bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Combined Application of Solid Pyrolysis Products and Digestate on Selected Soil Properties of Arenosol and Plant Growth and Composition in Laboratory Experiments
Authors: Gulyás Miklós , Someus Edward , Klátyik Szandra , Fuchs Márta , Varga Zsolt István , Dér Sándor , Fekete György , Czinkota Imre , Székács András , Gyuricza Csaba , Aleksza László
Published: June 2022

Biochars as soil amendments have been reported to improve soil properties and may have an important role in the mitigation of the consequences of climate change. As a novel approach, this study examines whether biochar and digestate co-application can be utilized as cost-effective, renewable plant nutrients. The effects of two types of biochar—wood chip biochar (WBC) and animal bone biochar (ABC), applied alone or in combination with an anaerobic digestate—on soil physicochemical properties, on the levels of selected elements, and on growth yields of ryegrass were studied in laboratory experiments. Most parameters were significantly affected by the treatments, and the investigated factors (biochar type, application rate, and the presence of digestate), as well as their interactions, were found to have significant effects on the characteristics investigated. The easily soluble phosphorus content (AL-P2O5) of the soil increased in all WBC and ABC biochar treatments, and the presence of digestate caused a further increase in AL-P2O5 in the case of anaerobic digestate-supplemented ABC treatment (ABCxAD). The pH increased in both ABC and WBC treatments, and also in the case of ABCxAD treatments. Similar increases in the salt content were detected in ABC-treated samples and in ABCxAD treatments at higher application rates. WBC increased the water holding capacity and carbon content of the soil. Phytotoxic effects of biochars were not detected, although higher doses resulted in slower germination. Combined biochar–digestate applications resulted in increased plant yields compared to sole biochar treatments. Thus, biochar–digestate combinations appear to be applicable as organo-mineral fertilizers.
Effect of Tillage and Residue-Returning Mode on Soil Carbon Mineralizability and Accumulation in a Wheat–Maize System
Authors: Duan Hongxuan , Cheng Jia , Han Mengqi , Zhang Hailin
Published: June 2022

Conservation tillage has been widely adopted to improve soil organic carbon (SOC) accumulation. To assess the effects of different tillage and residue-returning modes on SOC mineralizability and accumulation, a field experiment was conducted in 2015, including two tillage modes, i.e., no-tillage (NT) and rotary-tillage (RT), as well as three straw-returning modes, i.e., no straw returning (N0), wheat straw returning (WR), and wheat–maize straw retuning (WM). Wheat–maize root, maize straw, and wheat straw were added to soil samples during laboratory incubation. The results showed that NT significantly increased SOC concentration by 26.75% at a depth of 0–10 cm (p < 0.01). Although NT significantly decreased SOC mineralizability at soil depths of 0–10 cm and 10–20 cm (p < 0.05), the residue did not significantly affect SOC mineralizability in the 0–20 cm layer. The potentially mineralized C (Cp) and organic labile C fraction (C1) increased with the increase of residue amount. A significant correlation (R2 = 0.662) was observed between C1 and SOC concentration. Notably, SOC concentration and mineralizability without straw returning were insignificant between N0 and WR in the 0–10 cm and 0–20 cm layers, respectively. This suggests that NT with WR is a promising strategy to increase SOC concentration and decrease mineralizability.
DNA Methylation Correlates with the Expression of Drought-Responsive Genes and Drought Resistance in Rice
Authors: Ding Guohua , Cao Liangzi , Zhou Jinsong , Li Zhugang , Lai Yongcai , Liu Kai , Luo Yu , Bai Liangming , Wang Xueyang , Wang Tongtong , Wang Rongsheng , Yang Guang , Sun Shichen
Published: June 2022

The δ13C value is regarded as an important indicator for tolerance to drought stress (DS), which is a severe abiotic stress that influences rice productivity. However, exploration of drought-responsive genes (DRGs) related to δ13C regulation is less reported. In this study, we investigated the natural variation in δ13C values in 102 japonica rice accessions. Among them, two rice accessions with contrasting δ13C values, Longdao 10 (LD10, DS-tolerant) and Binxu (BX, DS-sensitive), were used for further analysis. LD10 possesses better drought resistance with 2% lower δ13C values, 35% lower stomatal length and density, 33% lower water loss, and 11% lower stomatal conductance in comparison to BX. Transcriptome analysis shows that there are 2325 and 1378 differentially expressed genes (DEGs) induced by DS in LD10 and BX at the tillering stage, respectively, while there are 1076 and 492 DEGs in LD10 and BX at the graining stage, respectively. In total, 21 overlapped DEGs (defined as DRGs) were identified due to DS effects across two rice accessions over two stages. Among them, the expression levels of six genes, including chloride transporter (CLT1) and photosystem II polypeptide (PSBP), were further tested using qRT-PCR. Furthermore, we found that four methyltransferase genes were upregulated in BX compared to LD10 under DS. Consistently, the methylation levels of CLT1 and PSBP were higher along both promoter and CDS regions for CG, CHG, and CHH types. This study highlights the importance of the expression of these DRGs in response to DS and provides deep insights into DNA methylation-driven gene expression conferring different drought responses in rice.
Mathematical Models of the Dynamic Stability of Arid Pasture Ecosystems in the South of Russia
Authors: Salugin Aleksandr Nikolaevich , Vlasenko Marina Vladimirovna
Published: June 2022

