bluuebunny/crossref_metadata_2025 huggingface dataset:
Soybean is relatively nonresponsive to K fertilizer rate or placement in Manitoba soils
Authors: Bourns Megan A. , Flaten Don N.
Published: April 2022

There has been little comprehensive potassium (K) fertility research for soybeans in Manitoba despite recent, rapid, expansion of soybean production in the province. Our main objective was to assess the efficacy of K fertilizer rate and placement combinations to increase K uptake and seed yield of soybeans grown on low K soils. Even though the seven sites had low concentrations of ammonium acetate-extractable soil test K (STK), midseason tissue K concentration increases with K fertilization, and, at several sites, visual deficiency symptoms in or near control plots, soybean seed yield did not respond to K fertilization, regardless of K fertilizer placement and rate. In a complementary field trial, barley, a crop known historically to respond well to K fertilization in Manitoba, had substantial (>20%) increases in yield with K fertilization where soybean did not respond. Ammonium acetate STK and the current 100 mg kg−1 threshold for recommending K fertilization for soybean and barley predicted barley yield response to K fertilization in our study, but did not predict soybean yield response.
Revisiting jet clustering algorithms for new Higgs Boson searches in hadronic final states
Authors: Chakraborty A. , Dasmahapatra S. , Day-Hall H. A. , Ford B. G. , Jain S. , Moretti S. , Olaiya E. , Shepherd-Themistocleous C. H.
Published: April 2022

We assess the performance of different jet-clustering algorithms, in the presence of different resolution parameters and reconstruction procedures, in resolving fully hadronic final states emerging from the chain decay of the discovered Higgs boson into pairs of new identical Higgs states, the latter in turn decaying into bottom-antibottom quark pairs. We show that, at the large hadron collider (LHC), both the efficiency of selecting the multi-jet final state and the ability to reconstruct from it the masses of the Higgs bosons (potentially) present in an event sample depend strongly on the choice of acceptance cuts, jet-clustering algorithm as well as its settings. Hence, we indicate the optimal choice of the latter for the purpose of establishing such a benchmark Beyond the SM (BSM) signal. We then repeat the exercise for a heavy Higgs boson cascading into two SM-like Higgs states, obtaining similar results.
Experiential and Stochastic Learning Algorithms Based on the Probability of a Fuzzy Event and Modified Fuzzy Metric Distance in Intelligent Robotic Part Micro-Assembly
Authors: Son Changman
Published: April 2022

Experiential and stochastic learning algorithms for improving the time consumed in robot’s part micro-assembly procedures are presented. A comprehensive comparison of the results with other recent algorithms is described. The experiential learning algorithm unites the neighboring sections in a manner that is very similar to the larger united section based on the specified criterion. If the experiential learning algorithm is consecutively repeated, the micro-assembly dynamics analysis chart will be not only minimized in the number of sections, but also formed such that each united section will have one selected optimal plan with the lowest modified fuzzy metric distance within its own section. Thus, if the new input appertaining to the section is merely identified, the final selected plan within the section will execute the related task quickly and accurately thus resulting to a significant improvement in the time spent in the part insertion. Through the stochastic learning algorithm extended from the crisp set domain to the fuzzy set domain, based on the probability of a fuzzy event and the modified fuzzy metric distance, to deal well with the uncertainties arising during the micro-assembly procedure, if one specific plan among the feasible plans is successively selected to solve its confronting micro-assembly problems, the probability that the selected plan will be continuously chosen will increase as a consequence of compensation, namely, a higher value of membership degree is continuously allocated to the selected plan. As a result, the selected control plan with the highest probability of success and the lowest degree of uncertainty acquired by the stochastic learning facilitates the stabilization of the execution phase of the part insertion task. The degree of uncertainty, which is measured by a modified fuzzy metric distance and related to the task execution of the micro-assembly, is used as a criterion to select the most valid plan. The results demonstrate that the micro-assembly stuck problems can be successfully resolved by the experiential and stochastic learning algorithms.
Stochastic n-point D-bifurcations of stochastic Lévy flows and their complexity on finite spaces
Authors: Da Costa Paulo Henrique , Högele Michael A. , Ruffino Paulo R.
Published: April 2022

This paper refines the classical notion of a stochastic D-bifurcation to the respective family of n-point motions for homogeneous Markovian stochastic semiflows, such as stochastic Brownian flows of homeomorphisms, and their generalizations. This notion essentially detects at which level the support of the invariant measure of the k-point bifurcation has more than one connected component. Stochastic Brownian flows and their invariant measures were shown by Kunita (1990) to be rigid, in the sense of being uniquely determined by the 1-and 2-point motions. Hence, only stochastic n-point bifurcation of level n = 1 or n = 2 can occur. For general homogeneous stochastic Markov semiflows this turns out to be false. This paper constructs minimal examples of where this rigidity is false in general on finite space and studies the complexity of the resulting n-point bifurcations.
Moderate deviations for stochastic Kuramoto–Sivashinsky equation
Authors: Amali Paul Rose Gregory , Suvinthra Murugan , Balachandran Krishnan
Published: April 2022

