bluuebunny/crossref_metadata_2025 huggingface dataset:
Modular access to multi-substituted allenones via environmentally friendly organocatalytic C–H allenylation of aldehydes
Authors: Hu Xinying , Jialingbieke Ayisenbati , Ren Yuzhi , Yang Yifan , Wei Donghui , Gao Jian , Du Ding
Published: October 2024

Organocatalytic direct C–H allenylation of aldehydes with propargyl bromides has provided a straightforward and step-economical strategy for allenone modular synthesis.
Evaluation of miniaturized ultrasonic nebulization for high-efficiency sampling in characterization of silver nanoparticles by single particle inductively coupled plasma mass spectrometry
Authors: Dong Junhang , Zhu Zhenli , Li Lujie , Xing Pengju , Li Shuyang , Ouyang Lei , Liu Xing , Guo Wei , Zheng Hongtao , Qian Rong
Published: October 2024

Miniaturized ultrasonic nebulization (MUN) provides high sampling efficiency for single particle inductive coupled plasma mass spectrometry (sp-ICPMS).
Perylene diimide functionalized nano-silica: green emissive material for selective probing and remediation of 4-nitrocatechol, Ru3+, and Cu2+ with biosensing applications
Authors: Yadav Sanjay , Choudhary Nishu , Vasave Avinash T. , Sonpal Vasavdutta , Paital Alok Ranjan
Published: October 2024

A novel nano-silica functionalized material developed for dual functions of sensing and remediation of specific analytes, offering benefits of sensitive detection, high adsorption capacity with recyclability & biosensing capability.
A chemical method for the complete components recovery from the ferric phosphate tailing of spent lithium iron phosphate batteries
Authors: Wu Zeguang , Mei Huaxian , Wan Xiaoxia , Shen Fanxi , Peng Cong
Published: October 2024

After acid leaching of ferric phosphate tailings, the filtrate was used to synthesize hydrated ferric phosphate, while the filter residue served as a filler for natural SBR, achieving the resourceful utilization of solid waste materials.
Vanadium Chemistry in the 21st Century
Authors: Pombeiro Armando J. L. , Sutradhar Manas , Correia Isabel
Published: October 2024

Guest Editors Armando J. L. Pombeiro, Manas Sutradhar and Isabel Correia introduce the New Journal of Chemistry “Vanadium Chemistry in the 21st Century” themed collection.
Orotic acid-capped Tb(iii)-doped calcium sulphate nanorods for the selective detection of tryptophan
Authors: Kumar Jaydeep , Yadav Neha , Mishra Viplove , Koppisetti Heramba V. S. R. M. , Roy Avishek , Mitra Antarip , Mahalingam Venkataramanan
Published: October 2024

This study presents a luminescence sensor using orotic acid-capped Tb3+-doped CaSO4 nanorods for the selective detection of tryptophan, with a LOD of 0.61 μM, showcasing potential for biomedical applications.
A chromium cobaltite based ternary composite as an efficient electrode material for hybrid supercapacitors with theoretical investigation
Authors: Kour Simran , Kour Pawanpreet , Sharma A. L.
Published: October 2024

The synergistic integration of PPY and AC with chromium cobaltite leads to the development of advanced electrode materials for hybrid supercapacitors.
The clinical potential of l-oligonucleotides: challenges and opportunities
Authors: Shearer Victoria , Yu Chen-Hsu , Han Xuan , Sczepanski Jonathan T.
Published: October 2024

We discuss the challenges and practical considerations currently associated with the use of l-oligonucleotides and explore potential solutions that pave the way towards the broader adoption of l-oligonucleotides in clinical applications.
Cation migration in layered oxide cathodes for sodium-ion batteries: fundamental failure mechanisms and practical modulation strategies
Authors: Jian Zhuang-Chun , Guo Jun-Xu , Liu Yi-Feng , Zhu Yan-Fang , Wang Jingqiang , Xiao Yao
Published: October 2024

We comprehensively review the research advances in cation migration of sodium layered oxides, systematically revealing the fundamental mechanisms and practical modulation strategies for irreversible cation migration leading to battery failure.
Dichlorine–pyridine N-oxide halogen-bonded complexes
Authors: Limberg Niklas , Rautiainen J. Mikko , Lundell Jan , Riedel Sebastian , Rissanen Kari , Puttreddy Rakesh
Published: October 2024

The first structural evidence of extremely unstable Cl⋯O halogen bonds formed by dichlorine and pyridine N-oxide has been successfully characterized by X-ray diffraction analysis.
Placental small extracellular vesicles from normal pregnancy and gestational diabetes increase insulin gene transcription and content in β cells
Authors: Seedat Faheem , Kandzija Neva , Ellis Michael J. , Jiang Shuhan , Sarbalina Asselzhan , Bancroft James , Drydale Edward , Hester Svenja S. , Fischer Roman , Wade Alisha N. , Stefana M. Irina , Todd John A. , Vatish Manu
Published: October 2024

