bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-linear Adaptive Wiener Filter for Time Series Smoothing
Authors: Levada Alexandre 
Published: November 2022

Abstract
Often, in the analysis of time series and digital signals, a smoothing procedure is required to filter undesired random perturbations as noise and outliers in data. Among the most widely known techniques for time series smoothing, we have convolutional filters, simple exponential smoothing, triple exponential smoothing (Holt-Winters method) and linear adaptive filters, such as the Wiener filter. In this paper, we propose the NoLAW filter (Non-Linear Adaptive Wiener filter), a higher-order nonlinear extension for the adaptive Wiener filter, which is a linear statistical smoothing technique that assumes the hypothesis that the underlying series is corrupted by a zero mean, additive and independent Gaussian noise. Numerical experiments show that the proposed method is a computationally efficient and viable approach for filtering time series. Quantitative metrics show that the NoLAW filter is capable of producing better results than the usual linear Wiener filter, simple exponential smoothing and Holt-Winters method. Moreover, the computational cost of the proposed NoLAW is linear in the number of samples, which means that, asymptotically, it is equivalent to the regular Wiener filter.
Role of the membrane spanning 4A (MS4A) gene family in lung adenocarcinoma
Authors: Zheng Zijun , Li Huiping , Guo Hui 
Published: November 2022

Abstract
Lung adenocarcinoma, which is the second most prevalent cancer in the world, has a poor prognosis and a low five-year survival rate. The MS4A protein family is crucial to disease development and progression, particularly for cancers, allergies, metabolic disorders, autoimmune diseases, infections, and neurodegenerative disorders. However, its involvement in lung adenocarcinoma remains unclear. In this study, we found that 12 MS4A family genes were up- or downregulated in lung adenocarcinoma. Furthermore, we described the genetic variation landscape of the MS4A family in lung adenocarcinoma. Notably, according to a function enrichment study of the MS4A family, the immune response-regulating signaling pathway and receptor signaling pathway on the surface of immune response-regulating cells were both found to be associated with the binding of an immunoglobulin. According to the Kaplan–Meier curve, patients with lung adenocarcinoma having poor expression of MS4A2, MS4A4A, MS4A7, MS4A14, and MS4A15 had a low overall survival rate. These five prognostic genes are substantially associated with immune-infiltrating cells, and a prognosis model incorporating them may more accurately predict the overall survival rate of patients with lung adenocarcinoma than current models. The findings of this study may offer creative suggestions and recommendations for the identification and management of lung adenocarcinoma.
Improving the quality attributes of mayonnaise enriched with phenolic extracts of fenugreek seeds and leaves during storage
Authors: Turker Izzet , Isleroglu Hilal 
Published: November 2022

Abstract

In this study, mayonnaise was enriched with phenolic extracts of fenugreek seeds (FSE) and fenugreek leaves (FLE), and quality attributes of the enriched mayonnaises were investigated during storage. FSE and FLE were added to mayonnaises at three different levels (0.05, 0.10 and 0.20%) and the samples were stored at 4°C for 12 weeks, and at 25°C for 6 weeks. Antioxidant activity, peroxide value, titratable acidity, color change, microbial and sensorial analysis were carried out. As a result, enrichment of mayonnaise with FSE and FLE improved its quality properties during storage. FSE and FLE added samples’ antioxidant activities were preserved ~ 87 and ~ 47% at 4°C, 81% and ~ 27% at 25°C, respectively. In addition, the increase in peroxide values of enriched mayonnaise with the extracts during storage was less than that of synthetic antioxidant (EDTA) added samples. The highest total color change (ΔE) was observed for FLE added samples for all the addition levels. Addition of FLE and FSE at the highest level (0.20%) was prevented the total aerobic mesophilic bacteria growth as 2.1 and 3.2 logs, and the total yeast/mold growth as 1.0 and 1.3 logs at 25°C, respectively. Sensory attributes (color, odor, taste, texture, overall acceptance) of the FSE and FLE added mayonnaises had higher scores than the control sample at the end of the storage.
Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction
Authors: Wei Zhang Wei , P P. Pushpa 
Published: November 2022

