bluuebunny/crossref_metadata_2025 huggingface dataset:
Cooperative Slack Management: Saving Energy of Multicore Processors by Trading Performance Slack Between QoS-Constrained Applications
Authors: Nejat Mehrzad , Manivannan Madhavan , Pericàs Miquel , Stenström Per
Published: January 2022

Processor resources can be adapted at runtime according to the dynamic behavior of applications to reduce the energy consumption of multicore processors without affecting the Quality-of-Service (QoS). To achieve this, an online resource management scheme is needed to control processor configurations such as cache partitioning, dynamic voltage-frequency scaling, and dynamic adaptation of core resources.

Prior State-of-the-art has shown the potential for reducing energy without any performance degradation by coordinating the control of different resources. However, in this article, we show that by allowing short-term variations in processing speed (e.g., instructions per second rate), in a controlled fashion, we can enable substantial improvements in energy savings while maintaining QoS. We keep track of such variations in the form of performance slack. Slack can be generated, at some energy cost, by processing faster than the performance target. On the other hand, it can be utilized to save energy by allowing a temporary relaxation in the performance target. Based on this insight, we present Cooperative Slack Management (CSM). During runtime, CSM finds opportunities to generate slack at low energy cost by estimating the performance and energy for different resource configurations using analytical models. This slack is used later when it enables larger energy savings. CSM performs such trade-offs across multiple applications, which means that the slack collected for one application can be used to reduce the energy consumption of another. This cooperative approach significantly increases the opportunities to reduce system energy compared with independent slack management for each application. For example, we show that CSM can potentially save up to 41% of system energy (on average, 25%) in a scenario in which both prior art and an extended version with local slack management for each core are ineffective.
Optimal Rate List Decoding over Bounded Alphabets Using Algebraic-geometric Codes
Authors: Guruswami Venkatesan , Xing Chaoping
Published: January 2022

We give new constructions of two classes of algebraic code families that are efficiently list decodable with small output list size from a fraction 1-R-ε of adversarial errors, where
R
is the rate of the code, for any desired positive constant ε. The alphabet size depends only ε and is nearly optimal.

The first class of codes are obtained by
folding
algebraic-geometric codes using automorphisms of the underlying function field. The second class of codes are obtained by restricting evaluation points of an algebraic-geometric code to rational points from a
subfield
. In both cases, we develop a linear-algebraic approach to perform list decoding, which pins down the candidate messages to a subspace with a nice “periodic” structure.

To prune this subspace and obtain a good bound on the list size, we pick subcodes of these codes by pre-coding into certain
subspace-evasive
sets that are guaranteed to have small intersection with the sort of periodic subspaces that arise in our list decoding. We develop two approaches for constructing such subspace-evasive sets. The first is a Monte Carlo construction of
hierearchical subspace-evasive
(h.s.e.) sets that leads to excellent list size but is not explicit. The second approach exploits a further
ultra-periodicity
of our subspaces and uses a novel construct called
subspace designs
, which were subsequently constructed explicitly and also found further applications in pseudorandomness.

To get a family of codes over a fixed alphabet size, we instantiate our approach with algebraic-geometric codes based on the Garcia–Stichtenoth tower of function fields. Combining this with pruning via h.s.e. sets yields codes list-decodable up to a 1-R-ε error fraction with list size bounded by
O
(1/ε), matching the existential bound for random codes up to constant factors. Further, the alphabet size can be made exp (
Õ
(1/ε
2
)), which is not much worse than the lower bound of exp (Ω (1/ε)). The parameters we achieve are thus quite close to the existential bounds in all three aspects (error-correction radius, alphabet size, and list size) simultaneously. This construction is, however, Monte Carlo and the claimed list-decoding property only holds with high probability. Once the code is (efficiently) sampled, the encoding/decoding algorithms are deterministic with a running time
O
_ε (

N
c

) for an absolute constant
c
, where
N
is the code’s block length.

Using subspace designs instead for the pruning, our approach yields the
first deterministic
construction of an algebraic code family of rate
R
with efficient list decoding from 1-R-ε fraction of errors over an alphabet of constant size exp (Õ(1/ε
2
)). The list-size bound is upper bounded by a very slowly growing function of the block length
N
; in particular, it is at most O(log

(
r
)

N
) (the
r
th iterated logarithm) for any fixed integer
r
. The explicit construction avoids the shortcoming of the Monte Carlo sampling at the expense of a slightly worse list size.
2023 EAAI mentored undergraduate research challenge
Authors: Freedman Richard G.
Published: January 2022

The topic for EAAI 2023's Mentored Undergraduate Research Challenge is Human-Aware AI in Sound and Music. What does that mean? Where are the applications? How can you get started? We break down the topic, discuss applications, and explore project ideas in this column.
Preserving Addressability Upon GC-Triggered Data Movements on Non-Volatile Memory
Authors: Ye Chencheng , Xu Yuanchao , Shen Xipeng , Jin Hai , Liao Xiaofei , Solihin Yan
Published: January 2022

