The proliferation of cloud computing has given rise to challenges in security that established perimeter-based models cannot easily adapt to. The new buzzword Zero Trust, which is based on the principle of "never trust, always verify," has been looked up as one of the promising solutions. Is Zero Trust a certainty or pie in the sky for security in the cloud? This is discussed further in this paper. This philosophical approach leads us through the earlier work and scientific literature available on the subject, presenting Zero Trust's capabilities, advantages when introduced into cloud security architecture, and obstacles about its incorporation. The study finds that Zero Trust holds the potential to enhance cloud security significantly, but it is no silver bullet and demands careful implementation and further studies to understand its intricacies as well as pitfalls.

As Cybercrimes continue to rise, researchers and industry players have continued to work hard to come up with solutions that could reduce and possibly eradicate these criminalities. This research has been able to discover the major factor that usually causes people to fall prey to these cyber criminals. This was why a school of thought came up with the Zero Trust Access model to strengthen the network framework. While previous network assets relied on the "trust but verify" philosophy, this technology's primary tenet is "never trust, always verify," which applies to every user and device connected to the network. This study interrogated the existing network models of the Integrative Trust Model (ITM) and Zero Trust Access (ZTA) in addition to the trust model (TA) to understand their concepts, implementation needs, strengths, and shortcomings. This made it possible for the study to establish the position of the ZTA model in repositioning the network security architectures vis-a-vis protecting the users

In response to the increasing complexity and sophistication of cyber threats, particularly those enhanced by advancements in artificial intelligence, traditional security methods are proving insufficient. This paper explores the zero-trust cybersecurity framework, which operates on the principle of "never trust, always verify" to mitigate vulnerabilities within organizations. Specifically, it examines the applicability of zero-trust principles in environments where large volumes of information are exchanged, such as schools and libraries, highlighting the importance of continuous authentication, least privilege access, and breach assumption. The findings highlight avenues for future research that may help preserve the security of vulnerable organizations.

The large and interconnected nature of the Internet of Things (IoT) presents unique security challenges, even as it revolutionizes various sectors. With numerous devices, often limited in resources, traditional perimeter-based security methods struggle to keep pace. The "never trust, always verify" principle of zero trust security offers a viable solution. Zero trust security is a concept which has become increasingly popular, using key exchange techniques to ensure secure and authenticated communication within the network, especially in managing risks in critical infrastructure. Authentication is a process to identify an entity, a prerequisite for authorization, and essential for granting access control. It fundamentally relies on trust management and various methods to generate and manage cryptographic keys for authentication. The aim of this study is to enhance zero trust security in the context of the Internet of Things by investigating authentication methods and discussing several potential solutions for successful implementation. This study also presents the performance evaluation criteria for authentication in IoT and introduces advanced approaches for different scenarios, including lightweight cryptography, mutual authentication, and blockchain technology. Finally, we address challenges related to implementation and future directions for research.

Modern organizations have migrated from localized physical offices to work-from-home environments. This surge in remote work culture has exponentially increased the demand and usage of Virtual Private Network (VPN), which permits remote employees to access corporate offices effectively. However, the technology raise concerns including security threats, latency, throughput, and scalability, among others. These Newer generation threats are more complex and frequent, which makes the legacy approach to security ineffective. The research paper gives an overview of contemporary technologies used across enterprises including VPN and Zero Trust Network Access (ZTNA), Proxy Servers, Secure Shell (SSH) Tunnels, Software-defined wide area network (SD-WAN), and Secure Access Service Edge (SASE). This paper also presents a comprehensive cybersecurity framework named Zero Trust VPN (ZTVPN), which is a VPN solution based on Zero Trust principles. The proposed framework is aimed to enhance IT security and privacy for modern enterprises in remote work environments and addresses concerns of latency, throughput, scalability, and security. Finally, the paper demonstrates the effectiveness of the proposed framework in various enterprise scenarios, highlighting its ability to prevent data leaks, manage access permissions, and provide seamless security transitions. The findings underscore the importance of adopting ZTVPN to fortify cybersecurity frameworks, offering an effective protection tool against contemporary cyber threats. This research serves as a valuable reference for organizations aiming to enhance their security posture in an increasingly hostile threat landscape.

The Zero Trust Architecture (ZTA) security system follows the "never trust, always verify" principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation is time-consuming and difficult. Azure's extensive services and customizations require careful design and implementation. Azure administrators struggle to navigate and change configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTA's comprehensive policy definitions, multi-factor and pass-wordless authentication, and other advanced features are tested in a mid-size business scenario. These configuration changes require a solid grasp of Azure's architecture and Zero Trust. Implementing an Azure ZTA reduces vulnerabilities and restricts authorized users' access to critical resources. Implementation is time-consuming and expensive, requires a significant commitment to changing IT procedures, and can be confusing because the same features are available in multiple places. Azure ZTA has shown great potential for both hybrid and cloud-native companies.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files ( greater than 50 MB) or Big data file ( grater than 1 TB) computational complexity is more we leverage on sharding concept to enhance both space and time complexity in cloud storage, hence the large files are divided into shards and stored in different database servers. We also employed blockchain smart contract to enhance secure retrieval of the file and also a secure access method which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data itself. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies require to store data. As the environment maintains privacy of the user data and by not storing the raw data of the user anywhere, the entire environment is setup as a Zero trust model.

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

The advent of Web3 technologies, leveraging blockchain and smart contracts, promises a paradigm shift toward decentralized and autonomous economic interactions. However, the lack of robust trust and reputation mechanisms among pseudonymous participants hinders its evolution into a fully functional economic system. This paper introduces the Dmany Nexus Protocol, a decentralized reputation system that quantifies user trustworthiness through verified on-chain and off-chain actions, addressing critical issues of information asymmetry, moral hazard, and adverse selection inherent in decentralized networks. Building upon the live data and insights from the Dmany Quest Engine, the Nexus Protocol integrates advanced principles from information economics, game theory, and mechanism design to develop a quantifiable reputation metric—the Social Capital Score ((SCS)). This metric aggregates multiple facets of user behavior, employing sophisticated anti-collusion algorithms and zero-knowledge proofs for privacy preservation. Unlike existing solutions, the Nexus Protocol offers a standardized, interoperable, and tamperresistant reputation system that enhances economic efficiency and fosters mass adoption by enabling secure, privacy-preserving interactions among pseudonymous actors. Empirical analyses demonstrate that the protocol effectively mitigates security risks such as Sybil attacks and reduces information asymmetry, leading to improved task quality and reduced fraudulent activities in decentralized platforms. By establishing a foundation for trust and cooperation in the Web3 ecosystem, the Dmany Nexus Protocol significantly advances the potential for scalable and efficient decentralized economies.

The rise of abusive language on social media is a significant threat to mental health and 1 social cohesion. For Bengali speakers, the need for effective detection is critical. However, current 2 methods fall short in addressing the massive volume of content. Improved techniques are urgently 3 needed to combat online hate speech in Bengali. Traditional machine learning techniques, while 4 useful, often require large, linguistically diverse datasets to train models effectively. This paper 5 addresses the urgent need for improved hate speech detection methods in Bengali, aiming to fill the 6 existing research gap. Contextual understanding is crucial in differentiating between harmful speech 7 and benign expressions. Large language models (LLMs) have shown state-of-the-art performance in 8 various natural language tasks due to their extensive training on vast amounts of data. We explore the 9 application of LLMs, specifically GPT-3.5 Turbo and Gemini 1.5 Pro, for Bengali hate speech detection 10 using Zero-Shot and Few-Shot Learning approaches. Unlike conventional methods, Zero-Shot 11 Learning identifies hate speech without task-specific training data, making it highly adaptable to new 12 datasets and languages. Few-Shot Learning, on the other hand, requires minimal labeled examples, 13 allowing for efficient model training with limited resources. Our experimental results show that 14 LLMs outperform traditional approaches. In this study, we evaluated GPT-3.5 Turbo and Gemini 1.5 15 Pro on multiple datasets. To further enhance our study, we considered the distribution of comments 16 in different datasets and the challenge of class imbalance, which can affect model performance. The 17 BD-SHS dataset consists of 35,197 comments in the training set, 7,542 in the validation set, and 7,542 18 in the test set. The Bengali Hate Speech Dataset v1.0 & v2.0 includes comments distributed across 19 various hate categories: personal hate (629), political hate (1,771), religious hate (502), geopolitical hate 20 (1,179), and gender abusive hate (316). The Bengali Hate Dataset comprises 7,500 non-hate and 7,500 21 hate comments. GPT-3.5 Turbo achieved impressive results with 97.33%, 98.42%, and 98.53% accuracy. 22 In contrast, Gemini 1.5 Pro showed lower performance across all datasets. Specifically, GPT-3.5 Turbo 23 excelled with significantly higher accuracy compared to Gemini 1.5 Pro. These outcomes highlight a 24 6.28% increase in accuracy compared to traditional methods, which achieved 92.25%. Our research 25 contributes to the growing body of literature on LLM applications in natural language processing, 26 particularly in the context of low-resource languages.

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.

High rates of phosphorus (P) currently being applied to soils for the production of vegetables in the Mekong Delta, Vietnam has led to the concern of its negative effect on the economics and the environment. This research presents a comprehensive study on the determination of P supplying capacity in this region of Vietnam to examine the possibility of reducing P fertilizer input. One hundred twenty (120) soil samples were collected to evaluate total P and Bray 1 available P in the soils. Phosphorus maximum sorption, degree of P saturation, P release, and the effect of P fertilizer on corn (Zea mays L.) yield in greenhouse and fields were also determined. Total P concentrations of 56.7% soil samples evaluated yielded high P concentrations (>560 mg P/kg), while 74.2% of the samples had high Bray 1 available P concentrations (>20 mg P/kg soil). Maximum P sorption ranged from 149 to 555 mg P/kg soil, respectively and has negative correlation to available P (r = - 0.63*), degrees of P saturation ranged from 0.63 to 5.46% correlated to available P (r = 0.98**) and maximum P release ranged from 1.2 to 61.9 mg P/kg soil, respectively correlated to available P (r = 0.96**). Corn grown in soils with available P concentrations >15 mg P/kg did not respond to P fertilizer in greenhouse or field experiments. We conclude that many farmers in this region can reduce P fertilizer input, thus increasing their profits and reducing negative environmental impacts associated with excess soil P for sustainable agriculture.

Orthostatic hypotension (OH) is a condition marked by a significant drop in blood pressure upon standing, resulting from impaired autonomic regulation of vascular tone and blood volume. This study investigates the prevalence, physiological mechanisms, and occupational risk factors for OH among high-risk professions in Nepal, such as traffic police, security guards, and shopkeepers. Using a cross-sectional design, participants were evaluated for blood pressure variability, autonomic function, and fluid balance. The findings reveal a high incidence of OH, linked to reduced baroreceptor sensitivity and impaired sympathetic activation due to prolonged standing and inadequate hydration. This study highlights the need for tailored clinical interventions and workplace modifications to mitigate OH in vulnerable occupational groups.

