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Nanosensitizers having long-lived upconversion emission under near-infrared (NIR) excitation offer unique advantages in terms of reduced background noise and prolonged signal detection for deep tissue therapy of cancer. Herein, we demonstrate a systematic mechanism of energy migration toward achieving long-lived Mn2+ upconversion emission in the multilayered core–shell–shell lattice of NaGdF4:Yb3+,Tm3+,Ca2+/NaGdF4:Yb3+,Ca2+/NaGdF4:Mn2+ upconversion nanoparticles (NPs), following the Yb3+ → Tm3+ → Gd3+ → Mn2+ intermetal ions energy transfer pathway. Furthermore, a rational design of nanosensitizer was achieved by incorporating Er3+ ions into the intermediate shell of multishell NPs, which was subsequently conjugated with the Rose Bengal sensitizer to enable the enhancement in singlet molecular oxygen (1O2) generation under excitation of a 980 nm NIR laser. An intense higher-energy emission in the UV–blue visible region from Tm3+ was achieved by optimizing the amount of Ca2+ in the core–shell NPs, followed by subsequent energy migration to the Mn2+ ion incorporated at the outer shell. The Mn2+ ions were strategically doped in the outer shell of NPs to leverage the catalytic activities of Mn2+ for H2O2 decomposition and decrease the backward energy transfer to the Tm3+ ion. Hence, this approach resulted in a long lifetime of Mn2+ (∼34 ms), attributed to the spin-forbidden 4T1g → 6A1g transition within 3d5 configuration. Additionally, the nanosensitizer demonstrated high 1O2 (∼0.39 μM) generation even at a very low concentration (5 μg/mL) under a laser power of 2 mW cm–2. The hydrogenase-like catalytic activities of Mn2+ exhibited significant oxygen production through decomposition of H2O2. Hence, these findings might contribute to the development of convenient multifunctional nanosensitizers for multimodal bioimaging and therapeutic features, including efficient 1O2 generation and catalytic decomposition of H2O2 (found excessively in a tumor environment) to oxygen for alleviating the hypoxia. [ABSTRACT FROM AUTHOR]

Abstract The use of wild food ingredients has been inextricably linked to each human culture; therefore, any cultural shift or transformation also mutates the knowledge. Particularly cross-cultural interactions have been playing a significant role in reshaping the knowledge within a given community. The study aimed to cross-culturally research the use of wild food plants among four different ethnolinguistic groups, i.e. Muhajir, Pathans, Punjabi, and Saraiki residing in the peri-urban area of Mianwali district, Punjab Pakistan. Data were taken through semi-structured interviews, and the results of cross-cultural comparison were visualized through Venn diagrams and statistically tested through Jaccard index. A total of 59 plants were recorded, which were used mostly as cooked vegetables (29 species) and snacks (21 species). About one-fourth of the quoted plants were commonly used by all the studied groups. High similarity on the use of the quoted plants was found among Muhajir, Punjabi, and Saraiki. Punjabi reported higher numbers of plants, and Pathans quoted comparatively lesser number. Pathans reported a comparatively high number of idiosyncratic foods uses followed by Muhajir. Punjabi, Muhajir, and Saraiki have a very close affinity and have comparatively rich knowledge after comparing those food uses which were reported by more 50% of participants. Punjabi frequently reported some plant uses which were rarely reported by the other four groups. Additionally a large number of plants were also quoted along with their medicinal uses which were prepared in the same way for both food and medicine. Despite the fact that most of the food ingredients were prepared in a simialr way but still the distinct names of several plants were retained across the studied groups. In the current context, the ethnobotanies of the studied communities are a blend as they might have learned the knowledge on the use of these plants from one another. It is concluded that since the knowledge is still in the memory of the people and has no serious threat to its extinction at hand, however it is necessary to frame policy programs in order to use this knowledge for the sustainability of future food and medical system otherwise it may be lost.