Abstract
In the digital era, Economic and Finance information is huge and attainable. The financial industry is a key to stimulating the evolution of the national economy, and with the immense availability of huge data makes the process of financial data analysis both a time consuming and tedious decision making in the financial market. By analyzing this Economic and Finance information significantly and easily, valuable perceptions can be provided. For rational economic growth, information pertaining to economic affairs and likelihoods is critical to decision-makers like governmental bodies, financial institutions and banks. However, precise predictions have been demand owing to the intricacy and unpredictability of financial and economic systems in the midst of persistent changes in economic environments. This work provides to approaches for better economic prediction and decision-making using a novel method called, Multivariate Box Jenkins Neural Network-based Keynecian Reinforcement Learning (MBJNN-KRL). In this work, Financial Data Analysis Deep Economic Prediction model that contains a Multivariate Box Jenkins Long Short Term Memory Forecasting model, for better economic prediction and convergence speed even in case of large data is first designed. The deep reinforcement learning technique is then adapted to retrain neural networks and rebalance the aggregate expenditure periodically. We combined the multivariate Multivariate Box Jenkins Long Short Term Memory Forecasting and the Keynecian Reinforcement Learning models into a single integrated reinforcement learning model named Multivariate Box Jenkins Neural Network-based Keynecian Reinforcement Learning (MBJNN-KRL). Finally, distinct sets of experiments are carried out on real large-scale data set, and the results entirely prove the efficiency and robustness of the proposed method in arbitrary economic prediction based on financial data. Our empirical data revealed that the MBJNN-KRL method could attain combative financial performance compared to traditional methods in terms of predictive accuracy, convergence speed and prediction error.
Pathological Evaluation of Corneal Regrafts During Ten Years at a Tertiary Referral Center in Tehran, Iran
Authors: Amoli Fahime Asadi , Abdi Parisa , Nozarian Zohreh , Toosang Moones , Mohammadi Abbas , Sadeghi Reza 
Published: November 2022

Abstract
Purpose
To evaluate the histopathological and epidemiologic features of corneal regrafts over ten years.
Methods
A single-center retrospective analysis was performed on corneal specimens diagnosed as corneal graft failure retrieved from The Farabi Eye Hospital (Tehran, Iran) over a 10-year period. Also, Demographic data including sex, age, laterality, primary diagnosis, type of graft, cause of corneal regraft, associated eye diseases and systemic comorbidities were evaluated.
Results
Among a total of 3120 corneal grafts, 267 of them (8.55%) were regrafts. Main cause of graft failure was corneal ulcer. The most common type of grafting was penetrating keratoplasty (PK), but most common type of regrafting surgery was DSAEK. The Main causes of graft failure was Infectious keratitis. The most common histopathological finding was Bowman layer disruption (81%) and stromal scar (80%). Stromal edema, retro corneal fibrous, sub epidermal bulla and stromal vascularization were the other present findings. The inflammatory reaction was considered discrete in 59.45% of the cases and dominant cell type was polymorphonuclear neutrophils (PMNs).
Conclusion
Infectious keratitis was the major cause of failure in our study. Considerable presence of histopathological changes such as retro corneal fibrous, sub epidermal bulla needs further investigations. Perhaps, proper education of infectious keratitis risk factors, as well as regular examinations and proper antibiotic prophylaxis can reduce the rate of the corneal graft failure
China’s Financial System - a Comparative Analysis of its Characteristics with Banking- or Market-based Financial Systems
Authors: Gottfried Oliver , Haake Anthony 
Published: November 2022

