bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System
Authors: Sumathy B. , Kumar Anand , Sungeetha D. , Hashmi Arshad , Saxena Ankur , Kumar Shukla Piyush , Nuagah Stephen Jeswinde
Published: February 2022

The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user’s view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people’s decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words.
Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach
Authors: Ogwara Noah Oghenefego , Petrova Krassie , Yang Mee Loong
Published: February 2022

Attacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed in this study (CCAID: cloud computing, attack, and intrusion detection) aims to improve the performance of intrusion detection systems (IDS) operating in CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). The selected features used to train the ML (machine learning) model of the intrusion detection component comprised a binary detection engine for the identification of malicious/attack packets and a multiclassification detection engine for the identification of the type of attack. Both detection engines deploy ensemble classifiers. Experiments were carried out using the NSL KDD dataset. The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). These results compare very favourably with the results reported in the literature and indicate the feasibility of the framework implementation.
Research on Electrical Equipment Monitoring and Early Warning System Based on Internet of Things Technology
Authors: Lei Tianxiang , Lv Fangcheng , Liu Jiaomin , Feng Jiahao
Published: February 2022

With the rapid increase of global population, the power load increases rapidly, and the electrical equipment such as generator, transformer, and power line in the power system is an important basis for production and life. Its safe operation is of great significance because it is related to people’s economic development and life stability. In recent years, with the rapid development of Internet of Things technology, the Internet of Things and electrical equipment safety early warning are combined to use their respective advantages to provide a new way for electrical equipment monitoring and early warning system. Through a variety of key technologies of the Internet of Things, such as sensor technology, network communication technology, and cloud computing technology, data, information exchange, and communication between electrical equipment and Internet technology are carried out according to the agreed protocol, and the safety of the operation status of electrical equipment is monitored in real time, so as to prevent power equipment failure and other problems. Based on this background, this paper studies the Internet of Things technology and electrical equipment monitoring and early warning system and analyzes its three-tier network architecture mode from the Internet of Things technology, namely, perception layer, network layer, and application layer. The combination of cloud computing and edge computing is studied and analyzed to provide theoretical support for the research of electrical equipment monitoring and safety early warning system. The wireless sensor network equipment is also installed on the electrical equipment through the Internet of Things technology to transmit data to the base station, so as to monitor whether the equipment operates safely. The monitoring and early warning system of wireless sensor system based on Internet of Things is given through case experiment. This system realizes relevant intelligent application services, which can not only ensure the stability of information transportation but also real-time monitoring of electrical equipment, early warning, shorten troubleshooting time, reduce the workload of power station staff, and achieve the functions of safety early warning, emergency command, and control. It is of great significance to the monitoring and early warning system of electrical equipment.
An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation
Authors: Cao Zijian , Jia Haowen , Zhao Tao , Fu Yanfang , Wang Zhenyu
Published: February 2022

Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet parameters to elevate the performance of wavelet denoising. Firstly, based on the original SOMA, an adaptive step size adjustment method is proposed by recording the step information of successful individuals, which improves the search ability of the SOMA. Secondly, an exploratory selection method of leader is proposed to effectively balance the exploration and exploitation of the SOMA. Finally, ASOMA is compared with the original SOMA and its variants using wavelet general threshold denoising on classical test images in denoising performance, which is evaluated by the indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results demonstrate that ASOMA has better denoising performance than the wavelet general threshold, the original SOMA, and the related variants of SOMA.
A Study on the Impact of Regional Total Factor Production in Digital Economy Based on Fuzzy Hierarchical VISC Algorithm
Authors: Li Haixiang , Xue Weixian
Published: February 2022

The classification inaccuracy occurs to the evaluation calculation of the total factor production impact in the traditional digital economy development area. This paper applies the fuzzy hierarchical (Visualization in Scientific Computing, VISC) algorithm to the calculation method of the total factor production impact evaluation in the digital economy development area. The quantitative recursive method is used to evaluate the ability of the data information model, to achieve the ability of controlling the acquisition of characteristic resources, and complete the classification and summary of the index parameters of the total factor production impact of the digital economy development area. Finally, the experiment proves that this calculation method is used to develop the evaluation of the impact of total factor production digital economy development area and improve the information integration and analysis capabilities, the accuracy of the evaluation of the impact of total factor production, and the efficiency of the use of digital economy development resources.
Magnetic Resonance Imaging of Contrast-Induced Acute Renal Injury and Related Pathological Alterations In Vivo
Authors: Li Yanfei , Shi Dafa , Zhang Haoran , Yao Xiang , Ren Ke
Published: February 2022

