bluuebunny/crossref_metadata_2025 huggingface dataset:
Proceedings of the Dual Use Research of Concern Panel Discussion: challenges and perspectives
Authors: Rohden Fabian , Nelson Christopher J. , Yost Christopher K. , Anderson Cody , Moritz Rebecca , Vinke Svenja , Wieden Hans-Joachim
Published: March 2022

To address real and perceived emerging risks originating from the ever-accelerating breakthroughs in life science research, the Dual Use Research of Concern (DURC) Panel Discussion, organized by Synbio Canada and the Alberta RNA Research and Training Institute (ARRTI), took place on June 23rd, 2021. It brought together six stakeholders from different levels of academic research, administration, governance, and science publishing to explore the current and future challenges in addressing DURC. Technological advancements within the life sciences, especially within the field of omics technology, make it difficult to apply a simple checklist for dual-use assessment and require continuous and integrated effort. Bottom-up approaches from within the scientific community are suggested by all stakeholders to enable efficient governance and address the true risks resulting from DURC, not just the alleged risks. To address such alleged risks, open and broadscale communication of DURC and its oversight policies may be required. At the same time, any form of open communication also contains the risk of information hazards, defined as potentially creating public fear or informing malicious actors. Here, an overview of the DURC panel and its outcomes is provided.
Recent advances in carbon footprint studies of urban ecosystems: overview, application, and future challenges
Authors: Chen Kexin , Yang Mingxia , Zhou Xiaolu , Liu Zelin , Li Peng , Tang Jiayi , Peng Changhui
Published: March 2022

Urban ecosystems are complex systems with anthropogenic features that generate considerable CO2 emissions, which contributes to global climate change. Quantitative estimates of the carbon footprint of urban ecosystems are crucial for developing low-carbon development policies to mitigate climate change. Herein, we reviewed more than 195 urban carbon footprint and carbon footprint related studies, collated the recent progress in carbon footprint calculation methods and research applications of the urban ecosystem carbon footprint, analyzed the research applications of the carbon footprint of different cities, and focused on the need to study the urban ecosystem carbon footprint from a holistic perspective. Specifically, we aimed to: (i) compare the strengths and weaknesses of five existing carbon footprint calculation methods [life cycle assessment, input–output analysis, hybrid life cycle assessment, carbon footprint calculator, and Intergovernmental Panel on Climate Change (IPCC)]; (ii) analyze the status of current research on the carbon footprint of different urban subregions based on different features; and (iii) highlight new methods and areas of research on the carbon footprint of future urban ecosystems. Not all carbon footprint accounting methods are applicable to the carbon footprint determination of urban ecosystems; although the IPCC method is more widely used than the others, the hybrid life cycle assessment method is more accurate. With the emergence of new science and technology, quantitative methods to calculate the carbon footprint of urban ecosystems have evolved, becoming more accurate. Further development of new technologies, such as big data and artificial intelligence, to assess the carbon footprint of urban ecosystems is anticipated to help address the emerging challenges in urban ecosystem research effectively to achieve carbon neutrality and urban sustainability under global change.
Peptidases: promising antifungal targets of the human fungal pathogen, Cryptococcus neoformans
Authors: Gutierrez-Gongora Davier , Geddes-McAlister Jennifer
Published: March 2022

Cryptococcus neoformans is a globally important fungal pathogen, primarily inflicting disease on immunocompromised individuals. The widespread use of antifungal agents in medicine and agriculture supports the development of antifungal resistance through evolution, and the emergence of new strains with intrinsic resistance drives the need for new therapeutics. For C. neoformans, the production of virulence factors, including extracellular peptidases (e.g., CnMpr-1 and May1) with mechanistic roles in tissue invasion and fungal survival, constitute approximately 2% of the fungal proteome and cover five classes of enzymes. Given their role in fungal virulence, peptidases represent promising targets for anti-virulence discovery in the development of new approaches against C. neoformans. Additionally, intracellular peptidases, which are involved in resistance mechanisms against current treatment options (e.g., azole drugs), as well as capsule biosynthesis and elaboration of virulence factors, present additional opportunities to combat the pathogen. In this review, we highlight key cryptococcal peptidases with defined or predicted roles in fungal virulence and assess sequence alignments against their human homologs. With this information, we define the feasibility of the select peptidases as “druggable” targets for inhibition, representing prospective therapeutic options against the deadly fungus.
Calibration of Mismatches in Time-Interleaved ADCs Using Teacher Learner-Based Optimization Algorithm
Authors: Chakravarthi M. V. N. , Chandramohan B.
Published: March 2022

