Unmanned Aerial Vehicles (UAVs) have become central to precision agriculture, where efficient trajectory planning over large fields directly determines mission feasibility, energy use, and the quality of acquired data. We address the problem of generating an optimal, fully covering trajectory for a single UAV operating over a plantation field modeled as a graph of waypoints. The problem is a constrained variant of the Travelling Salesman Problem in which all waypoints must be visited, total path length and turning cost must be minimized, and an energy budget must be respected. Existing metaheuristics such as Particle Swarm Optimization, Grey Wolf Optimizer, and Harris’s Hawks Optimization struggle to balance exploration with the strict coverage constraint of plantation scenarios, and tend to converge prematurely on irregular layouts. We propose the Hawk-Inspired Trajectory Optimizer (HITO), a new population-based metaheuristic whose search behavior is inspired by the soaring, patrolling, and stooping phases of hawks. HITO introduces a thermal-soaring exploration operator, a territorial-patrol transition operator, and a stoop-dive exploitation operator, supported by a coverage-repair mechanism that guarantees feasibility at every iteration. We provide a Markov-chain argument for convergence and an O(N·n²) per-iteration complexity bound, where N is population size and n are the number of waypoints. HITO is benchmarked on simulated plantation fields of 1, 3, and 5 hectares against seven baselines, and on twelve CEC 2017 benchmark functions. Across thirty independent runs, HITO reduces path length by 12.4 % on average over the best baseline, lowers energy consumption by 14.1 %, improves coverage robustness, and converges in fewer generations. Statistical significance is confirmed using Wilcoxon signed-rank and Friedman tests.
The drug delivery acceleration is done by employing the deep learning (DL) process through huge data recognition for probable drug target discovery, physicochemical properties prediction, drug design optimization, and probable toxicity estimation directed to quicker and more capable expansion procedures than conventional schemes. According to the in-vitro data, systems have been designed for drug release patterns inside the human body. Multifarious data associated with the tablets, excipients, and mechanized parameters, frequently previous to general execution in-vitro experiments, fundamentally with the help of an artificial intelligence (AI) scheme to form and forecast how a medicine is liberated from its mover over time. In this paper, for the drug release uniqueness evaluation, dissolution, hardness, and disintegration parameters are discovered efficiently by applying an efficient DL system. The non-negative Matrix Factorization (NMF)-based RNN-LSTM (Recurrent Neural Network-Long Short-Term Memory) method is presented in this work for the drug’s release profiles efficiently through multifarious chronological dynamics recognition of how a tablet could be released from its deliverance scheme, frequently characterized as a data point’s series. The performance of the proposed DL system was evaluated using Accuracy, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) to assess the prediction of drug hardness, disintegration time, and dissolution profiles. Experimental results of the presented scheme have demonstrated that the probability of a DL-based AI method has solved nonlinear time-series discovery issues in drug product growth.
Retinopathy caused by diabetes is still a primary factor contributing to vision impairment in the world today, and prompt treatment depends on early detection and precise diagnosis. Despite their effectiveness, traditional machine learning and deep learning-based methods frequently have problems such poor generalization across a variety of patient data, restricted interpretability, and static decision-making. In order to improve autonomy in making decisions, dynamic flexibility, and background comprehension of retinal fundus pictures, this study presents an Agentic-AI-Powered Diabetic Retinopathy Analysis Framework that makes use of clever learning systems based on agents. Adaptive feature learning and real-time analysis using patient-specific changes are made possible by the new integration of DR detection systems that incorporate agentic intelligence principles, autonomy, reactivity, and proactivity. DR detection systems that incorporate, autonomy, reactivity, and proactivity. For reliable classification, the suggested AI system combines a coordinated multi-agent ensemble of transformer-based and convolutional networks by a layer for decision fusion. Categorization accuracy, Interpretability of the model and effectiveness of decision fusion layer is evaluated. classification precision (up to 95.8%), increased model effectiveness using lower computing high above. This study demonstrates the revolutionary potential applications of agentic AI in medical imaging, opening in the door to learn more independent moreover comprehensible clinical decision-making tools.