This article points out an important threat that application-level Garbage Collection (GC) creates to the use of non-volatile memory (NVM). Data movements incurred by GC may invalidate the pointers to objects on NVM and, hence, harm the reusability of persistent data across executions. The article proposes the concept of movement-oblivious addressing (MOA), and develops and compares three novel solutions to materialize the concept for solving the addressability problem. It evaluates the designs on five benchmarks and a real-world application. The results demonstrate the promise of the proposed solutions, especially hardware-supported Multi-Level GPointer, in addressing the problem in a space- and time-efficient manner.
17th Workshop in Primary and Secondary Computing Education (WiPSCE 2022) call for papers
Authors: Grillenberger Mareen
Published: January 2022

We invite you to submit a paper, report, or poster for the 17th Workshop in Primary and Secondary Computing Education organized by the Schwyz University of Teacher Education and to join us in Morschach, Switzerland.
As a CS educator, how do you think we can address inequity issues that exist in the field?
Authors: Miller Jeffrey , Wallace Charles , Klawe maria
Published: January 2022

We have been inviting CS education researchers to offer brief remarks to spark discussion and provide ideas for actions we can all take to address inequity issues.
Current Trends in Data Summaries
Authors: Cormode Graham
Published: January 2022

The research area of data summarization seeks to find small data structures that can be updated flexibly, and answer certain queries on the input accurately. Summaries are widely used across the area of data management, and are studied from both theoretical and practical perspectives. They are the subject of ongoing research to improve their performance and broaden their applicability. In this column, recent developments in data summarization are surveyed, with the intent of inspiring further advances.
Accelerating Video Analytics
Authors: Arulraj Joy
Published: January 2022

MOTIVATION. The advent of inexpensive, high-quality cameras has led to a rapid increase in the volume of generated video data [19, 16]. It is now feasible to automatically analyze these video datasets at scale due to two developments over the last decade. First, researchers have designed complex, computationally-intensive deep learning (DL) models that capture the contents of a given set of video frames (e.g., objects present in a particular frame [11]) [15]. Second, the computational capabilities of hardware accelerators for evaluating these DL models have increased over the last decade (e.g., TPUs) [8]. We anticipate that automated analysis of videos will reduce the labor cost of analyzing video
The Impact of Water Concentration on the Electro-Oxidation of Formic Acid on Platinum
Authors: de Oliveira M. G. , Melle G. B. , Romano R. L. , Varela H.
Published: January 2022

Water plays a pivotal role in several electrocatalytic reactions. In the electrooxidation of small organic molecules, water can for instance assist the oxidation of adsorbed species or inhibit the reaction through the oxidation of surface sites. This paper is the third in a series of studies aiming at evaluating the impact of water concentration on the electro-oxidation of small organic molecules on polycrystalline platinum. The study was performed in phosphoric acid electrolyte, and the water concentration was varied from 14 to 50 mol l−1. Voltammetric profiles and potential oscillations were studied at distinct [H2O]/[H3PO4] ratios and constant concentration of formic acid. There is a diminution in the catalytic activity accompanying the decrease in the water concentration. The removal of carbon monoxide demands oxygenated species and can proceed in the absence of water for ethanol and methanol, but not for formic acid. Under oscillatory regime, the impact of water concentration, it is seen that higher water concentration implies higher oscillation frequencies, shorter and less stable time-series, and sharper transitions from low to high potential, where the adsorbed carbon monoxide is oxidized by adsorbed oxygenated species. Results are discussed in connection with the voltammetric study and also compared with other parent systems.
Anodically Pretreated Electrodes Using a Nontoxic Reagent: A New Voltammetric Sensing for Selective and Simultaneous Determination of Synthetic Dyes
Authors: Hirunsak Supasit , Kaewjua Kantima , Charoenkitamorn Kanokwan , Chailapakul Orawon , Siangproh Weena
Published: January 2022

A novel voltammetric electrode based on a screen-printed graphene electrode pretreated by the anodically pretreated process using a nontoxic reagent was first developed. A phosphate buffer solution (0.1 M) at pH 7 was used as a supporting electrolyte for the anodic pretreatment process. After the oxidizing process, the pretreated electrode was used to investigate the electrochemical properties of sunset yellow (SY) and tartrazine (TAR). The well-defined oxidation peaks of SY and TAR were clearly observed at +0.45 and +0.70 V (vs Ag/AgCl), respectively. Under the selected experimental variables, the calibration curves of SY and TAR were obtained in the ranges of 0.01–5.0 μM and 0.1–10.0 μM, with the LOD values being experimentally obtained (S/N = 3) and found to be 10 and 100 nM for SY and TAR, respectively. The developed electrode was successfully applied to the simultaneous determination of SY and TAR in nonalcoholic beverages, with the recovery values being in the range of 97.96%–102.76% and % relative errors are 1.5%–6.99%, which can be accepted. This new finding offers a simple, environmentally friendly process and cost-effectiveness for electrode fabrication. Moreover, this procedure can be considered as an alternative method for the routine analysis of food colorants.
Highly Sensitive Voltammetric Immunosensing of Cancer Biomarkers HER2 and CA125 Using Gold Nanoparticles Anchored Reduced Graphene Oxide Enzyme-Free Nanolabel
Authors: Vargis Vidhu Sara , Suneesh P. V. , Nair Bipin G. , Satheesh Babu T. G.
Published: January 2022

