bluuebunny/crossref_metadata_2025 huggingface dataset:
Bifidobacterium animalis MSMC83 Improves Oxidative Stress and Gut Microbiota in D-Galactose-Induced Rats
Authors: Vitheejongjaroen Porntipha , Kasorn Anongnard , Puttarat Narathip , Loison Fabien , Taweechotipatr Malai
Published: October 2022

The development of many chronic diseases is associated with an excess of free radicals leading to harmful oxidative stress. Certain probiotic strains have been shown to have antioxidant and anti-aging properties and are an important resource for development of microbial antioxidants. The present study aimed to explore the protection offered by Bifidobacterium animalis strain MSMC83 in a model of oxidative stress induced by D-galactose (D-gal). Male Sprague Dawley rats were randomly allocated to four groups: a control group injected with saline, a group injected subcutaneously with D-galactose, a probiotic group injected with D-galactose and administered B. animalis MSMC83 (109 CFU/mL) via daily oral gavage, and an ascorbic acid group. The probiotics significantly increased the superoxide dismutase, catalase, and glutathione peroxidase and significantly decreased the malondialdehyde in the plasma and livers of D-galactose-treated rats. Moreover, tumor necrosis factor-alpha level in the liver was significantly decreased. Furthermore, the treatment with B. animalis MSMC83 restored the microbiota diversity after D-galactose injection. Therefore, our results supported a beneficial role of B. animalis MSMC83 in alleviating oxidative stress through the increased expression of antioxidant enzymes and reduction of pro-inflammatory cytokines in rats. Our study suggests that B. animalis MSMC83 may be part of a healthy diet to prevent oxidative stress-associated diseases.
Vitamin K2 (MK-7) Intercepts Keap-1/Nrf-2/HO-1 Pathway and Hinders Inflammatory/Apoptotic Signaling and Liver Aging in Naturally Aging Rat
Authors: El-Sherbiny Mohamed , Atef Hoda , Helal Ghada M. , Al-Serwi Rasha Hamed , Elkattawy Hany A. , Shaker Gehan Ahmed , Said Eman , Abulfaraj Moaz , Albalawi Marzough A. , Elsherbiny Nehal M.
Published: October 2022

Aging is a naturally occurring physiological process with a deleterious impact on various body organs and humans’ well-being. The aging population is increasing worldwide, which imposes the need for the exploration of nutritional options that can intercept the impact of the aging processed on various body organs. Vitamin K2 (VK2) is a fat-soluble vitamin with emerging evidence on its therapeutic merits. In the current study, natural aging induced a significant liver deterioration with a disrupted Keap-1/Nrf-2/HO-1 axis and increased COX-2, iNOS and TNF-α expression and apoptotic and fibrotic changes. VK2 administration, on the other hand, improved the biochemical indices of liver function (total protein, albumin, ALT and AST); the suppressed hepatic expression of Keap-1 and increased the hepatic expression of Nrf-2 with a parallel increase in the hepatic activity of HO-1. Subsequently, the liver content and hepatic expression of TNF-α, COX-2 and iNOS were significantly retracted. In context, the liver content and hepatic expression of the fibrotic biomarkers TGFβ and TIMP significantly retracted as well. Moreover, the TUNEL assay confirmed the retraction of liver apoptotic changes. Of notice, electron transmission microscope examination confirmed the preservation of mitochondrial functions and preservation of the ultra-microscopical structures. In conclusion, the VK2-mediated interception of aging-induced Keap-1/Nrf-2/HO-1 signaling suppressed the hepatic contents of inflammatory and fibrotic biomarkers, as well as apoptotic changes with preservation of the hepatic architectural and functional status. VK2 can be presumed to be an effective nutritional supplement to the aging population to spare the liver, amongst other body organs, against aging-induced deleterious injury.
Microglial Hemoxygenase-1 Deletion Reduces Inflammation in the Retina of Old Mice with Tauopathy
Authors: Fernández-Albarral José A. , Salobrar-García Elena , Matamoros José A. , Fernández-Mendívil Cristina , del Sastre Eric , Chen Lejing , de Hoz Rosa , López-Cuenca Inés , Sánchez-Puebla Lidia , Ramírez José M. , Salazar Juan J. , Lopez Manuela G. , Ramírez Ana I.
Published: October 2022

