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Low cost energy management for demand side integration
Numerous batteries are outfitted with a state ofcharge (SoC) demonstrating the relaxation about the charge. Building a beneficial BMS is to check while thinking about, to that amount regardless we work now not holds a reliable method in imitation of study condition state of-charge, the nearly imperative measurement concerning a battery. Perusing the relaxation about the vigour of a battery is more unpredictable than administering thin fuel as in automobiles. An electrochemical cell diminishes its greatness and the in-and-streaming coulombs are counted for SOC. The BMS together which offers commitment while charging yet releasing; that detaches the battery if the SOC is below certain percentage. Various laws like Peukerts Law for battery capacity have been employed to determine the discharge rate of the battery. Arduino Uno is used for the input parameters required for Peukerts Law and various other calculations for significant monitoring. To address the existing complicated BMS, a new approach has been provided using IoT platform and making the understanding of BMS in much similar perspective. 2018 IEEE. -
Low cost lignite derived mesoporous nanocarbon for simultaneous electrochemical detection of heavy metal IONs cadmium and lead /
Patent Number: 202241042032, Applicant: Ashlin M Raj.The present invention provides a facile, scalable, and cost-effective method for preparing mesoporous nanocarbon extracted from low grade coal, lignite that possesses an excellent electrochemical activity. The synthesized nanostructure modified electrode is used for the rapid simultaneous quantitative detection of heavy metals ions cadmium and lead in a unique triple linear detection range over the concentration ranges from 2.08 to 129 nM. -
Low Latency based Smart Ambulance Model in Emergency Healthcare
Smart ambulance represents a significant evolution in emergency healthcare,using most advance and innovative new devices, machine and techniques to improve and create new opportunities by the help of engineering methods or any formula or scientific principles to offer solutions to the limitation of traditional emergency medical services. This work focuses on the combination of Artificial Intelligence, IoT, Telemedicine and real time monitoring system to ameliorate response time, enhance patient care and optimize resource allocation in smart ambulance. By examining the performance of smart ambulance compared to traditional ones ,the research emphasize their ability and focuses on strategies to navigate challenges such as traffic congestion, communication delays and inefficient decision making ,and suggest relevant ways to resolve the issue. The result showed a positive and steady increase in the performance of smart ambulance in ensuring faster, more efficient and higher quality of emergency medical services. This paper emphasizes breakthrough ability of smart ambulance system in improving in emergency healthcare outcome and build a bedrock for their wider usages in the medical field. 2025 IEEE. -
Low temparature synthesis of non-toxic monoclinic yttrium oxide quantum dots for display and biomedical applications /
Patent Number: 202141053609, Applicant: Soorya G Nath.
Monoclinic Y203 quantum dots were synthesized at low temperature using urea as the fuel. The sample preparation was done using simple laboratory hot air oven and the synthesis temperature was maintained at 90°C throughout the experiment. Prepared samples were characterized using x ray diffraction (XRD), Raman spectroscopy, high resolution transmission electron microscopy (HRTEM), UV- Vis absorption spectroscopy and photoluminescence (PL) Studies. -
Low temperature performance evaluation of asphalt binders and mastics based on relaxation characteristics
Low temperature cracking is one of the main distresses of asphalt pavement in cold regions. Stress relaxation characteristics is critical for cracking resistance of asphalt materials, especially at low temperatures, but there are few studies on the relaxation characteristic of asphalt mastics. To evaluate the effects of relaxation characteristics of asphalt binders and mastics on its low temperature performance, beam bending relaxation test was carried out through dynamic thermomechanical analyzer at low temperatures. Relaxation rate and relaxation time were proposed to illustrate the relaxation characteristics of asphalt binders and mastics. Then, the low-temperature performance of asphalt binders and mastics was evaluated by bending beam rheometer (BBR), glass transition temperature (Tg), and single edge notch beam bending test. Finally, the correlation of relaxation characteristics with low-temperature properties was analyzed based on Pearsons correlation coefficient and Spearman rank correlation