bluuebunny/crossref_metadata_2025 huggingface dataset:
The Source Structure Design of the Rotating Magnetic Beacon Based on Phase-Shift Direction Finding System
Authors: Li Bo , Yang Binfeng , Xiang Fenghua , Guo Jiaojiao , Li Hailin
Published: October 2022

Target azimuth information can help further improve the accuracy of magnetic orientation, but the current periodic magnetic field generated by the magnetic beacon is multivalued, so it is not suitable for azimuth measurement. According to the distribution of a rotating magnetic field and the phase angle measuring principle, we put forward a new magnetic source structure design of a multiple rotating permanent magnet array by adjusting the spacing d, the rotating speed ω and the initial rotation angle φ, and then verified the mathematical model using COMSOL simulation software. A triple structure was obtained by comparison (d3=3d1=3d2=43 m, d3=3d1=3d2=43 m, φ1=0, φ2=4π5 rad. φ3=π rad), which can produce a strong characteristic magnetic signal similar to a heart-shaped field pattern. Finally, a signal transceiver system was set up for the experiment. The experimental result shows that the waveform of the magnetic signal generated by the real beacon meets the requirement of having a unique maximum value and good directivity within a period, which proves the practical application effect of the structure.
xRatSLAM: An Extensible RatSLAM Computational Framework
Authors: de Souza Muñoz Mauro Enrique , Chaves Menezes Matheus , Pignaton de Freitas Edison , Cheng Sen , de Almeida Ribeiro Paulo Rogério , de Almeida Neto Areolino , Muniz de Oliveira Alexandre César
Published: October 2022

Simultaneous localization and mapping (SLAM) refers to techniques for autonomously constructing a map of an unknown environment while, at the same time, locating the robot in this map. RatSLAM, a prevalent method, is based on the navigation system found in rodent brains. It has served as a base algorithm for other bioinspired approaches, and its implementation has been extended to incorporate new features. This work proposes xRatSLAM: an extensible, parallel, open-source framework applicable for developing and testing new RatSLAM variations. Tests were carried out to evaluate and validate the proposed framework, allowing the comparison of xRatSLAM with OpenRatSLAM and assessing the impact of replacing framework components. The results provide evidence that the maps produced by xRatSLAM are similar to those produced by OpenRatSLAM when they are fed with the same input parameters, which is a positive result, and that implemented modules can be easily changed without impacting other parts of the framework.
Electric Impedance Spectroscopy in Trees Condition Analysis: Theory and Experiment
Authors: Astashev Maxim E. , Konchekov Evgeny M. , Kolik Leonid V. , Gudkov Sergey V.
Published: October 2022

Electric impedance spectroscopy is an alternative technology to existing methods that shows promising results in the agro-food industry and plant physiology research. For example, this technology makes it possible to monitor the condition of plants, even in the early stages of development, and to control the quality of finished products. However, the use of electric impedance spectroscopy is often associated with the need to organize special laboratory conditions for measurements. Our aim is to extract information about the state of health of the internal tissues of a plant’s branches from impedance measurements. Therefore, we propose a new technique using the device and model developed by us that makes it possible to monitor the condition of tree branch tissues in situ. An apple tree was chosen as the object under study, and the dependence of the impedance of the apple tree branch on the signal frequency and branch length was analyzed. The change in the impedance of an apple tree branch during drying was also analyzed. It was shown that, when a branch dries out, the conductivity of the xylem mainly decreases. The developed technique was also applied to determine the development of the vascular system of an apple tree after grafting. It was shown that the processing of the scion and rootstock sections with the help of cold atmospheric plasma and a plasma-treated solution contributes to a better formation of graft unions.
Multi-Class Skin Lesions Classification Using Deep Features
Authors: Usama Muhammad , Naeem M. Asif , Mirza Farhaan
Published: October 2022

Skin cancer classification is a complex and time-consuming task. Existing approaches use segmentation to improve accuracy and efficiency, but due to different sizes and shapes of lesions, segmentation is not a suitable approach. In this research study, we proposed an improved automated system based on hybrid and optimal feature selections. Firstly, we balanced our dataset by applying three different transformation techniques, which include brightness, sharpening, and contrast enhancement. Secondly, we retrained two CNNs, Darknet53 and Inception V3, using transfer learning. Thirdly, the retrained models were used to extract deep features from the dataset. Lastly, optimal features were selected using moth flame optimization (MFO) to overcome the curse of dimensionality. This helped us in improving accuracy and efficiency of our model. We achieved 95.9%, 95.0%, and 95.8% on cubic SVM, quadratic SVM, and ensemble subspace discriminants, respectively. We compared our technique with state-of-the-art approach.
StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information
Authors: Son Seungwook , Ahn Hanse , Baek Hwapyeong , Yu Seunghyun , Suh Yooil , Lee Sungju , Chung Yongwha , Park Daihee
Published: October 2022

