Search Results (91)

Objectives: To assess the safety and efficacy of adenotonsillectomy (AT) for treating uncomplicated pediatric obstructive sleep apnea (OSA) in children of different ages. Methods: A systematic search was conducted in four electronic databases, and 71 studies with a total of 9087 participants were included in the analysis. The studies were all before-and-after studies, cohort studies, and randomized controlled trials. Surgical results were analyzed according to age, disease severity, and follow-up duration. Results: Children younger than 7 years at the time of AT had a significantly greater decrease in disease severity, a greater decrease in hypoxemic burden, improved sleep quality, and improved cardiovascular function than children older than 7 years. Both cognitive and behavioral performance improved postoperatively, although these changes were more significantly associated with the duration of follow-up than with age at surgery. Notably, the rate of surgical complications was much greater in children under the age of 3. Conclusions: The current evidence indicates that AT is performed optimally between the ages of 3 and 7 years, offering the greatest chance of disease resolution and remission of associated conditions, balanced with a reduction in surgical risk. We highly recommend conducting high-quality randomized controlled trials to further inform the clinical guidelines for pediatric AT. Full article

Breast cancer is the most common malignancy affecting women, manifesting as a heterogeneous disease with diverse molecular characteristics and clinical presentations. Recent studies have elucidated the role of epigenetic modifications in the pathogenesis of breast cancer, including drug resistance and efflux characteristics, offering potential new diagnostic and prognostic markers, treatment efficacy predictors, and therapeutic agents. Key modifications include DNA cytosine methylation and the covalent modification of histone proteins. Unlike genetic mutations, reprogramming the epigenetic landscape of the cancer epigenome is a promising targeted therapy for the treatment and reversal of drug resistance. Epidrugs, which target DNA methylation and histone modifications, can provide novel options for the treatment of breast cancer by reversing the acquired resistance to treatment. Currently, the most promising approach involves combination therapies consisting of epidrugs with immune checkpoint inhibitors. This review examines the aberrant epigenetic regulation of breast cancer initiation and progression, focusing on modifications related to estrogen signaling, drug resistance, cancer progression, and the epithelial–mesenchymal transition (EMT). It examines existing epigenetic drugs for treating breast cancer, including agents that modify DNA, inhibitors of histone acetyltransferases, histone deacetylases, histone methyltransferases, and histone demethyltransferases. It also delves into ongoing studies on combining epidrugs with other therapies and addresses the upcoming obstacles in this field. Full article

Atopic dermatitis (AD) or eczema is an important inflammatory chronic skin disease that brings many complications in its management and treatment. Although several chemical agents are used for treatment, the search for better anti-inflammatory and antibacterial agents of plant origin has been ongoing, since natural compounds, it is commonly believed, are less dangerous than synthetic ones. Therefore, the present study explored a medicinal plant—Eclipta prostrata (L.) L.—for its anti-inflammatory activity alone and in combination with a non-steroidal anti-inflammatory drug (NSAID), diclofenac. The plant extract was used to make a cream formulation for treating atopic dermatitis and as an antibacterial agent against Staphylococcus aures, the major infectious agent associated with AD. The phytochemical analysis of the E. prostrata extract showed the presence of various phytochemicals, including flavonoids, Tannin, saponin, terpenoids, glycosides, phenol, alkaloids, quinone, and protein. The GC-MS profiling of methanolic E. prostrata extract was performed predicted the presence of twenty important phytochemicals, including 2-[5-(2-Hydroxypropyl) oxolan-2-yl]propanoic acid, dl-Menthol, dodecane, undecane, 4,7-dimethyl-, dodecane, 2,6,10-trimethyl-, decane, 2,3,5,8-tetramethyl-, cholest-5-en-3-ol, (3.alpha.)-, TMS derivative, cyclopropane carboxylic acid, 1-hydroxy-, (2,6-di-t-butyl-4-methylphenyl) ester, alpha.-farnesene, propanoic acid, 2-methyl-, 2-ethyl-1-propyl-1,3-propanediyl ester, diethyl phthalate, corticosterone, 2-methylpropionate, hentriacontan-13-ol, O-TMS, phthalic acid, 2,4-dimethylpent-3-yl dodecyl ester, hexasiloxane, 1,1,3,3,5,5,7,7,9,9,11,11-dodecamethyl-, acetic acid, 4-t-butyl-4-hydroxy-1,5-dimethyl-hex-2-ynyl ester, octadecane, 2-methyl- octacosane, 1-iodo-, nonacosane, and eicosyl isopropyl ether. Using an egg albumin denaturation inhibition assay, the anti-inflammatory activities of E. prostrata alone and in combination with diclofenac were investigated, and they showed 93% and 99% denaturation inhibition at 5 mg concentration of E. prostrata in alone and combination with diclofenac, respectively. Heat-induced haemolysis showed 2.5% and 2.4% of haemolysis at 5 mg of E. prostrata alone and in combination with diclofenac, respectively. An MTT assay performed using L₉₂₉ cells proved that the extract has no cytotoxic effect. The plant extract displayed potential antibacterial activity against Staphylococcus aureus; the growth was inhibited at 1 mg/mL of E. prostrata extract. Thus, based on this evidence, the authors suggest that E. prostrata extract should be studied further for its anti-inflammatory and antibacterial activities and topical application in the treatment of atopic dermatitis. Full article

