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Abstract Background Postpartum depression (PPD) is categorized by the Disorders-Fifth Edition as depression that begins during pregnancy or within the first month after giving birth. Ketamine and esketamine have shown promising results in the treatment of several depressive disorders, which suggests that they may have a role in the prevention of PPD. This systematic review and meta-analysis aim to update evidence about the efficacy and safety of using ketamine and esketamine to reduce PPD incidence. Methods We searched four databases, PubMed, Scopus, Web of Science, and Cochrane, to collect relevant studies. We included studies which investigated the preventive effect of ketamine or esketamine on PPD among women after giving birth through caesarean or vaginal delivery. We extracted PPD occurrence rate, PPD score, pain score and side effects. Finally, a meta-analysis was conducted using RevMan software. Results Twenty-one eligible studies were incorporated in the current systematic review and meta-analysis involving 4,389 pregnant women. Esketamine was the intervention in 14 studies, and ketamine was used in 7 studies. In subgroup analysis, both ketamine and esketamine were significantly effective in reducing the incidence of short-term PPD (ketamine: RR = 0.72, 95% CI [0.56, 0.93], P = 0.01; esketamine: RR = 0.43, P

Assessing Environmental, Social, and Governance (ESG) practices in the banking sector is becoming increasingly important. This study aims to analyze the correlation between ESG scores and the performance of banks. The ESG data were gathered using a Bloomberg database. Using fixed-effect estimation for a static model, this study examines a balanced panel sample of 15 Jordanian-listed banks from 2009 to 2023. Based on multivariate regression, the study outcomes suggest that Jordanian banks with higher ESG scores perform better in operating and market performance. Stakeholder theory supports this. Accordingly, the R2 values for the study models were 23.9% for the ROA model and 18.7% for Tobin’s Q, respectively, showing the high explanatory power of both models. Therefore, an increase of one point in ESG scores leads to a corresponding rise in ROA and Tobin’s Q 0.496 and 0.370, respectively. Regarding control variables, leverage has a negative correlation coefficient of –0.169 and –0.253, respectively, in both the ROA and Tobin’s Q models. According to the ROA model, a one-unit increase in bank size leads to a 0.309-unit increase in bank performance and a 0.115-unit increase, according to Tobin’s Q model. Similarly, as the bank ages by one year, its performance improves, with the ROA and Tobin’s Q models showing increases of 0.216 and 0.116 units, respectively. Additionally, the financial development showed correlation coefficients of 0.108 and 0.045 for the ROA and Tobin’s Q models, respectively. However, the ESG committee does not affect the performance of banks. AcknowledgmentThis research was funded through the annual funding track by the Deanship of Scientific Research, from the Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant NO. KFU242703].

Summary: Background: Acute malnutrition is a nutritional insufficiency resulting from either inadequate energy or protein intake. It is categorized into severe acute malnutrition and moderate acute malnutrition. Globally, acute malnutrition is still a prevalent public health issue. Objective: To identify determinants of acute malnutrition among 6 to 59-month-old children in public health facilities. Methods and materials: A hospital-based, unmatched case-control study was conducted from 1st March to 30th April 2023. Three hundred ninety (130 cases and 260 controls) participants were included in the study. Data was collected using pretested, semi-structured questionnaires by using Kobo Collect. The analysis was done using SPSS version 20. Both bi-variable and multivariable logistic regressions were done. An adjusted odds ratio with a 95% confidence interval was computed to determine levels of significance. All variables with a p-value less than 0.05 were declared as determinants of acute malnutrition. Results: In this study, estimated average family income, 1001–5000 Ethiopian birr (AOR=3.32, 95%CI=1.27, 8.72), 5001–10000 Ethiopian birr (AOR=3.82, 95%CI=1.56, 9.36), family size ≥5 (AOR=2.33, 95%CI=1.33, 4.084), private employee (AOR=4.62, 95%CI=2.18, 9.76), government employee (AOR=3.77, 95%CI=1.69, 8.42), non-governmental organization employee (AOR= 17.14, 95%CI=2.53, 16.27), food insecurity (AOR=1.46, 95%CI=1.08,1.97), bottle-feeding (AOR=2.64, 95%CI=1.22,5.68), and lack of latrines (AOR=3.30, 95%CI=1.04, 10.49) were identified as determinants of acute malnutrition. Conclusion: Estimated average family income, family size, lack of latrine, maternal or caregiver occupation, food security status, and bottle feeding were all predictors of acute malnutrition in this study. Promoting exclusive breastfeeding, raising awareness about the health issues of bottle-feeding and open defecation, improving access to clean water and sanitation, strengthening healthcare systems, and encouraging collaboration among agencies can effectively address acute malnutrition.

Several multimedia applications encounter difficulties due to concerns about the security of the underlying digital images. The peculiar properties of digital images, such as voluminous data, redundant data, and significant correlation between neighboring pixels, are typically strenuous to be handled by conventional encryption techniques. Further, several well-known image cryptosystems are flawed by different security issues, including fixed encryption parameters, inadequate plainimage sensitivity, independence of keystream generation from the plainimage, equivalent decryption keys, and a limited key space. Hence, this paper suggests an effective chaotic-based image cryptosystem to remedy these defects and accomplish the requirements of secured image exchange. The suggested scheme adopts the confusion-diffusion architecture which incorporates a new scrambling mapping and a novel substitution mapping. During the scrambling stage, the plainimage is first decomposed into four non-overlapping regions, and then the information of each region is mixed with the others by swapping operations through cross-interaction mapping. While in the diffusion stage, the shuffled pixels are masked by a strongly plainimage-related keystream, allowing each bit of the plainimage to influence the entire enciphered image. The suggested architecture employs several chaotic maps, including 1D logistic map, 2D logistic map and 4D hyper-chaos system, which boost the dynamic behavior of the cipher. Moreover, both stages are fully parameterized mappings that are wholly dependent on the plainimage data, including the pixel summation, histogram variance, and entropy features of the input image in addition to the previously substituted pixel. The inclusion of these features in computing all encryption parameters makes the corresponding keystreams for two images that differ by only one bit completely distinct. Specifically, the suggested cryptosystem does not directly employ the parameters or initial seeds of the chaos maps used as a secret key. Instead, an external secret key-based mechanism is suggested to produce these parameters, which employs several non-linear operations, resulting in a ''butterfly effect'' where a small input change affects subsequent keystream bits. As a result, it can achieve several advantages, such as key sensitivity support, security enhancement, dynamic encryption, resistance to attacks, efficiency, scalability, and flexibility, addressing chaotic system key distribution issues. While the suggested cryptosystem initially employs a static external key for all plainimages, both the generated scrambling sequence and the diffusion keystream are unique (representing a one-time key) to each image. Moreover, our architecture adjusts the external key used for each pixel based on the previously enciphered information. So, even a small change in the external key or input image results in completely different computed parameters. This creates a unique chaotic behavior that controls both the confusion and diffusion operations through different chaotic sequences. Consequently, the suggested cryptosystem can properly withstand different attacks involving the most potent known/chosen plainimage attack. Visually and computationally, the conducted experimental simulations attest that the suggested strategy delivers a superior security level and a noteworthy encryption quality compared to other techniques.

In this paper, we explore multi positive solutions together with their existence and uniqueness, which is properly defined for delta fractional version of Riemann–Liouville difference operators. Our exploration encompasses two distinct directions. In the first direction, we construct the Green’s function formula for the proposed delta fractional boundary value problems of order δ∈(1,2), and we present some essential properties of this function. The last and main results suggest using the well-known fixed point theorems in a Banach space for testing the existing and uniqueness of multi-positive solutions of such problems.

Highlights Two-dimensional mono- and few-layered Ti3CNTx MXene nanosheets with extremely high nitrogen content were synthesized. Better performance for hydrogen evolution reaction (HER) than Pt/C catalyst in acidic, neutral and alkaline solutions. Exceptional performance of HER in both acidic and alkaline solutions. A large current density (> 500 mA cm−2) has been achieved for HER.

