bluuebunny/crossref_metadata_2025 huggingface dataset:
Two-loop improved predictions for $$\mathbf {M_W}$$ and $$\mathbf {sin^2\theta _{eff}}$$ in Two-Higgs-Doublet models
Authors: Hessenberger Stephan , Hollik Wolfgang
Published: October 2022

We present the currently most precise predictions for the W-boson mass and the leptonic effective mixing angle in the aligned Two-Higgs-Doublet Model. The evaluation includes the full one-loop result, all known higher-order corrections of the Standard Model, and the non-standard two-loop contributions that increase with mass splittings between charged and neutral THDM Higgs bosons. They depend on $$\tan \beta $$

tan
β

and the soft $$Z_2$$

Z
2

-symmetry breaking parameter $$m_{12}^2$$

m

12

2

of the scalar potential, in addition to the non-standard boson masses. Whenever the one-loop corrections become large, the two-loop contributions yield substantial modifications of the predictions, which is of particular importance for the W mass where large mass shifts are required to reach the recently published final result of the CDF collaboration. Numerical results are shown for the dependence on the various non-standard parameters and in comparison with experimental data.
Resolving muon g-2 anomaly with partial compositeness
Authors: Xu Shuai , Zheng Sibo
Published: October 2022

We consider the scenario of composite Higgs with partial compositeness to address the muon g-2 anomaly. We show that this anomaly is resolved by one-loop correction due to composite muon partners with mass scale of order TeVs and large Yukawa coupling to composite Higgs, which is different from interpretations of vectorlike lepton models. We present parameter space by imposing indirect constraints from precise measurements on Higgs, Z and oblique electroweak parameters. We analyze direct constraints in light of both Drell–Yan and Higgs-associated productions of the composite muon partners at high-luminosity LHC. It turns out that the parameter regions with the lighter composite muon mass below $$\sim 325$$

∼
325

GeV can be excluded at 2$$\sigma $$
σ
order by the Drell–Yan processes.
Analysis for advection–diffusion problem subject to memory effects and local and nonlocal kernels: A fractional operators approach
Authors: Ali Qasim , Al-Khaled Kamel , Omar Jiyan , Raza Ali , Khan Sami Ullah , Khan M. Ijaz , Najati S. A. , Oreijah Mowffaq , Guedri Kamel , Galal Ahmed M.
Published: October 2022

In this communication, a familiar physical phenomenon along with a time-dependent concentration source in a one-dimensional fractional differential advection–diffusion has been worked out. The problem is supported with the boundary with initial and boundary conditions. First of all, the results for the nondimensional classical advection–diffusion process are deliberated utilizing the Laplace coupled with finite sine-Fourier transforms analytically. Later on, the analysis is expanded for different fractional operators. The inspection of memory factors is presented through Mathcad. The impacts of the fractional (memory) parameter upon the solute concentration are discussed by making use of Mathcad15. A detailed physical significance of the fractional problem in view of the parameters is studied. It is noted that the decreasing change in concentration is associated with the larger values of noninteger parameter.
Advanced Practical Strategies to Enhance Table Egg Production
Authors: El-Sabrout Karim , Aggag Sarah , Mishra Birendra
Published: October 2022

The global demand for table eggs has increased exponentially due to the growing human population. To meet this demand, major advances in hen genetics, nutrition, and husbandry procedures are required. Developing cost-effective and practically applicable strategies to improve egg production and quality is necessary for the development of egg industry worldwide. Consumers have shown a strong desire regarding the improvement of hens’ welfare and egg quality. They also become interested in functional and designer foods. Modifications in the nutritional composition of laying hen diets significantly impact egg nutritional composition and quality preservation. According to previous scientific research, enriched egg products can benefit human health. However, producers are facing a serious challenge in optimizing breeding, housing, and dietary strategies to ensure hen health and high product quality. This review discussed several practical strategies to increase egg production, quality, and hens’ welfare. These practical strategies can potentially be used in layer farms for sustainable egg production. One of these strategies is the transition from conventional to enriched or cage-free production systems, thereby improving bird behavior and welfare. In addition, widely use of plant/herbal substances as dietary supplements in layers’ diets positively impacts hens’ physiological, productive, reproductive, and immunological performances.
A General Epidemic Model and Its Application to Mask Design Considering Different Preferences towards Masks
Authors: Wang Chaoqian , Kavak Hamdi
Published: October 2022