Sewage sludge is an organic waste generated in waste-water treatment plants with certain content of nutrients, what may potentially be used as a source of slow release fertilizer, especially as phosphorus (P) source, but it demands some pre-treatment and the content of P is much lower compared to soluble mineral fertilizers. For these reasons, composted sewage sludge was used to manufacture pelletized organomineral fertilizers, mixing it with the inorganic sources monoammonium phosphate (MAP) and AshDec® (ASD) (thermochemically incinerated sewage sludge). The fertilizers were physiochemically characterized and evaluated as to the dynamics of soil P solubilization, and the forms of P remaining according to its lability. The sources evaluated were: organic compost of sewage sludge powder (SSC), pelletized SSC (SCP), pelletized organominerals SSC+MAP (S+MAP) and SSC+ASD (S+ASD), ASD alone, all compared to a soluble mineral fertilizer (MAP), and a control without P fertilizer. A test was conducted in leaching columns with 50 g of soil (Oxisol), where the fertilizers were applied at a dose of 100 mg P/column, and 30 mL of water or 2% citric acid were added daily for 30 consecutive days. The collected leachates had pH measured and P content determined. Pelletizing process resulted in denser products and promoted more gradual P release. The organomineral S+MAP was the most water-soluble recycled source, solubilizing about 70% of the total P, while the others presented much lower solubilization (<20%). In contrast, all fertilizers showed high solubility in 2% citric acid (except for S+ASD). After leaching, the greatest amount of P remaining in the soil as labile and moderately labile. Composting and the AshDec process produced materials with slow P solubilization, which would be favoured to solubilize in acidic soils and acidic rhizosphere conditions. In turn, the organomineral mixture of SSC with a highly soluble mineral fertilizer (MAP) resulted in a promising product with intermediate nutrient solubility, better synchronized with crop demand, thus increasing efficiency in P use.

A cross-sectional survey was distributed to 1322 members of a 1-on1 personalized strength training studio. 366 respondents (n=134 male and n=232 female), all aged over 20 years reported considerable training experience; 55% of males and 42% of females reporting 5+ years. Data were analyzed and reported descriptively with differences >5% identified based on use of the 5-point likert scale, sample size and nature of the observations. Disparity between males and females were identified; males reported higher perceptions of managing effort, technique, and programming without supervision compared to females. Safety was noted as being more important to females compared to males. Qualitatively, additional themes were raised including analogy of the personal relationship between trainer and trainee being similar to that of medical professionals. This was validated where participants discussed their adaptations from supervised strength training including quality of life in aging, and recovery from medical conditions and injury. Data is discussed in context of a previous body of literature suggesting males falsely report higher confidence in tasks compared to females, particularly in relation to effort, role models, and verbal encouragement. This research proves beneficial for strength training practitioners in enhancing their understanding and expectations of clients, as well as hopefully proving insightful in engaging more people in strength training.

In this paper, we introduce the (ρ,q)-analogue of the p-adic factorial function. By utilizing some properties of (ρ,q)-numbers, we obtain several new and interesting identities and formulas. We then construct the p-adic (ρ,q)-gamma function by means of the mentioned factorial function. We investigate several properties and relationships belonging to the foregoing gamma function, some of which are given for the case p = 2. We also derive more representations of the p-adic (ρ,q)-gamma function in general case. Moreover, we consider the p-adic (ρ,q)-Euler constant derived from the derivation of p-adic (ρ,q)-gamma function at x = 1. Furthermore, we provide a limit representation of aforementioned Euler constant based on (ρ,q)-numbers. Finally, we consider (ρ,q)-extension of the p-adic beta function via the p-adic (ρ,q)-gamma function and we then investigate various formulas and identities.

In this study, we propose a conservative and compact finite difference scheme designed to preserve both the mass change rate and energy for solving the sixth-order Boussinesq equation with the surface tension. Theoretical analysis confirms that the proposed scheme achieves second-order accuracy in temporal discretization and fourth-order accuracy in spatial discretization. The solvability, convergence, and stability of the difference scheme are rigorously established through the application of the discrete energy method. Additionally, a series of numerical experiments are conducted to illustrate the effectiveness and reliability of the conservative scheme for long-time simulations.

In this paper, we introduce a new class of $(p,q)$-analogue type of Fubini numbers and polynomials and investigate some properties of these polynomials. We establish summation formulas of these polynomials by summation techniques series. Furthermore, we consider some relationships for $(p,q)$-Fubini polynomials associated with $(p,q)$-Bernoulli polynomials, $(p,q)$-Euler polynomials and $(p,q)$-Genocchi polynomials and $(p,q)$-Stirling numbers of the second kind.

Formaldehyde is generated via the biochemical and chemical pathways, and it widely exists in foods. Its chronic exposure causes carcinogenic, neurotoxic, reproductive, allergic, immunological, genetic and respiratory toxicities. However, food-sourced formaldehyde is usually neglected due to its rapid metabolism after intake. Compared with its residual level in processed foods, a large proportion of formaldehyde disappears because of its high reactivity. Nevertheless, its elimination reaction mechanism and the potential consequences remain unclear. In this review, the disappearing pathway of formaldehyde from two aspects, the formation of formaldehyde-derived toxins and aromas, was reviewed. This review article may help food scientists take appropriate strategies to use the beneficial reactions of formaldehyde or divert its harmful effects to beneficial ones.

Employee turnover is a significant issue for financial institutions, impacting productivity, increasing recruitment costs, and disrupting critical operations. In this project, this study aimed to predict employee turnover using a dataset containing attributes such as employee satisfaction, performance, and tenure. By framing the task as a binary classification problem, this study employed CatBoost and XGBoost, two advanced regression-based algorithms, to develop predictive models. This paper's analysis demonstrated that CatBoost outperformed XGBoost across all evaluation metrics, including MAE, MSE, RMSE, and R², making it the more effective model for predicting turnover in the financial sector. The study highlights key factors contributing to employee attrition, such as job satisfaction, tenure, and promotion opportunities, offering actionable insights for retention strategies. Additionally, by predicting probabilities rather than binary outcomes, this study aims to make more detailed decisions about employee retention. This research provides valuable tools for financial institutions to mitigate the risk of turnover, retain critical talent, and ensure operational continuity.

Granular material is one of the most common in nature. Recently, there has been a significant amount of interest in the granular materials, such as powders or sand. It is challenging to describe the motion of such kind of macroscopic size matter, because it behaves like solid, fluid or gas. Size division, pattern formation, avalanches, packing and convection - these are just a few examples from a wide range of observed phenomena that occur during the motion process of granular matter. Most of these processes are based by flow and no wonder that much effort has been devoted to obtain a hydrodynamic description in which granular materials are treated as a continuous medium. During the research of vertically shaken granular medium motion in an opened container it was found that experimental and numerical results are explained by hydrodynamic theory. This paper investigates the motion of a granular matter for a shallow, vertically shaken bed. We assume the Leidenfrost state as initial and the matter resembles a fluid heated up from below.A one-dimensional isothermal and a non-isothermal problem of granular material motion,simulatedby a hydrodynamic model are considered, and the material is assumed to be a continuous medium. The local temporal solvability of isothermal and non-isothermal initial boundary-value problem in Sobolev’s spaces is proved. This work presents a numerical study of the one-dimensional model of granular medium flow. The feature of this model is consideration of the Navier-Stokes equations accounting an interpolation form of VanderWaals constitutive equation for pressure. A finite-difference approximation for numerical solution is proposed.

This bibliometric study analysed breast cancer research trends and outputs from 2014 to 1 2023, utilising data from the Scopus database. The primary objectives include analysing publication 2 growth, citation metrics, document type distribution, leading countries, preferred journals and 3 authorship patterns. The study reveals a significant increase in breast cancer research publications, 4 with a total of 12,073 documents and 216,769 citations over the ten-year period. The United States 5 leads in publication output and citation impact, followed by China and Japan. Articles are the 6 predominant document type for total 73.4% of publications, while reviews and book chapters also 7 contribute notably. The most preferred journals for publishing breast cancer research include Breast 8 Cancer Research and Treatment, Clinical Breast Cancer, and Breast Cancer Research. Authorship 9 patterns show a trend towards multi-authored papers, with a substantial number of papers having 10 more than five authors. The study underscores the global collaborative effort in advancing breast 11 cancer research and highlights key areas for future exploration.

While the knowledge about the development of understanding positive integers is rapidly growing, the development of understanding zero is not well-known. Here we tested several components of preschoolers’ understanding zero: whether they can use empty sets in numerical tasks, whether they can use empty sets soon after they understand the cardinality principle, whether they know what the word “zero” refers to, and whether they categorize zero as a number. The results show that preschoolers can handle empty sets in numerical tasks as soon as they understand the cardinality principle or even earlier, and some of them know that these sets are labeled as “zero.” However, they are unsure whether zero is a number. These results identify three components of knowledge about zero: operational knowledge, linguistic knowledge, and meta knowledge. To account for these results we propose that preschoolers might understand numbers as the properties of items or objects in a set. In this view, zero cannot be a number, because an empty set does not include any items, and the missing items cannot have any property, excluding also the number property. This model might explain why zero is handled correctly in numerical tasks, while it is not regarded to be a number.

proposal for a fundamental theory is described in which classical and quantum physics as a representation of the universe as a gigantic dendrogram are unified. The latter is the explicate order structure corresponding to the purely number-theoretical implicate order structure given by p-adic numbers. This number field was zero-dimensional, totally disconnected, and disordered. Physical systems (such as electrons, photons) are sub-dendrograms of the universal dendrogram. Measurement process is described as interactions among dendrograms; in particular, quantum measurement problems can be resolved using this process. The theory is realistic, but realism is expressed via the the Leibnitz principle of the Identity of Indiscernible. The classical-quantum interplay is based on the degree of indistinguishability between dendrograms (in which the ergodicity assumption is removed). Depending on this degree, some physical quantities behave more or less in a quantum manner (versus classic manner). Ideologically, our theory is very close to Smolin’s dynamics of difference and Rovelli’s relational quantum mechanics. The presence of classical behavior in nature implies a finiteness of the Universe-dendrogram. (Infinite Universe is considered to be purely quantum.) Reconstruction of events in a four-dimensional space type is based on the holographic principle. Our model reproduces Bell-type correlations in the dendrogramic framework. By adjusting dendrogram complexity, violation of the Bell inequality can be made larger or smaller.

Zero-shot learning ability robotics enables robots to generalize their knowledge and perform tasks for which they have not been explicitly trained. This ability is extremely helpful because in classic learning approaches, robots are trained on specific tasks and require extensive data and labeled examples to perform accurately. However, in real-world scenarios, robots often encounter new or unseen tasks that were not part of their training data. We briefly survey works in this area to give the reader a perspective about progresses in this area.

While there is a coordinated effort around reaching zero dose children and closing existing equity gaps in immunization delivery, it’s important that there is agreement and clarity around how ‘zero dose status’ is defined and what is gained and lost by using different indicators for zero dose status. There are two popular approaches used in research, program design, and advocacy to define zero dose status: one uses a single vaccine to serve as a proxy for zero dose status, while another uses a subset of vaccines to identify children who have missed all routine vaccines. We provide a global analysis utilizing the most recent publicly available DHS and MICS data to compare the number, proportion, and profile of children 12 to 23 months who are ‘penta-zero dose’ (have not received the pentavalent vaccine), ‘truly’ zero dose (have not received the BCG, polio, pentavalent, and measles vaccines), and ‘misclassified’ zero dose children (those who are penta-zero dose but have received at least one other vaccine). Our analysis includes 194,829 observations from 82 low- and middle-income countries. Globally, 14.2% of children are penta-zero dose and 7.5% are truly zero dose, suggesting that 46.5% of penta-zero dose children have had at least one contact with the immunization system. While there are similarities in the profile of children that are penta-zero dose and truly zero dose, there are key differences between the proportion of key characteristics among truly zero dose and misclassified zero dose children, including access to maternal and child health services. By understanding the extent of connection zero dose children may have with the health and immunization system and contrasting it with how much the use of a more feasible definition of zero dose may underestimate the level of vulnerability in the zero dose population, we provide insights that can help immunization programs design strategies that better target the most disadvantaged populations. If the vulnerability profiles of the truly zero dose children are qualitatively different from that of the penta zero dose children, then failing to distinguish the truly zero dose populations, and how to optimally reach them, may lead to the development of misguided or inefficient strategies for vaccinating the most disadvantaged population of children.