Sandwich type voltammetric immunosensors were developed for the quantitative determination of cancer biomarkers, HER2 (human epidermal growth factor receptor 2), and CA125 (cancer antigen 125). Specific antibodies were immobilized on the gold electrodes by using a self-assembled monolayer of mercaptoundecanoic acid (MUA) activated by EDC-NHS ((1-ethyl-3-(3-dimethylamino propyl) carbodiimide and N-hydroxysuccinimide). Antibody conjugated gold nanoparticles decorated reduced graphene oxide (anti-HER2-Au/rGO and anti-CA125-Au/rGO) were used as enzyme-free nano labels for signal amplification. The sensors were characterized thoroughly by spectroscopic, microscopic, and electrochemical methods. Differential pulse voltammetric (DPV) studies in the presence of thionine revealed that the current signal produced is directly related to the biomarker concentration. A linear range of 0.2 pg ml−1 to 100 ng ml−1 was obtained, which indicated the immunosensors to be highly sensitive in lower concentrations of cancer biomarkers. Finally, the fabricated immunosensors were tested with serum samples spiked with HER2 and CA125. The results obtained from the test confirmed the data collected from clinical labs.
The Substrate Morphology Effect for Sulfur-Rich Amorphous Molybdenum Sulfide for Electrochemical Hydrogen Evolution Reaction
Authors: Medina Marina , Corradini Patricia G. , de Brito Juliana F. , Sousa Santos Hugo L. , Mascaro Lucia Helena
Published: January 2022

Amorphous molybdenum sulfide (MoSx) is a promising material for hydrogen evolution reaction (HER) due to its nearly zero hydrogen adsorption free energy at the sulfur (S) edge-sites. To prepare more efficient MoSx-based electrocatalysts, new attempts are required to increase the exposure of the MoSx lateral size and, therefore, increase the S atom’s contents. The majority of studies reported in the literature investigate MoSx over conductive substrates. However, MoSx can be electrodeposited over inexpensive and chemically stable platforms, such as semiconductors. This work presents the semiconductor substrate morphology effect for prepared sulfur-rich MoSx for electrochemical hydrogen evolution reaction. The electrodes are prepared by cyclic voltammetry with 25 cycles over TiO2 film and TiO2 nanotubes (TiO2NT) substrates. The MoSx deposit on TiO2NT presents an increase S atoms contents and exhibits excellent HER activity with a low overpotential of 93 ± 7.5 mV to reach −10 mA cm−2 and a higher exchange current density equal to 91 μA cm−2, and a smaller Tafel slope of 43 mV dec−1.
Laboratory Investigation of Using Calcium Lignosulfonate as an Oxidation Inhibitor in Bitumen
Authors: Fatemi Saeed , Bazaz Jafar Bolouri , Ziaee Seyed Ali
Published: January 2022

Asphalt mixtures are the major constituent of road constructions. Those mixtures expose to high‐temperature levels during the construction process and their life cycle. The bitumen aging occurred because of the harmful effects of elevated temperatures upon the asphalt mixtures, which leads to oxidation, evaporation, and physical hardening within the bitumen structure. The aging phenomenon can intensify the hardness and brittleness of bitumen, which negatively affects the performance of asphalt mixtures. Researchers have investigated different types of bitumen additives to diminish the negative effects of bitumen aging, among which biomass additives are more attractive for researchers because that additives are organic and mainly emanate from waste materials. The current study investigated the effect of calcium lignosulfonate (CLS) at four quantities (i.e., 5%, 10%, 15%, and 20% by the weight of base bitumen) as an antiaging bitumen additive on physical, workability, thermal susceptibility, rheological, and chemical properties of 60/70 penetration grade bitumen. The results revealed that although the addition of CLS into the bitumen enhanced the rigidity of bitumen against rutting failure at high‐temperature conditions, the CLS‐modified bitumen was more susceptible to fatigue failure than the virgin bitumen at low‐temperature regions. The workability analysis showed that the CLS powder could improve the bitumen’s consistency against permanent deformation at high‐temperature levels. Although the bitumen’s viscosity increased because of CLS modification, the CLS‐modified bitumen provided acceptable workability based on the Superpave specifications. Outcomes from the rheological test revealed that the addition of CLS into the bitumen improved the rutting resistance of bituminous mixtures at high‐temperature levels. The SARA analysis indicated that the hardening of CLS‐modified samples emanated from the formation of asphaltenes within the bitumen because of CLS incorporation. Different types of aging indices, including PRP, SPI, VAI, and AIRF, which were calculated in this research, showed that the presence of CLS within the bitumen could retard the bitumen’s aging process.
Crashworthiness Analysis and Multi-Objective Optimisation of Multi-Cell Windowed Structures under Dynamic Impact Loading
Authors: Chen Jiehao , Liang Xifeng , Xu Ping , Yao Shuguang
Published: January 2022