Insulin secretion increases progressively during pregnancy to maintain normal maternal blood glucose levels. The placenta plays a crucial role in this process by releasing hormones and extracellular vesicles into the maternal circulation, which drive significant changes in pregnancy physiology. Placental extracellular vesicles, which are detectable in the plasma of pregnant women, have been shown to signal peripheral tissues and contribute to pregnancy-related conditions. While studies using murine models have demonstrated that extracellular vesicles can modulate insulin secretion in pancreatic islets, it remains unclear whether these effects translate to human biology. Understanding how placental signals enhance insulin synthesis and secretion from β cells could be pivotal in developing new therapies for diabetes. In our study, we isolated placental small extracellular vesicles from human placentae and utilised the human β cell line, EndoC-βH3, to investigate their effects on β-cell function in vitro. Our results indicate that human β cells internalise placental small extracellular vesicles, leading to enhanced insulin gene expression and increased insulin content within the β cells. Moreover, these vesicles up-regulated the expression of Annexin A1, a protein known to increase insulin content. This up-regulation of Annexin A1 holds promise as a potential mechanism by which placental small extracellular vesicles enhance insulin biosynthesis.
ExoLyn: A golden mean approach to multispecies cloud modeling in atmospheric retrieval
Authors: Huang Helong , Ormel Chris W. , Min Michiel
Published: October 2024

Context. Clouds are ubiquitous in exoplanets’ atmospheres and play an important role in setting the opacity and chemical inventory of the atmosphere. Understanding clouds is a critical step in interpreting exoplanets’ spectroscopic data.

Aims. The aim is to model the multispecies nature of clouds in atmospheric retrieval studies. To this end, we developed ExoLyn – a 1D cloud model that balances physical consistency with computational efficiency.

Methods. ExoLyn solves the transport equation of cloud particles and vapor under cloud condensation rates that are self-consistently calculated from thermodynamics. Exolyn is a standalone, open source package capable of being combined with optool to calculate solid opacities and with petitRADTRANS to generate transmission or emission spectra.

Results. With ExoLyn we find that the compositional structure of clouds in hot Jupiter planets’ atmospheres is layered with a cloud dominated by magnesiumsilicates on top of an iron cloud. This finding is consistent with more complex cloud formation models but can be obtained with ExoLyn in only a few seconds. The composition of the cloud particles can be constrained from the spectrum, for example, MgSiO3 and Mg2SiO4 components give rise to an absorption feature at 8–10 μm. We investigate the dependence of the cloud structure on the bulk elemental composition of the planet and find that SiO2-dominated clouds form on metal-rich planets and Fe clouds with a strong extinction effect form on C-rich planets.

Conclusions. Designed toward maximum flexibility, ExoLyn can also be used in retrieval analysis of sub-Neptunes and self-luminous planets. The efficiency of ExoLyn opens the possibility of joint retrieval of exoplanets’ gas and cloud components.
Reconciling concentration to virial mass relations
Authors: Leier Dominik , Ferreras Ignacio , Negri Andrea , Saha Prasenjit
Published: October 2024

Context. The concentration-virial mass (c-M) relation is a fundamental scaling relation within the standard cold dark matter (ΛCDM) framework well established in numerical simulations. However, observational constraints of this relation are hampered by the difficulty of characterising the properties of dark matter haloes. Recent comparisons between simulations and observations have suggested a systematic difference of the c-M relation, with higher concentrations in the latter.

Aims. In this work, we undertake detailed comparisons between simulated galaxies and observations of a sample of strong-lensing galaxies.

Methods. We explore several factors of the comparison with strong gravitational lensing constraints, including the choice of the generic dark matter density profile, the effect of radial resolution, the reconstruction limits of observed versus simulated mass profiles, and the role of the initial mass function in the derivation of the dark matter parameters. Furthermore, we show the dependence of the c-M relation on reconstruction and model errors through a detailed comparison of real and simulated gravitational lensing systems.

Results. An effective reconciliation of simulated and observed c-M relations can be achieved if one considers less strict assumptions on the dark matter profile, for example, by changing the slope of a generic NFW profile or focusing on rather extreme combinations of stellar-to-dark matter distributions. A minor effect is inherent to the applied method: fits to the NFW profile on a less well-constrained inner mass profile yield slightly higher concentrations and lower virial masses.
Compact ellipticals in the Antlia cluster of galaxies
Authors: Pablo Caso Juan , Vega Neme Luis , De Bórtoli Bruno J. , Bassino Lilia P. , Richtler Tom , Smith Castelli Analía
Published: October 2024

Context. Compact ellipticals (cEs) are rare objects in the local Universe, but relevant for the evolution of galaxies. Their origin is not completely understood, with evidence equally suggesting that they are relics of early epochs, and remnants of tidal disruptions.

Aims. This article aims to characterise two cEs, members of the nearby Antlia cluster, providing insights into the formation channels of cEs residing in high-density environments.

Methods. This dataset consists of archival ACS/HST images and Gemini/GMOS slit spectra. The isophotal analysis of the cEs was compared with their kinematics and stellar population synthesis. The results were also compared with a sample of cEs and ‘relic’ galaxies from the literature, to put them into context.

