bluuebunny/crossref_metadata_2025 huggingface dataset:
One-step hydrothermal synthesis of a Ni3S2–FeMoO4 nanowire–nanosheet heterostructure array for synergistically boosted oxygen evolution reaction
Authors: Chen Han , Zhang Jing , Wan Rui , Zhang Xiang , Pan Qijun , Li Mingtao , Chen Bin
Published: March 2024

We propose a one-step hydrothermal strategy to fabricate hierarchical Ni3S2–FeMoO4 nanowire–nanosheet heterostructures on NiFe-foam as structurally-integrated electrodes for synergistically boosted OER at high current density.
Incorporation of Ir(C^N)2(N^N)-NiCl2 in a (N^N)-covalent organic framework for transcendent dual catalysis in photochemical cross-coupling synthesis
Authors: Li Yan-Lin , Wang Fuke , Vittal Jagadese J. , Jin Peng , Huang Sheng-Li , Yang Guo-Yu
Published: March 2024

The incorporation of Ir(C^N)2(N^N)-NiCl2 in quinoline-based (N^N)-covalent organic framework exhibits remarkable photocatalytic activities in cross-coupling synthesis.
Metal-modified C3N1 monolayer sensors for battery instability monitoring
Authors: Gu Mingyang , Tao Lin , Dastan Davoud , Dang Jie , Fang Timing , An Baigang
Published: March 2024

High-performance sensors achieved highly selective monitoring of toxic gases produced after the battery becomes unstable.
New insights into Gasdermin D pore formation
Authors: Kappelhoff Shirin , Margheritis Eleonora G. , Cosentino Katia
Published: March 2024

Gasdermin D (GSDMD) is a pore-forming protein that perforates the plasma membrane (PM) during pyroptosis, a pro-inflammatory form of cell death, to induce the unconventional secretion of inflammatory cytokines and, ultimately, cell lysis. GSDMD is activated by protease-mediated cleavage of its active N-terminal domain from the autoinhibitory C-terminal domain. Inflammatory caspase-1, -4/5 are the main activators of GSDMD via either the canonical or non-canonical pathways of inflammasome activation, but under certain stimuli, caspase-8 and other proteases can also activate GSDMD. Activated GSDMD can oligomerize and assemble into various nanostructures of different sizes and shapes that perforate cellular membranes, suggesting plasticity in pore formation. Although the exact mechanism of pore formation has not yet been deciphered, cysteine residues are emerging as crucial modulators of the oligomerization process. GSDMD pores and thus the outcome of pyroptosis can be modulated by various regulatory mechanisms. These include availability of activated GSDMD at the PM, control of the number of GSDMD pores by PM repair mechanisms, modulation of the lipid environment and post-translational modifications. Here, we review the latest findings on the mechanisms that induce GSDMD to form membrane pores and how they can be tightly regulated for cell content release and cell fate modulation.
Asteroid reflectance spectra from Gaia DR3: Near-UV in primitive asteroids
Authors: Tinaut-Ruano F. , de León J. , Tatsumi E. , Morate D. , Mahlke M. , Tanga P. , Licandro J.
Published: March 2024

Context. In the context of charge-coupled devices (CCDs), the ultraviolet (UV) region has mostly remained unexplored after the 1990s. Gaia DR3 offers the community a unique opportunity to explore tens of thousands of asteroids in the near-UV as a proxy of the UV absorption. This absorption has been proposed in previous works as a diagnostic of hydration, organics, and space weathering.

Aims. In this work, we aim to explore the potential of the NUV as a diagnostic region for primitive asteroids using Gaia DR3.

Methods. We used a corrective factor over the blue part of Gaia spectra to erase the solar analog selection effect. We identified an artificial relation between the band noise and slope and applied a signal-to-noise ratio (S/N) threshold for Gaia bands. Meeting the quality standards, we employed a Markov chain Monte Carlo (MCMC) algorithm to compute the albedo threshold, maximizing primitive asteroid inclusion. Utilizing one- and two-dimensional (1D and 2D) projections, along with dimensionality-reduction methods (such as PCA and UMAP), we identified primitive asteroid populations.

Results. We uncovered: (a) the first observational evidence linking UV absorption to the 0.7 µm band, tied to hydrated iron-rich phyllosilicates; and (b) a 2D space revealing a split in C-type asteroids based on spectral features, including UV absorption. The computed average depth (3.5 ± 1.0 %) and center (0.70 ± 0.03 µm) of the 0.7 µm absorption band for primitive asteroids observed with Gaia is in agreement with the literature values.

