bluuebunny/crossref_metadata_2025 huggingface dataset:
Multisensor Feature Fusion-Based Model for Business English Translation
Authors: Zheng Pingfei
Published: January 2022

With the wide use of computers, machine translation has been gradually applied in many fields from natural language processing, such as industry, education, and so on. Due to the increasing demand for multilanguage translation, it is an urgent problem to effectively improve the quality of text translation. Driven by the upsurge of artificial intelligence, neural network technology is increasingly integrated into the field of machine translation, which gradually expands the traditional machine translation method into neural machine translation method. With the continuous improvement of deep learning technology, machine translation has gradually integrated these methods and strategies and achieved good results in multiple tasks, but there are still some shortcomings. The most prominent problem is that, since word vector is the basis for the model to obtain semantic and grammatical information, the existing methods cannot obtain semantic and grammatical feature information, which greatly reduces the accuracy of English translation. Based on this, this paper proposed a method of splicing word vector with character- level and word-level encoding vector. The characterization of fusion of more word vector can effectively solve the word does not appear in the table, the word with some low frequency, can express meaning more complete information, performance directly affects the whole translation model, the results can be seen through the experiment, we put forward the characteristics of the fusion method and strategy, can effectively enhance the overall translation performance of the model.
Detection of Deficiency of Nutrients in Grape Leaves Using Deep Network
Authors: Ali Asad , Ali Sikandar , Husnain Mujtaba , Saad Missen Malik Muhammad , Samad Ali , Khan Mukhtaj
Published: January 2022

It is quite natural that the crops may be affected from a number of diseases due to many factors namely, change in climate, variations in environmental changings, deficiency of urea etc. Among these factors, the deficiency of the natural nutrients is one of the common reasons that may effect on the overall production of a certain crop. The grape leaves are one of crops that are affected by the deficiency of nutrients like Potassium, Magnesium, Nitrogen and Phosphorus. Furthermore, the effects of these nutrients may have similar disorders on the grape leaves like tilting off the leaf from edges, change in color, rusting off from the root etc. and it is hard to find the identify the nutrient which is likely to be deficient in the grape leaf. In order to ensure the good quality and high production, it is necessary to design an automated system that helps in classifying the affected grape leaf in any of the four classes namely, Potassium-deficient (K-deficient), Phosphorus-deficient (P-deficient), Nitrogen-deficient (N-deficient) or Magnesium-deficient (Mg-deficient). To achieve this target, we performed a series of experiment in which we first created a dataset of grape leaves affected from the deficiency of nutrients, from the crop fields in a controlled environment. The dataset is also augmented since the data instances were not in appropriate amount to achieve the negotiable results. After preprocessing, the Convolution Neural Network (CNN) classifier is used to achieve the average individual accuracies of 77.97%, 77.74%, 81.81% and, 78.09% for K-, Mg-, P- and N-deficient grape leaves, respectively using conventional training testing ratio and while for the same sequence individual accuracies achieved are 95.95%, 92.70%, 90.91% and , 94.76% using n-fold cross validation approaches on the original dataset. These accuracies were improved when these approaches are applied on the augmented dataset. The results were also compared with recent studies concluding that our proposed approach outperformed the previous studies. Our experimental results are equally applicable and beneficial when implemented on mobile devices for getting real-time results.
The Role of Acupoint Application of Herbal Medicine for Asthma: Meta-Analysis of Randomized Double-Blind Placebo-Controlled Trials
Authors: Wu Zhenhu , Zheng Yanchan , Chen Yaoxin , He Bing , Wu Lei , Lin Lin , Xu Yinji , Chen Yuanbin
Published: January 2022

Background. Acupoint application of herbal medicine (AAHM) has been widely used in China. At present, there is no systematic review of AAHM versus placebo in the treatment of asthma. This systematic review aims to assess the efficacy of AAHM for asthma. Methods. Searches were conducted in five English databases and four Chinese databases from their inceptions until December 2020. Randomized double-blind placebo-controlled trials were screened, and included studies evaluated routine pharmacotherapy (RP) plus AAHM versus RP plus placebo or AAHM versus placebo. The Cochrane risk of bias tool and Grading of Recommendations Assessment, Development and Evaluation (GRADE) were performed to evaluate the methodological quality and quality of evidence separately. Results. Sixteen studies involving 1,730 participants were included in this review. Compared with placebo plus RP, participants receiving long-term AAHM plus RP showed improvement in asthma quality of life questionnaire (AQLQ) with moderate-quality evidence (MD 6.53 points, 95% CI 2.70 to 10.36). Low-quality evidence indicated that AAHM plus RP was associated with improved FEV1 (%) compared with placebo plus RP, whether long- or short-term use (MD 11.80%, 95% CI 2.84 to 20.76; MD 10.57%, 95% CI 8.40 to 12.74; respectively). Moderate-quality evidence showed that participants receiving short-term AAHM were associated with a higher AQLQ score (MD 6.57 points, 95% CI 3.76 to 9.38) and a lower frequency of acute exacerbations (MD –1.84, 95% CI –2.32 to –1.36) compared with placebo. Low-quality evidence also indicated that AAHM was associated with improved FEV1 (L) compared with placebo, whether long- or short-term use (MD 0.35 litres, 95% CI 0.03 to 0.67; MD 0.66 litres, 95% CI 0.59 to 0.73; respectively). Conclusions. Moderate-quality evidence is promising that AAHM can improve the quality of life and reduce acute exacerbations in patients with asthma. AAHM also shows a positive role in improving lung function, but the evidence is so indefinite due to low quality.
An Optimization Design Method of Express Delivery Service Based on Quantitative Kano Model and Fuzzy QFD Model
Authors: Shan Hongmei , Fan Xinmeng , Long Siyu , Yang Xuejing , Yang Shuhan
Published: January 2022

