bluuebunny/crossref_metadata_2025 huggingface dataset:
Heavy Metal Levels in Milk and Serum of Dairy Cows from Different Farms Located near an Industrial Area
Authors: Monteverde Vincenzo , Camilleri Gaetano , Arfuso Francesca , Pennisi Melissa , Perillo Laura , Patitò Gioacchino , Gioia Gianluca , Castronovo Calogero , Piccione Giuseppe
Published: September 2022

Background: Heavy metals are toxic, non-biodegradable substances able to enter the food chain of cows and then transfer to their milk. This study investigated the relationship between the heavy metal concentrations in serum and milk and the environmental pollutants exposure at two different farms in Ragusa, Italy. Methods: To evaluate the concentrations of aluminum (Al), chromium (Cr), iron (Fe), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb), milk and serum samples were collected from 40 Friesian dairy cows from farm 1 at about 3.7 km from an industrial area (group 1) and 40 Friesian dairy cows from farm 2 at about 400 mt from a greenhouse and 6.0 km from a chemical fertilizer factory (group 2). Results: The concentrations of heavy metals measured in serum and milk showed no statistically significant differences between group 1 and group 2. No significant correlation between heavy metals in serum and milk in group 1 was observed. A positive correlation between Zn concentrations measured in milk and serum samples was observed in group 2 (r = 0.35, p = 0.03). Conclusions: The determination of heavy metals in serum and milk can be an important tool to detect the exposure to environmental pollutants and in monitoring the hygienic state of the environment in which milk is produced.
A Novel A > G Polymorphism in the Intron 1 of LCORL Gene Is Significantly Associated with Hide Weight and Body Size in Dezhou Donkey
Authors: Wang Tianqi , Shi Xiaoyuan , Liu Ziwen , Ren Wei , Wang Xinrui , Huang Bingjian , Kou Xiyan , Liang Huili , Wang Changfa , Chai Wenqiong
Published: September 2022

Several studies have shown the association between the ligand-dependent nuclear receptor compression-like protein (LCORL) gene and body size in horses, pigs and donkeys. Based on previous studies, the LCORL gene was hypothesized to be associated with growth traits and hide weight in Dezhou donkeys. In this study, we aimed to reveal the variation of the LCORL gene in the Dezhou donkey and explore whether the gene is associated with hide weight and body size. In this study, genetic polymorphisms in the LCORL gene of the Dezhou donkey were studied using targeted sequencing technology, and single nucleotide polymorphisms (SNPs) of the LCORL gene were analyzed for association with hide weight and body size in Dezhou donkeys. The results showed that there was an SNP locus situated in intron 1 of the LCORL gene. Association analysis revealed that individuals with the GG genotype had significantly higher body height, body length, chest circumference and hide weight than those with the AA genotype (p < 0.05). Therefore, the g.112558859 A > G locus can be used as a potential candidate marker affecting body size and hide weight. This study provides the foundation for breeding high-quality donkeys with high hide yield.
Review on Preventive Measures to Reduce Post-Weaning Diarrhoea in Piglets
Authors: Canibe Nuria , Højberg Ole , Kongsted Hanne , Vodolazska Darya , Lauridsen Charlotte , Nielsen Tina Skau , Schönherz Anna A.
Published: September 2022

In many countries, medical levels of zinc (typically as zinc oxide) are added to piglet diets in the first two weeks post-weaning to prevent the development of post-weaning diarrhoea (PWD). However, high levels of zinc constitute an environmental polluting agent, and may contribute to the development and/or maintenance of antimicrobial resistance (AMR) among bacteria. Consequently, the EU banned administering medical levels of zinc in pig diets as of June 2022. However, this may result in an increased use of antibiotic therapeutics to combat PWD and thereby an increased risk of further AMR development. The search for alternative measures against PWD with a minimum use of antibiotics and in the absence of medical levels of zinc has therefore been intensified over recent years, and feed-related measures, including feed ingredients, feed additives, and feeding strategies, are being intensively investigated. Furthermore, management strategies have been developed and are undoubtedly relevant; however, these will not be addressed in this review. Here, feed measures (and vaccines) are addressed, these being probiotics, prebiotics, synbiotics, postbiotics, proteobiotics, plants and plant extracts (in particular essential oils and tannins), macroalgae (particularly macroalgae-derived polysaccharides), dietary fibre, antimicrobial peptides, specific amino acids, dietary fatty acids, milk replacers, milk components, creep feed, vaccines, bacteriophages, and single-domain antibodies (nanobodies). The list covers measures with a rather long history and others that require significant development before their eventual use can be extended. To assess the potential of feed-related measures in combating PWD, the literature reviewed here has focused on studies reporting parameters of PWD (i.e., faeces score and/or faeces dry matter content during the first two weeks post-weaning). Although the impact on PWD (or related parameters) of the investigated measures may often be inconsistent, many studies do report positive effects. However, several studies have shown that control pigs do not suffer from diarrhoea, making it difficult to evaluate the biological and practical relevance of these improvements. From the reviewed literature, it is not possible to rank the efficacy of the various measures, and the efficacy most probably depends on a range of factors related to animal genetics and health status, additive doses used, composition of the feed, etc. We conclude that a combination of various measures is probably most recommendable in most situations. However, in this respect, it should be considered that combining strategies may lead to additive (e.g., synbiotics), synergistic (e.g., plant materials), or antagonistic (e.g., algae compounds) effects, requiring detailed knowledge on the modes of action in order to design effective strategies.
Correlation and Influence of Seasonal Variation of Diet with Gut Microbiota Diversity and Metabolism Profile of Chipmunk
Authors: Teng Wei , Maqsood Iram , Wang Huan , Ma Jianzhang , Rong Ke
Published: September 2022

