bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Skin Carotenoid Levels in Young Students through Brief Dietary Education Using the Veggie Meter
Authors: Obana Akira , Asaoka Ryo , Miura Ayako , Nozue Miho , Takayanagi Yuji , Nakamura Mieko
Published: August 2022

The antioxidant and anti-inflammatory effects of carotenoid have been determined to aid in the prevention of a wide range of oxidative disorders, arteriosclerosis, obesity, and various types of cancers. In order to keep high carotenoid levels in the body, much of the vegetable and fruit (V/F) intake is mandatory. However, the actual intake of V/F is not enough in many countries. The aim of this study was to assess whether brief dietary education using the Veggie Meter (VM) that could measure skin carotenoid (SC) levels could induce the increase in carotenoid levels via V/F intake. Two hundred and sixty-one elementary and junior high school students (ages 7–14 years old) received brief educational session and SC evaluation by VM, and the changes in SC levels were examined after 6 months. The baseline VM scores ranged from 131 to 825, and the average significantly increased from 400.0 ± 124.7 (standard deviation) to 447.4 ± 140.4 at Month 6 (p < 0.0001). The percentage of increase at month 6 was negatively correlated with the baseline values (r = −0.36, p < 0.0001). This finding implies that subjects who became aware of their inferiority tended to make a significant effort to change their behavior. The multivariate logistic regression analysis demonstrated that subjects taking much of green and yellow vegetables, drinking vegetable/tomato juice, and eating any fruit had higher VM scores than the average value. In conclusion, the educational approach using VM was supposed to be an effective method of raising awareness of the V/F shortage and increasing V/F intake that could indue the increase in SC levels.
Selenium-Enriched Spirulina (SeE-SP) Enhance Antioxidant Response, Immunity, and Disease Resistance in Juvenile Asian Seabass, Lates calcarifer
Authors: Siddik Muhammad A. B. , Vatsos Ioannis N. , Rahman Md. Arifur , Pham Hung Duc
Published: August 2022

The present study examined the efficacy of dietary selenium-enriched spirulina (SeE-SP) on growth performance, antioxidant response, liver and intestinal health, immunity and disease resistance of Asian seabass, Lates calcarifer. A total of 480 seabass juveniles with an initial weight of 9.22 ± 0.09 g/fish were randomly assigned to four dietary groups. The fish were fed a fishmeal protein replacement diets with SeE-SP at 5%, 10%, and 20%, namely SeE-SP5, SeE-SP10, and SeE-SP20, and a fishmeal-based diet as control for 8 weeks. The results indicated that seabass juveniles fed SeE-SP5 and SeE-SP10 diets grew at the same rate as the fish fed a fishmeal-based control diet after 8 weeks of feeding, while SeE-SP20 grew at a significantly lower rate than the control (p < 0.05). Although most of the measured biochemical parameters were not influenced by the Se-SP diets, serum antioxidant-enzyme glutathione peroxidase (GPx) and immunological indices, such as lysozyme activity and immunoglobulin-M, were found significantly higher in the SeE-SP5 and SeE-SP10 diets compared to control. In addition, the fish fed the SeE-SP5 diet showed significantly lower mortalities after the 14-day of bacterial challenge with V. harveyi. These outcomes indicated that up to 10% inclusion of SeE-SP in the diet of juvenile Asian seabass does not compromise growth, while SeE-SP5 enhanced disease resistance in juvenile seabass.
The Effect of Different Types of Feedback on Learning of Aerobic Gymnastics Elements
Authors: Lamošová Anita , Kyselovičová Oľga
Published: August 2022

The aim of our study was to discover the effect of different types of feedback in teaching/learning of difficulty elements of aerobic gymnastics. The study was designed as a one-group comparative interrupted time study. For this purpose, eight gymnasts competing in the national development category were selected (average age 9 ± 1.5 years; average sport age 4 ± 1.5 years). The design of the study included two intervention programs; each lasting 23 days interrupted by an 8-week break. In intervention program 1, the group practiced a straddle jump using self-modeling followed by verbal feedback, and in intervention program 2 the group practiced a split jump using expert modeling followed by verbal feedback. The experimental group practiced three times a week for a period of 30 min per usual training session (normally lasting 90 min). The results showed that the execution of the elements in both intervention programs improved at the 5% level of significance. In intervention program 1, we noted a significant improvement (p ≤ 0.05) in subphase approach and culmination. In intervention program 2, we noted a significant improvement (p ≤ 0.05) in culmination only. There was no significant difference between the changes of the final scores of the executed elements in intervention programs 1 and 2. Comparing the results of individual subphases, we noted no significant difference either.
A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4
Authors: Thwe Yamin , Jongsawat Nipat , Tungkasthan Anucha
Published: August 2022