In this study, the issue of the ecological stability of the soil and vegetation cover of the North-Western Near-Caspian arid zones within the borders of the Black Lands (BL) of the Republic of Kalmykia was considered. Modeling was carried out of the open systems’ thermodynamics principles using continuous and discrete formalisms. Models presented in the form of systems of ordinary differential equations, Markov’s circuits, and autonomous impulse processes show the possibility of their application in the management of pasture feed stocks. Modeling with the systems of differential equations makes it possible to identify points of stable states of pasture systems. Markov circuits are able to establish an optimal animal load on these systems. A computational algorithm for determining the stability of pasture ecosystems using autonomous impulse processes allows optimization problems to be solved to determine the conditions for the spread of external influences. Computational experiments were carried out to determine the parameters of the model corresponding to the sustainable mode of operation in arid pasture ecosystems.
Variability of Bioactive Substances in Potatoes (Solanum Tuberosum L.) Depending on Variety and Maturity
Authors: Franková Hana , Musilová Janette , Árvay Július , Harangozo Ľuboš , Šnirc Marek , Vollmannová Alena , Lidiková Judita , Hegedűsová Alžbeta , Jaško Edina
Published: June 2022

Potatoes (Solanum tuberosum L.) are an essential food for the human diet and thus represent an important source of biologically active substances. This study aimed at investigating the content of bioactive substances (total anthocyanin and polyphenol contents and chlorogenic acid) in seven potato varieties (Belana, Cecile, Magenta Love, Mozart, Talentine, Toscana, and Violet Queen) with various flesh and skin colors. To evaluate the impact of potato maturity on the analyzed parameters, potato samples were harvested in two different periods (1st harvest—July and 2nd harvest—September). Total anthocyanin and polyphenol contents were determined spectrophotometrically, and chlorogenic acid by the HPLC-DAD method. Varieties with a colored flesh (Magenta Love and Violet Queen) showed the highest content of bioactive substances. The maturity level significantly (p < 0.05) affected the total content of anthocyanins and polyphenols in potatoes. Early to medium early varieties (Belana and Magenta Love) showed an increase in chlorogenic acid content during maturation, while in other varieties, chlorogenic acid decreased due to higher maturity. In terms of the content of bioactive substances with a possible positive effect on human health, the consumption of potatoes with colored flesh could be recommended.
Rodents in Agriculture: A Broad Perspective
Authors: Witmer Gary
Published: June 2022

There are thousands of rodent species in the world. While they provide a number of ecosystem functions, unfortunately, some species cause significant damage to agriculture. Rodent damage occurs to crops in the field, but also to stored foods, livestock feed, and structures. There are many methods available to reduce rodent populations and/or damage, including both lethal and non-lethal methods. There are advantages and disadvantages to most methods, and many are regulated by federal, state, and local ordinances. Public acceptance of the various methods also varies greatly. Examples and details of these topics are presented in this review.
Detection of Weeds Growing in Alfalfa Using Convolutional Neural Networks
Authors: Yang Jie , Wang Yundi , Chen Yong , Yu Jialin
Published: June 2022

Alfalfa (Medicago sativa L.) is used as a high-nutrient feed for animals. Weeds are a significant challenge that affects alfalfa production. Although weeds are unevenly distributed, herbicides are broadcast-applied in alfalfa fields. In this research, object detection convolutional neural networks, including Faster R-CNN, VarifocalNet (VFNet), and You Only Look Once Version 3 (YOLOv3), were used to indiscriminately detect all weed species (1-class) and discriminately detect between broadleaves and grasses (2-class). YOLOv3 outperformed other object detection networks in detecting grass weeds. The performances of using image classification networks (GoogLeNet and VGGNet) and object detection networks (Faster R-CNN and YOLOv3) for detecting broadleaves and grasses were compared. GoogLeNet and VGGNet (F1 scores ≥ 0.98) outperformed Faster R-CNN and YOLOv3 (F1 scores ≤ 0.92). Classifying and training various broadleaf and grass weeds did not improve the performance of the neural networks for weed detection. VGGNet was the most effective neural network (F1 scores ≥ 0.99) tested to detect broadleaf and grass weeds growing in alfalfa. Future research will integrate the VGGNet into the machine vision subsystem of smart sprayers for site-specific herbicide applications.
Effects of Different Moisture Levels and Additives on the Ensiling Characteristics and In Vitro Digestibility of Stylosanthes Silage
Authors: Bao Jinze , Wang Lei , Yu Zhu
Published: June 2022

