bluuebunny/crossref_metadata_2025 huggingface dataset:
Climate change and human influences on sediment fluxes and the sediment budget of an urban delta: the example of the lower Rhine—Meuse delta distributary network
Authors: Cox J. R. , Dunn F. E. , Nienhuis J. H. , van der Perk M. , Kleinhans M. G.
Published: December 2021

Deltas require sufficient sediment to maintain their land area and elevation in the face of relative sea-level rise. Understanding sediment budgets can help in managing and assessing delta resilience under future conditions. Here, we make a sediment budget for the distributary channel network of the Rhine—Meuse delta (RMD), the Netherlands, home to the Port of Rotterdam. We predict the future budget and distribution of suspended sediment to indicate the possible future state of the delta in 2050 and 2085. The influence of climate and anthropogenic effects on the fluvial and coastal boundaries was calculated for climate change scenarios, and the effects of future dredging on the budget were related to port development and accommodation of larger ships in inland ports. Suspended sediment rating curves and a 1D flow model were used to estimate the distribution of suspended sediment and projected erosion and sedimentation trends for branches. We forecast a negative sediment budget (net annual loss of sediment) for the delta as a whole, varying from −8 to −16 Mt/year in 2050 and −11 to −25 Mt/year by 2085, depending on the climate scenario and accumulated error. This sediment is unfavourably distributed: most will accrete in the northern part of the system and must consequently be removed by dredging for navigation. Meanwhile, vulnerable intertidal ecosystems will receive insufficient sediment to keep up with sea-level rise, and some channels will erode, endangering bank protection. Despite increased coastal import of sediment by estuarine processes and increased river sediment supply, extensive dredging for port development will cause a sediment deficit in the future.
Hadronic currents and form factors in three-body semileptonic $$\tau $$ decays
Authors: Krinner Fabian , Paul Stephan
Published: December 2021

Three-body semileptonic $$\tau $$
τ
-decays offer a path to understand the properties of light hadronic systems and CP symmetry violations through searches for electric dipole moments. In studies of electro-weak physics, the hadronic part of the final states has traditionally been described using the language of form factors. Spectroscopic information, resolved in terms of orbital angular momentum quantum-numbers, is best being derived from an explicit decomposition of the hadronic current in the orbital angular momentum basis. Motivated by the upcoming large data samples from $$\mathrm {B}$$
B
factories, we present the full description of the hadronic currents decomposed into quantum numbers of the hadronic final state using the isobar picture. We present formulas for orbital angular momenta up to three and apply the rules derived from hadron spectroscopy to formulate the decay chain of hadronic three-body systems of arbitrary mass. We also translate this formalism to the language of form factors and thereby correct insufficiencies found in previous analyses of three-body hadronic final states.
Dark matter dilution scenarios in the early universe
Authors: Chaudhuri Arnab , Khlopov Maxim Yu.
Published: December 2021

When the vacuum like energy of the Higgs potential within the standard model undergoes electroweak phase transition, an influx of entropy into the primordial plasma can lead to a significant dilution of frozen out dark matter density that was already present before the onset of the phase transition. The same effect can take place if the early universe was dominated by primordial black holes of small mass, evaporating before the period of Big Bang Nucleosynthesis. In this paper, we calculate the dilution factor for the above-mentioned scenarios.
Effect of magnetic screening mass on the diffusion of heavy quarks
Authors: Rahaman Mahfuzur , Das Santosh K. , Alam Jan-e , Ghosh Sabyasachi
Published: December 2021

The drag and diffusion coefficients of heavy quarks propagating through quark–gluon plasma (QGP) have been estimated by shielding the infra-red divergences using electric and magnetic screening masses. The electric-type screening in perturbative quantum chromodynamics (pQCD) has been widely studied and used in evaluating the diffusion coefficient of heavy quarks (HQs). The impact of magnetic screening on diffusion coefficients of HQs is not studied before to the best of our knowledge. We explore the effect of magnetic screening mass on the drag and diffusion coefficients of HQs and found it to be non-negligible. Therefore, the effect of magnetic screening should be taken into consideration to characterize hot and dense matter formed in the collisions of nuclei at ultra-relativistic energies. We estimate the suppression of heavy flavored mesons in heavy ion collisions compared to proton+proton collisions at high transverse momenta and found that the suppression is less with the inclusion of magnetic screening. The value of the magnetic screening mass is not known exactly because of its nonperturbative nature. Moreover, it may not be possible to single out the effect of magnetic mass because of the uncertainties in other parameters involved in the diffusion process of HQs. Still it is important to include the effects of magnetic screening because of its physical origin in QCD.
IoT Assisted Machine Learning Model for Warehouse Management
Authors: Wang Lanjing , Hamad Abdulsattar Abdullah , Sakthivel V.
Published: December 2021

