bluuebunny/crossref_metadata_2025 huggingface dataset:
DBSCAN Clustering Algorithm for Efficient Container Allocation in Cloud Computing Environment
Authors: Saeed Amany AbdElSamea
Published: October 2024

The deployment, management, and scalability of applications in cloud computing environment have all been revolutionized using containerization by encapsulating the applications and their dependencies into lightweight, portable units called containers. Although containers have many advantages, such as isolation, consistency across environments, and quick deployment, effective resource allocation is still a significant barrier in dynamic and heterogeneous cloud systems. Clustering techniques are crucial for cloud computing environments because they efficiently allocate and regulate resources to satisfy varying demands of workload. Clustering algorithms aggregate related workloads or containers into clusters allowing for more efficient resource utilization and better performance isolation. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular clustering algorithm that effectively discovers clusters of arbitrary shapes in spatial data while effectively handling noise. DBSCAN is very versatile and suitable to a wide range of datasets because it does not need a set quantity of clusters, in contrast to other standard clustering techniques. In order to improve load balancing and reduce resource execution times while simultaneously increasing resource utilization rates, this paper proposes the DBSCAN clustering algorithm for containers. The experimental findings demonstrate that the suggested algorithm performs better than FCFS in terms of response time, and throughput.
Classification and Detection of Chicken Disease Using CNN with Image Classification Technique
Authors: R Ms. Likitha , P Ms. Harshitha , M Ms. Rashmi
Published: October 2024

Artificial intelligence technology in deep learning is one of the popular classification methods. The development of deep learning technology is expected to assist farmers in identifying the types of infectious diseases that attack chickens based on feces images so as to increase production yields. Several infectious diseases that attack chickens can be identified through their feces, including newcastle disease caused by a virus, pullorum caused by bacteria, and coccidiosis caused by parasites. To identify, it is necessary to classify the types of diseases that attack by using images of chicken feces. With deep learning based on Convolutional Neural Network (CNN) in Keras/TensorFlow, the percentage of images that were accurately predicted according to the classification of infectious diseases suffered by chickens was 95.28 percent and less accurate was percent
Comparative Analysis of PV-Connected Power Quality Conditioners under the Source Voltage Disturbances
Authors: Kurmeti Nidhi , Agrawal Dr. Deepak , Vishwakarma Saurabh , Chouhan Sakshee
Published: October 2024

A new face of research is witnessed in recent decade to incorporate the dual functionality in designing the PV inverter which are merged with the benefits of customized power conditioner. The benefits of reactive power control strategies with PV inverters are provided. These inverters do the dual function of Grid interconnection as well as PV inverter behaves as a source for DC supply of CPD. In literature a special class of theses inverters are sited frequently which can perform both function of integrating solar with the grid as well as mitigating power quality issues. In the view of present scenario this paper presents the performance analysis of such customized converters with PV integration namely; PV-DSTATCOM, PV-DVR, PV-UPQC. The combination of solar PV systems and CPDs offers multiple solutions of power quality enhancement and clean energy generation. This thesis presents most popularly used three types of compensating devices namely STATCOM, DVR and UPQC. These devices in conjunction with solar energy presents an energy efficient compensation system which can supply active as well as reactive power as per the requirement. These devices are discussed in detail in the coming section with simulation results to presents their utility in enhancing power quality of distribution system
Digital Twin of Tube Hydroforming Press and Process
Authors: Islam Md Rakibul , Wang Minghui
Published: October 2024

Several constructed models have already been introduced in the tube hydroforming process which are great resorts to develop forming and quality improvement. However, in the tube hydroforming process, digital twin implementation can increase the quality of the product by inline monitoring as the digital twin is a virtual representation of the physical object and provides insights into performance, behavior, and conditions. In this paper, for a case study, a 350-ton hydraulic press machine was used as a physical twin with a symmetric die to deform the tube blank into the die shape. The real-time punch displacement, and internal pressure data were acquired by the sensors installed, and based on that monitoring and comparing of the parameters was done based on LabVIEW with the optimized loading path. The press and process digital twin uses finite element modeling to predict maximum and minimum thickness and optimization results show that 19% of maximum thickness was achieved
Statistical Analysis for Land Use Plastic Waste in India using AI
Authors: Akaash J N Guru , Tripathy Varun , R. L Priya , Nair Ajay , Kasimani Manigandan
Published: October 2024

India faces a major problem of Plastic Waste Management, where urbanization, industrialization, and economic growth have contributed to an increase in plastic waste generation. The rapidly growing population and the improved living standards have only worsened this problem. As per details provided by 35 Indian States/Union Territories, estimated plastic waste generation during the financial year 2019-20 is approximately 34,69,780 tonnes per annum. Solid waste collection, transportation, treatment, and disposal costs for Urban Local Bodies (ULBs) range from Rs.500 to Rs.1,500 per tonne. However, very little money is invested in scientific plastic waste disposal. Statistics on plastic waste generation are insufficient since quantified treatment of environmental issues is difficult. The problem with the data on plastic waste generation in India is that it has extrapolated values from a study prepared by the Central Pollution Control Board (CPCB) [12] . Moreover, most of the existing systems discuss the analysis of the amount of plastic waste generated in a specific area and fail to discuss the rate of growth and amount of plastic waste generation in the next few years. The proposed work calculated the amount of plastic generated in different states and ULBs of India for the past 10 years and thereby predicted the amount of plastic that will be generated in the following 10 years, thus suggesting mitigation measures in places having high amounts of plastic waste generation
Anti-Biofilm Potential of Secondary Metabolites from Microbes: A clinical approach
Authors: Chakraborty Tamoghna , Chakraborty Tamalika
Published: October 2024

