Roney das Mercês Cerqueira, Vitaly Félix Rodriguez-Esquerre, Anderson Dourado Sisnando
Multimode interference photonic nanodevices have been increasingly used due to their broad functionality. In this study, we present a methodology based on machine learning algorithms for inverse design capable of providing the output port position (<i>x</i>-axis coordinate) and MMI region length (<i>y</i>-axis coordinate) for achieving higher optical signal transfer power. This is sufficient to design Multimode Interference 1 × 2, 1 × 3, and 1 × 4 nanodevices as power splitters in the wavelength range between 1350 and 1600 nm, which corresponds to the E, S, C, and L bands of the optical communications window. Using Multilayer Perceptron artificial neural networks, trained with <i>k</i>-fold cross-validation, we successfully modeled the complex relationship between geometric parameters and optical responses with high precision and low computational cost. The results of this project meet the requirements for photonic device projects of this nature, demonstrating excellent performance and manufacturing tolerance, with insertion losses ranging from 0.34 dB to 0.58 dB.
Early detection of disease-causing antigens plays a crucial role in preventive healthcare. Biosensors are devices that monitor and diagnose human health by converting biological interactions into measurable signals. In recent years, microcantilever-based biosensors have gained significant attention due to their high sensitivity, miniaturization capability, and rapid response characteristics. This paper focuses on detecting the RSV-G protein of Respiratory Syncytial Virus using a paddle-type trapezoidal microcantilever with different stress concentration regions. The cantilever is designed using SU-8 polymer material with a density of 1123 kg/m³, Young’s modulus of 5 GPa, and Poisson’s ratio of 0.22. The sensing mechanism is modeled in static mode, where antigen–antibody binding is represented as an equivalent surface-stress-induced loading condition in the finite element simulation. Comparative analysis of different SCR geometries shows that the rectangular SCR configuration yields a maximum displacement of 6.8 × 10⁻¹⁸ µm, demonstrating a nearly fourfold enhancement over the conventional design without an SCR. This improvement is attributed to localized stress concentration and reduction in effective structural stiffness under identical loading conditions. The results indicate that geometry-driven stress concentration significantly enhances the mechanical sensitivity of the microcantilever sensor and provides an effective approach to structural optimization for viral biosensing applications.
The scope of this paper is to present a comprehensive approach for simulating low-temperature atmospheric dielectric barrier discharge plasmas. The proposed methodology categorizes the primary physical phenomena: (i) discharge dynamics, (ii) ionic motion, and (iii) fluid flow, according to their respective time scales and simulates each independently. This allows for the use of distinct solution procedures tailored to each of the three stages of the problem. Such separation offers significant flexibility in choosing appropriate models and numerical schemes for each stage, enabling the simulation of complex geometries and large-scale applications without the excessive computational costs associated with a monolithic approach. As a case study, we apply the proposed algorithm to the surface dielectric barrier discharge plasma actuator for flow control, which is powered by alternating high voltages. The algorithm successfully described the actuator’s behavior while maintaining low computational cost. Additionally, a parametric study is conducted to examine the effect of key input parameters on the generated electrohydrodynamic force and the resulting velocity. Finally, an overall assessment of the three-stage model is provided, highlighting its efficiency and accuracy.
Hicham Saber, Md. Mamunur Roshid, Mohamed Bouye
et al.
