HKBK College of Engineering

The development of alternatives to soil stabilization through mechanical and chemical stabilization has paved the way for the development of biostabilization methods. Since its development, researchers have used different bacteria species for soil treatment. Soil treatment through bioremediation techniques has been used to understand its effect on strength parameters and contaminant remediation. Using a living organism for binding the soil grains to make the soil mass dense and durable is the basic idea of soil biotreatment. Bacteria and enzymes are commonly utilized in biostabilization, which is a common method to encourage ureolysis, leading to calcite precipitation in the soil mass. Microbial-induced calcite precipitation (MICP) and enzyme-induced calcite precipitation (EICP) techniques are emerging trends in soil stabilization. Unlike conventional methods, these techniques are environmentally friendly and sustainable. This review determines the challenges, applicability, advantages, and disadvantages of MICP and EICP in soil treatment and their role in the improvement of the geotechnical and geoenvironmental properties of soil. It further elaborates on their probable mechanism in improving the soil properties in the natural and lab environments. Moreover, it looks into the effectiveness of biostabilization as a remediation of soil contamination. This review intends to present a hands-on adoptable treatment method for in situ implementation depending on specific site conditions.

The internet of things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi-dimensional data, and low computational cost. Prediction in energy consumption is essential for the enhancement of sustainable cities and urban planning, as buildings are the world's largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting prediction performance, best N window size, and model uncertainty estimation. Deep learning models for household energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty.

This research highlights the significant role of green synthesis in the production of copper oxide (CuO) nanoparticles by using natural extracts as reducing agents. These nanoparticles have shown promising potential in two key applications: photocatalytic degradation of industrial dye effluents and electrochemical sensing of ciprofloxacin. The study found that Arundinaria gigantea leaf extract is an effective reducing agent for synthesizing well-defined crystalline structure CuO nanoparticles, with an average size of 36 nm. The CuO nanoparticles have demonstrated high efficiency in photocatalytic applications, effectively degrading AR88 dye under UV irradiation, making them a viable solution for eco-friendly water purification. Additionally, when incorporated into an electrochemical sensor, these CuO nanoparticles have improved sensitivity and selectivity in detecting ciprofloxacin in aqueous solutions with high accuracy and precision. This study emphasizes the versatility and effectiveness of green-synthesized CuO nanoparticles for various practical uses.

In bioinformatics research, cancer classification is a crucial domain. The use of microarray technology to identify specific illnesses is common. A small number of genes uncovered in clinical applications can lead to low-cost medicines that can help estimate a patient's survival time or diagnose cancer. Because there are more genes and fewer samples in microarray data, high dimensionality is a serious concern. The genes in the microarray data were evaluated using F-statistics, T-Statistics, and Signal-to-Noise Ratio (SNR) in this study. The top-m rated genes are analyzed using optimization approaches to retrieve useful information. The genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and shuffling frog leaping with rapid flying are among the methods employed (SFLLF). Classification is done using the Support vector machine (SVM), the K-Nearest Neighbor classifier (KNN), and the Naive Bayes classifier (NBC). Lung Cancer Michigan, AMLALL, Colon Tumour, Lung Harvard2, and others are among the datasets utilized for experimental analysis. The classifiers are assessed using a 5-fold cross-validation approach. The findings demonstrate that the suggested two-step feature selection approaches are effective in selecting relevant genes from microarray data for cancer classification.