The Internet of Medical Things (IoMT) is one technology quite likely to change healthcare. Combining medical devices with the Internet of Things (IoT) permits them to be remote patient health monitors. Still, the precise expectation of patient health issues based on IoMT technology remains a difficult task. This present work intends to solve this challenge by means of an ensemble Deep Belief Network (DBN) framework, which incorporates Support Vector Machines (SVM), Feedforward Neural Networks (FFNN), Naive Bayes (NB), and the Deep Belief Network (DBN). This project intends to create a solid framework based on IoMT data that can reasonably forecast patient health issues. The ensemble DBN framework aims to maximize the advantages of many machine learning models thereby enhancing the prediction accuracy. This allows the utilization of the complementing characteristics of these models to increase the dependability and accuracy of health status prognosis. The ensemble DBN framework and single SVM, FFNN, and NB models were compared using large-scale simulations. The prediction powers of models are evaluated using criteria including but not restricted to accuracy and f-measure. The results reveal that the ensemble DBN performs better than the single models, thereby raising accuracy and an f-measure.
A solar power plant is a large-scale facility that converts sunlight into electricity using photovoltaic (PV) panels or to supply renewable energy to the grid or commercial users. The proposed solution offers a solar power plant’s intelligent energy management framework along with cutting-edge optimization methods and hybrid energy sources. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller, the architecture integrates a solar PV module, utility grid supply, and battery storage. By managing solar irradiance uncertainty and dynamic demand situations, the ANFIS controller effectively controls power flow between generating, storage, and load components. To guarantee steady functioning and longer battery life, a charger subsystem controls battery charging and discharging. Additionally, a Multi-Agent System (MAS) based on Group Teaching Optimization (GTO) is used to optimize energy distribution among various loads, including EVs, drones, and portable gadgets, enhancing overall system dependability and efficiency. In contemporary smart grid contexts, the combination of intelligent control and metaheuristic optimization improves energy efficiency, reduces reliance on grid power, and promotes sustainable and autonomous energy management.
Smartphones have developed into more than just communication tools in the current digital era, becoming essential to many facets of daily life. Because of its large user base and open-source nature, Android is a dominant mobile operating system. But because of its extensive use, it has become a prominent target for more complex malware attacks. This efficient deep learning-based framework for Android virus detection uses a proposed You Only Look Once based Neural Architecture Search (YOLO-NAS) model in conjunction with feature selection techniques. The system first receives a dataset of Android malware, after which data preparation is carried out to enhance data quality, remove noise, and standardize features. A Histogram of Oriented Gradients (HOG) model is used to identify the most pertinent and discriminative features from the processed dataset in order to decrease dimensionality and increase computing efficiency. The YOLO-NAS model, which classifies malware in a two-class environment as either benign or malicious, is then fed the enhanced feature set. The proposed YOLO-NAS concept aims to improve detection accuracy without compromising robustness or scalability. Performance is assessed using common measures such as F1-Score, Accuracy, Precision, and Recall. The suggested method is appropriate for real-time Android security applications since testing results show that YOLO-NAS and enhanced feature extraction greatly enhance malware detection capabilities.
Skin disease classification has become a critical task in modern medical diagnostics, where accurate detection plays a vital role in patient care. This study introduces a hybrid deep learning framework that combines Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) to enhance diagnostic precision. Current methods in skin disease classification often suffer from high computational costs and limited accuracy due to data heterogeneity and insufficient feature extraction. These challenges impede reliable diagnosis and delay timely treatment. To address these issues, the proposed Skin Disease Prediction using Deep Learning (SDP-DL) approach integrates deep feature extraction with robust classification techniques. The framework leverages CNNs to capture complex visual patterns in skin lesions, while SVMs refine decision boundaries to reduce false positives. The proposed method systematically processes pre-processed images through CNN layers, extracting hierarchical features before applying SVM for final classification. Experimental results demonstrate improved accuracy, reduced misclassification rates, and enhanced robustness compared to conventional models. The hybrid framework shows promise in supporting clinical decision-making and advancing automated skin disease diagnostics. Findings suggest that the SDP-DL approach significantly improves detection reliability, paving the way for integration into medical imaging systems for early and precise skin disease diagnosis.