Abstract This study explores in detail the bifurcation and optical solitons of the third-order nonlinear chiral (2 + 1)-dimensional nonlinear Schrödinger equation (M-fCNLSE) with the M-fractional derivative in nonlinear media. We also discuss the properties of fractional derivatives in this context. Initially, bifurcation theory is utilized to analyze critical points and phase portraits, identifying transitions that give rise to new dynamical behaviors, such as stability shifts or the onset of chaotic motion. The first figure depicts the dynamics of soliton solutions undergoing a saddle-node bifurcation. There are two techniques, namely the polynomial expansion (PE) and the unified solver (US) techniques, that are applied to explore wave propagation in telecommunication systems, nonlinear optics, plasma physics, and quantum mechanics. These methods enable the creation of new optical soliton solutions using hyperbolic, rational, and trigonometric functions. Numerical results, presented in 2D and 3D diagrams, demonstrate the behavior of the solutions. The polynomial expansion technique generates diverse periodic optical soliton solutions, including double-periodic and lump wave solitons. The unified solver technique produces periodic breather waves, double-periodic waves, and other complex wave structures. Additionally, two-dimensional graphs display the effects of the truncated M-fractional parameters for $$P=0.1, 0.5, 0.75, 0.9$$ . Overall, this investigation and the proposed techniques provide valuable tools for generating precise optical soliton solutions, which have significant applications in optical communications, nonlinear optics, and engineering.
Gusnul Mahesa, Ahmad Tajri, Eflan Ananda Pujito
et al.
Integrating Artificial Intelligence (AI) and network technology represents a transformative advancement in modern networks’ protection, management, and optimisation. This literature review presents a comprehensive overview of current developments, existing challenges, and future directions for AI applications in computer networking. The primary aim is synthesising recent research to illustrate how AI-driven technologies reshape traditional network models and drive the shift toward more intelligent, autonomous, and resilient infrastructures, particularly in emerging 5G and forthcoming 6G networks. Network systems have evolved from simple analogue designs into complex digital ecosystems that support high-speed communication, intelligent devices, and data-intensive applications. However, this rapid growth has outpaced the capabilities of traditional rule-based network management approaches, highlighting the need for adaptive, real-time solutions. AI through machine learning (ML) and deep learning (DL) offers powerful data processing, pattern recognition, and autonomous decision-making capabilities, positioning it as a key enabler for managing growing complexity, enhancing security, and supporting autonomous operations. A systematic review was employed to ensure methodological rigour, focusing on peer-reviewed journal articles, leading conference papers, and expert analyses related to AI use in network security, administration, and optimisation. Thematic and comparative analyses were conducted to identify key trends, performance indicators, and innovative developments across various network layers, particularly emerging AI paradigms such as dynamic graph learning, federated learning, and explainable AI (XAI). The review finds that AI significantly improves network performance, including enhanced intrusion detection, advanced threat analysis, intelligent traffic routing, predictive maintenance, and autonomous resource allocation. Furthermore, AI is instrumental in enabling the full potential of 5G and future 6G technologies, supporting features like network slicing, ultra-low latency communication, and novel use cases such as real-time remote healthcare and immersive extended reality (XR) experiences. Despite these advancements, several research gaps remain. These include the lack of standardisation, challenges balancing model interpretability with accuracy, real-time explainability, developing lightweight AI models suited for constrained networking hardware, and concerns around privacy and ethical use. This review ultimately underscores the importance of continued interdisciplinary collaboration to ensure responsible, effective, and sustainable integration of AI into networking. As the digital landscape continues to grow, AI will be essential in driving the development of faster, more intelligent, and more secure network environments.
The building industry plays a leading role in the macroeconomic development of sovereign states, particularly in the provision of essential infrastructure such as bridges, motorways, and other structures that underpin commercial activity. However, the same sector in emerging economies often struggles to deliver projects that fully meet planned performance targets, largely due to inadequate contractor selection procedures. This paper examined the relationship between contractor selection standards and construction project performance outcomes. The survey tool consisted of 29 items on a five-point Likert scale and was administered to 132 officers of the Rivers State Universal Basic Education Board (RSUBEB). Data analysis was conducted using descriptive statistics and Pearson correlation analysis with SPSS V.23. The results revealed a strong positive relationship (r = 0.846; p = 0.000) between the rigor of contractor selection requirements and positive project results. Based on that, the research proposes that standardized evaluation metrics that emphasize financial soundness, technical skills, prior experience, and resource availability should be prioritized as key factors associated with improved construction project performance.