Catheter-associated urinary tract infections (CAUTIs) cause serious complications among hospitalized patients due to biofilm-forming microorganisms which make treatment ineffective by forming antibiotic-resistant strains. As most CAUTI-causing bacterial pathogens have already developed multidrug resistance, there is an urgent need for alternative antibacterial agents to prevent biofilms on catheter surfaces. As a trial to find out such a potential agent of natural origin, the bark of Tamarix ericoides Rottl., a little-known plant from the Tamaricaceae family, was examined for its antibacterial and antibiofilm activities against one of the major, virulent, CAUTI-causing bacterial pathogens: Enterococcus faecalis. The methanolic T. ericoides bark extract was analyzed for its antibacterial activity using the well diffusion method and microdilution method. Killing kinetics were calculated using time–kill assay, and the ability of biofilm formation and its eradication upon treatment with the T. ericoides bark extract was studied by crystal violet assay. GC-MS analysis was performed to understand the phytochemical presence in the extract. A in vitro bladder model study was performed using extract-coated catheters against E. faecalis, and the effect was visualized using CLSM. The changes in the cell morphology of the bacterium after treatment with the T. ericoides bark extract were observed using SEM. The biocompatibility of the extract towards L₉₂₉ cells was studied by MTT assay. The anti-E. faecalis activity of the extract-coated catheter tube was quantified by viable cell count method, which exposed 20% of growth after five days of contact with E. faecalis. The anti-adhesive property of the T. ericoides bark extract was studied using CLSM. The extract showed potential antibacterial activity, and the lowest inhibitory concentration needed to inhibit the growth of E. faecalis was found to be 2 mg/mL. The GC-MS analysis of the methanolic fractions of the T. ericoides bark extract revealed the presence of major phytochemicals, such as diethyl phthalate, pentadecanoic acid, methyl 6,11-octadecadienoate, cyclopropaneoctanoic acid, 2-[(2-pentylcyclopropyl) methyl]-, methyl ester, erythro-7,8-bromochlorodisparlure, etc., that could be responsible for the antibacterial activity against E. faecalis. The killing kinetics of the extract against E. faecalis was calculated and the extract showed promising antibiofilm activity on polystyrene surfaces. The T. ericoides bark extract effectively reduced the E. faecalis mature biofilms by 75%, 82%, and 83% after treatment with 1X MIC (2 mg/mL), 2X MIC (4 mg/mL), and 3X MIC (6 mg/mL) concentrations, respectively, which was further confirmed by SEM analysis. The anti-adhesive property of the T. ericoides bark extract studied using CLSM revealed a reduction in the biofilm thickness, and the FDA and PI combination revealed the death of 80% of the cells on the extract-coated catheter tube. In addition, SEM analysis showed extensive damage to the E. faecalis cells after the T. ericoides bark extract treatment, and it was not cytotoxic. Hence, after further studies, T. ericoides bark extract with potential antibacterial, antibiofilm, and anti-adhesive activities can be developed as an alternative agent for treating CAUTIs. Full article

Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with diffusion models to address these challenges effectively, aligning with Sustainable Development Goal (SDG) 14: Life Below Water. The methodology leverages the Convolutional Block Attention Module (CBAM), Modified Swin Transformer Block (MSTB), and Diffusion model to enhance the quality of underwater images, thereby improving the accuracy of object detection and classification tasks. This study utilizes the TrashCan dataset, comprising diverse underwater scenes and objects, to validate the proposed method’s efficacy. This study proposes an advanced imaging technique YOLO (you only look once) network (AIT-YOLOv7) for detecting objects in underwater images. This network uses a modified U-Net, which focuses on informative features using a convolutional block channel and spatial attentions for color correction and a modified swin transformer block for resolution enhancement. A novel diffusion model proposed using modified U-Net with ResNet understands the intricate structures in images with underwater objects, which enhances detection capabilities under challenging visual conditions. Thus, AIT-YOLOv7 net precisely detects and classifies different classes of objects present in this dataset. These improvements are crucial for applications in marine ecology research, underwater archeology, and environmental monitoring, where precise identification of marine debris, biological organisms, and submerged artifacts is essential. The proposed framework advances underwater imaging technology and supports the sustainable management of marine resources and conservation efforts. The experimental results demonstrate that state-of-the-art object detection methods, namely SSD, YOLOv3, YOLOv4, and YOLOTrashCan, achieve mean accuracies (mAP@0.5) of 57.19%, 58.12%, 59.78%, and 65.01%, respectively, whereas the proposed AIT-YOLOv7 net reaches a mean accuracy (mAP@0.5) of 81.4% on the TrashCan dataset, showing a 16.39% improvement. Due to this improvement in the accuracy and efficiency of underwater object detection, this research contributes to broader marine science and technology efforts, promoting the better understanding and management of aquatic ecosystems and helping to prevent and reduce the marine pollution, as emphasized in SDG 14. Full article

Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%. Full article

The use of unmanned aerial vehicles (UAVs), or drones, as mobile aerial base stations (MABSs) in Disaster Response Networks (DRNs) has gained significant interest in addressing coverage gaps of user equipment (UE) and establishing ubiquitous connectivity. In the event of natural disasters, the traditional base station is often destroyed, leading to significant challenges for UEs in establishing communication with emergency services. This study explores the deployment of MABS to provide network service to terrestrial users in a geographical area after a disaster. The UEs are organized into clusters at safe locations or evacuation shelters as part of the communication infrastructure. The main goal is to provide regular wireless communication for geographically dispersed users using Long-Term Evolution (LTE) technology. The MABS traveling at an average speed of 50 km/h visits different cluster centroids determined by the Affinity Propagation Clustering (APC) algorithm. A combination of graph theory and a Genetic Algorithm (GA) was used through mutators with a fitness function to obtain the most efficient flyable paths through an evolution pool of 100 generations. The efficiency of the proposed algorithm was compared with the benchmark fitness function and analyzed using the number of serviced UE performance indicators. System-level simulations were used to evaluate the performance of the proposed new fitness function in terms of the UEs served by the MABS after the MABS deployment, fitness score, service ratio, and path smoothness ratio. The results show that the proposed fitness function improved the overall service of UEs after MABS deployment and the fitness score, service ratio, and path smoothness ratio under a given number of MABS. Full article