Background. The definitive mechanisms of CI-AKI include contrast medium (CM) nephrotoxicity and CM disturbances in renal blood flow, but how the immune system responds to CM has rarely been mentioned in previous studies, and different cell death pathways have not been clearly distinguished. Aim. To confirm whether MRI detect early CI-AKI and to investigate whether immunity-related responses, pyroptosis, and mitophagy participate in contrast-induced acute renal injury (CI-AKI). Methods. C57BL/6 mice with CI-AKI were established by tail vein injection of iodixanol 320. Magnetic resonance imaging of 9.4 T scanner and microscopic appearance of renal H&E staining were tools to test the occurrence of CI-AKI at different times. Immunohistochemistry and NGAL were used to examine the immune responses in the kidneys with CI-AKI. Transmission electron microscopy and western blot methods were used to distinguish various cell death pathways in CI-AKI. Key Results. The densitometry of T2WI, DTI, and BOLD presents CI-AKI in a regular way. The microscopic appearance presents the strongest renal damage in CI-AKI mice that existed between 12 h (

P
<
0.0001

) and 24 h (

P
<
0.05

) after contrast medium (CM) injection. Strong correlation may exist between MRI densitometry (T2WI, DTI, and BOLD) and pathology. Neutrophil and macrophage chemotaxis occurred in CI-AKI, and we observed that Ly6G was the strongest at 48 h (

P
<
0.0001

). Pyroptosis (Nlrp3/caspase-1,

P
<
0.05

), mitophagy (BNIP/Nix,

P
<
0.05

), and apoptosis (Bax,

P
<
0.05

) occurred in CI-AKI. Conclusions. fMRI can detect early CI-AKI immediately after CM injection. NLRP3 inflammasomes are involved in CI-AKI, and mitophagy may play a role in mitigating kidney injury. The mitochondrion is one of the key organelles in the tubular epithelium implicated in CI-AKI.
Green Synthesis, Characterization of Gold Nanomaterials using Gundelia tournefortii Leaf Extract, and Determination of Their Nanomedicinal (Antibacterial, Antifungal, and Cytotoxic) Potential
Authors: Keskin Cumali , Baran Ayşe , Baran Mehmet Fırat , Hatipoğlu Abdulkerim , Adican Mehmet Tevfik , Atalar Mehmet Nuri , Huseynova Irada , Khalilov Rovshan , Ahmadian Elham , Yavuz Ömer , Kandemir Sevgi İrtegün , Eftekhari Aziz
Published: February 2022

Introduction. Fighting against cancer and antibiotic resistance are important challenges of healthcare systems, and developing new treatment methods has become the most concentrated area of researchers. Method and Materials. Green synthesis, characterization, and some biological activities of gold nanomaterials (AuNPs) obtained with Gundelia tournefortii (kenger) leaf extract were investigated in this study. Fourier scanning electron microscope, UV‐visible spectrophotometer, Fourier transform ınfrared spectroscopy, energy‐dispersive X‐ray spectrophotometer, X‐ray diffraction diffractometer, transmission electron microscope, and Zetasizer instrument data were used to elucidate the structures of nanoparticles. Results. The maximum surface plasmon resonance was observed at 532.15 nm after 1 hour. With the powder XRD model, the mean cubic crystallite size was determined as 23.53 nm. It was observed that the shapes of the obtained AuNPs were spherical, and the dimensions were 5‐40 nm and hexagonal. Surface charges (‐27 mV) and average size (365.3 nm) of gold nanoparticles were measured with a zeta analyzer. Conclusion. The suppressive effects of AuNPs on the growth of pathogenic microorganisms and healthy and cancer cell lines were determined using the MIC and MTT methods, respectively.
Digital Computer Modulation Signal Classification Based on Neural Network
Authors: Wang Guisheng
Published: February 2022

Computer is one of the indispensable tools in the human world, and human needs for it are increasing, so the emergence and application of more advanced computers are needed. The current computers do not respond intelligently, and it is difficult to meet people’s needs for information processing in the era of big data. In order to solve these problems, this paper proposes the application of a neural network-based data classification algorithm in computers, aiming to study the practical application of the algorithm in computers. The research method of this paper is to introduce the BP neural network, select the appropriate method of classification features, and then study the data classification algorithm. The function of the research method is to compare the classification error and convergence speed of the BP network composed of different hidden layer nodes, to study whether a certain feature item of the data exists and the difference in the amount of information classification of the entire document, and to select high efficiency, accuracy, and scalability algorithm. This paper compares the forward reasoning time of the model before and after cutting through experiments based on neural network model design, algorithm design, and man-machine dialogue model design. The results show that, in terms of computing speed, the adaptive model compression method based on the accuracy and redundancy ratio compresses the model after the forward reasoning time is greatly reduced, and the reasoning time becomes 35% of the original, and in terms of calculation accuracy, the absolute error after using the SOM method in this article has not reached 0.5.
[Retracted] Dynamic Analysis of Network Protocol Hiding Behavior Based on Perceptual Mining
Authors: Xu Xiaoyan
Published: February 2022

