bluuebunny/crossref_metadata_2025 huggingface dataset:
A Bibliometric Review Study on Reading Literacy over Fourteen Years
Authors: Lan Xinya , Yu Zhonggen
Published: December 2022

Reading literacy has been considered one of the essential vital competencies in modern society and has thus gained increasing attention in research. With both qualitative and quantitative research methods, this study aimed to investigate the overall picture in this research field and investigate the role of reading motivation and online reading activities and how online reading literacy was assessed. The top ten organizations, countries with the highest publications, author keywords, all keywords, cited references, cited sources, and cited authors were visualized via VOSviewer clustering and counting techniques. Reading motivation, online reading activities, and digital reading literacy assessment tests were also explored through the visualization citation network in CitNetExplorer. In conjunction with the citation network, 13 peer-reviewed articles were selected for further analysis based on the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P). The results show that reading motivation played an essential role in reading literacy achievement in formal schooling, while online reading activities could both positively and negatively affect digital reading literacy due to their multifaceted nature. The digital reading literacy assessment tests vary across the world. Implications for widely investigating adults and L2 reading literacy and relevant support or interventional measures to boost reading literacy were also discussed.
Identifying the Characteristics of Non-Digital Mathematical Games Most Valued by Educators
Authors: Russo James , Bragg Leicha , Russo Toby , Minas Michael
Published: December 2022

Non-digital games are frequently used to support primary mathematics instruction. Moreover, we know from the literature that to increase the likelihood that a chosen mathematical game is educationally rich it should reflect specific principles, such as offering a balance between skill and luck and ensuring that a key mathematical focus is central to gameplay. However, there is limited research informing us, from a teacher’s perspective, of the specific characteristics of mathematical games that are most indicative of a game’s value for supporting learning, and the likelihood that teachers will use the game with students in the future. To help address this gap, the current study invited 122 educators to complete an on-line questionnaire, including 20 Likert-scale items designed to assess the characteristics of educationally-rich mathematical games (CERMaGs) that aligned with six ‘good practice’ principles previously identified in the literature, in relation to a specific mathematical game of their choosing. In total, educators chose a broad range of mathematical games to be evaluated (n = 53). On average, they reported that their chosen game was highly valuable for supporting mathematics learning and that they were very likely to use this game with students if given the opportunity. Our results revealed that the extent to which educators perceived a game to be suitably challenging, engaging, enjoyable, modifiable to support different learners, and transformable into an investigation or broader mathematical inquiry, were particularly important characteristics associated with perceptions of a game’s educational value. Similarly, perceived levels of student enjoyment, engagement and a game’s potential to lead to a rich mathematical investigation were important characteristics for evaluating the likelihood that an educator would use a particular game in the future with students if given the opportunity, as was the capacity of a game to support mathematical discussion. The implications of these findings for supporting classroom practice and teacher professional learning are discussed.
LSTM-Based IoT-Enabled CO2 Steady-State Forecasting for Indoor Air Quality Monitoring
Authors: Zhu Yingbo , Al-Ahmed Shahriar Abdullah , Shakir Muhammad Zeeshan , Olszewska Joanna Isabelle
Published: December 2022

Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO2) can lead to a series of negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be monitored for a variety of health reasons. Various air quality monitoring systems are available on the market. However, since they are expensive and difficult to obtain, they are not commonly employed by the general population. With the advent of the Internet of Things (IoT), the Indoor Air Quality (IAQ) monitoring system has been simplified, and a number of studies have been conducted in order to monitor the IAQ using IoT. In this paper, we propose an improved IoT-based, low-cost IAQ monitoring system using Artificial Intelligence (AI) to provide recommendations. In our proposed system, the IoT sensors transmit data via Message Queuing Telemetry Transport (MQTT) protocol which can be visualised in real time on a user-friendly dashboard. Furthermore, the AI technique referred to as Long Short-Term Memory (LSTM) is applied to the collected CO2 data for the purpose of predicting future CO2 concentrations. Based on the predicted CO2 concentration, our system can compute CO2 steady state in advance with an error margin of 5.5%.
Differentiable Constraints’ Encoding for Gradient-Based Analog Integrated Circuit Placement Optimization
Authors: Gusmão António , Alves Pedro , Horta Nuno , Lourenço Nuno , Martins Ricardo
Published: December 2022