Soil treatment methods to cope with ever-growing demands of construction industry and environmental aspects are always explored for their suitability in different in-situ conditions. Of late, enzyme induced calcite precipitation (EICP) is gaining importance as a reliable technique to improve soil properties and for contaminant remediation scenarios. In the present work, swelling and permeability characteristics of two native Indian cohesive soils (Black and Red) are explored. Experiments on the sorption and desorption of multiple heavy metals (Cd, Ni and Pb) onto these soils were conducted to understand the sorptive response of the heavy metals. To improve the heavy metal retention capacity and enhance swelling and permeability characteristics, the selected soils were treated with different enzyme solutions. The results revealed that EICP technique could immobilize the heavy metals in selected soils to a significant level and reduce the swelling and permeability. This technique is contaminant selective and performance varies with the nature and type of heavy metal used. Citric acid (C6H8O7) and ethylene diamine tetra-acetic acid (EDTA) were used as extractants in the present study to study the desorption response of heavy metals for different EICP conditions. The results indicate that calcium carbonate (CaCO3) precipitate deposited in the voids of soil has the innate potential in reducing the permeability of soil up to 47-fold and swelling pressure by 4-fold at the end of 21 days of curing period. Reduction in permeability and swell, following EICP treatment can be maintained with one time rinsing of the treated soil in water to avoid dissolution of precipitated CaCO3. Outcomes of this study have revealed that EICP technique can be adopted on selected native soils to reduce swelling and permeability characteristics followed by enhanced contaminant remediation enabling their potential as excellent landfill liner materials.

Today's critical goals in sensor network research are extending the lifetime of wireless sensor networks (WSNs) and lowering power consumption. A WSN necessitates the use of energy-efficient communication networks. Clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computation are also some of the energy limitations of WSNs. Moreover, cluster head selection remains problematic for WSN energy minimization. Sensor nodes (SNs) are clustered in this work using the Adaptive Sailfish Optimization (ASFO) algorithm with K-medoids. The primary purpose of research is to optimize the selection of cluster heads through energy stabilization, distance reduction, and latency minimization between nodes. Because of these constraints, achieving optimal energy resource utilization is an essential problem in WSNs. An energy-efficient cross-layer-based expedient routing protocol (E-CERP) is used to determine the shortest route, dynamically minimizing network overhead. The proposed method is used to evaluate the packet delivery ratio (PDR), packet delay, throughput, power consumption, network lifetime, packet loss rate, and error estimation, and the results were superior to existing methods. PDR (100%), packet delay (0.05 s), throughput (0.99 Mbps), power consumption (1.97 mJ), network lifespan (5908 rounds), and PLR (0.5%) for 100 nodes are the performance results for quality-of-service parameters.

The Internet of Medical Things (IoMT) has been applied to provide health care facilities for elders and parents. Remote health care is essential for providing scarce resources and facilities to coronavirus patients. Ongoing IoMT communication is susceptible to potential security attacks. In this research, an artificial intelligence-driven security model of the IoMT is also proposed to simulate and analyses the results. Under the proposed plan, only authorized users will be able to access private and sensitive patient information, and unauthorized users will be unable to access a secure healthcare network. The various phases for implementing artificial intelligence (AI) techniques in the IoMT system have been discussed. AI-driven IoMT is implemented using decision trees, logistic regression, support vector machines (SVM), and k-nearest neighbours (KNN) techniques. The KNN learning models are recommended for IoMT applications due to their low consumption time with high accuracy and effective prediction.

Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

The Internet of Medical Things (IoMT) is one of the most promising technology solutions that is currently being developed to monitor health status remotely. A risk-stratified data transmission protocol has been used to construct the IoMT architecture for remote patient monitoring. All the sub-systems have undergone performance tests as well as clinical validation. Clinical validation of IoMT software on 100 patients was successful. Digital representations' size and complexity are reduced by up to 80%, making them appropriate for use in developing narrow-band IoT networks. In particular for low-power devices, performance measurement revealed that bandwidth and energy were reduced to 97% and 95%, respectively.

