In the present work, we report on developing an electrochemical dopamine sensor using a novel material of nitrogen-rich sulfur dual-doped reduced graphene oxide (N-rich SRGO). Nitrogen and sulfur heteroatoms were incorporated into graphene sheets through a one-step, cost-effective hydrothermal approach to synthesize N-rich SRGO. Experimental investigations were carried out to compare the electrochemical properties of N-rich SRGO with nitrogen sulfur-doped reduced graphene oxide (NSRGO), nitrogen-doped reduced graphene oxide sheets (NRGO), and reduced graphene oxide (RGO) by modifying the glassy carbon electrode. Electrochemical studies demonstrated that N-rich SRGO exhibited a notably higher oxidation current (345 mu A) compared to NSRGO (219 mu A), NRGO (173 mu A), and RGO (160 mu A). We developed a dopamine sensor by utilizing the superior chemical reactivity and enhanced charge carrier density of the proposed N-rich SRGO-modified electrode. Experimental results reveal a high sensitivity of 142 mu A/mM, with a limit of detection of 9.3 mu M and a wide dynamic range of 150- 350 mu M. This N-rich SRGO-based sensor displayed excellent repeatability and selectivity, even in the presence of other electroactive interferents, showcasing its potential for practical applications.

New potential for healthcare has been made possible by the development of the Internet of Medical Things (IoMT) with deep learning. This is applied for a broad range of applications. Normal medical devices together with sensors can gather important data when connected to the Internet, and deep learning uses this data to reveal symptoms and patterns and activate remote care. In recent years, the COVID-19 pandemic caused more mortality. Millions of people have been affected by this virus, and the number of infections is continually rising daily. To detect COVID-19, researchers attempt to utilize medical imaging and deep learning-based methods. Several methodologies were suggested utilizing chest X-ray (CXR) images for COVID-19 diagnosis. But these methodologies do not provide satisfactory accuracy. To overcome these drawbacks, a recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm (RERNN-GEO) is proposed in this paper. The intention of this work is to provide IoT-based deep learning method for the premature identification of COVID-19. This paradigm can be able to ease the workload of radiologists and medical specialists and also help with pandemic control. RERNN-GEO is a deep learning-based method; this is utilized in chest X-ray (CXR) images for COVID-19 diagnosis. Here, the Gray-Level Co-Occurrence Matrix (GLCM) window adaptive algorithm is used for extracting features to enable accurate diagnosis. By utilizing this algorithm, the proposed method attains better accuracy (33.84%, 28.93%, and 33.03%) and lower execution time (11.06%, 33.26%, and 23.33%) compared with the existing methods. This method can be capable of helping the clinician/radiologist to validate the initial assessment related to COVID-19.

Carbon-carbon composite manufactured by deposition of pyrocarbon (PyC) through chemical vapor infiltration (CVI) has the key issue of being process parametric sensitive which necessitates the detailed study of the effect of process parameters on the rate of PyC deposition. Conventional method of studying the parametric effect by changing one variable at a time keeping the other variables constant has a limitation of more number of experiments and missing the interaction effect among the variables. Here, the effect of process parameters including temperature, pressure, methane gas flow rate, and nitrogen gas flow rate on the mass gain and PyC deposition was studied by Taguchi method, a statistical optimization method, which has the advantage of very few experiments performed at specific pairs of process parameters only. The experiments were performed at three levels of the process parameters. Carbon-Carbon composite material is processed through the CVI process where PyC was deposited on porous carbon fiber preforms at various process conditions as per the Taguchi method. The impact of gas residence time, Reynolds number, Prandtl number, and Peclet number were also investigated. It was observed that the CVI process parameters significantly affect the rate of PyC deposition. Optimized CVI process parameters are essential for achieving a high rate of PyC deposition to reduce the processing time. The findings have revealed that a higher PyC deposition rate arises under high temperatures, pressure, methane gas flow rate, and optimal nitrogen gas flow rate. The effect of the critical interaction of the CVI process parameters on the rate of PyC deposition was also obtained. Based on the experimental studies, process guidelines are proposed for the densification of carbon fibers preform to realize C/C composite products.