Conclusions. In this paper, we shed light on the importance of the UV absorption feature to discriminate among different mineralogies (i.e., iron-rich phyllosilicates vs. iron-poor) or to identify taxonomies that are conflated in the visible (i.e., F-types vs. B-types). We have shown that this is a promising region for diagnostic studies of the composition of primitive asteroids.
A review paper on an empirical method of solar roof tiles for thermal energy analysis applied in residential structures with phase change materials
Authors: Singha Soumya S. , Das Bhumika , Sukh Ashish V. , Mishra Priya , Haranatti Jagadish Shrisaila , Suneetha T.V. , Nikolai Ivanovich Vatin
Published: March 2024

The integration of solarcellsinto construction materials, such roof tiles, isgaining traction. Nevertheless, solarcells' conversion efficiencyistemperature-dependent, and a high temperaturewouldlowerit. This study examines the functionality of mortar roof tilesthat have builtinsolarcells and safety glass. To control the temperature of the solar cells, phase modifying materials (P CM) are added to the first roof tiles at a concentration of 3% by weight.For solar roof tiles, the impact ofPhase change materials on the generation of solar-to-electrical power isassessed, and life cycle costanalysisiscarried out to determine the viabilityfrom an economicstandpoint.During six summerdays, the electricalenergy production of the solartileswith PCM appliedwas 4.1% higherthanthat of the tileswithout the component modification material. In contrast, during six winterdays, the improvementisbetween 2.2 and 4.3%. With the cost of the inverterincluded, the solar roof tileswith Phase change materials have an economicalpaybackperiod of 5.7 years. The application of Phase change materialsraises the initial cost of roof tiles by 1.2% ;nevertheless, the paybackperiodispredicted to bethreemonths shorter thanthat of the equivalentwithout PCM, given an overallimprovement in energy production of 4.1%.Whilemany countries are takingsteps to reducetheiremissions, the IEA’slatest report on global and carbonemissionsshowed an increase of 1.7 percent in emissionsyear-onyear2018 to 33.1 billion tons (IEA, 2019). Australia's 2018 increase in carbondioxideequivalentemissions of 0.7% raisesseriousconcerns about the countrysability to meetitsemissionreductioncommitmentunder the Paris Agreement (Cox, 2019).
Pore structure characterization of bacterial concrete
Authors: Vempada Srinivasa Reddy , Seshagiri Rao Meduri V. , Saduwale Shrihari , Mahesh Pothula , Suneetha T.V. , Nemova Darya Viktorovna
Published: March 2024

This work describes an experimental investigation on the impact of calcifying bacteria on permeation properties (such as absorptivity and sorptivity) of bacterial concrete. In order to evaluate the improvement of permeation properties owing to biomineralization in bacterial concrete, tests for sorptivity, porosity, water absorption capacity, and ordinary (M20), standard (M40), and high strength (M60 and M80) grade bacteria were then conducted on the concrete samples treated with the bacteria. The findings demonstrate the bacterial concrete's porosity, sorptivity, and absorptivity are significantly less than those of the comparable properties of the reference concrete. The addition of Bacillus subtilis JC3 to the concrete notably improves these properties. The chemical study also revealed that bacterial concrete's cement-phase composition contains a sizable amount of calcium precipitate component. Understanding the processes of water transport within pore structure and the interactions between concrete and water require a thorough understanding of porosity. Because a solid contains interconnected channels (pores) that allow water to pass through and promote its transit, water can pass through solids. Concrete's permeability has a significant impact on how long it will last. As a result, the key variables to research in regard to concrete durability are the permeation qualities rather than the mechanical capabilities. Consequently, a great deal of work needs to go into looking into these qualities in the current study.
Evaluation of reduction of accidents in different regions of India
Authors: Durga Maligi , Jagan Mohan Reddy Kandukuru , Sanjay  , Chary Rathna , Lakshmi Prasanna Y. , Usanova Kseniia Iurevna
Published: March 2024

Road accidents and fatalities are leading causes of death, which is leading the burden to the public heath in India. This study aims at understanding the road accidents and reduction in road accidents in different regions of India. It has been found that Tamil Nadu has the highest number of road accidents followed by Kerala, Madhya Pradesh and others. Furthermore, the major factors like poor condition of roads, lack of use of safety equipments and poor traffic rules lead to increasing the number of road accidents. The government initiatives like awareness campaigns, identifying and rectifying black sports, vehicle engineering has contributed to improving road safety in India.
Evaluation of Grain Size Distribution by Digital Image Processing
Authors: Venkata Sai Prasad Garapati , Chenna Hari Naga Prasad , Naga Sai Baba Akella , Hugar Prashant , Pavani P. , Ivanovich Vatin Nikolai
Published: March 2024

Sieve analysis tests are frequently used to determine the grain size distribution of granular materials. This project proposes an ImageJbased image analysis approach for evaluating aggregate particle size distribution. Grain size in image analysis should be estimated to compare the graduation curves between the two methods.. Black sheets were more effective than white sheets for particle placement, perhaps due to light effects. This technology may be utilized for in-suit testing, as it requires a camera and computer. The study used monochromatic light and a highdefinition camera to capture grain photos while controlling for background, light direction, and intensity. A ground truth was established to evaluate errors in determining grain areas. All grain shape parameters are obtained using the ImageJ program. The grain size distribution curve is generated using image analysis.
Comparative study of basalt fiber and steel fiber as additives to concrete
Authors: Vijayakumar Miryala , Shrisaila Haranatti Jagadish , Neelappa Paruti Mahantesh , Sravanthi Jajimoggala , Maksudovna Vafaeva Khristina
Published: March 2024