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, have become increasingly prevalent across all human generations. Despite advances in diagnosis, effective long-term therapeutic options remain limited, with many patients experiencing recurrent symptoms after treatment. The multifactorial origins of ulcerative colitis are widely recognized, but the intestinal microbiome, particularly bacteria from the Desulfovibrionaceae family, is thought to play a central role in the pathogenesis of the disease. These bacteria contribute significantly to gut microbial functions, yet their cytotoxic and viability characteristics under disease conditions remain poorly understood. Our review provides insights on recent advancements in methodologies for assessing the cytotoxicity and viability of anaerobic intestinal bacteria, with a specific focus on their relevance to gut health and disease. We introduce overview from current literature on modern techniques including flow cytometry, high-throughput screening, and molecular-based assays, highlighting their applications in understanding the role of Desulfovibrionaceae and other gut microbes in IBD pathogenesis. By bridging methodological advancements with functional implications, this review aims to enhance our understanding of gut microbiota-host interactions, which are crucial for maintaining health and preventing disease through immune modulation, where microbiota help regulate immune responses and prevent excessive inflammation; nutrient metabolism, including the breakdown of dietary fibers into short-chain fatty acids that support gut health; and colonization resistance, where beneficial microbes outcompete harmful pathogens to maintain microbial balance.

The Motion artifacts in ECG data can lead to inaccurate circulatory state analysis. Recently, there has been growing interest in methods to mitigate motion distortion in ECG signals. One major challenge is to reduce motion artifacts without affecting the underlying ECG signal, as the motion artifacts and the ECG signal often overlap. Adaptive noise cancellers have proven effective in reducing motion artifacts, provided that an appropriate noise reference, which correlates with the noise in the ECG signal, is available. However, the correlation between motion distortion and the noise reference is not always consistent, and using an inappropriate noise reference can contaminate the ECG data. In this research, we present NARX_CSOFC, an advanced approach that significantly enhances the capabilities of the nonlinear autoregressive network with exogenous inputs (NARX) architecture by incorporating nonlinear combinations of input variables to globally estimate any nonlinear function. The method employs cat swarm optimization with fractional calculus (CSOFC) to find optimal solutions. The proposed NARX_CSOFC demonstrates substantial improvements in artifact reduction: achieving a 12 dB improvement for ECG artifacts, a 16 dB improvement for EMG artifacts, and a 15 dB improvement for random noise artifacts, as measured by Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE) compared to existing techniques.

This paper presents the design and analysis of two dual circularly polarized stacked Yagi-Uda antenna arrays, referred to as Type A and Type B. The proposed Type A Yagi-Uda antenna array consists of four printed bow-tie-shaped elements arranged at different angles: a director, a reflector, and two orthogonal driven elements. The driven elements are connected to coaxial cables via LC baluns. The Type B configuration introduces a complementary slotted bow-tie director element in place of the printed one, while the driven and reflector elements remain unchanged. Exciting the upper driven element generates right-hand circular polarization (RHCP), whereas left-hand circular polarization (LHCP) is achieved when the lower driven element is excited. Both proposed antenna arrays share the same dimensions of $0.39\lambda _{0} \times 0.39\lambda _{0} \times 0.27\lambda _{0}$ , where $\lambda _{0}$ represents the free-space wavelength at the operating frequency of 2.45 GHz. Measured results confirm that Type B outperforms Type A in several aspects. The Type B antenna array achieves a fractional impedance bandwidth of 8%, a 3-dB axial ratio (AR) bandwidth of 19.6%, and a peak gain of 4.49 dBic. In comparison, Type A exhibits a fractional impedance bandwidth of 2.45%, a 3-dB axial ratio bandwidth of 2.45%, and a peak gain of 3.5 dBic. These findings highlight the effectiveness of the complementary slotted bow-tie director in enhancing antenna performance, making Type B a promising candidate for Internet of Things (IoT) applications.

Hybrid Polymer Optical Fiber and Visible Light Communication (POF-VLC) systems are emerging as a promising solution for high-speed, interference-free connectivity, especially in environments where traditional RF communication is constrained. This paper investigates key nonlinear impairments in POF-VLC systems, such as chromatic dispersion (CD), self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), and stimulated scattering, which severely degrade signal quality and limit transmission range. We review advanced modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) and Discrete Multitone Modulation (DMT), alongside traditional methods like Non-Return-to-Zero (NRZ) and On-Off Keying (OOK), evaluating their effectiveness in overcoming these challenges. Furthermore, the application of machine learning, particularly neural network-based equalizers like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), is highlighted for their potential to enhance signal quality and system performance. This review emphasizes the transformative role these advanced strategies can play in optimizing hybrid POF-VLC systems, paving the way for their integration into high-demand communication environments. Moreover, this paper presents several promising research directions, such as optimizing training algorithms, exploring deeper neural network architectures, and integrating POF-VLC systems with emerging technologies like beyond 5G, improving energy efficiency, and addressing scalability and complexity in real-time adaptive POF-VLC systems.

In this study, a novel ultra-wideband antipodal Vivaldi antenna with a Chebyshev tapering profile and exponential slots is designed and measured. The dimensions of the proposed antenna are 95mm $ \times 100$ mm $ \times 0.127$ mm and the relative dimensions are $0.31\lambda _{0} \times 0.327\lambda _{0} \times 0.0004\lambda _{0}$ where $\lambda _{0}$ is the free space wavelength at the lowest operating frequency at 0.98 GHz for a $VSWR \leq 1.9$ . Several array configurations are proposed and all elementary array sources are combined in a mirror configuration, including the feeding part, to drastically reduce cross-polarization. First, a planar array is created at a distance $d=150mm$ between elementary sources. Then, from this primary source, an oblique array is created by inclining the two antennas at an angle $\theta $ , which cancels the ambiguity lobes in the upper frequency band. Finally, the single antenna is bent along a Vivaldi curvature, creating a mechanical tilt depending on the curvature parameters in H-plane. An array is formed by two curved Vivaldi antennas and exhibits a tilting effect with better compactness than the oblique array. For measurements, several foams with Vivaldi curvatures were created, allowing us to shape the Vivaldi antennas along various array distances, resulting in various beam shapes depending on the configuration used. The optimal distance $d=50mm$ provides a more stable radiation pattern across frequencies and cancels out ambiguity lobes for distances > $2\lambda _{0}$ . These Vivaldi-shaped arrays are measured and show good agreement with simulations.

This study introduces a novel approach to sentiment classification of Arabic tweets regarding educational reforms in Saudi Arabia. The complexity of the Arabic language, with its numerous dialects, poses challenges for natural language processing tasks, particularly when large volumes of data require manual annotation. To overcome the limitations of traditional labeling methods, we developed a weakly supervised learning framework that combines LLMs (GPT-3.5) and pre-trained language models (MarBERT and XLM-RoBERTa) to generate high-quality weakly labeled training data using the Snorkel framework. We fine-tuned the AraBERT model with this weakly labeled data for sentiment classification. Our experimental results demonstrated the effectiveness of the proposed approach, achieving 83% precision, 76% recall, and an 85% F1 score in classifying tweets as positive, negative, or neutral. Comparative analysis showed that GPT-3.5 outperformed Llama 2 in prompting tasks, and our weakly supervised model surpassed baseline machine learning methods. These findings highlight the potential of weakly supervised learning in analyzing public opinion on Arabic social media platforms without relying on large, labeled datasets.

Uncrewed aerial systems (UASs) were popularly used by hobbyists in the past, but they have now become critical enablers for managing disasters, handling emergencies, and so on. For example, one of their most critical applications is to provide seamless wireless communication services in remote rural areas. Thus, it is substantial to identify and consider the different security challenges in the research and development associated with advanced UAS-based B5G/6G architectures. Catering to this requirement, this article conducts a comprehensive review of the security aspects of UASs with respect to the 5G/6G system architecture, its enabling technologies, and privacy issues. It exhibits security integration at all the protocol stack layers and analyzes the existing mechanisms to secure UAS-based B5G/6G communications and its energy and power optimization factors. Last, this article also summarizes modern technological trends for establishing security and protecting UAS-based systems, along with the open challenges and strategies for future research work.