This paper aims to establish the central limit theorem and moderate deviation principle for the stochastic Kuramoto–Sivashinsky equation driven by multiplicative noise on a bounded domain. The moderate deviation principle is investigated using the weak convergence approach based on a variational representation for expected values of positive functionals of the Brownian motion. The approach relies on proving basic qualitative properties of controlled versions of the original stochastic partial differential equation which is under consideration.
A New Approach to Reduce Transverse Shear Locking of Reissner–Mindlin Plate Based on The Cell-Centered Finite Element Method
Authors: Ong Thanh Hai , Do Vuong Nguyen Van
Published: April 2022

This study investigates a new approach to solve a shear locking free on a low-order cell-centered finite element method (FECC) of the Reissner–Mindlin plate. The proposed method pursues the Lagrange piecewise of linear polynomial functions to attain the transverse displacement and rotations on the dual sub-mesh derived from the general one. The suggested theory is employed in numerical computation to verify the effectiveness and accurate performance. Detailed studies are carried out on several mesh types to compare with the analytical solutions reported by the low-order mimetic finite difference method [Beir ao Da Veiga, L., Lovadina, C. and Mora, D. [2013] “Numerical results for mimetic discretization of Reissner–Mindlin plate problems,” Calcolo 50, 209–237] and high-order mixed finite element methods [Chinosi, C. and Lovadina, C. [1995] “Numerical analysis of some mixed finite element methods for Reissner–Mindlin plates,” Comput. Mech. 16, 36–44]. The numerical results indicated that the present scheme based on the low-order FECC approximation can effectively predict on even the distorted meshes, satisfy the optimized convergence and be uniformly stable to the thin plate thicknesses.
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
Authors: Tian Haoye , Li Yinghua , Pian Weiguo , Kaboré Abdoul Kader , Liu Kui , Habib Andrew , Klein Jacques , Bissyandé Tegawendé F.
Published: April 2022

How do we know a generated patch is correct? This is a key challenging question that automated program repair (APR) systems struggle to address given the incompleteness of available test suites. Our intuition is that we can triage correct patches by checking whether each generated patch implements code changes (i.e., behavior) that are relevant to the bug it addresses. Such a bug is commonly specified by a failing test case. Towards predicting patch correctness in APR, we propose a novel yet simple hypothesis on how the link between the patch behavior and failing test specifications can be drawn:
similar failing test cases should require similar patches
. We then propose
BATS
, an unsupervised learning-based approach to predict patch correctness by checking patch
B
ehavior
A
gainst failing
T
est
S
pecification.
BATS
exploits deep representation learning models for code and patches: For a given failing test case, the yielded embedding is used to compute similarity metrics in the search for historical similar test cases to identify the associated applied patches, which are then used as a proxy for assessing the correctness of the APR-generated patches. Experimentally, we first validate our hypothesis by assessing whether ground-truth developer patches cluster together in the same way that their associated failing test cases are clustered. Then, after collecting a large dataset of 1,278 plausible patches (written by developers or generated by 32 APR tools), we use
BATS
to predict correct patches:
BATS
achieves AUC between 0.557 to 0.718 and recall between 0.562 and 0.854 in identifying correct patches. Our approach outperforms state-of-the-art techniques for identifying correct patches without the need for large labeled patch datasets—as is the case with machine learning-based approaches. While
BATS
is constrained by the availability of similar test cases, we show that it can still be complementary to existing approaches: When combined with a recent approach that relies on supervised learning,
BATS
improves the overall recall in detecting correct patches. We finally show that
BATS
is complementary to the state-of-the-art PATCH-SIM dynamic approach for identifying correct patches generated by APR tools.
Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework
Authors: Yuan Geng , Dong Peiyan , Sun Mengshu , Niu Wei , Li Zhengang , Cai Yuxuan , Li Yanyu , Liu Jun , Jiang Weiwen , Lin Xue , Ren Bin , Tang Xulong , Wang Yanzhi
Published: April 2022

Efficient deployment of Deep Neural Networks (DNNs) on edge devices (i.e., FPGAs and mobile platforms) is very challenging, especially under a recent witness of the increasing DNN model size and complexity. Model compression strategies, including weight quantization and pruning, are widely recognized as effective approaches to significantly reduce computation and memory intensities, and have been implemented in many DNNs on edge devices. However, most state-of-the-art works focus on ad hoc optimizations, and there lacks a thorough study to comprehensively reveal the potentials and constraints of different edge devices when considering different compression strategies. In this article, we qualitatively and quantitatively compare the energy efficiency of FPGA-based and mobile-based DNN executions using mobile GPU and provide a detailed analysis. Based on the observations obtained from the analysis, we propose a unified optimization framework using block-based pruning to reduce the weight storage and accelerate the inference speed on mobile devices and FPGAs, achieving high hardware performance and energy-efficiency gain while maintaining accuracy.
HLS-based High-throughput and Work-efficient Synthesizable Graph Processing Template Pipeline
Authors: Ahangari Hamzeh , Özdal Muhammet Mustafa , Öztürk Özcan
Published: April 2022

