bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smart Framework for Automatic Control of Home Appliances Using IoT
Authors: Gundavarapu Mallikarjuna Rao , Ineni Srineeth Kumar , Sathvika K. , Keshava Gannavaram Shiva , Charan Uppala Rishik 
Published: September 2022

Abstract
With the advent of the 21st century, comfort and efficiency started becoming a part of a common man’s life. Virtual assistants like Amazon Alexa and mobile applications like Google Assistant have tremendously helped in making Home Automation Systems more effortless and orderly. Efforts are still being made for better intra-connection of our homes. Home automation has created a great impact in the field of the Internet Of Things. In this paper, we have used the pre-existing technology of Alexa Echo to make Home Automation more cost-effective by enabling non-smart devices to react to the virtual assistant. In this framework, we proposed a smart node (relay) that can remotely control the connected electrical gadget using text/voice commands. The status can be monitored and controlled by the Alexa app remotely without the need for physical voice commands to Alexa. Further, room ambience, as well as emotion-based lighting, can be provided.
Moving Object Detection and Speed Estimation by Digital Image Processing
Authors: Khasim K N V , Anilkumar G , Vamshi G , Ch Sai kumar , Nikhil Yadav M 
Published: September 2022

Abstract
Intelligent network systems and security surveillance are used for smart city development. Manual and heritage technology is used in surveillance systems to detect speeds higher than usual in developing countries. This process can be automated with ease by leveraging the potential of image processing. Moving object detection is one of the essential tasks in image processing because of its prominent role in many real-world applications. Vehicle speed detection can be achieved by employing image and video processing methods. In this work, a unique approach is proposed for moving object detection and speed estimation based on an integrated approach in video sequence frames as opposed to the most commonly used RADAR and LIDAR devices for traffic law enforcement. Video data is collected and analyzed for speed in real-time without any sensor calibrations,thereby it removes any external hardware dependency requirements. Moving vehicles are segmented out by using frame-subtraction and masking techniques. The proposed algorithm tracks the time taken by the car to cover a predetermined fixed distance in order to calculate its speed.
Comparison of Deep Learning & Adaptive Algorithm Performance for De-Noising EEG
Authors: Al Imran Ibrahim , Rabbani Mamun 
Published: September 2022

Abstract
Various forms of artifacts can readily contaminate an electroencephalogram recorded using surface electrodes. A comparison of several electroencephalogram (EEG) de-noising methods is shown here. Five distinct forms of noise are reduced using three different strategies, and the results are compared. These three procedures are Recursive Least Squares (RLS) adaptive algorithm, Least Mean Squares (LMS) method, and Fully Connected Neural Network (FCNN). The results are shown using time-domain plots of the real EEG signal, noisy EEG signal, and forecasted EEG signal. For comparing the performance of the three de-noising techniques here relative-root-mean-square-error (RRMSE) and signal-to-noise-ratio were used. Here, exploring the values of the parameters, we find that FCNN predicts a better result than other two algorithms.
Wireless Communication for Optical Link in Network
Authors: Sebastian Vindro Jude P. , Kathirvel C. , Venkatesh Kumar S. 
Published: September 2022

Abstract
Laser, as a correspondence medium can give a decent substitute to the present-day correspondence frameworks as the issue of connection point looked on account of EM waves, isn’t there and a high arrangement of mystery is accessible. Optical correspondence offers a practical option in contrast to RF interchanges for between satellite connections and different applications where superior execution joins are a need. This paper is planned to give an outline of advancement that has been achieved with the assistance of lasers to send sound and explains on the way things are being utilized alongside future turns of events. In the transmitter, the power of the laser point of support is managed by the consequence of the code oscillator. Utilizing a push to on switch the tone oscillator is initiated to alarm the individual at the collector end prior to beginning a voice correspondence utilizing the mouthpiece. The beneficiary gets the power changed. We will dissect the outcome utilizing sun powered chargers and standard optical identifiers (Light-dependent resistors).
Driver Drowsiness Detection and Monitoring System using Machine Learning
Authors: Chinthalachervu Rohith , Teja Immaneni , Ajay Kumar M. , Sai Harshith N. , Santosh Kumar T. 
Published: September 2022