Abstract Nuclear fusion is entering the era of power plant-scale devices, which are now undergoing extensive studies to support the design phase. Plasma disruptions pose a high risk to these classes of devices because of the large stored thermal and magnetic energy which might jeopardize machine integrity and availability. Therefore, disruptions within these devices must be virtually eliminated, and any disruptions which do happen must be highly mitigated. However, the characterisation, prediction and technology used to mitigate disruptions is still an area of active development. In this paper, the authors investigate the disruptions within ST40, with particular attention at the identification of causes and effects associated with disruptions, both from a physics basis and an engineering standpoint. This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021–2022 experimental campaign. The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. The classification was validated by benchmarking the numerical reconstruction of the plasma dynamics with experimental data recovered from the plasma diagnostics. Subsequent Machine Learning analyses allowed the extrapolation of new disrupted plasma configurations for preliminary predictive simulations of the plasma column displacement. Thanks to the numerical simulations performed in MAXFEA environment, it is possible to investigate the plasma vertical displacement both during disrupted and regularly terminated plasma scenarios and to provide lessons to be learnt for the next ST40 experimental campaign and for the design of future ST devices.
Martin Mikulics, Andreas Winden, Joachim Mayer
et al.
LED devices are increasingly gaining importance in lithography approaches due to the fact that they can be used flexibly for mask-less patterning. In this study, we briefly report on developments in mask-free lithography approaches based on nano-LED devices and summarize our current achievements in the different building blocks needed for its application. Individually addressable nano-LED structures can form the basis for an unprecedented fast and flexible patterning, on demand, in photo-chemically sensitive films. We introduce a driving scheme for nano-LEDs in arrays serving for a singularly addressable approach. Furthermore, we discuss the challenges facing nano-LED fabrication and possibilities to improve their performance. Additionally, we introduce LED structures based on a hybrid nanocrystal/nano-LED approach. Lastly, we provide an outlook how this approach could further develop for next generation lithography systems. This technique has a huge potential to revolutionize the field and to contribute significantly to energy and resources saving device nanomanufacturing.
Electrochemically anodized TiO<sub>2</sub> nanotube arrays (NTAs) were used as a support material for the electrodeposition of zinc nanoparticles. The morphology, composition, and crystallinity of the materials were examined using scanning electron microscopy (SEM). Electrochemical impedance spectroscopy (EIS) was performed to evaluate the electrochemical properties of TiO<sub>2</sub> NTAs. Annealing post-anodization was shown to be effective in lowering the impedance of the TiO<sub>2</sub> NTAs (measured at 1 kHz frequency). Zinc nanohexagons (NHexs) with a mean diameter of ~300 nm and thickness of 10–20 nm were decorated on the surface of TiO<sub>2</sub> NTAs (with a pore diameter of ~80 nm and tube length of ~5 µm) via an electrodeposition process using a zinc-containing deep eutectic solvent. EIS and CV tests were performed to evaluate the functionality of zinc-decorated TiO<sub>2</sub> NTAs (Zn/TiO<sub>2</sub> NTAs) for glucose oxidation applications. The Zn/TiO<sub>2</sub> NTA electrocatalysts obtained at 40 °C demonstrated enhanced glucose sensitivity (160.8 μA mM<sup>−1</sup> cm<sup>−2</sup> and 4.38 μA mM<sup>−1</sup> cm<sup>−2</sup>) over zinc-based electrocatalysts reported previously. The Zn/TiO<sub>2</sub> NTA electrocatalysts developed in this work could be considered as a promising biocompatible electrocatalyst material for in vivo glucose oxidation applications.
Danielle van der Heijden, Anna Casimiro, Jan Matthijs ter Meulen
et al.