The growing field of anti-biofilm research focuses on secondary metabolites from microorganisms, revealing a promising approach for the development of innovative medicinal and biocontrol methodologies to combat biofilm-related problems. Biofilms, complex conglomerates of microorganisms encased in a self-secreted polymeric scaffold, present significant challenges in medical, industrial, agricultural, and marine environments due to their resistance to standard antimicrobial interventions and ability to evade host immune responses. This study emphasizes the critical role of secondary metabolites derived from actinomycetes, fungi, and marine microorganisms in disassembling pre-formed biofilm architectures, inhibiting quorum sensing pathways, and impeding microbiological entity attachment to substrates. Such discoveries emphasize the potential of these organic compounds in developing novel ways to mitigate the harmful effects of biofilms. The potential for using anti-biofilm secondary metabolites is enormous, with significant benefits in the medical sector by potentially reducing the occurrence and severity of biofilm-related infections through their inclusion into coatings for medical devices, oral hygiene products, and healing applications. In the field of agriculture, the use of these metabolites has the potential to improve plant defense and soil vitality, supporting ecologically responsible farming methods by reducing reliance on synthetic chemicals. The growing exploration of microbial diversity, particularly in understudied or harsh habitats, highlights the potential for the identification of novel bioactive chemicals.
Experimental Gas Leakage Detection Using Integrated Sensor Arrays Using Deep Learning
Authors: Masali Milad Hadizadeh , Mobtahej Mohammadamin , Zargarzadeh Hassan
Published: October 2024

Gas leaks pose significant risks to both industrial safety and environmental conservation, necessitating the use of reliable detection methods. This study details a method for detecting gas leaks by integrating multiple sensors and applying advanced machine learning algorithms, focusing on enhancing detection accuracy and reliability through data-driven analysis rather than relying on conventional single-point sensor systems. Sensor arrays based on environmental susceptibility data detect conditions prone to gas leakage. The proposed system employs deep learning techniques and demonstrates adaptability to detect and respond to gas leaks in real-time. By integrating data from multiple sensors measuring gas concentration, temperature, and pressure, the development of a comprehensive and context-aware detection system is enabled. The system analyzes temporal patterns to issue early warnings and insights into gas dispersion, substantiated by its effectiveness in predicting gas dispersion dynamics. This allows for proactive risk management and the creation of efficient mitigation strategies. The design is structured for practicality and scalability, demonstrated by compatibility tests with existing infrastructure and a framework that supports integration with future advancements in sensor technology and machine learning. Previously, the technology was characterized using MATLAB simulation. The effectiveness and reliability of the system in detecting gas leaks in various operational settings will now be demonstrated through real-world testing and observation
Dataset analysis for energy efficiency to use Machine Learning
Authors: Erick Vázquez Rufino
Published: October 2024

The analysis of the cooling and heating load through software tools such as Weka through classifiers is essential to design efficient, sustainable and economical air conditioning systems in buildings, which could have also an impact to reduce a high initial investment
Artificial Intelligence of Pushpaka Vimana used in Ramayana
Authors: Vaddadi Kavya
Published: October 2024

The ancient Indian texts describe the Pushpaka Vimana as an advanced aerial vehicle, often attributed to advanced civilizations in the distant past. This paper explores the possibility of artificial intelligence (AI) by looking into the descriptions of vimaans from Ramayana Pushpaka Vimana, examining how AI could have been used in such technical capabilities. The paper discusses the role of AI in real-time data analysis, decision-making processes, and automated control systems that could potentially mirror the legendary attributes of the Pushpaka Vimana, such as seamless flight, adaptive navigation, and intelligent response to environmental conditions. Additionally, the paper explores the ethical implications and technical challenges of recreating such an advanced system using AI, emphasizing the need for interdisciplinary research that bridges ancient wisdom and modern technology. This exploration not only offers a unique perspective on the synthesis of history and technology but also provides insights into the potential future of aerospace engineering. By investigating the application of AI in a historical context, this paper aims to inspire innovative approaches in the development of next-generation aerial vehicles, encouraging a deeper understanding of both our technological heritage and future possibilities
Effect of Ageing in Various Bituminous Mixes
Authors: Swarnkari Monika , Upadhyay Abhishek , Khan Toqir
Published: October 2024

