring time (1–15 min) on the creaminess of honey. We found that the stirring temperature is the most important factor for honey creaminess. At the optimal temperature of 14 °C, other factors like seed honey, stirring time and stirring interval have only a neglectable effect. If the optimal temperature of 14 °C cannot be maintained, as it may happen in beekeepers’ practice, sieving the honey with a mesh size of 200 µm before stirring, the addition of seed honey prepared with a kitchen food processor, and using a stirring screw and stirring several times per day is recommended.
Structural Property, Immunoreactivity and Gastric Digestion Characteristics of Glycated Parvalbumin from Mandarin Fish (Siniperca chuaisi) during Microwave-Assisted Maillard Reaction
Authors: Tai Jingjing , Qiao Dan , Huang Xue , Hu Huang , Li Wanzheng , Liang Xinle , Zhang Fuming , Lu Yanbin , Zhang Hong
Published: December 2022

This study was aimed to investigate the structural and immunological properties of parvalbumin from mandarin fish during the Maillard reaction. The microwave-assisted the Maillard reaction was optimized by orthogonal designed experiments. The results showed that the type of sugar and heating time had a significant effect on the Maillard reaction (p < 0.05). The SDS-PAGE analysis displayed that the molecular weight of parvalbumin in mandarin fish changed after being glycated with the Maillard reaction. The glycated parvalbumin was analyzed by Nano-LC-MS/MS and eleven glycation sites as well as five glycation groups were identified. By using the indirect competitive ELISA method, it was found that microwave heating gave a higher desensitization ability of mandarin fish parvalbumin than induction cooker did. In vitro gastric digestion experiments showed that microwave-heated parvalbumin was proved to be digested more easily than that cooked by induction cookers. The microwave-assisted Maillard reaction modified the structure of parvalbumin and reduced the immunoreactivity of parvalbumin of mandarin fish.
Two-Phase Extraction Processes, Physicochemical Characteristics, and Autoxidation Inhibition of the Essential Oil Nanoemulsion of Citrus reticulata Blanco (Tangerine) Leaves
Authors: Rashed Marwan M. A. , You Ling , Ghaleb Abduljalil D. S. , Du Yonghua
Published: December 2022

Combined ultrasound–microwave techniques and pre-enzymatic treatment (hemicellulase and cellulase) enhance essential oil isolation from Citrus reticulata Blanco (tangerine) leaves (CrBL). Subsequently, synergistic effects of modified amorphous octenyl succinic anhydride starch (OSA-MS), almond oil, and high-energy microfluidics were studied in synergy with ultrasound techniques in the production of CrBL essential oil (CrBL-EO) nanoemulsion (CrBL-EONE). GC–MS was used to study the extraction technique. Dynamic light scattering (DLS) analysis was used with confocal laser scanning microscopy (CLSM) techniques to investigate the nanoemulsion matrices’ physical and chemical properties. The D-limonene nanoemulsion (D-LNE) reached the optimal size of droplets (65.3 ± 1.1 r.nm), polydispersity index (PDI) (0.167 ± 0.015), and ζ-potential (−41.0 ± 0.4 mV). Besides, the CrBL-EONE obtained the optimal size of droplets (86.5 ± 0.5 r.nm), PDI (0.182 ± 0.012), and ζ-potential (−40.4 ± 0.8 mV). All the nanoparticle treatments showed significant values in terms of the creaming index (CI%) and inhibition activity (IA%) in the β-carotene/linoleate system with a low degradation rate (DR). The current study’s findings showed that integrated ultrasound–microwave techniques and pre-enzymatic treatment could enhance the extraction efficiency of the CrBL-EO. In addition, OSA-MS and almond oil can also be employed to produce CrBL-EONE and D-LNE.