bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drowning Alert System using RF Communication and GPRS/GSM
Authors: Monish P , Darshan R , Ponvalavan K , Bharathi Dr. M 
Published: August 2021

Abstract
Drowning is the third leading cause of unintentional injury death worldwide, accounting for seven percent of all injury-related deaths. There are an estimated 320,000 annual drowning deaths worldwide. Globally, the highest drowning rates are among children below four years, followed by children of five to nine years. The available solutions for the problem involve wristband with RF connections, drowning alert system using RFID tag and RFID reader, a video based drowning alert system, monitoring system using LASER and LDR, etc. The current idea in the proposed system here helps in knowing the status of the person who is in water by detecting the heartbeat. The system will continuously monitor the heart rate of the person using a heartbeat sensor when he or she is in the water and if there is a sudden increase or decrease in heart rate with reference to the standard range, the system will send a traumatic signal using an RF transmitter to the lifeguard station that will notify alarm alerts using a light bulb and buzzer at the end of the receiver. The distress signal can also be sent manually at the touch of a button. The system also has the ability to send SMS or call to the monitors and also provides live tracking using GPS and GSM technique. The requirements are met and manageable. The proposed system has many advantages over the existing system such as the inclusion of the buzzer, the light indication system, the transmission through RF, manual control, SMS and call feature and finally, live tracking. This will help in preventing many drowning deaths in the future.
A novel chaotic teaching learning based optimization algorithm and its application in optimization of extreme learning machine
Authors: Ma Yunpeng , Tang Haoheng , Wang Heqi , Wang Zhenying , Zhang Xinxin , Li Lipeng 
Published: August 2021

Abstract
Recent ten years, the teaching learning based optimization algorithm (TLBO) has been widely concerned and successfully applied to solve various constraints and non-constraints problems. However, its convergence accuracy and convergence speed should be further improved. Therefore, a novel chaotic teaching learning based optimization algorithm (called CTLBO) is proposed. Firstly, chaotic variables are applied to initialize population individuals for increasing the diversity of population. Secondly, a kind of self-adaptive acceleration coefficient is introduced into teaching phase to enhance the convergence speed and solution quality. Finally, two population updating mechanisms are proposed to balance the exploration and exploitation capabilities in the learning phase. One is neighbor elitist search mechanism, another is chaos optimization mechanism. The performance of CTLBO is compared with five state-of-the-art optimization algorithms by several CEC mathematical problems. The experiment results show that the CTLBO yields better convergence rate than other algorithms on most testing functions. Additionally, the proposed CTLBO is applied to optimize the model parameters of extreme learning machine(ELM) and the tuned ELM is adopted to establish the NOx emissions model. Experiment results reveal that the NOx emissions model has good accuracy and meets the engineering requirement.
User online purchase behavior prediction based on fusion model of CatBoost and Logit
Authors: Cao Wenchao , Wang Ke , Gan Hongcheng , Yang Mingcheng 
Published: August 2021

Abstract
Analyzing users’ consumption preferences and willingness based on massive online behavior data can help optimize user experience and improve product purchase rate. For the online platform user data, feature engineering methods were used to pre-process, visualize, analyze and construct features. Then, a prediction and evaluation model of users’ online purchase behavior was developed based on the CatBoost algorithm, and the important features affecting users’ purchase behavior were quantitatively analyzed using the Logit Model. Compared with Random Forest, Support Vector Machine and XGBoost, the prediction model has better performance with prediction accuracy of 98.38% and F1 score of 0.775. The results show that user basic information, user access data and user login data have impact on user online purchase behavior. Especially the number of visits with or without coupons, unfinished orders of annual classes, number of coupons received, repeated learning of courses, public number following, days of login, city and number of learning classes sections are important features affecting purchase behavior.
Unified Distillation: Supervising the Student to Make Fewer Mistakes
Authors: Chen Yuan , Rong Pan 
Published: August 2021

