bluuebunny/crossref_metadata_2025 huggingface dataset:
Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives
Authors: Saboor Abdul , Hassan Mohd Fadzil , Akbar Rehan , Shah Syed Nasir Mehmood , Hassan Farrukh , Magsi Saeed Ahmed , Siddiqui Muhammad Aadil
Published: June 2022

Cloud computing is a rapidly growing paradigm which has evolved from having a monolithic to microservices architecture. The importance of cloud data centers has expanded dramatically in the previous decade, and they are now regarded as the backbone of the modern economy. Cloud-based microservices architecture is incorporated by firms such as Netflix, Twitter, eBay, Amazon, Hailo, Groupon, and Zalando. Such cloud computing arrangements deal with the parallel deployment of data-intensive workloads in real time. Moreover, commonly utilized cloud services such as the web and email require continuous operation without interruption. For that purpose, cloud service providers must optimize resource management, efficient energy usage, and carbon footprint reduction. This study presents a conceptual framework to manage the high amount of microservice execution while reducing response time, energy consumption, and execution costs. The proposed framework suggests four key agent services: (1) intelligent partitioning: responsible for microservice classification; (2) dynamic allocation: used for pre-execution distribution of microservices among containers and then makes decisions for dynamic allocation of microservices at runtime; (3) resource optimization: in charge of shifting workloads and ensuring optimal resource use; (4) mutation actions: these are based on procedures that will mutate the microservices based on cloud data center workloads. The suggested framework was partially evaluated using a custom-built simulation environment, which demonstrated its efficiency and potential for implementation in a cloud computing context. The findings show that the engrossment of suggested services can lead to a reduced number of network calls, lower energy consumption, and relatively reduced carbon dioxide emissions.
EssayGAN: Essay Data Augmentation Based on Generative Adversarial Networks for Automated Essay Scoring
Authors: Park Yo-Han , Choi Yong-Seok , Park Cheon-Young , Lee Kong-Joo
Published: June 2022

In large-scale testing and e-learning environments, automated essay scoring (AES) can relieve the burden upon human raters by replacing human grading with machine grading. However, building AES systems based on deep learning requires a training dataset consisting of essays that are manually rated with scores. In this study, we introduce EssayGAN, an automatic essay generator based on generative adversarial networks (GANs). To generate essays rated with scores, EssayGAN has multiple generators for each score range and one discriminator. Each generator is dedicated to a specific score and can generate an essay rated with that score. Therefore, the generators can focus only on generating a realistic-looking essay that can fool the discriminator without considering the target score. Although ordinary-text GANs generate text on a word basis, EssayGAN generates essays on a sentence basis. Therefore, EssayGAN can compose not only a long essay by predicting a sentence instead of a word at each step, but can also compose a score-rated essay by adopting multiple generators dedicated to the target score. Since EssayGAN can generate score-rated essays, the generated essays can be used in the supervised learning process for AES systems. Experimental results show that data augmentation using augmented essays helps to improve the performance of AES systems. We conclude that EssayGAN can generate essays not only consisting of multiple sentences but also maintaining coherence between sentences in essays.
EmotIoT: An IoT System to Improve Users’ Wellbeing
Authors: Navarro-Alamán Javier , Lacuesta Raquel , García-Magariño Iván , Lloret Jaime
Published: June 2022

IoT provides applications and possibilities to improve people’s daily lives and business environments. However, most of these technologies have not been exploited in the field of emotions. With the amount of data that can be collected through IoT, emotions could be detected and anticipated. Since the study of related works indicates a lack of methodological approaches in designing IoT systems from the perspective of emotions and smart adaption rules, we introduce a methodology that can help design IoT systems quickly in this scenario, where the detection of users is valuable. In order to test the methodology presented, we apply the proposed stages to design an IoT smart recommender system named EmotIoT. The system allows anticipating and predicting future users’ emotions using parameters collected from IoT devices. It recommends new activities for the user in order to obtain a final state. Test results validate our recommender system as it has obtained more than 80% accuracy in predicting future user emotions.
Weighted Averaging Federated Learning Based on Example Forgetting Events in Label Imbalanced Non-IID
Authors: Hong Mannsoo , Kang Seok-Kyu , Lee Jee-Hyong
Published: June 2022

Federated learning, a data privacy-focused distributed learning method, trains a model by aggregating local knowledge from clients. Each client collects and utilizes its own local dataset to train a local model. Local models in the connected federated learning network are uploaded to the server. In the server, local models are aggregated into a global model. During the process, no local data is transmitted in or out of any client. This procedure may protect data privacy; however, federated learning has a worse case of example forgetting problem than centralized learning. The problem manifests in lower performance in testing. We propose federated weighted averaging (FedWAvg). FedWAvg identifies forgettable examples in each client and utilizes that information to rebalance local models via weighting. By weighting clients with more forgettable examples, such clients are better represented and global models can acquire more knowledge from normally neglected clients. FedWAvg diminishes the example forgetting problem and achieve better performance. Our experiments on SVHN and CIFAR-10 datasets demonstrate that our proposed method gets improved performance compared to existing federated learning algorithm in non-IID settings, and that our proposed method can palliate the example forgetting problem.
Long-Term Performance of the Water Infiltration and Stability of Fill Side Slope against Wetting in Expressways
Authors: Wu Yuedong , Zhou Xiangyu , Liu Jian
Published: June 2022