Tauopathies such as Alzheimer’s disease are characterized by the accumulation of neurotoxic aggregates of tau protein. With aging and, especially, in Alzheimer’s patients, the inducible enzyme heme oxygenase 1 (HO-1) progressively increases in microglia, causing iron accumulation, neuroinflammation, and neurodegeneration. The retina is an organ that can be readily accessed and can reflect changes that occur in the brain. In this context, we evaluated how the lack of microglial HO-1, using mice that do not express HO-1 in microglia (HMO-KO), impacts retinal macro and microgliosis of aged subjects (18 months old mice) subjected to tauopathy by intrahippocampal delivery of AAV-hTauP301L (TAU). Our results show that although tauopathy, measured as anti-TAUY9 and anti-AT8 positive immunostaining, was not observed in the retina of WT-TAU or HMO-KO+TAU mice, a morphometric study of retinal microglia and macroglia showed significant retinal changes in the TAU group compared to the WT group, such as: (i) increased number of activated microglia, (ii) retraction of microglial processes, (iii) increased number of CD68+ microglia, and (iv) increased retinal area occupied by GFAP (AROA) and C3 (AROC3). This retinal inflammatory profile was reduced in HMO-KO+TAU mice. Conclusion: Reduction of microglial HO-1 could be beneficial to prevent tauopathy-induced neuroinflammation.
Treatment with Hydrogen-Rich Water Improves the Nociceptive and Anxio-Depressive-like Behaviors Associated with Chronic Inflammatory Pain in Mice
Authors: Coral-Pérez Santiago , Martínez-Martel Ignacio , Martínez-Serrat Maria , Batallé Gerard , Bai Xue , Leite-Panissi Christie , Pol Olga
Published: October 2022

Chronic inflammatory pain is manifested in many diseases. The potential use of molecular hydrogen (H2) as a new therapy for neurological disorders has been demonstrated. Recent studies prove its analgesic properties in animals with neuropathic pain, but the possible antinociceptive, antidepressant, and/or anxiolytic actions of H2 during persistent inflammatory pain have not been investigated. Therefore, using male mice with chronic inflammatory pain incited by the subplantar injection of complete Freud’s adjuvant (CFA), we assessed the actions of hydrogen-rich water (HRW) systemically administered on: (1) the nociceptive responses and affective disorders associated and (2) the oxidative (4-hydroxy-2-nonenal; 4-HNE), inflammatory (phosphorylated-NF-kB inhibitor alpha; p-IKBα), and apoptotic (Bcl-2-like protein 4; BAX) changes provoked by CFA in the paws and amygdala. The role of the antioxidant system in the analgesia induced by HRW systemically and locally administered was also determined. Our results revealed that the intraperitoneal administration of HRW, besides reducing inflammatory pain, also inhibited the depressive- and anxiolytic-like behaviors associated and the over expression of 4-HNE, p-IKBα, and BAX in paws and amygdala. The contribution of the nuclear factor erythroid 2-related factor 2/heme oxygenase 1 and NAD(P)H: quinone oxidoreductase 1 pathway in the analgesic activities of HRW, systemically or locally administered, was also shown. These data revealed the analgesic, antidepressant, and anxiolytic actions of HRW. The protective, anti-inflammatory, and antioxidant qualities of this treatment during inflammatory pain were also demonstrated. Therefore, this study proposes the usage of HRW as a potential therapy for chronic inflammatory pain and linked comorbidities.
An SQWRL-Based Method for Assessing Regulatory Compliance in the Pharmaceutical Industry
Authors: Lallas Efthymios N. , Santouridis Ilias , Mountzouris Georgios , Gerogiannis Vassilis C. , Karageorgos Anthony
Published: October 2022

Nowadays, data integrity has become a critical issue in the pharmaceutical regulatory landscape, one that requires data to be compliant to ALCOA principles (i.e., data must be Attributable, Legible, Contemporaneous, Original, and Accurate). In this paper, we propose a method which exploits semantic web technologies to represent pharma manufacturing data in a unified manner and evaluate in a systematic manner their ALCOA compliance. To this purpose, in the context of a pharma manufacturing environment, a data integrity ontology (DIOnt) is proposed to be utilized as the basis for the semantic representation of pharma production data and the associated regulatory compliance management processes. We further show that semantic annotations can be used to represent the required ALCOA compliance information, and that semantic reasoning combined with SQWRL queries can be used to evaluate ALCOA compliance. The proposed approach has been implemented in a proof-of-concept prototype and validated with real world pharma manufacturing data, supporting the combined execution of SWRL rules and SQWRL queries with the aim to support the ALCOA compliance assessment and calculate non-compliance percentages per each ALCOA principle.
The Presence of Impacted Teeth (Except for Wisdom Teeth) in Orthodontic Patients in Israel
Authors: Shpack Nir , Afarsemon Kisoss Chaya , Laviv Amir
Published: October 2022

The research objectives were to determine the prevalence of impacted teeth in orthodontic patients in Israel, examining the epidemiological and genetic linkage. Methods: Data was collected from the cohort of patients treated in Tel-Aviv University between 2010–2017. Forty-five patients treated for impacted teeth were randomly selected, compared to 45 control patients, paired by age and sex. Collected data included: week of birth, type of birth, medical history, dental anomalies, treatment referee, chief complaint, treatment modalities and treatment duration. Results: The periodic prevalence of impacted teeth was 11.9%. There was a statistical tendency in the week of birth and type of birth (earlier birth week and more caesarean sections in the study group). Statistical significance was found for systemic disease, chief complaint, referee for treatment and treatment duration. Dental anomalies for the patient or first-degree relatives found odds ratio for the presence of an impacted tooth up to 6 times higher. Conclusions: There is epidemiological data related to impacted teeth, such as preterm births and caesarean sections, with the need for further research.
Correction: Cheng, T.-M.; Hou, H.-Y. Intelligent Educational Evaluation of Research Performance between Digital Library and Open Government Data. Appl. Sci. 2022, 12, 791
Authors: Cheng Tao-Ming , Hou Hsing-Yu
Published: October 2022