This study evaluated the efficacy of enzyme induced calcite precipitation (EICP) in restricting the mobility of heavy metals in soils. EICP is an environmentally friendly method that has wide ranging applications in the sustainable development of civil infrastructure. The study examined the desorption of three heavy metals from treated and untreated soils using ethylene diamine tetra-acetic acid (EDTA) and citric acid (C6H8O7) extractants under harsh conditions. Two natural soils spiked with cadmium (Cd), nickel (Ni), and lead (Pb) were studied in this research. The soils were treated with three types of enzyme solutions (ESs) to achieve EICP. A combination of urea of one molarity (M), 0.67 M calcium chloride, and urease enzyme (3 g/L) was mixed in deionized (DI) water to prepare enzyme solution 1 (ES1); non-fat milk powder (4 g/L) was added to ES1 to prepare enzyme solution 2 (ES2); and 0.37 M urea, 0.25 M calcium chloride, 0.85 g/L urease enzyme, and 4 g/L non-fat milk powder were mixed in DI water to prepare enzyme solution 3 (ES3). Ni, Cd, and Pb were added with load ratios of 50 and 100 mg/kg to both untreated and treated soils to study the effect of EICP on desorption rates of the heavy metals from soil. Desorption studies were performed after a curing period of 40 days. The curing period started after the soil samples were spiked with heavy metals. Soils treated with ESs were spiked with heavy metals after a curing period of 21 days and then further cured for 40 days. The amount of CaCO3 precipitated in the soil by the ESs was quantified using a gravimetric acid digestion test, which related the desorption of heavy metals to the amount of precipitated CaCO3. The order of desorption was as follows: Cd > Ni > Pb. It was observed that the average maximum removal efficiency of the untreated soil samples (irrespective of the load ratio and contaminants) was approximately 48% when extracted by EDTA and 46% when extracted by citric acid. The soil samples treated with ES2 exhibited average maximum removal efficiencies of 19% and 10% when extracted by EDTA and citric acid, respectively. It was observed that ES2 precipitated a maximum amount of calcium carbonate (CaCO3) when compared to ES1 and ES3 and retained the maximum amount of heavy metals in the soil by forming a CaCO3 shield on the heavy metals, thus decreasing their mobility. An approximate improvement of 30% in the retention of heavy metal ions was observed in soils treated with ESs when compared to untreated soil samples. Therefore, the study suggests that ESs can be an effective alternative in the remediation of soils contaminated with heavy metal ions.

We report the synthesis of medicinal plant, Vernonia amygdalina Del. mediated green copper oxide nanoparticles (VeA-CuO NPs). The presence of two absorbance maxima, λmax 1 and λmax 2 at 436 nm and 452 nm, respectively confirms a mixture of biomolecules surface amalgamated CuO NPs with different morphological features. The FT-IR spectra of the plant leaf extract and VeA-CuO confirmed the efficient role of biomolecules as capping and stabilising agents. The XRD patterns of NPs approved high crystallinity of CuO. The purity of the NPs was corroborated by SEM-EDAX analysis. The average particle size of the NPs was found to be 19.68 nm. In addition, the combined TEM, HRTEM and SAED analysis substantiated the presence of CuO with a d-spacing value of 0.2854 nm, which conformed to CuO (1 1 1). The antibacterial assay revealed that VeA-CuO NPs were synergistic in their influence versus bacterial strains, S. aureus, E. coli, P. aeruginosa, and E. aerogenes. The uppermost zone of inhibition of 15 mm was observed for E. aerogenes. The bioactive compounds capped around the CuO NPs served the effective role in disrupting the cell wall of bacterial strains. The degradation efficiencies for Indigo carmine (IC) and Malachite green (MG) dyes by NPs were found to be 95% and 91%, respectively. The lowest degradation half-life was recorded to be 16.55 min for MG dye. In addition, the better electrode stability revealed by CV and EIS studies, confirms the multi-functional nature of VeA-CuO NPs, these CuO NPs exhibited multifunctional applications.

Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods.

In this paper, the egg fertilisation is one of the major factors to be considered in the poultry farms. The smart incubation system is designed to combine the IoT technology with the smart phone in order to make the system more convenient to the user in monitoring and operation of the incubation system. The incubator is designed first with both setter and the hatcher in one unit and incorporating both still air incubation and forced air incubation which is a controller and monitored by the controller keeping in mind the four factors: temperature, humidity, ventilation and egg turning system. Here we are setting with three different temperatures for the experimental purpose at T1 = 36.5°C, T2 = 37.5°C and T3 = 38°C. The environment is maintained same in all the three cases and which is the best temperature for the incubation of the chicken eggs is noted.