Different settlements and instabilities of unsaturated subgrade subjected to wetting have been paid increasing attention in the southeast coastal areas of China. However, the treatments are costly when they are used in engineering. In addition, the long-term performances of the treatments are unclear. Based on seepage theory for unsaturated soils, a novel subgrade using a capillary barrier was proposed in this study to reduce the different settlements and stabilities. Compared with previous studies, a capillary barrier was merely applied in the landfill. The long-term performance and feasibility of a capillary barrier applied in a tilted subgrade slope is worthy of study, particularly in humid climates. Using Geo-Studio, the feasibility was verified by comparing a conventional subgrade with a subgrade using a capillary barrier in southeast coastal areas in terms of pore-water pressure, water content, settlement, and the safety factor. The numerical results showed that the subgrade using a capillary barrier could provide significant improvements in the performance of reducing the impact of pore-water pressure distribution it suffered from, so as to lead to smaller different settlements. The vertical settlement of the pavement using a capillary barrier over a 1 year period was 1 cm. Compared with a conventional subgrade, the settlement fell by 94%, and the safely factor increased by 15% for the subgrade using the capillary barrier.
Three-Dimensional Distance Mapping Method to Evaluate Mandibular Symmetry and Morphology of Adults with Unilateral Premolar Scissors Bite
Authors: Xie Yajuan , Lyu Xinwei , Liu Yuyao , Zeng Runling , Liao Yuwei , Tan Jiali
Published: June 2022

(1) Objective: This study aimed to evaluate the association between unilateral premolar scissors bite and mandibular symmetry of adults via the 3D distance mapping method. (2) Methods: A total of 53 cone-beam computed tomography (CBCT) images of adults with unilateral premolar scissors bite were set as study samples. A total of 53 age- and sex-matched samples without scissors bite were in the control group. Three-dimensional mandibular models and seven mandibular functional units, including condylar process (Co), coronoid process (Cr), mandibular ramus (Ra), mandibular angle (Ma), alveolar process (Ap), mandibular body (Mb), and chin process (Ch) were constructed and mirrored. After superimposition of the original and the mirrored models, 3D distance maps and deviation analysis were performed to evaluate the mandibular symmetry and morphology. (3) Results: In the study group, the matching percentages of the entire mandible (50.79 ± 10.38%), Ap (67.00 ± 12.68%), Mb (66.62 ± 9.44%), Ra (62.52 ± 11.00%), Ch (80.75 ± 9.86%), and Co (62.78 ± 13.56) were lower than that of the entire mandible (58.60 ± 5.52) (p < 0.01), Ap (73.83 ± 8.88%) (p < 0.01), Mb (72.37 ± 8.69%) (p < 0.01), Ra (68.60 ± 7.56%) (p < 0.01), Ch (85.23 ± 6.80%) (p < 0.01), and Co (67.58 ± 10.32%) (p < 0.05) in the control group. However, Cr and Ma showed no significant difference (p > 0.05). (4) Conclusions: The 3D distance mapping method provided a qualitative and quantitative mandibular symmetry and morphology assessment. Mandibular asymmetry was found in adults with unilateral premolar scissors bites. Mandibular functional units, including the alveolar process, mandibular body, mandibular ramus, chin process, and condylar process, showed significant differences, while no significant difference was observed in the coronoid process and mandibular angle.
A Molecular View on Biomaterials and Dental Stem Cells Interactions: Literature Review
Authors: Aguilar-Ayala Fernando J. , Aguilar-Pérez Fernando J. , Nic-Can Geovanny I. , Rojas-Herrera Rafael , Chuc-Gamboa Gabriela , Aguilar-Pérez David , Rodas-Junco Beatriz A.
Published: June 2022

Biomaterials and stem cells are essential components in the field of regenerative medicine. Various biomaterials have been designed that have appropriate biochemical and biophysical characteristics to mimic the microenvironment of an extracellular matrix. Dental stem cells (DT-MSCs) represent a novel source for the development of autologous therapies due to their easy availability. Although research on biomaterials and DT-MSCs has progressed, there are still challenges in the characteristics of biomaterials and the molecular mechanisms involved in regulating the behavior of DT-MSCs. In this review, the characteristics of biomaterials are summarized, and their classification according to their source, bioactivity, and different biological effects on the expansion and differentiation of DT-MSCs is summarized. Finally, advances in research on the interaction of biomaterials and the molecular components involved (mechanosensors and mechanotransduction) in DT-MSCs during their proliferation and differentiation are analyzed. Understanding the molecular dynamics of DT-MSCs and biomaterials can contribute to research in regenerative medicine and the development of autologous stem cell therapies.
Trace Metal Partitioning in the Salinity Gradient of the Highly Stratified Estuary: A Case Study in the Krka River Estuary (Croatia)
Authors: Marcinek Saša , Cindrić Ana Marija , Pađan Jasmin , Omanović Dario
Published: June 2022

