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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.

Heavy metal pollution is one of the most devastating abiotic factors, significantly damaging crops and human health. One of the serious problems it causes is a rise in cadmium (Cd) toxicity. Cd is a highly toxic metal with a negative biological role, and it enters plants via the soil–plant system. Cd stress induces a series of disorders in plants’ morphological, physiological, and biochemical processes and initiates the inhibition of seed germination, ultimately resulting in reduced growth. Fiber crops such as kenaf, jute, hemp, cotton, and flax have high industrial importance and often face the issue of Cd toxicity. Various techniques have been introduced to counter the rising threats of Cd toxicity, including reducing Cd content in the soil, mitigating the effects of Cd stress, and genetic improvements in plant tolerance against this stress. For decades, plant breeders have been trying to develop Cd-tolerant fiber crops through the identification and transformation of novel genes. Still, the complex mechanism of Cd tolerance has hindered the progress of genetic breeding. These crops are ideal candidates for the phytoremediation of heavy metals in contaminated soils. Hence, increased Cd uptake, accumulation, and translocation in below-ground parts (roots) and above-ground parts (shoots, leaves, and stems) can help clean agricultural lands for safe use for food crops. Earlier studies indicated that reducing Cd uptake, detoxification, reducing the effects of Cd stress, and developing plant tolerance to these stresses through the identification of novel genes are fruitful approaches. This review aims to highlight the role of some conventional and molecular techniques in reducing the threats of Cd stress in some key fiber crops. Molecular techniques mainly involve QTL mapping and GWAS. However, more focus has been given to the use of transcriptome and TFs analysis to explore the potential genomic regions involved in Cd tolerance in these crops. This review will serve as a source of valuable genetic information on key fiber crops, allowing for further in-depth analyses of Cd tolerance to identify the critical genes for molecular breeding, like genetic engineering and CRISPR/Cas9.

Recent studies have highlighted the growing integration of Natural Language Processing (NLP) techniques and Large Language Models (LLMs) in healthcare. These technologies have shown promising outcomes across various healthcare tasks, especially in widely studied languages like English and Chinese. While NLP methods have been extensively researched, LLM applications in healthcare represent a developing area with significant potential. However, the successful implementation of LLMs in healthcare requires careful review and guidance from human experts to ensure accuracy and reliability. Despite their emerging value, research on NLP and LLM applications for Arabic remains limited particularly when compared to other languages. This gap is largely due to challenges like the lack of suitable training datasets, the diversity of Arabic dialects, and the language’s structural complexity. In this study, a panel of medical experts evaluated responses generated by LLMs, including ChatGPT, for Arabic healthcare inquiries, rating their accuracy between 85% and 90%. After fine tuning ChatGPT with data from the Altibbi platform, accuracy improved to a range of 87% to 92%. This study demonstrates the potential of LLMs in addressing Arabic healthcare queries especially in interpreting questions across dialects. It highlights the value of LLMs in enhancing healthcare communication within the Arabic-speaking world and points to a promising area for further research. This work establishes a foundation for optimizing NLP and LLM technologies to achieve greater linguistic and cultural adaptability in global healthcare settings.

Osteoporosis (OP), a condition marked by reduced bone mineral density and increased fracture risk, can arise either as a primary disorder or secondary to other diseases and medications. While primary OP typically relates to age-related or postmenopausal changes, secondary OP results from underlying conditions or drug exposures, complicating diagnosis and management. This review explores the pathophysiology, prevalence, and treatment approaches for secondary OP arising from endocrine, renal, gastrointestinal, hematological, and autoimmune disorders, as well as medication side effects. The findings highlight that secondary OP is frequently undiagnosed, particularly in premenopausal women and men, with conditions such as chronic kidney disease, glucocorticoid use, and diabetes among the primary contributors. Management strategies must be tailored to address the underlying conditions to effectively reduce fracture risk and improve outcomes. Ultimately, this review underscores the necessity for increased clinical awareness and more targeted interventions for optimal management of secondary OP.

Short-term electric load forecasting is critical for power system planning and operations due to demand fluctuations driven by variable energy resources. While deep learning-based forecasting models have shown strong performance, time-sensitive applications require improvements in both accuracy and convergence speed. To address this, we propose a hybrid model that combines long short-term memory (LSTM) with a modified particle swarm optimisation (mPSO) algorithm. Although LSTM is effective for nonlinear time-series predictions, its computational complexity increases with parameter variations. To overcome this, mPSO is used for parameter tuning, ensuring accurate forecasting while avoiding local optima. Additionally, XGBoost and decision tree filtering algorithms are incorporated to reduce dimensionality and prevent overfitting. Unlike existing models that focus mainly on accuracy, our framework optimises accuracy, stability, and convergence rate simultaneously. The model was tested on real hourly load data from New South Wales and Victoria, significantly outperforming benchmark models such as ENN, LSTM, GA-LSTM, and PSO-LSTM. For NSW, the proposed model reduced MSE by 91.91%, RMSE by 94.89%, and MAPE by 74.29%. In VIC, MSE decreased by 91.33%, RMSE by 95.73%, and MAPE by 72.06%, showcasing superior performance across all metrics.

Malware detection, targeting the microarchitecture of processors, has recently come to light as a potentially effective way to improve computer system security. Hardware Performance Counter data are used by machine learning algorithms in security mechanisms, such as hardware-based malware detection, to categorize and detect malware. It is crucial to determine whether or not a file contains malware. Many issues have been brought about by the rise in malware, and businesses are losing vital data and dealing with other issues. The second thing to keep in mind is that malware can quickly cause a lot of damage to a system by slowing it down and encrypting a large amount of data on a personal computer. This study provides extensive details on a flexible framework related to machine learning and deep learning techniques using few-shot learning. Malware detection is possible using DT, RF, LR, SVM, and FSL techniques. The logic is that these algorithms make it simple to differentiate between files that are malware-free and those that are not. This indicates that their goal is to reduce the number of false positives in the data. For this, we use two different datasets from an online platform. In this research work, we mainly focus on few-shot learning techniques by using two different datasets. The proposed model has an 97% accuracy rate, which is much greater than that of other techniques.

Background and Objective: ATP-binding cassette (ABC) transporters are prominent drug targets due to their highly efficient trafficking capabilities and their significant physiological and clinical roles. Gaining insight into their biophysical and biomechanistic properties is crucial to maximize their pharmacological potential. Materials and Methods: In this study, we present the biochemical and biophysical characterization, and phylogenetic analysis of the domains of Mycobacterium tuberculosis (M. tuberculosis) ABC transporters: the exporter Rv1348 (IrtA) and the importer system Rv1349-Rv2895c (IrtB-Rv2895c), both involved in siderophore-mediated iron uptake. Results: Our findings reveal that the substrate-binding domain (SBD) of IrtA functions as an active monomer, while Rv2895c, which facilitates the uptake of siderophore-bound iron, exists in a dynamic equilibrium between dimeric and monomeric forms. Furthermore, ATP binding induces the dimerization of the ATPase domains in both IrtA (ATPase I) and IrtB (ATPaseII), but only the ATPase domain of IrtA (ATPase I) is active independently. We also analyzed the stability of substrate binding to the domains of the two transporters across varying temperature and pH ranges, revealing significant shifts in their activity under different conditions. Our study highlights the conformational changes that accompany substrate interaction with the transporter domains, providing insights into the fundamental mechanism required for the translocation of siderophore to the extracytoplasmic milieu by IrtB and, subsequently, import of their ferrated forms by the IrtB-Rv2895c complex. Phylogenetic analyses based on ATPase domains reveal that IrtA shares features with both archaeal and eukaryotic transporters, while IrtB is unique to mycobacterial species. Conclusions: Together, these findings provide valuable insights, which could accelerate the development of intervention strategies for this critical pathway pivotal in the progression of M. tuberculosis infection.