In the digital world of today, any enterprise that deals with the amounts of data in Warehouse Management Systems (WMS) are an important component. Furthermore, the amount of data being raisedand its complexity have become more challenging to maintain the WMS efficiency. Therefore, a device is required, which can manage such complexities autonomously with no human intervention. In this paper, Hybrid Machine Learning with the Internet of Things (HML-IoT) improves isolated doors. Furthermore, operating machine performance in the factory of hazardous goods. Decision-Making Algorithm (DMA) Data from the customer’s holding space’s dangerous goods warehouses shall be checked using separated doors. This paper’s significant aspect is that inventory and inventory operation’s organizational performance can be increased, further logistics costs minimized utilizing the fair use of isolated doors. Finally, the HML-IoT model integrated hazardous goods warehouse with isolated doors has been contrasted with the current one, demonstrating that the previous one has greater efficacy.
Fish Swarm Optimized Deep Hopfield Neural Network-Assisted HCI System for Augmentative Communication Using a Visual Feedback System
Authors: Li Shuo , Li Jianjun , Kumar Priyan Malarvizhi , Luhach Ashish Kr.
Published: December 2021

In this context, the uses of computers, the Human-computer interface (HCI) system, can assist interaction on-demand services. HCI method facilitates ventilated patients to interact with computers about their needs using their brain’s electrical activity. To accomplish this, an HCI framework is developed in this research to facilitate visual feedback system (VFS) using an augmentative communication approach. Augmentative communication (AC.) or icon-based services are incorporated with a portable monitor placed in front of a patient; they can look at the screen to select (ask) their appropriate needs-related icons. The services have been achieved by capturing and processing patients’ electromagnetic brain activates during the icon selection by their eye flickering moment recording using wearable Electroencephalogram (EEG). The flickering icons on the screen conveying an appropriate message to the monitoring unit computer, and the monitoring unit can respond to the patient’s request using VFS. The HCI system is comprised of the following methodologies to achieve augmentative communication-based services such as EEG signals acquisition, filtering, partition-based feature extraction, and fusion and fish swarm optimized Deep Hopfield neural network FSODHNN based classifier. The evolution results of the VSF based HCI framework are demonstrated successfully. It obtained the highest accuracy of 99.11%, specificity of 99.05%, the sensitivity of 99.09%, and the lowest RMSE of 0.98, MSE of 0.92 in icon identification/ selection.
Marvel: A Data-Centric Approach for Mapping Deep Learning Operators on Spatial Accelerators
Authors: Chatarasi Prasanth , Kwon Hyoukjun , Parashar Angshuman , Pellauer Michael , Krishna Tushar , Sarkar Vivek
Published: December 2021

A spatial accelerator’s efficiency depends heavily on both its mapper and cost models to generate optimized mappings for various operators of DNN models. However, existing cost models lack a formal boundary over their input programs (operators) for accurate and tractable cost analysis of the mappings, and this results in adaptability challenges to the cost models for new operators. We consider the recently introduced Maestro Data-Centric (MDC) notation and its analytical cost model to address this challenge because any mapping expressed in the notation is precisely analyzable using the MDC’s cost model.

In this article, we characterize the set of input operators and their mappings expressed in the MDC notation by introducing a
set of conformability rules
. The outcome of these rules is that any loop nest that is perfectly nested with affine tensor subscripts and without conditionals is conformable to the MDC notation. A majority of the primitive operators in deep learning are such loop nests. In addition, our rules enable us to automatically translate a mapping expressed in the loop nest form to MDC notation and use the MDC’s cost model to guide upstream mappers. Our conformability rules over the input operators result in a
structured mapping space
of the operators, which enables us to introduce a mapper based on our
decoupled off-chip/on-chip
approach to accelerate mapping space exploration. Our mapper decomposes the original higher-dimensional mapping space of operators into two lower-dimensional off-chip and on-chip subspaces and then optimizes the off-chip subspace followed by the on-chip subspace. We implemented our overall approach in a tool called
Marvel
, and a benefit of our approach is that it applies to any operator conformable with the MDC notation. We evaluated Marvel over major DNN operators and compared it with past optimizers.
Joint Program and Layout Transformations to Enable Convolutional Operators on Specialized Hardware Based on Constraint Programming
Authors: Rieber Dennis , Acosta Axel , Fröning Holger
Published: December 2021

The success of Deep Artificial Neural Networks (DNNs) in many domains created a rich body of research concerned with hardware accelerators for compute-intensive DNN operators. However, implementing such operators efficiently with complex hardware intrinsics such as matrix multiply is a task not yet automated gracefully. Solving this task often requires joint program and data layout transformations. First solutions to this problem have been proposed, such as TVM, UNIT, or ISAMIR, which work on a loop-level representation of operators and specify data layout and possible program transformations before the embedding into the operator is performed. This top-down approach creates a tension between exploration range and search space complexity, especially when also exploring data layout transformations such as im2col, channel packing, or padding.

In this work, we propose a new approach to this problem. We created a bottom-up method that allows the joint transformation of both computation and data layout based on the found embedding. By formulating the embedding as a constraint satisfaction problem over the scalar dataflow, every possible embedding solution is contained in the search space. Adding additional constraints and optimization targets to the solver generates the subset of preferable solutions.

An evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark shows that our approach can automatically generate code competitive to reference implementations. Further, we show that dynamically determining the data layout based on intrinsic and workload is beneficial for hardware utilization and performance. In cases where the reference implementation has low hardware utilization due to its fixed deployment strategy, we achieve a geomean speedup of up to × 2.813, while individual operators can improve as much as × 170.
Computational Design of Knit Templates
Authors: Jones Benjamin , Mei Yuxuan , Zhao Haisen , Gotfrid Taylor , Mankoff Jennifer , Schulz Adriana
Published: December 2021

We present an interactive design system for knitting that allows users to create template patterns that can be fabricated using an industrial knitting machine. Our interactive design tool is novel in that it allows direct control of key knitting design axes we have identified in our formative study and does so consistently across the variations of an input parametric template geometry. This is achieved with two key technical advances. First, we present an interactive meshing tool that lets users build a coarse quadrilateral mesh that adheres to their knit design guidelines. This solution ensures consistency across the parameter space for further customization over shape variations and avoids helices, promoting knittability. Second, we lift and formalize low-level machine knitting constraints to the level of this coarse quad mesh. This enables us to not only guarantee hand- and machine-knittability, but also provides automatic design assistance through auto-completion and suggestions. We show the capabilities through a set of fabricated examples that illustrate the effectiveness of our approach in creating a wide variety of objects and interactively exploring the space of design variations.
SecNVM: An Efficient and Write-Friendly Metadata Crash Consistency Scheme for Secure NVM
Authors: Lei Mengya , Li Fan , Wang Fang , Feng Dan , Zou Xiaomin , Xiao Renzhi
Published: December 2021

Data security is an indispensable part of non-volatile memory (NVM) systems. However, implementing data security efficiently on NVM is challenging, since we have to guarantee the consistency of user data and the related security metadata. Existing consistency schemes ignore the recoverability of the SGX style integrity tree (SIT) and the access correlation between metadata blocks, thereby generating unnecessary NVM write traffic. In this article, we propose SecNVM, an efficient and write-friendly metadata crash consistency scheme for secure NVM. SecNVM utilizes the observation that for a lazily updated SIT, the lost tree nodes after a crash can be recovered by the corresponding child nodes in NVM. It reduces the SIT persistency overhead through a restrained write-back metadata cache and exploits the SIT inter-layer dependency for recovery. Next, leveraging the strong access correlation between the counter and DMAC, SecNVM improves the efficiency of security metadata access through a novel collaborative counter-DMAC scheme. In addition, it adopts a lightweight address tracker to reduce the cost of address tracking for fast recovery. Experiments show that compared to the state-of-the-art schemes, SecNVM improves the performance and decreases write traffic a lot, and achieves an acceptable recovery time.
TLB-pilot: Mitigating TLB Contention Attack on GPUs with Microarchitecture-Aware Scheduling
Authors: Di Bang , Hu Daokun , Xie Zhen , Sun Jianhua , Chen Hao , Ren Jinkui , Li Dong
Published: December 2021

Co-running GPU kernels on a single GPU can provide high system throughput and improve hardware utilization, but this raises concerns on application security. We reveal that translation lookaside buffer (TLB) attack, one of the common attacks on CPU, can happen on GPU when multiple GPU kernels co-run. We investigate conditions or principles under which a TLB attack can take effect, including the awareness of GPU TLB microarchitecture, being lightweight, and bypassing existing software and hardware mechanisms. This TLB-based attack can be leveraged to conduct Denial-of-Service (or Degradation-of-Service) attacks. Furthermore, we propose a solution to mitigate TLB attacks. In particular, based on the microarchitecture properties of GPU, we introduce a software-based system, TLB-pilot, that binds thread blocks of different kernels to different groups of streaming multiprocessors by considering hardware isolation of last-level TLBs and the application’s resource requirement. TLB-pilot employs lightweight online profiling to collect kernel information before kernel launches. By coordinating software- and hardware-based scheduling and employing a kernel splitting scheme to reduce load imbalance, TLB-pilot effectively mitigates TLB attacks. The result shows that when under TLB attack, TLB-pilot mitigates the attack and provides on average 56.2% and 60.6% improvement in average normalized turnaround times and overall system throughput, respectively, compared to the traditional Multi-Process Service based co-running solution. When under TLB attack, TLB-pilot also provides up to 47.3% and 64.3% improvement (41% and 42.9% on average) in average normalized turnaround times and overall system throughput, respectively, compared to a state-of-the-art co-running solution for efficiently scheduling of thread blocks.
Editors' introduction
Authors: Talgam-Cohen Inbal , Weinberg S. Matthew
Published: December 2021

This issue of SIGecom Exchanges brings to our readers a letter from the SIGecom executive committee, the job candidate profiles for 2022, two surveys, and three research letters.
Constraints in fair division
Authors: Suksompong Warut
Published: December 2021

The fair allocation of resources to interested agents is a fundamental problem in society. While the majority of the fair division literature assumes that all allocations are feasible, in practice there are often constraints on the allocation that can be chosen. In this survey, we discuss fairness guarantees for both divisible (cake cutting) and indivisible resources under several common types of constraints, including connectivity, cardinality, matroid, geometric, separation, budget, and conflict constraints. We also outline a number of open questions and directions.
Exposing T Cell Secrets Inside and Outside the Thymus
Authors: Fink Pamela J.
Published: December 2021

