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This study explores the potential of cold atmospheric plasma (CAP) to facilitate the delivery of large-molecule drugs to the brain. The blood–brain barrier (BBB) restricts the passage of most drugs, hindering treatment for neurological disorders. CAP generates reactive oxygen and nitrogen species (RONS) that may disrupt the BBB’s tight junctions, potentially increasing drug permeability. An in vitro BBB model and an immortalized cell line (bEND.3) were used in this experiment. Fluorescein isothiocyanate dextran (FD-4), a model drug, was added to the cells to determine drug permeability. Custom microplasma was used to produce reactive oxygen species (ROS). Trans-endothelial electrical resistance (TEER) measurements assessed the integrity of the BBB after the CAP treatment. A decrease in TEER was observed in the CAP-treated group compared to the controls, suggesting increased permeability. Additionally, fluorescence intensity measurements from the basal side of the trans-well plate indicated higher drug passage in the CAP-treated group. Moreover, the higher presence of ROS in the plasma-treated cells confirmed the potential of CAP in drug delivery. These findings suggest that CAP may be a promising approach for enhancing brain drug delivery.

Background: The provision of dental care in Pakistan is limited, with less than 5% of the population having access to qualified dental practitioners. The lack of contextually relevant local dental guidelines further adds to the problem. We developed clinical practice guidelines (CPGs) and referral pathways to improve primary care for common oral diseases. Methods: Using the GRADE-ADOLOPMENT approach, recommendations from source guidelines (developed in Europe and the United States) were adopted (retained as is or with minor changes), adapted (modified according to the local context), or excluded (omitted due to lack of local relevance). The guidelines included diseases such as periodontitis, dental pain, intraoral swelling, and oral cavity malignancies. The end result was a set of locally relevant CPGs, which were then used to formulate referral pathways, with the incorporated suggestions being based on a thorough evidence review process. Results: We included four recommendations, three of which were adopted with minor modifications to the referral pathway. These changes focused on assessing potentially malignant oral conditions and counseling for risk factors. No content changes were needed for the CPGs of the other two disorders. We developed referral pathways for three specific oral conditions, detailing primary care physicians’ roles in diagnosis, initial treatment, and timely referral. Conclusion: Contextually relevant dental CPGs and referral pathways can improve patient outcomes in Pakistan. Our study produced four additional recommendations focused on risk factor counselling and mitigation, which could potentially reduce the burden of oral malignancies in our local population.

Halophytes are naturally salt-tolerant plants with immense potential to become alternate crops for saline lands. While their economic benefits have gained increasing attention, often, the roles of halophytes in addressing different climate change-related issues are overlooked. Halophytes can be a renewable resource for clean ‘carbon-neutral’ energy by serving as biofuel or biogas feedstock, help in the sequestration of rising CO2 as well as the phytoremediation of various pollutants, can be a good source of food and fodder thereby help in achieving food security in arid/saline areas, can help in protection and biodiversity conservation in various ecosystems, and can provide livelihood to poor local communities inhabiting barren lands. This review also attempts to highlight various usages of halophytes in connection with a global change perspective. However, there are still many challenges such as economic viability, customer preferences, environmental impacts, and scale-up challenges, which need further research, innovation, effective policies, and collaboration. In general, this review provides a synthesis of various biological, environmental, and socioeconomic aspects of halophytes to fully exploit the potential of halophytes for human welfare and combating global climate changes.

Background and Objectives: Rosiridin is a monoterpene with outstanding monoamine inhibitory activity that is useful to treat depressive episodes and rapid-onset dementia. The current investigation aims to evaluate the neurologically protective impact of rosiridin, which opposes aluminum chloride (AlCl3) and causes memory dysfunction in rats. Materials and Methods: Memory impairment was developed in Wistar rats by administering AlCl3 (100 mg/kg p.o.) orally for 42 days and then supplemented with rosiridin at 10 and 20 mg/kg/p.o. Upon completion of the investigation, the behavior factor was performed utilizing the Y-maze, Morris Water Maze, and open field tests. Estimating numerous biological factors, such as nitric oxide (NO), oxidative stress (malondialdehyde MDA), acetylcholinesterase (AChE), butyrylcholinesterase levels (BuChE), antioxidants (glutathione GSH, catalase CAT, and superoxide dismutases SODs) and neurotransmitter (serotonin-5HT, dopamine-DA, acetylcholine-Ach) in the brain. Furthermore, interleukin-6 (IL-6), IL-1β, tumor necrosis factor (TNF-α), brain-derived neurotrophic factor (BNDF), nuclear factor kappa B (NFᴋB), phosphatidylinositol 3-kinase (PI3K), and pAkt were assessed in the diffused brain cells. Results: The rosiridin-treated group significantly improved in terms of behavioral parameters, including in the Y-maze, Morris Water Maze, and open field tests. Further, rosiridin restored biochemical parameters, including NO, oxidative stress AChE, BuChE, antioxidants, neurotransmitters, IL-6, IL-1β, TNF-α, BNDF, NFᴋB, PI3K, and pAkt compared to AlCl3. Conclusions: The current investigation reveals that rosiridin could ameliorate the impairment of memory that AlCl3 causes in rats via improvements in behavioral and restored biochemical parameters.