Abstract Alzheimer’s disease (AD) is a generic form of dementia causing memory loss and environmental responses. AD detection is pursued using the different protein structures and their intensity based on different physical behaviors. Using the time-series protein structures the detection and is eased through the proposed neural method for structural protein filling (NC-SSF). Structural differentiations are performed using the high and low intensity profiles observed. This analysis identifies the missing inputs and thereby the fillable sequences are identified. The protein biomarker determines the maximum filling requirement as per the changes observed. The neural network is trained using this sequence required under the low and high intensity variations. This process is recurrent until maximum false rate is confined through accuracy improvements. The AD progression detection is performed by estimating the intensity under different profile filling levels. The proposed method improves accuracy, sensitivity, and specificity by 8.74%, 10.29%, and 8.84% respectively. This method reduced the false rate and MMSE by 9.85% and 10.78% respectively.

The purpose of this paper is to offer a unique adaptive path planning framework to address a new challenge known as the Unknown environment Persistent Monitoring Problem (PMP). To identify the unknown events’ occurrence location and likelihood, an unmanned ground vehicle (UGV) equipped with a Light Detection and Ranging (LIDAR) and camera is used to record such events in agriculture land. A certain level of detecting capability must be the distinct monitoring priority in order to keep track of them to a certain distance. First, to formulate a model, we developed an event-oriented modelling strategy for unknown environment perception and the effect is enumerated by uncertainty, which takes into account the sensor’s detection capabilities, the detection interval, and monitoring weight. A mobile robot scheme utilizing LIDAR on integrative approach was created and experiments were carried out to solve the high equipment budget of Simultaneous Localization and Mapping (SLAM) for robotic systems. To map an unfamiliar location using the robotic operating system (ROS), the 3D visualization tool for Robot Operating System (RVIZ) was utilized, and GMapping software package was used for SLAM usage. The experimental results suggest that the mobile robot design pattern is viable to produce a high-precision map while lowering the cost of the mobile robot SLAM hardware. From a decision-making standpoint, we built a hybrid algorithm HSAStar (Hybrid SLAM & A Star) algorithm for path planning based on the event oriented modelling, allowing a UGV to continually monitor the perspectives of a path. The simulation results and analyses show that the proposed strategy is feasible and superior. The performance of the proposed hyb SLAM-A Star-APP method provides 34.95%, 27.38%, 33.21% and 29.68% lower execution time, 26.36%, 29.64% and 29.67% lower map duration compared with the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP.