Background: Nanotechnology has emerged as a promising field of study, aiming to revolutionize healthcare through the innovative development of drugs, diagnostic tools, and medical devices. As a result, healthcare professionals in training are constantly faced with the challenge of understanding and applying these emerging technologies. The objective of this study is to determine the factors to consider for including the teaching of nanotechnological tools in medical specialty postgraduate programs. Methods: A study based on the Nominal Group Technique (NGT) was conducted to identify the factors that promote and hinder the implementation of nanotechnology in certain postgraduate programs where nanotechnology is employed, such as Internal Medicine, Critical Care Medicine, Family Medicine, and Clinical Pharmacology at the School of Medicine of Universidad de La Sabana. A framework analysis was used to analyze the collected data. Results: Based on the two research questions, several aspects were identified for the inclusion of nanotechnology in the medical practice of specialties. Key challenges include lack of knowledge and resource availability, misinformation, and lack of knowledge, as well as paradigmatic analysis as the main obstacles to the use of nanotechnology. Conclusions: The findings provide a first-time study on nanotechnology curricula within medical specialty programs in Colombia, with potential extrapolation to similar situations in other medical schools.

The species and bio-availability of phosphorus (P) in primary, secondary and digested sludge were fractionated and further analyzed in this study. Results showed that inorganic P (IP) was the primary P fraction in the secondary sludge and digested sludge, in which non-apatite IP (NAIP) amounted to 91.6% and 69.3% of IP, respectively. Organic P (OP), accounting for about 71.7% of total P (TP), was the dominant P composition in primary sludge. The content of bio-available P was about 9.7, 43.4, 29.8 mg-P/g-TS in primary sludge, secondary sludge and digested sludge, respectively, suggesting secondary sludge is the optimal choice when land application of sewage sludge is taken into consideration, followed by digested sludge and primary sludge. Polyphosphate and orthophosphate, comprising approximately 54.3% and 89.2% of TP, was the dominant P species in the secondary sludge and digested sludge, respectively. Monoester-P (54.6% of TP in extract) and diester- P (24.1%) were identified as OP species in primary sludge by Phosphorus-31 nuclear magnetic resonance (31PNMR). The present results would be helpful for P recovery and recycle from sewage sludge in wastewater treatment plant.

We conducted a comparative study on the development of two synthetic methods. The solvent-free synthesis is a green chemistry method developed in a bid to ameliorate environmental adverse effects of the conventional solvent-based synthesis. The synthesis of novel dichloro and polymethoxy p-nitrophenylhydrazones through solvent free technique gave moderate to high yields which were however lower than those of the solvent-based method. The established solvent-free approach has several benefits, including universality and simplicity of the approach, catalyst-free conditions, non-use of an organic solvent, quick reaction time, fast and efficient workup, and un-solvated pure products in moderate to high yields.

The zero-catch problem is a key issue in CPUE(Catch Per Unit Effort) standardization, and previous studies have treated all zero-catch data uniformly, but this actually loses some correctly-labeled samples. On the other hand, for the main catches with few zero-catch samples, the problem of low performance of fisheries forecasting remains unsolved even though the forecasting model structure is updating constantly, since we cannot know whether the samples are correctly recorded. In this paper, we propose a method based on confident learning theory to detect anomalous samples in the datasets and unify zero-catch and non-zero samples as noise through an overarching framework of learning with noisy labels, which reveals the heterogeneity among zero-catch samples (as well as among non-zero samples) and the homogeneity between zero-catch samples and non-zero samples. Using three species of tuna in the tropical Atlantic Ocean with the spatial resolution of 0.5 ◦ × 0.5 ◦ and time resolution of days from 2016 to 2019 as experimental material, performance on all three classical machine learning models(Random forest, Support Vector Machine and XGBoost) is significantly improved compared to each baseline.This confirms that we propose a self-adaptive, effective method for detecting and repairing anomalous samples in the fishery dataset.

This paper presents an approach to assess and improve the cybersecurity infras- tructure of developing countries. We have highlighted and addressed the critical gaps through a hybrid approach encompassing SWOT analysis, benchmarking using Data Envelopment Analysis (DEA) and, Root Cause Analysis (RCA). This approach has provided a strong basis for evaluating cybersecurity performance by combining qualitative insights with quantitative metrics. As a case study, we have compared an underdeveloped country’s (Pakistan) cybersecurity pos- ture to five highly developed countries (Lithuania, Estonia, Singapore, Spain,and Norway). Our analysis has identified cybersecurity shortcomings and pro- vided actionable recommendations within a flexible framework, applicable for all developing countries to identify and address vulnerabilities, and align their cybersecurity infrastructure with international standards.

The increasing energy consumption and industrial activities that release carbon dioxide (CO2) emissions into the atmosphere have become one of the most serious environmental issues of today. This article discusses the current status in Azerbaijan regarding the development of effective approaches to reduce carbon emissions and ensure sustainable development, and it proposes relevant strategies for managing the carbon footprint. By exploring the interrelationship between the economy and the environment, it aims to contribute to the formation of more effective policies for managing CO2 emissions in Azerbaijan. According to the results of the econometric model established within the research for the period of 1990-2023, as the Azerbaijan economy grows, per capita CO2 emissions tend to increase, which can be linked to rising industrial activity, energy consumption, and other factors driving economic growth. The presence of a positive long-term relationship between population growth and per capita CO2 emissions can be attributed to higher overall energy consumption, increased demand for goods and services, and rising transportation needs. The strong correlation between economic growth and CO2 emissions suggests that it may be too early to assert the validity of the Kuznets curve for Azerbaijan economy as a developing country.

Diospyros kaki, the most widely cultivated species of persimmon, has been long used in traditional medicine since its leaves extracts contain high amounts of flavonoids and terpenoids, endowed with potential antioxidant, anti-inflammatory, anticancer, antidiabetic and antimicrobial effects. Re-cently, many in vitro and in vivo studies have assessed different potential health benefits of per-simmon leaves; however, its anticancer activity towards colorectal cancer (CRC), the second deadliest cancer in Western countries, has not been investigated in depth. Since CRC current therapies are associated with serious side effects and show no efficacy towards patients carrying RAS/BRAF mutations, the search for new and more effective therapies has turned to plant extracts, which could help reduce conventional drugs dosages and toxicity. The effect of Diospyros kaki al-coholic extract has been investigated on E705 CRC cell line, representative of most CRC patients, and on SW480 cells, carrying a KRAS activating mutation. This extract is effective in reducing tumor cells viability, without affecting the healthy mucosa cell line CCD 841, and it triggers apoptosis in CRC cells, by disrupting mitochondrial functionality and increasing oxidative stress.

Pre-transport management activities can cause stress, injuries, and mortality in poultry. This study aimed to gather information about the current pre-transport practices (selection of unfit chickens, catching preparations, catching, and crating) for spent hens and broilers by questioning Flemish poultry farmers. The results showed that catching preparations such as an extra selection of chickens unfit for transport was performed by a minority of the poultry farmers, and that layer farmers were less in line with the EU-legislation for water and feed withdrawal than broiler farmers. All birds were caught inverted except for one broiler farmer who used mechanical catching (not yet common in Flanders). Additionally, mechanical catching may involve extra costs, increased biosecurity risks, and specific recommendations for the stable (height and width). The broiler farmers preferred mechanical catching for broiler catchers' well-being, while upright catching was considered better for animal welfare than catching more than three chickens by one/two legs, mechanically, or by wings. Awareness of the need to perform an extra selection by the poultry farmers before catching is required. Preparations like closing areas under the aviary system and removing litter (layers) can further streamline the process and reduce animal suffering.

This stability analysis method is simply represented and understood on a Bode plot of two impedances without complex equations. The external impedance of the charging system is properly quantified and designed based on the stability criteria by analyzing.

Arbutus unedo (strawberry tree) is a small Mediterranean tree capable of vigorous regrowth after disturbances like fire. Traditionally used for biomass fuel, its bark and branches hold potential for higher-value products through ecovalorisation into liquid mixtures that could replace petro-leum-based materials. This study aimed to explore the chemical composition of various compo-nents of Arbutus unedo and to produce a liquefied material from its internal (IB) and external bark (EB). Chemical compositions of internal and external bark were determined using TAPPI standards including ash, extractive content, lignin, and cellulose. Metal cations were analyzed by ICP. Liquefaction of bark was optimized in a PARR reactor, evaluating factors such as particle size, temperature, and time and the best polyols were monitored by FTIR-ATR. Polyurethane foams were made with internal and external bark liquefied materials by polymerization with isocyanate, a catalyst and water as blowing agent. Results showed that EB has a higher extractive and lignin content, while IB contains more cellu-lose. Liquefaction yields were higher for IB (74%) than EB (68%), with IB yielding polyols that produced stronger and more resilient foams. Mechanical properties of the foams were influenced by the NCO/OH ratio and catalyst levels. Overall, the internal bark demonstrated superior performance for foam production, highlighting its potential as an eco-friendly alternative to petroleum-derived materials.

Introduction: Leptin and ghrelin are two hormones that play a role in weight homeostasis. Leptin, which is produced primarily by adipocytes and is dependent on body fat mass, suppresses appetite and increases energy expenditure. Conversely, ghrelin is the “hunger hormone”, it stimulates appetite and promotes fat storage. Bariatric surgery significantly alters the levels and activity of these hormones, contributing to weight loss and metabolic improvements. Clarifying the interplay between bariatric surgery, weight loss, physical exercise, leptin, and ghrelin is essential for developing comprehensive strategies for optimizing long-term outcomes for candidates for bariatric surgery, especially sarcopenic patients. Methods: This was a randomized controlled study with two groups (n=22). The patients in both groups have obesity and sarcopenia. A Roux-en-Ygastric bypass (RYGB) procedure was performed in all patients. The intervention group participated in a structured exercise program three times per week beginning one month after surgery and lasting 16 weeks. Patient assessment was performed before surgery (baseline) and after completion of the exercise program. The control group received the usual standard of care and was assessed similarly. Results: After surgery, weight, BMI and lean mass decreased significantly in both groups from baseline to the second assessment. Leptin was not significantly different from baseline to the second assessment in the physical exercise group but was significantly lower in the control group (p=0.05). Ghrelin increased over time in both groups, but the differences were not significant. When we associated leptin (the dependent variable) with weight (the independent variable), we found that lower weight was associated with lower leptin levels. A similar relationship was also observed between leptin and sarcopenia parameters (muscle strength and mass), as well as with bone health parameters (bone mineral density and t-score). Higher ghrelin levels were significantly associated with higher t-scores and z-score (p<0.05). Conclusion: Exercise has been shown to have a significant effect on leptin and ghrelin levels after bariatric surgery. By incorporating regular physical activity into their lifestyle, bariatric patients can optimize their weight loss outcomes and improve their overall health. After the physical exercise protocol, patients in the intervention group revealed more established leptin levels, which may indicate a protected pattern concerning decreased leptin levels. An unfavorable profile was evidenced, according to which greater weight loss, sarcopenia, and osteoporosis were associated with lower leptin levels.