Background/Objectives: Inflammation and analgesia are two prominent symptoms and often lead to chronic medical conditions. To control inflammation and analgesia, many marketed drugs are in practice but the majority of them have severe side effects. Methods: This study involved the synthesis of a pivalate-based Michael product and evaluated it for in vitro COX-1, COX-2, and 5-LOX inhibitory potentials using specific assays. Molecular docking studies were also assessed. Based on the in vitro results, the compound was also subjected to in vivo anti-inflammatory and antinociceptive studies. Results: The pivalate-based Michael product (MAK01) was synthesized by an organocatalytic asymmetric Michael addition of ethyl isobutyrate to N-phenylmaleimide with an isolated yield of 96%. The structure of the compound was confirmed through 1H and 13C NMR analyses. The observed IC50 values for COX-1, COX-2, and 5-LOX were 314, 130, and 105 μg/mL, respectively. The molecular docking studies on the synthesized compound showed binding interactions with the minimized pockets of the respective enzymes. In a carrageenan model, a percent reduction in edema when administered at 10 mg/kg (a reduction of 33.3 ± 0.77% at the second hour), 20 mg/kg (a reduction of 34.7 ± 0.74% at the second hour), and 30 mg/kg (a reduction of 40.58% ± 0.84% after the fifth hour) was observed. The compound showed a significant response at concentrations of 50, 100, and 150 mg/kg with latency times of 10.32 ± 0.82, 12.16 ± 0.51, and 12.93 ± 0.45 s, respectively. Conclusion: In this study, we synthesized a pivalate-based Michael product for the first time. Moreover, based on its rationality and potency, it was found to be an effective future medicine for the management of analgesia and inflammation.

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.

It is evident that skew polynomials offer promising directions for developing cryptographic schemes. This paper focuses on exploring skew polynomials and studying their properties with the aim of exploring their potential applications in fields such as cryptography and combinatorics. We begin by deriving the concept of resultants for bivariate skew polynomials. Then, we employ the derived resultant to incrementally eliminate indeterminates in skew polynomial systems, utilising both direct and modular approaches. Finally, we discuss some applications of the derived resultant, including cryptographic schemes (such as Diffie–Hellman) and combinatorial identities (such as Pascal’s identity). We start by considering a bivariate skew polynomial system with two indeterminates; our intention is to isolate and eliminate one of the indeterminates to reduce the system to a simpler form (that is, relying only on one indeterminate in this case). The methodology is composed of two main techniques; in the first technique, we apply our definition of a (bivariate) resultant via a Sylvester-style matrix directly from the polynomials’ coefficients, while the second is based on modular methods where we compute the resultant by using evaluation and interpolation approaches. The idea of this second technique is that instead of computing the resultant directly from the coefficients, we propose to evaluate the polynomials at a set of valid points to compute its corresponding set of partial resultants first; then, we can deduce the original resultant by combining all these partial resultants using an interpolation technique by utilising a theorem we have established.

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OSs). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OSs, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OSs; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model’s accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems.

The relationship between plant productivity, measured according to biomass and species richness, is a fundamental focal point in community ecology, as it provides the basis for understanding plant responses or adaptive strategies. Although studies have been conducted on plant biomass and environmental factors, research concerning mountainous grassland areas is scarce. Therefore, the aim of the present study was to examine the influence of environmental factors on aboveground plant biomass in the mountainous grassland of the Mountain Zebra National Park, South Africa. Biomass distribution was uneven within the park, owing to certain species having relatively higher biomass values. These differences may be attributed to the chemical and physical properties of the soil, including carbon and nitrogen content, soil pH, and soil texture (sand, silt, and coarse fragments). A disc pasture meter was used to collect biomass data. Multiple regression analysis revealed that most environmental factors did not significantly influence plant biomass. The only environmental factor influencing plant biomass was soil pH; the influences of other factors were not statistically significant. The results of this study elucidate the interactions of environmental factors with plant biomass. Future research could investigate how environmental factors influence plant biomass, both below and above the ground in mountainous grassland.

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

The research purpose of this work is to examine the adsorption interaction of gaseous molecules (GMs), such as NO, NO2, SO, SO2, and SO3, with the surface of sodium magnesium phosphate NaMgPO4 (033), in a neutral medium, using two different computational methods: density functional theory (DFT) and Monte Carlo dynamic simulation (MCDS). Various quantum and dynamic descriptors, such as global and local quantum descriptors and the radial distribution function (RDF), are also evaluated and discussed. The data obtained revealed that the NO2 molecule has a small energy gap (0.363 eV) when compared to the other molecules, which means that it is highly reactive and is liable to adsorb, or stick, to the surface of NaMgPO4 (033). Furthermore, this NO2 molecule exhibits good adsorption in aqueous media, returning to the lowest global hardness value (0.1815 eV). MCDS predicted adsorption energies of −874.03, −819.94, −924.81, −876.33, and −977.71 kcal/mol for NO, NO2, SO, SO2, and SO3, respectively. These energies are negative, implying that adsorption occurs spontaneously. Thus, the side views indicated which SO, NO, and SO3 molecules are adsorbed in parallel to NaMgPO4 and the other SO2 and NO2 molecules are adsorbed horizontally. Eventually, the theoretical results reveal that the studied gaseous molecules interact strongly with NaMgPO4. The result obtained by radial distribution function (RDF) analysis for all complexes below 3.5 Å confirm that the adsorption is of the chemi1cal type.

Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads. However, the packet delivery ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose the Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in the case of node failures, compared with other forwarding-based protocols that require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments.

Lower back pain (LBP) is a widely prevalent global health issue, affecting over half a billion people and remaining the leading cause of years lived with disability (YLDs). LBP significantly impacts healthcare systems, with substantial costs related to surgical procedures and lost workdays. Vertebrogenic back pain (VBP), characterized by specific clinical symptoms and associated with Modic changes (MC) in vertebral endplates, best seen on MRI, is a significant subset of LBP. This paper explores the pathophysiology, diagnosis, and current reports and studies focusing on VBP and the role of basivertebral nerve (BVN) ablation as a therapeutic intervention. Multiple studies, including randomized controlled trials (RCTs) and meta-analyses, demonstrate the efficacy of BVN ablation in reducing pain and improving function in patients with chronic LBP associated with MC.