The automatic detection of individual pigs can improve the overall management of pig farms. The accuracy of single-image object detection has significantly improved over the years with advancements in deep learning techniques. However, differences in pig sizes and complex structures within pig pen of a commercial pig farm, such as feeding facilities, present challenges to the detection accuracy for pig monitoring. To implement such detection in practice, the differences should be analyzed by video recorded from a static camera. To accurately detect individual pigs that may be different in size or occluded by complex structures, we present a deep-learning-based object detection method utilizing generated background and facility information from image sequences (i.e., video) recorded from a static camera, which contain relevant information. As all images are preprocessed to reduce differences in pig sizes. We then used the extracted background and facility information to create different combinations of gray images. Finally, these images are combined into different combinations of three-channel composite images, which are used as training datasets to improve detection accuracy. Using the proposed method as a component of image processing improved overall accuracy from 84% to 94%. From the study, an accurate facility and background image was able to be generated after updating for a long time that helped detection accuracy. For the further studies, improving detection accuracy on overlapping pigs can also be considered.
Low Complexity Adaptive Detection of Short CPM Bursts for Internet of Things in 6G
Authors: Pan Zihao , Wang Heng , Zhang Bangning , Guo Daoxing
Published: October 2022

With the standardization and commercialization of 5G, research on 6G technology has begun. In this paper, a new low-complexity soft-input–soft-output (SISO) adaptive detection algorithm for short CPM bursts is proposed for low-power, massive Internet of Things (IoT) connectivity in 6G. First, a time-invariant trellis is constructed on the basis of truncation in order to reduce the number of states. Then, adaptive channel estimators, recursive least squares (RLS), or least mean squares (LMS), are assigned to each hypothetical sequence by using the recursive structure of the trellis, and per-survivor processing (PSP) is used to improve the quality of channel estimation and reduce the number of searching paths. Then, the RLS adaptive symbol detector (RLS-ASD) and LMS adaptive symbol detector (LMS-ASD) could be acquired. Compared to using a least-squares estimator, the RLS-ASD avoids matrix inversion for the computation of branch metrics, while the LMS-ASD further reduces the steps in the RLS-ASD at the cost of performance. Lastly, a soft information iteration process is used to further improve performance via turbo equalization. Simulation results and analysis show that the RLS-ASD improves performance by about 1 dB compared to the state-of-the-art approach in time-variant environments while keeping a similar complexity. In addition, the LMS-ASD could further significantly reduce complexity with a power loss of approximately 1 dB. Thus, a flexible choice of detectors can achieve a trade-off of performance and complexity.
Capturing Conversational Gestures for Embodied Conversational Agents Using an Optimized Kaneda–Lucas–Tomasi Tracker and Denavit–Hartenberg-Based Kinematic Model
Authors: Močnik Grega , Kačič Zdravko , Šafarič Riko , Mlakar Izidor
Published: October 2022

In order to recreate viable and human-like conversational responses, the artificial entity, i.e., an embodied conversational agent, must express correlated speech (verbal) and gestures (non-verbal) responses in spoken social interaction. Most of the existing frameworks focus on intent planning and behavior planning. The realization, however, is left to a limited set of static 3D representations of conversational expressions. In addition to functional and semantic synchrony between verbal and non-verbal signals, the final believability of the displayed expression is sculpted by the physical realization of non-verbal expressions. A major challenge of most conversational systems capable of reproducing gestures is the diversity in expressiveness. In this paper, we propose a method for capturing gestures automatically from videos and transforming them into 3D representations stored as part of the conversational agent’s repository of motor skills. The main advantage of the proposed method is ensuring the naturalness of the embodied conversational agent’s gestures, which results in a higher quality of human-computer interaction. The method is based on a Kanade–Lucas–Tomasi tracker, a Savitzky–Golay filter, a Denavit–Hartenberg-based kinematic model and the EVA framework. Furthermore, we designed an objective method based on cosine similarity instead of a subjective evaluation of synthesized movement. The proposed method resulted in a 96% similarity.
Learning Moiré Pattern Elimination in Both Frequency and Spatial Domains for Image Demoiréing
Authors: Liu Chenming , Wang Yongbin , Zhang Nenghuan , Gang Ruipeng , Ma Sai
Published: October 2022

Recently, with the rapid development of mobile sensing technology, capturing scene information by mobile sensing devices in the form of images or videos has become a prevalent recording method. However, the moiré pattern phenomenon may occur when the scene contains digital screens or regular strips, which greatly degrade the visual performance and image quality. In this paper, considering the complexity and diversity of moiré patterns, we propose a novel end-to-end image demoiré method, which can learn moiré pattern elimination in both the frequency and spatial domains. To be specific, in the frequency domain, considering the signal energy of moiré pattern is widely distributed in the frequency, we introduce a wavelet transform to decompose the multi-scale image features, which can help the model identify the moiré features more precisely to suppress them effectively. On the other hand, we also design a spatial domain demoiré block (SDDB). The SDDB module can extract moiré features from the mixed features, then subtract them to obtain clean image features. The combination of the frequency domain and the spatial domain enhances the model’s ability in terms of moiré feature recognition and elimination. Finally, extensive experiments demonstrate the superior performance of our proposed method to other state-of-the-art methods. The Grad-CAM results in our ablation study fully indicate the effectiveness of the two proposed blocks in our method.
Selection of a Potting Material and Method for Broadband Underwater Cymbal Arrays
Authors: Wang Wenbo , Shim Hayeong , Roh Yongrae
Published: October 2022

