bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Precision Iterative Preconditioned Gauss–Seidel Detection Algorithm for Massive MIMO Systems
Authors: Ahmad Mushtaq , Zhang Xiaofei , Khoso Imran A. , Shi Xinlei , Qian Yang
Published: November 2022

Signal detection is a serious challenge for uplink massive multiple-input multiple-output (MIMO) systems. The traditional linear minimum-mean-squared error (MMSE) achieves good detection performance for such systems, but involves matrix inversion, which is computationally expensive due to a large number of antennas. Thus, several iterative methods such as Gauss–Seidel (GS) have been studied to avoid the direct matrix inversion required in the MMSE. In this paper, we improve the GS iteration in order to enhance the detection performance of massive MIMO systems with a large loading factor. By exploiting the property of massive MIMO systems, we introduce a novel initialization strategy to render a quick start for the proposed algorithm. While maintaining the same accuracy of the designed detector, the computing load is further reduced by initialization approximation. In addition, an effective preconditioner is proposed that efficiently transforms the original GS iteration into a new one that has the same solution, but a faster convergence rate than that of the original GS. Numerical results show that the proposed algorithm is superior in terms of complexity and performance than state-of-the-art detectors. Moreover, it exhibits identical error performance to that of the linear MMSE with one-order-less complexity.
Epistemic-Uncertainty-Based Divide-and-Conquer Network for Single-Image Super-Resolution
Authors: Yang Jiaqi , Chen Shiqi , Li Qi , Jiang Tingting , Chen Yueting , Wang Jing
Published: November 2022

The introduction of convolutional neural networks (CNNs) into single-image super-resolution (SISR) has resulted in remarkable performance in the last decade. There is a contradiction in SISR between indiscriminate processing and the different processing difficulties in different regions, leading to the need for locally differentiated processing of SR networks. In this paper, we propose an epistemic-uncertainty-based divide-and-conquer network (EU-DC) in order to address this problem. Firstly, we build an image-gradient-based divide-and-conquer network (IG-DC) that utilizes gradient-based division to separate degraded images into easy and hard processing regions. Secondly, we model the IG-DC’s epistemic uncertainty map (EUM) by using Monte Carlo dropout and, thus, measure the output confidence of the IG-DC. The lower the output confidence is, the more difficult the IG-DC is to process. The EUM-based division is generated by quantizing the EUM into two levels. Finally, the IG-DC is transformed into an EU-DC by substituting the gradient-based division with EUM-based division. Our extensive experiments demonstrate that the proposed EU-DC achieves better reconstruction performance than that of multiple state-of-the-art SISR methods in terms of both quantitative and visual quality.
DEF-Net: A Dual-Encoder Fusion Network for Fundus Retinal Vessel Segmentation
Authors: Li Jianyong , Gao Ge , Yang Lei , Liu Yanhong , Yu Hongnian
Published: November 2022

The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so the automatic retinal vessel segmentation serves as an essential stage for efficient detection of eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential in retinal vessel segmentation tasks, but have limited feature representation ability. In addition, they don’t effectively consider the information at multiple scales when performing feature fusion, resulting in low fusion efficiency. In this paper, a newly model, named DEF-Net, is designed to segment retinal vessels automatically, which consists of a dual-encoder unit and a decoder unit. Fused with recurrent network and convolution network, a dual-encoder unit is proposed, which builds a convolutional network branch to extract detailed features and a recurrent network branch to accumulate contextual features, and it could obtain richer features compared to the single convolution network structure. Furthermore, to exploit the useful information at multiple scales, a multi-scale fusion block used for facilitating feature fusion efficiency is designed. Extensive experiments have been undertaken to demonstrate the segmentation performance of our proposed DEF-Net.
An Empirical Framework for Assessment of the Effects of Digital Technologies on Sustainability Accounting and Reporting in the European Union
Authors: Vărzaru Anca Antoaneta
Published: November 2022

Sustainability accounting and reporting is an emerging area of accounting that is receiving increasing attention as a result of sustainability requirements. In this paper, we examine the effects of implementing digital technology on sustainability accounting and reporting. This research consists of an empirical study at the level of 21 European Union countries using data provided by Eurostat. Transversal research emphasizes the impact of digital technologies (cloud computing, Big Data, the Internet of things, and artificial intelligence) on sustainability accounting and reporting. In this paper, we highlight the relationships between variables using artificial neural network analysis and cluster analysis. The study findings indicate that digital technologies significantly influence the sustainability accounting and reporting and sustainability-oriented culture of the countries included in the empirical study. A cluster analysis reveals a group of countries at the top of the sustainability reporting rankings as a result of advances in digital technologies. This study demonstrates that the digital transformation produced by Industry 4.0 contributes to the potential improvement of sustainability accounting and reporting, with significant links between sustainability and digitization.
Multi-Task Learning for Scene Text Image Super-Resolution with Multiple Transformers
Authors: Honda Kosuke , Kurematsu Masaki , Fujita Hamido , Selamat Ali
Published: November 2022