The durability and sustainability of bituminous mixes in road construction are critical for ensuring the long-term functionality and cost-effectiveness of infrastructure projects. This research delves into the aging processes of bituminous mixes, examining both chemical and physical transformations over time. Environmental factors such as temperature, moisture, and traffic loads significantly impact aging, necessitating thorough investigation. Challenges include understanding the intricate chemical reactions that harden the asphalt binder and the redistribution of binder and aggregate components, affecting stiffness and structural integrity. Regional variations in environmental conditions further complicate aging dynamics, requiring tailored strategies for different geographic locations. Mix design plays a pivotal role, demanding the selection of materials and proportions that balance durability with other performance attributes. The research objectives encompass investigating aging mechanisms, analyzing environmental influences, assessing traffic-related effects, and evaluating mix design parameters. The scope encompasses various bituminous mixes, aging mechanisms, environmental variables, traffic burdens, and mix design factors relevant to road construction. By addressing these challenges and objectives, this research aims to enhance understanding of bituminous mix aging and contribute to the development of durable and sustainable road infrastructure
Interplay of Mobile Computing and Smart Grid Manuscript: Enhancing Energy Efficiency and Management
Authors: Ganguly Ayan , Nath Anusha , Banerjee Ishita
Published: October 2024

This manuscript explores the symbiotic relationship between mobile computing and smart grid technologies, elucidating their combined potential to revolutionize energy efficiency and management. Mobile computing, with its ubiquitous presence and advanced capabilities, offers novel avenues for optimizing smart grid operations, facilitating real-time monitoring, control, and communication. The integration of these technologies holds promise for fostering a more resilient, reliable, and sustainable energy infrastructure. Through a comprehensive review and analysis, this paper delves into the synergistic benefits, challenges, and future directions of leveraging mobile computing within smart grid frameworks. The interplay between mobile computing and smart grid technologies heralds a transformative era in energy efficiency and management. In this manuscript, we explore the synergistic relationship between these two domains, elucidating their combined potential to revolutionize the modern energy landscape. Mobile computing, characterized by its pervasive presence and advanced capabilities, offers a conduit for real-time communication, data exchange, and decision-making within smart grid infrastructures. Leveraging mobile devices and applications, utilities can optimize grid operations, engage consumers, and facilitate dynamic demand response mechanisms. Conversely, smart grid technologies enable the seamless integration of renewable energy sources, grid automation, and advanced monitoring and control functionalities, augmenting the capabilities of mobileenabled energy management systems. Through a comprehensive review and analysis, we delve into the foundational principles, intersections, challenges, and future directions of integrating mobile computing within smart grid frameworks. Case studies and best practices illustrate the practical benefits and complexities encountered in deploying mobile-enabled smart grid solutions. Moreover, we discuss emerging trends, such as artificial intelligence and edge computing, which hold promise for further enhancing the efficiency and resilience of mobile-enabled smart grids. By shedding light on this dynamic interplay, we aim to inspire collaboration, innovation, and action towards building a more sustainable and efficient energy ecosystem for generations to come
Online Food Delivery App: MEALO
Authors: Basak Anirban , Dutta Sagnik , Ghosh Priyanka , Das Pritam , Rakshit Angshuman , Basu Pal Dr. Sudipta
Published: October 2024

MEALO is a website designed primarily for use in the food delivery sector. It aims to unite all the Cloud Kitchens / Home kitchens of the area and provide a platform to increase their scope of business. Only for customer orders or delivery, Cloud Kitchen is a business establishment equipped with a food processing area. Known under various names, including shared kitchen, virtual kitchen, ghost kitchen, and satellite kitchen. A cloud kitchen simply produces food in a commercial kitchen for takeaway or delivery; dine-in customers do not exist there. The system also allows for quick and easy management of an online menu that customers can browse and use to place orders with just a few clicks. This website was designed with the motive of helping home cooks so that not having a hotel/restaurant won’t limit their business. Furthermore, having Fresh home Food will help a lot of bachelor’s and students living outside their homes to manage their food expenses and also maintain a healthy lifestyle.
The use of mobile telephony among university students: a case study in Colombia
Authors: Henao Ríos José León , Noguera Claudia Alejandra Rosero , Echavarría Gladis Helena Vásquez
Published: October 2024

This article addresses the evolution and impact of mobile telephony in today's society, especially in the university educational environment. It highlights that 78% of the world's population over 10 years old owns a cell phone according to United Nations data for 2023. Similarly, it raises the importance of educating university students in Colombia on the appropriate use of mobile devices, taking advantage of digital strategies to improve education, while also recognizing the potential problems that excessive use of technology can generate, such as relationship issues, disconnection from the real world, and changes in behavior. Therefore, the research question of this project is: What is the impact of mobile telephony on the students of Data Systematization Technology at the Politécnico Colombiano Jaime Isaza Cadavid
“Study on Enhancing M25 and M30 Grade Concrete for Sand Replacement and Addition of Fibers”
Authors: Patel Pradeep , Goud Rupali
Published: October 2024