Analog IC design is characterized by non-systematic re-design iterations, often requiring partial or complete layout re-design. The layout task usually starts with device placement, where the several performance figures and constraints to be met escalate its complexity immensely, and, due to the inherent tradeoffs, an “optimal” floorplan solution does not usually exist. Deep learning models are now establishing for the automation of the placement task of analog integrated circuit layout design, promising to bypass the limitations of existing approaches based on: time-consuming optimization processes with several constraints; or placement retargeting from legacy designs/templates, which rely heavily on legacy layout data. However, as the complexity of analog design cases tackled by these methodologies increases, a broader set of topological constraints must be supported to cover the different layout styles and circuit classes. Here, model-independent differentiable encodings for regularity, boundary, proximity, and symmetry island constraints are formulated for the first time in the literature, and an unsupervised loss function is used for the artificial neural network model to learn how to generate placements that follow them. The use of a deep learning model makes push-button speed placement generation possible, additionally, as only sizing data are required for its training, it discards the need to acquire legacy layouts containing insights into this vast set of, often neglected, constraints. The model is ultimately used to produce floorplans from scratch at push-button speed for real state-of-the-art analog structures, including technology nodes not used for training. A case-study comparison with a floorplan design made by a human-expert presents improvements in the fulfillment of every constraint, reaching an overall improvement of around 70%, demonstrating the approach’s value in placement design.
SHO-CNN: A Metaheuristic Optimization of a Convolutional Neural Network for Multi-Label News Classification
Authors: Nadeem Muhammad Imran , Ahmed Kanwal , Li Dun , Zheng Zhiyun , Naheed Hafsa , Muaad Abdullah Y. , Alqarafi Abdulrahman , Abdel Hameed Hala
Published: December 2022

News media always pursue informing the public at large. It is impossible to overestimate the significance of understanding the semantics of news coverage. Traditionally, a news text is assigned to a single category; however, a piece of news may contain information from more than one domain. A multi-label text classification model for news is proposed in this paper. The proposed model is an automated expert system designed to optimize CNN’s classification of multi-label news items. The performance of a CNN is highly dependent on its hyperparameters, and manually tweaking their values is a cumbersome and inefficient task. A high-level metaheuristic optimization algorithm, spotted hyena optimizer (SHO), has higher advanced exploration and exploitation capabilities. SHO generates a collection of solutions as a group of hyperparameters to be optimized, and the process is repeated until the desired optimal solution is achieved. SHO is integrated to automate the tuning of the hyperparameters of a CNN, including learning rate, momentum, number of epochs, batch size, dropout, number of nodes, and activation function. Four publicly available news datasets are used to evaluate the proposed model. The tuned hyperparameters and higher convergence rate of the proposed model result in higher performance for multi-label news classification compared to a baseline CNN and other optimizations of CNNs. The resulting accuracies are 93.6%, 90.8%, 68.7%, and 95.4% for RCV1-v2, Reuters-21578, Slashdot, and NELA-GT-2019, respectively.
VW-SC3D: A Sparse 3D CNN-Based Spatial–Temporal Network with View Weighting for Skeleton-Based Action Recognition
Authors: Lin Xiaotian , Xu Leiyang , Zhuang Songlin , Wang Qiang
Published: December 2022

In recent years, human action recognition has received increasing attention as a significant function of human–machine interaction. The human skeleton is one of the most effective representations of human actions because it is highly compact and informative. Many recent skeleton-based action recognition methods are based on graph convolutional networks (GCNs) as they preserve the topology of the human skeleton while extracting features. Although many of these methods give impressive results, there are some limitations in robustness, interoperability, and scalability. Furthermore, most of these methods ignore the underlying information of view direction and rely on the model to learn how to adjust the view from training data. In this work, we propose VW-SC3D, a spatial–temporal model with view weighting for skeleton-based action recognition. In brief, our model uses a sparse 3D CNN to extract spatial features for each frame and uses a transformer encoder to obtain temporal information within the frames. Compared to GCN-based methods, our method performs better in extracting spatial–temporal features and is more adaptive to different types of 3D skeleton data. The sparse 3D CNN makes our model more computationally efficient and more flexible. In addition, a learnable view weighting module enhances the robustness of the proposed model against viewpoint changes. A test on two different types of datasets shows a competitive result with SOTA methods, and the performance is even better in view-changing situations.
A Carrier Synchronization Lock Detector Based on Weighted Detection Statistics for APSK Signals
Authors: Li Yaoyao , Shi Xuesen , Zhan Jie , Wang Yongqing
Published: December 2022

To solve the application limitations of conventional detectors caused by discrete phase distribution of high-order APSK signals, and the problem that the detection performance will degrade when the automatic control gain is unideal, a carrier synchronization lock detector based on weighted detection statistics is proposed for APSK signals. Based on the detection statistics of the Linn detector, the proposed detector calculates a weighted factor according to the amplitude difference of the signal on the APSK constellation to adjust the weight of detection statistics for different rings. The proposed detector solves the detection performance degradation problem of the Linn detector caused by uneven phase distribution. In order to further improve detection performance, the detection threshold and statistical signal length are reasonably designed. The expectation and variance properties are derived, and the lock detection probability is analyzed. The performance of the proposed detector is verified through simulations. Simulation results show that the proposed carrier synchronization lock detector has better performance than the Linn detector.
Design and Experiment for N = 3 Wineglass Mode Metal Cylindrical Resonator Gyroscope Closed-Loop System
Authors: Guo Xiaolin , Cui Rang , Yan Shaochen , Cai Qi , Wei Wenqiang , Shen Chong , Cao Huiliang
Published: December 2022