Abstract Several bacterial strains have developed resistance against commercial antibiotics, and interestingly, supramolecular nanomaterials have shown considerable advantages for antibacterial applications. However, the main challenges in adopting nanotechnology for antibacterial studies are random aggregation, compromised toxicity, multi-step preparation approaches, and unclear structure-function properties. Herein, we designed the amphiphilic tripeptide that acts as a reducing and capping agent for silver metal to form silver-peptide colloidal nanohybrids with the mild assistance of UV light (254 nm) through the photochemical reduction method. The nanohybrids are characterized by different spectroscopic and microscopic techniques, and non-covalent molecular interactions between metal and peptide building blocks confirm their central role in the formation of nanohybrids. The tripeptide is biocompatible and can reduce the toxicity of silver ions (Ag+) by reducing to Ag0. These colloidal nanohybrids showed antibacterial activity against gram-negative and gram-positive bacterial strains, and the possible mechanism of killing bacterial cells could be membrane disruption. This synthetic strategy is facile and green, which helps avoid using toxic chemicals or reagents and complicated methods for colloidal nanohybrid preparation for biomedical applications.

Abstract In the twenty-first century, global warming has become one of the severe challenges. Around one-fourth of the total carbon footprint is contributed by transport vehicles. It is crucial to provide alternative solutions to unsustainable transportation systems. This study compares the present travel modes of students of the Capital University of Science and Technology, Islamabad, with the proposed sustainable travel mode. A campus survey (N = 565) was conducted to understand travel choices, travel times, origin and destinations, and student preferences, considering university buses and private vehicles. A comparison was made between the travel time analysis of university bus users and non-bus users. A sustainability analysis has been conducted to evaluate the effects of the present travel modes on students and the proposed university bus transport system. Five main factors were compared: fuel consumption and associated fuel costs, carbon and vehicular emissions, traffic noise pollution, traffic congestion, and accident ratio. The results show that 38% of students commute by private cars and 18% by motorbikes. Around 25% of the students avoid university buses due to long travel times, and 30% prefer because their parents ask them. On average, university buses take 56.8 min, and non-bus transports take 38.8 min for one-way travel. If all students travel by university busses, they could be 86% more sustainable in fuel costs and carbon emissions than non-bus travel modes. These results give useful policy implications for providing sustainable travel alternatives to educational institutions.

Abstract Medical image machines serve as a valuable tool to monitor and diagnose a variety of diseases. However, manual and centralized interpretation are both error-prone and time-consuming due to malicious attacks. Numerous diagnostic algorithms have been developed to improve precision and prevent poisoning attacks by integrating symptoms, test methods, and imaging data. But in today’s digital technology world, it is necessary to have a global cloud-based diagnostic artificial intelligence model that is efficient in diagnosis and preventing poisoning attacks and might be used for multiple purposes. We propose the Healthcare Federated Ensemble Internet of Learning Cloud Doctor System (FDEIoL) model, which integrates different Internet of Things (IoT) devices to provide precise and accurate interpretation without poisoning attack problems, thereby facilitating IoT-enabled remote patient monitoring for smart healthcare systems. Furthermore, the FDEIoL system model uses a federated ensemble learning strategy to provide an automatic, up-to-date global prediction model based on input local models from the medical specialist. This assures biomedical security by safeguarding patient data and preserving the integrity of diagnostic processes. The FDEIoL system model utilizes local model feature selection to discriminate between malicious and non-malicious local models, and ensemble strategies use positive and negative samples to optimize the performance of the test dataset, enhancing its capability for remote patient monitoring. The FDEIoL system model achieved an exceptional accuracy rate of 99.24% on the Chest X-ray dataset and 99.0% on the MRI dataset of brain tumors compared to centralized models, demonstrating its ability for precision diagnosis in IoT-enabled healthcare systems.

Abstract Hyperspectral image (HSI) data has a wide range of valuable spectral information for numerous tasks. HSI data encounters challenges such as small training samples, scarcity, and redundant information. Researchers have introduced various research works to address these challenges. Convolution Neural Network (CNN) has gained significant success in the field of HSI classification. CNN’s primary focus is to extract low-level features from HSI data, and it has a limited ability to detect long-range dependencies due to the confined filter size. In contrast, vision transformers exhibit great success in the HSI classification field due to the use of attention mechanisms to learn the long-range dependencies. As mentioned earlier, the primary issue with these models is that they require sufficient labeled training data. To address this challenge, we proposed a spectral-spatial feature extractor group attention transformer that consists of a multiscale feature extractor to extract low-level or shallow features. For high-level semantic feature extraction, we proposed a group attention mechanism. Our proposed model is evaluated using four publicly available HSI datasets, which are Indian Pines, Pavia University, Salinas, and the KSC dataset. Our proposed approach achieved the best classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient. As mentioned earlier, the proposed approach utilized only 5%, 1%, 1%, and 10% of the training samples from the publicly available four datasets.