Background: This study aimed to evaluate the anti-inflammatory effects of a Zephyranthes rosea in an ovalbumin-induced asthma model. Methods: Allergic asthma was induced in mice via intraperitoneal injection, followed by intranasal ovalbumin challenge. Methanolic extract of Z. rosea bulb was orally administered to asthmatic mice for 14 days. Hematological parameters for bronchoalveolar lavage fluid (BALF) and blood were analyzed. The mRNA expression levels of interleukins and transforming growth factor beta (TGF-β1) in lung tissues were determined using reverse transcriptase–polymerase chain reaction (RT–PCR). Network pharmacology analysis was used to find possible Z. rosea targets. After building a protein–protein interaction network to find hub genes, GO and KEGG enrichment analyses were carried out to determine the potential mechanism. In silico analysis was performed by Molecular Operating Environment. Results: GC-MS analysis of Z. rosea extract detected major classes of phytochemicals. Hematological parameters in blood and BALF from Z. rosea extract-treated animals were significantly reduced in a dose-dependent fashion. Histopathology revealed that Z. rosea bulb had an ameliorative effect on lung tissues. Moreover, treatment with Z. rosea bulb extract significantly restored the normal levels of IL-4, IL-6, IL-1β, IL-10, IL-13, and TGF-β1 in allergic asthmatic mice compared to the diseased group. In silico analysis, particularly of the binding affinities of Z. rosea bulb phytoconstituents for IL6, AKT1, and Src, supported in vivo results. Conclusions: These findings indicated that Z. rosea bulb extract significantly ameliorates cellular and molecular biomarkers of bronchial inflammation and could be a potential candidate for treating allergic asthma.

The invasion of non-native plant species presents a significant ecological challenge worldwide, impacting native ecosystems and biodiversity. These invasive plant species significantly affect the native ecosystem. The threat of invasive plant species having harmful effects on the natural ecosystem is a serious concern. Invasive plant species produce secondary metabolites, which not only help in growth and development but are also essential for the spread of these plant species. This review highlights the important functions of secondary metabolites in plant invasion, particularly their effect on allelopathy, defense system, interaction with micro soil biota, and competitive advantages. Secondary metabolites produced by invasive plant species play an important role by affecting allelopathic interactions and herbivory. They sometimes change the soil chemistry to make a viable condition for their proliferation. The secondary metabolites of invasive plant species inhibit the growth of native plant species by changing the resources available to them. Therefore, it is necessary to understand this complicated interaction between secondary metabolites and plant invasion. This review mainly summarizes all the known secondary metabolites of non-native plant species, emphasizing their significance for integrated weed management and research.

LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). Using metrics such as accuracy, precision, recall, and F1-score, CAE-FL emerged as the top performer, achieving 97.27% accuracy and a balanced precision, recall, and F1-score of 0.97, with IF-FL close behind at 96.84% accuracy. Competitive performance from OCSVM-FL and LOF-FL, along with the comparable results of K-Means-FL, highlighted the robustness of clustering-based detection methods in this context. Visual analyses using confusion matrices and ROC curves provided further insights into each model’s effectiveness in detecting tampered signals. This research underscores the capability of federated learning to enhance privacy and security in anomaly detection for LoRa networks, even against unknown attacks, marking a significant advancement in securing IoT communications in sensitive applications.

The adaptation of plants to land requires sophisticated biological processes and signaling. Transcription factors (TFs) regulate several cellular and metabolic activities, as well as signaling pathways in plants during stress and growth and development. The WUSCHEL-RELATED HOMEOBOX (WOX) genes are TFs that are part of the homeodomain (HD) family, which is important for the maintenance of apical meristem, stem cell niche, and other cellular processes. The WOX gene family is divided into three clades: ancient, intermediate, and modern (WUS) based on historical evolution linkage. The number of WOX genes in the plant body increases as plants grow more complex and varies in different species. Numerous research studies have discovered that the WOX gene family play a role in the whole plant’s growth and development, such as in the stem, embryo, root, flower, and leaf. This review comprehensively analyzes roles of the WOX gene family across various plant species, highlighting the evolutionary significance and potential biotechnological applications in stress resistance and crop improvement.

In this article, our objective is to define and study a new subclass of analytic functions associated with the q-analogue of the sine function, operating in conjunction with a convolution operator. By manipulating the parameter q, we observe that the image of the unit disc under the q-sine function exhibits a visually appealing resemblance to a figure-eight shape that is symmetric about the real axis. Additionally, we investigate some important geometrical problems like necessary and sufficient conditions, coefficient bounds, Fekete-Szegö inequality, and partial sum results for the functions belonging to this newly defined subclass.

This study investigated the antioxidant potential of sugarcane vinegar, an emerging functional food, by analyzing its polyphenols and underlying molecular mechanisms that intervene in oxidative stress. Using a 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH) assay combined with UPLC-MS analysis, six key polyphenols were identified: chlorogenic acid, caffeic acid, ferulic acid, luteolin, protocatechuic acid, and syringic acid. These compounds showed a positive correlation with antioxidant capacity. In a simulated sugarcane vinegar environment, these polyphenols exhibited synergistic antioxidant effects, while in methanol, antagonistic interactions were predominant. Network pharmacology revealed five key polyphenols targeting 10 critical proteins involved in oxidative stress, including the PI3K-Akt and IL-17 signaling pathways. Molecular docking confirmed strong binding affinities between these polyphenols and core targets like PTGS2, STAT3, and GSK3B. This study establishes a reference for the antioxidant mechanisms of sugarcane vinegar and highlights its potential for developing functional products.

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.

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms—Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks.

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that integrates data from the Inertial Measurement Unit (IMU) with LiDAR sensors to correct tilt-induced distortions in LiDAR point cloud data and reduce ripple effects around objects. The adjusted data are grouped using clustering algorithms and bounding boxes for precise object localization. Additionally, a specialized Kalman filter tailored for maritime environments mitigates object discontinuities between successive frames and addresses data sparsity caused by boat tilting. The methodology was evaluated using the VRX simulator, with experiments conducted on the Volga River using real USVs. The preprocessing effectiveness was assessed using the Root Mean Square Error (RMSE) and tracking accuracy was evaluated through detection rate metrics. The results demonstrate a 25% to 30% improvement in detection accuracy and show that the pipeline can aid industry even with sparse object representation across different frames. This study highlights the potential of integrating sensor fusion with specialized tracking for accurate dynamic object detection in maritime settings, establishing a new benchmark for USV navigation systems’ accuracy and reliability.