This paper studies a kind of gyro structure of N = 3 Wineglass Mode Metal Cylindrical Resonator Gyroscope (WMMCRG). Compared with traditional Cylindrical Vibrating Gyroscope (CVG), the designed structure has higher scale factor and lower frequency split. This paper provides a more specific processing method and the parameters of resonator materials. A closed-loop controlling system with low error and low noise is designed for WMMCRG. The system is composed of three independent closed-loop systems: drive closed-loop, sensing closed-loop, and quadrature error correction closed-loop. Through the test of the high-precision turntable, under the premise of the same material and processing technology, the bias instability, bias stability, zero bias, Angular Random Walk (ARW), and frequency split of WMMCRG is 1.974°/h, 10.869°/h, 10.3323°/s, 16 (°)/√h, 0.02 Hz, respectively.
Parasitic-Aware Simulation-Based Optimization Design Tool for Current Steering VGAs
Authors: Mansour Nehad , Elnozahi Mohamed , Ragaai Hani
Published: December 2022

Designing millimeter-wave variable gain amplifiers (VGAs) is very challenging owing to the parasitic effects of the interconnects of both active and passive devices. An automated parasitic-aware optimization RF design tool is proposed in this paper to address this challenge. The proposed tool considers the parasitic effects prior to layout. It employs a knowledge-aware optimization technique. The augmentation between parasitic-aware and knowledge-aware techniques speeds up the design process and leads to a design as close to the final design after finalizing the layout. The proposed tool gives limitless and guaranteed converged solutions in a wide range of RF frequencies. A four-bits current steering VGA design is used as a validation of the tool. The tool is tested on three different frequencies using the 65 nm-technology node. The three tested frequencies (7, 10, and 13 GHz) show a root mean square gain error at approximately 0.1 dB and a phase variation at approximately 3.5° within a 16-dB gain control range. To our knowledge, it is the first reported automated design tool for a current steering VGA.
Prototype-Based Self-Adaptive Distribution Calibration for Few-Shot Image Classification
Authors: Du Wei , Hu Xiaoping , Wei Xin , Zuo Ke
Published: December 2022

Deep learning has flourished in large-scale supervised tasks. However, in many practical conditions, rich and available labeled data are a luxury. Thus, few-shot learning (FSL) has recently received boosting interest and achieved significant progress, which can learn new classes from several labeled samples. The advanced distribution calibration approach estimates the ground-truth distribution of few-shot classes by reusing the statistics of auxiliary data. However, there is still a significant discrepancy between the estimated distributions and ground-truth distributions, and artificially set hyperparameters cannot be adapted to different application scenarios (i.e., datasets). This paper proposes a prototype-based self-adaptive distribution calibration framework for estimating ground-truth distribution accurately and self-adaptive hyperparameter optimization for different application scenarios. Specifically, the proposed method is divided into two components. The prototype-based representative mechanism is for obtaining and utilizing more global information about few-shot classes and improving classification performance. The self-adaptive hyperparameter optimization algorithm searches robust hyperparameters for the distribution calibration of different application scenarios. The ablation studies verify the effectiveness of the various components of the proposed framework. Enormous experiments are conducted on three standard benchmarks such as miniImageNet, CUB-200-2011, and CIFAR-FS. The competitive results and compelling visualizations indicate that the proposed framework achieves state-of-the-art performance.
Blockchain Federated Learning for In-Home Health Monitoring
Authors: Farooq Komal , Syed Hassan Jamil , Alqahtani Samar Othman , Nagmeldin Wamda , Ibrahim Ashraf Osman , Gani Abdullah
Published: December 2022

This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients’ health. This paper details ongoing research towards individualized health monitoring using wearable gadgets. The goal of improving healthcare facilities and improvement of the quality of life of citizens naturally brings up Internet of Things (IoT) technologies for consideration. Health observation is exceptionally critical in terms of avoidance, especially since the early determination of illnesses can minimize trouble and treatment costs. The cornerstones of intelligent, integrated, and individualized healthcare are continuous monitoring of physical signs and evaluation of medical data. To build a more reliable and robust IoMT model, the study will monitor the application of blockchain technology in federated learning (FL). A viable way to address the heterogeneity problem in federated learning is to design the system, data, and model tiers to lessen heterogeneity and produce a high-quality, tailored model for each endpoint. Blockchain-based federated learning allows for smarter simulations, lower latency, and lower power consumption while maintaining privacy at the same time. This solution provides another immediate benefit: in addition to having a shared model upgrade, the updated model on phones will now be used automatically, giving personalized knowledge about the phone is used.
Blockchain-Based Method for Pre-Authentication and Handover Authentication of IoV Vehicles
Authors: Li Qiang , Su Wenlong , Zhang Peng , Cheng Xinzhou , Li Mingxin , Liu Yuanni
Published: December 2022