Scene text image super-resolution aims to improve readability by recovering text shapes from low-resolution degraded text images. Although recent developments in deep learning have greatly improved super-resolution (SR) techniques, recovering text images with irregular shapes, heavy noise, and blurriness is still challenging. This is because networks with Convolutional Neural Network (CNN)-based backbones cannot sufficiently capture the global long-range correlations of text images or detailed sequential information about the text structure. In order to address this issue, this paper proposes a Multi-task learning-based Text Super-resolution (MTSR) Network to approach this problem. MTSR is a multi-task architecture for image reconstruction and SR. It uses transformer-based modules to transfer complementary features of the reconstruction model, such as noise removal capability and text structure information, to the SR model. In addition, another transformer-based module using 2D positional encoding is used to handle irregular deformations of the text. The feature maps generated from these two transformer-based modules are fused to attempt improvement of the visual quality of images with heavy noise, blurriness, and irregular deformations. Experimental results on the TextZoom dataset and several scene text recognition benchmarks show that our MTSR significantly improves the accuracy of existing text recognizers.
Differential Optimization Federated Incremental Learning Algorithm Based on Blockchain
Authors: Chen Xuebin , Luo Changyin , Wei Wei , Xu Jingcheng , Zhang Shufen
Published: November 2022

Federated learning is a hot area of concern in the field of privacy protection. There are local model parameters that are difficult to integrate, poor model timeliness, and local model training security issues. This paper proposes a blockchain-based differential optimization federated incremental learning algorithm, First, we apply differential privacy to the weighted random forest and optimize the parameters in the weighted forest to reduce the impact of adding differential privacy on the accuracy of the local model. Using different ensemble algorithms to integrate the local model parameters can improve the accuracy of the global model. At the same time, the risk of a data leakage caused by gradient update is reduced; then, incremental learning is applied to the framework of federated learning to improve the timeliness of the model; finally, the model parameters in the model training phase are uploaded to the blockchain and synchronized quickly, which reduces the cost of data storage and model parameter transmission. The experimental results show that the accuracy of the stacking ensemble model in each period is above 83.5% and the variance is lower than 10−4 for training on the public data set. The accuracy of the model has been improved, and the security and privacy of the model have been improved.
Denial of Service Attack Classification Using Machine Learning with Multi-Features
Authors: Rustam Furqan , Mushtaq Muhammad , Hamza Ameer , Farooq Muhammad , Jurcut Anca , Ashraf Imran
Published: November 2022

The exploitation of internet networks through denial of services (DoS) attacks has experienced a continuous surge over the past few years. Despite the development of advanced intrusion detection and protection systems, network security remains a challenging problem and necessitates the development of efficient and effective defense mechanisms to detect these threats. This research proposes a machine learning-based framework to detect distributed DOS (DDoS)/DoS attacks. For this purpose, a large dataset containing the network traffic of the application layer is utilized. A novel multi-feature approach is proposed where the principal component analysis (PCA) features and singular value decomposition (SVD) features are combined to obtain higher performance. The validation of the multi-feature approach is determined by extensive experiments using several machine learning models. The performance of machine learning models is evaluated for each class of attack and results are discussed regarding the accuracy, recall, and F1 score, etc., in the context of recent state-of-the-art approaches. Experimental results confirm that using multi-feature increases the performance and RF obtains a 100% accuracy.
Emerging Technologies’ Contribution to the Digital Transformation in Accountancy Firms
Authors: Tiron-Tudor Adriana , Donțu Adelina Nicoleta , Bresfelean Vasile Paul
Published: November 2022

Digitalization becomes a key strategy for the success of businesses, which in today’s critical times, are under remarkable pressures and diffused uncertainty. The rapid pace of digitization is forcing deep changes in the modus operandi of organizations. This phenomenon is even more so true for accounting organizations considering that, by implementing blockchain, RPA, cloud, big data, cybersecurity, and AI, accountants might have the most digitized workplace of all. The purpose of this paper is to explore how these emergent technologies are contributing to the digital transformation of accounting firms. Based on a qualitative approach, the methodology consists of a thematic analysis of the academic literature to reveal the synergic effect of the most disruptive emergent technologies for accountancy firms. In addition to the topic of research, the originality of this study is ensured by the fact that it presents both technical and conceptual information, easily digestible for academicians and practitioners skilled in the ICT field, or not. The paper is intended to be a building brick for the literature related to this topic.
Analysis of Impulse Responses Measured in Motion in a Towing Tank
Authors: Czapiewska Agnieszka , Luksza Andrzej , Studanski Ryszard , Zak Andrzej
Published: November 2022

The growing interest in developing autonomous underwater vehicles (AUVs) and creating underwater sensor networks (USNs) has led to a need for communication tools in underwater environments. For obvious reasons, wireless means of communication are the most desirable. However, conducting research in real conditions is troublesome and costly. Moreover, as hydroacoustic propagation conditions change very significantly, even during the day, the assessment of proposed underwater wireless communication methods is very difficult. Therefore, in the literature, there are considered simulators based on real measurements of underwater acoustic (UWA) channels. However, these simulators make an assumption that, during the transmission of elementary signals, the impulse response does not change. In this article, the authors present the results of the measurements realized in a towing tank where the transmitter could move with a precisely set velocity and show that the analyzed channel was non-stationary, even during the time of the transmission of a single chirp signal. The article presents an evaluation method of channel stationarity at the time of the chirp transmission, which should be treated as novelty. There is also an analysis of the impulse responses measured in motion in a towing tank.
HfOx/Ge RRAM with High ON/OFF Ratio and Good Endurance
Authors: Wei Na , Ding Xiang , Gao Shifan , Wu Wenhao , Zhao Yi
Published: November 2022