It is widely acknowledged that Basalt Fiber (BF) and Steel Fiber (SF) concrete possess a significant characteristic of superior resistance to cracking and crack propagation. This unique ability to arrest cracks results in fiber composites having enhanced extensibility and Ts, both at the initial crack and at the ultimate stage, particularly under flexural loading. Moreover, these fibers are capable of maintaining the integrity of the matrix even after extensive cracking. This research paper delves into the mechanical properties, technologies, and applications of BF and SFs. The study involved experimental investigations using M40 grade concrete mix, and tests were conducted in accordance with recommended procedures outlined in relevant codes. The laboratory experiments included the design of cubes, beams, and cylindrical specimens with varying percentages of BF and SF concrete ranging from 0% to 1.25% (0%, 0.25%, 0.5%, 0.75%, 1% and 1.25%). The obtained data was analyzed and compared with a control specimen (0% Fiber), and ultimately, the test results for BF and SF were compared. Additionally, a graphical representation was provided to illustrate the relationship between Cs, Fs, Ts, and Age i.e. 7 days and 28 days.
Comparison study on cold formed steel of coupled channel section based on abaqus and cufsm by simple-simple end condition
Authors: Naidu Gandi Mounika , Patil Ushadevi , Nandihalli Poonam , Kothapally T. Hangamani , Devi T. Aswini , Usanova Kseniia Iurevna
Published: March 2024

Cold formed steel (CFS) channel sections are the sections which are widely using from last few decades and are designated as light gauge sections. Such sections are being widely utilized in industrial, commercial and residential purposes as a secondary bearing member. Further developments of thin walled members made it possible to use the CFS Sections as preliminary load carrying structural members. The reason is hot-rolled steel sections posses’ very low strength to weight ratio on CFS sections, and the production process of these sections is cost-effective and simple. This paper is about comparing the analysis results from CUFSM and ABAQUS of different channel sections. The loads will be applied at the edges of the members axially i.e., as a compression member with different boundary conditions.
Investigation of Al-7075/Al2O3/SiC by swirl casting and WEDM
Authors: Seshappa Angadi , Sreedhar chalicheemala , Subbaratnam Bhavanasi , Prudhvi Raj Konka , Boya Divya , Sobti Rajeev
Published: March 2024

The research, engineering, military, automotive, sports, and telecommunications industries are just a few of the many that make heavy use of aluminium metal matrix composites. The unique synergistic qualities of these composites, such as reduced weight density, improved performance, and thermal conductivity, make them highly desired. Because of its reputation for small but noticeable structural improvements, Al-7075 aluminium has seen extensive use in the aerospace industry. This research followed international criteria for evaluating specimens in terms of positional stiffness, deformation, impact resistance, and wear. It used the stir casting process to add SiC/Al2O3 to Al-7075 in percentages ranging from 2% to 8%. Hybrid Al-HMCs are the product of an aluminium metal matrix composite that was able to include 8% SiC/Al2O3via the stirring process. Because it combined aluminium oxide with silicon carbide, this Al-HMC showed improved mechanical qualities and stiffness. Aluminium metal composites with increasing amounts of Al2O3/ SiC showed reduced fatigue and frictional strengths. The effect on hardness remained small even when the MMC's SiC/Al2O3 proportions grew significantly. To further investigate the Wire Electrical Discharge Machining (WEDM) process, researchers used the L9 Arrays Design of Experiments (DOE) technique to incorporate new process variables. Our objective was to enhance the rate of material removal from Al-7075/SiC/Al2O3 while simultaneously improving the marginal surface roughness. Finding the best WEDM process parameters, including the ideal percentages of SiC/Al2O3, to achieve the desired surface roughness while minimising material removal rate was the goal of this investigation. To produce Al- 7075/SiC/Al2O3 composite materials with varying additive percentages in a production environment, this study will determine the best and most accurate WEDM process variables to use.
Investigations of Al-Manufacturing while 7075/RHA/Al2O3 Composite by Squeeze Casting Approach"
Authors: Seshappa Angadi , Prudhvi Raj Konka , Subbaratnam Bhavanasi , Pranavi Uppu , srihithagunapriya N. , Chhabra Soosan
Published: March 2024

The superior performance. metal matrix nano-composite substance containing agro-waste reinforcements is the subject of this study's manufacturing and characterization efforts. Composites made using aluminum 7075 as their basis substance with reinforced with rice husk ash (RHA) and Al2O3 are very desirable. The revolutionary composite was made using squeeze casting as the fabrication technique. The overall size % of ash from rice husks on its metal interface was adjusted from 0% to 15% with a constant increase of 5%, while the Al2O3 percentage was set at 3. Many qualities, including hardness, toughness, tensile strength, XRD analysis, XDS analysis, Surface Morphology and wear, define the manufactured composite. According to the findings, the produced cost-effective composite outperforms the raw material in terms of hardness, toughness, and tensile strength.
An empirical study on examining the influence of mutual fund investment growth on equity market performance
Authors: Aaluri Suresh , Priyadarshini Pulibandla Keerthi , Ganesh Lalisetti , Kumar C Vivek , Sehgal Archana
Published: March 2024