Service quality is the soul of express enterprises forever. It is of great practical significance for winning customer satisfaction, improving the market competition, and realizing sustainable performance. Unlike tangible products, express delivery service has the characteristics of intangibility, heterogeneity, indivisibility, and instability. While customer demands are complex and changeable and unpredictable, incorporating complex customer requirements into service design has been a growing interest of researchers and practitioners. This paper proposed an optimization design framework based on the quantitative Kano (QKNO) and the fuzzy quality function deployment (FQFD) to effectively achieve the best matching of enterprise service elements under the uncertainty and imprecise judgment information. An empirical study is conducted to verify the feasibility of the proposed approach. The results show that the framework could guide the express company to prioritize the enterprise service elements to maximize customer satisfaction and provide a reasonable budget allocation scheme to set the best resources match. It has theoretical and practical meaning for express enterprises to implement customization service strategy and improve service quality under the limited budget, which could be further extended to other service industries to make optimization decisions.
Automatic Guided Vehicles Introduction Impacts to Roll-On/Roll-Off Terminals: Simulation and Cost Model Analysis
Authors: Park Sang Hyung , Hwang Jeho , Yun Sohyun , Kim Sihyun
Published: January 2022

Automatic guided vehicles (AGVs) have been successfully applied to cargo terminals to reduce operating costs and improve productivity. However, the focus was on container terminal operations. Ports with roll-on/roll-off (RORO) terminals still heavily depend on human resources for the loading/unloading processes. Work operations are affected by human errors and safety issues. In particular, terminals where vehicles cannot be stacked pressure workers to handle cargo more rapidly, which induces more errors. In this study, we propose automating RORO terminal operations by using AGVs. We assessed the impact of AGVs on the productivity, cost efficiency, and environment. A series of simulation models was developed on the basis of the current loading system at an actual port to test the impact of AGVs. Then, we developed a cost model to analyze the economic benefit of AGVs compared with the current loading system. The environmental benefits were also analyzed. Results revealed that a system using 29 AGVs matched the productivity of the current loading system, and using more AGVs increased the productivity. For a given productivity level, the total operating cost of the AGV system was three times less than that of the current system over a 15-year period. The AGV system also showed great potential for improving the environmental friendliness of terminal operations. This is the first study to propose automating RORO terminal operations to improve productivity and sustainability through AGV technology rather than human factors. AGVs are expected to become a good option in the future to address labor shortages and the “untact” era.
Semantic Role Labeling Integrated with Multilevel Linguistic Cues and Bi-LSTM-CRF
Authors: Wan Fucheng , Yang Yimin , Zhu Dengyun , Yu Hongzhi , Zhu Ao , Che Guoyi , Ma Ning
Published: January 2022

Chinese Semantic Role Labeling (SRL) is the core technology of semantic understanding. In the field of Chinese information processing, where statistical machine learning is still the mainstream, the traditional labeling methods rely heavily on the parsing degree of syntax and semantics of sentences. Therefore, the labeling precision is limited and cannot meet the current needs. This paper adopts the model based on a bidirectional long short-term memory network combined with the Conditional Random Field (Bi-LSTM-CRF). In the feature processing stage, pooling technology is combined with sampling and selecting multifeature vector groups to improve the performance of the sequence labeling model. Lexical, syntactic, and other multilevel linguistic features are integrated into the training to realize in-depth improvement of the original labeling model. Through several groups of experiments, the precision of model annotation in this paper has been significantly improved combined with linguistic-assisted analysis, which proves that it can optimize the annotation performance of the model by integrating relevant linguistic features into the model based on Bi-LSTM-CRF and sampling and extracting multifeature groups; the evaluation of F increases to 82.18 percent.
Complete Blood Count Alterations of COVID‐19 Patients in Riyadh, Kingdom of Saudi Arabia
Authors: Elderdery Abozer Y. , Elkhalifa Ahmed M. E. , Alsrhani Abdullah , Zawbaee Khalid I. , Alsurayea Saad M. , Escandarani Faisal K. , Alhamidi Abdulaziz H. , Idris Hadeil M. E. , Abbas Anass M. , Shalabi Manar G. , Mills Jeremy
Published: January 2022

