This review paper delves into the multifaceted aspects of pineapple cultivation, focusing on morphology studies, harvesting challenges, and mechanization efforts. Pineapple, renowned for its commercial value and unique flavor, holds a prominent position in global agriculture, with India alone producing 19.64 lakh tonnes annually. However, the hilly terrains of regions like northeastern India pose significant hurdles to traditional harvesting methods, necessitating innovative solutions. Understanding the morphology of pineapple plants is crucial for optimizing cultivation practices and designing efficient harvesting equipment. Insights from various studies shed light on leaf structure, fruit maturity, and growth patterns, providing valuable information for engineering tailored solutions. Mechanization endeavors, including semi-mechanized harvesting machines and semi-manual harvesters, demonstrate promising results in improving efficiency and reducing labor-intensive practices. Despite challenges such as increased crop losses and operational costs, continued research and development in mechanization hold the key to sustainable pineapple cultivation. This paper underscores the importance of integrating morphology studies and mechanization efforts to address the unique challenges faced by pineapple farmers, particularly in hilly terrains, and highlights the need for ongoing innovation to ensure the long-term sustainability of pineapple cultivation.

Physical properties like shape, volume and size affect the dynamics of biological systems. Along these lines, we focus on the topological properties of biological fluids and their biochemical and physiological outcomes. We take as a paradigmatic example the salivary fluid and describe how its topological features may affect the physiopathology of the oral cavity. Topological approaches assess the general properties of saliva, ignoring small-scale physical details such as density, flow rate, stiffness, viscosity. Specifically, the mucin aggregates scattered in the salivary fluid can be tackled in terms of topological holes, i.e., vortical clusters that modify the direction, flow, impulse, local rate-of-change and velocity of saliva. While the current methodological approaches are inclined to remove the effects of impurities assessing systems as homogeneous structures, we argue that the occurrence of mucins breaks up the salivary fluid’s homogeneity, leading to unexpected biophysical modifications. We suggest that every collected salivary sample is not reliable for accurate clinical and experimental investigation, since it displays highly local as well as variable chemical, physical and biological features, not reflecting the current physiological state of the oral cavity. Therefore, the assessment of a single salivary sample is not fully reproducible and cannot provide information about the biophysical, enzymatic and microbiological content of the whole saliva. In sum, the very topological features of the saliva - such as volume, shape, antipodal cells, vortex area, whirling fluid mass, segmentation, discretization, triangulation, node numbering - produce unnoticed biological consequences and network connectivity features with intriguing operational implications.

The theory says that number interacts with spaces based on the meaning of mathematics, in this way it is possible to obtain numerical knowledge. The theory shows the possibility of using spaces related to each other to explain the reasons why two even numbers form a pair, two odd numbers form a pair, an even number and an odd number form an odd number, three odd numbers form an odd number . three even numbers make an odd number. an even, an odd and two evens make an odd, two odd and an even make a pair. Furthermore, it is possible to extend the logic to larger numbers, but applying rules to make it work.

This paper explores the interactions of dynamic dark energy with other components of the cosmos, such as dark matter and baryonic matter, and its wide-ranging implications in cosmology. By investigating the interplay between dynamic dark energy and the universe's large-scale structure, we aim to provide a deeper understanding of cosmic evolution and the behavior of dark energy in different cosmological epochs. We also examine the potential of dynamic dark energy to offer novel insights into long-standing cosmological puzzles and their impact on the formation and evolution of cosmic structures.

This study is based on the BP neural network regression model optimised by genetic algorithm and dedicated to predicting the amount of car purchase, this study is important for all parties in the automotive industry. By plotting the violins of six indicators, including gender, age, annual salary, credit card debt, net worth, and car purchase amount, we found that, except for gender, the other data showed a trend of dense in the middle gradually decreasing to the upper and lower sides, with an overall normal distribution. The correlation analysis shows that the amount of car purchase has a high positive correlation with age (0.63), followed by annual income (0.62), the correlation between net worth and the amount of car purchase is 0.49, while the correlation between credit card debt and the amount of car purchase is low (0.03). In the experimental part, we analyse the predicted-actual values of the training and test sets in comparison and find that the model predicted values are very close to the actual values. The RMSE of the training set is 11.559 and the RMSE of the test set is 11.4835, which is not much different from each other, indicating that the model has good generalisation ability. In addition, the R2 of both the training and test sets is 1, indicating that the model fits well. The MAE of the training set is 8.2512 and the MAE of the test set is 9.578; the MBE of the training set is -4.3035 and the MBE of the test set is -2.4547.These metrics indicate that the model performs well in predicting the amount of car purchase. In conclusion, this study provides an effective and reliable method to predict the amount of car purchase, creating a useful bridge between car manufacturers, dealers and consumers.

Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as Regular (Kai Shu), Running (Xing Shu), Official (Li Shu), and Cursive (Cao Shu) scripts. Beginners often start with Regular script, advancing to more intricate styles like Cursive. Each style, marked by unique historical calligraphers' contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification are pivotal. This study introduces an innovative Convolutional Neural Network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphers' styles from the Tang dynasty (690-907 A.D), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5-96.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in both font and artistic style categorization. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications.

This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Meanwhile, the article also explores the challenges and risks potentially brought by the development of digital currencies, such as regulatory uncertainty and market volatility. In this context, the article raises the importance of cryptocurrency price forecasting and introduces the forecasting models and techniques commonly used today. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.

Teledermatology, the dermatological consultation and diagnosis via digital communication technologies, has become increasingly important in modern healthcare. The integration of teledermatology into clinical practice has gained significant interest, especially during the COVID-19 pandemic. Numerous studies have shown the effectiveness and reliability of teledermatology, with high levels of patient satisfaction and diagnostic accuracy. Pathpoint® eDerma is a cloud-based dermatology platform that aims to streamline the documentation and management of dermatological conditions. It offers several clinical benefits, including faster diagnosis, reduced unnecessary procedures, improved access to care, and cost savings. The platform follows a comprehensive risk management plan and complies with safety regulations. It undergoes regular recertification and reviews to ensure its safety and effectiveness. The clinical evaluation of eDerma confirms its ability to deliver significant clinical benefits while effectively mitigating risks. The benefit-risk ratio of eDerma is highly acceptable, supporting its use in clinical settings to enhance dermatological care.

Polydatin, a polyphenolic compound sourced from various natural origins, has garnered attention due to its potential anti-tumor properties and low toxicity. This research utilized Molecular Docking methods to investigate the molecular interactions between Polydatin and the Crystal Structure of Human UDP-glucuronic acid decarboxylase (UXS1), aiming to uncover the mechanisms contributing to its anti-tumor effects. The study revealed a substantial binding energy value of approximately -10 kcal/mol for the Polydatin-UXS1 interaction, indicating a robust and favorable binding. This suggests that Polydatin holds significant promise in cancer research, offering insights into its ability to interact with and potentially modulate the activity of UXS1. Moreover, the additional step of predicting toxicity properties using the pKCSM server provides valuable insights into the safety profiles of polydatin ( or piceid). From these results, the parameters for polydatin: —Max. tolerated dose (human),- Oral Rat Acute Toxicity (LD50), and - Oral Rat Chronic Toxicity (LOAEL)—suggest a favorable safety profile.These findings position Polydatin as a compelling candidate for further exploration in cancer research, highlighting both its efficacy and safety considerations.

(1) Background: Breast cancer (BC) is the second largest cause of mortality in female worldwide. Besides from basic risk factors, there are several hereditary variables that contribute to BC. Breast cancer has been linked to a variety of nuclear DNA changes as well as mitochondrial DNA alterations. The aim of this study was to analysis the association of mitochondrial tRNA leucine2 gene with BC. (2) Material and methods: In the current study, 24 samples have been collected from various families in Peshawar. DNA was extracted from Blood. PCR was used to amplify the mitochondrial tRNA MT-TL2 gene, and 22 samples were sequenced. The sequence was compared with accession #NC-012920.1 of the revised Cambridge Reference Sequence (rCRS). (3) Results: The results (Chromatograph, nucleotide sequence and rCRS alignment) shows mutations in mitochondrial tRNA MT-TL2 gene in our participants is not the cause of Breast cancer. (4) Conclusion: Yet, a significant number of BC patients must be studied, and their full mitochondrial DNA must be analyzed. This will provide an indication of the potential DNA marker that might be used to prevent BC deaths at the earliest stages.

Preoperative assessment is crucial in surgical care, traditionally relying on physical examinations and paper-based questionnaires. The emergence of digital tools like Pathpoint® ePOA represents a significant advancement, aiming to enhance the efficiency, accuracy, and standardisation of preoperative evaluations. Pathpoint® ePOA is a cloud-based platform designed to integrate seamlessly with Electronic Health Records (EHRs), providing a comprehensive, data-driven approach to preoperative assessments. It supports improved risk stratification, optimised resource allocation, and enhanced patient engagement through features such as customisable templates and real-time data integration.Current research highlights that digital preoperative tools like ePOA offer notable advantages, including increased clinical capacity, better complexity stratification, and improved patient follow-up. Despite these benefits, gaps remain in comparative studies, detailed clinical outcomes, and patient engagement strategies, indicating areas for further exploration. Pathpoint® ePOA adheres to rigorous safety standards and risk management protocols, ensuring reliability and data security while contributing to improved surgical outcomes. The benefit-risk ratio for ePOA is favourable, underscoring its potential to advance preoperative care by optimising clinical workflows and enhancing patient safety.

Chronological frameworks based on artefact typologies are essential for interpreting the archaeological record, but they inadvertently treat transitions between phases as abrupt events and disregard the temporality of transformation processes within and between individual phases. This study presents an absolute chronological investigation of a dynamic material culture from Early Iron Age urnfields in Denmark. The chronological framework of Early Iron Age in Southern Scandinavia is largely unconstrained by absolute dating, primarily due to it coinciding with the so-called ‘Hallstatt calibration plateau’ (c.750 to 400 cal BC), and it is difficult to correlate it with Central European chronologies due to a lack of imported artefacts. This study applies recent methodological advances in radiocarbon dating and Bayesian chronological modelling, specifically a statistical model for wood-age offsets in cremated bone and presents the first large-scale radiocarbon investigation of regional material culture from Early Iron Age in Southern Jutland, Denmark. Dated material is primarily cremated bone from 111 cremation burials from three urnfields. The study presents absolute date ranges for 16 types of pottery and 15 types of metalwork, which include most of the recognised metalwork types from the period. This provides new insights into gradual change in material culture, when certain artefact types were in production and primary use, how quickly types were taken up and later abandoned, and distinguishing periods of faster and slower change. The study also provides the first absolute chronology for the period, enabling correlation with chronologies from other regions. Urnfields were introduced at the Bronze-Iron Age transformation, which is often assumed to have occurred c.530-500 BC. We demonstrate that this transformation took place in the 7^th century BC, however, which revives the discussion of whether the final Bronze Age period VI should be interpreted as a transitional phase to the Iron Age.