For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports. Full article

This paper presents a mathematical model for stacks of direct methanol fuel cells (DMFCs) using an optimised method. In order to reduce the sum of squared errors (SSE) in calculating the polarisation profile, the suggested technique makes use of simulated experimental data. Given that DMFC is one of the viable fuel cell choices, developing an appropriate model is essential for cost reduction. However, resolving this issue has proven difficult due to its complex and highly nonlinear character, particularly when adjusting the DMFC model to various operating temperatures. By combining the algorithm and the objective function, the current work introduces a novel method called LSHADE (ELSHADE) for determining the parameters of the DMFC model. This technique seeks to accurately identify DMFCs’ characteristics. The ELSHADE method consists of two stages, the first of which is controlled by a reliable mutation process and the latter by a chaotic approach. The study also recommends an improved Newton–Raphson (INR) approach to deal with the chaotic nature of the I-V curve equation. The findings show that, when used on actual experimental data, the ELSHADE-INR technique outperforms existing algorithms in a variety of statistical metrics for accurately identifying global solutions. Full article

The increasing prevalence of the Internet of Things (IoT) as the primary networking infrastructure in a future society, driven by a strong focus on sustainability and data, is noteworthy. A significant concern associated with the widespread use of Internet of Things (IoT) devices is the insufficient availability of viable strategies for effectively sustaining their power supply and ensuring their uninterrupted functionality. The ability of RF energy-harvesting systems to externally replenish batteries serves as a primary driver for the development of these technologies. To effectively mitigate concerns related to wireless technology, it is imperative to adhere strictly to the mandated limitations on electromagnetic field emissions. A TA broadband polarization-reconfigurable Y-shaped monopole antenna that is improved with a SADEA-tuned smart metasurface is one technique that has been proposed in order to accomplish this goal. A Y-shaped printed monopole antenna is first taken into consideration. To comprehend the process of polarization reconfigurability transitioning from linear to circular polarization (CP), a BAR 50-02 V RF PIN Diode is employed to shorten one of the parasitic conducting strips to the ground plane. A SADEA-driven metasurface, which utilizes the artificial intelligence-driven surrogate model-assisted differential evolution for antenna synthesis, is devised and positioned beneath the radiator to optimize performance trade-offs while increasing the antenna’s gain and bandwidth. The ultimate prototype achieves the following: an impedance bandwidth of 2.58 GHz (3.27–5.85 GHz, 48.45%); an axial bandwidth of 1.25 GHz (4.19–5.44 GHz, 25.96%); a peak gain exceeding 8.45 dBic; and when a highly efficient rectifier is integrated, the maximum RF-DC conversion efficiency of 73.82% and DC output of 5.44 V are obtained. Based on the results mentioned earlier, it is considered appropriate to supply power to intelligent sensors and reduce reliance on batteries via RF energy-harvesting mechanisms implemented in hybrid wireless applications. Full article

Profit maximization is critical in the control of power system networks for both power providers and users. Electrical energy is freely accessible in the electrical grid during off-peak hours, with storage units helping to store excess energy and assist the electrical grid during high-demand situations. Such techniques promote grid stability and ensure safe operation. Because renewable resources are intermittent, energy storage technologies are especially significant in renewable-associated power systems. Vehicle-to-grid (V2G) technology has recently acquired popularity in preserving power grid stability in the presence of renewable resources.V2G technology employs automobiles as mobile storage devices and focuses on the efficient utilization of extra power available during off-peak hours. The goal of this work is to improve the functioning of a V2G system in a power network to reduce energy production costs while increasing system profitability. This study for deregulated power environments also depicts the influence of V2G mixing on system voltage profile and locational marginal pricing (LMP), as well as the performance of the Unified Power Flow Controller (UPFC) on system economics. The MiPower simulation program is used in the study to find the best placement of the power storage unit for the modified IEEE 14-bus system. Full article