Hardware systems composed of diverse execution resources are being deployed to cope with the complexity and performance requirements of Artificial Intelligence (AI) and Machine Learning (ML) applications. With the emergence of new hardware platforms, system-wide programming support has become much more important. While this is true for various devices ranging from CPUs to GPUs, it is especially critical for specific neural network accelerators implemented on FPGAs. For example, Intel’s recent HARP platform encompasses a Xeon CPU and an FPGA, which requires an intense software stack to be used effectively. Programming such a hybrid system will be a challenge for most of the non-expert users. High-level language solutions such as Intel OpenCL for FPGA try to address the problem. However, as the abstraction level increases, the efficiency of implementation decreases, depicting two opposing requirements. In this work, we propose a framework to generate HLS-based, FPGA-accelerated, high-throughput/work-efficient, synthesizable, and template-based graph-processing pipeline. While a fixed and clock-wise precisely designed deep-pipeline architecture, written in SystemC, is responsible for processing graph vertices, the user implements the intended iterative graph algorithm by implementing/modifying only a single module in C/C++. This way, efficiency and high performance can be achieved with better programmability and productivity. With similar programming efforts, it is shown that the proposed template outperforms a high-throughput OpenCL baseline by up to 50% in terms of edge throughput. Furthermore, the novel work-efficient design significantly improves execution time and power consumption by up to 100×.
LA-MQTT
: Location-aware Publish-subscribe Communications for the Internet of Things
Authors: Montori Federico , Gigli Lorenzo , Sciullo Luca , Felice Marco Di
Published: April 2022

Nowadays, several Internet of Things (IoT) deployments use publish-subscribe paradigms to disseminate IoT data to a pool of interested consumers. At the moment, the most widespread standard for such scenarios is MQTT. We also register an increasing interest in IoT-enabled Location-Based Services, where data must be disseminated over a target area and its spatial relevance and the current positions of the consumers must be taken into account. Unfortunately, the MQTT protocol does not support location awareness, and hence it may result in notifying consumers that are geographically far from the data source, causing increased network overhead and poor Quality of Service (QoS). We address the issue by proposing
LA-MQTT
, an extension to standard MQTT supporting spatial-aware publish-subscribe communications on IoT scenarios.
LA-MQTT
is broker-agnostic and fully backward compatible with standard MQTT. As monitoring the position of subscribers over time may cause privacy concerns,
LA-MQTT
carefully supports location privacy preservation, for which the optimal tradeoff with the QoS of the spatial notifications is addressed via a learning-based algorithm. We demonstrate the effectiveness of
LA-MQTT
by experimentally evaluating its features via large-scale hybrid simulations, including real and virtual components. Finally, we provide a Proof of Concept real implementation of an
LA-MQTT
scenario.
EdgeWise: Energy-efficient CNN Computation on Edge Devices under Stochastic Communication Delays
Authors: Ghasemi Mehdi , Rakhmatov Daler , Wu Carole-Jean , Vrudhula Sarma
Published: April 2022

This article presents a framework to enable the energy-efficient execution of convolutional neural networks (CNNs) on edge devices. The framework consists of a pair of edge devices connected via a wireless network: a performance and energy-constrained deviceDas the first recipient of data and an energy-unconstrained deviceNas an accelerator forD. DeviceDdecides on-the-fly how to distribute the workload with the objective of minimizing its energy consumption while accounting for the inherent uncertainty in network delay and the overheads involved in data transfer. These challenges are tackled by adopting the data-driven modeling framework of Markov Decision Processes, whereby an optimal policy is consulted byDinO(1) time to make layer-by-layer assignment decisions. As a special case, a linear-time dynamic programming algorithm is also presented for finding optimal layer assignment at once, under the assumption that the network delay is constant throughout the execution of the application. The proposed framework is demonstrated on a platform comprised of a Raspberry PI 3 asDand an NVIDIA Jetson TX2 asN. An average improvement of 31% and 23% in energy consumption is achieved compared to the alternatives of executing the CNNs entirely onDandN. Two state-of-the-art methods were also implemented and compared with the proposed methods.
Maintaining Transcriptional Specificity Through Mitosis
Authors: Ito Kenji , Zaret Kenneth S.
Published: April 2022

Virtually all cell types have the same DNA, yet each type exhibits its own cell-specific pattern of gene expression. During the brief period of mitosis, the chromosomes exhibit changes in protein composition and modifications, a marked condensation, and a consequent reduction in transcription. Yet as cells exit mitosis, they reactivate their cell-specific programs with high fidelity. Initially, the field focused on the subset of transcription factors that are selectively retained in, and hence bookmark, chromatin in mitosis. However, recent studies show that many transcription factors can be retained in mitotic chromatin and that, surprisingly, such retention can be due to nonspecific chromatin binding. Here, we review the latest studies focusing on low-level transcription via promoters, rather than enhancers, as contributing to mitotic memory, as well as new insights into chromosome structure dynamics, histone modifications, cell cycle signaling, and nuclear envelope proteins that together ensure the fidelity of gene expression through a round of mitosis.
Review—A Pencil Drawing Overview: From Graphite to Electrochemical Sensors/Biosensors Applications
Authors: Ataide Vanessa N. , Arantes Iana V. S. , Mendes Letícia F. , Rocha Danielly S. , Baldo Thaísa A. , Coltro Wendell K. T. , Paixão Thiago R. L. C.
Published: April 2022