Results. Their characterisations highlight clear differences between the two cEs. Although the stellar populations for both cEs are mainly old, with mass-weighted ages above 10 Gyr, FS90 110, the most massive one, has a minor contribution from an intermediate-age population. Besides, it presents rotational evidence, a remarkably bright nucleus for its luminosity, and a clear dichotomy between the inner and outer components. It is probably the remnant of a low-mass disc galaxy that has been tidally disrupted by the close massive elliptical NGC 3258. On the contrary, FS90 192 does not present evidence for either rotation or discy isophotes, and its population synthesis leads to a stellar population with ages above 12 Gyr. It is probably a low-mass analogue to ‘relic’ galaxies.
Life Cycle Assessment of Biofuels using Monte Carlo Simulation
Authors: Krotov Alexandr , Karuna Gotlur , Bhattaru Sarathsimha , Singh Dhiraj , Joshi Ankita , Bhalla Lalit , Singh Amanveer , Khandelwal Utkal , Al-Farouni Mohammed
Published: October 2024

Biofuels are a viable alternative to fossil fuels, providing potential advantages in mitigating greenhouse gas emissions and improving energy security. Nonetheless, the environmental sustainability of biofuels requires thorough evaluation across their whole life cycle. This report provides a thorough Life Cycle Assessment (LCA) of biofuels, emphasizing greenhouse gas emissions, energy use, and resource depletion. Monte Carlo simulation is used to address variability and uncertainty in critical parameters, yielding a reliable assessment of environmental consequences. Findings demonstrate that biofuels may decrease greenhouse gas emissions by 45-60% relative to fossil fuels; however, considerable heterogeneity is seen based on feedstock type, agricultural methods, and conversion technology.
Circular Economy Assessment of Recycled Concrete with Life Cycle
Authors: Orlov Alexandr K. , Nautiyal Rishi Dev , Chandana Ch. , Parashar Komal , Al-Jawahry Hassan M. , Verma Vikas , Rathour Abhinav , Gudainiyan Jitendra , Lakshmi Prasanna Javvadi
Published: October 2024

This research examines the life cycle costing (LCC) of concrete produced using recycled concrete aggregates (RCA) combined with sustainable fibers, including carbon nanofibers and natural sisal fibers, as substitutes for traditional natural aggregates. A series of concrete mixtures were formulated with RCA content levels of 0%, 50%, and 100%, incorporating various fiber combinations, while some mixtures were further enhanced with supplementary cementitious materials such as fly ash (FA) or silica fume (SF). The study included a thorough assessment of mechanical properties, including compressive strength, flexural strength, and split tensile strength, to evaluate the performance of these concrete mixtures. The findings indicated that the integration of sisal and carbon nanofibers significantly improved mechanical properties, suggesting that while using 100% RCA (Mix C9) resulted in a slight reduction in strength, it still outperformed conventional concrete. This research highlights the economic advantages of utilizing RCA and sustainable fibers in concrete formulations, demonstrating how life cycle costing can provide a framework for promoting sustainable construction practices that enhance resource efficiency and minimize overall project costs in the context of a circular economy.
Energy Efficiency in Buildings using Internet of Things (IoT) and Big Data Analytics
Authors: Kankhva Vadim , Negi Anup Singh , Aaluri Suresh , Sharma Nittin , Husain Saif O. , Sobti Rajeev , Brar Manvinder , Varshney Neeraj , Anuradha Kodali
Published: October 2024

In order to assess the energy efficiency of building activities in real-time, this research offers a data-driven methodology. Efficiently managing building energy usage while minimizing negative effects on the environment is the focus of this study. Using a large dataset that includes efficiency ratings obtained from sophisticated analytics and continuous monitoring, as well as specific energy consumption (SEC) measurements, our study reveals intricate patterns in energy use. Reducing energy consumption by 15% during peak hours is possible with the use of predictive modeling tools, which show the possibility of proactive actions. With dynamic modifications resulting in a 20% reduction in total energy use, there are substantial benefits to implementing adaptive techniques based on real-time data. The method’s dependability is confirmed by comparing it to industry-standard standards, which highlights how strong the real-time evaluation system is. Building managers may benefit greatly from this research’s findings on energy efficiency, which will help to create more sustainable and financially feasible building systems.
Assessing the Environmental Impact of Plastic Waste using Life Cycle Assessment
Authors: Butyrin Andrey Y. , Shnain Ammar Hameed , Keerthi Reddy G. , Singh Takveer , Pandey Alok Kumar , Singh Navdeep , Jagga Megha , Sharma Prashant , Venkatraman Akila
Published: October 2024

This research use a life cycle assessment (LCA) paradigm to investigate the environmental effects of plastic waste management practices. The environmental impacts of these processes are measured using experimental data. The acquisition of raw materials, particularly in plastic manufacturing, results in considerable environmental consequences, including an energy expenditure of 1200 MJ and the release of 300 kg of CO2. Likewise, waste processing activities, such as plastic shredding and molding, need 1500 MJ of energy and produce 400 kg of CO2 emissions. The operational lifespan of the product is underscored in its usage phase, wherein Plastic Product A and Plastic Component B exhibit cumulative energy consumption of 100 MJ/year and 120 MJ/year, alongside emissions of 20 kg CO2/year and 25 kg CO2/year, respectively, thereby accentuating the significance of a product’s lifecycle. The end-of-life phase underscores the variety in recycling rates, emphasizing the need for more effective recycling techniques. This comprehensive LCA methodology delineates critical areas for improvement, directing sustainable plastic waste management methods and fostering environmentally responsible decision-making within the sector. The results provide a more sustainable method for handling plastic garbage and diminishing its ecological impact.
Renewable Energy Forecasting using Deep Learning Techniques
Authors: Alpackaya Irina , Fallah Mohammed Hussein , Mounika Nomula , Sood Saksham , Rajvanshi Saurabh , Lakhanpal Sorabh , Cajla Pancham , Sharma Aman , Lalitha Yaragudipati Sri
Published: October 2024