Tamias Sibiricus is the only member of the genus Tamias, a significant and vigorous seed distributor and vital food for their predators. No information is known about the strict diet, gut microbiota structure, and metabolism profile of chipmunks and how they diversify seasonally. The above factors, as well as flexibility toward seasonal shifts, are critical in defining its growth rates, health, survivorship, and population stability. This study explored the diet, gut microbiota composition, and chipmunk metabolism. Additionally, the influence of different seasons was also investigated by using next-generation sequencing. Results revealed that seasons strongly affected a diet: streptophyte accounted for 37% in spring, which was lower than in summer (34.3%) and autumn (31.4%). Further, Ascomycota was observed at 43.8% in spring, which reduced to 36.6% in summer and the lowest (31.3%) in autumn. Whereas, nematodes showed maximum abundance from spring (15.8%) to summer (20.6%) and autumn (24.1%). These results signify the insectivorous nature of the chipmunk in summer and autumn. While herbivorous and fungivorous nature in spring. The DNA analysis revealed that chipmunk mainly feeds on fungi, including Aspergillus and Penicillium genus. Similar to diet composition, the microbiome also exhibited highly significant dissimilarity (p < 0.001, R = 0.235) between spring/autumn and spring/summer seasons. Proteobacteria (35.45%), Firmicutes (26.7%), and Bacteroidetes (23.59%) were shown to be the better discriminators as they contributed the most to causing differences between seasons. Moreover, PICRUSt showed that the assimilation of nutrients were also varied seasonally. The abundance of carbohydrates, lipids, nucleotides, xenobiotics, energy, terpenoids, and polyketides metabolism was higher in spring than in other seasons. Our study illustrates that seasonal reconstruction in the chipmunk diet has a significant role in shaping temporal variations in gut microbial community structure and metabolism profile.
A Study on Deriving Improvements through User Recognition Analysis of Artificial Intelligence Speakers
Authors: Yoon Seong-Jeong , Kim Min-Yong
Published: September 2022

Recently, artificial intelligence speakers have been used a lot in homes and offices. However, users say that it is an automated speaker, not an artificial intelligence speaker. Regression analysis was performed by applying the Value-Based Acceptance Model (VAM) to see if there are any improvements to the negative perceptions of users mentioned above. As a result of the regression analysis, improvements were needed for convenience and security threats, and it did not reach the level of anthropomorphism such as with humans. In addition, it is concluded that the factors that positively affect the perceived value are usefulness and enjoyment and that they are somewhat satisfied with the burden of technical difficulties, cost, and reliability of the information. In conclusion, artificial intelligence should continuously collect various data and provide information or suggest choices and alternatives through the process of analysis, learning, and inference. However, as a result of this study, it is concluded that it is similar to an automated machine that simply finds the data among many data connected to the Internet, plays music, and connects to a site where you can shop and process it non-face-to-face. The rationale for being similar to an automated machine is that it has not reached the level of anthropomorphism.
Displacement Monitoring Requirements and Laser Displacement Monitoring Technology of Bridges with Short and Medium Spans
Authors: Li Xiaoya , Li Fangyuan
Published: September 2022