In the published study, there was an error regarding the affiliation for Hsing-Yu Hou [...]
Scalable Fabrication of Si-Graphene Composite as Anode for Li-ion Batteries
Authors: Lou Ding , Chen Shuyi , Langrud Strauss , Razzaq Amir Abdul , Mao Mingyang , Younes Hammad , Xing Weibing , Lin Tim , Hong Haiping
Published: October 2022

A facile and scalable method is reported to fabricate Si-graphene nanocomposite as anode material for Li-ion batteries (LIBs) with high capacity and capacity retention performance. The Si-graphene electrode showed an initial discharge capacity of 1307 mAh g−1 at a current rate of 0.1C. At the 25th cycle, the electrode retained a discharge capacity of 1270 mAh g−1, with an excellent capacity retention of 97%. At the 50th cycle, the electrode still retained high capacity retention of 89%. The improved capacity retention of Si-graphene anode compared with Si anode is attributed to the mechanical flexibility of graphene that compromises the volume expansion of Si during the lithiation/delithiation process. The electrochemical impedance measurement further confirms the enhanced electrical conductivity and the denser solid-electrolyte-interface of the Si-graphene electrode. This fabrication approach is cost-effective and easy to scale up compared to known techniques, making it a promising candidate for commercializing Si-based anode for LIBs.
Hybrid GA-SVM Approach for Postoperative Life Expectancy Prediction in Lung Cancer Patients
Authors: Nagra Arfan Ali , Mubarik Iqra , Asif Muhammad Mugees , Masood Khalid , Ghamdi Mohammed A. Al , Almotiri Sultan H.
Published: October 2022

Medical outcomes must be tracked in order to enhance quality initiatives, healthcare management, and mass education. Thoracic surgery data have been acquired for those who underwent major lung surgery for primary lung cancer, a field in which there has been little research and few reliable recommendations have been made for lung cancer patients. Early detection of lung cancer benefits therapy choices and increases the odds of a patient surviving a lung cancer infection. Using a Hybrid Genetic and Support Vector Machine (GA-SVM) methodology, this study proposes a method for identifying lung cancer patients. To estimate postoperative life expectancy, ensemble machine-learning techniques were applied. The article also presents a strategy for estimating a patient’s life expectancy following thoracic surgery after the detection of cancer. To perform the prediction, hybrid machine-learning methods were applied. In ensemble machine-learning algorithms, attribute ranking and selection are critical components of robust health outcome prediction. To enhance the efficacy of algorithms in health data analysis, we propose three attribute ranking and selection procedures. Compared to other machine-learning techniques, GA-SVM achieves an accuracy of 85% and a higher F1 score of 0.92. The proposed algorithm was compared with two recent state-of-the-art techniques and its performance level was ranked superior to those of its counterparts.
Experimental Investigation of Crystal Blocking in Drainage Pipes for Tunnels in the Karst Region
Authors: Xu Chongbang , Chen Yang , Yang Yunxuan , Li Pengfei , Wang Siqing , Li Lei
Published: October 2022

Crystal blockage of tunnel drainage pipes is one of the main causes of problems such as lining cracking and water leakage. The study of the crystal development rule is of great significance for the design of tunnel drainage systems and the long-term safety of tunnel support structures. In this paper, a series of experimental studies on the crystallization development law of drain pipes are conducted. The effects of the connection method of the drain, the diameter of the pipe, the spacing of the circular drain, and the material of the drain on the crystallization development pattern are investigated. The results show that the groundwater environment has a great influence on the crystallization development of the drain pipe. As the drain diameter and the spacing between two adjacent circular drains increased, the time for complete blockage of the drain is prolonged. The rate of crystallization on the drainage pipe can be effectively reduced by changing the material of the drainage pipe from polyamide (PA) to polypropylene (PP). The present study provides a reference for research work related to crystallization blockage in tunnel drainage pipes.
Research Advances on User Interactions in Social Media Using Data Science Approaches
Authors: García-Sánchez Felicidad , Cruz-Benito Juan
Published: October 2022

We are delighted to welcome readers to this Special Issue on “Research Advances on User Interactions in Social Media Using Data Science Approaches” [...]
A Frame Theory of Energetic Life: A Twisting Energy Solidified on the Holographic Fractal Structure
Authors: Wei Yanju , Yang Yajing , Zhang Yajie , Mu Zhiqiang , Bu Fanlu
Published: October 2022