This study is aimed at evaluating the association between Coronavirus disease 19 (COVID‐19) and the primary complete blood count (CBC) parameters in confirmed positive patients. In a retrospective cross‐sectional study, 384 files of patients with confirmed COVID‐19 diagnosis hospitalized at King Saud Medical City, Riyadh, were chosen randomly as a study group for hematological parameters, with another 384 non‐COVID‐19 files of patients without history of any disease which could influence the hematological profile were selected as a control group. The gender, age, and nationality of the control group were matched with those of the study group. Anemia and thrombocytopenia prevalence was significantly higher in COVID‐19 positive patients compared with negative. However, the positive cases were 3.4 times more likely to be anemic and approximately 5.3 times as likely to be thrombocytopenic, while the prevalence of leukopenia showed no statistical significance between the two groups. However, the Mean Cell Volume (MCV), Total White Blood Cell (WBC) count, lymphocyte count, and basophil count median values showed a nonsignificant difference between the two groups. Anemia and thrombocytopenia may be highly developed in severe positive cases, and therefore, further studies are recommended to validate these findings.
Joint Doppler Shift and Channel Estimation for High-Speed Railway Wireless Communications in Tunnel Scenarios
Authors: Wang Zhigang , Wang Zihao , Zhu Fusheng , Luo Zezhou , Li Fang , Liu Haopeng
Published: January 2022

In this work, we study the problem of Doppler shift and channel estimation for wireless communication systems on high-speed railways (HSRs). We focus on tunnel scenario, one of the classical scenarios of HSRs. We first build up the mathematical system model, design a joint Doppler shift and channel estimator, and compare its performance with the typical Moose algorithm. We show that our estimator outperforms the Moose algorithm in Doppler estimation. Besides, since wireless channels in tunnel scenarios often contain several or multiple taps, we suggest an adaptive frame structure to improve transmission efficiency. Simulations are then provided to corroborate our proposed studies.
Enterprise Human Resources Recruitment Management Model in the Era of Mobile Internet
Authors: Yang Yanhua , Wang Yu
Published: January 2022

In view of the low quality, low efficiency, and excessive restrictions of traditional talent recruitment and management methods, which can no longer meet the needs of talent recruitment and management under the current new economic situation, this urgent problem needs to be solved. With the development of mobile communication, intelligent terminal, and Internet technology, human resource signboard management is gradually transformed into the Internet model. This paper adopts the enterprise human resource recruitment management mode in the mobile Internet era. In order to prove the effectiveness of the algorithm proposed in this article, we carried out a large number of related experiments. The results show that the enterprise human resource recruitment management model in the mobile Internet era increased recruitment efficiency by 18%. Finally, the content studied in this article can provide some reference ideas for subsequent research.
Research on the Development of Digital Creative Sports Industry Based on Deep Learning
Authors: Chen Junmeng , Xu Shaofeng
Published: January 2022

The core of the digital entrepreneurial sports culture creative industry lies in innovation, which emphasizes the new impetus brought by the digital entrepreneurial sports culture to the social economy. The digital entrepreneurial sports cultural creative industry is rooted in the cultural creative industry. The digital entrepreneurial sports cultural creative industry is also an important part of the sports industry, and its development highly depends on the development of the sports industry. The digital entrepreneurial sports cultural creative industry has the characteristics of both the sports industry and the cultural creative industry. This paper uses the deep learning technology to study the development of the digital creative sports industry and build an intelligent model. Moreover, this paper assigns weights to the input multidimensional features, extracts the most relevant data features, and analyzes the performance of the proposed model through simulation experiments. From the experimental analysis results, we can see that the model proposed in this paper has certain practicality.
Network Pharmacology- and Molecular Docking-Based Identification of Potential Phytocompounds from Argyreia capitiformis in the Treatment of Inflammation
Authors: Obaidullah Ahmad J. , Alanazi Mohammed M. , Alsaif Nawaf A. , Alanazi Ashwag S. , Albassam Hussam , AZ Alanazi , Alwassil Osama I. , Alqahtani Ali M. , Tareq Abu Montakim
Published: January 2022