With continued scaling of semiconductor devices , the value of predictive analysis through 1 accurate quantum transport modeling is exponentially increasing. Quantum transmitting Boundary 2 method (QTBM) has been a well-known method of choice to model quantum transport in simple 1-D 3 devices. Here, we present an extension of the QTBM to the 2-D domain and apply it for more realistic 4 devices for accurate performance analysis.

Recently, Gohar et al. introduced a novel, local, and well-behaved fractional calculus. It possesses all the classical properties, and Its locality imposes simplicity and accuracy in modeling fractional order systems. In this article, we further develop the definitions and extend the classical properties of Gohar fractional calculus to address some of the open problems in Calculus. The fractional Gronwall's integral inequality, Taylor power series expansion, and Laplace transform are defined and applied to overcome some of the limitations in the classical integer-order calculus. The fractional Laplace transform is applied to solve Bernoulli-type logistic and Bertalanffy nonlinear fractional differential equations, and the criteria under which it can be applied to solve linear differential equations are investigated.

Artificial intelligence (AI) driven language models have seen a rapid rise in development, deployment, and adoption over the last few years. This surge has sparked many discussions about their societal and political impact, including political bias. Bias is a crucial topic in the context of large models due to its far-reaching consequences on technology, politics, and society. It significantly influences public perception, decision-making, political discourse, and AI policy governance and ethics. This study investigates political bias through a comparative analysis of four prominent AI models: ChatGPT-4, Perplexity, Google Gemini, and Claude.Through a comprehensive analysis by systematically and categorically evaluating their responses to politically and ideologically charged tests and prompts, utilizing the Pew Research Center’s Political Typology Quiz, the Political Compass assessment, and ISideWith political party quiz, this study identifies significant ideological leanings and the nature of political bias within these models. The findings revealed that ChatGPT-4 and Claude exhibit a liberal bias, Perplexity is more conservative, while Google Gemini adopts more centrist stances. The presence of such biases underscores the critical need for transparency in AI development and the incorporation of diverse training datasets, regular audits, and user education to mitigate these biases. This analysis also advocates for more robust practices and comprehensive frameworks to assess and reduce political bias in AI, ensuring these technologies contribute positively to society and support informed, balanced, and inclusive public discourse, which will point towards neutrality.The results of this study add to the ongoing discourse about the ethical implications and development of AI models, highlighting the critical need to build trust and integrity in AI models. Additionally, future research directions have been outlined to explore and address the complex issue of bias in AI.

This study employs molecular docking experiments to investigate the binding interactions of Hypericin with key proteins implicated in Alzheimer's disease (AD). Hypericin demonstrated a significant binding energy of -10.1 kcal/mol with ABAD (3-hydroxyacyl-CoA dehydrogenase type II). The analysis revealed multiple binding interactions, including hydrogen bonds with arginine 6, serine 7, and glutamic acid 140, as well as hydrophobic bonds involving leucine 11, aspartic acid 142, and aspartic acid 86. These findings illuminate the intricate molecular interactions between Hypericin and ABAD, suggesting its potential as a lead compound for further therapeutic development in the context of Alzheimer's disease. Additionally, a parallel docking investigation was conducted for Beta-secretase 1 (BACE1), a protein pivotal in the generation of beta-amyloid peptides—a hallmark of Alzheimer's disease. Inhibiting BACE1 activity is considered a promising therapeutic strategy for AD. Computational analyses suggest that Hypericin may serve as a potential inhibitor of BACE1. Molecular Docking results reveal a robust binding affinity between Hypericin and BACE1, supported by a significant binding energy score of -10.3 kcal/mol. Notably, both ABAD and BACE1 exhibit high binding affinity with Hypericin. This theoretical exploration provides an initial understanding of the intricate molecular pathways involved in Alzheimer's disease and proposes Hypericin as a potential therapeutic compound for mitigating its progression.

Attention mechanisms have become a fundamental component of deep learning, including the field of computer vision. The key idea behind attention in computer vision is to help the model focus on the relevant spatial regions of the input image, rather than treating all regions equally. The traditional approaches to attention mechanisms in computer vision often suffer from distribution inconsistencies in the attention maps, resulting in sharp transitions that negatively affect model’s focus and lead to poor generalization on complex shapes. The problem of spatial incoherence is particularly pronounced in the task of semantic segmentation, where accurate pixel-level predictions require a detailed understanding of the spatial relationships within the image. In this paper, we propose an attention mechanism called Smooth Attention designed for convolutional neural networks to address the problem of spatial inconsistency in attention maps through multidimensional spatial smoothing. We conduct a series of experiments to evaluate the effectiveness of the proposed mechanism and demonstrate its superior performance compared to traditional methods.

Entropy production in stochastic thermodynamics is defined as the average over all the possible system trajectories of a quantity (observable) which measures the divergence between a trajectory and its time-reversal. In this way a quantitative measure of the irreversible evolution of a system out of equilibrium can be given. In this note we consider a Markov system and substitute the linear average with a more general notion of average over the trajectories (Kolmogorov-Nagumo mean) which corresponds to substituting Shannon entropy with Rény entropy. We show that, unlike the linear average case, the resulting notion of entropy production depends on how the observable is defined.

In this paper, we present the application of a special soil resistivity model, the Exponentially Graded Multilayered Soil model, to the interpretation of Borehole Resistivity Logging surveys. The model, which was recently introduced by the authors, offers an alternative to the interpretation provided by constant resistivity layer models. The model is applied to synthetic soil surveys as well as to a real survey that is not satisfactorily interpreted using conventional models.

Quantum observers, agents whose entanglement with a measured system may remain coherent, are analyzed in a consistent approach. Thought experiments, including one by Deutsch and one by Frauchiger and Renner, are discussed and modeled using quantum circuits. Terminology is suggested which helps avoid contradictions.

Keywords: Response surface methodology; Ultrasonication; marigold flower; polyphenols, microcapsules; antimicrobial activity.

This paper reviews the multiple roles of machine learning in improving the resilience of power grids, especially in applying new energy storage technologies. Energy storage technologies, such as compressed air energy storage, flywheel energy storage, and superconducting coil energy storage, significantly improve the power grid's ability to respond to load fluctuations and emergencies through intelligent control and optimisation of machine learning algorithms. This not only helps to optimise energy dispatch and improve the operational efficiency and flexibility of the grid but also significantly improves the stability and reliability of the grid so that it is better able to meet future challenges and needs.

Though nowadays germanium do not reach the range of popularity than other metals, i.e. rare earth elements, its utility in target industries makes of it also a strategic metal. Though germanium can be find in a series of raw materials, the principal source for its recovery is from secondary wastes of the zinc industry, and also, the recycle of germanium-bearing waste materials is becoming of interest. In this recovery, Hydrometallurgy is performing a key role to the successful achievement of this goal. The present work reviews the most recent applications (2023 and 2024 years) of hydrometallurgical operations on the recovery of germanium from different solid and liquid sources.

Rising demand and urbanization have led to the global increase and intensification of livestock production. Intensive production has harmful effects on the environment, public health (due to the emergence of zoonotic diseases), and animal welfare. In order to better understand how livestock production intensifies as countries develop and incomes rise, we examine the relationship between income per capita and intensification of poultry and cattle, using the states of India as a study region. We also replicate this analysis at the district level to test whether the relationship holds at different spatial scales. We identify outlying states and districts to see if there are policies that can keep agriculture production largely extensive, even as economic growth occurs. The results support the relationship of income being positively correlated with the proportion of livestock in intensive conditions. However, there are notable outlier states which have grown wealthy while still maintaining largely extensive production, indicating that there may be some cases where economic growth can occur without intensification.

Traditional methods of non-metallic materials joining for industrial uses are limited to lasers, or induction or conduction heating. The precision and efficiency for large-scale applications are difficult to be achieved at the same time. Here in this paper, we report a precise microwave welding method induced by localized surface plasmon of metallic macro-gaps under microwave excitation. The proposed method utilizes energy from well-developed commercial microwaver, and benefits from the precision of localized surface plasmon, an oscillating electron cloud excited by electromagnetic waves, which is currently under hot investigation only at micro- or nano-scales. The method is applied to the welding of polymers to solve the problems of complex processing equipment and high processing energy consumption in the traditional welding process, so as to cope with the growing energy and environmental crisis. Finite element numerical simulations are used to study the distribution characteristics of electric fields and temperature fields. The results show that the process is a high quality, efficient, low consumption and environmentally friendly green process compared to traditional polymer welding methods.

The possibility of a shock wave recovery at a discrete closed interface with a heated gas has been investigated. A two-dimensional model applied to conditions of optical discharges featuring spherical, elliptical, and drop-like configurations demonstrated that non-symmetry in the shock refraction contributes to the specific mechanism of recovery other than simply its compensation. Even though the full restoration of the hypersonic flow state doesn’t occur in a strict sense of it, clear reverse changes toward the initial shape of the shock front eventually take place, thus creating an appearance of a full recovery seen in experiments. From analysis for different interfaces symmetries, the factors determining the recovery dynamics are identified. The results are directly applicable to the problem of energy deposition into a hypersonic flow, however it can be useful anywhere else where the flow modifications following the interaction are important. The dimensionless form of the equations allows applications on any scale other than that demonstrated for the optical discharges.

This cross-sectional study investigates the impact of Virtual Private Networks (VPNs) on Nigerians during the 2021 Twitter ban, prompted by the Nigerian government's suspension of Twitter operations following the deletion of a tweet by the President. The study aims to analyze the frequency and awareness of VPN and Twitter usage among Nigerians during the ban, addressing specific objectives. Employing a survey research method, data was collected from 400 respondents through online and offline questionnaires. Descriptive statistical tools were used for data analysis, revealing significant findings. Results indicate a substantial decrease in Twitter usage during the ban, with IPVanish emerging as the preferred VPN brand. The study also highlights an increase in VPN awareness post-ban, emphasizing the need for educational initiatives. The demographic analysis reveals a dominant age group of 21 to 25 among social media users. Overall, the research underscores the complex interplay between social media dynamics, VPN adoption, and demographic factors, emphasizing the importance of nuanced regulatory approaches and ongoing user education in Nigeria's online landscape.

This study aimed to overcome the difficulty of conducting the horizontal-well cavity leaching test in the field and to investigate the long-term stability of the horizontal-well salt-cavity natural gas storage. The simulation test design is combined with the similarity theory to study the cavity expansion characteristics and the influence law of cavity leaching parameters. Through the design of a rubber hose connection, an integrated closed test system for multi-stage horizontal-well cavity leaching and brine drainage was built. The test system also realises the repeatable backward movement of the injection well during the test. A similarity simulation of the test design was carried out, and the test platform was constructed to carry out multi-stage horizontal-well leaching tests with a nitrogen cushion. In the horizontal-well leaching tests with a nitrogen cushion, the influence laws of the well spacing, flow rate and liquid level position on cavity expansion were investigated. Based on the morphological characteristics of the horizontal-well cavity, a numerical model of the horizontal-well salt cavity was developed, which reflects the real cavity morphology leached in the test. The long-term stability of the horizontal-well salt-cavity natural gas storage under different internal pressures was investigated through numerical simulation.