Over the past few decades, research on object detection has developed rapidly, one of which can be seen in the fashion industry. Fast and accurate detection of an E-commerce fashion product is crucial to choosing the appropriate category. Nowadays, both new and second-hand clothing is provided by E-commerce sites for purchase. Therefore, when categorizing fashion clothing, it is essential to categorize it precisely, regardless of the cluttered background. We present recently acquired tiny product images with various resolutions, sizes, and positions datasets from the Shopee E-commerce (Thailand) website. This paper also proposes the Fashion Category—You Only Look Once version 4 model called FC-YOLOv4 for detecting multiclass fashion products. We used the semi-supervised learning approach to reduce image labeling time, and the number of resulting images is then increased through image augmentation. This approach results in reasonable Average Precision (AP), Mean Average Precision (mAP), True or False Positive (TP/FP), Recall, Intersection over Union (IoU), and reliable object detection. According to experimental findings, our model increases the mAP by 0.07 percent and 40.2 percent increment compared to the original YOLOv4 and YOLOv3. Experimental findings from our FC-YOLOv4 model demonstrate that it can effectively provide accurate fashion category detection for properly captured and clutter images compared to the YOLOv4 and YOLOv3 models.
Implementing Circular Economy Techniques for the Optimal Management of Recyclable Solid Waste Using the M-GRCT Decision Support Model
Authors: Vargas-Terranova Camilo-A. , Rodrigo-Ilarri Javier , Rodrigo-Clavero María-Elena , Parra-Saad Alejandro
Published: August 2022

This article analyzes the implementation of a circular economy model for the management of reusable solid waste in the Colombian municipalities of Arbeláez (province of Cundinamarca) and Tibasosa (province of Boyacá). The analysis is conducted using M-GRCT, a circular economy decision support model for the design of recyclable waste management systems in low-income municipalities. The model allows for performing calculations on a set of two scenarios integrating a sociocultural dynamics assessment—this being a characteristic feature of this type of municipalities. Results show that both the linear and circular models of waste management are economically viable. However, the particular conditions of each municipality, the tariff system, the number of subscribers and the variations in costs and inflation in each municipality affect the results of economic viability. In addition, the waste production scale and the volumes of recoverable waste also affect the results. All these factors are reflected in the scenarios analyzed. In terms of economic viability, the circular model presents better results in Arbeláez, while in the municipality of Tibasosa, the best results are obtained with a linear economy approach.
Mechanistic Study on the Influence of Stratigraphy on the Initiation and Expansion Pattern of Hydraulic Fractures in Shale Reservoirs
Authors: Li Shuangming , Hu Zhiwen , Chang Xin
Published: August 2022

The unique depositional characteristics of shale reservoirs lead to the extreme development of shale laminae, natural fractures, and other weak surfaces, which have an important influence on the hydraulic fracture extension behavior and the final fracture geometry. Therefore, because the deep shale laminar development and hydraulic fracture interaction theory and influence mechanism are not clear, this paper focuses on the analysis of the influence law of the ground stress difference, laminar surface cementation characteristics, laminar surface dip angle, and other factors on the hydraulic fracture extension pattern in shale reservoirs based on the cohesive unit method. Numerical calculation results show that (1) the hydraulic cracks are first expanded along the direction perpendicular to the minimum horizontal principal stress after starting to crack from the injection point and then turn to expand along the side of the laminar surface when encountering the laminar surface. (2) Three laminar dips of 5°, 30°, and 60° were compared and found. A certain ground stress difference is conducive to communicating more laminated joints and forming a more complex fracture network, while too large of a ground stress difference reduces the complexity of artificial fractures. (3) The smaller the inclination of the laminate surface is, the greater the positive stress it is subjected to, the more difficult it is for the laminate joints to undergo shear slip, and the more difficult it is to open. (4) Under the same ground stress combination state and laminar surface inclination, laminated cementation strengths of 2 and 0.5 MPa were compared, and it was found that the lower the cementation strength of the laminate is, the more likely the hydraulic fracture will be activated to shear slip damage. At the same time, the reliability of the simulation study on the effect of laminar surfaces on hydraulic fracture extension based on the cohesive unit method is verified by combining with the corresponding indoor hydraulic physical model test, which provides a reference for the accurate description of the interaction mechanism between laminar surfaces and hydraulic fractures.
The Effects of Synthesizing Music Using AI for Preoperative Management of Patients’ Anxiety
Authors: Hong Yeong-Joo , Han Jaeyeon , Ryu Hyeongju
Published: August 2022

Before a patient undergoes surgery, they are likely to complain of anxiety to various degrees. To address this issue, we designed and implemented a composition program using TensorFlow Recurrent Neural Networks (RNNs) to select music for learning. The nurses’ preferences and needs were assessed using the Geneva Emotional Music Scales-9 (GEMS-9) tool and focus group interview (FGI) methods for currently used sound sources and nurses at the operating room entrance. An FGI and GEMS-9 for preference analysis were conducted by nurses who currently work in the operating room, had experience with managing the operating room’s background music, and wished to participate voluntarily in this study on 31 January 2019 in an operating room simulation center. Interviews were held with a total of three nurse. The data were analyzed using a qualitative thematic analysis. Using GEMS-9 to evaluate 16 sample sources, the average of the sad–happy values was highest at four points, with a lower tension of 1.48. Happy, Joy, and Peaceful were classified as appropriate for background music in the operating room. Additionally, the top six songs were selected as suitable songs by calculating the difference in values among Sad, Tension, Tender, Nostalgia, and Trance, which were judged to be inappropriate along with Power and Wonder. The songs selected were two jazz songs, three bossa nova songs, and two piano classical songs. The results of this study show that music used in the operating room should contain a slow tempo such as slow classical, piano, strings, natural acoustics, and new age music. Music consisting of only musical instruments (preferably containing smaller arrangements of less than five instruments) is preferred over music containing human vocals. Based on the study findings, the conditions of the sound source to be used for learning were suggested after consulting with a music expert.
The Effects of a Natural Polyphenol Extract from Sugarcane (Saccharum officinarum) on the Growth, Survival, and Feed Conversion Efficiency of Juvenile Black Tiger Shrimp (Penaeus monodon)
Authors: Penglase Sam , Ackery Thomas , Kitchen Barry , Flavel Matthew , Condon Kelly
Published: August 2022

