bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Approach to Reduce Energy Consumption By Clustering With Genetic Algorithm and Sleeping Time in The IoT
Authors: Babaei Negin , Hedayati Alireza 
Published: July 2021

Abstract
Internet of things is one of the most important technologies in the last century which covers various domains such as wireless sensor networks. Wireless sensor networks consist of a large number of sensor nodes that are scattered in an environment and collect information from the surrounding environment and send it to a central station. One of the most important problems in these networks is saving energy consumption of nodes and consequently increasing lifetime of networks. Work has been done in various fields to achieve this goal, one of which is clustering and the use of sleep timing mechanisms in wireless sensor networks. Therefore, in this article, we have examined the existing protocols in this field, especially LEACH-based clustering protocols. The proposed method tries to optimize the energy consumption of nodes by using genetic-based clustering as well as a sleep scheduling mechanism based on the colonial competition algorithm. The results of this simulation show that our proposed method has improved network life (by 18%) and average energy consumption (by 11%) and reduced latency in these networks (by 17%).
Do Bio-Insecticides Affect Only Insect Species? Behavior, Regeneration and Sexual Reproduction of A Non-Target Freshwater Planarian
Authors: Silva Laila Cristina Rezende , Dornelas Aline Silvestre Pereira , Saraiva Althiéris de Souza , Gravato Carlos , Pestana João Luís Teixeira , Soares Amadeu Mortágua Velho da Maia , Sarmento Renato Almeida 
Published: July 2021

Abstract
Bio-insecticides have been increasingly used worldwide as ecofriendly alternatives to pesticides, but data on their effects in non-target freshwater organisms is still scarce and limited to insects. The aim of this study was to determine the lethal and sub-lethal effects of the bio-insecticides Bac Control (based on Bacillus thuringiensis kurstaki - Btk) and Boveril (based on Beauveria bassiana - Bb) on regeneration, behavioral and reproductive endpoints of the freshwater planarian Girardia tigrina. The estimated LC50 − 48h were > 800 mg a.i./L for Btk and 60.74 mg a.i./L for Bb. In addition, exposure to Btk significantly decreased locomotion and feeding activities of planarians (lowest observed effect concentration (LOEC) of 12.5 mg a.i./L Btk) and fecundity rate (LOEC = 3.12 mg a.i./L Btk), whereas exposure to Bb significantly delayed regeneration (LOEC = 0.75 mg a.i./L Bb) and decreased fecundity rate (1.5 mg a.i./L Bb) of planarians. Thus, both bio-insecticides induced deleterious sub-lethal effects on a non-insect freshwater invertebrate species. However, only Bb-based formulation affected the survival, fecundity rate and regeneration at concentrations below the maximum predicted environmental concentration (PEC = 247 mg/L). Thus, care should be taken when using such formulations as alternatives to chemical insecticides near aquatic ecosystems.
The Circular Economy and The Green Jobs Creation
Authors: Sulich Adam , Sołoducho-Pelc Letycja 
Published: July 2021

Abstract
The Circular Economy (CE) is a proposal for new, more sustainable and durable economy model. As consequence, of this pro-environmental economic model induces visible changes on the labor market which are Green Jobs (GJ). This paper is focused on the creation of Green Jobs in the CE. The GJ are most visible in the Environmental Goods and Services Sector (EGSS). This study aims to investigate EGSS among 28 European Union countries in years 2009–2019. The adopted method was literature research complemented by the statistical analysis of secondary data from Eurostat in the linear regression method. Then, some Sustainable Development Goals (SDGs) and their measure were used as main indicators reflecting changes on the labor market. Results are presented as a model indicating which of the SDGs can support CE and enhance number of the ‘Green Jobs’. Presented results contribute to the science, because combine factors influencing GJ creation in EGSS, in CE perspective. This study underlines a lack of uniform methods for measuring and forecasting the effects of green jobs creation and indicate future research directions.
Least Square Support Vector Regression-Based Model for Whiteness Index of Cotton Fabric Prediction
Authors: Yeo Wan Sieng 
Published: July 2021