Short and medium-span bridges account for more than 90% of the bridges in China, and it is not suitable to use conventional health monitoring technology to monitor them. Based on an analysis of the proportion of live load, displacement under load, the action time of live load and other factors affecting short and medium-span bridges, this paper determines a feasible technical solution using mid-span deflection to monitor bridges with requirements including measuring range, resolution and accuracy. A set of wireless laser displacement monitoring technologies and equipment is studied by using the phase laser ranging method and the principle of pulsed laser ranging, and the reliability of the data is verified by experimental tests. Using both practical application cases and economic analysis, this paper proves that the technology has significant application advantages for short and medium-span bridges.
A Novel Mechanical Fault Diagnosis Based on Transfer Learning with Probability Confidence Convolutional Neural Network Model
Authors: Lin Hsiao-Mei , Lin Ching-Yuan , Wang Chun-Hung , Tsai Ming-Jong
Published: September 2022

For fault diagnosis, convolutional neural networks (CNN) have been performing as a data-driven method to identify mechanical fault features in forms of vibration signals. However, because of CNN’s ineffective and inaccurate identification of unknown fault categories, we propose a model based on transfer learning with probability confidence CNN (TPCCNN) to model the fault features of rotating machinery for fault diagnosis. TPCCNN includes three major modules: (1) feature engineering to perform a series of data pre-processing and feature extraction; (2) transferring learning features of heterogeneous datasets for different datasets to have better generality in model training and reduce the time for modeling and parameter tuning; and (3) building a PCCNN model to classify known and unknown fault categories. In addition to solving the problem of an imbalanced sample size, TPCCNN self-learns and retrains by iterating with unknown classes to the original model. This model is verified with the use of the open-source datasets CWRU and Ottawa. The experimental results showing the feature transfer of heterogeneous datasets are of average accuracy rates of 99.2% and 93.8% respectively for known and unknown categories, and TPCCNN is then proven effectively in training heterogeneous datasets. Likewise, similar feature sets can also be applied to reduce the training of predicting models by 34% and 68% of the time.
Black Garlic and Pomegranate Standardized Extracts for Blood Pressure Improvement: A Non-Randomized Diet-Controlled Study
Authors: Fogacci Federica , Di Micoli Antonio , Grandi Elisa , Fiorini Giulia , Borghi Claudio , Cicero Arrigo F. G.
Published: September 2022

Recently released position papers by the European Society of Hypertension (ESH) and the Italian Society of Hypertension (SIIA) provide therapeutic recommendations for the use of nutraceuticals in the management of high blood pressure (BP) and hypertension, opening up new perspectives in the field. This not-randomized diet-controlled clinical study aimed to evaluate if daily dietary supplementation with black garlic and pomegranate (namely SelectSIEVE® SlowBeat) could advantageously affect BP in individuals with high-normal BP or stage I hypertension. Enrolled subjects were adhering to a Mediterranean DASH (Dietary Approaches to Stop Hypertension) diet for two weeks before deciding whether to continue following Mediterranean DASH diet alone or in association with SelectSIEVE® SlowBeat. At the end of the study, dietary supplementation with SelectSIEVE® SlowBeat was associated with significant improvement in systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to baseline (Pre-treatment: SBP = 134.3 ± 4.2 and DBP = 88.2 ± 3.4; 4-Week Follow-up: SBP = 130.1 ± 2.8 and DBP= 83.7 ± 2.6). SBP improved also in comparison with control. In conclusion, the study shows that dietary supplementation with extracts from black garlic and pomegranate safely exert significant improvements in BP in healthy individuals adhering to a Mediterranean DASH diet.
Benchmarking 4G and 5G-Based Cellular-V2X for Vehicle-to-Infrastructure Communication and Urban Scenarios in Cooperative Intelligent Transportation Systems
Authors: Petrov Tibor , Pocta Peter , Kovacikova Tatiana
Published: September 2022

Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario.
The Positive Effects on Volleyball Receiving Skills When Training with Lighter Balls
Authors: Lin Kuo-Chuan , Chang Chi-Yao , Hung Min-Hao , Lin Wei-Ming
Published: September 2022

The purpose of this study was to investigate the receiving ability and efficacy of volleyball players, which can be effectively improved after practicing with volleyballs of different weights. In this study, 36 elite male volleyball players were recruited and participated in a four-week training program in four groups: a lighter-ball training group (LBTG), a heavier-ball training group (HBTG), a normal-ball training group (NBTG), and a control group (CG). All subjects were required to evaluate their receiving ability during the initial training (week-0) and then to reevaluate it after two (week-2) and four (week-4) weeks of training. The statistical analysis revealed that the time factor (F2, 64 = 209.346, p < 0.001) and the time × group factor (F6, 64 = 17.463, p < 0.001) were significant but the group factor (F3, 32 = 0.340, p = 0.797) was not. The results revealed that the LBTG experienced a significant positive effect on the receiving test after four weeks of training, with an 8.6% improvement in skill performance after two weeks and a 16.4% improvement after four weeks. The HBTG, NBTG, and CG improved by 2.6%, 5.3%, and 3.8% after two weeks and by 6.3%, 9.3%, and 4.3% after four weeks, respectively. The main findings were that four weeks of volleyball training with a lighter ball resulted in significant improvements in player performance. In contrast, training with a ball with 70 g of added mass did not effectively enhance motor ability development.
Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
Authors: Jiao Niangang , Wang Feng , Chen Bo , Zhu Jingxing , You Hongjian
Published: September 2022