Background: Prevalence of seropositivity following SARS-CoV-2 infection is vital in evaluating herd immunity. However, depending on illness severity, it remains unclear whether the breadth and magnitude of immune response to SARS-CoV-2 infection is for short or long term. Objective: To test the persistence of humoral antibody responses after SARS-CoV-2 exposure in patients with different illness severity and among volunteers who had been vaccinated. Methods: This study was conducted in two Saudi Arabian tertiary hospitals. Participants were categorized as critically ill COVID-19 patients, non-critically ill COVID-19 patients, or vaccinated volunteers. We collected demographic data, COVID-19 exposure history, symptoms, vaccination details, and serum samples to analyze antibody persistence. We evaluated SARS-CoV-2 antibody concentrations in COVID-19 patients with varying disease severity and age groups, as well as in BNT162b2-vaccinated individuals, focusing on IgG levels against the S.FL and S1 domains of the spike protein. Results: The study included 172 adults: 92 unvaccinated hospitalized COVID-19 patients and 80 vaccinated volunteers. All vaccinated subjects demonstrated seropositivity to the SARS-CoV-2 spike protein, with nearly 80% having a median antibody titer of 13,500 AU/mL. Notably, vaccinated subjects exhibited significantly higher IgG levels than naturally infected patients (P < 0.001), including higher S.FL and S1 titers, regardless of severity. Age, comorbidities, and previous infections influenced S-specific antibody levels. Among hospitalized patients, 58% required intensive care, with 28- and 90-day mortality rates of 23% and 43%, respectively. Conclusion: These findings shed light on the immune response dynamics following SARS-CoV-2 infection compared to vaccinated individuals, where the latter showed significantly higher level of antibodies response, providing crucial insights for evaluating short-term herd immunity and the effectiveness of natural infection-induced immunity.

In this paper, we proposed an advanced multimodal fusion technique for fingerprint and finger vein recognition algorithms, incorporating novel improvements to established methods. Leveraging Scale-Invariant Feature Transform (SIFT) and Fast Library for Approximate Nearest Neighbors (FLANN), our approach integrates preprocessing enhancements such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and a robust descriptor alignment mechanism to optimize feature extraction and matching. This innovation enhances security, robustness, accuracy, and privacy in biometric systems. Additionally, the fusion of fingerprint and finger vein modalities is implemented at both score-level and feature-level, where feature-level fusion demonstrates superior performance by effectively addressing compatibility issues between modalities and reducing information leakage. Extensive evaluations using databases such as SOCOFing, FVC, CASIA, FV-USM, PLUSVein-FV3, and UTFVP validate the effectiveness of the proposed system. Our results show that feature-level fusion outperforms traditional approaches, achieving higher accuracy and resilience against environmental factors. This study provides a scalable and practical solution for contemporary biometric verification needs, particularly in border control and security applications.

Modern oncology is rapidly evolving, driven by recent advances in RNA-based therapeutics. As new emerging cutting-edge technology, mRNA vaccines hold excellent promise for encoding immunostimulatory molecules, tumor-associated antigens, neoantigens, and chimeric antigen receptors for T-cell reprogramming. RNA interference tools enable highly effective post-transcriptional gene silencing that has rapidly progressed towards more tailored antitumor treatments targeting key molecular players in tumor progression and drug resistance. The inherent challenges and limitations of RNA-based tools, such as size, low stability and surface charges hindering direct cell entry, along with the short circulatory half-life and rapid clearance, call for new and improved RNA delivery systems enabling enhanced gene delivery. Nanoplatforms, particularly certain types of lipid, polymeric nanoparticles and inorganic nanoparticles, provide designed means to address the challenges of RNA delivery and cellular uptake. This paper explores the challenges and barriers while giving insight into the future perspective of RNA-based cancer therapeutics in the context of delivery nanoplatforms and the challenges during development.

As the field of cybersecurity has experienced continual changes, up-to-date techniques have become increasingly necessary to analyze and defend against threats. Furthermore, the current methods consistently produce false alarms and sometimes completely miss real threats. This paper proposes an approach that integrates secure blockchain technology with data preprocessing, deep learning, and reinforcement learning to enhance threat detection and response capabilities. To secure the exchange of threat intelligence information, a safe blockchain network is used, which comprises Byzantine Fault Tolerance for high data integrity and Zero-Knowledge Proofs for access control. All relevant information is cleaned and standardized prior to analysis. Subsequently, graph convolutional neural networks with autoencoders are trained on large unlabeled sets of threat data to automatically label various types of threats, with the system employing fuzzy logic to rank and score possible threats. Furthermore, we implemented a feedback loop that incorporates reinforcement learning, thereby improving model performance over time according to guidance provided by cybersecurity specialists. The proposed system achieved high accuracy, precision, negative predictive value, and MCC, as well as notably low FPR and FNR values. The results establish that the proposed system is a reliable and effective measure for detecting cyberthreats.

Introduction/purpose: Temperature and time-dependent effects such as concrete shrinkage and creep significantly affect the behavior of steelconcrete composite beams. Hence, taking into account the demands brought by these additional effects is necessary. This necessity has resulted in various theoretical and numerical research studies. This article proposes an analytical tool capable of predicting a new redistribution of stresses brought by the combined action of temperature and concrete shrinkage in composite steel-concrete beams in partial shear connection. In this work, the partial shear connection at the steel-concrete interface is taken into account according to the degree of connection (N/Nf). Methods: This involves reformulating the model proposed in 2024 by Rahal et al analyzing the behavior of composite steel-concrete beams in full shear connection under the effect of temperature and concrete shrinkage. In this present study, the main contribution is the introduction of the effect of the connection degree (N/Nf) at the steel-concrete interface, thus leading to an analytical model capable of predicting additional stresses brought by shrinkage and temperature in composite steel-concrete beams in partial shear connection. Results: When referred to the model proposed in 2024 by Rahal et al, the results from this current approach are satisfactory. They clearly show that the degree of connection significantly affects the forces brought about by the combined action of concrete shrinkage and temperature. Conclusion: The results of the present approach and those of the existing model developed by Rahal et al are in good agreement. They clearly show the effect of concrete shrinkage and temperature as a function of the connection degree N/Nf on the behavior of composite steel-concrete beams.

Introduction/purpose: The use of supplementary cementitious materials (SCM) in construction has gained popularity due to their ability to improve the mechanical properties and environmental sustainability of concrete. This study aimed to investigate the potential of utilizing waste materials, specifically marble powder (MP) and dam sediment (DS), as partial replacements for cement in self-compacting concrete (SCC). The primary objectives were to recycle these waste materials and assess the durability and strength of SCC exposed to aggressive chemical environments. Methods: In this study, cement was partially replaced with 40% MP, 40% DS, and a combination of 20% MP and 20% DS. The performance of such concrete was evaluated through compressive strength tests conducted for 28 days. Durability was assessed by exposing the concrete to chemical attacks from hydrochloric acid (HCl), sulfuric acid (H₂SO₄), and sodium sulfate (Na₂SO₄) solutions. Mass loss due to these chemical attacks was also measured. Results: The concrete incorporating MP demonstrated compressive strengths similar to that of the control concrete, achieving 37.61 MPa at 28 days. The concrete with DS exhibited lower strength (31.81 MPa) and showed higher resistance to HCl (ML = 38.78%) compared to the MP concrete (ML = 40.74%). Additionally, all concrete samples exhibited good resistance to sulfuric acid due to the formation of expansive ettringite which protected the concrete from further degradation. Conclusions: The results indicated that both marble powder and dam sediment are viable supplementary materials for improving the mechanical properties and durability of SCC. The concrete with marble powder showed superior strength, while dam sediment contributed to enhanced acid resistance. The combination of these materials offers a sustainable solution for concrete exposed to aggressive environments.

Introduction/purpose: The consumption of natural sand in Algeria is high due to its extensive use in mortar, while sediments and rubber waste pose significant environmental and societal challenges. This study investigates the effects of incorporating rubber waste content in mortars mixed with crushed sand and sediment. The primary goal is to valorize crushed sand particles through physical and mechanical tests, evaluating their potential as an alternative to natural sand in mortar mixtures. Methods: Experimental work was carried out to study the impact of partially and fully replacing sediments with crushed sand particles in mortar mixes. Mortar mixtures were prepared using different sediment-to-crushed sand ratios (10%, 25%, 35%, 50%, and 100%) to observe their influence on physical and mechanical properties. Additionally, the effects of adding 2%, 4%, and 6% granulated rubber to the optimal mortar were analyzed. Various tests, including those testing compressive strength, flexural strength, and ultrasonic pulse velocity, were performed to evaluate the performance of the mixtures. Results: The results indicated that replacing sediment with crushed sand improved the strength properties of mortar, particularly due to better particle packing. The mortar containing 65 wt% sediments and 35 wt% crushed sand showed properties similar to the reference mortar. The addition of rubber waste increased compressibility but enhanced mechanical properties when used in moderation. Ultrasonic pulse velocity decreased with higher crushed sand content, and the porosity of the mixtures was reduced. Conclusion: Crushed sand and sediment particles are effective fillers for mortar, ensuring good performance and improved strength. The efficiency of these materials depends on their morphology and genesis. The study demonstrates that crushed sand can be a viable alternative to natural sand, and rubber waste can be used as a reinforcing material in mortar, though its proportions should be carefully controlled to avoid negative effects on mechanical properties.