A size partitioning of several trace metals (Zn, Cd, Pb, Cu, Ni, Co, Mn, Fe and Al) between five size fractions (<3 kDa, 3 kDa–0.1 µm, 0.1 µm–1.2 µm, 1.2 µm–5 µm and >5 µm) was studied in the vertical salinity gradient of the highly stratified Krka River estuary. The results indicated a dominant river source for Zn, Co, Mn, Fe and Al and a diluting effect on Cd, Pb and Ni. The truly dissolved fraction (<3 kDa) dominated the Zn, Cd, Cu, Ni and Co pool, and a large part of Pb, Mn, Fe and Al was present in >5 µm particles. Pb, Mn, Fe and Al were closely related, showing a precipitation and colloidal aggregation in the surface layers and dissolution in the seawater layer. The highest percentage (30–37%) of colloids (3 kDa–0.1 µm) in the dissolved pool was found for Pb, Cu, Fe and Al. Differences in size distribution between low and high river flow periods revealed that Zn, Pb, Co, Mn, Fe and Al are introduced by the river mostly in the 3 kDa–5 µm size range. Therefore, a low percentage of colloidally bound metals compared to other coastal areas can be explained by a limited riverine input of terrigenous material, characteristic for this estuary. Correlation with PARAFAC components revealed associations of Cu with protein-like substances and Co with humic-like substances. The accumulation of Cu at the freshwater-seawater interface coupled with an increase of its colloidal fraction was observed, apparently governed by biologically produced organic ligands.
Improving on Atmospheric Turbulence Profiles Derived from Dual Beacon Hartmann Turbulence Sensor Measurements
Authors: Boeckenstedt Alexander , McCrae Jack , Bose-Pillai Santasri , Wilson Benjamin , Fiorino Steven
Published: June 2022

Atmospheric turbulence is an inevitable source of wavefront distortion in all fields of long range laser propagation and sensing. However, the distorting effects of turbulence can be corrected using wavefront sensors contained in adaptive optics systems. Such systems also provide deeper insight into surface layer turbulence, which is not well understood. A unique method of profile generation by a dual source Hartmann Turbulence Sensor (HTS) technique is introduced here. Measurements of optical turbulence along a horizontal path were taken to create Cn2 profiles. Two helium-neon laser beams were directed over an inhomogeneous horizontal path and captured by the HTS. The measured differential tilt variances imposed on the laser wavefronts were used in conjunction with a set of computed weighting functions to profile the turbulence over the sensing path. The weighting function matrix is inherently ill-conditioned, therefore, Tikhonov regularization was applied to produce accurate Cn2 profiles. A distribution of sonic anemometers and a co-located boundary layer scintillometer (BLS) collected independent Cn2 measurements to add confidence to the HTS profiles. The Cn2 profiles generated by this approach agree very well with the auxiliary anemometer and scintillometer measurements. This method of producing turbulence profiles may be useful in future multi-conjugate adaptive optics applications.
Experimental Research on Dynamic Filtering Characteristics of Filter Materials for Electrostatic-Fabric Integrated Precipitator
Authors: Chen Kuixu , Huang Yaji , Wang Sheng , Zhu Zhaoping , Cheng Haoqiang
Published: June 2022

In recent years, the electrostatic-fabric integrated precipitator has been widely used, and the dust filtration performance of the core component filter bag is the most important factor affecting its dust removal efficiency. In this work, the dynamic dust removal performance of different types of filter media and different experimental conditions were studied on the filter media filtration performance test platform. The experimental study of the filtration performance of different types of filter media showed that the filtration performance stability of polyphenylene sulfide (PPS) filter media was better than that of polyimide (PI) and polytetrafluoroethylene (PTFE) filter media. Increasing the mass per unit area of the filter media had obvious advantages in the short term, and the impregnation process was beneficial to the filter performance of the filter media. Membrane-coated filter media had the best filtration performance, gradient filter media filtration performance was the second, followed by conventional filter media, ordinary blended, and ultrafine blended filter media. Studies with different experimental conditions found that the filtration efficiency increased with increasing resistance, was not significantly affected by changes in inlet dust concentration, but decreased with the increasing filtering velocity. This experimental results provided an important basis for the selection of filter bags for the electrostatic-fabric integrated precipitator project.
The Relationship between Process Capability and Quality of Measurement System
Authors: Markulik Štefan , Petrík Jozef , Šolc Marek , Blaško Peter , Palfy Pavol , Girmanová Lenka
Published: June 2022