A trade-off between the memory window and the endurance exists for transition-metal-oxide RRAM. In this work, we demonstrated that HfOx/Ge-based metal-insulator-semiconductor RRAM devices possess both a larger memory window and longer endurance compared with metal-insulator-metal (MIM) RRAM devices. Under DC cycling, HfOx/Ge devices exhibit a 100× larger memory window compared to HfOx MIM devices, and a DC sweep of up to 20,000 cycles was achieved with the devices. The devices also realize low static power down to 1 nW as FPGA’s pull-up/pull-down resistors. Thus, HfOx/Ge devices act as a promising candidates for various applications such as FPGA or compute-in-memory, in which both a high ON/OFF ratio and decent endurance are required.
Growth Hormone Deficiency
Authors: O'Neill Colleen , Gangat Mariam , Radovick Sally
Published: November 2022

Short stature is a common reason for a child to visit the endocrinologist, and can be a variant of normal or secondary to an underlying pathologic cause. Pathologic causes include growth hormone deficiency (GHD), which can be congenital or acquired later. GHD can be isolated or can occur with other pituitary hormone deficiencies. The diagnosis of GHD requires thorough clinical, biochemical, and radiographic investigations. Genetic testing may also be helpful in some patients. Treatment with recombinant human growth hormone (rhGH) should be initiated as soon as the diagnosis is made and patients should be monitored closely to evaluate response to treatment and for potential adverse effects.
Genome-Wide Identification and Expression Analysis of the MADS-Box Family in Ginkgo biloba
Authors: Yang Ke , Liu Zhongbing , Chen Xueyin , Zhou Xian , Ye Jiabao , Xu Feng , Zhang Weiwei , Liao Yongling , Yang Xiaoyan , Wang Qijian
Published: November 2022

As the most significant transformation stage of plants, the flowering process has typically been the focus of research. MADS-box gene plays an important regulatory role in flower development. In this study, 26 MADS-box genes were identified from Ginkgo biloba, including 10 type-I genes and 16 type-II genes, which were distributed on eight chromosomes. There was no collinearity between the GbMADS genes, and the homology with genes from other species was low. All GbMADS proteins contain conserved MADS domains. The gene structures of GbMADS in the same gene family or subfamily differed, but the conserved protein motifs had similar distributions. The microRNA (miRNA) target sites of the GbMADS genes were predicted. It was found that the expression of 16 GbMADS genes may be regulated by miRNA. The results of cis-acting element analysis showed that the 26 GbMADS genes contained a large number of hormones regulated and light-responsive elements as well as stress-response elements. Furthermore, the quantitative real-time PCR (qRT-PCR) experimental results showed that most GbMADS genes were differentially expressed in the male and female flowers at different developmental stages. Among them, the only MIKC * gene GbMADS16 has the highest expression in the metaphase development of the microstrobilus (M2) stage and is almost not expressed in female flowers. Taken together, these findings suggest that the MADS-box genes may play an important role in the development and differentiation of G. biloba flowers.
Sooty Bark Disease in Sycamore: Seasonal and Vertical Variation in Spore Release of Cryptostroma corticale
Authors: Burgdorf Nicole , Härtl Lisa , Hahn W. Andreas
Published: November 2022

Fungal pathogens that benefit from climate change have become more prevalent as a consequence of extended drought periods and higher temperatures. Since the dry and hot years of 2018/2019, the causative agent of sooty bark disease Cryptostroma corticale led to an increasing die back in sycamore forest stands in Germany. Thus, in 2019, spore traps were set up in one forest stand to investigate the dispersal of the pathogen under temporal and spatial aspects. The mortality rate in the study site due to sooty bark disease was quite high: 31% in spring, increasing to 49% in autumn 2019. Quantifying the airborne spores of C. corticale in consecutive periods of the year, a correlation with seasonal aspects was detected. In winter and spring, spore release was relatively low compared with summer months. In summer, high abundances of conidia of C. corticale were released. In July, 399 spores were detected per cm2 per day. From June to August, spore release was 12.6 times higher than in springtime. In autumn 2019, a decrease was visible, although the number of conidia was still higher than in spring. The vertical gradient of spore abundances changed over time: the lowest spore release was found along a gradient at 14 m of height in spring, which shifted down to 2 m in autumn. According to our results of spore release, sooty bark disease is strongly driven by the impact of seasonal factors.
The Macroeconomic Implications of the Transition of the Forestry Industry towards Bioeconomy
Authors: Artene Alin Emanuel , Cioca Lucian-Ionel , Domil Aura Emanuela , Ivascu Larisa , Burca Valentin , Bogdan Oana
Published: November 2022