Spliced optical satellite cameras suffering from low stitching accuracy are influenced by various factors which can greatly restrict their applications. Most previous studies have focused on the geometric precision of stitched images, which is influenced by the stitching consistency and the relationships between different inner CCD (Charge-Coupled Device) images. Therefore, the stitching accuracy is of great significance in multiple CCD image production. Traditionally, the line-time normalization method has been applied for inner CCD image stitching based on designed line-times with the assumption of uniform sampling during imaging. However, the misalignment of the designed and actual line-time affected by various factors can lead to image distortion. Therefore, this paper investigates the performance of different normalization methods to produce stitched images with higher geometric performance using the actual line-time. First, the geometric distortions caused by misalignments between the designed and actual line-time are analyzed to show the differences in sampling rate and step-points. To overcome the distortions introduced by the fitting error of the designed line-time, three fine normalization methods based on the actual line-time, respectively called scene-based, block-based, and line-based line-time normalization methods, are introduced and compared with the traditional method. The scene-based and block-based line-time normalization methods fit the actual line-time section-by-section, while the line-based method builds the relationships between adjacent inner CCD images line-by-line. Images obtained from the Sustainable Development Goals Satellite 1 (SDGSAT-1) satellite are used for verification of different methods. The performance of the designed line-time normalization method and three fine actual line-time normalization methods is compared; the stitching accuracy can reach about 0.8, 0.56, 0.5, and 0.45 pixels, respectively. The time consumption of these four compared methods is about 5.5 s, 4.9 s, 5.4 s, and 58.9 s, respectively. Therefore, the block-based actual line-time normalization method utilized in practice can provide a good balance between running time and accuracy. In the future, we intend to find a new way to improve the efficiency of line-based line-time normalization methods to produce stitched images with higher geometric consistency and accuracy.
Risk Factors for the Occurrence of Feather Pecking in Non-Beak-Trimmed Pullets and Laying Hens on Commercial Farms
Authors: Schwarzer Angela , Rauch Elke , Bergmann Shana , Kirchner Anna , Lenz Alice , Hammes Adriane , Erhard Michael , Reese Sven , Louton Helen
Published: September 2022

Severe feather pecking (SFP) is a behavioral disorder, for which there are multifactorial reasons. Various aspects of pullet and laying-hen husbandry—including housing conditions, management, feeding, and genetics—must be considered, to prevent negative outcomes, such as severe plumage damage, skin injuries, and high mortality rates due to SFP. The aim of this study was to identify housing and management factors in the occurrence of feather pecking, so as to reduce the risk of this behavioral disorder in non-beak-trimmed laying hens on commercial farms with aviaries. Beak trimming may reduce the extent of plumage damage, but it does not prevent feather pecking and cannibalism. Thirty non-beak-trimmed flocks (16 in the first, and 14 in the second laying period) were investigated on 16 commercial farms in Germany. Each flock was visited twice during rearing, and three times during the laying period. During each visit, individual plumage and integument scoring were performed, and data were collected, regarding the housing and management conditions of the flocks. To analyze the influence of management and housing on the plumage condition of the rearing and laying flocks, models were calculated, using univariate multifactorial analysis. In the rearing period, high stocking density and poor litter quality were significant risk factors in plumage damage due to SFP. In the laying period, a lack of free range, poor litter quality, insufficient enrichment, and plumage damage during rearing were significant risk factors for the development of SFP. An individual risk analysis of pullet and layer farms is therefore strongly recommended, to prevent outbreaks of SFP and cannibalism, especially in non-beak-trimmed birds.
Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health
Authors: Gyrard Amelie , Boudaoud Karima
Published: September 2022

Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health.
Packet Loss Concealment Based on Phase Correction and Deep Neural Network
Authors: Ji Qiang , Bao Changchun , Cui Zihao
Published: September 2022