Abstract
The textile bleaching process uses a hydrogen peroxide (H2O2) solution in alkali pH associated with high temperature is the commonly used bleaching procedure in cotton fabric manufacture. The purpose of the bleaching process is to remove the natural colour from cotton to obtain a permanent white colour before dyeing or shape matching. Normally, the visual ratings of whiteness on the cotton are measured by the whiteness index (WI). Notice that lesser research study is focusing on chemical predictive modelling of the WI of cotton fabric than its experimental study. Predictive analytics using predictive modelling can forecast the outcomes that can lead to better-informed cotton quality assurance and control decisions. Up to date, limited study applying least square support vector regression (LSSVR) based model in the textile domain. Hence, the present study was aimed to develop the LSSVR-based model, namely multi-output LSSVR (MLSSVR) using bleaching process variables to predict the WI of cotton. The predictive accuracy of the MLSSVR model is measured by root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), and its results are compared with other regression models including partial least square regression, predictive fuzzy model, locally weighted partial least square regression and locally weighted kernel partial least square regression. The results indicate that the MLSSVR model performed better than other models in predicting the WI as it has 60–1209% lower values of RMSE and MAE as well as it provided the highest R2 values which are up to 0.9985.
A Comparative Study on Probabaility Analysis of Annual Maximum Daily Streamflow Using Conventional and L-Moments
Authors: D Srinivasa Murthy , S Aruna Jyothy , P Mallikarjuna 
Published: July 2021

Abstract
The study aims at the probabilistic analysis of annual maximum daily streamflows at the gauging sites of Godavari upper, Godavari middle, Pranahitha, Indravathi and Godavari lower sub-basins. The daily streamflow data at Chass, Ashwi and Pachegaon of Godavari upper, Manjalegaon, Dhalegaon, Zari, GR Bridge, Purna and Yelli of Godavari middle, Gandlapet, Mancherial, Somanpally and Perur of Pranahitha, Pathagudem, Chindnar, Sonarpal, Jagdalpur and Nowrangpur of Indravathi, and, Sardaput, Injaram, Konta, Koida and Polavaram of Godavari lower sub-basins for the period varying between 1965–2011, collected from Central Water Commission (CWC), India were used in the analysis. Statistics of annual maximum daily streamflow series during the study period at the gauging sites of sub-basins indicated moderately variedand positively skewed streamflows, and flows with sharp peaks at the upstream gauging sites. Probabilistic analysis of streamflows showed that lognormal or gamma distribution with conventional moments fitted the maximum daily streamflow data at the gauging sites of Godavari sub-basins.Among 2-parameter distributions with L-moments,GPA2 followed by GAM2/LN2 fitted annual maximum daily streamflow data at most of the gauging sites.At the downstream-most gauging sites of Pranahitha, Indravathi and Godavari lower sub-basins, the data followed W2 probability distribution. Among 3-parameter distributions with L-moments, GPA3 at seven gauging sites, W3 and P3 at five gauging sites each, GLOG at four gauging sites and GEV at two gauging sites fitted the data. Based on the performance evaluation, 2 – parameter distributions using L-moments at the upstream, 3 – parameter distributions at the middle and probability distributions using conventional moments at the downstreamgauging sites performed better in the Godavari upper and middle sub-basins. Probability distributions based on conventional moments/ 3-parameter distributions using L-momentsfitted the annual maximum daily streamflow data at the gauging sites in the Pranahitha, Indravathi and Godavari lower sub-basins satisfactorily.
Finite-Time Stability of Hadamard Fractional Differential Equations in Weighted Banach Spaces
Authors: Ma Li , Wu Bowen 
Published: July 2021

Abstract
The main purpose of this paper is to investigate the finite-time stability of Hadamard fractional differential equations (HFDEs). Firstly, the standard definition of finite-time stability of HFDEs in compatible Banach space are proposed. In light of the method of successive approximation and Beesack inequality with weakly singular kernel, the criteria for finite-time stability of linear and nonlinear HFDEs are established, respectively. Then with regard to linear HFDEs with pure delay, a novel fractional delayed matrix function (also called delayed Mittag-Leffler matrix function) is given. Specific to nonlinear HFDEs with constant time delay, both Beesack inequality and Hölder inequality are utilized in the framework of the generalized Lipschitz condition. Finally, several indispensable simulations are implemented to verify the effectiveness and practicability of the main results.
High Capacity 64-Quadrature Amplitude Modulation Based Optical Coherent Transceiver for 60 GHz Radio Over Fiber System
Authors: Chack Devendra , THOOL SUNIL NARAYAN 
Published: July 2021