With the escalating intricacy and expansion of the interconnected electrical grid, the likelihood of power system (PS) collapse has escalated dramatically. There is an increased emphasis on immunizing renewable-dominated power systems from large-scale cascading failures and cyberattacks through optimal power grid partitioning (PGP). By altering the network’s topology, partitioning aims to create areas within the PS that are not only robust but also have increased flexibility in generation and improved controllability over variable demand. This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. To this end, an in-depth exploration of the basic principles of PGP and performance quantification is provided. The coherency adequacy and controlled islanding within the power network are comprehensively discussed. Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. Finally, key research gaps and future directions for effective PGP are outlined. This paper provides PS researchers with a bird’s eye view of the current state of mainstream PGP implementations. Additionally, it assists stakeholders in selecting the most appropriate clustering algorithms for PGP applications.

Abstract Cardiac injury initiates repair mechanisms and results in cardiac remodeling and fibrosis, which appears to be a leading cause of cardiovascular diseases. Cardiac fibrosis is characterized by the accumulation of extracellular matrix proteins, mainly collagen in the cardiac interstitium. Many experimental studies have demonstrated that fibrotic injury in the heart is reversible; therefore, it is vital to understand different molecular mechanisms that are involved in the initiation, progression, and resolution of cardiac fibrosis to enable the development of antifibrotic agents. Of the many experimental models, one of the recent models that has gained renewed interest is isoproterenol (ISP)–induced cardiac fibrosis. ISP is a synthetic catecholamine, sympathomimetic, and nonselective β‐adrenergic receptor agonist. The overstimulated and sustained activation of β‐adrenergic receptors has been reported to induce biochemical and physiological alterations and ultimately result in cardiac remodeling. ISP has been used for decades to induce acute myocardial infarction. However, the use of low doses and chronic administration of ISP have been shown to induce cardiac fibrosis; this practice has increased in recent years. Intraperitoneal or subcutaneous ISP has been widely used in preclinical studies to induce cardiac remodeling manifested by fibrosis and hypertrophy. The induced oxidative stress with subsequent perturbations in cellular signaling cascades through triggering the release of free radicals is considered the initiating mechanism of myocardial fibrosis. ISP is consistently used to induce fibrosis in laboratory animals and in cardiomyocytes isolated from animals. In recent years, numerous phytochemicals and synthetic molecules have been evaluated in ISP‐induced cardiac fibrosis. The present review exclusively provides a comprehensive summary of the pathological biochemical, histological, and molecular mechanisms of ISP in inducing cardiac fibrosis and hypertrophy. It also summarizes the application of this experimental model in the therapeutic evaluation of natural as well as synthetic compounds to demonstrate their potential in mitigating myocardial fibrosis and hypertrophy.

Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) have recently been recognized as innovative technologies for future beyond fifth generation (B5G) wireless networks due to their ability to improve the spectral efficiency and the wireless propagation properties, respectively. This paper presents an integration between RIS and NOMA to communicate with distant vehicular users. To alleviate the complexity of coherent phase shifting design at RIS, we propose a dynamic gain adaptation RIS selection scheme based on discrete phase shifting (i.e., termed as GADPS) to serve two vehicular users paired to perform NOMA. Regarding channel state information (CSI), the adaptation mechanism determines which user can be considered as a near user and the other as a far user with respect to RIS. To characterize the benefits of the GADPS scheme, the system outage performance is derived and compared with the cases of random discrete phase shifting (i.e., termed as GARPS) and conventional RIS-NOMA schemes; then the diversity order is also obtained. Furthermore, the impact of network parameters on the throughput and asymptotic average sum rate is discussed in both delay-limited and delay-tolerant transmission modes, respectively. Monte Carlo simulations emphasize the accuracy of the analytical work and investigate that: 1) The proposed GADPS scheme is more flexible than conventional RIS-NOMA scheme; 2) By joint optimization of RIS selection and phase shifting, the proposed GADPS scheme is capable of achieving superior performance over GARPS and conventional RIS-NOMA schemes with a superior diversity gain; 3) The throughput performance of the proposed GADPS scheme outperforms GARPS, conventional RIS-NOMA and other relay-assisted NOMA schemes, whereas full-duplex (FD) relay selection NOMA schemes suffer from throughput floor values; 4) By increasing the number of reflecting elements of RIS, the proposed GADPS scheme achieves the enhanced average sum rate performance over benchmark schemes in all scenarios of relative distance difference between users and the selected RIS.

Purpose – This paper aims to examine the devastating effects of the ongoing conflict in Gaza on its education system, a phenomenon termed “Educide”. The study documents the systematic destruction of educational infrastructure, including schools and universities, as well as the targeted killing of students and educators, which impedes current and future access to education. The paper argues that this deliberate dismantling of Gaza’s educational institutions aims to erase Palestinian culture, identity and resilience, thus hindering any prospects for SDG4. The authors emphasize the urgent need for international accountability and support to rebuild Gaza’s education sector, preserving its role in societal development, identity preservation and human rights. Design/methodology/approach – This research paper uses a qualitative design, using data gathered through a comprehensive review of both grey and scholarly literature to assess the impact of the ongoing conflict on Gaza’s education system. By analyzing official reports, working papers and conference abstracts, the study compiles evidence of the systematic targeting and destruction of educational infrastructure in Gaza. This approach allows for an in-depth understanding of the conflict’s immediate and long-term effects on educational access, infrastructure and cultural preservation. The methodology underscores the critical implications of these findings and calls for urgent international intervention and accountability to protect education in conflict zones. Findings – The paper’s findings reveal severe, systematic destruction of Gaza’s educational infrastructure, including schools and universities, with extensive loss of life among students and educators. The research shows that this “Educide” disrupts educational access for current and future generations, stifles intellectual growth and threatens the preservation of Palestinian identity and heritage. Evidence indicates that such deliberate targeting aims to destabilize Gaza’s social structure and hinder long-term development. The findings highlight the urgent need for international accountability to halt these acts, safeguard education as a fundamental right and rebuild Gaza’s education system to ensure resilience and continuity amid conflict. Research limitations/implications – The research faces several limitations, including reliance on secondary sources, such as gray literature and official reports, which may limit firsthand insights and real-time data accuracy. In addition, the ongoing nature of the conflict restricts access to updated information and complicates direct assessment of damage. The study’s qualitative approach may also limit the ability to quantify the full extent of destruction and long-term impacts on educational outcomes. Finally, the challenging political environment may affect the availability and reliability of certain sources, potentially leading to gaps in comprehensive data on Gaza’s educational infrastructure and human resource losses. Practical implications – The practical implications of this paper underscore the urgent need for international intervention to protect Gaza’s education sector. Highlighting the concept of “Educide”, the study calls for policies to prevent the targeting of educational institutions in conflict zones and mechanisms to hold perpetrators accountable. It advocates for comprehensive support to rebuild Gaza’s educational infrastructure, ensuring students and educators can safely return to learning environments. This research also serves as a foundation for policymakers, NGOs and human rights organizations to implement sustainable educational recovery programs to preserve Palestinian identity, cultural heritage and long-term development amidst ongoing challenges. Social implications – The social implications of this paper are profound, as it highlights the erosion of Gaza’s educational foundations and its broader effects on Palestinian society. The systematic destruction of schools and universities disrupts education and impacts community resilience, cultural preservation and intergenerational knowledge transfer. This “Educide” undermines social cohesion by depriving young Palestinians of safe learning environments, vital support networks and prospects for socioeconomic stability. The study emphasizes the need for global support to restore these educational spaces, ensuring that education can continue to foster community strength, identity and hope within Gaza’s society amid conflict. Originality/value – This paper is original in its focus on the concept of “Educide” in Gaza, a term describing the systematic targeting and destruction of educational infrastructure as a means of cultural and societal erasure. By framing the deliberate dismantling of Gaza’s education sector within a genocidal context, the paper offers a novel perspective on the intersection of conflict, education and cultural preservation. It contributes uniquely to academic and policy discourse by combining documented evidence with a call for international accountability, emphasizing the critical role of education as both a human right and a foundation for resilient societies in conflict zones.