The pervasive use of screens, averaging nearly 7 h per day globally between mobile phones, computers, notebooks and TVs, has sparked a growing desire to minimize reflections from ambient lighting and enhance readability in harsh lighting conditions, without the need to increase screen brightness. This demand highlights a significant need for advanced anti-glare (AG) technologies, to increase comfort and eventually reduce energy consumption of the devices. Currently used production technologies are limited in their texture designs, which can lead to suboptimal performance of the anti-glare texture. To overcome this design limitation and improve the performance of the anti-glare feature, this work reports a new, cost-effective, high-volume production method that enables much needed design freedom over a large area. This is achieved by combining mastering via large-area Laser Beam Lithography (LBL) and replication by Nanoimprint Lithography (NIL) processes. The environmental impact of the production method, such as regards material consumption, are considered, and the full cycle from design to final imprint is discussed.
Aris E. Giannakas, Vassilios K. Karabagias, Amarildo Ndreka
et al.
Active food packaging incorporated with natural plant extracts as food preservatives, which will totally replace chemical preservatives gradually, are of major interest. Sequentially to our and other scientists’ previous work, in this paper we present the results of a study on the development of a novel active food packaging film based on the incorporation of a natural-halloysite/carvacrol-extract nanohybrid with the commercially used low-density polyethylene. The corona-treatment procedure was employed to incorporate a natural preservative on to the optimum final film. Packaging films are formatted with and without incorporation of natural-halloysite/carvacrol-extract nanohybrid and are coated externally, directly or via corona-treatment, with carvacrol essential oil. Mechanical, physicochemical, and preservation tests indicated that the low-density polyethylene incorporated perfectly with a natural-halloysite/carvacrol-extract nanohybrid. The extra external coating of the film with pure carvacrol extract using the corona-treatment technique led to approximately 100% higher Young Modulus values, slightly decreased ultimate strength by 20%, and exhibited almost stable elongation at break properties. The water vapor and oxygen properties were increased by 45 and 43%, correspondingly, compared to those of pure low-density polyethylene film. Finally, the antioxidant activity of the corona-treated film increased by 28% compared to the untreated film coated with carvacrol because of the controlled release rate of the carvacrol.
Andrey A. Knizhnik, Pavel V. Komarov, Boris V. Potapkin
et al.
We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for fast and adequate prediction of polymer properties. We have developed the programs (a) GenStruc, for generating the new polymer SRUs using the enumeration and Monte Carlo algorithms, and (b) PolyPred, for predicting properties for a given input polymer as well as for multiple structures stored in the database files. The structure database from the original Bicerano publication is used to create databases of backbones and pendant groups. A database of 5,142,153 unique SRUs is generated using the scaffold-based combinatorial method. We show that using only known backbones of the polymer SRU and varying the pendant groups can significantly improve the predicted extreme values of polymer properties. Analysis of the obtained results for the dielectric constant and refractive index shows that the values of the dielectric constant are higher for polyhydrazides than for polyhydroxylamines. The high value predicted for the refractive index of polythiophene and its derivatives is in agreement with the experimental data.
Parastoo Pourali, Mahnaz Nouri, Tana Heidari
et al.
Silver nanoparticles (SNPs) can be produced by active and inactive forms of biomass, but their properties have not been compared. Recent research is attempting to reveal their differences in shape, size, amount, antibacterial activity, cytotoxicity, and apoptosis induction. The biomass of <i>Fusarium oxysporum</i> was divided into four groups and pretreated in the following devices: room temperature (RT) and refrigerator (for preparation of active biomass forms), autoclave, and hot air oven (for preparation of inactive biomass forms). Samples were floated in ddH<sub>2</sub>O, and SNPs were produced after the addition of 0.1699 g/L AgNO<sub>3</sub> in the ddH<sub>2</sub>O solution. SNP production was confirmed by visible spectrophotometry, transmission electron microscopy (TEM) and X-ray diffraction (XRD). SNPs were washed, and their concentration was determined by measuring atomic emission spectroscopy with inductively coupled plasma (ICP-OES). For antibacterial activity, the plate-well diffusion method was used. MTT and Annexin V-FITC/propidium iodide assays were used for cytotoxicity and apoptosis induction, respectively. The maximum absorbance peaks for SNPs pretreated in RT, refrigerator, autoclave, and hot air oven were 404, 402, 412, and 412 nm, respectively. The SNPs produced were almost the same shape and size, and the XRD results confirmed the presence of SNPs in all samples. Due to the differences in the type of bacterial strains used, the SNPs produced showed some differences in their antibacterial activity. The MTT assay showed that the amounts of SNPs in their IC<sub>50</sub> dose based on the results of ICP-OES were 0.40, 0.45, 0.66, and 0.44 ppm for the samples pretreated in the hot air oven, autoclave, and refrigerator, and RT, respectively. The apoptosis induction results showed that the biologically engineered SNPs induced more apoptosis (about 34.25%) and less necrosis (about 13.25%). In conclusion, the type and activity of SNPs produced by the active and inactive forms of fungal biomass did not change. Therefore, use of the inactive form of biomass in the future to avoid environmental contamination is reccommended.
