bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach
Authors: Liu Lan-Fen , Yang Xin-Feng 
Published: December 2021

AbstractThe diversity of products and fierce competition make the stability and production cost of manufacturing industry more important. So, the purpose of this paper is to deal with the multi-product aggregate production planning (APP) problem considering stability in the workforce and total production costs, and propose an efficient algorithm. Taking into account the relationship of raw materials, inventory cost and product demand, a multi-objective programming model for multi-product APP problem is established to minimize total production costs and instability in the work force. To improve the efficiency of the algorithm, the feasible region of the planned production and the number of workers in each period are determined and a local search algorithm is used to improve the search ability. Based on the analysis of the feasible range, a genetic algorithm is designed to solve the model combined with the local search algorithm. For analyzing the effect of this algorithm, the information entropy strategy, NSGA-II strategy and multi-population strategy are compared and analyzed with examples, and the simulation results show that the model is feasible, and the NSGA-II algorithm based on the local search has a better performance in the multi-objective APP problem.
A Deep-Fusion Network for Crowd Counting in High-Density Crowded Scenes
Authors: Khan Sultan Daud , Salih Yasir , Zafar Basim , Noorwali Abdulfattah 
Published: December 2021

AbstractPeople counting has been investigated extensively as a tool to increase the individual’s safety and to avoid crowd hazards at public places. It is a challenging task especially in high-density environment such as Hajj and Umrah, where millions of people gathered in a constrained environment to perform rituals. This is due to large variations of scales of people across different scenes. To solve scale problem, a simple and effective solution is to use an image pyramid. However, heavy computational cost is required to process multiple levels of the pyramid. To overcome this issue, we propose deep-fusion model that efficiently and effectively leverages the hierarchical features exits in various convolutional layers deep neural network. Specifically, we propose a network that combine multiscale features from shallow to deep layers of the network and map the input image to a density map. The summation of peaks in the density map provides the final crowd count. To assess the effectiveness of the proposed deep network, we perform experiments on three different benchmark datasets, namely, UCF_CC_50, ShanghaiTech, and UCF-QNRF. From experiments results, we show that the proposed framework outperforms other state-of-the-art methods by achieving low Mean Absolute Error (MAE) and Mean Square Error (MSE) values.
Chinese Named Entity Recognition Method in History and Culture Field Based on BERT
Authors: Liu Shuang , Yang Hui , Li Jiayi , Kolmanič Simon 
Published: December 2021

AbstractWith rapid development of the Internet, people have undergone tremendous changes in the way they obtain information. In recent years, knowledge graph is becoming a popular tool for the public to acquire knowledge. For knowledge graph of Chinese history and culture, most researchers adopted traditional named entity recognition methods to extract entity information from unstructured historical text data. However, the traditional named entity recognition method has certain defects, and it is easy to ignore the association between entities. To extract entities from a large amount of historical and cultural information more accurately and efficiently, this paper proposes one named entity recognition model combining Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). First, a BERT pre-trained language model is used to encode a single character to obtain a vector representation corresponding to each character. Then one Bidirectional Long Short-Term Memory (BiLSTM) layer is applied to semantically encode the input text. Finally, the label with the highest probability is output through the Conditional Random Field (CRF) layer to obtain each character’s category. This model uses the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model to replace the static word vectors trained in the traditional way. In comparison, the BERT pre-trained language model can dynamically generate semantic vectors according to the context of words, which improves the representation ability of word vectors. The experimental results prove that the model proposed in this paper has achieved excellent results in the task of named entity recognition in the field of historical culture. Compared with the existing named entity identification methods, the precision rate, recall rate, and $$F_1$$

F
1

value have been significantly improved.
Z-Hesitant Fuzzy Network Model with Reliability and Transparency of Information for Decision Systems
Authors: Yaakob Abdul Malek , Shafie Shahira , Gegov Alexander , Rahman Siti Fatimah Abdul 
Published: December 2021