coefficient. The results show that the elasticity of asphalt mastics increases with incorporation of mineral fillers and thus the viscous deformation potential is reduced, which affects the stress relaxation capability. The low-temperature cracking performance of asphalt mastics is indeed compromised as compared with asphalt binders, and the asphalt mastics prepared with fly ash performs the worst since it presents a stronger hardening effect. Fracture energy is determined not to be suitable for evaluating the low-temperature performance of asphalt mastics since its results contradict the BBR and Tg tests. The maximum displacement at fracture can better characterize the brittleness of asphalt materials at low temperatures. The relaxation characteristic index has the strongest correlation with Tg of asphalt binders and mastics, followed by maximum displacement at fracture and comprehensive compliance parameter (Jc). The correlation coefficients are almost larger than 0.5, suggesting that relaxation time and relaxation rate can characterize the low-temperature properties of asphalt binders and mastics. 2022, RILEM. -
Low temperature synthesis of MoO3 nanoparticles by hydrothermal method: Investigation on their structural and optical properties
Molybdenum trioxide nanoparticles have recently achieved notable attention in optoelectronic and biomedical applications by virtue of their excellent structural, optical, electrical, and catalytic characteristics. The work presented here demonstrates the synthesis of orthorhombic MoO3 through the facile hydrothermal method at low temperature. Structural and optical characterization of the prepared sample was examined. XRD studies and Raman spectroscopy were carried out to study the structural behavior of the sample. The XRD peaks were concordant with the standard peaks of MoO3, which corresponds to the orthorhombic structure of MoO3. Micro-strain effects were also verified by the W-H method using UDM, UDEDM, and USDM. Raman spectroscopic data ascertained the orthorhombic phase of MoO3. From Tauc plot, a wide bandgap value (4.9 eV) was evaluated. In photoluminescence spectroscopy, peaks are related to the transition among the sub-bands of Mo5+ defects. Being a wide bandgap oxide semiconductor, MoO3 is a promising and worthy material for luminescence applications. 2022 -
Low-cost bio-waste carbon nanocomposites for sustainable electrochemical devices: A systematic review
Innovative brains have always drawn inspiration from nature while creating new designs. Animals and plants offer a variety of structures that are stronger, have higher energy sorption capacities, and have lower densities. These structures can inspire the creation of new, functional designs. Scientists have created structures by drawing inspiration from biological structures seen in nature. These structures have been demonstrated to significantly outperform conventional structures for use in the environmental and energy sectors. Due to their simple synthesis, adaptability, excellent performance, and variety of uses, including in light-harvesting systems, batteries, catalysis, bio-fuels, water, and air purification, and environmental monitoring, bio-fabricated materials have demonstrated several advantages. However, sensitive fabrication tools that can create bio-inspired structures and scale up manufacturing from laboratory-scale synthesis are urgently needed. A quick rundown of recent developments in bionanomaterials for different electrochemical systems, particularly the extensively researched rechargeable batteries, sensors, and supercapacitors, provided a discussion of the design principles for bionanomaterials, synthesis, and strategies for low-cost bio-inspired nanomaterial synthesis and device integration. A quick overview of the future research priorities is then suggested, followed by a critical analysis of the current problems. This review is anticipated to provide some understanding of biowaste-nanocomposites for electrochemical applications by taking cues from nature. 2024 Elsevier Ltd -
Low-frequency pulse-jitter measurement with the uGMRT I: PSR J0437-4715
High-precision pulsar timing observations are limited in their accuracy by the jitter noise that appears in the arrival time of pulses. Therefore, it is important to systematically characterise the amplitude of the jitter noise and its variation with frequency. In this paper, we provide jitter measurements from low-frequency wideband observations of PSR J0437 4715 using data obtained as part of the Indian Pulsar Timing Array experiment. We were able to detect jitter in both the 300-500 MHz and 1 260-1 460 MHz observations of the upgraded Giant Metrewave Radio Telescope (uGMRT). The former is the first jitter measurement for this pulsar below 700 MHz, and the latter is in good agreement with results from previous studies. In