I've had serious misgivings about writing this article, because from living the experience day by day, it's hard to believe my accomplishments merit the attention. To skirt this roadblock, I forced myself to pretend I was in a conversation with my trainees, trying to distill the central driving forces of my career in science. The below chronicles my evolution from would-be astronaut/ballerina to budding developmental biologist to devoted T cell immunologist. It traces my work from a focus on intrathymic events that mold developing T cells into self–major histocompatibility complex (MHC)-restricted lymphocytes to extrathymic events that fine-tune the T cell receptor (TCR) repertoire and impose the finishing touches on T cell maturation. It is a story of a few personal attributes multiplied by generous mentors, good luck, hard work, perseverance, and knowing when to step down.
Carotid Body Inflammation: Role in Hypoxia and in the Anti-inflammatory Reflex
Authors: Iturriaga Rodrigo , Del Rio Rodrigo , Alcayaga Julio
Published: December 2021

Emergent evidence indicates that the carotid body (CB) chemoreceptor may sense systemic inflammatory molecules and is an afferent arm of the anti-inflammatory reflex. Moreover, a proinflammatory milieu within the CB is involved in the enhanced CB chemosensory responsiveness to oxygen following sustained and intermittent hypoxia. In this review, we focus on the physiopathological participation of CBs in inflammatory diseases, such as sepsis and intermittent hypoxia.
Distributed Super Nested Arrays: Reduce the Mutual Coupling between Array Antennas
Authors: Wang Hongyong , Deng Weibo , Suo Ying , Zhang Xin , Hu Yanmo , Wu Xiaochuan
Published: December 2021

In array, mutual coupling between the antennas is inevitable, which has an adverse effect on the estimation of parameters. To reduce the mutual coupling between the antennas of distributed nested arrays, this paper proposes a new array called the distributed super nested arrays, which have the good characteristics of the distributed nested arrays and can reduce the mutual coupling between the antennas. Then, an improved multiscale estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm is presented for the distributed super nested arrays to improve the accuracy of direction-of-arrival (DOA) estimation. Next, we analyze the limitations of the spatial smoothing algorithm used by the distributed super nested arrays when there are multiple-source signals and the influence of the baseline length of distributed super nested arrays on the accuracy of DOA estimation. The simulation results show that the distributed super nested arrays can effectively reduce the mutual coupling between the array antennas, improve the DOA estimation performance, and significantly increase the number of detectable source signals.
Comparison between Vascular Endothelial Growth Factor and Platelet-Derived Growth Factor Levels in Rhegmatogenous Retinal Detachment
Authors: Ichsan Andi Muhammad , Windy Dyah Ayu , Muhiddin Habibah Setyawati , Budu  , Massi Muhammad Nasrum , Islam Itzar Chaidir
Published: December 2021

Introduction. This study aimed to assess vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) levels within vitreous and blood serum samples of patients with rhegmatogenous retinal detachment (RRD) and their relationship to the development of proliferative vitreoretinopathy (PVR). Methods. Seventeen eyes of patients with RRD were included in the RRD group and divided into three subgroups: RRD without PVR, RRD with PVR grades A and B, and RRD with PVR grade C. Five control eyes (nucleus and intraocular lens drop) were included in this study. Blood serum and vitreous samples were collected during vitrectomy. VEGF-A and PDGF-AA levels were determined by enzyme-linked immunosorbent assay. Results. The mean vitreous VEGF-A level in the RRD group was 131.71 ± 58.25 pg/mL, and the mean vitreous PDGF-AA level was 174.62 ± 65.17 pg/mL. Both levels were significantly higher in the RRD group compared with the control group (

p
<
0.05

). Vitreous VEGF-A and PDGF-AA levels were the highest in RRD with PVR grade C subgroup, with mean levels of 179.87 ± 21.02 pg/mL and 229.44 ± 14.09 pg/mL, respectively (

p
<
0.05

). The vitreous VEGF-A/PDGF-AA ratios in the RRD subgroups were completely different. Conclusion. Based on the tendency of VEGF-A and PDGF-AA levels, RRD surgery has to be performed as soon as possible prior to retinal cell death and membrane proliferative formation.
Prediction of Agricultural Water Consumption in 2 Regions of China Based on Fractional-Order Cumulative Discrete Grey Model
Authors: Xu Yunhong , Wang Huadong , Hui Nga Lay
Published: December 2021

In this paper, a new forecasting method of agricultural water demand, fractional-order cumulative discrete grey model, is proposed. Firstly, the best fitting of historical data is used to construct the optimization model. MATLAB programming is applied to solve the optimization model and obtain the optimal order. Secondly, the fractional-order cumulative discrete grey model in this paper is compared with GM (1, 1) model to verify the performance of the model. Finally, Handan region of Hebei Province and Jingzhou region of Hubei Province were selected as the study areas to predict their agricultural water consumptions. The results show that the fractional-order cumulative discrete grey model has better prediction performance than the GM (1, 1) model. It can be used as an effective method for forecasting agricultural water consumption.
Nonlinear Dynamic Modelling of a Suspension Seat for Predicting the Vertical Seat Transmissibility
Authors: Yin Weitan , Ding Juyue , Qiu Yi
Published: December 2021