Farmed shrimp feeds are under continuous development to maximise shrimp growth, health, and feed efficiency. There is evidence in aquaculture species that botanical ingredients may be capable of improvements in each of these traits. However, the full potential of these ingredients remains largely unexplored. We investigated the effect of dietary additions of a polyphenol-rich sugarcane extract (PRSE) at the rate of 0, 2, 4, or 6 g kg−1 of diet, on the growth, survival, feed conversion ratio, and pathogen loading of black tiger shrimp (Penaeus monodon) over a 10-week feeding period in a small-scale intensive RAS system. Shrimp fed the highest level of PRSE compared to the control were 54% heavier (15.4 vs. 10.0 g; p < 0.05), had 39% lower feed conversion ratios (1.9 vs. 3.1; p < 0.05), had a higher survival rate (50 vs. 26%), and an increased carapace length (27.5 vs. 23.3 mm; p > 0.05). These findings demonstrate that polyphenol-rich sugarcane extracts have the potential to improve shrimp growth and FCR when added into feed formulations. Further research should be conducted in commercial rearing conditions for validation.
Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
Authors: Qin Liming , Zhou Xianliang , Wu Xuefeng
Published: August 2022

Obtaining the complete wear state of the milling cutter during processing can help predict tool life and avoid the impact of tool breakage. A cylindrical model of tool collection is proposed, which uses the collected partial pictures of the side edge to construct a panoramic picture of tool wear. After evaluating the splicing accuracy, the fully convolutional neural network (FCN) segmentation algorithm of the VGG16 structure is used to segment the panorama of the side edge of the end mill after splicing. The FCN model is built using Tensorflow to complete the image segmentation training and testing of the side edge wear area. Experimental results show that the FCN model can segment the side wear image and effectively solve the illumination change problem and different tool wear differences. Compared with the Otsu threshold adaptive segmentation algorithm and K-means clustering algorithm, the error of the extracted wear value is 1.34% to 8.93%, and the average error rate is 5.23%. This method can obtain a more intuitive panorama of the cutter side edge wear of the end milling and provide technical support for improving tool utilization rate, machining quality, and tool selection and optimization.
Measurement and Analysis of Radar Signals Modulated by the Respiration Movement of Birds
Authors: Gong Jiangkun , Yan Jun , Li Deren , Hu Huiping , Kong Deyong , Bao Wenjing , Wu Shangde
Published: August 2022

Once, bird respiration was thought to be responsible for the 10 dB-level fluctuations in the radar signals of birds. Although, recently, many researchers provide evidence against this, there are almost no quantification measurements of the contribution of respiration to bird signals in microwave anechoic chambers. Here, we first measured the radar signals modulated by the respiration of birds in a microwave anechoic chamber. Theoretically, the simulated signal fluctuation caused by the respiration of a 1 kg standard avian target (SAT) duck is approximately 1.2 dB based on the water sphere model. Then, experimentally, in a microwave anechoic chamber, we measured the signal fluctuations produced by the respiration movement of ducks using a dynamic system composed of a network analyzer and a high-speed camera. We tracked continuous radar data of a living duck and a dead duck within the S-band, X-band, and Ku-band, and then presented them using low-resolution range profiles (LRRP) and high-resolution range profiles (HRRP). The results indicate that respiration movement causes periodic signal fluctuation with a respiration rate of approximately 0.7 Hz, but the amplitudes within S-band, X-band, and Ku-band are approximately 1 dB level, much less than the 10 dB level. Respiration is not responsible for the 10 dB-level periodic signal fluctuation in radar echoes from birds.
Large-Scale 3D Multi-Story Enterprise Layout Design in a New Type of Industrial Park in China
Authors: Liu Xuemin , Huang Guozhong , Gao Xuehong , Li Haoxuan , Ou Shengnan , Hezam Ibrahim M.
Published: August 2022