While most masks have a limited effect on personal protection, how effective are they for collective protection? How to enlighten the design of masks from the perspective of collective dynamics? In this paper, we assume three preferences in the population: (i) never wearing a mask; (ii) wearing a mask if and only if infected; (iii) always wearing a mask. We study the epidemic transmission in an open system within the Susceptible‐Infected‐Recovered (SIR) model framework. We use agent‐based Monte Carlo simulation and mean‐field differential equations to investigate the model, respectively. Ternary heat maps show that wearing masks is always beneficial in curbing the spread of the epidemic. Based on the model, we investigate the potential implications of different mask designs from the perspective of collective dynamics. The results show that strengthening the filterability of the mask from the face to the outside is more effective in most parameter spaces because it acts on individuals with both preferences (ii) and (iii). However, when the fraction of individuals always wearing a mask achieves a critical point, strengthening the filterability from outside to the face becomes more effective because of the emerging hidden reality that the infected individuals become too few to utilize the filterability from their face to outside fully.
Tea (Camellia sinensis) Production, Opportunities, Challenges, and Future Prospects in Ethiopia
Authors: Dechassa Nagassa , Merga Misgana
Published: October 2022

Tea is one of the most commonly consumed stimulants in the world. It is cultivated commercially at Wushwush, Gumero, and Chewaka tea estate plantations, in Ethiopia. Over 150,000 hectares of Ethiopian land are available for the production of high-quality tea, but only 3,099 hectares are currently under cultivation. In Ethiopia, tea clones Mlk-1, Mlk-2, 11/56, S-15/10, FNF, 11/4, 6/8, L6, B9, Chai, BB-35, and SR-18 are commercially grown. Ethiopia’s favorable environmental conditions for high-quality tea production increased the availability of tea throughout the year, and the availability of a sufficient labor force is a significant opportunity for tea companies. However, tea production is still infant because it needs huge capital to establish the tea plantations and production, the pest problems, limitations of improved technologies like cultivars, and lack of tea genetic resources. Besides, there is a limitation of comprehensive research to develop innovative technologies in the case of tea agronomic, breeding, and pest management for Ethiopian conditions. Thus, the government should invite investors and facilitate necessary things for the investors to increase the tea industry in the country. The tea research should be strengthened for the development and implementation of the appropriate technologies.
Arabic Syntactic Diacritics Restoration Using BERT Models
Authors: Nazih Waleed , Hifny Yasser
Published: October 2022

The Arabic syntactic diacritics restoration problem is often solved using long short-term memory (LSTM) networks. Handcrafted features are used to augment these LSTM networks or taggers to improve performance. A transformer-based machine learning technique known as bidirectional encoder representations from transformers (BERT) has become the state-of-the-art method for natural language understanding in recent years. In this paper, we present a novel tagger based on BERT models to restore Arabic syntactic diacritics. We formulated the syntactic diacritics restoration as a token sequence classification task similar to named-entity recognition (NER). Using the Arabic TreeBank (ATB) corpus, the developed BERT tagger achieves a 1.36% absolute case-ending error rate (CEER) over other systems.
Driver’s Attention Allocation and Mental Workload at Different Random Hazard Points on Prairie Highway
Authors: Lyu Zhen , Qi Chunhua , Zhu Shoulin
Published: October 2022

To identify the characteristics of driver’s visual perception and measure the mental workload at different random hazard points on prairie highway, an on-road study was conducted with 28 drivers. The I view X HED eye tracker and MP150 multichannel physiological recorder were used to collect the driver’s eye movement and ECG data at different hazard scenarios synchronously. The gaze transfer theory and statistical methods were used to make comparative analysis of typical visual and mental workload evaluation indicators of drivers at different random risk points. The results show that no matter what kind of random risk is confronted, the percentage of drivers’ fixation duration to the current lane drops, where random risk belongs to increase. The distribution of eye glance transition proportions shows that drivers highly bias their scanning attention by only focusing on transferring between forward and the areas where the random belongs to. Compared with off-road risk points, the driver’s gaze transfer is more frequent when facing on-road risk points, and the gaze transfer path is fixed, indicating that on-road risks have higher requirements for drivers’ perception and greater information processing load. There are obvious differences in the degree of influence of the types of random risk points on driver’s psychology. The heart rate growth rate is the largest when drivers were confronted with overtaking cut-in (37.9%) and forward parking (38%), whereas the index RMSSD changes in the opposite way. It reaches the minimum value when the random risks are overtaking cut-in (22.679 ms) and forward parking (22.907 ms). Meanwhile, the driving speed shows larger fluctuation at risk points on the road. This study reveals that on-road hazards pose greater threats to drivers, and it can contribute to a better understanding of the potential hazards on the prairie highways and provide suggestions for future application of advanced driver assistance systems which can warn drivers about potential hazards.
A Meta‐Analysis of 13 Randomized Trials on Traditional Chinese Medicine as Adjunctive Therapy for COVID‐19: Novel Insights into Lianhua Qingwen
Authors: Lei Yi , Guan Huizhen , Xin Wenqiang , Yang Bi-Cheng
Published: October 2022

