bluuebunny/crossref_metadata_2025 huggingface dataset:
On the error term in the explicit formula of Riemann–von Mangoldt
Authors: Cully-Hugill Michaela , Johnston Daniel R.
Published: November 2022

We provide an explicit [Formula: see text] error term for the Riemann–von Mangoldt formula by making results of Wolke [On the explicit formula of Riemann–von Mangoldt, II, J. London Math. Soc. 2(3) (1983) 406–416] and Ramaré [Modified truncated Perron formulae, Ann. Math. Blaise Pascal 23(1) (2016) 109–128] explicit. We also include applications to primes between consecutive powers, the error term in the prime number theorem and an inequality of Ramanujan.
Red deer optimization for automatic modulation classification using hybrid extreme learning machine with bagging classifier
Authors: Durga Indira N. , Venu Gopala Rao M.
Published: November 2022

Automatic modulation classification (AMC) became an important process in various communication systems including commercial, telecommunication and military applications. Further, the accuracy of AMC impacts the performance of these applications. Various machine learning approaches were developed to improve the performance of AMC. However, they failed to classify the different modulation schemes, which needs to satisfy all the spectrum requirements under multipath fading environment. Further, the conventional methods suffer with computational complexity in training to satisfy the real-time operational requirements. So, this paper focuses on implementation of extreme learning machine (ELM) for reduction of training complexities and improves the classification performance. Initially, deep leaning convolutional neural network (DLCNN) model is introduced for extracting the interdependent modulation features based on different modulation types. Then, red deer optimization algorithm (RDOA) is introduced for selecting the best features from DLCNN extracted features. Further, the hybrid ELM with bagging classifier (HELM-BC) is used to classify the various modulation types, i.e., families. The simulation results show that the performance of the proposed AMC system using RDOA-based HELM-BC approach is superior to the conventional AMC systems.
Ordovician of the conterminous United States
Authors: McLaughlin Patrick I. , Stigall Alycia L.
Published: November 2022

The Ordovician rocks of the conterminous United States (US) have a complex history, spanning multiple ancient basins, shifting palaeoclimate and evolving tectonic regimes. The US portion of the palaeocontinent of Laurentia occupied a relatively stable and isolated position around the southern tropics during the Ordovician. In general, Lower Ordovician rocks form a vast autochthonous blanket of fine-grained (tropical) carbonates that covered much of Laurentia, named the ‘Great American Carbonate Bank’. Outboard, ribbon carbonates and graptolitic shales are found in allochthonous fragments of the ancient continental margin. Middle Ordovician strata are more lithologically diverse, including the addition of several regionally distributed sandstones of the inner detrital belt, mostly overlying the Sauk–Tippecanoe unconformity. Upper Ordovician strata show the greatest lithologic and faunal diversity, reflecting steepening topography resulting from regional compression along the south Laurentian (Appalachian) margin. Recent advances in the interpretation of the US Ordovician come primarily from studies of carbon and oxygen stable isotopes, sequence stratigraphy, palaeoecology, tephrochronology, redox geochemistry, strontium isotopes and geochronology.
Exact 3D Path Generation via 3D Cam-Linkage Mechanisms
Authors: Cheng Yingjie , Song Peng , Lu Yukun , Chew Wen Jie Jeremy , Liu Ligang
Published: November 2022

Exact 3D path generation
is a fundamental problem of designing a mechanism to make a point
exactly
move along a prescribed
3D path
, driven by a single actuator. Existing mechanisms are insufficient to address this problem. Planar linkages and their combinations with gears and/or plate cams can only generate 2D paths while 1-DOF spatial linkages can only generate 3D paths with rather simple shapes. In this paper, we present a new 3D cam-linkage mechanism, consisting of two 3D cams and five links, for exactly generating a continuous 3D path. To design a 3D cam-linkage mechanism, we first model a 3-DOF 5-bar spatial linkage to exactly generate a prescribed 3D path and then reduce the spatial linkage's DOFs from 3 to 1 by composing the linkage with two 3D cam-follower mechanisms. Our computational approach optimizes the 3D cam-linkage mechanism's topology and geometry to minimize the mechanism's total weight while ensuring smooth, collision-free, and singularity-free motion. We show that our 3D cam-linkage mechanism is able to exactly generate a continuous 3D path with arbitrary shape and a finite number of
C
0
points, evaluate the mechanism's kinematic performance with 3D printed prototypes, and demonstrate that the mechanism can be generalized for exact 3D motion generation.
Interactive and Robust Mesh Booleans
Authors: Cherchi Gianmarco , Pellacini Fabio , Attene Marco , Livesu Marco
Published: November 2022

