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Novel Applications of Graphene and its Derivatives: A Short Review
Graphene, a layered allotropic form of graphitic carbon, has fascinated the scientific world since its discovery. Its unique structural, physical, chemical, mechanical, and electrical properties find application in many areas. Because of its large surface area and its apt electrical property, it is used in electromagnetic interference shielding. With excellent carrier mobility, it is used for sensing purposes. Mechanical strength and elastic properties coupled with its lightweight make graphene a promising material as a supercapacitor. The 2-dimensional structural properties of the graphene layers can be used for the purification treatment of water and gas. The number of research in graphene applications is increasing every day, showing the importance and excellency of graphene properties. This short review provides a comprehensive understanding of graphene's properties and progress in electromagnetic interference shielding, sensors, water treatment, energy production, storage, and conversion applications such as supercapacitors, fuel cells, solar cells and electrocatalysts. 2023 Bentham Science Publishers. -
Development and Analysis of Current Collectors for Proton Exchange Membrane Fuel Cells
Hydrogen fuel cells are gaining popularity in power-consuming devices due to their zero-emission characteristics. However, ohmic resistance, which arises from the resistance to electron flow through the electrodes and external circuit, can cause reduced efficiency and voltage drops in a fuel cell. This research aims to develop current collector plates for proton exchange membrane fuel cells with optimal design, high electrical conductivity, and thermal conductivity to mitigate ohmic resistance. Six different designs and five different materials-copper, brass, aluminum, stainless steel 316, and stainless steel 304 were considered for this purpose. The study involved experimental electrical conductivity and fuel cell performance tests to identify the best material and design for the current collector. Results indicated that brass and copper exhibited the least resistivity and favorable material characteristics. Consequently, all six current collector plate designs were developed using brass and copper with various machining and finishing processes. Performance testing on a fuel cell test station revealed that brass current collector plate design 5, featuring open ratios, demonstrated superior performance. Ultimately, the optimum design and material selection of the current collector plates have led to the development of fuel cells with reduced ohmic resistance and improved overall performance. 2024, Politechnika Lubelska. All rights reserved. -
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Health diagnosis of mango trees using image processing techniques
A Mango disease detection artificial intelligent model needs robust and effective newlinefeature extraction methods. The machine vision system has been designed for the newlineidentification of disease in plants from color leaf images. The research done proposes newlinenovel algorithms to extract color features Pseudo Color Regions and Texture Features newlineusing Pseudo Color Co-Occurrence Matrix. A new Mango dataset has been created and newlinealgorithms tested on it. An artificial intelligence model has also been created and tested on an existing disease dataset of Apple and Tomato plants. Results were compared with existing methods in the literature. The effectiveness of each statistical function was studied in classifying the pattern using a Support Vector Machine. For textures that are newlinedifferent like smooth new leaves, dry leaves, growth a Gray Level Co-occurrence based newlinestatistics was effective but values failed to discriminate in certain diseases. The proposed and implemented novel method which uses second-order statistics on a pseudo-color-based co-occurrence matrix has resulted in better classification. Pseudo Color Region feature is created using a novel intermediate data structure and found to be more effective than hue-based color features. It identifies dots, spots, patches and regions of different colors on the leaf and uses that as a feature vector to classify plant diseases. This generic method can be applied for early disease detection for plants and help farmers take corrective measures to avoid loss of yield. -
Optimizing supercapacitor electrodes via lithium-induced JahnTeller modulation in CuO