The present study aims to estimate the dynamic effects of moisture levels and inoculants on the fermentation quality and in vitro degradability of Stylosanthes silage. In this experiment, Stylosanthes was ensiled with (1) no additive (control), (2) Lactobacillus plantarum (LP), (3) Lactobacillus plantarum carrying heterologous genes encoding multifunctional glycoside hydrolases (xg), or (4) LP + xg and was wilted until different moisture levels (60% and 72%) were attained. The ensiled bags were unpacked after different storage periods to determine the chemical composition and fermentation quality of the Stylosanthes silage. Moreover, the in vitro degradability was also determined 45 days after the ensiling process. The results show that the silage prepared with freshly mowed Stylosanthes also had a lower pH and NH3- N content. Adding transgenic engineered lactic acid bacteria xg not only decreased the NDF and ADF content of the silage, but also improved the in vitro digestibility significantly. We concluded that the addition of xg to Stylosanthes silage can improve its quality and increase in vitro digestibility and gas production. The results provide technical support and a theoretical basis for the utilization of warm-season forage silage.
Chemical Composition of the “Galo de Barcelos” (Barcelos Rooster Raw Meat)
Authors: Afonso Isabel Maria , Casal Susana , Lopes Júlio César , Domingues Jéssica , Vale Ana Paula , Meira Márcio , Marinho Maria Conceição , Vaz Pedro Santos , Brito Nuno V.
Published: June 2022

Ten roosters produced according to “Barcelos Confraria” rules and ten roosters of autochthonous “Amarela” breed, reared on a similar traditional production system, were analyzed, and the chemical profile of two of the most significant meat portions, breast and drumstick, was determined. The results demonstrated that the “Barcelos” rooster raw meat is rich in proteins (22.3%) and fat (4.31%), particularly in monounsaturated fatty acids (39.1%). Significant differences (p ≤ 0.01) were observed, with the breast having a higher protein content (25.1 vs. 19.7%) and less fat (1.9% vs. 6.7%), compared to the drumstick. The fatty acid profile revealed (SFA 30.0%, MUFA 39.1%, and PUFA 24.6%) a similar composition to the roosters reared in the traditional or organic production systems, such as the “Amarela” autochthonous rooster. The “Barcelos” rooster can be regarded as a highly nutritional meat, with an interesting chemical profile ensuring a high-quality traditional product to consumers.
Relationship between Plasma Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) Level and Proteome Profile of Cows
Authors: Czegledi Levente , Csosz Eva , Gulyas Gabriella
Published: June 2022

Pituitary adenylate cyclase-activating polypeptide (PACAP) is a pleiotropic and multifunctional neuropeptide; it takes part in the regulation of various physiological processes, such as feeding, reproduction, catecholamine synthesis, thermoregulation, motor activity, brain development and neuronal survival. Since PACAP plays important regulatory roles, we hypothesized that the level of PACAP in blood is associated with expression of other proteins, which are involved in different metabolic pathways. The objective of the present study was to compare plasma protein profiles of cows with high and low plasma PACAP levels. Differential proteome analyses were performed by two-dimensional gel electrophoresis (2D-PAGE) followed by tryptic digestion and protein identification by liquid chromatography–mass spectrometry (LC-MS). A total of 210 protein spots were detected, and 16 protein spots showed statistically significant differences (p < 0.05) in the expression levels between groups. Ten spots showed a higher intensity in the high-PACAP-concentration group, while six spots were more abundant in the low-PACAP-concentration group. The functions of the differentially expressed proteins indicate that the PACAP level of plasma is related to the lipid metabolism and immune status of cattle.
The Microbiota and Cytokines Correlation between the Jejunum and Colon in Altay Sheep
Authors: Ye Mengjun , Hou Meng , Peng Qimin , Jia Sheng , Peng Bin , Yin Fangfang , Li Na , Wang Jinquan
Published: June 2022

Both the jejunum and colon release cytokines that interact with intestinal microbiota. However, it is largely unclear which cytokines and microbial populations are involved in the homeostasis of the intestinal ecosystem for sheep health. To address this, we collected contents for isolating microbiota and tissues for determining cytokines from the jejunum and colon of 7-month-old Altay sheep. We used the techniques of 16S rRNA sequencing and ELISA to detect microbial population and cytokine level, respectively. Correlations between microbial population and cytokines were analyzed by Spearman correlation coefficient. The correlation analysis revealed higher populations of Bacteroides, Fibrobacteres and Spirochetes in the colon than in the jejunum, and IL-6 and IL-12 levels were higher in the jejunum than in the colon. Association analysis further revealed a positive association between IL-10 level and both Ruminococcus_2 and norank_f_Bifidobacteriaceae population in the jejunum. The analysis also revealed positive associations between IL-6 level and Ruminococcaceae_UCG-014 and Ruminococcaceae_UCG-013 population, IL-10 and Prevotellaceae_UCG-004, as well as TNF-α and Prevotellaceae_UCG-003 in the colon. These results indicate a potential interaction between the intestinal microbiota and the host immune system that needs to be further clarified for considering dietary formulations to maintain animal health and disease prevention.
Iturin A Rescued STb-R-Induced Pork Skeletal Muscle Growth Restriction through the Hypothalamic-Pituitary-mTORC1 Growth Axis
Authors: Ye Mao , Liu Zhenhua , Gao Chunqi , Yan Huichao , Wang Xiuqi , Wen Liufa , Jin Chenglong
Published: June 2022