Boolean operations are among the most used paradigms to create and edit digital shapes. Despite being conceptually simple, the computation of mesh Booleans is notoriously challenging. Main issues come from numerical approximations that make the detection and processing of intersection points inconsistent and unreliable, exposing implementations based on floating point arithmetic to many kinds of degeneracy and failure. Numerical methods based on rational numbers or exact geometric predicates have the needed robustness guarantees, that are achieved at the cost of increased computation times that, as of today, has always restricted the use of robust mesh Booleans to offline applications. We introduce an algorithm for Boolean operations with robustness guarantees that is capable of operating at interactive frame rates on meshes with up to 200K triangles. We evaluate our tool thoroughly, considering not only interactive applications but also batch processing of large collections of meshes, processing of huge meshes containing millions of elements and variadic Booleans of hundreds of shapes altogether. In all these experiments, we consistently outperform prior robust floating point methods by at least one order of magnitude.
PopStage
Authors: Lee Dawon , Yoo Jung Eun , Cho Kyungmin , Kim Bumki , Im Gyeonghun , Noh Junyong
Published: November 2022

StageMix
is a mixed video that is created by concatenating the segments from various performance videos of an identical song in a visually smooth manner by matching the main subject's silhouette presented in the frame. We introduce
PopStage
, which allows users to generate a StageMix automatically. PopStage is designed based on the StageMix Editing Guideline that we established by interviewing creators as well as observing their workflows. PopStage consists of two main steps: finding an editing path and generating a transition effect at a transition point. Using a reward function that favors visual connection and the optimality of transition timing across the videos, we obtain the optimal path that maximizes the sum of rewards through dynamic programming. Given the optimal path, PopStage then aligns the silhouettes of the main subject from the transitioning video pair to enhance the visual connection at the transition point. The virtual camera view is next optimized to remove the black areas that are often created due to the transformation needed for silhouette alignment, while reducing pixel loss. In this process, we enforce the view to be the maximum size while maintaining the temporal continuity across the frames. Experimental results show that PopStage can generate a StageMix of a similar quality to those produced by professional creators in a highly reduced production time.
DeepJoin
Authors: Lamb Nikolas , Banerjee Sean , Banerjee Natasha Kholgade
Published: November 2022

We introduce DeepJoin, an automated approach to generate high-resolution repairs for fractured shapes using deep neural networks. Existing approaches to perform automated shape repair operate exclusively on symmetric objects, require a complete proxy shape, or predict restoration shapes using low-resolution voxels which are too coarse for physical repair. We generate a high-resolution restoration shape by inferring a corresponding complete shape and a break surface from an input fractured shape. We present a novel implicit shape representation for fractured shape repair that combines the occupancy function, signed distance function, and normal field. We demonstrate repairs using our approach for synthetically fractured objects from ShapeNet, 3D scans from the Google Scanned Objects dataset, objects in the style of ancient Greek pottery from the QP Cultural Heritage dataset, and real fractured objects. We outperform six baseline approaches in terms of chamfer distance and normal consistency. Unlike existing approaches and restorations generated using subtraction, DeepJoin restorations do not exhibit surface artifacts and join closely to the fractured region of the fractured shape. Our code is available at: https://github.com/Terascale-All-sensing-Research-Studio/DeepJoin.
Asynchronous Collaborative Autoscanning with Mode Switching for Multi-Robot Scene Reconstruction
Authors: Guo Junfu , Li Changhao , Xia Xi , Hu Ruizhen , Liu Ligang
Published: November 2022

When conducting autonomous scanning for the online reconstruction of unknown indoor environments, robots have to be competent at exploring scene structure and reconstructing objects with high quality. Our key observation is that different tasks demand specialized scanning properties of robots: rapid moving speed and far vision for global exploration and slow moving speed and narrow vision for local object reconstruction, which are referred as two different scanning modes:
explorer
and
reconstructor
, respectively. When requiring multiple robots to collaborate for efficient exploration and fine-grained reconstruction, the questions on when to generate and how to assign those tasks should be carefully answered. Therefore, we propose a novel asynchronous collaborative autoscanning method with mode switching, which generates two kinds of scanning tasks with associated scanning modes, i.e., exploration task with
explorer
mode and reconstruction task with
reconstructor
mode, and assign them to the robots to execute in an asynchronous collaborative manner to highly boost the scanning efficiency and reconstruction quality. The task assignment is optimized by solving a modified Multi-Depot Multiple Traveling Salesman Problem (MDMTSP). Moreover, to further enhance the collaboration and increase the efficiency, we propose a
task-flow
model that actives the task generation and assignment process immediately when any of the robots finish all its tasks with no need to wait for all other robots to complete the tasks assigned in the previous iteration. Extensive experiments have been conducted to show the importance of each key component of our method and the superiority over previous methods in scanning efficiency and reconstruction quality.
Motion Guided Deep Dynamic 3D Garments
Authors: Zhang Meng , Ceylan Duygu , Mitra Niloy J.
Published: November 2022

Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the previous state of the garment as well as its relative position with respect to the underlying body. Technically, we model garment dynamics, driven using the input character motion, by predicting per-frame local displacements in a canonical state of the garment that is enriched with frame-dependent skinning weights to bring the garment to the global space. We resolve any remaining per-frame collisions by predicting residual local displacements. The resultant garment geometry is used as history to enable iterative roll-out prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.
Code and data is released in https://geometry.cs.ucl.ac.uk/projects/2022/MotionDeepGarment/
Differentiable Simulation of Inertial Musculotendons
Authors: Wang Ying , Verheul Jasper , Yeo Sang-Hoon , Kalantari Nima Khademi , Sueda Shinjiro
Published: November 2022

We propose a simple and practical approach for incorporating the effects of muscle inertia, which has been ignored by previous musculoskeletal simulators in both graphics and biomechanics. We approximate the inertia of the muscle by assuming that muscle mass is distributed along the centerline of the muscle. We express the motion of the musculotendons in terms of the motion of the skeletal joints using a chain of Jacobians, so that at the top level, only the reduced degrees of freedom of the skeleton are used to completely drive both bones and musculotendons. Our approach can handle all commonly used musculotendon path types, including those with multiple path points and wrapping surfaces. For muscle paths involving wrapping surfaces, we use neural networks to model the Jacobians, trained using existing wrapping surface libraries, which allows us to effectively handle the Jacobian discontinuities that occur when musculotendon paths collide with wrapping surfaces. We demonstrate support for higher-order time integrators, complex joints, inverse dynamics, Hill-type muscle models, and differentiability. In the limit, as the muscle mass is reduced to zero, our approach gracefully degrades to traditional simulators without support for muscle inertia. Finally, it is possible to mix and match inertial and non-inertial musculotendons, depending on the application.
Neural Parameterization for Dynamic Human Head Editing
Authors: Ma Li , Li Xiaoyu , Liao Jing , Wang Xuan , Zhang Qi , Wang Jue , Sander Pedro V.
Published: November 2022

Implicit radiance functions emerged as a powerful scene representation for reconstructing and rendering photo-realistic views of a 3D scene. These representations, however, suffer from poor editability. On the other hand, explicit representations such as polygonal meshes allow easy editing but are not as suitable for reconstructing accurate details in dynamic human heads, such as fine facial features, hair, teeth, and eyes. In this work, we present Neural Parameterization (NeP), a hybrid representation that provides the advantages of both implicit and explicit methods. NeP is capable of photo-realistic rendering while allowing fine-grained editing of the scene geometry and appearance. We first disentangle the geometry and appearance by parameterizing the 3D geometry into 2D texture space. We enable geometric editability by introducing an explicit linear deformation blending layer. The deformation is controlled by a set of sparse key points, which can be explicitly and intuitively displaced to edit the geometry. For appearance, we develop a hybrid 2D texture consisting of an explicit texture map for easy editing and implicit view and time-dependent residuals to model temporal and view variations. We compare our method to several reconstruction and editing baselines. The results show that the NeP achieves almost the same level of rendering accuracy while maintaining high editability.
Curl-Flow
Authors: Chang Jumyung , Partono Ruben , Azevedo Vinicius C. , Batty Christopher
Published: November 2022

We propose to augment standard grid-based fluid solvers with
pointwise
divergence-free velocity interpolation, thereby ensuring exact incompressibility down to the sub-cell level. Our method takes as input a discretely divergence-free velocity field generated by a staggered grid pressure projection, and first recovers a corresponding discrete vector potential. Instead of solving a costly
vector
Poisson problem for the potential, we develop a fast parallel sweeping strategy to find a candidate potential and apply a gauge transformation to enforce the Coulomb gauge condition and thereby make it numerically smooth. Interpolating this discrete potential generates a point-wise vector potential whose analytical curl is a pointwise incompressible velocity field. Our method further supports irregular solid geometry through the use of level set-based cut-cells and a novel Curl-Noise-inspired potential ramping procedure that simultaneously offers strictly non-penetrating velocities and incompressibility. Experimental comparisons demonstrate that the vector potential reconstruction procedure at the heart of our approach is consistently faster than prior such reconstruction schemes, especially those that solve vector Poisson problems. Moreover, in exchange for its modest extra cost, our overall
Curl-Flow
framework produces significantly improved particle trajectories that closely respect irregular obstacles, do not suffer from spurious sources or sinks, and yield superior particle distributions over time.
Rapid Face Asset Acquisition with Recurrent Feature Alignment
Authors: Liu Shichen , Cai Yunxuan , Chen Haiwei , Zhou Yichao , Zhao Yajie
Published: November 2022

We present
Re
current
F
eature
A
lignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603
mm
in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.
Characterizing and Forecasting Urban Vibrancy Evolution: A Multi-View Graph Mining Perspective
Authors: Liu Hao , Guo Qingyu , Zhu Hengshu , Fu Yanjie , Zhuang Fuzhen , Ma Xiaojuan , Xiong Hui
Published: November 2022

Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is vital to a city’s socio-economic development and sustainability. While many efforts have been made for statically measuring and evaluating urban vibrancy, there are few studies on the evolutionary process of urban vibrancy, yet we know little about the relationship between urban vibrancy evolution and sophisticated spatiotemporal dynamics. In this article, we make use of multi-sourced urban data to develop a data-driven framework,
U-Evolve
, to investigate urban vibrancy evolution. Specifically, we first exploit the spatiotemporal characteristics of urban areas to create multi-view time-dependent graphs. Then, we analyze the contextual features and graph patterns of multi-view time-dependent graphs in terms of informing future urban vibrancy variations. Our analysis validates the informativeness of multi-view time-dependent graphs for characterizing and informing future urban vibrancy evolution. After that, we construct a feature based model to forecast future urban vibrancy evolution and quantify each feature’s importance. Moreover, to further enhance the forecasting effectiveness, we propose a graph learning based model to capture spatiotemporal autocorrelation of urban areas based on multi-view time-dependent graphs in an end-to-end manner. Finally, extensive experiments on two metropolises, Beijing and Shanghai, demonstrate the effectiveness of our forecasting models. The
U-Evolve
framework has also been deployed in the production environment to deliver real-world urban development and planning insights for various cities in China.
Using Incremental Many-to-One Queries to Build a Fast and Tight Heuristic for A* in Road Networks
Authors: Strasser Ben , Zeitz Tim
Published: November 2022

We study exact, efficient, and practical algorithms for route planning applications in large road networks. On the one hand, such algorithms should be able to answer shortest path queries within milliseconds. On the other hand, routing applications often require integrating the current traffic situation, planning ahead with predictions for future traffic, respecting forbidden turns, and many other features depending on the specific application. Therefore, such algorithms must be flexible and able to support a variety of problem variants. In this work, we revisit the A* algorithm to build a simple, extensible, and unified algorithmic framework applicable to many route planning problems. A* has been previously used for routing in road networks. However, its performance was not competitive because no sufficiently fast and tight distance estimation function was available. We present a novel, efficient, and accurate A* heuristic using Contraction Hierarchies, another popular speedup technique. The core of our heuristic is a new Contraction Hierarchies query algorithm called
Lazy RPHAST
, which can efficiently compute shortest distances from many incrementally provided sources toward a common target. Additionally, we describe A* optimizations to accelerate the processing of low-degree vertices, which are typical in road networks, and present a new pruning criterion for symmetrical bidirectional A*. An extensive experimental study confirms the practicality of our approach for many applications.
Instance Space Analysis for Algorithm Testing: Methodology and Software Tools
Authors: Smith-Miles Kate , Muñoz Mario Andrés
Published: November 2022

Instance Space Analysis (ISA) is a recently developed methodology to (a) support objective testing of algorithms and (b) assess the diversity of test instances. Representing test instances as feature vectors, the ISA methodology extends Rice’s 1976 Algorithm Selection Problem framework to enable visualization of the entire space of possible test instances, and gain insights into how algorithm performance is affected by instance properties. Rather than reporting algorithm performance on average across a chosen set of test problems, as is standard practice, the ISA methodology offers a more nuanced understanding of the unique strengths and weaknesses of algorithms across different regions of the instance space that may otherwise be hidden on average. It also facilitates objective assessment of any bias in the chosen test instances and provides guidance about the adequacy of benchmark test suites. This article is a comprehensive tutorial on the ISA methodology that has been evolving over several years, and includes details of all algorithms and software tools that are enabling its worldwide adoption in many disciplines. A case study comparing algorithms for university timetabling is presented to illustrate the methodology and tools.
Machine Learning for Software Engineering: A Tertiary Study
Authors: Kotti Zoe , Galanopoulou Rafaila , Spinellis Diomidis
Published: November 2022

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009 and 2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions, including conducting further empirical validation and industrial studies on ML, reconsidering deficient SE methods, documenting and automating data collection and pipeline processes, reexamining how industrial practitioners distribute their proprietary data, and implementing incremental ML approaches.
Video Conferencing Tools: Comparative Study of the Experiences of Screen Reader Users and the Development of More Inclusive Design Guidelines
Authors: Leporini Barbara , Buzzi Marina , Hersh Marion
Published: November 2022

Since the first lockdown in 2020, video conferencing tools have become increasingly important for employment, education, and social interaction, making them essential tools in everyday life. This study investigates the accessibility and usability of the desktop and mobile versions of three popular video conferencing tools, Zoom, Google Meet, and MS Teams, for visually impaired people interacting via screen readers and keyboard or gestures. This involved two inspection evaluations to test the most important features of the desktop and mobile device versions and two surveys of visually impaired users to obtain information about the accessibility of the selected video conferencing tools. Sixty-five people answered the survey for desktop and 94 for mobile platforms. The results showed that Zoom was preferred to Google Meet and MS Teams but that none of the tools was fully accessible via screen reader and keyboard or gestures. Finally, the results of this empirical study were used to develop a set of guidelines for designers of video conferencing tools and assistive technology.
A Half Century Defining the Logic of Cellular Life
Authors: Shapiro Lucy
Published: November 2022