AbstractThe development of advanced electrode materials with superior electrochemical properties is essential to meet the growing demand for efficient energy storage technologies. While surface engineering is common to address this fundamental challenge, the present work shifts the focus from external morphology to internal structural stabilization. Through an integrated experimental and density functional theory (DFT) approach, we demonstrate that a moderate lithium incorporation of 4at. % achieves an optimal balance in CuO properties by suppressing subtle JahnTeller distortions, enhancing crystallite size, narrowing the band gap, and improving both optical and electrical conductivity. X-ray Absorption Spectroscopy (XAS) confirms that Li-ion incorporation increases local symmetry around Cu sites, while EXAFS analysis identifies localized structural disorder associated with dopant substitution. This dual effect stabilizes the CuO lattice while simultaneously creating additional redox-active sites. Electrochemical testing validates this approach, as the optimized 4at. % Li-doped CuO electrode delivers a high specific capacitance of 656F/g at 1 A g?1. The fabricated symmetric supercapacitor device delivers an energy density of ~7Whkg?1 at a power density of ~700Wkg?1, demonstrating the feasibility of Li-doped CuO thin films for supercapacitor applications, although further optimization is required to improve long-term cycling stability. This synergistic experimentaltheoretical framework provides both fundamental insight and practical guidelines for the rational design of doped transition-metal oxides, offering a cost-effective and scalable strategy for next-generation energy storage applications. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Evolution of Virtual Prey using a Virtual Predator
Can evolution happen on a computer is it possible for Images to evolve? This paper tries to show that Mother Nature has got it right and evolution is the future for programs. How many times will a new release come to solve my previous problem and in the process create new problems! Evolution can happen on a computer is very clear in this work titled Evolution of a Virtual Prey using a Virtual Predator. The Virtual Prey can evolve its skin color based on an Artificial Intelligent Virtual Predator. Monochrome patterns have evolved very successfully. Some repeatable patterns in the Brodatz Texture Database have also evolved. Charles Darwin in his famous book Origin of Species suggests that evolution takes place using a method he termed Natural Selection. Survival of fittest has to be considered in context of the environment in which the species are living. One of the criteria of survival depends on whether the specie was able to propagate before it was hunted down by a predator. For example the skin color of a prey like an insect evolves to match the background it is living on such that the predators like birds and frogs will not detect them. Assume there are thousand insects on a surface and if nine hundred and ninety are eaten over a period of time are eaten. Then the last 10 will be those which have got skin color, pattern, and texture closest to the original. Some findings have been in the comparison of different genetic operators to evolve specie. Use of Mutation and Crossover operator is critical. Some level of continuation is required in the next generation to maintain and not lose the quality which has been already attained. The number of generations required depend on DNA length and on the correct use of these three parameters.. The runtime from thirty minutes has been reduced to ten minutes to evolve specie of 100 plus variables in the DNA of the images. If we are ready to run the program for a day on multiple machine in parallel then the possibilities of great diversity is very much possible. The predator will be an algorithm which tries to do a basic computer vision operation of correlating two images and ranking them. Then it eliminates a certain percentage of the population. The dissertation is broken down in chapters. Chapter 1 gives the Introduction and Chapter 2 gives an idea of what all research is happening in this area. Chapter 3 is the core content of my work and it shows the different approaches I have used to evolve textures, the use of different operators. This chapter is followed by the testing I had to do due to the complexity of the application and high chance of error creeping into the results. An idea of the application can be got from the Appendix where I have given a walk through of the Zing World -
Pseudo Color Region Features for Plant Disease Detection
This study reports a novel pseudo color region features for a computer vision system for the identification of diseases in Tomato Plants. The HSV based algorithm identifies eccentric and non- eccentric dots, spots, patches and region of different pseudo colors. Proposed method uses region properties and creates an enhanced and effective feature vector for plant disease detection. The features are more intuitive for humans to understand and help in tuning the underlying Artificial Intelligence Model better. The algorithm uses a scalable data structure to store regions counts using a hash function. It has wide application in the Computer Vision domain. 2020 IEEE. -