A detailed research on deep learning in renewable energy forecasting shows how sophisticated algorithms may improve prediction accuracy. The research explores deep learning models and finds intriguing aspects that improve predictions. Long Short-Term Memory (LSTM) networks can capture temporal relationships in energy data, making them successful in predicting short-term variations with a prediction accuracy boost of 18.18% over ARIMA. Convolutional Neural Networks (CNNs) capture spatial correlations in huge datasets with up to 13% accuracy. With its capacity to analyze sequential data, Recurrent Neural Networks (RNNs) can capture long-term patterns and improve forecasting accuracy by 29.41% over Support Vector Machines. In addition, LSTM’s better handling of non-linear connections in wind energy data has improved prediction accuracy by 14.29% over feedforward networks. These results demonstrate how deep learning approaches improve renewable energy forecasting with unparalleled precision and dependability. As shown in diverse applications, LSTM, CNN, and RNN models improve renewable energy forecasting efficiency and efficacy, boosting sustainable energy solution innovation.
Evaluating the Effectiveness of Carbon Tax Policies with Econometric Modeling
Authors: Matusevich Alexander , Hassan Muntather Muhsin , Vaishnavi P. , Sudan Prerak , Chhetri Abhishek , Chhabra Soosan , Sharma Prabhat , Sharma Gunjan , Ashlin Deepa R.N.
Published: October 2024

This study uses econometric modeling to assess carbon tax programs’ efficacy in reducing carbon emissions across economic sectors. Multiple case studies, tax incentive analysis, and energy-efficient technology promotion reduced carbon intensity per unit of production by 25%. Additionally, dynamic pricing schemes matched with carbon taxation frameworks reduced emissions by 15%. These policies integrated into economic models outperformed standard regulatory methods, reducing emissions by 30% while maintaining or improving economic efficiency. The policy ideas have been tested in real-world settings, proving their practicality and applicability across sectors. This work contributes to climate change mitigation discourse and helps policymakers and business stakeholders combine environmental responsibility with economic development in a constantly changing global context.
Optimizing Smart Grids with Advanced AI Algorithms for Real-time Energy Management
Authors: Yuri Geraskin , Adnan Myasar M. , subbarayudu Yerragudipadu , Mahajan Sulabh , Dabral Upendra , Singla Tara , Khurana Sahil , Badhoutiya Arti , Ramesh Gajula
Published: October 2024

Using optimization techniques based on neural networks, this study explores how microgrids might integrate renewable energy sources. Dealing with problems caused by the uncertainty and unpredictability of renewable energy generation is the primary goal. Renewable energy generation has been showing encouraging trends, according to data analysis spanning many time periods. From 120 kWh to 140 kWh, there was a steady rise of 16.67% in solar energy utilization. Also, there was an 18.75% rise, from 80 kWh to 95 kWh, in the use of wind power. There was a 30% rise, from 50 kWh to 65 kWh, in the output of biomass energy. Microgrid load utilization analysis shows rising energy demands in commercial, industrial, and residential areas. Commercial and industrial loads climbed by 15% and 10%, respectively, while residential energy use increased by 10%, from 150 kWh to 165 kWh. With solar predictions at 98.4%, wind predictions at 95.5%, and biomass predictions at 97.3%, predictions made using neural networks were highly congruent with actual output of renewable energy.
Circular Economy in Manufacturing: Optimization using Lean Six Sigma
Authors: Orlov Alexandr K. , Hussein Ramy Riad , Subbarayudu Yerragudipadu , Kapoor Tarun , Kumar Pramod , Kansal Lavish , Bhardwaj Nishant , Kumar Yogesh , Lakshmi K. Prasanna
Published: October 2024

This research examines the amalgamation of Circular Economy concepts with Lean Six Sigma approaches to enhance manufacturing operations, emphasizing waste minimization, resource efficiency, and economic performance. The study assesses the influence of Lean Six Sigma on production efficiency and environmental sustainability via empirical analysis and process optimization methodologies. Conventional manufacturing procedures, including machining, milling, and grinding, were assessed for resource utilization and waste production, revealing that Lean Six Sigma methodologies markedly decreased material waste, especially in milling operations, where waste was curtailed by 20%. Advanced precision techniques, including CNC and laser cutting, were evaluated for their efficiency, with CNC cutting realizing a 15% decrease in energy usage and a 30% enhancement in material utilization. Laser cutting exhibited improved accuracy, resulting in a 35% decrease in rework and scrap. The economic study indicated that the application of Lean Six Sigma in CNC operations resulted in a 25% reduction in operating expenses over time, albeit greater initial expenditures. These results provide significant insights into the enhancement of industrial efficiency and the promotion of sustainable production practices via the integration of Circular Economy concepts with Lean Six Sigma methodologies. Keywords: Circular Economy, Precision Manufacturing, Comparative Analysis, Tool Life, Economic Implications.
Analysis of Carbon Footprint Reduction in Supply chains using Blockchains
Authors: Nechetnyy Nikita , Balassem Zaid Ajzan , subbarayudu Yerragudipadu , Goyal Wamika , Singh M.P. , Mittal Vaibhav , Sobti Sakshi , Sharma Gunjan , Nagaraju Krishna Chythanya
Published: October 2024

