The physical properties of a structure such stiffness although can be determined by some statical tests, the identification of damping parameter requires a dynamic test. In general, both theoretical prediction and experimental identification of damping are quite difficult. There are many different techniques available for damping identification, and each method gives a different damping parameter. The dynamic indentation method, rheometry, atomic force microscopy, and resonant vibration tests are commonly used to identify the damping of materials, including soft materials. While the viscous damping ratio, loss factor, complex modulus and viscosity are quite common to describe damping of materials, there are also other parameters such as the specific damping capacity, loss angle, half-power bandwidth, and logarithmic decrement to describe damping of various materials. Often one of these parameters is measured in practical applications and the measured damping parameter needs to be converted into another damping parameter for comparison purposes. In this review, the theoretical derivations of different parameters for the description and quantification of damping and their relationships, and the methods for damping identification are presented. The expressions for both high damping and low damping are included and evaluated. This paper could be a primary resource for damping research and teaching.

We define the function $Col: \mathbb{N} \to \mathbb{N}$ as the Collatz function, given by $3n + 1$ if $n$ is odd and $\displaystyle\frac{n}{2}$ if $n$ is even \cite{Lagarias1990}. The conjecture postulates that for any positive integer, at some point, its iteration will reach 1, or equivalently, every orbit will fall into the periodic cycle $\{4, 2, 1\}$. Two conditions would invalidate the conjecture: the existence of a divergent orbit or the presence of another cycle. In 2019 Terence Tao \cite{tao} proved that almost all orbits converge to the trivial cycle of $\{1,4,2\}$, in this work we will prove the non-existence of divergent orbits. The central idea is to decompose the dynamic system $Col$ into a binary dynamic system, generated by different compositions of two functions. This differs from a typical dynamic system, which is generated by the iteration of a single function. We consider the system generated by the functions $\theta, \psi: \mathbb{R} \to \mathbb{R}$, defined as $\displaystyle\theta(x) = \frac{x}{2}$ and $\displaystyle\psi(x) = \frac{3x + 1}{2}$, denoted as $\langle \theta, \psi \rangle$. We examine sequences of functions in $\langle \theta, \psi \rangle$ of the form $S_k(x) = s_k \circ S_{k-1}(x)$ with $s_k \in \{\theta, \psi\}$. We define a function that assigns to each element of these sequences an integer, called the minimum value positive integer or simply the minimum value. This corresponds to the smallest positive integer $n$ such that $S_k(n)$ is an integer. As we will prove later, the minimum value is monotonically increasing, meaning that increasing the terms of the sequence will never result in a value lower than the previous one. Based on this behavior, we distinguish two types of sequences: stable ones, where the minimum value is constant from a certain point, and unstable ones, where the minimum value is divergent. The main result for unstable sequences is that when the slope of the sequence is divergent, the sequence is unstable. From this result, we can prove that there are no divergent orbits for the function $Col$.

Let's define $\delta(x) = (\sum_{{q\leq x}}{\frac{1}{q}}-\log \log x-B)$, where $B \approx 0.2614972128$ is the Meissel-Mertens constant. The Robin theorem states that $\delta(x)$ changes sign infinitely often. Let's also define $S(x) = \theta(x) - x$, where $\theta(x)$ is the Chebyshev function. It is known that $S(x)$ changes sign infinitely often. We define the another function $\varpi(x) = \left(\sum_{{q\leq x}}{\frac{1}{q}}-\log \log \theta(x)-B \right)$. We prove that when the inequality $\varpi(x) \leq 0$ is satisfied for some number $x \geq 3$, then the Riemann hypothesis should be false. The Riemann hypothesis is also false when the inequalities $\delta(x) \leq 0$ and $S(x)\geq 0$ are satisfied for some number $x \geq 3$ or when $\frac{3 \times \log x + 5}{8 \times \pi \times \sqrt{x} + 1.2 \times \log x + 2} + \frac{\log x}{\log \theta(x)} \leq 1$ is satisfied for some number $x \geq 13.1$.

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2,892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG16, Detectron-121, MobileNet and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10-30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.