Detection of Disease in Mango Trees Using Color Features of Leaves
The goal has been to detect disease in mango trees. This paper compares different approaches to extract color features and check the accuracy and applicability for mango trees. The paper proposes variations which helped in increasing the accuracy of features extracted for mango trees: firstly, a customized method of splitting leaf into layers while doing K-means clustering, and secondly, segmenting the region of interest to blocks to help in applying statistical functions more accurately over a region. 2020, Springer Nature Singapore Pte Ltd. -
Yoga Posture Recognition Using Image Processing
Yoga is an ancient Indian practice that focuses on maintaining balance through various techniques like asanas and meditation. Traditional Indian yoga involves physical postures, regulated breathing, meditation, and relaxation techniques. The practice, rooted in physical, mental, and spiritual disciplines, offers numerous benefits. In this paper, we present an approach for classifying four prominent yoga poses: Goddess Pose (Utkata Konasana), Tree Pose(Vrksasana), Dead Body Pose (Savasana), and Downward Dog Pose (Adho Mukha Svanasana) using image processing techniques. The proposed methodology leverages sophisticated feature extraction techniques that analyse the posture's shape to help capture the details of the posture like the centroid, eccentricity, convex hull, etc. The subsequent classification process employs Support Vector Machines (SVM) enabling accurate categorization based on the extracted features. This blend of traditional wisdom and modern technology offers a promising tool for automating posture recognition, benefiting yoga practitioners and instructors, and can be extended to other real-life scenarios like odd posture detection. 2024 IEEE. -
Effect of Substrate Temperature on Properties of Copper Oxide Thin Films Coated by Spray Pyrolysis
Copper oxide shows a wide range of optical as well as electrical characteristics depending upon the preparation parameters. This wide range turning capability makes it a preferable candidate for effective use in various application fields like optical filters, light energy harvesting, gas sensing and semiconducting device fabrication. Spray pyrolysis technique with manual spray system was used to deposit a thin layer of copper oxide on glass substrates at temperatures of 300oC, 350o C, and 400o C. X-ray diffraction analysis shows that all the thin films obtained have monoclinic phase. A change of grain size from 15 nm to 25 nm was observedas the substrate temperature was varied from 300oC to 400o C.The Hall coefficient analysis confirms p-type conductivity in films obtained at 300o C and 350oC and N type conductivity with high resistivity for film coated at 400o C. Optical band gap increases from 1.75 to 2.17 eV with the increase in substrate temperature due to energy band tailing. 2023, Books and Journals Private Ltd.. All rights reserved. -
Study of the structural, optical, electrical and electrochemical properties of copper oxide thin films synthesized by spray pyrolysis
In our present study we focus on characterizing copper oxide (CuO) thin films synthesized at various substrate temperatures and to assess the electrochemical performance of the optimized sample. The spray pyrolysis method was used to fabricate CuO thin film samples, with the substrate temperatures ranging from 250 to 400C. The coatings underwent characterization through different analytical techniques, including X-ray diffraction, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, field emission scanning electron microscopy, Raman spectroscopy, and Hall effect measurements. All the thin film samples were confirmed to have a monoclinic phase. The presence of Cu=O was confirmed by Raman spectroscopy. All the samples exhibited P type conductivity except the one synthesized at 400C. Galvanostatic chargedischarge studies revealed a pseudocapacitive nature for the optimized sample synthesized at 350C. The symmetrical charging and discharging curves imply excellent material reversibility, indicating long-term cyclic stability. The Nyquist plot exhibited a semicircle at high frequencies, representing the materials intrinsic resistance and a linear behavior at low frequencies, depicting the ion transfer resistance. The electrode demonstrated favorable electrochemical properties and potential use of the material in supercapacitor applications. 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Na+ doped CuO: A new paradigm electrode material for high performance supercapacitors