Much effort was made in the literature to put pencil-drawing as an attractive way to fabricate electrochemical sensors. The pencil drawing is one of the easiest, cost-effective, unique single-step methods for fabricating paper-based devices without necessitating any expensive instrumentation by merely transferring the carbon by exfoliation abrasion on paper. Additionally, much has to be done to improve the quality of this transfer carbon to achieve a suitable sensing platform for electrochemical measurements. Hence, this review article aims to bring the new approaches developed in the last five years to circumvent some fabrication problems and enhance this “carbonization” step for electrochemistry detection using paper-based sensors. We will start the discussion on the carbon materials moving to the substrate and finally discuss the strategies to produce the conductive tracks and enhance their properties for sensing applications.
Review—Metal-Organic Frameworks Composites for Electrochemical Detection of Heavy Metal Ions in Aqueous Medium
Authors: J. Lavanya , R. Srinivasan , A. Ravi Sankar , M. V. Varsha , N. Gomathi
Published: April 2022

The development of precise sensors to detect hazardous heavy metal ions (HMIs) in an aqueous medium has recently attracted much attention. Metal-Organic Frameworks (MOFs) with large specific surface areas, more active sites, and flexible chemical structures have received tremendous research interest in many applications. MOFs composites blended with various functional materials are well suited for the electrochemical sensing of HMIs, because of their synergetic properties, including abundant active sites, enhanced electrical conductivity, and increased chemical stability. While MOFs composites’ synthesis protocols, modification techniques, and potential applications have been addressed in the previous reports, there is an increasing awareness of electrochemical detection of HMIs using MOFs composites. This review explicitly summarizes the electrochemical sensing of HMIs using various MOFs-based composites, including MOFs-carbon, MOFs-polymer, MOFs-metal nanoparticles, and MOFs-other composites. In addition, the challenges and opportunities towards electrochemical sensing with the MOFs composite-based materials are discussed.
Exploring the Relationship between Interactions and Learning Performance in Robot-Assisted Language Learning
Authors: Ao Yutong , Yu Zhonggen
Published: April 2022

Robot-assisted language learning is a pattern that uses a social robot in a class to enhance learning performance through human-robot interaction. While this pattern receives increasing attention, few review articles generalize whether each interactive behavior improves learning performance. This study delves into the answers to three research questions. The results show that the synthesized and prerecorded human voices are appropriate for different teaching activities. Verbal communication except for egocentric small talk and L1 translation between the robot and learners are indispensable to improve students’ confidence and learning gains. Moderate employment of nonverbal interactions helps students increase concentration, motivation, and retention of vocabulary, while undue interactions give rise to counterproductive effects. Based on the findings, future research on robot-assisted language learning is suggested to pay attention to the effectiveness of an independent interaction. Another valuable focus is the proper combination of interactive behaviors that suit different teaching tasks. A suitable level of robotic sociability is also worthy of exploration. Educators teaching with a robot should make full use of verbal and nonverbal interactive behaviors to boost students’ confidence, motivation, engagement, and learning gains. Meanwhile, they need to be cautious about the excessive employment of interactions.
An Optimization Model for Landscape Planning and Environmental Design of Smart Cities Based on Big Data Analysis
Authors: Yu LiWei , Zhang Lei , Gong Zhen
Published: April 2022

This paper adopts “digital landscape design logic,” analyzes and researches smart city and digital landscape design, and builds a digital city based on design logic, design basis, environment analysis, and results in a presentation based on the dilemma of landscape garden planning and design at this stage and the development trend of the smart garden and digital landscape design. The optimization model of the landscape and environment design is constructed based on design logic, design basis, environment analysis, and result presentation. First, on the Hadoop distributed computing platform based on the MapReduce parallel processing framework, we implement the massive small file processing methods (Hadoop Archives, CombineFileInputFormat, and Sequence Files) to compensate for the inherent defects of Hadoop and experimentally compare the memory consumption and execution efficiency of the three methods to propose a choice. The memory consumption and execution efficiency of the three methods are experimentally compared to propose a selection strategy. Finally, based on the MR-PFP algorithm, we parallelize the frequent itemset in-cab trajectory big data to generate interesting strong association rules. The experimental results show that the MR-PFP algorithm has better speedup ratio performance and higher mining efficiency than the parallel frequent pattern (PFP) growth algorithm. The research and analysis focused on the digital implementation of the standalone environmental analysis, using Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The study explores the use of digital landscape design methods and technologies in the landscape design process. Using Rhino + Grasshopper parametric and visualization programming software, we built parametric analysis models around elevation, slope, slope direction, water catchment, and viewable area and used mapping and overlay techniques to quantify the urban space. Finally, the purpose of collecting, monitoring, analyzing, simulating, creating, and reproducing landscape information is achieved.
The Existence of Periodic Solutions of Delay Differential Equations by