Abstract The gradual decline of production of the major fields of the Indus Basin has influenced the field pressure. Therefore, the prospects need to be assessed by the petro-elastic relationship. This study highlights the value-addition for identifying gas pockets within the E-Sand of the Lower Goru Formation through estimating and populating petro-elastic properties. A probabilistic neural network (PNN) was employed to develop the relationship of pore pressure to the inverted properties. The outcomes will help identify the high-pressure areas in accordance with the petro-elastic relationship for optimizing production strategy. In order to evaluate the petro-elastic relationship, stochastic seismic inversion was employed to holistically capture the reservoir’s variability. Pore pressure volumetric was estimated from inverted attributes using a PNN, a non-linear algorithm that was more suited to heterogeneous reservoirs. The main objective of the research is achieved via enhanced resolution of elastic properties attained through the integration of stochastic inversion and PNN processes. The high-resolution elastic properties including P-impedance, S-impedance, and Vp/Vs ratio can delineate the heterogeneities more profoundly. The petrophysical volumes obtained via enhanced inverted properties combined with pore-pressure through PNN illuminated the potential facies and the correlations in predicting the properties. The procedure provides a linkage of elastic, petrophysical, and geomechanical properties that is helpful for professionals covering a broad field of the petroleum sector. The developed workflow can be followed globally where problems occur regarding heterogeneities and early depletion.

Bioretention systems can efficiently eliminate microplastics (MPs) from stormwater and prevent their potential pollution in surface water. However, MPs dynamics in bioretention systems and their effects on microbes, plants, and organics removal are unknown. In this study, five lab-scale bioretention columns (i.e., control and four treatments) were established and filled with soil and fillers (zeolite and ceramsite). Various sorbents were utilized in columns, including biochar, kaolin and kaolin-biochar (KBC) composites for MPs adsorption. This study examines how biochar/kaolin amendment affects MPs and organics (COD and TOC) removal, plant health, and microbial community structure in bioretention systems. In the 60-day time-series column experiment, all amended columns removed over 90% of MPs compared to the control. The biochar, kaolin and their combined composite eliminated MPs by 90%, 94%, and 97%, respectively. Adding vegetation to the columns improved MPs removal. Moreover, bioretention systems were more effective in removing MPs ranging from 0.6 to 1 mm with a 71% removal rate than MPs ranging from 0.3 to 0.6 mm, resulting in a 54% removal. Organics were removed contrarily in the soil and filler layer of the bioretention system, with the soil layer removal higher due to increased microbial activity. The removal rate of total organic carbon was higher (90%) than that of chemical oxygen demand (80%). The most dominant phylum of the bacteria in the soil of treatment columns were Proteobacteria and Acidobacteriota, which constituted 16‐27% and 41‐58%, respectively. While the dominant phylum in that of fillers were Bacteroidota and Firmicutes, which constituted 18‐42% and 42‐65%, respectively. The maximum microbial enrichment was observed in the biochar and KBC vegetated columns. This work advances our understanding of the complex dynamics between microplastics and organic matter in stormwater and how, individually and in combination, vegetation, biochar, and kaolin vegetation, biochar, and kaolin, individually and in combination, enhance bioretention systems' effectiveness in managing multiple pollutants.

The Unani system of medicine (USM) is one of the popular traditional systems of medicine. World widely, this system of medicine becomes an important health care system. Since many years back Unani medicines were used by the Unani physicians for treating the diseases ailments. In USM most of the drugs are of plant origin and their therapeutic effects are due to the presence of active phytochemicals. The proper standardization methods must be necessary for preparation of formulations otherwise secondary metabolites or active constituents might not be up to the quality. The standard operating procedures (SOP's) are adopted for the preparation of Unani formulation. This study evaluated the quality of polyherbal unani formulation Habb-i-Shahtara, which is used in the treatment of skin diseases. The standardization parameters which were used for the development of quality standard of the study formulation were pharmacognostical studies, physicochemical and phytochemical parameters, HPTLC analysis, microbial load, aflatoxins, heavy metals and pesticide residues determination to evaluate the pharmacopoeial standard. The polyherbal Unani formulation Habb-i-Shahtara have been successfully standardized and the data and procedures which were used may serve the guidelines and standard reference in future for the preparation of formulation and for further study. [ABSTRACT FROM AUTHOR]