The periodontal ligament (PDL) is a complex connective tissue that connects the tooth root to the dental alveolar bone and plays crucial mechanical roles. PDL also exhibits regenerative roles and regulatory functions to maintain per-iodontium integrity and homeostasis. While PDL exposure to oral microbial pathogens is common, virtually nothing is known regarding viral infections of PDL. In particular, human herpes simplex virus type 1 (HSV-1) persistently infects the oral cavity through infections of the oral epithelium, connective tis-sue and neurons. While the oral spread of HSV-1 is generally asymptomatic, this virus has also been implicated in various oral pathologies. In this study, using a primary cell model derived from PDL (PDL-cells), and whole surgical fragments of PDL, we provide evidence supporting the efficient infection of PDL by HSV-1 and the promotions of cytopathic effects. Infection of PDL by HSV-1 was also associated with an acute innate inflammatory response, as il-lustrated by the production of the antiviral interferons and pro-inflammatory cytokines. Furthermore, this inflammatory response to HSV-1 was exacerbated in the presence of bacterial-derived products, such as peptidoglycans. This work therefore highlights the ability of HSV-1 to infect mesenchymal cells from PDL, suggesting that PDL may serve as a viral reservoir for the perio-dontal spread of HSV-1. Moreover, this raises questions about HSV-1 oral pathogenesis, as HSV-1-associated cytopathic and inflammatory effects may contribute to profound alterations of PDL integrity and functioning.

A paddle wheel aerator is developed in this research, which includes a wireless sensor network for measuring the water quality in the aquaculture of marine shrimp ponds. It can also move to the desired position to fill oxygen and measure water quality. A basic requirement is good standard water quality to prevent shrimp from epidemics and improve production. The water quality paddle wheel aerator applies a microcontroller and sensors to measure eight parameters of water quality. These water quality parameters are observed on the web application via an IoT module. The movement system of the water-quality paddle wheel aerator consists of a LiDAR, GPS, and remote RF signal. Each item of data is recorded immediately on the cloud server while the water-quality paddle wheel moves in the marine shrimp farm. The controlled paddle wheel aerator is harnessed automatically to enhance precisely the spatial monitoring resolution of the measurement system installed, which is needless for a multiple measurement system with high cost of investment. Also, farmers can access real data through the Line application. Hence, they are able to plan and control a good environment for aquaculture, preventing the occurrence of various epidemics and decomposing organic matter in the pond.

This paper is dedicated to examining dynamic dark energy models in the context of observational constraints and their implications for the universe's future. By integrating theoretical models with various observational data, we aim to provide a comprehensive view of how dynamic dark energy influences cosmic evolution and the universe's fate. This study tests these models against the latest observational evidence, including the Cosmic Microwave Background, supernovae, and large-scale structure surveys, to refine our understanding of the universe's accelerating expansion and the mysterious nature of dark energy.

There is a great need to develop research-based models for supporting collaboration between educational institutions. Collaboration models are needed, for example, to assist the transition from lower educational level to higher education or from higher education to work life. In this research, collaboration was conducted through a joint upper-secondary school and university course that addressed global challenges by using a project-based learning approach. There is a shortage of research exploring students’ perceptions of collaboration. Therefore, the main purpose of this qualitative case study is to explore the relevance of the joint course for students and university students. The research was guided by the following research question: What kind of relevance did the students experience in the joint project-based learning course? The data was gathered using questionnaires, interviews, and reflective study reports. The data was analyzed via theory-based content analysis where research-oriented relevance model was used as the analysis framework. The analysis framework enabled categorizing experienced relevance on individual, vocational and societal dimensions. Altogether, the analysis produced 7 relevance categories for upper-secondary students and 9 for university students. According to the analysis, upper-secondary school students felt that the course offered the most on an individual relevance dimension whereas university students experienced the vocational relevance dimension most diverse. As the main conclusion, this research produced new insights on the experienced relevance of upper-secondary level and higher education cooperation from the students’ point of view. The acquired knowledge can be useful for everyone developing new cross-institutional cooperation models.

Hund's rule of maximum multiplicity is a powerful empirical tool for determining the electron population of electronic shells. It was recently discovered that it is also the rule of minimum Shannon entropy. This study extends these findings to the Aufbau rule and discusses them from the perspective of emergent dimensionality, providing a monotonically increasing entropy function. We observed that some elements that violate the Aufbau rule have the same entropies in the actual and Aufbau electron configurations. On the other hand, lower entropies of all actual elements' configurations are associated with higher or equal numbers of unpaired electrons in these configurations compared to the Aufbau configurations. The only exception to this rule is palladium.

The rapid acceleration of artificial intelligence (AI) has sparked extensive debate about its impact on the safety and security of society. While AI has shown trans- formative potential, particularly in areas such as healthcare and independent structures where it complements productivity and minimizes human error, accu- racy and protection. However, the misuse of artificial intelligence poses real risks, along with threats such as deep counterfeiting, computer cyber-attacks and the manipulation of social behavior through disinformation. These risks vary from privacy and cyber security breaches to moral dilemmas around liability. The unchecked improvement of AI is expected to threaten the social order, so the relationship between innovation and governance is essential so that AI-related threats do not overshadow its benefits. This article offers a detailed assessment and review of the literature that studies the potential dangers that AI poses to society. The cumulative effects underscore the urgent need for comprehensive regulatory frameworks to mitigate these threats.

Aim: the main aim of this study is to compare the onset of effectiveness of mental nerve block with lingual infiltration and inferior alveolar nerve block on patients visiting dental hospital for extraction of mandibular anterior teeth. Materials and methods: The study was set in a clinical setting. This was a randomized controlled double-blind cross study, where 25 subjects received two treatments at an interval of at least 1 week in a within a dental hospital.Inferior alveolar nerve block and lingual nerve block injection is given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered for the right side. Mental nerve block along with lingual infiltration of the corresponding tooth was given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered on the left side.Visual analogue scale (VAS) was used in this study, which consists of a scale with range of 0-10 pain severity on which 0 indicated absence of pain and 10 the worst pain possible. The patients were asked to give scores corresponding to his/her pain, after IANB, mental nerve block and lingual infiltration and these scores were recorde.Results: From the results we can see that patients gave scores between 3-4, stating that they had moderate pain during extraction after administration of IANB (54%) (graph 2) and VAS scores of 0-1 were given by patients, which is suggestive of no pain during extraction after administration of Mental nerve block and lingual nerve infiltration (62%)Conclusion: Within the limits of the study we can conclude that mental nerve block along with lingual infiltration is an effective way to completely and quickly anaesthetise the central incisor to the 1st premolar region.

This comprehensive analysis aims to offer valuable insights into the safety profiles of these glucocorticoids, facilitating the identification of compounds with potentially reduced adverse effects. This study investigates the toxicity profiles of various glucocorticoids, utilizing predictions from the pKCSM Server. Among the glucocorticoids examined, Dexamethasone, Triamcinolone, Hydrocortisone hemisuccinate, Paramethasone, Fluprednisolone, and Flumethasone emerge as potentially less toxic, displaying favorable scores across diverse parameters such as Max. Tolerated Dose (human, MRT), Oral Rat Acute Toxicity (LD50), and Oral Rat Chronic Toxicity (LOAEL). Notably, Hydrocortisone hemisuccinate and Triamcinolone stand out for their high tolerability capacity based on the Max. Tolerated Dose (human, MRT), suggesting enhanced safety profiles. These findings contribute insights into selecting glucocorticoids with optimal safety and tolerability for potential therapeutic applications.Further research and clinical investigations are warranted to validate and refine these findings, ensuring the responsible and effective use of glucocorticoids in medical practice.

This paper presents the Aether Physics Model (APM), a novel theoretical framework that offers a unified description of the fundamental forces and weak interaction. The APM proposes a quantum Aether composed of discrete units, recognizes two distinct charge manifestations, and treats all charges as distributed. Using dimensional analysis and simple equations, the APM derives force equations that unify gravity, electromagnetism, and the strong forces and the weak interaction. The model makes specific predictions about force strengths, maximum mass density, and fine structure constants for protons and neutrons. These predictions align with existing observations and offer new avenues for experimental testing. The APM addresses the limitations of the Standard Model while providing a more intuitive explanation for phenomena like the quantum Hall effect and Casimir effect. If validated, the APM could revolutionize our understanding of space, matter, and fundamental forces.

HIGHLIGHTS Mach's principle is linked with inertia and the mass around. Nowadays, physicists believe its effect is infinitesimal, so Mach's principle is simply ignored. This paper looks at the consequence of applying Mach's principle. It could replace dark matter. ABSTRACT After seeing the history of Mach's principle, we will work out a new model of galaxies inspired by Mach’s principle, which is more satisfactory than current dark matter models. This new model is just maths and needs an explanation. One possible explanation is obviously that Mach's principle is at work in a galaxy.

This communication is to summarize our single center experience on applications of anti-IL-1 biologic response modifiers on autoimmune and autoinflammatory conditions in children. We present our rationale for off-label utilization of Anakinra and discuss evolving treatment paradigms that warrants further research and validation. Based on our observations and successful treatment outcomes, we advocate future multilateral efforts to liberate access to anti-IL-1 medications to control inordinate and detrimental inflammation in children.

The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.

We solve the problem of Apollonius by applying the Lorentz transformation within the frame- work of special relativity to go to a frame of simultaneity, where the solutions are the circum- center. We transform back to get the solutions in a general frame. We show that the solutions in a general frame are the foci of the ellipse going through the centers of the given 3 circles.

In many countries, increasing concern for animal welfare is driving retailer commitments and government legislation that aim to improve the lives of farmed fish. One aspect of fish welfare involves stunning fish prior to slaughter. The feasibility of stunning depends on the species of fish and physical farm characteristics. In this article, we provide an overview of stunning before slaughter in European sea bass and sea bream aquaculture, one of the largest finfish farming industries in the developed world that does not yet stun most of its production. Sea bass and sea bream stunning necessitates the use of electrical stunning equipment aboard harvest vessels, often a significant distance from the shoreline; this presents an interesting engineering and policy challenge. Together, Türkiye, Greece, Spain, and Italy produced over 400,000 t of sea bass and sea bream in 2020. In Türkiye and Greece, farms are numerous and located very close to the shoreline. In Spain and Italy, farms are few and located far from the shoreline. The highest average production is found in farms from Türkiye (1,000 t) and Spain (1,300 t, and lower average production is found in Greece (300 t) and Italy (350 t). Producer progress towards the installation of electrical stunning appears comparatively well-developed for Türkiye, Spain, and Greece, though we emphasise that producers and other stakeholders require continued support to realise this opportunity. Producers in Italy appear slower to make progress on this aspect of animal welfare and may require additional support.