The engineered STb-Rosetta Escherichia coli (STb-R) was designed to investigate the effects of Iturin A on the skeletal muscle growth of weaned piglets. A total of 28 piglets were randomly divided into 4 groups (7 piglets per group): the control group (100 mL PBS), the Iturin A group (100 mL 320 mg/kg body weight (BW) Iturin A), the STb-R group (100 mL 1 × 1010 CFU/mL STb-R), and the Iturin A + STb-R group (100 mL 320 mg/kg BW Iturin A + 1 × 1010 CFU/mL STb-R). Compared with the control, STb-R-reduced body weight gain were rescued by Iturin A. The semimembranosus muscle weight recovered to normal level in the Iturin A + STb-R group. The level of relevant genes of the growth axis were elevated by Iturin A, including GHRH in the hypothalamus, GHRHR and GH in the pituitary, and GHR, IGF-1 and IGF-1R in the semimembranosus muscle. Moreover, Iturin A increased the mean fiber area and the number of proliferating cells in the semimembranosus muscle, which were decreased by STb-R. Additionally, the mTORC1 pathway was reactivated by Iturin A to relieve the suppression of STb-R. Collectively, the hypothalamic-pituitary growth axis-mediated Iturin A reactivated the mTORC1 pathway to rescue STb-R-restricted pork skeletal muscle growth.
A Development of Visualization Technology through AR-Based Design Checklist Connection
Authors: Park Hyejin , Choo Seungyeon
Published: June 2022

Since the initial design review has the effect of minimizing the design changes needed in the later stages of an architectural project, a process of collaboration between architects, clients, and engineers in this design review is very important. Recently, design review using 3D models rendered in VR or AR, going beyond simple simulation, has been addressed in many studies. However, a synchronization function that provides immediate visualization of design changes is the focus, which has limitations in its ability to review factors required by law related to safety and design. The purpose of this study was, therefore, to develop AR-based design checklist linkage and visualization review technology that facilitates decision making by clients and architects during design review. For this purpose, a method for linking a design checklist in an AR environment using a game engine (Unity 3D) is proposed and various design review visualization functions are developed with consideration paid to the user interface. The efficiency of the design checklist linkage technology and visualization function developed in this study was verified in the pilot project.
Dynamic Modeling and Robust Adaptive Sliding Mode Controller for Marine Cable-Driven Parallel Derusting Robot
Authors: Chen Yizong , Li Jian , Wang Shenghai , Han Guangdong , Sun Yuqing , Luo Weirong
Published: June 2022

Ship derusting has the characteristics of a complex operation environment, high labor intensity and low efficiency. In order to better cope with this situation, a new type of cable-driven parallel derusting robot (CDPDR) is proposed in this article. To improve the positioning accuracy and anti-interference capacity of the motion platform where the end effector is mounted, the system’s dynamic model, considering wave excitation, is established. Further, the controllable workspace and cable tension optimization algorithm are studied. In addition, a fast non-singular terminal sliding-mode controller is designed. Meanwhile, the adaptive technique is used to estimate the disturbance upper bound. Then, the Lyapunov theory is applied to prove the stability of the system. Finally, the performance of the controller is verified by high-fidelity simulations in two different scenarios. The results show that the proposed controller can converge in finite time and maintain small error under multiple external disturbances. The relevant research in this article can provide theoretical guidance for the application of CDPDRs on ships.
Predicting the Biological Effects of Human Salivary Gland Tumour Cells for Scanned 4He-, 12C-, 16O-, and 20Ne-Ion Beams Using an SOI Microdosimeter
Authors: Lee Sung Hyun , Mizushima Kota , Yonai Shunsuke , Matsumoto Shinnosuke , Mizuno Hideyuki , Nakaji Taku , Kohno Ryosuke , Iwata Yoshiyuki , Shirai Toshiyuki , Pan Vladimir , Kok Angela , Povoli Marco , Tran Linh T. , Rosenfeld Anatoly B. , Suzuki Masao , Inaniwa Taku
Published: June 2022

