This article examines the biological characteristics and potential medicinal uses of the endemic plant species Scorzonera uzbekistanica, found only in Uzbekistan. The plant contains significant amounts of bio active compounds such as inulin, flavonoid, and oxyacetylene in its roots. These compounds are known for their antioxidant, anti-inflammatory, and prediction properties. Traditionally used to alleviate digestive issues, this plant's potential in modern medicine should be further supported by scientific research. The study calls upon the scientific community to explore the potential applications of this endemic plant.

Sustainability is a concept which engages the weighted balance between economic, environmental and social domains for all life forms and man. Within the frame of sustainability, it is important to realize that coexistence is part of a single life equation. Damage to the environment, social spectrum, and economic sectors (through deviant practices) reap havoc upon all life forms. Sustainability insures the existence of all within the single equation of life. Unsustainable, colonial practices prevail upon the present and the future of life.Sustainability should advocate for healthy environs for all life forms (including, but not exclusive to humans) to sustain themselves. The neglect and/or omission of concern for this mandate will lead to the deuteriation and eventual expiration of any such life forms. Life cannot be sustained in an imposed, artificial domain which is suited solely for mercantile purposes. History has taught us that the cost of dismissing the errors of the past and forging on results in error. This article proposes to explore the impact of the exploitation of agricultural practices, food availability, deforestation, dislocation, and "voluntary tourism" upon "Others" stemming from colonial occupation.

This study aims to analyze the effectiveness of implementing the Teaching Factory (TEFA) in improving the professional competence of teachers in State Vocational Schools (SMK) in Jakarta. Teaching Factory is an educational approach that integrates the learning environment with real work practice to bridge the gap between theory and practice. This study uses a quantitative method with surveys and interviews as data collection tools. The respondents are vocational school teachers involved in the Teaching Factory program. The results show that the implementation of Teaching Factory significantly improves teachers' abilities in both technical pedagogical aspects and soft skills, contributing positively to the development of their professional competence. The study concludes that Teaching Factory is an effective method for enhancing the quality of teaching and aligning teachers' skills with industry needs.

Blockchain-based games have introduced novel economic models that blend traditional gaming with decentralized ownership and financial incentives, leading to the rapid emergence of the GameFi sector. However, despite their innovative appeal, these games face significant challenges, particularly in terms of market stability, player retention, and the sustainability of token value. This paper explores the evolution of blockchain games and identifies key shortcomings in current tokenomics models using entropy increase theory. We propose two new models—ServerFi, which emphasizes Privatization through Asset Synthesis, and a model focused on Continuous Rewards for High-Retention Players. These models are formalized into mathematical frameworks and validated through group behavior simulation experiments. Our findings indicate that the ServerFi is particularly effective in maintaining player engagement and ensuring the long-term viability of the gaming ecosystem, offering a promising direction for future blockchain game development.

The Ali-Cesaro Stolz theorem, introduced in this paper, extends the classical Cesaro-Stolz theorem by addressing cases where the latter does not yield results or provides a "Does Not Existe (DNE) form. This novel theorem leverages the properties of limits and Z-transforms, offering a robust framework for analyzing sequences and series. The theorem is particularly effective in scenarios where traditional approaches fail, providing new insights and tools for mathematical analysis. This paper presents a detailed formulation of the Ali-Cesaro Stolz theorem, including proofs, applications, and a discussion on its implications and advantages over existing methods.

This study introduces a new approach to modeling galaxy rotation curves by integrating concepts from Perelman’s functional with modifications to General Relativity. While initial numerical simulations indicate that our P-E Integration model may offer a closer fit to observed data than existing theories, we recognize the preliminary nature of these findings [14]. The results, though promising, should be interpreted cautiously. Further validation across different galactic systems and refinement of the model are necessary to confirm its broader applicability [15]. This work aims to contribute to the understanding of galactic dynamics, with the hope of opening new avenues for future research.

This literature review presents a comparative analysis of cranial morphology in Allosaurus, focusing on its implications for feeding mechanics. Allosaurus, a large theropod dinosaur from the Late Jurassic period, exhibits distinct cranial features that are pivotal for understanding its predatory behaviors and ecological role. By examining fossilized skulls and employing advanced imaging techniques, this research identifies key morphological traits such as tooth structure, jaw articulation, and cranial robusticity. The analysis reveals that Allosaurus had a specialized cranial architecture that supported a powerful bite force and efficient processing of prey. Comparative metrics are applied to assess similarities and differences with other theropods, such as Tyrannosaurus rex, to elucidate variations in feeding strategies among theropods. The findings suggest that Allosaurus possessed adaptations that allowed it to exploit a diverse range of prey, indicating a versatile feeding strategy. These results enhance our understanding of theropod evolution and ecological interactions, providing insights into the functional significance of cranial morphology in dinosaurian predation. This review underscores the importance of cranial features in reconstructing the behavior and diet of extinct species and contributes to the broader field of vertebrate functional morphology.

While in the past, especially numerical approaches such as regression models and p-values were utilized to investigate whether the functional form between age and happiness is linear or U-shaped in a given country, recent advances have shown that such approaches can be misleading, and graphical analyses should amend the analyses. However, these have the downside that they rely to some extent on subjective interpretations and are hardly quantifiable. If applied carelessly, they can be misleading as well. We suggest two easily computed statistics to combine graphical and numerical approaches. We demonstrate their usage with ESS data (N > 440,000) and show how they enable a more nuanced investigation of functional forms. Furthermore, we discuss how statistical uncertainty can be handled in the age-happiness debate and emphasize practical significance, which needs to be kept in mind.

The inability to delineate a unified physical ontology that accounts simultaneously for the laws of special relativity and the results of quantum experiments has been a defining problem in physics for more than 100 years. This analysis addresses the problem by positing a novel mixed ontology composed of a “discrete” 4D spacetime and a physical, ultra-high dimensional (3 x N) “Planck Space.” Together, Planck Space and the three spatial dimensions of 4D spacetime form a tightly integrated ((3 x N) + 3) hyperspace (the “Dual Ontology”).Critically, the Dual Ontology’s structure replaces 1) the continuous, differentiable manifold of 4D spacetime with a discrete, background-independent 4D spacetime that is intrinsically curved and 2) mathematical spaces with a physical (3 x N) Planck Space. Moreover, the Dual Ontology is structurally and dynamically predicated on the one-to-one mapping and identity between the discrete spatial units that simultaneously form 4D spacetime and Planck Space. The one-to-one mapping and identity of the discrete spatial units support an integrated quantum dynamics based upon the dynamic evolution of single and N-body quantum states in 4D spacetime in full compliance with the laws of special relativity and the instantaneous collapse of all quantum states in an ontic Planck Space, where special and general relativity and more generally, 4D spacetime’s laws of physics, do not apply.

I present a personal account of self-organizing systems. As such, it is necessarily biased and partial. Nevertheless, it should be useful to motivate useful discussions. The relevant contribution is not my attempts at answering questions (maybe all my answers are wrong), but the steps towards framing relevant questions to better understand self-organization, information, complexity, and emergence. With this aim, I start with a notion and examples of self-organizing systems (what?), continue with their properties and related concepts (how?), and close with applications (why?).

Understanding creation as divine kenosis, which has become rather widespread since the last century, may emphasize the strong connection between the creation theology and the key Christian doctrine of the Incarnation while contributing to the dialogue between theology and science. At the same time, this theological project requires a thorough rethinking of divine action to avoid representing divine agency and natural causality as the competitive factors and to affirm divine kenosis as the definitive trait of God without compromising divine simplicity and freedom. To this end, the concept of “divine kenotic creativity” is suggested and discussed in this paper.

Gigantism in theropods represents one of the most intriguing phenomena in dinosaur evolution, showcasing extreme adaptations in body size and ecological roles. This literature review focuses on Giganotosaurus carolinii, one of the largest known theropods to have ever existed, to explore its unique aspects of gigantism and the subsequent ecological impacts. By analyzing features such as its skeletal morphology, comparative anatomy, and paleoenvironmental data, we assess the evolutionary pressures and biological constraints that shaped the gigantism observed in Giganotosaurus. We discover that the species exhibited a combination of physiological and ecological adaptations that allowed it to thrive as an apex predator in its Cretaceous environment. We will also further examine the implications of its size on prey dynamics, competition with other large theropods, and the overall ecosystem structure. Through a thorough, multidisciplinary approach, this research enhances our understanding of how gigantism influenced theropod behavior, interspecies interactions, and the broader paleoecological landscape.

Formal Calculation uses an auxiliary form to calculate various nested sums and provides results in three forms. It is also a powerful tool for analysis. This article studies the symmetry of the coefficients in Formal Calculation. Three types of extended numbers were introduced, and many formulas for symmetric functions were obtained.

The COVID-19 pandemic has revealed significant disparities in disease severity and mortality across various population groups and regions. These disparities cannot be attributed solely to healthcare systems, socioeconomic factors, living conditions, or individual immune landscapes; environmental factors also play a crucial role. This work explores how variations in nasal morphology related to age, gender, and race can affect the respiratory system's ability to filter, warm, and humidify inhaled air. These variations may contribute to differences in how cold, dry, polluted, and infected air impact the respiratory system, thus influencing the observed patterns of disparities in COVID-19 outcomes. The analysis suggests that individuals with genetically determined wider nasal cavities, often found in populations originating from warmer climates, have decreased filtration and air conditioning efficiency, leading to increased susceptibility to respiratory infections and disproportionately high mortality rates from COVID-19. Moreover, age-related changes in nasal morphology, such as increased nasal volume and involution atrophy of the mucosa, contribute to impaired air conditioning and filtration, thereby increasing the risk of severe outcomes in the elderly. Gender differences in nasal morphology, with men typically having larger nasal passages compared to women, are linked to poorer air conditioning and filtration, potentially contributing to the higher risk of severe COVID-19 in men. Preliminary statistical analyses of the U.S. population suggest that men with favorable nasal structures may have a longer life expectancy than those with less optimal nasal anatomy; however, this should be regarded as a hypothesis. The findings underscore significant gaps in our understanding and highlight the urgent need for further research to unravel the complex interplay between nasal morphology, respiratory system dynamics, and environmental factors in shaping disease susceptibility and outcomes.

Commercial banks are financial institutions that accept public deposits and provide loans for consumption and investment to generate profit. The term "bank" originates from the Italian word "banco," meaning "desk" or "bench," which was used by Florentine bankers during the Renaissance for conducting transactions. Banking activities, however, date back to ancient times. The primary objective of this study is to examine the impact of internal control components on the organizational performance of local commercial banks in Burao, Somaliland. A descriptive research design was employed, utilizing primary data collected from these banks to assess the role of internal controls on organizational performance.The findings indicate a strong influence of internal control systems on organizational performance, revealing a significantly positive relationship between the two. Effective internal control systems contribute to the administration, completeness, and accuracy of records, and provide a safeguard against fraud and collusion, particularly among those in positions of authority or trust. For optimal performance, internal control systems must be robust and well-implemented. It is recommended that local commercial banks conduct regular internal audits of their accounting systems to mitigate risk and enhance internal control measures. Strengthening internal control systems tailored to the organization's operations and ensuring strict authorization protocols for access to bank assets are essential for improving organizational performance.