Life, as the most mysterious and unique phenomenon on the Earth, has confused humans since time began. Why does life exist as it does and how has the diversity of life developed? We, herein, propose a new theory of energetic life, based on existing energy laws, to interpret the evolution and categorization of physical life forms, from microscopic life to macroscopic life. According to this theory, life is a process in which a mass of energy flows and diffuses in the environment. This energy takes DNA as the three-dimensional blueprint, protein as the basic material unit, and fractal network structure as the framework, so as to solidify from energy and form a semi-solid structure. DNA base pairs simultaneously have dual properties as protein pointers and spatial coordinates, and the multi-level self-similar fractal helix structure ultimately guides the formation of different levels of the fractal structure of organisms. This theory organically links the life phenomenon from microscopic to macroscopic levels, from gene, cell and organ to organism, and it provides a new perspective on life, which may inspire biologists to better reveal the mystery of life.
Hybrid Algorithms for Inbound and Outbound Truck Scheduling in Cross-Docking Systems
Authors: Wu Gen-Han , Chen Yi-Ting , Chen Ke-Hsuan
Published: October 2022

A cross-docking system is a kind of facility design for the purpose of enhancing the time efficiency of a distribution center. In this study, we attempt to consider a cross-docking system without temporary storage and obtain great system performance by addressing the scheduling problem of inbound and outbound trucks, in which the total operation time of trucks is minimized. In order to reduce computational efforts, three hybrid metaheuristic approaches based on particle swarm optimization, simulated annealing, and a variable neighborhood search are proposed. By the computational experiments, the three optimized approaches are analyzed and compared with each other. The experimental results show that all of these three approaches can obtain pretty good solutions, even in the large-scale examples. Moreover, one of these approaches—a hybrid metaheuristic with particle swarm optimization and a variable neighborhood search—can usually obtain the best solutions.
Safety Evaluation Method and Management Strategy for Aviation Flight Plans
Authors: Hao Siqi , Ye Jian , Liu Ruiwei , Zhang Yaping
Published: October 2022

Aviation resources in the post-pandemic era are still in short supply. The increasing air traffic flow aggravates flight delays and makes it difficult to ensure aviation safety. Instead of focusing on the economic benefits, this paper proposes a quantitative method for evaluating the safety of flight plans. A flight plan safety index system is constructed through airspace environment modelling and a conflict probability calculation. The proposed method provides a quantitative basis for the management and adjustment of flight plans at the strategic level. Improving the flight plan management strategy from the perspective of conflict avoidance is expected to fundamentally reduce the potential conflict and workload of controllers and pilots during flight and improve the safety level of the whole air transport system. Finally, the performance of the proposed flight plan safety evaluation method is demonstrated through an illustrative air traffic scenario.
A New Dual Image Based Reversible Data Hiding Method Using Most Significant Bits and Center Shifting Technique
Authors: Solak Serdar , Tezcan Gökhan
Published: October 2022

In this article, a new reversible data hiding method using most significant bits and center shifting technique in dual images is proposed. The proposed reversible data hiding method aims to securely hide high-capacity secret data. Instead of directly embedding the secret data, the method calculates new values with (n + 1) bits secret data and the n most significant bits of the cover image pixel values. Thus, it is impossible to extract secret data without obtaining the original cover image. Also, the center shifting process is performed to minimize the mean square error. After this process, the secret data to be hidden in the dual images are in the range [−3×2(n−1)+1, 3×2(n−1)−1]. The pixel values of stego images are obtained by using the secret data in this range value and cover image pixel values. As a result of experimental studies, when the payload is 2.5 bits per pixel (bpp), the peak signal-to-noise ratio value (PSNR), which expresses the visual quality, is above 34 (dB). In addition, the proposed method has proven secure against RS (regular and singular) analysis attacks.
SIS Epidemic Propagation on Scale-Free Hypernetwork
Authors: Wang Kaijun , Gong Yunchao , Hu Feng
Published: October 2022

The hypergraph offers a platform to study structural properties emerging from more complicated and higher-order than pairwise interactions among constituents and dynamical behavior, such as the spread of information or disease. Considering the higher-order interaction between multiple nodes in the system, the mathematical model of infectious diseases spreading on simple scale-free networks is extended to hypernetworks based on hypergraphs. A SIS propagation model based on reaction process strategy in a universal scale-free hypernetwork is constructed, and the theoretical and simulation analysis of the model is carried out. Using mean field theory, the analytical expressions between infection density and hypernetwork structure parameters as well as propagation parameters in steady state are given. Through individual-based simulation, the theoretical results are verified and the infectious disease spread process under the structure of the hypernetwork and simple scale-free network is compared and analyzed. It becomes apparent that infectious diseases are easier to spread on the hypernetworks, showing the clear clustering characteristics of epidemic spread. Furthermore, the influence of the hypernetwork structure and model parameters on the propagation process is studied. The results of this paper are helpful in further studying the propagation dynamics on the hypernetworks. At the same time, it provides a certain theoretical basis for the current COVID-19 prevention and control in China and the prevention of infectious diseases in the future.
Treatment of Palatally Displaced Canines Using Miniscrews for Direct or Indirect Anchorage: A Three-Dimensional Prospective Cohort Study on Tooth Movement Speed
Authors: Migliorati Marco , Drago Sara , Bocchino Tecla , Michelotti Ambra , D’Antò Vincenzo
Published: October 2022