As the world braces for an era of ubiquitous and seamless connectivity, hybrid beamforming stands out as a beacon guiding the evolutionary path of wireless communication technologies. Several hybrid beamforming technologies are explored for millimeter-wave multiple-input multi-output (MIMO) communication. The aim is to provide a roadmap for hybrid beamforming that enhances wireless fidelity. In this systematic review, a detailed literature review of algorithms/techniques used in hybrid beamforming along with performance metrics, characteristics, limitations, as well as performance evaluations are provided to enable communication compatible with modern Wireless Intelligent Transport Systems (WITSs). Further, an in-depth analysis of the mmWave hybrid beamforming landscape is provided based on user, link, band, scattering, structure, duplex, carrier, network, applications, codebook, and reflecting intelligent surfaces to optimize system design and performance across diversified user scenarios. Furthermore, the current research trends for hybrid beamforming are provided to enable the development of advanced wireless communication systems with optimized performance and efficiency. Finally, challenges, solutions, and future research directions are provided so that this systematic review can serve as a touchstone for academics and industry professionals alike. The systematic review aims to equip researchers with a deep understanding of the current state of the art and thereby enable the development of next-generation communication in WITSs that are not only adept at coping with contemporary demands but are also future-proofed to assimilate upcoming trends and innovations.

This study aims to propose a central composite design (CCD) combined with response surface methodology (RSM) to create a statistical experimental design. A new parametric optimization of entropy generation is presented. The flow behavior of magnetohydrodynamic hybrid nanofluid (HNF) flow through two flat contracting expanding plates of channel alongside radiative heat transmission was considered. The lower fixed plate was externally heated whereas the upper porous plate was cooled by injecting a coolant fluid with a uniform velocity inside the channel. The resulting equations were solved by the Homotopic Analysis Method using MATHEMATICA 10 and Minitab 17.1. The design consists of several input factors, namely a magnetic field parameter (M), radiation parameter (N) and group parameter (Br/A1). To obtain the values of flow response parameters, numerical experiments were used. Variables, especially the entropy generation (Ne), were considered for each combination of design. The resulting RSM empirical model obtained a high coefficient of determination, reaching 99.97% for the entropy generation number (Ne). These values show an excellent fit of the model to the data.

Tchebichef polynomials (TPs) play a crucial role in various fields of mathematics and applied sciences, including numerical analysis, image and signal processing, and computer vision. This is due to the unique properties of the TPs and their remarkable performance. Nowadays, the demand for high-quality images (2D signals) is increasing and is expected to continue growing. The processing of these signals requires the generation of accurate and fast polynomials. The existing algorithms generate the TPs sequentially, and this is considered as computationally costly for high-order and larger-sized polynomials. To this end, we present a new efficient solution to overcome the limitation of sequential algorithms. The presented algorithm uses the parallel processing paradigm to leverage the computation cost. This is performed by utilizing the multicore and multithreading features of a CPU. The implementation of multithreaded algorithms for computing TP coefficients segments the computations into sub-tasks. These sub-tasks are executed concurrently on several threads across the available cores. The performance of the multithreaded algorithm is evaluated on various TP sizes, which demonstrates a significant improvement in computation time. Furthermore, a selection for the appropriate number of threads for the proposed algorithm is introduced. The results reveal that the proposed algorithm enhances the computation performance to provide a quick, steady, and accurate computation of the TP coefficients, making it a practical solution for different applications.

Fungal keratitis is a rare yet severe infection of the cornea. Fungal species distribution depends on the climate and socioeconomic status and can show regional variation. This retrospective single-center study was conducted at a tertiary eye care center and the collaborating Institute of Medical Microbiology in Switzerland. On investigating all fungal-positive corneal scrapings and contact lens assessments of patients with keratitis from January 2012 to December 2023, 206 patients were identified, of which 113 (54.9%) were female. The median age was 38 (IQR 29.8, [18–93]), and 154 (74.8%) applied contact lenses. The most commonly found pathogen was Candida spp., followed by Fusarium spp. Molds were 1.8 times more common than yeasts. Linear regression showed no significant increase or decrease in the infection rate over time (p = 0.5). In addition, 10 patients (4.9%) were found to have coinfections with Acanthamoeba, 11 (5.3%) with HSV-1, none with HSV-2, and 4 (1.9%) with VZV. This study provides a long-term overview of fungal-positive corneal scrapings and contact lens specimens of patients with fungal keratitis. Based on our results, coinfections with Acanthamoeba, HSV, and VZV are frequent, especially in patients wearing contact lenses. Thus, wearing contact lenses may facilitate coinfection in fungal keratitis.

Bovine tuberculosis is endemic in Nigeria with control measures as provided by the laws of the country being minimally enforced mostly at the abattoirs only. This study focused on bovine tuberculosis in Adamawa and Gombe States. Tuberculosis lesions were observed in 183 of 13,688 slaughtered cattle in the regions between June and December 2020. Analysis of tissue samples resulted in 17 Mycobacterium bovis isolates, predominantly from Gombe State. Spoligotyping identified four spoligotypes, including SB0944, SB1025, SB1104, and one novel pattern. MIRU-VNTR analysis further differentiated these spoligotypes into eight profiles. All isolates belonged to the Af1 clonal complex. The study emphasises the need for broader coverage and more isolates to comprehensively understand the molecular epidemiology of bovine tuberculosis in Nigeria. To enhance research and surveillance, a cost-effective approach is proposed, utilising a discriminatory VNTR panel comprising five or nine loci. The five-locus panel consists of ETR-C, QUB26, QUB11b, MIRU04, and QUB323. Alternatively, the nine-locus panel includes ETR-A, ETR-B, QUB11a, and MIRU26. Implementing this approach would provide valuable insights into the genetic diversity of M. bovis strains in Nigeria. These findings are crucial for developing effective control measures and minimising the impact of bovine tuberculosis on both animal and human health.