The methanolic extract of Argyreia capitiformis stem was examined for anti-inflammatory activities following network pharmacology analysis and molecular docking study. Based on gas chromatography-mass spectrometry (GC-MS) analysis, 49 compounds were identified from the methanolic extract of A. capitiformis stem. A network pharmacology analysis was conducted against the identified compounds, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology analysis of biological processes and molecular functions were performed. Six proteins (IL1R1, IRAK4, MYD88, TIRAP, TLR4, and TRAF6) were identified from the KEGG pathway analysis and subjected to molecular docking study. Additionally, six best ligand efficiency compounds and positive control (aspirin) from each protein were evaluated for their stability using the molecular dynamics simulation study. Our study suggested that IL1R1, IRAK4, MYD88, TIRAP, TLR4, and TRAF6 proteins may be targeted by compounds in the methanolic extract of A. capitiformis stem to provide anti-inflammatory effects.
Influence of Digital Technology on Ideological and Political Education in Colleges and Universities under 5G Era
Authors: Lin Shanwei
Published: January 2022

With the advancement of digital technology, new means and methods are being offered in education to enhance teaching-learning. The use of technology in educational interventions has a positive impact on the students’ functional, cognitive, and psychomotor skills. This study aims to improve the teaching mode of ideological and political education (IPE). The research work intends to enhance enthusiasm of college students for ideological and political course (IPC). Based on 5G technology, the rising virtual reality (VR) technology is proposed in this study to improve IPC in colleges and universities. To begin with, the background of IPE and VR technology is introduced. Next, the current college students’ attitudes toward IPE and VR technology are investigated by a questionnaire survey. Finally, based on the red hero culture, the VR system for IPE is designed. Results of the evaluation show that the students who have been the members of the Communist Party have the highest degree of interest for IPC in colleges and universities. Endorsing the fact, about 29.7% college students and 26.81% university students who were members of the communist youth league favored for IPC, while only 14.29% of the masses showed interest for IPC. As a whole, 25.2% of all the students participated in the survey showed their interest for IPC. One of the main reasons behind the results is the teaching mode and means that attract students for IPC. At present, 84.11% of the ideological and political teachers give a lecture of IPC via PPT, while 67.29% of the students prefer the Internet platform as a means of IPC. About 73.4% of the students know VR and are interested in the application of VR to IPC. They believe that VR can help them understand the knowledge of IPC and has broad prospects in improving the teaching quality of IPE. The designed VR system for IPE which is used in colleges and universities has four modules, namely, the preamble module of graphic history, the hero wall module, the hero art sculpture module, and the scene experience module. This system can help realize the objective of IPE in colleges and universities and provides a reference for the application of VR in enhancing IPE.
Series Arc Fault Detection in a Low-Voltage Power System Based on CEEMDAN Decomposition and Sensitive IMF Selection
Authors: Fu Guixia , Chen Guizhen , Wang Wei , Wang Qinbing , Zou Guofeng
Published: January 2022

In the series arc fault detection of a low-voltage distribution network, the features of the fault current signal are easily submerged and arc fault features are difficult to be represented, which greatly increases the difficulty of fault arc detection based on current signals. To solve these problems, a series arc fault detection method combining CEEMDAN decomposition and sensitive IMF selection is proposed. In this paper, the CEEMDAN algorithm is first applied to complete decomposition of the arc current in series faults. Then, 12 feature indicators of the arc current are defined and the frequency band division of the IMF component is realized according to the kurtosis index and energy feature which are more sensitive. The time window-based feature calculation method is proposed to obtain the local features of the time scale of each high-frequency IMF component. Accurate selection of sensitive IMF components is realized by comparing feature indexes such as the variance and root mean square value. Finally, for the current feature set, the second dimension reduction is realized by the subspace transformation algorithm and the series arc fault detection is realized based on the SVM. The actual experiments show that the optimal detection accuracy of the proposed method is 91.67% and the average accuracy of 10 crossvalidation experiments is 88.33%. It shows that the proposed sensitive IMF selection method can effectively capture the fault component signals in the current and the proposed fault feature description method has good representation and discrimination ability.
Towards an Analytic Framework for System Resilience Based on Reaction Networks
Authors: Veloz Tomas , Maldonado Pedro , Bussseniers Evo , Bassi Alejandro , Beigi Shima , Lenartowicz Marta , Heylighen Francis
Published: January 2022