In the realm of financial markets, understanding the interplay between mutual fund investment growth and equity market performance is paramount for investors, fund managers, and policymakers alike. This empirical study embarks on a comprehensive exploration of this intricate relationship, employing advanced econometric techniques to dissect historical data and uncover underlying patterns and dynamics. Building upon hypotheses positing significant associations between mutual fund investment growth and equity market performance, the study delves into regression analysis and statistical modelling to discern causality and quantify the impact of mutual fund investments on equity market behavior. In addition, the study investigates the influence of mutual fund performance metrics, such as annualized return, on equity market returns, shedding light on the part of mutual funds in shaping market outcomes. Despite inherent limitations, including data constraints and potential endogeneity issues, the findings contribute valuable empirical evidence and actionable insights for stakeholders navigating financial markets. Through its rigorous empirical approach and robust analytical framework, this study advances financial research and informs decision-making processes, empowering stakeholders to navigate the complex landscape of investments and market participation effectively.The findings contribute to a deeper understanding of how mutual fund investments impact equity market performance, offering valuable implications for the various players in navigating financial markets.
Movie recommendation app with cosine similarity and flask
Authors: Vedhavyas P. , Srikar V. , Ashwarda N. , Begum Ruqqaiya
Published: March 2024

People are constantly busy with their professions, businesses, and other endeavours in the world in which we currently live. The majority of them find that watching movies is the greatest way to unwind during the limited free time they have between jobs. However, with so many movies available in different languages, choosing which one to watch may be a time-consuming task. The recommendation may be collaborative filtering or content-based. As the name implies, collaborative filtering bases its filtering method on the interactions between relevant user behaviour and that of other users. In order to correlate movies with recommendations, this study describes cosine similarity. To receive the top 5 recommendations, the user must enter the name of a movie they have previously enjoyed. We've also introduced a feature that allows the user to optionally provide the year data that they want the engine to use to propose movies that were published after that specific year. For example, if the user wants to filter recommendations for new movies, they can provide a recent year.
ECharge – An Electric Vehicle Charging Station Finder Application
Authors: Bharath Reddy Sadda , Nookaraju Yella , Kiran Goud Shivaghoni , Usman Sayyed , Sandeep B. , Madhavi K. , Viktorovna Nemova Darya
Published: March 2024

Even though electric cars are growing more and more common, many drivers still have trouble finding charging stations. Because of this, customers need an EV charging station finder app that is user-friendly and dependable, and that can provide them with the most recent information on the location and availability of charging stations specific to the type of electric vehicle they are driving. An dependable and user-friendly app that can assist EV drivers in finding charging stations is becoming increasingly necessary as more and more people choose to use electric vehicles (EVs). Although there are now a few apps for this usage, they frequently have limited functionality, lack real-time information, or are challenging to use. For EV drivers, this might cause aggravation and annoyance as they can find it difficult to map out their routes or locate. This app would be a helping hand for EV drivers.
Fine-Tunining the Future: Optimizing svm hyper-parameters or enhanced diabetes prediction
Authors: Bommala Harikrishna , Vamshi Krishna Kannedari , Supriya Avusula , Biradar Rama Krishna , Mayabrahma Bharath , Ushasree D. , Vladimirovich Kotov Evgeny
Published: March 2024

Millions of people throughout the globe suffer from diabetes mellitus, a debilitating illness that increases the risk of severe complications and early death. To take preventative measures and tailor treatment to each individual's needs, it is essential to identify diabetes early and estimate risk accurately. This research provides a data-driven strategy for predicting diabetes based on SVM models. This work uses a large dataset, including clinical and demographic data from a wide range of people, including those with and without diabetes, to conduct our analysis. A prediction model that divides people into diabetes and non-diabetic groups based on their input attributes is constructed using the SVM algorithm. Engineers use feature selection and other engineering methods to improve the model's efficacy and readability. The results of the research show that the SVM algorithm is capable of producing reliable predictions of diabetes risk. Measures of the model's efficacy include its sensitivity to false positives, specificity in identifying true positives, and area under the Receiver Operating Characteristics curve (AUC-ROC). In addition, feature significance analysis improves the model's interpretability by illuminating the most critical risk variables for diabetes. The accuracy and interpretability of the proposed SVM-based diabetic prediction model are promising, making it a valuable tool for healthcare practitioners and policymakers to identify those at high risk of developing diabetes and modify preventative measures and interventions appropriately.
Primary Colour Detection in An Image
Authors: Fatima Nasra , Barath Reddy Sada , Rani Kura Swapna , Psalms Shanya , Nithin Padmam , Kudakala Saikrishnai , Vaishnavi Pisipaty , Raju NV Ganapathi , Nijhawan Ginni
Published: March 2024