Sampling a signal at elevated sampling rates can be easily achieved by using time-interleaved analog-to-digital converters (TIADCs). TIADCs have more than one ADC in parallel. Each ADC samples the signal with a time gap of one sampling period and hence known as TIADC. The samples from all these ADCs are combined to reconstruct the signal. But the disadvantage of TIADCs is that they have mismatches like sampling time, gain and phase offset. The proposed work focuses on estimation and correction of these mismatches. For estimation of mismatches, teacher learner-based optimization (TLBO) algorithm was used and the estimated mismatches were used for correction by applying suitable operations. The proposed algorithm was applied for four-channel TIADCs. For estimation, a pilot signal is used which in this case is a monotonic sinusoidal signal. The estimation of mismatches was accurate and the correction was implemented for TIADCs with a sinusoidal input signal and the enhancement in signal quality was evaluated by finding signal-to-noise ratio (SNR) and signal-to-noise and distortion ratio (SNDR). There is a significant enhancement in SNR and SNDR. The average enhancements in SNR and SNDR are 50 and 46[Formula: see text]dB, respectively.
Propagation phenomena in a diffusion system with the Belousov-Zhabotinskii chemical reaction
Authors: Sheng Wei-Jie , Wang Mingxin , Wang Zhi-Cheng
Published: March 2022

This paper is concerned with the propagation phenomena in a diffusion system with the Belousov–Zhabotinskii chemical reaction in [Formula: see text] under the bistability assumption. We prove that there is a new type of entire solution originated from three moving planar traveling fronts, and evolved to a V-shaped traveling front as time changes, which means that the profile of this solution is not invariant at all. Here an entire solution is referred to a solution that is defined for all time [Formula: see text] and in the whole space [Formula: see text]. Furthermore, we show that not only the entire solution but also all transition fronts share the same global mean speed by constructing suitable radially symmetric expanding and retracting super- and subsolutions.
Bifurcation and chaos analysis of tumor growth
Authors: Liu Haiying , Yang Hongli , Liu Nan , Yang Liangui
Published: March 2022

In this paper, a dynamic model given by three-dimensional ordinary differential equations is studied to determine how the dynamics of tumor growth is controlled by some key parameters. By varying the competition coefficient between healthy host cells and tumor cells, a Hopf bifurcation occurs in this system, leading to the creation of a stable limit cycle. Through numerical analysis of the continuity of this limit cycle, we find that the cascade of period-doubling bifurcations leads to the generation of a chaotic attractor. Moreover, the region of attractors is shown in the parameter space. Numerical simulations, bifurcation diagrams, Lyapunov exponent graph and phase portraits permit to highlight the rich and complex phenomena presented by the model.
The mechanics of dynamic behaviors for SMEs’ growth by using fractional-order dynamic system approach
Authors: Ren Ruibin , Yuan George X.
Published: March 2022

The goal of this paper is to establish a general framework for the development of small- or medium-sized enterprises’ (SMEs) growth path by using star-coupled stochastic dynamic system approach. The dynamic behaviors are related to the stochastic resonance (SR) phenomenon subject to multiplicative fluctuation and periodic force which could be interpreted as uncertain environments faced by SMEs (enterprises) during their growth process. The multiplicative noise is modeled as dichotomous noise (uncertainty under the innovation and capital paradigm) and the memory of risk environments is characterized by using fractional kernel function. By using the mathematical derivation, the analytical expressions for the first moment of the steady state response, the stability in the long-time limit in terms of (enterprises) systems’ asymptotic stability is obtained. Theoretic and simulation results show the nonmonotonic dependence between the output gain and the input signal frequency, noise parameters for SMEs dynamic behaviors. Furthermore, the fluctuation noise, the number of related parties (partners) for SMEs, and the fractional-order work together, producing more complex dynamic phenomena compared with the traditional integral-order systems (for enterprises). Finally, theoretical analyses with corresponding numerical simulations results established in this paper would provide a possible fundamental mathematical framework for the study of Schumpeter’s theory on the development for SMEs’ growth under the “innovation and capital paradigm” and related disciplines. In particular, the framework allows us, for the first time, to logically conclude that “in general, the ratio for SMEs’ growth successfully is around less than one-third”, this is actually consistent with what the market has been observing commonly in general.
A Systematic Literature Review of Virtual, Augmented, and Mixed Reality Game Applications in Healthcare
Authors: Fu Yu , Hu Yan , Sundstedt Veronica
Published: March 2022