AbstractDecision-making environment often encounters complexity along its processes, especially in the context of multidisciplinary scientific research. This can commonly be seen in engineering, computing, finance, astrology and other different areas. It is of great restriction in dealing with the practical problems which have diverse demands and properties. There is a growing body of literature that recognizes the importance of dealing with the complexity in decision making environment. The reliability and the transparency are the dominant feature of the integration of fuzzy network and Z-numbers. However, much of the research up to now has been descriptive in nature of the features. Hence, this proposed method is unique and novel because it offers some interesting insight of dealing with reliability and transparency of information in Z-hesitant fuzzy network decision-making environment. The fuzzy networks have the functionality under rule bases of fuzzy systems where it is recognized by its transparency and precision. The proposed method makes use of fuzzy network with the incorporation of hesitant fuzzy sets to assimilate decision information towards alternatives. For the validation and applicability purposes of the proposed method, the case study of stock evaluation assessed by a number of decision makers has been utilized as a real-world problem. The performance of the proposed method is evaluated respectively by applying the Spearman’s rho correlation. The result shows that the proposed method performs as the established method with the consideration of additional dominant features.
UAV Intelligent Coverage Navigation Based on DRL in Complex Geometrical Environments
Authors: Liu Shuai , Bai Yuebin 
Published: December 2021

AbstractUnmanned aerial vehicle (UAV) is one of the preferred tools for coverage detection missions, because of its maneuverability and flexibility. It is challenging for the UAV to decide a track by itself in a complex geometrical environment. This paper presents a UAV intelligent navigation method based on deep reinforcement learning (DRL). We propose using geographic information systems (GIS) as the DRL training environment to overcome the inconsistency between the training environment and the test environment. We creatively save the flight path in the form of an image. The combination of the knowledge-based Monte Carlo tree search method and local search method can not only effectively avoid falling into local search, but also ensure learning the optimal search direction under the limitation of computing power. Experiments show that the trained UAV can find an excellent flight path by intelligent navigation, and able to make effective flight decisions in a complex geometrical environment.
Enhancing ELECTRE I Method with Complex Spherical Fuzzy Information
Authors: Akram Muhammad , Al-Kenani Ahmad N. , Shabir Maria 
Published: December 2021

AbstractThis article is concerned to delineate the strategic approach of ELiminating Et Choice Translating REality (ELECTRE) method for multi-attribute group decision-making (MAGDM) in terms of complex spherical fuzzy sets. The feasible, well-suited, and marvelous structure of complex spherical fuzzy set compliments the decision-making efficiency and ranking calibre of ELECTRE I approach to present a beneficial and supreme aptitude strategy for MAGDM. Beside the proposed methodology, a few non-fundamental properties of complex spherical fuzzy weighted averaging (CSFWA) operator inclusive of shift invariance, homogenous, linearity, and additive property are also explored. The proposed procedure validates the individual opinions into an acceptable form by the dint of CSFWA operator and the aggregated opinions are further analyzed by the proposed complex spherical fuzzy- ELECTRE I (CSF-ELECTRE I) method. Within the consideration of proposed methodology, normalized Euclidean distances of complex spherical fuzzy numbers are also contemplated. In CSF-ELECTRE I method, the score, accuracy, and refusal degrees determine the concordance and discordance sets for each pair of alternatives to calculate the concordance and discordance indices, respectively. Based on aggregated outranking matrix, a decision graph is constructed to attain the ELECTREcally outranked solutions and the best alternative. This article provides supplementary approach at the final step to profess a linear ranking order of the alternatives. The versatility and feasibility of the presented method are embellished with two case studies from the business and IT field. Moreover, to ratify the intensity and aptitude of the presented methodology, we provide a comparative study with complex spherical fuzzy-TOPSIS method.
Few-Shot Abnormal Network Traffic Detection Based on Multi-scale Deep-CapsNet and Adversarial Reconstruction
Authors: Ma Wengang , Zhang Yadong , Guo Jin , Yu Qian 
Published: December 2021