This study investigates the influence of sodium doping on the properties of cupric oxide (CuO) thin films synthesized via spray pyrolysis. Comprehensive characterization was conducted using X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDAX), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM), Raman spectroscopy, Hall effect measurements, and electrochemical studies. All films exhibited p-type conductivity, with an optical band gap variation from 1.53 to 1.73 eV. XRD analysis confirmed the dominance of monoclinic CuO, with minor phases of Cu2O and Cu4O3. EDAX and XPS verified the incorporation of Cu, O, and Na elements. FESEM revealed a densely packed morphology with uniform particle distribution and rough surfaces in the electrically optimized film. The Raman spectra of doped samples showed increased intensity and sharpness, attributed to Na + ion-induced polarizability enhancement. Hall effect measurements indicated a tenfold decrease in carrier concentration and a more than tenfold increase in mobility upon sodium doping. Films doped with 4 at.% sodium exhibited the lowest resistivity. Additionally, Na doping enhanced the electrochemical performance of CuO. These findings demonstrate that sodium doping significantly enhances the electrical, optical and electrochemical properties of CuO thin films, making them suitable for applications in optoelectronic devices and supercapacitors. 2024 Elsevier Ltd and Techna Group S.r.l. -
Evaluation of the inhibition efficiency of Pogonatum microstomum for mild steel in acid medium using gravimetric, kinetics, electrochemical studies and statistical modeling
Mosses from a distinct lineage of bryophyte family are found as thick green carpet on the moist rocks, trees, soil or streams. It is acclaimed for its good antimicrobial properties and is a reservoir of various phytochemicals. The nontoxicity nature and abundant availability in nature was exploited for the first time to investigate its effectiveness as novel and green corrosion. Present study deals with the evaluation of corrosion inhibition efficiency of the moss, Pogonatum microstomum using the electrochemical studies and weight loss studies. The moss extract showed a maximum corrosion inhibition efficiency of 95.28 % for 3hrs of immersion period at 303 K. Increase in the inhibition efficiency with concentration of moss extract is the result of adsorption of the constituents which are active on the surface of the metal. Tafel polarization and electrochemical impedance studies gave results on par with the weight loss measurements. The experimental results obtained were further validated by statistical analysis and statistical modeling using SPSS 20 software. 2020 American Institute of Physics Inc.. All rights reserved. -
Analysis of passive bloodstain morphology across surface textures and drop heights using deep learning
Bloodstain pattern analysis (BPA) is a critical forensic science tool for reconstructing crime scene events. In this study, the effect of substrate type and drop height on the morphology of passive bloodstains was examined under controlled laboratory conditions. Blood samples were dropped vertically at 90 angle from three different heights, and the drops were permitted to strike five different surfaces, including curved cups, crushed chart paper, jute cloth, jelly stone, and concrete. These substrates were chosen to represent a realistic range of porous, semi-porous, non-porous, textured, and curved materials that are commonly encountered in crime scenes. The features of the substrate affect stain morphology, including shape irregularity and satellite formation, but not the measured angle of impact. These findings validate the consistency of impact angle determination using BPA, wherein the nature of the substrate primarily affects stain morphology but not necessarily the accuracy of angles. The large image data sets were tested using deep learning approaches, which effectively differentiate bloodstain patterns generated from varying fall heights. MobileNet model, leveraging pretrained ImageNet features, achieved superior accuracy and generalisation, underscoring the value of transfer learning for small forensic datasets. Future extensions of this work will include multiple impact angles, motion-related effects and temperature-controlled conditions to represent the actual crime scene scenarios. Deep learningbased analysis of these data may improve the understanding of bloodstain morphology and strengthen the forensic applicability. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Real-Time Network Monitoring: Integrating Machine Learning and Custom Packet Sniffer Using Python