This article deals with the design of appropriate measures that had to be taken for the implemented measurement system. The measurement result was significantly negatively affected by several factors. Forty-five samples of shafts used in the production of surgical drills were measured. Measurements were performed by metrological appraisers with a calibrated digital micrometer. Measurement and subsequent data processing revealed low process capability (CP and PP indices). A large portion of the shafts had an observed size below the lower specific limit (LSL). Therefore, it was necessary to take corrective action. This paper focuses on the corrective measures implemented in the measurement system. The micrometer met the requirements of the standard and was metrologically capable. The shafts were measured by eight metrological appraisers, so attention was focused on the potential impact of the metrological appraiser. The measured data were evaluated by uncertainty analysis, paired t-tests, measurement systems analysis (MSA) and Cohen’s kappa. The number of non-compliant shafts was shown to increase with decreasing measurement capability. The measurement system was evaluated as conditionally capable, even incapable. One possibility was to identify the optimal pair of metrological appraisers. The pair of metrological appraisers E and F appeared to be the most suitable combination for most methods. Due to the relatively high %EV index, the second option was to improve the work with the measuring instrument, that is, improve the training and supervision of metrological appraisers in the measurement process. Repeated measurements by the pair with the highest capability (metrological appraisers E and F) resulted in an increase in the value of the capability indices and a decrease in the number of shafts out of tolerance for the same shafts. As the value of these indices was lower than 1.33 during repeated measurements, corrective measures had to be taken, not only in the measurement system, but also in the production system.
Discrimination, Bias, Fairness, and Trustworthy AI
Authors: Varona Daniel , Suárez Juan Luis
Published: June 2022

In this study, we analyze “Discrimination”, ”Bias”, “Fairness”, and “Trustworthiness” as working variables in the context of the social impact of AI. It has been identified that there exists a set of specialized variables, such as security, privacy, responsibility, etc., that are used to operationalize the principles in the Principled AI International Framework. These variables are defined in such a way that they contribute to others of more general scope, for example, the ones studied in this study, in what appears to be a generalization–specialization relationship. Our aim in this study is to comprehend how we can use available notions of bias, discrimination, fairness, and other related variables that will be assured during the software project’s lifecycle (security, privacy, responsibility, etc.) when developing trustworthy algorithmic decision-making systems (ADMS). Bias, discrimination, and fairness are mainly approached with an operational interest by the Principled AI International Framework, so we included sources from outside the framework to complement (from a conceptual standpoint) their study and their relationship with each other.
Study on the Position Deviation between the Iron Roughneck’s Spin-Rollers and the Drilling Tool
Authors: Sha Yongbai , Li Quan , Zhao Xiaoying
Published: June 2022

The spinner mechanism is one of the main working mechanisms of the iron roughneck, which is used to realize the rapid screwing-in or screwing-out of the drill pipe thread. The main problems of the spinner mechanism and the existing solutions are analyzed in this paper, and the spinner mechanism driven by a single hydraulic cylinder and transmitted by a wedge plate is designed. The upper and lower drilling tools are not concentric and affect the spin effect when the spinner mechanism works, and to address this problem, the influence of deviation in different directions is analyzed, and it is proposed that the lateral position deviation of drilling tools is the key factor affecting the spin performance. The relationship between the position deviation of the spin-roller and the position deviation of the drilling tool is established and solved. A method to reduce the lateral position deviation of drilling tools is proposed, and the effectiveness of this method is verified by experiments. The analysis shows that a spinner mechanism with a single follow-up roller easily causes the lateral position deviation of the drilling tool; the deviation of the spin-roller increases with the increase of the lateral deviation of the drilling tool, and the increase of the longitudinal deviation is more obvious; and with the increase of the drilling tool’s diameter, the deviation increases further. The symmetrical double follow-up roller structure can effectively reduce the lateral deviation of drilling tools and spin-rollers and ensures centering performance and the realization of reliable spin.
Learning Robust Shape-Indexed Features for Facial Landmark Detection
Authors: Wan Xintong , Wu Yifan , Li Xiaoqiang
Published: June 2022

In facial landmark detection, extracting shape-indexed features is widely applied in existing methods to impose shape constraint over landmarks. Commonly, these methods crop shape-indexed patches surrounding landmarks of a given initial shape. All landmarks are then detected jointly based on these patches, with shape constraint naturally embedded in the regressor. However, there are still two remaining challenges that cause the degradation of these methods. First, the initial shape may seriously deviate from the ground truth when presented with a large pose, resulting in considerable noise in the shape-indexed features. Second, extracting local patch features is vulnerable to occlusions due to missing facial context information under severe occlusion. To address the issues above, this paper proposes a facial landmark detection algorithm named Sparse-To-Dense Network (STDN). First, STDN employs a lightweight network to detect sparse facial landmarks and forms a reinitialized shape, which can efficiently improve the quality of cropped patches when presented with large poses. Then, a group-relational module is used to exploit the inherent geometric relations of the face, which further enhances the shape constraint against occlusion. Our method achieves 4.64% mean error with 1.97% failure rate on COFW68 dataset, 3.48% mean error with 0.43% failure rate on 300 W dataset and 7.12% mean error with 11.61% failure rate on Masked 300 W dataset. The results demonstrate that STDN achieves outstanding performance in comparison to state-of-the-art methods, especially on occlusion datasets.
Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
Authors: Lin Junjie , Liu Xu , Zhou Yalan , Zhu Baishu , Wang Yuanxin , Cui Wei , Peng Yan , Wang Bin , Zhao Chen , Zhao Renqing
Published: June 2022