Gastric Cancer (GC) remains a major global health concern, with early-stage detection being critical for improving survival rates. However, identifying GC in its early stages is challenging due to subtle and diverse clinical manifestations. This research aims to develop an optimized deep learning framework to enhance the accuracy, efficiency, and robustness of GC detection, particularly in early-stage cases. A novel hybrid optimization technique, the Aquila Inherited Dragonfly Optimizer (AI-DFO), is proposed to fine-tune a Convolutional Neural Network (CNN) for GC classification. The approach incorporates advanced image pre-processing using median filtering and CLAHE, precise lesion segmentation with Mask R-CNN, and AI-DFO-based optimization to enhance feature learning and model generalization. The proposed AI-DFO+CNN model achieved state-of-the-art performance, with 99% accuracy for endoscopic images and 98% accuracy for histopathological images. Stage-wise analysis confirmed superior sensitivity in detecting both early and advanced GC. Furthermore, the model demonstrated strong robustness to noise and image deformation. Computational efficiency was improved with reduced training time and lower energy consumption compared to baseline models. The results validate the effectiveness of the proposed framework for reliable, early GC detection. The generalizable design of AI-DFO and the segmentation-classification pipeline make this approach scalable to other medical imaging tasks. Future research will focus on large-scale multi-center validation, integration with real-time clinical workflows, and the application of the AI-DFO framework to other complex medical image analysis tasks beyond gastric cancer.
Face recognition (FR) technology is increasingly being used in clinical diagnostics and customized medicine, in addition to typical security applications. However, reliably identifying patients based on face traits in big and heterogeneous datasets remains a major difficulty. The study proposed a novel framework, the Multistage Regression Capsule Network with Modified Grey Wolf Optimization (MRCN-MGWO), to improve the accuracy and efficiency of patient identification in healthcare settings. The MRCN-MGWO model uses deep learning to increase diagnostic accuracy by assessing facial features and using specialized preparation techniques for medical photos. Facial images are first denoised using a median filter (MF) before being enhanced with contour-based image enhancement (EIC) to improve clarity. The Multistage Regression Capsule Network (MRCN) generates robust feature vectors to detect distinct facial patterns, whereas the Modified Grey Wolf Optimization (MGWO) approach optimizes the weights and biases of a stacked autoencoder (SAE). The MRCN-MGWO architecture is tested on the benchmark FEI dataset and shows promise for accurate patient identification in a variety of clinical settings by outperforming current face recognition techniques. As a result, the proposed MRCN-MGWO model improves precision and increases the rate of facial recognition while maintaining high accuracy.
The timely identification of coconut leaf diseases is essential to reduce the loss of yields and be able to intervene in precision agriculture. The present paper suggests an Earlier Disease Diagnosis in Coconut Leaves (DD-CL) model to add together transfer learning-based convolutional neural networks and symptom progression analysis to attain precise disease identification and severity prediction at the initial stages. The model uses a pre-trained VGG16 architecture with a SGD and Adam optimizer and is trained on a Kaggle coconut leaf dataset with five classes, inclusive of: healthy, flaccidity, yellowing, drying of leaflets and CCI-leaflets. To support robustness and minimize overfitting, the images are 300 × 300 pixel, normalized and augmented. In addition to classification of the disease, DD-CL does severity estimation by examining changes in the symptoms of flaccidity to uneven yellowing and tip browning. Experimental analysis shows that the highest classification accuracy is 97.3, and precision, recall, and F1-score are 97.3, 96.5 and 96.9, respectively, which is higher than ResNetV2 and MobileNet by 3–5%. The analysis of confusion matrices proves that there is minimum misclassification between the visually similar stages of the disease, especially during early disease transitions. The suggested DD-CL framework offers a scalable, interpretable, and reliable solution to automated surveillance of coconut disease and the severity at an early stage. N performs better than the single models, thereby raising accuracy and an f-measure.