E

+

-Conley Index Theory
Authors: Xiao Huafeng
Published: April 2022

In this paper, the

E

+

-Conley index theory has been used to study the existence of periodic solutions of nonautonomous delay differential equations (in short, DDEs). The variational structure for DDEs is built, and the existence of periodic solutions of DDEs is transferred to that of critical points of the associated function. When DDEs are

2
π

-nonresonant, some sufficient conditions are obtained to guarantee the existence of periodic solutions. When the system is

2
π

-resonant at infinity, by making use a second disturbing of the original functional, some sufficient conditions are obtained to guarantee the existence of periodic solutions to DDEs.
Surface Plasmon Resonance Sterilization 3D Imaging Technology Considering the Engineering Hue Algorithm
Authors: Xu Liang , Wang Hongwei , Si Wenhui
Published: April 2022

The high-resolution dynamic observation of the phenomenon of impossible surface plasmon resonance sterilization is conducted which resulted from the quality problems in the imaging process of traditional surface plasmon resonance sterilization 3D imaging technology. Surface plasmon resonance (SPR) technology is mainly based on the physical-optical properties generated by the optical coupling of metal thin films, and flexible optical analysis methods are used to improve the quality and efficiency of SPR sterilization 3D imaging. In this paper, the engineering hue algorithm is introduced into the 3D imaging process of surface plasmon resonance sterilization, and the front-end imaging system composed of the objective lens, distributed elements, focusing mirrors, and probes is used to obtain the corresponding surface plasmon resonance sterilization spectrum data on the back-end processor and quickly send the imaging calculation amount from the front-end to the back-end. Meanwhile, combined with 3D imaging, dislocation data processing technology, and multiframe reconstruction method, the reconstruction accuracy is improved, and memory space is released to speed up data processing. Finally, the experimental analysis shows that the engineering hue algorithm is used in the process of surface plasmon resonance sterilization 3D imaging, which can complete the superresolution plasmon resonance sterilization 3D imaging, and the obtained imaging effect is good, the data processing speed is fast, and it can be observed in surface plasmon resonance sterilization imaging with wide amplitude, high resolution, and low power consumption.
Upregulation of Centromere Proteins as Potential Biomarkers for Esophageal Squamous Cell Carcinoma Diagnosis and Prognosis
Authors: Wang Xiao , Lai Minshan , Wang Yue , Chai Ruihuan , Li Nan , Ou Ling , Zheng Kai , Li Jieling , Xu Guifeng , Wang Shaoqi , Dong Yun , Wang Shaoxiang
Published: April 2022

Esophageal squamous cell carcinoma (ESCC) has a high incidence and low survival rate, necessitating the identification of novel specific biomarkers. Centromere‐associated proteins (CENPs) have been reported to be biomarkers for many cancers, but their roles in ESCC have seldom been investigated. Here, the potential clinical roles of CENPs in ESCC patients were demonstrated by a systematic bioinformatics analysis. Most CENP‐encoding genes were differentially expressed between tumor and normal tissues. CENPA, CENPE, CENPF, CENPI, CENPM, CENPN, CENPQ, and CENPR were upregulated universally in the three datasets. Survival analysis demonstrated that high expression of CENPE and CENPQ was positively correlated with the outcomes of ESCC patients. The CENPE‐based forecast model was more accurate than the tumor‐node‐metastasis (TNM) staging‐based model, which was classified as stage I/II vs. III/IV. More importantly, the forecast model based on the commonly upregulated CENPs exhibited a much higher area under the curve (AUC) value (0.855) than the currently known TTL, ZNF750, AC016205.1, and BOLA3 biomarkers. The nomogram model integrating the CENPs, TNM stage, and sex was highly accurate in the prognosis of ESCC patients (AUC = 0.906). Besides, gene set enrichment analysis (GSEA) demonstrated that CENPE expression is significantly correlated with cell cycle, G2/M checkpoint, mitotic spindle, p53, etc. Finally, in validation experiments, we also found that CENPE and CENPQ were significantly overexpressed in esophageal cancer cells. Taken together, these results clearly suggest that CENPs are clinically promising diagnostic and prognostic biomarkers for ESCC patients.
Cost-Aware Resource Optimization for Efficient Cloud Application in Smart Cities
Authors: Gupta Punit , Kaikini Ravindra R. , Saini Dinesh Kumar , Rahman Salma
Published: April 2022

In this generation of smart computing environment, every device in the system and multple system are interconnected to each other, which allows users to view, analyze data, and make smart decisions. Smart cities are one example of smart environments where every device is connected and computing is performed on the cloud. In such a situation, the system requires an efficient system to handle huge requests and deliver data. Cloud computing plays an essential role in solving this issue but suffers from resource optimization, cost optimization, and load balancing. This work is aimed at solving the issue of resource and cost optimization in cloud infrastructure to provide a high service rate and sustainable infrastructure to cloud applications in smart cities. The proposed model is inspired by artificial neural networks and nature-inspired algorithm to reduce execution cost, average start time, and finish time and to make the system power efficient at the same time to improve the utilization of the system. The result shows that the proposed model completes more tasks in the least time and execution cost as compared to the existing models. This showcases the smart cloud applications are cost efficient and can complete the tasks in less time.
Vietnamese Primary School Teachers’ Needs for Professional Development in Response to Curriculum Reform
Authors: Nguyen Yen Thi Xuan , Ha Xuan Van , Tran Ngoc Hai
Published: April 2022

