bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent Prediction and Face Recognition sensors for Next Generation Machines
Authors: Gowdham R , Harish G , Madavan C A Brijesh 
Published: May 2021

Abstract
A new generation of ATM machines has been developed. Face recognition based on the system proposed. Meanwhile, the recognition process plays an important role with a high-grade image with many details. Face image is used in this paper for authentication. At first, a specific person’s face image is differentiated with the database portrayal. Then outcome of the comparison is dispatched by serial communication to control block. If the individual is unauthorized, then the communication will be sent to the individual. The controlling part uses a micro-controller.
Privacy Preserving Verification Scheme for Cloud Platform Using DML
Authors: Sujaritha  , Akshara D , Ashfak Ahamed A , Chandhini shri V S 
Published: May 2021

Abstract
Deduplication technology is familiar in cloud-based services. It is used to reduce the space and the bandwidth requirements as a result, it reduces the redundancy and stores only the original copy. Deduplication finds its usage when a group of users stores the same data to the cloud storage service, but by using this method there is an issue regarding the ownership and security, and before getting into this process many users first encrypt their data and then share it with the cloud storage so that there is minimum or no privacy issue found. Recently so many models were released to solve this problem. That was actually done by sharing the same encrypted data for the same data which is shared by each owner. But by following this described method many flaws were found. We present a novel server-side deduplication model for all encrypted data in this paper. By following this model there is a proper control over the delegated data even after the control changes from person to person. So, Random convergent encryption and stable ownership key distribution are used. Through this method, the data theft can be prevented not only to the reward user but also to the old user who previously owned that data.
Big data analytics and Mining for effective Visualization and trends forecasting of Crime data
Authors: Dhanasri V , Dheekshitha S , Soundarya M 
Published: May 2021

Abstract
Big Data Analytics (BDA) is an excellent procedure for investigating assorted models. In this paper, we use BDA to crook records and exploring the records exam has directed for illustration. A magnificent records mining is done profound getting to know techniques are utilized. Following measurable research and illustration, a few exciting realities and examples are observed from the records of San Francisco, Chicago and Philadelphia. The prescient display that the Prophet version and Keras stateful LSTM carry out a manner, but is not higher than neural company models, where an appropriate length of the practice records is observed to be 3 years. These promising consequences will help for police divisions and regulation requirement institutions for extra recognizing of crimes and supply with a bit more understanding with a view to empower them to observe exercises, foresee the opportunity of occurrences, safely ship the property and enhance the dynamic cycle. Through reading large records implementation cases, we sought to recognize how large records analytics skills rework organizational practices, thereby producing ability benefits. In addition to conceptually defining 4 large records analytics skills, the version gives a strategic view of large records
Intelligent Monitoring and Forecasting Using Machine Learning Techniques
Authors: Sathyabama S , Stemina S C , SumithraDevi T , Yasini N 
Published: May 2021

Abstract
Stock market prediction is a crucial task and a prominent research area in the financial domain as investing in the stock market involves greater risk. Various events may affect public sentiments and emotions differently, which may affect the trend of stock market prices. Because of dependency on numerous factors, the stock prices are dynamic, and not static, highly noisy and nonlinear time series data. Due to its great learning ability, machine learning has been applied to this research area. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors such as news in stock prediction. Machine learning algorithms have made a great impact in predicting stocks accurately. Methods based on learning for stock price prediction are found to be popular and a lot of strategies have been used to improve the performance of the learning-based predictors. However, performing successful predictions in the stock market is still a challenge. In this paper, we use machine learning algorithms and predict the stock market trade to examine if it increases or decreases. Compared with existing learning-based methods, the effectiveness of this new enhanced learning-based method is demonstrated by using a Naïve Bayes classifier which is found to be consistent and gives the maximum efficiency.
Fire Detection System Using Machine Learning
Authors: Arul A , Hari Prakaash R S , Gokul Raja R , Nandhalal V , Sathish Kumar N 
Published: May 2021

Abstract
The modern age of electronics has seen a lot of development. But the development of electrical and electronic devices has led to a growing rate of fire accidents mainly due to the carelessness. The main problem in the fire accidents is that they are not being able to detect the fire as early as possible. By the time we detect the fire it becomes difficult to safely evacuate all or even reduce the damage caused. Our aim is to detect the fire as early as possible. The main objective is to detect the fire as early as possible. This can be attained by capturing the fire and motions of the spreading fire by using CCTV system and processing it to produce an alert. The processed image is compared with the pre fed image using ML and checked for fire accident. If the processed image matches the pre fed image then an alert message along with a buzzer is enabled. In simple word, this fire monitoring system is an addition to the existing CCTV cameras to detect the fire effectively.
Design and Fabrication of Printed Circuit Board for IoT Applications
Authors: Ram Prasanna K , Seetharaman R , Mani Lakshmanan H , Karthik G , Anandan K 
Published: May 2021