This study investigates the enhancement of M25 and M30 grade concrete by replacing sand and adding fibers. The research focuses on evaluating the impact of these modifications on the mechanical properties and durability of concrete. A comprehensive analysis is conducted to determine the optimal mix design that balances strength and cost-effectiveness. The results show significant improvements in compressive strength, tensile strength, and flexural strength, making the modified concrete a viable option for construction projects requiring enhanced performance
Analysis of stress on crankshaft of an I.C Engine
Authors: Chandekar Akash H. , Bhope Dr. D.V.
Published: October 2024

In this work, a four-cylinder four-stroke engine's crankshaft was statically analyzed. Finite element analysis was used to determine the variation in stress magnitude at important places. A three-dimensional model of the crankshaft was generated using PRO/E software. The crankshaft was subjected to finite element analysis using ANSYS. The load was then applied to the FE model, with boundary conditions based on the engine's mounting circumstances in ANSYS. The analysis is performed to identify crucial locations in the crankshaft. The investigation looked at stress variation during the engine cycle as well as the effect of torsion and bending strain. Von-mises stress is estimated theoretically and numerically using the FEA software ANSYS. The stresses obtained from these analyses using the analytical approach and the FEA results are compared. The results obtained from the aforementioned analysis were utilized to compare the analytical and FEA results. The value of VonMisses Stresses obtained from the analysis was significantly lower than the material yield stress, indicating that the design was safe and that optimization could be performed to minimize material and hence cost
Studying the Microhardness Characteristics of Stir-cast Al 7075 (To2/SiC/Graphene 200 nano) NMMCs Experimentally
Authors: Sruthi G.
Published: October 2024

This study investigates the microhardness characteristics of Al 7075 reinforced with Titanium Dioxide (TiO₂), Silicon Carbide (SiC), and 200-nanometer Graphene nanoplatelets to form novel metal matrix composites (NMMCs) using the stir casting method. The Taguchi experimental design is employed to systematically analyze and optimize the influence of various process parameters on the composite's microhardness. An L9 orthogonal array is utilized to optimize experimental conditions, while Minitab software facilitates the statistical analysis. The stir casting process involved melting Al 7075 alloy at 750°C, incorporating pre-weighed TiO₂, SiC, and Graphene nanopowders, and mechanically stirring the molten mixture for uniform dispersion. The composites were then cast into preheated molds and solidified. Microhardness testing was performed using a Vickers hardness tester, and the indentation dimensions were measured to calculate the microhardness values. The results show significant improvements in the microhardness of the Al 7075 matrix with the addition of TiO₂, SiC, and Graphene reinforcements. The Taguchi method and ANOVA identified the optimal combination of parameters, including stirring speed, temperature, and reinforcement percentage, to achieve maximum microhardness. This research highlights the potential of Al 7075/TiO₂/SiC/Graphene NMMCs for advanced engineering applications requiring superior mechanical properties.
An enhanced Ensemble Methods for Predicting Concept Drift Labels from a High-Dimensional Data Stream
Authors: Yallini S. K. Komagal , Mahendiran Dr. N.
Published: October 2024

In information science, Multi-Label Learning (MLL) has emerged as a method for identifying samples according to certain features related to a number of tags. When an information flow introduces new perspectives, advanced training necessitates the classification of characteristics using New Labels (NLs). To address this challenge, a classification algorithm called MuEMNL-Ensemble Neural Network (ENN) was developed for MLL with Emerging Multiple NLs (MuEMNL). This algorithm effectively handles large amounts of data and resolves concept drift issues. However, the memory usage required to preserve previously learned information in the ENN was found to be excessive. This article presents a Generative Adversarial Network (GAN) model for classifying data streams using the MuEMNL-ENN classifier as a solution. The GAN model eliminates the need to store previously learned information, resulting in reduced memory usage. By including the GAN model, the GAN-MuEMNL-ENN model solves the problem of not having enough historical data and prevents data streams from forgetting everything in the MLL process. This model is capable of performing online MLL using the MuEMNL-ENN classifier and adapting to new data classes without losing previously learned information. The GAN consists of generator and discriminator networks, with the generator model regenerating historical training data that cannot be retained. This model eliminates the need to store and reuse previous observations, making it advantageous for applications dealing with large datasets. Extensive experiments have proven that the GANMuEMNL-ENN model outperforms existing models for MLL on a variety of multi-label datasets
Understanding the Complex Roots of Addiction: An Integrative Perspective on Biological, Psychological, and Social Influences on Substance Use Disorders
Authors: Al-Mahmoud Dr. Meshari , E AIChem
Published: October 2024

Substance Use Disorders (SUDs) are a global public health crisis, affecting millions of individuals and families each year. Traditionally, addiction has been attributed to isolated causes—be it genetics, environmental factors, or personal behavior. However, mounting evidence points to a far more intricate reality. This paper delves into the interplay of biological, psychological, and social factors in addiction, offering insights into prevention strategies with a particular emphasis on cannabis use disorder (CUD). By adopting a holistic understanding of these elements, we can craft more effective and sustainable interventions to curb the growing addiction epidemic.
An Optimization of Occlusion robust Spatio Temporal Hybrid Cue Network
Authors: Muthusamy Prema , Murugan Gomathi Pudupalayam
Published: October 2024