Among the theoretical problems with the prevailing theory of cosmology, Lambda Cold Dark Matter (λCDM), the most serious appears to be the twofold problem with the cosmological constant lambda: Dark Energy and Hubble Tension. Despite a two-decade search for systematic errors in astrometry the problems have not been resolved. This paper posits a potential source of error that may have affected astrometry related to the current and past measurements of lambda.

Differential beamforming has attracted much research since it can utilize the array with small aperture size to form frequency-invariant beampatterns and achieve high directional gains. It has recently been applied to the linear loudspeaker array to produce a broadside frequency-invariant radiation pattern. However, designing steerable frequency-invariant beampatterns for the linear loudspeaker array has yet to be explored. This paper proposes a method to design the steerable differential beamformer with the linear loudspeaker array. We first determine the target differential beampatterns according to the desired direction, the main lobe width, and the beampattern order. Then, we transform the target beampattern into the modal domain for representation. The Jacobi-Anger expansion is subsequently used to design the beamformer so that the resulting beam pattern matches the target differential beam pattern. Furthermore, based on the criterion of minimizing the mean square error between the synthesized beam pattern and the ideal one, a multi-constraint optimization problem is formulated to calculate the optimal desired weighting vector. Simulations and experimental results show that the proposed method can achieve steerable frequency-invariant beamforming over 300 Hz – 4 kHz.

N-(diisopropylphosphanyl)benzamide, PhC(O)NHPiPr2, has been synthesized in good yield following two alternative procedures that employ benzamide as the starting material. The first one is a two-step preparation, in which N-(trimetilsilyl)benzamide is reacted with PiPr2Cl, to give the title compound in good yield, whereas the second one is a straightforward synthesis which converts benzamide into N-(diisopropylphosphanyl)benzamide by reaction with PiPr2Cl in the presence of N,N-dimethylpyridin-4-amine (DMAP) and triethylamine. NMR spectroscopy and X-ray diffraction analyses have been performed to characterize the new compound and elucidate its molecular structure in the solid state. N-(diisopropylphosphanyl)benzamide adds to the limited family of amido-substituted phosphines, RC(O)NHPR’2, which can be classified as bidentate hybrid P,O-ligands, both in their neutral and anionic forms, the latter achievable by deprotonation of the NH group.

Zero-dose children, or children who have not received any routine vaccination, are a priority population for global health policy makers as these children are at high risk of mortality from vaccine-preventable illnesses. We conducted a narrative review to identify potential interventions, both within and outside of the health sector, to reach zero-dose children. We reviewed peer-reviewed and grey literature and identified 59 relevant resources. Additionally, we interviewed six key informants to enhance synthesis of our findings. Data were organized into three priority settings: (1) urban slums, (2) remote or rural communities, and (3) conflict settings. We found that zero-dose children in the three-priority settings face differing barriers to vaccina-tion and, therefore, require context-specific interventions, such as leveraging slum health commit-tees for urban slums or integrating with existing humanitarian response services for conflict set-tings. Three predominant themes emerged for grouping the various interventions: (1) community engagement, (2) health systems strengthening and integration, and (3) technological innovations. The barriers to reaching zero-dose children are multifaceted and nuanced to each setting, there-fore, no one intervention is enough. Technological interventions especially must be coupled with community engagement and health systems strengthening efforts. Evaluations of the suggested interventions are needed to guide scale up, as the evidence-base around these interventions is rel-atively small.

We assess simultaneous adoption and impacts of multiple improved technologies promoted as a bundle and recommended for legumes intensification systems for smallholder farmers in Ethiopia. We use DNA fingerprinting data to precisely identify our key treatment - “adoption to improved bean varieties” in the study. The results show significant positive impacts of adopting bundled interventions on, agricultural incomes and household food security but vulnerability to food insecurity persists. We find that growing improved varieties with fertilizers increased household agricultural revenue, allowing for more legume consumption and enhancing their likelihood of achieving adequate food consumption and food security outcomes. However, the vulnerability to food insecurity of the adopters remains high due to pre-existing resource degradation issues. Given similarity in production contexts in Sub Saharan Africa, our results provide perspective for similar development interventions. We use the results of our analysis to discuss potential policy implications and programmes to support technology intensification among small holder farmers.

Background: Gait analysis systems are crucial for rehabilitation but are often time-consuming and less accessible in low and middle-income countries like Brazil. GAITWell^® was developed to address these challenges with its modular and portable design for automated gait data extraction and analysis. This study evaluates its methodological properties. Methods: GAITWell^® uses discrete binary sensors on interconnected plates to collect gait data, analyzed via the DBSCAN algorithm. Each plate measures 44 cm x 37 cm and features an 11 x 7 discrete sensor array with 4 cm spacing. Test-retest reliability was assessed using the intraclass correlation coefficient (ICC), standard error of the mean (SEM), and Bland-Altman plots. Concurrent validity was evaluated by comparing the GAITWell^® measurements with those obtained from the Qualisys Pro-Reflex through correlations. Results: Participated 38 healthy young adults, including 18 females (47.4%). The sample had an average age of 33.2 years (SD 13.0) and an average BMI of 26.6 kg/m² (SD 5.4). Correlations between GAITWell^® and Qualisys ranged from moderate to very high for most gait variables, except stride length (r = .360, p < .05). ICC_2,1 showed moderate to good agreement for most variables, except for double support times, cadence, and base of support. Preliminary analysis suggests that increasing the sensor resolution to 1.2 cm x 1.2 cm could potentially decrease the measurement error from 2 cm to 0.6 cm, representing a theoretical reduction of 70%. Conclusions: While low resolution affected GAITWell^® reliability, it remains a promising tool. Future research should focus on enhancing sensor accuracy to improve reliability.

Accumulated data indicate that epigenetic regulations, including histone modifications and DNA methylation, are important means for adjusting genes’ expression in response to various stimuli. In contrast to the success in studying the role of DNA methylation in laboratory rodents, the role of DNA methylation in terrestrial snail Helix lucorum has been studied only in behavioral experiments. This prompted us to further investigate the role of DNA methylation and the interaction between DNA methylation and histone acetylation in the mechanisms of neuroplasticity in terrestrial snails using in vitro experiments. Dysregulation of DNA methylation by DNMT inhibitor RG108 suppressed significantly the long-term potentiation (LTP) of synaptic inputs in identified neurons. We then tested whether the RG108-induced weakening of potentiation will be rescued under co-application of histone deacetylase inhibitors sodium butyrate or trichostatin A. RG108-induced LTP deficiency was significantly compensated by increased histone acetylation. These data bring important insight to the functional role of DNA methylation as an important regulatory mechanism and a necessary condition for the development and maintenance of long-term synaptic changes in withdrawal interneurons of terrestrial snails. Moreover, these results support the idea of the interaction of DNA methylation and histone acetylation in the epigenetic regulation of synaptic plasticity.

The gut, often referred to as the "second brain," has significant contributions in the maintaining of the good bacteria contribute enormous role such as digestion, produce essential vitamins, support the immune system, and protect against harmful bacteria. The beneficial flora including Akkermansia muciniphila, Adlercreutzia equolifasciens, Barnesiella, Christensenella minuta, and Oxalobacter formigenes, along with their derived bioactive metabolites emerged as a key player in maintaining host metabolic and immune health. Dietary choices such as blending of prebiotic, fermented, symbiotic, anti-inflammatory foods, and secondary metabolites from a wide variety of plants and fruits promotes the diversity, composition, and stability of beneficial intestinal microbes. The colourful plant foods rich in phytochemicals bioactive compounds such as carotenoids, flavonoids, polyphenols, alkaloids, anthocyanins, and capsaicin offer a wide array of unique properties such as analgesics, antioxidants, anti-inflammatory, antimicrobials effect and promoting the abundance of beneficial gut bacteria and their bioactive metabolites confer numerous health-promoting effects. Here, we present knowledge about most beneficial gut bacteria and their derived metabolites in terms of their sources and health benefits. Finally, we discuss best foods that skew towards promoting healthy intestinal microbes.

This article presents a method for detecting P, QRS, T, and U waves in electrocardiogram (ECG) signals through advanced signal processing techniques, including a Butterworth bandpass filter and peak finding algo- rithms. The results are evaluated against the voltage ranges recommended in the literature, underscoring the importance of accurate wave detection for the diagnosis of various cardiac conditions.

Additive Manufacturing (AM) methods are increasingly beign abopted in the industry as an alternative for mass production. In particular, Fused Deposition Modeling (FDM) technology is leading the way in this field. However, the adhesion of layers in products produced using FDM technology is an important issue. These products are particularly vulnerable to forces acting parallel to the layers and specially to impact strength. This study aims to optimize the impact strength against loads acting parallel to the layers (ZX orientation) of PA6, one of the most widely used materials in the industry. This orientation is critical in term of mechanical properties, and the mechanical characteristics are significantly lower compared to other orientation. In this study, filaments containing pure PA6 with 15% short carbon fiber and 30% glass fiber were utilized. Additionally, printing temperature, layer thickness and heat treatment duration were used as independent variables. An L9 orthogonal array was employed in this study, and each experiment was repeated three times to conduct impact strength tests. Characterization, Taguchi optimization, a factor analyses were performed, followed by fracture surface characterization by SEM. As a result, the highest impact strength was achieved with pure PA6 at 8.9 kJ/m2, followed by PA6 GF30 at 8.1 kJ/m2 and the lowest impact strength was obtained with PA6 CF15 at 6.258 kJ/m2. When compared to the literature and manufacturer datasheets, it was concluded that the impact strength values have significantly increased, and the chosen experimental factors and their levels, particularly nozzle temperature, were effective.

For Sir Ronald Fisher, it is important to consistently obtain significant p-values to support an experimental hypothesis. So, replicating experiments to obtain independent p-values is a legitimate and desirable research practice. Several simple statistics have been proposed to meta-analyze p-values, all assuming that they are genuine, i.e. observations from independent standard Uniform random variables. But, as publication bias favors the studies that report "significant" p-values, when a p&gt;0.05 is obtained for the outcome of an experiment, some researchers will "fall into temptation" and decide to replicate the experiment in the hope of getting a smaller second p-value, ideally a significant one. Consequently, if the smallest of two p-values is reported, this is a Beta(1,2) distributed "fake" p-value, not a uniformly distributed genuine p-value. This is an unacceptable scientific research practice, and moreover the detection of fake p-values is unpractical. Even when it is possible, the analytic results to accommodate their existence in combined tests are cumbersome. For an informed decision, inclusive when the presence of fake p-values in a sample of p-values to be meta-analyzed is probable, tables with simulated critical values for the usual combined testing are supplied. This will also allow comparisons to be made between several combined tests.

The global malaria community has picked up the theme of malaria elimination in greater than 90 % of the world’s population in the next decade. The recent reports of P. vivax in all sub-Saharan Africa, including Duffy negative individuals, threaten the efforts to achieve elimination. This is not only in view of strategies that are tailored only to P. falciparum elimination, but also due to currently revealed biological characteristics of P. vivax concerning the relapse patterns of hypnozoites and conservation of a large biomass at cryptic sites in the bone marrow and spleen. A typical scenario has been observed in Botswana from the period between 2008 to 2018, which palpably projects how P. vivax could endanger efforts at malaria elimination where the two parasites co-exist. The need for the global malaria community, NMPs, funding agencies and relevant stakeholders to engage in a forum to discuss and recommend clear pathways for sub-Saharan Africa regarding malaria elimination including P. vivax, is warranted.