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 literature review examines the function and evolution of cranial crests in Dilophosaurus wetherilli, a notable theropod dinosaur from the Early Jurassic period. The review synthesizes morphological data and behavioral theories to provide a comprehensive analysis of the crests' roles in the dinosaur's life. Cranial crests, characterized by their distinctive, elongated structures extending from the skull, have intrigued paleontologists due to their unique appearance and apparent function. This review utilizes findings from recent fossil discoveries, comparative anatomy, and biomechanical studies to explore hypotheses regarding the crests' primary functions. Key theories suggest these structures may have served for display purposes, thermoregulation, or defensive mechanisms. Evidence from cranial and postcranial anatomy, as well as comparisons with extant analogs, provides insights into the possible evolutionary pressures influencing the development of crests in Dilophosaurus wetherilli. Additionally, the review considers the role of sexual dimorphism and social behavior in shaping these features. By integrating these diverse lines of evidence, the review aims to offer a nuanced understanding of how cranial crests might have contributed to the survival and social interactions of Dilophosaurus. This analysis not only advances our knowledge of Dilophosaurus wetherilli but also contributes to broader discussions on the evolution and functional morphology of early theropods.

This paper focuses on the concepts used in Quantum Cryptography and recent progress made. We look at the different categories of key distribution protocols that have been proposed and issues related to those. We then take an overview of two main approaches of communication using quantum cryptography concepts, i.e., ``Quantum Secure Direct Communication'' and ``Deterministic Secure Quantum Communication'', and limitations related to the usage of these schemes in a real-world system. At last, we list down the problems identified in the research and conclude our discussion by proposing areas of focus for future research.

This literature review provides a comprehensive analysis of the functional morphology and paleoecology of Pteranodon, a prominent genus of Pterosaur from the Late Cretaceous period. By synthesizing recent research findings with classic studies, this paper elucidates the aerodynamic and ecological adaptations of Pteranodon, which was one of the largest pterosaurs to ever soar the skies. The review examines the distinctive cranial features, including its elongated cranial crest and its implications for sexual dimorphism and species identification. Detailed analysis of wing morphology and limb proportions reveals insights into its flight mechanics, suggesting that Pteranodon was adapted for dynamic soaring, leveraging its expansive wingspan for efficient long-distance travel. Additionally, the paper explores Pteranodon's feeding habits and dietary preferences based on dental and jaw structure, indicating a diet primarily consisting of fish and other aquatic prey. The review also integrates fossil evidence with ecological models to reconstruct the paleoenvironment of the Late Cretaceous, highlighting the role of Pteranodon in its ecosystem as both a predator and a competitor. This synthesis not only enhances our understanding of Pteranodon’s physiological and ecological roles but also contributes to broader discussions on pterosaur evolution and behavior.

Magnesium monofluoride is a polar molecule amenable to laser cooling which has caused renewed interest in its spectroscopy. In this work we consider the case of three low-lying electronic excitations, namely $\rm X \, {}^{2}\Sigma \to A \, {}^{2}\Pi, \ \to B \, {}^{2}\Sigma, \ \to C \,{}^{2}\Sigma$, using well developed quantum chemistry approaches, i.e., without reference to the spin-orbit splitting of the $\rm A \, {}^{2}\Pi$ states. Accurate experimental data for these transitions have been available for over 50 years. Here we explore the linear response method at the level of CC2 theory, as well as equation of motion methods at the level of CCSD and CC3, using two families of basis sets. Excellent agreement is obtained for the first three transitions when using the correlation-consistent basis sets and extrapolation to the complete basis limit within EOM-CC3 (at a relative precision of $10^{-4}$), and qualitative agreement for the other two methods. The purpose of this paper is to serve as a guide in how to approach the accurate calculation of excitations in polar diatomic molecules.

We provide a generalisation of Pythagoras’ theorem, which enables us to calculate the absolute value of a hypersurface of arbitrary dimensions in terms of its projections. For a one dimensional hyperplane the theorem reduces to Pythagoras’ theorem.

Previous studies have examined the impact of Internet use on health in China, but they have not adequately addressed the issue of reverse causality or conducted a detailed analysis of health status. This study uses three waves of longitudinal data from the China Family Panel Studies conducted in 2014, 2016, and 2018 to investigate the association between Internet use and health status in China, aiming to address these gaps. The results indicate that Internet use may improve health status, including self-rated health, mental health, and outpatient visits. These effects vary by gender and age group: the positive effect of Internet use on health outcomes is more pronounced for women, and for middle-aged and older generations, compared to men and younger generations. These findings provide new evidence of the beneficial impact of Internet use on health outcomes in China, suggesting that policies promoting Internet utilization could enhance individuals’ health status, particularly among women and middle-aged and older populations.

Therizinosaurs, a unique clade of theropod dinosaurs, are characterized by their distinct morphological features and enigmatic ecological role. This review provides a comprehensive analysis of the functional morphology and ecological significance of these remarkable dinosaurs. Therizinosaurs are distinguished by their elongated, often curved claws, robust postcranial skeletons, and herbivorous diet, which starkly contrasts with their carnivorous theropod relatives. We explore the evolutionary implications of their morphology, focusing on adaptations such as their specialized feeding apparatus and limb structure. These features suggest a complex interplay between their herbivorous lifestyle and ecological niche. The review synthesizes recent findings from fossil evidence, including detailed studies of cranial and postcranial anatomy, to elucidate the functional mechanisms behind their unique adaptations. Additionally, we discuss the broader ecological role of therizinosaurs within their Mesozoic ecosystems, highlighting their interactions with contemporaneous flora and fauna. By integrating paleontological data with comparative anatomy and ecological modeling, this review aims to shed light on the evolutionary trajectory of therizinosaurs and their impact on our understanding of theropod diversity and adaptation. This focused review contributes to a more nuanced appreciation of therizinosaurs' place in dinosaur evolution and their adaptive strategies in prehistoric environments.

Ankylosaurus stands as a quintessential example of Cretaceous armored dinosaurs, renowned for its formidable defensive adaptations. This study explores the defensive mechanisms and paleoecology of Ankylosaurus, focusing on its unique armor and lifestyle. Through detailed morphological analysis of fossil specimens, we examine the structure and function of the robust osteoderms and the prominent tail club, evaluating their roles in defense against predators. The distribution and arrangement of these defensive features suggest a sophisticated strategy to deter and survive attacks from contemporary theropods. Additionally, we investigate the paleoenvironmental context of Ankylosaurus, assessing how its habitat and ecological interactions influenced its evolutionary adaptations. Comparative analysis with other ankylosaurids and contemporaneous species provides insights into the evolutionary pressures shaping its armor. Our findings highlight the balance between offensive and defensive strategies in the Late Cretaceous ecosystem, underscoring the role of Ankylosaurus as both a well-protected herbivore and a critical component of its ecological niche. This study contributes to a deeper understanding of the evolutionary dynamics of armored dinosaurs and their interaction with their environment, offering a comprehensive perspective on the adaptations that defined Ankylosaurus' survival strategy.

Waste management is a critical issue globally, and Bangladesh is no exception, facing significant challenges due to rapid urbanization, population growth, and inadequate infrastructure. This article explores comprehensive waste management strategies, including waste segregation, recycling, composting, and waste-to-energy technologies, emphasizing their environmental and health benefits. It examines the past and present waste management conditions in Bangladesh, highlighting the progress made and the persistent challenges. The article also compares waste management practices in neighboring countries like India, Bhutan, Sri Lanka, and Nepal, offering insights into potential improvements for Bangladesh. Emphasizing the need for public awareness, community involvement, and modern infrastructure, it calls for collaborative efforts to achieve sustainable waste management and environmental preservation in Bangladesh. This article examines the critical issue of waste management, emphasizing its importance in mitigating environmental pollution, conserving resources, and protecting public health. It provides a detailed analysis of various waste management strategies, including waste segregation, recycling, composting, and waste-to-energy technologies, highlighting their benefits and implementation challenges. Focusing on Bangladesh, the article traces the evolution of the country's waste management system, from past inadequacies to recent improvements, while acknowledging persistent issues such as rapid urbanization and insufficient infrastructure. Comparisons with waste management practices in neighboring countries like India, Bhutan, Sri Lanka, and Nepal offer valuable insights and potential strategies for Bangladesh to adopt. The article underscores the necessity of public awareness, community involvement, and robust infrastructure to achieve effective waste management. It calls for a collaborative approach involving government, private sector, NGOs, and local communities to develop sustainable solutions. By investing in modern waste management technologies and fostering responsible consumption and disposal habits, Bangladesh can address its waste management challenges, ensuring environmental preservation, public health, and overall national well-being.

Dendritic spines are protruding membrane organelles from neuronal dendrites. The quantity of dendritic spines formed seems to be regulated with the -BAR family proteins, as they would initiate the membrane curvature to form the spines; however it is unclear whether the positions of these spines are selected based on spatial information due to the location of pre-existing spines or are selected via random molecular interactions. Through measuring the distance between dendritic spines from the images of Prada et. al (6), a distribution graph was created to determine whether the spine formations followed a random distribution pattern. Two groups of images were measured: dendritic spines when extracellular vesicles (EV) neuron contact was normal (control) and when EV-neuron contact was inhibited (experimental), preventing the transfer of MicroRNA (miRNA). Two randomly generated simulation groups were then created with the same spine density as the control and experimental conditions. Distribution graphs were created for the two randomly generated simulation groups as well. It was found that the randomly generated control simulation and the control group had significantly similar distribution; however, the randomly generated experimental simulation and the experimental simulation showed less of a similar distribution. These findings show that an element of randomness was removed when EV-neuron contact was inhibited.

The durability of bridge decks is a critical factor in ensuring the safety and longevity of transportation infrastructure. This article explores advanced materials and innovative techniques designed to mitigate cracking in bridge decks, a common issue that can compromise structural integrity and increase maintenance costs. It provides a comprehensive review of cutting-edge solutions such as Fiber-Reinforced Polymers (FRP), High-Performance Concrete (HPC), Ultra-High Performance Concrete (UHPC), and self-healing concrete technologies. Each material is analyzed for its ability to enhance strength, reduce permeability, and improve resistance to environmental stressors. Additionally, the article examines the role of surface treatments, sealants, and routine maintenance practices in prolonging the life of bridge decks. By synthesizing current research and practical applications, this article offers valuable insights and recommendations for engineers, contractors, and policymakers seeking to advance bridge deck durability through innovative materials and methodologies.

Let an odd integer be represented as $\sum \limits_{n > m} 2^n + 2^m - 1$ for $m \geq 1$. To transform $2^m - 1$ according to the Collatz rules, the $3x + 1$ step is applied, resulting in the even integer $2^{m + 1} + 2^m - 2$. This even integer, being exactly once divisible by 2, becomes the next odd integer $2^m + 2^{m - 1} - 1$. This represents the growth phase, as exactly one even step follows an odd step. However, while the overall value of the integer increases, terms with decreasing indices are generated after each odd-even cycle. After $m$ such cycles, a term $2^{m - m}$ (which equals 1) is generated, canceling the negative 1. Additional even steps are then performed until the integer becomes odd again, which occurs when the next smallest index becomes zero. The positive 1 thus obtained is written as $2^1 - 1$. If uninterrupted, the term $2^1 - 1$ has the trivial cycle $2^1 - 1 \rightarrow 2^2 \rightarrow 2^1 \rightarrow 2^1 - 1$. This represents the shrinkage phase, as two even steps follow an odd step. To interrupt the trivial cycle, a sequence such as $2^1 + 2^2 + \cdots$ must be available to combine with $2^1 - 1$ and produce $2^{\mathcal{M}} - 1$. This starts another growth phase that lasts for $\mathcal{M}$ odd-even cycles. Finally, there is an attempt to craft an integer that can escape the shrinkage phase in the Collatz dynamics.