The strong ferromagnetic nanoparticles are analyzed within the band structure-based shell model, accounting for discrete quantum levels of conducting electrons. As is demonstrated, such an approach allows for the description of the observed superparamagnetic features of these nanocrystals. Assemblies of such superparamagnets incorporated into nonmagnetic insulators, semiconductors, or metallic substrates are shown to display ferromagnetic coupling, resulting in a superferromagnetic ordering at sufficiently dense packing. Properties of such metamaterials are investigated by making use of the randomly jumping interacting moments model, accounting for quantum fluctuations induced by the discrete electronic levels and disorder. Employing the mean-field treatment for such superparamagnetic assemblies, we obtain the magnetic state equation, indicating conditions for an unstable behavior. Respectively, magnetic spinodal regions and critical points occur on the magnetic phase diagram of such ensembles. The respective magnetodynamics exhibit jerky behavior expressed as erratic stochastic jumps in magnetic induction curves. At critical points, magnetodynamics displays the features of self-organized criticality. Analyses of magnetic noise correlations are proposed as model-independent analytical tools employed in order to specify, quantify, and analyze the magnetic structure and origin of superferromagnetism. We discuss some results for a sensor-mode application of superferromagnetic reactivity associated with spatially local external fields, e.g., the detection of magnetic particles. The transport of electric charge carriers between superparamagnetic particles is considered tunneling and Landau-level state dynamics. The tunneling magnetoresistance is predicted to grow noticeably with decreasing nanomagnet size. The giant magnetoresistance is determined by the ratio of the respective times of flight and relaxation and can be significant at room temperature. Favorable designs for superferromagnetic systems with sensor implications are revealed.
Shao-Wen Yao, Naeem Ullah, Hamood Ur Rehman
et al.
In this study, our focus is on the construction of novel soliton solutions of non-linear Schrödinger equation with parabolic law and non-local law non-linearities via new extended hyperbolic function method. The acquired solutions are original and could be helpful in the field of nonlinear optics, fluid dynamics, and plasma physics. From these outcomes, it is believed that this method is a promising technique to handle a wide variety of such type of equations. For physical understanding and better realization of our constructed solutions, some of the obtained solutions under appropriate parameters are demonstrated by 2D and 3D plots. Analysis discloses that the proposed scheme is consistent and can be applied to more advanced models in engineering and physics.
C. Venkataiah, D. Rajesh Setty, N. Ramanjaneyulu
et al.
As technology extended from deep sub-micron technology to nanometer regimes, the conventional copper (Cu) wire will not be able to continue. Now a substitute approaches such as Carbon Nano Tube (CNT) interconnects have been suggested to ignore the problems associated with global interconnects. Hence in this work, crosstalk analysis of Complementary metal oxide semiconductor (CMOS) buffer-driven of different interconnects have been analyzed for peak overshoot and overshoot width of Cu and CNTs for 16nm technology. For analyzing peak overshoot, the interconnect lengths are varied from 100um to 500um in 16 technology node for Cu, single walled carbon nanotube (SWCNT) and multi-walled carbon nanotube (MWCNT). The values of the peak overshoot and overshoot width changes, as the interconnect length increases, the peak overshoot and width is going to be increases. As Compared to Cu, SWCNT and MWCNT, the peak overshoot and width for SWCNT is lesser than copper and MWCNT. The MWCNT interconnect is less than that of conventional Copper interconnects.