AbstractDetecting various attacks and abnormal traffic in the network is extremely important to network security. Existing detection models used massive amounts of data to complete abnormal traffic detection. However, few-shot attack samples can only be intercepted in certain special scenarios. In addition, the discrimination of traffic attributes will also be affected by the change of feature attitude. But the traditional neural network model cannot detect this kind of attitude change. Therefore, the accuracy and efficiency of few-shot sample abnormal traffic detection are very low. In this paper, we proposed a few-shot abnormal network traffic detection method. It was composed of the multi-scale Deep-CapsNet and adversarial reconstruction. First, we designed an improved EM vector clustering of the Deep-CapsNet. The attitude transformation matrix was used to complete the prediction from low-level to high-level features. Second, a multi-scale convolutional capsule was designed to optimize the Deep-CapsNet. Third, an adversarial reconstruction classification network (ARCN) was proposed. The supervised source data classification and the unsupervised target data reconstruction were achieved. Moreover, we proposed an adversarial training strategy, which alleviated the noise interference during reconstruction. Fourth, the few-shot sample classification were obtained by combining multi-scale Deep-CapsNet and adversarial reconstruction. The ICSX2012 and CICIDS2017 datasets were used to verify the performance. The experimental results show that our method has better training performance. Moreover, it has the highest accuracy in two-classification and multi-classification. Especially it has good anti-noise performance and short running time, which can be used for real-time few-shot abnormal network traffic detection.
Application of Improved Differential Evolution Algorithm in Solving Equations
Authors: Ning Guiying , Zhou Yongquan 
Published: December 2021

AbstractThe problem of finding roots of equations has always been an important research problem in the fields of scientific and engineering calculations. For the standard differential evolution algorithm cannot balance the convergence speed and the accuracy of the solution, an improved differential evolution algorithm is proposed. First, the one-half rule is introduced in the mutation process, that is, half of the individuals perform differential evolutionary mutation, and the other half perform evolutionary strategy reorganization, which increases the diversity of the population and avoids premature convergence of the algorithm; Second, set up an adaptive mutation operator and a crossover operator to prevent the algorithm from falling into the local optimum and improve the accuracy of the solution. Finally, classical high-order algebraic equations and nonlinear equations are selected for testing, and compared with other algorithms. The results show that the improved algorithm has higher solution accuracy and robustness, and has a faster convergence speed. It has outstanding effects in finding roots of equations, and provides an effective method for engineering and scientific calculations.
Epidemiology of Dermatophytes Isolated from Clinical Samples in a Hospital in Eastern Saudi Arabia: A 20-Year Survey
Authors: Alshehri Bashayer Ali , Alamri Aisha M. , Rabaan Ali A. , Al-Tawfiq Jaffar A. 
Published: December 2021

Abstract
Background
Dermatophytes are group of fungi that cause superficial infections via enzymes that degrade keratin in human skin. Several factors, including climate, gender, age, lifestyle, human migration, cultural habits, and socioeconomic status influence the prevalence of dermatophyte infections. We analyzed the prevalence of dermatophyte isolates in a hospital in Eastern Saudi Arabia from 2000 to 2019.

Methods
The data on fungal cultures were obtained from the Laboratory Information System of the Mycology Laboratories at Johns Hopkins Aramco Healthcare, and were used for the analysis. Fungal isolates were examined microscopically for the presence of specialized hyphal structures and conidia. The Vitek® MS microbial identification system (biomerieux) was used if the culture type was not identified microscopically.

Results
Among the 10,021 samples analyzed, 3040 (30.33%) were positive for fungi and only 398 (3.97%) were dermatophytes. Microsporum species was the most common dermatophyte accounting for 50.5% (n = 201) followed by trichophyton with 36.9% (n = 147). The most common positive samples were scrapping (251, 63%) and hair (68, 17%). Culture positivity relative to the age groups revealed a cluster of positive dermatophyte species in children < 10 years of age with 215 (54%) of all cases and among 10–19 years of age with 60 (15) of the cases (p < 0.001). Microsporum species were the prevalent dermatophytes in patients  < 10 years of age, while Epidermophyton species were the most frequent dermatophyte species in age groups 10–19, 20–29, and 30–39 years. However, Trichophyton species were the most frequent dermatophyte species in individuals 70–79 years. The percentage of Microsporum and Trichophyton species decreased significantly over time (p < 0.001). In addition, there was a significant seasonal variation in relation to Trichophyton species. A comparison between the most frequent species showed that there was no difference in relation to gender, but there was a difference in relation to the specimen type and age group.