Abstract
In the present world, lots of road accidents take place due to the lack of attention and alertness of driver. This is termed as driver drowsiness. This leads to a lot of unfortunate situations causing adverse damage to human lives. The main goal of this research is the detection of driver drowsiness and an appropriate response to the detection. There are many methods which are based on the motion of the vehicle or based on the driver’s behavior. One of the methods is the physiological method which helps in distracting the driver from drowsiness and making him alert. And few methods require expensive sensors and deals with a lot of data. Therefore, this paper develops a system for detecting drowsiness in real time with proper procedure and accuracy which is acceptable. In this system, the driver’s facial expressions are captured and recorded using a webcam. Every movement in each frame is detected using few techniques of image processing. The Eye Aspect Ratio, Mouth Opening Ratio, and Nose Length Ratio are calculated using the landmark points on the face. The calculated values are compared to the threshold values developed by the system and the difference in value leads to the detection. At the same time, the machine learning algorithms are also implemented in offline manner. Based on the classification, the system has successfully achieved 95.58% of sensitivity and 100% of specificity using Support Vector Machine. This model system is compatible with all kinds of vehicles.
Investigation of chordwise functionally graded flexural rigidity in flapping wings using a two-dimensional pitch–plunge model
Authors: Reade Joseph , Jankauski Mark 
Published: September 2022

Abstract
Insect wings are heterogeneous structures, with flexural rigidity varying one to two orders of magnitude over the wing surface. This heterogeneity influences the deformation the flapping wing experiences during flight. However, it is not well understood how this flexural rigidity gradient affects wing performance. Here, we develop a simplified 2D model of a flapping wing as a pitching, plunging airfoil using the assumed mode method and unsteady vortex lattice method to model the structural and fluid dynamics, respectively. We conduct parameter studies to explore how variable flexural rigidity affects mean lift production, power consumption and the forces required to flap the wing. We find that there is an optimal flexural rigidity distribution that maximizes lift production; this distribution generally corresponds to a 3:1 ratio between the wing’s flapping and natural frequencies, though the ratio is sensitive to flapping kinematics. For hovering flight, the optimized flexible wing produces 20% more lift and requires 15% less power compared to a rigid wing but needs 20% higher forces to flap. Even when flapping kinematics deviate from those observed during hover, the flexible wing outperforms the rigid wing in terms of aerodynamic force generation and power across a wide range of flexural rigidity gradients. Peak force requirements and power consumption are inversely proportional with respect to flexural rigidity gradient, which may present a trade-off between insect muscle size and energy storage requirements. The model developed in this work can be used to efficiently investigate other spatially variant morphological or material wing features moving forward.
The American electric utility industry’s role in promoting climate denial, doubt, and delay
Authors: Williams Emily L , Bartone Sydney A , Swanson Emma K , Stokes Leah C 
Published: September 2022

Abstract
It is now well established that fossil fuel companies contributed to undermining climate science and action. In this paper, we examine the extent to which American electric utilities and affiliated organizations’ public messaging contributed to climate denial, doubt, and delay. We examined 188 documents on climate change authored by organizations in and affiliated with the utility industry from 1968 to 2019. Before 1980, electric utilities’ messaging was generally in-line with the scientific understanding of climate change. However, from 1990 to 2000, utility organizations founded and funded front groups that promoted climate doubt and denial. After 2000, these front groups were largely shut down, and utility organizations shifted to arguing for delayed action on climate change, by highlighting the responsibility of other sectors and promoting actions other than cleaning up the electricity system. Overall, our results suggest that electric utility industry organizations have promoted messaging designed to avoid taking action on reducing pollution over multiple decades. Notably, many of the utilities most engaged in communicating climate doubt and denial in the past currently have the slowest plans to decarbonize their electricity mix.
Do long-term high nitrogen inputs change the composition of soil dissolved organic matter in a primary tropical forest?
Authors: Niu Guoxiang , Yin Gege , Mo Xiaohan , Mao Qinggong , Mo Jiangming , Wang Junjian , Lu Xiankai 
Published: September 2022