This qualitative study explored the needs for teacher professional development (PD) of Vietnamese primary school teachers. The participants of the study were ten teachers at ten different primary schools in a large city in Vietnam. The data included in-depth individual interviews with the participating teachers. Qualitative analysis of the interview transcripts revealed several interesting insights into the teachers’ perspectives on PD, their regular PD activities, and their needs for PD. All the teachers considered PD essential for their demanding teaching job. They reported having attended various PD programs and activities provided by their schools, and the department of education and training at the province and district levels. They also participated in several teacher professional learning communities available on the Internet. However, the teachers expressed a need for more PD programs and activities, especially in the context of curriculum reforms. Notably, they preferred to attend PD courses which were more practical and relevant to their work. In addition, they highly appreciated the hands-on activities and opportunities to interact with teacher trainers and educators. These findings were interpreted and discussed in relation to the sociocultural contexts of Vietnamese primary education. The study also discussed implications for PD course designers and teacher educators in Vietnamese primary school contexts and beyond.
A Case of Systemic AL Amyloidosis Diagnosed by Screening Colonoscopy
Authors: Alnimer Lynna , Zakaria Ali , Patel Jalpa , Samhouri Yazan , Ahsan Samira , Goldman Lyle , Sorser Serge
Published: April 2022

Amyloidosis encompasses several diseases associated with deposition of low-molecular-weight proteins in an abnormal configuration. In light-chain amyloidosis (AL), monoclonal free lambda (λ) or kappa (κ) light chains are the amyloid proteins involved and can deposit in almost any organ. Symptoms vary depending on presence and extent of organ involvement, and thus, clinical presentation varies. Diagnosis requires biopsy of the affected tissue, and sometimes, fat pad or bone marrow biopsy is completed initially. Prognosis of AL amyloidosis depends on the presence of cardiac involvement. Treatment of AL amyloidosis involves systemic chemotherapy and evaluation for autologous stem cell transplant. Herein, we present a case report of an asymptomatic middle-aged female who was diagnosed with AL amyloidosis during an average-risk screening colonoscopy, which is an unusual setting. We discuss the workup involved, clinical presentation, and gastroenterology-related organ involvement.
Isolated Femoral LCL Avulsion Fracture in the Adult Knee: Two Patient Cases and Literature Review
Authors: Dietrich Gilles , Maeder Benoît , Nyland John , Maeder Yaël , Akiki Alain , Martin Robin
Published: April 2022

Avulsion injuries of the LCL most commonly occur at the fibular insertion. Femoral LCL avulsion injuries have only been previously described in pediatric patients or as multiligament knee injury components among adults. This case series with comprehensive literature review describes for the first time 2 cases of isolated LCL femoral avulsion fractures in adults including conservative treatment outcomes. Both patients sustained a strong varus stress mechanism to their right knee, following sport injury or road traffic accident. For both patients, a complete radiographic evaluation including X-rays, MRI, and CT scan confirmed no other associated knee lesions. The femoral LCL avulsion fractures that were observed were minimally displaced and noncomminuted. Furthermore, imagery suggested preserved integrity at the superior lateral genicular artery, adjacent articular capsule, and IT band. Based on clinical and imaging evaluations, the decision was made to follow conservative treatment. By 10 weeks postinjury, both patients were asymptomatic with early radiological fracture healing evidence. Comparative varus stress radiographs at 20° knee flexion revealed no side-to-side differences and clinical exam showed no posterolateral rotatory instability. The second patient case presented with mild femoral LCL attachment calcification on follow-up CT-scan. Following a detailed analysis of anatomic injury characteristics, we suggest that patients with isolated femoral LCL avulsion fractures have low secondary displacement risk provided SLGA, articular capsule, and IT band integrity are present. In contrast to high-grade ligamentous and distal avulsion LCL injuries, we recommend conservative treatment for patients who sustain these lesions.
A Personalizedα,β,l,k-Anonymity Model of Social Network for Protecting Privacy
Authors: Ren Xiangmin , Jiang Dexun
Published: April 2022

By mining the data published on social network, we can discover the hidden value of information including the privacy of individuals and organizations. Protecting privacy of individuals and organizations on social network has become the focus of more and more researchers. Based on the actual privacy protection need of edge sensitive attribute and vertexes sensitive attribute, we propose a new personalizedα,β,l,k-anonymity technology of privacy preserving to reduce distortion extent of the data in the privacy processing of data of social network. Experimental results of personalizedα,β,l,k-anonymity algorithm show thatd-neighborhood attack of graph, background knowledge attack, and homogeneity attack can be prevented effectively by using anonymous vertexes and edges, as well as the influence matrix based on background knowledge. The diversity of vertex sensitive attribute can be achieved. Personalized protecting privacy requirements can be met by using such parameter asα,β,l,k.
Review of an Improved Longwall Panel Layout for Ground Control in Longwall Mining
Authors: Cui Z. M. , Wang P. F. , Li Z. , Yang Y.
Published: April 2022