Purpose – This paper aims to provide the integrated model with artificial intelligence (AI) awareness for the betterment of the higher education system in crisis i.e. fear of contagious viruses (different kinds of flu, monkeypox, chickenpox, COVID-19, etc.) corollaries in developing nations where the quality of education depends on teachers’ commitment, stress and the turnover intention. Design/methodology/approach – This empirical investigation employs a self-administered survey distributed among the faculty members within higher education institutions (HEIs) of the Punjab province and the Federal Capital Territory (FCT) Islamabad, Pakistan. The final sample of 622 faculty members was collected through convenience sampling, and structural equation modeling was performed with SmartPLS to assess the proposed model. Findings – The study reveals that remote work significantly enhances organizational commitment while concurrently lowering the turnover intention. Conversely, perceived work stress negatively impacts organizational commitment but positively influences turnover intention. Organizational commitment partially mediates between perceived work stress and turnover intention but exhibited no mediation between remote work and turnover intention. Notably, fear of contagious viruses and AI awareness positively moderate and amplify both the perceived work stress with turnover intention and remote working with organizational commitment, respectively. Research limitations/implications – The current study extends the AI-mediated social exchange theory (MET) by observing faculty members of HEIs in the context of remote working, perceived work stress, commitment, turnover intention, fear of contagious viruses and AI awareness. Moreover, the successful application of AI-MET extended the researcher’s understanding of quality education in crisis. Practical implications – The study offers several contributions including applications of technical skills with AI awareness among faculty members to provide quality education for society’s welfare. Moreover, HEIs should arrange training programs for performance enhancement. Originality/value – This research provided a quality-based model for HEIs for developing nations to deal with forthcoming calamities of contagious viruses and deliver quality education through remote working during lockdown. Nowadays, off-campus education during calamity situations has been an alternative to on-campus education. Therefore, HEIs must introduce AI awareness to increase the dedication of faculty members toward society’s welfare with the utilization of full effort.

Alaa Abdulhafiz Khushhal,1 Ashraf Abdelaal Mohamed,1 Mohammad Matook Alsegame,2 Atif Mohammed Alsaedi2 1Department of Medical Rehabilitation Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia; 2Department of Nursing, Umm Al-Qura University Medical Center, Umm Al-Qura University, Makkah, Saudi ArabiaCorrespondence: Alaa Abdulhafiz Khushhal, Department of Medical Rehabilitation Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia, Tel +966536025182, Email aakhushhal@uqu.edu.saBackground: Electrocardiography (ECG) is a standard method for studying the heart rate and heart electricity. The Apple Watch has recently been used to measure heart electricity via 30 seconds of ECG monitoring, but its validity is not known.Methods: A cross-sectional study included a sample of 112 patients with chronic disorders, including cardiac patients with sinus rhythm, cardiac patients with irregular heart rhythm, and patients with diabetes, hypertension, and dyslipidemia; their cardiac activity was investigated using the Apple Watch and ECG.Results: The correlations were strong between the Apple Watch and the 12-lead ECG in the heart rate, PR intervals, QT intervals, QRS complex, and RR intervals in all patients’ subcategories (cardiac, hypertension, diabetic, and dyslipidemia patients).Conclusion: The Apple Watch is valid for carrying out ECG in measuring the heart rate, PR intervals, QT intervals, QRS complex, and RR intervals of patients with cardiac and chronic diseases at rest; this may help in initial diagnosis for patients with cardiac diseases or those who are at risk of developing cardiac disease in the future.Keywords: Apple Watch, electrocardiography, cardiac diseases, chronic diseases

This article introduces a new family of orthogonal moments, the fractional Racah moments (FrROMs), developed to meet the demands of copyright protection for medical images, a field requiring robust and secure methods that preserve the integrity of the original image. The FrROMs are derived from a matrix of fractional Racah orthogonal polynomials (FrROPs), which in turn are obtained from the matrix of classical Racah polynomials (ROPs) using the spectral decomposition theorem. To compute the ROPs, we enhanced a method for calculating the initial terms of these polynomials to avoid instability issues associated with higher-order ROPs. Subsequently, the FrROMs are used as descriptors for embedding and extracting a zero-watermark. Experimental results demonstrate that the proposed zero-watermarking scheme efficiently leverages the FrROMs, surpassing classical ROMs in terms of robustness and reconstruction capability. This scheme provides strong protection against various image processing attacks while maintaining the original content intact. Compared to existing methods, it stands out for its enhanced robustness and reliability, making a significant contribution to the secure protection of medical images.

The increasing complexity of Internet of Things (IoT) networks demands scalable and adaptive solutions to ensure reliable and efficient connectivity. As IoT ecosystems grow increasingly complex, multiple Internet Service Providers (ISPs) aggregation and dynamic traffic optimization have become critical for ensuring reliable, cost-effective, and high-performance network services. This paper proposes the Dynamic Connectivity Hub (DCH), a novel system that aggregates services from multiple ISPs - ranging from terrestrial (e.g., fiber optics, mobile 4G/5G), and satellite networks (e.g., LEO, GEO) - to dynamically distribute traffic to IoT devices. DCH uses an Artificial Neural Network (ANN) model to continuously optimize load balancing by assigning data rates to the most efficient ISPs in real time. By analyzing performance metrics such as latency, data rate, congestion, and cost, the ANN ensures that traffic is routed through the best-performing ISPs while discarding sub-optimal connections. The DCH is optimized for urban deployments, filling gaps in connectivity and providing backup coverage in areas with limited ISP infrastructure. While for rural deployments, DCH is optimized to serve as a primary connectivity provider. Extensive simulations demonstrate that the DCH outperforms traditional static ISP models in terms of latency reduction, cost efficiency, and resilience to outages. Validation through MATLAB simulations highlights the DCH’s ability to outperform baseline models by demonstrating a 50% reduction in latency, a 25% improvement in data rate allocation, and a 20% reduction in operational costs compared to traditional static models. This paper not only highlights the DCH’s potential as a flexible, scalable, and efficient solution but also presents it as a transformative solution for next-generation IoT networks, addressing connectivity challenges across diverse deployment scenarios.

Organizational cybersecurity relies heavily on security operation centers (SOCs) to protect businesses and institutions from emerging cyber threats. In recent years, the complexity and sophistication of cyber threats have increased, pushing SOCs to their limits. As a result, SOCs struggle to address the evolving threat landscape due to their reliance on isolation technologies and reactive strategies. However, advanced technologies, such as artificial intelligence (AI) and machine learning (ML), have the potential to revolutionize SOCs by enhancing threat identification and response capabilities, as well as predicting and preempting risks. To address these challenges and highlight the full potential of SOC, this study provides a detailed overview through a comprehensive literature review that identifies gaps in existing research and examines the latest technologies used in the SOC environment to help address different operational and technical challenges and bring out their capabilities. Various methods, ranging from automated incident response and behavioral analytics to neural networks and deep learning, have been classified and compared. In addition, an in-depth reference architectural model, which is a blueprint for SOC integrating AI and ML into SOCs, is introduced. The proposed model provides a structured framework for implementation and offers insights into different SOC components and their interactions. Moreover, this systematic review emphasizes the benefits of these technologies for enhancing security operations. Finally, a case study is presented to describe the function of ML- and AI-powered SOC components to achieve optimum security. This paper concludes by discussing additional challenges and future research directions that may help advance the cybersecurity sector and provide insights into improving SOCs.