The production of pro-inflammatory and anti-inflammatory cytokines, as well as adipocyte differentiation and fat accumulation in the 3T3-L1 mouse embryo fibroblast cell line and the human monocytic cell line THP-1 were measured to determine the anti-inflammatory and antiadipogenic effects of ethanolic extracts of verjuice (unripe grape juice (Vitis vinifera L.)), Salvia officinalis L., and Alchemilla vulgaris L. On both cell lines, the three extracts had much greater cytostatic effects than cytotoxic effects. With an IC50 of 505 μg/mL, S. officinalis had the highest cytostatic effect on THP-1-derived macrophages. After treatment with 125 μg/mL, the three extracts dramatically reduced the LPS-induced NO generation in THP-1-derived macrophages from 80 μM to control values after treatment with 125 µg/mL. Furthermore, the extracts reduced the levels of TNF-α and IL-6 production in a dose-dependent manner with the highest effects reached at 250 µg/mL. The production of TNF-α decreased at higher levels compared to IL-6 production. V. vinifera, S. officinalis, and A. vulgaris extracts improved the production levels of IL-10 from 32 pg/mL to 86 pg/mL, 98 pg/mL, and 80 pg/mL at an extract concentration of 125 µg/mL, respectively. The adipocyte differentiation and fat accumulation in 3T3-L1 were decreased to 20% of control values after treatment with plant extracts. Taken together, these results suggest that V. vinifera, S. officinalis, and A. vulgaris likely exert their anti-obesity effects through cytostatic actions and modulation of pro-inflammatory and anti-inflammatory cytokine production, as well as by reducing adipocyte differentiation and fat accumulation.

Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various recurrence algorithms have been proposed so far to address the challenge of numerical instability for large values of orders and signal sizes. The computation of DKraP coefficients was typically computed using sequential algorithms, which are computationally extensive for large order values and polynomial sizes. To this end, this paper introduces a computationally efficient solution that utilizes the parallel processing capabilities of modern central processing units (CPUs), namely the availability of multiple cores and multithreading. The proposed multi-threaded implementations for computing DKraP coefficients divide the computations into multiple independent tasks, which are executed concurrently by different threads distributed among the independent cores. This multi-threaded approach has been evaluated across a range of DKraP sizes and various values of polynomial parameters. The results show that the proposed method achieves a significant reduction in computation time. In addition, the proposed method has the added benefit of applying to larger polynomial sizes and a wider range of Krawtchouk polynomial parameters. Furthermore, an accurate and appropriate selection scheme of the recurrence algorithm is introduced. The proposed approach introduced in this paper makes the DKraP coefficient computation an attractive solution for a variety of applications.

Ophthalmologists’ diagnostic and treatment competence in Acanthamoeba keratitis varies widely. This investigator-initiated, retrospective, single-center chart review examined the electronic patient files regarding PCR-positive Acanthamoeba keratitis. We included corneal and contact lens assessments. We further reviewed the patient’s medical history, corneal scraping results regarding viral or fungal co-infections, and the duration from symptom onset to final diagnosis. We identified 59 eyes of 52 patients from February 2010 to February 2023, with 31 of 52 (59.6%) being female patients. The median (IQR, range) patient age was 33 (25.3 to 45.5 [13 to 90]) years, and the mean (SD, range) time to diagnosis after symptom onset was 18 (10.5 to 35 [3 to 70]) days. Overall, 7 of 52 (7.7%) patients displayed a bilateral Acanthamoeba infection, and 48 (92.3%) used contact lenses at symptom onset. Regarding other microbiological co-infections, we found virologic PCR testing in 45 of 52 (86.5%) patients, with 3 (6.7%) positive corneal scrapings. Fungal cultures were performed in 49 of 52 (94.2%) patients, with 5 (10.2%) positive corneal scrapings. The medical treatment success rate was 45/46 (97.8%). This study raises awareness of patient education in contact lens handling and screens for further microbial co-infections in suspected Acanthamoeba cases.

This study examines the behavior of single-walled carbon nanotubes (SWCNTs) suspended in a water-based ionic solution, driven by the combined mechanisms of electroosmosis and peristalsis through ciliated media. The inclusion of nanoparticles in ionic fluid expands the range of potential applications and allows for the tailoring of properties to suit specific needs. This interaction between ionic fluids and nanomaterials results in advancements in various fields, including energy storage, electronics, biomedical engineering, and environmental remediation. The analysis investigates the influence of a transverse magnetic field, thermal radiation, and mixed convection acting on the channel walls. The novel physical outcomes include enhanced propulsion efficiency due to SWCNTs, understanding the influence of thermal radiation on fluid behavior and heat exchange, elucidation of the interactions between SWCNTs and the nanofluid, and recognizing implications for microfluidics and biomedical engineering. The Poisson–Boltzmann ionic distribution is linearized using the modified Debye–Hückel approximation. By employing real-world approximations, the governing equations are simplified using long-wavelength and low-Reynolds-number approximation. Conducting sensitivity analyses or exploring the impact of higher-order corrections on the model’s predictions in recent literature might alter the results significantly. This acknowledges the complexities of the modeling process and sets the groundwork for further enhancement and investigation. The resulting nonlinear system of equations is solved through regular perturbation techniques, and graphical representations showcase the variation in significant physical parameters. This study also discusses pumping and trapping phenomena in the context of relevant parameters.

In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2 = 0.952 and a cross-validation value of Q2 = 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity.