Under the wave of technological advancement and industrial upgrading, new productivity is deeply influencing the field of vocational education and promoting fundamental changes in teachers' roles. Modern teachers must not only keep pace with technological development but also master innovative teaching modes and guide students to form correct values. They are gradually evolving from mere knowledge transmitters to guides for learning, stimulators of interest, explorers of in-depth research, and promoters of comprehensive growth. Through interdisciplinary knowledge structures and innovative abilities, combined with advanced tools such as information technology and artificial intelligence, teachers innovate teaching methods to meet students' diverse learning needs and focus on cultivating students' professional skills, comprehensive qualities, and innovative spirits. This multi-dimensional reshaping of roles is not only an inevitable choice for the development of vocational education but also provides important insights for educational reform.

The use of entropy concepts in the field of stochastic electrodynamics is briefly reviewed here. Entropy calculations to date that have been fully carried out are discussed in two main situations: first, where electric dipole oscillators interact with zero-point, or zero-point plus Planckian, or Rayleigh-Jeans radiation, and second where only these radiation fields exist within a cavity. The emphasis here will be on the first more difficult situation where both charged particles and radiation fields are present and interacting. Unlike the usual exposition on entropy in clasical statistical mechanics involving probabilistic notions of phase space occupation, the calculations to date for both particles and fields or for fields alone, follow the caloric entropy method where the notions of heat flow, adiabatic surfaces, and isothermal conditions, are utilized. Probability notions certainly still enter into the calculations, as the fields and charged particles interact stochastically together, following Maxwellian electrodynamics. Examples of phase-space calculations for harmonic oscillators and classical hydrogen atoms are carried out, emphasizing how much farther caloric entropy calculations have been successfully utilized.

In the context of the era of New Quality Productive Forces, technological innovation has become the core force to promote the development of social productivity, which is closely linked with vocational education, and together create a strong demand for high-quality technical and skilled personnel. In order to meet this demand, vocational education must synchronize with the development of the New Quality Productive Forces and form a mechanism of mutual promotion. The key to promoting the high-quality development of vocational education lies in taking technological innovation as the guide, and comprehensively improving the quality of education through measures such as innovating the education model, deepening the integration of industry and education, and enhancing the adaptability of specialties. To this end, vocational education should actively build bridges for the integration of industry and education, enhance the flexibility and adaptability of the education system, innovate the mechanism of talent cultivation, and establish a comprehensive quality assurance system. Together, these efforts will promote vocational education to enter a new stage of high-quality development and provide a solid talent guarantee for the sustainable development of the social economy.

Internet of Things (IoT) devices are going to be the primary data source in smart cities, often generating and communicating critical, sensitive, and private data that face threats of confidentiality breaches due to the rise of quantum computers. Even though NIST has approved post-quantum cryptography (PQC) schemes that promise to protect from quantum computer threats, these schemes can not be implemented efficiently on IoT devices, which often have computational and memory constraints. Hence, due to the substantial key size requirements and computationally intensive nature of PQC schemes, their direct deployment on IoT devices within smart city architecture is impeded by inherent computational and storage limitations. Consequently, it is imperative to devise PQC-based, resource-efficient authentication protocols tailored to IoT devices operating within smart cities. In this paper, we propose an offloading scheme that uses PQ Edge Server (PQES) for the network that performs PQC-related computation and memory operations for IoT devices in the network. The suggested technique transfers all storage and processing responsibilities from IoT devices in a network to ensure their normal operation and performance without any compromise. PQES generates a distinct PQ key pair for every network device and oversees a separate stream for each device. Therefore, IoT devices in the network may function without cryptographic processes, improving their efficiency by removing the need for conventional cryptography. Our results show that the proposed scheme reduces RAM usage to 1.05 KB and CPU usage to 1.75%, with maximum CPU context switches of 92.8. The proposed scheme uses TCP, a transport layer protocol, which is applicable to lightweight end devices without application-level protocols like HTTP or MQTT. When benchmarked on a Raspberry Pi, the PQES can handle 1000 concurrent connections and use a maximum of 73.5% of RAM. In comparison, previous research used at least 5.1 MB of RAM for one concurrent connection and may use up to 552.6 MB of RAM for 1000 concurrent connections. The maximum CPU usage for our method was 1.75%, whereas the previous proposal had a utilization of 67.1%. The greatest CPU context switches for our system were 92.8, far lower than the 3449 observed for the usual approach. Hence, our proposed scheme reduces the computational, memory, and network load for IoT devices by manifolds and increases device performance while providing PQC security.

This literature review investigates the role of plumage in the hunting tactics of Velociraptor, integrating anatomical analysis and biomechanical modeling. Velociraptor, a swift and agile theropod from the Late Cretaceous, exhibited advanced intelligence and sensory capabilities that contributed to its effective predatory strategies. Recent discoveries have confirmed that Velociraptor was covered in feathers, with quill knobs indicating well-developed plumage. This review explores the potential functions of feathers beyond flight, focusing on their impact on hunting behavior. Evidence suggests that Velociraptor’s plumage likely facilitated camouflage and enhanced aerodynamic control, improving its stealth and maneuverability during high-speed chases. Additionally, feathers might have contributed to stability during incline running, further supporting its adaptability in diverse environments. Velociraptor’s hunting strategies, characterized by agility and precision, were complemented by its feathered anatomy, which may have also played a role in pack hunting and social interactions. Overall, the integration of plumage with Velociraptor’s anatomical features and behavioral adaptations underscores its efficiency as a predator, demonstrating how feathers may have been a critical factor in its successful hunting tactics.

This literature review investigates how the diverse habitats of Baryonyx walkeri influenced its hunting strategies, with a focus on adaptations for piscivory and generalist predation. The spinosaurid theropod Baryonyx walkeri, known for its distinctive elongated snout and conical teeth, inhabited a variety of environments during the Early Cretaceous period. Fossil evidence suggests that Baryonyx frequented fluvial and lacustrine environments, leading to the hypothesis that its primary diet consisted of fish and other aquatic organisms. Detailed analysis of its cranial morphology reveals a suite of adaptations, including robust forelimbs with a large, sickle-shaped claw, ideal for catching slippery prey. Isotopic analysis of oxygen in Baryonyx indicates a significant reliance on fish and other similar organisms. Comparisons with modern analogs, such as crocodilians, provide further insight into the behavioral ecology of Baryonyx. This review integrates paleontological data with modern ecological analogs to reconstruct the hunting strategies of Baryonyx walkeri. The findings underscore the importance of habitat diversity in shaping the evolutionary pathways of predatory dinosaurs and contribute to a broader understanding of spinosaurid ecology and behavior.

This paper proposes a theoretical silicon dioxide (SiO2) solar cell based dielectric metamaterial unit surface consisting of zinc oxide (ZnO) periodic nanorods of bandgap energy of 2.2-2.4eV. It evaluates the absorptive efficiency of the film using S-parameter and Z-parameter testing of magnitude, return/insertion loss, and impedance matching with respect to a frequency band of 0.0014-10.5THz. Terahertz applications of the result are considered due to potential scope for infrared (IR) and microwave (MW) spectral selectivity with ZnO. Calculations of input n-impedance and loss tangents are also presented with emphasis on the exhibition of antireflection to gauge the performance of the thin-film meta-surface in tandem solar cells. The data for the reflectance parameters of this structure, primarily the S11 loss and impedance matching (dB/dB10) are compared to those of prior investigations with different metamaterial coatings on SiO2 solar photovoltaic cells, as well as ZnO nanorods used in photovoltaic applications, to ascertain the cylindrical meta-surface structure with the preset thicknesses of the study being an effective anti-reflector. Application can be extended to other broadband domains requiring plasmonic graded refractive surfaces, in further work.

A Physical Unclonable Framework (PUF) is a detached hardware component or, the structural arrangement of hardware that provides natural variations in the physical parts of semiconductor devices to generate unique and unpredictable identifiers based on challenging-to-replicate. In this case, PUFs are giving us a tool to utilise these variations to create an unclonable identifier for your device. PUFs generate a response or a unique pattern based on the physical characteristics of the device, and this response is difficult to predict or duplicate. This study aims to introduce a PUK device to provide security research on the Internet of Things technology. The elliptic curve cryptography algorithm was modified with an advanced equation to calculate a private key and K value. In this study, a PUF device will be employed with a modified elliptic curve cryptography algorithm on a novel security system to be used in current and future communication technology.

Newton's kinetic energy equation delineates the laws of motion for objects travelling at speeds much less than the speed of light, an equation extensively validated at these lower velocities. Any new, more complete formulation of kinetic energy must approximate Newton's kinetic energy equation under these conditions. Einstein's special relativity, utilizing the Lorentz factor, introduces a new perspective on spacetime, adhering to the requirement that the Newtonian kinetic energy equation serves as an approximation at low velocities. In this paper, we identify all factor expressions satisfying the condition that their approximate solutions at low velocities align with Newton's kinetic energy equation. We introduce the Yuyunrui factor, incorporating two real number hyperparameters, $\alpha$ and $\beta$, encompassing all potentially correct factors, with the Lorentz factor as a specific instance where $(\alpha=0$, $\beta=1)$. We classify all potentially correct $(\alpha, \beta)$ combinations into six categories based on their distinct scaling effects and present the corresponding scaling effects. The combinations of $(\alpha, \beta)$ values applicable in the real world are necessarily included within these candidates, with the precise values requiring rigorous experimental validation. This work aims to refine our understanding of special relativity beyond the Lorentz factor.

This literature review investigates the morphological and behavioral significance of the horns of Carnotaurus sastrei, a theropod dinosaur from the Late Cretaceous period. Through a comprehensive analysis of fossil specimens, comparative anatomy, and biomechanical modeling, this review explores the evolutionary adaptations and potential functions of these distinctive cranial structures. The horns of Carnotaurus are hypothesized to serve multiple roles, including species recognition, thermoregulation, and intraspecific combat. Our findings suggest that its cranial horns likely served a primary role in species recognition, intimidating rivals and signaling mating readiness. It also could have been used occasionally in head-butting contests, potentially as a display of dominance, but likely did not play a role in thermoregulation. Additionally, we examine the ecological context in which Carnotaurus lived, assessing how environmental factors could have influenced the development of such features. The implications of this study extend to broader discussions on the functional morphology and adaptive strategies of theropod dinosaurs, highlighting the complex relationship between form and function in the evolutionary history of predatory dinosaurs like Carnotaurus sastrei.

Virtual commerce has experienced significant growth in recent years, especially in emerging markets such as Peru. This boom has led to an increase in the number of online stores and with it a dispersion of prices which makes it difficult for consumers to make decisions. Comparing prices has become a crucial need to maximize savings and optimize online shopping. This platform uses web scraping techniques to collect price data of desktop products from various Peruvian online stores, presenting the information in a clear and accessible way for users. By centralizing and comparing prices, it not only facilitates purchasing decisions, but also promotes transparency and competitiveness in the market. For this according to the metrics used show 56% of success of the algorithm.

Beginning in December 2019, a novel coronavirus known as SARS-CoV-2 has caused an international outbreak of respiratory illness termed COVID-19. This disease may cause mild to moderate respiratory illness. However, older individuals and those with underlying medical conditions like diabetes, cancer, and cardiovascular disease are more likely to develop serious illnesses (progressive pneumonia, multi-organ failure, and even death) (1). The main target for antiviral therapy for COVID-19 is the 3CL protease (3CLpro, Main protease, Mpro, Nsp5). The 3CL protease plays an integral role in viral replication and is often known as the “Achilles heel” of the SARS-CoV-2 virus (2). Thus, NLC Pharma utilized a unique and novel technology termed Quantum Core Technology (QCT) to design a mechanism-based potent inhibitor against the SARS-Cov-2 3CL protease. 3CL Pharma has designed an oral anti-viral for COVID-19 called Tollovir. Biochemical assays have revealed that 40uM of Tollovir in PBS can achieve 90% maximum inhibition of 3CL protease. Furthermore, the oral anti-viral has completed clinical trial phase 2b/3. Tollovir has proven to be safe and significantly decreases mortality rates of hospitalized patients with COVID-19 infection.