Cymbal transducers are often used in arrays for underwater communication and detection systems. The working environment of a cymbal array is underwater; therefore, waterproofing, salt-corrosion prevention, and impact resistance are necessary for stable operation of the array. Hence, we simulated potting a cymbal array with 15 different rubber and epoxy materials available in the market, using the finite element method, and analyzed their effect on the transmitting voltage response spectrum of the array. From the analysis results, we selected the material that would achieve the widest frequency bandwidth, while preserving the structural stability of the array. A potting method corresponding to the selected material was suggested as well. This study provides guidelines for the selection of a potting material for use in underwater transducer arrays.
Anthropogenic Landforms Derived from LiDAR Data in the Woodlands near Kotlarnia (Koźle Basin, Poland)
Authors: Waga Jan Maciej , Szypuła Bartłomiej , Sendobry Kazimierz , Fajer Maria
Published: October 2022

Unlike farmland or urban areas, forests have long been regarded as environments that favour the preservation of valuable geological and historical sites. However, due to invasive forestry methods, the implementation of large investment projects and the development of mining, they are increasingly no longer safe spaces for the relics of human activities recorded in landforms. Data collection, including using LiDAR technology, presents an opportunity to preserve knowledge about these landforms. Through the analysis of shaded images of a 37-hectare woodland area near Kotlarnia, landforms of various ages documenting 21 different human activities were identified, including remnants of reforestation activities, the expansion and modification of hydraulic structures and road infrastructure, charcoal burning and tar distilling, exploitation of mineral resources and military activities. The results of the remote sensing work were verified in the field.
Design of 3D Virtual Reality in the Metaverse for Environmental Conservation Education Based on Cognitive Theory
Authors: Lo Shih-Che , Tsai Hung-Hsu
Published: October 2022

Background: Climate change causes devastating impacts with extreme weather conditions, such as flooding, polar ice caps melting, sea level rise, and droughts. Environmental conservation education is an important and ongoing project nowadays for all governments in the world. In this paper, a novel 3D virtual reality architecture in the metaverse (VRAM) is proposed to foster water resources education using modern information technology. Methods: A quasi-experimental study was performed to observe a comparison between learning involving VRAM and learning without VRAM. The 3D VRAM multimedia content comes from a picture book for learning environmental conservation concepts, based on the cognitive theory of multimedia learning to enhance human cognition. Learners wear VRAM helmets to run VRAM Android apps by entering the immersive environment for playing and/or interacting with 3D VRAM multimedia content in the metaverse. They shake their head to move the interaction sign to initiate interactive actions, such as replaying, going to consecutive video clips, displaying text annotations, and replying to questions when learning soil-and-water conservation course materials. Interactive portfolios of triggering actions are transferred to the cloud computing database immediately by the app. Results: Experimental results showed that participants who received instruction involving VRAM had significant improvement in their flow experience, learning motivation, learning interaction, self-efficacy, and presence in learning environmental conservation concepts. Conclusions: The novel VRAM is highly suitable for multimedia educational systems. Moreover, learners’ interactive VRAM portfolios can be analyzed by big-data analytics to understand behaviors for using VRAM in the future to improve the quality of environmental conservation education.
Pose Mask: A Model-Based Augmentation Method for 2D Pose Estimation in Classroom Scenes Using Surveillance Images
Authors: Liu Shichang , Ma Miao , Li Haiyang , Ning Hanyang , Wang Min
Published: October 2022

Solid developments have been seen in deep-learning-based pose estimation, but few works have explored performance in dense crowds, such as a classroom scene; furthermore, no specific knowledge is considered in the design of image augmentation for pose estimation. A masked autoencoder was shown to have a non-negligible capability in image reconstruction, where the masking mechanism that randomly drops patches forces the model to build unknown pixels from known pixels. Inspired by this self-supervised learning method, where the restoration of the feature loss induced by the mask is consistent with tackling the occlusion problem in classroom scenarios, we discovered that the transfer performance of the pre-trained weights could be used as a model-based augmentation to overcome the intractable occlusion in classroom pose estimation. In this study, we proposed a top-down pose estimation method that utilized the natural reconstruction capability of missing information of the MAE as an effective occluded image augmentation in a pose estimation task. The difference with the original MAE was that instead of using a 75% random mask ratio, we regarded the keypoint distribution probabilistic heatmap as a reference for masking, which we named Pose Mask. To test the performance of our method in heavily occluded classroom scenes, we collected a new dataset for pose estimation in classroom scenes named Class Pose and conducted many experiments, the results of which showed promising performance.
Vector Sensor Steering-Dependent Performance in an Underwater Acoustic Communication Field Experiment
Authors: Bozzi Fabricio A. , Jesus Sérgio M.
Published: October 2022