Maintaining breathable air in human space flight missions is a challenge since there will be a gradual increase in the concentration of CO 2 due to the exhalation of the spacecraft crew. Modern air revitalization technology for space missions uses temperature swing adsorption with zeolite-based materials as the adsorbent. However, the major drawbacks of zeolites are difficulty in regenerating used adsorbent and low selectivity towards CO 2 . Besides, frequent replacement of adsorption beds is necessary because of their low adsorption capacity towards CO 2 . This requires carrying a few spare beds in the spacecraft, which is not preferred because of the low-weight requirements for space missions. Hence, shifting to alternate materials with more selectivity and adsorption capacity toward CO 2 becomes essential. Metal-organic frameworks (MOFs) are a class of crystalline materials found to be potential adsorbents for CO 2 capture. Due to their ultra-high porosity, tunable pore characteristics, selective capture, and synergistic performance, they could replace the existing zeolite-based adsorbents. Further enhancement in the adsorption capacity could be achieved by introducing a secondary metal into the framework. This will bring heterogeneity in the structure, creation of defects, and open metal sites, thus enhancing the adsorption behavior. Incorporating a secondary metal into the framework and an additional filler material like activated carbon, making a BMOF composite, would lead to an exemplary improvement in the adsorption activity. The addition of filler material will lead to an increase in pore volume, which will enhance the adsorption capacity. In the current work, activated carbon was synthesized from sawdust via chemical activation method using KOH as the activating agent. It was carried out at low temperatures to boost the porous structure formation. Then the composite of Cu-Ni bimetallic MOF with activated carbon was prepared through one-pot solvothermal synthesis. The incorporation of activated carbon created an additional number of pores in the system leading to enhanced adsorption. N 2 adsorption-desorption analysis was carried out to find the surface area. The results showed that the surface area of activated carbon and Cu-Ni-Activated carbon BMOF composite was found to be 576 m 2 /g and 1321 m 2 /g, respectively. The analysis also revealed the presence of microporous system in the material. Several characterization techniques, such as IR, SEM, XRD etc., were used to study morphology and characteristic features. The SEM shows that the incorporation of nickel and activated carbon have made a profound modification on the octahedral morphology of copper. CO 2 adsorption studies were carried out at 1 bar, 25°C. The BMOF composite adsorbent was found to adsorb ~4.52 mmol/g of CO 2 whereas the as-synthesized activated carbon and ZSM-5 were found to adsorb ~2.71 mmol/g and ~1.68 mmol/g respectively. To understand the level of microporosity in the supermicropores range, CO 2 adsorption analysis at 1 bar and 0°C was carried out since N 2 adsorption at -196°C have limitations due to kinetic diffusion related problems at low temperatures. It revealed a micropore volume (<1.066nm) of 0.252 cm 3 /g and a pore size range of 4-10 angstroms, which is ideal for selective CO 2 adsorption. Also, the BMOF composite was found to adsorb ~8.27 mmol/g at 0°C, which showed that adsorption capacity increases with a decrease in temperature. Hence, the BMOF composites could serve as a promising adsorbent for selective CO 2 capture. Figure 1

Metal-organic frameworks (MOFs) are an attractive class of highly ordered, crystalline, porous materials that exhibit large specific surface area, porosity, tunable structure and ease of functionalization. The presence of a number of uniformly dispersed metal components i.e. catalytic molecular units throughout the framework make MOF based materials a potential candidate for electrochemical sensing studies. However, pristine MOFs have the inherent drawbacks such as the inferior electrical conductivity that need to be addressed for its direct application in sensing platforms. In this context, the design of redox-active and highly conducting MOF is a research hotspot in the material science since it can overcome the inferior electrocatalytic activity of chemically modified electrodes (CME) based on pristine MOF. The integration of highly conducting metals into the framework is an efficient method to enhance the electric conductivity and thereby the electrochemical activity of synthesized material. The synergistic effect arising from the combination of different metal ions changes the surface electronic structure of MOF and thereby improve the mobility of charge carriers. Herein, an electrochemical sensing platform based on ruthenium doped Cu-MOF was developed for the sensitive detection of ciprofloxacin antibiotic. This work focuses on the synthetic strategy of Ru-Cu-TMA where the parent MOF, Cu-TMA, was synthesised by a facile room temperature mixing at ambient pressure. The present synthetic method is found to be a simple and efficient method compared to the conventional solvothermal method reported commonly for MOF synthesis. The successful integration of ruthenium into Cu-MOF created a number of electrocatalytic active sites which can favour the interaction with analyte species in sensing studies. The structural features and morphology of the synthesized MOF materials were studied using different characterization techniques like XRD, IR, SEM, XPS etc. The porous structure of MOF combined with higher reaction kinetics due to the incorporation of ruthenium cause a synergistic effect which makes the mixed-valent MOF a promising candidate for sensing studies. The composite, Ru-Cu-TMA, prepared was used as an electrode modifier for the sensitive detection of ciprofloxacin by electrochemical technique. Initially, the electrochemical activity of the chemically modified electrodes were examined and Ru-Cu-TMA modified electrode shows the higher current and fast electron transfer which paves the way for its application as sensing material. In addition, more number of active sites formed in MOF by ruthenium doping considerably increases the sensing performance of Ru-Cu-TMA. The electrochemical oxidation of ciprofloxacin on electrode surface is confirmed as an irreversible, diffusion-controlled process. The sensor exhibited a wide linear dynamic range (2.5 – 100 µM) with a lower limit of detection (3.29 nM) and sensitivity 0.0524 µA/µM. Moreover, the sensor demonstrates excellent selectivity, adequate stability, repeatability and can be used for real sample analysis. The porous structure of MOF combined with the enhanced reaction kinetics due to ruthenium doping together increased the electrochemical activity of the developed sensor. The enhanced electrochemical surface area and the increased number of active sites generated with the doping of ruthenium facilitates electron transfer with the electrode surface. Further, the aromatic ring system of CIP can interact with the conjugated ᴨ-electrons in MOF through stacking which further makes the Ru-Cu-TMA modified electrode a promising sensing platform. Figure 1