Suspension seats are widely used in heavy vehicles to reduce vibration transmitted to human body and promote ride comfort. Previous studies have shown that the dynamics of the suspension seat exhibits nonlinear behaviour with changed vibration magnitudes. Despite various linear seat models developed in the past, a nonlinear model of the suspension seat capturing the nonlinear dynamic behaviour of the seat suspension and cushion has not been developed for the prediction of the seat transmissibility. This paper proposes a nonlinear lumped parameter model of the suspension seat to predict the nonlinear dynamic response of the seat. The suspension seat model comprises of a nonlinear suspension submodel integrated with a nonlinear cushion submodel. The parameters of the submodels are determined by minimizing the error between the simulated and the measured transmissibility of the suspension mechanism and the force-deflection curve of the seat cushion, respectively. The model of the complete seat is then validated using the seat transmissibility measured with inert mass under vertical vibration excitation. The results show that the proposed suspension seat model can be used to predict the seat transmissibility with various excitation magnitudes.
The Degree-Based Topological Indices for Two Special Families of Graphs of Diameter Three
Authors: Khalid Aqsa , Kausar Nasreen , Munir Mohammad , Aydi Hassen , Kousar Sajida , Gaba Yaé Ulrich
Published: December 2021

In this research article, we determine some vertex degree-based topological indices or descriptors of two families of graphs, i.e.,

G
=

C

4

K

n

and

G
=

C

4

K

n

+

v

1

v

3

, where

C

4

K

n

is a graph obtained by identifying one of the vertices of

K

n

with one vertex of

C

4

. Similarly, a graph formed by joining one of the vertices of

K

n

with one vertex of

C

4

+

v

1

v

3

is known as the

C

4

K

n

+

v

1

v

3

graph.
Exploration on Mechanical Behaviours of Hyacinth Fibre Particles Reinforced Polymer Matrix‐Based Hybrid Composites for Electronic Applications
Authors: Kumar Panda Pradeep , Jebastine J. , Ramarao Madhialagan , Fairooz Shaik , Reddy Chirra Kesava , Nasif Omaima , Alfarraj Saleh , Manikandan Velu , Jenish I.
Published: December 2021

Biocomposites with polylactic acid (PLA), nanosilica parts, and water hyacinth fibres have been developed in this experimental study. By changing the weight percentage of nanosilica particulate matter (0, 2, 4, 6, and 8 percent) with PLA and water hyacinth fibres, five composite mates were produced through a double screw extruder and compression moulding machine. According to the ASTM standards, the process to machine, the composite specimens have been adopted from the water jet machining process. The tensile, compression, flexural, impact, hardness, and water absorption tests were performed on the composite specimens to assess various mechanical properties and absorbance behaviour. The test findings reveal the significant improvement in the tensile and flexural properties of the composites. Composites contain 6 percent of the fine nanosilica particles by weight. Concerning adding the growing weight percentage (4 percent) of nanosilica particles to the composites, the water absorption properties of the composites have significantly improved. The tensile strength of 6% nanosilica mixed specimens showed the highest tensile stress rate as 36.93 MPa; the value was nearly 3.5% higher than the 4% nanosilica mixed composite specimens.
A Multiparameter Hardy–Hilbert-Type Inequality Containing Partial Sums as the Terms of Series
Authors: Liao Jianquan , Wu Shanhe , Yang Bicheng
Published: December 2021

In this study, a multiparameter Hardy–Hilbert-type inequality for double series is established, which contains partial sums as the terms of one of the series. Based on the obtained inequality, we discuss the equivalent statements of the best possible constant factor related to several parameters. Moreover, we illustrate how the inequality obtained can generate some new Hardy–Hilbert-type inequalities.
Systematic Review of Tongxinluo Capsule on the Therapeutic Effect and Hemorheology of Patients with Transient Ischemic Attack
Authors: Yang Peng , Liu Peng , Yang Ruijin
Published: December 2021

Objectives. This study aims to systematically evaluate the clinical efficacy of Tongxinluo capsule in the treatment of transient ischemic attack (TIA) and its effect on hemorheology, thereby providing scientific basis for clinical decision making. Methods. A comprehensive and systematic literature retrieval was conducted in the CNKI, Wanfang database, SinoMed, EMbase, and PubMed to screen the randomized controlled trials (RCTs) of Tongxinluo capsule in the treatment of TIA. The retrieval time was from the inception of each database to September 10, 2020. Endnote X9 was used to screen the literature. Cochrane Collaboration tool for assessing risk of bias was used to evaluate the quality of the included studies. Stata16.0 statistical software was used for meta-analysis. Results. A total of 12 RCTs were included, involving 946 subjects. (1) The clinical efficacy of the Tongxinluo group was better than that of the control group (RR = 1.19, 95% CI (1.09, 1.30),

P

 ≤ 0.001). (2) The hemorheological characteristics of the Tongxinluo group were significantly improved compared with those of the control group (whole blood high shear viscosity: SMD = −1.61, 95% CI (−1.89, −1.34);

