Keywords: Innovation ecosystems, Economic sustainability, Hydrocarbon-based economies, Economic diversification, Qualitative approach

Innovation is rapidly growing and affecting various industries, including hydrocarbon processing. This study aims to conduct a comprehensive and organized review of the literature on innovation ecosystems and their performance. It will examine existing definitions of innovation ecosystems and related concepts and identify metrics and indicators for measuring innovation and entrepreneurial ecosystems. The term "innovation ecosystem" has gained considerable attention from scholars and practitioners over the past fifteen years. Despite the proliferation of research in this area, there are concerns about its fragmented knowledge base. While previous reviews have highlighted the theoretical connections between innovation ecosystems and related concepts, there is still a need for a more comprehensive understanding of the current state of innovation ecosystem research. The study used a systematic literature review approach that combines bibliographic coupling and content analysis methods, drawing on over 100 studies to identify five streams of current innovation ecosystem research: technology innovation, platform innovation ecosystems, regional development, innovation ecosystem conceptualization and theorization, and entrepreneurship and innovation. The study's contribution lies in decoding the intellectual structure of current innovation ecosystem research and providing targeted recommendations for future research.

Appropriate diagnostic testing to identify persons infected with SARS-COV-2 is a vital part of a health system’s ability to control the global pandemic of COVID-19 disease. The primary purpose of this review is to provide an overview of the mass testing strategy implemented throughout the UAE and the overall impact it has made on containing and controlling the spread of the disease. This study describes the mass testing strategy and capacity of the UAE during the pandemic of the new coronavirus SARS-COV-2. The UAE has conducted 15 million polymerase chain reaction (PCR) tests to SARS-COV-2, as of 15 November 2020. The number of tests per day varied from 10,000 by the end of March to 120,000 tests per day in November 2020. The mass testing initiative across the entire UAE forms an integral part of a bigger strategy focusing on testing, tracing contacts and isolating positive cases.

As an on-going pandemic caused by the out-break of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or simply COVID-19 sweeps through the globe at an unprecedented rate leaving behind trails of high infection and mortality, it is crucial to understand the propagation dynamics of the virus in a host population in order to take urgent and effective remedial and mitigating steps to save life. It is already observed in many countries and communities that accurate and timely testing, tracing, and tracking of the infection lead to better containment and slowing down of the spread. In this exploratory research, the early growth dynamics of infection within a population is pursued based on real data. The study posits that the early growth in a homogenous population follows an exponential pattern motivating further rigorous quantitative treatment based on a number of analytical models such as logistic model, Richard’s model, and Gompertz model– the acceleration pattern of the outbreak is ascertained from the daily inflection data, and regression analysis against population models yields dynamic growth indices which allow very accurate prediction of the successive outbreak size when calibrated continually with updated data. The performance of the various models is evaluated with the real dataset. More, the basic reproduction number of the COVID-19 virus propagation in the community is estimated based on the on-set phase dataset using multi-compartmental epidemiological model. Also, the maximum infection size, infection doubling time and the scope of the herd immunity are also inferred for COVID-19 in a population.

In this paper, we establish certain Lp bounds for several classes of rough Marcinkiewicz integrals over surfaces of revolution on product spaces. By using these bounds and using an extrapolation argument, we obtain the Lp boundedness of these Marcinkiewicz integrals under very weak conditions on the kernel functions. Several previous results on Marcinkiewicz operators are essentially extended or improved. Our results represent natural extensions and improvements of several known results on Marcinkiewicz integrals over symmetric spaces.

We prove Lp estimates of a class of generalized Marcinkiewicz integral operators with mixed homogeneity on product domains. By using these estimates along with an extrapolation argument we obtain the boundedness of our operators under very weak conditions on the kernel functions. Our results in this paper improve and extend several known results on both generalized Marcinkiewicz integrals and parabolic Marcinkiewicz integrals on product domains.

In this paper, several classes of rough Marcinkiewicz integral operators along surfaces of revolution on product spaces are investigated. We prove the Lp boundedness of these operators when their kernels functions belong to a class of functions related to a class of functions introduced by Grafakos-Stefanov. The results in this paper extend and improve several known results on Marcinkiewicz integrals.

Brain disorders pose a significant global healthcare challenge due to their complexity and profound impact on individuals and society. This review explores recent advancements in brain disease research, deepening our understanding and progress towards treatments. Technological innovations, like high-resolution imaging and optogenetics, play a pivotal role in illuminating cellular structures and dissecting neural circuits. Genetic analysis identifies dis-ease-associated genes and mutations, yet translating findings to clinical applications remains challenging. Epigenomics, transcriptomics, and proteomics provide molecular insights, un-covering disrupted pathways in conditions like Alzheimer's disease. Stem cell models elucidate developmental mechanisms, and immunotherapies, including CAR T cells, emerge as prom-ising strategies for brain cancers like glioblastoma. Genome editing tools, exemplified by CRISPR, enable precise modification of disease genes in models, deepening our understanding of pathogenesis. Multifaceted approaches integrating human studies, animal models, and bi-oinformatics propel the field forward. However, complexities persist, as disease specificity for cell types or brain regions remains an enigma, and genetic factors interact with environmental influences, highlighting disease intricacies. Ultimately, interdisciplinary efforts combining bi-ophysical and psychospiritual perspectives are essential to fully understand brain health and unlock transformative treatments for these debilitating disorders. A comprehensive under-standing of brain disorders is vital to improve the quality of life for millions and alleviate the global burden of neurological afflictions. Continued collaborative research will lead to an era of improved brain health worldwide.

Lie algebra plays an important role in the study of singularity theory and other field of sciences. Finding numerous invariants linked with isolated singularities is always a main interest in the classification theory of isolated singularities. Any Lie algebra that characterizes simple singularity produces a natural question. The study of properties such as to find the dimensions of newly defined algebra is a remarkable work. Hussain, Yau and Zuo \cite{HYZ10} have been found a new class of Lie algebra \texorpdfstring{ $ \mathcal{L}_ k (V)$}{LG}, \texorpdfstring{$k\geq 1$}{LG} i.e., Der ($M_{k}(V), M_{k}(V)$) and purposed a conjecture over its dimension $\delta_{k}(V)$ for $k\geq0$. Later they proved it true for $k$ up to $k=1,2,3,4,5$. In this work, the main concern is whether it's true for a higher value of $k$. According to this, we calculate first, the dimension of Lie algebra \texorpdfstring{$\mathcal{L}_ k (V)$}{LG} for $k=6$ and then compute the upper estimate conjecture of fewnomial isolated singularities. Along with, we also justify the inequality conjecture: \texorpdfstring{$\delta_{k+1}(V) < \delta_{k}(V)$}{LG} for $k=6$. Our calculated results are innovative and a new addition to the study of singularity theory.

In this study, we surveyed over 600 participants to determine: a) major causes to mental stress during the pandemic and its future impacts, and b) diversity in public perception and acceptance (specifically for children) of Covid-19 vaccination. Statistical results and intelligent clustering outcomes indicate significant relationships between sociodemographic diversity, mental stress causes, vaccination perception, and Covid-19 infections. For instance, statistical results indicate significant dependence between mental stress due to Covid-19 and gender (p = 1.7e-05). Over 25% of males indicated work related stress comparing 35% in females however, females indicated more stressed (17%) due to relationships comparing to males (12%). Around 30% of Asian/Arabic participants don’t feel vaccination being safe as compared to 8% of white-British and 22% of white-European indicating significant dependence (p = 1.8e-08) with ethnicity. More specifically, vaccination acceptance for children is significantly dependent to ethnicity (p = 3.7e-05) where only 47% participants show willingness towards children’s vaccination. Primary dataset in this study along with experimental outcomes identifying sociodemographic information diversity with respect to public perception and acceptance of vaccination to children and potential stress factors might be useful for public and policy makers to be better prepared for future epidemics as well as working globally to combat mental health issues and running more effective vaccination campaigns.

With the global increase of photovoltaic (PV) modules deployment in recent years, the need to explore and realize their reported failure mechanisms has become crucial. Despite PV modules being considered reliable devices, failures and extreme degradations often occur. Some degradations and failures can be minor and cause no critical harm if within the expected range. Others, may start mildly and then deteriorate faster to become catastrophic, especially in harsh environments. This paper conducts a state-of-the-art literature review to scan PV failures, types, and their root cause based on PV’s constructed components (from protective glass to junction-box). It outlines the hazardous consequences beyond PV module failures, describing what harm they can bring to the PV system. As we delve into the literature, it becomes clear that every component is vulnerable to certain types of failures; some can deteriorate within themselves, and others infect further PV components resulting in emerging more severe failures. In the end, the review briefly summarises PV failure detection techniques, emphasising electrical characterization techniques, and disclosing the need to engage more electrical parameters. Most importantly, this review can prepare the stage for the PV research community to identify the most prevalent degradation processes. This, in turn, encourages researchers to investigate them throughout modelling and experimental studies to forecast them at the early onset in order to protect the PV systems from hazardous malfunctions.

This review explores the field of retreatment strategies for the cases filled with calcium silicate-based root canal sealers. Since the introduction of calcium silicate-based materials in dentistry, calcium silicate-based root canal sealers have become popular among dentists because of their biocom- patibility, bioactivity, and sealing ability. Therefore, effective retreatment strategies are indispensable. This article aims to identify the challenges associated with the removal of calcium silicate-based sealers itself and gutta-percha with the sealers during retreatment, evaluate current techniques and materials, and provide future directions for research in this field. Regarding the strategies of removal of root canal sealers, calcium silicate-based sealers are still relatively new materials for clinicians compared with the traditional sealers such as epoxy- or eugenol-based sealers. First, no clinically established solvents have been reported. Second, calcium silicate-based sealers are currently utilized by clinicians in either cold sealer-based technique or warm vertical condensation technique. Third, the setting process of calcium silicate-based sealers generates byproducts primarily calcium hydroxide and secondarily hydroxyapatite, which could interact with dentine. Lastly, there is a lack of clinical studies evaluating the efficacy of retreatment protocols for teeth filled with calcium silicate-based sealers. Therefore, it is important to investigate chemo-mechanical properties of calcium silicate- based sealers itself and their reactions to solvents and/or mechanical instruments, and identify the interfacial properties of calcium silicate-based sealers in respect to dentine and gutta-percha. In addition, researchers in clinical field need to actively gather and report data on retreatments of teeth filled with calcium silicate-based sealers.

Polarimetric Synthetic Aperture Radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep Learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convolutional Neural Networks (CNNs) play a crucial role in capturing PolSAR image characteristics by leveraging kernel capabilities to consider local information and the complex-valued nature of PolSAR data. In this study, a novel three-branch fusion of complex-valued CNN named Shallow to Deep Feature Fusion Network (SDF2Net) is proposed for PolSAR image classification. To validate the performance of the proposed method, classification results are compared against multiple state-of-the-art approaches using the Airborne Synthetic Aperture Radar (AIRSAR) datasets of Flevoland, San Francisco, and ESAR Oberpfaffenhofen dataset. The results indicate that the proposed approach demonstrates improvements in OA, with 1.3% and 0.8% enhancement for the AIRSAR datasets and 0.5% improvement for the ESAR dataset. The analyses conducted on the Flevoland data underscore the effectiveness of the SDF2Net model, revealing a promising OA of 96.01% even with only 1% sampling ratio.