This study aimed to assess the biomechanical behavior of endocrown-restored mandibular molars according to “margin design” and “coverage extent” using finite element analysis (FEA). Six 3D solid models were fabricated, namely, those with complete occlusal coverage: A (butt joint), B (anatomic margin); partial coverage (two mesial cusps): C (butt joint), D (anatomic margin); and partial coverage with mesial class II cavity: E (butt joint), F (anatomic margin). All models received lithium disilicate endocrowns (2.0 mm thickness and 4.0 mm central retainer cavity depth). A 300 N vertical load was applied to the occlusal surface, while a 250 N oblique load was applied at 45° to the lingual inclined planes of the buccal cusps. The maximum von Mises stress (VMS) distribution patterns were calculated for the endocrown, tooth structure, and cement layer. The VMS on the prepared teeth and cement layer showed subtle differences between the tested models under vertical loads. The anatomic margin (partial and complete coverage) exhibited a more homogeneous stress distribution and offered a more adhesive area of the tooth structure. Under oblique loading, the anatomic margin (complete and partial), except Model D, exhibited the lowest VMS in the cement layer. An anatomically based endocrown could be a promising alternative to the butt joint design, providing better-devised endocrown restorations, which could potentially yield a more benign stress dissipation.

The COVID-19 pandemic profoundly disrupted traditional education systems worldwide, prompting a rapid shift to online platforms and the emergence of innovative teaching strategies. This paper critically reviews the extensive body of research on post-COVID-19 education, focusing on the practical and feasible solutions proposed to maintain and enhance educational continuity. The review categorizes and examines studies on various approaches, including simulation-based training, project-based learning, and hybrid models, highlighting their effectiveness during and after the pandemic. Special attention is given to the role of information technology, the challenges faced by educators and students, and the importance of mental health support in the new educational landscape. The findings suggest that while digital tools such as virtual reality and 3D environments show promise, their implementation remains limited, particularly in resource-constrained settings. The study also identifies a significant gap in empirical research on these innovations in the post-pandemic era. Furthermore, the paper highlights the need for systemic changes in curriculum design, educator training, and policy development to address the long-term impacts of the pandemic on education. This review provides a comprehensive overview of the lessons learned from the COVID-19 pandemic, offering insights into how educational institutions can better prepare for future crises.

This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 24.1.0.2603908) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method’s potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems.

In our current study, we apply differential subordination and quantum calculus to introduce and investigate a new class of analytic functions associated with the q-differential operator and the symmetric balloon-shaped domain. We obtain sharp results concerning the Maclaurin coefficients the second and third-order Hankel determinants, the Zalcman conjecture, and its generalized conjecture for this newly defined class of q-starlike functions with respect to symmetric points.

The Asian dung beetle (Catharsius molossus L.; Coleoptera: Scarabeidae) has been shown to positively affect soil bacterial diversity and the agronomic features of crop plants. In this study, we used bioinformatic tools to investigate the differences in bacterial functional phenotypes and ecological functions between control soil, cow dung-amended soil (CD), and cow dung-amended soil composted by dung beetles (DB). The soil bacterial metagenomes were sequenced and analyzed with the bioinformatic packages BugBase, PICRUSt2, Tax4Fun, and FAPROTAX to evaluate the effects of dung beetle-mediated composting on bacterial functions such as human and plant pathogenicity, trophic strategies, and soil nutrient transformation. BugBase proved useful for the determination of differences in major functional phenotypes, whereas FAPROTAX was effective at identifying differences in bacterial ecological functions between the treatments. Both tools suggested a relative decrease in human pathogens in the DB soil. This was corroborated by the pairwise comparison of abundances in bacterial species, which showed a significant reduction in the abundance of the broad-host-range pathogen Pseudomonas aeruginosa in the DB soil. In addition, FAPROTAX suggested a decrease in plant pathogens and an increase in chitinolytic bacteria, meaning that the DB treatment might be beneficial to the plant-growth-promoting bacteria involved in biological control. Finally, FAPROTAX revealed an array of ecological functions related to trophic strategies and macro- and micronutrient metabolism. According to these results, the activity of C. molossus beetles enhanced methanotrophy, ammonification, nitrification, sulfate reduction, and manganese oxidation, whereas iron respiration was decreased in the DB-treated soil. Our results represent a collection of general insights into the effects of C. molossus beetles on soil bacterial functions, which also reflect on the nutrient composition of dung beetle-composted soil.

An estimated 2000 plant species have been employed for pest control worldwide. The use of these botanical derivatives is thought to be one of the most cost-effective and sustainable options for pest management in stored grain. The present study was designed to assess the efficacy of five plant extracts viz; Nicotiana tabacum L., Nicotiana rustica L., Azadirachta indica A. Juss., Thuja orientalis L., and Melia azedarach L. against Callosobruchus maculatus L. Plant species extracts were applied at six different concentrations, i.e., 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0% in four replications. The phytochemical analyses of ethanolic extracts of five plant species showed variable amounts of phytochemicals i.e., alkaloids, flavonoids, saponins, diterpenes, phytosterol, and phenols. Total phenolic and flavonoid compounds were also observed. The efficacy of A. indica was highest, characterized by the lowest infestation rate (16.65%), host seed weight loss (7.85%), mean oviposition (84.54), and adult emergence (58.40%). In contrast, T. orientalis was found to be the least effective against C. maculatus, with the highest infestation rate of 25.60%, host seed weight loss of 26.73%, mean oviposition of 117.17, and adult emergence rate of 82.01%. Probit analysis was performed by estimating LC50 and LC90. The toxicity percentages of N. tabacum (LC50 = 0.69%, LC90 = 14.59%), N. rustica (LC50 = 0.98%, LC90 = 22.06%), and A. indica (LC50 = 1.09%, LC90 = 68.52%) were notable in terms of the lower LC50 and LC90 values after the 96-h exposure period against C. maculatus. Repellency was assessed by using the area preference and filter paper method. The repellency of C. maculatus on plant extracts increased with the increasing dose and time, such that it was the highest after 48 h. Likewise, at a 3% concentration, A. indica demonstrated 100.00% (Class-V) repellency followed by N. tabacum (96.00%, Class-V), N. rustica (74%, Class-IV), M. azedarach (70.00%, Class-IV), and T. orientalis (68.00%, Class-IV). Based on the findings of this study, we recommend integrating N. rustica, N. tabacum, A. indica, and M. azedarach for effective management of C. maculatus and highlight the potential of these plant species in the formulation of new biocidal agents.