This paper analyzes the typical abnormal behaviors such as hidden and invisible attacks of network protocols and proposes a new method to perceive and mine the abnormal behaviors of protocols so as to evaluate the operational security of protocols. Aiming at the problems of long latency and difficulty to expose the hidden behavior of network protocols, a scheme combining dynamic stain analysis and instruction clustering analysis is proposed. The proposed method cannot only quickly distinguish the hidden behavior but also accurately grasp the hidden behavior instruction sequence. Experimentation results show that the proposed method can mine unknown protocols for abnormal behavior with high accuracy.
Design and Implementation of Fully Convolutional Network Algorithm in Landscape Image Processing
Authors: Pan Yinan , Li Yuan , Jin Jing
Published: February 2022

With the gradual improvement of the quality of life, taste, and ecological and environmental awareness of urban residents in China, the environmental landscape of residential areas has gradually become a hot spot. At present, the level of the residential environmental landscape has become a necessary means for real estate developers to publicize products and improve economic benefits. Although many residential areas have invested a high cost in constructing environmental landscapes, there are always some deficiencies and defects in the design and implementation of environmental landscapes in residential areas due to various reasons. Therefore, to ameliorate the low efficiency and high cost of manual processing of landscape images, a Fully Convolutional Network (FCN) model based on the traditional Convolutional Neural Network (CNN) is designed for semantic segmentation of landscape images to deal with the excessive amount of landscape elements in landscape image processing. The deconvolution method is utilized to realize pixel-level semantic segmentation. Besides, the image preprocessing method enhances the data to prevent overfitting from commonly occurring in FCN. Moreover, the model two-stage training method ameliorates long training time and complex convergence in deep learning. Finally, three upsampling network structures, i.e., FCN-32s, FCN-16s, and FCN-8s, are selected for a comparative experiment to determine the most suitable network. The experimental results demonstrate that the FCN-8s upsampling network structure is the most prominent; it attains a pixel accuracy of more than 90%, an average accuracy of 88%, and an average Image Understanding of 75%. The three values are the highest among the three upsampling structures, indicating that the FCN-8s can realize accurate landscape image processing. Besides, the recognition accuracy of FCN for landscape elements reaches 90%, 25% higher than that of CNN. This method is effective and accurate in classifying landscape elements, improves the classification accuracy intelligently, and significantly reduces the cost of landscape element classification, which is feasible.
Industrial Design Capability Evaluation System under Cloud Service Model
Authors: Yi Shanshan
Published: February 2022

This study focuses on the problem that the multiobjective optimization decision-making process of the design scheme under the cloud service model fails to fully consider the needs of users’ diversity, due to the lack of research on quantitative modeling and evaluation methods of designers’ ability. It affects the reasonable matching between complex design tasks and design ability and hinders the socialized aggregation of design ability. Based on the competency model, facing the industrial designers in the cloud manufacturing model, our paper constructs the design capability evaluation system from the perspectives of Yuan Shengren capability, industry technology competency, design task competency, and learning capability competency and discusses the design capability evaluation method based on a network analysis method. Through the design capability evaluation of the industrial design cloud service platform, feasibility and scientificity of the method in this paper are verified. This paper proposes a design scheme optimization decision-making method combining quality function configuration and rough set theory. Based on the analysis of the cloud service model, this method introduces the design decision-making process model of user participation and fuzzy evaluation mechanism to carry out cluster analysis and the intelligent transformation of user requirements. A design scheme evaluation index system oriented to user requirements is established, and the importance of design scheme evaluation indexes in the process of networked collaborative design is accurately quantified. Following the principle of combining scientificity and systematicness, a third-order decision-making model for design scheme optimization under the cloud service model is proposed. The model is solved by combining the rough set theory and rough approximation ideal solution.
The Role of Early Vitrectomy in the Healing of Retinal Lesions in Progressive Outer Retinal Necrosis
Authors: Riazi-Esfahani Hamid , Mirzaei Arash , Mirghorbani Masoud , Ghassemi Fariba , Zarei Mohammad , Khalili Pour Elias , Ebrahimiadib Nazanin
Published: February 2022