Virtual reality, augmented reality, and mixed reality (VR/AR/MR) as information and communication technologies have been recognised and implemented in healthcare in recent years. One of the popular application ways is games, due to the potential benefits of providing an engaging and immersive experience in a virtual environment. This study presents a systematic literature review that evaluates the state-of-the-art on VR/AR/MR game applications in healthcare by collecting and analysing related journal and conference papers published from 2014 through to the first half of 2020. After retrieving more than 3,000 papers from six databases, 88 articles, from both computer science and medicine, were selected and analysed in the review. The articles are classified and summarised based on their (1) publication information, (2) design, implementation, and evaluation, and (3) application. The presented review is beneficial for both researchers and developers interested in exploring current research and future trends in VR/AR/MR in healthcare.
Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future
Authors: Pourebadi Maryam , Riek Laurel D.
Published: March 2022

Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.
Non-invasive Techniques for Monitoring Different Aspects of Sleep: A Comprehensive Review
Authors: Hussain Zawar , Sheng Quan Z. , Zhang Wei Emma , Ortiz Jorge , Pouriyeh Seyedamin
Published: March 2022

Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We finally discuss several open issues and future research directions in the area of sleep monitoring.
Optimizing Storage Performance with Calibrated Interrupts
Authors: Tai Amy , Smolyar Igor , Wei Michael , Tsafrir Dan
Published: March 2022

After request completion, an I/O device must decide whether to minimize latency by immediately firing an interrupt or to optimize for throughput by delaying the interrupt, anticipating that more requests will complete soon and help amortize the interrupt cost. Devices employ adaptive interrupt coalescing heuristics that try to balance between these opposing goals. Unfortunately, because devices lack the semantic information about which I/O requests are latency-sensitive, these heuristics can sometimes lead to disastrous results.

Instead, we propose addressing the root cause of the heuristics problem by allowing software to explicitly specify to the device if submitted requests are latency-sensitive. The device then “calibrates” its interrupts to completions of latency-sensitive requests. We focus on NVMe storage devices and show that it is natural to express these semantics in the kernel and the application and only requires a modest two-bit change to the device interface. Calibrated interrupts increase throughput by up to 35%, reduce CPU consumption by as much as 30%, and achieve up to 37% lower latency when interrupts are coalesced.
Weakly Supervised Video Object Segmentation via Dual-attention Cross-branch Fusion
Authors: Wei Lili , Lang Congyan , Liang Liqian , Feng Songhe , Wang Tao , Chen Shidi
Published: March 2022

Recently, concerning the challenge of collecting large-scale explicitly annotated videos, weakly supervised video object segmentation (WSVOS) using video tags has attracted much attention. Existing WSVOS approaches follow a general pipeline including two phases, i.e., a pseudo masks generation phase and a refinement phase. To explore the intrinsic property and correlation buried in the video frames, most of them focus on the later phase by introducing optical flow as temporal information to provide more supervision. However, these optical flow-based studies are greatly affected by illumination and distortion and lack consideration of the discriminative capacity of multi-level deep features. In this article, with the goal of capturing more effective temporal information and investigating a temporal information fusion strategy accordingly, we propose a unified WSVOS model by adopting a two-branch architecture with a multi-level cross-branch fusion strategy, named as dual-attention cross-branch fusion network (DACF-Net). Concretely, the two branches of DACF-Net, i.e., a temporal prediction subnetwork (TPN) and a spatial segmentation subnetwork (SSN), are used for extracting temporal information and generating predicted segmentation masks, respectively. To perform the cross-branch fusion between TPN and SSN, we propose a dual-attention fusion module that can be plugged into the SSN flexibly. We also pose a cross-frame coherence loss (CFCL) to achieve smooth segmentation results by exploiting the coherence of masks produced by TPN and SSN. Extensive experiments demonstrate the effectiveness of proposed approach compared with the state-of-the-arts on two challenging datasets, i.e., Davis-2016 and YouTube-Objects.
A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams
Authors: Yan Runze , Liu Xinwen , Dutcher Janine , Tumminia Michael , Villalba Daniella , Cohen Sheldon , Creswell David , Creswell Kasey , Mankoff Jennifer , Dey Anind , Doryab Afsaneh
Published: March 2022

This paper presents a computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We evaluate the framework with two case studies using datasets of smartphone, Fitbit, and OURA smart ring to evaluate the framework’s ability to (1) detect cyclic biobehavior, (2) model commonality and differences in rhythms of human participants in the sample datasets, and (3) predict their health and readiness status using models of biobehavioral rhythms. Our evaluation demonstrates the framework’s ability to generate new knowledge and findings through rigorous micro- and macro-level modeling of human rhythms from mobile and wearable data streams collected in the wild and using them to assess and predict different life and health outcomes.
What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data
Authors: Hu Yang , Chapman Adriane , Wen Guihua , Hall Dame Wendy
Published: March 2022