The efficacy and safety of traditional Chinese medicine (TCM) paired with western medicine in the treatment of patients with COVID‐19 remains controversial. This meta‐analysis was performed to identify the effects of TCM. Seven electronic databases were reviewed from the inception of these databases to 30 June 2022. A quality assessment of the included studies was performed with the Cochrane Collaboration’s tool to provide a score of high, unclear, or low risk of bias. The standard software program (Stata, version 12.0, statistical software) was used for endpoint analyses. A total of 13 RCTs involving 1398 patients conducted in China were included. The cross‐sectional data from various studies were plotted, and the results illustrated that the statistically higher rates of total effectiveness (RR, 1.357; 95% CI, 1.259 to 1.464; P < 0.001), improvement of chest CT (RR, 1.249; 95% CI, 1.143 to 1.356; P < 0.001), and cough improvement (RR, 1.228; 95% CI, 1.057 to 1.570; P = 0.012) and a lower incidence of conversion to severe cases (RR, 0.408; 95% CI, 0.275 to 0.605; P < 0.001) were demonstrated in the TCM group than that of the control group. Of note, the subgroup on specific TCM of Lianhua Qingwen (LQ) revealed that the experiment group was associated with a higher rate of total effectiveness (RR, 1.248; 95% CI, 1.136 to 1.371; P < 0.001) and improvement of chest CT (RR, 1.226; 95% CI, 1.110 to 1.356; P < 0.001) and a lower rate of conversion to severe cases (RR, 0.469; 95% CI, 0.311 to 0.707; P < 0.001). However, there was no significant difference in fever improvement (RD, 0.110; 95% CI, ‐0.063 to 0.283; P = 0.213). The findings of this meta‐analysis suggest that TCM combined with western medicine is more effective in treating COVID‐19 via relieving symptoms, promoting patients’ recovery, and cutting the rate of patients developing into severe conditions. However, given the relevant possible biases in our study, adequately powered and better‐designed studies with long‐term follow‐up are required to reach a firmer conclusion.
A New Image Encryption Technique Based on Sine Map, Chaotic Tent Map, and Circulant Matrices
Authors: Shamsa Kanwal  , Inam Saba , Hajjej Fahima , Cheikhrouhou Omar , Nawaz Zainab , Waqar Ayesha , Khan Majid
Published: October 2022

Image encryption is one of the sensitive areas used recently to save information over the Internet and confirms the security on a higher level. A new cryptosystem has been proposed for the encoding and decoding of images using sine map, chaotic tent map, and circulant matrices. The process involves three stages. The sine map is used for the permutation phase. In the substitution phase, the Hill cipher method together with prime circulant matrix are used and a chaotic tent map is used in the diffusion phase. The algorithm becomes impenetrable to resist differential- and statistical-type attacks. The algorithm's key space is sufficient in size to withstand brute-force attacks. This symmetric image encryption algorithm indicates good results for correlation analysis, key space analysis, entropy analysis, key sensitivity analysis, histogram analysis, number of pixel change rate (NPCR), unified average changing intensity (UACI), and peak signal-to-noise ratio (PSNR). From all these simulation results, we come to know that the constructed image encryption cryptosystem is systematic and vigorous, supplying better protection for image encryption.
Robust Allocation of FACTS Devices in Coordinated Transmission and Generation Expansion Planning considering Renewable Resources and Demand Response Programs
Authors: Mahmoudi Rashid Sara , Akbari Ehsan , Khalafian Farshad , Hossein Atazadegan Mohmmad , Shahmoradi Saeid , Zare Ghaleh Seyyedi Abbas
Published: October 2022

Nowadays, the use of demand response programs (DRPs) in a variety of long-term and short-term planning problems has been explored. In this paper, a generation and transmission expansion planning (GTEP) model along with FACTS device allocation is presented. Furthermore, demand response programs are taken into account for more load flexibility. The proposed model is presented as a multi-objective minimizing problem considering emission, cost, and voltage security index. Furthermore, the conventional Pareto optimization is adopted using fuzzy weighted sum method (FWSM) to achieve a single-objective model. The final problem is constrained by equations of alternative current power flow, operation and voltage security limits, planning model of shunt FACTS devices, and operation of the DRP. Adaptive robust optimization (ARO) is used to reach suitable models for the active power of renewable resources and power consumption. As a main search algorithm, a hybrid combination of water cycle algorithm (WCA) and ant lion optimization (ALO) is proposed to find the optimum solution with a small standard deviation. The problem is tested on different standard IEEE systems, and the results validate the operation and network security improvement due to optimal location of FACTS devices. According to the results, the economic and environmental status of the network has also improved.
Noise Modulation-Based Reversible Data Hiding with McEliece Encryption
Authors: Wang Zexi , Zhang Minqing , Kong Yongjun , Ke Yan , Di Fuqiang
Published: October 2022