Abstract
Today’s world demands for maximum bandwidth utilization in the area of optical fiber network to achieve serious progress due to data consumption has been grown by > 25% as compared to last year. 5G system is one of the promising technology cater for high bandwidth utilization between multiple transceivers and have the advantages to operate in the milli-meter wave (mm-wave) of 60 GHz. There is much interest of photonic technique such as optical heterodyne coherent detection to produce mm-wave frequencies. We propose a 60 GHz radio over fiber (RoF) link using 168 Gb/s high capacity input data rate based 64-Quadrature amplitude modulation (64-QAM), coherent detection using optical heterodyning and advanced digital signal processing (ADSP) based system. An optimized version of optical heterodyne technique is used to derive 60 GHz radio frequency (RF) signal for connecting radio nodes over wireless link. In order to compensate the irregularities effect of standard signal mode fiber (SSMF), a novel ADSP technique is used which allows good improvement in the spectral signal efficiency and achieved longer distance communication. The ADSP technique plays a key role to achieve high capacity data rate requirements for forthcoming 5G technologies. The proposed RoF system shows that an error free transmission is accomplished over 170 km SSMF and achieved BER is 2.7×10-3 against 7.015 % error vector magnitude (EVM) with 30 dB Signal to Noise ratio (SNR).
Temporal Variation in Leachate Composition of A Newly Constructed Landfill Site in Lahore in Context To Human and Environmental Risks
Authors: Ashraf Mahsoon , Zeshan Muhammad , Hafeez Sadia , Hussain Rahib , Qadir Abdul , Majid Muzaffar , Ahmad Farman , Ahmad Sajid Rashid 
Published: July 2021

Abstract
The present study was aimed to explore the seasonal and temporal variation in the extent and sources of physiochemical and trace elements in the Lakhodair solid waste Landfill site in Lahore, Pakistan. For the seasonal and temporal study of leachates, systematic composite samples were collected monthly (for one year) and analyzed for physiochemical and trace elements. The concentration of TDS, TSS, COD, NH3-N, BOD5, sulfate, sulfides, phenolic compounds, oil and grease were higher than the National Environmental Quality standard (NEQs). The trace elements, namely Mn (1.7 mg/L), and Cd (0.05 mg/L), while in a few samples Fe (14 mg/L), Ni (1.6 mg/L), and Zn (6.7 mg/L) were higher than the NEQs. In Lakhodair leachates, TDS, COD, NH3-N, BOD5, sulfides, and Cl have a high concentration coefficient (i.e., CC 3 to > 6), which may cause considerable to high contamination, while remaining parameters may cause low to moderate contamination (CC 1 to ≤ 3). The lower BOD5/COD ratio (< 0.1) in the Spring and Autumn seasons, represents the active methanogenesis and anaerobic activities in the Lakhodair landfill site. The anaerobic and methanogenesis enhance the redox reaction as a result CO2 is emitted and alternatively increases pH, TDS, COD, Cl, BOD5, NH3-N, sulfides, phenolic compounds in leachates. However, the lower concentration of some trace elements may be because of an anaerobic process that may immobilize the trace elements. It is presumed that the trace elements in the Lakhodair landfill may be in a metastable state, which is not easy to leach out. It’s hereby recommended that Leachate produced in the Lakhodair landfill site need to handle carefully so as to limit the environmental and health implication.
Clinical Outcomes of Genotype-Matched Therapy For Recurrent Gynecological Cancers
Authors: Sawada Kiyoka , Nakayama Kentaro , Nakamura Kohei , Yoshimura Yuki , Razia Sultana , Ishikawa Masako , Yamashita Hitomi , Ishibashi Tomoka , Sato Seiya , Kyo Satoru 
Published: July 2021

