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Innovation has been made in the caisson foundation to support a very large structure resting over a soft soil stratum known as the root-caisson foundation. This technique was executed in China-Yangtze River Bridge. The root caisson foundation was first implemented in the Yangtze River bridge and discovered that the root improves soil structure interaction and increases vertical bearing capacity by 100%. In the present research, a numerical study of root-caisson foundation under combined vertical and horizontal loading was performed using ABAQUS software. Analysis was performed by varying the parameters of root caisson such as the inclination of roots (30°, 45°, 60°, and 90°) spacing(S) between the root floors with respect to diameter (D) of the caisson as (0.5°D, 0.6°D, and 0.75°D) with different loading conditions and results were compared with normal traditional caisson with the same length (L) to diameter (D) ratio. From the test results, the load-settlement behavior of caisson is nonlinear for individual vertical, and lateral load tests and also the same for the combined loading conditions. It is found that the root-caisson has a higher load bearing capacity as compared to that of the traditional caisson. Moreover, in combined loading, the load bearing capacity increases considerably, compared with the ultimate vertical and lateral load capacity.

Bone-conducted microphone (BCM) senses vibrations from bones in the skull during speech to electrical audio signal. When transmitting speech signals, bone-conduction microphones (BCMs) capture speech signals based on the vibrations of the speaker’s skull and have better noise-resistance capabilities than standard air-conduction microphones (ACMs). BCMs have a different frequency response than ACMs because they only capture the low-frequency portion of speech signals. When we replace an ACM with a BCM, we may get satisfactory noise suppression results, but the speech quality and intelligibility may suffer due to the nature of the solid vibration. Mismatched BCM and ACM characteristics can also have an impact on ASR performance, and it is impossible to recreate a new ASR system using voice data from BCMs. The speech intelligibility of a BCM-conducted speech signal is determined by the location of the bone used to acquire the signal and accurately model phonemes of words. Deep learning techniques such as neural network have traditionally been used for speech recognition. However, neural networks have a high computational cost and are unable to model phonemes in signals. In this paper, the intelligibility of BCM signal speech was evaluated for different bone locations, namely the right ramus, larynx, and right mastoid. Listener and deep learning architectures such as CapsuleNet, UNet, and S-Net were used to acquire the BCM signal for Tamil words and evaluate speech intelligibility. As validated by the listener and deep learning architectures, the Larynx bone location improves speech intelligibility.

This study work is related to exploring the role of connected wing walls in changing the behaviour of a metakaolin geopolymer wall type abutment when acted upon by all the forces that are generally applied on a short-span bridge. The modelling of abutment with connected wing walls is done using the STAAD Pro V8i SS6 software and all the loading applied for the analysis as per IRC: 6-2016. The modelling is done using the 4-noded plate elements for all the members, and the plate elements here are meshed using the quadrilateral meshing feature. The behaviour of the metakaolin geopolymer wall-type abutment is analyzed using various models with changing the basic parameters such as length of the wing walls, height of the walls, number of lanes on the bridge, and type of live load on the bridge. The various results are obtained in the form of bending moments from all the cases, which show us some really interesting behaviour of the abutment wall and the wing walls. As the length of the wing walls is increased, they take up more horizontal moments than the abutment wall and the deflection behaviour of the wing walls is way different than that of cantilever wall, and hence, it shows that the design aspects of the wing walls need to be checked. Also, the connected wing walls cause horizontal moments in the middle of the abutment wall, which is an interesting result; as now, it proves that after casting wing walls monolithically to the abutment wall, the design of the abutment wall cannot be done as cantilever wall, and we need to take care of this horizontal moment by providing required reinforcement. Also, as the length of the wing walls is short, the torsional moments become critical.

Triticum aestivum (Family: Poaceae), Ocimum sanctum (Family: Lamiaceae), and Tinospora cordifolia (Family: Menispermaceae) are commonly known as wheatgrass, tulsi, and giloy, respectively, which are the plants used as medicines for the treatment of various diseases. All three medicinal plants possess phenolic compounds with other important chemical constituents such as polysaccharides, aliphatic compounds, and alkaloids. The extract of these plants has been prepared and investigated for antioxidant, total phenolic content, total flavonoid content, and antimicrobial study in order to discover potential sources for new pharmaceutical formulations. To determine the antioxidant activity, a free radical scavenging assay for 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydrogen peroxide was performed using ascorbic acid as the standard. The R 2 value of the prepared extract was found to be 0.9964 and 0.990 in DPPH and hydrogen peroxide scavenging activity, respectively. The phenolic and flavonoid content was found to be 87.50 μl/ml and 58.00 μl/ml, respectively. The diffusion method was used to screen the antimicrobial activity of the prepared extract sample against various microorganisms. This extract showed better results for antioxidant and antimicrobial activity.

Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.

This article examines distinctive techniques for monitoring the condition of fishes in underwater and also provides tranquil procedures after catching the fishes. Once the fishes are hooked, two different techniques that are explicitly designed for smoking and drying are implemented for saving the time of fish suppliers. Existing methods do not focus on the optimization algorithms for solving this issue. When considering the optimization problem, the solution is adequate for any number of inputs at time t. For this combined new flanged technique, a precise system model has been designed and incorporated with a set of rules using contention protocols. In addition, the designed system is also instigated with a whale optimization algorithm that is having sufficient capability to test the different parameters of assimilated sensing devices using different sensors. Further to test the effectiveness of the proposed method, an online monitoring system has been presented that can monitor and in turn provides the consequences using a simulation model for better understanding. Moreover, after examining the simulation results under three different scenarios, it has been observed that the proposed method provides an enhancement in real-time monitoring systems for an average of 78%.

In the current-generation wireless systems, there is a huge requirement on integrating big data which can able to predict the market trends of all application systems. Therefore, the proposed method emphasizes on the integration of nanosensors with big data analysis which will be used in healthcare applications. Also, safety precautions are considered when this nanosensor is integrated where depth and reflection of signals are also observed using different time samples. In addition, to analyze the effect of nanosensors, six fundamental scenarios that provide good impact on real-time applications are also deliberated. Moreover, for proving the adeptness of the proposed method, the results are equipped in both online and offline analyses for investigating error measurement, sensitivity, and permeability parameters. Since nanosensors are introduced, the efficiency of the projected technique is increased by implementing media access control (MAC) protocol with recurrent neural network (RNN). Further, after observing the simulation results, it is proved that the proposed method is more effective for an average percentile of 67% when compared to the existing methods.

In the area of Nano technological research, green synthesis of Nanoparticles (NPs) has pulled in a ton of interest in light of the fact that the green-synthesized Ag NPs show more prominent antimicrobial and inhibitory qualities, in perspective on which they could be utilized in various applications in the areas of medical and drug delivery. It might be the most appropriate option for the conventional techniques that are commonly conflicting and exert dangerous impacts on the earth. In this research, green synthesis of silver NPs using Ziziphus mauritiana leaf extract was carried out. The synthesized Ag NPs were characterized using UV–V is spectrophotometry, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) techniques. The consequences of UV-Vis spectroscopy showed plasma resonance peaks around 413 nm, that exhibited the existence of Ag NPs. The result observedfromSEM demonstrated thatNPs werefound inspherical and in the 4–96 nm range. The practical gatherings forNPs synthesis using organic compounds with minimized procedure of biosynthesis and stabilization of silver NPs were studied with FTIR and were observed to be phenols, alcohols, primary amines, and alkenes. The XRD pattern demonstrated the FCC structure of AgNO3 and average particle was observed to be 12.0 nm.

Honey has medicinal benefits due to its substantial nutritional profile, wholesome, sweet, and active ingredient that is used for interdisciplinary purposes in the food industry. Honey quality can be characterized by its engineering properties besides potential functional features, such as antioxidant, antibacterial, and antimicrobial properties. However, there are many problems associated with the design of processes, product, equipment, and process control. Therefore, food engineers, scientists, processors, and beekeepers have a duty to understand the concept of engineering properties and to fix the problems occurred during harvesting, storage, transport, mixing, heating, cooling, refining and pumping honey and honey products. In the case of the future-line work, there are few compressive reviews concerning with engineering properties of honey. However, in the present review, an emphasis has been given to explore the knowledge of some engineering properties of Algerian and Ethiopian honeys, such as viscosity, moisture content, minerals, specific gravity, hydroxymethylfurfural, glass transition, color, degree brix and sugar components.

The food waste management has become a very important part in the modern world. In the olden days, the population was very low, so that the low waste produced was easily disposed by dumping it under the soil. However, nowadays, due to high population, amount of waste is produced high which could not be dumped due to the land pollution. To overcome this problem, the food waste must be managed or utilized properly to give zero waste. Therefore, this work focused on the production of biogas from food waste through the anaerobic digestion process using iron oxide nanoparticles. The iron oxide nanocatalyst from red mud reduced the process time for the anaerobic digestion was by 5.38% compared to the noncatalytic process. The produced biogas analysis done through GCMS analysis and calculated by comparing the both anaerobic degradation setup with and without nanocatalyst. Nanocatalyzed degradation contains 50% high amount of methane and 23.5% of total amount of biogas when compared to nonnanocatalyzed degradation.

Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

Parkinson’s disease (PD) is a neurodegenerative illness that progresses and is long-lasting. It becomes more difficult to talk, write, walk, and do other basic functions when the brain’s dopamine-generating neurons are injured or killed. There is a gradual rise in the intensity of these symptoms over time. Using Parkinson’s Telemonitoring Voice Data Set from UCI and deep neural networks, we provide a strategy for predicting the severity of Parkinson’s disease in this research. An unprocessed speech recording contains a slew of unintelligible data that makes correct diagnosis difficult. Therefore, the raw signal data must be preprocessed using the signal error drop standardization while the features can be grouped by using the wavelet cleft fuzzy algorithm. Then the abnormal features can be selected by using the firming bacteria foraging algorithm for feature size decomposition process. Then classification was made using the deep brooke inception net classifier. The performances of the classifier are compared where the simulation results show that the proposed strategy accuracy in detecting severity of the Parkinson’s disease is better than other conventional methods. The proposed DBIN model achieved better accuracy compared to other existing techniques. It is also found that the classification based on extracted voice abnormality data achieves better accuracy (99.8%) over PD prediction; hence it can be concluded as a better metric for severity prediction.

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

A rising proportion of older people has more demand on services including hospitals, retirement homes, and assisted living facilities. Regaining control of this population’s expectations will pose new difficulties for lawmakers, medical professionals, and the society at large. Smart technology can help older people to have independent and fulfilling lives while still living safely and securely in the community. In the last several decades, the number of sectors using robots has risen. Comrade robots have made their appearance in old human life, with the most recent notable appearance being in their care. The number of elderly individuals is increasing dramatically throughout the globe. The source of the story is the use of robots to help elderly people with day-to-day activities. Speech data and facial recognition model are done with AI model. Here, with the Comrade robotic model, elder people’s healthcare system is designed with better analysis state. The aim is to put in place a simple robotic buddy to determine the health of the old person via a headband that has been given to them. Comrade robot may do things like senior citizens home automation, home equipment control, safety, and wellbeing sensing, and, in emergency situation, routine duties like navigating in the outside world. The fear that robotics and artificial intelligence would eventually eliminate most of the jobs is increasing. It is anticipated that, in order to survive and stay relevant in the constantly shifting environment of work, workers of the future will need to be creative and versatile and prepared to identify new business possibilities and change industry to meet challenges of the world. According to the research, reflective practice, time management, communicating, and collaboration are important in fostering creativity.

Concrete’s binder can be substituted with rice husk ash, metakaolin, kaolin clay, GGBS, and other agricultural and industrial byproducts and waste materials, which lowers greenhouse gas emissions, namely carbon dioxide (CO2). When employed in the manufacturing of concrete, the aforementioned items reduce the amount of water required and have no discernible impact on the material’s long-term performance. Combination of metakaolin and class-F fly ash is taken for this experimental study to create geopolymer concrete, as both the materials are very good sources of alumino-silicates, which is the foremost requirement for the material to be the binder in geopolymer concrete. Also, the percentage replacement of crushed aggregates with crushed E-waste plastic (EWP) is are created for making the concrete eco-friendlier as the EWP is a big problem for us to dispose of it. To activate raw ingredients and mixing of concrete, alkaline solution is used, made from sodium hydroxide (NaOH) and water glass solution, i.e., sodium silicate (Na2SiO3). Low molarity metakaolin geopolymer concrete (MGPC) was created to have benefits over standard cement concrete in terms of performance, durability, and mechanical properties. This study describes the findings of a comprehensive experimental test and analysis to investigate the characteristics of MGPC-EWP with a change in different factors of the mix design. The study’s findings demonstrated that MGPC performance is closely correlated with its molarity, as the molarity increases, the mechanical strengths of the MGPC also increases. Also, the optimum percentages of the EWP are to be incorporated in the MGPC. This study also uses the petrographic image analysis to validate the geopolymerization products forms in the MGPC and check the microstructure growth of geopolymerization of particles with age. The petrographic image analysis provides photographs of the microstructure of hydration products of the samples with good details. As the petrographic microscope is very affordable as compared to the SEM setup and requires less skilled worker, it comes out to be a good approach for the microstructural analysis.