Applying a windowed design in a thin-walled structure is a method to further enhance the crashworthiness of the structure. In this study, two kinds of windowed designs were introduced into different regions of a multi-cell energy absorption structure to form a multi-cell windowed structure, and its crashworthiness under dynamic impact was studied. Dynamic impact testing and LS-DYNA finite element modelling were combined to calculate the crashworthiness response of the structure. The simulation results show that the use of the windowed multi-cell structure can improve the crashworthiness response of the multi-cell structure. The influence of the windowed design on the structural deformation mode was analysed in detail. In addition, the multi-objective optimization design of different windowed structures was carried out to obtain a comprehensive crashworthiness evaluation, and the results show that the windowed design at the front end of the structure yields good results regarding the initial peak force (PCF), while the windowed design in the middle of the structure can effectively improve the specific energy absorption (SEA) and energy absorption (EA).
A Melatonin Treatment Delays Postharvest Senescence, Maintains Quality, Reduces Chilling Injury, and Regulates Antioxidant Metabolism in Mango Fruit
Authors: Bhardwaj Renu , Pareek Sunil , Mani Saravanan , Domínguez-Avila J. Abraham , González-Aguilar Gustavo A.
Published: January 2022

The effects of an exogenous application of various concentrations and dipping duration of melatonin (MT) treatment on postharvest senescence, quality, chilling tolerance, and antioxidant metabolism of mango fruit cv. “Dashehari” were examined. Fruits were treated with three concentrations of MT (50, 100, or 150 µM), each applied for three times (60, 90, or 120 min), followed by storage at 5 ± 1°C. The MT concentration of 100 µM with a dipping duration of 120 min was efficient in reducing the chilling injury and maintaining the quality of mango fruit for 28 d. Effects of this treatment were due to its effectiveness in reducing metabolic activity, specifically, respiration rate and ethylene production, resulting in higher firmness, titratable acidity, and ascorbic acid content and lower weight loss, total soluble solids, pH, and total soluble solid : acidity ratio. Moreover, it maintained a higher concentration of total phenolics and total flavonoids, as well as antioxidant capacity (2,2-diphenyl-1-picrylhydrazyl and cupric reducing antioxidant power assays), as compared to other treatments. This was further confirmed with higher activities of antioxidant enzymes superoxide dismutase and catalase and membrane stability (according to a lower malondialdehyde content and lipoxygenase activity). Thus, our data show that a 100 µM MT administered for 120 min appears to be the most appropriate treatment to maintain the quality of mango fruits stored at chilling temperatures.
Central Extensions and Nijenhuis Operators of Hom-
δ
-Jordan Lie Triple Systems
Authors: Li Qiang , Ma Lili
Published: January 2022

In this paper, the equivalence of central extensions and

H

α
,

α

V

3

T
,
V

is proven in the study in Hom-

δ

-Jordan Lie triple systems. The concepts of Nijenhuis operators of Hom-

δ

-Jordan Lie triple systems are given. Moreover, a trivial deformation is got.
A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning
Authors: Bukhari Maryam , Yasmin Sadaf , Sammad Saima , Abd El-Latif Ahmed A.
Published: January 2022

Leukemia is a fatal category of cancer-related disease that affects individuals of all ages, including children and adults, and is a significant cause of death worldwide. Particularly, it is associated with White Blood Cells (WBC), which is accompanied by a rise in the number of immature lymphocytes and cause damage to the bone marrow and/or blood. Therefore, a rapid and reliable cancer diagnosis is a critical requirement for successful therapy to raise survival rates. Currently, a manual analysis of blood samples obtained through microscopic images is done to diagnose this disease, which is often very slow, time-consuming, and less accurate. Furthermore, in microscopic analysis, the appearance and shape of leukemic cells seem very similar to normal cells which make detection more difficult. In the past decades, deep learning utilizing Convolutional Neural Networks (CNN) has provided state-of-the-art approaches for image classification problems; however, there is still a gap to improve their efficacy, learning procedure, and performance. Therefore, in this research study, we proposed a new variant of deep learning algorithm to diagnose leukemia disease by analyzing the microscopic images of blood samples. The proposed deep learning architecture emphasizes the channel associations on all levels of feature representation by incorporating the squeeze and excitation learning that recursively performs recalibration on channel-wise feature outputs by modeling channel interdependencies explicitly. In addition, the incorporation of the squeeze-and-excitation process enhances the feature discriminability of leukemic and normal cells, and strategically assists in exposing informative features of leukemia cells while suppressing less valuable ones as well as improving feature representational power of deep learning algorithm. We show that piling these learning operations of squeeze and excite together in a deep learning model can improve the performance of the model in diagnosing leukemia from microscopic images based on blood samples of patients. Furthermore, an extensive set of experiments are performed on both cropped cells and full-size microscopic images as well as with data augmentation to address the problem of fewer data and to further boost their performance. The proposed model is tested on two publicly available datasets of blood samples of leukemia patients, namely, ALL_IDB1 and ALL_IDB2. The suggested deep learning model exhibits good results and can be utilized to make a reliable computer-aided diagnosis for leukemia cancer.
Orthodontic Rubber Band-Assisted Endoscopic Submucosal Dissection: An Efficient Method for Treating Superficial Colorectal Tumors
Authors: Zheng Linfu , Wang Wen , Li Dazhou , Chen Junguo , Chen Longping , Wang Rong , Jiang Chuanshen , Zhang Guanpo , Hou Yaping , Zheng Jin , Bai Yang
Published: January 2022