Integrating carbon footprint reduction into contemporary supply chain networks necessitates strong cybersecurity and data integrity protocols to safeguard communication infrastructures against threats. This study examines the use of blockchain technology as a cybersecurity and traceability framework in supply chain systems, using simulated data that represents logistical information, shipping quantities, carbon emissions, and blockchain transactions. An investigation indicates varied carbon-emitting mechanisms, with air freight exhibiting the greatest emissions and rail transit the lowest. Transportation nodes in Supply varying emission rates, with urban distribution centres generating more emissions than rural logistics hubs, underscoring disparities in carbon footprints across different areas. Moreover, risks such data breaches, illegal access, and cyberattacks underscore the fragility of centralized supply chain systems. Blockchain transactions exemplify secure and transparent exchanges throughout all of the supply chain, highlighting blockchain’s capacity to provide safe and verifiable monitoring of carbon emissions inside the network. This research Supply the potential of blockchain technology to diminish carbon footprints in supply chains, emphasizing its ability to enhance data security, authenticate carbon emission assertions, and augment traceability. The findings highlight the need for customized cybersecurity frameworks for Supply demonstrate blockchain’s capacity to improve the transparency, security, and sustainability of global supply chain operations.
Solar Energy Harvesting in Urban Areas using Photovoltaic Thermal Systems
Authors: Lapteva Svetlana , Priyanka Daggubati Sri Sai , subbarayudu Yerragudipadu , Gandhi Ayush , Prasad Rajendra , Singh Harminder , Kalele Girish , Badhoutiya Arti , Obaid Saif
Published: October 2024

An exhaustive investigation of the effect that urban pollution and heat have on the efficiency of Photovoltaic Thermal (PVT) systems was carried out by means of a comparative field study that included empirical data analysis. Pollutant accumulation on PVT systems in urban environments showed a significant increase over the course of a five-day period. These rates increased from an initial value of 0.03 g/m² to 0.07 g/m², representing a significant increase of 133.33%. As a direct result, the performance of PVT systems saw a considerable decline, with electricity generation falling from an average of 18% to 14% after exposure to urban conditions, a 22.22% loss in efficiency. Concurrently, the examination of urban climate factors revealed correlations between increased pollutant accumulation and higher temperatures (25-30°C), reduced air quality, decreased humidity (50-62%), higher wind speeds (2-4 meters per second), and varying solar irradiation (780-900 watts/m²). The environmental impacts on PVT system performance were further elucidated via correlation analysis, which highlighted probable connections between urban climate and system inefficiency. The research also emphasized the varying effectiveness of cooling interventions and airflow enhancements in reducing the performance losses of PVT systems in urban areas. Our findings underscore the importance of specific cooling tactics and maintenance procedures in maintaining optimal solar energy harvesting performance in urban environments. These results also provide valuable insights into the development of effective mitigation measures for PVT systems in challenging urban conditions. Keywords: Urban pollution, solar panel efficiency, Dust deposition, Environmental factors, Cleaning interventions
Energy Storage Optimization in Renewable Energy Systems using Particle Swarm Optimization
Authors: Nidziy Elena , Bobba Phaneendra babu , Seshapp Angadi , Taneja Madhur , Saini Neha , Sharma Vishal , Gupta Tannmay , Sharma Ashish , Al-Farouni Mohammed
Published: October 2024

This research examines the application of Particle Swarm Optimization (PSO) to optimize energy storage optimizations with the objectives of improving energy generation, cost-efficiency, system dependability, and environmental sustainability. The optimisation of solar panel and energy storage capacities was conducted using empirical data from various microgrid locations: Site 1, which had a capacity of 90 kW solar and 40 kW wind, Site 2, which had a capacity of 50 kW wind and 80 kW solar, Site 3, which had a capacity of 60 kW wind and 110 kW solar, and Site 4, which had a capacity of 45 kW wind and 85 kW solar. The findings suggest that energy generation increased significantly by 15% to 25% across all sites following optimization. Furthermore, significant decreases in the levelized cost of energy (LCOE) between 10% and 14% were noted, providing confirmation of the economic feasibility. Increased grid stability of 17% to 24% during periods when microgrids were operating under stable conditions demonstrates that PSO-optimized configurations are dependable. The positive environmental effects of solutions derived from PSO were apparent, as evidenced by the conservation of carbon emissions and ecological footprints, which decreased by 7% to 15%. The sensitivity analysis validated the optimized configurations' robustness, establishing their ability to withstand changes in parameters. In summary, the utilization of PSO to optimize energy storage optimizations showcases its capacity to enhance the efficiency, dependability, cost-effectiveness, and environmental impact of these systems. This advances the possibility of constructing microgrids that exclusively utilize sustainable renewable energy sources.
Sustainable Material Selection in Construction using Multi Criteria Decision Analysis (MCDA)
Authors: Sudakova Anna , Lalitha Yaragudipati Sri , Reddy K. Ramakrishna , Kataria Ansh , Prakash Sunil , Sharma Gazal , Mittal Sachin , Kumar Yogesh , Almusawi Muntather
Published: October 2024