Alzheimer’s disease is the neuro disorder which characterized by means of Amyloid– β (A β ) in brain. However, accurate detection of this disease is a challenging task since the pathological issues of brain are complex in identification. In this paper, the changes associated with the retinal imaging for Alzheimer’s disease are classified into two classes such as wild-type (WT) and transgenic mice model (TMM). For testing, optical coherence tomography (OCT) images are used to classify into two groups. The classification is implemented by support vector machines with the optimum kernel selection using a genetic algorithm. Among several kernel functions of SVM, the radial basis kernel function provides the better classification result. In order to deal with an effective classification using SVM, texture features of retinal images are extracted and selected. The overall accuracy reached 92% and 91% of precision for the classification of transgenic mice.

Eragrostis tef (Teff) is one of the most important staple crops in Ethiopia. The straw obtained from teff is a plentiful source of lignocellulosic biomass. In the present study, straw obtained from teff grass was exploited as a carbon precursor to synthesise the activated carbon via chemical activation followed by microwave-aided pyrolysis. The prepared activated carbon by microwave aided method from teff straw (MATSAC) was utilised as a bio-adsorbent to examine the lead II ions adsorption potential from the aqueous medium. RSM technique was employed to explore a process model which correlates the four independent variables namely Pb(II) ions initial concentration, MATSAC dose, adsorption time, and solution pH. Further, the model was statistically optimised to achieve optimum Pb(II) ions removal. They were discovered to be Pb(II) ions initial concentration: 94.35 mg/L, MATSAC dose: 0.655 g/100 mL, adsorption time: 87.6 min, and solution pH: 5.4 to achieve the maximised removal of Pb(II) (90.89%). In the investigation on the models of isotherm, it was inferred that Langmuir isotherm fitted excellently to the equilibrium data of the adsorption. The adsorption capacity of Pb(II) on MATSAC was 42.97 mg/g. In addition, the kinetic analysis confirmed that the process of adsorption was statistically significant to pseudo 2nd order. The thermodynamic study indicated that the negative value of ���Go deep-rooted the practicability and spontaneity of MATSAC for Pb(II) ions removal. In a nutshell, MATSAC, which is derived from locally available agricultural waste, can remove toxic Pb(II) ions from contaminated water at a minimal cost.

Hydrothermal liquefaction (HTL) is identified as a promising thermochemical technique to recover biofuels and bioenergy from waste biomass containing low energy and high moisture content. The wastewater generated during the HTL process (HTWW) are rich in nutrients and organics. The release of the nutrients and organics enriched HTWW would not only contaminate the water bodies but also lead to the loss of valued bioenergy sources, especially in the present time of the energy crisis. Thus, biotechnological as well as physicochemical treatment of HTWW for simultaneous extraction of valuable resources along with reduction in polluting substances has gained significant attention in recent times. Therefore, the treatment of wastewater generated during the HTL of biomass for reduced environmental emission and possible bioenergy recovery is highlighted in this paper. Various technologies for treatment and valorisation of HTWW are reviewed, including anaerobic digestion, microbial fuel cells (MFC), microbial electrolysis cell (MEC), and supercritical water gasification (SCWG). This review paper illustrates that the characteristics of biomass play a pivotal role in the selection process of appropriate technology for the treatment of HTWW. Several HTWW treatment technologies are weighed in terms of their benefits and drawbacks and are thoroughly examined. The integration of these technologies is also discussed. Overall, this study suggests that integrating different methods, techno-economic analysis, and nutrient recovery approaches would be advantageous to researchers in finding way for maximising HTWW valorisation along with reduced environmental pollution.

Leather is one among the significant commodities carry extensive fare profit to the Federal Democratic Republic of Ethiopia. In leather industries, chrome tanning is one of the main process for producing less susceptible to decomposition and more durable leather. Chrome tanning results in a huge amount of Cr content toxic sludge waste which poses a risk to the earth. In the present work, the optimization process parameters, initial pH, temperature, and elemental sulfur (S0) concentration were examined by Response Surface Methodology (RSM) for the bacterial evacuation (bioleaching) of Cr from the tannery sludge. From the RSM analysis, an equation of polynomial model was produced for the Cr removal effectiveness. Further, the optimized parameters have been recognized by solving the polynomial model through RSM. So as to get the worldwide optimized conditions, the model was settled using a technique dependent on a MATLAB tool, Genetic Algorithm (GA). Moreover, an investigation was led at various temperatures with fixed optimized initial pH and S0 concentration to assess the impact of temperature on the Cr removal by bioleaching. Using these exploratory outcomes, the kinetics for the activation energy, reaction rate-constant, rate-controlling step, and thermodynamic parameters were resolved. Such a study will be useful for designing and building up the ideal bioleaching system for sludge detoxification in leather processing industries.