Abstract
Knowledge distillation has become a popular task in modern deep learning applications by performing knowledge transfer from a cumbersome neural network commonly called “teacher model” to a much smaller network called “student model”. In traditional knowledge distilling process, there are usually two objectives for training the student model, namely hard target and soft target. However, sometimes it’s hard to find a trade-off between them. We unify the two objectives into one making it easier to perform knowledge distillation, and propose a novel distilling method called “Unified Distillation” to supervise the student to make fewer mistakes. The model can correct the wrong predictions according to the hard target, and maintain the advantage of knowledge distillation. Although our method can be used in almost all fields suitable for knowledge distillation, we choose neural machine translation as a study object for its complexity. We conducted experiments on three neural machine translation tasks, using a finetuned language model BERT as the teacher, and a Transformer base model as the student. The experimental results indicate that our method is better than traditional knowledge distillation method.
Differential evolution algorithm based on PCA to determine the objective function to optimize the configuration of microgrid
Authors: Wang Yunhu , Xu Ruobing , Duan Qing , Zhang Guobao , Huang Yongming 
Published: August 2021

Abstract
Recently, microgrids are increasingly used in our lives. The purpose of this paper is to solve the uncertainties of multi-objective decision-making and the instability of algorithms in the optimal configuration of traditional microgrids. This paper proposed a differential evolution algorithm based on PCA to determine the objective function. Firstly, a multi-objective optimization model of the micro-grid was established on the basis of fully considering the economy, environmental protection and stability of the micro-grid. Next, the first principal component of the multi-objective was extracted based on PCA combining innovatively the Pareto optimal solution set, and the multi-objective optimization problem was transformed into a single-objective optimization problem. Finally, the experimental simulation result shows that the algorithm proposed in this paper can comprehensively consider the economy, environmental protection and stability of the microgrid on the basis of ensuring the efficiency and stability of the algorithm to realize the optimized configuration of the microgrid.
Sequential Mechanical Fault Diagnosis in High Voltage Circuit Breaker using Attention Mechanism
Authors: Zhang Baoyu , Wang Xinru , Wang Jin 
Published: August 2021

Abstract
To address the shortcomings of traditional shallow-based model in terms of feature extraction and generalization capacity, this paper provides a high-voltage circuit breaker mechanical condition online detection scheme by using attention mechanism to locally weight the sample correlation and combining convolutional neural network (CNN) and long short-term memory (LSTM) network. The network uses convolutional layers for feature transformation of the raw vibration data, combined with the local time-domain feature representation capability of the gated unit, to extract fault-sensitive features. The temporal deep learning model using the attention mechanism enables it to extract global features of long-time sequences while making important features obtain higher weight parameters. The resulting model improves the overall learning and recognition efficiency. The experiments on the vacuum circuit breaker show that the proposed model has better performance than the classical diagnostic method based on the support vector machine.
Collaborative exploration of multiple unmanned surface vessels in complex areas based on PPO algorithm
Authors: Wang Kuo , Peng Haowen , Zhao Mingyang , Guan Wei 
Published: August 2021

Abstract
The increasing congestion of maritime traffic leads to frequent maritime traffic accidents. Unmanned search and rescue equipment provides a guarantee for rapid rescue operations. In marine accidents, the waiting time is short and the rescue risk factor is high. Unmanned surface vessel (USV) can transport rescue equipment to the accident site in the first time and carry out searching and rescuing, which will greatly improve rescue efficiency. With the development of technology, USV can autonomously conduct area exploration to assist in the discovery of survivors. This paper proposes a complex area exploration of multiple unmanned surface vessels based on the Proximal Policy Optimization algorithm (PPO). The PPO algorithm is used to improve training efficiency, achieve 100% search coverage in complex water surface areas, and make searching and rescuing operations safe, simple and efficient. Experimental simulations show that the algorithm can realize 100% area exploration, have high search efficiency, and consumes less energy. This is a new intelligent ship application that can complete 100% exploration without human involvement.
Cartilaginous spheroid-assembly design considerations for endochondral ossification: towards robotic-driven biomanufacturing
Authors: Nilsson Hall Gabriella , Rutten Iene , Lammertyn Jeroen , Eberhardt Jens , Geris Liesbet , Luyten Frank P , Papantoniou Ioannis 
Published: August 2021