Which machine-learning model is the best at winning the prisoner’s dilemma? Which models create the best cumulative outcomes? Is there a model that perfectly captures both winning and cumulative points? These are questions generated from a simple 2 x 2 payoff matrix of the prisoner’s dilemma. Imagine you and your partner in crime are caught and sent independently into questioning. You can either collaborate or defect, but you don’t know what your partner will do. If you both collaborate, cumulatively, you’ll each get one year in jail. If you defect and your partner collaborates, you’ll serve no time and they will serve 10 years, and vice versa. If you both defect, you’ll both serve 5 years in jail. Placing AIs against each other to play just one round doesn’t reveal much about their code, strategy, and end goal. So the Reinforcement Learning, Pattern Learning, Tit For Tat, and other models were put up against each other in a 100 round game where their behavior, convergence, and learning were analyzed to reveal the most effective ways strategies to beat the prisoner’s dilemma. All code and data is open sourced here.

This literature review aims to gain a deeper understanding of the anatomy behind the jaw strength of Tyrannosaurus rex and analyze how these features contribute to its predatory behavior. This review will examine various studies and fossil excavations, exploring ideas such as the bite force of T. rex and its variations across different life stages. This will be obtained from numerous biomechanical models of both juvenile and adult Tyrannosaurus rex, as well as reference animals and dinosaurs for comparison. To provide additional information behind T. rex’s hunting techniques, we will also discuss the fauna of herbivorous dinosaurs that were part of T. rex’s diet. Furthermore, we will explore how mandibular stress concentration can vary between ages, a crucial topic in determining what T. rex could consume at certain stages of its life. Finally, we obtain a well-evidenced analysis of the various aspects of Tyrannosaurus rex’s hunting strategies throughout its lifetime.

In previous papers we have shown how Schr\"{o}dinger's equation which includes an electromagnetic field interaction can be deduced from a fluid dynamical Lagrangian of a charged potential flow that interacts with an electromagnetic field. The quantum behaviour was derived from Fisher information terms which were added to the classical Lagrangian. It was thus shown that a quantum mechanical system is drived by information and not only electromagnetic fields. This program was applied also to Pauli's equations by removing the restriction of potential flow and using the Clebsch formalism. Although the analysis was quite successful there were still terms that did not admit interpretation, some of them can be easily traced to the relativistic Dirac theory. Here we repeat the analysis for a relativistic flow, pointing to a new approach for deriving relativistic quantum mechanics.

This literature review aims to gain a deeper understanding of Spinosaurus aegyptiacus’s dorsal sail regarding its function as thermoregulation, display, or hydrodynamic. The function of S. aegyptiacus’s dorsal sail has been widely debated for many years. The three most popular hypotheses are that it was used as a biological mechanism of thermoregulation, display, or hydrodynamic. The display hypothesis includes attracting mates, intimidating rivals, and social communication with other members. This literature review delves into each of the three hypotheses using past studies and excavation sites to analyze their validity. For the hydrodynamic aspect, the lifestyle of S. aegyptiacus must first be established. Based on existing research, we cannot conclude that there is a definitive primary function of Spinosaurus aegyptiacus’s dorsal sail. However, it is likely that its sail served the crucial function of display, since it would have a large surface area and could thus be a distinguishable characteristic of S. aegyptiacus that could determine mating success. Additionally, it is probable that the sail had supplementary roles in thermoregulation and hydrodynamics.

Environmental responsibility is crucial in both consumption and production processes. Increasing environmental concerns of societies require eco-innovative and nature-based solutions (NBS) for entrepreneurs. Young people's intentions as potential innovators and entrepreneurs on eco-entrepreneurship with mediating effects have not been investigated is a shortcoming in the literature. This study was conducted with 920 respondents from a public university (Düzce University) students to determine the underlying motivations of eco-entrepreneurship intentions using structural equation modeling (SEM) and bootstrap methodology. SEM results show that environmental concerns have a positive direct effect (β=0.62) on students’ eco-entrepreneurship intention. This study can serve the several sustainable development goals (SDGs) making suggestions for the efficient and effective use of production factors in terms of environmentally responsible behavior. Study results provide data for sustainable business applications and eco-innovative thinking. Study results intend the direction of modern environmental governance, policy makers and SEM developers.

The Mediterranean Corn Borer (MCB), Sesamia nonagrioides, poses a significant threat to maize crops, necessitating effective pest management strategies. This study investigates the compatibi-lity of two entomopathogenic nematode (EPN) isolates, Steinernema feltiae KV6 and Heterorhabditis bacteriophora EO7, with four registered insecticides for MCB control: deltamethrin, flubendiamide, spinetoram, and betacyfluthrin. The impact of these insecticides on EPN mortality, infectivity, and reproduction was assessed. Results indicate that deltamethrin exhibits the lowest toxicity to EPNs, with mortality rates of 1.3% for S. feltiae and 0.63% for H. bacteriophora at field dose (FD) after 24 hours, and 4.63% and 1.96% respectively after 48 hours. In contrast, spinetoram and flubendiamide showed higher toxicity, with mortality rates of 38.04% and 14.17% for S. feltiae at FD after 48 hours. The infectivity assays demonstrated that deltamethrin-treated EPNs caused up to 100% mortality in MCB larvae, while the reproduction capacity varied significantly between the EPN species and insecticides. H. bacteriophora exhibited higher progeny production, especially in the presence of Deltamethrin (87,900 IJs/larva). The findings suggest that integrating EPNs with selective insec-ticides like deltamethrin can enhance pest control efficacy and support sustainable agricultural practices. This study provides valuable insights for developing integrated pest management (IPM) strategies aimed at mitigating MCB infestations in maize while minimizing environmental im-pacts.

Metabolic engineering in plants offers powerful strategies to enhance crop yield and quality, addressing global challenges in agriculture and food security. This study explores cutting-edge approaches in manipulating primary and secondary metabolic pathways, utilizing advanced genetic modification tools to improve key traits in crops. We examine successful applications that have enhanced photosynthetic efficiency, nutrient use, stress tolerance, and the production of valuable metabolites. The study also delves into emerging trends, such as synthetic biology approaches and multi-gene trait stacking, which are revolutionizing the field. By integrating omics technologies and computational modeling, researchers are optimizing metabolic engineering designs with unprecedented precision. As the field rapidly evolves, we consider the regulatory and biosafety aspects of genetically modified crops, providing insights into the future of sustainable agriculture and crop improvement.

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share commonalities: historic, conceptual, methodological, and philosophical. Although their origins trace back to the 1940s birth of cybernetics, they were only able to properly develop as modern information technology became available. In this perspective, I offer a personal (and thus biased) account of the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. Even when the trajectory of the paper is influenced by my personal experience, the general questions posed (which outweigh the answers) hopefully will be useful to align efforts in these fields toward overcoming --- or accepting --- their limits.

This paper explores the increasing use of big data in education to create personalized learning experiences tailored to individual student needs. The trend, driven by vast amounts of educational data, requires the development of new techniques to optimize learning outcomes while addressing the ethical implications of data collection and utilization. The paper emphasizes the necessity for robust policy frameworks to ensure transparency and trust, based on clear guidelines and parental consent. It also discusses how educational institutions are updating curricula to prepare learners for a rapidly evolving workforce, incorporating contemporary AI technologies that impact education systems. The dual influence of AI presents both opportunities and challenges, necessitating a balanced and thoughtful approach. The paper advocates for a holistic strategy in integrating AI in education, emphasizing the importance of collaboration between the AI industry and other sectors to develop comprehensive policy frameworks for the ethical and effective use of AI in education.

In this paper, we investigate the proftability of investing in movies for retail investors, focusing on the return on investment (ROI) metric, which is calculated as revenue divided by budget. Our analysis encompasses 5000 movies from the movie database TMDB, examining factors such as production scale, genres, key actors, directors, and more. Additionally, we employ textual analysis techniques like Latent Semantic Analysis (LSA) on movie overviews and tags to incorporate movie themes. We then evaluate various supervised classifcation models including Logistic Regression, Random Forest, and Light Gradient Boosting (LightGBM), comparing their performance. Our fndings highlight the signifcance of production scale, team structure, and movie themes in identifying potential high-return opportunities for investors.

Background:Patients with chronic kidney disease (CKD), especially those undergoing dialysis, encounter complex nutritional challenges. Key dietary factors such as protein and fluid intake, potassium and sodium levels, and co-morbidities significantly influence dietary planning for CKD patients. A standardized approach to dietary recommendations for CKD patients is ineffective due to the diverse needs of individual patients. Social media platforms have become a popular source of dietary information, often advocating low-carb, high-protein diets, and other combinations purported to be universally beneficial. However, these generalized online dietary recommendations lack the individualized approach necessary for CKD patients. Unlike these social media-driven suggestions, established medical guidelines offer a more nuanced view of CKD dietary needs. This study investigates the discrepancies between these sources and their potential impact on patient health. Objective:The primary goal of this study is to identify and analyze the flaws in the dietary recommendation system for CKD patients that might adversely affect their health. By critically evaluating the content of YouTube videos, we aim to compare the dietary advice provided by social media influencers and content creators with established medical guidelines. Our approach combines qualitative and quantitative methods to thoroughly assess the accuracy and reliability of online dietary advice available to CKD patients. Method:We conducted four YouTube searches using keywords related to "diet plan" and "chronic kidney disease (CKD)." From each search, the first 30 videos were selected based on relevance, resulting in an initial pool of 120 videos. A stepwise screening process was used to filter out the most relevant and accessible content, ultimately narrowing the sample to 30 videos for in-depth analysis. The videos were evaluated using two scoring systems: the Journal of the American Medical Association (JAMA) score and the Global Quality Scale (GQS) score, which assess video quality. To ensure impartiality, three independent scorers conducted the evaluations. Additionally, two six-point scorecards were used to evaluate the inclusion and accuracy of information on various foods and eating behaviors affecting CKD risk, with higher scores indicating more comprehensive coverage. Data on video characteristics, such as view count, number of likes, and video age, were also collected to provide context and further insight into their reach and influence. Results:The study included 30 videos, with 60% (18 out of 30) posted by or featuring nephrology health professionals. Scorer 1 reported a mean JAMA score of 2.3 (SD 0.61) out of 4 points and a mean GQS score of 2.5 (SD 0.64) out of 5 points. Scorer 2 found a mean JAMA score of 2.5 (SD 0.62) and a mean GQS score of 2.8 (SD 0.73). Scorer 3 reported a mean JAMA score of 2.6 (SD 0.55) and a mean GQS score of 2.8 (SD 0.69). The dietary six-point scorecards revealed a mean score of 3.6 (SD 0.85) for the presence of information and 2.8 (SD 0.88) for the accuracy of the information. Conclusion:The videos analyzed in this study related to diet plans for CKD patients demonstrated generally low scores, indicating a lack of comprehensive and accurate information. Many videos provided incomplete or partial information, which could mislead viewers and potentially cause harm due to incomplete knowledge. This study highlights the need for careful scrutiny of online content, particularly on platforms like YouTube, due to the potential for contradictory information and its harmful consequences. The findings emphasize the necessity for greater accuracy and vigilance in disseminating health-related information to ensure the safety and well-being of CKD patients.

In this work, we will study at a basic level, a series of possible new three-dimensional monoclinic allotropes of silicon, with sp3 hybridization, of the bct type; where hexahedrons and octahedrons are combined, with other cluster shapes, with basic silicon geometries such as: triangles ( Si3 ), squares ( Si4 ), pentagons ( Si5 ) and hexagons ( Si6 ); similar to two-dimensional, sp2 hybridized graphene allotropes. Calculating its electron density, bandgap energy and electronic mobility.