This study investigates resource adequacy and renewable energy integration in the United States, European Union, and Pakistan amid global energy market liberalization and greenhouse gas re-duction efforts. It explores how these regions are adapting to the surge in renewable sources like wind and solar, which, despite their financial and environmental benefits, challenge resource adequacy and the economic viability of traditional energy sources. In the US and EU, significant improvements have been introduced in wholesale electricity markets and capacity accreditation mechanisms, which enhanced the large-scale deployment of renewables. This shift has prompted a reevaluation of resource adequacy, leading to increased deployment of battery storage and demand response. Presently, gas-based generation is largely upholding resource adequacy, however, future trends indicate a move towards greater consumer participation, energy efficiency, and utility-scale storage, with a decline in fossil fuel use. Pakistan aims to adopt a liberalized market structure by balancing competitive markets with legacy contracts. Public pressure is driving a shift from costly fossil-based generation to renewables. Similarly, a trend in the rise of behind-the-meter solar generation can be witnessed. In future, Pakistan may also experience resource adequacy challenges. It will likely need to implement battery storage, demand response, and modern capacity accreditation tools, by drawing lessons from developed markets.

Given the conceptual confusion between resilience and sustainability in organizations in the literature, the present study reviews the 27-year global literature on resilience and sustainability in organizations to clarify the relationship between the two, and derive a framework as cutting-edge knowledge on how to ensure resilience and sustainability in organizations. Findings uncover five schools of thought: Sustainable Business Strategy, Predictive Analytics, Sustainable Supply Chain, Ecological Resilience and Sustainable Leadership. A cutting-edge framework, comprising sustainability practices, sustainability outputs, sustainability and resilience outcomes, is derived to guide future research and inform practitioners on how to ensure resilience and sustainability in their organizations.

Steroidogenic factor 1 (SF-1) is a nuclear receptor that regulates steroidogenesis and reproductive development. NR5A1/SF-1 variants are associated with a broad spectrum of phenotypes across individuals with disorders of sex development (DSD). Oligogenic inheritance has been suggested as an explanation. However, testing the impact of specific gene variants involved in a network remains difficult. To confirm the hypothesis that NR5A1/SF-1-related DSD follow an oligogenic mode of inheritance, we investigated a constellation of gene variants identified in a 46,XY severely undervirilized individual. Candidate genes were revealed by whole exome sequencing, and pathogenicity was predicted by different in silico tools. We found variants in NR1H2 and INHA associated with steroidogenesis, sex development, and reproduction. Functional testing was conducted in cell models. Novel SF-1 and NR1H2 binding sites in the AR and INHA gene promoters were found. Transactivation studies showed that wild-type NR5A1/SF-1 regulates INHA and AR gene expression, while the NR5A1/SF-1 variant had decreased transcriptional ac-tivity. NR1H2 was found to regulate AR gene transcription; however, the NR1H2 variant showed normal activity. This study expands the NR5A1/SF-1 network of interacting partners while strengthening the hypothesis that the broad phenotype observed in 46,XY DSD individuals may be caused by oligogenic pathogenicity.

This paper proposes an optimization method for the scheduling of a multi-energy virtual power plant supply-demand balance in the power market environment of Jiangxi Province. The objective of this method is to enhance the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method comprehensively considers the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Additionally, real-time market price feedback is integrated to achieve accurate allocation of power supply and demand. Through a case study of a multi-energy virtual power plant in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

The impact of financial risks extends beyond the normal operation and survival of industrial and commercial enterprises and financial institutions. They also have the potential to impede the stable development of a country's and even the world's financial economy. This is clearly demonstrated by the severe consequences of the frequent financial crises that have occurred in recent years. It thus follows that the prevention of financial risks has become one of the core tasks of the operation and management of industrial and commercial enterprises and financial institutions. Portfolio optimization techniques are employed in the construction of a variety of asset allocation models, with reinforcement learning algorithms used to dynamically adjust investment strategies with the objective of maximizing returns and minimizing risks. The preliminary construction of an asset allocation model is achieved through the utilization of a genetic algorithm. Genetic algorithms emulate the processes of natural selection and genetic variation to identify the optimal portfolio of assets that will yield the greatest returns at a pre-established level of risk. Subsequently, the Deep Q-Learning algorithm is introduced to facilitate dynamic adjustments and optimization of the asset allocation, based on the initial construction. Deep Q-learning employs deep neural networks to forecast the prospective returns of disparate investment strategies, thereby optimizing the decision-making process through continuous learning and updating. The combination of genetic algorithms and deep Q-learning enables the system to identify the optimal investment strategy under a diverse range of initial conditions and to adapt it in real-time to respond to market fluctuations and uncertainties. The experimental analysis demonstrates that the proposed method exhibits an exemplary capacity for risk control and a robust ability to generate stable income growth across diverse market environments. In simulation experiments, portfolios constructed using this method demonstrate lower volatility and higher average returns than those generated through traditional methods.