Over more than fifty years, I have studied how the logic that controls and integrates cell function is built into the dynamic architecture of living cells. I worked with a succession of exceptionally talented students and postdocs, and we discovered that the bacterial cell is controlled by an integrated genetic circuit in which transcriptional and translational controls are interwoven with the three-dimensional deployment of key regulatory and morphological proteins. Caulobacter's interconnected genetic regulatory network includes logic that regulates sets of genes expressed at specific times in the cell cycle and mechanisms that synchronize the advancement of the core cyclical circuit with chromosome replication and cytokinesis. Here, I have traced my journey from New York City art student to Stanford developmental biologist.
Pseudo-Potential Calculations of AlxIn1−xPySb1−y Alloys Under the Effect of Hydrostatic Pressure: Phonon Frequencies and Related Parameters
Authors: Elkenany Elkenany B. , Degheidy A. R. , Alfrnwani O. A.
Published: November 2022

Based on the empirical pseudopotential method (EPM) modified with virtual crystal approximation (VCA), we report a detailed study of the pressure dependence of the phonon frequencies, sound velocity, and Debye temperature of AlxIn1−xPy Sb1−y alloys lattice-matched to GaSb, InAs, and InP substrates. There is a good agreement between our calculated results and the available data in the literature for the constituent’s binary compounds which gives support for those of the studied alloys. The phonon frequencies and sound velocity are increased nonlinearly by enhancing pressure and giving high results for the InP substrate than those of other substrates. The results in this work seem likely to be useful as a reference that we report for the first time.
Reduced muscle strength in patients with long-COVID-19 syndrome is mediated by limb muscle mass
Authors: Ramírez-Vélez Robinson , Legarra-Gorgoñon Gaizka , Oscoz-Ochandorena Sergio , García-Alonso Yesenia , García-Alonso Nora , Oteiza Julio , Ernaga Lorea Ander , Correa-Rodríguez María , Izquierdo Mikel
Published: November 2022

The causes of post-COVID-19 syndrome are uncertain. Limb muscle wasting common to patients with COVID-19 limits daily activities and exercise. In this cross-sectional study, we found that patients with long-COVID-19 syndrome had significantly lower absolute and relative muscle strength measurements than control participants. Interestingly, we identified that these relationships were mostly mediated by limb muscle mass. Our data thus suggest that the evident reduced upper and lower muscle mass is a putative cause of—or contributor to—the functional limitation of patients with long-COVID-19 syndrome.
Bioprotection on Chardonnay Grape: Limits and Impacts of Settling Parameters
Authors: Simonin Scott , Honoré-Chedozeau Carole , Monnin Ludovic , David-Vaizant Vanessa , Bach Benoît , Alexandre Hervé , Chatelet Bertrand , Tourdot-Marechal Raphaëlle
Published: November 2022

Background and Aims. If bioprotection has already been proven to limit the development of spoilage flora on musts, its effectiveness against oxidation depends on the winemaking process. To optimize its application, this study analyzed the evolution of the chemical composition of the bioprotected musts and wines, according to different settling routes. Their impacts on the organoleptic characteristics of wines were also studied. Methods and Results. A bioprotected must was subjected to 6 different maceration routes before AF (triplicates), varying the duration and temperature parameters. A temperature value ≤12°C was the main factor independently of the duration which allowed a good implantation of the bioprotectant. An increase of the maceration duration at 12°C led to browning of the must, without significant effect on the final color of the wine, which was felt as more “floral,” with more length in the mouth. Conclusions. The bioprotectant implantation and its effectiveness on the sensory profile of the wine was guaranteed at maceration temperature values lower than 12°C. Significance of the Study. This study participates in the improvement of the bioprotection management in white winemaking, with the guarantee of a positive impact of the prefermentation maceration without sulphites on the organoleptic profile of the wines.
Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features
Authors: Yazdani Azita , Fekri-Ershad Shervan , Jelvay Saeed
Published: November 2022

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person’s chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope.
Biomechanical Comparison of Multilevel Lumbar Instrumented Fusions in Adult Spinal Deformity According to the Upper and Lower Fusion Levels: A Finite Element Analysis
Authors: Son Dong-Min , Lee Soo-Bin , Lee Sung-Jae , Park Tae-Hyun , Jang Ji Eun , Jeong Seung Jo , Kang Young-Mi , Lee Byung Ho
Published: November 2022