Abstract
Dissolved organic matter (DOM) plays a key role in forest carbon biogeochemistry by linking soil organic carbon (SOC) sequestration and water fluxes, which is further shaped by elevated atmospheric nitrogen (N) deposition. Although enhanced SOC sequestration was evidenced in tropical forests due to rising N deposition, it remains unclear how long-term N inputs affect soil DOM composition, which regulates SOC sequestration capability due to its mobility and biological instability. Here, the quantity, optical properties, and molecular-level characteristics of soil DOM based on a simulative N deposition experiment with four N addition levels (0, 5, 10, and 15 g m−2 yr−1) were studied in a primary tropical forest in south China. Results showed that 18 year N additions significantly altered soil DOM composition, with an increasing trend in soil dissolved organic carbon content. Medium- (10 g m−2 yr−1) and high-N addition (15 g m−2 yr−1) markedly elevated DOM average molecular weight by 12% and aromaticity, with specific ultraviolet absorbance at 254 nm increasing by 17%, modified aromatic index by 35%, and condensed aromatics by 67%. Medium- and high-N addition also increased recalcitrant DOM components but decreased other DOM components, with increasing percentages of lignin-like, tannin-like, and carboxylic-rich alicyclic molecule-like compounds, and decreasing percentage of more bioavailable contributions with H/C ratio >1.5. Importantly, significant correlations of the SOC content of the heavy fraction with optical properties and with recalcitrant DOM components were observed. These findings suggest that long-term N additions may alter soil DOM composition in a way to benefit soil OC storage in the primary tropical forests. It merits focusing on the mechanisms to association of soil DOM dynamics with SOC sequestration.
Projecting the impact of air pollution on child stunting in India—synergies and trade-offs between climate change mitigation, ambient air quality control, and clean cooking access
Authors: Dimitrova Asya , Marois Guillaume , Kiesewetter Gregor , Rafaj Peter , Pachauri Shonali , KC Samir , Olmos Sergio , Rasella Davide , Tonne Cathryn 
Published: September 2022

Abstract
Many children in India face the double burden of high exposure to ambient (AAP) and household air pollution, both of which can affect their linear growth. Although climate change mitigation is expected to decrease AAP, climate policies could increase the cost of clean cooking fuels. Here, we develop a static microsimulation model to project the air pollution-related burden of child stunting in India up to 2050 under four scenarios combining climate change mitigation (2 °C target) with national policies for AAP control and subsidised access to clean cooking. We link data from a nationally representative household survey, satellite-based estimates of fine particulate matter (PM2.5), a multi-dimensional demographic projection and PM2.5 and clean cooking access projections from an integrated assessment model. We find that the positive effects on child linear growth from reductions in AAP under the 2 °C Paris Agreement target could be fully offset by the negative effects of climate change mitigation through reduced clean cooking access. Targeted AAP control or subsidised access to clean cooking could shift this trade-off to result in net benefits of 2.8 (95% uncertainty interval [UI]: 1.4, 4.2) or 6.5 (UI: 6.3, 6.9) million cumulative prevented cases of child stunting between 2020–50 compared to business-as-usual. Implementation of integrated climate, air quality, and energy access interventions has a synergistic impact, reducing cumulative number of stunted children by 12.1 (UI: 10.7, 13.7) million compared to business-as-usual, with the largest health benefits experienced by the most disadvantaged children and geographic regions. Findings underscore the importance of complementing climate change mitigation efforts with targeted air quality and energy access policies to concurrently deliver on carbon mitigation, health and air pollution and energy poverty reduction goals in India.
Wintertime marine extreme temperature events modulate phytoplankton blooms in the North Pacific through subtropical mode water
Authors: Tak Yong-Jin , Song Hajoon , Park Jong-Yeon 
Published: September 2022