In a packet switching network, the performance of packet loss concealment (PLC) is often affected by inaccurate estimation of phase spectrum of speech signal in the lost packet. In order to solve this problem, two kinds of PLC methods in the scene of continuous packet loss are proposed in this paper based on phase correction. One of them is based on waveform similarity overlap-add (WSOLA) and deep neural network (DNN), and the other one is based on the Griffin–Lim algorithm (GLA) and DNN. In the first method, considering the correlation of adjacent frames of speech signal, the periodicity of speech signal is well retained by the WSOLA method so that the phase spectrum of the lost signal is recovered. Combined with the prediction of amplitude spectrum of the lost signal by the DNN, the performance of the PLC in the case of continuous packet loss is effectively improved. In the second method, the phase spectrum of the lost signal is modified iteratively by the amplitude spectra estimated via the DNN based on the consistency of Fourier transform so that the phase spectra match the amplitude spectra. The PLC is greatly achieved as well. The experimental results show that the proposed PLC methods provide better speech quality than the reference methods.
LaneFormer: Real-Time Lane Exaction and Detection via Transformer
Authors: Yang Yinyi , Peng Haiyong , Li Chuanchang , Zhang Weiwei , Yang Kelu
Published: September 2022

In intelligent driving, lane line detection is a basic but challenging task, especially in complex road conditions. The current detection algorithms based on convolutional neural networks perform well for simple scenes with plenty of light, and the lane lines are clean and unobstructed. Still, they do not perform well for complex scenes such as damaged, blocked, and lack-of-light scenes. In this article, we have exceeded the above restrictions and propose an attractive network: LaneFormer; We use an end-to-end network for up and down sampling three times each, then fuse them in their respective channels to extract the slender lane line structure. At the same time, a correction module is designed to adjust the dimensions of the extracted features using MLP, judging whether the feature is completely extracted through the loss function. Finally, we send the feature into the transformer network, detect the lane line points through the attention mechanism, and design a road and camera model to fit the identified lane line feature points. Our proposed method has been validated in the TuSimple benchmark test, showing the most advanced accuracy with the lightest model and fastest speed.
An Efficient FPGA Implementation of MUSIC Processor Using Cyclic Jacobi Method: LiDAR Applications
Authors: Ghayoula Ridha , Amara Wided , El Gmati Issam , Smida Amor , Fattahi Jaouhar
Published: September 2022

LiDAR is a technology that uses lasers to measure the position of elements. Measuring the laser travel time and calculating the distance between the LiDAR and the surface requires the calculation of eigenvalues and eigenvectors of the convergence matrix. SVD algorithms have been proposed to solve an eigenvalue problem, which is computationally expensive. As embedded systems are resource-constrained hardware, optimized algorithms are needed. This is the subject of our paper. The first part of this paper presents the methodology and the internal architectures of the MUSIC processor using the Cyclic Jacobi method. The second part presents the results obtained at each step of the FPGA processing, such as the complex covariance matrix, the unitary and inverse transformation, and the value and vector decomposition. We compare them to their equivalents in the literature. Finally, simulations are performed to select the way that guarantees the best performance in terms of speed, accuracy and power consumption.
Integrated Estimation Strategy of Brake Force Cooperated with Artificial Neural Network Based Road Condition Classifier and Vehicle Mass Identification Using Static Suspension Deflections
Authors: Minh Nhat Nguyen , Jung DaeYi
Published: September 2022

Brake forces and maximum static road friction coefficients for each wheel of the vehicle are essential information for vehicle safety systems including adaptive cruise control, electronic stability control (ESC), and collision avoidance system, etc. Many studies have been performed to estimate brake force and road friction using well-known model-based approaches, but none have unambiguously guaranteed an accurate performance in all ranges of driving conditions and road ones. In addition, the investigation of the integrated estimation approach of road friction and brake force including mass estimation has not been clearly addressed so far. Therefore, in this study, a novel integrated estimation strategy based on a data-driven technique and artificial neural network (ANN) classifier along with a compact mass identification has been proposed to acquire the accurate road friction and brake force of individual wheel. Specifically, it includes an instant mass estimation by monitoring static suspension deflections, an artificial neural network (ANN) classifier for road friction coefficient based on the average data set from available standard sensors, and a brake force estimation using the data-driven technique. The performance of the proposed technique is validated by a co-simulation environment between Carsim and MATLAB/Simulink. It is found that the integrated estimation strategy guaranteed an accurate estimation of brake forces and road friction for a wide range of variations of road frictions, vehicle velocities, and masses. This work will be a valuable asset for those who wish to develop an integrated estimation system for such crucial parameters of the vehicle system.
Lightweight and Parameter-Optimized Real-Time Food Calorie Estimation from Images Using CNN-Based Approach
Authors: Haque Rakib Ul , Khan Razib Hayat , Shihavuddin A. S. M. , Syeed M. M. Mahbubul , Uddin Mohammad Faisal
Published: September 2022