Microplasma irradiation is a promising technique for the transdermal delivery of high-molecular-weight drugs. In this technique, microplasma components interact with the skin surface or cell membranes, allowing the drugs to penetrate. For efficient and safe drug delivery, it is crucial to understand these interactions. To this end, this study investigated the effects of microplasma irradiation on cellular lipids, particularly those associated with cell membranes. Rat intestinal epithelial cells were treated with microplasma irradiation at two different voltages (4.0 kV or 4.5 kV). An untargeted lipidomic was conducted using liquid chromatography–mass spectrometry (LC/MS) technique. The results revealed that microplasma irradiation at 4.0 kV induces a significant increase in cell membrane lipids within 10 min post-irradiation. All major cell membrane lipids, including phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, and sphingomyelin, exhibited increases of over 25% within this short timeframe. Notably, this effect is transient, as lipid levels return to their baseline after 12 h. Furthermore, no significant differences in live and apoptotic cell percentages were observed between the control and 12 h post-irradiated cells. In contrast, irradiation at 4.5 kV did not elicit significant changes in cell membrane lipids, correlating with the absence of drug absorption under this condition. Hence, our study unveiled a correlation between the rapid increase in cell membrane lipids and enhanced drug absorption in microplasma-irradiated cells. This lipid augmentation potentially enhances membrane fluidity and permeability, thus facilitating drug absorption. Beyond elucidating the mechanisms and safety of microplasma-based drug delivery, our research provides valuable insights for advancing various microplasma-based biomedical technologies.

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals’ haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.

Fructilactobacillus sanfranciscensis is a significant and dominant bacterial species of sourdough microbiota from ecological and functional perspectives. Despite the remarkable prevalence of different strains of this species in sourdoughs worldwide, the drivers behind the genetic diversity of this species needed to be clarified. In this research, 14 F. sanfranciscensis strains were isolated from sourdough samples to evaluate the genetic diversity and variation in metabolic traits. These 14 and 31 other strains (obtained from the NCBI database) genomes were compared. The values for genome size and GC content, on average, turned out to 1.31 Mbp and 34.25%, respectively. In 45 F. sanfranciscensis strains, there were 162 core genes and 0 to 51 unique genes present in each strain. The primary functions of core genes were related to nucleotide, lipid transport, and amino acid, as well as carbohydrate metabolism. The size of core genes accounted for 41.18% of the pan-genome size in 14 F. sanfranciscensis strains, i.e., 0.70 Mbp of 1.70 Mbp. There were genetic variations among the 14 strains involved in carbohydrate utilization and antibiotic resistance. Moreover, exopolysaccharides biosynthesis-related genes were annotated, including epsABD, wxz, wzy. The Type IIA & IE CRISPR-Cas systems, pediocin PA-1 and Lacticin_3147_A1 bacteriocins operons were also discovered in F. sanfranciscensis. These findings can help to select desirable F. sanfranciscensis strains to develop standardized starter culture for sourdough fermentation, and expect to provide traditional fermented pasta with a higher quality and nutritional value for the consumers.

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This study investigated three key factors (technological-related, organisational-related and environmental-related barriers) affecting the adaptation to or integration of developed ICT. It also examined how SMEs in less developed countries can explore the different stages of developed ICT by moving from one stage to the other. The integration of ICT in SMEs is important as technologies have become competitive tools in contemporary business practices. This study is based on a survey of 322 Nigerian SMEs which was successfully validated using the SmartPLS3 software. The quantitative analysis centred on the three hypothesised barriers to measure the extent to which SMEs’ internal and external variables could limit their competitiveness in relation to business expansion and organisational growth. The analysis helped explain some of the critical challenges faced by rural SMEs in an emerging economy such as Nigeria despite the literature’s previous emphasis on the impacts of ICT on the SMEs’ growth and expansion. A major contribution of the study is the development of a distinct model to help SMEs identify the significance of developed ICT and propose a strategy for SMEs to navigate the stages of developed ICT.

This study investigates techniques for medical image classification, specifically focusing on COVID-19 scans obtained through computer tomography (CT). Firstly, handcrafted methods based on feature engineering are explored due to their suitability for training traditional machine learning (TML) classifiers (e.g., Support Vector Machine (SVM)) when faced with limited medical image datasets. In this context, I comprehensively evaluate and compare 27 descriptor sets. More recently, deep learning (DL) models have successfully analyzed and classified natural and medical images. However, the scarcity of well-annotated medical images, particularly those related to COVID-19, presents challenges for training DL models from scratch. Consequently, I leverage deep features extracted from 12 pre-trained DL models for classification tasks. This work presents a comprehensive comparative analysis between TML and DL approaches in COVID-19 image classification.

Dietary protein supplementation has emerged as a promising strategy in combating sarcopenia. Furthermore, searching for alternatives of animal proteins has been a hot topic. The present study aimed to investigate the effects of zein peptides on C2C12 myoblasts and explore their potential molecular mechanisms. The proliferative, cell cycle, and anti-apoptotic activities of zein peptides were evaluated. Peptidomics analysis and transcriptome sequencing were employed to explore the structure-activity relationship and underlying molecular mechanisms. The results indicated that zein peptides (0.05–0.2 mg/mL) exerted a significant proliferation-promoting impact on C2C12 cells, via increasing cell viability by 33.37 to 42.39%. Furthermore, zein peptides significantly increased S phase proportion and decreased the apoptosis rate from 34.08% (model group) to 28.96% in C2C12 cells. In addition, zein peptides exhibited a pronounced anti-apoptotic effect on C2C12 cells. Zein peptides are abundant in branch-chain amino acids, especially leucine. Transcriptomics analysis revealed that zein peptides can promote proliferation, accelerate cell cycle, and improve protein synthesis of muscle cells through mTORC1 and mTORC2 signaling pathways.

This study was designed to determine the fragmentation of sub-lakes in winter and its effects on wintering waterbirds in Poyang Lake. Poyang Lake becomes fragmented in winter, which forms many seasonal sub-lakes every year, and have different environmental characteristics. These sub-lakes significantly impact winter bird habitats and result in susceptibility to various changes, because birds have different distribution responses. A total of 24 sub-lakes were surveyed from one to five vantage points using point count methods in each sub-lake with binoculars, monocular, and a spotting scope for four consecutive winter seasons. The multi-site dissimilarity Sorensen index measures overlapped between two populations, and the R software “iNEXT” package was used to evaluate the sample coverage test of the study area. We observed 58 wintering waterbird species belonging to 9 orders and 15 families from 2016 to 2020. Spearman correlation analysis showed that the species richness of wintering waterbirds was significantly positively correlated with the sub-lake areas and associated with the richness of habitat type. The WNODF analyses were considerably correlated for sites of waterbirds, mainly with the abundance of forage and conservation of habitat form. The outcomes of this study showed that Maying Lake has the highest local beta diversity, whereas Dacha Lake has the lowest local beta diversity contribution (0.007). This study’s findings demonstrate Poyang Lake’s role in waterbird the habitat suitability of waterbirds, especially for foraging and conservation.