Background. Colorectal endoscopic submucosal dissection (ESD) is a complex operation. Effective traction is crucial. We have successfully used an orthodontic rubber band (ORB) combined with the clip traction method to assist ESD (ORB-ESD). The aim of this retrospective study is to describe the method and to compare the efficacy and safety of ORB-ESD versus conventional ESD in the treatment of superficial colorectal tumors. Methods. We retrospectively analyzed the data of patients with superficial colorectal tumor (with

diameter
≥
20
 
mm

) who received either ORB-ESD (

n
=
34

) or conventional ESD (

n
=
90

) between January 2019 and September 2020. Propensity score matching (PSM) was used to match the clinical data of 31 pairs of patients in each group. Results. Operation time was significantly shorter for ORB-ESD than for conventional ESD (34.5 minutes vs. 56 minutes,

P
≤
0.001

). In the propensity-matched cohorts, the operation time remained significantly shorter in the ORB-ESD patients (35 minutes vs. 50 minutes,

P
=
0.001

). Postoperative adverse events, en bloc resection rate, and R0 resection rate were comparable between the two groups (

P
>
0.05

), both before and after propensity score matching. In the ORB subgroup analysis, the trainee and expert ESD operation times were similar (37 (26–53) vs. 33.5 (26–37) minutes, respectively;

P
=
0.274

). Conclusion. ORB-ESD appears to be an effective technique for ESD of colorectal cancer. Our findings need to be confirmed in large prospective multicenter studies.
Design of a Regional Economic Forecasting Model Using Optimal Nonlinear Support Vector Machines
Authors: Zhang Tong
Published: January 2022

Forecasting regional economic activity is a progressively significant element of regional economic research. Regional economic prediction can directly assist local, national, and subnational policymakers. Regional economic activity forecast can be employed for defining macroeconomic forces, such as prediction of stock market and cyclicality of national labor market movement. The recent advances of machine learning (ML) models can be employed to solve the time series prediction problem. Since the parameters involved in the ML model considerably influence the performance, the parameter tuning process also becomes essential. With this motivation, this study develops a quasioppositional cuckoo search algorithm (QOCSA) with a nonlinear support vector machine (SVM)-based prediction model, called QOCSO-NLSVM for regional economic prediction. The goal of the QOCSO-NLSVM technique is to identify the present regional economic status. The QOCSO-NLSVM technique has different stages such as clustering, preprocessing, prediction, and optimization. Besides, the QOCSO-NLSVM technique employs the density-based clustering algorithm (DBSCAN) to determine identical states depending upon the per capita NSDP growth trends and socio-economic-demographic features in a state. Moreover, the NLSVM model is employed for the time series prediction process and the parameters involved in it are optimally tuned by the use of the QOCSO algorithm. To showcase the effective performance of the QOCSO-NLSVM technique, a wide range of simulations take place using regional economic data. To determine the current economic situation in a region, the QOCSO-NLSVM technique is used. The simulation results reported the better performance of the QOCSO-NLSVM technique over recent approaches. The QOCSO-NLSVM technique generated effective results with a minimal mean square error of 70.548 or greater. Astonishingly good results were obtained using the QOCSO-NLSVM approach, which had the lowest root mean square error (RMSE) of 8.399.
Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques
Authors: Hessen Shrouk H. , Abdul-kader Hatem M. , Khedr Ayman E. , Salem Rashed K.
Published: January 2022

Recently, artificial intelligence (AI) domain increased to contain finance, education, health, mining, and education. Artificial intelligence controls the performance of systems that use new technologies, especially in the education environment. The multiagent system (MAS) is considered an intelligent system to facilitate the e-learning process in the educational environment. MAS is used to make interaction easily among agents, which supports the use of feature selection. The feature selection methods are used to select the important and relevant features from the database that could help machine learning algorithms produce high performance. This paper aims to propose an effective and suitable system for multiagent-based machine learning algorithms and feature selection methods to enhance the e-learning process in the educational environment which predicts pass or fail results. The univariate and Extra Trees feature selection methods are used to select the essential attributes from the database. Five machine learning algorithms named Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and K-nearest neighbors algorithm (KNN) are applied to all features and selected features. The results showed that the learning algorithm that has been measured by the Extra Trees method has achieved the highest performance depending on the evaluation of cross-validation and testing.
A Method of Planning Disaster Emergency Rescue Paths in Road-Free Environment
Authors: Xu Qing , Feng Shisheng , Sun Qun , Zhu Xinming , Chen Ruoxu , Lihua Xiaoyang , Wu Beibei
Published: January 2022

The occurrence of natural disasters has caused great loss of lives and properties that affected many people. To improve the efficiency of the emergency rescue efforts, the problem of road planning for emergency workers, vehicles, and other mobile objects should be overcome. Due to the characteristics of the emergency planning for rescue paths, timely, effective, and rapid efforts should be taken into effect based on geographical data in the road-free environment. This paper analyzes trafficability, researches both access ability and traffic efficiency, and uses traffic efficiency concerning the road-free environment as an influential weight and timeliness to improve the A∗ algorithm, which realizes the rapid planning of the mobile objects’ emergency rescue paths in the road-free environment, providing decision-making services for the rescue path planning of disaster emergencies in the environment of no road network. Finally, the experiments were carried out using available data for Shuozhou City, Shanxi Province. The results show that the research can provide a reference for the rescue path planning of disaster emergencies and has contributed to theoretical and applicable aspects of the research.
Effect of Application of Treadmill Training on Metabolic Control and Vitamin D Level in Saudi Patients with Type 2 Diabetes Mellitus
Authors: El Askary Ahmad , Shafie Alaa , Almehmadi Mazen , Allam Hatem H. , Elsayyad Lamiaa K. , Hassan Asmaa F. , Althobaiti Bader B. , Khalifa Mohamed Mansour , Saber Tamer , Alharthi Aisha H. , Gharib Amal F.
Published: January 2022