In a global economic system where essential resources are limited, demand is increasing and environmental degradation is more pronounced, the only viable option to ensure sustainable development is to create an environmentally friendly and efficient economy in order to produce more economic value with the same or fewer resources. The aim of this paper is to provide insight into the macroeconomic implications determined by the transition to a forest bioeconomy, with a focus on the impact on the national gross value added. More specifically, this analysis assesses the relationship between the potential of the macroeconomic value creation on the forestry industry and the measures of progress on the transition towards sustainable forest management and long-term economic growth. The analysis refers to a period between 2013 and 2019, summing-up 133 observations, data that were reported by Eurostat for 23 European Union members. We propose a model that describes a construct of the potential of the value creation that can be generated by each country included in our sample, translated into an efficiency score determined using the Data Envelopment Analysis(DEA)methodology. The results highlighted that the evolution of economic, social, and environmental (ESG) context positively impacted the efficiency score. This positive evolution in time was mainly driven by the higher awareness of governments, companies, and people on the need for a transition to sustainable economic growth and sustainable forest management. Furthermore, this study highlights that the transition to sustainable economic growth implies negative changes to the cost structure of the economies, which lead to higher operational costs and lower gross value added. Moreover, our study provides more insight, from an econometric methodology perspective, regarding the synergy effect as determined by the transformation of business models in the forestry sector towards sustainable forest management.
Efficient Purification of 2′-Fucosyllactose by Membrane Filtration and Activated Carbon Adsorption
Authors: Wang Yuanyifei , Hu Jiaqiang , Sun Mengyu , Peng Bo , Wang Shuo
Published: November 2022

With the rapid development of synthetic biology, the production of 2′-fucosyllactose by biological fermentation gradually has the basis for industrialization. However, the lack of efficient downstream technology of biological fermentation, especially purification technology, has become the main factor limiting its commercialization. In this study, based on the general E. coli biosynthesis of 2′-fucosyllactose fermentation broth, most of the impurities were removed and concentrated using membrane filtration technology after simple flocculation. The target 2′-fucosyllactose was eluted in a targeted manner using activated carbon adsorption and ethanol gradient elution technology. The 2′-fucosyllactose product with 90% or even higher purity could be prepared efficiently. This study explored a new direction for the industrial production of 2′-fucosyllactose.
Sustainable Production and Characteristics of Dried Fermented Vegetables
Authors: Janiszewska-Turak Emilia , Rybak Katarzyna , Pobiega Katarzyna , Nikodem Anna , Gramza-Michałowska Anna
Published: November 2022

The current fashion for healthy food and the increasing number of people with lactose intolerance make fermented vegetables increasingly important. On top of this, surpluses unused in the vegetable harvest can become a potential source of “green waste”. The use of fermentation and freeze-drying can result in a valuable, sustainable product that can solve the problems of spoiled vegetables and the need for refrigerated storage. Therefore, this study aimed to obtain sustainable dried fermented vegetables and to compare their selected physical and structural properties. Beetroot, carrot, and red pepper were selected for this purpose. These vegetables were subjected to a spontaneous lactic fermentation process. After the process, the vegetables were freeze-dried, and their structure and selected properties (color, dry weight, and the number of lactic acid bacteria) were determined. Fermented vegetables were found to differ from their raw sources in structure and color, the main discrepancies being shown by the b* factor (yellow-blue). Root vegetables had smaller pores of structure in the freeze-dried samples than red peppers. The freeze-drying process did not affect the number of bacteria. It can be concluded that both the fermentation and the freeze-drying processes affected the structure of the selected vegetables. All tested vegetables can be fermented and freeze-dried without major changes in color and microbiological properties and can be used as a potential source of lactic acid bacteria and health-promoting pigments, e.g., in the form of chips. In addition, their shelf life is extended.
Effects of Different Additives on Fermentation Quality, Microbial Communities, and Rumen Degradation of Alfalfa Silage
Authors: Ling Wenqing , Zhang Lei , Feng Qixian , Degen Abraham Allan , Li Jue , Qi Yue , Li Yan , Zhou Yi , Liu Yijia , Yang Fulin , Zhou Jing
Published: November 2022

This study examined the effects of different additives on the fermentation quality, nutrient composition, microbial communities, and rumen degradation of ensiled alfalfa. Six treatments were employed in which additives were applied to alfalfa on a fresh weight basis: CK (no additive), FA (0.6% formic acid), CaO (3% calcium oxide and 3% urea), LB (1 × 106 cfu/g Lentilactobacillus buchneri), GLB (2% glucose and 1 × 106 cfu/g L. buchneri), and FLB (2% fucoidan and 1 × 106 cfu/g L. buchneri). After 60 days of ensiling, all treatments altered the bacterial communities, improved the fermentation quality, reduced dry matter (DM) and crude protein (CP) losses, and enhanced the rumen degradation of nutrients. The addition of LB increased the relative abundance of Lactobacillus spp. (p < 0.05), whereas GLB reduced (p < 0.05) the NH3-N:TN ratio and elevated (p < 0.05) the concentrations of Lactobacillus and lactic acid content. The FA treatment reduced (p < 0.05) the pH, as well as the DM and CP degradations, while the CaO treatment increased the degradations of DM, acid detergent fiber, and neutral detergent fiber. We concluded that FA, LB, GLB, and FLB had beneficial effects on alfalfa fermentation, and that CaO increased alfalfa silage rumen degradation.
A Dynamic Federated Identity Management Using OpenID Connect
Authors: Alsadeh Ahmad , Yatim Nasri , Hassouneh Yousef
Published: November 2022