Experimental microdosimetry along with the microdosimetric kinetic (MK) model can be utilized to predict the biological effects of ions. To predict the relative biological effectiveness (RBE) of ions and the survival fraction (SF) of human salivary gland tumour (HSGc-C5) cells, microdosimetric quantities measured by a silicon-on-insulator (SOI) MicroPlus-mushroom microdosimeter along the spread-out Bragg peak (SOBP) delivered by pencil beam scanning of 4He, 12C, 16O, and 20Ne ions were used. The MK model parameters of HSGc-C5 cells were obtained from the best fit of the calculated SF for the different linear energy transfer (LET) of these ions and the formerly reported in vitro SF for the same LET and ions used for calculations. For a cube-shaped target of 10 × 10 × 6 cm3, treatment plans for 4He, 12C, 16O, and 20Ne ions were produced with proprietary treatment planning software (TPS) aiming for 10% SF of HSGc-C5 cells over the target volume and were delivered to a polymethyl methacrylate (PMMA) phantom. Afterwards, the saturation-corrected dose-mean lineal energy derived based on the measured microdosimetry spectra, along with the physical dose at various depths in PMMA phantoms, was used for the estimation of the SF, RBE, and RBE-weighted dose using the MK model. The predicted SF, RBE, and the RBE-weighted dose agreed with what was planned by the TPS within 3% at most depths for these ions.
MALDI-TOF/MS Analysis of Extracellular Vesicles Released by Cancer Cells
Authors: Zambonin Carlo
Published: June 2022

The direct shedding of extracellular vesicles (EVs) from the plasma membrane is a recognized fundamental method for the intercellular transfer of properties in both physiological and pathological conditions. EVs are classified according to origin, biogenesis, size, content, surface markers, and/or functional properties, and contain various bioactive molecules depending on the physiological state and the type of the cells of origin including lipids, nucleic acids, and proteins. The presence of tumor-derived EVs in body fluids such as blood, ascites, urine, and saliva, together with the important role played in the tumor microenvironment where they intervene at different levels from oncogenesis to metastasis, make EVs a priority target for cancer studies. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) can play a leading role in the analysis and characterization of EVs and their load due to its intrinsic advantages such as high throughput, low sample consumption, speed, the cost-effectiveness of the analysis, and the ease of use. This work reviews the main MALDI-TOF applications for the analysis and characterization of extracellular vesicles in the tumor field.
Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
Authors: Elejla Omar E. , Anbar Mohammed , Hamouda Shady , Faisal Serri , Bahashwan Abdullah Ahmed , Hasbullah Iznan H.
Published: June 2022

Internet Protocol version six (IPv6) is more secure than its forerunner, Internet Protocol version four (IPv4). IPv6 introduces several new protocols, such as the Internet Control Message Protocol version six (ICMPv6), an essential protocol to the IPv6 networks. However, it exposes IPv6 networks to some security threats since ICMPv6 messages are not verified or authenticated, and they are mandatory messages that cannot be blocked or disabled. One of the threats currently facing IPv6 networks is the exploitation of ICMPv6 messages by malicious actors to execute distributed denial of service (DDoS) attacks. Therefore, this paper proposes a deep-learning-based approach to detect ICMPv6 flooding DDoS attacks on IPv6 networks by introducing an ensemble feature selection technique that utilizes chi-square and information gain ratio methods to select significant features for attack detection with high accuracy. In addition, a long short-term memory (LSTM) is employed to train the detection model on the selected features. The proposed approach was evaluated using a synthetic dataset for false-positive rate (FPR), detection accuracy, F-measure, recall, and precision, achieving 0.55%, 98.41%, 98.39%, 97.3%, and 99.4%, respectively. Additionally, the results reveal that the proposed approach outperforms the existing approaches.
Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
Authors: Wang Suzhen , Hu Zhongbo , Deng Yongchen , Hu Lisha
Published: June 2022

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.
Sasa borealis Ethanol Extract Protects PC12 Neuronal Cells against Oxidative Stress
Authors: Kang Min Hye , Ji Yun-Jeong , Han Yu Mi , Jang Gwi Yeong , Kim Dong Hwi , Lee Jeong Hoon , Kim Geum-Soog , Choi Su Ji , Kim Hyung Don
Published: June 2022

The overproduction of reactive oxygen species (ROS) can cause oxidative stress to biomolecules such as nucleic acids, proteins, and lipids, leading to neurodegenerative disorders. Sasa borealis (SB) has antioxidant, anti-inflammatory, antidiabetic, and anti-obesity effects. We evaluated the neuroprotective activity of SB on hydrogen peroxide (H2O2)-induced oxidative stress. We investigated the antioxidant and neuroprotective effects of SB water extract (SBW) and SB ethanol extract (SBE) by measuring the radical scavenging activities and intracellular ROS production. SBE, which had a high level of isoorientin, had higher antioxidative activities than SBW in 2,2′-azino-bis-(3-ethylbenzothiazolin-6-sulfonic acid) diammonium salt (ABTS+) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays. It also reduced ROS generation in pheochromocytoma 12 (PC12) cells more significantly than SBW. It increased the translation of heme oxygenase-1 (HO-1), superoxide dismutase 2 (SOD2), catalase (CAT), and glutathione peroxidase (GPx) with a corresponding increase in the translation of NF-E2-related factor-2 (Nrf-2). In conclusion, SBE with high levels of phenolic compounds such as isoorientin shows promise for preventing neurodegenerative diseases.
A Comparison of Electrical Breakdown Models for Polyethylene Nanocomposites
Authors: Xing Zhaoliang , Zhang Chong , Han Mengyao , Gao Ziwei , Wu Qingzhou , Min Daomin
Published: June 2022