Abstract
Purpose: This study analyzes the future development of the Chinese Financial System (CFS) based on the theory of financial market development and the past regulatory changes within the Chinese financial system. The result of our study, a framework for the CFS’s development, may hold predictive power over future regulatory changes.
Design/Methodology/Approach: To assess the state of the CFS theory predicted evolution from being banking-based to market-based, we apply primary research and conduct expert interviews to analyze the evidence from internal stakeholders about the systems development. We conduct five interviews with three stakeholder groups: a.) Manager of growth-stage startups (Demand public/private equity and debt); b.) Equity investors (Supply private/public equity and debt); c.) Managers at State-owned banks (Supply debt).
Findings: We demonstrate that the CFS has the characteristics of a banking-based system as it relies on traditional debt financing provided by banks but also equity funding from stock markets. Those banks allocate capital based on market forces and aligned with the Chinese government’s long-term 5-year plan and are effective in stimulating growth on an industry level. While debt markets are more dominant there is also a strong equity funding system based on the respective stock markets.
Research and Practical implications/Originality: Our framework aids the reader in putting past and upcoming changes of financial systems into perspective. The detailed overview of the CFS offers an entry point to understanding Chinese financial regulation. It provides a theoretical perspective on the Chinese government’s long-term goals when applying our framework.
JEL: E51, E59, E61, E62
The Dynamics of Climate Change Science and Policy in Panama: A Review
Authors: Castillero Gustavo Cárdenas , Paton┼ Steven , Noriega┼ Rodrigo , Calderón Adriana 
Published: November 2022

Abstract
The local studies and reports indicate that the temperature of Panama has increased by approximately 1°C since the 1970s. More evidence shows a constantly rising sea level in the Guna Yala archipelago, coral bleaching on both coasts, and increasingly more frequent and extreme precipitation events throughout Panama. This study includes an analysis of over 400 scientific publications made by researchers from multiples centres and more than 20 Panamanian official reports due to Panama’s mandate and duties under the international climate accords. To summarise the results, the studies were gathered according to the climate change effects by Panamanian locations and analysed posteriorly using Rstudio and ArcMAP. The results indicate a significant increase in climate change research beginning in 2007. This study identified and examined the essential findings per hydroclimatic region, showing the trends, limitations, collaborations, and international contributions. Climate change research in Panama includes some of the longest-term meteorological, hydrological, oceanographic, and biological studies in the neotropics. The greatest number of identified climate change-related studies were conducted, at least in part, in the Barro Colorado Natural Monument located in central Panama. Other frequently used sites include Metropolitan Natural Park, Soberanía Park, the Panama Canal Watershed, and the Caribbean coast of the province of Colón and Bocas del Toro, primarily due to research carried out by Smithsonian Tropical Research-affiliated investigators. The tropical forests of Panama are some of the bests studied in the world, however, research has been concentrated in a relatively small number of locations and should be expanded to include additional areas to achieve a more complete and comprehensive understanding of how climate change will impact Panama in the future.
Automation, Education, and Population: Dynamic Effects in an OLG Growth and Fertility Model
Authors: Peralta Catarina , Gil Pedro 
Published: November 2022

Abstract
We address two main structural changes occurring in developed countries: the rise of automation and population ageing. We use an R&D-based growth model in an OLG framework with endogenous education and fertility, and automation in the production process. Our model is able to combine the growth of real wages over time and either a fall or an increase in birth rates, consistent with recent data regarding the birth rate by skill group. Moreover, our model allows for the study of the interplay between the effects of population ageing and those of automation. The results show a dynamics consistent with the US trends for the period covering 1970 to 2019. We also show the model's response to automation and demographic (fertility vs longevity) exogenous shocks and to policy instruments aiming to mitigate inequality.
JEL Codes: J11, J23, J24, O3, O4
First thrust measurements in ionic multi-propeller rotational engines
Authors: Ieta Adrian , Chirita Marius 
Published: November 2022