In the new-type clustered industrial park, the closer distance between enterprises leads to risk aggregation, and the layout of enterprises affects the safety and economy of the park. However, previous studies have often paid insufficient attention to safety, and few studies have considered park profits. To address this issue, a bi-level three-dimensional layout optimization model was proposed to minimize the overall association risk of the park and maximize the rental profit. In particular, this article explained the enterprise association risks and provided calculation formulas, considering multiple risk types. To solve the proposed nonlinear model, a specific variable conversion method was presented to reduce the problem scale. Subsequently, an improved genetic algorithm was developed and applied to obtain the layout results. Furthermore, a case study of an industrial park was conducted, and the computational results indicated the validity of the model and methods. Finally, two different scenarios were implemented, and critical parameters were tested to provide valuable management insights.
AAC-IoT: Attribute Access Control Scheme for IoT Using Lightweight Cryptography and Hyperledger Fabric Blockchain
Authors: Alshehri Suhair , Bamasag Omaimah
Published: August 2022

The Internet of Things (IoT) is an integrated environment as it merges physical smart objects to the Internet via wireless technologies to share data. The global connectivity of IoT devices brings the needs to ensure security and privacy for data owners and data users. In this paper, an attribute-based access control scheme for IoT (AAC-IoT) using Hyperledger Fabric (HLF) blockchain is proposed to address the security challenges. In the AAC-IoT scheme, data owners are registered and authenticated using identities, certificates and signatures. Data users, however, are registered with identities, certificates, signatures and physical unclonable function (PUF); then a credence score is computed for users to predict the originality during authentication. For access control, attribute-based access control (ABAC) is used, and the number of attributes is selected based on the sensitivity of the data. In accordance with the attributes count, the access control policies are generated. The novel concept of attribute count is determined from a fuzzy logic method using data type and preference. Hyperledger Fabric (HLB) blockchain is presented to manage meta-data and security credentials from data owners and data users, respectively, using a lightweight hashing algorithm. The AAC-IoT model using HLF blockchain is developed with Java programming language and iFogSim simulator. The performance metrics are measured based on latency, throughput and storage overhead, and the results show better outcome than the previous research work.
The Optimal Erection of the Inverted Pendulum
Authors: Massaro Matteo , Lovato Stefano , Limebeer David J. N.
Published: August 2022

The erection of the inverted pendulum is a classic control problem, which has appeared in several variants. One of the most challenging is the minimum-time erection of a pendulum that is mounted on a moving cart. The aim is to erect the pendulum from the ‘straight-down’ (stable equilibrium) to a ‘straight-up’ (unstable equilibrium) position in minimum time. The swing-up maneuver is usually addressed using a pre-defined control strategy, e.g., energy-based control or selecting the switching times in a bang-bang structure. The aim of this paper is to show that the minimum-time solution may have a singular arc, with the optimal control taking a bang-singular-bang form. The singular arc segment of the control law is a feedback law that is derived herein with the solution discussed. A sensitivity analysis of the solution structure is also performed by varying the model parameters. Finally, the time-optimal solution is compared with that obtained using an energy-based control strategy.
Integroly: Automatic Knowledge Graph Population from Social Big Data in the Political Marketing Domain
Authors: Guedea-Noriega Héctor Hiram , García-Sánchez Francisco
Published: August 2022

Social media sites have become platforms for conversation and channels to share experiences and opinions, promoting public discourse. In particular, their use has increased in political topics, such as citizen participation, proselytism, or political discussions. Political marketing involves collecting, monitoring, processing, and analyzing large amounts of voters’ data. However, the extraction, integration, processing, and storage of these torrents of relevant data in the political domain is a very challenging endeavor. In the recent years, the semantic technologies as ontologies and knowledge graphs (KGs) have proven effective in supporting knowledge extraction and management, providing solutions in heterogeneous data sources integration and the complexity of finding meaningful relationships. This work focuses on providing an automated solution for the population of a political marketing-related KG from Spanish texts through Natural Language Processing (NLP) techniques. The aim of the proposed framework is to gather significant data from semi-structured and unstructured digital media sources to feed a KG previously defined sustained by an ontological model in the political marketing domain. Twitter and political news sites were used to test the usefulness of the automatic KG population approach. The resulting KG was evaluated through 18 quality requirements, which ensure the optimal integration of political knowledge.
Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
Authors: Liu Zhiyuan , Ye Huihui , Liu Huafeng
Published: August 2022

High-quality and fast reconstructions are essential for the clinical application of positron emission tomography (PET) imaging. Herein, a deep-learning-based framework is proposed for PET image reconstruction directly from the sinogram domain to achieve high-quality and high-speed reconstruction at the same time. In this framework, conditional generative adversarial networks are constructed to learn a mapping from sinogram data to a reconstructed image and to generate a well-trained model. The network consists of a generator that utilizes the U-net structure and a whole-image strategy discriminator, which are alternately trained. Simulation experiments are conducted to validate the performance of the algorithm in terms of reconstruction accuracy, reconstruction efficiency, and robustness. Real patient data and Sprague Dawley rat data were used to verify the performance of the proposed method under complex conditions. The experimental results demonstrate the superior performance of the proposed method in terms of image quality, reconstruction speed, and robustness.
Digital Reconstruction of Fragmented Cultural Heritage Assets: The Case Study of the Dacian Embossed Disk from Piatra Roșie
Authors: Comes Radu , Neamțu Călin Gheorghe Dan , Grec Cătălin , Buna Zsolt Levente , Găzdac Cristian , Mateescu-Suciu Liliana
Published: August 2022