Sustainable material is an innovative combination of hybrid engineering methods and advanced Multi Criteria Decision that seeks to transform the properties of materials on different surfaces. This scientific inquiry uncovers the transformative framework of Sustainable material. In addition, the study uncovers transformation factors (TF) for the integration of nanomaterials (TF = 1.8), Multi Criteria Decision that resemble biological processes (TF = 2.5), deposition aided by plasma (TF = 1.6), and Multi Criteria Decision made using a sol-gel hybrid method (TF = 2.0). These results demonstrate significant enhancements via the use of substantial experimental data. Biomimicry Multi Criteria Decision surpass conventional Multi Criteria Decision in terms of adhesion strength (22 MPa), abrasion resistance (0.3 mm3), and corrosion protection (300 hours). This is shown by the remarkable improvements seen in coating performance metrics. Sustainable material's adaptability is shown via experiments into material transformation, enhancing surface hardness, hydrophobicity, transparency, and tensile strength over a diverse spectrum of substrates. Environmental resistance assessments emphasize the longevity of Multi Criteria Decision, whereas biomimicry Multi Criteria Decision demonstrate remarkable resistance to UV radiation (600 hours), thermal stability (350 degrees Celsius), and chemical resistance. The precise and comprehensive information provided by percentage change values highlights the distinctive characteristics of Sustainable material, establishing it as a disruptive presence in the world of material science and engineering. This work contributes to the continuing discourse on advanced materials and offers valuable insights that may be used to many sectors such as aeronautical engineering, biomedical engineering, and other related areas.
Modeling the Impact of Deforestation on Global Warming using System Dynamics
Authors: Singh Balpreet , Reddy V. Mallikarjuna , Reddy K. Ramakrishna , Sharma Pratibha , Nautiyal Mansi , Bhatnagar Sanjay , Islam Raisul , Alabdeli Haider
Published: October 2024

This study aims to analyze the evolving dynamics of environmental sustainability via a thorough investigation of deforestation, atmospheric carbon levels, ecological equilibrium, and global climate trends. A conclusion reached is that a positive association exists between deforestation and increasing global temperatures, shown by a twenty percent rise in CO2 emissions. The loss of forests has markedly contributed to ecological imbalance, leading to detrimental impacts on biodiversity, with a total increase of fifteen percent in habitat damage. Initiatives aimed at reducing deforestation have resulted in a major 10% decrease in global carbon absorption rates, emphasizing forest regeneration and markedly diminishing carbon sequestration capabilities. The major focus is on the degradation of ecosystems, illustrating a collective struggle, shown by a 12% reduction in forest coverage and a 15% increase in global warming indices. These discoveries enhance the knowledge of environmental changes that may impact future climate systems. Furthermore, they provide crucial direction for environmental policy-makers and establish a basis for further research initiatives.
Optimization of Wind Farm Layout using Genetic Algorithms
Authors: Bhardwaj Nitin , Vittalaiah A. , Seshapp Angadi , Makhija Himanshu , Singh Rajat , Verma Raman , Gupta Manish , Al-Jawahry Hassan M.
Published: October 2024

In order to increase the economic feasibility, sustainability, and efficiency of energy production, this research proposes an improved optimization framework for hybrid wind-solar energy systems that use an augmented Genetic Algorithm (GA). Wind turbine size and photovoltaic (PV) panel orientation were optimized using historical data on wind and solar resources, system load profiles, and component specifications. There was an 18% increase in energy production, a 14% improvement in wind turbine efficiency, and a 16% increase in solar panel output because to the GA's outstanding performance. An 18% reduction in the payback time and a 12% reduction in the Levelized Cost of Energy (LCOE) were achieved. Results from the evaluation of the project's social and environmental consequences showed that community acceptability increased by 9 percentage points and land-use efficiency by 12 percentage points. A sensitivity study verified that the system could withstand several economic and environmental scenarios. The results demonstrate the promise of GA-based optimization in improving the efficiency of renewable energy hybrid systems.
Life Cycle Analysis of Biodegradable Plastics using Cradle-to-Grave Assessment
Authors: Pushkarna Gaurav , Hemalatha K. , Seshapp Angadi , Singh Jaskirat , Rawat Babita , Gupta Tannmay , Kulshreshtha Kushagra , Adnan Myasar Mundher
Published: October 2024

This research examines the impact of ergonomic adjustments on production settings, focusing specifically on the concept of biodegradable plastic. The study used a mixed-methods approach, combining quantitative and qualitative analyses derived from the collected data. The Life cycle assessment data reveal a department-specific improvement of 28.57% in the Degradable department, demonstrating the efficacy of the implemented ergonomic solutions. Research on biodegradable plastic in workstations uncovers inconsistencies and advocates for the standardization of features to provide uniform ergonomic comfort. The Productivity and Comfort Survey demonstrates a significant correlation between perceived comfort and productivity. Based on the survey findings, there is a direct correlation between a 10% increase in productivity and a one-point increase in comfort ratings. Analysis of Training and Feedback data reveals that the introduction of training initiatives resulted in a 20% improvement in outcomes linked to attention. The analysis of this information necessitates the integration of ergonomic interventions as a fundamental aspect of Life cycle industrial practices. This integration has the potential to enhance both workplace comfort and productivity, as well as job satisfaction.
Assessing Climate Change Adaptation Strategies in Agriculture using Agent-Based Modeling
Authors: Punia Ankit , Kumar Yalla Jeevan Nagendra , Lakshmi Adidela Rajya , Shnain Ammar Hameed , Saxena Archana , Kumar Suresh , Kundra Danish , Tiwari Deepak Kumar
Published: October 2024