Herein, an electrochemical sensing platform based on ruthenium doped copper- metal organic framework (CuMOF) was constructed for the sensitive detection of ciprofloxacin antibiotic. A facile room temperature method was used for the synthesis of parent Cu-MOF. The structural features and morphology of the synthesized MOF materials were studied using different characterization techniques like XRD, IR, SEM, XPS etc. The porous structure of MOF combined with higher reaction kinetics due to the incorporation of ruthenium cause a synergistic effect which makes the mixed-valent MOF a promising candidate for sensing studies. The composite, RuCu-TMA (ruthenium doped copper-trimesic acid), prepared was used as an electrode modifier for the sensitive detection of ciprofloxacin by electrochemical technique. Initially, the electrochemical activity of the chemically modified electrodes were examined and Ru-Cu-TMA modified electrode shows the higher current and fast electron transfer which paves the way for its application as sensing material. In addition, more number of active sites formed in MOF by ruthenium doping considerably increases the sensing performance of Ru-Cu-TMA. The electrochemical oxidation of ciprofloxacin on electrode surface is confirmed as an irreversible, diffusioncontrolled process. The sensor exhibited a wide linear dynamic range (2.5 - 100 mu M) with a lower limit of detection (3.29 nM) and sensitivity 0.0524 mu A/mu M. Moreover, the sensor demonstrates excellent selectivity, adequate stability, repeatability and can be used for real sample analysis.

Today, there is a great need for 3D instance segmentation, which has several uses in robotics and augmented reality. Unlike projective observations like 2D photographs, 3D models offer a metric reconstruction of the sceneries without occlusion or scale ambiguity of the environment. In agriculture, understanding Plant growth phenotyping enhances comprehension of complex genetic features and accelerates the advancement of contemporary breeding and smart farming. A reduction in crop production quality is caused by leaf diseases in agriculture. In order to increase productivity in the agricultural industry, it is therefore possible to automate the recognition of leaf diseases. Diverse leaf disease patterns affect the detection’s accuracy in the majority of systems. During phenotyping, 3D PCs (PC) of components of plants like the stems and leaves are segmented in order to follow autonomous growth and estimate the level of stress the crop has experienced. This research proposed a Point Sampling Method with occupancy grid representation for segmenting PCs of different plant species, which was developed. To handle unordered input sets, this approach mainly relies on the application of the single symmetric function max pooling. In reality, a set of optimization functions are used by the network to choose points which is more curious or instructive from the PC and encapsulate the selection reason, and the fully connected layers, used for shape classification or shape segmentation, integrate these learned ideal significances hooked on a global descriptor regarding the overall shape. After being trained on the Point Sampling Network-created plant dataset, the network can simultaneously realize semantic and leaf instance segmentation.