Primary Colour Detection in an Image, Traditional methods of color cast detection do not discriminate between images with true cast and those with dominant colors. This may result in an inaccuracy of the color cast measurement. In order to overcome the limitation of traditional methods, an approach based on image semantic is present. It can improve the accuracy and reliability of the detecting results by the means of recognizing and removing the dominant color objects · and analyzing the color distribution of the whole image. Class specific color detection is already implemented in some systems, but image classification usually relies on global image features only. The method of recognizing the dominant color objects is based on block-features and region-features by using K-Means Clustering in this project. It shows a significant improvement over previously achieved classification for a variety of critical image classes. The comparison of the results of the approach proposed to that of people shows that it is reliable and effective. This project proposes the use of Machine Learning Techniques and Deep Learning Algorithms.
An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm
Authors: Subbarayudu Yerragudipadu , Vijendar Reddy Gurram , Raj Kumar Masuram , Aravind Naik Mudavath , Nagini R.V.S.S. , Singh Balpreet
Published: March 2024

Glaucoma, a neuro-degenerative eye disease, is the result of an increase in intraocular pressure inside the retina. It is the second-leading cause of blindness worldwide, and if an early diagnosis is not made, it can lead to total blindness. There is a critical need to develop a system that can work well without a lot of equipment, qualified medical professionals, and requires less time about this core issue. This article provides a thorough examination of the main machine learning (ML) techniques employed in the processing of retinal images for the identification and diagnosis of glaucoma. Machine learning (ML) has been demonstrated to be a crucial technique for the development of computer-assisted technology. Machine learning (ML) techniques can be used to construct predictive models for the early diagnosis of glaucoma. Our objective is to develop a machine learning algorithm that can accurately forecast the likelihood of developing glaucoma using patient data. Ophthalmologists have also conducted a significant amount of secondary research over the years. Such characteristics emphasise the importance of ML while analysing retinal pictures.
A brain tumor identification using convolution neural network in the deep learning
Authors: Krishna Bandi , Vankdothu Ramdas , Revuri Varun , Prashanth B.
Published: March 2024

Brain tumors cause a lot of suffering; resulting in many illnesses they are properly handled. The diagnosis is part of the treatment of tumors. Proper and appropriate tumor identification is used to identify benign and malignant tumors. The key issue that leads to a rise in cancer affecting people across the globe is the irresponsible conduct towards the handling of gossip in its early stages. Includes noise reduction, image sharpening along with certain morphological functions, dilution, and erosion to get the context. The negative of the background-subtracted from the separate picture sets resulting in an isolated representation of the brain tumor. Image ting the contour vs. the c-label of growth is the border that makes it all the more useful to imagine and diagnose in order relevant to the tumors. The method determines the form, position, and size of the brain tumor.
A study on routing metrics to improve the quality of service in MANET environment
Authors: Kumar C. Satya , Sambasiva Rao N. , Venkata Rami Reddy G.
Published: March 2024

MANETs, infrastructure-free, multi-hop networks with an in height grade of complexity and dispersed nodes. Without a permanent and dedicated infrastructure, a node can communicate with other nodes directly or via intermediary nodes. Routing procedures play a crucial role in enhancing the overall presentation of a dynamic network environment like a MANET, which is referred to as Quality of Service (QoS). Simply put, a number of routing protocols may employ a composite routing measure to determine the best path(s) for data transfer. The development of routing protocols for MANETs will be affected by routing metrics. Therefore, we look at quality-of-service-based MANETs. In this overview, we will examine the classification scheme for ad hoc routing methods, and we will evaluate the similarities and differences between various routing protocols. As such, we will give priority to suggestions that will result in the highest possible network performance and energy competence. In this work, we provide a summary of our research and recommendations for future directions for the subject.
Neural network-assisted integration of renewable sources in microgrids: A case study
Authors: Vladimirovich Kotov Evgeny , Ramesh Banoth
Published: March 2024

This study examines the incorporation of renewable energy sources into microgrids using neural network-assisted optimization methods. The objective is to tackle the difficulties related to the fluctuation and uncertainty of renewable energy production. An examination of the collected data over various time periods indicates encouraging patterns in the production of renewable energy. The solar energy use shows a steady rise from 120 kWh to 140 kWh, representing a 16.67% increase. Similarly, wind energy usage also demonstrates an upward trend, increasing from 80 kWh to 95 kWh, marking an 18.75% expansion. The biomass energy production has seen a substantial increase from 50 kWh to 65 kWh, representing a significant 30% rise. The examination of microgrid load consumption demonstrates the increasing energy needs in residential, commercial, and industrial sectors. The household load consumption has increased from 150 kWh to 165 kWh, representing a 10% spike. Additionally, the commercial load and industrial load have also seen a surge of 15%. The predictions made by the neural network demonstrate a high level of accuracy, closely matching the actual output of renewable energy. The accuracy rates for solar, wind, and biomass projections are 98.4%, 95.5%, and 97.3% correspondingly. The assessment of improved energy distribution emphasizes the effective usage of renewable sources, guaranteeing grid stability and optimal resource utilization. The results highlight the capacity of neural network-assisted methods to precisely predict renewable energy outputs and efficiently incorporate them into microgrids, hence promoting sustainable and resilient energy solutions. This report provides valuable insights on improving microgrid operations, decreasing reliance on traditional energy sources, and accelerating the shift towards sustainable energy systems.
Evolutionary algorithms for dynamic optimization of plug-in charging station networks
Authors: Kumar Manish , Annapoorna E.
Published: March 2024