Conventional longwall layout (CLL) for mining coal, trona, etc. is still fraught with problems including low resource recovery, wavy ground surface subsidence, bumps or rockbursts, gateroad support and maintenance, poisonous air hazard, spontaneous combustion in coal mines, instability of equipment at inclined, or steeply inclined working face, etc. In order to improve longwall mining, a novel alternate longwall panel layout that has been employed for over 20 years in China, Australia, and Russia, etc. is proposed termed “split-level longwall layout” (SLL). Gateroads on either end of the same SLL panel are split level, i.e., placed in different levels. This paper provides a review of existing research literature and insights on the problems encountered in CLL and the improvement of the new approach. Theoretical, numerical analysis, and field observation results of the new strategy demonstrate that the problems mentioned above in CLL are mitigated, and the development entries using the improved longwall system have a better stress environment with associated reduction in large roadway convergence, coal bumps or outbursts, and many other issues on ground control. Based on the current development of SLL and the future requirement of mining industry, the prospects as well as its applications, limitations, and concerns for SLL are presented.
Fault Diagnosis Method of Rotating Machinery Based on Collaborative Hybrid Metaheuristic Algorithm to Optimize VMD
Authors: Guifan Zhou
Published: April 2022

With the improvement of the complexity and reliability of mechanical equipment, it has been difficult for the commonly used variational modal decomposition method of vibration signal of rotating machinery to meet the current practical engineering requirements. In order to further improve the adaptability, processing efficiency, and robustness of rotating machinery fault diagnosis methods, a collaborative hybrid element heuristic to multiobjective optimization algorithm is introduced in this paper. Combined with variational modal decomposition (VMD) method, the fault diagnosis method of rolling bearing under complex working conditions is studied. This paper mainly uses a collaborative hybrid metaheuristic algorithm to improve the nondominated sorting genetic algorithm II (NSGA II) and multiobjective particle swarm optimization (MOPSO), which improves the convergence efficiency of multiobjective optimization method and solves the problem of uneven distribution of optimal solutions. Then, the improved multiobjective optimization algorithm is combined with VMD to solve the problem of parameter selection of the VMD method under complex working conditions of rotating machinery. At the same time, the variation relationship between various signal features and VMD decomposition results is compared and studied, and the features with good effect are taken as the objective function of the optimization algorithm; the ability of denoising and feature extraction of VMD in rotating machinery fault diagnosis is improved. In this paper, the proposed method is explored by using analog signals and experimental data of rolling bearings. Through comparison, the improvement of adaptive ability, operation speed, and robustness of the proposed method in rotating machinery fault diagnosis is verified.
Cardiovascular Autonomic Responses to Aerobic, Resistance and Combined Exercises in Resistance Hypertensive Patients
Authors: Fraccari-Pires Nayara , Coelho-Júnior Hélio José , Gambassi Bruno Bavaresco , Cabral de Faria Ana Paula , Ritter Alessandra Mileni Versuti , Gasparetti Carolina Souza , Pioli Mariana Rodrigues , Ruberti Olívia Moraes , Ferreira-Melo Silvia Elaine , Moreno Heitor , Rodrigues Bruno
Published: April 2022

Here, we report the acute effects of aerobic (AER), resistance (RES), and combined (COM) exercises on blood pressure, central blood pressure and augmentation index, hemodynamic parameters, and autonomic modulation of resistant (RH) and nonresistant hypertensive (NON‐RH) subjects. Twenty participants (10 RH and 10 NON‐RH) performed three exercise sessions (i.e., AER, RES, and COM) and a control session. Hemodynamic (Finometer®, Beatscope), office blood pressure (BP), and autonomic variables (accessed through spectral analysis of the pulse‐to‐pulse BP signal, in the time and frequency domain‐Fast Fourrier Transform) were assessed before (T0), one‐hour (T1), and twenty‐four (T2) hours after each experimental session. There were no changes in office BP, pulse wave behavior, and hemodynamic parameters after (T0 and T1) exercise sessions. However, AER and COM exercises significantly reduced sympathetic modulation in RH patients. It is worth mentioning that more significant changes in sympathetic modulation were observed after AER as compared to COM exercise. These findings suggest that office blood pressure, arterial stiffness, and hemodynamic parameters returned to baseline levels in the first hour and remained stable in the 24 hours after the all‐exercise sessions. Notably, our findings bring new light to the effects of exercise on RH, indicating that RH patients show different autonomic responses to exercise compared to NON‐RH patients. This trial is registered with trial registration number NCT02987452.
The Data Analytics of Finance Impact on the Rural Development Combining Financial Constraint and Economic Growth Theory
Authors: Guo Xiaoxiao
Published: April 2022