Background: The present is an exploratory study, to evaluate the immune effect of a bacterial chemical lysate: Lantigen-B (Lan-B). PBMCs were stimulated with the concentration of the drug utilized in the clinical setting to evaluate its effects on lymphocyte subpopulation and cytokine release. Methods: PBMCs, from 7 healthy donors, were cultured alone or incubated with Lan-B at 199.45 Antigen Units/mL concentrations. Lymphocyte subpopulations were evaluated by Facs-Scan and cytokines by Cytometer. Results: A significant increase of IL-1b, IFN-g, TNF-a, IL-17, and IL-18 release and of CD19+ and CD16+/56+cells proportion was found in stimulated compared to unstimulated cultures along with a significant decrease of MCP1 and a trend for an increase of CD8+ cells and IL12-p40. Conclusions: The study shows that Lantigen-B drives a Th1-reaction and stimulates the differentiation/activation of B and NK cells. Changes in lymphocyte subpopulations and in cytokines are well correlated. The observed changes can result in a state of “prealert” of the immune system able to successfully fight infections also induced by bacteria and viruses different from those administered, the so-called “Trained Immunity”. Finally, the reduction of MCP1, which drives a Th2 reaction, can explain the beneficial effects on allergies observed in some clinical studies.
Lung cancer leads to mortality worldwide, and hence, accurate cancer grading is essential for prognosis and treatment planning. Existing tumor grading approaches are time-consuming and prone to inter-observer inconsistency. Also, existing Deep Learning (DL) approaches focus on binary classification and fail to identify the fine-grained tumor aggressiveness, which is crucial for accurate prognosis and treatment planning. This study develops a hybrid attention-based DL grading framework for accurate detection and grading of lung cancer into adenocarcinoma, squamous cell carcinoma, and normal tissues, which differentiates into grade 1, grade 2, and grade 3 tumor grades. The proposed methodology utilizes a Macenko color normalization method to preprocess the high-resolution histopathological images. The proposed work introduces a hybrid attention-based DL framework, ResCBAM-ViTFSG, that integrates ResNet18 with Convolutional Block Attention Module (CBAM) for extracting local spatial features and Vision Transformer with Feature Selection Gate (FSG) for extracting global features. To improve robustness, introduced a novel Uncertainty-Aware Reweighting Adam (UAR-Adam) optimizer that dynamically adjusts learning rates. The proposed work achieved fine-grained grading of lung cancer through per-class grading analysis, effective feature extraction counts, and optimizer benchmarking, achieving an accuracy of 98.34 %, precision of 98.50 %, recall of 98.18 %, F1-score of 98.34 %, and an inference time of 7.8 ms. These results outperform baseline models in lung cancer grading, suitable for deployment in pulmonary pathology clinical diagnostic systems.
This study presents an energy-efficient mixed-mode solar drying system integrating nano enhanced thermal storage and desiccant dehumidification to improve the continuous drying of agricultural products. A dual-axis photovoltaic (PV)-powered forced convection dryer was developed and coupled with paraffin wax phase change material (PCM) incorporated with 2 wt% Al₂O₃ nanoparticles and a solar-regenerated bentonite–CaCl₂ desiccant bed. Material characterization using SEM, DSC, TGA, and FTIR confirmed stable nanoparticle dispersion, enhanced thermal conductivity, and preservation of chemical integrity within the composite PCM. Drying experiments for turmeric (Curcuma longa) rhizomes were conducted under three modes: (i) forced convection, (ii) forced convection with nano-PCM storage, and (iii) nano-PCM combined with desiccant-assisted dehumidification. The hybrid system significantly reduced drying time, with unsliced turmeric drying in 48, 24, and 22 h, and sliced samples in 36, 18, and 14 h across the respective modes. Compared to traditional open sun drying (95 h), the system improved energy utilization, sustained drying during off-sunshine h, and delivered superior moisture management. The integration of nano-PCM and desiccant technology demonstrated substantial potential for energy recovery, extended operational duration, and improved environmental performance, marking a practical and sustainable solution for post-harvest agro-processing.
Precision agriculture endures significant hurdles in accurately identifying and classifying plant diseases. Since different plant diseases display subtle and varied symptoms, traditional procedures involving manual inspection are time-consuming and result in high false positives. To overcome these obstacles, a lightweight Shallow-CNN classification framework tailored specifically to potato leaf disease was proposed. The proposed framework is further combined with formidable ensemble learning methods, Random Forest and XGBoost, to ensure better classification of potato leaf disease and reduce overfitting. The proposed algorithm was trained and evaluated on the benchmark Plant Village dataset and real-world images taken from the crop fields. The experimental results show that the proposed framework achieves better accuracy, precision, F1-score and recall when compared with several state-of-the-art approaches. This shows that it is particularly effective and scalable for real-time agricultural disease surveillance systems.