Fluorescent-based sensors attract enormous attention due to their facile nature, simplicity, superior selectivity and sensitivity, and better reproducibility and reliability. The sensing parameters, such as detection limit, dynamic range, and the possibility for multiplexed detection, rely hugely on the physicochemical properties of the fluorophore used. Therefore, the choice and rational design of fluorescent probes demand considerable attention. The growing interest in highly fluorescent semiconductor quantum dots as sensors or imaging probes is evident from the numerous reports available in the literature. Unlike conventional organic dyes, novel fluorescent quantum dots-inorganic nanocrystals have manifold advantages. QDs have symmetric and narrow emission profiles, high absorption efficiency in the UV region, high quantum yields, especially in the NIR region, exorbitant photo and temporal stability, and shallow photo-bleaching effect. The optical properties of QDs are primarily defined by the nature of constituent materials, size and shape, surface chemistry, and the nature of dangling bonds. The applicability of CdTe QDs was limited in the biological realm mainly due to the highly toxic nature of its constituent materials. The use of benign ligands is one procedure to be adopted to overcome this impediment. Also, the stability of the QDs warrants the limited bleaching of the material. The aqueous solubility of the material is another requirement to be satisfied. The functional groups in ligands also play a significant role in determining the stability of QDs and the utility of the QDs synthesized, as it can advocate the possibility of reactions and recognition of suitable analytes in sensing schemes. This talk will discuss the choice of organic ligands with side methyl chains that can supplement or be used in alternatives to the existing ligands for synthesizing QDs. We have employed easy and facile colloidal synthetic procedures for an extended period for manipulating the size of QDs formed, which can be further implemented for the sensing of various analytes. The side methyl chain added several benefits for the QDs synthesis, such as providing better stability of the QDs and in the sensing scenario.

Autonomous vehicles require in-depth knowledge of their surroundings, making path segmentation and object detection crucial for determining the feasible region for path planning. Uniform characteristics of a road portion can be denoted by segmentations. Currently, road segmentation techniques mostly depend on the quality of camera images under different lighting conditions. However, Light Detection and Ranging (LiDAR) sensors can provide extremely precise 3D geometry information about the surroundings, leading to increased accuracy with increased memory consumption and computational overhead. This paper introduces a novel methodology which combines LiDAR and camera data for road detection, bridging the gap between 3D LiDAR Point Clouds (PCs). The assignment of semantic labels to 3D points is essential in various fields, including remote sensing, autonomous vehicles, and computer vision. This research discusses how to select the most relevant geometric features for path planning and improve autonomous navigation. An automatic framework for Semantic Segmentation (SS) is introduced, consisting of four processes: selecting neighborhoods, extracting classification features, and selecting features. The aim is to make the various components usable for end users without specialized knowledge by considering simplicity, effectiveness, and reproducibility. Through an extensive evaluation of different neighborhoods, geometric features, feature selection methods, classifiers, and benchmark datasets, the outcomes show that selecting the appropriate neighborhoods significantly develops 3D path segmentation. Additionally, selecting the right feature subsets can reduce computation time, memory usage, and enhance the quality of the results.

Summary Internet of things (IoT) is the reliable alternative among the networking technologies for achieving high performance with improved potentialities of flexible adoptions, data exchanges, resource allocations, and system controls. The existing IoT suffers from the limitations of resource allocation ranging between complicated service provisioning environments and networking service quality mismatching. IoT environment needs to handle the resource allocation issue for attaining satisfactory degree of quality of experience (QoE) that maps multiple resources to gateways. This problem of mapping multiple resources to gateways belongs to the class of NP‐complete problem and can be ideally solved through intelligent metaheuristic algorithms. In this paper, galactic swarm‐improved whale optimization algorithm‐based resource management (GSIWOA‐RM) scheme is proposed for efficient mapping of multiple resources to gateways in IoT. It specifically utilizes galactic swarm optimization algorithm (GSOA) for establishing global control with inherited multiple adaptive cycles of exploitation and exploration during resource allocation. It further prevents the limitations of early convergence in the exploitation phase by utilizing the evolution‐based whale optimization algorithm (WOA) that aids in better balance between exploitation and exploration. The simulation results of the proposed GSIWOA‐RM scheme confirm a better throughput of 28.32% with minimized delay and energy consumptions of 19.24% and 21.82%, when compared to the baseline resource management schemes.