Palatally impacted canines are usually challenging to treat in terms of both biomechanics and possible side effects. Different therapeutic approaches have been reported with or without the use of temporary anchorage devices, including the canine-first approach. Two groups of patients with palatally impacted canines were compared, observing their canine movement measured on consecutive CBCTs after three months of treatment. In the control group, impacted canines were treated with direct anchorage on miniscrews, and in the test group with indirect anchorage using a miniscrew-supported transpalatal arch. The primary outcome was the canine movement speed; the secondary outcome was the change in the root length of adjacent teeth. The median monthly apex speed was 1.05 mm in the control group (IR [0.74, 1.64]) and 0.72 mm in the test group (IR [0.27, 1.30]). The median monthly cusp displacement was 1.89 mm in the control group (IR [1.04, 2.84]) and 1.08 mm in the test group (IR [0.81, 1.91]). Approximately 50% of teeth adjacent to an impacted canine underwent a negative root length change of less than 1 mm in the majority of cases, but no significant differences were found in root length changes between groups. No statistically significant differences in the reported speeds were found, and no miniscrew failures were observed in either group.
Adapting Neural-Based Models for Position Error Compensation in Robotic Catheter Systems
Authors: Akinyemi Toluwanimi O. , Omisore Olatunji M. , Chen Xingyu , Duan Wenke , Du Wenjing , Yi Guanlin , Wang Lei
Published: October 2022

Robotic catheter systems with master–slave designs are employed for teleoperated navigation of flexible endovascular tools for treating calcified lesions. Despite improved tool manipulation techniques, patient safety and lowering operative risks remain top priorities. Therefore, minimizing undesirable drifts and imprecise navigation of flexible tools during intravascular catheterization is essential. In the current master–slave designs, finite displacement lag between position command and actual navigation action at the slave device affects smooth catheterization. In this study, we designed and developed a compact 2-DOF robotic catheter system and characterized the influence of displacement step values, velocity, and motion gap on the position error at the slave device. For uniform and varying motion commands from the master platform, the results indicate that the overall position error increases with the distance traveled and the displacement step values, respectively. Hence, we proposed using recurrent neural networks—long short-term memory and gated recurrent unit controllers to predict the slave robot’s position and appropriate compensation value per translation step. An analysis of in-silico studies with CoppeliaSim showed that the neural-based controllers can ensure uniform motion mapping between the master–slave devices. Furthermore, we implemented the models within the RCS for a catheterization length of 120 mm. The result demonstrates that the controllers suitably aid the slave robot’s stepwise displacement. Thus, the neural-based controllers help match the translational motion and precise tool navigation by the slave robotic device. Therefore, the neural-based controllers could contribute to alleviating patients’ safety concerns during robotic interventions.
A Theoretical and Experimental Investigation of High-Frequency Ultrasonic Vibration-Assisted Sculpturing of Optical Microstructures
Authors: Zhang Canbin , Cheung Chi-Fai , Liang Xiaoliang , Bulla Benjamin
Published: October 2022

Ultrasonic vibration-assisted cutting (UVAC) has been regarded as a promising technology to machine difficult-to-machine materials. It allows for a sub-micrometer form accuracy and surface roughness in the nanometer range. In this paper, high-frequency vibration-assisted sculpturing is used to efficiently fabricate quadrilateral microlens array with sharp edges, instead of using slow-slide-servo diamond turning with vibration. The machining principle of diamond sculpturing, the cutting dynamics of ultrasonic vibration, and the tool edge on the theoretical form error between the designed structure and the machined structure were investigated for this technique. Then, the quadrilateral microlens array was machined by means of conventional sculpturing (CS) and high-frequency ultrasonic vibration-assisted sculpturing (HFUVAS), respectively, followed by a study of the cutting performances including form accuracy, the surface morphology of the machined structure, and the tool wear. Results showed that conventional sculpturing fabricated microlens array with poor form accuracy and surface finish due to couple effect of material adhesion and tool wear, while the high-frequency ultrasonic vibration-assisted sculpturing achieved optical application level with sub-micrometer form accuracy and surface roughness of nanometer due to reduction of material adhesion and tool wear resulted from high-frequency intermittent cutting.
Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation
Authors: Dai Yanyan , Park Subin , Lee Kidong
Published: October 2022

To prevent or deal with chronic diseases, using a smart device, automatically classifying food categories, estimating food volume and nutrients, and recording dietary intake are considered challenges. In this work, a novel real-time vision-based method for solid-volume food instance segmentation and calorie estimation is utilized, based on Mask R-CNN. In order to address the proposed method in real life, distinguishing it from other methods which use 3D LiDARs or RGB-D cameras, this work applies RGB images to train the model and uses a simple monocular camera to test the result. Gimbap is selected as an example of solid-volume food to show the utilization of the proposed method. Firstly, in order to improve detection accuracy, the proposed labeling approach for the Gimbap image datasets is introduced, based on the posture of Gimbap in plates. Secondly, an optimized model to detect Gimbap is created by fine-tuning Mask R-CNN architecture. After training, the model reaches AP (0.5 IoU) of 88.13% for Gimbap1 and AP (0.5 IoU) of 82.72% for Gimbap2. mAP (0.5 IoU) of 85.43% is achieved. Thirdly, a novel calorie estimation approach is proposed, combining the calibration result and the Gimbap instance segmentation result. In the fourth section, it is also shown how to extend the calorie estimation approach to be used in any solid-volume food, such as pizza, cake, burger, fried shrimp, oranges, and donuts. Compared with other food calorie estimation methods based on Faster R-CNN, the proposed method uses mask information and considers unseen food. Therefore, the method in this paper outperforms the accuracy of food segmentation and calorie estimation. The effectiveness of the proposed approaches is proven.
An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks
Authors: Wang Juan , Sun Lijuan , Zhou Jian , Han Chong
Published: October 2022