P

 ≤ 0.001, whole blood low shear viscosity: SMD = −1.06, 95% CI (−1.31, −0.80),

P

 ≤ 0.001, fibrinogen: SMD = −1.12, 95% CI (−1.94, −0.29),

P

 = 0.008, plasma specific viscosity: SMD = −1.00, 95% CI (−1.69, −0.31),

P

 = 0.004, and hematocrit: SMD = −1.47, 95% CI (−2.16, −0.77),

P

 ≤ 0.001). (3) There was no significant difference in the incidence of adverse reactions between the Tongxinluo group and control group (RR = 7.76, 95% CI (0.98, 61.28),

P

 = 0.052). Conclusion. Tongxinluo capsule is superior to conventional treatment in improving clinical overall response rate and hemorheological indexes and is relatively safe. Due to the deficiencies of the existing studies, more high-quality studies with rigorous design are required for further verification.
On the Matrix Versions of Incomplete Extended Gamma and Beta Functions and Their Applications for the Incomplete Bessel Matrix Functions
Authors: Zou Chaojun , Yu Mimi , Bakhet Ahmed , He Fuli
Published: December 2021

In this paper, we first introduce the incomplete extended Gamma and Beta functions with matrix parameters; then, we establish some different properties for these new extensions. Furthermore, we give a specific application for the incomplete Bessel matrix function by using incomplete extended Gamma and Beta functions; at last, we construct the relation between the incomplete confluent hypergeometric matrix functions and incomplete Bessel matrix function.
Olfm4 Is Highly Expressed in HCC Patients and as a Biomarker and Therapeutic Target for HCC
Authors: Wei Yulong , Song Qingzhu , Zhang Fenglan , Yuan Tian
Published: December 2021

Hepatocellular carcinoma (HCC) is one of the primary types of cancer that claims many lives worldwide, and its incidence continues to increase. Conventional therapies against liver cancer are inadequate, and the pathogenesis of HCC remains unclear. Thus, not only are more effective therapies to treat HCC required but also identification of the key genes involved in its pathogenesis is important for developing such therapies. This study found that olfactomedin 4 (OLFM4) level is higher in HCC patients than in healthy individuals. Furthermore, HCC patients also have higher messenger ribonucleic acid (mRNA) expression level in HCC tissues than in liver paracancerous tissues. OLFM4 has high predictive capacity as a biomarker for HCC and closely correlates to tumor size. It is confirmed that OLFM4 contributes to cancer cell proliferation, and HIF1α is involved in this process. Thus, the OLFM4/HIF-1α axis might be a target signaling pathway for developing novel drugs to treat HCC.
The Anti-Inflammatory and Antiapoptotic Effects of Nicorandil in Antisepsis Cardiomyopathy
Authors: Lu Jinshuai , Liu Fen , Yu Xia , Xu Likun , Zhang Lingling
Published: December 2021

Objective. To observe the effect of nicorandil on septic rats and explore the possible mechanism of its myocardial protection, so as to provide theoretical basis for the treatment of septic cardiomyopathy. Methods. Sixty male clean SD rats were selected as the research objects and randomly divided into 3 groups by random number method: sham operation group (sham group), cecal ligation and perforation group (CLP group), nicorandil treatment group (nicorandil+CLP group). After the operation, the nicorandil group was pumped with nicorandil diluent 1 ml/h (2 mg/kg/h) with a micropump for 6 hours. The sham group and CLP group were pumped with the same amount of normal saline 1 ml/h for a total of 6 hours. After 24 hours, the survival of the rats in each group was observed. The expression of troponin I (cTnI), tumor necrosis factor α (TNF-α), and interleukin-1β (IL-1β) in the serum was detected. Then, the ventricle was harvested for the observation of the pathological changes of myocardium. Quantitative real-time polymerase chain reaction and immunostaining were used to detect myocardial tissue apoptosis, and Western blot methods were used to detect protein expression changes in nuclear factor-κB (NF-κB) pathways. Results. 24 hours after operation, the survival rate of the rats in the CLP group was 60%. There was a large amount of necrosis of myocardial cells and inflammatory cell infiltration. The survival rate of rats in the nicorandil+CLP group was 75%. Compared with the CLP group, the necrosis of myocardial cells was reduced, and there was still a small amount of inflammatory cell infiltration. In the CLP group, myocardial inflammation and apoptosis were significant, and NF-κB pathway was activated. On the contrary, the NF-κB pathway in the nicorandil+CLP group was inhibited, and the expression of inflammatory factors and apoptosis factors was inhibited. Conclusion. Nicorandil can reduce the release of inflammatory factors in septic rats, improve the inflammatory response, reduce myocardial damage, and play a myocardial protective effect. Its mechanism may be related to the inhibition of the activation of NF-κB signaling pathway.
An Efficient Hybrid Numerical Scheme for Nonlinear Multiterm Caputo Time and Riesz Space Fractional-Order Diffusion Equations with Delay
Authors: Omran A. K. , Zaky M. A. , Hendy A. S. , Pimenov V. G.
Published: December 2021