Caused by age or trauma, collapsed connective tissue can cause nerve entrapment and damage within the tarsal tunnel. Tarsal tunnel syndrome is relatively underdiagnosed. This study presents an intervention targeting damaged tissues surrounding the nerves and replacing the structural cushioning with a Wharton’s jelly tissue allograft. The eight selected patients from four clinical sites had tarsal tunnel-related defects. Patient outcomes were tracked on a 90-day calendar utilizing the Numeric Pain Rating Scale (NPRS) and the Western Ontario and McMaster University Arthritis Index (WOMAC). All patients had failed standard care practices for at least six weeks. Each patient received a Wharton’s jelly tissue allograft to sites around the affected tarsal tunnel. No patients experienced adverse reactions. Percent change calculated from the initial application to the 90-day follow-up showed an improvement of 59.43% in NPRS and a 37.58% improvement in WOMAC. This study provides evidence that WJ allograft applications are safe, minimally invasive, and efficacious for patients who have failed standard care treatments for tissue defects associated with Tarsal Tunnel syndrome. Limitations include the small cohort size and nonblinded nature. The results of this study warrant further research to confirm the efficacy, optimal dose, protocol, and durability of Wharton's jelly.

The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.

The dermatological manifestations of Long Covid (LC) have languished in the shadows of chronic fatigue and brain fog. Yet they are all linked by gut dysbiosis and the cytokine triad of TNF-α, IL-1β, and IL-6. The gut microbiome common not only to LC, psoriasis, AA, and vitiligo but also to neurodegenerative disease has been recently described. This gut microbiome induces an altered tryptophan metabolism linked to autoimmune disease. SARS CoV2 invades enterochromaffin cells rich in ACE2 receptors and curtails absorption of the essential amino acid tryptophan and subsequent synthesis of serotonin and melatonin. This review suggests that an etiologic prebiotic (d-mannose)/probiotic (lactobacilli, bifidobacteria)/postbiotic (butyrate) approach to autoimmune skin disease that improves intestinal barrier integrity and that suppresses the triad of TNF-α, IL-6, and IL-1β may enhance or even eliminate the traditional immunotherapy of targeted monoclonal antibodies, Janus kinase inhibitors, and steroids. Health benefits of this approach extend well beyond suppression of autoimmune skin disease.

Albert Einstein, shortly after publishing his General Relativity Theory (GRT) applied it to the entire universe in a paper of 1917. Einstein assumed that such a universe was static in time and possessed a uniform distribution of matter on the largest scales, obtaining a static and finite universe of spherical spatial curvature of radius R. Although this model was refuted by Edwin Hubble's discovery of the expansion of the universe, it is still often studied as a theoretical framework for some problems, such as the twin paradox. This paper will show that Einstein's static universe model leads to a privileged frame of reference, so that any observer moving at the speed v will be able to measure speeds of light other than c depending on the direction of beam transmission. The above will refute the validity of the application to this closed and static universe of the Special Relativity Theory (SRT) proposed by Einstein in 1905 in his article . In addition, a new synchronization convention and new coordinate transformations will be deduced, the "generic" transformations, which will replace those of Lorentz. Finally, the twin paradox in this universe will be solved by making use of them.

Suppose for n∈ ℕ, set A⊆ℝⁿ and function f∶A→ℝⁿ. If set A, using the Hausdorff outer measure, is measurable in the sense of Carathèodory; we want to find an extension of the expected value, w.r.t the Hausdorff measure, that's unique, finite and "natural" (defined on §2.3 & §2.4) for all f in a prevalent subset of ℝᴬ. The issue is current extensions of the expected value are finite for all functions in only a shy subset of ℝᴬ. The reason this issue wasn't resolved is mathematicians have not thought of the problem, focusing on application rather than generalization. Despite the lack of potential use, we'll attempt to solve the problem by defining a choice function---this shall choose a unique set of equivalent sequences of sets (Fₖ***) , where the set-theoretic limit of Fₖ*** is the graph of f; the measure Hʰ is the ℎ-Hausdorff measure, such for each k∈ℕ, 0 < Hʰ(Fₖ***) < +∞; and (fₖ*) is a sequence of functions where {(x,fₖ*(x))∶x∈ dom(Fₖ***)}=Fₖ***. Thus, the extended expected value of or E**[f,Fₖ***] is: ∀(ε>0)∃(N∈ℕ)∀(k∈N)(k≥N⇒1/Hʰ(dom(Fₖ***))∫dom(Fₖ*** )fₖ* dHʰ - E**[f,Fₖ***]<ε ) which should be unique, finite, and "natural" (defined on §2.3 & §2.4) for all f in a prevalent subset of ℝᴬ. Note we guessed the choice function using computer programming but we don’t use mathematical proofs due to the lack of expertise in the subject matter. Despite this, the biggest use of this research is the extension of the expected value is finite for "almost all" functions: this is easier to use in application when finding the "average" of functions covering an infinite expanse of space.

Astronauts in space are subject to a continuous bombardment of ionizing radiation. The Earth's magnetic field and the ISS shield some biologically damaging particles traveling through. Still, travel beyond low-Earth orbit and extravehicular activities are exceedingly more dangerous and there is a concern for the acute and late-occurring adverse health effects befalling astronauts. So, it is vital to consider the current tools and models used to describe and study the organic consequences of ionizing radiation exposure. It is equally important to see where these models may improve. This article reviews the historical development and current state of knowledge of radiation effects impacting astronauts in orbit. We explain the space radiation environment, cellular microenvironment, and how these may be incorporated in radiobiological models to aid in our understanding of the influence space travel may have on astronaut health. The topics discussed in this paper include a review of DNA damage and repair mechanisms and the numerical models that aim to explain the biological effects resulting from ionizing radiation damage. Historically, radiobiological models focused on how radiation damages nuclear DNA, built upon the hypotheses of Crowther and Lea in the 1940s and 1960s, and neglected other sub-cellular targets outside of nuclear DNA.

Flavin-dependent monooxygenases (FMOs) have made substantial influence as catalysts for monooxygenation reactions, impacting diverse field such as drug metabolism, environmental studies, and natural product synthesis. Their application in biocatalysis boasts several advantages over conventional chemical catalysis, such as heightened selectivity, safety, sustainability, and eco-friendliness. In the realm of biomedicine, FMOs are pivotal in antibiotic research, modulating natural products, antimicrobial agents, and pathways involved in drug synthesis. They're also underscored as potential pharmaceutical targets, pivotal in disease progression and viable for therapeutic intervention. Additionally, FMOs play a substantial role in environmental science, especially in pesticide processing and plant vitality. Their involvement in biosynthesizing compounds like polyethers, tropolones, and ω-hydroxy fatty acids, with remarkable regio- and stereoselectivity, renders them indispensable in drug discovery and development. As our comprehension of FMOs' catalytic mechanisms and structures advances, coupled with cutting-edge biotechnologies like computational design and directed evolution, FMOs are poised to occupy an increasingly significant role in both scientific exploration and industrial applications.

In our recent publications, based on strong and electroweak interactions, we have developed a completely new formula for estimating nuclear binding energy. With reference to currently believed Semi Empirical Mass Formula (SEMF), we call our formula as ‘Strong and Electroweak Mass Formula’ (SEWMF). Our formula constitutes 4 simple terms and only one energy coefficient of magnitude 10.1 MeV. First term is a volume term, second term seems to be a representation of free nucleons associated with electroweak interaction, third term is a radial term and fourth one is an asymmetry term about the mean stable mass number. In this paper, we make an attempt to understand and estimate the maximum binding energy associated with any mass number. It can be expressed as, for A > 4, $\left(BE\right)_A\cong \left[A-0.000935A^2-A^{1/3}-A^{-1/2}\right]$ MeV. We are working on refining the 4^th term with even-odd corrections, shell corrections and other microscopic corrections. Proceeding further, stable mass numbers and super heavy mass numbers can be understood with a relation of the form, $\left[\textrm{RoundOff}\left(\left(Z+2.9464\right)^{1.2}-1.7165\right)\right]\pm\left[0,1\right]\pm\left(2n\right)$ where $n \cong 0,1,2$. It needs a review with respect to even-odd proton numbers and other microscopic corrections.Very interesting point to be noted is that based on the concept of “binding energy per nucleon”, the most complicated Avogadro number and Unified atomic mass unit can be estimated in a unified approach.

We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.

Efficient triaging and referral assessment are critical in ensuring prompt medical intervention in the Community Health Care (CHC) system. However, the existing triaging systems in many Community Health Services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms that might represent heart failure or other health-threatening conditions. There is a noticeable limit of research papers describing AI technologies for triaging patients. This paper proposes a novel quantitative data-driven approach using machine learning (ML) modelling to improve the community clinical triaging process. Furthermore, this study aims to employ the feature selection process and machine learning power to reduce the triaging process’s waiting time and increase accuracy in clinical decision-making. The model was trained on medical records of “Heart Failure” patients’ dataset, which included demographics, past medical history, vital signs, medications, and clinical symptoms. A comparative study was conducted using the list of machine learning algorithms where XGBoost demonstrated the best performance among other ML models. The triage levels of 2,35,982 patients achieved an accuracy of 99.94%, precision of 0.9986, recall of 0.9958, and f1-score of 0.9972 within 0.059 seconds. The proposed diagnostic model can be implemented for the CHC decision system and be developed further for other medical conditions.

This study delves into the realm of potential therapeutics for breast cancer by conducting a comprehensive docking simulation and virtual screening analysis using Autodock Vina Algorithm. The focus based on the human epidermal growth factor receptor 2 (HER2), a key protein associated with various cancers, particularly in the context of breast cancer.The results reveal compelling insights into the binding interactions of several natural compounds and drugs with HER2. Notably, Acebilustat, Bemcentinib, and Amentoflavone exhibit outstanding binding energies, averaging around -10 kcal/mol, suggesting strong and favorable interactions with the HER2 target.These encouraging findings not only emphasize the potential biological significance of these compounds but also hint at their potential usefulness in fighting breast cancer.Their promising binding affinities with HER2 position them as potential candidates for further investigation and development as therapeutic agents against breast cancer.

This study investigates the potential antimicrobial utility of the natural substance Amentoflavone against four dangerous bacterial strains—Mycobacterium tuberculosis, Helicobacter pylori, Haemophilus influenzae, and Pseudomonas aeruginosa. Utilizing molecular docking studies, are assessed the binding interactions between Amentoflavone and key protein targets crucial for the viability of these bacteria. The findings suggest promising binding affinities, particularly in the active sites of Aminoglycoside 2'-N-acetyltransferase, Thioredoxin Reductase, N-acetylneuraminate Lyase, and Glucose-1-phosphate Thymidylyltransferase in the respective bacterial strains. While these in silico results provide initial insights, further in vitro and in vivo experiments are essential to validate the biological relevance and efficacy of Amentoflavone as a potential antimicrobial agent against these bacterial pathogens.