Abstract Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identification and monitoring of risk factors can assist to reduce pregnancy complications. The primary goal of this research is to use real-world datasets to identify and predict Maternal Health Risk (MHR) factors. As a result, we developed and implemented the Quad-Ensemble Machine Learning framework to predict Maternal Health Risk Classification (QEML-MHRC). The methodology used a vacxsriety of Machine Learning (ML) models, which then integrated with four ensemble ML techniques to improve prediction. The dataset collected from various maternity hospitals and clinics subjected to nineteen training and testing tests. According to the exploratory data analysis, the most significant risk factors for pregnant women include high blood pressure, low blood pressure, and high blood sugar levels. The study proposed a novel approach to dealing with high-risk factors linked to maternal health. Dealing with class-specific performance elaborated further to properly understand the distinction between high, low, and medium risks. All tests yielded outstanding results when predicting the amount of risk during pregnancy. In terms of class performance, the dataset associated with the “HR” class outperformed the others, predicting 90% correctly. GBT with ensemble stacking outperformed and demonstrated remarkable performance for all evaluation measure (0.86) across all classes in the dataset. The key success of the models used in this work is the ability to measure model performance using a class-wise distribution. The proposed approach can help medical experts assess maternal health risks, saving lives and preventing complications throughout pregnancy. The prediction approach presented in this study can detect high-risk pregnancies early on, allowing for timely intervention and treatment. This study’s development and findings have the potential to raise public awareness of maternal health issues.

Introduction: Children who are born with disability, more so intellectual disability are dependant on their parents for their daily needs. The presence of disabled child in the family adds to the caregiver burden which may read to emotional distress manifesting as anger, depression, stigma, etc. The study aimed to assess both the positive and negative impacts of having a disabled child in the family on caregivers. Methodology: The present, cross-sectional study was conducted in a rural area of Jammu district using simple random sampling, two health zones out of eight were selected for the conduct of the study. All the disabled children were identified and their parents were interviewed using National Institute of Mental Health-Disability Impact Scale Scale after taking an informed consent from them (IECGJ)(NO: IEC/GMC/2019/892). Results: During the course of the study, a total of 101 caregivers of disabled children were interviewed. Both negative and positive impact on the caregivers was found to be in close approximation to the tune of 15%–16%. Among the negative impacts, financial burden was the most dominant domain followed by physical care of the child and caregiver health being affected. Conclusion: It would be pertinent to conclude that we need to empower the caregivers as an important step for the inclusion of these special children in the society. In this context, few recommendations have been discussed in the present study.

In recent years, there has been increased interest in hydrogen storage due to the numerous advantages hydrogen offers as an energy source. This study explores the structural, hydrogen storage, electronic, elastic, mechanical, and optical properties of Ca1-xTixFeH3 through DFT. After Ti substitution in the host material, structure phase transformation occurs: cubic → pseudo-cubic → cubic. Study compounds are thermodynamically stable and experimentally synthesizable according to the calculated negative formation energies. Gravimetric hydrogen storage capacities for different concentrations of Ti (x = 0, 0.11, 0.33, 0.55, 1) were calculated as 2.963, 2.938, 2.843, and 2.753 wt%, respectively. The investigation focuses on understanding the material's BS, Fermi level, and DOS, elucidating its metallic characteristics, and non-zero DOS at the Fermi level contributing to electrical and thermal conductivity. Elemental partial density of states reveals hybridization effects with Ti doping, affecting the valence band's atomic contributions. Using cubic symmetry, C11, C12, and C14 elastic constants were used to describe mechanical characteristics, and the Born criteria were met. Thus, mechanical stability was demonstrated for these compounds. Optical analyses exhibit changes concerning Ti concentrations and energy levels, including absorption spectra, conductivity, reflectivity, dielectric function, refractive index, energy loss function, and extinction coefficient. These findings shed light on the material's response to incident radiation and potential applications in hydrogen storage and optoelectronic devices.