McEliece cryptosystem is expected to be the next generation of the cryptographic algorithm due to its ability to resist quantum computing attacks. Few research studies have combined it with reversible data hiding in the encrypted domain (RDH-ED). In this article, we analysed and proved that there is a redundancy in the McEliece encryption process that is suitable for embedding. Then, a noise modulation-based scheme is proposed, called NM-RDHED, which is suitable for any signal and not only for images. The content owner scrambles the original image and then encrypts it with the receiver’s public key. The data hider generates a load noise by modulating additional data. After that, the load noise is added to the encrypted image, which achieves the data embedding. The reconstructed image is without any distortion after the direct decryption of the marked image, and the extracted data are no errors. The experimental results demonstrate our scheme has a higher embedding rate and more security, which is superior to existing schemes.
Identification of a New Prediction Model for Bladder Cancer Related to Immune Functions and Chemotherapy Using Gene Sets of Biological Processes
Authors: Zhou Huidong , Huang Guo , Chen Tao , Zhang Jianglei , Ouyang Jun
Published: October 2022

Background. Biological processes serve crucial functions in the initiation and development of cancer. Therefore, we constructed and validated a model for bladder cancer (BLCA) with good predictive power for immunity, prognosis, and therapy. Methods. Using the expression of the gene sets based on biological processes, BLCA patients were divided into three clusters by consensus cluster analysis. By performing LASSO regression analysis twice, key genes were selected, and the biological processes‐related genes’ (BPRG) score was calculated. Differences in immune infiltration, tumor microenvironment, tumor mutation burden, immunotherapy, and sensitivity towards chemotherapy were analyzed between two groups divided by BPRG score. Results. Good accuracy was observed for the three clusters. They showed different prognoses and levels of immune cell infiltration. The selected key genes were mainly enriched in immune‐related pathways. The high‐BPRG score group was related to poor prognosis, higher immune cell infiltration, interstitial scores, and increased tumor mutation. Moreover, the effects of immunotherapy were good, and those of chemotherapy were poor. Conclusion. Overall, key genes may be involved in various complex immune regulation processes. Therefore, the quantification and verification of the BPRG score are expected to facilitate the understanding of the immunosuppressive microenvironment in BLCA and guide the choice of chemotherapeutic drugs and immunotherapeutic regimens and help predict the prognoses of patients with BLCA.
An Updated Review on Properties and Indications of Calcium Silicate-Based Cements in Endodontic Therapy
Authors: Eskandari Fateme , Razavian Alireza , Hamidi Rozhina , Yousefi Khadije , Borzou Susan
Published: October 2022

Regarding the common use of calcium silicate cements (CSCs) in root canal therapy, their position in the context of past and present dentistry agents can provide a better understanding of these materials for their further improvement. In this context, the present review article addresses a wide range of recent investigations in the field of CSC-based products and describes details of their composition, properties, and clinical applications. The need for maintaining or reconstructing tooth structure has increased in contemporary endodontic treatment approaches. This research thus discusses the attempts to create comprehensive data collection regarding calcium ion release, bond strength, alkalinizing activity and bioactivity, and the ability to stimulate the formation of hydroxyapatite as a bioactive feature of CSCs. Sealing ability is also highlighted as a predictor for apical and coronal microleakage which is crucial for the long-term prognosis of root canal treatment integrity. Other claimed properties such as radiopacity, porosity, and solubility are also investigated. Extended setting time is also mentioned as a well-known drawback of CSCs. Then, clinical applications of CSCs in vital pulp therapies such as pulpotomy, apexification, and direct pulp capping are reviewed. CSCs have shown their benefits in root perforation treatments and also as root canal sealers and end-filling materials. Nowadays, conventional endodontic treatments are replaced by regenerative therapies to save more dynamic and reliable hard and soft tissues. CSCs play a crucial role in this modern approach. This review article is an attempt to summarize the latest studies on the clinical properties of CSCs to shed light on the future generation of treatments.
Progress in Control‐Actuation Robotic System for Gastrointestinal NOTES Development
Authors: Du Huijiang , Liu Xiaotong , Sun Hudie , Zhu Qi , Sun Liping
Published: October 2022