Reaction network is a promising framework for representing complex systems of diverse and even interdisciplinary types. In this approach, complex systems appear as self‐maintaining structures emerging from a multitude of interactions, similar to proposed scenarios for the origin of life out of autocatalytic networks. The formalism of chemical organization theory (COT) mathematically specifies under which conditions a reaction network is stable enough to be observed as a whole complex system. Such conditions specify the notion of organization, crucial in COT. In this paper, we show that the structure and operation of organizations can be advanced towards a formal framework of resilience in complex systems. That is, we show that there exist three fundamental types of change (state, process, and structural) defined for reaction networks, and that these perturbations not only provide a general representation of perturbations in the context of resilience but also pave the ground to formalize different forms of resilient responses. In particular, we show that decomposing the network’s operational structure into dynamically decoupled modules allows to formalize what is the impact of a perturbation and to what extent any potential compensation to that perturbation will be successful. We illustrate our approach with a toy model of a farm that operates in a sustainable way producing milk, eggs, and/or grains from other resources. With the help of simulations, we analyze the different types of perturbations and responses that the farm can undergo and how that affects its sustainable operation.
FDA Approval Summary: Pembrolizumab, Atezolizumab, and Cemiplimab-rwlc as Single Agents for First-Line Treatment of Advanced/Metastatic PD-L1–High NSCLC
Authors: Akinboro Oladimeji , Larkins Erin , Pai-Scherf Lee H. , Mathieu Luckson N. , Ren Yi , Cheng Joyce , Fiero Mallorie H. , Fu Wentao , Bi Youwei , Kalavar Shyam , Jafri Samina , Mishra-Kalyani Pallavi S. , Fourie Zirkelbach Jeanne , Li Hongshan , Zhao Hong , He Kun , Helms Whitney S. , Chuk Meredith K. , Wang Min , Bulatao Ilynn , Herz Jonathan , Osborn Blaire L. , Xu Yuan , Liu Jiang , Gong Yutao , Sickafuse Sharon , Cohen Rebecca , Donoghue Martha , Pazdur Richard , Beaver Julia A. , Singh Harpreet
Published: January 2022

FDA's approval of cemiplimab-rwlc on February 22, 2021, follows prior approvals of pembrolizumab and atezolizumab for similar indications as first-line treatment for patients with programmed death ligand-1 (PD-L1)–high advanced non–small cell lung cancer (NSCLC). Approvals of these anti–PD-L1 agents were supported by statistically significant and clinically meaningful improvements in overall survival (OS) in international, multicenter, active-controlled randomized trials. In KEYNOTE-024, the OS HR was 0.60 [95% confidence interval (CI), 0.41–0.89; P = 0.005] favoring pembrolizumab over platinum-doublet chemotherapy. In IMpower110, the OS HR was 0.59 (95% CI, 0.40–0.89; P = 0.0106) favoring atezolizumab over platinum-doublet chemotherapy. In Study 1624, the OS HR was 0.68 (95% CI, 0.53–0.87; P = 0.0022) favoring cemiplimab-rwlc over platinum-doublet chemotherapy. The progression-free survival (PFS) effect sizes for these anti–PD-L1 antibodies were also comparable across their respective registrational trials, and their safety profiles were consistent with the anti–PD-L1 class adverse event profile. The consistent survival benefits and manageable toxicity profiles of these single-agent anti–PD-L1 antibodies have established them as important treatment options in the PD-L1–high NSCLC treatment landscape. FDA approvals of these anti–PD-L1 antibodies, based on their favorable benefit-risk profiles, present effective chemotherapy-free therapeutic options for patients with advanced PD-L1–high NSCLC in the United States.
Giant Colonic Diverticulum of the Sigmoid with an Unusual Localization
Authors: Frenken Misha R.M. , Koenjer Lisanne M. , Versteegen Marieke G.J. , Vogelaar Jeroen F.
Published: January 2022

Diverticular disease is a common disorder of the colon. A rare complication of diverticulosis is a giant colonic diverticulum (GCD) defined as a diverticulum measuring >4 cm. It most often affects the sigmoid colon. We report a case of a 68-year-old male presenting at the emergency department with a giant diverticulum of the sigmoid with an unusual size and migration to the right hemi-abdomen. Segmental colectomy with en-bloc diverticular resection and primary anastomosis seems to be the best treatment according to the available current literature.
The Genome Reduction Excludes the Ribosomal Rescue System in Acholeplasmataceae
Authors: Zübert Christina , Ilic Anna-Marie , Duduk Bojan , Kube Michael
Published: January 2022

The trans-translation process is a ribosomal rescue system for stalled ribosomes processing truncated mRNA. The genes ssrA and smpB fulfil the key functions in most bacteria, but some species have either lost these genes or the function of the ribosomal rescue system is taken over by other genes. To date, the ribosomal rescue system has not been analysed in detail for the Acholeplasmataceae. This family, in the Mollicutes class, comprises the genus Acholeplasma and the provisional taxon “Candidatus Phytoplasma”. Despite their monophyletic origin, the two clades can be separated by traits such as not representing primary pathogens for acholeplasmas versus being phytopathogenic for the majority of phytoplasmas. Both taxa share reduced genomes, but only phytoplasma genomes are characterised by a remarkable level of instability and reduction. Despite the general relevance of the ribosomal rescue system, information is lacking on coding, the genomic context and pseudogenisation of smpB and ssrA and their possible application as a phylogenetic marker. Herein, we provide a comprehensive analysis of the ribosomal rescue system in members of Acholeplasmataceae. The examined Acholeplasmataceae genomes encode a ribosomal rescue system, which depends on tmRNA encoded by ssrA acting in combination with its binding protein SmpB. Conserved gene synteny is evident for smpB, while ssrA shows a less conserved genomic context. Analysis of the tmRNA sequences highlights the variability of proteolysis tag sequences and short conserved sites at the 5′- and 3′-ends. Analyses of smpB provided no hints regarding the coding of pseudogenes, but they did suggest its application as a phylogenetic marker of Acholeplasmataceae – in accordance with 16S rDNA topology. Sequence variability of smpB provides sufficient information for species assignment and phylogenetic analysis.
Expression of the α7 nAChR Subunit Duplicate Form (CHRFAM7A) Was Down-Regulated in Patients with Intracranial Infection and Reduced Inflammation in in vitro Model by p38 MAPK
Authors: Hu Xuefei , Hu Keao , Xu Xinping , Zhang Wei , Xu Fei
Published: January 2022