This paper shows the performance resulting from combining vector sensor directional components in an underwater acoustic communication experiment. The objective is to relate performance with transmission direction and range. Receiver structures based on beamforming and passive time-reversal are tested in order to quantify and compare the steerability impact of vector sensor directional components. A shallow water experiment is carried out with a bottom fixed two-axis pressure-gradient vector sensor. A ship suspended acoustic source transmits coherent modulated communication signals at various ranges and from several directions. Results show that one vector sensor can provide an up to 10 times smaller error bit rate than a pressure sensor, favoring communication robustness without size penalty.
Trends of Augmented Reality for Agri-Food Applications
Authors: Xie Junhao , Chai Jackey J. K. , O’Sullivan Carol , Xu Jun-Li
Published: October 2022

Recent years have witnessed an increasing interest in deploying state-of-the-art augmented reality (AR) head-mounted displays (HMDs) for agri-food applications. The benefits of AR HMDs to agri-food industry stakeholders (e.g., food suppliers, retail/food service) have received growing attention and recognition. AR HMDs enable users to make healthier dietary choices, experience novel changes in their perception of taste, enhance the cooking and food shopping experience, improve productivity at work and enhance the implementation of precision farming. Therefore, although development costs are still high, the case for integration of AR in food chains appears to be compelling. This review will present the most recent developments of AR HMDs for agri-food relevant applications. The summarized applications can be clustered into different themes: (1) dietary and food nutrition assessment; (2) food sensory science; (3) changing the eating environment; (4) retail food chain applications; (5) enhancing the cooking experience; (6) food-related training and learning; and (7) food production and precision farming. Limitations of current practices will be highlighted, along with some proposed applications.
Application of Smoothing Spline in Determining the Unmanned Ground Vehicles Route Based on Ultra-Wideband Distance Measurements
Authors: Rykała Łukasz , Typiak Andrzej , Typiak Rafał , Rykała Magdalena
Published: October 2022

Unmanned ground vehicles (UGVs) are technically complex machines to operate in difficult or dangerous environmental conditions. In recent years, there has been an increase in research on so called “following vehicles”. The said concept introduces a guide—an object that sets the route the platform should follow. Afterwards, the role of the UGV is to reproduce the mentioned path. The article is based on the field test results of an outdoor localization subsystem using ultra-wideband technology. It focuses on determining the guide’s route using a smoothing spline for constructing a UGV’s path planning subsystem, which is one of the stages for implementing a “follow-me” system. It has been shown that the use of a smoothing spline, due to the implemented mathematical model, allows for recreating the guide’s path in the event of data decay lasting up to a several seconds. The innovation of this article originates from influencing studies on the smoothing parameter of the estimation errors of the guide’s location.
Blockchain-Based Access Control and Behavior Regulation System for IoT
Authors: Song Haoxiang , Tu Zhe , Qin Yajuan
Published: October 2022

With the development of 5G and the Internet of things (IoT), the multi-domain access of massive devices brings serious data security and privacy issues. At the same time, most access systems lack the ability to identify network attacks and cannot adopt dynamic and timely defenses against various security threats. To this end, we propose a blockchain-based access control and behavior regulation system for IoT. Relying on the attribute-based access control model, this system deploys smart contracts on the blockchain to achieve distributed and fine-grained access control and ensures that the identity and authority of access users can be trusted. At the same time, an inter-domain communication mechanism is designed based on the locator/identifier separation protocol and ensures the traffic of access users are authorized. A feedback module that combines traffic detection and credit evaluation is proposed, ensuring real-time detection and fast, proactive responses against malicious behavior. Ultimately, all modules are linked together through workflows to form an integrated security model. Experiments and analysis show that the system can effectively provide comprehensive security protection in IoT scenarios.
Scoring the Sit-to-Stand Performance of Parkinson’s Patients with a Single Wearable Sensor
Authors: Marin Frédéric , Warmerdam Elke , Marin Zoé , Ben Mansour Khalil , Maetzler Walter , Hansen Clint
Published: October 2022

Monitoring disease progression in Parkinson’s disease is challenging. Postural transfers by sit-to-stand motions are adapted to trace the motor performance of subjects. Wearable sensors such as inertial measurement units allow for monitoring motion performance. We propose quantifying the sit-to-stand performance based on two scores compiling kinematics, dynamics, and energy-related variables. Three groups participated in this research: asymptomatic young participants (n = 33), senior asymptomatic participants (n = 17), and Parkinson’s patients (n = 20). An unsupervised classification was performed of the two scores to differentiate the three populations. We found a sensitivity of 0.4 and a specificity of 0.96 to distinguish Parkinson’s patients from asymptomatic subjects. In addition, seven Parkinson’s patients performed the sit-to-stand task “ON” and “OFF” medication, and we noted the scores improved with the patients’ medication states (MDS-UPDRS III scores). Our investigation revealed that Parkinson’s patients demonstrate a wide spectrum of mobility variations, and while one inertial measurement unit can quantify the sit-to-stand performance, differentiating between PD patients and healthy adults and distinguishing between “ON” and “OFF” periods in PD patients is still challenging.
Pilot Validation Study of Inertial Measurement Units and Markerless Methods for 3D Neck and Trunk Kinematics during a Simulated Surgery Task
Authors: Zhang Ce , Greve Christian , Verkerke Gijsbertus Jacob , Roossien Charlotte Christina , Houdijk Han , Hijmans Juha M.
Published: October 2022