In this paper, we construct and analyze a linearized finite difference/Galerkin–Legendre spectral scheme for the nonlinear multiterm Caputo time fractional-order reaction-diffusion equation with time delay and Riesz space fractional derivatives. The temporal fractional orders in the considered model are taken as

0
<

β

0

<

β

1

<

β

2

<

⋯

<

β

m

<
1

. The problem is first approximated by the

L
1

difference method on the temporal direction, and then, the Galerkin–Legendre spectral method is applied on the spatial discretization. Armed by an appropriate form of discrete fractional Grönwall inequalities, the stability and convergence of the fully discrete scheme are investigated by discrete energy estimates. We show that the proposed method is stable and has a convergent order of

2
−

β

m

in time and an exponential rate of convergence in space. We finally provide some numerical experiments to show the efficacy of the theoretical results.
Application of Design Structure Matrix to Simulate Surgical Procedures and Predict Surgery Duration
Authors: Li Zhaoxuan , Tate Derrick , McGill Thomas , Griswold John , Chyu Ming-Chien
Published: December 2021

Background. The complexities of surgery require an efficient and explicit method to evaluate and standardize surgical procedures. A reliable surgical evaluation tool will be able to serve various purposes such as development of surgery training programs and improvement of surgical skills. Objectives. (a) To develop a modeling framework based on integration of dexterity analysis and design structure matrix (DSM), to be generally applicable to predict total duration of a surgical procedure, and (b) to validate the model by comparing its results with laparoscopic cholecystectomy surgery protocol. Method. A modeling framework is developed through DSM, a tool used in engineering design, systems engineering and management, to hierarchically decompose and describe relationships among individual surgical activities. Individual decomposed activities are assumed to have uncertain parameters so that a rework probability is introduced. The simulation produces a distribution of the duration of the modeled procedure. A statistical approach is then taken to evaluate surgery duration through integrated numerical parameters. The modeling framework is applied for the first time to analyze a surgery; laparoscopic cholecystectomy, a common surgical procedure, is selected for the analysis. Results. The present simulation model is validated by comparing its results of predicted surgery duration with the standard laparoscopic cholecystectomy protocols from the Atlas of Minimally Invasive Surgery with 2.5% error and that from the Atlas of Pediatric Laparoscopy and Thoracoscopy with 4% error. Conclusion. The present model, developed based on dexterity analysis and DSM, demonstrates a validated capability of predicting laparoscopic cholecystectomy surgery duration. Future studies will explore its potential applications to other surgery procedures and in improving surgeons’ performance and training novices.
Research on Multimodal Music Emotion Recognition Method Based on Image Sequence
Authors: Yu Zhao
Published: December 2021

The work of music performance system is to control the light change by identifying the emotional elements of music. Therefore, once the identification error occurs, it will not be able to create a good stage effect. Therefore, a multimodal music emotion recognition method based on image sequence is studied. The emotional characteristics of music are analyzed, including acoustic characteristics, melody characteristics, and audio characteristics, and the feature vector is constructed. The recognition and classification model based on neural network is trained, the weight and threshold of each layer are adjusted, and then the feature vector is input into the trained model to realize the intelligent recognition and classification of multimodal music emotion. The threshold of the starting point range of a specific humming note is given by the center clipping method, which is used to eliminate the low amplitude part of the humming note signal, extract the short-time spectral structure features and envelope features of the pitch, and complete the multimodal music emotion recognition. The results show that the calculated kappa coefficient k is greater than 0.75, which shows that the recognition and classification results are in good agreement with the actual results, and the classification and recognition accuracy is high.
A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System
Authors: Huang Min , Liu Dandan , Ma Liyun , Wang Jingyang , Wang Yuming , Chen Yazhou
Published: December 2021

With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.
Pathophysiologic Mechanisms of Insulin Secretion and Signaling-Related Genes in Etiology of Polycystic Ovary Syndrome
Authors: Shaaban Zahra , Khoradmehr Arezoo , Amiri-Yekta Amir , Nowzari Fariborz , Jafarzadeh Shirazi Mohammad Reza , Tamadon Amin
Published: December 2021

Polycystic ovary syndrome (PCOS) is a common endocrinopathy in women. PCOS is characterized by anovulation, hyperandrogenism, polycystic ovaries, insulin resistance, and obesity. Despite the finding that the genetic origin of PCOS is well demonstrated in previous twin and familial clustering studies, genes and factors that can exactly explain the PCOS pathophysiology are not known. Objective(s). In this review, we attempted to identify genes related to secretion and signaling of insulin aspects of PCOS and their physiological functions in order to explain the pathways that are regulated by these genes which can be a prominent function in PCOS predisposition. Materials and Methods. For this purpose, published articles and reviews dealing with genetic evaluation of PCOS in women from peer-reviewed journals in PubMed and Google Scholar databases were included in this review. Results. The genomic investigations in women of different populations identified many candidate genes and loci that are associated with PCOS. The most important of them are INSR, IRS1-2, MTNR1A, MTNR1B, THADA, PPAR-γ2, ADIPOQ, and CAPN10. These are mainly associated with metabolic aspects of PCOS. Conclusions. In this review, we proposed that each of these genes may interrupt specific physiological pathways by affecting them and contribute to PCOS initiation. It is clear that the role of genes involved in insulin secretion and signaling is more critical than other pathways.
Exploring the Emerging Evolution Trends of Probabilistic Service Life Prediction of Reinforced Concrete Structures in the Chloride Environment by Scientometric Analysis
Authors: Zhang Junzhi , Wang Meng , Zhou Xiaoyun , Yu Weilong , Gao Yanhong , Zhang Yurong
Published: December 2021