Understanding student academic performance is a cornerstone for developing sustainable educational practices that benefit students, teachers, policymakers, and society. This analysis directly impacts students’ ability to engage in and promote sustainable practices, thereby shaping their future academic success. While many studies focus on predicting student performance based on a set of features, our study takes an approach by reducing these features into factors and analyzing their impact. We aim to identify the factors influencing student performance within the middle school education system using a combined approach of Factor Analysis for Mixed Data (FAMD) and Multiple Linear Regression (MLR). Our analysis is based on a robust and reliable large dataset of 1,073,450 observations, encompassing qualitative and quantitative features. FAMD analysis identified four underlying factors: prior academic performance, academic delay, socioeconomic status, and class environment; all these factors have good to excellent reliability, with Cronbach’s Alpha values ranging from 0.809 to 0.930. Feeding these factors into the MLR produces a robust model that explains 88.53% of the variance in the CGPA, indicating a strong fit. Prior Academic Performance factor emerges as the most powerful predictor, accounting for 76.6% of the explained variance. Academic Delay follows, explaining 14.34% of the variance. Socioeconomic Status contributes 6.02%, and Class Environment adds 3.03%, reflecting smaller but meaningful impacts. All predictors are statistically significant (p

In this paper, we introduce a new and accurate approximation for the Beckmann distribution, a widely employed model for describing generalized pointing errors in the context of free-space optical (FSO) communication systems. More specifically, this four-parameter distribution, which considers distinct jitter variances along horizontal and vertical displacements and accounts for nonzero boresight errors at the receiver, is approximated using a Gamma distribution with shape and scale parameters. By applying this approximation to the generalized pointing error model and incorporating angle-of-arrival (AOA) variations, we present novel analytical equations for the cumulative distribution function and the probability density function of the composite Gamma-Gamma turbulence channel. These unified formulations are applicable to intensity modulation with direct detection and heterodyne detection methods, and are expressed using the generalized Meijer's-G function. Utilizing these results, we provide expressions for the moments, the ergodic capacity, the average bit-error rate for several modulations, and the outage probability. Additionally, employing a moments-based approach, we derive a highly accurate asymptotic approximation for the ergodic capacity for high signal-to-noise ratio (SNR) regimes using simple functions. All analytical expressions are validated using Monte-Carlo simulations.

In the past three decades, there has been a sweeping trend in Western and developed countries worldwide to transform the vertically integrated electricity supply chain into competitive electricity markets to diversify investment in the system and ultimately drive down operation costs. Nonetheless, due to some geopolitical and economic reasons, many developing countries adopted a modestly liberalized version of the power market (imperfect market). With the trend of privatization, specifically at the generation level, to leverage the hypothetical competitiveness, countries that did not adopt a full-fledged market structure face a dilemma. The system operators of incumbent imperfect market models find it increasingly difficult to deal with multiple private ownership of Independent Power Producers who are unwilling to share their detailed operational parameters for long-term generation scheduling (lasting for years). In this paper, Blockchain (BC) is being advocated as a platform that simulates a virtual market environment to address such issues. The proposed BC-based structure allows generators to participate in the short-term scheduling mechanism (such as day-ahead) in a trust-free environment without sharing their vital data yet achieving efficient, market-grade solutions. The feasibility of this new proposition is demonstrated through three different application scenarios, utilizing real-world load and renewable generation profiles sourced from the respective Grid System Operators databases. Python library (PYPSA) and Ethereum Testnet are being used for grid simulation and BC platform implementation respectively. The results of BC-assisted generation scheduling are presented and compared with the imperfect market model to highlight the viability of the proposed new approach.

Sand and dust storms significantly challenge microwave and millimeter-wave communications, particularly in arid and semi-arid regions. Various models have been developed to predict attenuation caused by these storms theoretically and empirically based on two meteorological parameters, namely visibility and humidity. However, these models are found unable to predict most of the attenuation measurements. This study presents a hybrid Machine Learning (ML) model that predicts dust storm attenuation for 22 GHz terrestrial links using meteorological data. The received signal levels were measured for a 22 GHz link over a month in Khartoum, Sudan. The visibility, humidity, atmospheric pressure, temperature and wind speed were also monitored simultaneously by Automatic Weather Station (AWS). The proposed model incorporates XGBoost for feature selection and combines Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers to capture both short-term and long-term dependencies in meteorological data. The results demonstrate a strong correlation between meteorological parameters and dust storm attenuation. The model’s performance is validated against the measured data at 22 GHz, outperforming existing empirical and theoretical models. The RMSE for the proposed model is 0.07, while all existing theoretical and empirical models are higher than 0.25. Furthermore, the proposed model demonstrates significant enhancements over the available ML model for dust attenuation prediction. This hybrid ML approach offers a more accurate and robust solution for predicting microwave and millimetre wave attenuation during dust storms, enhancing the reliability of communication systems in affected regions.

Abstract Background Our understanding of osteoarthritis (OA) has evolved from a degenerative disease to one in which low-grade, chronic inflammation plays a central role. In addition, evidence suggests that OA is accompanied by both peripheral and central nervous system sensitization that can cause pain. It has been demonstrated that transcutaneous vagus nerve stimulation (tVNS) can relieve pain, inflammation, and central sensitization in other conditions including fibromyalgia, pelvic pain, and headaches. We aimed to assess the efficacy and safety of tVNS on nociceptive pain, central sensitization, and physical function in knee OA. Methods In this 12-week study, we stimulated the auricular branch of the vagus nerve with an auricular electrode connected to a transcutaneous electrical nerve stimulation device once a day for 3 days each week for 12 weeks. A total of 68 patients with chronic knee OA were randomly assigned to the active and sham groups (34 patients in each group). We used a variety of outcome measures, including the visual analog scale (VAS), pressure pain threshold (PPT), knee injury and osteoarthritis outcome score (KOOS), PainDETECT (PD-Q) and Douleur Neuropathique 4 (DN4) questionnaires. Outcome measures were recorded at baseline, At the end of the stimulation period, and then after 4 weeks. Results In the active group, compared to baseline, there was a significant improvement in VAS scores between the first and second follow-up visits (P

Abstract Background Hexaflumuron (HFM), a common pesticide, can disrupt the immune system and cause oxidative stress. This study investigated the potential of Physalis peruviana L. calyces extract (PP) to counteract these effects in rats. Methods Rats were divided into 6 groups including control, PP-treated, HFM-exposed, and co-treated (HFM + PP) groups. Immune function, antioxidant activity, and organ damage were assessed. Furthermore, UPLC-MS/MS analysis identified potential bioactive compounds in PP extract. Results HFM exposure suppressed immune responses and caused organ damage. Notably, the co-administration of PP extract with HFM reversed these effects, indicating its ability to reduce oxidative stress and protect the immune system. UPLC-MS/MS analysis of PP calyces ethanolic extract revealed its richness in various health-promoting metabolites, including acyl sucrose sugar, withanolides, and flavonoids, which may provide valuable insight into the underlying mechanisms of PP’s calyces protective effects against HFM toxicity. Conclusions This study provides novel insights into the potential of P. peruviana L. calyces ethanolic extract as a natural agent to counteract the harmful effects of HFM exposure. These findings have significant implications for developing effective strategies to mitigate pesticide-induced toxicity and promote human health.

Abstract Introduction This study compared the clinical outcomes of allogenic cultured limbal epithelial transplantation (ACLET) and cultivated oral mucosal epithelial transplantation (COMET) in the management of limbal stem cell deficiency (LSCD). Methods Forty-one COMET procedures in 40 eyes and 69 ACLET procedures in 54 eyes were performed in the Corneoplastic Unit of Queen Victoria Hospital, East Grinstead. Data were examined for demographics, indications, ocular surface stability, absence of epithelial defect, ocular surface inflammation, visual outcomes, and intra- and postoperative complications. Results Kaplan–Meier analysis showed that patients in the ACLET group with longer follow-up had a significantly higher graft survival rate (81.7%, n = 56) than the COMET group (60.7%, n = 25) and the difference was statistically significant (p = 0.01). In the COMET group, there was no statistically significant improvement in the visual acuity (VA) while in the ACLET group there was statistically significant improvement in the final VA. Elevated intraocular pressure (IOP) developed in 9 eyes (22.0%) in the COMET group and in 18 eyes (26.1%) in the ACLET group; infection developed in 4 eyes (9.8%) in the COMET group and in 10 eyes (14.5%) in the ACLET group; and perforation or melting happened in 4 eyes (9.8%) in the COMET group and in 1 eye (1.4%) in the ACLET group. Postoperative immunosuppression complications were noted in 9 eyes (13.0%) in the ACLET group. No graft rejection was observed in either group. Conclusion Both ACLET and COMET are effective therapeutic procedures for managing advanced and bilateral cases of LSCD. Although COMET has lower graft survival rate than ACLET, it does not mandate systemic immunosuppression therapy to protect against potential graft rejection.