Insulin resistance is recognized as one major feature of metabolic syndrome, and frequently emerges as a difficult problem encountered during long-term pharmacological treatment of diabetes. Insulin resistance often causes organs or tissues, such as skeletal muscle, adipose, and liver, to become less responsive or resistant to insulin. Exercise can promote the physiological function of those organs and tissues and benefits insulin action via increasing insulin receptor sensitivity, glucose uptake, and mitochondrial function. This is done by decreasing adipose tissue deposition, inflammatory cytokines, and oxidative stress. However, understanding the mechanism that regulates the interaction between exercise and insulin function becomes a challenging task. As a novel myokine, irisin is activated by exercise, released from the muscle, and affects multi-organ functions. Recent evidence indicates that it can promote glucose uptake, improve mitochondrial function, alleviate obesity, and decrease inflammation, as a result leading to the improvement of insulin action. We here will review the current evidence concerning the signaling pathways by which irisin regulates the effect of exercise on the up-regulation of insulin action in humans and animals.
Four-Channel Buck-Type LED Driver with Automatic Current Sharing and Soft Switching
Authors: Jiang Wen-Zhuang , Hwu Kuo-Ing , Shieh Jenn-Jong
Published: June 2022

A buck-type LED driver together with automatic current sharing and high step-down voltage conversion ratio but without complex control is proposed. The proposed LED driver can not only achieve zero voltage switching (ZVS) turn-on by adding only one resonant coupled inductor, which resonates with parasitic capacitors of active switches, but also can obtain lower voltage gain and better conversion efficiency. In this paper, the operating principles and design considerations of the proposed converter are discussed in detail. In addition, the number of LED strings can be extended to more than four channels. Finally, the theoretical analysis and performance of the proposed LED driver are verified by simulations and experiments using a field-programmable logic gate array (FPGA) named EP3C5E144C8N as a circuit control kernel.
Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
Authors: Long Xiangan , Ma Chongjun , Sheng Han , Chen Liwen , Fei Yiyan , Mi Lan , Han Dongmei , Ma Jiong
Published: June 2022

Subvisible particles are an ongoing problem in biotherapeutic injectable pharmaceutical formulations, and their identification is an important prerequisite for tracing them back to their source and optimizing the process. Flow imaging microscopy (FIM) is a favored imaging technique, mainly because of its ability to achieve rapid batch imaging of subvisible particles in solution with excellent imaging quality. This study used VGG16 after transfer learning to identify subvisible particle images acquired using FlowCam. We manually prepared standards for seven classes of particles, acquired the image information through FlowCam, and fed the images over 5 µm into VGG16 consisting of a convolutional base of VGG16 pre-trained with ImageNet data and a custom classifier for training. An accuracy of 97.51% was obtained for the test set data. The study also demonstrated that the recognition method using transfer learning outperforms machine learning methods based on morphological parameters in terms of accuracy, and has a significant training speed advantage over scratch-trained CNN. The combination of transfer learning and FIM images is expected to provide a general and accurate data-analysis method for identifying subvisible particles.
On Hyperbolic Complex Numbers
Authors: Richter Wolf-Dieter
Published: June 2022

For dimensions two, three and four, we derive hyperbolic complex algebraic structures on the basis of suitably defined vector products and powers which allow in a standard way a series definitions of the hyperbolic vector exponential function. In doing so, we both modify arrow multiplication, which, according to Feynman, is fundamental for quantum electrodynamics, and we give a geometric explanation of why in a certain situation it is natural to define random vector products. Through the interplay of vector algebra, geometry and complex analysis, we extend a systematic approach previously developed for various other complex algebraic structures to the field of hyperbolic complex numbers. We discuss a quadratic vector equation and the property of hyperbolically holomorphic functions of satisfying hyperbolically modified Cauchy–Riemann differential equations and also give a proof of an Euler type formula based on series expansion.
Hydraulic Fracture Propagation in Fractured Rock Mass
Authors: Liu Taoying , Sheng Yeshan , Li Qing , Zhang Chaoyang , Cui Mengyuan , Yu Zhanfu , Cao Ping
Published: June 2022