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include heavy reliance on massive training data, limited generalizability, and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.
Traveling Transporter Problem: Arranging a New Circular Route in a Public Transportation System Based on Heterogeneous Non-Monotonic Urban Data
Authors: Lin Fandel , Hsieh Hsun-Ping
Published: March 2022

Hybrid computational intelligent systems that synergize learning-based inference models and route planning strategies have thrived in recent years. In this article, we focus on the non-monotonicity originated from heterogeneous urban data, as well as heuristics based on neural networks, and thereafter formulate the traveling transporter problem (TTP). TTP is a multi-criteria optimization problem and may be applied to the circular route deployment in public transportation. In particular, TTP aims to find an optimized route that maximizes passenger flow according to a neural-network-based inference model and minimizes the length of the route given several constraints, including must-visit stations and the requirement for additional ones. As a variation of the traveling salesman problem (TSP), we propose a framework that first recommends new stations’ location while considering the herding effect between stations, and thereafter combines state-of-the-art TSP solvers and a metaheuristic named
Trembling Hand
, which is inspired by self-efficacy for solving TTP. Precisely, the proposed Trembling Hand enhances the spatial exploration considering the structural patterns, previous actions, and aging factors. Evaluation conducted on two real-world mass transit systems, Tainan and Chicago, shows that the proposed framework can outperform other state-of-the-art methods by securing the Pareto-optimal toward the objectives of TTP among comparative methods under various constrained settings.
Local-Oxide-Thinning-Induced Deep Depletion Phenomenon in MOS Capacitors
Authors: Lin Kuan-Wun , Hwu Jenn-Gwo
Published: March 2022

The influence of local oxide thinning (LOT) spots on the electrostatics of MOS capacitors was studied in this work. The capacitors are found to suffer from severe deep depletion (DD) above threshold once LOT spots that cause significant gate leakage are introduced, making them inapplicable for MOSFETs. With the help of simulation, we proposed the presence of a lateral electric field at the spot edge, which effectively drifts inversion charge toward the spot from its exterior, leading to severe depletion of inversion charge underneath the entire gate area. Capacitor size, spot size and spot thickness effects on the DD behavior were also investigated. This work demonstrates the high influence of LOT spots on device operations, as well as affirming the importance of oxide structural defect control.
Design, Fabrication and Characterization of Inter-Layer Microheaters Using LTCC Technology
Authors: Kharbanda D. K. , Suri N. , Khanna P. K.
Published: March 2022

This paper presents design, fabrication and characterization of novel inter-layer microheaters based on low temperature co-fired ceramics (LTCC) technology. LTCC microheater structures (S1 to S3) with three different heater configurations has been presented. Microheater structure S1 has a heater pattern generated only on the top LTCC layer while S2 and S3 structures have inter-layer heater patterns. These structures have been simulated using COMSOL software to depict the temperature distribution over the active area. LTCC being a multilayer technology, heater patterns are generated in two different layers of LTCC tapes and connected through vias (3D interconnections) to fabricate inter-layer microheaters. By distributing the heater pattern of S1 equally in two LTCC tape layers (as in S3), this method allows possibility to develop miniature LTCC microheaters using conventional screen-printing process. The developed microheaters are characterized and the results are compared. At an input power of ∼1 W, structure S3 reaches a peak temperature of 316 °C as compared to 272 °C achieved with S1 configuration. Thermal imaging results shows better temperature uniformity in the active area for inter-layer microheaters as compared to microheater having heater pattern only on the top LTCC layer.
Role of parvalbumin in fatigue-induced changes in force and cytosolic calcium transients in intact single mouse myofibers
Authors: Nogueira Leonardo , Gilmore Natalie K. , Hogan Michael C.
Published: March 2022

Parvalbumin (PV) is a cytosolic Ca2+buffer that is present in mouse myofibers but not in human muscle. We show that inducible knockout of PV leads to increases in myofiber cytosolic free Ca2+concentrations and slowing of Ca2+pumping during fatigue versus control mice. However, PV ablation does not interfere with fatigue-induced slowing in relaxation or fatigue resistance. These data support the use of mouse muscle as a suitable model to investigate human muscle fatigue.
A New Class of the Power Function Distribution: Theory and Inference with an Application to Engineering Data
Authors: Al Mutairi Alya , Iqbal Muhammad Z. , Arshad Muhammad Z. , Afify Ahmed Z.
Published: March 2022