Automated object identification has seen significant progress during the last decade with close to human-level accuracy, aided by deep learning methods. With the rapid rise of obesity and other lifestyle-related diseases worldwide, the availability of fast, automated, and reliable image-based food calorie estimation is becoming a necessity. With the help of a deep learning-based automated object identification system, it is possible to introduce accurate and intelligent solutions in the form of a mobile app. However, for these kind of applications, processing speed is an important concern as the images should be processed in real time. Although plenty of studies have been conducted that focus on food image detection-based calorie estimation, there is still a lack of an image-driven, lightweight, fast, and reliable food calorie estimation system. In this paper, we propose a method based on the parameter-optimized Convolution Neural Networks (CNN) for detecting food images of regular meals using a handheld camera. Once identification process of the food items are complete, the corresponding calories and nutritional facts can be calculated using prior knowledge about the food class. Through our findings, we demonstrate that our proposed approach ensures high accuracy and can significantly simplify the existing manual calorie estimation procedures by converting them into a real-time automated process.
Two-Layer Security Algorithms to Prevent Attacks on Data in Cyberspace
Authors: Nadeem Muhammad , Arshad Ali , Riaz Saman , Zahra Syeda Wajiha , Dutta Ashit Kumar , Alruban Abdulrahman , Almutairi Badr , Almotairi Sultan
Published: September 2022

The ratio at which the number of users is increasing in cloud computing, the same as the number of data destruction, has doubled. Various algorithms have been designed to secure cloud servers to prevent an attacker from attacking, but they cannot protect the data. With the help of cryptography, data are converted into a format that is difficult for an attacker to understand, and these data can be broadcasted in a reliable format. If the attacker obtains access to the cryptographic algorithm, then the attacker can easily decrypt the data, but if the data are encrypted with the help of the key, it is difficult for the attacker to decrypt the data without the key. When the data are encrypted with the same key at different times, there is a possibility to obtain the actual key with the help of the Kasiski test. Different researchers have encrypted the data with the help of the same key or have developed an algorithm by combining different algorithms, which is a problem. This paper developed an efficient algorithm that cannot be broken easily. Even if an attacker obtains access to the algorithm, the attacker cannot identify the keys derived from that text, and the key only works in the text from which it is generated, which is the novelty of this paper. Data are secured in two different layers. In Layer-1, the proposed algorithm implements plaintext and a static key to obtain the encrypted text and a Vigenère key. In Layer-2, the encrypted text and the Vigenère key are implemented on the Vigenère cipher algorithm to obtain the ciphertext. At the end of the paper, a comparison of different papers is made with the proposed paper, and a conclusion is made based on different techniques and results.
Correction: Ananyev et al. Selection of Fuel Isotope Composition in Heating Injectors of the FNS-ST Compact Fusion Neutron Source. Appl. Sci. 2021, 11, 7565
Authors: Ananyev Sergey , Dnestrovskij Alexey , Kukushkin Andrei
Published: September 2022

We, the authors, wish to make the following corrections to our paper [...]
Investigating Wearable Fitness Applications: Data Privacy and Digital Forensics Analysis on Android
Authors: Hutchinson Shinelle , Mirza Mohammad Meraj , West Nicholas , Karabiyik Umit , Rogers Marcus K. , Mukherjee Tathagata , Aggarwal Sudhir , Chung Haeyong , Pettus-Davis Carrie
Published: September 2022

Wearable devices are becoming more and more prevalent in our daily lives as people become more curious about how well they are doing in monitoring, improving, or maintaining their health and fitness. Fitness trackers and smartwatches have become almost ubiquitous, so these devices have begun to play a critical role in forensic investigations. In this paper, the authors conducted a forensic analysis of the controlling applications for three popular fitness bands and smartwatches (i.e., Amazon Halo, Garmin Connect, and Mobvoi) on an Android smartphone device to (1) provide forensic investigators with a road-map of forensically relevant data that are stored within these applications and (2) highlight any privacy concerns that the stored data within these applications may present to the applications’ users. Our findings indicate that the three fitness applications store a wealth of user data. In particular, the Amazon Halo app stores daily, weekly, and monthly activity-related data for at least the last 13 days. The user’s Tone Analysis results were also recovered. The Garmin Connect application also records detailed user activity information, as it was possible to recover the last 15 days worth of user activity data. The Garmin Connect user’s general location was also determined via the application’s weather notification feature. Lastly, the Mobvoi application records all data points from the time the device is first used until the last time the device is used. These data points may include heart rates taken every 5 min and step counts. Our findings highlight the possibility of collecting personally identifiable information about users of these devices and apps, including their profile information, habits, location, and state of mind. These findings would be pertinent to forensic investigators in the event that these or similar applications are part of an investigation.
Dynamics of the Response of Vegetation Activity to Air Temperature Change in Temperate China
Authors: Qin Mingxing , Jin Ning , Zhao Jie , Feng Meichen , Wang Chao
Published: September 2022