Purpose. Natural orifice transluminal endoscopic surgery (NOTES) is a minimally invasive surgical procedure that reduces patient trauma, infection probability, and rehabilitation time. This paper reviews the progress made in the control‐actuation robotic systems for gastrointestinal NOTES development. Material and Methods. A survey on both existing and state‐of‐the‐art control‐actuation robotic systems for gastrointestinal NOTES was conducted in December 2021. Results. Nine control‐actuation robotic systems for gastrointestinal NOTES were identified. The structures and specifications of these robotic systems were reported. The technical parameters were also discussed. Special attention was directed to systems using a control‐actuation structure and tendon‐driven mechanism. The control‐actuation robotic systems typically deploy a control‐actuation structure and tendon‐driven mechanism. Control‐actuation robotic systems for gastrointestinal NOTES show great ability to improve operational accuracy and flexibility and flatten the learning curve of procedures. These characteristics suggest that the use of control‐actuation robotic systems is worth exploring in future development.
UAV with Vision to Recognise Vehicle Number Plates
Authors: Kaimkhani Naveed Ali Khan , Noman Muhammad , Rahim Sabit , Liaqat Hannan Bin
Published: October 2022

The trend of using Unmanned Aerial Vehicles (UAVs) in industries is rapidly growing. Nowadays, they are used in many applications, from surveillance to disaster assistance. Almost all applications of UAVs require cameras either to perform specific vision tasks such as facial recognition and vehicle number plate identification or to avoid obstacles in the flight path of the UAV. The most emerging application of UAVs today is to provide security and surveillance, and they are mostly used for vehicle number identification. Typical Automatic Number Plate Recognition (ANPR) uses static high-resolution cameras mounted in specific places to identify the vehicle’s number plates. Identifying the characters on the number plate becomes a very crucial task when the number plate is at an arbitrary angle to the drone camera. The camera gimble angle, the height of the UAV from the vehicle, and the relative speed of the UAV w.r.t vehicle play a very key role in identifying the vehicle license plate correctly. This study explains how Automatic Number Plate Recognition (ANPR) is performed on real-time images using MATLAB with the help of a UAV to analyze the effect of the above-mentioned key factors. The process is completed in three steps: collecting visual data from the drone, processing that data, and obtaining the recognized number plate.
Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques
Authors: Yang Sheng-xiong , Xu Hong-feng , Mao Yong-jia , Liang Zu-hua , Pan Chun-liu
Published: October 2022

Tuberculosis (TB) is one of the world’s deadliest infectious disease killers today, and despite China’s increasing efforts to prevent and control TB, the TB epidemic is still very serious. In the context of the COVID-19 pandemic, if reliable forecasts of TB epidemic trends can be made, they can help policymakers with early warning and contribute to the prevention and control of TB. In this study, we collected monthly reports of pulmonary tuberculosis (PTB) in Guiyang, China, from January 1, 2010 to December 31, 2020, and monthly meteorological data for the same period, and used LASSO regression to screen four meteorological factors that had an influence on the monthly reports of PTB in Guiyang, including sunshine hours, relative humidity, average atmospheric pressure, and annual highest temperature, of which relative humidity (6-month lag) and average atmospheric pressure (7-month lag) have a lagging effect with the number of TB reports in Guiyang. Based on these data, we constructed ARIMA, Holt-Winters (additive and multiplicative), ARIMAX (with meteorological factors), LSTM, and multivariable LSTM (with meteorological factors). We found that the addition of meteorological factors significantly improved the performance of the time series prediction model, which, after comprehensive consideration, included the ARIMAX (1,1,1) (0,1,2)12 model with a lag of 7 months at the average atmospheric pressure, outperforms the other models in terms of both fit (

RMSE
=
37.570

,

MAPE
=
10.164
%

,

MAE
=
28.511

) and forecast sensitivity (

RMSE
=
20.724

,

MAPE
=
6.901
%

,

MAE
=
17.306

), so the ARIMAX (1,1,1) (0,1,2)12 model with a lag of 7 months can be used as a predictor tool for predicting the number of monthly reports of PTB in Guiyang, China.
The Role of Ferroptosis-Related Molecules and Significance of Ferroptosis Score in Cervical Cancer
Authors: Li Pengxiang , Lv Xuefeng , Liu Lu , Peng Mengle , Qin Dongchun
Published: October 2022