Abstract
Printed circuit boards are used for various product manufacturing and assisting purposes. This paper deals with a multifaceted trainer board for integrating various modules for Internet of Things (IoT) applications. As IoT plays a major role in the upcoming technological growth, the next generation of students need to learn about the hardware and software involved in a more effective manner. Also hardware is vital for hands-on training. To guide the students and aspirants to apply various ideas onto one board requires a perfect trainer board. Moreover, this trainer board should be well flexible to handle a vast variety of modules. Arduino Mega, is one example, where programming and interfacing of modules are easy and well-suited to develop IoT applications. This novel trainer board is designed for interfacing with the Arduino Mega, HC-05 (Bluetooth module) and Node MCU (Wi-Fi module) that are embedded on the board. The main purpose is to interface various Analog/Digital/PWM pins with various devices. Each Digital or analog pins are connected with 3-port or 4-port female headers to enable various modules to be linked with the board. Also, each 3 or 4-port header pins are connected with power supply and ground by default and there are interlinks between Analog/digital I/O. The board and external modules can be powered with 5V, 9V and 12V which can be selected based on the application.
What determines the effectiveness of national protected area networks?
Authors: Shah Payal , Baylis Kathy , Busch Jonah , Engelmann Jens 
Published: May 2021

Abstract
More than 15% of global terrestrial area is under some form of protection and there is a growing impetus to increase this coverage to 30% by 2030. But not all protection is effective and the reasons some countries’ protected areas (PAs) are more effective than others’ are poorly understood. We evaluate the effectiveness of national PA networks established between 2000 and 2012 globally in avoiding forest loss, taking into account underlying deforestation threats using a combination of matching methods and cross-sectional regressions. We then assess which demographic, agricultural, economic, and governance factors are most strongly associated with national PA effectiveness using machine learning methods. We estimate that national PAs established between 2000 and 2012 reduced deforestation in those areas by 72%, avoiding 86 062 km2 of forest loss. The effectiveness of national PAs varied by strictness of protection based on International Union for Conservation of Nature category. Strictly PAs reduced forest loss by 81% compared to what would have occurred without protection, while less strictly PAs reduced forest loss by 67%. Thus, the 26% of new PAs that were strictly protected contributed 39% of the total forest loss avoided within PAs between 2000 and 2012. If every country’s PAs were as effective as the country with the most effective PAs within the same region, they would have increased the area of deforestation avoided by 38%, saving a further 119 082 km2 of forest. Part of the variation in PA effectiveness across countries is explained by the placement of PA in areas facing higher deforestation threat. Countries with lower agricultural activity, higher economic growth and better governance are most strongly associated with greater country-level PA effectiveness.
Multiple cropping alone does not improve year-round food security among smallholders in rural India
Authors: Mondal Pinki , DeFries Ruth , Clark Jessica , Flowerhill Nicole , Arif Md. , Harou Aurelie , Downs Shauna , Fanzo Jessica 
Published: May 2021

Abstract
Achieving and maintaining food and nutrition security is an important Sustainable Development Goal, especially in countries with largely vulnerable population with high occurrence of hunger and malnutrition. By studying a small-scale agricultural system in India, we aim to understand the current state of dietary diversity and food insecurity among the farmer communities. The study landscape has witnessed a steady rise in multiple cropping (i.e. harvesting more than once a year) along with irrigation over the last two decades. Whether this multiple cropping can be expected to improve year-round food security is not well understood. We specifically examine if planting multiple food crops within a year is associated with dietary diversity and food security. We collected information on demographic and economic variables, farming activities and livelihood choices, from 200 unique households for three seasons (monsoon/rainy, winter, summer) during 2016–2018 (n = 600). Based on both a 24 h and a 30 days recall, we calculated several indicators, including the household dietary diversity score, the minimum dietary diversity for women, and household food insecurity access scale. At least 43% of the sample population experiences moderate to severe food insecurity in all seasons. Cereals (mainly rice) remain the most important food item irrespective of the season, with negligible consumption of other nutrient-rich food such as tubers, fish, eggs, and meats. Around 81% of women in all seasons do not consume a minimally diverse diet. Multiple cropping is associated with higher food security only during monsoon, while selling monsoon crops is associated with winter food security. Households practicing multiple cropping consume more pulses (a plant-based protein source) compared to single-cropping or non-farming households (p < 0.05). We find that multiple cropping cannot be used as a cure-all strategy. Rather a combination of income and nutrition strategies, including more diverse home garden, diverse income portfolio, and access to clean cooking fuel, is required to achieve year-round dietary diversity or food security.
News selection and framing: the media as a stakeholder in human–carnivore coexistence
Authors: Arbieu U , Chapron G , Astaras C , Bunnefeld N , Harkins S , Iliopoulos Y , Mehring M , Reinhardt I , Mueller T 
Published: May 2021