In this study, a new class that generates optimal univariate models called a new exponentiated‐G class of distributions is developed. Numerous complementary statistical properties are derived and discussed in detail for the newly exponentiated power function (EPF) distribution. All possible shapes of the probability density and hazard rate functions are sketched for selected values of parameters. Six accredited estimation methods are discussed, and their performance is assessed and compared by a simulation study. The applicability of the new class is evaluated by analyzing the automotive engineering sector data.
Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning
Authors: Yuan Wang , Xiwen Zhang , Rong Zhou , Shangqin Tang , Huan Zhou , Wei Ding
Published: March 2022

With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently to acquire air superiority. In this paper, the UCAV maneuver decision problem in continuous action space is studied based on the deep reinforcement learning strategy optimization method. The UCAV platform model of continuous action space was established. Focusing on the problem of insufficient exploration ability of Ornstein–Uhlenbeck (OU) exploration strategy in the deep deterministic policy gradient (DDPG) algorithm, a heuristic DDPG algorithm was proposed by introducing heuristic exploration strategy, and then a UCAV air combat maneuver decision method based on a heuristic DDPG algorithm is proposed. The superior performance of the algorithm is verified by comparison with different algorithms in the test environment, and the effectiveness of the decision method is verified by simulation of air combat tasks with different difficulty and attack modes.
Distance Field-Based Convolutional Neural Network for Edge Detection
Authors: Hu Dadan , Yang Hongbo , Hou Xia
Published: March 2022

In this paper, we first propose an accurate edge detector using a distance field-based convolutional neural network (DF-CNN). In recent years, CNNs have been proved to be effective in image processing and computer vision. As edge detection is a fundamental problem among them, we try to improve the accuracy of edge detection based on the deep learning framework. The proposed network combines a feature extraction backbone that can fully exploit the multiscale and multilevel information of the edge with the supervised training of the distance field branch to realize the accurate end-to-end object edge detection. The distance field branch is applied to predict the Euclidean distance from nonedge points to the nearest edge point in the feature maps. And the distance information embedded in the predicted distance field map can effectively improve the accuracy of edge detection. The network is trained to minimize the weighted sum of the distance field branch loss and the cross-entropy loss. Our experimental results show that the proposed edge detector achieves better performance than previous approaches and the effectiveness of the proposed distance field branch.
Intelligent Crack Detection and Quantification in the Concrete Bridge: A Deep Learning‐Assisted Image Processing Approach
Authors: Yu Licun , He Shuanhai , Liu Xiaosong , Jiang Shuqing , Xiang Shuiying
Published: March 2022

We proposed a modified concrete bridge crack detector based on a deep learning‐assisted image processing approach. Data augmentation technology is introduced to extend the limited dataset. In our proposed method, the bounding box for the crack is detected by YOLOv5. Then, the image covered by the bounding box is processed by the image processing techniques. Compared with the conventional image processing‐based crack detection method, the deep learning‐assisted image processing approach leads to higher detection accuracy and lower computation cost. More precisely, the mask filter is employed to remove handwritten marks, and the ratio filter is adopted to eliminate speckle linear noises. When a single crack is detected by several bounding boxes, we proposed a novel fusion method to merge these bounding boxes. Furthermore, we proposed a connected component search approach based on the crack trend of the area to improve the connection accuracy. With the crack detector, the cracks that are wider than 0.15 mm can be correctly detected, quantified, and visualized. The detection absolute error of the crack width is less than 0.05 mm. Thus, we realized fast and precise detection and quantification of bridge crack based on the practical engineering dataset.
Ultrasound Evaluation of the Diaphragm in Clinical Anesthesia
Authors: He Yina , Zhao Tiantian
Published: March 2022