Surgeons are at high risk for developing musculoskeletal symptoms (MSS), like neck and back pain. Quantitative analysis of 3D neck and trunk movements during surgery can help to develop preventive devices such as exoskeletons. Inertial Measurement Units (IMU) and markerless motion capture methods are allowed in the operating room (OR) and are a good alternative for bulky optoelectronic systems. We aim to validate IMU and markerless methods against an optoelectronic system during a simulated surgery task. Intraclass correlation coefficient (ICC (2,1)), root mean square error (RMSE), range of motion (ROM) difference and Bland–Altman plots were used for evaluating both methods. The IMU-based motion analysis showed good-to-excellent (ICC 0.80–0.97) agreement with the gold standard within 2.3 to 3.9 degrees RMSE accuracy during simulated surgery tasks. The markerless method shows 5.5 to 8.7 degrees RMSE accuracy (ICC 0.31–0.70). Therefore, the IMU method is recommended over the markerless motion capture.
Analysis of Gyro Bias Depending on the Position of Inertial Measurement Unit in Rotational Inertial Navigation Systems
Authors: Seo Yeong-Bin , Yu Haesung , Ryu Kyungdon , Lee Inseop , Oh Juhyun , Kim Cheonjoong , Lee Sang Jeong , Park Chansik
Published: October 2022

In this paper, a calibration method for gyro bias that changes depending on the position of the IMU (inertial measurement unit) is proposed to improve the navigation performance of RLG-based RINS (ring-laser-gyro-based rotational inertial navigation system). RINS is a navigation device that compensates for the inertial sensor errors by utilizing the rotation of the IMU. In previous studies, the rotation scheme of the IMU is designed assuming that inertial sensor errors are not affected by position of the IMU. However, changes in temperature distribution, direction of gravity, and dithering according to the rotation of the IMU affect the inertial sensor errors, such as gyro bias. These errors could degrade the long-term navigation performance of RLG-based RINS. To deal with this problem, this paper proposed a compensation method of the gyro bias that changes depending on the position of the IMU. First, RINS is reviewed using a dual-axis 16-position rotation scheme and RLG. Next, the attitude error of RLG-based RINS is derived utilizing navigation equations. The effect of the gyro bias change caused by the change in the IMU attitude for the navigation performance of RINS is analyzed based on navigation equations and simulations. Finally, system-level indirect calibrations for the Z–axis up position and Z–axis down position are performed to calculate the gyro bias change caused by the IMU attitude. The accuracy of the proposed calibration method is verified by long-term navigation test. The test results show that the proposed calibration method improves the navigation performance of RINS compared with the conventional calibration method.
Shielding without a Shield—Older People under COVID-19: A Comparison of Four Cities
Authors: Wong Johnston H. C. , Ho Ghee W.
Published: October 2022

Older people were globally the most vulnerable during the pandemic. This paper examines the mortality and psychological crisis of older people during the pandemic in four cities, namely Wuhan, Milan, London, and Hong Kong, in order to explain their high death rates. The sample cities were purposefully selected to cover different degrees of social connectedness and outbreak periods. Quantitative data were employed to account for death and suicide data. Qualitative data analyses in government reports, public press releases, NGO announcements, and journal articles were used to study government responses and disruptions of essential healthcare services. Published scientific studies provided important information on the psychological stress and resilience of older people. Findings based on a welfare regime perspective showed that society and governments have not learned from the experiences of other countries, which resulted in high numbers of preventable deaths. Older people have successfully coped with living difficulties while serious psychological disturbances and suicidal ideations were absent at least for the period up to 2022. Older care home residents, unfortunately, were not protected with proper shielding and available vaccinations. Public inefficiency and negligence contributed to a disproportionately high rate of mortality in nursing homes.
Security Requirements Prioritization Techniques: A Survey and Classification Framework
Authors: Khanneh Shada , Anu Vaibhav
Published: October 2022