addition, at 300-500 MHz, we investigated the frequency dependence of the jitter by calculating the jitter for each sub-banded arrival time of pulses. We found that the jitter amplitude increases with frequency. This trend is opposite as compared to previous studies, indicating that there is a turnover at intermediate frequencies. It will be possible to investigate this in more detail with uGMRT observations at 550-750 MHz and future high-sensitive wideband observations from next generation telescopes, such as the Square Kilometre Array. We also explored the effect of jitter on the high precision dispersion measure (DM) measurements derived from short duration observations. We find that even though the DM precision will be better at lower frequencies due to the smaller amplitude of jitter noise, it will limit the DM precision for high signal-to-noise observations, which are of short durations. This limitation can be overcome by integrating for a long enough duration optimised for a given pulsar. The Author(s), 2024. Published by Cambridge University Press on behalf of Astronomical Society of Australia. -
Low-Profile Metasurface-Integrated Ultra-Wideband Antenna with Enhanced Gain
In this paper a metamaterial-integrated compact antenna is proposed, and the design, simulation, and implementation are presented which works in Ultra-Wideband frequency (UWB) range. The FR4 substrate has been used to design a compact, flexible, wearable antenna. Metamaterial structure comprises of periodic arrangement of unit cells termed as metasurface, to achieve higher gain. The proposed integrated antenna exhibits maximum gain of 8.1 dBi with overall dimensions of 50 mm 40 mm. Also, the gain enhancement of 3.2 dBi along with 0.2 GHz increment in bandwidth is observed after adding the metamaterial array. Thus, the proposed antenna is suitable for wearable applications. 2025 IEEE. -
Low-Velocity Impact Characteristics of GLARE Laminates with Different Sheet Thickness
Fiber reinforcement with metallic face sheets is one of the recently implemented advanced materials in distinctive applications such as fender, bonnet, and chassis used in automotive sectors. While the reinforcement enhances the sustenance property of the laminate, the face sheets provide resistance to impact force. In most automotive sectors, drop-weight analysis at varying velocity range is performed to evaluate the damage characteristics of the vehicle body. The present work is aimed at studying the influence of low-velocity impact (LVI) on glass laminate aluminum-reinforced epoxy (GLARE) laminate. Three distinct thicknesses of Al-2024 T3 aluminum alloy (0.2, 0.3, and 0.4 mm) were chosen as the face sheet, the overall thickness was kept at 2.0 mm for all the cases. Absorbed energy and damage characteristics of GLARE for different energy was experimentally determined using drop-weight impact tester. From the results, it was found that GLARE laminate can sustain a maximum impact energy of around 20 J, beyond which damage in the form of cracks begin to occur at bottom face sheet also. It was also evident that laminate can sustain impact at a velocity of 3.13 m/s and beyond which it leads to delamination damage at 3.49 m/s. Further, it is noticed that GLARE laminate with 0.3 mm face sheet thickness has best results with reference to both absorbed energy and damage when compared with other thicknesses. Also, the sample B indicates the optimal surface texture when subjected to LVI damage obtained through scanning electron microscope (SEM). 2021 SAE International. -
LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
LRD: Loop Free Routing Using Distributed Intermediate Variable in Mobile Adhoc Network
One of the critical challenges in the design of the mobile adhoc networks is to design an efficient routing protocol. Mobility is an unique characteristics of wireless network, which leads to unreliable communication links and loss of data packets. We present a new algorithm, Loop Free Routing with DIV (LRD) is introduced which prevents loops and count to infinity problem using intermediate variables. In addition it finds the shortest path between source and destination. The analysis shows that DIV is compatible with all the routing protocol as it is independent of the underlying environment. The proposed algorithm LRD is compared with the existing algorithm of DIV to prove its applicability in the any routing environment. The simulation results show that LRD excels AODV routing protocol while considering throughput and packet delivery ratio. The new algorithm assures that the routing protocol is shortest loop-free path and outperforms all other loop-free routing algorithms previously proposed from the stand point of complexities and computations. Springer Nature Switzerland AG 2020. -
LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools. 2024 The Authors -
LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm
One of the most prevalent and severe tumors in women, breast cancer, remains a major global health issue despite a notable increase in incidence over the last ten years. It is the second leading cause of cancer-related death among women. Identifying breast cancer in its early stages has the potential to save lives; however, current screening techniques for the illness require several laboratory procedures involving medical experts. Automated solutions with rapid and reliable diagnostic capabilities are needed to minimize human error and expedite breast cancer diagnosis. The projected accuracy of cancer diagnosis remains far from matching the precision offered by existing approaches, even with the research on automated systems for the disease being studied. This work suggests a long short-term memory-based modified Gorilla troop optimization (LSTM-MGTO) method for breast cancer classification in order to address these issues. The Mastectomy Koibra Dataset (BCCD) and Wisconsin Diagnostic Mastectomy (WDBC) datasets were used to test the suggested methods. First, the proposed system employs contrast-limited adaptive histogram equalization (CLAHE) to enhance the quality of digital mammograms. Furthermore, employ a semantic deep learning (SDL) model to extract features. After the feature selection process, a recursive feature elimination technique was implemented to determine the crucial WDBC and BCCD characteristics that are relevant to breast cancer detection. Moreover, recommend a modified U-Net architecture for partitioning in both unmapped and guided contexts. The experimental findings indicate that the newly developed partitioning model surpasses existing advanced techniques, yielding superior results in both Dice and IoU score evaluations. On the WDBC and BCCD datasets, the suggested U-Net segmentation produces maximum Dice scores of 97.65% and 96.24%, respectively. Additionally, the model obtained the greatest IoU scores of 95.43% and 90.65% on the WDBC and BCCD datasets, respectively, according to the experimental findings. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025. -
LULC Analysis of Green Cover Loss in Bangalore
Urbanization of the cities especially the Indian City of Bangalore has led to the creation of an important discourse concerning development and conservation. The study carries out a detailed LULC study with special reference to Green Cover Loss in city of Bangalore. Using satellite images from 2014 to 2023 period and machine learning tools, the study establishes declines in green spaces with economic, environmental and health consequences of the city's uncontrolled expansion. The innovations afforded to the study regard methodologically on the use of ResNet50 for accurate LULC classification with an accuracy of 92% Hence the study reveals the interaction between urbanization and conservation, the efficiency of which requires policy adjustments that depend on existing knowledge. The study not only accustomizes the progression in the geography of Bangalore but it also shapes the technology and methodology for the further geospatial research in the areas under rapidly urbanizing in the future. 2024 IEEE. -
Luminescence and energy storage characteristics of coke-based graphite oxide
The substantial escalation in both energy consumption and ecological crisis prompts the utilization of conventional pollution-causing energy resources towards a proficient mode of energy production and storage. The most polluting fossil fuel like coal possesses a highly ordered sp2 carbon clusters, that can be easily tailored into graphene derivatives promising for energy-related applications. However, the impact of crystallinity and quality of the precursor coke on the physicochemical characteristics of extracted carbon nanostructures need to be identified. Herein, we have prepared graphite oxide structures (GrO) from high-quality coal, coke via Improved Hummers' method eliminating the need for toxic NaNO3. The inherent defect states own by coke are also of high significance as it performs the role of various photoluminescence emission centers. The sp2 domains and different surface defects promote excitation independent and dependent luminescence substantiating the distinct multi-emission property of GrO. The extent of functionalization during the oxidation process has also significantly affected the thermal stability of the carbogenic structure. The symmetric galvanostatic charge-discharge curves and lower internal resistance present superior stability and fatser ion transport of as-synthesized GrO. A specific capacitance of 193F/g was obtained at 0.2A/g with excellent capacitance retention over 2500 cycles. The versatile attributes of the coke derived GrO validate its realizable optoelectronics and energy storage applications. 2020 Elsevier B.V. -