This project seeks to analyze the complex dynamics of climate change adaptation techniques in agriculture by investigating the synergistic link between diverse agricultural practices and technology intervention s via agent-based modeling. The experimental data, consisting of synthetic datasets, clarifies the iterative adaptation process by illustrating examples such as “Drought Resistant Crop-alpha” and “Water Conservation System-beta,” which demonstrate the effectiveness of swift modifications to evolving climatic circumstances. The revolutionary influence of technology adoption is shown by developments such as “Smart Irrigation-Enhanced,” which underscores its ability to improve resilience and production. The seamless integration of these methods across all agricultural phases, from initial acceptance to extensive application, highlights their interrelated and mutually reinforcing connection. Confronting difficulties, especially those associated with socio-economic dynamics and resource allocation, requires the integration of ethical and policy considerations. This study offers critical insights into the essential comprehension of climate adaptation techniques, which will inform future research and underscore the need for ethical frameworks in enhancing the efficacy of agent-based models for agricultural resilience.
Increasing Solar cell Efficiency using Quantum Dot Sensitization
Authors: Parashar Komal , Bobba Phaneendra babu , Samyuktha Penta , Fallah Mohammed Hussein , Chauhan Sanjay Singh , Verma Vikas , Rathour Abhinav , Gupta Manish
Published: October 2024

This research examines the effectiveness of swarm intelligence approaches in improving the functioning of solar cell hybrid microgrids, specifically focusing on the difficulties caused by the irregularity of renewable energy sources. Analyzed were simulated data that represented the creation of solar cell and solar cell electricity, the status of charge of batteries, and the outputs of swarm optimization. The solar cell power data exhibited oscillations in power output ranging from 85 kW to 150 kW, corresponding to changes in solar cell speed ranging from 6.5 m/s to 9.0 m/s. On the other hand, solar cell power saw a marginal decrease from 95 kW to 88 kW, which may be attributed to variations in solar cell irradiation ranging from 850 W/m² to 780 W/m². The battery's level of charge varied between 70% and 95%, indicating the fluctuating rates of charging and discharging, which ranged from 20 kW to 30 kW and 12 kW to 25 kW, respectively. The swarm optimization rounds showed a decrease in the cost of the optimum solution from 3200 to 2000, and an improvement in the convergence rate from 80% to 100%. The analysis indicated a significant 76.5% surge in solar cell power output at peak periods, whereas there was an 8% decline in solar cell power. The state of charge (SoC) of the battery exhibited an average rise of 35.7%, while swarm optimization demonstrated a 37.5% reduction in cost and a steady 25% improvement in convergence rate. The results emphasize the natural fluctuation of renewable sources and demonstrate the potential of swarm intelligence in improving microgrid operation. These results highlight the significance of adaptive control methods in the management of renewable-based microgrids, providing valuable insights for improving their stability, dependability, and cost-effectiveness. The study's findings have practical applications, highlighting the importance of swarm intelligence in promoting the development of sustainable energy systems in the context of integrating renewable energy sources.
Reuse of Industrial By-products in Construction using Geopolymerization
Authors: Manukhina Lyubov , Sreejyothsna Ankam , Logabiraman Govardhan , Dhawan Aashim , Kumar Rakesh , Anand Sami , Sarpal Sumeet Singh , Sharma Prashant , Hassan Muntather Muhsin
Published: October 2024

This study explores the reuse of industrial by-products in construction through geopolymerization, positioning it as a sustainable alternative to conventional construction materials. In light of the growing demand for environmentally friendly building practices, this research aims to evaluate geopolymer concrete made with industrial by-products as a viable substitute for traditional concrete. The introduction highlights the importance of sustainable construction and the potential of geopolymer technology in reducing environmental impacts. A comprehensive review of the composition, properties, and previous studies on geopolymer concrete using industrial waste materials is provided. The research methodology outlines the objectives, materials used, experimental framework, and testing protocols. Results on compressive strength, flexural strength, and durability of the geopolymer concrete samples are discussed in detail in the results and analysis section. The discussion interprets the findings, focusing on the enhanced strength and durability achieved through the incorporation of industrial by-products. The study concludes with a summary of key outcomes, implications for the construction industry, and recommendations for future applications and research on geopolymer concrete using industrial waste.
Climate Change Impact on Water Resources using Hydrological Modelling
Authors: Joshi Abhishek , Samatha Anthappagudem , Narsaiah M.N. , Thakur Rahul , Ahuja Sunaina , Choudhary Sunila , Yadav Rajat , Abbas Haider Mohammed
Published: October 2024

