bluuebunny/crossref_metadata_2025 huggingface dataset:
Tetrathiafulvalene-based covalent organic frameworks as high-voltage organic cathodes for lithium batteries
Authors: Valente Gonçalo , Dantas Raquel , Ferreira Pedro , Grieco Rebecca , Patil Nagaraj , Guillem-Navajas Ana , Rodríguez-San Miguel David , Zamora Félix , Guntermann Roman , Bein Thomas , Rocha João , Braga M. Helena , Strutyński Karol , Melle-Franco Manuel , Marcilla Rebeca , Souto Manuel
Published: August 2024

We report a series of 2D tetrathiafulvalene (TTF)-based COFs incorporating different organic linkers between the electroactive moieties that were investigated as p-type organic cathode materials for lithium-organic batteries.
Improved performance of perovskite solar cells by fine-tuning dibenzofuran-based hole transporting materials
Authors: Liu Xuepeng , Zhang Xianfu , Han Mingyuan , Chen Jianlin , Rahim Ghadari , Liang Yongpeng , Li Botong , Dai Songyuan
Published: August 2024

Spiro-4 exhibits more matched energy levels with perovskite, a higher glass transition temperature, higher hole mobility, and enhanced hole extraction ability than other positional isomers, delivering a power conversion efficiency of 23.38%.
From a candle to content creation
Authors: Grand Ann
Published: August 2024

This article gives a short tour through the changing faces of science communication, including dissemination, public understanding of science and public engagement with science and technology to engaged research.
A predictive SD‐WAN traffic management method for IoT networks in multi‐datacenters using deep RNN
Authors: Nazemi Absardi Zeinab , Javidan Reza
Published: August 2024

Deploying the Internet of Things (IoT) in integrated edge‐cloud environments exposes the IoT traffic data to performance issues such as delay, bandwidth limitation etc. Recently, Software‐Defined Wide Area Network (SD‐WAN) has emerged as an architecture that originates from the Software‐Defined Network (SDN) paradigm and provides solutions for networking multiple data centers by allowing network administrators to manage and control network layers. In this article, an SDWAN‐based policy for traffic management in IoT is introduced in which the Quality of Service (QoS) metrics such as end‐to‐end delay and bandwidth utilization are optimized. The proposed method implements the traffic management policy in the SDWAN controller. When the IoT traffic flows reach the SDWAN infrastructure network, graph search algorithms are performed to find the near‐optimal paths that affect the end‐to‐end delay of traffic flows. Because of the ability of deep learning to process complex data, a deep RNN model is used to predict the network state information, such as link latency and available bandwidth, before the traffic flows reach the infrastructure network. The proposed method consists of four key modules to predict the routes for future time intervals: (a) an SD‐WAN topology updater unit that checks the link changes and availability, (b) the network state information collector, which collects the network state information to create a dataset, (c) the learning unit, which trains a deep RNN model using the created dataset, and (d) the route predictor unit, which uses the trained model to predict the network state information using a heuristic algorithm to determine the routes. The simulation results showed that the deep RNN model can achieve high accuracy and low Mean Absolute Error (MAE), and the proposed method outperforms shortest‐path algorithms in terms of latency. At the same time, the available bandwidth is almost fairly distributed among all network links.
Global information aware network with global interaction graph attention for infrared small target detection
Authors: Yang Ruimin , Zhang Yidan , Gao Guangshuai , Liao Liang , Li Chunlei
Published: August 2024

Detecting small targets in infrared images is crucial for ground surveillance and air traffic control. However, distinguishing small infrared targets from similar backgrounds is challenging due to their lack of structural and textural characteristics. To address these challenges, this study proposes a novel global information‐aware network with global interaction graph attention (GIGA) for infrared small target detection. The GIGA consists of a global interaction layer (GILayer), graph attention weights (GAW), and a global relational learning (GRL) module. Specifically, the GILayer dynamically learns global inter‐pixel relationships of small target images by enhancing the dependencies between feature dimensions. The GAW component calculates pixel‐by‐pixel similarity across the entire feature map using graph attention mechanisms, while the GRL module retains critical similarity features in the feature extraction network, thereby facilitating small target detection. Additionally, the multi‐scale context fusion module utilises self‐attention and dilation convolution to complement richer feature details at different scales. Experimental results on both natural and synthetic datasets demonstrate the proposed method's superiority over other state‐of‐the‐art conventional and deep learning approaches in infrared small target detection.
Comfortable driving control for connected automated vehicles based on deep reinforcement learning and knowledge transfer
Authors: Wu Chuna , Chen Jing , Yao Jinqiang , Chen Tianyi , Cao Jing , Zhao Cong
Published: August 2024