The current study explored the differential interaction between ethylene (ET) and abscisic acid (ABA) in relation to salt stress in mustard (Brassica juncea L.) plants. Significant reductions in seed germination, growth, and photosynthesis were observed with 100 mmol NaCl. Among the cultivars tested, the Pusa Vijay cultivar was noted as ET-sensitive. Pusa Vijay responded maximally to an application of 2.0 mmol ethephon (Eth; 2-chloethyl phosphonic acid-ethylene source), and exhibited the greatest growth, photosynthesis, activity of 1-aminocyclopropane carboxylic acid (ACC) synthase (ACS), and ET evolution. Notably, Eth (2.0 mmol) more significantly improved the seed germination percentage, germination and vigor index, amylase activity, and reduced H2O2 content under salt stress, while ABA (25 µmol) had negative effects. Moreover, the individual application of Eth and ABA on Pusa Vijay under both optimal and salt-stressed conditions increased the growth and photosynthetic attributes, nitrogen (N) and sulfur (S) assimilation, and antioxidant defense machinery. The addition of aminoethoxyvinylglycine (0.01 µmol AVG, ET biosynthesis inhibitor) to ABA + NaCl-treated plants further added to the effects of ABA on parameters related to seed germination and resulted in less effectiveness of growth and photosynthesis. In contrast, the effects of Eth were seen with the addition of fluoridone (25 µmol Flu, ABA biosynthesis inhibitor) to Eth + NaCl. Thus, it can be suggested that ET is crucial for alleviating salt-induced inhibition in seed germination, growth, and photosynthesis, while ABA collaborated with ET to offer protection by regulating nutrient assimilation and enhancing antioxidant metabolism. These findings provide insight into the complex regulatory processes involved in ET–ABA interaction, enhancing our understanding of plant growth and development and the mitigation of salt stress in mustard. It opens pathways for developing hormonal-based strategies to improve crop productivity and resilience, ultimately benefiting agricultural practices amidst a challenging environment.

Improving energy efficiency in listed buildings to reduce greenhouse gas emissions (GHGs) by using a monocrystalline solar PV system with the Dar-Al-Arqam mosque in Edinburgh as a case study. Leveraging PVsyst simulation software, various solar PV design scenarios are evaluated. The results suggest the west wing of the roof is optimal for solar PV installation, demonstrating that PV panels can economically reduce emissions while preserving the building’s integrity. Different cleaning methods were investigated and the one best suited for the location is the natural cleaning method.

Tissue engineering, a rapidly advancing field in medicine, has made significant strides with the development of artificial tissue substitutes to meet the growing need for organ transplants. Three-dimensional (3D) porous scaffolds are widely utilized in tissue engineering, especially in orthopedic surgery. This study investigated the fluidic properties of diamond and gyroid structures with varying porosity levels (50–80%) using Computational Fluid Dynamics (CFD) analysis. The pressure and velocity distributions were analyzed, and it was observed that the pressure decreased gradually, whereas the velocity increased in the central area of the surface structures. Specifically, the pressure drop ranged from 2.079 to 0.984 Pa for the diamond structure and from 1.669 to 0.943 Pa for the gyroid structure as the porosity increased from 50% to 80%. It was also found that the permeability increased as the porosity level increased, with values ranging from 2.424×10−9 to 5.122×10−9 m2 for the diamond structure and from 2.966×10−9 to 5.344×10−9 m2 for the gyroid structure. The wall shear stress (WSS) was also analyzed, showing a consistent decrease with increased porosity for both types of structures, with WSS values ranging from 9.903×10−2 to 9.840×10−1 Pa for the diamond structure and from 1.150×10−1 to 7.717×10−2 Pa for the gyroid structure. Overall, this study provides insights into the fluidic properties of diamond and gyroid structures, which can be useful in various applications such as tissue engineering.

Africa, home to the world’s second-largest population of approximately 1.3 billion, grapples with significant challenges in meeting its medical needs, particularly in accessing quality healthcare services and products. The continent faces a continuous onslaught of emerging infectious diseases, exacerbating the strain on its already fragile public health infrastructure. The COVID-19 crisis highlighted the urgency to build local vaccine production capacity and strengthen the health infrastructure in general. The risks associated with a heavy reliance on imported vaccines were exposed during the COVID-19 pandemic, necessitating the need to nurture and strengthen the local manufacturing of vaccines and therapeutic biologics. Various initiatives addressing training, manufacturing, and regulatory affairs are underway, and these require increasing dedicated and purposeful financial investment. Building vaccine manufacturing capacity requires substantial investment in training and infrastructure. This manuscript examines the current state of education in vaccinology and related sciences in Africa. It also provides an overview of the continent’s efforts to address educational needs in vaccine development and manufacturing. Additionally, it evaluates the initiatives aimed at strengthening vaccine education and literacy, highlighting successful approaches and ongoing challenges. By assessing the progress made and identifying the remaining obstacles, this review offers insights into how Africa can enhance its vaccine manufacturing capacity to respond to vaccine-preventable disease challenges.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to be a global threat due to its ability to evolve and generate new subvariants, leading to new waves of infection. Additionally, other coronaviruses like Middle East respiratory syndrome coronavirus (MERS-CoV, formerly known as hCoV-EMC), which first emerged in 2012, persist and continue to present a threat of severe illness to humans. The continued identification of novel coronaviruses, coupled with the potential for genetic recombination between different strains, raises the possibility of new coronavirus clades of global concern emerging. As a result, there is a pressing need for pan-CoV therapeutic drugs and vaccines. After the extensive optimization of an HCV protease inhibitor screening hit, a novel 3CLPro inhibitor (MK-7845) was discovered and subsequently profiled. MK-7845 exhibited nanomolar in vitro potency with broad spectrum activity against a panel of clinical SARS-CoV-2 subvariants and MERS-CoV. Furthermore, when administered orally, MK-7845 demonstrated a notable reduction in viral burdens by >6 log orders in the lungs of transgenic mice infected with SARS-CoV-2 (K18-hACE2 mice) and MERS-CoV (K18-hDDP4 mice).

The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently.

This paper describes an empirical experiment of Internet of Things (IoT)’s integration in the Post-Occupancy Evaluation (POE) process. The experiment aimed to trial a novel IoT approach to enabling building user responsiveness to prevalent IEQ for individualised comfort. The purpose is to provide a system that mitigates a common issue of centralised air conditioning that limits occupants’ control over their immediate environment. To achieve this, an IoT platform was developed with smart IEQ monitoring sensors and wearable devices and trialled with PhD researchers in a shared university workspace. The findings provided empirical evidence of IoT’s enhanced benefits to improving user control over their individual comfort and enabling positive energy behaviour in buildings. Specifically, the IoT system provided real-time insight into CO2 concentration data while enabling responsive occupant interaction with their immediate environment and at-the-moment mitigation actions. Outputs of the experiment showed that the perceptions of participants about the stuffiness of the air, productivity, and healthy environment were significantly better after taking the mitigation action compared to before. Also, we found a significant relationship between measured CO2 concentration readings and perceived air stuffiness (p = 0.004) and productivity (p = 0.006) and a non-significant relationship between CO2 concentration readings and perceived healthy environment (p = 0.058). Interestingly, we observed that irrespective of the similarities in recorded CO2 concentration readings being within acceptable ranges (632–712 ppm), the perception of air stuffiness significantly differed (p = 0.018) before and after the mitigation actions. The effectiveness of the developed IoT platform was evidenced as most of the participants found the process very easy to participate in with little interruptions to their work as little time was consumed. The results are useful in modifying approaches to building occupant comfort and energy behaviour in commercial and residential settings.