Background. Ferroptosis, a form of cell death driven by iron-dependent lipid peroxidation, may be a potential treatment for many cancers, including cervical cancer (CC). However, the regulation of long noncoding RNAs (lncRNAs) in the process of ferroptosis and whether ferroptosis inducers could increase the cytotoxicity of conventional chemotherapy drugs remain to be further elucidated. Methods. We analyzed the variation of 55 differentially ferroptosis-related genes (FRGs) and the influence of mutations in CC patients. The patients with CC were classified into two ferroptosis clusters by the non-negative matrix factorization (NMF) algorithm. The principal components analysis (PCA) was used to measure the ferroptosis score (FerroScore) in patients with CC. Besides, FerroScore was used to predict the sensitivity of chemotherapy and responses to immunotherapy in patients with CC. Finally, experiments were performed to verify the regulatory effect of AC026790.1 on erastin-induced ferroptosis, as well as the effect of erastin in combination with cisplatin on the toxicity of CC cells (SiHa, HeLa). Result. There were significant differences in the overall survival and immune cell infiltration between the two ferroptosis clusters. Patients with low FerroScore were more sensitive to chemotherapy drugs such as cisplatin and docetaxel. The low-FerroScore group had higher CD8+ T cell infiltration and immune checkpoint expression, demonstrating that patients with lower FerroScores were more sensitive to immunotherapy, which was consistent with the result of the submap method. In vitro, overexpression of AC026790.1 could promote erastin-induced ferroptosis, and the combination of erastin and cisplatin could increase the toxicity of CC cells. Conclusion. FerroScore has a potential prognostic value for CC that may provide a reference for chemotherapy and immunotherapy. LncRNA AC026790.1 can influence ferroptosis, and the combination of ferroptosis inducers and chemotherapy drugs can provide a new perspective on cancer treatment.
Malaria Microscopy Competency in the Subnational Verification, China: Implications for Malaria Elimination and the Prevention of Malaria Reestablishment
Authors: Li Mei , Huang Fang , Yin Jianhai , Yan He , Zhou Shuisen , Xia Zhigui
Published: October 2022

Introduction. Qualified microscopy competency is a key indicator for certification of malaria elimination. To better prepare the country certification and identify the priorities that need improvement to prevent malaria reestablishment, microscopy competency at different levels were assessed in subnational verification of malaria elimination in China. Methodology. Microscopist representatives from centers for disease control and prevention (CDC)/institutes of parasitic diseases (IPD) and medical institutes for malaria diagnosis at the provincial and county levels in the subnational verification were analyzed. Specifically, five provincial microscopist representatives and ten county-level representatives were assessed in each of previously endemic provinces on qualitative identification (Plasmodium positive or negative) and Plasmodium species identification using standard slides from the National Malaria Diagnosis Reference Laboratory. Results. A total of 100 provincial-level representatives (60 from 42 CDCs/IPDs and 40 from 34 medical institutes) and 200 county-level representatives (61 from 41 CDCs and 139 from 118 medical institutes) were included. The qualitative accuracy was higher than 90% each (

P
=
0.137

), but slides with low parasite density were easy to be misdiagnosed as negative. Furthermore, the accuracy of species identification was 80.0% and 83.6% in medical institutes and centers for disease control and prevention (CDCs) at the provincial level (

P
=
0.407

) with relatively high misdiagnosis of P. vivax as P. ovale in the latter (16.2%) and 82.0% and 85.0% in medical institutes and CDCs at the county level (

P
=
0.330

) for the identification of P. falciparum and non-P. falciparum with higher false-negative in medical institutions (

P
<
0.001

). Conclusions. In conclusion, competent microscopy in subnational verification supported the quality in eliminating malaria in China, while the accurate identification of malaria parasites, especially slides with low parasite density still need to be improved through continuous diagnostic platform construction, continuous technological innovation, and targeted training to prevent reestablishment of malaria transmission.
Safe and Ultraconservative Rehabilitation of Worn Teeth Patients: How Sectional Clear Aligners May Enhance the Prosthetic Treatment Plan
Authors: D’Arcangelo Camillo , Vadini Mirco , Buonvivere Matteo , De Angelis Francesco
Published: October 2022

This study describes the clinical workflow for occlusal vertical dimension (OVD) increase in patients showing extensive tooth wear and mild teeth misalignment/crowding. A patient affected by dental erosion and occlusal abnormalities was treated to improve her situation. After ascertaining sound condyle and temporomandibular joint (TMJ) conditions, an OVD increase was sought to provide enough inter-occlusal space for the restorations. The use of TMJ three-dimensional imaging throughout the clinical procedures allowed to objectively track the condyle and disk position and confirm a steady condyle–glenoid cavity relationship before definitive restorations placement. Sectional clear aligner therapy prior to totally additive prosthetic rehabilitation allowed obtaining slight derotation and movements of anterior teeth, thus maximizing sound tissue preservation. Adhesively luted restorations were finally delivered on both anterior and posterior sectors. At the end of the treatment, the pre-operative TMJ balance appeared successfully preserved, and the patient was satisfied with the aesthetic and function achieved.
Research on Pedestrian Detection Algorithm Based on MobileNet-YoLo
Authors: Liu Lisang , Ke Chengyang , Lin He , Xu Hui
Published: October 2022