To reveal the global picture and emerging evolution trends with sufficiently large literature data of RC structure’s service life prediction, especially the time to corrosion initiation, the scientometric analysis on the corresponding evolution trends was performed by using visualization software CiteSpace and VOSviewer in this paper. First, the application of CiteSpace and VOSviewer and retrieval strategy for data collection were described. And then, information visualization analysis was carried out based on the papers related to RC structure’s service life evaluation from publications number evolution, journal distribution, and authors’ contribution. Finally, document cocitation network, cooccurring keywords network, and timeline view of keywords network were conducted to discuss the research hotspots on time to corrosion initiation evaluation of reinforcement. Research results reveal that the number of publications on RC structure’s service life evaluation reached its peak in 2011. Besides, Structural Safety is the journal that makes the most significant contributions, and Professor Li CQ is the author with the most contribution score. Moreover, the knowledge body of the time to corrosion initiation prediction consists of six clusters; the high‐citation articles in this field mainly focus on the multiple‐parameters model and probabilistic reliability method.
Real-Time Malware Process Detection and Automated Process Killing
Authors: Rhode Matilda , Burnap Pete , Wedgbury Adam
Published: December 2021

Perimeter-based detection is no longer sufficient for mitigating the threat posed by malicious software. This is evident as antivirus (AV) products are replaced by endpoint detection and response (EDR) products, the latter allowing visibility into live machine activity rather than relying on the AV to filter out malicious artefacts. This paper argues that detecting malware in real-time on an endpoint necessitates an automated response due to the rapid and destructive nature of some malware. The proposed model uses statistical filtering on top of a machine learning dynamic behavioural malware detection model in order to detect individual malicious processes on the fly and kill those which are deemed malicious. In an experiment to measure the tangible impact of this system, we find that fast-acting ransomware is prevented from corrupting 92% of files with a false positive rate of 14%. Whilst the false-positive rate currently remains too high to adopt this approach as-is, these initial results demonstrate the need for a detection model that is able to act within seconds of the malware execution beginning; a timescale that has not been addressed by previous work.
Semi-
I
-Expandable Ideal Topological Spaces
Authors: Boonpok Chawalit
Published: December 2021

Our purpose is to introduce the notion of semi-

I

-expandable ideal topological spaces. Some properties of semi-

I

-locally finite collections are investigated. In particular, several characterizations of semi-

I

-expandable ideal topological spaces are established.
Deep Learning-Based Correlation Analysis between Spine Surgery Lumbar Facet Joint and Lumbar Disc Herniation Using Magnetic Resonance Images
Authors: Wang Chao , Yuan Jiabin , Huang Zebin , Shi Zhicai
Published: December 2021

The research aimed at discussing the analytic function of convolutional neural network (CNN) algorithm-based magnetic resonance images (MRI) in the correlation between lumbar disc herniation (LDH) and angle and irregular variation of joint (IVJ) of lumbar facet-joint (LFJ). First, CNN-based MRI (CNNM) algorithm was constructed, and Markov random field (MRF) and fuzzy C-means (FCM) algorithms were introduced for comparison. Meanwhile, all patients received MRI examination of lumbar, and CNNM algorithm was adopted in MRI images. The results showed that the sensitivity, specificity, accuracy, and precision (98.53%, 93.65%, 99.56%, and 98.74%, respectively) of the CNNM algorithm were all superior to those of MRF algorithm (90.41%, 81.11%, 91.18%, and 91.13%, respectively) and of FCM algorithm (93.14%, 82.86%, 93.23%, and 93.08%, respectively) (

P
<
0.05

). Besides, the lumbar spine angles of L3-L4, L4-L5, and L5-S1 (6.03 ± 1.34°, 7.14 ± 1.18°, and 8.96 ± 3.26°, respectively) in the experimental group was obviously less than those in the control group (6.84 ± 1.15°, 9.85 ± 1.25°, and 17.34 ± 4.79°, respectively) (

P
<
0.05

). In the experimental group, there was irregular mutation of LFJ in 78 cases, while 8 cases suffered from irregular mutation of LFJ in the control group. The proportions of protrusion in L3/4, L4/5, and L5/S1 segments (11 cases, 53 cases, and 14 cases, respectively) was higher than that in the control group (1 case, 5 cases, and 2 cases, respectively) (

P
<
0.05

). In short, the constructed CNNM algorithm had excellent performance in diagnosing lumbar MRI images and had clinical research and promotion value. Moreover, the IVJ of patients with LDH was notably increased, most of the physiological angle of the lumbar spine changed, and facet joint was correlated with the occurrence of LDH.