The Internet of Vehicles (IoV) is an important supporting technology for intelligent transportation systems that connects traffic participants, such as vehicles, pedestrians, and roads, through wireless networks and enables information exchange to enhance traffic safety and improve traffic efficiency. The IoV is a unique network that involves many network security risks, which must be controlled through authentication, encryption, and other protective measures. To solve problems, such as high computing overhead and low handover authentication efficiency of the existing vehicle access authentication of the IoV, a compact consensus pre-authentication and handover authentication method was designed based on blockchain features such as decentralization and security. The proposed method is based on ensuring authentication security and reduces the consensus time, saves computing resources, and effectively solves the problems of high computing cost and high communication cost arising from frequent vehicle authentication handovers. A performance and security analysis demonstrates that our approach can reduce the computational overhead by up to 88.14% for a vehicle and by more than 60% for a roadside unit (RSU). The overall communication overhead of the solution is reduced by up to 71.31%. The data illustrate that the proposed method can safely and significantly improve the efficiency of vehicle handover authentication.
An SSD-MobileNet Acceleration Strategy for FPGAs Based on Network Compression and Subgraph Fusion
Authors: Tan Shoutao , Fang Zhanfeng , Liu Yanyi , Wu Zhe , Du Hang , Xu Renjie , Liu Yunfei
Published: December 2022

Over the last decade, various deep neural network models have achieved great success in image recognition and classification tasks. The vast majority of high-performing deep neural network models have a huge number of parameters and often require sacrificing performance and accuracy when they are deployed on mobile devices with limited area and power consumption. To address this problem, we present an SSD-MobileNet-v1 acceleration method based on network compression and subgraph fusion for Field-Programmable Gate Arrays (FPGAs). Firstly, a regularized pruning algorithm based on sensitivity analysis and Filter Pruning via Geometric Median (FPGM) was proposed. Secondly, the Quantize Aware Training (QAT)-based network full quantization algorithm was designed. Finally, a strategy for computing subgraph fusion is proposed for FPGAs to achieve continuous scheduling of Programmable Logic (PL) operators. The experimental results show that using the proposed acceleration strategy can reduce the number of model parameters by a factor of 11 and increase the inference speed on the FPGA platform by a factor of 9–10. The acceleration algorithm is applicable to various mobile edge devices and can be applied to the real-time monitoring of forest fires to improve the intelligence of forest fire detection.
Effect of Platinum Nanoparticles (PtNPs) Pollution on the Biological Properties of Haplic Cambisols Eutric of the Caucasus Forests
Authors: Kolesnikov Sergey , Timoshenko Alena , Kabakova Victoria , Minnikova Tatiana , Tsepina Natalia , Kazeev Kamil , Minkina Tatiana M. , Shende Sudhir S. , Mandzhieva Saglara S. , Tsitsuashvili Victoria , Sushkova Svetlana N.
Published: December 2022

Pollution by platinum (Pt) is an emerging threat to forest soil health. The widespread use of Pt nanoparticles (NPs) in gas neutralizers for automobile exhaust has sharply increased the amount of PtNP pollution in the environment, including forest ecosystems. Recently, territories with Pt concentrations greater than 0.3 mg/kg in soil have been discovered. This concentration is 750 times greater than the background content in the earth’s crust. Cambisols, the most prevalent forest soil type in boreal forests that determines the functioning of the entire forest ecosystem, occupy a significant share of the Earth’s soil cover, which is about 1.5 billion hectares worldwide, or 12% of the entire continental land area. This shows the importance of studying the effect of pollution on this type of soil. In this study, laboratory simulations of PtNP contamination of the Haplic Cambisols Eutric at concentrations of 0.01, 0.1, 1, 10, and 100 mg/kg were carried out. The effect of PtNPs on soil properties was assessed using the most sensitive and informative biological indicators. The total number of bacteria was studied by the methods of luminescent microscopy, catalase activity (gasometrically), dehydrogenases activity (spectrophotometrically), germination, and length of roots by the method of seedlings. It was found that at the concentrations of 0.01, 0.1, and 1 mg/kg of PtNPs, there was either no effect or a slight, statistically insignificant decrease in the biological state of Haplic Cambisols Eutric. Concentrations of 10 and 100 mg/kg of PtNPs had a toxic effect on all the studied parameters. No statistically significant stimulating effect (hormesis) of PtNPs on the biological properties of Haplic Cambisols Eutric was observed, which indicates the high toxicity of PtNPs and the importance of studying the consequences of soil and ecosystem contamination with PtNPs. However, when the content of Pt in the soil was 1 mg/kg, there was a tendency to stimulate germination, the length of radish roots, and the total number of bacteria. The toxicity of PtNPs measured by biochemical indicators (activity of catalase and dehydrogenases) starts at a concentration of 100 mg/kg for phytotoxic effects (germination and root length of radish) and 10 mg/kg for microbiological effects (total number of bacteria).
Effect of Fermented Meat and Bone Meal–Soybean Meal Product on Growth Performance in Broilers
Authors: Lo Weng-Keong , Ong Ping-Lin , Lee Yueh-Sheng , Hsu Yi-Tai , Chen Kuo-Lung
Published: December 2022