The integration of blockchain technology in educational institutions is gaining momentum due to its promise to improve the graduation system and the process of verifying educational qualifications. However, little research exists on the social aspects of incorporating blockchain technology in an educational environment. This study aims to evaluate the social impact of blockchain technology on internal and external stakeholders of a higher education institution, including educational leaders, management, graduates, and external organisations. Using the Social Impact Assessment methodology, this study reveals that the integration of blockchain technology can have an overall positive impact on industrial and alumni relations and resource optimization. This study also contributes to the literature on Social Impact Assessment by presenting a methodological model for the integration of blockchain technology in the context of a higher education environment.

In this article, the Lagrange expansion of the second kind is used to generate a novel zero-truncated Katz distribution; we refer to it as the Lagrangian zero-truncated Katz distribution (LZTKD). Notably, the zero-truncated Katz distribution is a special case of this distribution. Along with the closed form expression of all its statistical characteristics, the LZTKD is proven to provide an adequate model for both underdispersed and overdispersed zero-truncated count datasets. Specifically, we show that the associated hazard rate function has increasing, decreasing, bathtub, or upside-down bathtub shapes. Moreover, we demonstrate that the LZTKD belongs to the Lagrangian distribution of the first kind. Then, applications of the LZTKD in statistical scenarios are explored. The unknown parameters are estimated using the well-reputed method of the maximum likelihood. In addition, the generalized likelihood ratio test procedure is applied to test the significance of the additional parameter. In order to evaluate the performance of the maximum likelihood estimates, simulation studies are also conducted. The use of real-life datasets further highlights the relevance and applicability of the proposed model.

CXCL10 (IP-10) plays a key role in leukocyte homing to the inflamed tissues and its increased levels are associated with the pathophysiology of various inflammatory diseases including obesity and type 2 diabetes. IL-1β is a key proinflammatory cytokine that is found upregulated in meta-inflammatory conditions and acts as a potent activator, inducing the expression of cytokines/chemokines by immune cells. However, it is unclear whether IL-1β induces the expression of CXCL10 in monocytic cells. We, therefore, determined the CXCL10 induction using IL-1β in THP1 monocytic cells and investigated the mechanisms involved. Monocytes (human monocytic THP-1 cells) were stimulated with IL-1β. CXCL10 gene expression was determined with real-time RT-PCR. CXCL10 protein was determined using ELISA. Signaling pathways were identified by using Western blotting, inhibitors, siRNA transfections, and kinase assay. Our data show that IL-1β induced the CXCL10 expression at both mRNA and protein levels in monocytic cells (p = 0.0001). Notably, only the JNK inhibitor (SP600125) significantly suppressed the IL-1β-induced CXCL10 expression, while the inhibitors of MEK1/2 (U0126), ERK1/2 (PD98059), and p38 MAPK (SB203580) had no significant effect. Furthermore, IL-1β-induced CXCL10 expression was decreased in monocytic cells deficient in JNK/c-Jun. Accordingly, inhibiting the JNK kinase activity markedly reduced the IL-1β-induced JNK/c-Jun phosphorylation in monocytic cells. NF-κB inhibition by Bay-117085 and resveratrol also suppressed the CXCL10 expression. Our findings provide preliminary evidence that IL-1β stimulation induces the expression of CXCL10 in monocytic cells which requires signaling via the JNK/c-Jun/NF-κB axis.

The city represents a complex adaptive system consisting of a set of interconnected subsystems within self-organization and flexible structures that give it the ability to adapt to continue operating over time. This research discusses the adaptive flexibility of the complex urban system by measuring the degree of complexity and adaptation of secondary systems within the general system depending on its kinetic and visual system, making it self-organized and within a coherent and harmonious unit. Thus, the research problem arises from “the lack of knowledge on how to determine the appropriate degree of complexity and adaptation for urban systems, providing them with adaptive flexibility that allows them to maintain their energy and ensure their survival and continuity over time”. This research aims to determine the adaptive flexibility of the complex urban system, which keeps it self-organized and able to survive depending on its kinetic and visual system that links the secondary systems of the general system based on the degree of complexity and adaptation ideal. The research adopted quantitative analysis, which includes calculating the adaptive dimensions of the complex system based on the kinetic and visual system, as well as the nodes connecting the parts of the secondary system, in addition to determining both the area and the perimeter of the study area. The historic Karkh area in Baghdad was selected for implementation because it includes a model of the complex system that originated and developed within different time stages. The system’s adaptive flexibility was calculated to determine its ability to survive and continue. This research found that it is possible to determine the strength of the urban format and its ability to survive or not, as well as the possibility of developing it, by understanding the ideal degree of adaptive flexibility for the format. This ideal degree is 1, achieved through visual and kinetic coherence among the nodes that make up the parts of the format. The farther the format is from this degree, the less adaptive flexibility it has, which affects its ability to survive and continue. Therefore, it is necessary to maintain the strength of the interconnections between the parts of the kinetic and visual system and between the parts of the complex format, which directly affects its ability to self-regulate and survive.

Achieving the net zero emissions target that was set in the Paris Agreement to mitigate the risks of climate change seems increasingly difficult as countries and sectors of the economy are falling behind the expected trajectory. The building and construction industry, as one of the main contributors to global emissions, has an essential role to play toward this aim. Net zero emissions target has been introduced to this sector as well; however, achieving it is a very challenging and complex task. Many studies have been undertaken on implementing different measures and strategies to reduce the industry’s carbon footprint. These studies identified many challenges and barriers in transforming the industry. This paper aims to provide a systematic review of challenges that organisations face in delivering new net zero emissions buildings. The relevant journal articles published since the Paris Agreement were identified and analysed using mixed-method data analysis, including quantitative (science mapping) and qualitative (thematic) analysis. The result showed increased attention to the subject over this period, with China, the UK, and Australia being the top contexts for research. The most discussed groups of barriers were “economic”, “knowledge”, and “technical”, respectively, followed by “organisational”, “market”, “technological”, and “legal” barriers.

This paper presents a novel fuzzy-augmented model reference adaptive voltage regulation strategy for the DC–DC buck converters to enhance their resilience against random input variations and load-step transients. The ubiquitous proportional-integral-derivative (PID) controller is employed as the baseline scheme, whose gains are tuned offline via a pre-calibrated linear-quadratic optimization scheme. However, owing to the inefficacy of the fixed-gain PID controller against parametric disturbances, it is retrofitted with a model reference adaptive controller that uses Lyapunov gain adaptation law for the online modification of PID gains. The adaptive controller is also augmented with an auxiliary fuzzy self-regulation system that acts as a superior regulator to dynamically update the adaptation rates of the Lyapunov gain adaptation law as a nonlinear function of the system’s classical error and its normalized acceleration. The proposed fuzzy system utilizes the knowledge of the system’s relative rate to execute better self-regulation of the adaptation rates, which in turn, flexibly steers the adaptability and response speed of the controller as the error conditions change. The propositions above are validated by performing tailored hardware experiments on a low-power DC–DC buck converter prototype. The experimental results validate the improved reference tracking and disturbance rejection ability of the proposed control law compared to the fixed PID controller.