The growth in network traffic and the increasing complexity of cyber threats necessitate robust systems for detecting anomalies that indicate security breaches. This research presents a methodology for finding anomalies in packets sent when the connection is established. It uses a machine learning model and a packet sniffer. It captures Transmission Control Protocol (TCP), User Datagram Protocol (UDP), IPv4, and Internet Control Message Protocol (ICMP) segments to predict if any anomalies are present (Sanders in Practical packet analysis: using wireshark to solve real-world network problems, No Starch Press, San Francisco, 2017). An unsupervised learning model is utilized. The presence of unlabeled data to enhance the real-time prediction using isolation forest model. The data collected by packet sniffer undergoes avoiding null values and encoding addresses, and thus an isolation forest is used so that it predicts if anomalies are present using binary trees. The performance is evaluated on the basis of metrics like accuracy, precision, and F1-score (Goutte and Gaussier in European conference on information retrieval, Springer, New York, 2005). The result illustrates the model is accurate in predicting whether anomalies are present. Future work is focused on enhancing the models capabilities with more protocols and an active defense mechanism. The study addresses real-world deployment challenges especially in heterogeneous environments like IoT-based networks. While isolation forest is getting high accuracy, future research could explore hybrid approaches combining traditional statistical methods with deep learning techniques for enhanced industry applications (Ahmed et al. in J Netw Comput Appl 60:1931, 2016). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Authentic Pride versus Hubristic Pride: Mediating Role of FoMO-directed Consumer Conformity Consumption Behaviour in Young Adults
Purpose-Sustainability is a word that has carried fame and prominence in the global conversation for the pro-environmental movement to protect the environment. Even making sustainable buying choices has been associated with individuals sense of identity in the socio-cultural sphere, especially when brands worldwide strongly promote them. This cross-sectional study aims to inquire if sustainability consciousness could impact consumers pride and, if yes, can fear of missing out (FoMO)-directed conformity consumption mediate the relationship between them or not. Method-Three standardised scales: the Sustainability Consciousness Questionnaire, 14-item Hubristic and Authentic Pride Scale and Consumer Consumption-FoMO Questionnaire, were administered to 18 to 35-year-old Indian young adults (N=204) recruited online to identify their levels of sustainability consciousness, hubristic and authentic pride and FoMO-directed consumer conformity consumption behaviour. Thus, convenient sampling was employed to collect the data for the study. The analysis involved Pearsons product-moment correlation followed by regression using SPSS software. Further, Sobels tests were conducted to verify the mediating roles of FoMO-directed consumer conformity consumption behaviour in relationships across sustainability consciousness and pride. Results Statistical analyses revealed that sustainable behaviours positively related to authentic pride with no mediating effects by FoMO-directed consumption behaviour. Similarly, sustainability attitudes are inversely associated with hubristic pride, but no mediating effects results were significant. On the other hand, sustainability knowingness was negatively related to hubristic pride, and the relationship was mediated significantly by certain but not all dimensions of FoMO. Conclusion-The study instilled empirical evidence for adaptive and maladaptive types of pride derived from sustainable orientation and the significant role of FoMO in strengthening hubristic pride. 2024 RJ4All. -
Posttraumatic relationship experiences in women in South India
Marriage is a socially binding intimate relationship between two individuals which is expected to be stable and enduring. In many cases, there can be severe difficulties questioning the quality of ones married life such as IPV or other kinds of abuse or exploitation which could lead to a divorce. Although divorce legally dissolves the relationship, studies suggest that the stress caused by a traumatic relationship may not end after terminating the relationship. The resemblance of these symptoms to PTSD led to the proposed diagnosis of PTRS. In this study, seven participants who have been divorced due to domestic violence for at least a year were identified and interviewed regarding their past and present life situations. The emergent themes in the data pointed to several factors that may influence ones married life, the decision of divorce and current life situations which can affect the amount of stress an individual might face concerning their past traumatic relationship. The factors influencing stress experienced during a traumatic marriage included involvement and support from ones family and in-laws, nature, and cause of abuse, stress-related to children, social support and the very decision to get a divorce. The process of overcoming fear, mistrust, and grief, social and family support, child custody, and related legal processes were factors that affected stress related to the process of divorce. The grief related to child custody, ability to rationalize the decision, career, remarriage and childrens future were some factors that influenced the stress these individuals experienced currently in their life. 2019, 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Knowledge of sexual abuse and resistance ability among children with intellectual disability