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks. Full article

This study aims to design a compact antenna structure suitable for implantable devices, with a broad frequency range covering various bands such as the Industrial Scientific and Medical band (868–868.6 MHz, 902–928 MHz, 5.725–5.875 GHz), the Wireless Medical Telemetry Service (WMTS) band, a subset of the unlicensed 3.5–4.5 GHz ultra-wideband (UWB) that is free of interference, and various Wi-Fi spectra (3.6 GHz, 4.9 GHz, 5 GHz, 5.9 GHz, 6 GHz). The antenna supports both low and high frequencies for efficient data transfer and is compatible with various communication technologies. The antenna features an asynchronous-meandered radiator, a parasitic patch, and an open-ended square ring-shaped ground plane. The antenna is deployed deep inside the muscle layer of a rectangular phantom below the skin and fat layer at a depth of 7 mm for numerical simulation. Furthermore, the antenna is deployed in a cylindrical phantom and bent to check the suitability for different organs. A prototype of the antenna is created, and its reflection coefficient and radiation patterns are measured in fresh pork tissue. The proposed antenna is considered a suitable candidate for implantable technology compared to other designs reported in the literature. It can be observed that the proposed antenna in this study has the smallest volume (75 mm³) and widest bandwidth (181.8% for 0.86 GHz, 9.58% for 1.43 GHz, and 285.7% for the UWB subset and Wi-Fi). It also has the highest gain (−26 dBi for ISM, −14 dBi for WMTS, and −14.2 dBi for UWB subset and Wi-Fi) compared to other antennas in the literature. In addition, the SAR values for the proposed antenna are well below the safety limits prescribed by IEEE Std C95.1-1999, with SAR values of 0.409 W/Kg for 0.8 GHz, 0.534 W/Kg for 1.43 GHz, 0.529 W/Kg for 3.5 GHz, and 0.665 W/Kg for 5.5 GHz when the applied input power is 10 mW. Overall, the proposed antenna in this study demonstrates superior performance compared to existing tri-band implantable antennas in terms of size, bandwidth, gain, and SAR values. Full article

This manuscript presents a thorough review of unitized regenerative fuel cells (URFCs) and their importance in Remote Area Power Supply (RAPS). In RAPS systems that utilize solar and hydrogen power, which typically include photovoltaic modules, a proton exchange membrane (PEM) electrolyzer, hydrogen gas storage, and PEM fuel cells, the cost of these systems is currently higher compared to conventional RAPS systems that employ diesel generators or batteries. URFCs offer a potential solution to reduce the expenses of solar hydrogen renewable energy systems in RAPS by combining the functionalities of the electrolyzer and fuel cell into a single unit, thereby eliminating the need to purchase separate and costly electrolyzer and fuel cell units. URFCs are particularly well-suited for RAPS applications because the electrolyzer and fuel cell do not need to operate simultaneously. In electrolyzer mode, URFCs function similarly to stand-alone electrolyzers. However, in fuel cell mode, the performance of URFCs is inferior to that of stand-alone fuel cells. The presented review summarizes the past, present, and future of URFCs with details on the operating modes of URFCs, limitations and technical challenges, and applications. Solar hydrogen renewable energy applications in RAPS and challenges facing solar hydrogen renewable energy in the RAPS is discussed in detail. Full article

This paper presents the design and analysis of a modified double-ended forward converter (DEFC) for stepper motor-based robotic applications. The proposed converter topology provides galvanic isolation between the input and output while also higher efficiency with a smooth operative system, making it suitable for use in robotic systems that require both power and control signals to be transmitted. The paper also discusses the control strategy for the converter, which uses Proportional Integral (PI) to regulate the output voltage and current. The control strategy is implemented using a microcontroller-based system, which provides precise control of the output parameters. The converter is tested using a stepper motor-based load, and the results demonstrate the effectiveness of the proposed topology and control strategy. In addition to the experimental results, the paper also presents a detailed analysis of the converter’s performance. The analysis includes the input voltage and current, capacitor voltage, MOSFET parameters, output voltage and current, and calculation of efficiency. The analysis results show that the proposed converter topology and control strategy offer high efficiency comparing to existing converting approaches. Overall, the proposed double-ended forward converter offers a suitable solution for stepper motor-based robotic applications, providing efficient and reliable power and control signals. The results demonstrate the effectiveness of the proposed converter topology and control strategy, making it a promising option for use in future robotic systems. Full article