When the human body is anesthetized, the human nerve tissue will be greatly affected, which also affects the breathing of the human body. The respiration during anesthesia is a lack of initiative, and the energy efficiency of the diaphragm in the lungs is very important to the safety of anesthesia. In this paper, the application of the ultrasound evaluation of the diaphragm in clinical anesthesia was studied. In this paper, 24 patients who underwent lung examination under medical anesthesia at our hospital were evaluated by the ultrasound vertical mixed echo method. Through patient voluntary selection and consent, 16 patients were examined with B-mode ultrasound and the other 8 patients with M-mode ultrasound to compare the effects of different ultrasounds on diaphragm image quality. In addition, this paper also analyzes the differences between different ultrasounds and the strengths and weaknesses of diaphragmatic ultrasound evaluation in clinical anesthesia. The suggestions of using different ultrasounds in ultrasonic evaluation are given. The study showed that 16 cases of B-mode ultrasound evaluation of the diaphragm obtained ultrasound images which showed a large field of vision, acoustic frequency between 7 and 18 MHz, and thickness difference between 0.35 and 0.52 cm. In 8 patients with the diaphragm evaluated by M-mode ultrasound, the local features of M-mode ultrasound images were clearer than those of B-mode ultrasound images, but the visual field area was smaller, the acoustic frequency was between 10 and 15 MHz, and the thickness difference was between 0.12 and 0.18 cm. Based on the above data, this paper suggests that, in the ultrasonic evaluation of the diaphragm, B-mode ultrasound should be used to check the patients first, and then M-mode ultrasound should be used to check the parts with poor quality so that the accurate diaphragm quality of patients can be obtained in the vast majority of patients.
High‐Calcium Microenvironment during the Development of Kidney Calculi Can Promote Phenotypic Transformation of NRK‐52E Cells by Inhibiting the Expression of Stromal Interaction Molecule‐1
Authors: Li Li-Sha , Zhu Yun-Peng , Xia Qi-Dong , Wang Shao-Gang , He Deng
Published: March 2022

Objective. To explore whether Stromal Interaction Molecule‐1 (STIM1) participates in the phenotypic transformation of NRK‐52E cells under high‐calcium microenvironment. Materials and Methods. NRK‐52E cells were treated with high concentration of calcium. The viability and apoptosis of cells were detected by CCK‐8 (cell counting kit‐8) and flow cytometry, respectively. The expression changes of phenotypic marker proteins (E‐cadherin and OPN) and calcium channel proteins (STIMl and Orai1) in high‐calcium environment were detected by western blotting and real‐time quantitative polymerase chain reaction. The expression of STIMl protein in NRK‐52E cells was upregulated and downregulated by plasmid‐STIM1 and plasmid‐shRNA‐STIMl, respectively. The expressions of phenotypic marker proteins after upregulation or downregulation of STIMl were detected again. Besides, the intracellular calcium concentrations of NRK‐52E cells in different treatments were detected by flow cytometry. Results. High‐calcium microenvironment can promote the phenotypic transformation and the adhesion of calcium salts in NRK‐52E cells and simultaneously suppress the expression of STIMl protein in NRK‐52E cells. Downregulation of STIMl protein could also promote the phenotype transformation, while both the gene silence of matrix gla protein (MGP) and overexpression of STIMl showed reverse results. Conclusion. STIMl protein plays an important role in promoting phenotypic transformation of NRK‐52E cells in high‐calcium microenvironment.
New generation CAD‐CAM materials for implant‐supported definitive frameworks fabricated by using subtractive technologies
Authors: Cevik Pinar , Schimmel Martin , Yilmaz Burak
Published: March 2022

Innovations in digital manufacturing enabled the fabrication of implant‐supported fixed dental prostheses (ISFDPs) in a wide variety of recently introduced materials. Computer‐aided design and computer‐aided manufacturing (CAD‐CAM) milling allows the fabrication of ISFDPs with high accuracy by reducing the fabrication steps of large‐span frameworks. The longevity of ISFDPs depends on the overall mechanical properties of the framework material including its fit, and the physical properties of the veneering material and its bond with the framework. This comprehensive review summarizes the recent information on millable CAD‐CAM framework materials such as pre‐sintered soft alloys, fiber‐reinforced composite resins, PEEK, and PEKK in high‐performance polymer family, and 4Y‐TZP. Even though promising results have been obtained with the use of new generation millable CAD‐CAM materials for ISFDPs, clinical studies are lacking and future research should focus on the overall performance of these millable materials in both static and dynamic conditions.
Heat Transfer in a Fractional Nanofluid Flow through a Permeable Medium
Authors: Anwar Muhammad Shoaib , Irfan Muhammad , Hussain Majid , Muhammad Taseer , Hussain Zakir
Published: March 2022

This study examines viscoelastic fractional nanofluid flow through Darcy medium. Memory characteristics due to elasticity are explored with noninteger time derivatives. The unsteady motion of MHD flow is modeled by nonlinear differential equations. Buoyancy forces are incorporated via convection parameters in the flow domain. Fractional relaxation time is considered to control the propagation speed of temperature. A finite difference, along with finite element, a numerical algorithm has been developed for the computation of governing flow equations. Friction coefficient, Sherwood numbers, and Nusselt numbers are computed for the noninteger derivative model. Simulations revealed that noninteger numbers have congruous behavior for concentration, temperature, and velocity fields. It is also noted that heat flux,