The development of direct current high-voltage power cables requires insulating materials having excellent electrically insulation properties. Experiments show that appropriate nanodoping can improve the breakdown strength of polyethylene (PE) nanocomposites. Research indicates that traps, free volumes, and molecular displacement are key factors affecting the breakdown strength. This study comprehensively considered the space charge transport, electron energy gain, and molecular chain long-distance movement during the electrical breakdown process. In addition, we established three simulation models focusing on the electric field distortion due to space charges captured by traps, the energy gain of mobile electrons in free volumes, the free volume expansion caused by long-distance movement of molecular chains under the Coulomb force, and the energy gained by the electrons moving in the enlarged free volumes. The three simulation models considered the electrical breakdown modulated by space charges, with a maximum electric field criterion and a maximum electron energy criterion, and the electrical breakdown modulated by the molecular displacement (EBMD), with a maximum electron energy criterion. These three models were utilized to simulate the breakdown strength dependent on the nanofiller content of PE nanocomposites. The simulation results of the EBMD model coincided best with the experimental results. It was revealed that the breakdown electric field of PE nanodielectrics is improved synergistically by both the strong trapping effect of traps and the strong binding effect of molecular chains in the interfacial regions.
Analysis of Knowledge Graph Path Reasoning Based on Variational Reasoning
Authors: Tang Hongmei , Tang Wenzhong , Li Ruichen , Wang Yanyang , Wang Shuai , Wang Lihong
Published: June 2022

Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning divergent autoencoder (DIVA) model. The network structures and calculation processes of the models are analyzed. The GraphSAGE algorithm is introduced into the path reasoning module to solve the inability of the original model to perceive the features of the graph structure, which leads to a decline in the accuracy rate. Hence, GraphDIVA can achieve a higher accuracy rate with fewer learning iterations. The experiments show the efficiency and effectiveness of our model and proves that our method has a better effect on the accuracy rate and training difficulty than the baseline model on the FB15k-237 and NELL-995 benchmark datasets.
Selected Applications of Terahertz Pulses in Medicine and Industry
Authors: Taday Philip F. , Pepper Michael , Arnone Donald D.
Published: June 2022

This article contains a brief summary of areas where terahertz technology is making an impact in research and industrial applications. We cover some of its uses in the pharmaceutical setting, where both imaging and spectroscopy play important roles. Medical applications are also being pursued in many research laboratories, primarily for imaging purposes and following on from the first results just over 20 years ago. The three-dimensional imaging capability of pulsed terahertz allows for the observation of tumours below the surface of tissue, such as basal cell carcinoma of skin. The recent use of the technology in studies of cultural heritage has shown to increase our understanding of the past. The power of terahertz is exemplified by the discussion on its importance in different industries, such as semiconductor circuit manufacturing and automotive assembly.
Pollutants Concentration during the Construction and Operation Stages of a Long Tunnel: A Case Study of Lowari Tunnel, (Dir–Chitral), Khyber Pakhtunkhwa, Pakistan
Authors: Khan Jehanzeb , Ahmed Waqas , Yasir Muhammad , Islam Ihtisham , Janjuhah Hammad Tariq , Kontakiotis George
Published: June 2022

Long tunnels with significant overburden, changeable geological conditions, a steep gradient, water infiltration, and heavy traffic flow are susceptible to environmental concerns during both construction and operation. The availability of fresh air and visibility is the most important necessity in excavation for tunnel workers inside the tunnel during the construction phase, as well as those crossing the tunnel during operation. Lowari Tunnel’s tunnel air pollutants were researched. Carbon monoxide (CO), carbon dioxide (CO2), oxygen (O2), hydrogen sulfide (H2S), sulfur dioxide (SO2), nitrogen oxide (NO), ammonia (NH3), nitrogen dioxide (NO2), PM1, PM2.5, PM10, air velocity, dust morphological and particle size distribution analysis are among the parameters under consideration. The findings provide evidence for the development of tunnel air quality.
A Machine Learning Approach to Predict the Probability of Brain Metastasis in Renal Cell Carcinoma Patients
Authors: Kim Hyung Min , Jeong Chang Wook , Kwak Cheol , Song Cheryn , Kang Minyong , Seo Seong Il , Kim Jung Kwon , Lee Hakmin , Chung Jinsoo , Hwang Eu Chang , Park Jae Young , Choi In Young , Hong Sung-Hoo
Published: June 2022