Purpose. To report on the efficacy of early pars plana vitrectomy (PPV), silicone oil (SO) tamponade, and intravitreal ganciclovir injection in the treatment of a case with progressive outer retinal necrosis (PORN). Case Presentation. A 33-year-old man with a history of shingles on the chest skin 2.5 months ago presented with progressive vision loss in both eyes over the past 20 days. Fundus examination revealed retinal necrosis with perivascular clearance. Human immunodeficiency virus (HIV) infection was confirmed by western blot analysis. Treatment with intravenous acyclovir and intravitreal ganciclovir injections was unable to stop the progression of retinitis. Along with highly active antiretroviral therapy, the patient underwent PPV with SO tamponade and intravitreal ganciclovir injection in both eyes. A few days after surgery, retinal lesions started to improve. Conclusion. Early PPV, SO tamponade, and intravitreal ganciclovir injection may be considered an effective intervention in PORN patients with an unfavorable response to medical treatment.
Time-Domain Channel Estimation Scheme for OFDM over Fast Fading Channels
Authors: Asharjabi Sami , Sakran Hefdhallah , Al-nahari Azzam
Published: February 2022

In high-mobility scenarios, the time variation of mobile radio channels leads to a loss of orthogonality among subcarriers in orthogonal frequency division multiplexing (OFDM) systems, resulting in intercarrier interference (ICI) and performance deterioration. Conventional channel estimation schemes are usually based on pilot tones, which are distributed in each OFDM symbol to estimate the channel variation. Hence, the channel estimator itself suffers from ICI. In this study, a new estimation scheme, which does not suffer from ICI, is proposed to estimate the channel variation within OFDM symbols. The main idea is to zero-pad (ZP) the OFDM symbol in the time domain. Then, in the middle of the ZP interval, an impulse signal is inserted as a pilot sample, which is used to estimate the channel at the pilot signal in the OFDM symbol. Finally, a linear model is used to estimate the channel variation over an OFDM symbol. Additionally, we derive the mean squared error (MSE) of the proposed estimation technique under the constraint that the channel varies linearly within OFDM symbols. Simulation results show that our scheme can achieve a substantial improvement in the bit error rate (BER) performance of OFDM, in spite of the OFDM symbol length being increased. Moreover, in many cases, the new scheme can achieve the same BER performance as the perfect knowledge of channel state information (CSI). Theoretical analysis and numerical simulations show that our scheme achieves excellent performance with much lower computational complexity.
Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care
Authors: Wang Lina
Published: February 2022

Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP system uses MRI radio imaging for predicting colorectal cancer. During this process, high‐ and low‐level features are required to examine cancer in an earlier stage. Due to the limitation of the conventional feature extraction process, both features are difficult to extract from cancer suffered locations. Hence, a deep learning system (DLS) is used to examine the entire bowel MRI image to identify the cancer‐affected location, feature extraction, and feature training process. Furthermore, the DLS‐based IAECP system helps improve the overall colorectal cancer identification accuracy for further process. The derived bowel features are trained by applying the residual convolution network, which minimizes the error between predicted and actual values. Finally, the test query images are compared with the trained image by applying the sum, which is more absolute to the cross‐correlation template feature matching (SACC) algorithm. The experimental process is performed using 100,000 histological data sets, which is considered a publicly available data set. Moreover, the introduced method does not use generic features, whereas the deep learning features help improve the overall IAECP prediction rate (99.8%) ratio as predicted at lab‐scale analysis.
Research on the Influence of Natural Wind Pressure in Deep Mines on Ventilation Stability
Authors: Wang Enmao , Li Xiaoping , Huang Qiming , Wang Gang
Published: February 2022

Deep mines are greatly affected by changes in natural wind pressure because of their large buried depths and long ventilation paths. Changes in natural wind pressure do affect the air flow of the underground ventilation system, and even change the direction of individual branches. If the dynamic changes of natural wind pressure are not monitored constantly, it is very likely to cause disasters such as gas overrun and may even lead to heavy casualties. In this paper, the changes of natural wind pressure and the air volume entering the mine are measured on‐site in the 630 mining area in the south wing of Tangkou Coal Mine, Then, compare the change law of natural wind pressure with the change law of ventilation air volume. Finally, through numerical simulation by FLUENT, the change of internal flow in the gob where there is a loosely closed condition is simulated. Through research, the annual natural wind pressure change and the change of air intake in the 630 mining area of the south wing of Tangkou Coal Mine were obtained; The influence of changes in external conditions on the ventilation air volume of deep mines is obtained; The importance of the influence of natural wind pressure on the stability of the deep mine ventilation system is verified.
Short Video Copyright Storage Algorithm Based on Blockchain and Expression Recognition
Authors: Yang Yang , Yu Dingguo
Published: February 2022