With the development of connected automated vehicles (CAVs), preview and large‐scale road profile information detected by different vehicles become available for speed planning and active suspension control of CAVs to enhance ride comfort. Existing methods are not well adapted to rough pavements of different districts, where the distributions of road roughness are significantly different because of the traffic volume, maintenance, weather, etc. This study proposes a comfortable driving framework by coordinating speed planning and suspension control with knowledge transfer. Based on existing speed planning approaches, a deep reinforcement learning (DRL) algorithm is designed to learn comfortable suspension control strategies with preview road and speed information. Fine‐tuning and lateral connection are adopted to transfer the learned knowledge for adaptability in different districts. DRL‐based suspension control models are trained and transferred using real‐world rough pavement data in districts of Shanghai, China. The experimental results show that the proposed control method increases vertical comfort by 41.10% on rough pavements, compared to model predictive control. The proposed framework is proven to be applicable to stochastic rough pavements for CAVs.
A new understanding of the Gemini-Monoceros X-ray enhancement from discoveries with eROSITA
Authors: Knies J. R. , Sasaki M. , Becker W. , Liu T. , Ponti G. , Plucinsky P. P.
Published: August 2024

Aims. The Gemini-Monoceros X-ray enhancement is a rich field for studying diffuse X-ray emission and supernova remnants (SNRs). Most SNRs in this part of the sky are notoriously difficult to observe due to their large extent. With the launch of the extended ROentgen Survey with an Imaging Telescope Array (eROSITA) on board the Spektrum-Röntgen-Gamma platform in 2019, we are now able to fully study those objects for the first time with CCD resolution. Many of the SNRs in the vicinity are suspected to be very old remnants, which are severely understudied in X-rays due to numerous observational challenges. In addition, the identification of new faint large SNRs might help to solve the long-standing discrepancy between the observed and expected number of Galactic SNRs.

Methods. We performed a detailed X-ray spectral analysis of the entire Gemini-Monoceros X-ray enhancement and a detailed background analysis of the vicinity, which allowed us to model the background with a high precision inside the X-ray enhancement. We also made use of multiwavelength data to better understand the morphology and to constrain the distances to the different sources. Based on the spectral analysis, we estimated the properties of the sources and calculated a grid of model SNRs to determine the individual SNR properties.

Results. Most of the diffuse plasma of the Monogem Ring SNR is well described by a single nonequilibrium ionization (NEI) component with an average temperature of kT = 0.14 ± 0.03 keV. We obtain an age of ≈1.2 × 105 yr – consistent with PSR B0656+14 – for the Monogem Ring and an explosion energy typical for a core-collapse (CC) supernova (SN). In the southeast, we found evidence for a significant temperature enhancement and a second plasma component. Our findings show that a scenario of two SNRs at ≈300 pc is likely, with the new candidate having an age of ≈50 000 yr. We were also able to improve on previous results for the Monoceros Loop and PKS 0646+06 SNRs by disentangling the foreground diffuse emission of the Monogem Ring SNR. We obtained significantly higher temperatures than previous studies, and for PKS 0646+06 a much lower estimated age of the SNR. We also found a new SNR candidate G190.4+12.5 which most likely is located at D > 1.5 kpc, expanding into a low density medium at a high distance from the Galactic plane, with an estimated age of 40 000–60 000 yr.
COSMOS brightest group galaxies
Authors: Gozaliasl G. , Finoguenov A. , Babul A. , Ilbert O. , Sargent M. , Vardoulaki E. , Faisst A. L. , Liu Z. , Shuntov M. , Cooper O. , Dolag K. , Toft S. , Magdis G. E. , Toni G. , Mobasher B. , Barré R. , Cui W. , Rennehan D.
Published: August 2024

The unique characteristics of the brightest group galaxies (BGGs) serve as a link in the evolutionary continuum between galaxies such as the Milky Way and the more massive brightest cluster galaxies found in dense clusters. This research investigates the evolution of the stellar properties of BGGs over cosmic time (z = 0.08 − 1.30), extending the work from our prior studies. We analyzed the data of 246 BGGs selected from our X-ray galaxy group catalog within the COSMOS field, examining stellar age, mass, star-formation rate (SFR), specific SFR, and halo mass. We compared observations with the Millennium and Magneticum simulations. Additionally, we investigated whether stellar properties vary with the projected offset from the X-ray peak or the hosting halo center. We evaluated the accuracy of SED-derived stellar ages using a mock galaxy catalog, finding a mean absolute error of around 1 Gyr. Interestingly, the observed BGG age distributions exhibit a bias toward younger intermediate ages compared to both semi-analytical models and the Magneticum simulation. Our analysis of stellar age versus mass unveils intriguing trends with a positive slope, hinting at complex evolutionary pathways across redshifts. We observed a negative correlation between stellar age and SFR across all redshift ranges. We employed a cosmic time dependent main sequence framework to identify star forming BGGs and find that approximately 20% of BGGs in the local universe continue to exhibit characteristics typical of star forming galaxies, with this proportion increasing to 50% at z = 1.0. Our findings support an inside-out formation scenario for BGGs, where older stellar populations reside near the X-ray peak and younger populations at larger offsets indicate ongoing star-formation. The observed distribution of stellar ages, particularly for lower-mass BGGs in the range of 1010−11 M⊙, deviates from the constant ages predicted by the models across all stellar mass ranges and redshifts. This discrepancy aligns with the current models’ known limitations in accurately capturing galaxies’ complex star-formation histories.
Insensitizing control problems for the stabilized Kuramoto–Sivashinsky system
Authors: Bhandari Kuntal , Hernández-Santamaría Víctor
Published: August 2024