Background: Sexual abuse is a global concern among children with intellectual disabilities. Sexual abuse is frequent and long-lasting when the victim is a child with an intellectual disability. Moreover, the rate of sexual abuse is two to eight times the rate in the general population. Objective: This study aimed to investigate the knowledge of sexual abuse and resistance ability among children with intellectual disabilities. Participants and setting: The study was conducted among 120 children with mild or moderate intellectual disabilities attending twelve schools for specific purposes. Methods: We adopted a cross-sectional design to assess knowledge and resistance ability. Personal Safety Questionnaire and Modified What If Situation Test were administered verbally during individual interviews. Institutional Ethics Committee approved our study. Results: Current study suggests that children with intellectual disabilities have average knowledge (M = 6.6, SD = 1.6) regarding sexual abuse. More than 90 % of children demonstrated poor reporting skills. Although children exhibited good knowledge in differentiating appropriate from inappropriate touch requests, most children reported they would not disclose this incident to anyone. Conclusions: This study strongly suggests the need for a structured training program for children with intellectual disabilities to prevent sexual abuse. 2022 Elsevier Ltd -
Carbon dots derived from Averrhoa bilimbi fruit for the detection of cholesterol and chromium(vi)
Carbon dots (CDs) are a class of carbon-based nanomaterials, typically less than 10 nm in size, known for their unique optical and electronic properties. Their discovery led to the opening of new avenues in nanotechnology, particularly in the field of fluorescence-based sensing. Owing to their strong photoluminescence, excellent aqueous solubility, low cytotoxicity, and potential surface functionalization, CDs have been considered as effective fluorescent probes for the detection of a wide range of analytes. Herein, we report the hydrothermal synthesis of CDs from a natural source, Averrhoa bilimbi fruit, leading to the formation of CDs exhibiting useful photoluminescent properties and potential for selective detection of cholesterol and Cr(vi) ions. The average particle size of Averrhoa bilimbi fruit-derived CDs (AB-CDs) was found to be 6.022 nm. The properties of AB-CDs were unravelled from structural and optical characterization and the applicability of AB-CDs as sensors for heavy metals and biomarkers was studied. The selective fluorescence response towards cholesterol and Cr(vi) makes it an efficient fluoroprobe for practical applications. The limits of detection for the sensing of cholesterol and Cr(vi) were estimated to be 0.31 M and 1.71 M respectively. The sensor system using AB-CDs is economical, sustainable, and eco-friendly. This journal is The Royal Society of Chemistry, 2026 -
Using Time series analysis, analyze the impact of the wholesale price index on the price escalation in the automotive industry
The automobile industry is a crucial sector of the economy, contributing significantly to employment and economic growth. One of the major challenges faced by this industry is the problem of price escalation, which can affect both consumers and manufacturers. In this project, we explore the impact of wholesale price index (WPI) on the price escalation of automobiles using time series analysis. We analyze the historical data of WPI and automobile prices in India from 2010 to 2022. We use statistical techniques like stationarity tests, autocorrelation analysis, and Granger causality tests to understand the relationship between WPI and automobile prices. Furthermore, we employ a SARIMA model in predicting WPI value and Vector Auto regression (VAR) model to analyze the dynamic interactions between WPI and CPI value. Our findings suggest that WPI has a significant impact on the price escalation of automobiles in India. The VAR model shows that there is a positive feedback loop between WPI, CPI and automobile prices, implying that an increase in WPI leads to a corresponding increase in automobile prices and vice versa. This feedback loop can create an inflationary spiral in the automobile industry, which can be detrimental to the economy. Our project highlights the importance of monitoring WPI and its impact on the automobile industry. Policymakers and industry experts can use our findings to develop effective strategies to manage price escalation in the automobile industry and mitigate its negative impact on the economy. 2023 ACM.