The most peculiar characteristic of a cultural heritage is represented by its uniqueness. To ensure that an object is preserved against environmental deterioration, vandal attacks, and accidents, modern Cultural Heritage documentation involves 3D scanning technologies. In the case of fragmented artefacts, the digitization process represents an essential prerequisite for facilitating an accurate 3D reconstruction. The aim of this research paper is to present a framework that enables an accurate digital reconstruction of fragmented or damaged artefacts using ornament stencils obtained from 3D scan data. The proposed framework has been applied for the richly adorned ornaments of the fragmented Dacian embossed disk from Piatra Roșie. The case study makes use of the 3D dataset acquired, using a structured light scanner to extract vector displacement maps, which are then applied to the 3D computer-aided design (CAD) model. The output of the framework includes a proposed digital reconstruction of the aurochs fragmented Dacian embossed disk, as well as the ornaments’ stencils database. The proposed framework addresses problems that are associated with 3D reconstruction processes, such as self-intersections, non-manifold geometry, 3D model topology, and file format interoperability. Finally, the resulting 3D reconstruction has been integrated within virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications, as well as computer-aided manufacturing (CAM) based on additive manufacturing to facilitate the dissemination of the results.
Comparative Evaluation of the Antioxidative and Antimicrobial Nutritive Properties and Potential Bioaccessibility of Plant Seeds and Algae Rich in Protein and Polyphenolic Compounds
Authors: Miedzianka Joanna , Lachowicz-Wiśniewska Sabina , Nemś Agnieszka , Kowalczewski Przemysław Łukasz , Kita Agnieszka
Published: August 2022

Spice plants are not only a source of nutrition compounds but also supply secondary plant metabolites, such as polyphenols. Therefore, their bioaccessibility is an important issue. In order to understand the biological activity of polyphenols present in spice plants, it is necessary to broaden knowledge about the factors influencing their bioaccessibility, including nutritional factors. Therefore, the objective of this research was to determine the antioxidative and antimicrobial nutritive properties and potential bioaccessibility of plant seeds and microalgae rich in protein and polyphenolic compounds. Plant seeds rich in protein—i.e., black cumin, milk thistle, fenugreek, almonds, white sesame, white mustard, eggfruit and the two most popular algae, chlorella and spirulina—were analyzed for total polyphenolic compounds (TPC) and antioxidant properties (ABTS, FRAP), as well as their potential bioaccessibility, antimicrobial activity, basic chemical composition and amino acid profiles. With regard to the TPC, the highest levels were found in star anise, followed by milk thistle, white mustard and fenugreek, whereas the lowest were noted in white sesame, almonds, eggfruit, spirulina and chlorella. White mustard and milk thistle showed the highest antioxidant capacities and almonds, eggfruit, spirulina, and chlorella the lowest according to the ABTS and FRAP assays. The widest spectrum of microbial growth inhibition was detected for fenugreek extract, which showed antimicrobial activity against four analyzed microorganisms: B. subtilis, P. mirabilis, V. harveyi and C. albicans. The protein from seeds of black cumin, milk thistle, white mustard and eggfruit and chlorella was not limited by any essential amino acids. Among all analyzed plants, fenugreek seeds were judged to have potential for use in food formulation operations in view of their antioxidant activity and amino acid profile. Based on the results, intake of polyphenols together with protein in plant seeds does not have a major impact on the potential bioaccessibility of a range of polyphenols and phenolic metabolites.
The Proportional Characteristics of Daytime and Nighttime Precipitation Based on Daily Precipitation in Huai River Basin, China
Authors: Zhu Ying , Liu Xiaoli , Zhang Yuqing , Chen Changchun , Shen Liucheng , Ju Qin , Zhou Ting , Xia Ping
Published: August 2022

The daytime and nighttime precipitation proportions of daily total precipitation (especially extreme daily precipitation) are important indicators that help to understand the process of precipitation formation, which in turn helps to evaluate and improve models and reanalysis precipitation data. In this study, we used the Huai River Basin (HRB) as a case to explore the daytime and nighttime precipitation proportions of daily total precipitation based on 135 meteorological stations during 1961–2018. The total, daytime, and nighttime precipitation showed zonal distributions with high and low values in the southern and northern parts of the basin, respectively. The nighttime precipitation was slightly greater than the daytime precipitation. With the increase in precipitation intensity, the seasonal cycles of the total, daytime, and nighttime precipitation were more distinct, and precipitation mainly occurred in summer. The annual range of precipitation differences between daytime and nighttime in wet seasons showed a downward trend in 1961–2003 followed by an upward trend in 2003–2018. This reversal of annual range of precipitation around 2003 may be related to the changes in annual range of convective precipitation differences between daytime and nighttime in wet seasons. The decrease of light precipitation mainly depended on the decrease of nighttime precipitation. The contributions of nighttime precipitation events to torrential precipitation events were greater than those of daytime precipitation. The days of extreme precipitation events accounted for a very low proportion of total precipitation days, but their precipitation amount accounted for relatively high proportions of total precipitation amount. Annual extreme precipitation amount showed a slightly upward trend, which was caused by the increased nighttime precipitation. Under extreme precipitation conditions, large proportions of daytime precipitation were mainly concentrated in the southeastern parts of the HRB, whereas large proportions of nighttime precipitation were mainly concentrated in the northwestern parts of the basin. The concurrent daytime and nighttime precipitation showed slightly increasing trends, especially in the southeastern part of the basin. With the increase in daytime and nighttime precipitation, the risk of concurrent precipitation extremes in the southern part of the basin increased (shorter return period means higher risk).
Chemical Characterization and Health Risk Assessment of Particulate Matter from Household Activities in Bamako, Mali, Western Sub-Saharan Africa
Authors: Sidibe Alimata , Sakamoto Yosuke , Murano Kentaro , Sato Keiichi , Yuba Akie , Futami Mari , Koita Ousmane A. , Traore Ibrahim , Kajii Yoshizumi
Published: August 2022