Background. Diabetes mellitus type 2 and vitamin D deficiency are both prevalent in the Saudi Arabia. Vitamin D deficiency treatment with supplements carries a risk of intoxication. Aim. The present study is aimed at elucidating the effect of exercise on modulation of metabolic status and vitamin D level in patients with type 2 diabetes mellitus (T2DM). Methods. A sum of 110 type 2 diabetic patients were voluntarily enrolled for the present investigation by dividing them into two separate groups (55 individuals for each group), the diabetic study group and diabetic control group. The diabetic study group was engaged in the training program using treadmill exercise. Laboratory parameters were monitored before and after the training program. Results. There were significant elevation in the diabetic study group compared to diabetic control group regarding postexercise vitamin D level, high-density lipoprotein (HDL) (pvalue ≤ 0.001, 0.045; respectively). In addition, triglycerides, low-density lipoprotein (LDL), glycosylated hemoglobin (HbA1C), and homeostatic model assessment-insulin resistance (HOMA-IR) were significantly decreased (pvalue < 0.001 for all mentioned parameters). Moreover, there were significant higher level in postexercise parameters as compared to preexercise level in the diabetic study group. Conclusion. The exercise training program improved the metabolic control and vitamin D level after three months of intervention.
Clinical Study on the Relationship between the SNP rs8192675 (C/C) Site of SLC2A2 Gene and the Hypoglycemic Effect of Metformin in Type 2 Diabetes
Authors: Ye Weiwei , Wang Yi , Chen Feng , Zhao Qian , Meng Xiangying , Chen Jianyang , Liu Cong , Zhou Yong
Published: January 2022

This study investigates the correlation between the gene polymorphism of rs8192675 (C/C) locus of SLC2A2 in patients with type 2 diabetes (T2DM) and the efficacy of metformin. For this purpose, we have selected 110 T2DM patients (T2DM group) and 110 healthy people (control group) who were treated in our hospital from January 2019 to January 2020 as the research subjects. PCR-restriction fragment length polymorphism (PCR-RFLP) method detects the distribution frequency of gene polymorphism. The patients in the T2DM group were treated with metformin and followed up for 90 days to analyze the relationship between the efficacy of metformin and the SLC2A2 gene polymorphism. The genotypes of SLC2A2 rs8192675 in the control group and in the T2DM group conformed to the Hardy–Weinberg equilibrium law. Compared with the control group, the CT type and the CC type at rs8192675 in the T2DM group were significantly higher (

P
<
0.05

). For rs8192675, there was no significant difference in TT, CT, CC FPG, 2hPBG, and HbA1c levels before treatment (

P
>
0.05

); after metformin treatment, the reduction in FPG, 2hPBG, and HbA1c in CC patients was lower than that of TT and CT patients (

P
<
0.05

). SLC2A2 gene polymorphism site rs8192675 CC type T2DM patients are sensitive to metformin and have a better hypoglycemic effect.
Modelling and Analysis of Constant False Alarm Rate Performance in Presence of Jamming Environments
Authors: Chen Xiaozhou , Hu Mengzhong , Lu Shu
Published: January 2022

A novel “Bernoulli experiment model” of constant false alarm rate (CFAR) is presented in order to analyze CFAR performance in jamming condition. In this model, target detection can be treated as a sequence of independent subevents, and the detection process equals to pick up target energy from an energy pool diluted with noise, interference, and jamming pulses. In a multiple-jammer case, each jammer contributes independently, and the content of the energy pool only relates to the corresponding jammer. Impacts of jammer factors, such as signal-to-interference ratio (SIR), jamming-to-interference ratio (JIR), the number of false targets in reference cells, and the number of false targets in a cell under test (CUT), are analyzed and compared in detail, and two jamming operation principles are deduced as an application. The deduction procedure and conclusions drawn from the model can also be utilized on similar occasions.
Acute Oxalate Nephropathy Caused by Excessive Vegetable Juicing and Concomitant Volume Depletion
Authors: Chaudhari Harshad , Michaud Jennine , Srialluri Nityasree , Mahendrakar Smita , Granz Christine , Yudd Michael
Published: January 2022

Acute oxalate nephropathy (AON) induced by high dietary intake of oxalate-rich food is a rare cause of acute kidney injury and end-stage renal disease (ESRD). We describe a 68-year-old man with adequate baseline renal function who developed severe AON and ESRD. Six months earlier, he started a daily oxalate-rich fruit and vegetable juice diet high in spinach, with a calculated daily oxalate dietary intake of 1500 mg, about 10 times a typical diet. Renal biopsy showed extensive tubular oxalate deposits and acute tubular damage; the renal tissue was relatively free of chronic changes such as glomerulosclerosis, tubular atrophy, and interstitial fibrosis. A year later, he remains dialysis dependent.
LC-QTOF-MS Characterization, Antioxidant Activity, and In Vitro Toxicity of Medicinal Plants from the Tri-Than-Thip Remedy
Authors: Wetchakul Palika , Chonsut Piriya , Punsawad Chuchard , Sanpinit Sineenart
Published: January 2022