Abstract
The media widely covers large carnivores and their impacts on human livelihood and plays an important role in their conservation. Yet, we know little about how species identity affects news selection, framing, accuracy and information flow. We investigated the online coverage of two cases of attacks or alleged attacks on humans alternatingly attributed to wolves and dogs in Greece and Germany. The period during which wolves were considered the primary suspects for the attacks was covered by up to two times more articles than when dogs were suspected. Wolves were presented as more likely suspects for the attacks than dogs, and wolf articles contained more inaccuracies measured as title-text mismatches. Press agencies played a significant role in the selection and dissemination of wolf news. We suggest that conservation scientists, journalists and policy makers work together to ensure an accurate representation in the media of human–carnivore coexistence and its challenges.
Statistical seasonal forecasting of tropical cyclones over the western North Pacific
Authors: Chan Kelvin T F , Dong Zhenyuan , Zheng Minglin 
Published: May 2021

Abstract
Forecasting tropical cyclone (TC) activities has been a topic of great interest and research. Many studies and existing seasonal forecasting models have examined and predicted the number of TCs (including geneses and landfalls) mainly based on the environmental factors in the peak TC season. However, these predictions can be time-consuming, computationally expensive and uncertain, depending on the efficiency and predictability of the dynamical models. Therefore, here we propose an effective statistical seasonal forecasting model, namely the Sun Yat-sen University (SYSU) Model, for predicting the number of TCs (intensity at tropical storm or above) over the western North Pacific based on the environmental factors in the preseason. The nine categories comprising 103 candidate predictors in 1980–2015 (36 years) are systematically investigated. The best subset selection regression shows that the sea surface temperatures at the tropical North Atlantic and eastern North Pacific in April, the 500 hPa geopotential height difference between April and January at the open ocean southwest of Australia and the 700 hPa geopotential height at the North Pacific in April are the most significant predictors. The correlation coefficient between the modeled results and observations reaches 0.89. The model is successfully validated by leave-one-out, nine-fold cross-validations, and later 5 year (2016–2020) observations. The prediction of the SYSU Model exhibits a 95% hit rate in 1980–2020 (39 out of 41), suggesting an operational potential in the seasonal forecasting of TCs over the western North Pacific.
Development of Towed Radio Buoy for AUV
Authors: Yu Bykanova A , Kostenko V V , Michailov D N 
Published: May 2021

Abstract
One way to extend the functionality of autonomous underwater vehicle is to use a surface buoy-repeater that provides a high-speed data exchange channel between the operator station and AUV in real time. Also, the data of the receiver of the satellite navigation system of the buoy make it possible to correct errors accumulated by the on-board navigation system of the device without using hydroacoustic navigation devices. The report presents the design stages of the towed radio buoy, its composition and functional diagram, structural appearance and some results of field tests.
Effect of New Feed Additive on Growth Performance and Immunoglobulin of Broilers
Authors: Kareem K. Y. , Loh T. Ch. , Foo H. L. 
Published: May 2021

Abstract
Recently postbiotics and inulin combinations used as a new feed additives. Within current study, we examined the effect of new feed additives on growth performance and immunoglobulin of broiler. 216 one day-old male chicks were distributed into six treatments, six replicates/six birds in cage system, negative control (NC) without additive, positive control (PC) basal diet + antibiotic, Basal diet + 0.15% postbiotic + 1.0% inulin (T1), Basal diet + 0.3% postbiotic + 1.0% inulin (T2), Basal diet + 0.45% postbiotic + 1.0% inulin (T3), Basal diet + 0.6% postbiotic + 1.0% inulin (T4) and nourished for six weeks. Results demonstrated that birds that fed T3 and T1 had greater (p<0.05) body weight in the starter and finisher phase than negative control birds. The outcomes from the current study exhibited that the immune response increased especially at the starter phase for both types of immunoglobulin. We conclude that combinations of postbiotic and inulin are potential substitutes for antibiotic in poultry industry as growth promoters.
Study on Fracturing Parameters Optimization of Horizontal Wells in Low Permeability Layers
Authors: Sun Meifeng 
Published: May 2021

Abstract
Block A has poor and thin oil layers, and it is developed with horizontal wells to achieve Higher recovery. The horizonal initial production capacity 3-5 times the surrounding straight well initial production. However. As the development time goes on, some horizontal wells produce more water. This is mainly because of their well types. The horizontal wells have special layers and those layers are produced differently. In order to improve the production of different layers, we can use hydraulic fracturing to improve the production of horizontal wells. This paper uses numerical simulation technology to establish geological model of the block, to optimize the fracturing crack spacing, length, number of bars and other parameters, so we can maximize the effect of measures, while providing guidance for similar oil field level fracturing measures.
Quantitative identification of interlayer in delta phase oilfield
Authors: Liu Xinyu , Cong Lin , Ma Shizhong , Zheng Bin 
Published: May 2021