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of IoT on urban mobility, using empirical data obtained from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its alignment with actual data. The data reveals a link between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of anticipating congestion and AQI, hence confirming the effectiveness of the models. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban mobility, enhancing decision-making processes, and creating sustainable urban environments via the use of data-driven insights and proactive interventions.
Fuzzy Logic-Based Energy Storage Control in Smart Grids for Grid Stability
Authors: Kumar Singla Atul , Srilatha CH.
Published: March 2024

This study studies the usefulness of fuzzy logic-based control systems for improving energy storage control inside smart grids to promote grid stability. The study combines empirical data analysis, including energy storage system (ESS) specifications, smart grid operational data, fuzzy logic-based control rules, and ESS state variables, to demonstrate the suitability and efficiency of using fuzzy logic-based control mechanisms in dynamic grid environments. The examination of ESS specs revealed a wide range of maximum capacities, spanning from 100 kWh to 200 kWh. Additionally, the charge and discharge efficiencies exhibited variations, ranging from 85% to 96%. An analysis of operational data from the smart grid revealed significant variations in grid frequency, ranging from 50.0 Hz to 50.3 Hz. Voltage levels also exhibited fluctuations, ranging from 229 kV to 232 kV. Additionally, renewable energy generation from solar and wind sources showed fluctuations between 1400 kW to 1650 kW and 800 kW to 850 kW, respectively. The incorporation of linguistic factors and fuzzy rules based on grid parameters facilitated the adaptive control of ESS units in the construction of fuzzy logic-based control rules. The analysis of ESS state variables revealed dynamic changes in the state of charge, which ranged from 60% to 90%. Additionally, oscillations in available energy were observed across different timestamps and ESS units. An investigation of in state variables, revealed adaptive changes percentage change demonstrating varying degrees of variations in state of charge, available energy, and operational states at various timestamps. The results emphasize the flexibility and efficiency of control systems based on fuzzy logic in improving energy storage operations in smart grids, highlighting their capacity to improve grid stability and efficiently handle changing grid characteristics.
L’isotopie à l’épreuve des textes d’élèves. Étude réalisée à partir d’un extrait du corpus EcriScol
Authors: Sardier Anne
Published: March 2024

Nous souhaitons, dans cette contribution, questionner le concept d’isotopie grâce à une analyse d’un extrait du corpus ECRISCOL. Nous revenons d’abord sur ledit concept en considérant notamment les relations syntagmatiques de contigüité, puis sur le rôle de l’isotopie dans l’activité du lecteur et, enfin, sur les stratégies scripturales dont elle peut témoigner. Notre démarche, basée sur l’analyse du corpus, consiste ensuite à relever et classer les items responsables de l’isotopie dans 90 textes d’élèves de cours élémentaire première et deuxième années (7-9 ans). Les textes présentent la rédaction d’un récit à quête. Les analyses de ce carottage permettent, d’une part, de redessiner les contours du concept d’isotopie au vu de la cooccurrence lexicale et, d’autre part, d’envisager des pistes de travail pour la réécriture des textes, voire les stratégies de lecture, à l’école. Elles invitent également à mettre en regard les productions des élèves à deux niveaux de classe différents.
Exploring critical internal enablers to SMEs export performance: evidence from Qatar
Authors: Elsharnouby Tamer H. , Elbanna Said , Farha Allam Abu , Mauji Nasrina
Published: March 2024

This study seeks to provide a contextualized and nuanced understanding of what constitutes internal enablers among small and medium-sized enterprises (SMEs) in support of their export activities in international markets. We unpacked a unique set of critical enablers that can be cultivated as the internal impetus for enhancing the export performance of SMEs in a less-researched emerging market, Qatar. The study employs a qualitative approach for collecting, analyzing, and interpreting data, which were collected through in-depth interviews with 20 SMEs managers from 11 manufacturing sub-sectors. A thematic analysis of our data reveals five key internal enablers needed for better export performance. These comprise employees with networking skills, effective supply chain management, product diversification, digitalization, and market intelligence capabilities. This research offers empirical insights into what enables non-hydrocarbon manufacturing SMEs to enhance their export performance.
The Cambridge Structural Database and structural dynamics
Authors: Bürgi Hans-Beat
Published: March 2024