Abstract
Spheroids have become essential building blocks for biofabrication of functional tissues. Spheroid formats allow high cell-densities to be efficiently engineered into tissue structures closely resembling the native tissues. In this work, we explore the assembly capacity of cartilaginous spheroids (d ∼ 150 µm) in the context of endochondral bone formation. The fusion capacity of spheroids at various degrees of differentiation was investigated and showed decreased kinetics as well as remodeling capacity with increased spheroid maturity. Subsequently, design considerations regarding the dimensions of engineered spheroid-based cartilaginous mesotissues were explored for the corresponding time points, defining critical dimensions for these type of tissues as they progressively mature. Next, mesotissue assemblies were implanted subcutaneously in order to investigate the influence of spheroid fusion parameters on endochondral ossification. Moreover, as a step towards industrialization, we demonstrated a novel automated image-guided robotics process, based on targeting and registering single-spheroids, covering the range of spheroid and mesotissue dimensions investigated in this work. This work highlights a robust and automated high-precision biomanufacturing roadmap for producing spheroid-based implants for bone regeneration.
Improving the low-cycle fatigue properties of laser-welded Al–Zn–Mg–Cu alloy joints using double-sided ultrasonic impact treatment
Authors: Chen Furong , Liu Chenghao 
Published: August 2021

Abstract
To improve the low-cycle fatigue properties of Al–Zn–Mg–Cu 7075 aluminum alloy laser-welded joints, a post-weld treatment method for double-sided ultrasonic impact treatment (DSUIT) was used to treat the joints. The mechanism of different DSUITs on the microstructure and fatigue properties of welded joints was analyzed. The results showed that DSUIT reduced the welding defects and roughness of the joint surface, and the dendrite structure at the upper and lower surfaces of the joints was broken to form a plastic deformation layer (PDL) with an approximate thickness of 100 μm. Furthermore, the texture strength and grain size at the PDL were reduced. A beneficial residual compressive stress was introduced into the upper and lower surfaces of the joints after the DSUIT. When the number of cycles was 2 × 106, the maximum fatigue strength of the joints was 103.02 MPa after DSUIT, indicating an increase of 111.8% of the untreated joints of 48.62 MPa. Moreover, observance of the fatigue fracture of the joints revealed that the PDL produced by the DSUIT inhibited the initiation and propagation of cracks at the surface. Therefore, the DSUIT effectively improved the low-cycle fatigue properties of the joints.
Mapping forest disturbances across the Southwestern Amazon: tradeoffs between open-source, Landsat-based algorithms
Authors: Reygadas Yunuen , Spera Stephanie , Galati Valerie , Salisbury David S , Silva Sonaira , Novoa Sidney 
Published: August 2021

Abstract
Local and cross-continental road building, increased economic teleconnections, growing agricultural demands, logging and mining practices, and general development processes are putting pressure on even the least densely populated regions of the Amazon, where local, regional, and global demand for food, fuel and fiber are resulting in observable biophysical effects. It is essential, then, that stakeholders can both map and understand the effects of these forest disturbances on ecosystem services. Multiple remote sensing algorithms focused on detecting vegetation changes have been developed: the challenge now lies in understanding which algorithm best suits the user´s study area and research objective. Using Google Earth Engine, we compared the performance of three algorithms –Continuous Degradation Detection (CODED), Landsat-based detection of trends in disturbance and recovery (LandTrendr), and Multi-variate Time-series Disturbance Detection (MTDD)– to detect and characterize forest disturbances in the Southwestern Amazon (Ucayali, Peru and Acre, Brazil) during the 2000–2020 period. In general, the results of all of the algorithms agreed with the reference data: overall accuracies were 94% (± 0.6% LandTrendr), 95% (±0.6% MTDD), and 96% (± 0.6% CODED). Although the map products exhibit similar spatial patterns, they often differ on the specific disturbance extent. CODED works well in capturing disturbances associated with roads, MTDD excels best at capturing entire disturbance patches, and LandTrendr excels both in terms of user friendliness and range of output options. Through three case study regions, we highlight land-cover change dynamics that have occurred in this remote, transboundary region over the last two decades. We also describe the strengths and weaknesses of each algorithm and demonstrate that it would be incorrect to assume that any one algorithm is the most accurate. Our work, then, improves the capacity of the community to understand how well each algorithm is suited best to map various forest disturbances to promote sustainable decision making.
A sophisticated case of division of labour in the trimorphic stamens of theCassia fistula(Leguminosae) flower
Authors: Saab Gabriella da Silva , Mansano Vidal de Freitas , Nogueira Anselmo , Maia Isabele Carvalho , Bergamo Pedro Joaquim , Paulino Juliana Villela 
Published: August 2021