Abstract
Marine extreme temperature events (METs), including marine heatwaves (MHWs) and cold spells, have recently gained much attention owing to their vital influence on the marine ecosystem and social economy. Since METs can alter the upper ocean stratification and wintertime convective mixing in the northwestern North Pacific subtropical gyre (NPSG), their activities may modulate phytoplankton blooms by regulating entrainment of the subtropical mode water (STMW) with high NO

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. Furthermore, because STMW formed in the previous winter reemerges east of its formation site in the following winter, the METs activities imprinted in STMW affect phytoplankton blooms remote from its formation site. Here, we examined the relationship between the MET activities, STMW volume, and phytoplankton blooms using satellite observations and a data-assimilative coupled physical-biogeochemical model dataset. MET activities appearing in the STMW formation region during winter regulate the formation of STMW and the supply of NO

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from the subsurface, with the latter controlling the spring/autumn blooms in that region under NO

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-limited conditions. Subsequently, this water mass is transported eastward in the subsurface within the northern flank of the NPSG before reemerging east of the STMW formation site the following spring. This process results in a negative lag-correlation between MET activities and surface chlorophyll in the reemergence region; for example, MHWs in winter at the STMW formation site tend to lower the surface chlorophyll concentration one year later in the reemergence region. Our study suggests that the oceanic processes allow one year of predictability of the marine ecosystems by monitoring METs in the STMW formation site.
Future changes and seasonal variability of the directional wave spectra in the Mediterranean Sea for the 21st century
Authors: Lira-Loarca Andrea , Besio Giovanni 
Published: September 2022

Abstract
A state-of-the-art regional assessment of future directional wave spectra in the Mediterranean Sea and the projected changes with respect to hindcast is presented. A multi-model EURO-CORDEX regional ensemble of bias-adjusted wave climate projections in eleven locations of the Mediterranean are used for the assessment of future seasonal changes in the directional wave spectra under the high-emission scenario RCP8.5. This analysis allows us to identify climate change effects on the spectral energy of the swell and wind-sea systems and their seasonal variability which cannot be captured with the standard integrated wave parameters, such as significant wave height and mean wave direction. The results show an overall robust decrease in the predominant wave systems, resulting in a likely decrease in the significant wave height that is in agreement with previous studies. However, the results depict a robust increase in other less energetic frequencies and directions leading to a projected behavioral change from unimodal to bimodal/multimodal wave climate in many locations which has strong repercussions on the vulnerability of coastal assets and ports operability.
A feasibility study of Covid-19 detection using breath analysis by high-pressure photon ionization time-of-flight mass spectrometry
Authors: Zhang Peize , Ren Tantan , Chen Haibin , Li Qingyun , He Mengqi , Feng Yong , Wang Lei , Huang Ting , Yuan Jing , Deng Guofang , Lu Hongzhou 
Published: September 2022

Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused a tremendous threat to global health. polymerase chain reaction (PCR) and antigen testing have played a prominent role in the detection of SARS-CoV-2-infected individuals and disease control. An efficient, reliable detection tool is still urgently needed to halt the global COVID-19 pandemic. Recently, the food and drug administration (FDA) emergency approved volatile organic component (VOC) as an alternative test for COVID-19 detection. In this case-control study, we prospectively and consecutively recruited 95 confirmed COVID-19 patients and 106 healthy controls in the designated hospital for treatment of COVID-19 patients in Shenzhen, China. Exhaled breath samples were collected and stored in customized bags and then detected by high-pressure photon ionization time-of-flight mass spectrometry for VOCs. Machine learning algorithms were employed for COVID-19 detection model construction. Participants were randomly assigned in a 5:2:3 ratio to the training, validation, and blinded test sets. The sensitivity (SEN), specificity (SPE), and other general metrics were employed for the VOCs based COVID-19 detection model performance evaluation. The VOCs based COVID-19 detection model achieved good performance, with a SEN of 92.2% (95% CI: 83.8%, 95.6%), a SPE of 86.1% (95% CI: 74.8%, 97.4%) on blinded test set. Five potential VOC ions related to COVID-19 infection were discovered, which are significantly different between COVID-19 infected patients and controls. This study evaluated a simple, fast, non-invasive VOCs-based COVID-19 detection method and demonstrated that it has good sensitivity and specificity in distinguishing COVID-19 infected patients from controls. It has great potential for fast and accurate COVID-19 detection.
Primary production assessment on eco-engineering infrastructures: English Channel case study
Authors: Claquin P , Vivier B , Navon M , Rusig A M , Mussio I , Chasselin L , Deloor M , Dauvin J C , Boutouil M , Orvain F 
Published: September 2022

Abstract
In the Marineff project, experimental eco-engineering dyke modules were immersed in 2020 in the Bay of Cherbourg (English Channel). Eco-engineering and classic dyke modules were compared regarding biodiversity and productivity. This paper presents the monitoring strategy of primary production assessment applied. Primary production assessment was performed by coupling various technics in order to consider all the compartments of primary producers present on the dykes and in the associated surrounding water mass. Seaweeds, microphytobenthos and phytoplankton were studied. Benthic chambers measuring oxygen and pCO2 under controlled light, and technics based on variable fluorescence were applied on eco-engineering and classic dyke modules. Preliminary results are presented in order to illustrate the expected indicator and methodology improvement and limits were discussed. The whole data set will allow us to explore the relationship between productivity and biodiversity as a function of the sites and of the nature of the infrastructure, eco-engineering or not. At this stage of our study we pointed out that the macroalgae were 7.9 times more productive than biofilm per surface on eco-engineering module whilst only 1.5 times on classic module. Smooth surface appeared to favour biofilm productivity at this stage of colonisation while rough surface drove more quickly the system to a production dominated by macroalgae.
QRS complex detection using stationary wavelet transform and adaptive thresholding
Authors: Sharma Neenu , Sunkaria Ramesh Kumar , Sharma Lakhan Dev 
Published: September 2022

Abstract

Purpose. Electrocardiogram (ECG) signal is a record of the electrical activity of the heart and contains important clinical data about cardiovascular-related misfunctioning. The goal of the present work is to develop an improved QRS detection algorithm for the detection of heart abnormalities. Methods. In this present work stationary wavelet transforms (SWT) based method has been proposed for precise detection of QRS complex with ‘sym2’ mother wavelet. The stationary wavelet transform is a systematic mathematical tool to decompose the signal without downsampling using scale analysis and provides high detection of QRS complex and accurate localization of signal components. In the proposed method four level of decomposition is applied and the initial thresholding value is computed by the maximum amplitude of scale one at level four in SWT coefficients without the zero-crossing amplitude detection method. The multi-layered dynamic thresholding method has been applied to detect the true R-peak values and locate the QRS complex in the ECG signal. Results. For evaluation of results, the presented methodology is assessed on MIT-BIH, QTDB, and Noise stress test databases. In MIT-BIH, the sensitivity = 99.88%, positive predictivity = 99.93%, accuracy = 99.80% and detection error rate = 0.18% is achieved. In NSTD database, sensitivity = 97.46%, positive predictivity = 94.20%, accuracy = 91.95% and detection error rate = 8.47% and in QTDB, sensitivity = 99.95%, positive predictivity = 99.90%, accuracy = 99.71% and detection error rate = 0.16% is executed. Conclusion. In the presented proposed methodology, the computation complexity is low and exhibits a simple technique rather than an empirical approach. The proposed technique corroborates the performance for the detection of QRS complex with improved accuracy.