Conclusion
Dermatophytosis was common among children and adolescent with the most common samples were scrapping and hair. There was a significant reduction in Microsporum and Trichophyton species over time.
An Integrated Approach to Assess Knowledge/Perceptions and Attitudes/Practices (KAP) Regarding Major Neglected Tropical Diseases Endemic in the Mbengwi Health District, North West Region, Cameroon
Authors: Sumo Laurentine , Ntonifor Ngum H. , Lenou-Nanga Cédric G. , Chenkumo-Kengmoni Nicanor , Amana-Bokagne Vanessa T. , Awah Chembo G. , Niamsi-Emalio Yannick , Nana-Djeunga Hugues C. 
Published: December 2021

Abstract
Background and objectives
Preventive chemotherapy (PCT) is the main strategy currently used to control and/or eliminate onchocerciasis, lymphatic filariasis (LF) and soil transmitted helminthiasis (STH), and community participation (through implementation or adherence to PCT) is critical. This study aimed at investigating knowledge/perceptions of populations of the Mbengwi health district (North West Region, Cameroon), in relation to their attitudes/practices regarding the most prevalent neglected tropical diseases (NTDs).

Methods
A household-based cross-sectional survey was carried out in the Mbengwi health district (North West Region, Cameroon) using the cluster sampling technique. Clusters were selected using the probability proportionate to estimate size strategy. In each cluster, the random walk technique was used for the selection of households, and a structure questionnaire was administered to 2–3 of its members.

Results
A total of 254 households from 26 clusters were visited, and 514 individuals were interviewed. The sex ratio of interviewees (1.08) was unbiased, and their ages ranged between 10 and 99 years old. Though most of the respondents declared having already heard of these NTDs (41.6%, 73.9% and 90.5% for onchocerciasis, LF and STH, respectively), only a minority of them were aware of how they are acquired/transmitted (8.9%, 9.2% and 32.7% for onchocerciasis, LF and STH, respectively), or prevented (23.1%, 18.9% and 47.2% for onchocerciasis, LF and STH, respectively).

Conclusions
This study revealed poor knowledge/perceptions and wrong attitudes/practices of interviewees as regards to these NTDs, and these misconceptions can seriously affect the adherence and contribution of populations to the success of PCTs. It appears compulsory to reinforce information, education, and communication, with a focus on the rationale and importance behind PCTs, to optimize/improve community participation.
Comparison of the Characteristics of Asymptomatic and Presymptomatic Patients with Coronavirus Disease 2019 in the Republic of Korea
Authors: Hyun Miri , Lee Ji Yeon , Park Jae Seok , Kim Jin Young , Kim Hyun Ah 
Published: December 2021

Abstract
Purpose
This retrospective study aimed to evaluate the baseline characteristics of asymptomatic patients with coronavirus disease 2019 at admission and to follow-up their clinical manifestations and radiological findings during hospitalization.

Methods
Patients with coronavirus disease 2019 who were asymptomatic at admission were divided into two groups—those with no symptoms until discharge (group A) and those who developed symptoms after admission (group B). Patients who could not express their own symptoms were excluded.

Results
Overall, 127 patients were enrolled in the study, of whom 19 and 108 were assigned to groups A and B, respectively. The mean age and median C-reactive protein level were higher in group B than in group A. All patients in group A and one-third of patients in group B had normal initial chest radiographs; 15.8% and 48.1% of patients in groups A and B, respectively, had pneumonia during hospitalization. One patient in group B, whose condition was not severe at the time of admission, deteriorated due to aggravated pneumonia and was transferred to a tertiary hospital.