Household particulate matter (PM) is a major health concern, especially in developing regions, where biomass fuels are used quantitatively in households. Additionally, the combustion of incense (ICS) and insecticide (IST) is common. This study characterized the PM chemical composition to evaluate its health effects, as such information is lacking in developing regions, including Bamako, Mali. The composition of PM emitted from typical household activities, including cooking and combustion of ICS and IST, was characterized. These contained ions, organic carbon (OC), elemental carbon (EC), and metals. The results revealed that the chemical composition varied with emission source and combustion conditions. The dominant ions were Ca2+ (charcoal cooking), K+ (wood cooking) and F− (in ICS and IST). The OC/EC ratio for IST, ICS, wood, and charcoal cooking was 59, 30, 8, and 7, respectively. Moreover, US EPA (United States Environmental Protection Agency) health risk assessment models indicated a higher hazard index (≤6.04) than the recommended limit (1) for nearly all emission sources. Total-CR was higher than the lower boundary limit (10−6) in adults and children. Wood cooking activity and ICS combustion exceeded the unacceptable limit (10−4) in children. Our findings highlight the need to develop effective air pollution mitigation strategies for health safety.
Coupled and Synchronization Models of Rhythmic Arm Movement in Planar Plane
Authors: Machmudah Affiani , Dutykh Denys , Parman Setyamartana
Published: August 2022

Nonlinear dynamics have become a new perspective on model human movement variability; however, it is still a debate whether chaotic behavior is indeed possible to present during a rhythmic movement. This paper reports on the nonlinear dynamical behavior of coupled and synchronization models of a planar rhythmic arm movement. Two coupling schemes between a planar arm and an extended Duffing-Van der Pol (DVP) oscillator are investigated. Chaos tools, namely phase space, Poincare section, Lyapunov Exponent (LE), and heuristic approach are applied to observe the dynamical behavior of orbit solutions. For the synchronization, an orientation angle is modeled as a single well DVP oscillator implementing a Proportional Derivative (PD)-scheme. The extended DVP oscillator is used as a drive system, while the orientation angle of the planar arm is a response system. The results show that the coupled system exhibits very rich dynamical behavior where a variety of solutions from periodic, quasi-periodic, to chaotic orbits exist. An advanced coupling scheme is necessary to yield the route to chaos. By modeling the orientation angle as the single well DVP oscillator, which can synchronize with other dynamical systems, the synchronization can be achieved through the PD-scheme approach.
Marine Autotroph-Herbivore Synergies: Unravelling the Roles of Macroalgae in Marine Ecosystem Dynamics
Authors: Cheng Acga , Lim Wai Yin , Lim Phaik-Eem , Yang Amri Affendi , Poong Sze-Wan , Song Sze-Looi , Ilham Zul
Published: August 2022

Species invasion is a leading threat to marine ecosystems worldwide, being deemed as one of the ultimate jeopardies for biodiversity along with climate change. Tackling the emerging biodiversity threat to maintain the ecological balance of the largest biome in the world has now become a pivotal part of the Sustainable Development Goals (SDGs). Marine herbivores are often considered as biological agents that control the spread of invasive species, and their effectiveness depends largely on factors that influence their feeding preferences, including the specific attributes of their food–the autotrophs. While the marine autotroph-herbivore interactions have been substantially discussed globally, many studies have reported contradictory findings on the effects of nutritional attributes and novelty of autotrophs on herbivore feeding behaviour. In view of the scattered literature on the mechanistic basis of autotroph-herbivore interactions, we generate a comprehensive review to furnish insights into critical knowledge gaps about the synergies based largely on the characteristics of macroalgae; an important group of photosynthetic organisms in the marine biome that interact strongly with generalist herbivores. We also discuss the key defence strategies of these macroalgae against the herbivores, highlighting their unique attributes and plausible roles in keeping the marine ecosystems intact. Overall, the feeding behaviour of herbivores can be affected by the nutritional attributes, morphology, and novelty of the autotrophs. We recommend that future research should carefully consider different factors that can potentially affect the dynamics of the marine autotroph-herbivore interactions to resolve the inconsistent results of specific attributes and novelty of the organisms involved.
Diverse Roles of Ceramide in the Progression and Pathogenesis of Alzheimer’s Disease
Authors: Chowdhury Md Riad , Jin Hee Kyung , Bae Jae-sung
Published: August 2022