Identity federation allows one to link a user’s digital identities across several identity management systems. Federated identity management (FIM) ensures that users have easy access to the available resources. However, scaling FIM to numerous partners is a challenging process due to the interoperability issue between different federation architectures. This study proposes a dynamic identity federation model to eliminate the manual configuration steps needed to establish an organizational identity federation by utilizing the OpenID Connect (OIDC) framework. The proposed model consists of three major steps to establish dynamic FIM: first, the discovery of the OpenID service provider, which indicates the location of the partner organization; second, the registration of the OpenID relying party, which allows the organization and its partner to negotiate information for establishing the federation; finally, establishing the dynamic trust federation. The proposed dynamic FIM model allows applications to provide services to end-users coming from various domains while maintaining a trust between clients and service providers. Through our proposed dynamic identity federation model, organizations can save hundreds of hours by achieving dynamic federation in runtime and serving a large number of end-users.
Numerical Simulation of Ethanol Air Diffusion Flame Quenching under Transverse AC Electric Field
Authors: Zhao Shuai , Liu Boyun , Zhao Bo , Li Taiwei , Shu Qi
Published: November 2022

The electric field fire extinguishing technology is an efficient, clean, and new fire extinguishing technology that can be operated at a long distance. In order to study the synergistic mechanism of “electric-flow-heat” in the process of transverse AC electric field fire extinguishing, the ionic wind formed by the influence of electric field on each charged particle during the burning process of ethanol–air diffusion flame is simulated by the non-premixed combustion model, and the experimental phenomenon of flame quenching in the transverse AC electric field is reproduced by means of numerical simulation. The accuracy of the numerical model was verified by comparing the temperature and flow velocity in the region obtained from the simulation with the data measured in the experiment. According to both simulated and experimental phenomena, we present a hypothesis of how the flame is quenched under the influence of an electric field. The next research directions are: (1) improving the accuracy of numerical simulation by building fine models; (2) studying the dynamic mechanism of real flames by particle image velocimetry technology.
Recent Progress in the Synergistic Bactericidal Effect of High Pressure and Temperature Processing in Fruits and Vegetables and Related Kinetics
Authors: Zhang Sinan , Meenu Maninder , Hu Lihui , Ren Junde , Ramaswamy Hosahalli S. , Yu Yong
Published: November 2022

Background: Traditional thermal processing is a widely used method to ensure food safety. However, thermal processing leads to a significant decline in food quality, especially in the case of fruits and vegetables. To overcome this drawback, researchers are extensively exploring alternative non-thermal High-Pressure Processing (HPP) technology to ensure microbial safety and retaining the sensory and nutritional quality of food. However, HPP is unable to inactivate the spores of some pathogenic bacteria; thus, HPP in conjunction with moderate- and low-temperature is employed for inactivating the spores of harmful microorganisms. Scope and approach: In this paper, the inactivation effect of high-pressure and high-pressure thermal processing (HPTP) on harmful microorganisms in different food systems, along with the bactericidal kinetics model followed by HPP in certain food samples, have been reviewed. In addition, the effects of different factors such as microorganism species and growth stage, process parameters and pressurization mode, and food composition on microbial inactivation under the combined high-pressure and moderate/low-temperature treatment were discussed. Key findings and conclusions: The establishment of a reliable bactericidal kinetic model and accurate prediction of microbial inactivation will be helpful for industrial design, development, and optimization of safe HPP and HPTP treatment conditions.
Quality of Goji Berry Fruit (Lycium barbarum L.) Stored at Different Temperatures
Authors: Fatchurrahman Danial , Amodio Maria Luisa , Colelli Giancarlo
Published: November 2022

Goji berries are widely known for their outstanding nutritional and medicinal properties; they are usually found in the market as dried fruit or as juice because the fruit has a short shelf-life, and little information is available about its postharvest behavior at low temperatures. This study aimed to determine the storage performance of goji berry fruit by evaluating physicochemical, and sensorial attributes during storage at three different temperatures (0, 5, and 7 °C) for 12 days in a range that has not been extensively studied before. In addition, fruit respiration and ethylene production rates were also measured at the three temperatures. Fruit stored at 0 °C showed the lowest respiration rate and ethylene production (5.8 mg CO2 kg−1h−1 and 0.7 µg C2H4 kg−1h−1, respectively); however, at this temperature, the incidence and severity of pitting and electrolytic leakage were the highest. In contrast, 5 °C was found to be the best storage temperature for goji berry fruit; the fruit appeared fresh and healthy, had the highest scores during sensory analysis with an acceptable general impression, and had the lowest amount of damage attributable to chilling injury, with 17.1% fruit presenting with shriveling, 12.5% pitting, 6.7% mold, and 35% electrolytic leakage on day 9 of storage. Storage of goji berries at 7 °C resulted in the lowest marketability and the highest incidence of decay. Significant differences were also found in the phytochemical attributes, vitamin C content, soluble solid content (SSC), titratable acidity (TA), SSC/TA ratio, total polyphenol content, 2,2-diphenylpicrylhydrazy (DPPH), and anthocyanin content. This study revealed that a storage temperature of 5 °C for 9 days is recommended to maintain the quality of fresh goji berry. Thus, broadening the existing knowledge of the postharvest behavior of fresh goji berries; our results can help improve the commercial life of goji berries and ensure high-quality attributes throughout distribution.
Prebiotic, Antipathogenic Bacteria and Hypocholesterolemia Properties of Fermented Rice Bran Extracts Derived from Black Rice and Germinated Brown Rice
Authors: Sutthanut Khaetthareeya , Tippayawat Patcharaporn , Srijampa Sukanya , Phoksawat Wisitsak , Vachirodom Pornchanan , Wandee Roongrawee
Published: November 2022