Efficient task execution is critical to optimize the usage of computing resources in process scheduling. Various task scheduling algorithms ensure optimized and efficient use of computing resources. This article introduces an innovative dual-layer scheduling algorithm, Multi-Queue Adaptive Priority Scheduling (MQAPS), for task execution. MQAPS features a dual-layer hierarchy with a ready queue (RQ) and a secondary queue (SQ). New tasks enter the RQ, where they are prioritized, while the SQ contains tasks that have already used computing resources at least once, with priorities below a predefined threshold. The algorithm dynamically calculates the time slice based on process priorities to ensure efficient CPU utilization. In the RQ, the task’s priority level defines its prioritization, which ensures that important jobs are completed on time compared to other conventional methods where priority is fixed or no priority parameter is defined, resulting in starvation in low-priority jobs. The simulation results show that MQAPS better utilizes CPU resources and time than traditional round-robin (RR) and multi-level scheduling. The MQAPS showcases a promising scheduling technique ensuring a balanced framework for dynamic adjustment of time quantum and priority. The MQAPS algorithm demonstrated optimization, fairness, and efficiency in job scheduling.

Tropical cyclones, including surge inundation, are a common event in the coastal regions of Bangladesh. The surge washes out the area within a very short period and remains in flooded condition for several days. Spatial analysis to understand the surge susceptibility level can assist the cyclone management system. Surge susceptibility analysis could be one of the most essential parts of disaster risk reduction through which cyclone vulnerability can be minimized. A Geographic Information Systems-based analytical hierarchy process (AHP) multi-criteria analysis and bivariate frequency ratio (FR) techniques were conducted to understand the surge susceptibility level of a cyclone-prone area on the Bangladesh coast. A total of 10 criteria were considered influential to surge flooding, i.e. Topographic Wetness Index, elevation, wind velocity, slope, distance from sea and rivers, drainage density, Land Use and Land Cover, Normalized Difference Vegetation Index, precipitation, and soil types. The final surge susceptibility maps were categorized into five classes, i.e. very low, low, moderate, high, and very high. Conferring to these susceptibility classes, policymakers can make decisions for future land use management and disaster risk reduction activities. According to this research, AHP showed better precision (Receiver Operating Characteristic) than FR for surge susceptibility prediction on the Bangladesh coast.

This study examined the influence of different wastewater treatment processes on the physicochemical properties of Alginate-Like Exopolymers (ALE) extracted from waste sludge. Sludge samples were collected from wastewater treatment plants (WWTPs) processing both combined industrial and domestic wastewater, as well as domestic wastewater alone. Among the processes studied, aerobic granular sludge (AGS) produced the highest ALE yield (352 ± 50 mg/g-VSsludge), significantly exceeding that from membrane bioreactor (170 ± 41 mg/g-VSsludge) and conventional activated sludge (

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs.

The Pab Formation of Zamzama block, lying in the Lower Indus Basin of Pakistan, is a prominent gas-producing sand reservoir. The optimized production is limited because of water encroachment surrounding the gas-producing wells, thus it is required to distinguish the gas-sand facies from the remainder of the wet sands and shales for additional drilling zones. An approach is adopted based on the relation between petrophysical and elastic properties to characterize the prospect locations. Petro-elastic models (PEMs) for the identified facies were generated to distinguish lithologies in their elastic ranges. Several elastic properties, including p-impedance (11,600-12,100 m/s*g/cc), s-impedance (7,000-7,330 m/s*g/cc), and Vp/Vs ratio (1.57-1.62), were calculated from the simultaneous prestack seismic inversion (SPSI), allowing the identification of gas sands in the field. The inverted elastic attributes and well-based lithologies were incorporated into the Bayesian framework to evaluate the probability of gas sands. To determine the reservoir quality, bulk volumes of PHIE and clay were estimated using elastic volumes trained on well logs by employing Probabilistic neural networking (PNN), which effectively handles heterogeneity effects. The results showed that the channelized gas sands passing through the existing well locations exhibited reduced clay content and maximum effective porosities of 9%, confirming the reservoir's good quality. Such approaches can be widely implemented in producing fields to completely assess lithofacies and achieve maximum production with minimal risks. [ABSTRACT FROM AUTHOR]