Objective: In this study, we investigated that the effects and possible mechanisms of the α7 nAChR subunit duplicate form (CHRFAM7A) affected inflammation in the model of intracranial infection. Methods: Mice of the model group were injected (intracranial injection) with Staphylococcus aureus. Mouse microglial BV2 cell was exposed with 200 ng of LPS for 4 h. Results: CHRFAM7A mRNA expressions were reduced in patients with intracranial infection. CHRFAM7A mRNA and protein expressions were suppressed in mice with intracranial infection in a time-dependent manner. CHRFAM7A reduced inflammation in mice with intracranial infection. The inhibition of CHRFAM7A reduced inflammation in mice with intracranial infection. CHRFAM7A suppressed p38 MAPK in mice with intracranial infection. The inhibition of p38 MAPK shows the effects of CHRFAM7A in intracranial infection. Conclusion: Our data demonstrate that the expression of the CHRFAM7A was down-regulated in patients with intracranial infection and reduced inflammation in in vitro model by p38 MAPK, which suggests the potential role of CHRFAM7A as a diagnostic biomarker for intracranial infection.
Bazex Syndrome Associated with Squamous Cell Carcinoma of the Lip: A Rare Paraneoplastic Acrokeratosis with Nail Dystrophy
Authors: Gaurav Vishal , Grover Chander
Published: January 2022

Introduction: Acrokeratosis paraneoplastica of Bazex (APB) is a rare paraneoplastic dermatosis having definite association with malignancy, most commonly squamous cell carcinomas (SCC) of the upper aerodigestive tract. It is characterized by psoriasiform plaques involving the ears, nose, hands, and feet. Nail changes are commonly encountered, including onychauxis, subungual hyperkeratosis, xanthonychia, onycholysis, and onychorrhexis. Case Presentation: We report the details of a 40-year-old male who presented with cutaneous changes involving both ears, palms, and soles and multiple nails, suggestive of APB. These typical changes were succeeded by an SCC of the lip. Discussion/Conclusions: In 70% cases of APB, nail changes precede the diagnosis of the underlying neoplasm by up to 10 months, offering a good lead-time for early suspicion of malignancy. Our case highlights the importance of recognizing these changes early. An atypical location of malignancy (lower lip) and rare presentations, including painful acrokeratosis, melanonychia striata, and chloronychia, as seen in our case, should also be kept in mind.
Imaging of Hematological Patients in the Era of COVID-19
Authors: Eshet Yael , Avigdor Abraham , Kedmi Meirav , Tau Noam
Published: January 2022

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulted in changes in management and imaging routines for patients with hematological malignancies. Treating physicians had to familiarize themselves with a new disease, with distinct imaging manifestations, sometimes overlapping with other infections prevalent in this patient population. In some aspects, infected hematological patients might exhibit a different disease course, and routine imaging in asymptomatic hematological patients may result in unexpected COVID-19 findings, implying covert infection, that should be further explored. Furthermore, some complications of hematological diseases and treatments may present with findings similar to COVID-19 manifestations, and treating physicians must consider both possibilities in the differential diagnosis. In this review, we aimed to present the influence the COVID-19 pandemic had on hematological malignancy imaging.
A predictive maintenance system for wireless sensor networks: a machine learning approach
Authors: Almazaideh Mohammed , Levendovszky Janos
Published: January 2022

Predictive maintenance system (PdM) is a new concept that helps system operators evaluate the current status of their systems, and it also assists in predicting the future quality of these systems and scheduling maintenance action. This paper proposes a PdM model that utilizes machine learning to predict the system’s operational status after M active steps based on L previous observations implemented by a feedforward neural network (FFNN). We use quantization and encoding schemes to reduce the complexity of the system. We apply the proposed model to build a PdM system for wireless sensors networks (WSNs), where our concern is to predict the state of the system as far as the quality of data transfer is concerned. The FFNN provides a forward prediction of the operational status of the network after M consecutive time steps in the future, based on the previous L readings of quality of service (QoS) requirements of WSN. We also demonstrate the relation between complexity and accuracy. We found that larger M leads to higher complexity and larger prediction error, where larger L entails higher complexity and smaller prediction error. We also investigate how quantization and encoding can reduce complexity to implement a real-time PdM system.
Design and implementation of an S-band transmitter for nanosatellites with new configuration
Authors: Ha Bui Thi , Doan Tran Chinh , Duong Bach Gia
Published: January 2022