In order to understand the effects of continuous improvement techniques on water resources initiatives, process efficiency, equipment downtime, and hydrological modelling, this study synthesizes empirical data to examine the synchronized efficacy of these tactics. The results show that staff efficiency has increased, with an overall improvement of 10% and a significant 15% uptick in the Production division. Lean Water resources reduced waste by 12%, Six Sigma Implementation reduced defects by 18%, Predictive Maintenance reduced equipment downtime by 20%, and Supply Chain Optimization improved logistical efficiency by 10%; all of these initiatives demonstrated substantial positive outcomes as a result of continuous improvement. The efficacy of the techniques on reducing operational interruptions is shown by the equipment downtime log analysis, which shows a 25% decrease in downtime hours ascribed to Predictive Maintenance. Reductions of 15% in cycle times and 12% in defect rates are two examples of the impressive gains seen in process efficiency indicators. A complete optimization of water resources operations is shown by the cumulative percentage changes across projects and processes, which highlight the transforming power of strategic interventions. This research offers a path for businesses to create better productivity, resilience, and operational excellence by providing empirical insights into the interwoven symphony of continuous improvement.
Optimization of Bioenergy Production from Algae Using Bioreactor Design
Authors: Joshi Sanjeev Kumar , Madhavi Arelli , Reddy Vijaya Basher , Raj Nimesh , Kumar Manish , Bhalla Anubhav , Kumar Yogendra , Ali Hassan Mohamed
Published: October 2024

Bioenergy production from algae has gained significant attention due to its potential as a sustainable and renewable energy source. This research investigates the optimization of bioenergy production by focusing on bioreactor design to enhance the efficiency of algal biomass growth and lipid extraction. The study involves analyzing key bioreactor parameters such as light intensity, nutrient concentration, pH levels, and carbon dioxide (CO₂) supply. Experimental results demonstrated a 25% increase in lipid production through optimized conditions, showing promising improvements in biofuel yield. The findings of this research provide valuable insights into the potential scalability of algae-based bioenergy systems.
Sustainable Packaging Design using Life Cycle Thinking
Authors: Pant Ruby , Ramesh Banoth , Kalpana Kilaru , Sharma Nittin , Gupta Gaurav , Brar Manvinder , Kulshreshtha Kushagra , Adnan Myasar M.
Published: October 2024

This research examines environmental impact data, sustainable packaging qualities, consumer feedback surveys, and price comparisons to draw important findings. Research focuses on “Packaging Sustainability Revolution: Life Cycle Thinking Reveals Eco-Friendly Innovations.” This research examines sustainable packaging design evolution. Life cycle analysis (LCA) showed that packaging materials had an average carbon footprint of 120 grams of CO2 per unit and a 60% recycling rate. This shows the diverse environmental impacts of packing options. A study of sustainable package design shows that individuals have preferences. The favorability of biodegradable, recyclable, and minimalist packaging increased significantly. In subjective consumer feedback surveys, Packaging A and Packaging B scored 8.3 and 8.7 total satisfaction. In contrast, Packaging C and D do well. The cost increases among models in Cost comparisons expenditures show the economic effects of sustainable design. This emphasizes the tight balance between consumer satisfaction and sustainable practices' economic sustainability. The empirical findings improve scholarly discourse on life cycle thinking in Cost comparisons by revealing the sustainability variables driving the Packaging Sustainability Revolution.
Enhancing Circular Economy in Food Supply Chains using Industrial Symbiosis
Authors: Kostyshak Mikhail , Annapoorna Errabelli , Narasaiah B.P. , Sudan Prerak , Chandramauli Awadhesh , Modi Sanjay , Sharma Prabhat , Sharma Gunjan , Obaid Saif
Published: October 2024

This research used system dynamics modeling to assess the synergies generated by industrial symbiosis in food supply chains, focusing on resource recovery and waste reduction. The examined symbiotic systems included food waste valorization, energy recovery, and by-product interchange across sectors including agricultural, food processing, and bioenergy production. The findings indicated that the agriculture sector achieved a 15% decrease in waste via effective by-product exchange. Food processing showed a 20% reduction in energy use via waste-to-energy conversion. The statistical study revealed a substantial association between enhanced symbiotic relationships and improved resource efficiency. Furthermore, the significance of inter-industry cooperation was paramount, since food waste was reused more efficiently in areas with robust industrial networks. Moreover, system performance was affected by external influences, including market demand and regulatory rules. These results provide significant insights into the capacity of industrial symbiosis to improve circular economy practices within food supply chains, hence promoting sustainability and resource efficiency.
Energy-Efficient Urban Transportation Planning using Traffic Flow Optimization
Authors: Khandelwal Utkal , Karuna Gudapalli , Reddy Sadda Bharath , Ghumman Sukhman , Balmiki Vinod , Sharma Rajeev , Alhadrawi Merwa , Kumar Sourabh
Published: October 2024

This study examines how predictive analytics and the IoT might improve sustainable urban transportation systems. Using IoT device data, this study will explore how predictive analytics and IoT integration alter urban transportation. The data covers vehicle speed, traffic density, AQI, and weather. The research estimates traffic congestion, AQI, and volume using predictive modeling. This assesses prediction accuracy and data match. Unfavorable weather increases congestion, whereas traffic density decreases vehicle speed. Predictive methods accurately estimate congestion and air quality, but traffic volume is more difficult. The algorithms' accuracy in anticipating congestion and AQI is confirmed by comparing predicted and actual outcomes. Despite a 1.4% traffic flow increase, predictive analytics and IoT solutions reduce congestion by 25% and improve air quality by 12.7%. The impact research shows that these methods reduce congestion and promote sustainability. This research highlights the potential of predictive analytics and IoT to improve urban mobility, enable smarter decision-making, and create sustainable urban environments via data-driven insights and proactive actions.