Sign language (SL) is used by deaf-mute individuals for communication but normal people finds hard to understand the SL which causes a communication gap between them. It is crucial to reduce the gap between physically challenged people and normal people. In recent days, Deep Learning (DL) methods are used for the prediction of SLs. Amongst, Occlusion-resistant Spatio-Temporal Hybrid Cue Network (OSTHCN) method is developed which utilizes Skeleton Occupancy Likelihood Map estimation using B-Spline curve in Dynamic Dense Spatio-Temporal Graph Convolutional Neural Network (DDSTGCNN) to refine the skeleton extraction and VGG11+1D-Convolutinal Neural Network (CNN) for full frame data. It employs BLSTM encoders, Connectionist Temporal Classification (CTC), and Self-Attention based LSTM decoders for sequence learning in ISL recognition and translation. However, the networks utilized in the sequence learning are conceived manually by assigning values to a large number of hyper-parameters which results in high computational complexity and lower accuracy performance. Hence, an automated hyperparameter optimization model is developed in this paper using Dove Swarm Optimization (DSO) to lower the computational complexity and enhance the accuracy results. The DSO is adopted for selecting the optimal hyper-parameters in sequence learning like number of neurons, number of hidden units, learning rate, weight decay, number of epochs, batch size, dropout rate, number of partitions, number of clusters per batch, momentum, optimizer and loss function. The essential principle of DSO are derived from the foraging behaviors of doves adopted in real-time applications. The initial population of dove forages represents the initial hyper-parameters and computation of each dove crumbs subsequent locations represents the search for the best hyper-parameter to find the optimal values for the sequence learning. The BLSTM, CTC and SA-LSTM are completely optimized using DSO and it is termed as Optimized OSTHCN (OOSTHCN). Finally, the test findings revealed that the OOSTHCN model achieves 97.86% of accuracy on ISLCSLTR dataset compared to the existing models.
Study on Electrical Use for Growing 150 Grams of Lettuce in a Fully Controlled Agriculture Environment
Authors: Al Kulaib Hamad Jasem , Alhasainan Fahad A
Published: October 2024

Controlled Environment Agriculture (CEA) has emerged as a pivotal solution to addressing food security challenges in regions with harsh climatic conditions, such as the GCC (Gulf Cooperation Council) countries. In these regions, where temperatures often soar above 40°C, and water scarcity is a pressing concern, CEA allows for the cultivation of crops in optimal indoor environments, completely independent of external weather factors. This study examines the energy requirements for growing 150 grams of lettuce in a fully controlled CEA system, with specific emphasis on the GCC region, where cooling demands are high due to extreme heat. Lettuce is selected for its relatively short growth cycle and popularity as a crop within CEA systems. Key energy-consuming components of the system, including LED lighting, HVAC (Heating, Ventilation, and Air Conditioning), and water management, are analyzed in detail. In the GCC, due to the high ambient temperatures, the energy required for cooling (HVAC) can be significantly higher compared to regions with milder climates. Through this study, we aim to provide a comprehensive analysis of the energy inputs necessary for sustainable lettuce production in controlled environments, with particular attention to GCC-specific conditions. The findings highlight potential energy efficiency improvements and strategies for reducing the environmental impact of CEA, including the integration of renewable energy sources like solar power, which is abundant in the region. By adopting cutting-edge technologies in lighting and climate control, the GCC has the potential to reduce itsdependence on food imports, enhance food security, and promote sustainable agriculture practices, despite the region’s challenging environmental conditions
Downstream and seasonal changes of lithium isotope ratios in the Ganges‐Brahmaputra river system
Authors: Manaka Takuya , Araoka Daisuke , Yoshimura Toshihiro , Hossain H. M. Zakir , Nishio Yoshiro , Suzuki Atsushi , Kawahata Hodaka
Published: July 2017

The Li isotope ratio (δ7Li) is expected to be a useful tracer of silicate weathering in river and groundwater systems, which is an important contributor to the seawater compositional changes that accompany the evolution of the Earth's surface environment. To obtain accurate estimates of continental Li fluxes to the ocean, we determined δ7Li values of dissolved Li in the lower Ganges‐Brahmaputra river system in both the dry and rainy seasons, and in deep groundwater in the Bengal basin. Dissolved Li and δ7Li values in the lower reaches of the rivers (0.04–0.66 µmol kg−1 and +19.1‰ to +34.2‰, respectively) were predominantly derived from silicate weathering, as is the case in the upper parts of these rivers. We observed large changes in δ7Li over a distance of more than 1000 km downstream that were due mainly to Rayleigh‐type removal of Li from river water. Extremely high Li concentrations (1.15–1.67 µmol kg−1) and low δ7Li values (+5.1‰ to +11.6‰) in groundwater samples indicate congruent isotope leaching and dissolution of silicate minerals in the deep aquifer, where the water residence time is long. In the rainy season, Li concentrations and δ7Li values were lower than in the dry season, owing to the shorter residence time of river water and the substantial input of local subsurface flow through lowland alluvium. These results suggest that accurate estimation of continental Li fluxes to the ocean should take account of downstream and seasonal changes, as well as aquifer depth variations, in δ7Li values.
The evolution of scaling laws in the sea ice floe size distribution
Authors: Horvat Christopher , Tziperman Eli
Published: July 2017