According to fracture mechanics theory, the initial cracking law and wing crack propagation model of compression shear rock cracks subjected to hydraulic pressure and far field stresses are discussed. The results of the theoretical study show that crack initiation strength is inversely related to hydraulic pressure, that hydraulic pressure aggravates the wing crack’s growth, and that the wing crack’s behavior under high hydraulic pressure shifts from stable to unstable expansion. The confining pressure is proportional to the rock mass strength, the wing crack’s stress intensity factor drops as the remote field stress σ3 increases, and the wing crack tends to expand stably. As the crack angle increases, the cracking strength reduces at first, then increases. At the same time, damage fracture mechanics models are established for the occurrence of wing crack-wing crack failure, wing crack-shear crack failure, wing crack-shear crack-wing crack failure, and shear crack-shear crack failure in the compression-shear crack bridge under far-field stress and hydraulic pressure. The link between hydraulic pressure, confining pressure, fracture angle, stress intensity factor, and compression-shear factor λ12 is also clarified. The value of the stress intensity factor increases when the hydraulic pressure decreases, the confining pressure increases, and the crack angle increases, whereas the compression-shear factor decreases. This study lays the groundwork for a quantitative assessment of fractured rock mass destruction under hydraulic pressure.
LUMA: A Mapping Assistant for Standardizing the Units of LOINC-Coded Laboratory Tests
Authors: Vogl Kai , Ingenerf Josef , Kramer Jan , Chantraine Christine , Drenkhahn Cora
Published: June 2022

The coding system Unified Code for Units of Measure (UCUM) serves the unambiguous electronic communication of physical quantities and their measurements and has faced a slow uptake. Despite being closely related to popular healthcare standards such as LOINC, laboratories still majorly report results using proprietary unit terms. Currently available methods helping users create mappings between their units and UCUM are not flexible and automated enough to be of great use in trying to remedy this. We propose the “LOINC to UCUM Mapping Assistant” (LUMA) as a tool able to overcome the drawbacks of existing approaches while being more accessible even to inexperienced users. By mapping LOINC’s Property axis to representations within UCUM reflecting its semantics, we were able to formalize the association between the two. An HL7 FHIR back-end provides LUMA with UCUM unit recommendations sourced from existing lookup tables simply by providing it with a LOINC code. Additionally, the mappings users created may be used to perform unit conversions from proprietary units to UCUM. The tool was evaluated with five participants from the LADR laboratory network in Germany, who valued the streamlined approach to creating the mappings and particularly emphasized the utility of being able to perform unit conversions within the tool.
Dispersion of Knee Helical Axes during Walking after Maximal versus Resistant Strength Training in Healthy Subjects
Authors: Adamo Paola , Oddenino Francesco , De Leo Davide , Agnelli Miriana , Cescon Corrado , Temporiti Federico , Gatti Roberto
Published: June 2022

Knee joint stability can be estimated during functional tasks through the analysis of the helical axes (HAs) dispersion. The study aimed at investigating (1) the test–retest reliability of knee HAs dispersion during walking and (2) the effects of maximal versus resistant strength training on knee HAs dispersion during walking. Thirty healthy subjects (age: 22.6 ± 2.1 years) randomized into a maximal training (MT) group and a resistance training (RT) group underwent a 2-week quadricep–hamstring strength training at 90% or 30% of the maximum voluntary contraction, respectively. Participants walked on a treadmill with clusters of retro-reflective markers placed on thighs and shanks to detect knee kinematics with an optoelectronic system. Knee HAs dispersion was assessed using mean distance (MD) and mean angle (MA) at 1 week before training start, before and after the first training session, and before and after the last training session. Moderate to excellent reliability was found for MD and MA on the sagittal plane (ICCs ≥ 0.70). No differences over time were found for MD and MA between MT and RT. HAs dispersion indexes resulted in reliable parameters for the quantification of knee stability on the sagittal plane during walking. Maximal and resistance strength training induced no knee HAs dispersion changes during walking.
Hyperspectral Identification of Ginseng Growth Years and Spectral Importance Analysis Based on Random Forest
Authors: Zhao Limin , Liu Shumin , Chen Xingfeng , Wu Zengwei , Yang Rui , Shi Tingting , Zhang Yunli , Zhou Kaiwen , Li Jiaguo
Published: June 2022

The growth year of ginseng is very important as it affects its economic value and even defines if ginseng can be used as medicine or food. In the case of large-scale developments in the ginseng industry, a set of non-destructive, fast, and nonprofessional operations related to the growth year identification method is needed. The characteristics of ginseng reflectance spectral data were analyzed, and the growth year recognition model was constructed by a decision-tree-based random forest machine learning method. After independent verification, the accuracy of distinguishing ginseng food and medicine can reach 92.9%, with 6-year growth as the boundary, and 100%, with 5-year growth as the boundary. The research results show that the spectral change of ginseng is the most obvious in the fifth year, which provides a reference for the key research years based on chemical analyses and other methods. For the application of growth year recognition, the NIR band (1000–2500 nm) had little contribution to the recognition of ginseng growth years, and the band with the largest contribution was 400–650 nm. The recognition model based on machine learning provides a non-destructive, fast, and simple scheme with high accuracy for ginseng year recognition, and the spectral importance analysis conclusion of ginseng growth years provides a design reference for the development of special lightweight spectral equipment for year recognition.
ASAD: Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
Authors: Wang Hao , Zhang Yuanyuan , Liu Yijia , Liu Fenglin , Zhang Hanyang , Xing Bin , Xing Minghai , Wu Qiong , Chen Liangyin
Published: June 2022