AbstractBuzz-pollinated pollen flowers have pollen as the primary resource for pollinators and must deal with a conflict between the exploitation of pollen grains by bees and pollination success. It has been hypothesized that heterostemony allows division of labour between stamens as a solution to the pollen dilemma. To test the division of labour hypothesis, we chose Cassia fistula, which has a trimorphic androecium and analysed androecium development, pollen grain release mechanisms and visitor behaviour. We explored the reflectance of floral organs and carried out an exclusion experiment to test the attractiveness of each stamen morph to the bee species. Finally, we explored the structural, ultrastructural and functional variation between the pollen grains, including pollen viability across stamen morphs. The differences among the three stamen morphs, which is developed from two whorls of the stamen, are the first evidence of the division of labour in our study system. Large Bombus and Xylocopa bees actively and exclusively exploited the pollen grains from the central poricidal anthers generating pollen deposition on their bodies. The reflectance pattern of floral organs indicated a targeting of these large bees to the central anthers, corroborated by the anther manipulative experiment where only the exclusion of the anthers positioned in the flower centre, especially the intermediate stamens, reduced bee visits. Both results revealed a division of labour, in which the intermediate stamen morph was responsible for both floral attractiveness and pollen resources. Only the largest stamen morph produced germinable pollen grains, highlighting their role as pollinating stamens. The smallest stamen morph has a less clear function, likely representing an economy in pollen production for feeding function. Our findings suggest that the evolution of the trimorphic androecium is associated with division of labour in large pollen flowers and can represent a strong strategy for circumventing the pollen dilemma, optimizing the feeding function by reducing pollen grain investment from central anthers.
Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method
Authors: Gao Jianzhao , Zheng Shuangjia , Yao Mengting , Wu Peikun 
Published: August 2021

Abstract

Motivation
The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in the protein surface or inside of protein. However, this computation will fail when the residues information is missing.

Results
In this article, we proposed a novel method for estimation RSA using the Cα atom distance matrix with the deep learning method (EAGERER). The new method, EAGERER, achieves Pearson correlation coefficients of 0.921–0.928 on two independent test datasets. We empirically demonstrate that EAGERER can yield better Pearson correlation coefficients than existing RSA estimators, such as coordination number, half sphere exposure and SphereCon. To the best of our knowledge, EAGERER represents the first method to estimate the solvent accessible area using limited information with a deep learning model. It could be useful to the protein structure and protein function prediction.

Availabilityand implementation
The method is free available at https://github.com/cliffgao/EAGERER.

Supplementary information
Supplementary data are available at Bioinformatics online.
Rethinking the Rhetorical Epistemics of Gaslighting
Authors: Graves Clint G , Spencer Leland G 
Published: August 2021