Abstract Background Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal disease of the lungs. It is characterized by pulmonary and extrapulmonary comorbidities. So far, little is known as regards the prevalence of cardiovascular comorbidities in IPF patients. This study aims to investigate the prevalence of cardiovascular comorbidities in patients with IPF and correlate it with different radiological and laboratory indices of disease severity. Methods This prospective case–control study was performed on 134 IPF patients above 18 years. Diagnosis of IPF was based on diagnostic radiologic criteria addressed by ATS guidelines. Patients were recruited from the Pulmonology Department, Assiut University Hospitals, from June 2023 to June 2024. Cardiac comorbidities were assessed by ECG and echocardiography. All patients had C-reactive protein (CRP), Krebs von den Lungen-6 (KL-6), and lipid profile including cholesterol level, triglyceride level, HDL-cholesterol level, and LDL-cholesterol level measured. Patients were divided into two groups: IPF patients with cardiovascular comorbidities and IPF patients without cardiovascular comorbidities. Comparison between both groups as regards clinical, radiological, and laboratory criteria was carried out. Results The studied group consists predominantly of females (65%). Sixty out of the 134 documented IPF patients had cardiovascular comorbidities (44.7%), cardiomyopathy (mean ± SD: 43.63 ± 10.56), pulmonary hypertension (mean ± SD: 41.67 ± 15.32), ECG-ischemic changes (40.0%), and atrial fibrillation (13.3%). IPF patients with cardiovascular comorbidities vs. patients without had higher radiological HRCT total fibrosis score TFS (P value

Abstract Geothermal energy is a large, renewable, and clean source of energy from the earth in the form of heat. Exploring the deeper layers of the Williston Basin has revealed favorable reservoir temperatures, particularly in the western areas where high heat flows are prevalent. The quality of a geothermal hotspot hinges on the reservoir quality index (RQI), which is determined by the accuracy of calculating the field reservoir permeability. The primary goal of this study is to apply machine learning techniques to accurately calculate the field permeability, which is important for optimizing the RQI. To enhance accuracy, we initially applied various clustering algorithms, including the density-based spatial clustering of applications with noise (DBSCAN), K-means, K-median, and hierarchical clustering methods, to delineate hydraulic flow units (HFU) within the reservoir using porosity, permeability and water saturation core data. Subsequently, regression models including supervised ML regression methods such as neural networks, support vector machine (SVM) regression, Gaussian process regression (GPR), ensemble regression, linear regression, and decision trees were employed for each flow unit to establish correlations and calculate field permeability with each of these models validated using cross-validation. In comparison to the other clustering methods, the hierarchical clustering method showed the best performance by showing a strong correlation between the actual and predicted permeability values. Overall, the SVM and GPR regression methods were observed to show consistent results with the training and testing datasets, with the SVM regression technique yielding higher R-squared values through regression across the different clustering techniques. In addition, cross-plots were employed to successfully delineate the Red River formation into distinct regions, aiding in the definition of formation lithology and the estimation of field water saturation. Our study showcases an integrated approach to predicting reservoir permeability, considering limited core data. ML emerges as an effective tool for characterizing the Red River formation as a geothermal hotspot in North Dakota, showcasing the potential for sustainable energy exploration and utilization which reduces the reliance on extensive coring in order to enhance geothermal exploration accuracy.

Abstract Glass system of 45B2O3–20ZnO–30BaO–5X, (where X represents CaO, MgO, Al2O3, TiO2, CuO and Fe2O3) in mole percentage was investigated for gamma ray radiation shielding experimentally. Six glass composites were fabricated and the density was measured experimentally and the BZBCa glass sample has the least density with a value of 3.932 g cm−3 and this is due to the presence of CaO in it, and the sample BZBFe has the highest density with a value of 4.031 g cm−3. Through comparing the linear attenuation coefficient (LAC) data (experimental and Phy-X) for the BZBX glass samples, the LAC values for glass samples obtained experimentally and using Phy-X are in a very close range. All the glass samples have the greatest LAC values at 0.0595 MeV, the lowest energy value. Sample BZBCu has a LAC value of 16.203 1/cm, which is also the highest LAC value among all the studied glasses, this is as a result of the high density of this glass and due to the high atomic number of Cu. The glasses’ transmission factor (TF) at 1 cm thickness against energy was determined. The TF values of all the glasses were almost zero. The TF values increased significantly for all the glasses when the energy was increased to 0.662 MeV, and for sample BZBCa its TF value increases 74.08%, which was the highest TF value increase. The half-value layer and other shielding parameters have been determined experimentally.

Abstract The hyoid bone has been identified as sexually dimorphic in various populations. The current study is a forerunner analysis that used three-dimensional multidetector computed tomography (3D MDCT) images of the hyoid bone to examine sexual dimorphism in the Egyptian population. A total of 300 subjects underwent neck CT imaging, with an additional 60 subjects randomly selected for model validation. Ten hyoid variables were measured. Initially, the dataset was subjected to discriminant analysis to predict sex and the critical variables associated with sexual dimorphism. Subsequently, machine learning approaches were employed to enhance the accuracy of sex determination. The results indicated that all measured dimensions of the hyoid bone were substantially larger in males confront to females. Discriminant functions combining four measurements (major and minor axes of the hyoid body, the distance between the lesser horns, and hyoid bone length) achieved a higher accuracy of sex prediction compared to univariate functions. The accuracies of machine learning models ranged from 0.8667 to 0.933 with precision, recall, and F1-scores also showing improvements. These findings underscore the robustness and reliability of hyoid bone in sex discrimination among Egyptians, supported by both traditional statistical methods and machine learning approaches, and could prove invaluable in forensic cases.

Abstract Household kitchen waste (HKW) is produced in large quantity and its management is difficult due to high moisture content and complex organic matter. Aerobic composting of HKW is an easy, efficient, cost-effective and eco-friendly method. This study is designed to achieve a zero-waste concept and to convert HKW. We optimized the type and size of three different bulking agents to speed up the composting process. The tested bulking agents were fallen leaves, sawdust and fly ash. The results showed a higher and longer thermophilic phase (55oC) for 11 days in C2. Higher moisture content (69%) and higher organic matter degradation (38.4%) were also observed in C2. The pH range in all compost treatments was 7-8.5, Electrical conductivity range was 1.8–3.55 mS/cm, C/N ratio range was 15.4–18.1, water holding capacity range was 3.25–4.3 g water/g dry sample, total potassium range was 1.52–1.61%, total phosphorous range was 0.83–1.14%. The highest germination index (119.1%) was also obtained in C2. The highest chili height (16.7 cm), greater number of leaves (20), greater shoot fresh weight (4.75 g) and root fresh weight (1.2 g) was obtained in the presence of C2. Similarly, greater water WHC (2.8 g water/g DW), higher porosity (55.49%) and higher aggregate stability (54.14%) of soil was also obtained by C2. This research effectively reduced the maturation time to 32 days and converted kitchen waste into compost (resource). This is a very practical idea for home composting and kitchen gardening to combat food security issues in developing countries.

Abstract We report the fabrication and characterization of a Bi(III) oxide/polypyrrole (Bi2O3/Ppy) nanocomposite thin film optoelectronic photodetector synthesized by a simple one-pot method. The nanocomposite consists of spherical Bi2O3 nanoparticles embedded in a Ppy matrix, forming a porous structure with a high surface area. The XRD analysis reveals that the Bi2O3 nanoparticles have a poly-crystalline nature with a crystal size of 40 nm and an optical bandgap of 2.86 eV. The SEM images show that the nanoparticles are agglomerated into clusters of about 100 nm in diameter, with pores of about 200 nm in width. The device exhibits a high sensitivity to light, as evidenced by the increase of the photocurrent density (Jph) from − 0.06 mA.cm−2 in the dark to -0.12 mA.cm−2 under illumination at -2.0 V. The device also shows a wavelength-dependent response, with the highest responsivity (R) of 1.0 and 0.9 mA/W and detectivity (D) of 0.221 × 109 and 0.20 × 109 Jones at photon energies of 3.6 eV and 2.8 eV, respectively. The Bi2O3/Ppy nanocomposite is a promising material for low-cost, high-performance optoelectronic applications, making it a superior choice over many contemporary devices reported in recent literature.

Abstract Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing Tmax, Tmin, RHmean, Rs, and U2, with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R2), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.