With the availability of the computer readable information in the Cambridge Structural Database (CSD), wide ranging, largely automated comparisons of fragment, molecular, and crystal structures have become possible. They show that the distributions of interatomic distances, angles, and torsion angles for a given structural fragment occurring in different environments are highly correlated among themselves and with other observables such as spectroscopic signals, reaction and activation energies. The correlations often extend continuously over large ranges of parameter values. They are reminiscent of bond breaking and forming reactions, polyhedral rearrangements, and conformational changes. They map—qualitatively—the regions of the structural parameter space in which molecular dynamics take place, namely, the low energy regions of the respective (free) energy surfaces. The extension and continuous nature of the correlations provides an organizing principle of large groups of structural data and suggests a reconsideration of traditional definitions and descriptions of bonds, “nonbonded” and “noncovalent” interactions in terms of Lewis acids interacting with Lewis bases. These aspects are illustrated with selected examples of historic importance and with some later developments. It seems that the amount of information in the CSD (and other structural databases) and the knowledge on the nature of, and the correlations within, this body of information should allow one—in the near future—to make credible interpolations and possibly predictions of structures and their properties with machine learning methods.
Characterization and modeling of plasma sheath in 2.45 GHz hydrogen ECR ion sources
Authors: Asadi Aghbolaghi M. , Abbasi Davani F. , Yarmohammadi Satri M. , Riazi Mobaraki Z. , Ghasemi F. , Castro G.
Published: March 2024

In this article, we present a multi-fluid numerical model developed for application on electron cyclotron resonance ion sources (ECRIS). The 1D-model is matured to compute the density of the ion species in the plasma sheath in the presence of an inhomogeneous magnetic field of a 2.45 GHz ECRIS. The multi-fluid model in cylindrical coordinates is focused on solving the continuity and momentum equations of hydrogen plasma particles to characterize their sheath properties. In addition, 28 important processes, including volume and surface collisions, have been included in the COMSOL Multiphysics package to simulate the ECR plasma. We study the elementary processes containing electron–atom, electron–molecule, atom–molecule, molecule–molecule, and particle–wall interactions. Then, the results of the model and the simulation of a 2D-hydrogen plasma are reported, and future perspectives are discussed throughout the paper.
Strong electron correlation-induced Mott-insulating electrides of Ae5X3 (Ae = Ca, Sr, and Ba; X = As and Sb)
Authors: Xu Ya , Zheng Lu , Zhang Yunkun , Zhang Zhuangfei , Wang QianQian , Zhang Yuewen , Chen Liangchao , Fang Chao , Wan Biao , Gou Huiyang
Published: March 2024

The presence of interstitial electrons in electrides endows them with interesting attributes, such as low work function, high carrier concentration, and unique magnetic properties. Thorough knowledge and understanding of electrides are thus of both scientific and technological significance. Here, we employ first-principles calculations to investigate Mott-insulating Ae5X3 (Ae = Ca, Sr, and Ba; X = As and Sb) electrides with Mn5Si3-type structure, in which half-filled interstitial electrons serve as ions and are spin-polarized. The Mott-insulating property is induced by strong electron correlation between the nearest interstitial electrons, resulting in spin splitting and a separation between occupied and unoccupied states. The half-filled antiferromagnetic configuration and localization of the interstitial electrons are critical for the Mott-insulating properties of these materials. Compared with that in intermetallic electrides, the orbital hybridization between the half-filled interstitial electrons and the surrounding atoms is weak, leading to highly localized magnetic centers and pronounced correlation effects. Therefore, the Mott-insulating electrides Ae5X3 have very large indirect bandgaps (∼0.30 eV). In addition, high pressure is found to strengthen the strong correlation effects and enlarge the bandgap. The present results provide a deeper understanding of the formation mechanism of Mott-insulating electrides and provide guidance for the search for new strongly correlated electrides.
Sustained robust exciton emission in suspended monolayer WSe2 within the low carrier density regime for quantum emitter applications
Authors: Chen Zheng-Zhe , Chang Chiao-Yun , Tsai Ya-Ting , Tsai Po-Cheng , Lin Shih-Yen , Shih Min-Hsiung
Published: March 2024

The development of semiconductor optoelectronic devices is moving toward low power consumption and miniaturization, especially for high-efficiency quantum emitters. However, most of these quantum sources work at low carrier density regions, where the Shockley–Read–Hall (SRH) recombination may be dominant and seriously reduce the emission efficiency. In order to reduce the effect of carrier trapping and sustain a strong photoluminescence (PL) emission under low power pumping conditions, we investigated the influence of “suspending” a monolayer of tungsten diselenide (WSe2), a novel two-dimensional quantum material. Not only the PL intensity but also the fundamental photoluminescence quantum yield (PLQY) has exhibited a huge, order-scale enhancement through suspending; even surprisingly, we found the PLQY improvement to be far significant under small pumping powers and observed an exponential increase in tendency toward an even lower carrier density region. With its strong excitonic effect, suspended WSe2 offers a solution to reduce carrier trapping and participate in non-radiative processes. Moreover, in the low-power range, where SRH recombination dominates, suspended WSe2 exhibited a remarkably higher percentage of excitonic radiation compared to contacted WSe2. Herein, we quantitatively demonstrate the significance of the suspended WSe2 monolayer in a low carrier density region, highlighting its potential for developing compact, low-power quantum emitters in the future.
Charge transfer induced linear and nonlinear optical properties in polypyrrole/ZnO@MoS2 ternary composites
Authors: Paul Swati , Karthikeyan B.
Published: March 2024