Colour is considered as the basic visual perception of any space. There are numerous developed theories and assumptions related to the aesthetic comfort offered by colours, and to their effect on human psyche. In this paper, it is inspected how the choice of colour changes with age and its impact on that age group of people. This investigation will focus all the factors those are predicted to be more influential in architecture, interior spaces and the psychological status of users. The research aim of this study is to increase the concern about the importance of the interaction between interior space and human behaviour responding to the colour of the interior space. Paper talks about the importance of colour in an interior space where the different user groups spend most of their time like- schools for kids, offices for adults and meditation centre, old age homes for senior citizens. This research would help others to understand the importance of colour in the interior space and how people of different age groups respond to it. Based on the analysis of the conducted surveys and interviews, conclusions are drawn about the effect colours have on human behaviour in an interior space.
Fake currency is a critical factor affecting economies worldwide, including India. In this paper, a novel and structurally efficient approach for detecting and identifying duplication in currency notes is presented using Discrete Wavelet Transform (DWT). The system employs image processing algorithms to extract essential features such as security thread, intaglio printing (RBI logo), and identification mark, which serve as security measures for Indian currency. To identify fake portions in the currency notes and make informed decisions about their authenticity, the matching scores from all fake detection modules are fused together. A crucial aspect of the work lies in comparing the extracted features from various currency notes, enabling us to differentiate between fake and genuine notes effectively. To assess the performance, mean square error is employed as a metric for comparison between two images. A database is build containing authentic Indian notes of different denominations, extract their features, convert them into binary equivalents, and then calculate their mean square error. The proposed Fake Note Detection System takes a test currency note image, performs preprocessing operations to eliminate noise and negative artifacts, and then proceeds with the detection process. The system offers a promising solution to combat counterfeit currency issues and safeguard the integrity of the Indian economy.
This paper presents an in-depth examination of color cast in digital images, elucidating its fundamental principles, generation mechanisms, and real-world implications influenced by light absorption and scattering. The study explores diverse color cast correction methods and provides a detailed analysis of their respective outcomes. Foundational knowledge of color cast, rooted in the principles of light interaction, serves as the basis for understanding its manifestation in various real-world contexts. The research systematically investigates the intricate dynamics of color cast across diverse scenarios, shedding light on its complexities and impact. The paper evaluates a range of color cast correction techniques, including classic approaches such as the Gray World Algorithm, Max–RGB, and White Balance Correction, as well as advanced methods like Gamma Correction, Histogram-Based Method, and the Gray Edge Algorithm. Notably, simulation results underscore the consistent superiority of the Gray Edge Algorithm in effectively correcting color cast, showcasing its robustness across diverse scenarios. This comprehensive exploration contributes to a holistic understanding of color cast, covering its generation, consequences in real-world scenarios, and an in-depth analysis of correction methodologies. The findings provide valuable insights for professionals in image processing and computer vision seeking efficient correction strategies.
The increasing impact of social media for big brands nowadays are preferring influencer marketing over the traditional marketing techniques. Influencer advertising is a methodology that recognizes influential individuals on social media who impact a brand’s industry or target crowd. In an influencer advertising technique, a brand shapes an association with the influencer wherein the influencer consents to open their crowd to the brand’s informing or substance. Influencer marketing has become dramatically throughout the long term and the objective gathering for this promoting procedure is expanding step by step. Digital ad campaigns and online sketches made by the influencers get viral in no span of time. With hashtags, makes reference & other keywords the name of the brand stays in limelight for a longer period of time & impacts deeper in the minds of the audience. The research paper is based on secondary data. In this paper, the researcher has studied influencer market for promotions and various opportunities for market expansion.