Background and Objectives: A polymorphism in the promoter region of the IL-6 gene would influence the level of IL-6 expression in patients with HCV, resulting in a pro-inflammatory response. Few studies have shown the association between −174G>C (rs1800795) and −1363G>T (rs2069827) polymorphisms and HCV infection, and their results have been contradictory. There are no data published in our population to study such an IL-6 stimulus against HCV infection and its impact on RNA secondary structure. Therefore, we isolated human subjects from the province of Punjab, Pakistan. The objective was to screen for IL-6 gene promoter polymorphisms −174G/C and −1363G/T and those correlated with serum concentrations of IL-6 in patients with HCV and compared with a control. Materials and Methods: In conventional PCR, measurement of serum IL-6 by CLIA and statistical analysis were performed to observe the genotype association studies. By integrating bioinformatics and computational tools, our study aimed to provide a comprehensive understanding of how variations in the promoter region of IL-6 may have functional implications on gene expression. Results: The −174G>C and −1363G>T genotypes in the promoter region of patients with HCV were in strong allelic association (Δ = 0.97, p < 0.001). Interestingly, the bioinformatics analysis was well aligned with our experimental data. Conclusions: Based on the data, it can be inferred that IL-6 gene promoter polymorphisms are important in the dysregulation of IL-6 levels in patients with HCV.

Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an integral component of effective diagnosis, informing critical decisions made by cardiologists. This review paper surveys diverse computational intelligence methodologies employed for arrhythmia analysis within the context of the widely utilized MIT-BIH dataset. The paucity of adequately annotated medical datasets significantly impedes advancements in various healthcare domains. Publicly accessible resources such as the MIT-BIH Arrhythmia Database serve as invaluable tools for evaluating and refining computer-assisted diagnosis (CAD) techniques specifically targeted toward arrhythmia detection. However, even this established dataset grapples with the challenge of class imbalance, further complicating its effective analysis. This review explores the current research landscape surrounding the application of graph-based approaches for both anomaly detection and classification within the MIT-BIH database. By analyzing diverse methodologies and their respective accuracies, this investigation aims to empower researchers and practitioners in the field of ECG signal analysis. The ultimate objective is to refine and optimize CAD algorithms, ultimately culminating in improved patient care outcomes.

In running, step rate and step angle are important, but the relationship between the two parameters is not clear in the literature. This study aimed to investigate the effect of step rate manipulation on step angle in running. A group of twenty healthy recreational runners aged between 30 and 59 years who regularly run 15–90 km per week were recruited. Kinematic data were recorded using a motion capture system while running on a treadmill. Participants maintained a self-selected speed and then altered their step rate using a metronome and completed three thirty-second trials at the preferred step rate, 10% above the preferred step rate and 10% below the preferred step rate. The results showed that the step angle is not significantly correlated with the step rate and kept at roughly 37 degrees at the preferred step rate and 10% lower than the preferred step rate but increased to 42 deg when the step rate increased to 10% of the preferred step rate. The step angles were not significantly different between the male and female or between sides. This finding provides an understanding of the association of step rate re-training on swing phase parameters.

Recent advancements in computational fluid dynamics (CFD) have triggered research in the field of heat exchangers. Driven by the need to decrease the size of heat exchangers, many researchers have exploited the higher heat transfer achieved by replacing single-phase flow systems with boiling counterparts. The concept of using mini-channels to provide compact heat exchangers while maintaining heat transfer performance is relatively new. A minimal number of researchers have reported simulations of water-steam systems in mini-channels. This paper presents a numerical study of the heat transfer performance (HTP) of mini channels in a water-steam system using the volume of fluid (VOF) model coupled with the Lee phase change model on commercial CFD software ANSYS. The numerical model consisted of a 1 mm × 1.5 mm × 52 mm channel with boundary conditions: top adiabatic; constant heat flux at the bottom surface; left/right periodic; mass flow inlet and pressure outlet. A mesh independence study was carried out for the proposed model, and simulations were validated against the experimental results of heat transfer versus vapor quality for a wide range of mass and heat fluxes. The VOF model best predicts experimental HTC at high mass fluxes, although the results at low mass fluxes were predicted with reasonable accuracy. Based on the agreement of numerical and numerical results, the VOF model turned out to be a promising candidate for designing compact micro/mini channel heat exchangers.

Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables—gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.

In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques.

Communication technologies have drastically increased the number of wireless networks. Heterogeneous networks have now become an indispensable fact while designing the new networks and the way the data packet moves from device to device opens new challenges for transmitting the packet speedily, with maximum throughput and by consuming only confined energy. Therefore, the present study intends to provide a shrewd communication link among all IoT devices that becomes part of numerous heterogeneous networks. The scrumptious dataflow strategy (SDS) for IoT devices in the heterogeneous network environment is proposed and it would deal with all link selection and dataflow challenges. The SDS would accomplish the targeted output in five steps: Step 1 determines the utility rate of each heterogeneous link. Step 2 develops a link selection attribute (LSA) that gauges the loads of network features used for the link selection process. Step 3 calculates the scores of all heterogeneous networks. Step 4 takes the LSA table and computes the network preference for different scenarios, such as round trip time (RTTP), network throughput, and energy consumption. Step 5 sets the priority of heterogeneous networks based on the scores of network attributes. Performance of the proposed SDS mechanism with state of the art network protocols, such as high-speed packet access (HSPA), content-centric networking (CCN), and dynamic source routing (DSR), was determined by conducting a simulation with NS2 and, consequently, the SDS exhibited its shrewd performance. During comparative analysis, in terms of round trip time, the SDS proved that it utilized only 16.4 milliseconds to reach IoT device 50 and was first among all other protocols. Similarly, for network throughput, at IoT device 50, the throughputs of the SDS are recorded at 40% while the rest of other protocols were dead. Finally, while computing the energy consumption used to reach IoT device 50, the SDS was functional and possessed more than half of its energy compared to the other protocols. The SDS only utilized 302 joules while the rest of the protocols were about to die as they had consumed all of their energy.

Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date very challenging. Although advanced iterative algorithms have been developed to address this challenge, they exhibit slow convergence speed and thus deliver high latency and computational complexity. To overcome this challenge, we propose a computationally efficient conjugate gradient-descent (CGD) algorithm based on the Riemannian manifold in order to optimize the DL training sequence at base station (BS), while improving the convergence rate to provide a fast CSI estimation for an FDD m-MIMO system. To this end, the sum rate and the computational complexity performances of the proposed training solution are compared with the state-of-the-art iterative algorithms. The results show that the proposed training solution maximizes the achievable sum rate performance, while delivering a lower overall computational complexity owing to a faster convergence rate in comparison to the state-of-the-art iterative algorithms.

More than 600 million people globally are estimated to be living with chronic pain. It is one of the most common complaints seen in an outpatient setting, with over half of patients complaining of pain during a visit. Failure to properly diagnose and manage chronic pain is associated with substantial morbidity and mortality, especially when opioids are involved. Furthermore, it is a tremendous financial strain on the healthcare system, as over USD 100 billion is spent yearly in the United States on healthcare costs related to pain management and opioids. This exceeds the costs of diabetes, heart disease, and cancer-related care combined. Being able to properly diagnose, manage, and treat chronic pain conditions can substantially lower morbidity, mortality, and healthcare costs in the United States. This review will outline the current definitions, biopsychosocial model, subclassifications, somatosensory assessments, imaging, clinical prediction models, and treatment modalities associated with chronic pain.

In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures.

In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.

Ginseng has long been widely used for its therapeutic potential. In our current study, we investigated the impact of abiotic stress induced by infrared (IR) radiations and sodium silicate on the upregulation of antioxidant and anti-tyrosinase levels, as well as the total phenolic and total flavonoid contents of the Korean ginseng (Panax ginseng C.A. Meyer) variety Yeonpoong. The RSM-based design was used to optimize ultrasonic-assisted extraction time (1–3 h) and temperature (40–60 °C) for better anti-tyrosinase activity and improved antioxidant potential. The optimal extraction results were obtained with a one-hour extraction time, at a temperature of 40 °C, and with a 1.0 mM sodium silicate treatment. We recorded maximum anti-tyrosinase (53.69%) and antioxidant (40.39%) activities when RSM conditions were kept at 875.2 mg GAE/100 g TPC, and 3219.58 mg catechin/100 g. When 1.0 mM sodium silicate was added to the media and extracted at 40 °C for 1 h, the highest total ginsenoside content (368.09 mg/g) was recorded, with variations in individual ginsenosides. Ginsenosides Rb1, Rd, and F2 were significantly affected by extraction temperature, while Rb2 and Rc were influenced by the sodium silicate concentration. Moreover, ginsenoside F2 increased with the sodium silicate treatment, while the Rg3-S content decreased. Interestingly, higher temperatures favored greater ginsenoside diversity while sodium silicate impacted PPD-type ginsenosides. It was observed that the actual experimental values closely matched the predicted values, and this agreement was statistically significant at a 95% confidence level. Our findings suggest that the application of IR irradiation in hydroponic systems can help to improve the quality of ginseng sprouts when supplemented with sodium silicate in hydroponic media. Optimized extraction conditions using ultrasonication can be helpful in improving antioxidant and anti-tyrosinase activity.

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

Pathogens cause infections and millions of deaths globally, while antipathogens are drugs or treatments designed to combat them. To date, multifunctional nanomaterials (NMs), such as organic, inorganic, and nanocomposites, have attracted significant attention by transforming antipathogen livelihoods. They are very small in size so can quickly pass through the walls of bacterial, fungal, or parasitic cells and viral particles to perform their antipathogenic activity. They are more reactive and have a high band gap, making them more effective than traditional medications. Moreover, due to some pathogen’s resistance to currently available medications, the antipathogen performance of NMs is becoming crucial. Additionally, due to their prospective properties and administration methods, NMs are eventually chosen for cutting-edge applications and therapies, including drug administration and diagnostic tools for antipathogens. Herein, NMs have significant characteristics that can facilitate identifying and eliminating pathogens in real-time. This mini-review analyzes multifunctional NMs as antimicrobial tools and investigates their mode of action. We also discussed the challenges that need to be solved for the utilization of NMs as antipathogens.

Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of machine learning models offers a promising alternative. This study focuses on employing the multivariate adaptive regression splines (MARS) model algorithm, a machine learning method that presents a significant advantage over other models through generating human-understandable piecewise linear equations. The MARS model was trained and tested on a comprehensive dataset to predict essential soil compaction parameters, including optimum water content (wopt) and maximum dry density (ρdmax). The performance of the model was evaluated using coefficient of determination (R2) and root mean square error (RMSE) values. Remarkably, the MARS models showed excellent predictive ability with high R2 and low RMSE, MAE, and relative error values, indicating its robustness and reliability in predicting soil compaction parameters. Through rigorous five-fold cross-validation, the model’s predictions for wopt returned an RMSE of 1.948%, an R2 of 0.893, and an MAE of 1.498%. For ρdmax, the results showcased an RMSE of 0.064 Mg/m3, an R2 of 0.899, and an MAE of 0.050 Mg/m3. When evaluated on unseen data, the model’s performance for wopt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R2 of 0.948. Similarly, for ρdmax, the predictions were characterized by an MAE of 0.047 Mg/m3, RMSE of 0.062 Mg/m3, and R2 of 0.919. The results also indicated that the MARS model outperformed previously developed machine learning models, suggesting its potential to replace conventional testing methods. The successful application of the MARS model could revolutionize the geotechnical field through providing quick and reliable predictions of soil compaction parameters, improving efficiency for construction projects. Lastly, a variable importance analysis was performed on the model to assess how input variables affect its outcomes. It was found that fine content (Cf) and plastic limit (PL) have the greatest impact on compaction parameters.