Low Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite–ground links result in the challenge to design an efficient channel allocation algorithm for the LEO satellite network. Edge computing (EC) provides sufficient computing power for LEO satellite networks and makes the application of reinforcement learning possible. In this paper, an adaptive dynamic channel allocation algorithm based on a temporal–spatial correlation analysis for LEO satellite networks is proposed. First, according to the user mobility model, the temporal–spatial correlation of handoff calls is analyzed. Second, the dynamic channel allocation process in the LEO satellite network is formally described as a Markov decision process. Third, according to the temporal–spatial correlation, a policy for different call events is designed and online reinforcement learning is used to solve the channel allocation problem. Finally, the simulation results under different traffic distributions and different traffic intensities show that the proposed algorithm can greatly reduce the rejection probability of the handoff call and then improve the total performance of the LEO satellite network.
TMCrack-Net: A U-Shaped Network with a Feature Pyramid and Transformer for Mural Crack Segmentation
Authors: Wu Meng , Jia Min , Wang Jia
Published: October 2022

The detection of crack information is very important in mural conservation. In practice, the number of ancient murals is scarce, and the difficulty of collecting digital information about murals leads to minimal data being collected. Crack information appears in pictures of paintings, which resembles painting traces and is easy to misidentify. However, the current mainstream semantic segmentation networks directly use the features of the backbone network for prediction, which do not fully use the features at different scales and ignore the differences between the decoder and encoder features. This paper proposes a new U-shaped convolutional neural network with feature pyramids and a transformer called TMCrack-net. Instead of U-Net’s jump-join, an AG-BiFPN network is used, which consists of two modules: a channel cross-fusion (CCT) module with a transformer and a bidirectional feature pyramid network. While fully using the information in different network dimensions, the channel cross-fusion module optimizes the final features of each layer to reduce the confounding effect caused by the fusion of features. For the fusion of multi-scale channel information with decoder features, we designed a fusion module based on a channel attention (called FCA) to guide the fusion of enhanced encoder features with decoder features and reduce the ambiguity between the two feature sets. To demonstrate the effectiveness and generalization of the model, TMCrack-Net was evaluated on the Tang Dynasty tomb chamber mural dataset and Crack500. MIou values of 0.7731 and 0.7944 were achieved, respectively, which are better than those of other advanced crack detection methods. The method yields accurate segmentation performance and is advantageous for mural painting crack segmentation tasks.
Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses
Authors: Rodríguez-Mazahua Nidia , Rodríguez-Mazahua Lisbeth , López-Chau Asdrúbal , Alor-Hernández Giner , Machorro-Cano Isaac
Published: October 2022

Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method.
Walking and Sitting Time after a Stroke: A Comparison of Shifts and Changes over Time within an Acute Care Setting
Authors: Yaron Katz Tammuz , Hallevi Hen , Molad Jeremy , Kafri Michal
Published: October 2022

Early activity post-stroke reduces secondary complications and improves rehabilitation outcomes. This study aimed to describe the physical activities of stroke patients in an acute hospital setting, compare activity patterns between working shifts, and assess associations between activity and clinical status. Twenty-one patients (mean age 69.4 ± 33.4 years,13 men) admitted due to acute ischemic stroke wore activity monitors for two weeks or until discharge. During the morning and evening shifts, the activity monitor collected daily data on walking and body position. The study discovered that patients’ overall activity levels were low and that activity was higher during morning shifts than evening shifts (sitting time: 185.31 ± 109.31 min and 91.8 ± 98.46 min, p = 0.002; number of steps: 58.3 ± 32.73 and 30.4 ± 17.6 steps, p < 0.001). Upright and sitting time increased in morning shifts (p = 0.002), while the number of steps increased in both morning and evening shifts (p = 0.002). In the evening shift, there was a fair (r = 0.28, p = 0.02) positive correlation between grip strength and the number of steps, such that patients with higher grip strength took more steps. In addition, there were poor (r = −0.2, p = 0.02) correlations between motor function (Trunk Control Test and Functional Ambulation Category) and time in an upright position, such that patients with lower functional ability sat longer. Clinical characteristics and level of activity did not show any other correlations. To conclude, the main out-of-bed activity of patients was sitting during morning shifts. The findings highlight the temporal differences in activity throughout the day, as well as the disconnect between clinical characteristics and activity levels.
BEERL: Both Ends Explanations for Reinforcement Learning
Authors: Terra Ahmad , Inam Rafia , Fersman Elena
Published: October 2022