In this work, we address the existence of insensitizing controls for a nonlinear coupled system of fourth- and second-order parabolic equations known as the stabilized Kuramoto–Sivashinsky model. The main idea is to look for controls such that some functional of the states (the so-called sentinel) is locally insensitive to the perturbations of the initial data. Since the underlying model is coupled, we shall consider a sentinel in which we may observe one or two components of the system in a localized observation set. By some classical arguments, the insensitizing problem can be reduced to a null-controllability one for a cascade system where the number of equations is doubled. Upon linearization, the null-controllability for this new system is studied by means of Carleman estimates but unlike other insensitizing problems for scalar models, the election of the Carleman tools and the overall control strategy depends on the initial choice of the sentinel due to the (lack of) couplings arising in the extended system. Finally, the local null-controllability of the extended (nonlinear) system (and thus the insensitizing property) is obtained by applying the inverse mapping theorem.
Virtual Screening of candidate Bio-actives from selected medicinal plants against rilpivirine resistance in HIV
Authors: Kumar Anish , Sandhir Nidhi , Wahengbam Sanatombi , Khajuria Karuna , Mahajan Anshika
Published: August 2024

HIV, the causative virus of AIDS, has posed a severe threat to global health for an extensiveperiod. Recent statistics show the magnitude of this challenge, with approximately 2.4 million people inIndia living with HIV, along with 41.97 thousand deaths which were reported in 2021. In the face of thisongoing crisis, patients have relied heavily on anti-retroviral therapies, among which Rilpivirine standsout as a key treatment option, particularly for individuals exhibiting low viral loads. However, the efficacyof Rilpivirine diminishes significantly in patientsagainst high viral loads of HIV-1C, leading to theemergence of drug-resistant strains. Consequently, there exists an urgent need to identify and developmore potent therapeutic agents to combat HIV infection. To address this, we performed an in-silicoinvestigation, utilizing virtual screening tools to explore potential alternative drugs to Rilpivirine for HIVtreatment.Our methodology involved mining data from reputable sources such as the PubChemand Zincdatabases to identify compounds with potential antiretroviral activity. We subjected these compounds toscreening using the SwissADME tool, evaluating their pharmacokinetic characteristics and adherence toLipinski's rule of five. Compounds demonstrating favourable properties, as indicated by zero violations inOSIRIS Property Explorer, were then prioritized for further analysis. The structural frameworks ofRilpivirine protein-ligand complexes, both native (7Z2D) and mutant (7Z2E) variants, were procured fromthe Protein Data Bank (PDB). Further, ligand molecules' structural configurations were sourced fromdatabases such as PubChem and Corinasoftware. Employing molecular docking simulations facilitated byHDOCK docking software, the binding affinities of these ligands against the receptor protein-ligandcomplexes were studied, from which, Schumannificine emerged as the most promising candidate,exhibiting the most stable binding interactions.In summation, out of 65 phytochemicals, ourcomprehensive investigation underscores the potential of Schumannificine (CID: 184890), an alkaloidcompound derived from the Schumanniophytonmagnificum plant as a novel and viable therapeutic optionfor HIV patients.These findings hold significant implications for advancing the development of moreefficacious treatments for HIV/AIDS and warrant further experimental validation and clinicalexploration.In silico study has proven to increase the ease of drug discovery, and in vitro and in vivostudies can be further done to completely verify this drug candidature.
A Review on Quality Assurance in Aluminium Die Casting through Deep Learning-Based Defect Detection
Authors: Bhat Varun , Balikai Veeresh G. , Revankar P.P. , Gorwar M.B.
Published: August 2024

The materials constitute an important segment of engineering practice and their appropriate choice forms the utmost part of engineering practice. The heterogeneity in engineering material quality and composition induced during manufacturing stages has challenged engineers performing roles in material selection and purchase sections of industry. The defects in aluminium casting left unnoticed can affect component ability to operate and maintain structural integrity. The detection of minute faults can be difficulty through existing techniques and hence this study reviews deep learning-based flaw detection through radiographic imaging of aluminium castings. The availability of labelled radiography pictures of different case scenarios in castings data can help develop a strong mechanism using convolution neural network (CNN) architecture. The AI based predictor gets trained to recognise distinct characteristics within various defect categories like porosity, shrinkage, and cracks. The trained deep learning algorithm displays high accuracy and efficiency to ensure real-time analysis to quickly detect and classify irregularities. This immense technology ability can significantly improve quality control procedures in production of aluminium castings. Its impact goes beyond the realms of improved product quality and facilitates quick and accurate problem diagnosis, which results in significant savings in execution time to accelerate manufacturing quality assurance timeline. The Industry 4.0, which emphasises automation and data interchange, strongly connects with deep learning time economy that exhibits features of being more streamlined, automated, and data- informed. The lowered manual inspection results in cost efficiencies and better resource deployments. Cutting-edge technology combined with efficient procedures fuses innovation to efficacy, augmenting the foundation of Industry 4.0 mission. As a result, this strategy not only guarantees production of excellent, defect-free components but also supports broader goals of Industry 4.0by demonstrating how to effectively combine technological development with operational optimization.
Stenographic Approaches for Enhancing Data Security in Cloud Computing
Authors: Rajguru Sankalp Sudarsan , Singh Gursharan , Malhi Simarjit Singh , Kaur Gursimran
Published: August 2024