Blockchain technology is widely used in the field of digital right protection technology. The traditional digital right protection scheme is not only inefficient and highly centralized but also has the risk of being modified. Due to its own characteristics, blockchain cannot completely store all the original files of digital resources. In this paper, a convolutional neural network algorithm based on visual priority rule is proposed (CNNVP). This algorithm can recognize facial expressions in the original files of digital resources (for short video of face class). The algorithm extracts facial expression features accurately and makes these features form log files that can represent the original files of digital resources. Then, the paper proposes a short video copyright storage algorithm based on blockchain and facial expression recognition and stores the log file into the blockchain. The above methods not only improve the efficiency of short video copyright storage, reduce the degree of storage centralization, and eliminate the risk that copyright is easy to be modified. Moreover, the computing operation of deep learning technology on short video not only ensures the privacy of storage certificate information but also ensures the possibility of blockchain storage of video information. Experiments show that the algorithm proposed in this paper is more efficient than the traditional copyright storage method. Moreover, the algorithm proposed in this paper can provide technical support to the media resource management department.
The Effect of Cutibacterium acnes Infection on Nerve Penetration in the Annulus Fibrosus of Lumbar Intervertebral Discs via Suppressing Oxidative Stress
Authors: Shan Zhi , Wang Xianjun , Zong Wentian , Li Jie , Zheng Bingjie , Huang Bao , Zhang Xuyang , Chen Jian , Huang Yue
Published: February 2022

Modic changes (MCs) and low back pain are highly correlated and an economic burden to the society. Previous studies have shown that Cutibacterium acnes (C. acnes) infection can lead to MCs. The purpose of this study was to clarify whether and how C. acnes contributes to oxidative stress and nerve growth that potentially leads to low back pain. Neurons from the hippocampus or dorsal root ganglion (DRG) of Sprague-Dawley (SD) rats were cocultured with annulus fibrosus cells (AFCs) with or without the presence of the C. acnes supernatant in vitro. Cell viability, neurite length, oxidative stress, and neuro-related gene expression were examined. Furthermore, samples from the patients with MCs and SD rat model of MCs were used to validate the nerve growth results. Neurons from both the hippocampus and DRG showed neurites when cocultured with AFCs in the environment with/without the C. acnes supernatant. The average neurite length was significantly longer when exposed to the C. acnes supernatant in the hippocampal neuron (

217.1
±
90.0
 
μ
m

versus

150.1
±
68.1
 
μ
m

in the control group) and in the DRG neuron (

229.1
±
91.3
 
μ
m

versus

149.2
±
64.8
 
μ
m

in the control group). Hippocampal neurons showed upregulated expression levels of NeuN, Map2, and Psd95, while upregulation was only seen in Tuj-1 in DRG neurons. Suppressed oxidative stress could be observed using axon growth symbols. Degenerated disc structures and abnormal bone remodelling were found in animal models and clinical samples of MCs, with astrocytes, microglia, and neurons in the disc. Therefore, C. acnes infection was found to cause back pain in the presence of MCs by promoting nerve penetration into the annulus fibrosus by suppressing oxidative stress.
Analysis of the Effect of the Friction Coefficient on a Pavement Structure
Authors: Bai Lu , Zhang Yong-sheng
Published: February 2022

Insufficient skid resistance of pavement is one of the main causes of traffic accidents. In this study, the influence of the friction coefficient on the internal effect of the pavement structure is studied deeply by establishing the asymmetric finite element numerical model. The model analyzes the inner stress, strain, and displacement variations of the pavement, with friction coefficients under the conditions of 0.3, 0.5, 0.8, and 1.0 and the action of the nonlinear superposition between the single-point static vertical load and horizontal load. In addition, against the limitations of the scatter calculation results, this paper analyzes the distribution of the internal stress field and displacement field. The results show that, with the attenuation of the friction coefficient, the distribution law of stress, strain, and displacement on the road surface (Z = 0) remains unchanged, and the maximum principal strain and the third principal strain on the Z-axis decrease monotonically with increasing depth. With the attenuation of the friction coefficient, the horizontal displacement inside the pavement structure gradually decreases, the vertical displacement changes insignificantly, and the principal stress gradually decreases. The principal strain and maximum shear stress inside the pavement structure show different changing rules.
Enhanced Lorenz-Chaotic Encryption Method for Partial Medical Image Encryption and Data Hiding in Big Data Healthcare
Authors: Rashmi P. , Supriya M. C. , Hua Qiaozhi
Published: February 2022