Background. The world population has exhibited increased trust in folk medicine, including Thai folk medicine, for the treatment of various illnesses. However, the comparative antioxidant and cytoprotective activities against oxidative damage of Tri-Than-Thip (Tri-TT), a Thai folk remedy, have not been reported. Objectives. The purpose of this study was to evaluate the antioxidant capacities of Tri-TT and its herbal constituents, Cassia fistula, Pithecellobium dulce, and Ficus benjamina. Methods. Extracts were obtained from Tri-TT and its herbal constituents. The free radical scavenging activities, cytotoxicity, ferric-reducing antioxidant power (FRAP), metal chelating activities, total phenolic compound (TPC) contents, and total flavonoid (TF) contents of Tri-TT extract were investigated, and qualitative analysis of the chemical composition of Tri-TT extract was performed by LC-QTOF-MS. Results. Tri-TT extract exhibited remarkable scavenging activities toward DPPH, ABTS, and superoxide anion radicals, with IC50 values of 0.081 ± 0.00, 0.021 ± 0.00, and 0.205 ± 0.057 mg/mL, respectively. The oxygen radical antioxidant capacity (ORAC) and FRAP values of Tri-TT were 6.499 ± 0.67 μM TE/g extract and 1,919.71 ± 63.14 mM FeSO4/mg sample, respectively. P. dulce had the highest scavenging activities and antioxidant capacity followed by C. fistula and F. benjamina. The TPC and TF content of Tri-TT extract were 287.87 mg equivalence/g extract and 225.62 mg catechin equivalent/g extract, respectively. The highest TPC was obtained from P. dulce, and TF content was observed in C. fistula. Using LC-QTOF-MS, a total of 25 compounds were tentatively identified in Tri-TT, including polyphenols such as luteolin, gallic acid, baicalein, apigenin, epicatechin, and ellagic acid. In addition, Tri-TT extract demonstrated nontoxicity (cell viability >90%) to Vero cells at the highest tested concentration of 80 μg/mL. Conclusion. This study demonstrated that the Tri-TT remedy is a promising candidate as a natural source of antioxidant activity, suggesting that the polyphenol content of plants may contribute to antioxidant activities.
Finite Mixture of the Hidden Markov Model for Driving Style Analysis
Authors: Ding Lusa , Zhu Ting , Wang Yanli , Zou Yajie
Published: January 2022

Analyzing driving style is useful for developing intelligent vehicles. Previous studies usually consider the statistical features (e.g., the means and standard deviations of brake pressure) of the measured driving data or manually define the number of patterns divided by behavior semantics to characterize driving styles. In this paper, we propose a driving style analysis to describe the personalized driving styles from time-series driving data without specifying the levels in advance but by estimating them from the data. First, range, range rate, and acceleration are selected as three feature variables to describe car-following scenarios. Then, the car-following data are normalized to reduce the scale influence of different variables on the segmentation results. The hidden Markov model (HMM) and the finite mixture of the hidden Markov model (MHMM) are adopted to extract behavior semantics. Compared with the HMM, the MHMM can identify the heterogeneity of data and then provide more reasonable primitive driving patterns. Based on the results, this study uses the K-means clustering to label all the driving patterns semantically and identifies a total of 75 different driving patterns. We use the normalized frequency distributions to describe personalized driving behavior characteristics, and similarity evaluations of driving styles are applied using the Kolmogorov–Smirnov test. The proposed approach in this paper is useful for exploring the characteristics of driving habits.
Innovative Design of Compact Heavy-Load Independent Transfer Device for Nuclear Engineering System
Authors: Wan Hao , Weng Songfeng , Du Hua , Dong Dailin , Wang Bingyan , Yu Tianda
Published: January 2022

The transportation of heavy equipment in nuclear engineering has always been the focus of engineers, especially those transfer devices with the characteristics of small geometric size and heavy load. According to this kind of compact heavy-load transfer device and its engineering tasks, the core problems caused by excessive vertical and horizontal forces in the design process were analyzed. By introducing the theory of inventive problem solving (TRIZ) design method, these problems were creatively solved by the contradiction theory and substance-field model in TRIZ, and an innovative design scheme of the compact heavy load-independent transfer device was obtained. Through the analysis of the design scheme and the stability and rapidity of its hydraulic system, some key parameters were determined. The power of the transfer device was all from the hydraulic system, and it can carry up to 300 t weight of reactor equipment, while its geometric size was only 1600 × 400 × 500 mm. It was of great significance to improve the efficiency of the nuclear engineering system.
Machine Monitoring Protocols Based on Quality of Service (QoS) to Improve Performance of Real-Time Industrial Applications
Authors: Abualsauod Emad H.
Published: January 2022