Task scheduling in Mobile Edge Computing (MEC) is a challenging multi-objective optimization problem, where conflicting goals such as minimizing latency, reducing energy consumption, and controlling execution cost must be achieved under uncertain and unreliable resource conditions. This study proposes a Federated Learning (FL)-based trust evaluation framework combined with a novel hybrid metaheuristic, the Black Widow-Updated Jellyfish Search (BW-UJS) algorithm. By integrating the exploration-exploitation mechanisms of Black Widow Optimization with the adaptive search behavior of Updated Jellyfish Search, the proposed method enhances decision-making in complex scheduling environments. The problem is formulated as a multi-objective optimization model that incorporates trust constraints to ensure reliable resource provider selection. Computational experiments conducted on synthetic MEC offloading scenarios demonstrate that BW-UJS consistently outperforms benchmark algorithms (Original, Offload, MUCAO, FLO), achieving up to 2.1 % improvement in energy efficiency, 3 % reduction in execution cost, and 0.01 % decrease in delay. The findings highlight the effectiveness of BW-UJS as a robust optimization approach for task scheduling in distributed systems. Future work will focus on extending the method with real-time adaptive learning mechanisms to address dynamic and large-scale network conditions.
Background: Multiple sclerosis (MS) is an immune-mediated inflammatory disease of the central nervous system characterized by demyelination and axonal damage. This chronic neurological disorder affects millions worldwide and poses a significant economic and social burden. Current therapeutic strategies for MS primarily focus on mitigating neuroinflammation but do not effectively reverse demyelination. The GABAB receptor is expressed in oligodendrocytes and immune cells. GABAB receptors appear to play a role in modulating immune cells and promoting remyelination of damaged neurons. However, the effects of GABAB-receptor modulation in animal models of MS remain largely unexplored. Therefore, this study evaluates the therapeutic role of GABAB receptor activity in rats with experimental autoimmune encephalomyelitis (EAE) to mimic MS in humans. Methods: EAE was induced in rats by immunization with 200 μg myelin oligodendrocyte glycoprotein (35-55) peptide in complete Freund’s adjuvant containing killed Mycobacterium tuberculosis. Additionally, rats received injections of 200 ng pertussis toxin on the day of immunization and 48 h later. The EAE rats were treated with normal saline, CGP-55845, baclofen, or CGP-55845 + baclofen, i.p. Myelination in the lumbar spinal cord was assessed in control rats at 18- and 35-day postinduction. Results: CGP treatment significantly enhanced remyelination in EAE rats. This was evidenced by significant improvements in body weight and EAE clinical scores, as well as favorable histological changes. The observed increase in myelin expression in CGP-treated animals suggests that CGP-55845 promotes both remyelination and oligodendrocyte differentiation. Conclusions: Our results suggested that the selective GABAB receptor antagonist CGP-55845 plays a significant role in promoting neural stem cell proliferation, particularly oligodendrocyte cells and remyelination in the EAE model of MS
Cerebral Microbleeds (CMBs) are among the significant contributors to mortality worldwide and require accurate diagnosis for effective medical intervention. Owing to their wide variability in size, shape, and intensity, manual identification and classification of CMBs in brain imaging remain a complex and error-prone task. This study proposes an automated classification framework for brain MRI-filtered images, categorizing them as either normal or abnormal. The suggested methodology combines a tailored Convolutional Neural Network founded on the ResNet50 architecture, employing a blend of image processing and deep learning strategies. First, a number of preprocessing processes were implemented to increase the MRI pictures quality. One of these steps was the fusion of multi-focus images, which helped to make details more visible. These enhanced images were then processed through a 13-layer CNN architecture specifically designed for effective CMB classification. The strength of the proposed CNN-ResNet50 model was confirmed through validation with two independent datasets. Experiment one used a 10-fold cross-validation procedure, while experiment two split the dataset in half, with 80% used for training and 20% for testing. The model achieved a train-test split accuracy of 98.77% and a cross-validation accuracy of 98.33% while classifying Dataset 1. An accuracy of 92.22% and an accuracy of 93.33% were attained by the model in the two experimental setups for Dataset 2. All investigations used real-world MRI scans. This data set originated from Neyyoor, India's CSI Medical Mission Hospital's International Cancer Center (ICC). The efficacy of the suggested CNN-ResNet50 model was evaluated in comparison to established deep learning models, such as AlexNet and the original ResNet50. Experimental data indicate that our proposed method surpasses both comparative models regarding classification accuracy.