Multilevel lumbar fusion with posterior pedicle screw fixation is a widely performed surgical procedure for the management of adult spinal deformity. However, there has not been a comprehensive biomechanical study on the different types of fusion levels in terms of stability and possible complications. We aimed to investigate the biomechanical properties of multilevel lumbar fusion according to different types of upper and lower fusion levels. Six different types of fusions were performed using three‐dimensional finite element models. Type A and B referred to the group of which upper fusion level was L1 and T10, respectively. Subtype 1, 2, and 3 referred to the group of which lower fusion level was L5, S1, and ilium, respectively (A1, L1‐L5; A2, L1‐S1; A3, L1‐ilium; B1, T10‐L5; B2, T10‐S1; B3, T10‐ilium). Flexion, extension, axial rotation, and lateral bending moments were applied, and the risk of screw loosening and failure and adjacent segment degeneration (ASD) was analyzed. Stress at the bone‐screw interface of type B3 was lowest in overall motions. The risk of screw failure showed increasing pattern as the upper and lower levels extended in all motions. Proximal range of motion (ROM) increased as the lower fusion level changed from L5 to S1 and the ilium. For axial rotation, type B3 showed higher proximal ROM (16.2°) than type A3 (11.8°). In multilevel lumbar fusion surgery for adult spinal deformity, adding iliac screws and increasing the fusion level to T10‐ilium may lower the risk of screw loosening. In terms of screw failure and proximal ASD, however, T10‐ilium fusion has a higher potential risk compared with other fusion types. These results will contribute for surgeons to provide adequate patient education regarding screw failure and proximal ASD, when performing multilevel lumbar fusion.
Titanium Coated with Graphene and Niobium Pentoxide for Biomaterial Applications
Authors: Corado Hazel Paloma Reis , Moura de Souza Soraes Francielly , Barbosa Dyanni Manhães , Lima Andreza Menezes , Elias Carlos Nelson
Published: November 2022

Graphene and niobium oxide are used in biomaterial coatings. In this work, commercially pure titanium (cp Ti) was coated with graphene oxide (GO), niobium pentoxide (Nb2O5), and a mixture of both materials (NbGO) by the electrochemical deposition method. The surface morphology, roughness, wettability, and degradation of coated and uncoated samples were analyzed by scanning electron microscopy, interferometry, and contact angle. The results showed that the specimens coated with NbGO (cp Ti-NbGO) showed the highest surface roughness (Ra = 0.64 μm) and were hydrophobic. The contact (θ) angle between water and the surface of uncoated specimens (cp Ti), coated with GO (cp Ti-GO), coated with a mixture with GO and Nb2O5) (cp Ti-NbGO), and coated with Nb2O5 were 50.74°, 44.35°, 55.86°, and 100.35°, respectively. The electrochemical corrosion tests showed that coating with graphene oxide increased the corrosion resistance and coating with Nb2O5 decreased the corrosion resistance. The negative effect of the effect of Nb2O5 coating in corrosion resistance compensated for the release of Nb2O5, which helps osseointegration, increasing cell viability, and proliferation of osteoblasts. The NbGO coating may be a good way to combine the bactericidal effect of graphene oxide with the osseointegration effect of Nb2O5.
Fast Flexible Direct Power Flow for Unbalanced and Balanced Distribution Systems
Authors: Patel Debasis Kumar , Mansani Swapna , Goswami Arup Kumar
Published: November 2022

The study proposes a fast flexible direct power flow solution for radial distribution systems and a fast flexible direct weakly meshed power flow solution for weakly meshed distribution systems. The algorithm is based on the direct forward sweep power flow solution, which is an updated version of the backward/forward sweep solution. The fast flexible direct power flow uses a unique conversion matrix (CM) to rapidly determine the power flow solution. The inverted conversion matrix and its slide-modified matrix are used to solve the power flow problem in a single forward sweep step, which is a novel feature of this work. To ensure the invertibility of the conversion matrix, it is constructed to have a small condition number and a determinant of minus one, and all of its eigenvalues must be equal to that of minus one. Additionally, by modifying the conversion matrix to accommodate the loop branch using the break-point idea, a new weakly meshed conversion matrix (WMCM) is generated with the same following modification as for the radial network and employed in the fast flexible direct weakly meshed power flow (FFDWMPF) solution for the weakly meshed distribution network. The usage of a single matrix in the power flow solution and advanced direct techniques decreases the number of iterations and CPU execution time when MATLAB programming is executed. Furthermore, the proposed method is flexible enough to incorporate any changes in the radial or weakly meshed distribution system just by incorporating the changes in the CM and WMCM for any radial or weakly meshed system. Moreover, the robustness of FFDPF and FFDWMPF is evaluated under various loading scenarios on balanced radial and weakly meshed distribution networks. Finally, to validate the proposed algorithm, the proposed strategy is applied to numerous balanced and unbalanced distribution systems.
The Autologous Hematopoietic Stem Cells Transplantation Combination-Based Chimeric Antigen Receptor T-Cell Therapy Improves Outcomes of Relapsed/Refractory Central Nervous System B-Cell Lymphoma
Authors: Xue Fei , Zheng Peihao , Liu Rui , Feng Shaomei , Guo Yuelu , Shi Hui , Liu Haidi , Deng Biping , Xu Teng , Ke Xiaoyan , Hu Kai
Published: November 2022