Previous research has documented a tight positive relationship between vegetation activity and growing season air temperature in China’s temperate zone (TC). However, this relationship may change over time following alternations in other environmental factors. Using the linear regression analysis and the moving windows based on partial correlation analysis method, the temporal variations of responses of vegetation NDVI to rising air temperature during 1982–2015 in the TC were examined. The results showed that the interannual partial correlation between NDVI and air temperature (RNDVI−T, include RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin, represents the partial correlation between NDVI and Tmean, Tmax, and Tmin, respectively) for the growing season (GS) in a 17−year moving window showed a significant decreasing trend during the last 34 years, mainly due to decreasing RNDVI−T in summer and autumn. The area with a significant decrease of RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin for the GS approximately accounted for 52.36%, 45.63%, and 49.98% of the TC, respectively. For the seasonal patterns of RNDVI−T, the regions with a significant downward trend in all seasons were higher than those with a significant upward trend. We also found a more significant and accelerating decrease of RNDVI−T for warm years compared to cold years, implying a decoupling or even a reverse correlation between NDVI and air temperature with continuous climate warming over the TC. Overall, our study provided evidence that the impact of Tmean, Tmax, and Tmin on vegetation activities exhibited a weakening trend and cautioned using results from interannual time scales to constrain the decadal response of vegetation growth to future global warming.
Spectroscopic Peculiarities at Ionization of Excited 2p5(2PJf)3s[K]0,1,2 States of Ne: Cooper Minima and Autoionizing Resonances
Authors: Popova Maria M. , Kiselev Maksim D. , Burkov Sergei M. , Gryzlova Elena V. , Grum-Grzhimailo Alexei N.
Published: September 2022

An extensive study of photoionization from neon excited states was performed. The R-matrix approach was applied to calculate a photoionization cross-section from the metastable 2p5(2PJf)3s[K]0,2 and dipole-allowed 2p5(2PJf)3s[K]1 states. The resonance structures and Cooper minimum accessible in photoionization from the excited states by the photons with energy below 30 eV were analyzed. The parameters of the lowest autoionizing states (AISs) of even parity were extracted by fitting of the photoionization cross-section. For the dipole-allowed states, calculations are presented for unpolarized, linearly and circularly polarized radiation.
Upconversion Fluorescence Nanoprobe-Based FRET for the Sensitive Determination of Shigella
Authors: Chen Min , Yan Zhongyu , Han Lu , Zhou Dandan , Wang Yan , Pan Leiqing , Tu Kang
Published: September 2022

Shigella as a typical foodborne pathogen has strong survivability in the environment or food, leading to infectious diseases, yet its rapid detection technology with high selectivity and sensitivity remains challenging. In this study, complementary strand modified upconversion nanoparticles (UCNPs) can offer stable yellow-green fluorescence at 500–700 nm excited by a 980 nm laser. Importantly, Shigella aptamer modified gold nanoparticles (GNPs) formed by “Au−S” bond act as a fluorescence resonance energy transfer (FRET) donor and recognition element that can bind specifically to Shigella and significantly quench the fluorescence of complementary strand modified UCNPs. As a result, the fluorescence of our developed nanoprobe increased linearly with the increase in Shigella in a wide range from 1.2 × 102 to 1.2 × 108 CFU/mL and the detection limit was as low as 30 CFU/mL. Moreover, the fabricated upconversion fluorescence nanoprobe can achieve Shigella detection in contaminated chicken without enrichment in 1 h.
Design and Optimization of A Magneto-Plasmonic Sandwich Biosensor for Integration within Microfluidic Devices
Authors: Soroush Mona , Ait Mammar Walid , Wilson Axel , Ghourchian Hedayatollah , Salmain Michèle , Boujday Souhir
Published: September 2022