The sub‐gridscale floe size and thickness distribution (FSTD) is an emerging climate variable, playing a leading‐order role in the coupling between sea ice, the ocean, and the atmosphere. The FSTD, however, is difficult to measure given the vast range of horizontal scales of individual floes, leading to the common use of power‐law scaling to describe it. The evolution of a coupled mixed‐layer‐FSTD model of a typical marginal ice zone is explicitly simulated here, to develop a deeper understanding of how processes active at the floe scale may or may not lead to scaling laws in the floe size distribution. The time evolution of mean quantities obtained from the FSTD (sea ice concentration, mean thickness, volume) is complex even in simple scenarios, suggesting that these quantities, which affect climate feedbacks, should be carefully calculated in climate models. The emergence of FSTDs with multiple separate power‐law regimes, as seen in observations, is found to be due to the combination of multiple scale‐selective processes. Limitations in assuming a power‐law FSTD are carefully analyzed, applying methods used in observations to FSTD model output. Two important sources of error are identified that may lead to model biases: one when observing an insufficiently small range of floe sizes, and one from the fact that floe‐scale processes often do not produce power‐law behavior. These two sources of error may easily lead to biases in mean quantities derived from the FSTD of greater than 100%, and therefore biases in modeled sea ice evolution.
Searching for patterns in caldera unrest
Authors: Sandri Laura , Acocella Valerio , Newhall Chris
Published: July 2017

The ultimate goal of volcanology is forecasting eruptions. This task is particularly challenging at calderas, where unrest is frequent, affects wider areas and its evidence is often masked by the activity of hydrothermal systems. A recent study has compiled a database on caldera unrest, derived from seismicity, geodetic, gravity, and geochemical monitoring data at calderas worldwide, from 1988 to 2014. Here we exploit this database, searching for the most recurring features of unrest and, in turn, its possible dynamics. In particular, we focus on (a) the duration of unrest at calderas; (b) recurring patterns in unrest; (c) unrest episodes culminating in eruptions, including time‐predictability or size‐predictability and a multivariate regression analysis. Our analysis indicates that preeruptive unrest is shorter than noneruptive unrest, particularly with open or semiplugged calderas, calderas with mafic or mixed composition of past eruptive products, or unrest driven by mafic magma; conversely, lack of data on preeruptive unrest driven by felsic magma and/or at felsic or plugged calderas prevents an analysis of these specific subsets. In addition, 72% of preeruptive unrest lasts <10 months and shows high seismicity and degassing. The remaining 28% (a) is essentially aseismic in calderas with open‐conduit (17%), or (b) lasts between 10 and 18 months, with seismicity and degassing, constituting a longer‐duration tail of the preeruptive unrest with seismicity and degassing (11%). Surface deformation is not always reliable to characterize preeruptive unrest. Our analysis suggests that magma may withstand only a limited period of “eruptability,” before becoming stored in the upper crust.
Toward a new paradigm for boulder dislodgement during storms
Authors: Weiss Robert , Sheremet Alex
Published: July 2017

Boulders are an important coastal hazard event deposit because they can only be moved by tsunamis and energetic storms effects of storms. Storms and tsunami are competing processes for coastal change along many shorelines. Therefore, distinguishing the boulders that were moved during a storm from those moved by a tsunami is important. In this contribution, we present the results of a parameter study based on the TRIADS model for wave shoaling on mildly sloping beaches, coupled with a boulder‐dislodgement model that is based on Newton's Second Law of Motion. The results show how smaller slopes expose the waves longer to the nonlinear processes, thus increasing the energy in the infragravity wave band. More energy in the infragravity wave band means that there are more energy wave lengths that can dislodge larger boulders. At the same time, a steeper slope lowers the threshold for boulder dislodgement (critical angle of dislodgement), making it more likely for larger boulders to be dislodged on a steeper slope. The competition between these two processes govern boulder dislodgement during storms and is investigated inhere.
Mass balance reassessment of glaciers draining into the Abbot and Getz Ice Shelves of West Antarctica
Authors: Chuter S. J. , Martín‐Español A. , Wouters B. , Bamber J. L.
Published: July 2017

We present a reassessment of input‐output method ice mass budget estimates for the Abbot and Getz regions of West Antarctica using CryoSat‐2‐derived ice thickness estimates. The mass budget is 8 ± 6 Gt yr−1 and 5 ± 17 Gt yr−1 for the Abbot and Getz sectors, respectively, for the period 2006–2008. Over the Abbot region, our results resolve a previous discrepancy with elevation rates from altimetry, due to a previous 30% overestimation of ice thickness. For the Getz sector, our results are at the more positive bound of estimates from other techniques. Grounding line velocity increases up to 20% between 2007 and 2014 alongside mean elevation rates of −0.67 ± 0.13 m yr−1 between 2010 and 2013 indicate the onset of a dynamic thinning signal. Mean snowfall trends of −0.33 m yr−1 water equivalent since 2006 indicate recent mass trends are driven by both ice dynamics and surface processes.
The role of South Pacific atmospheric variability in the development of different types of ENSO
Authors: You Yujia , Furtado Jason C.
Published: July 2017