Image encryption is highly required in the big data healthcare cloud to improve the security of the medical image for remote access. Data hiding method is the process of storing the medical information of the patient in the medical image in the hidden format. Many existing data hiding methods are based on wavelet and chaotic map due to its effectiveness. Wavelet based methods have limitations of lack of phase information, poor directionality, and shift sensitivity. Chaotic map is applied to improve the security of the medical image and chaotic map has the limitation of low sensitive to control parameters and initial conditions. In this research, the Improved Chaos Encryption (ICE) is applied to improve the security based on randomness. The average energy is calculated in the images and compared with adaptive threshold to segment the Lorenz 96 model applied in the chaos encryption algorithm to improve the model security. Lorenz 96 increased the randomness of the chaos encryption method due to its high sensitivity. Medial images were used to test the performance of the ICE in the image encryption and image hiding. The proposed ICE model evaluated the quality of the recovered and decrypted image in the various embedding rate. The result shows that the proposed ICE model has the PSNR value of 104.7 dB compared to the LSB-ROI method which has 97.61 dB PSNR.
Nutritional, Functional, Physical, and Microstructural Properties of Ethiopian Emmer Wheat (Triticum dicoccum L.) Varieties as Affected by Growing Seasons and Grain Types (Hulled and Dehulled)
Authors: Melese Bethlehem , Satheesh Neela , Fanta Solomon Workneh , Bishaw Zewdie
Published: February 2022

This study was conducted to evaluate the effect of the growing seasons, grain types, and varieties on physical, proximate, functional, mineral, vitamins, amino acids, and microstructural properties of Ethiopian emmer wheat. One local landrace and three improved emmer wheat varieties (Hydroo, Sinana 01, and Lameso) grown during Meher and Belg seasons and grain types (hulled and dehulled) were used for analysis. The study showed that dehulled Sinana 01 variety from Belg season had the highest (17.82%) protein content. Varieties grown in Belg season showed the highest mineral compositions. The essential amino acids in emmer wheat were higher in the hulled grain type grown in Meher season. The highest vitamin B1 (0.17 mg/g), B2 (0.35 mg/g), and B6 (5.52 mg/g) contents were observed in hulled emmer wheat types grown in Meher season. The study concluded that seasonal variation, grain types, and varieties have a great effect on the proximate, physical, and functional properties of emmer wheat cultivated in Ethiopia.
Power Inverted Nadarajah–Haghighi Distribution: Properties, Estimation, and Applications
Authors: Ahsan-ul-Haq Muhammad , Ahmed Junaid , Albassam Mohammed , Aslam Muhammad
Published: March 2022

In this paper, a new three‐parameter power inverted Nadarajah–Haghighi distribution is proposed and studied. The new distribution is a generalization of the inverted Nadarajah–Haghighi distribution in which an additional shape parameter is included. Surprisingly, this additional shape parameter dominates the tails’ weights of distribution and also affects skewness and kurtosis. Such heavy‐tailed distributions are suitable for fitting actuarial and financial risk real problems. Important mathematical properties of this distribution are obtained. Characterization based on two truncated moments is also derived. The parameters are estimated using the maximum likelihood method. Further, a numerical simulation study is conducted for reinforcement of the results. Finally, the application of the proposed distribution to real‐life data sets is presented to prove its applicability.
[Retracted] A Blockchain Technique for Trade Credit Maintainability Using the Role of Information and Communication Technology
Authors: Shah Faiza , Liu Yumin , Shah Yasir , Ul Haq Ijaz , Mukred Muaadh , Hussain Saddam , Alasaly Mahfoudh S.
Published: February 2022

Recent advances in data analysis and processing methods can improve the ability of computational applications to perform complex steps of different tasks. With the progress of information and communication technologies (ICT), Blockchain‐based complex data processing for transaction analysis and smart contract agreement has become a new research area in the fields of mathematics and computation. Stability of financial sector based on the ICT is a core component for growing the economics of medium and small enterprises. This stability brings the innovation to businesses productivity, while the management of information takes more prospective for improving the efficiency and more ways for innovating the business of products. In this study, we use the autoregressive distribution lag (ARDL) model with Blockchain‐based complex data processing approach to emphasize the role of ICT in the field of trade credit maintainability. Actually, the ICT connects the industries in the entire world and makes business sectors that use its technologies be more advanced. Based on the ARDL model conducted on the records gathered from 2000 to 2019, the analysis concludes that the ICT‐based complex data processing is a critical component of trade credit. The statistics of ICT are chosen based on the economy penetrations through the Internet and mobile phones. The causality exposed between the trade credit and ICT is bidirectional in nature. Also, it is found that the usage of mobile phones has a substantial influence on the business sectors, as a substantial amount of trading and business transactions are conducted over the phone. Therefore, the primary concern is the association between the Blockchain and trade credit, which is thoroughly discussed in this work. The trade credit improves the stability of financial sector and the Blockchain supports its maintainability by the role of ICT. The results of the study can help the business stakeholders and investors to estimate the marketing for future useful execution.
Graphical Analysis of q-Rung Orthopair Fuzzy Information with Application
Authors: AlSalman Hussain , Alkhamees Bader Fahad
Published: February 2022