The increasing prevalence of spinal disorders and the subsequent rise in spinal fusion surgeries stress the necessity for optimizing spinal fusion cages, crucial for stabilizing the spine and supporting osseointegration. Despite the widespread attempts at material modifications and surface enhancements to improve these devices, failure rates and complications remain significant. This research explores an imaginitive approach to enhancing spinal fusion cages through the application of geometric tiling designs, a method scarcely employed . The study systematically investigates the potential of triangular, diamond, and hexagonal tiling patterns to improve the structural integrity and capability for osseointegration of spinal fusion cages. Through a finite element analysis simulation, each design's ability to withstand multi-directional loads and effectively distribute stress was tested under simulated conditions that mimic the complexities of spinal motion. Preliminary findings in fields of engineering indicate that geometric tiling can significantly enhance the load-bearing capabilities and stress distribution of spinal fusion cages, potentially reducing the likelihood of implant subsidence and failure. The triangular and hexagonal designs, in particular, demonstrate superior performance in maintaining structural stability under axial and anterior compressive forces compared to traditional cage designs. This research opens a new pathway for the development of more durable and efficient spinal fusion cages. By leveraging the structural advantages of geometric tiling, future spinal implants could achieve better clinical outcomes, leading to improved patient recovery and reduced rates of complications.

This paper presents an enhanced Fully Connected Neural Network (FCNN) framework that incorporates manifold learning and Renormalization Group (RG) methods. By integrating Christoffel symbols, the model adapts to the curvature and topology of the data manifold, improving learning from geometrically complex data. Additionally, RG methods enable effective multi-scale data processing. We demonstrate the enhanced model’s performance on synthetic and real-world datasets, showing improved accuracy compared to standard FCNNs, particularly in complex systems simulation.

This study investigates the latest advancements in utilizing machine learning techniques for predicting financial market movements. Financial markets are complex systems influenced by numerous factors, making accurate predictions challenging but highly valuable. The research explores various machine learning approaches, including supervised learning, unsupervised learning, reinforcement learning, and deep learning, to enhance the accuracy and reliability of financial market forecasts. The study provides a comprehensive review of current methodologies, highlighting key innovations and their applications in financial market prediction. Techniques such as neural networks, support vector machines, random forests, and ensemble methods are examined in the context of their ability to analyze vast amounts of financial data, identify patterns, and generate actionable insights. Additionally, the research delves into the integration of alternative data sources, such as social media sentiment, news articles, and macroeconomic indicators, to improve prediction models. Through empirical analysis and case studies, the study demonstrates the effectiveness of advanced machine learning models in predicting stock prices, currency exchange rates, and commodity prices. It also discusses the challenges associated with these techniques, including overfitting, data quality issues, and the need for interpretability in model outputs. Findings suggest that machine learning techniques can significantly enhance the predictive power of financial models, offering more accurate and timely insights for investors and financial analysts. The study concludes with recommendations for future research directions, emphasizing the importance of model transparency, the incorporation of diverse data sets, and the continuous adaptation of machine learning algorithms to evolving market conditions.

In this work, we will study metallic clusters equivalent to the organic ones intercalated with metals, of solid benzene type, Ba anthracene and K3.45picene; and we will try to calculate its superconducting Tc, for each of these compounds, from electronic correlations at room temperature. We will also study metal hydrides and clusters, totally composed of metals, which adopt forms analogous to molecular organic picene and anthracene; also calculating its TC.

This research addresses the challenge of integrating fairness considerations into optimization problems, focusing on the development of multi-objective optimization techniques that balance traditional efficiency metrics with fairness constraints. In many algorithmic decision-making processes, optimizing for efficiency alone can inadvertently perpetuate or exacerbate biases, leading to inequitable outcomes. This study explores how data analysis and machine learning can be leveraged to identify and mitigate these biases, ensuring that optimization models yield both efficient and fair solutions. The research presents a framework for incorporating fairness constraints into multi-objective optimization, utilizing various fairness metrics such as demographic parity, equal opportunity, and disparate impact. It investigates the application of this framework across different domains, including resource allocation in healthcare systems, loan approvals in finance, and personalized learning platforms in education. By analyzing real-world data sets, the study demonstrates how fairness-aware optimization can lead to more equitable outcomes without significantly compromising efficiency. Empirical results from case studies show that multi-objective optimization with fairness constraints can effectively balance the trade-off between efficiency and equity. The research also discusses the computational challenges and ethical considerations associated with implementing fairness constraints in optimization models. Strategies for addressing these challenges, such as regularization techniques and fairness-aware machine learning algorithms, are evaluated and presented. Findings highlight the importance of incorporating fairness into optimization processes to prevent algorithmic biases and promote social equity. The study concludes with practical recommendations for policymakers, practitioners, and researchers on adopting fairness-aware optimization techniques to ensure that algorithmic decision-making processes are both efficient and just.

.In his groundbreaking 1905 paper on special relativity, Einstein distinguished between local and global time in inertial systems, introducing his famous definition of distant simultaneity to give physical content to the notion of global time. Over the following decade, Einstein attempted to generalize this analysis of relativistic time to include accelerated frames of reference, which, according to the principle of equivalence, should also account for time in the presence of gravity. Characteristically, Einstein's methodology during this period focused on simple, intuitively accessible physical situations, exhibiting a high degree of symmetry. However, in the final general theory of relativity, the a priori existence of such global symmetries cannot be assumed. Despite this, Einstein repeated some of his early reasoning patterns even in his 1916 review paper on general relativity and in later writings. Modern commentators have criticized these arguments as confused, invalid, and inconsistent. Here, we defend Einstein in the specific context of his use of global time and his derivations of the gravitational redshift formula. We argue that a detailed examination of Einstein's early work clarifies his later reasoning and demonstrates its consistency and validity.

This study explores the limitations of current AI systems, which predominantly function in the digital realm, and investigates the transformative potential of embodied AI. Embodied AI involves AI agents equipped with physical bodies, enabling them to interact directly with the physical world. This research focuses on how embodied AI can bridge the "embodiment gap" and enhance human-machine collaboration and learning in dynamic environments. The research examines the fundamental differences between traditional AI systems and embodied AI, emphasizing the importance of physical interaction for contextual understanding, adaptive learning, and intuitive human-machine collaboration. It explores various applications of embodied AI, including robotics, autonomous vehicles, and assistive technologies, demonstrating how physical embodiment can improve performance, safety, and user experience. Through experimental studies and real-world case analyses, the study highlights the advantages of embodied AI in tasks requiring situational awareness, dexterity, and real-time decision-making. It also addresses the challenges associated with developing and deploying embodied AI systems, such as sensor integration, real-time processing, and human-machine interface design. Findings indicate that embodied AI can significantly improve the efficacy of AI systems in dynamic and unpredictable environments. By leveraging physical embodiment, AI agents can better understand and respond to their surroundings, facilitating more natural and effective interactions with humans. The research concludes with recommendations for advancing embodied AI, including interdisciplinary collaboration, investment in sensor and actuator technologies, and the development of standardized frameworks for embodied intelligence.

This paper discusses the Quantum Temporal Resonator (QTR), a theoretical device leveraging quantum entanglement and metamaterials with negative refraction indices to achieve controlled temporal displacement. By establishing a network of entangled particles within a Bose-Einstein Condensate (BEC) and utilizing a resonant cavity made of advanced metamaterials, the QTR creates localized distortions in space-time. The frequency and amplitude of temporal harmonics within this cavity are finely tuned to allow for precise temporal displacement of matter and information. Numerical simulations demonstrate the coupling between the quantum wave function and electromagnetic fields, validating the theoretical model. The results show distinctive patterns in the wave function and perturbations in the electric field, supporting the feasibility of achieving controlled temporal displacement. This study explores the theoretical framework, mathematical modeling, experimental setup, and potential applications of the QTR, providing a comprehensive analysis of its feasibility and implications.

There is a lot of pressures from organizations to continually redesign the business processes and the technology infrastructure to support the organization, but to also help organizations manage competing stakeholder needs, changing objectives and behaviour, and so on. A key part of this is making sure that all their activities in the different components of the business are interconnected and that they are moving in the same direction. Enterprise Architecture (EA) is an effective way of dealing with the challenge of designing business activities and supporting IT. This paper will summarize the organization of EA and look more closely at some major components of EA: governance architecture, business architecture, information architecture and technical architecture and consider the overlap and constructive interaction involved. Enterprise Architecture can be best understood through business process redesign. So, it is useful to briefly review the leading frameworks for business process redesign, including COBIT from ISACA, LEAN from the Toyota Group, TOGAF from the Object-Management Group, and the Capgemini Integrated Architecture Framework (IAF). All these frameworks allow us to more successful transformation, to return to the central idea of this paper.

Training artificial intelligence (AI) for reasoning presents significant challenges due to the lack of clear objectives and reward functions. In structured environments such as chess and Go, objectives are well-defined, and the optimal moves can be determined through search algorithms that maximize the reward function. This clarity enables AI to learn and improve autonomously through random trials and recursive calculation of rewards, mirroring human learning processes that depend on prior experiences and state-dependent actions. However, in real-world applications, AI lacks access to direct interaction and feedback, making it difficult to define proper reward functions. This limitation is particularly evident in the training of large language models (LLMs), where the quality of the output is constrained by the training data and the absence of a dynamic reward system. Simulated environments offer some utility but are inherently limited by their design and scope. Achieving artificial general intelligence (AGI) requires AI to function and receive feedback in real-world settings, similar to human cognitive and strategic development through interactive experiences. This paper explores the critical role of clear objectives and reward functions in AI training, the limitations posed by the current lack of real-world interaction, and the implications for future advancements in AI reasoning capabilities.

We will try to use the approach of labeled cycle cover sum to count the number of hamiltonian cycles in a special type of bipartite graphs, with 4k + 2 vertices which can also be considered as an alternative method to check hamiltonicity in such bipartite graphs. This graph is such that, there exists at least 1 vertex which is not a part of any cycle of size 4m + 2 where m < k, in O∗(1.414n) time. The algorithm is inspired by Bjorklund’s approach where instead of working on the field of GF(2k), we will be working on C.

Methylation in the estrogen receptor alpha (ESRα) promoter is an epigenetic abnormality in breast cancer (BCa) and hypermethylation results in the loss of ER expression. We used pyrosequencing to investigate if there is a direct link between P0/P1 promoters of ESRα aberrant methylation and risk of progression of benign tumors to BCa. Results showed a significant elevated level of DNA methylation in ESRα P1 promoter (p = 0.0001) in fibroadenoma than ER-negative BCa tissues, and a 2-fold increased ESRα expression in fibrocystic, and fibroadenoma. In addition, the methylation level of HIN-1 and RASSF1A promoters were elevated in ER-positive when compared to ER-negative BCa (p-value <0.5). ANOVA Mixed Model revealed a significant interaction between RASSF1A with fibroadenoma and ER-positive BCa (p = 0.004). Tumors with unclassified molecular subtype (ER-positive, PR-negative, HER2-negative) had elevated levels of methylation (p = 0.046) in the P0 promoter compared with luminal B (ER-positive, PR-positive, HER2-positive) tumors. Tumors with grade 3 showed a borderline association with ESRα P1 promoter methylation when compared with grade 2 tumors (p = 0.056). These results showed a highly methylated ESRα P0 promoter in the initial stages of breast carcinogenesis while the methylation in the P1 promoter occurs at later stages of BCa with poor prognosis. Therefore, methylation of ESRα promoter and tumor-related genes could serve as potential biomarker for prediction of fibroadenoma that are at elevated risk of progressing to BCa.