We designed a magneto-plasmonic biosensor for the immunodetection of antigens in minute sample volume. Both spherical gold nanoparticles (AuNP) and magnetic beads (MB) were conjugated to goat anti-rabbit IgG antibody (Ab) capable of recognizing a model target, rabbit IgG (rIgG). The AuNP bioconjugate was used as the optical detection probe while the MB one was used as the capture probe. Addition of the target analyte followed by detection probe resulted in the formation of a sandwich immunocomplex which was separated from the unbound AuNP-Ab conjugate by application of an external magnetic field. The readout was executed either in a direct or in indirect way by measuring the UV–Visible spectrum of each fraction in a specially designed microcell. Dose–response curves were established from the optical signal of the immunocomplex and unbound AuNP-Ab conjugate fractions. Finally, the assay was transposed to a microfluidic cell specially designed to enable easy separation of the immunocomplex and AuNP-Ab conjugate fractions and subsequent analysis of the latter fraction and achieve the quantification of the analyte in the ng/mL concentration range.
Ho2O3-TiO2 Nanobelts Electrode for Highly Selective and Sensitive Detection of Cancer miRNAs
Authors: Cui Jingjie , Wang Xuping , Chen Shaowei
Published: September 2022

The design and engineering of effective electrode materials is critical in the development of electrochemical sensors. In the present study, Ho2O3-TiO2 nanobelts were synthesized by an alkaline hydrothermal process. The structure and morphology were investigated by X-ray diffraction (XRD) and field emission scanning electron microscope (FESEM) measurements. The Ho2O3-TiO2 nanobelts showed a distinctly enhanced (004) reflection peak and rough surfaces and were used for the electrochemical selective sensing of various cancer miRNAs, such as prostate cancer miR-141, osteosarcoma miR-21, and pancreatic cancer miR-1290. Voltammetric measurements showed an oxidation peak at +0.4, +0.2, and +1.53 V for the three different cancer biomarkers, respectively, with the detection limit as low as 4.26 aM. The results suggest that the Ho2O3-TiO2 nanobelts can be used as active materials to detect early cancers, for in vitro screening of anticancer drugs, and molecular biology research.
Bird Communities and the Rehabilitation of Al Karaana Lagoons in Qatar
Authors: Draidia Ayaterahman , Tareen Momina , Bayraktar Nuran , Cramer Emily R. A. , Chen Kuei-Chiu
Published: September 2022

Qatar, a peninsular country in the Persian Gulf, is significant to avian species due to its location along the African–Eurasian Flyway, a key migratory path. Receiving untreated domestic and industrial liquid waste from Qatar in the past, Al Karaana Lagoons have since been reconstructed as an artificial wetland to address the growing environmental concern posed by contamination build-up. This study documents the changes in biodiversity at Al Karaana Lagoons following their environmental remediation. Data collected (2015 and 2017) by Ashghal (Public Works Authority) prior to project implementation was analyzed alongside data collected independently following project completion (2019–2021). There was a marked increase in bird biodiversity following remediation, including substantial use by migratory species and resident breeders. Further analysis of water quality data of the TSE (treated sewage effluent) ponds shows that they are eutrophic but still support substantial bird life. The project’s success demonstrates how reclaimed lands can provide important habitats to local and migratory birds and encourages similar restoration efforts in the future in both Qatar and elsewhere. We call for the continued monitoring of the site and the implementation of guidelines for the use of the site that balance human activities and habitat quality.
Transcutaneous Vagus Nerve Stimulation Could Improve the Effective Rate on the Quality of Sleep in the Treatment of Primary Insomnia: A Randomized Control Trial
Authors: Wu Yating , Song Lu , Wang Xian , Li Ning , Zhan Shuqin , Rong Peijing , Wang Yuping , Liu Aihua
Published: September 2022

(1) Background: The purpose of this study was to investigate the efficacy and safety of transcutaneous vagus nerve stimulation (t-VNS) in the treatment of primary insomnia. (2) Methods: This is a single center, randomized, double-blind study. A total of 30 patients diagnosed with primary insomnia were randomly divided into two groups to receive 20 Hz t-VNS in either the auricular concha area (treatment group) or periauricular area (control group), twice a day for 20 min during a one-month study period. The effective rate of treatment, defined as a ≥50% reduction of the Pittsburgh Sleep Index Scale (PSQI) after treatment, was compared between the two groups as the primary outcome. Response rate (defined as ≥10% change in the PSQI score), and changes in the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD) scores were also assessed. (3) Results: After one month of treatment, the PSQI score of the treatment group decreased significantly (p = 0.001). The effective rate of the treatment group (73% vs. 27%, p = 0.027) was significantly higher than that of the control group. No statistical differences in changes of HAMA and HAMD scores were detected between the two groups. There were no complications in all patients. (4) Conclusion: T-VNS appeared to be a safe and effective treatment for primary insomnia.