The breakthroughs in public healthcare infrastructure have resulted in a large influx of highly sensitive and critical healthcare information. The application of sophisticated data analysis techniques can aid in the early detection and prevention of a variety of fatal diseases. Diabetes can cause heart disease, renal disease, and nerve damage, all of which are life-threatening complications of the disease. The goal of this work is to identify, detect, and forecast the emergence of diabetes in its earliest stages by employing machine learning techniques and algorithms. When it comes to diabetes classification, an MLP is used. The experimental evaluation was carried out using the PIMA Indian Diabetes dataset. According to the study findings, MLP outperforms the competition in terms of accuracy, with an accuracy rate of 86.08%. Following this, a comparison of the suggested technique with the existing state of the art is carried out, proving the flexibility of the proposed approach to a wide range of public healthcare applications.

Nanocrystalline powders of ZnO, α-Fe2O3 and ZnFe2O4/ZnO were prepared by solution combustion method. The characterization of the nano powders was done by powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The ZnO and α-Fe2O3 nanopowders exhibited the wurtzite and hexagonal phases, respectively. The PXRD pattern of ZnFe2O4/ZnO indicated the spinel phase of zinc ferrite and wurtzite phase of zinc oxide. The three nanopowders were used as photocatalysts for the removal of the azodye brilliant yellow (BY) from its aqueous solution. A comparison of the results indicated that ZnFe2O4/ZnO showed better photocatalytic activity for the removal of BY than ZnO and α-Fe2O3. This was attributed to the synergistic effect between ZnFe2O4 and ZnO resulting in better charge separation and reducing the electron–hole recombination. The photocatalytic activity followed the order: ZnO < α-Fe2O3 < ZnFe2O4/ZnO.

The photocatalytic degradation of the organic pollutants using the green synthesized catalysts is an environmentally safe approach for the wastewater treatment. In this study, ZnO and CuO nanoparticles (NPs) and ZnO/CuO nanocomposites (NCs) with various CuO weight percents were synthesized using extract of Musa acuminata fruit peel as the stablizing and capping agent. The synthesized nanomaterials were characterized by TGA/DTA, XRD, SEM, TEM, SAED, HR-TEM, UV-DRS and FTIR techniques. The degradation of methylene blue (MB) dye using the synthesized catalysts was investigated under a visible light source. XRD analysed average crystalline sizes were 24.9, 17.0 and 22.6 nm for ZnO, CuO, and ZnO/CuO nanomaterials, respectively. The SEM and TEM analysis confirms that ZnO NPs, CuO NPs, and ZnO/CuO NCs possessed the spherically shaped monoclinic structure. The bandgap energies (Eg) of ZnO NPs, CuO NPs and ZnO/CuO NCs were found to be 3.25, 1.7 and 3.18 eV respectively. The FT-IR analysis confirms presence of various reducing and capping agents. The photocatalytic activities of ZnO NPs, CuO NPs, and ZnO/CuO NCs were evaluated using the degradation of MB dye under the visible light irradiation. The photocatalysts CuO, ZnO, and ZnO/CuO exhibited the degradation efficiencies of 50%, 57%, and 90%, respectively.

As society transitions into the digital era, the anticipated requirements and expectations placed on various sectors undergo profound transformations. This proposed study explores the shifting landscape, identifying key demands and expectations across various domains and highlighting the requirement for adaptability and innovation. The digital era has directed transformative changes, redefining the landscape of libraries into dynamic digital repositories. Anticipating the future requirements and expectations in this evolving domain is imperative for effectively catering to user needs. The term ‘digital library,' often referred to as a ‘digital repository,' is crucial in contemporary information. The process of transforming a digital repository into an institutional repository (IR) is of dominant importance. The variations observed in institutional repositories are designed to align with users' demands and expectations for digital information and services.