Experiential learning, defined as the result, source, or context of knowledge obtained from direct experience, is increasingly bolstered by technology and human-computer interaction (HCI) principles and protocols. This article will describe how technology-based human-computer interaction (HCI) has been used in experiments to enhance direct knowledge experience. In this paper, immersive technologies (such as Virtual Reality, Augmented Reality, and haptics) will be described based on cognitive learning theory, empirical research and practical experience gained from implementing cutting-edge technology and maximizing HCI principles in constructive learning. Additionally, this article will describe how AI can be used to create individualized learning environments according to a student's needs in the future. While cost, technical issues and specialised trainings are the obstacle to deliver personalized learning experiences especially to economically disadvantaged students, technological change opportunities are projected, and these technologies will take educational outcomes to the next level.

Recent research underscores the potential hazards that Backdoor Attacks pose to natural language processing (NLP) models. A thorough exploration of these attack methodologies is critical for comprehending the susceptibility of such models. Under normal circumstances, a model compromised by a backdoor attack will produce standard outputs; however, the presence of a specific trigger within the input leads to erroneous results. This paper focuses on the vulnerability of BERT, a widely recognized model in numerous NLP applications, by introducing a novel backdoor attack strategy that effectively compromises it. We manipulate the attention heads in BERT to enhance the backdoor attack. The efficacy of this method is demonstrated through experiments conducted on clean-label attack and a Sentiment Analysis task.

A surge of b-phenylethylamine (PEA), an endogenous monoamine found in traces in the human brain and naturally present in chocolate, has been commonly associated with romantic love earning it the nickname of “love molecule”. The origin of this association must be traced to a speculation dating back to the early eighties that has over time become something of a factoid, reaching a large diffusion through the sharing phenomenon of the Internet. What is disputed in this paper is the existence of any direct biochemical or physiological evidence of the role of PEA in any of the phases of romantic love in the human species. The actual and possible roles of PEA in human physiology are reviewed.

Event data loggers are frequently used to record the date and time of tip events in tipping-bucket rain gauges. The HOBO® pendant event data logger is one such commercially-available device commonly used for this purpose. It can record the contact closure of a TBGR reed switch at a maximum timing resolution of 1 second, tied to the timing of the logger clock, which is set each time the logger is launched. These event loggers are ideal for the routine recording of rainfall. This paper addresses the issue of whether they can also be relied upon when estimating short-term intensities, for which they were not designed. New experiments show that for a series of switch closures at fixed intervals other than exact multiples of 1 s, the HOBO® logger fails to record evenly-spaced tip events. Thus, for example, with pulses at fixed 2.75 s intervals, the logger records some events as occurring at 2 s intervals, and others at 3 s intervals. The duration between logged events is correct only when the mean is calculated for a long series of logged events. In natural rainfall, tip events can occur at any time, and inter-tip times, from which intensity can be estimated, will generally not be an integral number of seconds. Consequently, particularly in intense rain, the logger behaviour just described can lead to erroneous estimates of the rainfall rate estimated from the duration of individual inter-tip times. Possible solutions are discussed.

We present a novel benchmarking methodology for Large Language Models (LLMs) to evaluate their susceptibility to hallucinations, thereby determining their reliability for real-world applications involving greater responsibilities. This method, called Deception-Based Benchmarking, involves testing the model with a task that requires composing a short paragraph. Initially, the model performs under standard conditions. Then, it is required to begin with a misleading sentence. Based on these outputs, the model is assessed on three criteria: accuracy, susceptibility, and consistency. This approach can be integrated with existing benchmarks or applied to new ones, thus facilitating a comprehensive evaluation of models across multiple dimensions. It also encompasses various forms of hallucination. We applied this methodology to several small opensource models using a modified version of MMLU, DB-MMLU1 . Our findings indicate that most current models are not specifically designed to self-correct when the random sampling process leads them to produce inaccuracies. However, certain models, such as Solar-10.7B-Instruct, exhibit a reduced vulnerability to hallucination, as reflected by their susceptibility and consistency scores. These metrics are distinct from traditional benchmark scores. Our results align with TruthfulQA, a widely used benchmark for hallucination. Looking forward, DB-benchmarking can be readily applied to other benchmarks to monitor the advancement of LLMs.

This paper investigates the potential of utilising multilevel quasi-interpolation techniques on Chebyshev sparse grids for complex numerical computations. The paper starts by laying down the motivations for choosing Chebyshev sparse grids and quasi-interpolation methods with Gaussian kernels. It delves into the practical aspects of implementing these techniques. Various numerical experiments are performed to evaluate the efficiency and limitations of the multilevel quasi-sparse interpolation methods with dimensions two dimension and three dimension. The work ultimately aims to provide a comprehensive understanding of the computational efficiency and accuracy achievable through this approach, comparing its performance with traditional methods.

Water supply policy is closely linked to sustainability as it directly influences the conservation and management of vital natural resources. This policy focuses on developing regulations and designing various activities related to the preser-vation of water resources, which can promote both ecological and economic sustainability. This study focuses on the Challenges of the Israeli Water Sector in Times of Emergency. In a time of growing climate crisis and despite the global warming trend, Israeli citizens routinely enjoy a regular constant supply of clean fresh water, thanks to local desalina-tion plants. Establishment of the desalination plants has become a model of water management for many countries in an era of growing climate crisis. At the same time, Israel’s water sector is faced with challenges and threats related to earthquakes and various states of warfare and security confrontations. In such times of emergency, Israel’s water sector is particularly vulnerable to disruptions of the water infrastructure and it’s adequate operation, by both contamination of the water sources and damage to the desalination plants. This study examines the challenges of the Israeli water sec-tor that require it to contend with these emergency situations in an era of reliance on desalination plants. The research method combines analysis of statistical data extracted from Israel’s Central Bureau of Statistics, Government Water and Sewage Authority, and Meteorological Service, in addition to reading and analyzing primary documents generated by various government ministries and government organs related to Israel’s water policy. The research findings lead to the conclusion that public policy on managing the water sector, manifested in the development and establishment of water desalination plants, has resolved Israel’s water crisis, put an end to its dependency on the amount of precipitation and on natural water sources, and allowed an increase in water production to match the rise in consumption. Nonetheless, as successful as this public policy may be, it does not consider the possibility of extreme scenarios and does not develop the entire range of steps necessary to confront them, and thus undermines the ability of the Israeli water sector to provide its citizens with water in times of emergency.

Language models like BERT dominate current NLP research due to their robust performance, but they are vulnerable to backdoor attacks. Such attacks cause the model to consistently generate incorrect predictions when specific triggers are present in the input, while maintaining normal behavior on clean inputs. In this paper, we propose a straightforward data poisoning method targeting the BERT architecture. Our approach does not involve complex modifications to the model or its training process; instead, it relies solely on altering a small portion of the training data. By introducing simple perturbations into just 10% of the training dataset, we demonstrate the feasibility of injecting a backdoor into the model. Our experimental results show a high attack success rate, indicating that the model trained on the poisoned data can reliably associate the trigger with the attacker's desired outputs while its performance on clean data remains unaffected. This highlights the stealth and effectiveness of our method, emphasizing the need for improved defensive strategies to protect against such threats. Our study underscores the critical importance of ongoing research and development to safeguard AI systems from malicious exploitation, ensuring the security and reliability of NLP applications.

Introduction: It may be feasible to detect episodic memory change in preclinical Alzheimer’s disease (AD) to indicate navigational risks and AD prior to mild cognitive impairment. The establishment of episodic memory signatures may allow for biomarker mapping to inform diagnostic research and practiceMethods: Retrospective longitudinal mixed methods and cross-sectional effect size, to compare group differences, are applied to the Craft-21 memory scores from the preclinical years of persons who later developed AD (pre-AD) dementia , n = 175 with 112 females and 63 males. Their scores are compared to cognitively normal controls (non-AD) n = 6,814 with 4,232 females and 2,582 males. Pre-AD and non-AD groups are further analyzed by biological sex. The dataset is from the National Alzheimer’s Coordinating Center funded by NIA/NIH Grant U24 AG072122. Results: The pre-AD episodic memory scores decreased an average of -.510, p < .001 versus (vs.) the non-AD annual increase by .127, p < .001. The first Cohen’s d = .482 and last Cohen’s d = 976, p < .001. The pre-AD females had an average annual decreased of -.762, p < .001 vs. non-AD females increase of .117, p < .001. The first Cohen’s d = .576 and last Cohen’s d = 1.133, p <.001. The pre-AD males increased every year by .185, p = .996 vs. the non-AD males annual increase of .185, p < .001. The first Cohen’s d = 1.054 decreased by the last year to Cohen’s d = .680, p <.001Discussion: Distinct decline in episodic memory occurs for pre-AD females, but there is not a significant change in pre-AD males. However, the effect size difference between both pre-AD and non-AD groups suggests biomarker mapping in conjunction with memory trajectories may be feasible to determine potential navigational risks as well as biological and cognitive preclinical AD.

The paper describes schemes for the resource-efficient generation of twiddle factors for the fixed-radix version of the ubiquitous fast Fourier transform (FFT) algorithm. The schemes, which are targeted at a parallel implementation of the FFT, provide one with the facility for trading off arithmetic complexity, as expressed in terms of the required numbers of multiplications and additions (or subtractions), against the memory requirement, as expressed in terms of the amount of random access memory (RAM) required for constructing the look-up tables (LUTs) needed for the storage of the two twiddle factor components – one component being derived from the sine function and the other from the cosine function. Examples are provided which illustrate the advantages and disadvantages of each scheme – which are very much dependent upon the length of the FFT to be computed – for both the single-level and multi-level LUTs, highlighting those situations where their adoption might be most appropriate. More specifically, it is seen that the adoption of a multi‑level LUT scheme may be used to facilitate significant reductions in memory – namely, from an O(N) to an memory requirement, for the case of an N‑point FFT, where β ≥ 2 corresponds to the number of distinct angular resolutions used – at a relatively small cost in terms of increased FFT latency and arithmetic complexity.

Alleged practices of magical assault and vampirism are a recurrent feature of popular explanations of misfortune in Tuva Republic, South Siberia. Based on a field study of healing practices in an “Association of Shamans”, this article analyses rituals of countercursing one’s antagonists in a context of Russian political domination. A central purpose of this discussion is to foreground the centrality of kinds of parasitical worship and occult threat to structures of political power in – and beyond – the region of Tuva. Focusing on a “curse-scape”, which develops from the combative practices of shamans, occult specialists and office-holders, the article probes a repertoire of healing symbols in a shaman’s courtroom. It is argued that shamanic healing draws its efficacy from appropriating hunting symbols and animal spirits, which appear in Indigenous Siberian cosmologies. The analysis shows that ideas of playing with risk during countercursing rituals structure the process of symbolic resolution. The article concludes by highlighting indigenous perceptions of a “cursed” landscape as a field where the agencies of “darkness” (and their political sponsors) are confronted with an emancipating, Buddhist religious mindset.

Marine litter, comprising persistent and human-made solid materials discarded in marine environments, poses a severe threat to Nigeria's coastal and marine ecosystems. Despite existing international, regional, and national regulations, ineffective enforcement and compliance have led to widespread environmental degradation (UNEP, 2021). This study examines the factors contributing to the proliferation of marine litter in Nigeria, focusing on regulatory gaps, insufficient waste management infrastructure, and the lack of public awareness. The findings reveal that the marine ecosystem is deteriorating due to inadequate waste disposal practices and limited understanding of the impacts of marine litter on both the environment and human health (UNEP, 2021). The paper concludes with recommendations for improving regulatory enforcement, enhancing public education, and investing in waste management infrastructure to address the growing crisis of marine litter. Throughout this article, the limited information on the impacts of marine pollution across Africa is highlighted, emphasizing the value of these insights despite their scarcity.