Security requirements Engineering (SRE) is an activity conducted during the early stage of the SDLC. SRE involves eliciting, analyzing, and documenting security requirements. Thorough SRE can help software engineers incorporate countermeasures against malicious attacks into the software’s source code itself. Even though all security requirements are considered relevant, implementing all security mechanisms that protect against every possible threat is not feasible. Security requirements must compete not only with time and budget, but also with the constraints they inflect on a software’s availability, features, and functionalities. Thus, the process of security requirements prioritization becomes an integral task in the discipline of risk-analysis and trade-off-analysis. A sound prioritization technique provides guidance for software engineers to make educated decisions on which security requirements are of topmost importance. Even though previous research has proposed various security requirement prioritization techniques, none of the existing research efforts have provided a detailed survey and comparative analysis of existing techniques. This paper uses a literature survey approach to first define security requirements engineering. Next, we identify the state-of-the-art techniques that can be adopted to impose a well-established prioritization criterion for security requirements. Our survey identified, summarized, and compared seven (7) security requirements prioritization approaches proposed in the literature.
The Effects of a Weighted Football Intervention on Ball Velocity of a Standard Football Place-Kick among Elite Gaelic Football Goalkeepers: A Single-Subject Designed Study
Authors: Jermyn Sam , O’Neill Cian , Lacey Seán , Coughlan Edward K.
Published: October 2022

Weighted football place-kicking acutely enhances the ball velocity (BV) of subsequent standard football place-kicks. However, there is a dearth of research examining the long-term effects of such interventions, with less evidence in existence among elite athlete cohorts. Therefore, the purpose of this study was to investigate the individual effects of a 4 week, eight-session weighted Gaelic football intervention on BV of standard Gaelic football place-kicks among six elite male Gaelic football goalkeepers. This research design was based on a pre-, mid-, post-, and retention-test design. A linear mixed model analysis was employed, with time and participants as fixed effects, and the number of place-kicks per testing session as a random effect. Post hoc tests revealed significant changes in BV for five of the six participants (p < 0.05), with three participants experiencing significant BV increases from pre-test to post-test (p < 0.05), while no significant differences were found between post-test and retention-test. The remaining three participants experienced no significant BV differences from pre-test to post-test and retention-test. These findings suggest that a weighted football place-kicking intervention can be a time-efficient means of maintaining and enhancing BV and, thus, kick distance, among elite goalkeepers during pre-season and in-season phases.
Effect of 8 Weeks Aerobic Training and Saffron Supplementation on Inflammation and Metabolism in Middle-Aged Obese Women with Type 2 Diabetes Mellitus
Authors: Rajabi Ali , Khajehlandi Mojdeh , Siahkuhian Marefat , Akbarnejad Ali , Khoramipour Kayvan , Suzuki Katsuhiko
Published: October 2022

Background: This study aimed to investigate the effects of 8-week aerobic training (AT) and saffron supplementation on inflammation and metabolism in middle-aged obese women with type 2 diabetes mellitus (T2DM). Methods: Thirty-two obese women with T2DM were randomly divided into four groups (n = 8 in all groups): saffron + training (ST), placebo + training (PT), saffron supplementation (SS), and placebo (P). The ST and PT groups performed eight weeks of aerobic training (AT) (three sessions/week at 60–75% HRmax). A daily dose of 400 mg saffron powder was consumed by the ST and SS groups for 8 weeks. Blood samples were taken after 12 h of fasting, 48 h before the first AT session, 48 h and two weeks after the last AT session. Results: AT, saffron supplementation, and their combination affected body mass index (BMI), homeostatic model assessment for insulin resistance (HOMA-IR), and serum levels of insulin, adiponectin, interleukin-6 (IL-6), high-density lipoprotein cholesterol (HDL-C), cholesterol, and triglyceride (TG) (p < 0.05). However, body weight, body fat percentage, and serum levels of glucose, resistin, tumor necrosis factor-alpha (TNF-α), irisin, and low-density lipoprotein cholesterol (LDL-C) showed significant changes in the ST group only (p < 0.05). In addition, a significant difference was seen between all factors in post-training and follow-up in the ST group (p < 0.05). Conclusions: Saffron supplementation at a dose of 400 mg/day, when combined with AT, could improve inflammation, metabolism, glycemic status, and lipid profile in T2DM patients, and these changes are sustainable at up to 2 weeks of detraining.
Comparison of Positivity in Two Epidemic Waves of COVID-19 in Colombia with FDA
Authors: Urbano-Leon Cristhian Leonardo , Escabias Manuel
Published: October 2022

We use the functional data methodology to examine whether there are significant differences between two waves of contagion by COVID-19 in Colombia between 7 July 2020 and 20 July 2021. A pointwise functional t-test is initially used, then an alternative statistical test proposal for paired samples is presented, which has a theoretical distribution and performs well in small samples. Our statistical test generates a scalar p-value, which provides a global idea about the significance of the positivity curves, complementing the existing punctual tests, as an advantage.
A Novel Generalization of Zero-Truncated Binomial Distribution by Lagrangian Approach with Applications for the COVID-19 Pandemic
Authors: Irshad Muhammed Rasheed , Chesneau Christophe , Shibu Damodaran Santhamani , Monisha Mohanan , Maya Radhakumari
Published: October 2022