Steganography has become a well-known method for concealing data so that unauthorized users cannot identify it among other data. With the growing popularity of cloud computing, security of cloud databases has become a major problem, and the necessity to safeguard sensitive data stored in the cloud has become crucial. Cloud computing is an emerging technology that provides users with enormous storage and computational capabilities. In the modern world, cloud computing is become a necessary technology. Using the internet, individuals may remotely process, store, and access data. However, Security has become a major problem as more data is kept on the cloud. The difficulties in implementing steganography in cloud computing and suggest some future research areas in this area. Our review suggests that steganography can be a useful technique for strengthening the security of cloud databases, but its use calls for careful evaluation of the difficulties involved. Overall, the use of steganography techniques in cloud computing and their potential to improve the security of cloud databases are discussed in this study in useful details.
Eco-Conscious Packaging: Exploring Sugarcane Bagasse-Based Tableware Manufacturing in India
Authors: Khoisnam Nikita , Sandhu Harpinder Singh , Awatade Sudarshan C. , Subba Ranjit , Dulal Jaya Prakash
Published: August 2024

This research study presents a solution to combat plastic pollution by creating biodegradable tableware from bagasse, a byproduct of the sugar industry, as an eco-friendly alternative to conventional plastics. The purpose of the study is to establishing a technological manufacturing facility in India to produce tableware made of bagasse, showcasing its versatility and market viability. The optimal price, quality, and resources required for manufacturing bagasse-based biodegradable tableware were analyzed, aiming to replace Styrofoam in food packaging and tableware. The practical implications of this research include promoting lowcarbon economic solutions and contributing to reduce plastic waste. Moreover, the potential for widespread adoption of bagasse-based goods extends beyond tableware, presenting an opportunity for various industries both in India and globally to embrace sustainability. This research strives to pave the way for a greener and more sustainable future, revolutionizing packaging and tableware production through the adoption of biodegradable goods derived from bagasse.
Enhancing Mechanical Properties of Composites with Plasma-Treated Linear Low-Density Propylene Matrix, SiC Nanoparticles, and Carbon Fiber Filler
Authors: Al Ansari Mohammed Saleh , Kaliappan S. , Sree G. Vanya , Prabhakar Pranav Kumar , Maranan Ramya , Meshram Pawan Devidas
Published: August 2024

In this research, the optimization of composite materials for improving their mechanical properties is investigated. It is achieved by applying different compositions of the PTLLDPE matrix, SiC nanoparticles, and carbon fibre filler. For this purpose, six composite samples are prepared using different compositions of PTLLDPE from 40% to 60%, SiC nanoparticles from 0% to 3%, and carbon fibre filler from 10% to 20%, which are mechanically tested . Results show that tensile strength increases with increasing PTLLDPE contents, Sample 6 having the highest value of 62 MPa. As the SiC nanoparticles contents increase, the flexural strength and impact resistance increases, Sample 4 having the highest flexural strength at 75 MPa and impact resistance at 200 J/m2. The hardness increases with increasing carbon fiber fillers, Sample 6 having the highest hardness value at 88 shore D. This is important in the synthesis and the optimization of composite formulations, helping various industries in in their choice and application of the composites.
Revolution of E-Learning Up takes in Higher Education Institutions in India
Authors: Archana  , Kumar Amar , Lakhera Mishra Girish , Kumari Jyoti , Ojha Megha
Published: August 2024