Lung Cancer Classification from CT-Scan Images Using an Enhanced VGG16 Model
Lung cancer has been one of the most common and deadly types of cancer around the globe, for which early detection is quite crucial for patient survival. In this research work, a deep learning-based method for four-class classification of chest CT-scan images, such as Squamous Cell Carcinoma, Large Cell Carcinoma, Adenocarcinoma, and Normal, is presented. With a modified VGG16 architecture, adding Squeeze-and-Excitation (SE) blocks and residual connections, the enhanced SERES_VGG16 model enhances feature representation and classification accuracy. The dataset we used here contains preprocessed chest CT-scan images divided into a training set, validation set, and test set. It is trained with augmentation techniques in the data to improve generalization. Its performance is evaluated using measures of standard performances, such as F1-score, recall, precision, accuracy and confusion matrices. The model achieved over 95% accuracy, class-wise precision ranging from 94 to 99%, recall ranging from 88 to 99%, F1-score from 93 to 96%. The presented approach reached over 95% accuracy on the test set and can be a trusted second opinion for radiologists to assist with early and accurate lung cancer subtype classification. However, this study is constrained by the small size of the dataset and the lack of other clinical parameters like genetic information. Future studies will concentrate on expanding the dataset and integrating multi-modal clinical information for enhanced robustness. This work in this study justifies the importance of deep learning in the classification of the medical images and points out further ways toward improving automated diagnostic systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm
Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably. 2023 IEEE. -
Lung cancer detection and classification with optimal feature selection and two-fold-deep-learning-classifiers
The respiratory system is undoubtedly hampered by lung disorders. Also, one of the important reasons for death among people all around the world has been lung cancer. Early discovery can advance human survival probabilities. As a result, a unique ensemble-deep-learning paradigm for lung cancer detection and classification is established in the present research effort. The projected model includes five major phases: (a) image augmentation, (b) pre-processing, (c) segmentation, (d) feature extraction, (e) feature selection, and (f) lung cancer detection and classification, respectively. The collected raw CT images are augmentation with SMOTE. The augmented images are pre-processed via Median Filtering (for noise removal) and Contrast-limited adaptive histogram equalization (CLAHE) (for image contrast enhancement). Subsequently, from the pre-processed data, the ROI is identified via optimized U-NETS. The activation function (hyper-parameter) of U-NETS is optimized via a new hybrid optimization model-Digging Tunaswarm Optimizer (DTO). This DTO is the conceptual amalgamation of two standard meta-heuristic optimization models, namely Honey Badger Algorithm (HBA) and Tuna Swarm Optimization (TSO) models, respectively. Then, from the selected ROI area, the features like texture features (Manhattan Distance-based-GLCM, GLRM), Color features (Color Histogram), and Shape features (Moments, Area, Perimeter) are extracted. Among the extracted features, the optimal features are selected using DTO. This optimal feature selection reduces the computational complexity of the projected model. Finally, using these extracted optimal features, the two-fold-deep-learning-classifier framework is trained. This two-fold-deep-learning-classifiers framework encapsulates the Bidirectional long-short term memory (Bidirectional LSTM) and the Recurrent Neural Network (RNN) and the Modified Convolutional Neural Network (M-CNN). In the first phase, the Bi-LSTM and RNN are clamped, and they are trained with the selected optimal factors. The outcome from Bi-LSTM and also RNN was fed as input to M-CNN. Final detected findings based on the existence or absence of lung cancer are acquired from the M-CNN, whose loss function has been modified with RMSE. Finally, a comparative evaluation is undergone to validate the efficiency of the projected model. The proposed model has a higher overall accuracy (92.4%) detecting modelling accuracy (96.3%) and classification accuracy (92.4%) compared to other models such as HBA, TSO, CNN, 3D CenterNet, and TSCNN. The use of a two-fold deep learning framework is responsible for these improvements, and the model also has lower failure rates (FPR and FNR) in detecting lung cancer. It is suggested that the proposed approach is effective in early-stage lung cancer detection. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.