Recent advances in tropical Pacific climate variability have focused on understanding the development of El Niño–Southern Oscillation (ENSO) events, specifically the types or “flavors” of ENSO (i.e., central versus eastern Pacific events). While precursors to ENSO events exist, distinguishing the particular flavor of the expected ENSO event remains unresolved. This study offers a new look at ENSO predictability using South Pacific atmospheric variability during austral winter as an indicator. The positive phase of the leading mode of South Pacific sea level pressure variability, which we term the South Pacific Oscillation (SPO), exhibits a meridional dipole with with a(n) (anti)cyclonic anomaly dominating the subtropics (extratropics/high latitudes). Once energized, the cyclonic anomalies in the subtropical node of the SPO weaken the southeasterly trade winds and promote the charging of the eastern equatorial Pacific Ocean, giving rise to eastern Pacific ENSO events. Indeed, the type of ENSO event can be determined accurately using only the magnitude and phase of the SPO during austral winter as a predictor (17 out of 23 cases). The SPO may also play a role in explaining the asymmetry of warm and cold events. Collectively, our findings present a new perspective on ENSO‐South Pacific interactions that can advance overall understanding of the ENSO system and enhance its predictability across multiple timescales.
The 3‐D geological model around Chang'E‐3 landing site based on lunar penetrating radar Channel 1 data
Authors: Yuan Yuefeng , Zhu Peimin , Zhao Na , Xiao Long , Garnero Edward , Xiao Zhiyong , Zhao Jiannan , Qiao Le
Published: July 2017

High‐frequency lunar penetrating radar (LPR) data from an instrument on the lunar rover Yutu, from the Chang'E‐3 (CE‐3) robotic lander, were used to build a three‐dimensional (3‐D) geological model of the lunar subsurface structure. The CE‐3 landing site is in the northern Mare Imbrium. More than five significant reflection horizons are evident in the LPR profile, which we interpret as different period lava flow sequences deposited on the lunar surface. The most probable directions of these flows were inferred from layer depths, thicknesses, and other geological information. Moreover, the apparent Imbrian paleoregolith homogeneity in the profile supports the suggestion of a quiescent period of lunar surface evolution. Similar subsurface structures are found at the NASA Apollo landing sites, indicating that the cause and time of formation of the imaged phenomena may be similar between the two distant regions.
The free troposphere as a potential source of arctic boundary layer aerosol particles
Authors: Igel Adele L. , Ekman Annica M. L. , Leck Caroline , Tjernström Michael , Savre Julien , Sedlar Joseph
Published: July 2017

This study investigates aerosol particle transport from the free troposphere to the boundary layer in the summertime high Arctic. Observations from the Arctic Summer Cloud Ocean Study field campaign show several occurrences of high aerosol particle concentrations above the boundary layer top. Large‐eddy simulations suggest that when these enhanced aerosol concentrations are present, they can be an important source of aerosol particles for the boundary layer. Most particles are transported to the boundary layer by entrainment. However, it is found that mixed‐phase stratocumulus clouds, which often extend into the inversion layer, also can mediate the transport of particles into the boundary layer by activation at cloud top and evaporation below cloud base. Finally, the simulations also suggest that aerosol properties at the surface sometimes may not be good indicators of aerosol properties in the cloud layer.
Assimilating satellite‐based canopy height within an ecosystem model to estimate aboveground forest biomass
Authors: Joetzjer E. , Pillet M. , Ciais P. , Barbier N. , Chave J. , Schlund M. , Maignan F. , Barichivich J. , Luyssaert S. , Hérault B. , von Poncet F. , Poulter B.
Published: July 2017

Despite advances in Earth observation and modeling, estimating tropical biomass remains a challenge. Recent work suggests that integrating satellite measurements of canopy height within ecosystem models is a promising approach to infer biomass. We tested the feasibility of this approach to retrieve aboveground biomass (AGB) at three tropical forest sites by assimilating remotely sensed canopy height derived from a texture analysis algorithm applied to the high‐resolution Pleiades imager in the Organizing Carbon and Hydrology in Dynamic Ecosystems Canopy (ORCHIDEE‐CAN) ecosystem model. While mean AGB could be estimated within 10% of AGB derived from census data in average across sites, canopy height derived from Pleiades product was spatially too smooth, thus unable to accurately resolve large height (and biomass) variations within the site considered. The error budget was evaluated in details, and systematic errors related to the ORCHIDEE‐CAN structure contribute as a secondary source of error and could be overcome by using improved allometric equations.
Models are likely to underestimate increase in heavy rainfall in the extratropical regions with high rainfall intensity
Authors: Borodina Aleksandra , Fischer Erich M. , Knutti Reto
Published: July 2017