Objective. The objective is to explore the effectiveness and safety of CAR T-cell therapy in advanced relapsed/refractory central nervous system B-cell lymphoma and compare the impact of autologous stem cell transplantation (ASCT) plus CAR T-cell therapy versus sequential CART therapy on the survival of patients. Methods. The retrospective analysis was based on the data of 17 patients with advanced relapsed/refractory central nervous system B-cell lymphoma. Bridging chemotherapy was applied before CAR T-cell infusion to further reduce the tumor burden. For patients with autologous hematopoietic stem cell successful collection, CD19/20/22CAR T-cell immunotherapy following ASCT was performed with the thiotepa-containing conditioning regimen, while sequential CD19/CD20/CD22CAR T-cell therapy was applied. For lymphodepletion, patients received bendamustine or fludarabine monotherapy or fludarabine combined with cyclophosphamide pre-CART-cell infusion. Results. Out of the 17 patients, 8 completed ASCT plus CART cell therapy, while 9 patients completed CART cell alone therapy. In efficacy assessment at 3 months after infusion, the objective response rate (ORR) was 12/17 (71%) and the complete response rate (CRR) was 11/17 (65%). The CRR of the ASCT group and non-ASCT was 100% and 44.4%, respectively (

P
<
0.01

). The median progression-free survival was 16.3 (2.6–24.5) months, and the median overall survival was 19.3 (6–24.5) months. Patients who underwent ASCT plus CART cell therapy had significantly longer PFS (

P
<
0.01

) and OS (

P
<
0.01

). Grade 3 or higher immune effector cell-associated neurologic toxicity syndrome (≥grade 3 ICANS) and cytokine release syndrome (≥grade 3 CRS) events occurred in 29% and 41% of the patients, respectively. No treatment-related death occurred. Conclusion. The CAR T-cell therapy could augment its efficacy in the treatment of advanced relapsed/refractory CNS B-cell lymphoma, while ASCT in combination with CART can induce durable responses and OS with a manageable side effect.
Splenic Artery Infarct Requiring Surgery: A Rare Complication of COVID-19 Infection
Authors: Dimitriou Ioannis , Christodoulou Nikolaos , Chatzimargaritis Kleanthis , Kaikis Aristidis , Kasti Eirini , Triantos Georgios
Published: November 2022

Introduction. Coronavirus disease (COVID-19) from SARS-CoV-2 infection is linked to a hypercoagulable state, leading to arterial and venous thrombotic events, of which pulmonary embolism is the most frequent. However, arterial thromboembolisms may also occur as visceral infracts in unusual sites, such as the renal, splenic, and intestinal arteries. Case Report. A 46-year-old unvaccinated male with a COVID-19 infection was admitted to the COVID-19 isolation ward with symptoms of respiratory infection. He complained of epigastric pain and fever for several days; radiological imaging of the abdomen revealed complete splenic arterial occlusion due to a large infarct. He was treated with low molecular weight heparin (enoxaparin) in therapeutic doses, resulting in minimal improvement. However, the pain worsened, and eventually, a laparotomy and splenectomy were performed. He was hospitalized for another 36 days before he was discharged in good condition. A second surgery was performed to remove a noninfected encapsulated hematoma from the subdiaphragmatic space. The patient remained healthy afterward, with no relapses. Discussion. Although rare, the number of cases of visceral infarcts in COVID-19 patients has increased. Splenic artery infarct is an exceptional case of acute abdominal pain that can be treated successfully with anticoagulant medication. Splenectomy may be required to manage refractory pain after failure of conservative management.
Analysis of the Refrigeration Performance of the Refrigerated Warehouse with Ice Thermal Energy Storage Driven Directly by Variable Photovoltaic Capacity
Authors: Liang Junyu , Du Wenping , Wang Dada , Yuan Xingyu , Liu Mei , Niu Kunhao
Published: November 2022