The q-rung orthopair fuzzy graph (q-ROFG) is an expansion of the intuitionistic fuzzy graph (IFG) and Pythagorean fuzzy graph (PFG); q-rung orthopair fuzzy model is an influential model for describing vagueness and uncertainty as a comparison to an intuitionistic fuzzy model and Pythagorean fuzzy model. The research aims to illustrate the notion of the graph of q-rung orthopair fuzzy sets (q-ROFSs). Furthermore, in this article, we examine the ideas of domination theory (DT) and double domination theory (DDT) in q-ROFGs. Additionally, the structure of q-ROFG is developed and its associated concept is presented through the assistance of instructive instances. Furthermore, the DT of q-ROFGs is established, as are cardinality, power, and completeness on dominance in a q-ROFG and bipartite q-ROFG, and double domination set (DDS), as well as some results, is investigated in the concept of q-ROFGs. A political campaign is simulated using the proposed structure as an application, and the impact of double dominance (DD) on political campaigns is investigated. Finally, a comparison is given between the proposed study and actual studies, as well as the advantages of working in the q-ROFG scenario.
Graphene and g-C3N4-Based Gas Sensors
Authors: Kotbi Ahmed , Imran Mohammed , Kaja Khaled , Rahaman Ariful , Ressami El Mostafa , Lejeune Michael , Lakssir Brahim , Jouiad Mustapha
Published: February 2022

The efficient monitoring of the environment is currently gaining a continuous growing interest in view of finding solutions for the global pollution issues and their associated climate change. In this sense, two-dimensional (2D) materials appear as one of highly attractive routes for the development of efficient sensing devices due, in particular, to the interesting blend of their superlative properties. For instance, graphene (Gr) and graphitic carbon nitride g-C3N4 (g-CN) have specifically attracted great attention in several domains of sensing applications owing to their excellent electronic and physical-chemical properties. Despite the high potential they offer in the development and fabrication of high-performance gas-sensing devices, an exhaustive comparison between Gr and g-CN is not well established yet regarding their electronic properties and their sensing performances such as sensitivity and selectivity. Hence, this work aims at providing a state-of-the-art overview of the latest experimental advances in the fabrication, characterization, development, and implementation of these 2D materials in gas-sensing applications. Then, the reported results are compared to our numerical simulations using density functional theory carried out on the interactions of Gr and g-CN with some selected hazardous gases’ molecules such as NO2, CO2, and HF. Our findings conform with the superior performances of the g-CN regarding HF detection, while both g-CN and Gr show comparable detection performances for the remaining considered gases. This allows suggesting an outlook regarding the future use of these 2D materials as high-performance gas sensors.
Surgical Management of Postoperative Grade C Pancreatic Fistula following Pancreatoduodenectomy
Authors: Torres Orlando Jorge Martins , Moraes-Junior José Maria Assunção , Fernandes Eduardo de Souza Martins , Hackert Thilo
Published: March 2022

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Background - The incidence of Grade C postoperative pancreatic fistula ranges from 2 to 11% depending on the type of pancreatic resection. This complication may frequently require early relaparotomy and the surgical approach remains technically challenging and is still associated with a high mortality. Infectious complications and post-operative hemorrhage are the two most common causes of reoperation. Summary - The best management of grade C pancreatic fistulas remains controversial and ranges from conservative approaches up to completion pancreatectomy. The choice of the technique depends on the patient’s conditions, intraoperative findings, and surgeon’s discretion. A pancreas-preserving strategy appears to be attractive, including from simple to more complex procedures such as debridement and drainage, and external wirsungostomy. Completion pancreatectomy should be reserved for selected cases, including stable patients with severe infection complication or hemorrhage after pancreatic fistula who do not respond to pancreas-preserving procedures.
Key Messages - This review describes the current options for management of grade C pancreatic fistula after pancreatoduodenectomy with regard to indication, choice of procedure and outcomes of the different approaches.
A Space for the Subject: Tracing Garden Culture in Muslim Russia
Authors: Bustanov Alfrid K.
Published: February 2022

This article examines the place occupied by garden culture in the mental landscape of Russia’s Muslims from the early nineteenth century to the late Socialist era. First taken from the Qur’an as a symbol of eternal salvation, the idea that gardens might embody both aesthetic and metaphysical values was further articulated by traveling missionaries with Sufi affiliations. This idea was afterwards absorbed by the generation of students graduated from Central Asian madrasas who, in the first half of the nineteenth century, brought the fashion for having gardens back to their home villages in European Russia. Gardens built or imagined by Muslims in European Russia had a history of their own, developing from the classical vision of heavenly gardens in Qur’anic exegesis into what became a central spatial category in Sufi tradition. In post-war Soviet Russia a place of piety was rethought as dacha—the entire process reflecting the evolution of Muslim subjectivity over the last few centuries.
Pavilions to Celebrate Honest Officials: An Authenticity Dilemma in Fifteenth-Century China
Authors: Schneewind Sarah
Published: February 2022