Academic and corporate research has been focusing on analyzing stock performances, financial markets, and investment strategies for decades. According to Lynch and Rothchild (1989), certain investments can be described as plays. In this work, the authors use the term for investments in value stocks, spin-offs, and turnarounds to analyze the long-term stock performance and risk-adjusted excess returns of up to five years for those stocks. Additionally, stocks with a supe-rior return performance are identified within an investment play based on low P/E and P/B mul-tiples. The study finds that value stocks, spin-offs, and turnarounds outperform the S&P 500 on average on a one-, three- and five-year investment horizon. The analysis of the three investment plays confirms earlier studies that investment plays constitute a profitable investment opportunity. Moreover, the research exemplifies that private investors could achieve an excess return (alpha) through a passive buy-and-hold investment strategy. Overall, the analysis provides a deeper in-sight into the long-term performance of value stocks, spin-offs, and turnarounds.

This study presents the experimental and analytical evaluations on an innovative connection class under cyclic loading. The connection was an H- or I-shaped section as a short stub column (SSC) between the end-plate and the column. The stub section was bolted to the column flange on the one side and bolted to the beam along with the welded end-plate on the other side. Stiffeners were installed on the webs and flanges of the beam and stub to ensure the necessary stiffness of the connection. The variables included thickness of stub flange/web and height of stub web. The cyclic loading was applied on a sample of the proposed connection according to AISC ANSI/AISC 341-16 loading protocol. Experimental observations displayed that the proposed connection, one can either employ hot rolling sections or create the connection as a plate girder, in which case the flange thickness and stub web, as well as the distance between two flanges and their height, was obtained according to the demand and performance of the structure. The optimum cross-section of the stub was then obtained. Also, the analytical model was calibrated using the finite element method (FEM), and its performance was confirmed. The effective parameters of the proposed connection were determined by modeling in the FEM software, and the design method was presented.

Introduction: Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence (AI)--powered mobile apps for automated ulcer segmentation and their application in clinical settings. Methods: A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane and Google Scholas databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. Results: Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. Discussion: AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. Conclusion: AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems.

Migraine and stroke are neurological disorders with significant global prevalence and impact. Recent advances in migraine therapy have focused on calcitonin gene-related peptide (CGRP) pathway. This review examines the shared pathomechanisms between migraine and stroke, with emphasis on the role of CGRP. We analyze current literature on CGRP's functions in cerebrovascular regulation, edema formation, neuroinflammation, and neuroprotection. CGRP acts as a potent vasodilator and plays a crucial role in trigeminovascular activation during migraine attacks. In stroke, CGRP has demonstrated neuroprotective effects by improving collateral circulation and reducing ischemia-reperfusion injury. Concerns have been raised about the potential impact of CGRP inhibsitors on stroke risk and outcomes. Studies in animals suggest that CGRP receptor antagonists may worsen cerebral ischemia by impairing collateral flow. We discuss the implications of these findings for the use of CGRP-targeting therapies in migraine patients, especially those at increased risk of stroke. Additionally, we explore the complex interplay between CGRP, endothelial function, and platelet activity in both conditions. This review highlights the need for further research to elucidate the long-term cerebrovascular safety of CGRP pathway inhibitors and to identify potential subgroups of migraine patients who may be at higher risk of adverse cerebrovascular events with these novel therapies.

Phosphorus (P) is an essential element in food production, but it is often largely unavailable for plants as it persists in soils in immobilized forms. Farmers have been using P fertilizers extensively to overcome this unavailability, leading to the exhaustion of non-renewable deposits of phosphorite, rising fertilizers prices and causing several environmental impacts such as eutrophication. Its large-scale utilization is also inefficient since most of the added P becomes quickly unavailable for the plants. Therefore, farming solutions that improve P uptake, P use efficiency of plants and P mobilization in soils are highly important. In this narrative review, we summarized and explored the link between the Silicon (Si) and P cycles in soils. Silicon is quasi-essential for plants, and it has been established that its presence in soil and uptake by plants has several benefits, especially in toxic and nutrient-deficient conditions. Here, we established that Si in soils can affect crop production in P-deficient conditions through: 1) the pH effect – Si improves organic matter decomposition and decreases Al3+ availability; the Si uptake effect – the deposited Si in the plant improves its photosynthetic performance, improves the root ability to uptake P, improves organic matter decomposition and increases P/Mn ration in plant, improving P use; and 3) the Si soil effect – Si stimulates phosphorite rocks weathering and competes with P for the binding places at the surface of Ak and Fe hydroxides, releasing P into soil solution. This positive effect of Si on P mobilization in soils and consequent use by crops should be further disseminated to farmers and other relevant stakeholders.

Cystic fibrosis (CF) diagnosis can be a challenge due to the large spectrum of pathogenic variants in the CFTR gene and the effects of complex alleles. Next-generation sequencing has improved our understanding of the contribution of these complex alleles to the wide spectrum of CF clinical symptoms and to the response to medications. Herein, we studied nine CF patients from six unrelated families carrying the complex allele p.[Ile148Thr;Ile1023_Val1024del] with a frequency of 0.18%. All the patients were from Central Mexico. This complex allele was found in trans with Class I and II pathogenic variants (PVs) such as [c.1521_1523del, p.(Phe508del), and c.3231_3232del, p.(Phe1078Profs*77)]. A targeted search of a dataset of 2,217 exomes from healthy individuals revealed that eight individuals (0.18%) carried the p.(Ile148Thr) variant, but only one (0.022%), who was also born in Central Mexico, was a carrier of the complex allele. These findings led us to hypothesize a founder effect for the p.[Ile148Thr;Ile1023_Val1024del] complex allele in Mexican patients with CF, probably through migration from the French-Canadian population in Mexico. Finally, protein modeling revealed that this complex allele disrupts the secondary structure of the CFTR protein and might alter the ion flow.

In this paper, various Ni-P composite coatings containing toner, MoS2, and nano-SiO2 particles were deposited on steel substrates by the electroless method. Then, the electrochemical properties of these coatings after a heat treatment process were compared. The microstructural evaluations were also done by using the optical and electron microscopy methods. Both Tafel polarization and electrochemical impedance spectroscopy techniques were utilized to survey the electrochemical behavior of such coatings. The surface morphology of all coatings contained cauliflower-like nodules. The X-ray diffraction patterns showed the crystalline phases of Ni and Ni3P for all coatings after the heat-treatment step. Obtained results showed that all composite coatings exhibited lower corrosion rates with respect to Ni-P coatings. Such a reduction was about 21.6-92.2%. This behavior was attributed to the presence of reinforcement as barriers for corrosive ion diffusion through the coating plus the changes in detected phases and thickness. Electrochemical impedance spectroscopy test results also demonstrated that the increase in the polarization resistance for composites coatings was about 18.4-85.3% after 1 h immersion in a 0.6M NaCl solution; however, when the immersion time increased to 24 h, such increased resistance changed to 18.1 to 73.1%. Totally, despite the lower deposition rate, the presence of MoS2 and nano-SiO2 particles were more effective than toner particles to raise the corrosion rate of the Ni-P coating.

In this paper, we investigate p-adic q-integral (q-Volkenborn integral) on Z_p of p-adic gamma function via their Mahler expansions. We also derived two q-Volkenborn integrals of p-adic gamma function in terms of q-Daehee polynomials and numbers and q-Daehee polynomials and numbers of the second kind. Moreover, we discover q-Volkenborn integral of the derivative of p-adic gamma function. We acquire the relationship between the p-adic gamma function and Stirling numbers of the first kind. We finally develop a novel and interesting representation for the p-adic Euler constant by means of the q-Daehee polynomials and numbers.

It is known that the class of p-normed spaces (0 < p ≤ 1) is an important generalization of usual normed spaces with rich topological and geometrical structure, but the most of tools and general principles in nature with nonlinearity have not been developed yet, thus the main goal of this paper is to develop tools for nonlinear analysis under the framework of p-vector spaces. In particular, we first develop the general fixed point theorems which provide solutions to answer Schauder conjecture since 1930’s in the affirmative for p-vector spaces when p = 1 (which is just general topological vector spaces); then the one best approximation result for upper semi-continuous mappings is given, which is used as a powerful tool to establish fixed points for non-self set-valued mappings with either inward or outward set conditions; and finally we establish comprehensive existence results of solutions for Birkhoff-Kellogg Problems, and the general principle of nonlinear alternative by including Leray-Schauder alternative and related results. The results given in this paper not only include the corresponding results in the existing literature as special cases, but also expected to be useful tools for the study of nonlinear problems arising from theory to practice.

Correlated binary data in a 2x2 table has been analyzed from both the frequentist and Bayesian perspectives, but a fully Bayesian hierarchical model has not been proposed. This is a commonly used model for correlated proportions when considering, for example, a diagnostic test performance where negative subjects are tested a second time. We consider a new hierarchical Bayesian model for the parameters resulting from a 2x2 table with a structural zero. We investigate the performance of the hierarchical model via simulation. We then illustrate the usefulness of the model by showing how a set of historical studies can be used to build a predictive distribution for a new study that can be used as a prior distribution for both the rate ratio and marginal probability of a positive test. We then show how the prior based on historical 2x2 tables can be used to power a future study that accounts for pre-experimental uncertainty. High quality prior information can lead to better decision making by improving precision in estimation and by providing realistic numbers to power studies.

Energy market price volatility and an upward trajectory of prices per unit of electricity have sent all industrial sectors and many economies to the brink of recession. Alongside the urgent need for decarbonisation of all industries, achieving a higher level of energy independence across all sectors seems imperative. A multi-disciplinary approach with a proposed system of CO₂ emissions reduction and capture technologies has the potential for short-term emissions reduction to near-zero in absolute terms. The findings of this research showed CO₂ emissions reduction of ~30% from 977t of CO_2e to 684t in one single blast furnace production cycle, by switching the energy provider generating energy exclusively from renewable sources. Replacing coal with biomass (BECCS), resulted in an additional reduction of ~30%, to CO_2e to 479t CO_2e. Installing solar PV panels for energy generation on site resulted in a further ~30% reduction, to 335 tonnes CO_2e. Finally, the installation of CO₂ filters reduced the CO₂ emissions to near-zero. Simultaneously, energy-saving and process improvement measures implementation (up to 60% efficiency increase), excess heat recovery (&lt;30% of energy savings), and retrofitting renewable energy technology resulted in an energy independence of 88%. Short-term engineering solutions, partly subsidised in the UK, are readily available.

Recently, the focus of entity linking research has centered on the zero-shot scenario, where the entity mention to be labeled at the time of testing was never observed during the training phase or may belong to a different domain than the source domain. Current studies have used BERT as the base encoder as it effectively establishes distributional links between source and target domains. The currently available negative sampling methods all use an extractive approach, which makes it difficult for the models to learn diverse and more challenging negative samples. To address this problem, we propose a generative negative sampling method, Adaptive_mixup_hard, which generates more difficult negative entities by fusing the features of both positive and negative samples on top of hard negative sampling and introduces a transformable adaptive parameter W to increase the diversity of negative samples. Next, we fuse our method with the Biencoder architecture and evaluate its performance under three different score functions. Ultimately, experimental results on the standard benchmark dataset Zeshel demonstrate the effectiveness of our method.