This project investigates the behavior of multi-head attention in Transformer models, specifically focusing on the differences between benign and trojan models in the context of sentiment analysis. Trojan attacks cause models to perform normally on clean inputs but exhibit misclassifications when presented with inputs containing predefined triggers. We characterize attention head functions in trojan and benign models, identifying specific 'trojan' heads and analyzing their behavior.

In this paper, we explore the hypothesis that topological defects formed in the quark-gluon plasma (QGP) of the early universe, specifically flux tubes and domain walls, could serve as stable dark matter candidates. Theoretical analysis of the QCD vacuum structure reveals that these defects can form due to nontrivial topological configurations, with the Lagrangian density incorporating topological terms. We model the formation and stability of flux tubes and domain walls, estimating their energy densities and dynamics within the QGP. Flux tubes, with energy per unit length approximately given by $\mu \approx \frac{\sigma}{g_s^2}$, and domain walls, characterized by a surface energy density $\sigma_{\text{DW}} \sim \Lambda_{\text{QCD}}^3$, exhibit unique gravitational signatures. These signatures include distinct gravitational lensing patterns due to their linear and planar mass distributions, making them detectable through astrophysical surveys. Furthermore, their minimal interaction with conventional matter aligns well with observational constraints on dark matter properties. We discuss potential experimental approaches for detecting these defects, including high-energy collider experiments and detailed analysis of gravitational lensing data. Our findings suggest that topological defects in the QGP provide a compelling and novel solution to the dark matter problem, offering a new direction for both theoretical investigation and experimental verification.

This study focuses on the design, development, and evaluation of an educational mobile robot, named Robot-T2, to be used in educational activities within a technical school. The primary objective was to create a versatile platform that allows students to explore programming, engineering, and science concepts in a practical and collaborative manner. The methodology included an iterative design and testing approach, incorporating feedback from students and teachers to continuously improve the robot's functionality. The tests covered autonomous navigation, line following, and object manipulation, using the OnBotJava programming software and the Control Hub as the main interface. The results demonstrated that Robot-T2 is highly efficient and adaptable in various educational contexts, showing remarkable precision in navigation and object manipulation. The design iterations significantly improved the robot's performance, enriching the educational experience and promoting active learning. In conclusion, educational robotics, represented by Robot-T2, has great potential to enhance the teaching and learning process in technical schools, fostering teamwork, problem-solving, and the integration of technology in the classroom. This project is expected to inspire future developments in educational robotics.

This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learning, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services.

Although evolutionary theory has yet to furnish a comprehensive rationale for the underlying mechanism of natural selection, it is pertinent to note that the phenomenon transcends the confines of biological systems, exhibiting analogous patterns in physical systems as well. Specifically, isolated and closed systems tend to evolve towards states of progressively increasing entropy. In scenarios where multiple paths for entropy increase are accessible, such systems tend to favor combinations of paths that exhibit the most rapid entropy increase rates. Among the various processes in nature, life activities constitute a salient means of achieving entropy increase. Genetic variation within organisms generates individuals that vary in their rates of entropy increase. As these organisms interact, compete and combine, they form diverse combinations of entropy-increasing pathways. Nature, in turn, selects among these combinations, favoring those that culminate in the fastest entropy increase, thereby propelling the evolution of life. Essentially, the evolution of life is an ongoing exploration of diverse combinations of pathways that maximize entropy increase across various energy pools. Amidst continuous genetic variation and the selective pressure imposed by nature for maximum entropy production rates, information accumulates, leading to a corresponding acceleration in the rate of entropy increase. This natural selection, which favors maximizing the entropy at the fastest possible rate, serves as the ultimate driving force for the origin and evolution of life.

This paper presents an approach to the analysis of beams with rectangular cross-sections subjected to Saint-Venant torsion using the Fourier analysis, body forces being not considered. The Saint-Venant torsion of beams, also called free torsion or unrestrained torsion, is characterized by the absence of axial stresses due to torsion; only shear stresses are developed. The solution to this torsion problem consists of finding the Prandtl stress function that satisfies the governing equation and the boundary conditions, the governing equation being such that the Laplacian of the stress function has a constant value within the cross-section. In this paper, Fourier sine series was utilized to describe the constant value of the above mentioned Laplacian, while double sine series or simple sine series was utilized to describe the Prandtl stress function. Closed-form solutions in form of infinite series were derived for shear stresses, torsional constant and warping function. Those solutions were identical to the exact solutions.

Cerebral Palsy (CP) poses substantial challenges to individuals, affecting their physical capabilities and daily functioning, and necessitates tailored interventions to enhance their quality of life and independence. Hand functionality is often affected in more than 60% of individuals with CP; they (mostly children) may spend up to 2-3 hours tightening their shoes perfectly. This includes the tightening of laces, standing up to determine if it is sufficient, and having to repeat the process many times till desired throughout the day. This paper focuses on the development of such a system that will assist them in effectively reducing this time to an approximate maximum time of 10 minutes. In its proposed form, this would reduce the shoe-tying process and make it ~18 times faster. The system will allow the user to perform the process with one hand and be simple and reliable. This additional freedom, among utilization of other systems, could lead to a betterment in mental health regarding the desire to perform outdoor activities among individuals with CP

Introduction: Papatasi fever, or three-day fever, is a viral disease transmitted by a species of female sandfly called Phlebotomus papatasi that has symptoms similar to influenza. Robat Karim County, due to the high abundance of Sandfly and climate change in recent years, provides a suitable environment for the growth and reproduction of Sandfly, especially Phlebotomus papatasi Therefore, the aim of this study was molecular monitoring of Papatasi fever virus in Sandfly of Robat Karim County, Tehran province. Materials and methods: In 7 selected areas in Robat Karim County, 679 Sandfly using sampling methods including traps and aspirators were caught and 500 samples were randomly selected according to the fishing location and transferred to the laboratory of the Medical Entomology Department. After fixing Sandfly on the slide surface, identification and percentage of frequency of sandfly species were determined and then by Real-time PCR method, molecular examination of Papatasi virus infection was performed at Tehran Pasteur Institute. Findings: Out of 679 samples taken, 310 (45.65%) belonged to males and 369 (54.35%) belonged to females. 7 species including 3 species of Sandfly of the genus Phlebotomus and 4 species of the genus Sergentomyia were identified among the samples, the highest percentage of catch belonged to Phlebotomus sergenti (28.57%). Of the 500 samples sent to the laboratory for testing for phleboviruses by Real-time PCR, no positive cases of Papatasi virus infection were observed in them. Conclusion: Robat Karim is one of the endemic areas of infectious diseases such as leishmaniasis. Due to climate change in this region that conditions for the growth and reproduction of Sandfly, especially Phlebotomus papatasi as a Vector of sandfly fever and since the sandfly fever disease is not vaccinated therefore, preventive measures to control and reduce diseases transmitted by Sandfly are important.

Areca palm sheath leaves were an incredibly noble food packaging system. In ancient times, naturally available wood, leaves, stems, and barks were used to store food materials. But all these were turned down upon the rise of civilization. Today, many modern food packaging systems are used; paper and paperboard packaging materials are used mainly as a replacement for plastic materials. But somehow, paper-based materials have major drawbacks when temperature-dependent products are stored; leaching out components poses a higher threat to health. To alleviate it, bio-based components were used in the manufacturing of paper materials, but the migration of those components was still risky. This study focuses on how the areca palm sheath leaves can be effectively used to prevent this migration of components.

Swimming with Sharks: Can Small Investors Compete with Buffett? Pankaj Topiwala. FastVDO LLC, 3097 Cortona Dr., Melbourne, FL 32940 Abstract: Millions of small investors fare poorly in the financial markets. For them, the markets are like turbulent seas. Braving them in a mere skiff, a major storm can spell financial doom at any time. But the reality is that most investment funds also underperform the market. Against this backdrop, there are some savvy investors who have consistently beat the market, for decades! Names such Benjamin Graham, John Templeton, Peter Lynch, or Jim Simons come to mind. But no investor has beat the market as decisively or for as long as Warren Buffett, getting an annual return of nearly 20% since 1965! Is there a way that the small investor can swim with these sharks? These legendary investors developed a refined sense for valuing companies through fundamental analysis (hard work), investing in solid companies that are deep in value. The untrained small investor cannot hope to replicate their method. To enter these waters, she needs a new boat: algorithmic trading. Not the kind that trades in microseconds (high frequency trading), which is again out of her reach, but the type that trades only a few times a year (very low frequency). Using an approach developed and tested in previous papers, we go head-to-head with Buffett's famous BRK-A fund and compare performance over the 44-year period 1980-2024. Buffett's BRK-A earned 19% annual return over the 44 years. We also test a 50-year period, the historical limit for Nasdaq. By using simple algorithmic trading methods, we show that the small investor can indeed keep pace with Buffett, without the hard work. Our simple methods allow us to obtain both strong gains, and reduced drawdowns, helping us to survive the major market downturns known as Black Swan events. This win-win is good news for small investors worldwide.

We propose a novel framework for understanding quantum phenomena through the lens of a modified electromagnetic spacetime governed by Einstein field equations. By assuming the existence of an electromagnetic spacetime that adheres to these modified geodesic and Einstein field equations, provided the field equations satisfy the Coulomb force law in weak-field approximations, we explore the derivation of Maxwell’s equations and highlight the necessity of incorporating spin to validate these equations. We derive the wave equation for the normalized transverse metric and establish its equivalence with the de Broglie wave proposition, suggesting a relationship between this metric and the particle's wavefunction. This approach offers a classical explanation for several quantum phenomena, including the double-slit experiment, entanglement, tunneling, non-locality, and wavefunction collapse. Additionally, it posits a method to visualize atomic orbital structures in line with quantum mechanical predictions. These findings suggest that the foundational principles of quantum mechanics can be interpreted as linearized approximations of general relativity applied to electromagnetic spacetime. While this theory does not yet extend to the correlations between general relativity, quantum electrodynamics (QED), and quantum chromodynamics (QCD), it provides a valuable visualization tool for physicists and advances our understanding of the unresolved challenges linking quantum mechanics with general relativity.

The study of the emission, propagation, and reflection of balls in Newtonian mechanics, applying the laws of conservation of momentum and energy when the mass of the balls converges to zero, concludes that the kinematics of light are like those of massless balls. The kinematics of light explain why the speed of light is the constant c in each inertial frame in which the light source and reflective mirror are at rest, why the laws of physics have the same form in each inertial frame, and why any light experiment in an inertial frame cannot prove the motion of that inertial frame. The following law of mechanics governs the kinematics of balls and electromagnetic radiation: Each inertial frame drags at its velocity the balls and electromagnetic radiation emitted by bodies at rest in that inertial frame. The theory of special relativity misapplies the symmetry observed in phenomena to two inertial frames; therefore, it duplicates a physical phenomenon from one inertial frame, which is considered stationary, into another. There are multiple issues rooted in Lorentz's and Einstein's transformations. For example, transformations offer the speed of light at the constant c in the moving and opposite directions of the inertial frame. At the same time, it is variable in any other direction, converging to infinity. Time contraction in the moving direction of the inertial frame is different from time dilation in the opposite direction. It requires a ruler and time synchronization in each direction. There are no length contractions in Lorentz's and Einstein's transformations to support the fundamental concept of length contraction in special relativity. With these unacceptable conclusions, the theory of special relativity is self-negating.