Rice bran is a rich source of health-promoting nutrition and bioactive compounds; nevertheless, the properties of rice brans depend on cultivars, ages, and preparation methods, drawing the potential of raw materials for health benefits. Therefore, this research aimed to investigate the health-promoting properties of fermented rice bran extracts from cultivar black rice (H7F) and germinated brown rice (G13F), focusing on their prebiotic, antipathogenic bacteria activity and safety demonstrated in vitro and in vivo study models, respectively. Here, the screening of metabolites’ change after rice bran fermentation by ATR-FTIR spectra revealed specific peaks corresponding to the composited components of protein, carbohydrate, and lipid. Then, in the in vitro study, the prebiotic capability of H7F and G13F extracts was demonstrated by a growth-promoting effect on Lactobacillus delbrueckii subsp. lactis under specific acidic conditions. Furthermore, antipathogenic bacterial activity against Escherichia coli and Staphylococcus aureus was presented at 25 mg/mL of MIC values and 50 mg/mL of MBC of both fermented rice bran extracts, eliminating the bacteria by interfering with the biofilm formation. For safety, an acute and chronic toxicity study using Wistar rats was conducted, in which changes in the body and organ weights, histopathology of organs, blood chemistry, and hematological parameters were observed after H7F and G13F treatment. Desirably, they showed no toxicity, with a significant reduction in blood cholesterol levels in the chronic treatment of H7F and G13F. Conclusively, the overall results evidenced the health benefits of H7F and G13F related to their prebiotic and antipathogenic bacteria properties and hypocholesterolemia potential with a high level of safety. Therefore, the fermented rice bran extracts were demonstrated as potential materials for the further development of functional ingredients and health products.
The Effects of Storage Temperature, Light Illumination, and Low-Temperature Plasma on Fruit Rot and Change in Quality of Postharvest Gannan Navel Oranges
Authors: Sun Ying , Li Yuanyuan , Xu Yu , Sang Yali , Mei Siyi , Xu Chaobin , Yu Xingguo , Pan Taoyu , Cheng Chen , Zhang Jun , Jiang Yueming , Gao Zhiqiang
Published: November 2022

Gannan navel orange (Citrus sinensis Osbeck cv. Newhall) is an economically important fruit, but postharvest loss occurs easily during storage. In this study, the effects of different temperatures, light illuminations, and low-temperature plasma treatments on the water loss and quality of the Gannan navel orange were investigated. The fruit began to rot after 90 d of storage at 5 °C and 20–45 d at 26 °C. Navel oranges stored at 26 °C had 7.2-fold and 3.1-fold higher rates of water loss at the early and late storage stages, respectively, as compared with those stored at 5 °C. Storage at 5 °C decreased the contents of total soluble solids at the early storage stage and the contents of titratable acids at the late storage stage, whereas storage at 26 °C decreased the contents of total soluble solids at the late storage stage and the contents of titratable acids at the early storage stage, respectively. Application of low-temperature plasma produced by air ionization for 6 min, or continuous blue or red light illumination significantly inhibited water loss within 7 and 21 d of storage at 22 °C, respectively, but exhibited no significant effect on fruit quality. Furthermore, the low-temperature plasma treatment protected against fruit rot. Thus, treatment with low-temperature plasma followed by storage at a low temperature under continuous red or blue light illumination was of potential value as a green technology for preserving Gannan navel orange during storage.
Forecasting Food Innovations with a Delphi Study
Authors: Zickafoose Alexis , Lu Peng , Baker Mathew
Published: November 2022

Food innovations can create novel nutritious food, improve agricultural sustainability, and increase the agri-food industry’s market profits. Our study proposes a consensus definition of food innovations and forecasts food innovations that will be available to consumers in the next five years by using a Delphi study. Thirteen experts aged 35 to 85 from the US and the UK researching or working in agriculture and nutrition, public health, the agri-food industry, or food policy participated in three rounds of this Delphi study. The experts were chosen using the snowball sampling method. This study followed the implementation and data analysis guidelines popularized by the Rand Corporation. The consensus definition for food innovations (with 76.9% agreement) was that ‘food innovations aid in the development, production, or transportation of new food products, processes, or technology to promote human health, food security, or environmental sustainability’. The specific food innovations, which had over 69% agreement, are ranked as (1) plant-based meat alternatives, (2) personalized nutrition, (3) natural foods, (4) new genetically modified organisms, (5) regenerative agriculture, (6) urban agriculture, (7) packing innovations, (8) alternative flours, (9) improving shelf life, (10) supply chain technologies, (11) improved soil health, and (12) technology for traceability. The food innovation definition and identified specific food innovations could further connect the agricultural value chain to develop novel nutritious foods and improve agricultural sustainability. Agri-food industry specialists, practitioners, researchers, and policymakers can advance food innovation development and research pinpointing the specific food innovations along the agricultural value chain.
Dynamic Changes of Bacterial Communities and Microbial Association Networks in Ready-to-Eat Chicken Meat during Storage
Authors: Qiu Mengjia , Xiao Xingning , Xiao Yingping , Ma Jiele , Yang Hua , Jiang Han , Dong Qingli , Wang Wen
Published: November 2022