Conclusion
We summarize the clinical characteristics during hospitalization of patients with coronavirus disease 2019 who were purely asymptomatic at the time of admission. The majority of asymptomatic patients with coronavirus disease 2019 were discharged without significant events during hospitalization. However, it may be difficult to predict subsequent events from initial chest radiographs or oxygen saturation at admission.
Factors that Influence 2-Year Progression-Free Survival Among Head and Neck Cancer Patients
Authors: Irawan Cosphiadi , Benbella Larangga Gempa , Rachman Andhika , Mansjoer Arif 
Published: December 2021

Abstract
Objectives
The majority of patients with head and neck cancer (HNC) come to the hospital at advanced stages. This research was conducted to determine the mortality, 2-year progression-free survival (PFS) and factors that influenced PFS of HNC patients.

Methods
A retrospective cohort study was conducted among locally advanced HNC patients who underwent chemoradiation for the first time at RSCM from January 2015 to December 2017. Data were retrieved through medical records. Laboratory data were taken 2–4 weeks prior and 2–4 weeks after chemoradiation. PFS observation started from the first day of chemoradiation until disease progression or death. PFS data were recorded in two groups: ≤ 2 years and > 2 years. The Chi-square test was used for bivariate analysis with the Fischer-exact test as an alternative. Variables will be further tested using multivariate logistic regression tests.

Results
Among 216 subjects, there were 103 (47.69%) patients who did not reach overall survival (OS) > 2 years. There were 108 (50%) patients who had PFS > 2 years. Based on the results of multivariate analysis, it was found that smoking, hemoglobin level ≤ 12 g/dl, ECOG (Eastern Cooperative Oncology Group) 1–2, and negative therapeutic response were associated with poor PFS. Hazard ratio (HR) for 2-year PFS for Brinkman index > 250 was 1.36 (95% CI 0.93–2.00; p = 0.02); HR for Hb ≤ 12 g/dl was 1.65 (95% CI 1.13–2.42; p = 0.01); HR for ECOG 1–2 was 4.05 (95% CI 1.49–11.00; p < 0.01); and HR for negative therapeutic response was 2.37 (95% CI 1.43–3.94; p < 0.01).

Conclusion
Mortality of HNC patients within 2 years is 47.69%, with a 2-year PFS reaching 50%. Cigarette smoking, low hemoglobin levels, poor performance status, and negative therapeutic response (non-responders) negatively affect the 2-year PFS.
Combined Blood Indexes of Systemic Inflammation as a Mirror to Admission to Intensive Care Unit in COVID-19 Patients: A Multicentric Study
Authors: Hamad Dina Ali , Aly Mai Mostafa , Abdelhameid Marwa Ahmed , Ahmed Shimaa Abdalla , Shaltout Asmaa Salah , Abdel-Moniem Alaa Eldin , Ragheb Ahmed Mohamed Reda , Attia Mohammed Nahed , Meshref Taghreed Sayed 
Published: December 2021

Abstract
Background
The Coronavirus 2019 is a pandemic that has spread worldwide, threatening human health. The main cause of death in patients with COVID-19 is a systemic pro-inflammatory mechanism that quickly progresses to acute respiratory distress syndrome. Hematological ratios as affordable indicators of inflammatory response were studied in COVID-19 patients. The study aimed to study the importance of the blood cell indexes of the systemic inflammatory response, as the Aggregate Index of Systemic Inflammation (AISI), neutrophils lymphocyte to platelet ratio (NLPR), systemic immune-inflammation index (SII) and, systemic inflammation response index (SIRI) in predicting intensive care unit (ICU) admission of COVID-19 patients.

Methods
495 COVID-19 patients managed in four tertiary centers; divided into non-ICU and ICU groups.

Results
Total leucocyte count (TLC), AISI, NLPR, SII, and SIRI were more elevated in the ICU group (P < 0.001 for all except AMC P = 0.006), while this group had less absolute lymphocyte count (ALC) (P = 0.047).
We estimated the optimal cut-off values of the hematological ratio; AISI (729), NLPR (0.0195), SII (1346), and SIRI (2.5). SII had the highest specificity (95.6%), while NLPR had the highest sensitivity (61.3%). Age, AISI, CRP, D-dimer, and oxygen aid were the independent predictors for ICU admission in COVID-19 in multivariate logistic regression.

Conclusion
AISI is a predictor for severity and ICU admission in COVID-19 patients, SII is a predictor of survival, while NLPR and SIRI have an additive role that needs further evaluation.