Laser Powder Bed Fusion (LPBF), as a transformative technology in the field of additive manufacturing, offers unprecedented design flexibility and material efficiency. This review paper delves into the latest advancements in LPBF technology and the various alloys used in the process, including titanium alloys, aluminum alloys, nickel-based superalloys, steel alloys, copper alloys, and high-entropy alloys. For each alloy category, we discuss their specific applications, performance characteristics, and challenges faced in LPBF usage. The paper discusses important technological breakthroughs, including innovations in laser systems, scanning strategies, and in-situ monitoring technologies, which have enhanced the precision and reliability of LPBF.

The limited velocity in a geometry of Lorentzian signature, seem to prevent the universe to reach thermodynamic equilibrium as suggested by the cosmic microwave background. Thus it was suggested that the universe which was initially minuscule has reached a more considerable radius in a short duration by a process known as cosmic inflation. However, to drive such a process have led to the suggestion of an ad-hoc scalar field the inflaton, which has no purpose in nature other than driving the cosmic inflation field and then stopping it once the universe reached the right size. In a recent paper it was shown that rapid expansion can occur without postulating an inflation by following Hawking's suggestion and assuming that primordially the metric of the universe had an Euclidean signature, in which case velocity is not limited and thermalization and rapid expansion are derived without the need to assume an ad-hoc field. However, while in the previous work emphasis was put on the dynamics and physical statistics of the particles in a Euclidean space vs. Lorentzian space in which both spaces were given. No mathematical model was given regarding the development of current Lorentzian space-time from the early Euclidean space-time, and how fundamental problems such as the space-time singularity and the homogeneity of the CMB can be solved in the hybrid Euclidean-Lorentzian picture. This lacuna is to be rectified in the current paper.

There are hundreds, if not thousands, of pathways in a unicellular prokaryotic organism, and all these pathways will have to be controlled, regulated, and coordinated to ensure their proper functioning. As organisms evolve from unicellular eukaryotic organisms to multicellular organisms such as the metazoans, more pathways have evolved to support more diverse biological functions. As a result, fine-tuned control, regulation, and coordination of these pathways becomes an immensely complex and demanding task. Yet, consistent with Darwin’s insight that “nature is prodigal in variety, though niggard in innovation,” nature has produced only a few master coordination hubs for these pathways. Moreover, multicellular organisms such as metazoans have evolved two broader means for coordinating various pathways: cross-talk and communication. Cross-talk operates via having shared components and links between intracellular pathways whereas communication operates via transmitting intercellular signals such as hormones and other ligands with membrane receptors and downstream intracellular signal transductions pathways. Our framework points to a more complex network-based approach for understanding biological pathways and the consequences of their disruption, escape, and malfunction such as cancer, aging, and other diseases.

This study examines the association between dietary saturated fatty acid (SFA) intake and leukocyte telomere length (LTL) using data from the National Health and Nutrition Examination Survey (NHANES) 1999-2002. Telomeres, protective repeats of DNA at the ends of chromosomes, play a crucial role in cellular aging. Their length is shortened over time and is influenced by various factors, one of which is diet. The present study's objective was to evaluate whether increased SFA consumption is associated with shorter LTL. The analysis, conducted on a large, nationally representative sample of 6646 adults, revealed an inverse association between dietary SFAs and LTL. When examined further, this association was found to be significant in women, but not in men. Serum C-reactive protein (CRP) levels were tested as a potential mediator in this relationship, but no such mediation was found. These findings support the role of dietary factors in modulating cellular aging. However, further research is warranted to elucidate the underlying mechanisms driving the overall association, as well as the gender-specific effects observed.

The existence of palindrome numbers of arbitrary length greater than 2 that are divisible by the prime number 37 is proven in this paper. The method to construct such a palindrome is also described. It turns out that a palindrome number divisible by 37 is also divisible by 3 and, consequently, divisible by 111.

Health care operations in many hospitals are experiencing increasing demand which is however hindered by the limited resources available. The imbalance between the demand and service provision capacity within the hospitals have resulted in long waiting times for service delivery due to overcrowding, high costs of treatment as well as low levels of patient’s satisfaction. The above problems are attributed to the development of hospital management information system. This dissertation is focused on investigating some of the causes of the increased cost and time consumption as well as low levels of patient’s satisfaction in the application of hospital management information system. The study used comparative design to make a comparison of the respondents from two different hospital facilities where 400 health workers were drawn for research. The questionnaire was used as a survey tool for the study that involved both qualitative and quantitative analysis. The results of the study indicate various playing factors that arise due to the development of the hospital management information system. These factors include the large functionality of the system, the need for training of the end users, the increasing ageing population, and delays. The identified factors play a significant role in the way the information system impacts the time taken, the cost involved, and patient satisfaction. Keywords: health, hospital, doctors, technology, information system, time, and cost.

Since severe acute respiratory syndrome coronavirus (SARS-CoV)-2 emerged in December 2019, significant research has been conducted around the world to understand the virus- and vaccine-generated immune response, and to identify the drivers of severe coronavirus disease 19 (COVID-19). Although viral tropism is largely limited to the respiratory tract, SARS-CoV-2 infection can lead to severe COVID-19 disease, which is characterized by multiorgan failure and permanent cognitive disorders. The combined effects of virus-mediated inhibition of host antiviral mechanisms and the recognition of pathogen-associated molecules by pattern recognition receptors (PRRs) are the main drivers in the pathogenesis of severe disease. The innate immune system constitutes the first line of defense against SARS-CoV-2 by limiting the entry of the virus into the host cell, suppressing viral replication, detecting virus-infected cells, and accelerating the formation of the adaptive immune response. The adaptive immune system is composed of two basic cells, B and T cells, which serve different but complementary functions in viral infections. Although the critical role of adaptive immunity in clearing SARS-CoV-2 infection is well known, its function in the development of virus-induced immunopathogenesis remains poorly understood. The basic characteristic of the humoral immune response is the creation of specific antibodies to each foreign antigen encountered. Antibodies that sense pathogens are classified as neutralizing antibodies (nAbs) and non-neutralizing antibodies (non-nAbs) based on their capacity to neutralize pathogens and inhibit membrane fusion. While nAbs exert their activities by blocking ACE2-dependent viral entry into host cells and neutralizing pathogens, the functions of non-nAbs are often undetectable. The level of anti-SARS-CoV-2 antibodies in serum and mucosal tissues varies with the stage and severity of the COVID-19 infection. Although current COVID-19 vaccines elicit robust antibody responses, this effect decreases over time, due to the weakening of immunity and the emergence of novel variants that evade the antibody response, such as Delta and Omicron. SARS-CoV-2 may evade host innate immune surveillance by minimizing host recognition and interfering with cellular immune signaling. In this review article, the central role of host immune responses in the pathogenesis of COVID-19 and the effectiveness of COVID-19 vaccines, as well as the immune evasion strategies of SARS-CoV-2 are discussed in detail in the light of novel knowledge.

Abstract In This research we will describe how to deal the system with users to make the work in the company or in any work space . in application forms interact with users inside the system . application form of any system must creates a many graphical interface for user ant we should take care about who is dealing with the system , Additionally, application forms used from different type of user so that we must take care about this points during designing the system for different users . Application Forms in the system consists of many programs, or components and function we are designing it. the instructions inside program can be execute by clicking on the command button and we can create specific process in the system.

Depression is diagnosed when a specific set of symptoms appear over a defined period with a certain intensity. According to the DSM-5, Major Depressive Disorder (MDD) is diagnosed when five or more symptoms are present over a two-week period. Depression, a debilitating mental health disorder affecting millions worldwide, poses a complex challenge for researchers and clinicians. In children and adolescents, depression can result in negative outcomes such as substance abuse, academic problems, risky sexual behavior, physical health issues, impaired social relationships, and a thirty-fold increased risk of suicide. This paper presents the findings of a study that evaluated the effectiveness of Cognitive Behavioral Therapy (CBT) alone and CBT combined with selective ser-otonin reuptake inhibitors (SSRIs) in a remediation program. The study included 16 children and adolescents with depression (8 males and 8 females) and 16 typically developing peers (8 males and 8 females) aged 9 to 15 years (M = 11.94, SD = 2.02). Baseline evaluations using Event-Related Po-tentials (ERPs), the Children Depression Inventory (CDI), and reaction time tests showed that participants with depression had cognitive deficits in attention and memory, indicated by longer P300 latencies. After undergoing remediation with either CBT alone or CBT combined with med-ication, the children and adolescents with depression exhibited lower CDI scores and shorter P300 latencies and reaction times compared to before remediation and relative to the control group.

The Ramsey theory based approach to the phase transitions of the second order is suggested. The phase transitions of the second order are seen as switching of physical interactions/chemical bonds between the entities forming the primitive cell of the material. Such a switching is typical for a phase change materials. If the energy of the primitive cell is kept constant under the change in the spatial order of the chemical bonds, the phase transition of the second order takes place. The switching of interactions between the entities forming the primitive cell is accompanied by the breaking of the initial symmetry of the cell. The order parameter/the degree of ordering characterizing the ordering within the primitive cell is re-defined. The introduced degree of ordering quantifies ordering of links/interactions/chemical bonds between entities constituting the 2D lattice; whereas, the classical “Landau degree of order” quantifies the symmetry breaking under variation in spatial locations of these entities. The suggested approach is generalized easily for 3D primitive cells. Thermal capacity of the non-symmetrical phase is larger than that of the symmetrical phase. For the primitive cells built of six interacting entities, the Ramsey Theory predicts inevitable appearance of the unstable monochromatic triangles, when the links correspond to attraction or repulsion interactions. The situation becomes different for the primitive cells built of five interacting entities.

The article presents an accessible route into the field of neuroscience through ardEEG device. This device allows convert Arduino UNO R4 WIFI into a brain-computer interface, making it easy to measure EEG, EMG and ECG signals with 8 channels. Over the past decade, the proliferation of artificial intelligence has significantly impacted various industries, including neurobiology. The integration of machine learning techniques has opened avenues for practical applications of EEG signals across technology sectors. This surge in interest has led to the widespread popularity of low-cost brain-computer interface devices capable of recording EEG signals using non-invasive electrodes. ardEEG device demonstrates satisfactory noise levels and accuracy for use in applied tasks. Moreover, it successfully detects alpha brain waves, showcasing its effectiveness in capturing essential neurological signals

Methodologies for future-oriented research are mutually beneficial to highlight different methodological perspectives and proposals for extending higher education didactics towards sustainability. This study explores how different augmented reality applications can enable new ways of teaching and learning. It systematically investigates how student teachers (n=18) in higher education experienced ongoing realities while designing learning activities for a hybrid conference and interconnecting sustainability knowings via didactic modelling and design thinking. This qualitative study aims to develop a conceptual hybrid framework concerning the implications of student teachers incorporating the design thinking and inner transition into their professional work with future-oriented methodologies on didactic modelling for sustainability commitment. With a qualitative approach data was collected during and after a hackathon-like workshop through student teachers’ reflections, post-workshop surveys, and observation field notes. The thematic analysis shed light on transgressive learning and a transition in sustainability mindset through activation of inner dimensions. Findings reinforcing sustainability commitment evolved around the following categories: being authentic (intra-personal competence), collaborating co-creatively (inter-personal competence), thinking long-term-oriented (futures-thinking competence on implementing didactics understanding), relating with creative confidence (values-thinking competence as embodied engagement), and acting based on perseverant professional knowledge driven change (bridging didactics) by connecting theory-loaded empiricism and empirically loaded theory. The results highlight some of the key features of future-oriented methodologies – and approaches to future-oriented methodologies feature collaboration, boundary crossing and exploration – and show the conditions that can support or hinder methodological development and innovation.