In this paper, the author presents the design and implementation of an Sband transmitter for nanosatellites. By combining heterostructure field effect transistors (HFET) and laterally diffused metal–oxide–semiconductor (LDMOS) technology and using flexible structure and flexible control method, this research obtained 60 dB gain power when input is -14 dBm, output power is 46 dBm (more than 25 W) in 2,1 GHz -2,3 Ghz frequency; phase noise is -80 dBc/Hz at 100 KHz offset frequency. Unlike other traditional transmitters, this transmitter was designed with multi-stages which have multi-peaks resonance to expand bandwidth to respond to the requirement of generation of the complex signal in wide band. Moreover, the phase locked loop (PLL) in frequency synthesizer makes the frequency conversion more flexible and output frequency more stable; thermal problem in module also was solved by using thermistor and operation mode. Measurement results prove that the design does not only satisfy the requirements of nanosatellites but also can be applied to other satellites together with their ground station because it has open configure with flexible structure and flexible control method.
An efficient and robust parallel scheduler for bioinformatics applications in a public cloud: A bigdata approach
Authors: Ammanna Leena , Jagadeeshgowda Jagadeeshgowda , Pujari Jagadeesh
Published: January 2022

In bioinformatics, genomic sequence alignment is a simple method for handling and analysing data, and it is one of the most important applications in determining the structure and function of protein sequences and nucleic acids. The basic local alignment search tool (BLAST) algorithm, which is one of the most frequently used local sequence alignment algorithms, is covered in detail here. Currently, the NCBI's BLAST algorithm (standalone) is unable to handle biological data in the terabytes. To address this problem, a variety of schedulers have been proposed. Existing sequencing approaches are based on the Hadoop MapReduce (MR) framework, which enables a diverse set of applications and employs a serial execution strategy that takes a long time and consumes a lot of computing resources. The author, improves the BLAST algorithm based on the BLAST-BSPMR algorithm to achieve the BLAST algorithm. To address the issue with Hadoop's MapReduce framework, a customised MapReduce framework is developed on the Azure cloud platform. The experiment findings indicate that the suggested bulk synchronous parallel MapReduce-basic local alignment search tool (BSPMR-BLAST) algorithm matches bioinformatics genomic sequences more quickly than the existing Hadoop-BLAST method, and that the proposed customised scheduler is extremely stable and scalable.
Efficient processing of continuous spatial-textual queries over geo-textual data stream
Authors: Metre Kalpana Vivek , Kharat Madan
Published: January 2022

Due to the extensive use of social media and mobile devices, unbounded and massive data is generated continuously. The need to process this big data is increasing day by day. The traditional data processing algorithms fail to cater to the need of processing data generated by various applications such as digital geo-based advertising, and recommendation systems. There has been a high demand to process continuous spatial fuzzy textual queries over data stream of spatial-textual objects with high density by present locationbased and social network-based service applications. For the spatialkeyword data stream, the performance plays a vital role as the geo information and keyword description matching is needed for every incoming streaming object. The various continuous geo-keyword query processing methods normally lack the support for fuzzy keyword matching when processing the objects from the geo-textual data stream. The edit distancebased approach with the adaptive partitioning tree index for the queries is used for fuzzy string matching and it outperforms than the existing approaches in storage cost and query performance cost.
Network intrusion detection system: machine learning approach
Authors: Jaradat Ameera S. , Barhoush Malek M. , Easa Rawan S. Bani
Published: January 2022

The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.
Intelligent fault diagnosis for power distribution system-comparative studies
Authors: Hoang Thi Thom , Le Thi Huong
Published: January 2022

Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-to-ground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double line-to-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
Renewable energy based dynamic tariff system for domestic load management
Authors: Goswami Kuheli , Sil Arindam Kumar
Published: January 2022

To deal with the present power-scenario, this paper proposes a model of an advanced energy management system, which tries to achieve peak clipping, peak to average ratio reduction and cost reduction based on effective utilization of distributed generations. This helps to manage conventional loads based on flexible tariff system. The main contribution of this work is the development of three-part dynamic tariff system on the basis of time of utilizing power, available renewable energy sources (RES) and consumers’ load profile. This incorporates consumers’ choice to suitably select for either consuming power from conventional energy sources and/or renewable energy sources during peak or off-peak hours. To validate the efficiency of the proposed model we have comparatively evaluated the model performance with existing optimization techniques using genetic algorithm and particle swarm optimization. A new optimization technique, hybrid greedy particle swarm optimization has been proposed which is based on the two aforementioned techniques. It is found that the proposed model is superior with the improved tariff scheme when subjected to load management and consumers’ financial benefit. This work leads to maintain a healthy relationship between the utility sectors and the consumers, thereby making the existing grid more reliable, robust, flexible yet cost effective.
Topology network effects for DSSH circuit on vibration energy harvesting using piezoelectric materials
Authors: Hmamsy Youssef El , Ennawaoui Chouaib , Hajjaji Abdelowahed
Published: January 2022