Interest in developing novel crop protectants has gained attention in the recent decade due to the hazardous effects of synthetic pesticides on humans and the environment. The citrus industry worldwide is threatened by the D. citri, the primary vector of phlo-em-limited bacterium (HLB) or citrus greening. However, there is no cure for citrus greening available. Diaphorina citri management largely depends on synthetic insecticides, but their massive application leads to resistance in pest populations. Therefore, alternative pest management strategies are needed. Our results indicated fewer D. citri adults settled on plants treated with ABEO than on control 48 h after re-lease. The psyllid fed on citrus leaves treated with ABEO significantly secreted less honeydew than the control. The essential oil showed potent ovicidal activity against the D. citri eggs with LC50 5.88 mg/mL. Furthermore, we also explore the fitness of D. citri on ABEO-treated and untreated Citrus × sinensis by using two-sex life table tools. The results indicated that the intrinsic rate of increase (r) was higher on untreated seedlings (0.10 d−1) than those treated with a sub-lethal dose of ABEO (0.07 d−1). Similarly, the net re-productive rate (R0) was higher for untreated seedlings (14.21 offspring) than those treated (6.405 offspring). Notably, the ABEO were safer against Aphis melifera, with LC50 ranging from 31.05 to 55.86 mg/mL, which is relatively higher than the LC50 24.40 mg/mL values against D. citri. Our results indicate that ABEO exhibits toxic and behavioral effects on D. citri that could be useful for managing this pest.

For the task planning problem of the command and control architecture, the existing algorithms have problems such as low efficiency and poor re-planning quality under abnormal conditions. Based on the requirements of the current accusation architecture, this paper constructs a distributed command and control architecture model based on multi-agents, which makes use of the superiority of multi-agents in dealing with complex tasks. The concept of MultiAgents-HTN is proposed under the framework. The original hierarchical task network planning algorithm is optimized, the multi-agent collaboration framework is redefined, and the coordination mechanism of local conflict is designed. Taking the classical resource scheduling problem as the experimental background, the comparison between the proposed algorithm and the classical HTN algorithm is carried out. The experimental results show that the proposed algorithm has higher quality and higher efficiency than the existing algorithm, and the space anomaly is heavy during processing. The planning is more efficient, and the time is more complicated and superior in dealing with the same problem, with good convergence and adaptability. The conclusion proves that the distributed command and control architecture proposed in this paper has high practicability in related fields and can solve the problem of distributed command and control architecture in multi-agent environment.

Inorganic nanoparticles based on magnetite improve the mechanical, thermal, and magnetic properties of microporous cryogel polymer composites. Here we report the synthesis of microporous cryogel based on the crosslinked sodium vinyl sulfonate (Na-VS) and 2-acrylamido-2-methylpropane sulfonic acid sodium salt (Na-AMPS). The magnetite nanoparticles were inserted into Na-VS/Na-AMPS cryogel either during its crosslinking polymerization or by in-situ technique after its crosslinking. The morphology, particle sizes, thermal stability and magnetite contents of Na-VS/Na-AMPS cryogel and its magnetite composites were investigated. The prepared Na-VS/Na-AMPS cryogel and its magnetite composites were used as adsorbents for methylene blue (MB) cationic dye using optimum conditions. The magnetite Na-VS/Na-AMPS cryogel composite prepared by in-situ technique achieved the best adsorption MB removal capacity for 7 cycles among the other adsorbents via chemical adsorption mechanism at room temperature.

Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.

Peste des Petits Ruminants (PPR) is one of the major transboundary animal diseases that severely impact livestock production and trade worldwide. An investigation was carried into the seroprevalence and risk factors of PPR in small ruminants, camels, and wild ruminants in Bahrain. A total of 1240 serum samples were collected from 119 sheep flocks and 1224 serum samples from 125 goat herds. In addition, 100 sera from camels and 38 sera from wild ruminants, were collected. Serum samples were tested for the presence of PPR specific antibodies using a competitive ELISA. Flock management and health information of the flocks and herds were collected using a semi-structured questionnaire. The individual prevalence of PPR in sheep and goats was 26% and 25.5%, respectively. The flock or herd level prevalence of PPR was 22.7% and 29.6% in sheep and goats, respectively. None of the tested camels or wild ruminants were positive for PPR-specific antibodies. Sheep and goats from the Northern governorate were more likely to be seropositive to PPR than in other governorates. On the individual animal level, the univariate analysis suggested; age (&lt;12 months), sex (male), and health status (weak animals) as risk factors for PPR seropositivity. On the flock/herd level, the multivariable logistic regression analysis identified large flock/herd size and semi-intensive farming as risk factors for PPR seropositivity.

Chatbots are extensively needed in customer services to handle customer inquiries, such as tracking orders or providing information about products and services. One of the most reliable implementations of chatbots is using the common architectures of LSTM networks named Seq2Seq networks. The networks are using an encoder and a decoder. Seq2Seq chatbot is a type of chat system that is professional enough to pass the Turing test. The Turing test is a way of deciding the accuracy of the machine by examining its response, it should appear like a human response. In this research, we will introduce a novel architecture that can pass the Turing test. The seq2seq Accuracy is improved by making incremental training to the chatbot. The new proposal provides higher accuracy and high similarity to human chat responses.

In this project, four of fibers Bragg gratings were fabricated by injecting different volumes of liquids (star line Glass Mechanix optical adhesive material, olive oil diluted with ethanol) into the hollow core fiber. The amplitude splitting interferometric technique with a high resolution specially designed translation stage was used for the fabrication process. The fabrication was done using ultraviolet laser operated at wavelength 405nm. The fabricated Bragg length of the four fibers is equal to 3.8 cm. The results presented fiber Bragg grating (FBG) with successful fabrication at 653.3 nm Bragg reflected wavelength.

Pakistan relies mostly on fossil fuel for fulfilling its energy demand which is expensive as 11 well as not friendly for the environment. The need of the hour is to shift from fossil fuels to renew-12 able energy resources like geothermal, wind, solar etc. to cater for global warming issues. Pakistan 13 has a lot of potential geothermal sites because of several fault lines and hot springs. A sound 14 knowledge of ground temperature is essential to use geothermal energy, which is obtained by drill-15 ing boreholes and putting sensors making it an expensive procedure. Thus, to avoid huge cost for 16 drilling bore holes, particularly for ground temperature analysis, a numerical approach has been 17 considered for determining ground temperature. Furthermore, correlation charts between air and 18 ground temperatures have been developed as there were no proper studies on the ground temper-19 ature of Pakistan. Then, with the help of a boreholes drilled in National University of Sciences and 20 Technology Islamabad Pakistan, the actual ground and numerically calculated temperatures have 21 been compared which shows an error margin in the range between 0.27% for higher depths to 7.3% 22 near surface. Thus, it is shown that the proposed method is easy to implement and better than large 23 scale testing methods for the depths at which geothermal energy is extracted.

Background: Digital health significantly affects healthcare delivery. Moreover, empirical studies on the utilization of telehealth in Dubai are limited. Accordingly, this study examines the utilization of telehealth services in Dubai Health Authority (DHA) facilities and the factors associated with telehealth appointment completion and turnaround time. Methods: This cross-sectional study examines patients who used telehealth services in DHA from 2020 through 2021 using 241,822 records. A binary logistic regression model was constructed to investigate the association between appointment turnaround time as a dependent variable and patient and visit characteristics as independent variables. Results: Of the total scheduled telehealth visits, more than three-quarter (78.55%) were completed. Older patients, non-Emiratis, patients who had their visits in 2020, patients who had video visits, and those who sought family medicine as a specialty had a shorter turn-around time to receive their appointment. Conclusions: This study identifies several characteristics associated with the turn-around time. Moreover, technological improvements focusing on specialties that can readily be addressed through telehealth and further research in this domain will improve service provision and support building an evidence base.

The heritage of ancient buildings is an important part of the world's history and culture, which has an extremely rich historical-cultural value and artistic research value. Beijing has a large number of palace ancient buildings, and because of the age of their construction, many of them have problems of varying degrees of peeling and mold on the inner surfaces of the envelope. To solve the problems of the damp and moldy interior of palace buildings, a mathematical model of indoor heat and moisture transfer was established based on a wooden palace ancient building in Beijing. Through the indoor mold distribution validation model, the effects of outdoor humidity, soil moisture, wall humidity, and other factors on the indoor heat and moisture transfer of ancient buildings were simulated and analyzed by using the control variables method. The results showed that the molds were distributed at the indoor corners and floors, and the simulation of indoor humidity match the measured humidity. Thus, the simulation results were consistent with the actual situation. The variable trend of the relative humidity of the indoor environment with the outdoor humidity is inconsistent from plane to plane, i.e. it increases or remains constant with the increase of the outdoor humidity. The indoor ambient relative humidity increased with increasing the wall humidity. And the indoor average temperature is 23.3 ℃ and indoor relative humidity ranged between 90.9 % to 92.44 %. Soil moisture and wall humidity were the main factors affecting the indoor environmental relative humidity.

Microglial cells, the resident macrophages of the CNS, exist in a process-bearing, ramified/surveying phenotype under resting conditions. Upon activation by cell damaging factors they get transformed to an amoeboid phenotype releasing various cell products including pro-inflammatory cytokines, chemokines, proteases, reactive oxygen/nitrogen species and the excytotoxic ATP and glutamate. In addition, they engulf pathogenic bacteria or cell debris and phagocytose them. However, already resting/surveying microglia has a number of important physiological functions in the CNS; they e.g. shield small disruptions of the blood-brain barrier by their processes, dynamically interact with synaptic structures and clear surplus synapses during development. In neurodegenerative illnesses they aggravate the original disease by a microglia-based compulsory neuroinflammatory reaction. Therefore, the blockade of this reaction improves the outcome of Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, etc. The function of microglia is regulated by a whole array of purinergic receptors classified as P2Y12, P2Y6, P2Y4, P2X4, P2X7, A2A, A3, and being targets for endogenous ATP, ADP, or adenosine. ATP is sequentially degraded by the ecto-nucleotidases and 5’-nucleotidase enzymes to the weak adenosine agonist inosine as an end-product. The appropriate selective agonists/antagonists for purinergic receptors as well as the respective enzyme inhibitors may profoundly interfere with microglial functions and reconstitute the homeostasis of the CNS disturbed by neuroinflammation.

Despite the effectiveness of insulin injections in managing hyperglycemia in Type 1 Diabetes, they fall short in addressing autoimmunity and regenerating damaged islets. This review aims to explore the potential of various treatment modalities for T1DM, including mesenchymal stem cells (MSCs), MSC-derived exosomes, gene therapy, islet allotransplantation, and pancreatic islet cell transplantation. Results: MSCs demonstrate efficacy in T1DM treatment through immunomodulation and tissue regeneration, although further research is warranted to optimize their therapeutic potential. Gene therapy presents a promising approach despite challenges such as donor shortage and immune rejection. Pancreatic islet cell transplantation emerges as a viable option, albeit with associated risks including graft rejection and the need for immunosuppression. Teplizumab, an FDA-approved humanized monoclonal antibody, revolutionizes the treatment landscape of Type 1 Diabetes by significantly delaying its onset, offering a paradigm shift since the discovery of insulin Conclusion: Ongoing studies and technological advancements in T1DM management offer a promising outlook for improved treatments and possibly even eradication on a global scale. MSCs, MSC-derived exosomes, gene therapy, and pancreatic islet transplantation hold considerable potential.