The integration of fog computing and machine learning (ML) in digital healthcare has revolutionized patient care, operations, and personalized treatment. This chapter explores the potential of fog computing in telemedicine, remote monitoring, and personalized treatment. It highlights its role in addressing data processing challenges, enabling real-time data analytics, and ensuring secure transmission of medical information. Key case studies demonstrate how these integrated solutions are driving innovation in the healthcare industry. The combination of fog computing and ML offers a promising avenue for the future of digital healthcare, focusing on data-driven decision-making and precision medicine.

This paper introduces a novel approach for medical image reclamation, specifically focusing on enhancing chest image resolution. The proposed method introduces the Dual-Tree Complex Wavelet Transform (DT-CWT) with Edge Preservation Smoothing (EPS) filters to balance visual clarity. The resulting Image Reclamation system maintains high-quality results while preserving image edges. Performance validation using established metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Root Mean Square Error (RMSE), and entropy demonstrates substantial improvements: PSNR of 31, SSIM of 0.99, RMSE of 8.25, and entropy of 1.03. Furthermore, the algorithm extracts features from the enhanced chest image through symlet transform, allowing for Bhattacharya coefficient computation and unique bin analysis to enhance image retrieval. Experimental results show efficiency gains, increasing the top 5 matching images’ retrieval score from 320 to 512. This approach promises to enhance medical image reclamation in emergency settings, facilitating quicker and more accurate diagnoses and treatments for acute chest injuries. Ultimately, this work can potentially save lives, reduce complications, and improve patient outcomes in chest trauma emergencies.

The structural, thermal, linear, and femtosecond third-order nonlinear optical (NLO) properties of two pyridine-based anthracene chalcones, (2E)-1-(anthracen-9-yl)-3-(pyridin-2-yl)prop-2-en-1-one (2PANC) and (2E)-1-(anthracen-9-yl)-3-(pyridin-3-yl)prop-2-en-1-one (3PANC), were investigated. These two chalcones were synthesized following the Claisen–Schmidt condensation method. Optically transparent single crystals were achieved using a slow evaporation solution growth technique. The presence of functional groups in these molecules was established by Fourier transform infrared and NMR spectroscopic data. The detailed solid-state structure of both chalcones was determined from the single-crystal X-ray diffraction data. Both crystals crystallized in the centrosymmetric triclinic space group P1̅ with the nuance of unit cell parameters. The crystals (labeled as 2PANC and 3PANC) have been found to be transparent optically [in the entire visible spectral region] and were found to be thermally stable up to 169 and 194 °C, respectively. The intermolecular interactions were investigated using the Hirshfeld surface analysis, and the band structures (highest occupied molecular orbital–lowest unoccupied molecular orbital, excited-state energies, global chemical reactivity descriptors, and molecular electrostatic potentials) were studied using density functional theory (DFT) techniques. The ultrafast third-order NLO properties were investigated using (a) Z-scan and (b) degenerate four-wave mixing (DFWM) techniques using ∼50 fs pulses at 800 nm (1 kHz, ∼4 mJ) from a Ti:sapphire laser amplifier. Two-photon-assisted reverse saturable absorption, self-focusing nonlinear refraction, optical limiting, and optical switching behaviors were witnessed from the Z-scan data. 3PANC demonstrated a stronger two-photon absorption coefficient, while 2PANC depicted a stronger nonlinear refractive index among the two. The time-resolved DFWM data demonstrated that the decay times of 2PANC and 3PANC were ∼162 and ∼180 fs, respectively. The second hyperpolarizability (γ) values determined by DFT, Z-scan, and DFWM were found to be in good correlation (with a magnitude of ∼10–34 esu). The ultrafast third-order NLO response, significant NLO properties, and thermal stability of these chalcones brands them as potential candidates for optical power limiting and switching applications.