Deep Reinforcement Learning (RL) is a black-box method and is hard to understand because the agent employs a neural network (NN). To explain the behavior and decisions made by the agent, different eXplainable RL (XRL) methods are developed; for example, feature importance methods are applied to analyze the contribution of the input side of the model, and reward decomposition methods are applied to explain the components of the output end of the RL model. In this study, we present a novel method to connect explanations from both input and output ends of a black-box model, which results in fine-grained explanations. Our method exposes the reward prioritization to the user, which in turn generates two different levels of explanation and allows RL agent reconfigurations when unwanted behaviors are observed. The method further summarizes the detailed explanations into a focus value that takes into account all reward components and quantifies the fulfillment of the explanation of desired properties. We evaluated our method by applying it to a remote electrical telecom-antenna-tilt use case and two openAI gym environments: lunar lander and cartpole. The results demonstrated fine-grained explanations by detailing input features’ contributions to certain rewards and revealed biases of the reward components, which are then addressed by adjusting the reward’s weights.
Optimized Routing by Combining Grey Wolf and Dragonfly Optimization for Energy Efficiency in Wireless Sensor Networks
Authors: Younus Hiba Apdalani , Koçak Cemal
Published: October 2022

The rapid development of technology has resulted in numerous sensors and devices for performing measurements in an environment. Depending on the scale and application, the coverage and size of a wireless sensor network (WSN) is decided. During the implementation, the energy consumption and life of the nodes in the WSN are affected by the continuous usage. Hence, in this study, we aimed to improve the lifespan of the WSN and reduce energy consumption by the nodes during the data transfer using a hybrid approach. The hybrid approach combines Grey Wolf Optimization (GWO) and Dragonfly Optimization (DFO) for exploring a global solution and optimizing the local solution to find the optimum route for the data transfer between the target node and the control center. The results show that the proposed approach has effective energy consumption corresponding to the load applied. Our proposed system scored high in the average residual energy by the number of rounds compared to other methods such as k-means, LEACH-C, CHIRON, and Optimal-CBR. The first dead node was found after 500 rounds, showing that the proposed model has nodes with better reliability. It also showed a comparative analysis of the transmission rate of a packet concerning mobility speed among various methods. The proposed method has the highest ratio at all mobility speeds, i.e., 99.3, 99.1, 99, 98.8, and 98.6, and our proposed system has the lowest computational time of all the evaluated methods, 6 s.
Block-Wisely Supervised Network Pruning with Knowledge Distillation and Markov Chain Monte Carlo
Authors: Liu Huidong , Du Fang , Song Lijuan , Yu Zhenhua
Published: October 2022

Structural network pruning is an effective way to reduce network size for deploying deep networks to resource-constrained devices. Existing methods mainly employ knowledge distillation from the last layer of network to guide pruning of the whole network, and informative features from intermediate layers are not yet fully exploited to improve pruning efficiency and accuracy. In this paper, we propose a block-wisely supervised network pruning (BNP) approach to find the optimal subnet from a baseline network based on knowledge distillation and Markov Chain Monte Carlo. To achieve this, the baseline network is divided into small blocks, and block shrinkage can be independently applied to each block under a same manner. Specifically, block-wise representations of the baseline network are exploited to supervise subnet search by encouraging each block of student network to imitate the behavior of the corresponding baseline block. A score metric measuring block accuracy and efficiency is assigned to each block, and block search is conducted under a Markov Chain Monte Carlo scheme to sample blocks from the posterior. Knowledge distillation enables effective feature representations of the student network, and Markov Chain Monte Carlo provides a sampling scheme to find the optimal solution. Extensive evaluations on multiple network architectures and datasets show BNP outperforms the state of the art. For instance, with 0.16% accuracy improvement on the CIFAR-10 dataset, it yields a more compact subnet of ResNet-110 than other methods by reducing 61.24% FLOPs.
Resistance Coefficient Estimation for a Submarine’s Bare Hull Moving in Forward and Transverse Directions
Authors: Saghi Hassan , Parunov Joško , Mikulić Antonio
Published: October 2022

Resistance of the bare hull of the tourist submarine with spherical heads, moving in forward and transverse directions is analyzed in OpenFOAM using Computational Fluid Dynamics. The resistance coefficients of the submarine are estimated for different length-to-diameter ratios and Reynolds numbers. The Artificial Neural Network with the optimum number of neurons is then trained to predict the resistance coefficients. Two simplified Artificial Neural Network models and Nonlinear Least Squares Marquardt-Levenberg algorithm are employed to fit the results in the form of equations that may be used in the initial design of this type of submarines. The comparative analysis of different prediction models is performed and guidelines for their practical application are given.
Effect of Border Width and Micro-Sprinkling Hose Irrigation on Soil Moisture Distribution and Irrigation Quality for Wheat Crops
Authors: Wang Shengfeng , Ji Pengwei , Qiu Xinqiang , Yang Haochen , Wang Yanping , Zhu Hengkang , Wang Min , Li Hongdong
Published: October 2022