A major factor in the optimization of real-time industrial applications is Quality of Service (QoS). There is also a quality-of-service parameter (QoS). Normally, dynamic networks require QoS settings for real-time applications. The industrial production environment is changing due to rapid advances in ICT. Industries have increased output due to improved manufacturing chain efficiency, flexibility, and security. Establishing a secure, reliable, and fast network in the sector is therefore critical. In industrial applications, data collection is an important factor to consider. Various routing methods exist for collecting data from industrial environments and sending it to processing. In this situation, the energy consumption of device nodes and data transmission delay are important factors to consider. This research proposes to construct an energy-efficient delay-aware WSN routing (EDR) protocol for defect detection. The clustering group optimal approach is used to identify which clusters should be selected. A modified task selection method is proposed to calculate the most efficient path between industrial areas and data centres (monitoring rooms). The node selection approach is used to find the network’s aggregate node. The Network Simulator (NS2) programmed is used to model the projected IEM-EDR system. The proposed Industrial Energy Monitoring-Energy-Efficient Delay-Aware Routing (IEM-EDR) system’s performance is compared to that of existing techniques in hop count, latency, energy consumption, throughput, and delivery rate.
Analysis of Pedestrian Lane Change Behavior Spectrum Based on Video Data at Ticket Gate Facilities in Subway Stations
Authors: Fang Yong , Shi Qi , Hu Hua , Hao Yanxi , Liu Zhigang
Published: January 2022

Using motion unit tracking technology, the pedestrian motion parameters were extracted from the monitoring video data at the ticket gate facilities in subway stations and the indicators of the lane change behavior were determined. The pedestrian lane change behavior spectrum of ticket gate facilities in subway stations was constructed from the three elements of types of ticket gate facilities, pedestrian flow, and lane change behavior, and the quartile method was used to determine the upper and lower thresholds of the indicators. The results showed that when the pedestrian flow was (5, 10] ped/min, the average values of displacement, distance, and cumulative side shift distance were the largest and the thresholds were the largest. When the pedestrian flow was (10, 15] ped/min, pedestrians generally adopted a faster walking speed to change lanes. With the increase of the pedestrian flow, the average value of the change in direction of the movement increased and the longitudinal distance at the gate-type ticket gate facilities was greatly affected by the pedestrian flow. The number of lane changes was generally once. The research results can provide a basis for scientifically setting up ticket gate facilities and reducing congestion risks caused by abnormal lane change behavior.
Research on the Cooperative Control Method of Intermittent Bus Lane and Downstream Intersection
Authors: Zheng Guorong , Zhang Mingyan
Published: January 2022

Intermittent bus lane (IBL) used for bus priority is a lane in which the status of a given section changes according to the presence or absence of a bus in its spatial domain. Therefore, when bus services are not so frequent, general traffic will not suffer much, and bus priority can still be obtained. In order to further explore the road resource utilization, a signal timing optimization model was proposed considering coordinate intermittent bus lanes and downstream intersections. Firstly, the functional relationship was established between the general vehicle carrying capacity and the social vehicle arrival rates, the distribution of public transportation vehicles, and the downstream intersection timing plan; then, a bilevel programming model was built which maximizes the social vehicle carrying capacity and simultaneously minimizes the delay of general vehicle intersections. Finally, the optimal solution of carrying capacity under different scenarios was analyzed through experimental simulation, which effectively reduced the delay and proved the effectiveness of the proposed method.
Biofeedback Posture Training for Adolescents with Mild Scoliosis
Authors: Cheung Mei-chun , Yip Joanne , Lai Janelle S. K.
Published: January 2022

Adolescent idiopathic scoliosis (AIS) is characterized by uneven shoulders, spinal curvature, and uneven hips, and asymmetry in paraspinal muscle activities is common in AIS. This pilot study was aimed at examining the use of a surface electromyography (sEMG) biofeedback posture training program in adolescents with mild scoliosis (Cobb’s angle < 30°) to attenuate asymmetry in paraspinal muscle activities and control the curve progression. Seven female adolescents (age, 12–14 years) with mild scoliosis (Cobb’s angle < 30°) were recruited. The participants received 30 tailor‐made sessions of sEMG biofeedback posture training at a rate of one to two sessions per week for approximately 6 months. The activities of the paraspinal muscles (the trapezius, latissimus dorsi, thoracic erector spinae, and lumbar erector spinae) measured by sEMG during habitual sitting postures and spinal deformity evaluated by 3D ultrasound imaging were compared before and after training. The mean values of the root‐mean‐square sEMG ratio, an index of symmetry in paraspinal muscle activities of the muscle pairs between the concave and convex sides of the spinal curve, revealed significant asymmetry over the trapezius and lumbar erector spinae before the training (p <0.05). After the training, all seven adolescents achieved relatively more symmetrical paraspinal muscle activities over these two muscle pairs (p < 0.05). In two adolescents, the spinal curvature decreased by 5.7° and 5.6°, respectively, whereas the remaining adolescents showed a minimal curve progression with changes in the spinal curvature controlled under 5°. To conclude, sEMG biofeedback posture training can reduce asymmetry in paraspinal muscle activities and control curve progression in adolescents with mild scoliosis and can potentially be considered an alternative early intervention for muscle reeducation in this cohort.
On Edge H‐Irregularity Strength of Hexagonal and Octagonal Grid Graphs
Authors: Ibrahim Muhammad , Gulzar Ana , Fazil Muhammad , Naeem Azhar Muhammad
Published: January 2022

The edge H‐irregularity strength, ehs(Γ, H), of a graph Γ is the smallest integer k, such that Γ has an H‐irregular edge k‐labeling. In this study, we compute the exact value of edge H‐irregularity strength of hexagonal and octagonal grid graphs.