In response to the enhancing utilization of technologies and the present state of the field, this study aims to explore the E-learning revolution in Higher Educational institutes in India. Through a systematic review using bibliometric methods, data mining, and analytics, a total of 293 publications were examined. The study reveals a consistent growth in research on E-learning, particularly in recent years, with the USA, China, and India in this field. The primary contributors to this research area are Education and Information Technology, followed by an International review of research in open and distance. This study employs the Scopus database to identify trends in publications, influential papers, key themes, and keywords. It also explores futuristic themes and target concepts. The average citation value over three years stands at 6.768. Issues In Information Systems, T and D and Developments In Marketing Science: Proceedings Of The Academy Of Marketing Science. Research has shown that it is effective in increasing the effectiveness of e-learning in educational settings, especially in the areas of organizational behavior, corporate professionals, training and development, and finance, among others. It makes significant contributions to topics like financial crises, sustainability, COVID-19 issues, team challenges, organizational conflicts, and digital transformation. The study recognizes its limitations and explores possible directions for future investigation.The findings also highlight crucial research gaps, underscoring the need for further investigations. This knowledge can inform the development of strategic approaches to tackle challenges and leverage opportunities related to E-learning in higher educational institutes. Ultimately, the study aims to provide insights for strategies that support the advancement of E-Learning
Development of Data-Driven Predictive Framework for Nanofluid-based Solar Thermal Collector: A Machine Learning Approach
Authors: Sankar Anjana , Kumar Gupta Kritesh , Bhalla Vishal
Published: August 2024

The conventional approach for solar collector design often requires performing large-scale experimentations or computationally intensive simulations which hinders the comprehensive screening and optimization of process design. This creates a strong rationale for developing a computationally efficient framework capable of leveraging a relatively small number of samples to generate a machine-learning model with sufficiently high fidelity. In this regard, the present study aims to integrate the concepts of random sampling, Gaussian process(GP), and Bayesian optimization for developing a computationally efficient data-driven framework for capturing the complete continuous domain of the parametric variation and predicting the desired performance measure. The proposed framework is rigorously tested at different stages with the help of unknown samples (out-of-fold test samples) to ensure the sound generalization capability of the constructed model. The model assessment revealed that the increase in sample size for training the GP model from 35 samples to 105 samples resulted in ≈ 56% reduction in root mean square error (RMSE), which further reduces to ≈ 96.5% after performing Bayesian optimization based hyperparameter tuning. The proposed framework will be extremely helpful in designing the highly efficient nanofluid-based solar thermal collector, by preventing the need of performing large-scale experimentations/simulations for screening purpose.
Strategy to optimize the galvanising process for products in small batches in a galvanising plant
Authors: Purbhoonauth N. , Surnam B.Y.R.
Published: August 2024

This study was performed to optimize the zinc usage, through zinc pickup, for products to be galvanized in small batches or one-off. A strategy was also developed to that end. Five factors influencing the galvanising process, namely surface profile, immersion time, extraction rate, zinc temperature and chemical composition of steel were investigated through the Taguchi’s design of experiment approach. It was found that, for a good process capability, immersion time should be kept to a minimum. The best surface preparation method for reactive steels is by grinding and the optimum galvanising temperature for the latter is 445oC. A protocol for handling variations has been devised for the galvanization of small batches of products. Though many researches are being performed to improve the zinc coating, many galvanising factories face these issues. The results are expected to help all the developing galvanising companies which receive products to be galvanized in small batches.
Providing energy efficiency and ecological compatibility in organising special cargo delivery
Authors: Lytvynenko Sergiy , Vasylenko Igor , Voitsehovskiy Viktor , Viniukov-Proshchenko Artur , Gabrielova Tetiana , Lytvynenko Larysa
Published: August 2024

The relevance of ensuring energy efficiency and environmental compatibility in all sectors of the economy is growing every year. Transport is one of the key factors in the successful development of the global economy, which ensures, among other things, the continuous movement of goods to support global value chains. The methodological basis of the study is represented by the provisions of modern economic theory, logistics, transport systems, management, ecology, and energy efficiency. A number of general scientific and special methods were used to solve the tasks, in particular, methods of formalisation, system and statistical analysis, probability theory, typology, abstract and logical synthesis. As part of the study, an effective mechanism for ensuring energy efficiency and environmental compatibility in the delivery of special categories of cargo was proposed which can be used to most effectively solve the tasks set throughout the supply chain. The proposed mechanism is formed by environmental and energy-efficient tools, a support system, internal and external contradictions to achieve an environmental and energy balance. The toolkit is fully consistent with the public interest, as well as with the goals of Industry 5.0 in ensuring the environmental effect as part of the path to a circular economy, increasing the reliability and sustainability of value chains through the formation of increased responsibility of participants in the process of delivering special cargo, as well as the implementation of the principles of environmental and social management.
Investigation and comparison of sustainable cooling techniques for solar (PV) panel
Authors: Maskaren John , NK Beewal Verma Aditya , Wankhede Sagar , Patil Megha
Published: August 2024