Alzheimer’s disease (AD) is the most common neurodegenerative disorder, and is associated with several pathophysiological features, including cellular dysfunction, failure of neurotransmission, cognitive impairment, cell death, and other clinical consequences. Advanced research on the pathogenesis of AD has elucidated a mechanistic framework and revealed many therapeutic possibilities. Among the mechanisms, sphingolipids are mentioned as distinctive mediators to be associated with the pathology of AD. Reportedly, alteration in the metabolism of sphingolipids and their metabolites result in the dysfunction of mitochondria, autophagy, amyloid beta regulation, and neuronal homeostasis, which exacerbates AD progression. Considering the importance of sphingolipids, in this review, we discuss the role of ceramide, a bioactive sphingolipid metabolite, in the progression and pathogenesis of AD. Herein, we describe the ceramide synthesis pathway and its involvement in the dysregulation of homeostasis, which eventually leads to AD. Furthermore, this review references different therapeutics proposed to modulate the ceramide pathway to maintain ceramide levels and prevent the disease progression.
Short-Term Cortical Electrical Stimulation during the Acute Stage of Traumatic Brain Injury Improves Functional Recovery
Authors: Wang Liang-Chao , Wei Wei-Yen , Ho Pei-Chuan
Published: August 2022

Functional restoration is an important issue in the treatment of traumatic brain injury (TBI). Various electrical stimulation devices and protocols have been applied in preclinical studies and have shown therapeutic potential for brain trauma. Short-term invasive cortical electrical stimulation during the acute stage of TBI might be a feasible adjuvant therapy for patients with moderate-to-severe brain injury receiving neurosurgical treatment in the intensive care unit. However, the therapeutic effects of short-term multisession cortical electrical stimulation for brain trauma are not clear. This study explored the therapeutic effects of acute-stage short-term cortical electrical stimulation on TBI. We conducted seven sessions of one-hour cortical electrical stimulation from day 0 to day 6 in rats after brain trauma by controlled cortical impact and then evaluated the functional outcome and histopathological changes. Our data showed that short-term cortical electrical stimulation improved motor coordination, short-term memory, and learning ability and attenuated neurological severity after brain trauma. Lesion volume, apoptosis, and gliosis after brain trauma were reduced, and trauma-induced neurogenesis in the hippocampus for the innate neural reparative response was increased. Our study demonstrated that short-term cortical electrical stimulation applied in the acute stage of traumatic brain injury is a potential adjuvant therapy to improve the recovery of neurological deficits.
The Dose-Dependent Effects of Multifunctional Enkephalin Analogs on the Protein Composition of Rat Spleen Lymphocytes, Cortex, and Hippocampus; Comparison with Changes Induced by Morphine
Authors: Ujcikova Hana , Roubalova Lenka , Lee Yeon Sun , Slaninova Jirina , Brejchova Jana , Svoboda Petr
Published: August 2022

This work aimed to test the effect of 7-day exposure of rats to multifunctional enkephalin analogs LYS739 and LYS744 at doses of 3 mg/kg and 10 mg/kg on the protein composition of rat spleen lymphocytes, brain cortex, and hippocampus. Alterations of proteome induced by LYS739 and LYS744 were compared with those elicited by morphine. The changes in rat proteome profiles were analyzed by label-free quantification (MaxLFQ). Proteomic analysis indicated that the treatment with 3 mg/kg of LYS744 caused significant alterations in protein expression levels in spleen lymphocytes (45), rat brain cortex (31), and hippocampus (42). The identified proteins were primarily involved in RNA processing and the regulation of cytoskeletal dynamics. In spleen lymphocytes, the administration of the higher 10 mg/kg dose of both enkephalin analogs caused major, extensive modifications in protein expression levels: LYS739 (119) and LYS744 (182). Among these changes, the number of proteins associated with immune responses and apoptotic processes was increased. LYS739 treatment resulted in the highest number of alterations in the rat brain cortex (152) and hippocampus (45). The altered proteins were functionally related to the regulation of transcription and cytoskeletal reorganization, which plays an essential role in neuronal plasticity. Administration with LYS744 did not increase the number of altered proteins in the brain cortex (26) and hippocampus (26). Our findings demonstrate that the effect of κ-OR full antagonism of LYS744 is opposite in the central nervous system and the peripheral region (spleen lymphocytes).
Investigation of the Effect of the Same Polishing Protocol on the Surface Roughness of Denture Base Acrylic Resins
Authors: Quezada Margarida Martins , Salgado Helena , Correia André , Fernandes Carlos , Fonseca Patrícia
Published: August 2022