The global agricultural sector is increasingly pressured to adopt sustainable practices and reduce its environmental impact. In this context, greenhouses play a crucial role in enabling year-round crop production, ensuring food security, and minimizing reliance on traditional open-field farming. However, the energy consumption associated with greenhouse operations poses a significant challenge to achieving sustainability goals. As a result, there is a growing emphasis on transitioning greenhouses towards near-zero energy consumption. Near-zero energy consumption in greenhouses refers to the ambitious objective of minimizing energy usage to the greatest extent possible while maintaining optimal growing conditions for crops. This goal encompasses reducing energy consumption for heating, cooling, lighting, and other operational needs, as well as exploring renewable energy sources to power greenhouse operations. This review article offers a comprehensive overview of greenhouse energy consumption, with the main goal of analyzing the present situation, identifying key challenges, exploring potential opportunities, and proposing future perspectives for decreasing energy usage in greenhouse environments. As the focus on sustainable agricultural practices grows, the need to reduce energy consumption in greenhouses becomes increasingly important. The review critically examines current technological models and strategies applied in smart greenhouse applications, as well as the monitoring of microclimatic conditions inside the greenhouse, encompassing factors such as temperature, humidity, CO2 levels, soil quality, and crop cultivation. Moreover, it aims to present existing literature that investigates the advancement of greenhouses toward achieving significant reductions in energy consumption.

Epsilon-near-zero nanoantennas can be used to tune the far-field radiation pattern due to their exceptionally large intensity-dependent refractive index. Here, we propose hybrid optical antenna based on indium tin oxide (ITO) to optically tune the deflection of radiation. In particular, a hybrid structure antenna of ITO and dielectric material, which makes the deflection angle changes 17 ∘ as incident intensities increases. Finally, the array of ITO or hybrid nanodisks can enhance the unidirectionality to be needle-like, with the angular beam width α&lt; 8∘ of main lobe. The deflection angle of radiation pattern response with needle-like lobe pave the way for further studies and applications in beam steering and optical modulation where dynamic control of the nanoantennas is highly desirable.

Through history, particular attention has been paid of the study of the relationship between the energy use and the city structure. Improving energy efficiency in modern agglomerations is the most promising means to mitigate climate change and its impacts. In this current context of globalisation, European Union proposes initiatives and policy targets to rethink the urban development strategies towards the ‘zero energy objectives’. Providing a methodological approach with a simulation district analysis, the present article summarizes how the ‘zero energy’ challenge is analyzed in an existing district (Epinlieu) to articulate the users’ requirements in energy. This study contributes to the scientific discussion of the districts’ urban structure and energy planning by establishing a linkage among the beneficial influence of the KPIs of the districts’ form to increase their energy efficiency and its application in a real case study in Belgium.

N-shot learning has emerge in recent year as potential learning approach to solve the problem of data scarcity by learning underlying pattern from a few training sample. Despite recent state-of-the-art research on model-agnostic metal learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample, there remains a big challenge on an actual out-of-distribution zero-day without any similarity to previously known malware family or new variant of an existing malware family. This ultimately questions the effectiveness of current state-of-the-art few-shot learning approach. In this research, we did an in-depth investigation into the performance of state-of-the-art Zero-shot, Single-shot, and few-shot learning approaches on zero-day out-of-distribution malware attack detection based on their static properties using Malimg and Malevis malware dataset. We ensure our model was aware of an out-of-distribution class during training while varying the number of samples in the out-of-distribution class accordingly zero-shot(no sample), single-shot (1 sample), few-shot(5 samples) while using confusion matrix to get the actual number of correct prediction on out-of-distribution malware validation samples. we assert that the model should be smart enough to detect and classify previously unseen data into an empty family as an out-of-distribution considering that the model was made to be aware of the existence of such distribution during training. Result shows 0, 0, and 3 correct out-of-distribution predictions on Zero-shot, single-shot, and few-shot experiments respectively, thereby showing limitation of the current state-of-the-art N-shot approaches on out-of-distribution attack.

The main aim of this paper is to set some correlations between Boole polynomials and p-adic gamma function in conjunction with p-adic Euler contant. We develop diverse formulas for p-adic gamma function by means of their Mahler expansion and fermionic p-adic integral on ℤ_{p}. Also, we acquire two fermionic p-adic integrals of p-adic gamma function in terms of Boole numbers and polynomials. We then provide fermionic p-adic integral of the derivative of p-adic gamma function and a representation for the p-adic Euler constant by means of the Boole polynomials. Furthermore, we investigate an explicit representation for the aforesaid constant covering Stirling numbers of the first kind.

This study examines the significant impact of Houthi insurgent activities on maritime traffic within the strategic Red Sea and Suez Canal routes, essential conduits for global trade. It explores the correlation between regional instability, exemplified by Houthi actions from November 19, 2023, to February 5, 2024, and changes in maritime traffic patterns and operational efficiency. This study seeks to answer a critical question in transport geography: How does regional instability, exemplified by Houthi insurgent activities, affect maritime traffic patterns and the operational efficiency of the Red Sea and Suez Canal? Using descriptive statistics, qualitative analysis, and geospatial methods, the research highlights recent trends in maritime traffic and incidents, revealing spatial and geopolitical challenges in this crucial trade route.Findings indicate a notable decline in maritime activity in the Gulf of Aden and Suez Canal due to security concerns from Houthi attacks, prompting a significant shift to alternative routes, particularly around the Cape of Good Hope. This shift underscores the broader implications of regional instability on global trade and the importance of maintaining uninterrupted maritime flow. The study also emphasizes the economic ramifications, such as increased operational costs and freight rates due to longer transit times and enhanced security measures.The research concludes with a call for improved maritime security protocols and international cooperation to protect these strategic maritime pathways. It contributes to the discourse on transport geography by quantifying the direct impacts of regional conflicts on maritime logistics and proposing strategies for future resilience, highlighting the interconnected nature of global trade and security and the need for collective action against evolving geopolitical challenges.

The fall armyworm (FAW), Spodoptera frugiperda, a major pest in maize production, was assessed for its temporal and spatial distribution in maize fields during both the off and rainy seasons of 2021 and 2022. In this study, 30 and 50 maize plants were randomly sampled using a "W" pattern during the dry and rainy seasons, respectively. Larval density, infestation rates, and damage severity were monitored over time. Taylor's power law and the mean crowding aggregation index were applied to evaluate the dispersion patterns of the larvae. The results indicate a higher infestation rate and larval density in agroecological zone AZE 8 (fisheries zone of South Benin) compared to AZE 6 (ferralitic soils zone) during the off-season. In the rainy season, while the percentage of damaged plants was higher in AZE 8, no significant differences in larval density between the two zones were observed. The dispersion analysis revealed moderate aggregation (aggregation index = 1.25) with a basic colony of 2.08 larvae, reflecting the aggregation oviposition behavior of FAW. This study provides valuable monitoring data on the FAW's distribution, offering insights for further research on population dynamics and developing predictive models for integrated pest management strategies.

Objective: To explore the acceptability and feasibility of conducting comprehensive, community-based, hy-brid (in-person and virtual) mental health screening and feedback with diverse youth in the community. Methods: The Screening and Support for Youth intervention provides comprehensive assessment and thera-peutic feedback in community settings including symptoms, diagnoses, functional impairment, suicide, and clinical risk. Symptom severity, seven diagnoses and suicidality were assessed with the Kiddie Computer-ized Adaptive Test. Functional impairment was evaluated by youth self-report on the Weiss Functional Im-pairment Rating Scale. Measures were scored according to nationally developed norms and an algorithm was developed to aggregate symptoms and functioning to determine clinical risk. The results were discussed with participants in a motivational interview to promote insight and engagement in an online intervention. Results: Methodological innovations included using a QR code for recruitment, integrated in-person and virtual participation, and expansion from in-person recruitment within the schools to virtual engagement with youth throughout the community. The final sample included disproportionately more Black or African American and Hispanic youth as compared to school and community statistics, suggesting that hybrid re-search may facilitate recruitment of diverse populations. Qualitative interviews indicated that the screening and feedback raised youth awareness of their wellbeing and/or distress, its impact on their functioning, and engagement with options for improving wellbeing. Conclusion: Hybrid research, developed as a necessity during the pandemic, proved advantageous to the feasibility and acceptability of community-based recruit-ment of at risk, minoritized youth.

In order to deal with imprecision, ambiguity, and uncertainty in data analysis, Pawlak introduced rough set theory in 1982. This paper aims to expand the scope of basic set theory developed by presenting the notions of δP-upper and δP-lower approximations, that are based on the notion of δP-open sets, we additionally examine a few of their fundamental characteristics.

This work reports the role of structure and composition on the determination of the performances of p-type SnOx TFTs deposited by rf magnetron sputtering at room temperature, followed by a post-annealed step up to 200 °C at different oxygen partial pressures (Opp), between 0% and 20%, but where the p-type conduction was only observed between 2.8–3.8%. The role of structure and composition were evaluated by XRD and Mössbauer spectroscopic studies. The study allows to identify the best phases/compositions and thicknesses (around 12 nm) to be used that lead to the production of TFTs with a bottom gate configuration, on glasses coated with conductive Indium Tin Oxide, followed by Aluminium Titanium Oxide dielectric layer with saturation mobility of 4.6 cm²V⁻¹s⁻¹ and on-off ratio above 7 × 10⁴, operating at the enhancement mode with a saturation voltage of −10 V.

In this paper, we investigate several relations for p-adic gamma function by means of their Mahler expansion and fermionic p-adic q-integral on ℤ_{p}. We also derive two fermionic p-adic q-integrals of p-adic gamma function in terms of q-Boole polynomials and numbers. Moreover, we discover fermionic p-adic q-integral of the derivative of p-adic gamma function. We acquire a representation for the p-adic Euler constant by means of the q-Boole polynomials. We finally develop a novel, explicit and interesting representation for the p-adic Euler constant including Stirling numbers of the first kind.

In this paper, we investigate p-adic q-integral (q-Volkenborn integral) on ℤ_{p} of p-adic gamma function via their Mahler expansions. We also derived two q-Volkenborn integrals of p-adic gamma function in terms of q-Daehee polynomials and numbers and q-Daehee polynomials and numbers of the second kind. Moreover, we discover q-Volkenborn integral of the derivative of p-adic gamma function. We acquire the relationship between the p-adic gamma function and Stirling numbers of the first kind. We finally develop a novel and interesting representation for the p-adic Euler constant by means of the q-Daehee polynomials and numbers.

Leveraging the open-world understanding capacity of large-scale visual-language pre-trained models has become a hot-spot in point cloud classification. Recent approaches rely on transferable visual-language pre-trained models, classifying point clouds by projecting them into 2D images and evaluating consistency with textual prompts. These methods benefit from the robust open-world understanding capabilities of visual-language pre-trained models and require no additional training. However, they face several challenges summarized as prompt ambiguity, image domain gap, view weights confusion, and feature deviation. In response to these challenges, we propose PointBLIP, a zero-training point cloud classification network based on the recently introduced BLIP-2 visual-language model. PointBLIP is adept at processing similarities between multi-images and multi-prompts. We separately introduce a novel method for point cloud zero-shot and few-shot classification, which involves comparing multiple features to achieve effective classification. Simultaneously, we enhance the input data quality for both the image and text sides of PointBLIP. In point cloud zero-shot classification tasks, we outperform state-of-the-art methods on three benchmark datasets. For few-shot classification tasks, to the best of our knowledge, we present the first zero-training few-shot point cloud method, surpassing previous works under the same conditions and showcasing comparable performance to full-training methods.