To address the problem that large pedestrian detection networks cannot be directly applied to small device scenarios due to the heavyweight and slow detection speed, this paper proposes a pedestrian detection and recognition model MobileNet-YoLo based on the YoLov4-tiny target detection framework. To address the problem of low accuracy of YoLov4-tiny, MobileNetv3 is used to optimize its backbone feature extraction network, and the MFF model is proposed to fuse the output of the first two layers to solve the information loss problem, and the attention mechanism CBAM is introduced after strengthening the feature extraction network to further improve the detection efficiency; then the 3 × 3 convolution is replaced by the depth separable convolution, which greatly reduces the number of parameters and thus improves the detection rate, then propose Ordinary data augmentation to efficiently augment the dataset and dynamically adjust the target detection anchor frame using the k-means++ clustering algorithm. Finally, the model weights trained by the VOC2007 + 2012 dataset were applied to the pedestrian dataset for retraining by the transfer learning method, which effectively solved the problem of scarce samples and greatly shortened the training time. The experimental results on the VOC2007 + 2012 dataset show that the average means accuracy of the MobileNet-YoLo model compared to YoLov4-tiny, MobileNet-YoLov4, MobileNet-YoLov3, and YoLov5s by 5.00%, 1.30%, 3.23%, and 0.74%, respectively and have reached the level to realize the landed application.
Automatic identification system-based trajectory clustering framework to identify vessel movement pattern
Authors: Oka Widyantara I Made , Noven Hartawan I Putu , Ngurah Eka Karyawati Anak Agung Istri , Er Ngurah Indra , Artana Ketut Buda
Published: October 2022

Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.
Trafﬁc management in vehicular adhoc networks using hybrid deep neural networks and mobile agents
Authors: Sabri Yassine , El Kamoun Najib
Published: October 2022

The trafﬁc congestion in vehicular adhoc networks (VANETs) is a vital problem due to its dynamic increase in trafﬁc loads. VANETs undergo inefﬁcient routing capability due to its increasing trafﬁc demands. This has led to the need for intelligent transport system (ITS) to assist VANETs in enabling suitable trafﬁc loads between vehicles and road side units (RSU). Most conventional systems offer distributed solution to manage trafﬁc congestion but fail to regulate real-time trafﬁc ﬂows. In this paper, a dynamic trafﬁc control in VANETs is offered by combining deep neural network (DNN) with mobile agents (MA). An experimental analysis is carried out to test the efﬁcacy of the DNN-MA against conventional machine learning and a deep learning routing algorithm in VANETs. DNN-Mal is validated under various trafﬁc congestion metrics like latency, percentage delivery ratio, packet error rate, and throughput. The results show that the proposed method offers reduced energy consumption and latency.
Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network
Authors: Laabid Zineb , Moumen Aziz , Mansouri Khalifa , Siadat Ali
Published: October 2022

Today's world is no longer that of yesterday, the pace with which we live and also the speed is enormous and rapid, that overnight we discover the appearance of new technologies and solutions in all the fields, in particular, that of scientific research. Artificial intelligence plays the main role. Predicting the behavior of new materials using artificial neural networks has become a frequently adopted solution by researchers today. The performance of neural networks depends mainly on the activation functions used. This work was designed to mainly study the impact of these functions on the response speed of an artificial neural network in general, and particularly on the model we are working on to predict the thermomechanical behavior of innovative materials. By using TANSIG, PURELIN and LOGSIG in a feed forward back propagation training by Levenberg-Marquardt algorithm, we were able to generate 9 models. For each of these models, we were interested in analyzing the speed’s response of the network and studying its regression. Thus, this work was able to show us that choosing the right neuron activation function from one layer to another can clearly influence the performance of the results. Depending on the problem studied, the desired objective and the chosen architecture, the activation function can radically change the result and provide us with the expected efficiency. 
Low-cost convolutional neural network for tomato plant diseases classifiation
Authors: Bensaadi Soumia , Louchene Ahmed
Published: October 2022

Agriculture is a crucial element to build a strong economy, not only because of its importance in providing food, but also as a source of raw materials for industry as well as source of energy. Different diseases affect plants, which leads to decrease in productivity. In recent years, developments in computing technology and machine-learning algorithms (such as deep neural networks) in the field of agriculture have played a great role to face this problem by building early detection tools. In this paper, we propose an automatic plant disease classification based on a low complexity convolutional neural network (CNN) architecture, which leads to faster on-line classification. For the training process, we used more than one 57.000 tomato leaf images representing nine classes, taken under natural environment, and considered during training without background subtraction. The designed model achieves 97.04% classification accuracy and less than 0.2 error, which shows a high accuracy in distinguishing a disease from another.
Artificial neural network for cervical abnormalities detection on computed tomography images
Authors: Ratnasari Putri Erlinda , Zarkasi Ahmad , Prajitno Prawito , Soeharso Soejoko Djarwani
Published: October 2022