Anomaly detection is the foundation of intelligent operation and maintenance (O&M), and detection objects are evaluated by key performance indicators (KPIs). For almost all computer O&M systems, KPIs are usually the machine-level operating data. Moreover, these high-frequency KPIs show a non-Gaussian distribution and are hard to model, i.e., they are intricate KPI profiles. However, existing anomaly detection techniques are incapable of adapting to intricate KPI profiles. In order to enhance the performance under intricate KPI profiles, this study presents a seasonal adaptive KPI anomaly detection algorithm ASAD (Adaptive Seasonality Anomaly Detection). We also propose a new eBeats clustering algorithm and calendar-based correlation method to further reduce the detection time and error. Through experimental tests, our ASAD algorithm has the best overall performance compared to other KPI anomaly detection methods.
Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
Authors: Minopoulos Georgios M. , Memos Vasileios A. , Stergiou Christos L. , Stergiou Konstantinos D. , Plageras Andreas P. , Koidou Maria P. , Psannis Konstantinos E.
Published: June 2022

Current medical methods still confront numerous limitations and barriers to detect and fight against illnesses and disorders. The introduction of emerging technologies in the healthcare industry is anticipated to enable novel medical techniques for an efficient and effective smart healthcare system. Internet of Things (IoT), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), and Cloud Computing (CC) can play a vital role in the instant detection of illnesses, diseases, viruses, or disorders. Complicated techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) could provide acceleration in drug and antibiotics discovery. Moreover, the integration of visualization techniques such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) with Tactile Internet (TI), can be applied from the medical staff to provide the most accurate diagnosis and treatment for the patients. A novel system architecture, which combines several future technologies, is proposed in this paper. The objective is to describe the integration of a mixture of emerging technologies in assistance with advanced networks to provide a smart healthcare system that may be established in hospitals or medical centers. Such a system will be able to deliver immediate and accurate data to the medical stuff in order to aim them in order to provide precise patient diagnosis and treatment.
Sustainable Use of By-Products and Wastes from Greece to Produce Innovative Eco-Friendly Pervious Concrete
Authors: Petrounias Petros , Giannakopoulou Panagiota P. , Rogkala Aikaterini , Kalpogiannaki Maria , Laskaris Nikolaos , Lampropoulou Paraskevi , Mouzakis Panagiotis , Panagiotaras Dionisios , Koukouzas Nikolaos
Published: June 2022

This study was based on the reduction of the extraction of natural resources and, at the same time, was focused on the use of by-products and various wastes in construction applications by following the principles of circular economy. Sterile natural rocks (limestones, basalts), industrial by-products (slags), hotel construction wastes (bathroom wastes) and electronic wastes (e-wastes) were tested for pervious concrete aggregates. For this reason, ten concrete specimens were prepared and tested petrographically, structurally, and physically. The physical properties of the tested raw materials directly depended on their petrographic characteristics and played crucial role for the permeability of the produced concrete specimens, for their mechanical behavior, and for the freeze–thaw test results. Generally, from this study, strong encouraging results were achieved as concrete made by variable wastes and by-products can be compatible for concrete production as they show similar performance both in the mechanical strength test and in the freeze–thaw test with those made by natural aggregates. Another goal of this study was to recommend to other researchers the extended use of by-products, construction wastes, and e-wastes as concrete aggregates for producing eco-friendly constructions.
Correction: Seo, S. Line-Detection Based on the Sum of Gradient Angle Differences. Appl. Sci. 2020, 10, 254
Authors: Seo Suyoung
Published: June 2022

In the original publication [...]
GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
Authors: Zhou Fangming , Zhao Lulu , Jiang Xinglong , Li Limin , Yu Jinpei , Liang Guang
Published: June 2022

Due to the sparsity of GNSS signal in the correlation domain, compressed sensing theory is considered to be a promising technology for GNSS signal acquisition. However, the detection probability of the traditional compression acquisition algorithm is low under low signal-to-noise ratio (SNR) conditions. This paper proposes a GNSS compression acquisition algorithm based on sensing matrix optimization. The Frobenius norm of the difference between Gram matrix and an approximate equiangular tight frame (ETF) matrix is taken as the objective function, and the modified conjugate gradient method is adopted to reduce the mutual coherence between the measurement matrix and the sparse basis. Theoretical analysis and simulation results show that the proposed algorithm can significantly improve the detection probability compared with the existing compression acquisition algorithms under the same SNR.
Tea (Camellia sinensis): A Review of Nutritional Composition, Potential Applications, and Omics Research
Authors: Wang Cheng , Han Jingxue , Pu Yuting , Wang Xiaojing
Published: June 2022