It is increasingly common for experiments in biology and medicine to involve large numbers of hypothesis tests. A natural graphical method for visualizing these tests is to construct a histogram from the p-values of these tests. In this article, we examine the shapes, both normal and abnormal, that these histograms can take on, as well as present simple inferential procedures that help to interpret the shapes in terms of diagnosing potential problems with the experiment. We examine potential causes of these problems in detail, and discuss potential remedies. Throughout, examples of abnormal-looking p-value histograms are provided and based on case studies involving real biological experiments.

Recent studies with ApoE knockout (ApoE-/-) mice deficient for PACAP (pituitary adenylate cyclase-activating polypeptide) or its receptor PAC1 provide evidence for a possible involve-ment of immune- and vasoregulatoy PACAP signaling at the PAC1 receptor in atherogenesis and plaque associated vascular inflammation. Therefore, we tested the PAC1 receptor agonist Maxadilan and the PAC1 selective antagonist M65 on plaque development and lumen stenosis in the ApoE-/- atherosclerosis model for possible anti-atherogenic effects. Adult male ApoE-/- mice were fed a cholesterol-enriched diet (CED) or standard chow (SC) treat-ed with Maxadilan, M65 or Sham. Effects of treatment on atherosclerotic plaques, lumen stenosis, apoptosis and pro-inflammatory signatures were analyzed in the brachiocephalic trunk (BT). The percentage of Maxadilan treated mice exhibiting plaques under SC and CED was lower than that of Sham or M65 treatment indicating opposite effects of Maxadilan and M65. Maxadilan ap-plication inhibited lumen stenosis under SC and CED compared to Sham. In spite of significantly increased cholesterol levels, lumen stenosis of Maxadilan-treated mice was similar under CED and SC. In contrast, M65 under SC or CED did not reveal a significant influence on lumen steno-sis. Maxadilan significantly reduced the TNF-α+ area in the plaques under CED, but not under SC. In contrast, the IL-1β+ area was reduced after Maxadilan treatment under SC, but remained unchanged under CED compared to Sham. Maxadilan reduced the caspase-3+ area in the media under both SC and CED without affecting lipid content in plaques. Despite persisting hypercholesterolemia, Maxadilan reduces lumen stenosis, apoptosis and TNF-α driven inflammation. Our data suggest that Maxadilan provides atheroprotection by act-ing downstream of cholesterol-induced vascular inflammation. This implicates the potential of PAC1-specific agonist drugs against atherosclerosis even beyond statins and PCSK-9.

Background: Urinary tract infections (UTI) are a significant health concern, particularly in women, with almost half of them experiencing at least one UTI in their lifetime. This problem is further compounded by the high recurrence rate within 6-12 months. Postmenopausal women are especially vulnerable to recurrent UTI due to the natural decline in estrogen levels, which alters the urogenital epithelium and subsequently affects the urogenital microbiome. Methods: This study included 674 female patients that were admited at "Dr. Carol Davila" Central Military Emergency University Hospital in Bucharest over a period of 3 years. Results: Of the 674 women with urinary tract infection, 435 (79.3%) had more than one positive culture, and 239 (35.4%) experienced at least one recurrent urinary tract infection 6-12 months after the initial diagnosis. The mean age of women with urinary tract infection was 63 (± 15.61 years). Recurrent urinary tract infections were most prevalent in those aged 65-80 years (52%). Escherichia coli was detected in 71% of the positive cultures. Conclusions: The management of recurrent urinary tract infections in women presents a complex challenge that necessitates a multifaceted approach. The emerging understanding of the urobiome and its role in bladder health offers promising avenues for developing targeted interventions. As research in this field advances, it is imperative to integrate these new insights into clinical practice to balance traditional antibiotic treatments with novel strategies that consider the intricate microbial ecosystem of the urinary tract.

Disruptions in power quality have a negative impact on many energy consumers. These include lighting, where interference manifests itself, among others, in the form of light flickering. The article presents phenomena accompanying the operation of modern light sources against the background of exemplary results of studies on the flicker of conventional light sources, such as incandescent or fluorescent lamps. To assess the effects of these phenomena, it is necessary to use measures other than those traditionally used. The results of measurements carried out in accordance with different recommendations take into account both power supply disturbances and the properties of modern light sources.

The utilization of optical antennas for active control of far-field radiation at the subwavelength scale is crucial in various scientific and technological applications. We propose a thermally tunable disk design of indium tin oxide (ITO) and aluminum gallium nitride (Al0.18Ga0.82As), enabling a switch between absorption and scattering. Furthermore, the control of far-field radiation pattern can be easily realized by combining ITO and Al0.18Ga0.82As to enhance or suppress emission. Our results demonstrate that hybrid structure can be dynamically tuned with temperature variations. In the proposed design, a frequency is achieved at the epsilon-near-zero wavelength of 1240 nm. The thermal tunability of hybrid structure introduces new multifunctional possibilities for light manipulation, thereby enhancing the potential applications of new devices in the near-infrared range.

Background: Manoka Health District (MHD), an enclaved archipelago district in Cameroon, is home to about 1,732 under-2 children. Over 90% of these children have not received a single dose of any vaccine. In this paper, we explored the reasons for the high proportion of zero-dose children in this district so as to generate information that policymakers can use to develop context-specific interventions to boast coverage for routine immunization (RI) in this remote fishing island Methodology: We collected qualitative data via key informant interviews (KII) and focused group discussions (FGD) and analyzed it using thematic analysis. Participants for KII were selected using purposive sampling, and a snowballing approach helped to recruit an intermixed population of locals and immigrants for FGDs. Results: MHD is a hard-to-reach zone with a lone health facility that lack a functional cold chain equipment to cover the 47 islets of the district. Diurnal floods, long distance, sea turbulence, infrastructural and resource constraints, and immigrant population without residence permits hinders optimal childhood vaccination. Insufficient community health workers and lack of proper community engagement with feedback loops hamper effective communication and vaccine uptake during campaigns and outreaches. The reasons for vaccination hesitancy included hospital-based vaccination preference over home-based vaccination, fear of post-vaccination fever, rumors, repeated postponement of the vaccination schedules, and refusal by some ethnic group leaders. Conclusion: This study revealed context-specific reasons for zero-dose childhood vaccination status in MHD. These findings should be leveraged to design tailored interventions to raise RI in MHD and similar under-vaccinated communities. Keywords: zero-dose, childhood vaccination, missed communities, Cameroon

New setting is introduced to study failed zero-forcing number and failed zero-forcing neutrosophic-number. Leaf-like is a key term to have these notions. Forcing a vertex to change its color is a type of approach to force that vertex to be zero-like. Forcing a vertex which is only neighbor for zero-like vertex to be zero-like vertex but now reverse approach is on demand which is finding biggest set which doesn’t force. LetNTG : (V,E,σ,μ) be a neutrosophic graph. Then failed zero-forcing number Z(NTG) for a neutrosophic graph NTG : (V,E,σ,μ) is maximal cardinality of a set S of black vertices (whereas vertices in V (G) \ S are colored white) such that V (G) isn’t turned black after finitely many applications of “the color-change rule”: a white vertex is converted to a black vertex if it is the only white neighbor of a black vertex. Failed zero-forcing neutrosophic-number Zn(NTG) for a neutrosophic graphNTG : (V,E,σ,μ) is maximal neutrosophic cardinality of a set S of black vertices (whereas vertices in V (G) \ S are colored white) such that V (G) isn’t turned black afterfinitely many applications of “the color-change rule”: a white vertex is converted to a black vertex if it is the only white neighbor of a black vertex. Failed zero-forcing number and failed zero-forcing neutrosophic-number are about a set of vertices which are applied into the setting of neutrosophic graphs. The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs namely path-neutrosophic graphs, cycle-neutrosophic graphs, complete-neutrosophic graphs, star-neutrosophic graphs, bipartite-neutrosophic graphs, and t-partite-neutrosophic graphs are investigated in the terms of maximal set which forms both of failed zero-forcing number and failed zero-forcing neutrosophic-number. Neutrosophic number is reused in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number to compare with other vertices. Summation of three values of vertex makes one number and applying it to a comparison. This approach facilitates identifying vertices which form failed zero-forcing number and failed zero-forcing neutrosophic-number. In path-neutrosophic graphs, the set of vertices such that every given two vertices in the set, have distance at least two, forms maximal set but with slightly differences, in cycle-neutrosophic graphs, the set of vertices such that every given two vertices in the set, have distance at least two, forms maximal set. Other classes have same approaches. In complete-neutrosophic graphs, a set of vertices excluding two vertices leads us to failed zero-forcing number and failed zero-forcing neutrosophic-number. In star-neutrosophic graphs, a set of vertices excluding only two vertices and containing center, makes maximal set. In complete-bipartite-neutrosophic graphs, a set of vertices excluding two vertices from same parts makes intended set but with slightly differences, in complete-t-partite-neutrosophic graphs, a set of vertices excluding two vertices from same parts makes intended set. In both settings, some classes of well-known neutrosophic graphs are studied. Some clarifications for each result and each definition are provided. Using basic set not to extend this set to set of all vertices has key role to have these notions in the form of failed zero-forcing number and failed zero-forcing neutrosophic-number. The cardinality of a set has eligibility to form failed zero-forcing number but the neutrosophic cardinality of a set has eligibility to call failed zero-forcing neutrosophic-number. Some results get more frameworks and perspective about these definitions. The way in that, two vertices don’t have unique connection together, opens the way to do some approaches. A vertex could affect on other vertex but there’s no usage of edges. These notions are applied into neutrosophic graphs as individuals but not family of them as drawbacks for these notions. Finding special neutrosophic graphs which are well-known, is an open way to pursue this study. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

Theoretical analysis into the energetics of a novel putative electromagnetic field propulsion device by the author, found that it was able to impart momenergy to the ground state of the electromagnetic field; some rest-energy of the craft was converted to kinetic energy of the craft. Electrical analysis showed that the propulsor was always a net electrical load – if the device accelerated from one frame, then deaccelerated to the original frame, both processes would consume electrical work. The aim of this paper is to look further into this sinking of high-grade electrical energy into the field ground state and to show that an even more pernicious form of 2^nd Law of Thermodynamics exists.

Insurance companies need to calculate solvency capital requirements in order to ensure that they can meet their future obligations to policyholders and beneficiaries. The solvency capital requirement is a risk management tool essential for, when extreme catastrophic events occur, resulting in a high number of possibly interdependent claims. This paper studies the problem of aggregating the risks coming from several insurance business lines and analyses the effect of reinsurance in the level of risk. Our starting point is to use a Hierarchical Risk Aggregation method, which was initially based on 2-dimensional elliptical copulas. We then propose the use of copulas from the Archimedean family and a mixture of different copulas. Our results show that a mixture of copulas can provide a better fit to the data than an individual copula and consequently avoid over or underestimating of the capital requirement of an insurance company. We also investigate the significance of reinsurance in reducing the insurance company’s business risk and its effect on diversification. The results show that reinsurance does not always reduce the level of risk, but can also reduce the effect of diversification for insurance companies with multiple business lines.