In this study, we screen the proteolytic activity of Bacillus species in meat and bone meal (MBM) and investigate the effects of fermented MBM–soybean meal products (FMSMPs) on the growth performance of broilers. In Trial 1, FMSMPs were fermented using four strains—Bacillus siamensis M3 (M3), B. velezensis M5 (M5), B. subtilis M6 (M6), and B. subtilis M20 (M20)—all of which presented more total peptides and higher degrees of hydrolysis (DH) than Bacillus subtilis var. natto N21 (N21). In Trial 2, 280 0-day-old Arbor Acres broilers, with equal numbers of both sexes, were randomly assigned into 5% fish meal (FM), MBM–soybean meal (MSM, as control), and N21, M3, M5, M6, and M20 FMSMP groups. The results demonstrated that the crude protein, total amino acids, alkaline protease, trichloroacetic acid–soluble nitrogen (TCA-SN), TCA-SN/total nitrogen, total peptides, DH, and free-hydroxyproline levels in the M6 group were greater than those in any other group (p < 0.05). Furthermore, the weight gain in the M6 group was superior to that of the FM and MSM groups in 0–21 and 0–35-day-old broilers (p < 0.05). In conclusion, B. subtilis M6 likely efficiently decomposes MSM to improve the protein properties and nutritional value of the product after fermentation. Supplementation with 5% FMSMP may promote weight gain in broilers.
Effect of Complex Prebiotics on the Intestinal Colonization Ability of Limosilactobacillus fermentum DALI02
Authors: Liu Xiaoxiao , Chen Dawei , Li Qiming , Zhang Chenchen , Zhang Longfei , Qu Hengxian , Wang Wenqiong , Zhou Yuanyuan , Huang Yujun , Xiao Lixia , Gu Ruixia
Published: December 2022

Intestinal colonization is beneficial to the role of probiotics, and prebiotics can promote the adhesion and colonization of probiotics in the intestine. This study optimized the combination of complex prebiotics that could improve the growth ability and adhesion ability of Limosilactobacillus fermentum (L. fermentum) DALI02 to Caco-2 cells in vitro and determined the effect of its colonization quantity and colonization time in the immunocompromised rat model. The results showed that all five prebiotics (fructo-oligosaccharides (FOS), galacto-oligosaccharides (GOS), inulin, stachyose, and xylo-oligosaccharides (XOS)) significantly promoted the growth and adhesion of L. fermentum DALI02. It was found that 0.5% (w/w) inulin had the best growth promotion effect, and 0.5% FOS had the strongest adhesion promotion (the adhesion rate was increase by 1.75 times). In addition, 0.05% FOS, 0.20% GOS, 0.30% inulin, 0.20% stachyose, and 0.30% XOS could significantly improve the adhesion rate of L. fermentum DALI02 from 1.72% to 3.98%. After 1 w of intervention, the quantity of colonization in the fermented broth with prebiotics group was significantly higher than that in the fermented broth group. The intervention time was extended from 1 d to 4 w, and the amount of colonization of L. fermentum DALI02 in the fermented broth with prebiotics group increased significantly from 4.32 lgcopies/g to 5.12 lgcopies/g. After the intervention, the serum levels of lipopolysaccharide (LPS) and D-lactic acid in rats were significantly reduced, and the most significant was in the fermented broth with prebiotics group, with LPS and D-lactic acid levels of 74.11 pg/mL and 40.33 μmol/L, respectively. Complex prebiotics can promote the growth and adhesion of L. fermentum DALI02 and significantly increase the quantity of colonization and residence time of the strain in the intestine, which helps the restoration of intestinal barrier function and other probiotic effects.
Pedestrian Simulation with Reinforcement Learning: A Curriculum-Based Approach
Authors: Vizzari Giuseppe , Cecconello Thomas
Published: December 2022

Pedestrian simulation is a consolidated but still lively area of research. State of the art models mostly take an agent-based perspective, in which pedestrian decisions are made according to a manually defined model. Reinforcement learning (RL), on the other hand, is used to train an agent situated in an environment how to act so as to maximize an accumulated numerical reward signal (a feedback provided by the environment to every chosen action). We explored the possibility of applying RL to pedestrian simulation. We carefully defined a reward function combining elements related to goal orientation, basic proxemics, and basic way-finding considerations. The proposed approach employs a particular training curriculum, a set of scenarios growing in difficulty supporting an incremental acquisition of general movement competences such as orientation, walking, and pedestrian interaction. The learned pedestrian behavioral model is applicable to situations not presented to the agents in the training phase, and seems therefore reasonably general. This paper describes the basic elements of the approach, the training procedure, and an experimentation within a software framework employing Unity and ML-Agents.
Narrowband Internet-of-Things to Enhance the Vehicular Communications Performance
Authors: Hamarsheh Qadri , Daoud Omar , Baniyounis Mohammed , Damati Ahlam
Published: December 2022