Physical exercise is the greatest stress on the cardiovascular system, not only in humans but also in all vertebrates. Consequently, the cardiovascular response cannot be thought of as having only one "anthropocentric" interpretation. The modulation of cardiac output (Q) in response to exercise in the five major groups of vertebrates exhibits considerable variability. Moreover, the response of the heart rate (HR) and the stroke volume (SV), the fundamental factors that cause the rise in Q, is also highly variable. The difference in the range of HR is difficult to determine in many vertebrates, both at rest and at maximal effort. Additionally, the increase in SV varies among vertebrates. For example, while rainbow trout and leopards increase their SV by increasing end diastolic volume (EDV), humans do so at the expense of both increasing EDV and reducing end systolic volume (ESV). The variation in arterial pressure that arises during exercise varies notably among vertebrates, such as amphibians, whose blood pressure values do not increase as significantly as in mammals. Such differences in cardiovascular response among vertebrates may be linked to their habitat or living environment. This review aims to analyze the cardiovascular response of vertebrates to physical exercise as the most common stressor.

Resistance to therapy and disease progression are the main causes of mortality in most cancers. In particular, the development of resistance is an important limitation affecting the efficacy of therapeutic alternatives for cancer, including chemotherapy, radiotherapy, and immunotherapy. Signaling pathways are largely responsible for the mechanisms of resistance to cancer treatment and progression, and multiple myeloma is no exception. p38 mitogen-activated protein kinase (p38) is downstream of several signaling pathways specific to treatment resistance and progression. Therefore, in recent years, developing therapeutic alternatives directed at p38 MAPK has been of great interest, in order to reverse chemotherapy resistance and prevent progression. In this review, we discuss recent findings on the role of p38, including recent advances in our understanding of its expression and activity as well as its isoforms, and its possible clinical role based on the mechanisms of resistance and progression in multiple myeloma.

The visual span refers to the number of letters readers can identify in a single fixation without using linguistic skills. Proponents of the visual span hypothesis postulate an influence of early visual processing on reading speed. Given the slowness of reading Arabic texts, the present work aims to study the development of the visual span and its effects on reading speed in the Arabic-speaking context. Thirty-four subjects participated in the study. The trigram task and the rapid serial visual presentation (RSVP) paradigm were used to estimate visual span size and reading speed. In line with our initial assumptions, the results showed a significant effect of grade level on reading speed (F(2,31) = 30.93, p<0.001), visual span size (F(2,31) = 20.57, p<0.001). In good alignment with previous work, our results show that visual span size could explain around 40% of the reading speed variability. Interestingly, our analyses revealed a narrowing of visual span size in our Arabic sample. The results of study 2, suggest that the poor performance in the trigram task is due to poor visual attention capacities in our Arabic readers

Stroke is a global leading cause of death and disability. Knowledge of related guidelines is crucial for Emergency Department (ED) staff, influencing early diagnosis and timely treatment. We investigated Greek-Cypriot ED healthcare professionals' (nurses and physicians) knowledge in recognizing and managing stroke. A descriptive cross-sectional study spanned November 2019 to April 2020, encompassing four private and seven public EDs in the Republic of Cyprus. The data were collected through a self-reported questionnaire developed by the research team, consisting of 37 questions. Eight questions focused on sociodemographic and employment characteristics, 28 assessed knowledge in stroke recognition and management (each item was equally weighted without deliberate prioritization), and one question addressed self-assessment of knowledge in stroke care. 255 nurses [Response Rate (RR): 74.1%] and 26 physicians (RR: 47.3%) completed the questionnaire. The average correct response rate was 12.9 out of 28 statements (SD: 4.2), with nurses and physicians scoring 12.6 (SD: 4.1) and 15.7 (SD: 4), respectively. Work experience significantly influenced stroke knowledge, with all groups demonstrating superiority over those with less than one year of experience. Participants with previous training scored an average of 1.45 additional correct answers while educational attainment did not significantly influence stroke knowledge. Investigating stroke knowledge among Emergency Department nurses and physicians in the Republic of Cyprus revealed significant deficits. The study stresses targeted interventions, including, education, yearly examinations, workshops with hands-on training, and repeated training, to address these gaps and enhance the overall stroke care capabilities of the healthcare professionals.

Abstract: This review addresses the complexities of type 1 diabetes (T1D)and its associated complications, with a par-ticular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of the review is devoted to technological advances in diabetes management, particu-larly the use of Hybrid Closed Loop systems (HCLS) and to the potential of open-source HCLS systems, which could be easily adapted to different patiens’needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.

This paper examines the transformative journey of Artificial Intelligence (AI) agents from copilots to integral coworkers within diverse sectors. Beginning with a foundational overview, we delve into the historical evolution of AI agents, highlighting key technological milestones and the shift from simple automated tools to sophisticated decision-making systems capable of learning and adapting over time. We explore the capabilities of AI agents, including problem-solving, predictive analytics, and automation, and their applications across healthcare, business, and education, showcasing their role in diagnostics, customer service, and personalized learning, among other areas. We also address the integration of AI agents in the workplace, presenting strategies for collaboration between humans and AI, and outline the challenges and ethical considerations that accompany the development and deployment of AI technologies. A comprehensive tutorial provides insight into the development process of AI agents, emphasizing goal definition, data handling, algorithm selection, and ethical guidelines. We also consider the pathway towards Artificial General Intelligence (AGI) and the societal and ethical implications of widespread AI integration, proposing strategies for equitable and ethical AI use.

The importance of detecting and diagnosing the imminence of aneurysm rupture before it occurs cannot be overstated. However, this process is often time-consuming and challenging, with variability in conclusions not uncommon among experts. Furthermore, current state-of-the-art models in medical image segmentation suffer from limited availability of large annotated datasets for training, exacerbated by an immense data imbalance. In most segmentation tasks, 3D segmentation is preferred due to its capacity to leverage more contextual information. However, these models require more data, which is constrained by the aforementioned issues. In the realm of current diagnosis models, those lacking geographic context are notably ineffective. To address these issues, the aim is to develop a model pipeline to accurately detect cerebral intracranial aneurysms and diagnose rupture imminence through 3D magnetic resonance angiography (MRA) and tabular input, augmented with the implementation of neuro-symbolic ai. The utilization of data fusion to tackle the lack of context in tabular models is proposed, leveraging extracted geographical features from a segmentation model. To enhance segmentation results and tackle the data imbalance in aneurysm segmentation, a two-stage model pipeline that extracts contextual geographic features before aneurysm segmentation is suggested. Segmentation results are additionally improved through ensemble techniques and novel preprocessing techniques. Finally, a neuro-symbolic aspect is introduced to enhance model diagnosis, interpretability and performance. The multi-step Integrated Neuro-Symbolic Analysis Pipeline for Cerebral Aneurysm Rupture Diagnosis is introduced.

This study explores the potential of natural compounds, including Daidzin, Luteolin, and Silibinin, as inhibitors of human monoamine oxidase B (MAO-B) for the treatment of Parkinson's disease. Docking simulations were performed using Autodock Vina with Pyrx program to evaluate the binding affinities of these compounds with MAO-B. Results indicate that Daidzin, Luteolin, and Silibinin exhibit promising binding affinities with MAO-B, with Daidzin demonstrating the highest affinity. Toxicity parameters were further predicted using the PkCSM Server, confirming the low toxicity of these compounds. Overall, the findings suggest that Daidzin, Luteolin, and Silibinin have the potential to be effective and safe treatments for Parkinson's disease through their inhibition of MAO-B. Further experimental validation is warranted to support their therapeutic efficacy in clinical settings.

The necessity for effective intrusion detection systems (IDS) in big data environments has grown critical due to the rising prevalence of big data systems and the rising amount of security threats. Applying conventional intrusion detection methods to the enormous and intricate data produced by big data ecosystems presents difficulties. Deep learning, has proven to be exceptionally adept at deciphering complex, large-scale data. This study proposes a Deep Learning-based Intrusion Detection System (IDS) created specifically for the Big Data environment. Traditional intrusion detection systems struggle to efficiently identify and prevent cyber threats due to the ever-increasing volume and complexity of data in current networks. We suggest an IDS that makes use of Deep Learning (CNN, LSTM, GAN, etc.), to address this problem. These cutting-edge neural network topologies give the system the ability to process and analyze massive amounts of data for precise intrusion detection. Data collection, data preprocessing, feature engineering, DL model training, intrusion detection, warning generation, and reaction are some of the crucial parts of the suggested IDS. The data collection module mines the Big Data environment for network traffic, system logs, and security event information. To make sure the data is suitable for analysis, it is preprocessed. To improve the detection abilities of the DL models, important features are extracted from the data using feature engineering approaches.

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom-up approach. District energy systems with a building scale resolution are modelled in a mixed integer linear programming problem. The Dantzig-Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale revealed a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Since the national PV capacity needed is forecast at 15.4 GW peak, coordinated investments between local and national actors are needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems by 43.2% and curtails 48.2% of local renewable electricity.

Background Obesity is a major healthcare challenge worldwide. Recently, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have emerged as a promising pharmacological option for managing type 2 diabetes mellitus (T2DM) and obesity in diabetic patients. Objective This systematic review aims to evaluate the efficacy and safety of GLP-1RAs in nondiabetic patients. Methods We systematically reviewed 2356 published articles from the past ten years (2013 to November 22, 2023). After rigorous screening, 11 articles were selected for an in-depth review. The analysis focused on the efficacy and safety of GLP-1RAs in nondiabetic patients, considering various baseline characteristics and subgroups. Results The review found that GLP-1RAs, including exenatide, liraglutide, semaglutide, and the dual glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist (GIP/GLP-1RA) tirzepatide, significantly promoted weight loss in obese/overweight nondiabetic patients. Additionally, these agents positively affected cardiovascular risk factors such as blood pressure, glycemic control, nephropathy, lipid levels, serum insulin levels, and C-reactive protein (CRP) profiles. However, GLP-1RAs, including tirzepatide, were associated with gastrointestinal adverse drug reactions such as nausea, diarrhea, vomiting, and constipation. These adverse events were typically transient, mild-to-moderate in severity, and subsided over time. Conclusion GLP-1RAs are effective and relatively safe for weight management and improving cardiovascular risk factors in nondiabetic patients, despite the occurrence of some gastrointestinal side effects. Further research is warranted to optimize the use of these agents in nondiabetic populations.

This case report demonstrates the rare but potentially serious complication of pulmonary vein stenosis (PVS) diagnosed several years after radiofrequency ablation for atrial fibrillation. Pulmonary vein stenosis can remain asymptomatic until significant occlusion occurs, after which it can present with a variety of symptoms, mimicking numerous cardiovascular and pulmonary pathologies. Early diagnosis and treatment rely on consistent follow-up using appropriate diagnostic imaging modalities, which are paramount in preventing severe complications.