Abstract Background and objectives Gingivitis and periodontitis are common periodontal diseases that can significantly harm overall oral health, affecting the teeth and their supporting tissues, along with the surrounding anatomical structures, and if left untreated, leading to the total destruction of the alveolar bone and the connective tissues, tooth loss, and other more serious systemic health issues. Numerous studies have shown that propolis can help reduce gum inflammation, inhibit the growth of pathogenic bacteria, and promote tissue regeneration, but with varying degrees of success reported. For this reason, this comprehensive systematic review aims at finding out the truth concerning the efficacy of propolis mouthwashes in treating gingivitis and periodontitis, as its main objective. Data sources Research findings from 6 different databases: China National Knowledge Infrastructure (CNKI), PubMed®, Europe PMC, Cochrane Central Register of Controlled Trials (CENTRAL), BioMed Central, and Google Scholar, were retrieved and examined in addition to a manual search in the references lists. Study selection and synthesis The PICOS framework was used to select and exclude studies. The focus was on clinical randomized controlled trials (RCTs) that examined the effectiveness of propolis-containing mouthwashes in comparison with propolis-free ones for the treatment of gingivitis and periodontitis, employing related periodontal indices. Animal studies, microbiological studies, in-vitro studies, retrospective studies, case-control studies, cohorts, case reports, case series, reviews, letters, editorials, meta-analyses, and non-clinical randomized controlled trials (non-RCTs), all were excluded. A meta-analysis was not performed and data were only studied qualitatively due to the obvious heterogeneity amongst the studies. Data from the selected studies were extracted, and then the revised Cochrane’s risk of bias tool (RoB 2.0) was utilised by two of the authors, independently, to evaluate the risk of bias in each study. Results At first, 151 results were reached, but then after removing duplicates, 99 records remained, and were later screened, assessed, and studied in full details based on the set PICOS criteria. Out of these 99 articles, ten studies were included in this systematic review, encompassing a total of 453 patients with an age range of (13–70) years old. Propolis mouthwashes with different protocols of application were the intervention whereas placebo or the rest of the tested mouthwashes such as, chlorhexidine, sodium fluoride with cetylpyridinium chloride, sterile distilled water, hydrogen peroxide, were the ones to which propolis mouthwashes were compared. Treatment duration extended from 14 days to 3 months and the follow-up period differed from 14 days to 3 months. In general, propolis mouthwashes decreased plaque accumulations and gingival inflammation in gingivitis patients based on the employed indices. On the other hand, the aforementioned tested mouthwashes other than propolis were deemed equally effective or even superior to propolis in some studies. As an overall assessment for the risk of bias, four studies were assigned as having a low risk of bias. Two studies were deemed to have some concerns, while four studies were identified as having a high risk of bias. Conclusions Despite the fact that propolis has shown positive effects in terms of controlling gingival and periodontal inflammation especially when used with mechanical methods, studies lack certainty and their power of evidence is low with no agreed gold standards. These conclusions come, for sure, within the limitations of this review, like having substantial variability amongst the included studies and the presence of studies with a high risk of bias. The findings demonstrate that propolis-based mouthwashes showed promising clinical outcomes in reducing plaque and gingival inflammation. However, it is highly recommended to conduct more rigorous trials with patient-reported outcome measures, extended follow-up periods, larger samples sizes, better-designed methodologies, typified propolis use, and with the implementation of similar indices in order to obtain more reliable, conclusive, and generalisable results. Prospero Registration Number CRD42024524523.

Abstract Background Increasing demands, such as the COVID-19 pandemic, have presented substantial challenges to global healthcare systems, resulting in staff shortages and overcrowded emergency rooms. Health kiosks have emerged as a promising solution to improve overall efficiency and healthcare accessibility. However, although kiosks are commonly used worldwide for access to information and financial services, the health kiosk industry, valued at $800 million, accounts for just 1.9% of the $42 billion global kiosk market. This review aims to bridge the research-to-practice gap by examining the development of health kiosk technology from 2013 to 2023. Methods We conducted a systematic search across PubMed, IEEE Xplore, and Google Scholar databases, identifying 5,537 articles, with 36 studies meeting inclusion criteria for detailed analysis. We evaluated each study based on kiosk purpose, targeted diseases, measured vital signs, and user demographics, along with an assessment of limitations in participant selection and data reporting. Results The findings reveal that blood pressure is the most frequently measured vital sign, utilized in 34% of the studies. Furthermore, cardiovascular disease detection emerges as the primary motivation in 56% of the included studies. The United States, India, and the United Kingdom are notable contributors, accounting for 43% of the reviewed articles. Our assessment reveals considerable limitations in participant selection and data reporting in many studies. Additionally, several research gaps remain, including a lack of performance testing, user experience evaluation, clinical intervention, development standardization, and inadequate sanitization protocols. Conclusions This review highlights health kiosks’ potential to ease the burden on healthcare system and expand accessibility. However, widespread adoption is hindered by technical, regulatory, and financial challenges. Addressing these barriers could enable health kiosks to play a greater role in early disease detection and healthcare delivery.

Abstract Background Uveal melanoma (UM) is the most common intraocular tumor in adults, arises either de novo from normal choroidal melanocytes (NCMs) or from pre-existing nevi that stem from NCMs and are thought to harbor UM-initiating mutations, most commonly in GNAQ or GNA11. However, there are no commercially available NCM cell lines, nor is there a detailed protocol for developing an oncogene-mutated CM line (MutCM) to study UM development. This study aimed to establish and characterize premalignant CM models from human donor eyes to recapitulate the cell populations at the origin of UM. Results Given the precious value of human donor eyes for studying multiple ocular cell types, we validated a co-isolation protocol of both human NCMs and retinal pigment epithelial (RPE) cells from a single eye. To this end, NCMs and RPE cells were sequentially isolated from 20 donors, with success rates of 95% and 75%, respectively. MutCMs were generated from 10 donors using GNAQ Q209L -carried lentivirus with high mutant copies (up to 98.8% of total GNAQ copies being mutant). NCM growth and behavior were characterized under different culture conditions (i.e., supplementation with serum and 12-O-tetradecanoylphorbol-13-acetate) to determine optimized protocols. Particularly, Matrigel™ coating induced spheroid growth under certain coating thickness and cell seeding density but did not improve NCM metabolic activity. Current methodologies in NCM isolation, culture, and research applications were summarized. Proteomic profiling of 4 NCMs, 1 MutCM, and 3 UMs allowed to discover significant differences in UMs including a downregulation of proteins linked to melanocyte differentiation and an upregulation of proteins involved in RNA metabolism. RNA sequencing revealed enriched pathways related to cancer, notably PI3K-Akt and MAPK signaling pathways, in MutCMs and UM cells compared to NCMs, providing insights into molecular changes in GNAQ Q209L -mutated pre-cancer cell models and UM cells. Conclusions We successfully isolated and established NCM, RPE, and MutCM cell lines. We describe efficient methods for the isolation and growth of NCMs and report their phenotypic, proteomic, and transcriptomic characteristics, which will facilitate the investigation of UM development and progression. The co-isolated RPE cells could benefit research on other ocular pathologies, such as age-related macular degeneration. Graphical Abstract Created with https://www.biorender.com/ .

Abstract Tetanus is a serious toxin-mediated disease caused by Clostridium tetani, and it was well-known to ancient physicians and in Greece. The disease is still prevalent in developing countries including Egypt with high morbidity and mortality. It is preventable with a safe and cost-effective vaccine and effective immunization programs. Based on the remarkable success of the World Health Organization (WHO) in reducing maternal and neonatal tetanus throughout the world, lowering tetanus among adults is also possible if a plan for disease elimination is promoted.

Abstract Obesity is a modifiable risk factor for breast cancer. Yet, how obesity contributes to cancer initiation is not fully understood. The goal of this study was to determine if the body mass index (BMI) and metabolic hallmarks of obesity are related to DNA damage in normal breast tissue. In a mouse model of diet-induced obesity, weight gain was associated with elevated levels of DNA double-strand breaks in the mammary gland. We also found a positive correlation between BMI and DNA breaks in the breast epithelium of premenopausal women (but not postmenopausal women). High BMI was associated with elevated systemic and tissue-level oxidative DNA damage across the lifespan, and we propose that the breast epithelium undergoing menstruous proliferation waves is particularly prone to the generation of DNA breaks from oxidative lesions. Ancestry was an important modulator of the obesity-DNA break connection. Compared to non-Hispanic Whites, women identifying as African Americans had higher levels of DNA breaks, as well as elevated leptin and IGF-1. In 3D cultures of breast acini, both leptin and IGF-1 caused an accumulation of DNA damage. The results highlight a connection between premalignant genomic alterations in the breast epithelium and metabolic health modulated by obesity and ancestry. They call for attention on biological determinants of breast cancer risk disparities.

Introduction Bell palsy (BP) is an acquired, idiopathic facial palsy linked to lower motor neuron malfunction of the seventh cranial nerve. Several studies have identified BP as one of the many neuropathies that coronavirus disease 2019 (COVID-19) patients have developed, while other studies disagree.