The interest in vehicle-to-vehicle communication has gained a high demand in the last decade. This is due to the need for safe and robust smart communication, while this type of communication is vulnerable to latency and power. Therefore, this work proposes the Narrowband Internet-of-Things to enhance the robustness of the vehicular communication system. Accordingly, the system’s QoS is enhanced. This enhancement is based on proposing two parts to cover the latency and the harmonics issues, in addition to proposing a distributed antenna configuration for the moving vehicles under a machine learning benchmark, which uses the across-entropy algorithm. The proposed environment has been simulated and compared to the state-of-the-art work performance. The simulation results verify the proposed work performance based on three different parameters; namely the latency, the mean squared error rate, and the transmitted signal block error rate. From these results, the proposed work outperforms the literature; at the probability of 10−3, the proposed work reduces the peak power deficiency by almost 49%, an extra 23.5% enhancement has been attained from the self-interference cancellation side, and a bit error rate enhancement by a ratio of 31%.
Initial Assessment of Fire Response Time between Different Categories of Fire Stations in Malaysia
Authors: Wan Jusoh Wan Nursheila , Tharima Ahmad Faiz , Ghani Wahyunah , Mohamad Lukman Nur Hafizah , Visvasathan Sunthaar , Shamsudin Mohd Hafizi , Mahmud Zuhudi Nurul Zuhairah , Mohd Nur Nurhayati
Published: December 2022

Response time is an important factor in fire operations. A continuous assessment of response time is crucial in order to monitor firefighters’ performance level. An initial assessment of fire response time was conducted for fire stations in categories A-D throughout Malaysia from 2018 to 2020. The categories were determined based on risk profiling scores. In this study, the mean response time and distance travelled for the selected fire stations were calculated. To measure the fire station’s performance, a 10 min standard response time was used as a benchmark. One-way analysis of variance (ANOVA) was also applied to statistically determine any significant differences between mean response time and mean distance travelled. Among the four categories, category C and D fire stations recorded high values for mean distance travelled and mean response time. Category C fire stations recorded the mean response time, at 15.1 min, and distance travelled, 20.1 km. The areas where category D fire stations are located have low population density, resulting in greater coverage for the stations. Most of the fire stations in this category had approximately 13.8 km travel distance with a mean response time of 17.9 min. Category C and D fire stations require a substantial amount of time to reach incident locations due to the low-quality road network and the local topography. A new profiling method for minimizing fire risk based on constant development in these areas might be necessary for future improvement. Additionally, new category C and D fire stations would meet the demands of expanding communities. It is important to note that establishing a demand zone in Malaysia with specific response time could give a better indication of firefighters’ performance in the future.
Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review
Authors: Ye Weixin , Xu Wei , Yan Tianying , Yan Jingkun , Gao Pan , Zhang Chu
Published: December 2022

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.
Identification of Optimal Fermentation Temperature for Dry-Fermented Sausage Using Strains Isolated from Korean Fermented Foods
Authors: Jeong Chang-Hwan , Lee Sol-Hee , Yoon Yohan , Choi Hyung-Youn , Kim Hack-Youn
Published: December 2022

This study aims at identifying the optimal fermentation temperature for dry-fermented sausage using strains isolated from Kimchi (GK1, Pediococcus pentosaceus-GK1; NK3, P. pentosaceus-NK3), Doenjang (D1, Debaryomyces hansenii-D1), and commercial fermented sausage (S6, spontaneously generated Penicillium nalgiovense-S6). The microbial population, pH, moisture content, color, thiobarbituric acid reactive substance (TBARS), volatile basic nitrogen (VBN), and electronic nose (E-nose) were analyzed to identify the optimal fermentation temperature. The dry-fermented sausages were inoculated with three types of starter cultures [CS (commercial starter culture), GD (GK1 + D1 + S6), and ND (NK3 + D1 + S6)]. The fermentation was performed for 3 days at 20 °C and 25 °C, and dried for 28 days. The Lactobacillus spp. plate count and TBARS showed significantly higher values in the 25 °C group than in the 20 °C group (p < 0.05). The Staphylococcus spp. plate count of GD and ND were significantly higher than CS group at all temperatures. On day 31, the moisture content and VBN values of all groups were less than 35 % and 20 mg%, respectively. According to E-nose, the highest amount of acetoin was detected at the GD group fermented at 25 °C. Thus, the optimal fermentation temperature is expected at 25 °C after using GD in the manufacturing of dry-fermented sausages.
Protease-Assisted Mild Extraction of Soluble Fibre and Protein from Fruit By-Products: A Biorefinery Perspective
Authors: Fuso Andrea , Viscusi Pio , Larocca Susanna , Sangari Francesco Saverio , Lolli Veronica , Caligiani Augusta
Published: December 2022