Fintech is an industry that uses technology to enhance and automate financial services. Fintech firms use software, mobile apps, and digital technologies to provide financial services that are faster, more efficient, and more accessible than those provided by traditional banks and financial institutions. Fintech companies take care of processes such as lending, payment processing, personal finance, and insurance, among other financial services. A data breach refers to a security liability when unapproved individuals gain access to or pilfer susceptible data. Data breaches pose a significant financial, reputational, and legal liability for companies. In 2017, Equifax suffered a data breach that revealed the personal information of over 143 million customers. Combining federated learning (FL) and blockchain can provide financial institutions with additional insurance and safeguards. Blockchain technology can provide a transparent and secure platform for FL, allowing financial institutions to collaborate on machine learning (ML) models while maintaining the confidentiality and integrity of their data. Utilizing blockchain technology, FL can provide an immutable and auditable record of all transactions and data exchanges. This can ensure that all parties adhere to the protocols and standards agreed upon for data sharing and collaboration. We propose the implementation of an FL framework that uses multiple ML models to protect consumers against fraudulent transactions through blockchain. The framework is intended to preserve customer privacy because it does not mandate the exchange of private customer data between participating institutions. Each bank trains its local models using data from its consumers, which are then combined on a centralised federated server to produce a unified global model. Data is neither stored nor exchanged between institutions, while models are trained on each institution’s data.

The widespread global adoption of wind energy sources has established a significant presence in the existing power grid. However, the massive integration of intermittent wind energy poses forecasting errors, prompting the need for supplementary reserves from conventional energy sources with increased operational expenses and carbon emissions. Hence, to facilitate the seamless operation of large-scale wind-integrated power grids, it is imperative to harness the potential of renewable energy sources and leverage flexible loads to deliver power-balancing services. In this research, dynamic real-time power dispatch strategies have been developed for the Automatic Generation Control (AGC) system to integrate the reserve capacities of conventional generation units and wind power plants and utilize the demand response capabilities of flexible loads for power balancing services. A comprehensive power system grid model was developed in DigSilent PowerFactory software, consisting of coal-based energy systems, wind energy systems, gas turbines, and cold storage units as flexible loads. The study is divided into different case studies to assess the impact of each scenario on system operation in mitigating the forecasting errors of wind power plants. Further, a comparative analysis was performed to illustrate the effectiveness of each case study. The analysis showed that Case Study III, where reserves are provided by coal energy systems and cold storage units, yielded the highest reduction in Positive Regulation (PR) and Negative Regulation (NR) errors, at 89.0% and 94.15%, respectively. Conversely, Case Study IV demonstrated the least reduction in errors, with 67.82% in PR and 78.41% in NR. However, it indicates that reserves can be supplied from wind energy systems and flexible loads without the support of conventional power plants.

Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average f 1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and f 1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT.

Underwater Wireless Sensor Networks (UWSNs) are essential for a number of environmental and oceanographic monitoring applications. However, they face different and more complex challenges than terrestrial wireless sensor networks (TWSNs). The main challenges faced by UWSNs are limited include high propagation delays, poor bandwidth, low throughput, and high energy consumption. Replacing sensor batteries in such networks becomes extremely difficult as they are usually deployed in remote areas where limited human interaction is possible. The unbalanced and inefficient usage of energy by various network nodes poses another issue, as it may reduce the applicability and feasibility of the network. Therefore, proposing Energy-Efficient Routing Protocols (E-ER-Ps) is crucial to improve the performance and lifespan of these networks. Due to the challenges mentioned earlier, this research presents an extensive analysis of several different E-ER-Ps intended for UWSNs. We compare contemporary approaches that use machine learning (ML) with conventional protocols, as ML-based approaches have shown significant potential in resolving the intricate challenges faced by UWSNs. This paper aims to present a critical review of different E-ER-Ps from various prospects for UWSNs. To better comprehend the structure and uses of these protocols, we provide an innovative taxonomy for their classification. While ML-based protocols are evaluated for their flexibility, predictive power, and overall efficiency advancements, traditional protocols are evaluated based on their routing tactics and energy-efficiency improvements. A thorough comparative analysis highlights the advantages, disadvantages, and possible uses for different protocols. Furthermore, a critical analysis of ML’s function, incorporating intelligent and adaptive routing approaches, is presented, highlighting the technology’s potential to completely alter UWSN management. To formulate and implement E-ER-Ps for UWSNs, the article concludes by highlighting the present obstacles, including the need for real-time flexibility, resilience to environmental alters, and interaction with pre-existing network infrastructures. The development of ML-based approaches, hybrid approaches that combine conventional and ML-based methodologies, and the design of protocols that can adapt dynamically to the changing circumstances of underwater habitats are highlighted as future research objectives. This research provides the foundation for future advancements in this crucial field by presenting a comprehensive overview of the state-of-the-art UWSN E-ER-Ps.