AbstractGaslighting is defined as a dysfunctional communication dynamic in which one interlocutor attempts to destabilize another’s sense of reality. In this article, we advance a model of gaslighting based in an epistemic rhetoric perspective. Our model directs attention to the rhetorics used to justify competing knowledge claims, as opposed to philosophical models that tend to rely on objective truth-value. We probe the discursive manifestations of gaslighting in logocentric, ethotic, or pathemic terms. We then apply our model to explain sexist and racist gaslighting that derives power from normatively instantiated discourses of rape culture and White supremacy. Specifically, our analysis identifies the appeal structures used to legitimate such gaslighting in response to disclosures of sexual violence and testimony about racial injustice.
Critical oxygen delivery threshold during cardiopulmonary bypass in older cardiac surgery patients with increased frailty risk
Authors: Smoor Rosa M , van Dongen Eric P A , Verwijmeren Lisa , Schreurs Inge A A M , Vernooij Lisette M , van Klei Wilton A , Noordzij Peter G 
Published: August 2021

Abstract OBJECTIVESOlder patients have a higher cardiac surgery-associated acute kidney injury (CSA-AKI) related mortality. Low oxygen delivery (DO2) during cardiopulmonary bypass (CPB) is a risk factor for CSA-AKI, but critical DO2 thresholds for older patients are unknown. This study investigated critical DO2 thresholds for CSA-AKI in patients ≥70 years undergoing on-pump cardiac surgery.METHODSPatients were enrolled from July 2015 until August 2017. CPB data from 432 patients were collected, and DO2 values were calculated per minute. The primary outcome was CSA-AKI. The association between DO2 and CSA-AKI was analysed with multivariable regression analysis. Multiple DO2 thresholds were analysed. The association between CSA-AKI and the area below the DO2 thresholds (DO2 deficit) was evaluated, as was the association between frailty and CSA-AKI.RESULTSCSA-AKI occurred in 63 (14.6%) patients. Mean and nadir (lowest) DO2 values were lower in patients with CSA-AKI (283 vs 312 ml/min/m2; P-value &lt;0.001 and 238 vs 270 ml/min/m2; P-value &lt;0.001, respectively). The adjusted relative risk for CSA-AKI was 1.006 [99% confidence interval (CI) 1.001–1.012] per ml/min/m2 nadir DO2 decrease. The critical DO2 threshold was 270 ml/min/m2 [adjusted relative risk 2.06 (99% CI 1.33–2.80)]. The DO2 deficit below 270 ml/min/m2 was associated with CSA-AKI [adjusted relative risk 2.84 (99% CI 1.87–3.81)]. No association between frailty and CSA-AKI was found (P = 0.82).CONCLUSIONSLow DO2 increased the risk for CSA-AKI in older patients who had cardiac surgery. A critical DO2 threshold of 270 ml/min/m2 was applicable for frail and non-frail patients. The efficacy of a DO2 &gt;270 ml/min/m2 to reduce CSA-AKI in older patients needs further evaluation.
Cardinium inhibits Wolbachia in its mite host, Tyrophagus putrescentiae, and affects host fitness
Authors: Hubert Jan , Nesvorna Marta , Pekar Stano , Green Stefan J , Klimov Pavel B 
Published: August 2021

ABSTRACT
Interactions among endosymbiotic bacteria inside their eukaryotic hosts are poorly understood, particularly in mites. The mite Tyrophagus putrescentiae is a common, medically important generalist species that has many intracellular and gut bacterial symbionts. In the experiments, we examined bacterial abundances and composition in mite populations obtained by controlled mixing of stock mite populations that differed in the presence/absence of the major intracellular bacteria Wolbachia and Cardinium. Changes in microbial communities were characterized using 16S ribosomal RNA high-throughput sequencing (pooled mite individuals) and quantitative PCR for key microbial taxa (individual mites). Mite fitness was estimated as a parameter of population growth. We detected that in mixed mite populations, Cardinium and Wolbachia can co-occur in the same mite individual. The presence of Cardinium was negatively correlated with the presence of Wolbachia and Bartonella, while the Bartonella and Wolbachia were positively correlated in individual level samples. Since mixed populations had lower abundances of Wolbachia, while the abundance of Cardinium did not change, we suggest that the presence of Cardinium inhibits the growth of Wolbachia. The mixed mite populations had lower population growth than parental populations. The possible effect of symbionts on the fitness of mixed population is discussed.