By-products from the fruit supply chain, especially seeds/kernels, have shown great potential to be valorised, due to their high content of macronutrients, such as lipids, protein, and fibre. A mild enzymatic assisted extraction (EAE) involving the use of a protease was tested to evaluate the feasibility of a cascade approach to fractionate the main fruit by-products components. Protease from Bacillus licheniformis (the enzyme used in the AOAC 991.43 official method for dietary fibre quantification) was used, and besides protein, the conditions of hydrolysis (60 °C, neutral pH, overnight) allowed us to dissolve a portion of soluble fibres, which was then separated from the solubilized peptide fraction through ethanol precipitation. Good protein extraction yields, in the range 35–93%, were obtained. The soluble fibre extraction yield ranged from 1.6% to 71% depending on the by-product, suggesting its applicability only for certain substrates, and it was found to be negatively correlated with the molecular weight of the fibre. The monosaccharide composition of the soluble fibres extracted was also diverse. Galacturonic acid was present in a low amount, indicating that pectin was not efficiently extracted. However, a predominance of arabinose and galactose monomers was detected in many fractions, indicating the isolation of a fruit soluble fibre portion with potential similarity with arabinogalactans and gum arabic, opening up perspectives for technological applications. The residual solid pellet obtained after protease assisted extraction was found to be an excellent fibre-rich substrate, suitable for being subjected to more “hard” processing (e.g., sequential pectin and hemicellulose extraction) with the objective to derive other fractions with potential great added economic value.
Construction of a Fab Library Merging Chains from Semisynthetic and Immune Origin, Suitable for Developing New Tools for Gluten Immunodetection in Food
Authors: Garcia-Calvo Eduardo , García-García Aina , Rodríguez Santiago , Farrais Sergio , Martín Rosario , García Teresa
Published: December 2022

The observed increase in the prevalence of gluten-related disorders has prompted the development of novel immunological systems for gluten detection in foodstuff. The innovation on these methods relies on the generation of new antibodies, which might alternatively be obtained by molecular evolution methods such as phage display. This work presents a novel approach for the generation of a Fab library by merging semi-synthetic heavy chains built-up from a pre-existent recombinant antibody fragment (dAb8E) with an immune light chain set derived from celiac donors. From the initial phage population (107 candidates) and after three rounds of selection and amplification, four different clones were isolated for further characterization. The phage Fab8E-4 presented the best features to be applied in an indirect ELISA for the detection of gluten in foods, resulting in improved specificity and sensitivity.
Effect of Yeast Fermentation on the Physicochemical Properties and Bioactivities of Polysaccharides of Dendrobium officinale
Authors: Chen Hang , Shi Xueqin , Cen Lanyan , Zhang Lin , Dai Yifeng , Qiu Shuyi , Zeng Xiangyong , Wei Chaoyang
Published: December 2022

Fermentation is an effective method for enhancing the biological activity of polysaccharides, but research on its effect on Dendrobium officinal polysaccharides is rare. In this study, the effects of mono-fermentation (Saccharomyces cerevisiae FBKL2.8022, Sc; Wickerhamomyces anomalous FBKL2.8023, Wa) and co-fermentation (Sc+Wa) on the physicochemical properties and bioactivity of Dendrobium officinal polysaccharides were investigated. Meanwhile, the polysaccharide (DOP) obtained from Dendrobium officinale was used as a control. Four homogeneous polysaccharides were obtained by isolation and purification and named DOSCP, DOWAP, DOSWP, and DOP. The results showed that DOSCP, DOWAP, DOSWP, and DOP consisted of mannose and glucose with ratios of 3.31:1, 5.56:1, 2.40:1, and 3.29:1, respectively. The molecular weights (Mws) of the four polysaccharides were 25.73 kDa, 15.01 kDa, 17.67 kDa, and 1268.21 kDa. The antioxidant activity of DOSCP, DOWAP, and DOSWP was better than that of DOP. Additionally, all four polysaccharides were able to reduce the inflammatory response of LPS-induced RAW 264.7 macrophages in the mice without a significant difference. Yeast fermentation significantly reduced the molecular weight and improved the antioxidant activity of Dendrobium officinale polysaccharides, indicating a potential way to improve its antioxidant activity.
Attribute-Driven or Green-Driven: The Impact of Subjective and Objective Knowledge on Sustainable Tea Consumption
Authors: Zheng Manhua , Tang Decong , Xu Anxin
Published: December 2022

The market for green agricultural products has tremendous growth potential as the pressure on resources and the environment increases and the safety of agricultural products is garnering attention. The demand for green food (tea) is also rising as tea is among the top three beverages consumed worldwide. The study attempts to propose a model of the relationship between green food (tea) customers’ product knowledge, perceived product quality, trust, purchase intention, and purchase behaviour. In addition, we will provide an analysis of the role played by age, education, income, gender, etc. The study included 700 questionnaires on green food (tea) consumers that were collected through the Credemo questionnaire platform, and data analysis was carried out using the SmartPLS software to assess the model of product knowledge on green food (tea) consumption behaviour. The findings demonstrate that, concerning differences in age, education, income, and gender; product knowledge—including attribute knowledge and green knowledge—has a positive impact on perceived product quality and trust; perceived product quality has a positive impact on trust; perceived product quality and trust have a positive impact on purchase intention; and purchase intention has a positive impact on purchase behavior.
UHPLC-Triple-TOF-MS Characterization, Antioxidant, Antimicrobial and Antiproliferative Activity of Raspberry (Rubus idaeus L.) Seed Extracts
Authors: Marić Boško , Abramović Biljana , Ilić Nebojša , Bodroža-Solarov Marija , Pavlić Branimir , Oczkowski Michał , Wilczak Jacek , Četojević-Simin Dragana , Šarić Ljubiša , Teslić Nemanja
Published: December 2022