Abstract
Background: Recent advances in next-generation sequencing and genome medicine has contributed to treatment decisions in patients with cancer. Most advanced gynecological cancers develop resistance to chemotherapy and have a poor prognosis. Therefore, we conducted genomic tests in gynecological tumors to examine the efficacy and clinical feasibility of genotype-matched therapy. Methods: Target sequencing was performed in 20 cases of gynecological cancers (cervical cancer, 6; endometrial cancer, 6; and ovarian cancer, 6).Results: Both actionable and druggable genes were identified in 95% (19/20) of the cases. Among them, seven patients (35%) received genotype-matched therapy, which was effective in three patients. Of the three patients, one patient with a PTEN mutation received everolimus, another patient with a TSC2 mutation received everolimus and letrozole, and the patient with a BRIP1 mutation received olaparib. Subsequently, disease control in these three patients lasted for more than half a year. However, all patients relapsed between 9 and 13 months after the initiation of genotype-matched therapy. In this study, the response rate of genotype-matched therapy was 43% (3/7), which may have contributed to improved prognoses.Conclusions: Therefore, genotype-matched therapies may help patients with refractory gynecological cancers achieve better outcomes.
COVID19 Outcome Prediction: Differences Between Raw Laboratory and Human Pre-Processed Data
Authors: Principe Alessandro , Gómez-Zorrilla Silvia , López-Montesinos Inmaculada , Pérez-Almengor Eva , Pomés-Arnau Pau , Sorli María , Güerri-Fernández Robert , Grau Santiago , Márquez Albert , Martínez-Roldán Jordi , Horcajada Juan 
Published: July 2021

Abstract
Beyond health consequences, coronavirus disease 2019 (COVID19) has shown inadequacies and limitations of healthcare systems all over the world. Predictions of good and bad outcomes might improve lockdown and curfew policies and reduce the COVID19 health impact. We describe a multimodal machine learning (ML) approach through which we predict early hospital discharge and other key clinical outcomes as respiratory worsening, intensive care unit admission or the need of rescue therapy. Using this model, our hospital occupation might have decreased around 50% during the first wave of the pandemic. Our model used both, raw laboratory results (LR) and electronic health records (EHR), which allow a better anticipation of worsening through selective reporting of LRs. Thus, EHR models can be considered as a human preprocessing enhancing ML classification. Our approach may be a useful, customizable, and adaptable hospital resource management tool, especially to help interventions targeted at lowering COVID19 infection rates.
Association of MMP9 Polymorphism With Myocardial Infarction
Authors: Iqbal Riffat , Zafar Aneeqa , Habib Arslan , Mukhtar Maryam , Liaqat Iram , Aftab Kiran , Aslam Shaista , Jaffar Ghulam , Shehzad Muhammad 
Published: July 2021

Abstract
Myocardial Infarction (MI) is the most common type of coronary heart disease characterized by pain, nausea, sweating, shortness of breath and abnormal heartbeat. Current study was conducted to identify the susceptibility of the MMP-9 gene among families affected with myocardial infarction in the Pakistani population. A family clustering study based on 5 families having MI patients was conducted. Blood samples from MI patients and their family members were collected for further genetic analysis. Patients were with a mean BMI of ±30.2 kg/m2 which indicates that obese were at high risk of disease development. The mean age for diagnosis of disease was ±50 years, in which all patients had a positive family history. Most patients were diabetic with hypertension, most of them were smokers and their exposure to air pollution was higher. Genomic DNA was isolated from the blood through manual extraction. Primers were optimized and genotyping was done by polymerase chain reaction (PCR) which was followed by DNA sequencing and restriction fragment length polymorphism (RFLP). As a result of polymorphism, A into G and C into T conversions were identified on rs17576 and rs3918242 polymorphic sites on the MMP-9 gene respectively. In conclusion smoking, hypertension, diabetes, exposure to air pollution and polymorphism of rs17576 and rs3918242 were significantly associated with the onset of MI in the Pakistani population and males were at higher risk. Further studies should be conducted on large scale to evaluate the association of MMP-9 polymorphism with MI.
Spatial Prediction of Highway Slope Disasters Based on Convolution Neural Networks
Authors: Yin Chao , Wang Zhanghua , Zhao Xingkui 
Published: July 2021