Ready-to-eat (RTE) chicken is a popular food in China, but its lack of food safety due to bacterial contamination remains a concern, and the dynamic changes of microbial association networks during storage are not fully understood. This study investigated the impact of storage time and temperature on bacterial compositions and microbial association networks in RTE chicken using 16S rDNA high-throughput sequencing. The results show that the predominant phyla present in all samples were Proteobacteria and Firmicutes, and the most abundant genera were Weissella, Pseudomonas and Proteus. Increased storage time and temperature decreased the richness and diversity of the microorganisms of the bacterial communities. Higher storage temperatures impacted the bacterial community composition more significantly. Microbial interaction analyses showed 22 positive and 6 negative interactions at 4 °C, 30 positive and 12 negative interactions at 8 °C and 44 positive and 45 negative interactions at 22 °C, indicating an increase in the complexity of interaction networks with an increase in the storage temperature. Enterobacter dominated the interactions during storage at 4 and 22 °C, and Pseudomonas did so at 22 °C. Moreover, interactions between pathogenic and/or spoilage bacteria, such as those between Pseudomonas fragi and Weissella viridescens, Enterobacter unclassified and Proteus unclassified, or those between Enterobacteriaceae unclassified and W.viridescens, were observed. This study provides insight into the process involved in RTE meat spoilage and can aid in improving the quality and safety of RTE meat products to reduce outbreaks of foodborne illness.
Conserved Quantities for Constrained Hamiltonian System within Combined Fractional Derivatives
Authors: Song Chuanjing
Published: November 2022

Singular systems, which can be applied to gauge field theory, condensed matter theory, quantum field theory of anyons, and so on, are important dynamic systems to study. The fractional order model can describe the mechanical and physical behavior of a complex system more accurately than the integer order model. Fractional singular systems within mixed integer and combined fractional derivatives are established in this paper. The fractional Lagrange equations, fractional primary constraints, fractional constrained Hamilton equations, and consistency conditions are analyzed. Then Noether and Lie symmetry methods are studied for finding the integrals of the fractional constrained Hamiltonian systems. Finally, an example is given to illustrate the methods and results.
Triple-Positive Solutions for a Nonlinear Singular Fractional q-Difference Equation at Resonance
Authors: Yu Changlong , Li Shuangxing , Li Jing , Wang Jufang
Published: November 2022

Fractional q-calculus plays an extremely important role in mathematics and physics. In this paper, we aim to investigate the existence of triple-positive solutions for nonlinear singular fractional q-difference equation boundary value problems at resonance by means of the fixed-point index theorem and the q-Laplace transform, where the nonlinearity f(t,u,v) permits singularities at t=0,1 and u=v=0. The obtained theorem is well illustrated with the aid of an example.
Genome-Wide DNA Methylation Profiling Solves Uncertainty in Classifying NSD1 Variants
Authors: Ferilli Marco , Ciolfi Andrea , Pedace Lucia , Niceta Marcello , Radio Francesca Clementina , Pizzi Simone , Miele Evelina , Cappelletti Camilla , Mancini Cecilia , Galluccio Tiziana , Andreani Marco , Iascone Maria , Chiriatti Luigi , Novelli Antonio , Micalizzi Alessia , Matraxia Marta , Menale Lucia , Faletra Flavio , Prontera Paolo , Pilotta Alba , Bedeschi Maria Francesca , Capolino Rossella , Baban Anwar , Seri Marco , Mammì Corrado , Zampino Giuseppe , Digilio Maria Cristina , Dallapiccola Bruno , Priolo Manuela , Tartaglia Marco
Published: November 2022

Background: Inactivating NSD1 mutations causing Sotos syndrome have been previously associated with a specific genome-wide DNA methylation (DNAm) pattern. Sotos syndrome is characterized by phenotypic overlap with other overgrowth syndromes, and a definite diagnosis might not be easily reached due to the high prevalence of variants of unknown significance (VoUS) that are identified in patients with a suggestive phenotype. Objective: we performed microarray DNAm profiling in a set of 11 individuals with a clinical suspicion of Sotos syndrome and carrying an NSD1 VoUS or previously unreported variants to solve uncertainty in defining pathogenicity of the observed variants. The impact of the training cohort size on sensitivity and prediction confidence of the classifier was assessed. Results: The Sotos syndrome-specific DNAm signature was validated in six individuals with a clinical diagnosis of Sotos syndrome and carrying bona fide pathogenic NSD1 variants. Applying this approach to the remaining 11 individuals with NSD1 variants, we succeeded in confirming pathogenicity in eight subjects and excluding the diagnosis of Sotos syndrome in three. The sensitivity and prediction confidence of the classifier based on the different sizes of the training sets did not show substantial differences, though the overall performance was improved by using a data balancing strategy. Conclusions: The present approach solved uncertainty in cases with NDS1 VoUS, further demonstrating the clinical utility of DNAm profiling.
AKAP9-Related Channelopathy: Novel Pathogenic Variant and Review of the Literature
Authors: Huynh Minh-Tuan , Proust Alexis , Bouligand Jérôme , Popescu Elena
Published: November 2022