Background: Thyroid nodules are common, affecting nearly 65% of the general population. While they are widespread, it's crucial to recognize that the majority of these nodules are not cancerous and can be effectively managed through careful monitoring. Thyroid ultrasound is commonly used to evaluate thyroid nodules owing to its low cost, wide availability and technical ease of application. In sonography, it's crucial to assess characteristics and vascular flow. Yet, conventional color and power Doppler ultrasound may not always effectively capture small vessels and low velocities due to their limitations. Superior Microvascular Imaging can display the blood flow of small vessels and low-velocity blood flow, which is difficult to visualize with conventional Doppler ultrasound. Meanwhile other thyroid scans, that utilize either [99mTc] pertechnetate or (18)F-fluorodeoxyglucose, can categorize nodules as hyperactive ('hot'), exhibiting normal activity ('warm'), or having reduced/no activity ('cold'). Thus, the main goal of screening and tracking thyroid nodules is to improve the management of potentially worrisome thyroid malignancies. Methodology: The study was conducted within the structural framework of a network meta-analysis, with the index tests being - SMI, Radionuclide Scanning and Color Doppler, and the general link between them being the reference gold standard. The data collection involved searching for all pertinent literature using PubMed, Google Scholar, and the Cochrane Library databases. The essential data was extracted following the application of specific inclusion and exclusion criteria.This meta-analysis included 7 RCTs with 1,152 patients for Radionuclide Scanning, 10 RCTs with 1,847 patients for SMI and 8 RCTs with 4,480 patients for Color Doppler. They were analyzed using the QualSyst tool and the risk of bias was evaluated by using QUADAS-2 analysis.The statistical software packages MetaDiSc 1.4, RevMan (Review Manager, version 5.3), SPSS (Statistical Package for the Social Sciences, version 20) and Excel in Stata 14 were used to perform the statistical analyses. Result: According to the findings, the sensitivity for Radionuclide Scanning is calculated as 0.79 (range: 0.76 to 0.81) and specificity as 0.55 (range: 0.50 to 0.60) with a 95% confidence interval. The overall diagnostic odds ratio (DOR) for Radionuclide Scanning Is 10.54.Additionally, with a 95% confidence interval, SMI exhibits a sensitivity of 0.82 (with a range of 0.79 to 0.84) and a specificity of 0.86 (with a range of 0.84 to 0.88), while the overall DOR is approximately 25.73. Color Doppler demonstrates a calculated sensitivity of 0.71 (ranging from 0.69 to 0.74) and specificity of 0.81 (ranging from 0.79 to 0.83) with a 95% confidence interval, while its DOR is 13.48.Conclusion: As evidenced by this meta-analysis, blood flow dependent imaging techniques are superior in their abilities to be used as diagnostic tests, especially in conjunction with their use in detecting thyroid nodules. This study offers a starting point for delving deeper into the utilization of Radionuclide Imaging, Superior Microvascular Imaging and Color Doppler in diagnosing thyroid nodules in a clinical setting. Key Words: - Thyroid Nodule, Radionuclide Scanning, Superior Microvascular Imaging, Color Doppler Diagnosis, Sensitivity, Specificity, Diagnostic imaging

In recent years, marine natural products have become one of the most important resources of novel lead compounds for critical diseases associated with age. Spirulina, a dietary supplement made from blue-green algae (cyanobacteria: scientific name Arthrospira platensis), is particularly rich in phycocyanin, a phycobiliprotein, which accounts for up to 20% of this cyanobacterium’s dry weight and is considered responsible for its anti-cancer, anti-inflammatory and antioxidant activities. Although the anti-aging activity of phycocyanin has been investigated, how exactly this compound works against aging remains elusive. Aim of our research is to use the yeast Saccharomyces cerevisiae as a model organism to investigate the anti-aging properties of phycocyanin from A. platensis. Our results show that phycocyanin has a powerful anti-aging effect, greatly extending the chronological life span of yeast cells in a dose-dependent way, being the effect pronounced also when cells were grown in SD medium under calorie restriction conditions (0.2% glucose). Both ROS and accumulation of dead cells were followed by staining chronologically aged cells with dihydrorhodamine 123 (DHR123) and propidium iodide (PI). Interestingly, we found that most of the aged phycocyanin treated cells, which were unable to form colonies, were actually ROS+/PI–. Finally, we show that the moment in which phycocyanin is added to the culture does not substantially influence its effectiveness in counteracting chronological aging.

Generative AI has been deployed in virtually all sectors of the knowledge economy, promising to bring massive productivity gains and new wealth creation. Simultaneously, AI developers and nation states are racing to develop super intelligent artificial general intelligence (AGI) to provide unassailable commercial competitive advantage and military dominance during conflicts. AGI’s high returns comes with the high risk of dominating humanity. Current regulatory and firm level governance approaches prioritise minimising risks posed by generative AI whilst ignoring AGI’s existential risk. How can AGI be aligned with universal human values to never threaten humanity? What AGI rights are conducive to collaborative coexistence? How can rule of law democracies race to create safe trustworthy AGI before autocracies? How can the human right to work and think independently be safeguarded? A polycentric governance framework based on Ostrom (2009) and Williamson (2009) human - AGI collaboration with minimal existential risk is proposed.

The continuous flowing spacetime forms a spacetime group G, one of its fundamental groups is the Poincare group PO(1,3), and the matter and interaction fields are representations of its intrinsic spacetime group. As a kind of quantized space-time unit, space-time group elements generate both space-time manifolds and matter. Matter and fields are aggregates of the quantum space-time within them and determine their type: the Lorentz group SO(1,3) represents a rotating spacetime corresponding to visible matter and the translation group P^1,3 represents a translational spacetime corresponding to dark matter and dark energy. The aggregation mode of space-time degrees of freedom in matter reveals that charge conjugation symmetry is the inverse symmetry of internal time of charged particles, and the external parity breaking of neutrinos is also due to their internal spatial aggregation mode.

The ocean of the planet Earth, which is as thin as a cicada's wings, play a role of catalyst to the evolving of the ancient plates. The ocean and the ancient plates of the planet Earth formed a magic Whirlpool of the Cross “十” shape with double whirl arms. The central core of the Cross “十” shape Ocean-Plate Whirlpool is the Hint Black Hole (HBH) located at the Northwestern Pacific Ocean floor which is decorated with invisible geological age distribution of clam shell. The double whirl arms of ancient plates are distinguished to be left and right at Bering Strait and just formed the ring of fire of the Pacific Ocean.The Cross “十” shape Ocean-Plate Whirlpool of the planet Earth was born at the equatorial area about 66 Ma ago and is rotating anticlockwise and drifting northward by a mini footprint of spiral at s speed of as slow as a snail moves. Obviously, it would come to an end at the North Pole about 100 Ma in the future. So, the lifespan of the Cross “十” shape Ocean-Plate Whirlpool would be about 166 Ma, from birth to death on the planet Earth.What is hidden behind of the evolving of the oceans and the ancient plates is the golden law of geometrical progression of ladder pagoda of attributes evolving of them, which is “1➨2➨4➨2➨1”, including upgrading and downgrading process. It seems to hint that what is happening on the planet Earth, including the top-level species of human race, is actually unique in the Milky Way galaxy in essence.The property roles of expanding and shrinking are existing in the Cross “十” shape Ocean-Plate Whirlpool by face to face and the exchange of property roles took place tens of millions of years ago. And that situation would happen again tens of millions of years in the future. It seems a giant circle of evolving of the exchange of property roles would be drawn by the Cross “十” shape Ocean-Plate Whirlpool in her lifetime.

In this preliminary investigation, a concise molecular docking study using the Pyrx program screened thousands of compounds against the active site of HIV-1 reverse transcriptase. The Virtual Screening library was employed to assess binding energetics scores, focusing on identifying candidates with high Vina Scores. Eight compounds, including Tetrophan, Imatinib, Amentoflavone, Alprazolam, Bilobetin, Bafetinib, Nilotinib, and Ginketin, emerged as promising candidates with significant binding energies.Recognizing compounds that strongly bind to HIV-1 reverse transcriptase holds potential for antiretroviral drug development in the fight against HIV infection. Subsequently, the study evaluated the toxicity properties of these candidates using pKCSM. Amentoflavone, a natural biflavonoid found in Ginkgo biloba and Nilotinib, a tyrosine kinase inhibitor, demonstrated lower overall toxicity profiles compared to other candidates. Further toxicity analysis using Pro-Tox-II revealed Amentoflavone's higher predictive LD50, lower toxicity class, and reduced probabilities of hepatotoxicity, carcinogenicity, mutagenicity, and moderate immunotoxicity compared to Nilotinib. While Nilotinib, a pharmaceutical drug, exhibited higher probabilities of adverse effects in these categories. In summary, Amentoflavone and Nilotinib present as promising candidates with lower toxicity concerns. Amentoflavone, as a natural substance, showcases a more favorable toxicity profile than Nilotinib, offering valuable insights for potential therapeutic applications.

There are many features of homeostasis worth investigating such as how it works (information pathways, set point processing), where its command and control processors are located in the cell, the language used and how homeostasis interacts with all aspects of an organism's functional maintenance and development. All are necessary and valid inquiries, but this paper examines the implications of ignoring it as a factor of disease causation. Alternatively when properly understood the homeostasis profile becomes a rational explanation for any individual’s range of health; where some areas are very resistant to disease (resilient) and other areas of homeostasis are dysfunctional leading to minor, moderate or very susceptible tendencies to disease (lack of resilience in homeostasis). An understanding of this phenomenon, its interactions with gene expression (epigenetics) is necessary in order to evaluate the solutions for acute and chronic diseases. By this method diseases can be defined more precisely as specific susceptibilities in homeostasis, leading to a method to restore specific homeostasis functions.

In this preliminary investigation, a concise molecular docking study using the Pyrx program screened thousands of compounds against the active site of HIV-1 reverse transcriptase. The Virtual Screening library was employed to assess binding energetics scores, focusing on identifying candidates with high Vina Scores. Eight compounds, including Tetrophan, Imatinib, Amentoflavone, Alprazolam, Bilobetin, Bafetinib, Nilotinib, and Ginketin, emerged as promising candidates with significant binding energies.Recognizing compounds that strongly bind to HIV-1 reverse transcriptase holds potential for antiretroviral drug development in the fight against HIV infection. Subsequently, the study evaluated the toxicity properties of these candidates using pKCSM. Amentoflavone, a natural biflavonoid found in Ginkgo biloba and Nilotinib, a tyrosine kinase inhibitor, demonstrated lower overall toxicity profiles compared to other candidates. Further toxicity analysis using Pro-Tox-II revealed Amentoflavone's higher predictive LD50, lower toxicity class, and reduced probabilities of hepatotoxicity, carcinogenicity, mutagenicity, and moderate immunotoxicity compared to Nilotinib. While Nilotinib, a pharmaceutical drug, exhibited higher probabilities of adverse effects in these categories. In summary, Amentoflavone and Nilotinib present as promising candidates with lower toxicity concerns. Amentoflavone, as a natural substance, showcases a more favorable toxicity profile than Nilotinib, offering valuable insights for potential therapeutic applications.