Patients with brain metastasis (BM) have a better prognosis when it is detected early. However, current guidelines recommend brain imaging only when there are central nervous system symptoms or abnormal experimental values. Therefore, metastases are discovered later in asymptomatic patients. As a result, there is a need for an algorithm that predicts the possibility of BM using clinical data and machine learning (ML). Data from 3153 patients with renal cell carcinoma (RCC) were collected from the 11-institution Korean Renal Cancer Study group (KRoCS) database. To predict BM, clinical information of 1282 patients was extracted from the database and used to compare the performance of six ML algorithms. The final model selection was based on the area under the receiver operating characteristic (AUROC) curve. After optimizing the hyperparameters for each model, the adaptive boosting (AdaBoost) model outperformed the others, with an AUROC of 0.716. We developed an algorithm to predict the probability of BM in patients with RCC. Using the developed predictive model, it is possible to avoid detection delays by performing computed tomography scans on potentially asymptomatic patients.
Identification of Key Components of CNC Lathe Based on Dynamic Influence of Fault Propagation
Authors: Luan Lan , Shen Guixiang , Zhang Yingzhi , Guo Guiming
Published: June 2022

Identifying the key components of CNC lathe and analyzing the fault propagation behavior is a powerful guarantee for the fault diagnosis and health maintenance of CNC lathe. The traditional key component identification studies are mostly based on the feature parameter evaluation of the fault propagation model, disregarding the dynamics and influence of fault propagation. Therefore, this paper proposes a key component identification method based on the dynamic influence of fault propagation. Based on the CNC lathe architecture and fault data, the cascaded faults are analyzed. The improved Floyd algorithm is used to iterate and transform the direct correlation matrix expressing the cascaded fault information, and the fault propagation structure model of each component is constructed. The coupling degree function is introduced to calculate the dynamic impact degree between components, and the dynamic fault propagation rate of each component is calculated with the dynamic fault rate model. Based on this, the dynamic influence value of fault propagation is obtained by using the improved ASP algorithm. The key components of the system are identified by synthesizing the fault propagation structure model and the dynamic influence value of fault propagation. Taking a certain type of CNC lathe as an example, the proposed method is verified to be scientific and effective by comparing with the traditional identification method of key components based on fault propagation intensity.
Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
Authors: Grundler Alexander , Dazer Martin , Herzig Thomas
Published: June 2022

Statistical power analyses are used in the design of experiments to determine the required number of specimens, and thus the expenditure, of a test. Commonly, when analyzing and planning life tests of technical products, only the confidence level is taken into account for assessing uncertainty. However, due to the sampling error, the confidence interval estimation varies from test to test; therefore, the number of specimens needed to yield a successful reliability demonstration cannot be derived by this. In this paper, a procedure is presented that facilitates the integration of statistical power analysis into reliability demonstration test planning. The Probability of Test Success is introduced as a metric in order to place the statistical power in the context of life test planning of technical products. It contains the information concerning the probability that a life test is capable of demonstrating a required lifetime, reliability, and confidence. In turn, it enables the assessment and comparison of various life test types, such as success run, non-censored, and censored life tests. The main results are four calculation methods for the Probability of Test Success for various test scenarios: a general method which is capable of dealing with all possible scenarios, a calculation method mimicking the actual test procedure, and two analytic approaches for failure-free and failure-based tests which make use of the central limit theorem and asymptotic properties of several statistics, and therefore simplify the effort involved in planning life tests. The calculation methods are compared and their respective advantages and disadvantages worked out; furthermore, the scenarios in which each method is to be preferred are illustrated. The applicability of the developed procedure for planning reliability demonstration tests using the Probability of Test Success is additionally illustrated by a case study.
Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package
Authors: Shende Mayur Kishor , Salih Sinan Q. , Bokde Neeraj Dhanraj , Scholz Miklas , Oudah Atheer Y. , Yaseen Zaher Mundher
Published: June 2022

Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.
Impact of Steam Autoclaving on the Mechanical Properties of 3D-Printed Resins Used for Insertion Guides in Orthodontics and Implant Dentistry
Authors: Kirschner Anna , David Samuel , Brunello Giulia , Keilig Ludger , Drescher Dieter , Bourauel Christoph , Becker Kathrin
Published: June 2022

Guided implant placement has been shown to be more accurate than free-handed insertion. Still, implant position deviations occur and could possibly pose risks. Thus, there is a quest to identify factors that might impair the accuracy of implantation protocols using templates. This study aimed to investigate the influence of autoclaving cycles (cycle 1: 121 °C, 1 bar, 20.5 min; cycle 2: 134 °C, 2 bar, 5.5 min) on the Vickers hardness and flexural modulus of five different materials used for 3D-printed insertion guides. The specimens were subjected to Vickers hardness tests, showing significant changes in the Vickers hardness for two and three materials out of five for cycle 1 and 2, respectively. The results of the three-point bending tests (n = 15 specimens per material) showed decreasing flexural moduli after autoclaving. However, changes were significant only for one material, which presented a significant decrease in the flexural modulus after cycle 2. No significant changes were detected after cycle 1. In conclusion, our findings show that autoclaving can alter the mechanical properties of the templates to some extent, especially with cycle 2. Whether these modifications are associated with dimensional changes of the templates and reduced accuracy of the implantation protocols remains to be investigated.
Measuring Dependencies between Variables of a Dynamical System Using Fuzzy Affiliations
Authors: Wulkow Niklas
Published: June 2022