Tea (Camelliasinensis) is the world’s most widely consumed non-alcoholic beverage with essential economic and health benefits since it is an excellent source of polyphenols, catechins, amino acids, flavonoids, carotenoids, vitamins, and polysaccharides. The aim of this review is to summarize the main secondary metabolites in tea plants, and the content and distribution of these compounds in six different types of tea and different organs of tea plant were further investigated. The application of these secondary metabolites on food processing, cosmetics industry, and pharmaceutical industry was reviewed in this study. With the rapid advancements in biotechnology and sequencing technology, omics analyses, including genome, transcriptome, and metabolome, were widely used to detect the main secondary metabolites and their molecular regulatory mechanisms in tea plants. Numerous functional genes and regulatory factors have been discovered, studied, and applied to improve tea plants. Research advances, including secondary metabolites, applications, omics research, and functional gene mining, are comprehensively reviewed here. Further exploration and application trends are briefly described. This review provides a reference for basic and applied research on tea plants.
The Free-Energy Landscape of a Mechanically Bistable DNA Origami
Authors: Wong Chak Kui , Doye Jonathan P. K.
Published: June 2022

Molecular simulations using coarse-grained models allow the structure, dynamics and mechanics of DNA origamis to be comprehensively characterized. Here, we focus on the free-energy landscape of a jointed DNA origami that has been designed to exhibit two mechanically stable states and for which a bistable landscape has been inferred from ensembles of structures visualized by electron microscopy. Surprisingly, simulations using the oxDNA model predict that the defect-free origami has a single free-energy minimum. The expected second state is not stable because the hinge joints do not simply allow free angular motion but instead lead to increasing free-energetic penalties as the joint angles relevant to the second state are approached. This raises interesting questions about the cause of this difference between simulations and experiment, such as how assembly defects might affect the ensemble of structures observed experimentally.
Implementing Data-Driven Approach for Modelling Ultrasonic Wave Propagation Using Spatio-Temporal Deep Learning (SDL)
Authors: Gantala Thulsiram , Balasubramaniam Krishnan
Published: June 2022

In this paper, we proposed a data-driven spatio-temporal deep learning (SDL) model, to simulate forward and reflected ultrasonic wave propagation in the 2D geometrical domain, by implementing the convolutional long short-term memory (ConvLSTM) algorithm. The SDL model learns underlying wave physics from the spatio-temporal datasets. Two different SDL models are trained, with the following time-domain finite element (FE) simulation datasets, by applying: (1) multi-point excitation sources inside the domain and (2) single-point excitation sources on the edge of the different geometrical domains. The proposed SDL models simulate ultrasonic wave dynamics, for the forward ultrasonic wave propagation in the different geometrical domains and reflected wave propagation phenomenon, from the geometrical boundaries such as curved, T-shaped, triangular, and rectangular domains, with varying frequencies and cycles. The SDL is a reliable model, which generates simulations faster than the conventional finite element solvers.
Design of a Laser Driver and Its Application in Gas Sensing
Authors: Cong Menglong , Zhang Shanshan , Wang Yiding , Liang Dachao , Zhou Kunpeng
Published: June 2022

A laser driver which features high stability and a graphical user-interface was designed and used in trace gas sensing. The running of the laser driver was managed by an ARM processor which was embedded with a real-time operating system (RTOS). Through clicking on the touch screen that was configured with an emWin graphical user-interface (GUI), the parameters of the driving current can be graphically set and monitored. The circuit model of the distributed feedback laser diode (DFB-LD) was introduced into a TINA-SPICE simulation to evaluate the performance of the current source. Through simulation, the potential self-oscillation can be visually predicted, and the feedback loop can be appropriately compensated. To validate the applicability, the laser driver was used for driving a carefully selected DFB-LD and was employed in wavelength modulation spectroscopy (WMS) for CH4 detection at R(3) absorption line of the 2ν3 overtone. Under the conditions of room temperature, normal pressure and an effective absorption path of 15.4 cm, repetitive experiments were conducted using gas samples, with their concentrations ranging from 400 ppm to 1%, and the detection limit derived from the signal-to-noise ratio (SNR) was 7.2 ppm. The promising result indicates the high potential of this laser driver for use in absorption spectrum-based sensing applications.
Experiment and Analysis of Film-Soil Separation Motion Characteristics of a Chain Drive Residual Film Recovery Mechanism for the Tillage Layer
Authors: Shi Zenglu , Zhang Xuejun , Yan Jinshan , Jiang Yanwu , Yao Jieting
Published: June 2022

The working principle and key components of a chain drive residual film recovery machine were studied. A kinematic analysis of the chain drive soil separation mechanism was completed by applying ADAMS software to obtain the trajectory, displacement, velocity and acceleration of the center of mass of the chain plate link_1 in the x- and y-directions within the effective shaking displacement. A field test of the chain drive separation mechanism was carried out. When the shaking roller was in contact with the chain, the change in speed of the chain in the x- and y-directions was not significant, but the force in the y-direction was conducive to breaking the soil pieces and improving the rate of collecting the residual film. The change in the speed of link_1 in the y-direction speed with x-direction speed explained the action of the separation mechanism on conveying and throwing of the film-soil mixture in the horizontal and vertical directions, which revealed the mechanism of the chain drive film-soil separation equipment. The field test showed that the film collection rate of the chain drive separation mechanism was 68%, and the surface residue was 6%. The test results meet the relevant national and industry standards and design requirements. The results of this study provide guidance for the design and optimization of chain drive film-soil separation equipment.