Energy extraction takes place using several different technologies, depending on the type of energy and how it is used. The objective of this paper is to study topology influence for a smart network based on piezoelectric materials using the DSSH (Double Synchronized Switch Harvesting). In this work, has been presented network topology for circuit DSSH (DSSH Standard, Independent DSSH, DSSH in parallel, Mono DSSH, DSSH in series). Using simulation-based on a structure with embedded piezoelectric system harvesters, then compare different topology of circuit DSSH for knowledge is how to connect the circuit DSSH together and how to implement accurately this circuit strategy for maximizing the total output power. The network topology DSSH extracted power a technique allows again up to in terms of maximal power output compared with network topology standard extracted at the resonant frequency. The simulation results shows that by using the same input parameters the maximum efficiency for topology DSSH in parallel produces 120% more energy than topology DSSH-series. In addition, the energy harvesting by Mono-DSSH is more than DSSH-series by 650% and it has exceeded DSSH-ind by 240%.Energy extraction takes place using several different technologies, depending on the type of energy and how it is used. The objective of this paper is to study topology influence for a smart network based on piezoelectric materials using the DSSH (Double Synchronized Switch Harvesting). In this work, has been presented network topology for circuit DSSH (DSSH Standard, Independent DSSH, DSSH in parallel, Mono DSSH, DSSH in series). Using simulation-based on a structure with embedded piezoelectric system harvesters, then compare different topology of circuit DSSH for knowledge is how to connect the circuit DSSH together and how to implement accurately this circuit strategy for maximizing the total output power. The network topology DSSH extracted power a technique allows again up to in terms of maximal power output compared with network topology standard extracted at the resonant frequency. The simulation results shows that by using the same input parameters the maximum efficiency for topology DSSH in parallel produces 120% more energy than topology DSSH-series. In addition, the energy harvesting by Mono-DSSH is more than DSSH-series by 650% and it has exceeded DSSH-ind by 240%.
Robust least square approach for optimal development of quadratic fuel quantity function for steam power stations
Authors: Onyegbadue Ikenna , Ogbuka Cosmas , Madueme Theophilus
Published: January 2022

Ordinary Least Square (OLS) and Robust Least Square (RLS) consisting of Least Absolute Residual and Bi-square approaches were deployed to obtain the fuel consumption characteristic curve and the coefficients of the quadratic fuel consumption function for thermal stations in Nigeria. Results were compared based on convergence property, Root Mean Square Error, R-Square value, Adjusted R-Square value, and Width Interval of coefficients. Valve Point loading effects of Egbin and Sapele power stations were used to develop the quadratic fuel consumption characteristic curve and function. The average difference in width interval for the coefficients a, b, c, d, and e of the two stations, after comparing Bi-square and OLS technique, were 0.02084, 8.5055, 1856.565, 520.8855, and 0.0082, respectively. The R-square values obtained from the Bi-Square technique were superior to the OLS technique with arithmetic differences of 0.6196 and 0.5254 for Egbin and Sapele generating stations, respectively. Bi-Square technique also offered better adjusted R-Square value for Egbin and Sapele stations with arithmetic differences of 0.622 and 0.5287, respectively. Bi-Square technique offered smaller Root Mean Square Error than the Ordinary Least Square technique for both stations. The coefficients obtained from Bi-Square technique were used to develop the fuel quantity function for the studied stations.
Smart monitoring system using NodeMCU for maintenance of production machines
Authors: Pranowo Ignatius Deradjad , Artanto Dian
Published: January 2022

Maintenance is an activity that helps to reduce risk, increase productivity, improve quality, and minimize production costs. The necessity for maintenance actions will increase efficiency and enhance the safety and quality of products and processes. On getting these conditions, it is necessary to implement a monitoring system used to observe machines' conditions from time to time, especially the machine parts that often experience problems. This paper presents a low-cost intelligent monitoring system using NodeMCU to continuously monitor machine conditions and provide warnings in the case of machine failure. Not only does it provide alerts, but this monitoring system also generates historical data on machine conditions to the Google Cloud (Google Sheet), includes which machines were down, downtime, issues occurred, repairs made, and technician handling. The results obtained are machine operators do not need to lose a relatively long time to call the technician. Likewise, the technicians assisted in carrying out machine maintenance activities and online reports so that errors that often occur due to human error do not happen again. The system succeeded in reducing the technician-calling time and maintenance workreporting time up to 50%. The availability of online and real-time maintenance historical data will support further maintenance strategy.