Solar panels are essential for absorbing renewable energy, prolonged exposure to high temperatures can diminish their efficiency and durability. Research has been done on sustainable cooling methods as a potential fix for this issue. This study analyses many cooling methods, such as using Aloe Vera, Ficus, Rubber, and Snake plants, to enhance the efficiency of solar panels. The intent of the research is to evaluate how well these methodologies reduce panel temperature and increase overall efficiency. Temperature and voltage output were the primary performance metrics in the controlled experiments. The experiment’s results demonstrate that different cooling techniques have different effects on solar panels’ capacity to control temperature. Owing mainly to its capacity for evaporative cooling, the aloe vera plant demonstrated maximum voltage production and notable drop in temperature. Ficus plant, Rubber plant, and Snake plant in terms of temperature reduction and voltage production. The comparison study demonstrates how effectively each technique reduces temperature increase and improves the efficiency of solar panels. These findings provide helpful information to solar panel system stakeholders, aiding in the selection of suitable sustainable cooling techniques according to their efficacy and appropriateness for certain climatic conditions. All things considered, the experimental performance and comparison of different cooling solutions enhance the technology of renewable energy by improving the efficiency and reliability of solar panel systems.
Exploring the Potential of Floating Architecture & its Energy Challenges in the UAE
Authors: Kartheekeyan Kamalanandhini , Baig Alvira , Mahanta Niva Rana
Published: August 2024

The rising issues of climate change & the increase in global population call for an intensive approach to an underutilized canvas for reimagining the way we live, work, and connect with nature. With three-quarters of the earth’s surface covered with water bodies, it becomes inevitable to expand livelihood into the water thus, Floating architecture is a more sustainable approach as opposed to the conventional methods of land reclamation with minimal environmental disruption. The UAE is a nation characterized by its rapidly growing population, rising sea levels, and initiatives in making use of its strategic location and resources to explore renewable energy sources like solar panels and wind turbines, while incorporating with blues and greens in architecture. This has led to a drastic impact on the demand for sustainable homes, communities, and cities leading to the necessity for innovative solutions in developing land around coastal or marine space because of pressing current issues. This research paper aims to delve into the potential of floating architecture and blue economy strategies as pioneered by UAE, in addressing energy challenges. Through the process of primary research, literature review, case studies, and analysis, the paper examines the functionality, construction and design of floating structures in UAE, assessing their potential to generate renewable energy & contribute to positive climate impact.
Optimizing Sustainable Construction Costs: A Green Building Approach for Residential Development
Authors: Patil Vanishri A. , Wadalkar Shruti , Kale Vinayak , Sawant Rohan , Joshi Deepa A.
Published: August 2024

Green buildings are generally referred to as environmental friendly buildings. Globally, building is responsible for a huge share of energy, water, electricity, and material consumption. The green building concept is very popular worldwide. The adverse impact of construction on the environment significantly promotes the development of the green building concept worldwide.The growing awareness of the adverse environmental impacts of construction has led to the widespread adoption of the green building concept globally. This approach promotes sustainability and mitigates the negative effects of construction on the environment. This research project aims to perform the energy and cost saving comparison of green building over the conventional building. Green Building are designed to reduce the overall impact of the built environment on human health and natural environment. This study aims to investigate the cost premiums and cost performance of green building projects. After an extensive literature review and case study of G+4 residential building located at Bhusawal district, Maharashtra has been presented in detail. The land area spread over 6400 sqft and construction area is about 12000 Sqft. and it is designed as per green building design to save the cost and energy for the building. This case study shows that the average increase in the initial cost of green building is 7-10% for those with four-star rating building however 34-40% of energy can be saved by implementing the green building design and technique. This study can reduce the maintenance cost of green building over the conventional building which is very huge amount over the lifecycle of the project.
Enhancing Concrete Strength: The Impact of Silica Admixture
Authors: Aakash  , Vashistha Kapil , Kumar Saurabh , Kumar Soni Saurabh
Published: August 2024

Researchers set out to determine if and by what margins Nano silica reduces the compressive strength of concrete. From seven percent to seventeen percent, silica was an ingredient in the concrete mixture. The mix design for M20 grade concrete was cast in accordance with the Indian Standard Code IS 10262-1982. One interesting thing that came out of the experiments was that the concrete’s compressive strength was stronger after 28 days compared to 7 days. In addition, concrete mixtures containing nano silica at concentrations as high as 14% showed noticeably greater compressive strength compared to conventional concrete. The compressive strength of concrete was also shown to be consistently higher when 7, 11, or 14 percent silica was added to it. Compressive strength actually decreased with the addition of silica at a rate of 17%. The finding demonstrated a robust quadratic relationship (R-squared value approaching 1) between the compressive strength (or load) and the percentage of silica in the concrete mix. As can be seen from this correlation, the strength diminishes with increasing silica concentration. So, as a mechanical property enhancer for concrete, nano silica truly comes into its own at a certain point.
Analysis of current situation and future development trend of national carbon market construction under the background of Energy Internet
Authors: Chen Chen , Tan Jian , Xue Guiyuan , Meng Shiyu , Yin Junping
Published: August 2024

The Energy Internet is an important technical and institutional support to expand the coverage of carbon trading and reduce transaction costs. The carbon trading system is an important financial and marketbased means to promote the low-carbon Energy Internet and realize the revolution of energy production and consumption. The Energy Internet will provide necessary information access for the carbon trading market, and the carbon trading market will provide considerable financial support for the Energy Internet. The two will work together to build a big data and value chain platform for energy and environmental rights and interests transactions. Therefore, it is necessary to deeply study the current situation and future development trend of the national carbon market construction under the background of Energy Internet.
Forecast of new energy vehicle ownership in key cities in China
Authors: Liang Panchun , Chen Chuan , Li Hongwei
Published: August 2024