The importance of Lagrangian distributions and their applicability in real-world events have been highlighted in several studies. In light of this, we create a new zero-truncated Lagrangian distribution. It is presented as a generalization of the zero-truncated binomial distribution (ZTBD) and hence named the Lagrangian zero-truncated binomial distribution (LZTBD). The moments, probability generating function, factorial moments, as well as skewness and kurtosis measures of the LZTBD are discussed. We also show that the new model’s finite mixture is identifiable. The unknown parameters of the LZTBD are estimated using the maximum likelihood method. A broad simulation study is executed as an evaluation of the well-established performance of the maximum likelihood estimates. The likelihood ratio test is used to assess the effectiveness of the third parameter in the new model. Six COVID-19 datasets are used to demonstrate the LZTBD’s applicability, and we conclude that the LZTBD is very competitive on the fitting objective.
Parametric Study on the Behavior of Steel Tube Columns with Infilled Concrete—An Analytical Study
Authors: Shaker Fattouh M. F. , Daif Mohammed S. , Deifalla Ahmed Farouk , Ayash Nehal M.
Published: October 2022

Concrete-filled steel tube (CFST) columns are used in tall buildings and bridges, and they provide more rigidity and higher bearing capacity, but buckling affects their behavior. There is an exceptional need to study the behavior of these columns under various conditions. The numerical method is beneficial in supplementing the experimental works and is used to explore the effects of various parameters because of the limitations in cost, apparatus, and time of the experimental program. The various parameters, such as the different slenderness ratios, i.e., column-height-to-cross-section-dimension (H/D), different steel-tube-thickness-to-column-dimension (D/t), and different compressive strength of concrete to yield strength of steel tube ratio (fc/fy) under concentric axial loading are considered in this current study. Firstly, a finite element model used the “ANSYS” software program and was constructed to validate the results of the experimental works. The extensive numerical models were carried out to extensively widen the study in this field. The numerical work was conducted on sixty-four specimens. Moreover, the analytical calculations from the different international codes/standards were compared with the numerical results to test their reliability in predicting the ultimate carrying loads. The study provided results that show the improvement effect of CFST columns with the high compressive strength of infilled concrete, while no remarkable enhancement effect with the high yield strength of steel tube was observed. Increasing the columns’ diameter is more effective in enhancing the load capacity (about three times more) than increasing the tube thickness (about 1.3 times). Ring stiffeners for long CFST columns (H/D > 12) do not lead to any enhancement of the column behavior due to yielding occurring firstly at the location of the rings. ECP205-2007 is the most conservative design code in predicting the load capacity of CFST columns, while the AIJ design code is good at predicting the ultimate load failure compared to the other codes/standards. Eurocode 4 provides underestimation values of the load-carrying capacity of CFST columns.
Assessment of Hydrological Extremes for Arid Catchments: A Case Study in Wadi Al Jizzi, North-West Oman
Authors: Abushandi Eyad , Al Ajmi Manar
Published: October 2022

The objective of this research was to analyse hydrological variability by conducting an intensive analysis of extreme events, under dry and wet conditions. Drought conditions were assessed using the Standard Precipitation Index (SPI) and Rainfall Anomaly Index (RAI), while the Soil Conservation Service (SCS) method was used to simulate flooding at four stations. The SPI results indicated that the amount of rainfall within the catchment area is near to normal, ranging from 64% to 75%, with some extremely wet exceptions which may cause flash floods. The RAI results also indicated that the amount of rainfall within the catchment area is near to normal, but the extremely wet category obtained the largest percentage (ranging from 36% to 50%) and the very wet category had the lowest percentage (ranging from 9% to 36%). The simulated flooding, using SCS, tended to slightly underestimate the observed streamflow, while the performance showed some weaknesses when the observed flooding was less than 1 m3/s. The Nash–Sutcliffe Efficiency showed higher performance at closer rainfall stations to the outlet, with values of 0.92 and 0.94. Distant stations simulated floods that showed a lower level of efficiency, with values of 0.77 and 0.81. Given the fact that hydrological extremes (dry and wet conditions) are connected, the findings of the two indices and the SCS method are consistent and suitable for monitoring drought and flood events under climate change.
Examination of STEM Parent Awareness in the Transition from Preschool to Primary School
Authors: Mercan Zerrin , Papadakis Stamatios , Can Gözüm Ali İbrahim , Kalogiannakis Michail
Published: October 2022

This study, which aims to examine STEM parent awareness in the transition from preschool to primary school, used a survey model, one of the quantitative research designs. The study group consisted of 400 parents with children aged 5–7 years (preschool and primary school age) who participated in the study voluntarily from different provinces of Turkey. The STEM Parent Awareness Scale, was used to collect data. The STEM Parent Awareness Scale evaluated parents’ STEM awareness in knowledge and attitude. Data collection took place online using volunteer participants. Data analysis consisted of descriptive analyses using quantitative data and MANOVA to analyze the STEM awareness of parents with preschool children and the STEM awareness of parents with children in primary school. Data analysis revealed that STEM parent awareness did not differ according to the children’s education levels but the parents’ and the children’s STEM education status. To raise awareness, the authors recommend organizing training activities, courses, and workshops for families and increasing STEM studies in out-of-school activities.
Delivering Goods Using a Baby Pram: The Sustainability of Last-Mile Logistics Business Models
Authors: Kervola Henri , Kallionpää Erika , Liimatainen Heikki
Published: October 2022