Deficiencies in immune protection (both congenital and acquired through life) significantly influence a human’s life quality. Primary minor immunodeficiencies (PMDs) are more common in the population than classical immunodeficiencies and place a high burden on society. However, the evidence on PMDs is not systematized. The aim of the current research became the analysis and synthesis of the evidence on etiology, epidemiology, diversity, clinical manifestations, diagnosis, and treatment of PMD in humans to synthesize a scientific concept. In this way, the systematic review of publications from PubMed and SCOPUS databases has been conducted by the keywords. The time of analysis was the period from 1960 to 2025. Out of 2937 primary publications, 424 that met the selection criteria were included in the final list. As a result, terminology, genetic heterogeneity, epidemiology, spectrum of manifestation, structuring of clinical syndromes, and classification of PMD were clarified due to the current research. A distinction is made between PMD and classical immunodeficiencies. The algorithms of diagnostics and immunotherapeutic interventions were considered. The scientific concept of PMD diagnosis and treatment was proposed, which presents PMDs as a universal natural model of the development of different human immune-dependent pathologies on a population scale. PMDs, by their prevalence in the population, diversity, and degree of clinical manifestation, can explain the development of the entire described spectrum of immunodependent diseases in humans. The proposed PMD concept can allow optimizing the clinical management of patients with associated immunodependent pathology using an integrative personalized multidisciplinary approach with the availability of etiology estimation and etiotropic treatment providing.
Elderly individuals with dementia experience significant cognitive and emotional impairments, motivating research into technology-driven therapies to improve their quality of life. However, the effectiveness of such systems is often limited by poor-quality electroencephalographic (EEG) data, which can be distorted or incomplete due to artefacts. This study introduces a novel brain-computer interface (BCI)-based rehabilitation framework that combines neural network-assisted EEG data restoration with personalised therapy modules. The proposed method employs a multilayer perception (MLP) enhanced with a custom activation function to reconstruct missing EEG values by modelling spatial and temporal dependencies among adjacent electrodes. Experimental evaluation on benchmark EEG datasets shows that the proposed approach reduces Mean Absolute Error (MAE) by 15% and increases the Correlation Coefficient (CC) by 10% compared to traditional imputation techniques such as mean substitution and k-nearest neighbours (KNN). The restored EEG data are further integrated into a generative AI-powered rehabilitation system that delivers adaptive treatments through virtual reality (VR) environments and social interaction activities. By incorporating patient-specific affective profiles and preferences, the system dynamically personalises interventions such as cognitive games, reminiscence sessions, and immersive simulations. Overall, this framework bridges computational neuroscience and patient-centred healthcare, highlighting EEG imputation as a core technology for next-generation intelligent dementia care solutions, particularly in rural and resource-limited settings.
Due to rapid enhancement of digital communication in cloud paradigm, easier transmission & storage of the multimedia information in several platforms becomes challenging. The security of image information is vital since the images are considered as a major component of communication in cloud environment. The secret information is shared in the form of secured image which needs to be retrieved and send to user without losing the integrity and confidentiality of data. For this purpose, the proposed model is designed which employs feature extraction categorization process and transmitting extracted information securely via cryptographic process. Initially the input images are retrieved and parameter initialization is carried by bilinear matrix. An optimal feature extraction is carried using Rotational invariant Local Binary Pattern (RI-LBP) along with Enriched Shark smell optimization process for extracting features of secret information. E-IBE (Enhanced-Identity based encryption) is employed for private key generation followed by cryptographic process via Ensemble Improved Homomorphic Pailler and Quantized ElGammal Elliptic curve Cryptography (ECC) scheme. The decrypted outcome attained is then digitally verified by employing SHA3 verification model. Thus, retrieved data is provided to the user after validation in a secured manner. The simulation results are then observed by analyzing the proposed scheme performance on CIFAR-10 dataset &MNIST dataset attained outcomes are compared with traditional schemes to validate the enhancement of proposed model over other models. the performance is carried for various metrics like extraction accuracy, recall, precision F1-score, precision-recall curve, RoC curve, execution time, runtime & storage space of entire system.