The Cucurbitaceae family includes several edible species that are consumed globally as fruits and vegetables. These species produce high volumes of seeds that are often discarded as waste. In this study, we investigate the chemical composition and biological activity of three seed oils from Cucurbitaceae plants, namely, cantaloupe, honeydew, and zucchini, in comparison to the widely used pumpkin seed oil for their ability to enhance and accelerate wound healing in rats. Our results showed that honeydew seed oil (HSO) was effective in accelerating wound closure and enhancing tissue repair, as indicated by macroscopic, histological, and biochemical analyses, as compared with pumpkin seed oil (PSO). This effect was mediated by down-regulation of the advanced glycation end products (AGE) and its receptor (RAGE) cue, activating the cytoprotective enzymes nuclear factor erythroid 2 (Nrf2) and heme oxygenase-1 (HO-1), suppressing the inflammatory mediators tumor necrosis factor (TNF)-α, nuclear factor kappa B (NF-κB), and nod-like receptor protein 3 (NLRP3), and reducing the levels of the skin integral signaling protein connexin (CX)-43. Furthermore, immunohistochemical staining for epidermal growth factor (EGF) showed the lowest expression in the skin after treatment with HSO, indicating a well-organized and complete healing process. Other seed oils from cantaloupe and zucchini exhibited favorable activity when compared with untreated rats; however, their efficacy was comparatively lower than that of PSO and HSO. Gas chromatographic analysis of the derivatized oils warranted the superior activity of HSO to its high nutraceutical content of linoleic acid, which represented 65.9% of the fatty acid content. This study’s findings validate the use of honeydew seeds as a wound-healing fixed oil and encourage further investigation into the potential of Cucurbitaceae seeds as sources of medicinally valuable plant oils.

Diabetes, a multifactorial metabolic disorder, demands the discovery of multi-targeting drugs with minimal side effects. This study investigated the multi-targeting antidiabetic potential of quercetin and kaempferol. The druggability and binding affinities of both compounds towards multiple antidiabetic targets were explored using pharmacokinetic and docking software (AutoDock Vina 1.1.2). Our findings showed that quercetin and kaempferol obey Lipinski’s rule of five and exhibit desirable ADMET (absorption, distribution, metabolism excretion, and toxicity) profiles. Both compounds showed higher binding affinities towards C-reactive protein (CRP), interleukin-1 (IL-1), dipeptidyl peptidase-4 (DPP-IV), peroxisome proliferator-activated receptor gamma (PPARG), protein tyrosine phosphatase (PTP), and sodium–glucose co-transporter-1 (SGLT-1) compared to metformin (the positive control). Both quercetin and kaempferol inhibited α-amylase activity (in vitro) up to 20.30 ± 0.49 and 37.43 ± 0.42%, respectively. Their oral supplementation significantly reduced blood glucose levels (p < 0.001), improved lipid profile (p < 0.001), and enhanced total antioxidant status (p < 0.01) in streptozotocin–nicotinamide (STZ-NA)-induced diabetic mice. Additionally, both compounds significantly inhibited the proliferation of Huh-7 and HepG2 (cancer cells) (p < 0.0001) with no effect on the viability of Vero cell line (non-cancer). In conclusion, quercetin and kaempferol demonstrated higher binding affinities towards multiple targets than metformin. In vitro and in vivo antidiabetic potential along with the anticancer activities of both compounds suggest promise for further development in diabetes management. The combination of both drugs did not show a synergistic effect, possibly due to their same target on the receptors.

Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-associated enzyme-CAS holds great promise for treating many uncured human diseases and illnesses by precisely correcting harmful point mutations and disrupting disease-causing genes. The recent Food and Drug Association (FDA) approval of the first CRISPR-based gene therapy for sickle cell anemia marks the beginning of a new era in gene editing. However, delivering CRISPR specifically into diseased cells in vivo is a significant challenge and an area of intense research. The identification of new CRISPR/Cas variants, particularly ultra-compact CAS systems with robust gene editing activities, paves the way for the low-capacity delivery vectors to be used in gene therapies. CRISPR/Cas technology has evolved beyond editing DNA to cover a wide spectrum of functionalities, including RNA targeting, disease diagnosis, transcriptional/epigenetic regulation, chromatin imaging, high-throughput screening, and new disease modeling. CRISPR/Cas can be used to engineer B-cells to produce potent antibodies for more effective vaccines and enhance CAR T-cells for the more precise and efficient targeting of tumor cells. However, CRISPR/Cas technology has challenges, including off-target effects, toxicity, immune responses, and inadequate tissue-specific delivery. Overcoming these challenges necessitates the development of a more effective and specific CRISPR/Cas delivery system. This entails strategically utilizing specific gRNAs in conjunction with robust CRISPR/Cas variants to mitigate off-target effects. This review seeks to delve into the intricacies of the CRISPR/Cas mechanism, explore progress in gene therapies, evaluate gene delivery systems, highlight limitations, outline necessary precautions, and scrutinize the ethical considerations associated with its application.

COVID-19 has improved awareness of the importance of appropriate indoor air quality (IAQ) in indoor spaces, particularly in classrooms where children are expected to learn. Research has shown that poor IAQ and temperature levels affect the cognitive performance of children. In this paper, we critically compare IAQ standards for New Zealand’s Designing Quality Learning Spaces (DQLS Document) against international benchmarks from the Organization for Economic Co-operation and Development (OECD) countries, including ASHRAE 62.1, CIBSE TM57, EN-15251, WHO AQGs, and Building Bulletins 99 and 101. The aim was to ascertain the robustness of New Zealand’s DQLS document, identify areas of superiority, and recommend the required improvement for appropriate IAQ and thermal comfort in classrooms. This comparison review focuses on IAQ parameters: CO2 levels, temperature, ventilation rates, room size, occupant density, and occupancy rates. The findings illuminate a slight lag in New Zealand’s DQLS standards compared to her international counterparts. For instance, while New Zealand’s standards align closely with WHO standards for IAQ concerning temperature and ventilation rates, the recommended CO2 range appears slightly inadequate (800 to 2000 ppm) along with occupancy and classroom size for effectively controlling classroom pollutant growth. This paper emphasises the need to align New Zealand’s IAQ and thermal comfort standards with optimal OECD benchmarks. The identified disparities present opportunities for improving learning spaces in terms of CO2 concentration, size of classroom, and occupant density in schools in New Zealand to meet globally recognised standards, ultimately creating a healthier and more conducive learning environment.