Abstract
It is very difficult to accurately tap the potential of remaining oil in delta phase oilfield, and the analysis of geological origin of remaining oil is particularly critical. This paper analyzes and studies the types and genesis of interbeds and quantitative identification of interbeds in delta phase oilfield. The data analysis shows that: on the basis of the existing data, the core qualitative interlayer identification is carried out first, and the types and causes of the interlayer are studied. Then, the identification standard of interlayer is established to identify logging interlayer. If the coincidence rate is low, it is necessary to modify the identification standard, reestablish the identification standard of rock electrical corresponding interlayer, and carry out logging interlayer identification again. Finally, we must carry out manual inspection, interpretation inspection and rationality analysis.
Research on horizontal directional staged fracturing technology for roof of surface wells
Authors: Ge Chungui , Sun Dongling , Wang Xinkun , Yuan Benqing , Zhang Yongjiang , Cao Jianjun 
Published: May 2021

Abstract
In order to reduce the outburst risk of soft broken high gas coal seam, based on the gas geological conditions of Luling Mine field, the technology of segmented roof fracturing in water level section of U-shaped well in broken soft coal seam was tested. The safe drilling in the roof rock of horizontal section of coal seam was realized by directional drilling, and the trajectory of horizontal section was controlled in the roof rock of target coal seam 10000m3 / d, the pumping effect is good.
Experimental Study on Subgrade Performance of Collapsible Loess Mixed with Concrete Gravel
Authors: Zhen Yao , Liu Bailai 
Published: May 2021

Abstract
Collapsible loess-like and concrete breccia is a rare special soil type in China, which is widely distributed in Baoji area. The paper preliminarily studied its collapsibility mechanism and engineering properties. The experimental results show that mixing 30% concrete crushed gravel has the best compressive effect and the best bearing ratio in California. Scanning by electron microscope found that after collapsible loess was mixed with concrete gravel, as the proportion of concrete increased, its internal structural stability increased. Concrete gravel recycling projects in subgrade engineering in collapsible loess areas have a certain feasibility, and its economic value is great. In future studies, it can be studied for higher proportions of concrete gravel.
Calibration Method and Measurement Uncertainty Evaluation for the Zero Error of Level Rules
Authors: Yang Yi , Wang Tao , Dai Liwei 
Published: May 2021

Abstract
A calibration method for the zero error of level rules was proposed. According to the calibration method, the evaluation model of measurement uncertainty was established, and the sources of uncertainty were analyzed. Combined with the measurement example, the evaluation process of the measurement uncertainty for the zero error of level rules was described.
Failure Analysis of the Aircraft Accessories Graphite Seal Ring
Authors: Wang Zhanbin , Fan Jinjuan , Zhang Bing 
Published: May 2021

Abstract
After 1300 hours of operation, the aircraft accessories graphite seal ring parts were overhauled with the aircraft engine. Then the seal assemblies were reinstalled on the engine. The graphite seal ring fractured 1 hour of used. The failure cause of the graphite seal ring was analyzed by appearance inspection, structure relationship analysis of the seal parts, surface and fracture observation, physical performance testing and composition examination. The analysis results show that the physical performance of the failed graphite seal ring met the requirement, and no abnormality was observed in the structural composition. But in the long-term used process, there was mutual wear between the fixing groove of the graphite seal ring and the limiting bracket of the metal static ring seat. As a result, the operating space of graphite seal ring was increased. The overall coordination stability decreases after the secondary assembly. Therefore, when the engine starts, the fluctuating friction occurs between the graphite seal ring and the friction pair, and the lubrication condition was poor, resulting in the graphite ring carrying capacity was too large, and then led to the graphite ring fracture in a short time after maintenance. This leads to fracture of graphite ring in a short time after overhaul. In order to prevent the failure from happening again, this paper gives the corresponding improvement suggestions.
The main controlling factors of coal seam injection heat enhanced extraction technology
Authors: Jia Quanmin 
Published: May 2021

Abstract
In order to improve the gas extraction rate in low permeability coal seam, the coal injection thermal theory was proposed. Coal seam heat injection technology is still at the stage of theoretical and laboratory research. Thermal coal injection process is a very complex multi-physics coupling problem, involving factors such as temperature, humidity, pressure and other factors on the effect of heat injection. According to the research results of seepage mechanics, heat transfer, rock mechanics and predecessors, the main controlling factors of the coal seam heat injection process are analyzed in terms of gas adsorption, desorption and coal seam permeability, and a multi-field coupling model for coal seam heat injection is analyzed and the solution of the above provides research ideas.