The literature study delves into novel therapeutic strategies in transplantation medicine, highlighting unique approaches that could revolutionize current practices. It emphasizes novel patterns that have the potential to transform the current landscape. Commencing with a thorough examination of the historical environment, it traces crucial advances and changes in transplantation. The focus is on traditional techniques, emphasizing their limitations and challenges. An extensive examination of contemporary transplantation therapies offers valuable insights into the most recent progress in organ, cellular, and tissue transplantation. This analysis examines the immunological obstacles and outlines the current advancements and constraints. The exploration further navigates into novel models and concepts such as immunomodulation, developments in organ preservation techniques, gene editing, biomaterials, scaffold-based solutions, and the integration of precision medicine, artificial intelligence, and machine learning applications. Each section analyzes the potential for change and current progress in various areas, backed by relevant scientific sources up to 2023. The subsequent sections delve into innovative cellular therapies, biotechnological progress, current clinical trials, and case studies demonstrating effective applications. This examination delves into the ethical and regulatory considerations of innovative therapeutic procedures, emphasizing ethical implications, regulatory frameworks, and the importance of balancing innovation with patient welfare. The literature review concludes by offering a forward-looking perspective, emphasizing potential progress, anticipated challenges, and advocating for collaboration and interdisciplinary approaches to overcome limitations in the field of transplantation medicine.

The leptin-melanocortin pathway is pivotal in appetite and energy homeostasis. Pathogenic variants in genes involved in this pathway lead to severe early-onset monogenic obesity (MO). The MC4R gene plays a central role in leptin-melanocortin signaling, and variants predominantly heterozygous in this gene are the most common cause of MO. We identified two novel heterozygous variants c,253A>G p.Ser85Gly and c.802T>C p.Tyr268His in the MC4R gene in two unrelated patients with morbid obesity and evaluated the functional impact of these variants. A targeted gene panel consisting of 52 obesity-related genes was undertaken. Variants were analyzed and filtered to identify potential disease-causing and validated using Sanger sequencing. The impact of the variants on the MC4R gene using in silico prediction tools and molecular dynamics simulation was assessed. To further study the pathogenicity of the identified variants, GT1-7 cells were transfected with plasmid DNA encoding either wild-type or mutant MC4R variants. The effects of allelic variations in MC4R gene on cAMP synthesis, MC4R protein level, and activation of ERB and CREB signaling pathways in both stimulated and unstimulated ɑ-MSH paradigms were determined for their functional implications. In-silico analysis suggested that the variants destabilized the MC4R structure and affect the overall dynamics of the MC4R protein, possibly leading to intracellular receptor retention. In-vitro analysis of the functional impact of these variants showed a significant reduction in cell surface receptor expression and impaired extracellular ligand binding activity, leading to reduced cAMP production. In-vitro analysis assays and in silico analysis revealed the pathogenicity of the two novel MC4R variants identified. Our analysis shows that the variants do not affect total protein expression; however, they are predicted to affect the post-translational localization of MC4R protein to the cell surface and impair downstream signaling cascades. This finding might help our patients to benefit from the novel therapeutic advances for monogenic forms of obesity.

Demand for environmentally friendly types of energy is growing all over the world, which naturally increases the intensity of work on the production of hydrogen-containing fuel mixtures. Steam reforming of methanol is one of the most effective areas of research, as it is a process with low energy consumption. The results of steam reforming of methanol on the catalysts prepared by solution combustion synthesis (SCS) and impregnation by moisture capacity are presented. Investigation of the activity of Cu-Ce-Al catalysts of different composition was carried out for production of hydrogen and comparison of SCS with traditional impregnation method. Methanol conversion reaches 99%, while the selectivity of H2 was 88%, at 500 °C. It was found that CuAl2O4 spinel formation is due to substitution of Al3+ with Cu2+ Ce3+ cations. A consequence CuAl2O4 and CeAlO3 lattice parameters increase because of difference in ionic radius of Al3+ (0.53 Å) and Cu2+ (0.73 Å), Ce3+ (1.07 Å). Advantages of SCS catalysts in steam reforming of methanol were shown. The aim of this research is to develop a new catalyst for conversion of methanol into hydrogen containing fuel mixtures, which will further promote the organization of a new era of environmentally friendly energy-saving production methods.

In this study, the olive pomace was considered because of its importance in the environment. So, this study carried out to estimate the total nitrogen content and the fatty acids profile of olive pomace. The olive pomace samples were collected from Irbid, Jarash, Ajloun and Mafraq during the harvesting season 2014 to study the biochemical characteristics of fatty acid composition and the total nitrogen (TN) content. The total nitrogen content was determined by standard Kjeldahl method and the fatty acid profile was detected by gas chromatography (GC). The results show that the total nitrogen content ranged from (0.39 ± 0.0) to (0.62 ± 0.02) with statistically significant difference suggesting that the composition of matrices and their percentage may be responsible for composition of amendments. The calculated total protein percentage ranged between (2.43 ± 0.00) to (3.87 ± 0.17). Also, 13 different fatty acids were quantitatively profiled and quantitated. Oleic acid (C 18:1) was found to be the highest percentage of all other fatty acids and ranged between (59.03%) and (63.81%), moreover the C 18:1/C 18:2 (oil quality) was calculated and C.V% showed variation meaning that nutritional implication could affect the oxidative stability of oils. In conclusion, OP by-product could give a sustainable and alternative-cheap source for fertilizers, pharmaceutical industries, cosmetics and other industries.

Targets detection in synthetic aperture radar (SAR) remote sensing images, which is a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Besides, the ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of SAR image scene still remains a challenge. This paper analyzes the defects and shortcomings of traditional visual models applied to SAR images. Then a visual attention model designed for SAR images is proposed. The model draws the basic framework of classical ITTI model; selects and extracts the texture features and other features that can describe the SAR image better. We proposes a new algorithm for computing the local texture saliency of the input image, then the model constructs the corresponding saliency maps of features; Next, a new mechanism of feature fusion is adopted to replace the linear additive mechanism of classical models to obtain the overall saliency map; Finally, the gray-scale characteristics of focus of attention (FOA) in saliency map of all features are taken into account, our model choose the best saliency representation, Through the multi-scale competition strategy, the filter and threshold segmentation of the saliency maps can be used to select the salient regions accurately, thereby completing this operation for the visual saliency detection in SAR images. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of visual models. The results show that our model provides superior performance compared with classical visual models. By further contrasting with the classical visual models, Our model reduce the false alarm caused by speckle noise, and its detection speed is greatly improved, and it is increased by 25% to 45%.

The variability of ozone over Egypt has been studied in this work. Higher values of coefficient of variation (COV) at eight stations occur at winter while lowest values occur at summer. The COV is function of latitude in annual, winter and spring where it decreases gradually from the north to south of Egypt. The trend analysis of ozone over the eight stations indicates negative trends over northern stations for both annual and seasonal time series with the greatest one at winter. The horizontal distribution of ozone trend values is negative over all Egypt in winter while it positive over middle and south Egypt in the other seasons. The long-term variability of the behavior of the annual ozone shows positive trend values in ozone are the dominant features during the period 1979- 1989 at the most stations. Negative trend values in ozone are the dominant features during the period 1990- 2014 at all stations. The Mann–Kendall test confirms that there is an abrupt change towards decreasing of ozone occurs in 1981, 1984, 2012, 1999, 2000, 1998 and 2010, while a change towards increasing of ozone appears in 2006, 2014, 1993 and 1989.

By shifting eating hours from daytime to nighttime, Ramadan intermittent fasting (RIF) illustrates how religious beliefs influence human dietary behavior and subsequent anthropometric and metabolic measures. This study aimed to investigate socioeconomic characteristics and examine how observing RIF affects dietary intake, anthropometric indices, and metabolic markers in healthy premenopausal (PRE-M) and post-menopausal (POST-M) Saudi women. Before starting the study, the authors hypothesized that the RIF improves some nutritional aspects and health in respondents, but not after breaking the fast.. The study includes 62 women (31 PRE-M, 21-43 years, and 31 POST-M, 44-68 years). A structured questionnaire was used to collect socioeconomic data. Physical activity, anthropometric, dietary, and biochemical assessments were undertaken before RIF and at the end of the third week of Ramadan fasting month. Socioeconomic data varied among respondents. Observing RIF was associated with significantly (at either p ≤ 0.01 or p ≤ 0.05) lower intake of calories, macronutrients, minerals (except for Na), and vitamins than before RIF for both groups. In terms of body mass index (BMI), and waist-to-height ratio (WHtR) for PRE-M, the percentage of overweight decreased significantly (p≤0.01) while obesity remained unchanged. In contrast, for POST-M, the percentage of overweight increased significantly (p≤0.05), but obesity decreased after RIF. Both groups' waist-to-hip ratio (WHR), body fat (BF), and fat mass (FM) decreased after RIF. High-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), triglycerides (TG), and blood pressure (SBP, DBP) were generally maintained at acceptable normal levels in most responders before and after RIF. However, low-density lipoprotein cholesterol (LDL-C) was significantly decreased during RIF than before, especially for POST-M. Age, occupation, and monthly income were the most important factors influencing women’s nutritional status and body fat. In conclusion, observing RIF by PRE- and POST-M Saudi women was associated with significant improvements in variable health indicators, with a few exceptions, particularly POST-M, and may help lower risk factors for chronic diseases.

Background: Asthma is a widespread chronic respiratory disease that poses a significant public health challenge. The current study investigated the associations between air pollution and asthma severity among individuals residing near the Sohar industrial port (SIP) in Oman. Despite the presence of multiple major industrial complexes in Oman, limited knowledge regarding their impact on respiratory health is accredited. Hence, the primary objective of this study is to offer valuable insights into the respiratory health consequences of industrial air pollution in Al Batinah North. Methods: The state health clinics' records for patient visits related to asthma were collected for the timeframe spanning 2014 to 2022. Exposure was defined as the distance from SIP, Majan Industerial Area (MIA) and Sohar Industerial Zone (SIZ) to determine high, intermediate, and low exposure zones (12 km, respectively). Exposure effect modifications by age, gender, and smoking status were also examined. Results: The conducted cross-sectional study of 410 patients (46.1% males and 53.9% females) living in over 17 areas around SIP revealed that 73.2% of asthmatics were under 50 years old, with severity significantly associated with closeness to the port. Risk ratios were estimated to be (RR:2.42; CI95%: 1.01–5.78), (RR:1.91; CI95%: 1.01–3.6), and (RR:1.68; CI95%: 0.92–3.09) for SIP, MIP and SIZ areas, respectively, compared to the control area. Falaj Al Qabail (6.4 km) and Majees (6 km) had the highest number of asthma patients (N. 69 and N. 72), and highest percentages of severe asthma cases among these patients (28% and 24%) with significant risk ratios (RR:2.97; CI95%: 1.19–7.45 and RR:2.55; CI95%: 1.00–6.48), correspondingly. Moreover, severe asthma prevalence peaked in the 25-50 age group (RR:2.05; CI95%: 1.26–3.33), and this linkage between asthma and age was much more pronounced in males than females. Smoking and exposure to certain contaminant (dust and smoke) increased also the risk of severe asthma symptoms, but their effects were less important in the high risk zone, suggesting much more important risk factor. A neural network model accurately predicted asthma risk (94.8% accuracy), with proximity to Sohar Industrial Port as the most influential predictor. Conclusion: The study highlights the High asthma burden near Sohar Industrial Port, linked to port proximity and smoking and wind direction as major risk factors. These findings inform vital public health policies to reduce air pollution and improve respiratory health in the region, prompting national policy review.