Wildfires play a dual role in ecosystems by providing ecological benefits while posing catastrophic events; they also inflict non-catastrophic damage and yield long-term effects on biodiversity, soil quality, and air quality, among other factors, including public health. This study analysed the key determinants of wildland fires in Spain using openly available spatial data from 2008 to 2021, including fire perimeters, bioclimatic variables, topography, and socioeconomic datasets, at a resolution of 1 km2. Our methodology combined principal component analysis (PCA), linear regression analysis, and one-way analysis of variance (ANOVA). Our findings show that scrub/herbaceous vegetation (average 63 ± 1.45% SE) and forests (average 19 ± 0.76% SE) have been highly susceptible to wildfires. The population density exhibited a robust positive correlation with wildfire frequency (R2 = 0.88, p < 0.0001). Although the study provides insights into some fire-related climatic drivers over Spain, it includes only temperature- and precipitation-based variables and does not explicitly consider fuel dynamics. Therefore, a more advanced methodology should be applied in the future to understand the local specifics of regional wildfire dynamics. Our study identified that scrub/herbaceous areas and forests near densely populated regions should be prioritised for wildfire management in Spain, particularly under changing climate conditions.

This study unveils verapamil’s compelling cytoprotective and proliferative effects on pancreatic β-cells amidst diabetic stressors, spotlighting its unforeseen role in augmenting cholecystokinin (CCK) expression. Through rigorous investigations employing MIN6 β-cells and zebrafish models under type 1 and type 2 diabetic conditions, we demonstrate verapamil’s capacity to significantly boost β-cell proliferation, enhance glucose-stimulated insulin secretion, and fortify cellular resilience. A pivotal revelation of our research is verapamil’s induction of CCK, a peptide hormone known for its role in nutrient digestion and insulin secretion, which signifies a novel pathway through which verapamil exerts its therapeutic effects. Furthermore, our mechanistic insights reveal that verapamil orchestrates a broad spectrum of gene and protein expressions pivotal for β-cell survival and adaptation to immune-metabolic challenges. In vivo validation in a zebrafish larvae model confirms verapamil’s efficacy in fostering β-cell recovery post-metronidazole infliction. Collectively, our findings advocate for verapamil’s reevaluation as a multifaceted agent in diabetes therapy, highlighting its novel function in CCK upregulation alongside enhancing β-cell proliferation, glucose sensing, and oxidative respiration. This research enriches the therapeutic landscape, proposing verapamil not only as a cytoprotector but also as a promoter of β-cell regeneration, thereby offering fresh avenues for diabetes management strategies aimed at preserving and augmenting β-cell functionality.

This paper presents a multi-parameter ionospheric disturbance analysis of the total electron content (TEC), density (Ne), temperature (Te), and critical frequency foF2 variations preceding two significant earthquake events (2015 Mw 7.5 and 2023 Mw 6.3) that occurred in Afghanistan. The analysis from various ground stations and low-Earth-orbit satellite measurements involved employing the sliding interquartile method to process TEC data of Global Ionospheric Maps (GIMs), comparing revisit trajectories to identify anomalies in Ne and Te from Swarm satellites, applying machine learning-based envelope estimation for GPS-derived TEC measurements, utilizing the least square method for foF2 data and ionograms obtained from available base stations in the Global Ionosphere Radio Observatory (GIRO). After excluding potential influences caused by solar and geomagnetic activities, the following phenomena were revealed: (1) The GIM-TEC variations displayed positive anomalies one day before the 2015 Mw 7.5 earthquake, while significant positive anomalies occurred on the shock days (7, 11, and 15) of the 2023 Mw 6.3 earthquake; (2) the Swarm satellite observations (Ne and Te) for the two earthquakes followed almost the same appearance rates as GIM-TEC, and a negative correlation between the Ne and Te values was found, with clearer appearance at night; (3) there were prominent positive TEC anomalies 8 days and almost 3 h before the earthquakes at selected GPS stations, which were nearest to the earthquake preparation area. The anomalous variations in TEC height and plasma density were verified by analyzing the foF2, which confirmed the ionospheric perturbations. Unusual ionospheric disturbances indicate imminent pre-seismic events, which provides the potential opportunity to provide aid for earthquake prediction and natural hazard risk management in Afghanistan and nearby regions.

Background and Objectives: Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the quality of life of patients worldwide. Comorbidities such as chronic kidney disease (CKD), arterial hypertension, and diabetes mellitus (DM) are common among HF patients, as they share similar risk factors. This study aimed to identify the prognostic significance of multiple factors and their correlation with disease prognosis and outcomes in a Jordanian cohort. Materials and Methods: Data from the Jordanian Heart Failure Registry (JoHFR) were analyzed, encompassing medical records from acute and chronic HF patients attending public and private cardiology clinics and hospitals across Jordan. An online form was utilized for data collection, focusing on three kidney function tests, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), and creatinine levels, with the eGFR calculated using the Cockcroft–Gault formula. We also built six machine learning models to predict mortality in our cohort. Results: From the JoHFR, 2151 HF patients were included, with 644, 1799, and 1927 records analyzed for eGFR, BUN, and creatinine levels, respectively. Age negatively impacted all measures (p ≤ 0.001), while smokers surprisingly showed better results than non-smokers (p ≤ 0.001). Males had more normal eGFR levels compared to females (p = 0.002). Comorbidities such as hypertension, diabetes, arrhythmias, and implanted devices were inversely related to eGFR (all with p-values p ≤ 0.001). Higher creatinine levels were linked to hypertension, diabetes, dyslipidemia, arrhythmias, and previous HF history (all with p-values p = 0.049) and mortality (p ≤ 0.001), while BUN levels did not significantly affect these outcomes. Machine learning analysis employing the Random Forest Classifier revealed that length of hospital stay and creatinine >115 were the most significant predictors of mortality. The classifier achieved an accuracy of 90.02% with an AUC of 80.51%, indicating its efficacy in predictive modeling. Conclusions: This study reveals the intricate relationship among kidney function tests, comorbidities, and clinical outcomes in HF patients in Jordan, highlighting the importance of kidney function as a predictive tool. Integrating machine learning models into clinical practice may enhance the predictive accuracy of patient outcomes, thereby supporting a more personalized approach to managing HF and related kidney dysfunction. Further research is necessary to validate these findings and to develop innovative treatment strategies for the CKD population within the HF cohort.

Background: Peroxynitrite (ONOO−) is an oxidant linked with several human pathologies. Apigenin, a natural flavonoid known for its health benefits, remains unexplored in relation to ONOO− effects. This study investigated the potential of apigenin to structurally protect fibrinogen, an essential blood clotting factor, from ONOO−-induced damage. Methods: Multi-approach analyses were carried out where fibrinogen was exposed to ONOO− generation while testing the efficacy of apigenin. The role of apigenin against ONOO−-induced modifications in fibrinogen was investigated using UV spectroscopy, tryptophan or tyrosine fluorescence, protein hydrophobicity, carbonylation, and electrophoretic analyses. Results: The findings demonstrate that apigenin significantly inhibits ONOO−-induced oxidative damage in fibrinogen. ONOO− caused reduced UV absorption, which was reversed by apigenin treatment. Moreover, ONOO− diminished tryptophan and tyrosine fluorescence, which was effectively restored by apigenin treatment. Apigenin also reduced the hydrophobicity of ONOO−-damaged fibrinogen. Moreover, apigenin exhibited protective effects against ONOO−-induced protein carbonylation. SDS-PAGE analyses revealed that ONOO−treatment eliminated bands corresponding to fibrinogen polypeptide chains Aα and γ, while apigenin preserved these changes. Conclusions: This study highlights, for the first time, the role of apigenin in structural protection of human fibrinogen against peroxynitrite-induced nitrosative damage. Our data indicate that apigenin offers structural protection to all three polypeptide chains (Aα, Bβ, and γ) of human fibrinogen. Specifically, apigenin prevents the dislocation or breakdown of the amino acids tryptophan, tyrosine, lysine, arginine, proline, and threonine and also prevents the exposure of hydrophobic sites in fibrinogen induced by ONOO−.