In order to solve the predicament of rural economic development, China’s rural areas have spontaneously formed a rural financial system with rural characteristics. With the deepening reform of rural finance, Rural Commercial Bank was restructured on the basis of rural credit cooperatives in the past. It can be said that Rural Commercial Bank is a rural financial institution born in rural areas. Therefore, the development of the local rural commercial bank and the development of the local rural economy have been integrated, and the core force in rural finance is the Rural Commercial Bank. It is precisely because of the core position of China’s rural commercial banks in rural, when Chinese scholars conduct research on rural finance, they often choose rural commercial banks as the research object. Based on this, when the article studies the impact of rural finance on the rural economy, it also chooses rural commercial banks as the research object. Finance that the rural commercial banks are used as the starting point to carry out the research work on their local rural finance. This article takes D County Rural Commercial Bank as an example to introduce and analyze the development status and development process of D county’s rural finance. Using the method of empirical research, this paper studies the impact of the development of agricultural-related business of rural commercial banks on the rural economy. In addition, according to the empirical research results, a set of financial innovation countermeasures to promote rural economic development is proposed. Through the research on the influence of rural financial institutions in D county on the rural economy, it can be seen that providing new directions and new methods for the development of China’s rural economy from the perspective of finance. It provides a reference for promoting the stable and effective development of the domestic rural economy. The survey shows that rural deposits have maintained a rapid upward trend. The total amount increased rapidly from 2.219 billion yuan in 2012 to 6.039 billion yuan in 2018, with an average annual increase of 637 million yuan.
MAPK Pathway Genetic Alterations Are Associated with Prolonged Overall Survival in Low-Grade Serous Ovarian Carcinoma
Authors: Manning-Geist Beryl , Gordhandas Sushmita , Liu Ying L. , Zhou Qin , Iasonos Alexia , Da Cruz Paula Arnaud , Mandelker Diana , Long Roche Kara , Zivanovic Oliver , Maio Anna , Kemel Yelena , Chi Dennis S. , O'Cearbhaill Roisin E. , Aghajanian Carol , Weigelt Britta , Chui M. Herman , Grisham Rachel N.
Published: April 2022

Purpose:

To characterize the somatic mutational landscape, investigate associations between genetic alterations and clinical outcomes, and determine the prevalence of pathogenic germline mutations in low-grade serous ovarian carcinomas (LGSC).

Experimental Design:

Patients with LGSC tumors who underwent panel-based sequencing of up to 505 genes were identified. Data on somatic and germline mutations; copy-number alterations; and clinicopathologic features, including age at diagnosis, platinum sensitivity, and overall survival (OS), were collected.

Results:

Following central pathology rereview, 119 patients with LGSC were identified for analysis. Of these, 110 (92%) had advanced-stage disease (stages III/IV). Somatic KRAS (33%), NRAS (11%), EIF1AX (10%), and BRAF (11%) alterations were the most common; MAPK pathway alterations were found in 60% (n = 71) of LGSCs. KRAS mutations were significantly associated with age at diagnosis more than 50 years (P = 0.02) and platinum-sensitive disease (P = 0.03). On multivariate analysis, MAPK pathway alterations (P = 0.02) and platinum sensitivity (P = 0.005) were significantly associated with improved OS. Seventy-nine patients (66%) underwent germline genetic testing; seven pathogenic germline mutations were identified: MUTYH (n = 2), BAP1 (n = 1), RB1 (n = 1), CHEK2 (n = 1), APC (n = 1), and FANCA (n = 1). There were no germline BRCA1/2 mutations. One germline MUTYH-associated LGSC harbored loss-of-heterozygosity at the MUTYH locus, and the patient with the germline BAP1 mutation also harbored a somatic BAP1 frameshift mutation.

Conclusions:

This study showed that MAPK pathway alterations in LGSC, including KRAS mutations, are independently associated with platinum sensitivity and prolonged survival. Germline data, which were limited, identified few pathogenic germline mutations in patients with LGSC.

See related commentary by Veneziani and Oza, p. 4357
The Effect of Sevoflurane Low-Flow Anesthesia on Preserving Patient Core Temperature
Authors: Yüksek Ahmet , Talih Gamze
Published: April 2022

Introduction: Low-flow anesthesia (LFA) reduces the fresh gas flow (FGF) entering the anesthesia circuit and saves on the volatile agent used. In this study, the effect of LFA with sevoflurane on core temperature and the incidence of perioperative hypothermia were investigated. Methods: According to the FGF applied, patients were classified into three groups: LFA (1 L/min), medium-flow anesthesia (MFA = 2 L/min), and high-flow anesthesia (HFA = 4 L/min). Patients’ demographic data and the initial (T1) and final (T2) core temperatures during the operation were compared. Results: A total of 160 patients were included in the study. The T2 value of the HFA group was significantly lower than the LFA group (p = 0.028). Different flow values were found to have a significant effect on temperature change (F = 21.630, p < 0.001, partial eta squared = 0.216). There was a significant difference between the mean temperatures measured at two different times (F = 301.064, p < 0.001, partial eta squared = 0.657). The overall incidence of hypothermia was 32.5%, with 52 patients. Hypothermia (T2<36°C) incidences were not different between the LFA group and the MFA and HFA groups (p = 0.682). However, perioperative core temperature loss was significantly lower in the LFA group (p = 0.001). Conclusions: LFA using sevoflurane was not sufficient alone to significantly reduce the incidence of hypothermia. However, we have demonstrated that it may also have a beneficial effect on reducing perioperative temperature loss.