An independent solar photovoltaic (PV) refrigerated warehouse system with ice thermal energy storage is constructed in this paper. In this system, the vapour compression refrigeration cycle is directly driven by a PV array, and the frequency of the compressor varies with the solar radiation intensity. The refrigeration performance and the matching characteristics of the system driven by different PV capacities are studied. The results show that the intensity of solar radiation required for the compressor to work at the same frequency decreases by approximately 7.8% when the ratio of PV capacity to compressor-rated power increases by 10%, and the time required for the temperature in the refrigerated warehouse to drop from ambient temperature to 0°C is reduced by 32 min on average. The energy efficiency ratio of the vapour compression refrigeration subsystem and the coefficient of performance (COP) of the refrigerated warehouse system increase with the ratio of PV capacity to compressor-rated power α. When α increases from 1 to 1.3, the growth rate of the COP is very slow. For the PV direct-drive refrigerated warehouse system with a compressor-rated power of 4.4 kW, the suitable ratio of PV capacity to compressor-rated power α is about 1.3. When the refrigerated warehouse system is driven directly by a 5.4 kW PV array, the overall COP is approximately 0.19. In the cycle mode of refrigeration and cold energy storage during the day and cold energy release at night, the stored cold energy can still meet the refrigeration required by the load for 48 hours after eight days of continuous operation. According to the current market price of cold storage, during the service life of the system, the income per unit volume of cold storage is about 2.2 times the investment.
Smart City: Recent Advances in Intelligent Street Lighting Systems Based on IoT
Authors: Omar Amjad , AlMaeeni Sara , Attia Hussain , Takruri Maen , Altunaiji Ahmed , Sanduleanu Mihai , Shubair Raed , Ashhab Moh’d Sami , Al Ali Maryam , Al Hebsi Ghaya
Published: November 2022

Based on the importance of energy saving in terms of reducing the carbon impact and global warming problems, smart street lighting systems have been proposed in the past few years with different specifications. These systems include sensors for controlling the light intensity and connectivity for recording weather conditions and diagnosing lamp failure remotely. This paper discusses many published research studies regarding smart street lighting systems, providing a comparison between these systems which emphasizes the limitations of each one of them. Current and future trends are highlighted.
Nomogram for Prediction of Postoperative Delirium after Deep Brain Stimulation of Subthalamic Nucleus in Parkinson’s Disease under General Anesthesia
Authors: Ling Yu-Ting , Guo Qian-Qian , Wang Si-Min , Zhang Li-Nan , Chen Jin-Hua , Liu Yi , Xuan Ruo-Heng , Qu Bo , Liu Li-Ge , Wen Zhi-Shuang , Xu Jia-Kun , Jiang Lu-Lu , Xian Wen-Biao , Wu Bin , Zhang Chang-Ming , Chen Ling , Liu Jin-Long , Jiang Nan
Published: November 2022

Introduction. Postoperative delirium can increase cognitive impairment and mortality in patients with Parkinson’s disease. The purpose of this study was to develop and internally validate a clinical prediction model of delirium after deep brain stimulation of the subthalamic nucleus in Parkinson’s disease under general anesthesia. Methods. We conducted a retrospective observational cohort study on the data of 240 patients with Parkinson’s disease who underwent deep brain stimulation of the subthalamic nucleus under general anesthesia. Demographic characteristics, clinical evaluation, imaging data, laboratory data, and surgical anesthesia information were collected. Multivariate logistic regression was used to develop the prediction model for postoperative delirium. Results. A total of 159 patients were included in the cohort, of which 38 (23.90%) had postoperative delirium. Smoking (OR 4.51, 95% CI 1.56–13.02,

p
<
0.01

) was the most important risk factor; other independent predictors were orthostatic hypotension (OR 3.42, 95% CI 0.90–13.06,

p
=
0.07

), inhibitors of type-B monoamine oxidase (OR 3.07, 95% CI 1.17–8.04,

p
=
0.02

), preoperative MRI with silent brain ischemia or infarction (OR 2.36, 95% CI 0.90–6.14,

p
=
0.08

), Hamilton anxiety scale score (OR 2.12, 95% CI 1.28–3.50,

p
<
0.01

), and apolipoprotein E level in plasma (OR 1.48, 95% CI 0.95–2.29,

p
=
0.08

). The area under the receiver operating characteristic curve (AUC) was 0.76 (95% CI 0.66–0.86). A nomogram was established and showed good calibration and clinical predictive capacity. After bootstrap for internal verification, the AUC was 0.74 (95% CI 0.66–0.83). Conclusion. This study provides evidence for the independent inducing factors of delirium after deep brain stimulation of the subthalamic nucleus in Parkinson’s disease under general anesthesia. By predicting the development of delirium, our model may identify high-risk groups that can benefit from early or preventive intervention.
Coefficient Bounds for a Certain Family of Biunivalent Functions Defined by Gegenbauer Polynomials
Authors: Al-Shbeil Isra , Wanas Abbas Kareem , Benali Abdelkader , Cătaş Adriana
Published: November 2022

In the present work, by making use of Gegenbauer polynomials, we introduce and study a certain family of λ‐pseudo bistarlike and λ‐pseudo biconvex functions with respect to symmetrical points defined in the open unit disk. We obtain estimates for initial coefficients and solve the Fekete–Szeg problem for functions that belong to this family. Furthermore, we give connections to some of the earlier known results.