Abstract:High-pressure water jet technology has received extensive attention and application due to its high efficiency and environmental protection characteristics. The high-pressure water jet nozzle is its core component, and the research on the high-pressure water jet nozzle is conducive to improving the jet performance and improving the jet efficiency. This paper introduces the characteristics of water jet and the working principle of pure water jet, abrasive water jet, pulsed water jet and cavitation water jet, reviews the theoretical analysis and experiment of the predecessor's nozzle, briefly explains the application of high-pressure water jet and looks forward to the development trend of this technology.

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finish. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity vc, feed per tooth fz, milling depth ap, and milling width ae on tool wear during high-speed milling under SCCO2-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model's relevance and accuracy were confirmed using F-tests and t-tests. Ultimately, through the utilization of a genetic algorithm, the milling parameters were optimized, resulting in the identification of the most optimal combination of parameters. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials.

κ Draconis is a binary system with a classical Be star as the primary component. Its emission-line spectrum consists of hydrogen lines, notably the Hα line with a peak intensity ratio (V/R) variations phase-locked with the orbital period P = 61.55 days. Among binaries demonstrating the Be phenomenon, κ Dra stands out as one of a few systems with a discernible mass of its secondary component. Based on more than 200 spectra obtained in 2014 − 2023, we verified the physical parameters and constructed the mass function. We used part of these data obtained in 2014 − 2021 to investigate regions in the circumstellar disk of the primary component that emit the Hα line using the Doppler tomography method. The results show that the disk has a non-uniform density distribution with a prominent enhancement at Vy≈ 99 km s−1 and Vx≈−6 km s−1 that corresponds to a cloud-like source of the double-peaked Hα line profile. We argue that this enhancement’s motion is responsible for the periodic variations of the Hα V/R ratio, which is synchronised in orbital phase with the radial velocity (RV) of absorption lines from the atmosphere of the primary component.

The experience of ego dissolution and self-transcendence induced by psychedelic substances offers a profound challenge to our conventional understanding of the self and our relationship with the natural world. This paper delves into the philosophical underpinnings and implications of this phenomenon, exploring how the temporary breakdown of ego boundaries during a psychedelic experience may foster a sense of profound interconnectedness with nature. Drawing from Western and Eastern philosophical traditions, as well as recent insights from neuroscience and consciousness studies, we examine the potential of psychedelic-induced self-transcendence to catalyze a shift in our perspectives on the human-nature relationship. The paper investigates the philosophical implications of this experience, including its resonance with deep ecological philosophies and its potential to inspire more ecocentric ethical frameworks. Furthermore, we explore the possible practical implications of fostering a sense of ecological connectedness through psychedelics, such as promoting environmentally sustainable behaviors and addressing the pressing challenges of climate change and ecological degradation. Ultimately, this exploration highlights the value of engaging with altered states of consciousness as a means to reconsider our place within the intricate web of life on Earth.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

Background: Despite their generally favorable prognosis, hepatic fibrosis remains a possible complication for hepatitis B virus (HBV) inactive carriers. This study aimed to estimate the prevalence of significant fibrosis and associated risk factors among Omani HBV inactive carriers. Methods: A retrospective study was conducted of 200 Omani HBV inactive carriers visiting a tertiary hospital in Muscat, Oman, between January 2017 and December 2018. Significant fibrosis (stage F2 or higher) was identified using two-dimensional shear-wave elastography. Baseline clinical, laboratory, and radiological data were analyzed to determine associations. Results: Of the total sample, 106 patients (53%) were male, and the mean age was 44.6 ± 9.3 years. The prevalence of significant fibrosis was 20% (n = 40). Male gender (p = 0.007), age ≥60 years (p = 0.024), and fatty changes in liver ultrasound (p = 0.044) were found to be independent factors for significant fibrosis. Conclusion: the prevalence of significant fibrosis was relatively high among a sample of Omani HBV inactive carriers, with several sociodemographic and clinical risk factors identified. Periodic assessment of liver function is needed for patients with these risk factors; moreover, routine biochemical parameters could be useful predictors of disease prognosis. Further research is recommended to substantiate these findings.

This study aimed to investigate the fatigue performance and interfacial strengthening mechanisms of Al6061-to-Cu dissimilar lap joints via electromagnetic pulse welding (EMPW). The load-bearing capacity of the joints at discharge voltages of 14 kV and 16 kV was superior to that of 12 kV. Furthermore, the fatigue life of the 14 kV joints was one order of magnitude higher compared to the 12 kV joints. SEM observation of the fatigue fracture surface revealed the presence of typical "tire-mark" fatigue striations only in the 14 kV joint, indicating higher ductility and the ability to withstand extended plastic deformation. Notably, ultra-fine nanocrystalline Al2Cu phase, amorphous phase, and numerous dislocations hindered EBSD acquisition in the transition zone. Nevertheless, dense low-angle grain boundaries (LAGBs) and refined grains were examined at the bonding interface. This suggests that the high-velocity collision caused severe plastic deformation and led to the formation of substructures. The interfacial strength was attributed not only to the "wave-like" or "inverted hook-like" diffusion layer but also to the presence of refined grains along the interface and nanocrystalline Al2Cu in the transition zone. Furthermore, the hybrid nanocrystalline-amorphous microstructure strengthened the joint by regulating the balance between strength and ductility.

While microcredits are, by the development practitioners, repeatedly hailed as a useful measure of development policy, this empirical research in Bangladesh arrives at different results: the governing and disciplinary measures are necessary to trigger the best possible repayment success. Based on interviews with the microfinance beneficiaries, loan officers, and microfinance experts in Bangladesh, I chart some of the most frequently used governing and disciplinary techniques including fictitious proximity of loan officers, their fields of visibility and surveillance processes. The “success” of credit delivery is a neoliberal stance that ensures the ceaseless debt repayment process using the intentional and planned conduct, capitalizing social capital and credit honour reputation, surveillance over business enterprises, use of equivocal language and different forms of deception. The development strategy through microfinance in Bangladesh is a development “dispositif” with a changing shift in its declared social missions.

The objective of this research was to explore the impact of a gradual replacement of Ca2+ with Mn1+ on the crystal structure of CaHPO4·2H2O, which could be beneficial for producing Ca1−xMnxHPO4·nH2O compounds. In order to prepare the starting solutions required for this study, Ca(NO3) 2·4H2O, (NH4)·2HPO4, and MnNO3·4H2O were used at various molar concentrations. The findings reveal that Mn substitutes Ca partly at low Mn/Ca molar ratios, and Mn doping reduces the unit cell of CaHPO4·2H2O. The produced biphasic compound was composed of monoclinic CaHPO4·2H2O and triclinic/monoclinic nanostructured Mn3PO4O11 after a continuous increase in the Mn/Ca molar ratio to 1. When the Ca/Mn molar ratio is 1.5, a triphasic compound of monoclinic CaHPO4·2H2O, triclinic Mn3P4O11, and Orthorhombic MnHPO4·(H2O)3 phases is formed. The formation of the monophasic MnHPO4·(H2O)3 compound was achieved by increasing the Mn/Ca molar ratio above 4. By adjusting the Mn/Ca molar ratio in the initial solutions, it is possible to produce biomaterials with specific and bespoke characteristics in the future. The Mn/Ca ratio of the starting solution can be modified to produce Ca1-xMnxHPO4-nH2O phases with weight percent compositions, as established. For the first time, Ca1−xMnxHPO4·nH2O compounds were synthesized using novel methods in this manuscript.

A new class of nano-cube core-shell heterostructures containing Ag coating on the top of WO₃ was fabricated. Physical vapor deposition was used to produce WO₃ based nano-heterostructures. All kind of wet toxic chemical process was avoided to make the process simple and contaminant free. Sputtering of WO₃ and a subsequent thermal annealing process was done to create nano-cubes of WO₃. After that, sputtering of Ag was performed to form the Ag-WO₃ core-shell nano-heterostructures (CSNH). The CSNHs were characterized using field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), X-ray diffraction analysis (XRD) and UV-vis spectroscopy. The morphologies, elemental analysis, interfaces, crystallinity, phases, and chemical compositions were analyzed. The bottom-up growth of WO₃ nanocubes was studied using different time periods at 900°C. Ag coating was also studied before and after annealing. Finally, an optical property (band gap) was also analyzed using Tauc plot derive from absorption spectra. The tailoring the band gap of WO₃ from ~2.9eV to ~ 2.45 eV was observed while Ag-WO₃ CSNH formed.

This paper investigates the influence of magnetic field for a two dimensional deformations on a two temperature problem at the free surface of a semi-infinite semiconducting medium under the effect of mechanical force during a photothermal theory and the effect of hydrostatic initial stress on the medium. The Harmonic Wave Method (Normal Mode Analysis) has been used to obtain the equations of elastic waves, heat conduction equation, quasi-static electric field, carrier density, two temperature coefficient, ratios, and constitutive relationships for the thermo-magnetic-electric medium. The effects of several parameters as thermoelastic and thermoelectric coupling parameters and two temperature parameter of this force on the displacement component, force stress, carrier density and temperature distribution has been depicted graphically.

Gestational trophoblastic diseases (GTDs) encompass a spectrum of conditions characterized by abnormal trophoblastic cell growth, ranging from benign molar pregnancies to malignant trophoblastic neoplasms. This systematic review explores the molecular underpinnings of GTDs, focusing on genetic and epigenetic factors that influence disease progression and clinical outcomes. Based on 65 studies identified through systematic search and selection criteria, key findings include dysregulations in tumor suppressor genes such as p53, aberrant apoptotic pathways involving BCL-2, and altered expression of growth factor receptors and microRNAs. These molecular alterations not only differentiate molar pregnancies from normal placental development but also contribute to their clinical behavior, from benign moles to potentially malignant forms. The review synthesizes insights from immunohistochemical studies and molecular analyses to provide a comprehensive understanding of GTD pathogenesis and implications for personalized care strategies.

Background: COVID-19 was initially identified as a respiratory system disorder, but it has been discovered to interact with and influence the cardiovascular system. COVID-19-associated cardiovascular (CV) complications are common, resulting in high acute phase mortality and a large number of morbidities in the chronic phase, thus severely impacting patients' quality of life and health outcomes. Nevertheless, clinical, cellular, and molecular biological factors underlying the pathophysiology of cardiovascular complications associated with COVID-19 are poorly understood. Objective: This review investigates putative underlying clinical factors as well as cellular and molecular biological mechanisms by which COVID-19 leads to acute CV complications, including state-of-the-art genomic sequencing-based findings, assessing the long-term CV consequences of COVID-19, and aiming to shed light on developing strategies for differential diagnosis, risk prognostic stratification, prevention, and clinical management of CV sequlea in COVID-19 patients. Methods: For this purpose, a through review of literature and published data was carried out from first report of COVID-19 till October 2023 to find out a comprehensive account of clinical, cellular and molecular genetic factors underlying COVID-19-associated cardiovascular diseases. Results: We found that the relationship between COVID-19 and CV risk is complex and multifaceted. In addition to acute COVID-19 detertriuos effects, COVID-19 survivors may experience long-term CV effects. We provide a detailed account of the involvement of a large number of genomic alterations, microRNAs, and novel viral as well as host proteins in CVDs associated with COVID-19, which has helped identify some novel drug targets to treat COVID-19-related cardiovascular complications. Conclusions: The relationship between COVID-19 and CV risk is complex and multifaceted. While COVID-19 primarily affects the respiratory system, it can also significantly affect CV health. as compared to classical cardiovascular diseases, there are new clinical, cellular and molecular biological factors in CVDs related to COVID-19, that need specific diagnostic assays, prognostic stratification and treatment modules. Therefore, specail care is must taken to treat cardiovascular diseases associated with COVID-19.