Disease-associated pathogenic variants in the A-Kinase Anchor Protein 9 (AKAP9) (MIM *604001) have been recently identified in patients with autosomal dominant long QT syndrome 11 (MIM #611820), lethal arrhythmia (ventricular fibrillation, polymorphic ventricular tachycardia), Brugada syndrome, and sudden unexpected death. However, AKAP9 sequence variations were rarely reported and AKAP9 was classified as a “disputed evidence” gene to support disease causation due to the insufficient genetic evidence and a limited number of reported AKAP9-mutated patients. Here, we describe a 47-year-old male carrying a novel frameshift AKAP9 pathogenic variant who presented recurrent syncopal attacks and sudden cardiac arrest that required a semi-automatic external defibrillator implant and an electric shock treatment of ventricular arrhythmia. This study provides insight into the mechanism underlying cardiac arrest and confirms that AKAP9 loss-of-function variants predispose to serious, life-threatening ventricular arrhythmias.
Study of CTS DNA Proficiency Tests with Regard to DNA Mixture Interpretation: A NIST Scientific Foundation Review
Authors: Bille Todd , Coble Michael D. , Kalafut Tim , Buckleton John
Published: November 2022

The National Institute of Standards and Technology has released a document entitled DNA Mixture Interpretation: A NIST Scientific Foundation Review for public comment. This has become known as the Draft NIST Foundation Review. It contains the statement: “Across these 69 data sets, there were 80 false negatives and 18 false positives reported from 110,408 possible responses (27,602 participants × two evidence items × two reference items). In the past five years, the number of participants using PGS has grown.” We examine a set of proficiency test results to determine if these NIST statements could be justified. The summary reports for each relevant forensic biology test (Forensic Biology, Semen, and Mixture) in the years 2018–2021 were reviewed. Data were also provided to us by CTS upon our request. None of the false positives or negatives could be attributed to the mixture interpretation strategy and certainly not to the use of PGS.
Numerical Simulations of Large Martian Impact Ripples
Authors: Yizhaq Hezi , Kok Jasper F. , Silvestro Simone , Saban Lior , Katra Itzhak
Published: November 2022

Ripples made from unimodal fine sands can grow much larger on Mars than on Earth, reaching wavelengths of 1–3 m and heights exceeding 1 dm. Smaller decimeter-wavelength ripples can be superimposed on them. Classification and origins of these bedforms have been debated. They have been interpreted as analogous to subaqueous ripples on Earth, or as aeolian impact ripples with a range of grain sizes that reach large maximum sizes on Mars. This study uses a mathematical model to evaluate the formation of large Martian ripples as aeolian impact ripples to further investigate this hypothesis. The model parameters were computed using COMSALT for 100 µm grains under shear velocity of 0.65 m/s, which is a reasonable shear velocity for sand transport on Mars according to recent estimations of threshold Martian winds. The numerical experiments utilize a large grid 8 m long. Experiments also evaluate the development of secondary small ripples between the large ripples from random perturbations. The numerical simulations show the evolution of ripple wavelength and height. According to the results, the time scale for the formation of the large ripples is about 2–3 years, which is a much longer time scale compared to terrestrial impact ripples. Small secondary ripples develop only if the space between the large ripples is sufficiently large.
Population Based Average Parotid Gland Volume and Prevalence of Incidental Tumors in T1-MRI
Authors: Brzoska Tina , Ittermann Till , Ihler Friedrich , Koch Carmela , Blaurock Markus , Bülow Robin , Völzke Henry , Busch Chia-Jung , Beule Achim Georg
Published: November 2022

Representative epidemiologic data on the average volume of the parotid gland in a large population-based MRI survey is non-existent. Within the Study of Health in Pomerania (SHIP), we examined the parotid gland in 1725 non-contrast MRI-scans in T1 weighted sequence of axial layers. Thus, a reliable standard operating procedure (Intraclass Correlation Coefficient > 0.8) could be established. In this study, we found an average, single sided parotid gland volume of 27.82 cm3 (95% confidence interval (CI) 27.15 to 28.50) in male and 21.60 cm3 (95% CI 21.16 to 22.05) in female subjects. We observed positive associations for age, body mass index (BMI), as well as male sex with parotid gland size in a multivariate model. The prevalence of incidental tumors within the parotid gland regardless of dignity was 3.94% in the Northeast German population, slightly higher than assumed. Further epidemiologic investigations regarding primary salivary gland diseases are necessary.