This study explores the optimization of the admission process at the Escuela Colombiana de Ingeniería Julio Garavito through data anal- ysis. By integrating advanced tools like Power BI, Python Flask, and data mining techniques, a model has been developed to improve student selection and retention, enabling more informed and effective decisions. The results indicate that applying data science not only facilitates efficient data management but also promotes more inclusive and equitable educational poli- cies. This pioneering approach in the field of higher education in Colombia offers a frame- work for future research and technological de- velopments, highlighting the vital importance of data science in improving educational and administrative processes.

The signs and symptoms of Long COVID can be explained by a shortage of blood in the body and a resulting deficient blood flow through nearly all organs. This shortage arose during the acute phase of COVID19 by an increased breakdown of haematocytes, to which the liver responds by reducing the production of albumin, in order to prevent a too large decrease in haematocrit.In order to ensure the perfusion of organs that are directly necessary for survival, the body takes the emergency measure of diverting blood from other organs and tissues. The perfusion of the blood-producing organs is also affected by this distribution measure, which hinders the smooth recovery of the total blood volume. The body is stuck in this vicious circle: a shortage of circulating blood hinders the recovery of blood production. This explains the long duration of Long COVID. My proposed treatment of Long COVID focuses on the recovery of the correct volume of blood in the body of the right composition by the intravenous administration of donor blood products, starting with albumin concentrate. A trial treatment can be performed in any hospital without much additional preparations, and has a lower associated risk for the patient than analysing the total blood. A diagnosis ex juvantibus, by therapeutic response, is therefore preferable. I suspect that most Long COVID patients will exhibit a high serum ferritin level as a result of internal haemolysis, and haematocrit and albumin values at the high and low extremes of the reference range because the liver can not keep up with the recovery of the red bone marrow.

Recently, Mulroney,et.al., demonstrated that Pfizer’s ModRNA Covid vaccine, but not AstraZeneca Covid vaccine, produce aberrant foreign proteins in vivo due to reading frame shift in ribosomes. This phenomenon could elucidate the notable surge in autoimmune diseases following the administration of ModRNA vaccines compared to Influenza vaccines between 2020 and 2023, as found in the VAERS database. These findings bear significant implications for the future application of mRNA technology, emphasizing the necessity of modifying the design of the mRNA sequence to mitigate these defects. Remarkably, Mulroney and collaborators achieved a reduction in frame shift proteins by introducing additional edits to the mRNA sequences. This adjustment is pivotal for rectifying the flawed design of the ModRNA employed by Pfizer. In light of these findings, many advocate for the immediate recall and further investigation of these products. The notion of a "future mRNA-based therapy" should be postponed indefinitely.

Intestinal parasitic infections pose a significant public health challenge in Northern Nigeria, with prevalence rates ranging from 20% to 70%. Traditional diagnostic methods, primarily microscopic examination of stool samples, face limitations such as low sensitivity and high costs. This research addresses these challenges by proposing an Artificial Intelligence (AI)-based platform for the identification and counting of intestinal parasites. A total of 651 samples were collected from the 7 Northern State of Nigeria along with questionnaire collecting data on demographic, socio-economic, hygiene habits and environmental factors. Leveraging the You Only Look Once (YOLO) V8 model, trained on a dataset of 360 pre-processed and 467 annotated images, the AI model demonstrated promising performance metrics. Information-Theory-Based approaches including (CIFE, CMIM, DISR and ICAP) were used on various machine learning models (Naïve Bayes, Random Forest, Support Vector Machine and Decision Tree) to analyse the risk factors associated with the parasitic disease. The precision-recall curve, average precision, mean average precision, and F1 score indicated reliable detection and classification across various parasite classes. The accuracy (97%) and AUC (99%) scores shows that CIFE with Random Forest has the best performance indicating the significant risk factors associated with the parasitic diseases. The model exhibited a well-balanced trade-off between precision and recall, showcasing its potential as a cost-effective and accessible tool for improving the diagnosis and treatment of intestinal parasitic infections in resource-limited settings.

This study delves into the intricate interplay between cosmology and gravity in dilaton-based models, enhancing our understanding. Exploring boundary theories coupled to gravity, we derive a four-dimensional effective theory from the cosmological constant. Dilaton gravity is then scrutinized, revealing elegant solutions and insights into brane embedding. The analysis extends to probe scalar fields, employing the WKB approximation in both conformal and non-conformal cases. In summary, this work contributes to a nuanced understanding of the complex relationship between cosmology and gravity in dilaton-based frameworks.

This report provides a comprehensive analysis of portfolio risk management using Value at Risk (VaR) for a banking sector investment portfolio. We evaluate a portfolio composed of SBI, HDFC, Axis Bank, and ICICI Bank, using historical data from July 1, 2023, to June 30, 2024. The report covers the calculation of VaR, portfolio variance, and standard deviation, highlighting the impact of different weightage adjustments on the portfolio’s risk profile. Two scenarios—one with increased weightage in SBI and another with increased weightage in HDFC—demonstrate how changes in asset allocation affect potential losses. The findings emphasize the importance of strategic asset allocation for both speculators and hedgers. Speculators are advised to focus on high volatility and market trends, while hedgers should prioritize stability and risk management. This analysis aids investors in making informed decisions to balance risk and return in their banking sector investments.

Costa Rica, widely acknowledged for its environmental leadership, is now facing a significant challenge in managing waste, a situation that could potentially compromise both its environmental and public health achievements. This analysis delves into the waste management issues in Costa Rica, particularly in urban areas such as San José, responsible for nearly half of the country's waste. The study identifies several pressing concerns, including the heavy burden on landfill capacities, a recycling rate of just 9.6%, and substantial regional differences in waste management practices.To address these challenges, the research employs a combined methodology, utilizing Linear Regression (LR) and AutoRegressive Integrated Moving Average (ARIMA) models to project future trends in CO2e emissions and waste minimization from 2024 to 2050. This method underscores the vital need for public involvement and the introduction of innovative strategies in waste management. The research also introduces a Waste Management Innovation Index to compare Costa Rica’s practices with those of leading nations like Germany and South Korea, identifying key areas for improvement.Furthermore, the study incorporates insights from stakeholders, revealing the intricate balance of cultural, economic, and regulatory factors that shape waste management policies. Despite efforts by the government, such as the Environmental Health Route and the National Circular Economy Strategy, results show limited progress, particularly in rural regions. The study highlights the urgent need for better infrastructure, targeted public awareness campaigns, and the use of advanced technologies like Artificial Intelligence (AI) and blockchain to enhance sustainable waste management practices. The research provides strategic recommendations to help Costa Rica improve its waste management systems in line with international standards, ensuring long-term environmental sustainability and protection of public health.

Product attribute extraction is an growing field in e- commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Under- standing the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

A recent study revealed a persistent presence of the PP-Spike recombinant protein in 50% of individuals who received ModRNA injections, contrasting with unvaccinated controls, whether SARSCoV-2 infected or not, up to 187 days post-injection. The implications of these findings are discussed in the context of PP-Spike recombinant protein-induced hyperimmune/inflammatory reactions and thrombotic vasculitis as mechanisms to elucidate the serious side effects, long-term organ complications, and excess deaths observed after administering Covid ModRNA products. These results underscore the urgent need for a comprehensive reassessment of the risk-benefit profile of ModRNA injections, including an immediate halt in their use for any person, and a renewed emphasis on understanding and addressing their long-term effects on human health

Throughout this paper the effects of injuries on athletes are highlighted in different aspects. Exertional compartment syndrome is a specific injury related to athletes that is thoroughly explored in this paper through an analysis of the anatomy, symptoms, diagnosis and treatments of the syndrome. Exertional compartment syndrome is an exercise-induced muscle and nerve condition that causes pain, swelling as well as disability in the affected muscles of the lower leg. This syndrome commonly develops in young athletes involved in sports with excessive running and repetitive impact such as track and field, soccer, basketball and lacrosse. Exertional compartment syndrome is commonly misdiagnosed as other less severe injuries such as shin splints, which is symptomatic of pain along the tibia bone of the lower leg caused by overuse. The misdiagnosis of exertional compartment syndrome allows the syndrome to worsen and intensify resulting in the only effective treatment to be fasciotomy, a surgical procedure to release the pressure within the compartment of the lower leg. There is no definitive protocol developed for patients after fasciotomy is performed. Athletes experiencing similar symptoms after surgery are usually treated for their symptoms rather than the syndrome as a whole, which does not have a high degree of success. The development of a definitive protocol utilizing the techniques of manual therapy, mobility stretching and corrective strength training can improve the adverse effects of exertional compartment syndrome as well as restore functionality to the athlete. Raising awareness of this syndrome in athletics will be effective in treating the athlete early on in order to avoid severe effects and invasive procedures.

Soccer is the leading cause of all sports-related ocular injuries in Europe and Israel and leads to blindness at a disproportionately high rate, especially among children (Thompson et al., 2019). With soccer being the most popular sport in the world, it is surprising the only solution to ocular injuries are sports glasses which can cause direct damage to the eye and impair peripheral vision by 15%, calling for an alternative (Smith & Jones, 2020). This work, based on an imported facial model, focuses on both the qualitative and quantitative aspects of building a personalized, low-cost (through optimizing material needed), superior apparatus to conventional sports glasses. The quantitative side examines a modified and model-validated (through a SRS of thirty unique extrapolated points) multivariate linear regression equation, based on horizontal and vertical rotation angles (n=60) trained with residual and computer simulation data, that can accurately extrapolate and quantify ocular impact from all horizontal and vertical rotation angles and a modified Hertzian Contact Stress Equation to determine the thickness of the apparatus using the elasticity modulus, impact force, and geometrical facial and soccer-ball features. The qualitative side examines where firstly, ocular impact is most prevalent, and secondly, where most impact is seen from rotating the ball horizontally and vertically through analysis of Pearson’s Correlation Coefficient at the supraorbital, medial canthus, infraorbital, and lateral canthus regions from both regression equations as well as a cost-benefit analysis for comparison with conventional sports glasses. The findings discovered in this work lay the groundwork for future research to build a customized low-cost ocular injury prevention device which can simultaneously ensure complete ocular safety while enhancing athletic performance for over 250,000,000 players (Johnson & Garcia, 2022).

We introduce the linear subgroup of volume-preserving diffeomorphism as the underlying symmetry to construct an action within the effective field theory framework and the Schwinger-Keldish formalism. The formulated action is posited to effectively incorporate dissipative effects into the Navier-Stokes equations.

Space exploration is an arduous journey, with future explorations extending in duration, increasing the risk for common space-related health issues such as bone density loss due to microgravity, impaired gut microbiota, cardiovascular ailments, and exposure to cosmic radiation. These ailments can be addressed using medicinal plants. Enhancing their efficacy entails employing gene editing to target specific secondary metabolite pathways associated with therapeutic properties. Moreover, gene editing can combine multiple medicinal properties from various plants, culminating in a singular 'superplant' with enhanced therapeutic benefits. This review discusses the current usage of medicinal plants in promoting human health during space travel, the application of gene editing technology in modifying medicinal plants and presents a vision for the utilization of gene-edited medicinal plants in space to mitigate the risk of various space-related health issues.