The article centers on the celebration of Wenzhou Prefect He Wenyuan (1385–1457)’s moral integrity in a building called the “Lodge of Spurned Gold.” Building on historian Ying Zhang’s conception of late-Ming Confucian image politics, in which gentrymen and officials faced an “authenticity crisis,” the article introduces a new concept, the “authenticity dilemma.” A person faces an authenticity dilemma when his or her virtue is questioned precisely because it has been celebrated. The article shows that He rejected an illegal gift intended as a bribe, in a way that may have gone against social norms. The monument to his act accorded with orthodox encouragement of honesty in others, being based on celebrations of Han and Tang exemplars. Despite having clashed with bureaucratic colleagues and emperors, He won state recognition for his excellent service to the people of Wenzhou prefecture and until he was promoted to high office at court there was no question about his honesty. Yet his biographer worked hard to argue that He deserved the honors he won in Wenzhou, because changes in personnel policies and factional fighting meant that He was impeached at court for having “stolen a reputation,” based on the building of the Lodge. On the model of the Lodge, a number of other officials, beginning with envoys sent overseas, were honored for their honesty with “Spurned-Gold Pavilions” (que jin ting 卻金亭). Their multiplication and painter Wen Zhengming’s involvement further undermined He’s reputation, sharpening the authenticity dilemma. As a result, earlier stories of He’s life were reshaped to insist on his deep honesty, at the expense of historical details of the incident.
Capitalism in Khiva: Cash Waqf or Cash Loan?
Authors: Khaliyarov Alisher
Published: February 2022

This article examines the sharp increase of cash waqf endowments in the Khivan Khanate and the circumstances of their usage during the final decades of the nineteenth century. This research supports the conclusion that the rise of monetization in the economy prompted the rapid emergence of cash waqf endowments and led the administrators of such pious institutions to become personally involved in moneylending activities. Through an investigation of Khivan qāżī records, this article shows how cash waqf capital was utilized through bay‘-i jāʾiz, a type of moneylending agreement that became popular across much of the 19th-century Islamic world. It argues that the emergence of cash waqf and bayʿ-i jāʾiz agreements during this period is indicative of the influences of global industrial capitalism on the Khivan economy.
God as a Printer: On the Theological Status of Printing in the Kabbalistic Tradition of Israel Sarug
Authors: Baumgarten Eliezer
Published: March 2022

Recent decades have witnessed a broad scholarly discussion of the cultural influences of the printing press Invention. The core of this revolved around the new technological influence on the concept of knowledge, its methods of dispersion, and on social changes that it engendered in the 16th century, when it became an affordable widespread technology. This article presents the way in which the spread of the printing press influenced conceptual paradigms of Kabbalists in general, and Lurianic Kabbalists from Sarug’s tradition in particular. These Kabbalists exchanged the traditional conception of creation as an act of writing, within the conception of the world as a written text, for conceptions of creation as a printing act and the world as a printed text. I show how the professional term ‘letterpress printing’ entered these Kabbalists’ descriptions of divine emanation, alongside their conceptualization of printing as a divine activity, as writing had been conceptualized previously.
An actualized catalogue of the Psychodidae (Diptera) of Mexico and their known distribution by state
Authors: IBÁÑEZ-BERNAL SERGIO , DURÁN-LUZ JUANA
Published: February 2022

A catalogue of Psychodidae of Mexico is presented. A total of 99 species in 30 genera of the subfamilies Bruchomyiinae, Phlebotominae, Psychodinae, and Trichomyiinae are listed, with bibliographic citations and distributional data by country and in the case of Mexico by states.
On some Moroccan Pamphagidae, with the description of a new species of Euryparyphes (Orthoptera: Caelifera)
Authors: CHOBANOV DRAGAN , MASSA BRUNO
Published: February 2022

An update to the distribution records of the Moroccan Pamphagidae is presented. One new species to science, Euryparyphes minor sp. n., is described, and one new combination is proposed.
A new species of the genus Malaia Heller, 1892 (Scarabaeidae: Rutelinae: Anomalini) from Sulawesi Island, Indonesia
Authors: KANEKO NAOKI , WADA KAORU
Published: February 2022

New species of the genus Malaia Heller, 1892 (subgenus Malaia) found in Sulawesi Selatan, Indonesia, named M. (M.) medea Kaneko & Wada, new species, is described. This species is similar to M. (M.) simulatrix Heller, 1892 but is readily distinguished by several characters.