Atmospheric pressure (AP) plasma treatment, a proven direct and dry means of surface modification, was deployed for the functionalization of carbon nanotube (CNT). AP plasma treatment successfully incorporated the functional groups such as -COOH and -NH2 onto the surface of CNT without affecting its crystallinity. Reduction in tube diameter and increased purity through the removal of amorphous carbon were confirmed by transmission electron microscopy (TEM) and Raman analysis. As a result of these surface chemistry and morphological changes induced by AP plasma treatment, the modified CNT-cyanate ester nanocomposite exhibited enhanced dispersion via reduced van-der Walls interaction. At optimum concentration of 0.1 wt% loading, the functionalized CNT with its high aspect ratio showed a well wetting behavior with cyanate ester (BADCy) matrix leading to remarkable increase in the mechanical properties like tensile, impact and flexural strengths and thus rendering them for potential aerospace structural applications.

MOF derived porous carbon materials have emerged as a research hotspot in recent years due to its exceptional properties like high electrical conductivity, large specific surface area, presence of numerous accessible active sites, high porosity etc. which endows them with diverse applications. The unique properties associated with derived material are inherited from the precursor MOF and the provision of in situ heteroatom doping into the carbon structure further enhances its properties specific to diverse applications. In this review, we discuss the recent advancements in MOF derived carbon focusing on its properties and electrocatalytic applications. In the first part, an overview of the unique properties, methods to regulate the morphology and composition, and the structure of MOF derived carbon materials are discussed. Then, the application of MOF derived carbon as a promising electrode material for electrochemical sensing as well as electrocatalytic process focusing on OER and HER are explained in detail. Furthermore, the challenges that need to be addressed and future aspects of MOF derived carbon research are presented.

The disease diagnosis in the medical field enhances better medical service to patients and also leads to a decrease in their mortality rate. The prediction of the survival rate of the patients purely depends on the accurate diagnosis of the diseases, but still, it is a major challenge to the physicians as well as to medical domains. Besides, several researches have experimented related to the prediction and classification of heart diseases, but they are ineffective in providing accurate results. In this research, the performance analysis of the optimal clustering algorithm-based real-world heart dataset is carried out with the developed clustering methods. Here, three developed methods, such as kernel-based exponential grey wolf optimisation (KEGWO), enhanced kernel-based exponential grey wolf optimisation (EKEGWO), and whale grey clustering (WGC) algorithm obtained better performance and provided accurate results about the diagnosis of diseases. Moreover, the performance analysis is done by considering the evaluation metrics like the Jaccard coefficient, F-measure, MSE and Rand coefficient.

In the current scenario, about 25% of all cancer deaths reported globally are related to lung cancer. Despite the development of different diagnosing techniques, including X-ray, magnetic resonance imaging, biopsy, etc., there is still a significant challenge in dealing with lung cancer mortality and morbidity. Therefore, the early diagnosis and treatment of lung cancer is essential and holds a considerable place in lung cancer research. Biomarkers, an indicator, are overexpressed in the malignant tissues and are found in the body fluids. The existence of these biomolecules beyond the cut-off level can be used as an indicator for sensing tumour markers in the diagnosis, prognosis, and clinical management of lung cancer. Hence, fast and precise detection of biomarkers is helpful in the early detection of cancer. Moreover, the redox materials used for sensing biomarkers play a critical role in the sensitivity of electroanalytical devices. Therefore, we critically review the early diagnosis of lung cancer using an electrochemical biosensor. First, the importance of biomarkers and their application in determining various stages are discussed. A detailed section is devoted to recent electrochemical electrode materials, including graphene (Gr), carbon nanotube (CNT), and metal-based nanomaterials. Finally, the limitations and future prospects of electrochemical sensors are elaborated.