This investigation aims to determine the effect of the same polishing protocol on the surface roughness (Ra) of different resins obtained by different processing techniques. Acrylic resins obtained by CAD/CAM technology overcame the disadvantages identified in conventional materials. A total of thirty samples (six of each resin): self-cured, heat-polymerized, injection molded, CAD/CAM 3D-printed and CAD/CAM milled were prepared. JOTA® Kit 1877 DENTUR POLISH was used to polish the samples by two techniques: manual and mechanized, with a prototype for guided polishing exclusively developed for this investigation. The Ra was measured by a profilometer. The values were analyzed using ANOVA, Games–Howell post-hoc test and One-sample t-test, with p < 0.05. Manual polishing produces lower values of Ra compared to mechanized polishing, except for injected molded resins (p = 0.713). Manual polishing reveals significant differences between the resin pairs milling/3D-printing (p = 0.012) and thermopolymerizable/milling (p = 0.024). In the mechanized technique only, significant differences regarding the Ra values were found between the self-cured/3D-printed (p = 0.004) and self-cured/thermopolymerizable pair resins (p = 0.004). Differences in surface roughness values can be attributed to the inherent characteristics of the resin and the respective processing techniques.
The Impact of Practicing Open- vs. Closed-Skill Sports on Executive Functions—A Meta-Analytic and Systematic Review with a Focus on Characteristics of Sports
Authors: Heilmann Florian , Weinberg Henrietta , Wollny Rainer
Published: August 2022

Exercise modes can be categorized based on the skills required (open vs. closed skills), which implicates various demands on cognitive skills, especially executive functions (EFs). Thus, their practice may have varying effects on EFs. There is a lack of detailed analysis of cognitive requirements and suitable classification of sports. It is hypothesized that the amount and type of cognitive requirements of sports lead to small effect sizes when comparing open-skill exercising (OSE) and closed-skill exercising (CSE) athletes. The current meta-analysis evaluates the variances in EFs skills caused by particular sport modes. Four research databases (Web of Science, PubMed, ScienceDirect, PsychINFO) were searched for cross-sectional studies in which the authors compare the performance in EF tasks of OSE and CSE athletes. Risk of bias assessment was conducted using funnel plots and two reviewer selection process (overall and subgroup analysis; low risk of publication and selection bias). A total of 19 studies were included, revealing an overall effect size of Hedge’s g = 0.174 (p = 0.157), favoring OSE for the development of EFs. The subgroup analysis revealed the effects for the subdomains of EFs (cognitive flexibility: Hedge’s g = 0.210 > inhibitory control: Hedge’s g = 0.191 > working memory: Hedge’s g = 0.138; p > 0.05), which could be characterized as low to moderate. The hypothesis that studies with the smallest effect sizes compare sport modes with similar cognitive demands was rejected. The paper discusses the differentiation of sports into OSE and CSE and presents new approaches for their categorization.
Adaptive Cognitive Control in Prematurely Born Children: An HD-EEG Investigation
Authors: Mento Giovanni , Toffoli Lisa , Della Longa Letizia , Farroni Teresa , Del Popolo Cristaldi Fiorella , Duma Gian Marco
Published: August 2022

Preterm birth is a neurodevelopmental risk condition often associated with cognitive control (CC) impairment. Recent evidence showed that CC can be implicitly adapted through associative learning. In the present study we investigated the ability to flexibly adjust CC as a function of implicit stimulus-response temporal regularities in preterm (PT; N = 21; mean age 8 ± 1.3 years; gestational age 30 ± 18.5 weeks) and full-term (FT; N = 20; mean age 8 ± 1.3 years) school-age children. All children underwent an HD-EEG recording while undergoing the Dynamic Temporal Prediction (DTP) task, a simple S1–S2 detection task purposely designed to generate local-global temporal predictability of imperative stimuli. The Wisconsin card sorting test (WCST) was administered to measure explicit CC. The PT group showed more premature and slower (DTP) as well as perseverative (WCST) responses than the FT group. Moreover, pre-terms showed poor adaptive CC as revealed by less efficient global response-speed adjustment. This behavioral pattern was mirrored by a reduced and less sensitive to global manipulation anticipatory Contingent Negative Variation (CNV) and by different cortical source recruitment. These findings suggest that implicit CC may be a reliable endophenotypic marker of atypical cognitive development associated with preterm birth.
The Prediction of Consumer Behavior from Social Media Activities
Authors: Ali Hakami Nada , Hosni Mahmoud Hanan Ahmed
Published: August 2022

Consumer behavior variants are evolving by utilizing advanced packing models. These models can make consumer behavior detection considerably problematic. New techniques that are superior to customary models to be utilized to efficiently observe consumer behaviors. Machine learning models are no longer efficient in identifying complex consumer behavior variants. Deep learning models can be a capable solution for detecting all consumer behavior variants. In this paper, we are proposing a new deep learning model to classify consumer behavior variants using an ensemble architecture. The new model incorporates two pretrained learning algorithms in an optimized fashion. This model has four main phases, namely, data gathering, deep neural modeling, model training, and deep learning model evaluation. The ensemble model is tested on Facemg BIG-D15 and TwitD databases. The experiment results depict that the ensemble model can efficiently classify consumer behavior with high precision that outperforms recent models in the literature. The ensemble model achieved 98.78% accuracy on the Facemg database, which is higher than most machine learning consumer behavior detection models by more than 8%.