With the rapid development of the electric vehicle industry, the future number of new energy vehicle(NEV) will play a key role in urban planning and the infrastructure layout of NEV development such as charging piles. This paper aims to forecast the future vehicle ownership and NEV ownership in ten key cities in China through authoritative data and scientific forecasting methods, discuss the characteristics and differences of NEV development in different cities, and provide a reference for government policy formulation and enterprise industrial layout.
Digital and Intelligent Operation and Maintenance Technology for Thermal Injection Boilers
Authors: Liu Jiangling
Published: August 2024

The production of heavy oil in Liaohe Oilfield accounts for 57.2% of the total, with the main hot oil production development methods being steam injection, SAGD, fire driving, and steam driving. The annual steam injection is 25.35 million cubic meters, and the steam injection energy consumption accounts for 82% of the total energy consumption. How to adopt digital operation and maintenance technology to realize the intelligent control of hot injection boilers, not only saving labor costs but also reducing energy consumption, is the only way for the oilfield to achieve high-quality development. Therefore, based on the Dream Cloud management platform, using modern Internet of Things technology, information programming technology, and automation control technology, integrating real-time collection systems, real-time control systems, and safety operation and maintenance systems, etc., the study of the boiler automation control system is carried out to achieve safe and efficient production, promote economic benefits, and provide reference for other similar oilfields.
Research advances in environmental corrosivity mapping techniques
Authors: Chen Gang , Zhai Xing , Wang Yan , Zhang Yongwei , Sun Renchang , Bao Deyu , Jiang Bingshi , Du Haifeng , Zhao Wenjing , Huang Luyao
Published: August 2024

Corrosion map is the most direct form of environmental corrosion, which can provide data support for the anti-corrosion design, construction and operation and maintenance of engineering equipment intuitively and quickly. The research advancements in environmental corrosivity mapping techniques are reviewed here. It delineates the atmospheric and soil corrosivity classification methods through qualitative and quantitative analysis. The merits and constraints of these classification methods and interpolation methods when applied to corrosion mapping are critically assessed.
The Cause Analysis of Valve Stem Bolt Failures in Nuclear Power Plant
Authors: Song Shuaishuai , Qi Jun , Bai Zhankun , Fang Qishu
Published: August 2024

The shaft of the electric isolation valve at the outlet of the seawater tank pump in a nuclear power plant fractured due to bolt failure, resulting in detachment and loss of electrical switching function. Upon disassembly, it was discovered that all connecting bolts on the frame were fractured. Macroscopic examination, chemical composition analysis, hardness testing, metallographic examination, scanning electron microscope (SEM) observation, and energy spectrum analysis were performed on the fractured bolts. Results revealed significant deviation from design specifications for stainless steel austenite in terms of their chemical composition; instead they consisted of low-alloy steel (40Cr). High-strength bolts exposed to marine environments are susceptible to corrosion-induced stress cracking or hydrogen embrittlement leading to fracture. Replacement is recommended as a preventive measure against corrosion-induced bolt failure.
Top Management Team Environmental Attention and Corporate Cost Stickiness: Evidence from China
Authors: Ma Xinyu
Published: August 2024

Based on the attention-based view and the upper echelon theory, top management teams’ attention allocation plays a critical role in strategic decision-making and operational outcomes. We investigate whether a firm’s cost behaviour is influenced by the top management team environmental attention. The empirical results show that cost stickiness is common among listed firms in China. And the top management team’s environmental attention can significantly exacerbates cost stickiness. Furthermore, the positive relationship between top management team environment attention and cost stickiness is more pronounced in mature-stage firms. These results enriches the determinants of cost stickiness by providing evidence from the perspective of managers’ attention.
Transformation of Heavy Metals by periphyton
Authors: Huang Haoyu , Lin Yan , Yang Zhanpeng , Qu Keling , Li Zhimei , Lu Gangzhong , Hui Fujiang , Wei Jiaqing , Song Yufeng , Yao Yi
Published: August 2024

The role of periphyton in the transformation of heavy metal pollutants has attracted extensive attention.This paper reviews the research progress of periphyton in heavy metal transformation, including its species and characteristics, transformation mechanism, influencing factors, practical application, existing problems and future development direction.The results showed that heavy metals were effectively transformed by periphyton through adsorption, biotransformation and biodegradation.The main factors affecting heavy metal biotransformation include species and quantity of organisms, species and concentration of heavy metals and environmental conditions.In recent years, some achievements have been made in the application of periphyton in sewage treatment, soil remediation and water purification, but there are still some challenges and problems in the practical application process.Future research should further explore the transformation mechanism of periphyton, optimize its application methods, and promote its industrialization and commercialization, in order to provide more feasible solutions for heavy metal pollution control.
Research progress on optimization of light conditions during Chlorella culture
Authors: Pei Alang , Dong Guori , Chen Fang , Wang Haiwang , Peng Xinhong , Bi Yanli
Published: August 2024