δ

1

, and mass flux,

δ

2

, numbers have contradictory effects on the friction coefficient. Various flows, particularly in polymer industries and electrospinning for the production of nanofibers, can be tackled in a comparable pattern.
A Study on Parameters Influencing Blast-Induced Frequency Content and Dominant Frequency Attenuation
Authors: Aftabi Milad , Motamedi Mohammad hossein , Molladavoodi Hamed
Published: March 2022

Adverse effects of the blasting vibration on surrounding structures intensify importance of investigation on the blast frequency content while it has not been fully considered in blasting design. In this study, blast-induced frequency content and dominant frequency attenuation are investigated. The results of this study and its comparison with the previous studies demonstrate that investigation of different parameters influencing blast-induced frequency content including peak blast load pressure, rise time, and falling time by the experimental pressure-time history with its Fast Fourier Transform (FFT) analysis leads to the similar results of study by the velocity-time recorded data of an infield blasting. The outcomes of this study on dominant frequency attenuation for different types of explosives but in the same material show approximately the same dominant vibration frequency in far-field for different explosives, whereas a rapid drop of dominant frequency to half of the initial value in near distance to the blasthole can be observed. Investigation on dominant frequency attenuation versus distance for the materials with different natural ground frequencies demonstrates that the geomaterial tends to pass the vibration frequency closer to its natural frequency. If the earth has a lower natural frequency, it will pass the waves with lower dominant frequencies, but it will attenuate the waves with higher dominant frequency.
Type Synthesis Based on Modular Combination with Virtual Rotation Center and Application
Authors: Li Xinning , Zhang Qishuo , Zhang Zongsu , Yang Xianhai , Wu Hu , Li Yongbo , Qu Hailong
Published: March 2022

Type synthesis of mechanical structure is of great significance to the realization of mechanism target function, systematization, and stability of mechanical device. The type synthesis method of multilinkage robot has been given high demands with increasing number of degrees of freedom and high flexibility in special occasions. In order to improve the workspace and flexibility of mechanism, this paper studies the existing type synthesis theory and proposes a type synthesis method of modular combination with virtual rotation centers. Firstly, modular units are built. Secondly, modular units are expanded according to the needed paths. In the end, the expanded modular units are combined to form the kinematic linkages. Based on the proposed method, the configuration design of the aerial working platform and the self-adaptive levelling platform is completed. The stabilities of two platforms are checked by modal analysis. The prototype products are manufactured, respectively, for further verifying validity of the method. The designed aerial working platform with virtual rotation centers can achieve 360° rotating large workspace, more compact mechanical structure, and short arrival time at the same height than the common scissor-type and mast-type aerial working platforms. The designed adaptive levelling platform is tested that ensures the levelling of the upper surface at different inclinations. The method can provide new idea for the mechanism configuration and expand the application scope of new mechanisms.
Numerical Simulation of Large Compression Deformation Disaster and Supporting Behavior of Deep Buried Soft Rock Tunnel with High In Situ Stress Based on CDEM
Authors: Tang Hao , Ji Xiang , Zhang Hongyi , Li Tianbin
Published: March 2022

Large compressive deformation of tunnels is a phenomenon involving plastic deformation and failure of surrounding rocks and often refers to the weak surrounding rock self‐bearing capacity loss or partial loss. This research discusses the formation and evolution of large compressive deformation and effectiveness of the combined support of high in situ stress tunnel. From the new perspective of large deformation disaster caused by the structural failure of high in situ stress surrounding rock to clarify it, this paper illustrates the mechanism of progressive cracking and large deformation of high in situ stress soft rock tunnel from the aspects of the formation of self‐bearing system, deformation evolution of the surrounding rock, mechanical properties of the surrounding rock, and failure characteristics. Accordingly, the continuous and discontinuous numerical simulation methods are used. The following conclusions are drawn by comparing the simulation results of surrounding rock under combined support with no support. (1) The supporting structure constitutes the self‐supporting system with the surrounding rock and plays the roles of codeformation and load‐bearing. (2) The support structure has evident reinforcing effect on the rock mass in the relaxation zone, thereby leading to the phenomenon of weakened rock mass failure. Moreover, the shear area develops to the compaction zone. (3) The supporting structure improves the bearing capacity of rock mass in the relaxation zone. It also increases the surrounding rock stress and reduces the range of the compaction zone. Simulation results verify that the combined support measures have a good suppression effect on the large compressive deformation, thereby providing a reference for similar projects and research on the large compressive deformation of soft rock.
Real Estate Development Strategy Based on Artificial Intelligence and Big Data Industrial Policy Background
Authors: Liu Yu
Published: March 2022