Cellular demise is a pivotal event in both developmental processes and disease states, with mitochondrial regulation playing an essential role. Traditionally, cell death was categorized into distinct types, considered to be linear and mutually exclusive pathways. However, the current understanding has evolved to recognize the complex and interconnected mechanisms of cell death, especially within apoptosis, pyroptosis, and necroptosis. Apoptosis, pyroptosis, and necroptosis are governed by intricate molecular pathways, with mitochondria acting as central decision-makers in steering cells towards either apoptosis or pyroptosis through various mediators. The choice between apoptosis and necroptosis is often determined by mitochondrial signaling and is orchestrated by specific proteins. The molecular dialogue and the regulatory influence of mitochondria within these cell death pathways are critical research areas. Comprehending the shared elements and the interplay between these death modalities is crucial for unraveling the complexities of cellular demise.

Antimicrobial resistance (AMR), caused by microbial infections, has become a major contributor to morbid rates of mortality worldwide and a serious threat to public health. The exponential increase in resistant pathogen strains including Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) poses significant hurdles in the health sector due to their greater resistance to traditional treatments and medicines. Efforts to tackle infectious diseases caused by resistant microbes have prompted the development of novel antibacterial agents. Herein, we present selenium and copper oxide monometallic nanoparticles (Se-MMNPs and CuO-MMNPs), characterized using various techniques and evaluated for their antibacterial potential via disc diffusion, determination of minimum inhibitory concentration (MIC), antibiofilm, and killing kinetic action. Dynamic light scattering (DLS), scanning electron microscopy (SEM/EDX), and X-ray diffraction (XRD) techniques confirmed the size-distribution, spherical-shape, stability, elemental composition, and structural aspects of the synthesized nanoparticles. The MIC values of Se-MMNPs and CuO-MMNPs against S. aureus and E. coli were determined to be 125 μg/mL and 100 μg/mL, respectively. Time–kill kinetics studies revealed that CuO-MMNPs efficiently mitigate the growth of S. aureus and E. coli within 3 and 3.5 h while Se-MMNPs took 4 and 5 h, respectively. Moreover, CuO-MMNPs demonstrated better inhibition compared to Se-MMNPs. Overall, the proposed materials exhibited promising antibacterial activity against S. aureus and E. coli pathogens.

Deep neural networks (DNNs) have gained prominence in various applications, but remain vulnerable to adversarial attacks that manipulate data to mislead a DNN. This paper aims to challenge the efficacy and transferability of two contemporary defense mechanisms against adversarial attacks: (a) robust training and (b) adversarial training. The former suggests that training a DNN on a data set consisting solely of robust features should produce a model resistant to adversarial attacks. The latter creates an adversarially trained model that learns to minimise an expected training loss over a distribution of bounded adversarial perturbations. We reveal a significant lack in the transferability of these defense mechanisms and provide insight into the potential dangers posed by L∞-norm attacks previously underestimated by the research community. Such conclusions are based on extensive experiments involving (1) different model architectures, (2) the use of canonical correlation analysis, (3) visual and quantitative analysis of the neural network’s latent representations, (4) an analysis of networks’ decision boundaries and (5) the use of equivalence of L2 and L∞ perturbation norm theories.

In this research work, a new three-parameter lifetime distribution is introduced and studied. It is called the Harris extended Bilal distribution due to its construction from a mixture of the famous Bilal and Harris distributions, resulting from a branching process. The basic properties, such as the moment generating function, moments, quantile function, and Rényi entropy, are discussed. We show that the hazard rate function has ideal features for modeling increasing, upside-down bathtub, and roller-coaster data sets. In a second part, the Harris extended Bilal model is investigated from a statistical viewpoint. The maximum likelihood estimation is used to estimate the parameters, and a simulation study is carried out. The flexibility of the proposed model in a hydrological data analysis scenario is demonstrated using two practical data sets and compared with important competing models. After that, we establish an acceptance sampling plan that takes advantage of all of the features of the Harris extended Bilal model. The operating characteristic values, the minimum sample size that corresponds to the maximum possible defects, and the minimum ratios of lifetime associated with the producer’s risk are discussed.

In this research, we design the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, a bivariate analogue of the moment exponential distribution, using the Farlie–Gumbel–Morgenstern approach. With the analysis of real-life data, the competitiveness of the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution in comparison with the other Farlie–Gumbel–Morgenstern distributions is discussed. Based on the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, we develop the distribution theory of concomitants of order statistics and derive the best linear unbiased estimator of the parameter associated with the variable of primary interest (study variable). Evaluations are also conducted regarding the efficiency comparison of the best linear unbiased estimator relative to the respective unbiased estimator. Additionally, empirical illustrations of the best linear unbiased estimator with respect to the unbiased estimator are performed.

Background: D-dimer, generated upon the degradation of fibrin, is extensively used to detect thrombosis in various diseases. It is also explored as a marker for thrombosis in cases with COVID-19 disease. Few studies have confirmed its utility as a marker for assessing disease severity. Objectives: The current research was undertaken to determine the role of D-dimer in patients with COVID-19 and to investigate any association with the progression and severity of the disease in the Saudi population. Methods: Clinical indices in confirmed COVID-19 patients were collected from tertiary care hospitals in Aljouf and Qassim regions. The plasma D-dimer levels were quantified directly in the samples collected from COVID-19 patients (n = 148) using an immunofluorescence assay, and the data were presented in Fibrinogen Equivalent Units (mg/L). The collected data of D-dimer were analyzed based on COVID-19 severity, age, and the gender of patients. Results: The findings show that the plasma D-dimer concentrations were significantly (p = 0.0027) elevated in COVID-19 cases (n = 148), compared to in the normal healthy uninfected controls (n = 309). Moreover, the D-dimer levels were analyzed according to the severity of the disease in the patients. The data revealed that D-dimer concentrations were significantly increased in patients with mild infection to moderate disease, and the levels were the highest in patients with severe COVID-19 disease (p < 0.05). Our analysis demonstrates that the D-dimer levels have no association with the age or gender of COVID-19 patients (p > 0.05) in the study population. Conclusions: D-dimer can serve as a biomarker not only for the detection of COVID-19 infection, but also for determining the severity of infection of COVID-19 disease.