This study compares the differences in the attitudes of last-mile delivery companies operating with different business models toward economic, environmental, and social sustainability. The economic perspective focuses on productivity, the environment, and social sustainability, and where the environment and social sustainability are concerned, on carbon neutrality goals and means, and on what societal factors companies consider important, respectively. In this multi-case qualitative study, a literature review was carried out and 11 semi-structured expert interviews were conducted in companies representing three business model categories: crowdshipping companies, last-mile providers, and traditional logistics service providers. Based on this study, crowdshipping and last-mile companies were largely loss making, whereas logistics service providers were profitable. The first two groups invest heavily in growth, which explains the loss. All logistics service providers, half of the last-mile providers, and no crowdshipping companies set a carbon neutrality target. The biggest difference in environmental measures comes from the fact that in the crowdshipping business model deliveries are performed partly in conjunction with other travel, emphasizing job creation in society and freedom of work. In contrast, the last-mile and traditional logistics companies emphasized the responsibilities of the employer. For the scientific community, this study provides new insights into the subject from the perspective of different business models. For management, this research provides insight into the views of other companies that can be utilized in future decision making. For example, if the legislation of crowdshipping becomes clearer, then traditional companies could consider using it in their own operations to overcome labor shortages.
How Does Manufacturing Intelligentization Influence Innovation in China from a Nonlinear Perspective and Economic Servitization Background?
Authors: Tang Genghua , Mai Hongxun
Published: October 2022

Under the trend of high-quality economic development and economic servitization in China, it is of great significance to study the impact of manufacturing intelligentization on innovation and its mechanisms. This study uses a sample of 30 of China’s provinces from 2008 to 2020 to empirically test the effect of manufacturing intelligentization on innovation performance from a nonlinear perspective and examine the intermediary mechanism of diversified agglomeration of producer services against an economic servitization background. The study finds that intelligentization has a significant inverted U-shaped impact on innovation performance. It shows that the positive marginal intelligentization effect on innovation gradually decreases, and intelligence inhibits innovation when it exceeds the threshold. In addition, diversified agglomeration of producer services can improve innovation performance, and intelligentization has an inverted U-shaped effect on this diversified aggregation. Thus, the nonlinear influence of intelligence on innovation performance has a channel of diversified agglomeration of producer services. Furthermore, human capital has a reverse moderating effect on the inverted U-shaped relationship between intelligentization and innovation performance. This conclusion can help to promote the innovation-driven and sustainable development of China’s economy under the intelligent manufacturing strategy.
Modeling and Simulation of a Commercial Lithium-Ion Battery with Charge Cycle Predictions
Authors: Camas-Náfate Mónica , Coronado-Mendoza Alberto , Vega-Gómez Carlos Jesahel , Espinosa-Moreno Francisco
Published: October 2022

The analysis of the behavior of lithium-ion batteries has gained considerable interest in recent years. There are different alternatives for the analysis of this behavior; however, depending on the type of modeling, there are application and optimization restrictions. In this work, a hybrid model has been made that is capable of predicting the characteristics of a lithium-ion battery. As a novelty, the simplification, at the same time, facilitates the sampling of parameters for their prompt selection for optimization. A new model open to the user is proposed, which has proven to be efficient in simulation time. For example, one hour simulates it in 5 min, providing information detailing how these parameters, State of Health (SOH), Open Circuit Voltage (VOC), State of charge (SOC), and Number of charge and discharge cycles, in the face of temperature variations and charge and discharge cycles. Opening the possibility of optimizing the parameters with different techniques to estimate the performance and dynamics in the face of temperature change and charge and discharge cycles. A model based on linear regressions, manufacturer characteristics, and integrating equations in the electrical model of electrochemical phenomena is proposed.
Hydrogen Purification by Pressure Swing Adsorption: High-Pressure PSA Performance in Recovery from Seasonal Storage
Authors: Kalman Viktor , Voigt Johannes , Jordan Christian , Harasek Michael
Published: October 2022

Hydrogen storage in a depleted gas field is a promising solution to the seasonal storage of renewable energy, a key question in Europe’s green transition. The gas composition and pressure in the month-long storage and recovery phase can vary substantially; meanwhile, the recovered H2 has to be pure, especially for fuel cell applications. Pressure swing adsorption can be used for the purification of the recovered gas. A lab-scale, four-bed PSA unit was built to investigate its applicability by separating different H2-CH4 mixtures. The feed parameters in the experiments are based on a depleted gas reservoir with a pressure range of 25–60 bar and methane contamination between 0 and 35%. The change in the feed properties is modeled by four distinct stages and the twelve-step cycle is tailored to each stage. The high pressure did not have any irreversible effects on the process. A hydrogen purity of 99.95% was achieved in all stages with the average hydrogen recovery ranging from 60 to 80%. The experiments revealed the challenges of a cycle design when the feed parameters are not constant, but an adequate separation performance was shown, which supports the applicability of the PSA in seasonal storage and confirms the need for further investigation with multicomponent contaminants and large-scale projects.