With the role of Chlorella in bioenergy, big health, sewage treatment and carbon emission reduction, Chlorella attracts more and more attention. Chlorella is significantly affected by light during growth. For a long time, in order to improve the biomass, active substance production, and the application in environmental protection, the effect of light on the algae has been deeply studied. Literature research shows that the light intensity around 6000 lx can provide sufficient light energy for Chlorella, and its net photosynthetic oxygen release rate increases rapidly with the increase of light intensity, but is inhibited beyond the light saturation;although it is not conducive to photosynthesis and rapid growth under low light, it is conducive to the synthesis and accumulation of EPA and the removal of nitrogen and phosphorus by the symbiotic system of bacteria and algae. Different color light has different effects on promoting the growth of Chlorella algae and improving the production of active substances.14:10h is more suitable for the photosynthesis in Chlorella and algae; high frequency light can reduce the photoinhibition effect. Due to the different gene expression levels at different growth stages, corresponding light measures should be taken according to its adaptive differences. Based on the above views, the future light research of Chlorella should be conducted on the adaptive light device, automatic corresponding changes of light during the whole life cycle of chlorella and the characteristic light required by the characteristic active substances.
Multi-omics lights on the toxicological effects of Cr contamination on Penicillium janthinellum P1
Authors: Chen Qiuquan , Chi Binbin , Chen Huiying , Li Xia , Xiong Bixia , Guo Quan
Published: August 2024

Heavy chromium pollution is a global sticky problem. In this study, an built-in proteomic and metabolomic method used to be used to look at the possible organic toxicity of Cr-contaminated answer based totally on an publicity scan with Penicillium janthinellum P1. The results showed that the high-Cr exposures resulted in cell injuries and signaling disturbances in P. janthinellum P1. Furthermore, Cr-contaminated answer triggered an amplify in glutamine and a sizable minimize in dimethylglycine, betaine and taurine, suggesting that the osmoregulatory metabolism of the P. janthinellum may additionally have been disturbed. The alteration of ATPase and mitochondrial associated proteins, combined with the upregulation of amino acids and carbohydrates, revealed that Cr contamination may disrupt the energy metabolism system. In addition, the secretory effects were confirmed by the upregulation of cytochrome P450 and deoxypodophyllotoxin, and the downregulation of penicillopepsin via proteomics. Therefore, dimethylglycine, glutamine, aspartate and cytochrome P450, all with remarkably alteration, were supposed to be selected as potential biomarkers of Cr contamination. This work assume to furnished new perception into the proteomic and metabolic toxicity of excessive Cr-contaminated area of interest in filamentous fungi, highlighted the mechanisms underlying toxicity and extended our knowledge of multi-omics jointly.
Research on Ultrasonic Nondestructive Testing Algorithm for Metal Materials
Authors: Wei Lihong , Calderon Aldrin D. , Tian Peng
Published: August 2024

With the continuous development of science and technology, metal castings are widely used in aerospace, oil exploitation and other fields, and the safety and availability of casting quality has become a hot issue in current research. Ultrasonic nondestructive testing has good penetration and directivity, but nonstationary ultrasonic signals will be formed in the process of flaw detection, which has a certain impact on the extraction of effective information. Therefore, by processing and analyzing ultrasonic signals, this paper uses wavelet transform and empirical mode decomposition to make a comparative study. It is found that the wavelet transform denoising algorithm still has distortion after processing the noisy signal. Empirical mode decomposition is based on the signal itself, which has strong adaptability and better performance. Moreover, the improved threshold function has higher accuracy in signal processing, and the distortion degree of waveform is reduced, which is more suitable for noise reduction of metal echo signal, and can meet the requirements of engineering applications.
Introduction to the Proceedings of the 9th “Complexity-disorder” Meeting: Intermediate Modelling means classifying
Authors: Levy Jean-Claude Serge
Published: August 2024

This introduction to these proceedings is mainly devoted to comment a few intermediate models. The interest of intermediate models lies in their ability to define large classes of entities, and such general principles can go through distinct disciplines, the main goal of these days. The first examples of intermediate models with short range interactions are shown to occur in magnetism. This practical knowledge is then transferred to the case of structures where geometry and energy compete in defining crystals, quasicrystals and more disordered structures. Such studies based upon a simple variational treatment followed by a careful relaxation using classical numerical treatments enable us also to deal with defects, static and dynamic surface problems. This classification of structures leads also to classify metallic atoms. The rich field of lacunary systems and alloys is also evocated with its potential links with sociology for instance. Finally, long-ranged interactions such as dipolar ones are introduced as intermediate models. These dipolar interactions are sensitive to the size and shape of the sample and act even through vacuum between disconnected parts. This introduces a full complexity with rich static and dynamic properties. Finally, all contributions are briefly introduced.