The primary aim of this experiment was to investigate the bioactivity potential and polyphenolic profile of defatted raspberry seeds (DRS) extracts from three varieties (Willamette, Meeker, and Polka) using the in vitro tests HPLC-DAD and UHPLC-Triple-TOF-MS. Extracts were obtained using ultrasound-assisted extraction (UAE) or hydrolysis. The antioxidant activity of the extracts was tested using 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic) cation (ABTS), and ferric reducing antioxidant power (FRAP) assays. Furthermore, the extracts were tested for antimicrobial activity using the disk diffusion method for four bacterial cultures (Staphylococcus aureus, Escherichia coli, Listeria monocytogenes, and Salmonella enterica subsp. enterica Enteritidis). In vitro antiproliferative activity was tested using cervical carcinoma (HeLa), breast adenocarcinoma (MCF7), and fetal lung (MRC-5) human cell lines. In total, 32 phenolic compounds were detected in DRS extracts. A small quantity of ellagic acid (EA) was in free form, while EA content increased after the hydrolysis process. The extracts from the Meeker variety exhibited the highest antioxidant activity, analyzed with DPPH and FRAP assays, while extracts from the Polka variety had the highest activity towards ABTS•+ radical scavenging activity. The UAE samples expressed higher antiproliferative activity in comparison to hydrolysis extracts. The results indicate that DRS extracts have certain bioactivity, and their use in the food, cosmetic, and pharmaceutical industries is recommended.
Anoxia Tolerance in Four Forensically Important Calliphorid Species
Authors: Authement Melissa Lein , Higley Leon G. , Hoback William Wyatt
Published: December 2022

Forensically important blow flies, Diptera: Calliphoridae, are among the first organisms to colonize carrion. After eggs hatch, the larvae of most blow fly species feed in an aggregation or “mass”. While in this mass larvae may experience periods of hypoxia/anoxia, but the tolerance of blow fly larvae to anoxic conditions is poorly studied. We tested the anoxia tolerance of four species of calliphorids (Calliphora vicina, Cochliomyia macellaria, Lucilia sericata, and Phormia regina), by examining actively feeding third-stage larvae across five temperatures. Experiments were conducted by exposing larvae to pure nitrogen environments and determining mortality at set time intervals. All species show significant linear relationships between survival time and temperature under anoxic conditions. Of species tested, C. macellaria had the greatest tolerance to anoxia (LT50 of 9 h at 20 °C). In contrast, C. vicina was the least tolerant (LT50 of 2.2 h at 40 °C). With all species, survivorship decreased with increasing temperature. Unlike many other insects tested in severe hypoxia, the larvae of the calliphorids tested, which included members of three subfamilies, were not tolerant of anoxic conditions. From these findings, it seems likely that hypoxia is a significant limitation for maggots in a maggot mass, particularly when the mass temperature is high (>40 °C). Forensically, these data provide a limit on potential maggot survival on bodies that have been submerged or otherwise experience severe hypoxia before discovery.
Fractional Transformation-Based Intelligent H-Infinity Controller of a Direct Current Servo Motor
Authors: Rahman Muhammad Zia Ur , Leiva Víctor , Martin-Barreiro Carlos , Mahmood Imran , Usman Muhammad , Rizwan Mohsin
Published: December 2022

Direct current (DC) servo motors are central to many complex systems, such as electrical, electro-mechanical, and electro-hydraulic frameworks. In practice, these systems can have nonlinear characteristics and parameter variations. Accurate model representation and position tracking of DC motors are the main issues in many real systems, such as twin rotors, aircraft, airships, and robot manipulators. The precise position tracking of these systems has already been achieved using conventional H-infinity (H∞) controllers. However, the order and structure become more intricate when employing complex weights to shape the closed-loop system, which limits the current proposals. To overcome the above-mentioned limitations, in this article, we provide a precise angular position tracking of a DC servo motor utilizing an intelligent, robust linear controller based on a fixed-structure linear fractional transformation. The conventional H∞ controllers are based on the minimization of an unstructured linear fractional transformation objective function that leads to a complex design of these controllers. The main advantage of the proposed intelligent H∞ synthesis is the fixed and simple structure that increases its practical implementation. The methodology is formulated in the MATLAB software for the robust design of the proposed synthesis based on an intelligent fixed-structure H∞ optimization. Simulation results are compared with conventional H∞ and proportional-integral-derivative controllers. The results are also validated experimentally.
Approximating the First Passage Time Density of Diffusion Processes with State-Dependent Jumps
Authors: D’Onofrio Giuseppe , Lanteri Alessandro
Published: December 2022

We study the problem of the first passage time through a constant boundary for a jump diffusion process whose infinitesimal generator is a nonlocal Jacobi operator. Due to the lack of analytical results, we address the problem using a discretization scheme for simulating the trajectories of jump diffusion processes with state-dependent jumps in both frequency and amplitude. We obtain numerical approximations on their first passage time probability density functions and results for the qualitative behavior of other statistics of this random variable. Finally, we provide two examples of application of the method for different choices of the distribution involved in the mechanism of generation of the jumps.