Salmonella Typhimurium is a typical Gram-Negative bacterium associated with food-borne illness threatening public health. This pathogen is reported to have caused millions of infections and thousands of hospitalizations yearly in the United States alone. Salmonella Typhimurium has a sophisticated array of virulent factors, including its flagella, which contributes significantly to its pathogenicity. Cannabidiol (CBD), the non-psychoactive component of the cannabis plant, has attracted the interest of many researchers recently because of its antimicrobial properties. With the growing interest in exploring CBD’s antimicrobial properties, we, in this study, have delved into unravelling how prolonged exposure of Salmonella Typhimurium to CBD influences the expression of the flgA gene. We hypothesized that the exposure of Salmonella Typhimurium to CBD may result in flgA upregulation, causing the bacteria to withstand various stresses and potentially enhance its survival rates and pathogenicity. We have employed several assays, such as mRNA expression, motility assays, pH induction assays, nutrient starvation interventions, and biofilm analysis, to explore the influence of CBD on the pathophysiology of Salmonella Typhimurium. Our findings reveal that CBD-resistant strains exhibit enhanced resilience to extreme pH levels and motility and survive poorly under nutrient starvation, with more robust biofilm formation compared to the susceptible strains. The results contribute to the broader comprehension of CBD resistance mechanisms in bacteria. This work provides insight into the potential therapeutic interventions for controlling CBD-resistant bacterial infections. This study also underscores the essence of exploring novel alternative antimicrobial agents to tackle the ever-persistence of bacterial antibiotic resistance.

Objectives: One of the home-made herbal remedy being traditionally used against gout is evaluated to identify any new and characteristic marker compound which may be used as characteristic analytical marker to standardize this polyherbal remedy. Materials and method: A distinct and remarkable peak in the HPLC chromatogram of remedy was identified which was further isolated by fraction collector. This fraction was then evaporated and characterized for structure elucidation using set of various techniques such as RP-HPLC,UV, FTIR, TLC, EIMS and NMR. Results: The compound characterized was non polar compound, ester of betulin and palmitoleic acid. Although the remedy has been standardized using set of four markers as discussed in literature but this newly developed marker compound can be used to further standardize this remedy and other herbal preparations comprising of this marker compound.Conclusion: Hence the isolated compound can be used as novel analytical marker in the standardization of this herbal remedy and the other herbal preparations containing such marker compound

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

The endocannabinoid system (ECS) is a complex neuromodulatory network in the human body that have enormous impact in developing the central nervous system, as well as modulates various physiological processes. The ECS is made up of anandamide, 2-arachidonoylglycerol (2-AG), which are responsible for the synthesis and degradation of endocannabinoids, and enzymes such as FAAH and MAGL ensures the precise control over endocannabinoid levels. The ECS relies on CB1 receptors found in the central nervous system (CNS), and CB2 receptors found in the peripheral nervous system and immune cells. Dysregulation of the ECS has been associated with several health conditions, prompting investigations into its association with phytocannabinoids to harness their therapeutic potential. The ECS is intricately linked with the nervous system, and it is known to modulate neuronal activities in the CNS, influencing pain and mood modulation. The ECS is also associated with neuropathies-peripheral nerve damage leading to symptoms such as weakness, numbness and pain as evidenced in diabetic neuropathy, Guillain-Barré Syndrome, carpal tunnel syndrome, peripheral neuropathy, and postherpetic neuralgia. The focus of this review is on the interplay between 2-arachidonoylglycerol and anandamide amidohydrolase in neuropathy, elucidating their roles in pain regulation, inflammation, and specific neuropathies. The knowledge generated is expected to have broad implications, providing comprehensive insights into neuropathic conditions and how to improve on them. Ultimately, this understanding may lead to more effective treatments and improved quality of life for individuals affected by these conditions.

The paper's main goal is to introduce a novel type of interpolative convex contraction and build up some fresh findings for it using the interpolative convex contractions' progressive approach. We have established certain conclusions using orbitally S-complete and Suzuki type contractions in F-metric spaces. My research aims to examine the fixed point method's efficacy in solving fractional differential equation with boundary conditions.

Findings of new targeted treatments with adequate safety evaluations is essential for better cancer cures and mortality rates. Immunotherapy holds promise for patients with relapsed disease, with the ability to elicit long-term remissions. Emerging promising clinical results in B-cell malignancy using gene-altered T-lymphocytes uttering chimeric antigen receptors have sparked a lot of interest. This treatment could open the path for a major difference in the way we treat tumors that are resistant or recurring. Genetically altered T cells used to produce tumor-specific chimeric antigen receptors are resurrected field of adoptive cell therapy by demonstrating remarkable success in the treatment of malignant tumors. Because of the molecular complexity of chimeric antigen receptors -T cells, a variety of engineering approaches to improve safety and effectiveness are necessary to realize larger therapeutic uses. In this study, we investigate at new strategies for enhancing chimeric antigen receptors-T cell therapy by altering chimeric antigen receptors proteins, T lymphocytes, and their relations with other solid tumor microenvironment (TME) aspects.

Khabib Nurmagomedov versus Conor McGregor Ultimate Fighting Championship (UFC) 229 battle was among the most controversial mixed martial art fights of the past decade. In this study, the author examined how the various popular media outlets from the Eastern and Western world portrayed Khabib Nurmagomedov after UFC 229. The author used Huntington’s (1993) clash of civilization thesis by grounding it in the Orientalism and Occidentalism paradigms to examine the phenomenon. Fairclough’s (1989, 1995) model for critical discourse analysis was employed to investigate the various Western and Eastern popular press and digital media platforms (i.e., newspapers, blogs, and sporting news websites). The author analyzed 57 (Western n = 38, Eastern n = 19) media reports per the inclusion criteria. The study results unveiled conflicting predispositions present in the Western and Eastern media for Khabib Nurmagomedov. This study contributes to the limited knowledge about how a Muslim man athlete with diverse racial and ethnic backgrounds is seen contrarily in the Western and Eastern media. The author discusses the theoretical implications of the study.

This article delves into the enduring relevance of Liberal International Relations (IR) Theory amidst persistent criticisms regarding power dynamics and its applicability in emerging states. Despite its detractors, Liberal IR Theory offers a nuanced understanding of state behavior, blending social contexts with power dynamics. It recognizes the intricacies of economic interdependence and advocates for a balanced approach in advancing democracy and human rights. While nationalism poses challenges to international institutions, their adaptability endures. Leaders, albeit constrained, retain the capacity to foster peace through visionary leadership and global cooperation. By acknowledging its limitations and fostering open dialogue, Liberal IR Theory continues to evolve, poised to contribute to a just and peaceful global order.

Toll plaza optimization is essential for improving traffic flow and easing congestion on road networks. This study aims to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer facilitation. Different resources are used for data collection, like the National Highway Authority of Pakistan. Physical data is collected from the established Toll Plaza of Peshawar, along with the development of each line. The goal of this study is to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer happiness. The analysis of present traffic patterns, the detection of bottlenecks, and the creation of plans to ease congestion and enhance overall traffic flow are all part of the optimization process. The approaches used in this study include data analysis, simulation, and traffic modeling. PTV VISSIM is software for transportation planning, design, and analysis. The simulations consider various conditions, such as variable traffic volumes, toll collection methods, and lane configurations. When comparing the different optimization strategies, performance indicators like trip time, latency, and queue length are utilized to determine which approach is practical. Waiting time Vs. Existing each lane, etc, is explained. In the end, comparing queue lengths and throughput across different lane scenarios shows that maximizing throughput with existing lanes yields up to 45% improvement with E-tag lanes, 40% with existing E-tag lanes, 38% with E-tag plus additional toll and cash lanes, 32% with three cash lanes, and 28% with two E-tag lanes. Failure to regularly monitor and adjust the existing lane pattern scenario based on traffic patterns can lead to queues being 20% longer. Without proactive evaluation and optimization, there is a potential 63% opportunity for queue length improvement

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

Soft tactile sensors based on piezoresistive materials have various large-area sensing applications. However, their accuracy is often affected by hysteresis, a phenomenon that poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fibre-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units with different layer configurations (1, 3, 6, and 12) were designed. These sensor units underwent force sequences to collect corresponding output resistances. A backpropagation network was trained using these force sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement establishes the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for force measurement and control, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Notably, the complete elimination of hysteresis in tactile sensors may not be feasible. Nevertheless, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. Maintaining water quality is crucial for environmental well-being. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, and evaluate associated health risks pertaining to nitrate and flouride in AlHassa region, KSA. The physiochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021-4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate Health Quotient (HQ) values indicated chronic human health risks ranging from low to severe, with newborns being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The Hazard Index (HI) yielded moderate to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the Total Hazard Index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources and vulnerable human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans.

Identifying ambient noise-based (ANb) signatures of streams can help in the estimation of their erosive potential (EP) that promotes reverie landslides and soil losses in the fluvial valleys. This is particularly imperative on flooding or rainy days, leading to stronger erosion-prone conditions (colluvium and boulders) of the valley beds inferred from georadar attribute analysis. Developing such research direction can benefit the local communities, as is the case with the Cerrado region of Brazil, where these phenomena have high destructive potential with social, economic, and climatic implications. For the present study, a seasonal stream in the Federal District of Brazil was investigated by ANb monitoring supported by Ground Penetration Radar (GPR) for site characterization. The ANb monitoring was conducted (at a safe distance) with a seismometer over several durations of dry and rainy conditions. The power spectral density (PSDs) was computed as a function of several variables, including weather conditions (rainfall, wind speed, and pressure), time-frequency spectrograms, and ambient noise displacement root mean square (RMS). This analysis also considered the single station horizontal-to-vertical spectral ratio (HVSR), where rain, wind, pressure, river flow and anthropogenic signatures were evident (at selective frequency ranges). Multi-peaks that emerged on the HVSR curve were further analyzed to identify amplitude and frequency changes, and the three peaks shift on average to a lower position during the rainy period. The GPR amplitude and waveform variation features were attributed to the stratigraphy (i.e., the boundary between valid and invalid regions and coherence value) of the floodplain and regions susceptible to erosion (erosion-prone lithological spots). This approach provides the basis for non-destructive monitoring tools enabling the detection of 'seismic signatures' and weak spots of the fluvial channels for improving environmental management.