Abstract
Rotary ionic engines (RIEs) with multi-coaxial propellers (12.6 cm diameter) and their thrust-to-power ratios are investigated. Up to 40 mN thrust was obtained in single propeller RIE at 2600 rpm, 0.34mA, 37.5kV, and optimal ground diameter of 17cm. More thrust can be obtained with two and three propellers at constant current per propeller. Propeller interaction is minimal above 5 cm axial propeller-separation. Thrust-to-power ratios present a peak at low currents (maximum 30mN/W for 17cm ground diameter). Thrust-to-power variation can be assessed by the propeller kinetic energy-to-power ratio and also by the voltage- to-current ratio at currents larger than about 0.1mA.
Cycloserine-induced psychosis in patients with drug-resistant tuberculosis: a systematic review of case reports
Authors: Cotrina-Santome Alonso , Ulloa-Esqu Lizbeth , Vásquez-Quispe Shirley , Arevalo-Flores Martín , Pedraz-Petrozzi Bruno 
Published: November 2022

Abstract
Objectives

To describe the clinical characteristics and outcomes of cycloserine (CS) - induced psychosis in adults diagnosed with drug-resistant tuberculosis (DR-TB).

Materials and Methods

A systematic review of case reports was carried out according to PRISMA guidelines. Subsequently, information was extracted concerning sociodemographic variables, clinical characteristics of psychosis, treatment, and clinical outcomes, as well as the quality of the articles using a standardized tool (Joanna Briggs Institute -JBI- Case Reports Tool).

Results

Of 3416 articles, 20 reports from seven countries were included, encompassing 22 patients (68.18% male participants, mean age: 31.45 ± 10.88 years). Delusions (68.20%, primarily paranoid) were the most frequent psychotic symptom. The median duration of the psychotic episode was 13 days (interquartile range: 35). Other frequently appearing symptoms in CS-induced psychosis were aggressiveness (68.20%), insomnia (59.10%), hallucinations (54.50%), incoherent/disorganized speech (45.45%) and irritability (45.45%). After antipsychotic treatment (81.81% of the reported cases were treated with at least one antipsychotic), 95.50% presented improvement, while 4.54% died by suicide. Finally, after the quality assessment of studies using the JBI tool, 85.00% of the articles showed a low risk of bias.

Conclusions

CS-induced psychosis is a rare presentation, generally of short duration, that includes delusions (mostly paranoid) as its main psychotic symptom and shows mostly a symptom improvement after medical treatment.
PROSPERO registration number: CRD42022359551 (Date of registration: 22/09/2022)
Comparative Assessment of Seasonal Variations in the Quality of Surface water and its associated health hazards in Gold Mining Areas of Osun State, South-West Nigeria.
Authors: Olalekan Awogbami Stephen , Adewoye Solomon Olayinka , Henry Sawyerr Olawale , Raimi Morufu Olalekan 
Published: November 2022

Abstract

Industrial and urban development are contributing to an increase in global environmental degradation. Therefore, the release of heavy metals from mining-related operations into surface water is harmful to human health. But as anthropogenic influences on the environment grow, surface water characteristics are also altering, which has an impact on aquatic creatures. In order to analyze the acceptability of the surface water in the gold mining area for drinking and irrigation uses, different quality water assessment methodologies were incorporated in this research. In light of this, the purpose of the current study is to comprehend how the hydro-geochemistry and appropriateness of surface water for drinking and irrigation vary on a monthly basis. The study employed standardized analytical techniques. According to APHA recommendations, all sampling, conservation, transportation, and analysis were completed (2012). All collected samples were transported to the study lab while being kept in an icebox to prevent the degradation of the organic components. As a result, the study is focused on the contamination level in the surface water for a year. Overall, the study also highlights important pollutants that have an impact on the quality of the surface water as it passes through Osun State's gold mining regions. Finally, it has been determined that the following criteria are crucial for the stretch in each season of this research: DO, Hardness, Turbidity, Chloride, Potassium, Lead, TSS, Cadmium, Chromium, Manganese, Mercury, and Arsenic. Most of the physicochemical variables examined in this study fell within their corresponding standard limits. Based on the results of this study, the appropriate constituted authority is encouraged to continuously monitor and assess surface water quality suitability for drinking, domestic, and irrigation purposes in order to keep track of the effects of water contaminants and detect any changes in the water quality. To safeguard and maintain the groundwater quality and public health, it is advised that appropriate regulatory policies and water treatment procedures be employed in the area. Additionally, it is proposed that when enhancing water quality and investigating the sustainable use of water resources, surface water pollution should be taken into consideration. More research on report quality needs to be done in Nigeria's other mining environments in order to develop technical capacity. In the context of new contaminants and a changing climate, this study suggests additional research directions to enhance knowledge of surface water in a mining setting and sustainable surface water management in mining areas.
The Internet-of-Vehicle Traffic Condition System Developed by Artificial Intelligence of Things
Authors: Wu Hsin-Te 
Published: November 2022