This paper takes a cursory look at the problem of inadequate power supply in the rural healthcare centres of a developing country, specifically Nigeria, and proffers strategies to address this issue through the design of hybrid renewable energy systems combined with the existing unreliable grid in order to meet the healthcare load demand, thus ensuring higher reliability of available energy sources. The simulations, analysis and results presented in this paper are based on meteorological data and the load profiles of six selected locations in Nigeria, using which hybrid grid-connected systems integrating diesel, solar and wind energy sources are designed with configurations to give optimum output. The optimised design configurations in the considered case study, Ejioku, Okuru-Ama, Damare-Polo, Agbalaenyi, Kadassaka and Doso, produce very low energy costs of of 0.0791 $/kWh, 0.115 $/kWh, 0.0874 $/kWh, 0.0754 $/kWh, 0.0667 $/kWh and 0.0588 $/kWh, respectively, leveraging solar and wind energy sources which make higher percentage contributions at all sites. The load-following-dispatch strategy is adopted at all sites, ensuring that at every point in time, there is sufficient power to meet the needs of the healthcare centres. Further works on this topic could consider other strategies to optimise general energy usage on the demand side.

Dhaka city is experiencing rapid land cover changes, and the effects of climate change are highly visible. Investigating their combined influence on runoff patterns is vital for sustainable urban planning and water resources management. In this work, multi-date land cover classification was performed using a random forest (RF) algorithm. To validate accuracy of land cover classification, an assessment was conducted by employing kappa coefficient, which ranged from 85 to 96%, indicating a high agreement between classified images and the reference dataset. Future land cover changes were forecasted with cellular automata-artificial neural network (CA-ANN) model. Further, soil conservation service -curve number (SCS-CN) rainfall-runoff model combined with CMIP6 climate data was employed to assess how changes in land cover impact runoff within Dhaka metropolitan development plan (DMDP) area. Over the study period (2020–2100), substantial transformations of land cover were observed, i.e., built-up areas expanded to 1146.47 km2 at the end of 2100, while agricultural areas and bare land diminished considerably. Consequently, monsoon runoff increased from 350.14 to 368.24 mm, indicating elevated hydrological responses. These findings emphasized an intricate interplay between urban dynamics and climatic shifts in shaping runoff patterns, underscoring urgency of incorporating these factors into urban planning strategies for sustainable water resources management in a rapidly growing city such as Dhaka.

Diabetes mellitus (DM) is a metabolic complication and can pose a serious challenge to human health. DM is the main cause of many life-threatening diseases. Researchers of natural products have been continuously engaged in treating vital diseases in an economical and efficient way. In this research, we extensively used phytosteroids from Notholirion thomsonianum (Royle) Stapf for the treatment of DM. The structures of phytosteroids NtSt01 and NtSt02 were confirmed with gas chromatography–mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) analyses. Through in vitro studies including α-glucosidase, α-amylase, and DPPH assays, compound NtSt01 was found to be comparatively potent. An elevated dose of compound NtSt01 was also found to be safe in an experimental study on rats. With a dose of 1.0 mg/kg of NtSt01, the effect on blood glucose levels in rats was observed to be 519 ± 3.98, 413 ± 1.87, 325 ± 1.62, 219 ± 2.87, and 116 ± 1.33 mg/dL on the 1st, 7th, 14th, 21st, and 28th, days, respectively. The in vivo results were compared with those of glibenclamide, which reduced the blood glucose level to 107 ± 2.33 mg/dL on the 28th day. On the 28th day of NtSt01 administration, the average weights of the rats and vital organs (liver, kidney, pancreas, and heart) remained healthy, with a slight increase. The biochemical parameters of the blood, i.e., serum creatinine, blood urea, serum bilirubin, SGPT (or ALT), and serum alkaline phosphatase, of rats treated with NtSt01 remained in the normal ranges. Similarly, the serum cholesterol, triglycerides, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) levels also remained within the standard ranges. It is obvious from our overall results that the phytosteroids (specifically NtSt01) had an efficient therapeutic effect on the blood glucose level, protection of vital organs, and blood biochemistry.

Using multimodal imaging, the literature proposed the following risk factors for choroidal nevus growth into melanoma: increased tumor thickness, subretinal fluid, decreased visual acuity, presence of orange pigment, ultrasound acoustic hollowness, and increased tumor diameter. This study investigated the presence of the mentioned risk factors in choroidal nevi, choroidal melanomas, and indeterminate choroidal melanocytic lesions. This retrospective, single-center chart review assessed choroidal melanocytic tumors with multimodal imaging. We defined our primary outcome as the cumulative presence of mentioned risk factors. Further, we evaluated various optical coherence tomography (OCT), ultrasound, and autofluorescence findings. We analyzed 51 tumors from 49 patients during the period from April 2008 to June 2021. The median (IQR) age was 64.0 (56.0 to 70.5) years, with 23 of 49 (46.9%) patients being female. The follow-up time for all tumors was median (IQR) 25.0 (12.0 to 39.0) months. The choroidal nevi had a median (range) risk score of 0.0 (0.0 to 3.0), and the choroidal melanoma of 5.0 (3.0 to 6.0), with statistically significant different ratings (p < 0.001). Multimodal imaging creates a score that may help to distinguish choroidal nevi from choroidal melanomas objectively.