Abstract
In order to clarify the spatial differentiations of highway slope disasters (HSDs) in Boshan District, spatial prediction was carried out based on ECG-CNN with the support of GIS. Spatial prediction factors of HSDs were selected, the stabilities of the 147 highway slopes in Boshan District were determined. The spatial prediction model of HSDs was established by ECG-CNN, and the spatial susceptibility map of HSDs in Boshan District was drawn. Influences of the prediction factor combinations and the drill sample & verification sample combinations on the prediction success rates were verified. The results showed that low susceptible areas, medium susceptible areas and high susceptible areas account for 56.92%, 28.46% and 14.62% of the total areas of Boshan District respectively. Some sections of Binlai Expressway, G205, G309, S210 and S307 pass through high susceptible areas. The surface cutting depth has a small impact on the prediction success rate, while the elevation and gradient have great impacts on the prediction success rate. When the drill samples are small, network drill’s maturity has a great impact on the prediction success rate, while when there are many drill samples, the model’s logical structure itself has a large impact on the prediction success rate.
Robotic Obstacle Avoidance: A Virtual Modeling And Reinforcement Learning
Authors: Chen Ming-Fei , Tsai Han-Hsien , Hsiao Wen-Tse 
Published: July 2021

Abstract
This study developed a robotic arm self-learning system based on virtual modeling and reinforcement learning. Using the model of a robotic arm, information concerning obstacles in the environment, initial coordinates of the robotic arm, and the target position, this system automatically generated a set of rotational angles to enable a robotic arm to be positioned such that it can avoid all obstacles and reach a target. The developed program was divided into three parts. The first part involves robotic arm simulation and collision detection; specifically, images of a six-axis robotic arm and obstacles were input to the Visualization ToolKit library to visualize the movements and surrounding environment of the robotic arm. Subsequently, an oriented bounding box algorithm was used to determine whether collisions had occurred. The second part concerned machine-learning–based route planning. The TensorFlow was used to establish a deep deterministic policy gradient model, and reinforcement learning was employed for the response to environmental variables. Different reward functions were designed for tests and discussions, and the program’s practicality was verified through actual machine operations. Finally, the application of reinforcement learning in route planning for a robotic arm was proved feasible by the experiment. Such an application facilitated automatic route planning and achieved an error of less than 10 mm from the target position.
Accurate Training of The Cox Proportional Hazards Model on Vertically-Partitioned Data While Preserving Privacy
Authors: Kamphorst Bart , Rooijakkers Thomas , Veugen Thijs , Cellamare Matteo , Knoors Daan 
Published: July 2021

Abstract
Background: Analysing distributed medical data is challenging because of data sensitivity and various regulations to access and combine data. Some privacy-preserving methods are known for analyzing horizontally-partitioned data, where different organisations have similar data on disjoint sets of people. Technically more challenging is the case of vertically-partitioned data, dealing with data on overlapping sets of people. We use an emerging technology based on cryptographic techniques called secure multi-party computation (MPC), and apply it to perform privacy-preserving survival analysis on vertically-distributed data by means of the Cox proportional hazards (CPH) model. Both MPC and CPH are explained. Methods: We use a Newton-Raphson solver to securely train the CPH model with MPC, jointly with all data holders, without revealing any sensitive data. In order to securely compute the log-partial likelihood in each iteration, we run into several technical challenges to preserve the efficiency and security of our solution. To tackle these technical challenges, we generalize a cryptographic protocol for securely computing the inverse of the Hessian matrix and develop a new method for securely computing exponentiations. A theoretical complexity estimate is given to get insight into the computational and communication effort that is needed. Results: Our secure solution is implemented in a setting with three different machines, each presenting a different data holder, which can communicate through the internet. The MPyC platform is used for implementing this privacy-preserving solution to obtain the CPH model. We test the accuracy and computation time of our methods on three standard benchmark survival datasets. We identify future work to make our solution more efficient. Conclusions: Our secure solution is comparable with the standard, non-secure solver in terms of accuracy and convergence speed. The computation time is considerably larger, although the theoretical complexity is still cubic in the number of covariates and quadratic in the number of subjects. We conclude that this is a promising way of performing parametric survival analysis on vertically-distributed medical data, while realising high level of security and privacy.