Cervical cancer is the second deadliest after breast cancer in Indonesia. Sundry diagnostic imaging modalities had been used to decide the location and severity of cervical cancer, one among those is computed tomography (CT) Scan. This study handles a CT image dataset consisting of two categories, abnormal cervical images of cervical cancer patients and normal cervix images of patients with other diseases. It focuses on the ability of segmentation and classification programs to localize cervical cancer areas and classify images into normal and abnormal categories based on the features contained in them. We conferred a novel methodology for the contour detection round the cervical organ classified with artificial neural network (ANN) which was employed to categorize the image data. The segmentation algorithm used was a region-based snake model. The texture features of the cervical image area were arranged in the form of gray level co-occurrence matrix (GLCM). Support vector machine (SVM) had been added to determine which algorithm was better for comparison. Experimental results show that ANN model has better receiver operating characteristic (ROC) parameter values than SVM model’s and existing approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and 95.75% of accuracy. 
Motivation assessment model for intelligent tutoring system based on mamdani inference system
Authors: Thinakaran Rajermani , Chupra Suriayati , Batumalay Malathy
Published: October 2022

Many educators have used the benefit offer by intelligent tutoring system. To become more personalizing and effective tutoring system, student characteristics need to be considered. One of important student characteristic is motivation. Therefore, in this study a motivation assessment model based on self-efficacy theory was proposed. Refer to the theory, effort, choice of activities, performance and persistence were discussed as motivation attributes. Further, time spend, difficulty level, number of correct answers and number of questions skipped are the parameters was defined for each attribute. The model was designed by taking the advantages of Mamdani inference system as fuzzy logic technique to predict students’ motivation level. The model able to inmates like a human tutor does in the traditional classroom to understand students’ motivation level.
Single parent mating in genetic algorithm for real robotic system identification
Authors: Abd Samad Md Fahmi , Zainuddin Farah Ayiesya
Published: October 2022

System identification (SI) is a method of determining a mathematical model for a system given a set of input-output data. A representation is made using a mathematical model based on certain specified assumptions. In SI, model structure selection is a step where a model structure perceived as an adequate system representation is selected. A typical rule is that the final model must have a good balance between parsimony and accuracy. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the effectiveness of GA operators. This paper presents a mating technique named single parent mating (SPM) in GA for use in a real robotic SI. This technique is based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that eases the search for the optimal model. The results show that using three different objective functions (Akaike information criterion, Bayesian information criterion and parameter magnitude–based information criterion 2) respectively, GA with the mating technique is able to find more optimal models than without the mating technique. Validations show that the selected models using the mating technique are acceptable.
Comparative study of optimization methods for optimal coordination of directional overcurrent relays with distributed generators
Authors: El Idrissi Zineb , Haidi Touria , Elmariami Faissal , Belfqih Abdelaziz
Published: October 2022

Due to the growing penetration of distributed generators (DGs), that are based on renewable energy, into the distribution network, it is necessary to address the coordination of directional overcurrent relays (DOCR) in the presence of these generators. This problem has been solved by many metaheuristic optimization techniques to obtain the optimal relay parameters and to have an optimal coordination of the protection relays by considering the coordination constraints. In this article, a comparative study of the optimization techniques proposed in the literature addresses the optimal coordination problem using digital DOCRs with standard properties according to IEC60-255. For this purpose, the three most efficient and robust optimization techniques, which are particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are considered. Simulations were performed using MATLAB R2021a by applying the optimization methods to an interconnected 9-bus and 15-bus power distribution systems. The obtained simulation results show that, in case of distributed generation, the best optimization method to solve the relay protection coordination problem is the differential evolution DE. 
Device identification using optimized digital footprints
Authors: Chowdhury Rajarshi Roy , Idris Azam Che , Abas Pg Emeroylariffion
Published: October 2022

The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.
Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy
Authors: Ferdiansyah Ferdiansyah , Othman Siti Hajar , Md Radzi Raja Zahilah , Stiawan Deris , Sutikno Tole
Published: October 2022

Cryptocurrency is a virtual or digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency, decentralization, and conservation. Cryptocurrency prices have a high level of fluctuation; thus, tools are needed to monitor and predict them. RNN is a deep learning model that is capable of strongly predicting data time series. Some types of Recurrent Nureal Network layers, such as Long Short Term Memory, have been used in previous studies to prediction common used currency. In this study, we used the Gate Recurrent Unit and Bidirectional–LSTM hybrid model to predict cryptocurrency prices to improve the accuracy of previously proposed prediction LSTM Model to predict the Bitcoin,  Using four cryptocurrencies (Bitcoin, Ehtereum, Ripple, and Binance), we obtained very good results with RMSE after normalization the results get closer to 0 and with MAPE values all below <10%.