In recent years, as one of the pillar industries of the national economy, the real estate industry has achieved unprecedented development. Since the 1990s, my country’s real estate industry has experienced three decades of rapid development. The average annual growth of commercial housing area is nearly 20%, and the average annual growth of housing prices in second-tier cities is 11.87%. However, in the rapid development, there are also many problems. For example, there are more and more phenomena such as unreasonable development, serious environmental pollution, and shortage of resources. At the same time, artificial intelligence has made great achievements in the development process in recent years, and it has continued to grow with technological progress and social demand. At present, China is in a transitional period of economic and social development, and the real estate industry is also facing huge challenges. In this context, research and development of traditional Chinese cities is very necessary and important. Therefore, how to effectively control and coordinate the real estate development behavior in the big data environment is one of the major problems that China is facing and needs to be solved urgently. This article uses questionnaire surveys and data analysis methods to understand the elements of real estate development strategies and analyze consumer purchase intentions through questionnaires. Randomly select 120 citizens of P city as the survey objects, and carry out a questionnaire survey. According to the survey results, most of the interviewees believe that the resource integration strategy occupies an important position in the real estate development strategy, and the big data management strategy also exerts its advantages. Most people believe that internal demand motivation is the most important, followed by the characteristics of real estate. It can be seen that real estate development must fully consider the actual needs of consumers and improve the development process to highlight the characteristics of real estate.
Partner Phubbing as a Social Allergen: Support for a Dual Process Model
Authors: Roberts James A. , David Meredith E.
Published: March 2022

In a couple context, a social allergen is a behavior that irritates one’s partner and tends to increase as a romantic relationship continues. Given that smartphones are a constant companion for many people, their use in the presence of one’s romantic partner is pervasive and can have important implications for relationships. The present research focuses on relationship length and partner phubbing and investigates the mediating role of passion and deromantization on the social allergen of partner phubbing. Study 1 surveyed 250 married adults and found that relationship length is negatively associated with romantic passion which, in turn, is positively associated with perceptions of partner phubbing. Although the literature on social allergens would suggest a positive effect of relationship length on partner phubbing, Study 1 showed no significant main effect of relationship length on phubbing. As an attempt to explain this seeming anomaly, we drew from the attachment theory to propose an additional mechanism underlying this relationship. Study 2 (n=250married adults) then tests an expanded model that includes attachment anxiety as an additional mediator. Results show that relationship length is associated with lower attachment anxiety which is in turn associated with less perceived partner phubbing. Overall, the results show support for a dual process model, such that romantic passion and attachment anxiety differentially underlie the path between relationship length and perceived partner phubbing. The findings provide important insights into better understanding partner phubbing as a social allergen over the course of marital relationships.
The Effects of Additional Filtration on Image Quality and Radiation Dose in Cone Beam CT: An In Vivo Preliminary Investigation
Authors: Houfrar Jan , Ludwig Bjorn , Bister Dirk , Nienkemper Manuel , Abkai Ciamak , Venugopal Adith
Published: March 2022

Purpose. The aim of this study was to investigate the effect of reduced radiation doses on the image quality of cone‐beam computed tomography scans and the suitability of such imaging for orthodontics, oral surgery, dental implantology, periodontology, and endodontology. Materials and Methods. Cone‐beam computed tomography scans of a live patient were performed using seven attenuation filters with increased thickness to decrease the effective radiation dose from 22.4 to 1.8 μSv, and the effects of different radiation doses on image quality were further analysed. Quantitative image quality was calculated using dedicated measures, such as signal and contrast‐to‐noise ratio and sharpness. A panel of five certified raters assessed the cone‐beam computed tomography scans qualitatively. Nine anatomical structures relevant to dentistry were identified, and the overall acceptance was assessed. Results. Linear reduction of the effective radiation dose had a nonlinear effect on image quality. A 5‐fold reduction in the effective dose led to acceptable quantitative and qualitative image quality measures, and the identification rate of dental anatomical structures was 80% or greater. The use of less than 40% of the reference dose was unacceptable for all dental specialties. Conclusions. The ideal radiation dose for specific diagnostic requirements remains a patient‐related and specialty‐related decision that must be made on an individual basis. Based on the results of this study, it is possible to reduce exposure in selected patients, and at the same time obtain sufficient quality of images for clinical purposes.