Polymer-based ternary nanocomposites with tunable optical properties are the key components for optoelectronics applications. Here, we demonstrate the linear and nonlinear optical properties of polypyrrole (PPy) and its ternary composites with different concentrations of the MoS2–ZnO core–shell (ZnO@MoS2) heterostructure. The interesting observation of multiple excitons and their transportation through the interface of PPy and ZnO@MoS2 by using photoluminescence and fluorescence lifetime spectroscopy was studied. Furthermore, the third-order nonlinear optical properties of all samples were also measured by employing the z-scan technique at an excitation wavelength of 532 nm. The reverse saturable absorption of pure PPy was switched to saturable absorption after the addition of ZnO@MoS2 at the uniform linear transmittance. These ternary composites with good nonlinear responses provide an option for the development of high-performance nonlinear optical devices and open a new path for the future development of ternary systems in the optical fields.
Phase-field theory study on the modulation mechanism of oxygen vacancy concentration on charged domain wall in ferroelectric thin films
Authors: Zhu Bingyan , Yang Wanting , Deng Yuhui , Yang Qiong , Jiang Jie , Jiang Limei
Published: March 2024

This study analyzes the regulatory mechanism of oxygen vacancy concentration on tail-to-tail charged domain walls (T–T CDWs), along with the writing time, conduction current magnitude, and retention performance of through-type T–T CDWs. The research results show that the highest density and length of T–T CDWs are achieved when the oxygen vacancy concentration is 1 × 1020 cm−3. Moreover, the successful writing of through-type T–T CDWs is limited to a certain electric field range, which is controlled by oxygen vacancy concentration. An increase in the oxygen vacancy concentration leads to a decrease in the maximum and minimum threshold electric fields required for writing through-type charged domain walls. The writing time and conductivity of through-type T–T CDWs determine the information writing speed and signal strength of domain wall memories, and the oxygen vacancy concentration also plays a regulatory role in both aspects. When the oxygen vacancy concentration is 1 × 1020 cm−3, the through-type T–T CDW exhibits the fastest writing speed, requiring only 8 ns. The magnitude of the conduction current of through-type T–T CDWs is directly proportional to the oxygen vacancy concentration. The through-type T–T CDWs formed by the aggregation of oxygen vacancies exhibit excellent retention performance, making them highly promising for applications in ferroelectric domain wall memories. Our research demonstrates that oxygen vacancies have a significant regulatory effect on the morphology and current response of charged domain walls, opening up new avenues for the study of domain wall memories.
The degradation mechanism and stability enhancement of GaSe lateral memristors
Authors: Tang Junhao , Zhan Runze , Chen Enzi , Zhu Qing , Li Weijian , Li Dawei , Lu Zhenye , Wan Xi , Chen Kun
Published: March 2024

Memristors have attracted considerable attention in the fields of high-density memory and artificial intelligence. However, the performance and stability of memristors may undergo noticeable changes over time, particularly in the case of lateral memristors. In this study, based on two-dimensional (2D) GaSe lateral memristors, we observed that the performance of devices degrades rapidly within a week, accompanied by the appearance of numerous particles identified as AgxSey particles on the surface. Furthermore, we found a correlation between the severity of device degradation and the quantity of AgxSey components, which is related to the current compliance during device testing. After applying a protective layer to the device surface, both the morphology and electrical performance of the device were effectively preserved for more than half a year. Our results highlight the significant impact of conductance filament content in lateral memristors on device degradation. Moreover, we demonstrate the effectiveness of using a protective layer in inhibiting degradation and maintaining the long-term performance of lateral memristors. These findings are of significant importance for enhancing the performance stability of integrated lateral memristors.
An averaged mass correction scheme for the simulation of high subsonic turbulent internal flows using a lattice Boltzmann method
Authors: Ma Jingtao , Xu Lincheng , Jacob Jérôme , Serre Eric , Sagaut Pierre
Published: March 2024

This paper addresses the simulation of internal high-speed turbulent compressible flows using lattice Boltzmann method (LBM) when it is coupled with the immersed boundary method for non-body-fitted meshes. The focus is made here on the mass leakage issue. The recent LBM pressure-based algorithm [Farag et al. Phys. Fluids 32, 066106 (2020)] has shown its superiority on classical density-based algorithm to simulate high-speed compressible flows. Following our previous theoretical work on incompressible flows [Xu et al. Phys. Fluids 34, 065113 (2022)], we propose an averaged mass correction technique to mitigate mass leakage when simulating high-Mach-number compressible flows. It is adapted to deal here with a density, which is decoupled from the zero-moment definition. The simulations focus on two generic but canonical configurations of more complex industrial devices, the straight channel at different angles of inclination at Mach numbers (Ma) ranging from 0.2 to 0.8, and the National Aeronautics and Space Administration Glenn S-duct at Ma = 0.6. The present results show that mass leakage can be a critical issue for the accuracy of the solution and that the proposed correction technique effectively mitigates it and leads to significant improvements in the prediction of the solution.