Various heterogeneous devices or objects shall be integrated for transplant and seamless communication under the umbrella of internet of things (IoT). It would facilitate the open accession of data for the growth of a glut of digital services. To build a general framework of IoT is very complex task because of heterogeneity in devices, technologies, platforms and services, operating in the same system. In this paper, we mainly focus on the framework for big data analytics in smart city applications , which being a broad category specifies the different domains for each application. IoT is intended to support the vision of Smart City, where advance technologies will be used for communication for the quality life of citizens. A novel approach used in this paper, is for enhancing the energy conservation and to reduce the delay in big data gathering at tiny sensor nodes used in IoT framework. To implement the smart city scenario in terms of big data in IoT, an efficient (optimized in quality of service) WSN is required where communication of nodes is energy effcient. That is why, a new protocol QoS-IoT is proposed on the top layer of the architecture which is validated over the traditional protocols.

The paper delineates the developed vision system of the UR5 cobot and the operating algorithm of the robotic quality control station. The hardware-software architecture of the developed robotic station was shown, consisting of a UR5 cobot equipped with a web camera and a stationary industrial camera with a lighting system. Image processing and analysis algorithms were described, the method of control and communication between the station components was discussed, and two operating scenarios were presented as a single robotic station and a robotic line. Based on the results obtained, the level of measurement noise, accuracy, and repeatability of the developed vision system were estimated.

Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

In order to establish the formation patterns of the Co–Mg oxide system, samples with different Co:Mg ratios and heat treatment temperatures were synthesized and studied. Study of the sam-ples confirmed the phase transition of MgxCo2–xO4 spinels into corresponding solid solutions at 800–900 °C. The similarity of formation patterns for different compositions is shown. The rock-salt oxide in low-temperature samples is an anion-modified paracrystalline phase that forms a “true” solid solution only upon spinel decomposition. TPR profiles of decomposed Co3O4 spinel show surface Co3O4 peaks and a wide peak corresponding to well-crystallized CoO, while par-tial Co3O4 TPR up to 380 °C results in dispersed and amorphous CoO. High-temperature non-stoichiometric samples are poorly reduced, indicating their low oxygen reactivity. Spinel reoxidation after heat treatment to 1100 °C by calcination at 750 °C showed complete regenera-tion for MgCo2O4–Co3O4 samples and its absence in case of an excess of MgO relative to stoi-chiometry. Based on the data obtained, it can be concluded that the processes governing the formation of Co–Mg oxide systems are determined by both Co:Mg ratio and the heat treatment conditions, potentially providing the possibility of fine tuning for specific catalytic processes.

Dietary habits, including meal frequency, meal timing, and skipping meals, have been extensively studied due to their association with the development of non-communicable diseases (NCDs). This study describes dietary habits, meal timing, meal frequency, skipping meals, and late-night eating in Kuwaiti adults. The Kuwait National Nutrition Surveillance System data was utilized to reach the objectives of this study. The findings reveal that approximately 50% of the adults in Kuwait eat after 10 p.m., 29% skip breakfast, and 9.8% skip dinner. Furthermore, adults in Kuwait consume 4.4 meals per day on average. Women skip breakfast more often and have extended night fasting than men (p&lt;0.001). Married adults skip breakfast and dinner less than unmarried adults (p&lt;0.001). In conclusion, this descriptive study provides valuable insights into the dietary habits of Kuwaiti adults, emphasizing the importance of further investigating the association between meal timing, meal frequency, and the prevalence of NCDs in Kuwait.

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

Early-life adversity can profoundly impact individual development, yet some demonstrate resilience. Longitudinal studies tracking individuals over extended periods have aided our understanding of resilience and growth following adverse childhood experiences. This paper reviews insights from influential studies like the Kauai Longitudinal Study, the Adverse Childhood Experiences (ACE) Study, and the Dunedin Study. It explores key findings on risk and protective factors, adaptation trajectories, and long-term outcomes associated with resilience. Theoretical frameworks and methodological considerations are discussed. The paper examines how this research informs interventions, policies, and support systems for fostering resilience. It highlights the need for diverse samples, integrating biological and environmental factors, and investigating intergenerational impacts. By synthesizing longitudinal research, this paper elucidates complex pathways to resilience and positive development despite early adversity, offering insights for research and supporting vulnerable populations.

Longitudinal studies tracing high-risk samples over decades have illuminated powerful pathways of resilient adaptation among individuals who overcome adversities faced early in life. This research demonstrates resilience is a common phenomenon driven by dynamic interactions between individual traits and environmental resources that enable positive adjustment despite adversity exposure. For clinical social workers supporting clients impacted by childhood trauma, abuse, violence and other adversities, understanding these resilience processes is vital for developing strengths-based, trauma -informed interventions that enhance positive adaptation. This paper synthesizes key findings from landmark longitudinal resilience studies and explores specific practice implications for resilience-oriented clinical assessment, intervention planning, prevention efforts, and social worker training. By integrating resilience research insights, social workers can more effectively identify and cultivate clients' innate capacities for resilience, facilitating a journey towards posttraumatic growth.

Deep Learning (DL) and Machine Learning (ML) are widely used in many fields, but rarely used in Frequency Estimation (FE) and Slope Estimation (SE) of signals. Frequency and slope estimation for Frequency-Modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sonar, and radar measurements. In this work, artificial neural network (ANN) and convolutional neural network (CNN) are used in frequency and slope estimation for FM signals under Additive White Gaussian Noise (AWGN) and Additive Symmetric alpha Stable Noise (SαSN). SαS distributions are impulsive noise disturbances found in many communication environments like marine systems; their distribution lacks a closed-form Probability Density Function (PDF), except for specific cases, and infinite second-order statistic, hence Geometric SNR (GSNR) is used in this work to determine the impulsiveness of noise in a mixture of Gaussian and SαS noise processes. ANN is a machine learning classifier, designed with few layers for reducing FE and SE complexity while getting higher accuracy as compared with classical techniques. CNN is a deep learning classifier, designed with many layers for FE and SE, and proved to be more accurate than ANN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful for FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption, which is important in many systems such as some Internet of Things (IoT) sensor applications. After training DCNN for frequency and slope estimation of LFM signals, the performance of DCNN (in terms of accuracy) can give acceptable results at very low signal-to-noise ratios where TFD fails, giving more than 20dB difference in the GSNR working range.

Microbial composition across substrates in mangroves, particularly in the Middle East, remains unclear. This study characterized bacterial communities in sediment, water, Terebralia palustris snail guts, and plastic associated with Avicennia marina mangrove forests in two coastal lagoons in the Sea of Oman using 16S rDNA gene MiSeq sequencing. The genus Vibrio dominated all substrates except water, which featured unique genera Sunxiuqinia and Pseudomonas. There were significant differences in bacterial communities on different substrata, in particular plastic. Snail guts harbored the highest number of unique Operational Taxonomic Units (OTUs) in both lagoons. Plastic in the polluted Sawadi lagoon with low salinity harbored distinct genera like Vibrio, Aestuariibacter, Zunongwangia, and Jeotgalibacillus absent in Qurum lagoon with higher salinity and low pollution. Sawadi lagoon exhibited higher species diversity in sediment and plastic substrates, Qurum lagoon demonstrated lower overall species diversity. The PCA indicates that environmental factors like salinity, pH, and nutrient levels significantly influence bacterial community composition across substrates. Variations in organic matter and potential anthropogenic influences, particularly from plastics, further shape bacterial communities. This study highlights the complex microbial communities in mangrove ecosystems, emphasizing the importance of considering multiple substrates in mangrove microbial ecology studies.

This paper presents the deformation of the joined sheet after the clinch-riveting process. The DX51D steel sheet with zinc coating was used. The samples to be joined with clinch riveting technology had a thickness of 1±0.05 mm and 1.5±0.1 mm. The sheet deformation was measured before and after the joining process. The rivet was pressed in the sheets with the same dimension between the rivet axis and three sheet edges: 20, 30 and 40 mm. For fixed segments of the die, from the rivet side close to the rivet, the sheet deformation was greater than that of the area with movable segments. The movement of die sliding element caused more sheet material to flow in the space between the fixed part of the die and movable segments. Hence, the sheet deformation in these places was smaller than for the die fixed element – sheet material was less compressed. For sheets thickness 1.5 mm and width 20 mm, the bulk of the sheet was observed. For a sheets width of 20 mm, it was observed that the deformation of the upper and lower sheets in the area of the rivet is greater than for sheets width of 30 or 40 mm.

Generative artificial intelligence tools are flourishing across various sciences, particularly in the educational domain. ChatGPT, with its versions 3.5 and 4.0, stands out for its multilingual capabilities, catering to both students and educators. This research aimed to evaluate the performance differences between these versions, and importantly, to compare the accuracy of ChatGPT's responses in English versus Arabic. In our study, we presented ChatGPT with 39 chemistry problems related to 6th-7th grade curriculum, including twenty-five open questions and fourteen multiple-choice questions. Each response was categorized as accurate, partially accurate, or inaccurate. Our analysis, focused on trends across versions and languages, revealed significant enhancements in version 4.0, particularly in its ability to process Arabic. However, despite these improvements, our findings indicate that responses in English consistently outperformed those in Arabic in terms of accuracy. In light of this discrepancy, we suggest that ChatGPT either incorporates additional Arabic databases into its training regimen or develops a code that adopts the following strategy to ensure more accurate responses in science education: questions should first be translated from Arabic to English, addressed, and then retranslated into Arabic before being presented to the user. This approach takes advantage of the greater accuracy of responses in English to benefit Arabic-speaking users, and it may also improve outcomes for speakers of other languages who experience less precise answers in the fields of chemistry or science education.

Modern building automation systems implement plenty of advanced control and monitoring functions considering various parameters like users’ activity, lighting, temperature changes etc. Moreover, new solutions based on Internet of Things and cloud services are also being developed for smart buildings to ensure comfort of use, user safety, energy efficiency improvements and integration with smart grids and smart city platforms. Such a wide spectrum of technologies and functions requires a new approach in building automation systems design to provide effective way of their implementation and flexibility during operation. At the same time, in the building design and operation industries, tools based on building information modeling and digital twins are being developed. In this paper, the authors discuss development directions and application areas of these solutions, identifying new trends and possibilities of their use in smart homes and buildings. In particular, the focus is on procedures for selecting automation functions, effective integration, and interoperability of building management systems with Internet of Things considering organization of prediction mechanisms and dynamic functional changes in buildings and smart networks. Chosen solutions and functions should consider the requirements from EN ISO 52120 standard and the guidelines defined for the Smart Readiness Indicator.

Rice blast is the most harmful disease caused by Magnaporthe oryzae, leading to yield loss. The aim of the work is to improve resistance of rice to blast by introducing and pyramiding genes using conventional breeding technique and modern MAS analysis. Pyramiding is the combination of several resistance genes in one genotype, in this case it improves the resistance of rice to pathogens. MAS analysis revealed that 3 out of 4 studied domestic high-yielding rice cultivars (Bakanasski, Aisaule and Aru) contain 1 blast resistance gene (Pi-2). It was discovered 7 pyramidal lines with the introduction of a combination of 2 blast resistance genes (Pi-1 and Pi-ta) into 6 hybrids and 3 genes (Pi-1, Pi-33 and Pi-ta) into 1 line. Phytopathological test of local cultivars and pyramidal lines of rice showed that 3 out of 7 pyramidal lines have moderate resistance: 2 lines (F2 Bakanasski/7667 (var. vulgaris Koern.) and F2 Aisaule/7664 (var. italica Alef.)), containing 2 resistance genes (Pi-1 and Pi-ta) and 1 line – F2 Aisaule/7689 (var. zeravschanica Brsch.), carrying 3 genes (Pi-1, Pi-33 and Pi-ta), while the local cultivars Bakanasski, Aisaule and Aru are susceptible. As a result, the blast resistance of rice was improved by creating pyramidal lines. Pyramided lines can be used for germplasm exchange and in rice breeding programs to improve blast resistance in rice.