Micro-sprinkling irrigation is a small-flow irrigation technology that uses the grouped outlets on the micro-sprinkling hoses to spray the pressure water evenly in the field. Plants’ barriers during the middle to late growth period of winter wheat significantly reduce the irrigation quality of the micro-spray system. It is still unclear whether soil border width in wheat fields can alleviate the negative effect. In this study, a popularly-used variety (c.v. ZM 369) was adopted to test the mitigation effect of soil borders on irrigation quality, as well as soil moisture distribution, in wheat fields. Two irrigation quotas (i.e., 75 mm and 45 mm per time) and three border widths (i.e., 2.3 m, 3.3 m, and 5.3 m) were arranged in a randomized block design in the experimental years of 2020–2022. Soil moisture distribution and irrigation quality during the middle to late growth period of winter wheat (i.e., jointing to heading stage and grain filling stage) were investigated, as well as the effects on grain yield and water use efficiency (WUE). The results showed that irrigation water distribution in the direction perpendicular to micro-spray tapes generally decreased with the distance from tapes increasing. The maximum difference between the irrigation amount and water collected under the canopy was 134 mm. The uniformity coefficient of soil moisture distribution was increased by 25.8% with a 5.3 m border width compared to a 2.3 m width. Although an irrigation quota of 75 mm was beneficial for ensuring better irrigation uniformity and more stable grain yield, grain yield and WUE were produced with an irrigation quota of 45 mm. In conclusion, it is appropriate to increase border width and adopt a small quota for the micro-spray system in the North China Plain for wheat crops.
Hybrid Task Allocation of an AGV System for Task Groups of an Assembly Line
Authors: Hu Ya , Wu Xing , Zhai Jingjing , Lou Peihuang , Qian Xiaoming , Xiao Haining
Published: October 2022

An AGV system can be used to transport different-size materials in an assembly line. The hybrid task allocation problem is involved in the assembly line, where both single-AGV tasks and multi-AGV tasks exist. However, there is little research on this problem. The goal of solving this problem is to obtain a task allocation scheme with minimum idle time and maximum system throughput. Since all necessary materials must be delivered to the assembly station before the operation can start, the delivery tasks are not independent of each other in a task group serving the operation. To solve the problem above, a hybrid task allocation method based on a task binding strategy and an improved particle swarm optimization (IPSO) is proposed. Firstly, a mathematical model considering the punctuality of material delivery and the cooperative relationship between tasks is established. Secondly, a task binding strategy and four heuristic rules are devised to improve the quality of randomly- and heuristic-generated individuals in the initial population for model optimization. Thirdly, an IPSO is developed to help the optimization algorithm jump out of local optimums. Finally, a simulation is performed to verify the effectiveness of the proposed methods. The simulation results show that a better scheme can be obtained by our hybrid task allocation method, compared to conventional Genetic Algorithms and PSO algorithms.
Machine Learning-Assisted Prediction of Oil Production and CO2 Storage Effect in CO2-Water-Alternating-Gas Injection (CO2-WAG)
Authors: Li Hangyu , Gong Changping , Liu Shuyang , Xu Jianchun , Imani Gloire
Published: October 2022

In recent years, CO2 flooding has emerged as an efficient method for improving oil recovery. It also has the advantage of storing CO2 underground. As one of the promising types of CO2 enhanced oil recovery (CO2-EOR), CO2 water-alternating-gas injection (CO2-WAG) can suppress CO2 fingering and early breakthrough problems that occur during oil recovery by CO2 flooding. However, the evaluation of CO2-WAG is strongly dependent on the injection parameters, which in turn renders numerical simulations computationally expensive. So, in this work, machine learning is used to help predict how well CO2-WAG will work when different injection parameters are used. A total of 216 models were built by using CMG numerical simulation software to represent CO2-WAG development scenarios of various injection parameters where 70% of them were used as training sets and 30% as testing sets. A random forest regression algorithm was used to predict CO2-WAG performance in terms of oil production, CO2 storage amount, and CO2 storage efficiency. The CO2-WAG period, CO2 injection rate, and water–gas ratio were chosen as the three main characteristics of injection parameters. The prediction results showed that the predicted value of the test set was very close to the true value. The average absolute prediction deviations of cumulative oil production, CO2 storage amount, and CO2 storage efficiency were 1.10%, 3.04%, and 2.24%, respectively. Furthermore, it only takes about 10 s to predict the results of all 216 scenarios by using machine learning methods, while the CMG simulation method spends about 108 min. It demonstrated that the proposed machine-learning method can rapidly predict CO2-WAG performance with high accuracy and high computational efficiency under conditions of various injection parameters. This work gives more insights into the optimization of the injection parameters for CO2-EOR.
Special Issue on Advanced Applications of Bioencapsulation Technologies
Authors: Quarta Alessandra , Di Corato Riccardo
Published: October 2022

Bioencapsulation involves the envelopment of bioactive compounds or cells to host and protect them from chemical/physical degradation and biological attack from hazardous species or undesired immune responses [...]