A statistical, data-driven method is presented that quantifies influences between variables of a dynamical system. The method is based on finding a suitable representation of points by fuzzy affiliations with respect to landmark points using the Scalable Probabilistic Approximation algorithm. This is followed by the construction of a linear mapping between these affiliations for different variables and forward in time. This linear mapping, or matrix, can be directly interpreted in light of unidirectional dependencies, and relevant properties of it are quantified. These quantifications, given by the sum of singular values and the average row variance of the matrix, then serve as measures for the influences between variables of the dynamics. The validity of the method is demonstrated with theoretical results and on several numerical examples, covering deterministic, stochastic, and delayed types of dynamics. Moreover, the method is applied to a non-classical example given by real-world basketball player movement, which exhibits highly random movement and comes without a physical intuition, contrary to many examples from, e.g., life sciences.
Verifying Measures of Quantum Entropy
Authors: Pastor Giancarlo , Woo Jae-Oh
Published: June 2022

This paper introduces a new measure of quantum entropy, called the effective quantum entropy (EQE). The EQE is an extension, to the quantum setting, of a recently derived classical generalized entropy. We present a thorough verification of its properties. As its predecessor, the EQE is a semi-strict quasi-concave function; it would be capable of generating many of the various measures of quantum entropy that are useful in practice. Thereafter, we construct a consistent estimator for our proposed measure and empirically test its estimation error, under different system dimensions and number of measurements. Overall, we build the grounds of the EQE, which will facilitate the analyses and verification of the next innovative quantum technologies.
Immunoinformatic Approaches to Identify Immune Epitopes and Design an Epitope-Based Subunit Vaccine against Emerging Tilapia Lake Virus (TiLV)
Authors: Islam Sk Injamamul , Mahfuj Sarower , Alam Md. Ashraful , Ara Yeasmin , Sanjida Saloa , Mou Moslema Jahan
Published: June 2022

Tilapia tilapinevirus, known worldwide as tilapia lake virus (TiLV), is a single-stranded RNA virus that belongs to the Amnoonviridae family. The virus attacks the fish species’ external and internal organs, such as the eyes, brain, and liver. Syncytial cells develop in the liver cells of infected fish, which are characterized by widespread hepatocellular necrosis and karyolytic nuclei. It is a highly infectious virus that spreads both horizontally and vertically. Despite these devastating complications, there is still no cure or vaccine for the virus. Therefore, a vaccine based on epitopes developed using immunoinformatics methods was developed against TiLV in fish. The putative polymerase basic 1 (PB1) gene was used to identify immunodominant T- and B-cell epitopes. Three probable epitopes were used to design the vaccine: CTL, HTL, and LBL. Testing of the final vaccine revealed that it was antigenic, non-allergenic, and has improved solubility. Molecular dynamics simulation revealed significant structural compactness and binding stability. Furthermore, the computer-generated immunological simulation indicated that immunization might stimulate real-life immune responses following injection. Overall, the findings of the study imply that the designed epitope vaccine might be a good option for prophylaxis for TiLV.
Black Sea Freezing and Relation to the Winter Conditions in 2006–2021
Authors: Matov Mirna , Peneva Elisaveta , Galabov Vasko
Published: June 2022

Black Sea freezing in winter is observed regularly in its northern parts and near the Kerch Strait. The reason for this is the relatively shallow northwestern shelf part and the river inflow of the three major European rivers Danube, Dnieper, and Dniester, as well as Don through the Azov Sea, carrying a large amount of fresh water to this part of the Black Sea. The global warming that has been observed in recent decades has made these episodes less intense; nevertheless, they exist and impact people who live n the area. The aim of this study is to analyze the extent of sea-ice variability in the last 15 years, observed by satellite observations, and to describe the weather conditions favorable for freezing to occur. It is found that, in 2006, 2012 and 2017, sea ice extended unusually southward, which is related to the unusually cold winter and weather conditions in these years. The weather patterns associated with the periods of maximal sea ice in the Black Sea are discussed. In addition, we analyze how the winter conditions change in the period 1926–2021 by combining different data sources. The winter is classified as cold, moderate or mild through the Winter Severity Index following a previously published methodology. The findings in our paper could help to monitor and predict these events and to inform the interested end-users.
Ultradilute Quantum Droplets in the Presence of Higher-Order Quantum Fluctuations
Authors: Boudjemaa Abdelaali , Abbas Karima , Guebli Nadia
Published: June 2022

We investigate the effects of higher-order quantum fluctuations on the bulk properties of self-bound droplets in three-, two- and one-dimensional binary Bose mixtures using the Hartree–Fock–Bogoliubov theory. We calculate higher-order corrections to the equation of state of the droplet at both zero and finite temperatures. We show that our results for the ground-state energy are in a good agreement with recent quantum Monte Carlo simulations in any dimension. Our study extends to the finite temperature case where it is found that thermal fluctuations may destabilize the droplet state and eventually destroy it. In two dimensions, we reveal that the droplet occurs at temperatures well below the Berezinskii–Kosterlitz–Thouless transition temperature.