Abstract
An Internet-of-Vehicle (IoV) system primary transmits traffic information and various kinds of emergency notices through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I); however, the transmission of multimedia enables drivers to control route conditions better, such as road obstacles and the range of a construction site. Additionally, car accidents usually require relevant video records of the scene for investigation; surrounding cars could transfer the accident scene videos to help the police restore the detailed situation. Meanwhile, the multimedia messages of IoV need to go through security verification and privacy protection for the system to deliver push notifications and multimedia messages to social groups instantly.
The study aims to construct an IoV traffic condition system developed by Artificial Intelligence of Things (AIoT); the data transmitting method of this research is via the 6th Generation Network (6G Network), which has advantages of high transmission speed and Quality of Service (QoS) guarantee. Furthermore, the suggested system employs federated learning to ensure message security and privacy. The features of the researched system are: 1. Use Faster Region-based Convolutional Neural Networks (R-CNN) to recognize the objects in cameras and judge if there are road obstacles and any constructions; 2. Capture car accident videos through federated learning, and send the encrypted evidence to relevant legal units; 3. Use push notifications to send multimedia messages to social groups instantly, marking the locations and the road conditions to help drivers control the conditions with the surroundings. This study expects to delivering videos and Global Positioning System (GPS) data for road condition recognition, improving driving safety.
Benchmarking Multi-Task Learning for Sentiment Analysis and Offensive Language Identification in Under-Resourced Dravidian Languages
Authors: Hande Adeep , Hegde Siddhanth U , Priyadharshini Ruba , Ponnusamy Rahul , Kumaresan Prasanna Kumar , Thavareesan Sajeetha , Chakravarthi Bharathi Raja 
Published: November 2022

Abstract
To obtain extensive annotated data for under-resourced languages is challenging, so in this research, we have investigated whether it is beneficial to train models using multi-task learning. Sentiment analysis and offensive language identification share similar discourse properties. The selection of these tasks is motivated by the lack of large labelled data for user-generated code-mixed datasets. This paper works on code-mixed YouTube comments for Tamil, Malayalam, and Kannada languages. Our framework is applicable to other sequence classification problems irrespective of the size of the datasets. Experiments show that our multi-task learning model can achieve high results compared with single-task learning while reducing the time and space constraints required to train the models on individual tasks. Analysis of fine-tuned models indicates the preference of multi-task learning over single-task learning resulting in a higher weighted F1-score on all three languages. We apply two multi-task learning approaches to three Dravidian languages: Kannada, Malayalam, and Tamil. Maximum scores on Kannada and Malayalam were achieved by mBERT subjected to cross-entropy loss and with an approach of hard parameter sharing. Best scores on Tamil was achieved by DistilBERT subjected to cross-entropy loss with soft parameter sharing as the architecture type. For the tasks of sentiment analysis and offensive language identification, the best-performing model scored a weighted F1-score of (66.8% and 90.5%), (59% and 70%), and (62.1% and 75.3%) for Kannada, Malayalam, and Tamil on sentiment analysis and offensive language identification, respectively. &nbsp;The data and approaches discussed in this paper are Published in Github.