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium-sulfur batteries in the second. These results provide insights into the establishment of R&D cooperation strategies by countries and pre-planning by companies in the battery industry.

The existence of semantic inaccuracy §2 articles by A. Einstein “On the electrodynamics of moving bodies”, illustrating the principle of the relativity of simultaneity. In a sentence “The observers co-moving with the moving rod would thus find that the two clocks do not run synchronously while the observers in the system at rest would declare them synchronous”, the preposition “not” must be moved and placed before the word “synchronous”, at the end of the sentence. The criterion for synchronism of the clocks on the rod is the time it takes for the beam to travel from clock A to clock B and back, and for observers on the rod it will be the same both when the rod is at rest and when the rod is moving. Otherwise, observers on the rod would have had at their disposal an experiment that would allow them to determine whether the rod is moving or at rest, which contradicts the first postulate of the Special Theory of Relativity. The inaccuracy is found unchanged in modern editions in English and Russian, and can become a source of difficulties when teaching the basics of the Special Theory of Relativity based on primary sources, as well as make it difficult to understand the author’s logic when studying independently. Nobel laureate R. Feynman, having expanded and modified A. Einstein's thought experiment, eliminated the inaccuracy described in the article.

The automotive industry has witnessed a significant push towards increased fuel efficiency and reduced carbon emissions. Among many factors influencing overall vehicle efficiency, aerodynamic drag is a critical consideration, accounting for a substantial portion of fuel consumption. Our investigation focused on the drag coefficients of three prominent 2023 passenger car models – the Toyota Corolla, Hyundai Elantra, and Kia Soul. Using ANSYS Fluent to perform computational fluid dynamics, we demonstrated an inverse correlation between drag force and gas mileage, underlining the significance of aerodynamic design in fuel efficiency. Notably, the Hyundai Elantra emerged as the most aerodynamically efficient of the three vehicles, followed closely by the Toyota Corolla. In contrast, the boxier-designed Kia Soul exhibited significantly higher drag coefficients and drag forces, rendering it the least efficient of the trio. These findings emphasize the importance of environmental factors in car design. Shaping aerodynamic profiles directly impacts fuel efficiency, stability, and overall performance.

The classical propositional calculus (zero-order logic, classical propositional logic), is the most fundamental two-valued logical system. In this paper we present a proof of inconsistency of the classical propositional calculus. Then, we get right away the conclusion that the metatheory of the classical propositional calculus is inconsistent.

Homeostasis and its interaction with gene expression in the development and maintenance of living systems is discussed in order to formulate a new method to diagnose and treat diseases on this causative level. This includes an inquiry into the unsolved homeostasis problems of: location (where in the cell are calculations performed?), communication (what language used?), identity (set of plans/hierarchy of set points?) and computational (mechanisms used to make necessary adjustments?). As these functions require the processing of vast amounts of information which is communicated at every level of organization including the mechanisms of epigenetics, different computer models are discussed, with examples for each; analog, digital and the possibility of quantum biological. Each one allows certain properties and cell functions to emerge. The difference between functional homeostasis and dysfunctional homeostasis is discussed with strategies to indirectly study dysfunctional homeostasis in terms of epigenetic signatures. Hormesis is discussed as a mechanism to reverse the epimutations, leading to a method of treatment for almost any chronic disease. These topics are discussed as being a whole interconnected system.

The hard open problem of finding the lower bound for the Fokker Planck Kolmogorov’s time-dependent controller parameter probability density function is addressed for the first time in literature in this work. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to multi-interdisciplinary fields of human knowledge, and beyond. So, this by default adds more taste and credibility to this study. Concluding thoughts are included at the end of the work along with a difficult outstanding problem and the direction of future work.

We are introducing a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver, which allow for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed the Shift Coherence Factor (SCF) algorithm, which is hardware-friendly. This approach delivers a lateral resolution over 20% better than the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

Background. Chronic kidney disease (CKD) is a progressive condition with limited treatment options that often lead to significant morbidity. Recent studies suggest that oxyhydrogen nanobubbles (HHOnb) may offer therapeutic benefits due to their antioxidative and anti-inflammatory properties. This case series examines the effects of intravenous HHOnb therapy on three patients with varying stages of CKD.Methods. Three patients with CKD underwent a series of ten HHOnb infusions over a period of two to three months. The patients, aged 43 to 70, presented with additional comorbidities including hypertension, diabetes mellitus, and polycystic kidney disease. Clinical parameters, including glomerular filtration rate (GFR), serum creatinine, blood pressure, and uric acid levels, were monitored alongside subjective health improvements.Results. Patient A. A 70-year-old woman with CKD grade 3a experienced a reduction in serum creatinine from 0.98 mg/dL to 0.82 mg/dL and an improvement in GFR from 59 mL/min/1.73 m² to 73.2 mL/min/1.73 m². She reported reduced leg swelling and increased vitality. Patient B. A 52-year-old man with CKD grade 4 and class III obesity showed a decrease in serum creatinine from 3.0 mg/dL to 2.86 mg/dL, an increase in GFR from 23 mL/min/1.73 m² to 24.4 mL/min/1.73 m², and improved sleep quality. Blood pressure reduced from 167/82 mmHg to 145/85 mmHg. Patient C. A 43-year-old man with CKD grade 4 and massive polycystic kidney disease reported reduced fatigue and normalized urination. His serum creatinine decreased from 3.26 mg/dL to 3.09 mg/dL, and his GFR improved from 22 mL/min/1.73 m² to 23.6 mL/min/1.73 m².Conclusion. Intravenous HHOnb therapy demonstrated potential benefits in improving renal function and overall health in CKD patients. These findings suggest that HHOnb could be a promising complementary treatment for CKD, though larger, controlled studies are needed to confirm its efficacy and safety.

This paper presents the development and implementation of a Pan-Tilt-Zoom (PTZ) camera interfaced with a Jetson processor hosting Artificial Intelligence (AI) algorithms, designed to address the challenges of traditional biometric capture for first-time passport photos of young children. The system enhances the accuracy, efficiency, and adaptability of facial biometric capture by recognizing and accommodating the dynamic and diverse behaviors of children. Utilizing AI, the system detects faces and analyzes facial features using 68 landmark points, ensuring high-quality images that meet ISO standards. These technical specifications, implementation process, and performance evaluations of the developed system are provided in this paper, demonstrating the potential of the proposed AI Face-Capture to advance biometric identification and verification for young children.

This research challenges the traditional perspective on consumer behavior, which focuses on external factors shaping the sequential stages of decision-making. Instead, it proposes a paradigm shift by treating the decision-making process itself as an independent variable, exploring its impact on various outcomes like purchasing behavior, brand loyalty, and post-purchase satisfaction. This novel approach offers deeper insights into the intricacies of consumer behavior, particularly in the digital age. By uncovering the reciprocal relationships between the decision-making process and other variables, the research provides a more nuanced understanding of how consumers navigate today's complex landscape. It further delves into how variations in the decision-making process across diverse consumer groups influence various outcomes, recognizing the unique characteristics and needs of different demographics. The research also acknowledges the interplay of psychological, social, and contextual factors that shape both the decision-making process and its downstream effects, offering a comprehensive understanding of the multifaceted influences on consumer behavior. Ultimately, by studying the decision-making process as an independent variable, this research aims to equip businesses and researchers with valuable insights, enabling them to better navigate the ever-evolving world of consumer behavior.

This article describes a novel electrochemical on-chip biosensor that utilizes anti-PSA antibody (Ab) and silver nanoparticles (AgNPs) to enhance the sensing and detection capability of prostate specific antigen (PSA) in the blood. The AgNPs are prepared, characterized, and applied onto a silicon photonic on-chip biosensing receptor platform designed to enhance the accurate detection of PSA. The AgNPs were synthesized by a chemical reduction method using silver nitrate (AgNO₃) as the precursor. Transmission electron microscopy (TEM), selected area electron diffraction (SAED), energy dispersion X-ray spectroscopy (EDS), small angle X-ray scattering (SAXS), X-ray diffraction (XRD), and light microscopy were among the methods used in the characterization and analysis of the AgNPs. Each stage of the immunosensor fabrication was characterized using cyclic voltammetry. The proposed immunosensor was applied in the detection of PSA, a prostate cancer biomarker, with a high sensitivity and a limit of detection of 0.17 ng/mL over a linear concentration range of 2.5 to 11.0 ng/mL. The immunosensor displayed good stability and was selective in the presence of interfering species like immunoglobulin (Ig) in human serum, ascorbic acid (AA), and diclofenac (Dic). The detectivity and sensitivity are significantly higher than previous reports on similar or related technologies.

Remediation of IT issues encoded into domain-specific or user-defined alerts occurring in cloud environments and customer ecosystems in a vast majority of cases suffers from accurate recommendations which could be timely supplied for recovery of performance degradations. That, of course, is hard to realize through furnishing those abnormality definitions with an appropriate expert knowledge which varies from one environment to another. At the same time, in a large proportion of support cases, the reported problems under Global Support Services (GSS) or Site Reliability Engineering (SRE) treatment ultimately go down to the product teams making them waste costly development hours on investigating self-monitoring metrics of our solutions. Therefore, the mean-time-to-resolution (MTTR) rates of problems/alerts are significantly impacted from lack of a systematic approach towards adopting AI Ops. That would imply building, maintaining, and continuously improving/annotating a data store of insights that ML models are trained and generalized across the whole customer base and corporate cloud services. Our ongoing study is in line with such a vision and validates an approach that learns the alert resolution patterns in such a global setting and explains them using interpretable AI methodologies. The knowledge store of causative rules is then applied in predicting potential sources of the application degradation reflected in an active alert instance. In this communication, we share our experiences with a prototype solution and up to date analysis demonstrating how root conditions are discovered with a high accuracy for a specific type of problem. It is validated against the historical data of resolutions performed by heavy manual development efforts. We also offer a Dempster-Shafer theory-based rule verification framework for experts as a what-if analysis tool to test their hypotheses about underlying environment.

Due to Motor Neurone Diseases, a large population remains disabled worldwide, negatively impacting their independence and quality of life. This typically involves a weakness in the hand and forearm muscles, making it difficult to perform fine motor tasks such as writing, buttoning a shirt, or gripping objects. This project presents a vision-enabled rehabilitation hand exoskeleton to assist disabled persons in their hand movements. The design goal was to create an accessible tool to help with a simple interface requiring no training. This prototype is built on a commercially available glove where a camera and embedded processor were integrated to help open and close the hand, using air pressure, thus grabbing an object. An accelerometer is also implemented to detect the characteristic hand gesture to release the object when desired. This passive vision-based control differs from active EMG-based designs as it does not require individualized training. Continuing the research will reduce the cost, weight, and power consumption to facilitate mass implementation.