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4^th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

Solar pasteurization, as a means of treating milk in remote rural areas without electrical power, is based on using solar energy to thermally inactivate pathogenic microorganisms at temperatures below the boiling point to maintain or improve milk quality. A solar milk pasteurizer of flat‐plate water‐heating glass acting as the solar collector, connected to a stainless-steel cylindrical milk vat, was constructed and evaluated in Navrongo, in the tropical savanna zone of Ghana, West Africa. The novel approach taken in this project is to integrate the solar collection and heat exchanger into a single unit using materials and fabrication techniques readily available in the developing world. The vat comprises a 1.5 mm thick stainless steel cylindrical tank, a 2.2 cm wide hot water jacket, and an outer layer of 5.0 mm thick aluminium foil insulation. Hot water produced by the collector was used for pasteurizing milk. The optimum quantity of milk that this device could pasteurize under the study conditions was 8 L. The device could pasteurize raw milk to a maximum temperature of 74 °C at 14:00 h GMT. The ambient temperature during the pasteurization ranged from 30 °C to 43 °C. The microbial analysis before and after the pasteurization was done, and the result shows that the total bacteria count before pasteurization was 6.6x10⁶ log CFU/mL per unit volume, and the total bacteria count after pasteurization was 1.0x10² log CFU/mL. By acceptable standards, the total bacteria count after pasteurization is within a safe range for consumption, and coliform counts were negative. The solar milk pasteurization system is cost-effective. Hence, it is appropriate for milk producers in arid pastoral areas without electricity to adopt the solar pasteurizer as an alternative to firewood as a fuel source for milk pasteurization. This approach can potentially create a cottage industry that may also play a crucial role as a water treatment technology that will improve the health of rural populations.

Sustainable development and education in sustainable development (ESD) are crucial and interconnected topics under wide discussion today. Young adults need to be educated in how to help bring about change. Thus, the dynamic relationship between school and home is important. In education, use could be made of English language classes, especially since English is a global language that enables access to the sustainability literature and facilitates discussion in global conferences and meetings. Consequently, English as a foreign language (EFL) students could gain confidence from being able to understand and use the terminology of, for example, sustainability. In this paper, the use of competence-based language teaching (CBLT) is proposed as a means of introducing sustainability topics into EFL writing classes at a university in Saudi Arabia. Students’ competence in sustainable development is therefore considered based on their ability to write about suggested sustainable development topics, measured on a scale for five dimensions of ESD proposed by Michelsen and Fischer (2017). The results demonstrate that the students possessed competence in sustainable development and intended to participate in its implementation. Thus, recommendations are made for the stakeholders in the education sector to include sustainable development topics as an initial offering in the curricula of various specialties using the CBLT approach.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

The objective of this research is to analyze the effect of determinants of Business ethics (HR management ethics, ethics in corporate governance, and ethics in sales & marketing) on the organizational performance of the high-tech industry. The study further examines the influence of multiple serial mediations of human, structural, structural, and relational capital on the connection between Business ethics and organizational performance. Additionally, this research examines how technological change moderates the relationship between Business ethics and organizational performance. Data was collected from the information technology sector in various cities of South Asian countries through structured questionnaires. PLS-SEM modeling was used via Smart-PLS 4.0 software for analysis. The hypothesized & modified measurement and structural model were validated using the HTMT matrix, Fornell-Larcker criterion, R-square values, f-square values, outer loading, construct reliability and validity, path coefficient analysis, Blindfolding, and predictive relevance. The research results suggest that ethics in corporate governance, HR management ethics, and ethics in sales & marketing have a notable positive effect on business ethics, leading to a substantial and affirmative impact on the organizational performance of the high-tech industry. The findings further suggested that dimensions of intellectual capital (human capital, structural capital & relational capital) significantly moderate between exogenous and endogenous constructs. Moreover, technological change significantly & positively moderates the relationship between Business ethics and organizational performance of the high-tech industry. In conclusion, this research provides insights into how these variables interact and what potential mechanisms act upon them. Organizations can make informed decisions about improving their performance and ethical standards by understanding these relationships. This research offers substantial theoretical and managerial implications to research scholars and industry practitioners.

The Yb/Al multilayer films exhibit excellent theoretical reflectivity in the 54-90nm wavelength range. This study attempted to incorporate 1.5% wt.% of Si impurities into Al to suppress the crystallization of Al, reduce interfacial roughness, and enhance the actual reflectivity of the prepared Yb/Al multilayer films. The internal microstructure changes of the film layers before and after Si impurity doping were investigated using GIXRR, AFM, and XRD techniques. The reflectivity of two types of multilayer films, Yb/Al(1.5wt.%Si) and Yb/Al(pure), was tested to evaluate the effect of Si impurity on the film performance.

The leaves of Nicotiana glauca (Solanaceae) plant, are known for its major cause of health concern. This study investigated the antioxidant activity and polyphenols composition of aerial parts of N.glauca collected from its wild habitat in Jordan, using Methanol-Conventional (MC) and deep eutectic solvents (DESs) extraction methods in addition to nicotine content determination using UHPLC. The results have shown that the MC extract contains fewer total phenols and flavonoid content than the DES extract at ratio of 90% (0.1194 ±0.009 and 0.311± 0.020 mg/mL equivalent to gallic acid) and (0.01084±0.005 and 0.928 ± 0.09 mg/mL equivalent to rutin), respectively. Moreover, the study showed that the MC extract contains 635.07 ppm nicotine compared to the DES extraction method, which contains 1194.91 ppm. Both the MC and the DES extracts exhibited weak antioxidant activities with the highest was 33%inhibition equivalent to ascorbic acid was found for DES at (90%) ratio. The UHPLC-MS/MS analysis revealed the presence of variations in the detected compounds between the two extraction methods used. This study found that en-vironmentally friendly DES extraction of N. glauca produced higher phenol and flavonoid content than MC method, highlighting green chemistry methods' superior efficiency and environmental benefits for extracting valuable phytoconstituents.

Developments of new probability models for data analysis are keen interest of importance for all fields. The log-Dagum distribution has a prominent role in the theory and practice of statistics. In this article, a new family of continuous distributions generated from a log Dagum random variable called the log-Dagum Weibull distribution is proposed. The key properties of the proposed distribution are derived. Its density function can be symmetrical, left-skewed, right-skewed and reversed-J shaped and can have increasing, decreasing, bathtub hazard rates shaped. The model parameters are estimated by the method of maximum likelihood and illustrate its importance by means of applications to real data sets.

Management of chronic inflammation and wounds has always been a key issue in the pharmaceutical and healthcare sector. Curcumin (CCM) is an active ingredient extracted from turmeric rhizomes that has antioxidant, anti-inflammatory, and antibacterial activities, thus showing significant effectiveness toward wound healing. However, its shortcomings such as poor water solubility, poor chemical stability and fast metabolic rate limit its bioavailability and long-term use. In this context, hydrogels appear to be a versatile matrix for carrying and stabilizing drugs due to the biomimetic structure, soft porous microarchitecture, and pleasant biomechanical properties. The drug loading/releasing efficiencies can also be controlled by use of highly crystalline and porous metal organic frameworks (MOFs). Here, a flexible hydrogel composed of sodium alginate (SA) matrix and CCM-loaded MOFs was constructed for long-term drug release and antibacterial activity. The morphology and physicochemical properties of composite hydrogels were analyzed by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), ultraviolet visible spectroscopy (UV-Vis), Raman spectroscopy and mechanical property tests. The results showed that the composite hydrogel was highly twistable and bendable to mechanically comply with human skin. The as-prepared hydrogel could capture efficient CCM for slow drug release as well as effective killing of bacteria. Therefore, such composite hydrogel is expected to provide a new management system for chronic wound dressings.

Use of MS Excel as a platform for teaching simple iterative solution of partial differential equations runs into issues of reliability of the computation because of the large number of calculations required. This article documents the problem and an empirical solution. The cause of this unreliability was found by detailed examination of the numerical results from three simultaneous iterations. Computers of several types from different manufacturers were found to fail occasionally in updating variables in the numerical iteration. A simple work-around is suggested and was tested on several platforms.

Mosquito-borne diseases (MBDs) are a group of illnesses transmitted by mosquitoes and can be caused by bacteria, viruses, or parasites. These diseases represent a significant global burden of infectious diseases, including morbidity and mortality. This systematic review delves into the multifaceted factors contributing to the spread of mosquito-borne diseases (MBDs) in the Middle East and North Africa (MENA) region. Following PRISMA guidelines, a thorough analysis of peer-reviewed articles from May 1990 to Jan 2023 was conducted, highlighting the interplay of population, environmental, disease, and mosquito factors in disease transmission and prevalence. The review incorporated 31 studies that revealed a complex relationship between various risk factors and the presence of MBDs. Significant associations were observed with age, certain occupations, environmental conditions such as rainfall and temperature, sanitation practices, specific pathogen variants, clinical symptoms, and Aedes aegypti mosquitoes. Conversely, gender, socioeconomic status, educational status, and certain sanitation-related factors showed inconsistent association with the spread of MBDs. The review underscores the need for targeted interventions, including vector control, improved sanitation, and educational campaigns to mitigate the spread of MBDs in the MENA region. This review could guide research studies to address data gaps and assist in developing effective surveillance programs in the MENA region. This work emphasizes the need for region-specific public health strategies and further research to understand and curb the burden of these diseases effectively.

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition and intelligence. A core mystery is the MRI explanation of individual differences in neurocognition and intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation need to be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on machine intelligence inferring human intelligence.

Background and Objective: Segmentation of the femur in Dual Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models. Methods: Convolutional neural network (CNN) based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and its effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods. Results: Our study demonstrated that FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis. Conclusions: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.

Aluminum wire is a common material for wire bonding due to its resistance to oxidation and low price. It does not melt when becoming a free air ball (FAB) during the electronic flame-off (EFO) process with wettability, and is applied by wedge bonding. This study used 20μm Zn-Coated Al-0.5wt.%Si (ZAS) wires to improve the FAB shape after the EFO process, while maintaining stability of the mechanical properties, including the interface bonding strength and hardness. In order to test circuit stability after ball bonding, the current-tensile test was performed. During the experiment, it was found that 80nm ZAS with wire bonding has lower resistance and higher fusing current. For the bias tensile test, the thicker Zn film diffused into the Al-Si matrix easily, after which the strength was reduced. The ball-bond interfaces had no change in their condition before and after the bias. Accordingly, ZAS could be a promising candidate for ball bonding in the future.