The primary goal of Theory of Everything (ToE) is to unify General Relativity and Quantum Mechanics, establishing a theoretical framework that explains all natural phenomena and fundamental laws of the universe. The rapid development of artificial intelligence in the 21st century presents new opportunities for the establishment of TOE. Research has revealed that the observer and the agent, as key concepts in physics and intelligence science respectively, share intrinsic consistency. Using this as a breakthrough point, we first proposed Standard Agent Model, which, from the perspective of information processing, establishes a unified structure for agents. This model posits that any agent possesses five essential capabilities: input, output, storage, creation, and control of these four capabilities.By setting the values of these five essential capabilities in Standard Agent Model to either zero or infinity, we identify two extreme states of agents: Absolute Zero Agent (α Point) and Omniscient and Omnipotent Agent (Ω Point). Based on the evolution of agents towards these two extreme states, we derive two driving forces for agent evolution: α Gravity and Ω Gravity. These elements collectively form General Model of Agent Evolution.Building on the first two models, we further derive Intelligent Universe Evolution Model, proposing that Universe, as an agent, evolves dynamically between α Point and Ω Point under the influence of α Field and Ω Field. These three models together constitute "Theory of Universal Intelligent Evolution," abbreviated as Ω Theory. According to Ω Theory, we demonstrate that the observer in physics and a wide range of research objects can be unified within the framework of Standard Agent Model. Additionally, we argue that the four fundamental forces have the potential to be unified into α Gravity and Ω Gravity.Within the framework of Ω Theory, through theoretical analysis and experimental validation, it has been demonstrated that the differences between classical mechanics, relativity, and quantum mechanics stem from the varying intelligence levels assigned to observers. Furthermore, it has been discovered that the differences in intelligent characteristics between the theoretical Universe proposed by these three major physical theories and Universe in which humanity currently resides are the root cause of the varying precision and limitations of these theories.

As the global economy pays increasing attention to sustainable development and environmental protection, the role of green finance in promoting regional technological innovation is becoming more and more prominent. This study aims to analyse the impact of green finance on regional technological innovation and its mechanism of action, with special attention to the mediating effect of R&D inputs and the moderating role of regional characteristics. By analysing the panel data of 30 provinces in China from 2008 to 2021 with fixed and mediated effect models, the results show that green finance significantly promotes regional technological innovation. R&D investment intensity partially mediates this effect, while regional innovation and entrepreneurship capacity plays a significant moderating role in this relationship. Specifically, regional innovation and entrepreneurship capacity reinforces the positive impact of green finance on R&D investment, but the marginal effect of R&D investment is reduced in regions with stronger innovation and entrepreneurship capacity. The findings provide a theoretical basis for formulating relevant policies to promote green finance development and sustainable innovation in regional economies.

Here, we describe the bicomplex (p,q)- Fibonacci numbers and the bicomplex (p,q)- Fibonacci quaternions that are based on these numbers and give some of their equations, including the Binet formula, generating function, Catalan, Cassini, d’Ocagne’s identities, and some summation formulas for both of them. Finally, we create a matrix for bicomplex (p,q)- Fibonacci quaternions, and we obtain a determinant of a special matrix that gives the terms of that quaternion.

In this paper, some convexity properties and some inequalities for the (p,k)-analogue of the Gamma function, Г_p,k(x) are given. In particular, a (p,k)-analogue of the celebrated Bohr-Mollerup theorem is given. Furthermore, a (p,k)-analogue of the Riemann zeta function, ζ_p,k(x) is introduced and some associated inequalities are derived. The established results provide the (p,k)-generalizations of some known results concerning the classical Gamma function.

We introduce the notion of p-adic equiangular lines and derive the first fundamental relation between common angle, dimension of the space and the number of lines. More precisely, we show that if {τj} n j=1 is p-adic γ-equiangular lines in Qd p , then (1) |n| 2 ≤ |d| max{|n|, γ2 }. We call Inequality (1) as the p-adic van Lint-Seidel relative bound. We believe that this complements fundamental van Lint-Seidel [Indag. Math., 1966] relative bound for equiangular lines in the p-adic case. Keywords: Equiangular lines, p-adic Hilbert space.

This article presents a proposed system that enables energy transfer from the dc grid to a single-phase onboard grid, operating at an hi frequency. In addition to the energy transfer function, it additionally enables compensation of reactive power in the ac grid when the converter operates at less than rated power. To operate in compensator mode, it is necessary to decouple the current from the grid into active and reactive components and control them independently. To achieve good dynamics, synchronizers that operate in the dq system are used. In the case of a single-phase grid, it requires such a synchronizer to generate virtual quadrature signals. The used systems based on second-order generalized integrator are good, but to improve dynamics in the work a quadrature generator based on trigonometric calculations was proposed. The developed system was implemented in a proportional resonant current control system. Tests performed in steady state and in dynamic states related to typical grid disturbances showed better dynamic properties than the standard integrator-based system.Keywords: power transfer; compensator; P+R controller

Arc sensors have been used in seam tracking and widely studied since the 80s; commercial arc sensing products for T and V shaped grooves have been developed. However, it is difficult to use these arc sensors in narrow gap welding because arc stability and sensing accuracy are not satisfactory. Pulse gas melting arc welding (P-GMAW) has been successfully applied in narrow gap welding and all position welding processes, so it is worthwhile to research P-GMAW arc sensing technology. In this paper, we derived a linear mathematical P-GMAW model for arc sensing, and the assumptions for the model are verified through experiments and finite element methods. Finally, the linear characteristics of the mathematical model were investigated. In torch height changing experiments, uphill experiments, and groove angle changing experiments the P-GMAW arc signals all satisfied the linear rules. In addition, the faster the welding speed, the higher the arc signal sensitivities; the smaller the groove angle, the greater the arc sensitivities. The arc signal variation rate needs to be modified according to the welding power, groove angles, and swing or rotate speed.

Pardosa pseudoannulata is one of the most common wandering spiders in agricultural fields and a potentially good bioindicator for heavy metal contamination. However, little is known about the mechanism by which spiders respond to heavy metals at the molecular level. In this study, high-throughput transcriptome sequencing was employed to characterize the de novo transcriptome of the spiders and to identify differentially expressed genes (DEGs) after cadmium exposure. We obtained 60,489 assembled unigenes, 18,773 of which were annotated in the public databases. Ultimately, 3450 cDNA simple sequence repeats were identified and validated as potential molecular markers in the unigenes. A total of 2939, 2491 and 3759 DEGs were detected among the three libraries of two Cd-treated groups and the control. Functional enrichment analysis revealed that metabolism processes and digestive system function were predominately enriched in response to Cd stress. At the cellular and molecular levels, significantly enriched pathways in lysosomes and phagosomes as well as replication, recombination and repair demonstrated that oxidative damage resulted from Cd exposure. Based on the selected DEGs, certain critical genes involved in defence and detoxification were analysed. These results may elucidate the molecular mechanism underlying spiders' responses to heavy metal stress.

In the present study, we introduce the two dimensional Chlodovsky type Bernstein operators based on (p,q)−integer. We examine approximation properties of our new operator by the help of Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence by means of the modulus of continuity and the Lipschitz type maximal function. Also, we give a Voronovskaja type theorem for this operators. And, we investigate weighted approximation properties of these operators and estimate rate of convergence in the same space. Finally, with the help of Maple, illustrative graphics show the rate of convergence of these operators to certain functions . The optimization of approximation speeds by operators during system control provides significant improvements in stability and performance. As a result, the control and modeling of dynamic systems become more efficient and effective through innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications in these areas.

Polo-Like Kinase-1 (PLK1) is a key regulator of the G2/M cell cycle and has been linked to treatment resistance in prostate cancer (PCa). This study aimed to explore the correlation between PLK1 expression, determined through RNA-seq analysis, and clinical outcomes in PCa patients. We analyzed tumor samples from 452 patients diagnosed with prostate adenocarcinoma between 2014 and 2020, sourced from nine institutions affiliated with the Oncology Research Information Exchange Network (ORIEN). PLK1 expression levels were determined using RNA-seq data and stratified into high (≥75th percentile) vs. low (<75th percentile) expression. Univariate and multivariate Cox proportional hazard models compared overall survival between patients with low vs. high PLK1 expression, adjusting for age, race, PSA, and stage. Of the 452 tumors, 241 (53.3%) had high PLK1 and 211 (46.7%) had low PLK1 expression. Median follow-up was 2.74 years for low PLK1 and 2.71 years for high PLK1. No significant difference in overall survival was observed between low and high PLK1 groups in univariate (p=0.86; HR 1.09) or multivariate (p=1.0; HR 0.99) analyses. This study found no association between RNA-seq-based PLK1 expression and prognosis in PCa. PLK1 expression is not a prognostic marker for prostate cancer.

This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann's Limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.

The number zero is happily used by mathematics, but not physics for various reasons. It should be incorrect to say that $\phi(x)= 0=\gamma(x)$, when there are two, different, physical meanings, such as fields for two very different kinds of particles, etc. That could lead to some kind of confusion in physics. In that case, it could even be necessary to {\bf reject} using the zero number in certain expressions, and instead, at least try to `go around them'. Do not forget that the number zero was {\it banned} for 1,500 years, long, long ago!

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

Singapore, being a land-scarce country, is heavily dependent on imported food, which poses a challenge in ensuring food security. In addition, the substantial amount of food waste generated in Singapore further deepens food insecurity challenges, imposing enormous pressure on the country's food and land resources. In the recent years, valorising food waste streams into higher value products has been an increasing trend in tackling food wastage due to its economical and environmental benefits. This paper provides a thorough review on food waste valorisation in Singapore and summarizes critical information from the current food landscape to the food waste management strategies adopted. The review highlights the prototypes developed from food waste streams by the local research and development capabilities and the technological processing and techniques used for versatile applications ranging from food and nutraceuticals to urban agriculture. The challenges associated with the valorisation of food waste are also addressed. Two ambitious Initiatives by the Singapore government Zero Waste Plan and the 30-by-30 food security goal to encourage food waste valorisation are explored and presented. The review serves as a reference for other countries in ensuring food security and achieving sustainable development goals.

The photodegradation of azithromycin present was carried out in water using H2O2 under UV irradiation. The reaction variables considered in this study were the amount of H2O2 solution and the initial concentration of azithromycin to evaluate the performance of the photodegradation process. The azithromycin degradation was not observed in the dark during stirring for 20 minutes. The study showed efficient photodegradation of azithromycin using H2O2 as an oxidant in the presence of UV irradiation. The azithromycin degradation was affected significantly by the pH of the irradiated solution. The degradation was low at an acidic pH and showed an increasing trend as the pH changed to basic. The azithromycin degradation was increased with a higher amount (higher concentration) of H2O2. The degradation of azithromycin decreased with a higher concentration of azithromycin in the reacting solution. The highest degradation of AZT was achieved in 1 hour using a 1.0 ppm AZT solution containing 3 ml of H2O2. The experimental data obtained was well-fitted to zero-order reaction kinetics. The results of this study were found to be superior when compared with those reported in the literature, with photocatalysis using nanomaterials and photolysis using irradiation and H2O2. The UV/H2O2 system was found to be quite efficient for the photodegradation of azithromycin, and this system can be applied to degrade other organic pollutants present in industrial wastewater. To this point, such a study has not been reported specifically for AZT photodegradation.

This paper explores the potential of using the SAM segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM on GitHub allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by DeepLabv3+, then merging the segmentation masks provided by these two networks. Additionally, we examine the \enquote{oracle} method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from ground truth. In addition, we tested in the CAMO datasets an ensemble of PVTv2 transformers; combining the ensemble and SAM yields state-of-the-art performance in that dataset. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method.