Model projections of regional changes in heavy rainfall are uncertain. On timescales of few decades, internal variability plays an important role and therefore poses a challenge to detect robust model response in heavy rainfall to rising temperatures. We use spatial aggregation to reduce the major role of internal variability and evaluate the heavy rainfall response to warming temperatures with observations. We show that in the regions with high rainfall intensity and for which gridded observations exist, most of the models underestimate the historical scaling of heavy rainfall and the land fraction with significant positive heavy rainfall scalings during the historical period. The historical behavior is correlated with the projected heavy rainfall intensification across models allowing to apply an observational constraint, i.e., to calibrate multimodel ensembles with observations in order to narrow the range of projections. The constraint suggests a substantially stronger intensification of future heavy rainfall than the multimodel mean.
Ionospheric Alfvén resonator observed at low‐latitude ground station, Muroto
Authors: Nosé M. , Uyeshima M. , Kawai J. , Hase H.
Published: July 2017

The ionospheric Alfvén resonator (IAR) is found in a dynamic power spectrum of the geomagnetic field variations as spectral resonance structures in the frequency range of 0.1–10 Hz. To date, observations of IAR at low latitude have only been made by a few studies employing a single ground observatory and are not sufficient enough to reveal its general characteristics. We analyze data measured with an induction magnetometer installed at Muroto, Japan (24.40° geomagnetic latitude) for the period from 28 December 2013 to 13 August 2016. A statistical analysis reveals that the occurrence probability of the low‐latitude IAR is (1) dominant during nighttime with a gradual increase from dusk to midnight and a broad maximum at 00–05 LT followed by a sudden decrease at dawn, (2) slightly higher during May through September, and (3) independent of the Kp index for Kp ≤ 5. We also find that (4) the frequency separation between the harmonics (Δf) of the low‐latitude IAR is 0.1–0.5 Hz with a peak at 0.2–0.275 Hz. These observational results are compared with the model calculation results along with previous studies about possible energy sources. It is found that the global thunderstorm activity is a likely source for IAR at Muroto; that is, it produces electromagnetic disturbances that are converted into the Alfvén waves and that are then trapped in the ionospheric cavity bounded by the conductive E layer and a steep gradient of Alfvén velocity above the F2 layer to form the resonant Alfvén waves.
A direct estimate of poleward volume, heat, and freshwater fluxes at 59.5°N between Greenland and Scotland
Authors: Rossby T. , Reverdin Gilles , Chafik Leon , Søiland Henrik
Published: July 2017

The meridional overturning circulation (MOC) in the North Atlantic plays a major role in the transport of heat from low to high latitudes. In this study, we combine recent measurements of currents from the surface to >700 m from a shipboard acoustic Doppler current profiler with Argo profiles (to 2000 m) to estimate poleward volume, heat, and freshwater flux at 59.5°N between Greenland and Scotland. This is made possible thanks to the vessel Nuka Arctica that operates on a 3 week schedule between Greenland and Denmark. For the period late 2012 to early 2016, the deseasoned mean meridional overturning circulation reaches a 18.4 ± 3.4 Sv maximum at the σθ = 27.55 kg m−3 isopycnal, which varies in depth from near the surface in the western Irminger Sea to 1000 m in Rockall Trough. The total heat and freshwater fluxes across 59.5°N = 399 ± 74 TW and −0.20 ± 0.04 Sv, where the uncertainties are principally due to that of the MOC. Analysis of altimetric sea surface height variations along exactly the same route reveals a somewhat stronger geostrophic flow north during this period compared to the 23 year mean suggesting that for a long‐term mean the above flux estimates should be reduced slightly to 17.4 Sv, 377 TW, and −0.19 Sv, respectively, with the same estimate uncertainties. The ADCP program is ongoing.
Near‐surface salinity and temperature structure observed with dual‐sensor drifters in the subtropical South Pacific
Authors: Dong Shenfu , Volkov Denis , Goni Gustavo , Lumpkin Rick , Foltz Gregory R.
Published: July 2017

Three surface drifters equipped with temperature and salinity sensors at 0.2 and 5 m depths were deployed in April/May 2015 in the subtropical South Pacific with the objective of measuring near‐surface salinity differences seen by satellite and in situ sensors and examining the causes of these differences. Measurements from these drifters indicate that water at a depth of 0.2 m is about 0.013 psu fresher than at 5 m and about 0.024°C warmer. Events with large temperature and salinity differences between the two depths are caused by anomalies in surface freshwater and heat fluxes, modulated by wind. While surface freshening and cooling occurs during rainfall events, surface salinification is generally observed under weak wind conditions (≤4 m/s). Further examination of the drifter measurements demonstrates that (i) the amount of surface freshening and strength of the vertical salinity gradient heavily depend on wind speed during rain events, (ii) salinity differences between 0.2 and 5 m are positively correlated with the corresponding temperature differences for cases with surface salinification, and (iii) temperature exhibits a diurnal cycle at both depths, whereas the diurnal cycle of salinity is observed only at 0.2 m when the wind speed is less than 6 m/s. The amplitudes of the diurnal cycles of temperature at both depths decrease with increasing wind speed. The mean diurnal cycle of surface salinity is dominated by events with winds less than 2 m/s.