This paper investigates the improvement in solar energy collection and conversion using Al₂O₃-Water nanofluids in a solar flat plate collector (SFPC). The efficiency of a solar flat plate collector using water as the fluid medium is analyzed experimentally and theoretically. For theoretical analysis, a mathematical model in MATLAB is used to simulate and is validated by the experimental results. To enhance the solar energy collection and conversion efficiency of the SFPC, Al₂O₃-Water nanofluid was selected as the fluid medium. The nanofluid properties like density, specific heat capacity, thermal conductivity and viscosity are analyzed and compared for several models of Al₂O₃-Water nanofluids and the best model was selected to modify the simulation. Effect of particle diameter in the nanofluid was found to be marginal on the nanofluid properties. The optimum volume concentration of the nanofluid was found to be 4% giving an efficiency increase of 7.78% in the SFPC over the use of water. This reduces the area of the SFPC by 10.5%.

The Gulf Cooperation Council (GCC), locally known as Khaliji, is a group of six Arab nations, including Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, and the United Arab Emirates (UAE). Intimate partner violence (IPV) is a significant public health concern in the GCC region, but the research that synthesized the trend has received scant attention. The present narrative review examines existing research on the prevalence and frequency of IPV among Khaliji women in GCC nations. This review synthesized studies on physical violence, sexual violence, emotional abuse, and controlling behaviors perpetrated by an intimate partner. The prevalence rates of IPV among Khaliji women are observed to be high: women reported facing different types of abuse from their partners, namely physical (7% - 71%), sexual (3.7% - 81%), financial (21.3% - 26%) and psychological (7.5% - 89%), which is a culmination of controlling behavior (36.8%), emotional (22% - 69%) and social violence (34%). The extant studies in the GCC suggest that the most endorsed IPV was psychological abuse (89%) followed by sexual violence (81%). Qualitative content analysis of the associated factors resulted in four meaningful descriptors such as demographics of the victim, sociocultural factors, socioeconomic, and perpetrator-related issues. The study on IPV is still nascent and few. The way forward will require developing culturally appropriate interventions that address unique risk factors for IPV among Khaliji populations, strengthening institutional responses, and increasing awareness and social support for the victim of IPV.

In this project, our foremost intention is to analyze the angle of deflection of photon by NED black holes with many horizons in the context of non-linear electrodynamics. For the accomplishment of our intention at first we manipulated the optical metric of the desired black holes for the calculation of Gaussian curvature. After that, the angle of deflection in leading orders is attained from the desired black holes in weak field limit by using the famous Gauss-Bonnet theorem. Moreover, we evince the effect of plasma medium on the angle of deflection. At last, the graphical impact of the angle of deflection for NED black holes with many horizons is examined with respect to black hole charge and impact parameter in both the plasma and non plasma medium. In addition, we discuss the rigorous bound for grey body factor of the desired black hole and also observer its graphical behavior.

Gravel packing completion method for horizontal wells has the advantages of maintaining high oil production for a long time, maintaining wellbore stability and preventing sand production, so it has become the preferred completion method for horizontal wells. At present, this technology still faces the problems of high sand bed height and poor gravel migration. In order to improve the efficiency of gravel packing in horizontal wells, pulsed gravel packing technology for horizontal wells is proposed for the first time. Based on the mechanism of hydraulic pulse, the Eularian model, RNG K-ε model and CFD model are used to simulate the solid-liquid two-phase flow. By optimizing the parameters such as frequency and amplitude of pulse waveform, the optimal pulse waveform of pulsed gravel packing in horizontal wells is determined. The effects of parameters such as sand-carrying fluid displacement, sand-carrying fluid viscosity, sand-carrying ratio, gravel particle size and string eccentricity on pulsed gravel packing in horizontal wells are studied, and the distribution law of gravel migration velocity and volume fraction in horizontal wells is obtained. According to the results, it can be seen that with the increase of displacement and viscosity of carrier fluid, the volume fraction of fixed bed and moving bed decreases gradually, while that of suspension bed increases gradually. With the increase of sand-carrying ratio, gravel particle size and string eccentricity, the volume fraction of fixed bed and moving bed increases gradually, while that of suspended bed decreases gradually. Comparing the effects of conventional gravel packing and pulsed gravel packing in horizontal wells, it can be concluded that the efficiency of pulsed gravel packing in horizontal wells is higher. The volume fraction of fixed bed and moving bed decreased by 30% and 40% respectively, while the volume fraction of suspended bed increased by 20%. The migration velocity of moving bed and suspended bed increased by 40% and 25% respectively. And the migration ability of gravel improved obviously.

Multi-walled carbon nanotubes (MWCNTs) and graphene oxide (GO) reinforced carbon foam (CF) composite were prepared by direct pyrolysis of MWCNTs, GO and mesophase coal tar pitch. The effect of additive amount of the mixture of MWCNTs and GO on the microstruture and properties of carbon foam was analzyed by transmission electron miscroscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), Four-probe resistance meter, universal testing machine, and laser thermal conductivity tester respectively. The result shows that MWCNTs and GO had significant impact on the microstructure of carbon foam. Futhermore, the electrical, mechanical and thermal properties of carbon foam composites were significantly enhanced by increasing the additive amount. Maximum compressive strenght of 19.2 MPa and Young’s modulus of 56.8 MPa of CF composite were observed. Similarly, Highest thermal conductivity of 30.91 W/m.K and electrical conductivity of 27.2 ×103 S/m were observed at 2 wt. % of MWCNTs-GO additive loading.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

The aim of this paper is to develop a composite coating on St 304 steel employing TIG process. Ti wire cored with graphite powder is used as the means of coating material. The process parameters are varied in order to develop coating with optimum characteristics (i.e., hardness and wear resistance). The microstructure of the coating is analyzed with SEM and XRD. It is found that both of the hardness and wear resistance increase as the current increases while both of these properties decrease as the travelling speed increases. It is found that the coated samples with composite layers have more hardness than the substrate and it could range up to 1100 HV being almost 4.5 times higher than the hardness of St 304. Likewise, the wear resistance of coating is observed to be 4.5 times higher than that of substrate. The high performance of coating, as revealed by microstructural analysis, is due to formation of TiC and Cr₂₃C₆ .The optimum conditions to produce the coating are proposed to be 120 A current and 3.17 mm/s travel speed.

Osteopetrosis incidence is less than 1:200,000 birth in most populations (Worth, 1963) . It's more common in consanguineous people as it's unusual in two members of the same family. The defined treatment for Osteopetrosis is Hematopoietic stem cell transplantation (HSCT). With a 73% five years disease-free survival in donors. The incidence of Hypertrophic pyloric stenosis is 1 in 300–900 newborns. Hypertrophic pyloric stenosis is due to hypertrophy of the smooth muscle of the pyloric sphincter. The classic age of occurrence is the first few months of life, and the classic presentation is non-bilious projectile vomiting after feeding. We reported a four family members with Osteopetrosis and Pyloric stenosis and their three cousins with Osteopetrosis, which is a very rare presentation and association.

It is a theoretical exportation for mass transpiration and thermal transpiration of Casson nanofluid over an extending cylindrical surface. The Stagnation point flow through porous matrix is influenced by magnetic field of form strength. Appropriate similarity functions are availed to yield the transmuted system of leading differential equations. Existence for the solution of momentum equation is proved for various values of Casson parameter β, magnetic parameter M, porosity parameter Kp and Raynolds number Re in two situations of mass transpiration (suction/injuction). Moreover, uniqueness results are discussed and for skin friction factor are established to attain accuracy for large injection values. Thermal and concentration profiles are delineated numerically by applying Runge-Kutta method and shooting technique.

Hypervirulent Klebsiella pneumoniae (hvKp) is a variant that has been increasingly linked to severe, life threatening infections including pyogenic liver abscess and bloodstream infections. HvKp belonging to the capsular serotypes K1 and K2 have been reported worldwide, however very scarce studies are available on the their genomics and virulence. In the current study we report four hypermucoviscous hvKp ESBL clinical strains of capsular serotype K1 and K2 isolated from pus and urine of critically ill patients in tertiary care hospitals in Oman. These strains belong to diverse sequence types (STs) namely ST-23(K1), ST-231(K2), ST-881(K2), and ST-14(K2). To study their virulence, Galleria Mellonella model and resistance to human serum killing were used. The G. mellonella model revealed that the K1/ST-23 isolate is the most virulent as 50% of the larvae died in the first day, followed by isolate K2/ST-231 for which 75% of the larvae died in the second day and isolate K2/ST-14 for which 50% of the larvae died in the second day. Resistance to human serum killing showed there was complete inhibition of bacterial growth of all four isolates by the end of the first hour and up to the third hour. For ST-23(K1), ST-231(K2), ST-881(K2), and ST-14(K2), there was a gradual decrease in the number of colonies up to the second to third hour, which indicates neutralization of bacterial cells by the serum components. However, this was followed by sudden increase of bacterial growth, indicating possible resistance of bacteria against human serum bactericidal activity. Whole genome sequencing (WGS) revealed that hvKp strains display unique genetic arrangement of k-loci. Whole genome SNP-based phylogenetic analysis revealed that these hvKp isolates are phylogenetically distinct, belonging to diverse clades and belong to different STs in comparison to global isolates. This is the first report from Oman detailing the WGS of hvKp clinical isolates and assessing their resistance and virulence genomics, which reinforce our understanding of their epidemiology and dissemination in clinical settings.

Impure and pure casts have collected reasonable attentions over all the world. In this paper, Aluminum (Al) and Copper (Cu) cast alloys are considered to be studied. Multiple impure and pure Al-Cu cast alloys are established under the condition of very high temperatures. Then, all of the established alloys are examined by applying mechanical tests. More specifically, tests of hardness are exploited. Moreover, different quenching conditions are employed and analyzed. These are the water, air and oil. Consistent results are separately obtained for the impure and pure materials.

Keywords: Barriers; Facilitators; Sepsis; Sepsis Six Guidelines; Nurses.

Premenstrual syndrome (PMS) continues to impact the health outcomes and emotional well-being of reproductive-age women, globally. Several studies have provided conflicting evidence concerning the role of dietary approaches in improving PMS symptoms. Accordingly, this study aimed to evaluate the possible influence of a healthy diet and motivational strategies on PMS symptoms and health-related quality of life among Omani adolescents. This open-label, randomized, prospective controlled trial was conducted in two randomly selected secondary schools, in Al Seeb Willayah, in Muscat region. Adolescents with PMS symptoms, who were at grade 10 or 11, aged 16 years or above, had regular menstrual cycles and not known to have psychiatric disorder, were included in this study. Participants in the intervention group received an individual face-to-face dietary consultation and motivational phone consultation. The health outcomes, including the PMS symptoms in both groups, and quality of life, were recorded via a daily record of severity of problems questionnaire (DRSP) and the 14-item Self-Reporting-Based Perceived Stress Scale tools, respectively. The primary outcome was the differences in the mean premenstrual symptom scores between the two groups. Secondary outcomes included the quality of life and stress levels of participants. The study period was from 1 February and ended 30 June 2021. SPSS was used to analyze the data and intention-to-treat analysis was utilized.A total of 72 adolescents with PMS were randomized to the intervention and control groups (n=36 each). Both groups were similar at baseline (P-value&gt;0.05). No significant association was found between healthy-diet and PMS symptoms (P-value&gt;0.05). In addition, no significant association was found between healthy-diet and quality of life at follow-up (P-value=0.216). The outcomes of this study refuted any possible relationships between healthy diet and PMS symptoms. Accordingly, dietary consultations may not facilitate the clinical management of PMS symptoms in adolescent females.