The blood that remains in the placenta and umbilical cord after the birth of the infant is referred to as umbilical cord blood (UCB), which has the advantages of convenient source and less ethical controversy. MicroRNA (miRNA) is a class of non-coding RNAs that can regulate gene expression after translation and transcription. Alterations in miRNA expression involve changes in a range of gene expression pathways. An increasing body of research indicates that miRNAs are implicated in many human disorders and are involved in almost all biological medium processes. Due to their high stability in UCB, circulating miRNAs are considered promising biomarkers for diagnosis and disease prognosis. Herein, we conduct a comprehensive review to summarize the roles of diverse UCB miRNAs from different perspectives, including UCB miRNA as biomarker for developmental projections and disease diagnosis, the relationship between maternal gestational status and neonatal UCB miRNA expression, the therapeutic function of exosomal UCB miRNA, miRNA expression profiles in UCB cell lineages. Taken together, miRNAs in UCB are very promising biomarkers for indicating physiological states, disease states, drug responses, development states, or health conditions, which can help in diagnosing, classifying, and predicting diseases, prognosis, or providing personalized treatment plans for patients.

Anterior Cruciate Ligament (ACL) injuries rank among the most prevalent and severe types of injuries, significantly impacting both athletes and non-athletes alike. These injuries not only result in immediate physical impairment, such as intense pain, substantial swelling, and a marked loss of mobility, but also carry long-term health consequences that can alter a person's quality of life. Chronic pain, persistent instability, and an increased risk of developing osteoarthritis are among the lasting effects that can follow an ACL injury. An in-depth understanding of the biophysics behind ACL injuries is paramount for devising effective prevention and treatment protocols. Biophysics, which combines principles from physics with biological systems, provides crucial insights into the mechanical and structural integrity of the ACL and its susceptibility to injury under various conditions. This systematic review aims to collate and synthesize the current knowledge surrounding the biophysical mechanisms that underlie ACL injuries. The review encompasses a range of factors, including the biomechanical forces that place stress on the ligament, anatomical structures that may predispose individuals to injury, and physiological conditions that affect ligament health and resilience. Each of these factors plays a crucial role in the incidence and severity of ACL injuries. Biomechanical forces, for example, can involve sudden changes in direction or impact during physical activity, leading to excessive stress on the ACL. Anatomical factors might include variations in bone structure or ligament alignment that inherently increase the risk of injury. Additionally, physiological conditions such as muscle strength, flexibility, and overall ligament health can influence the likelihood and extent of an ACL injury. The findings of this review underscore the necessity of adopting integrated approaches in both injury prevention and rehabilitation. Such approaches must consider the multifaceted nature of ACL injuries, involving not only mechanical and anatomical aspects but also physiological and possibly even genetic factors. By emphasizing a multi-faceted understanding, interventions can be more effectively tailored to address the complex interplay of elements that contribute to ACL injuries. This holistic approach can lead to better outcomes for those at risk of or recovering from ACL injuries, enhancing the efficacy of prevention strategies and rehabilitation protocols.

Most research on sustainable small-scale, local producer lifestyles emphasises healthy food production, lacking integration of mental and spiritual well-being into a holistic concept of a healthy lifestyle. This study explores the motives behind the production activity of producers engaged in sustainable food production in several Western Hungarian sub-regions, aiming to identify their attitudes and motivations. The small-scale entrepreneurial mindset encompasses needs beyond physical and ethical aspects, such as involvement, socio-cultural attachment to the past, tradition, nature, place, and local culture. An online questionnaire was conducted with 73 local producers in the second quarter of 2024. The results of the factor and cluster analyses were used to classify the producers into two clusters: the "Value-Creator" and the "Proud" clusters. The main features of these two clusters were illustrated in persona profiles. The "Value-Creator" cluster, mainly women, feel a strong connection to the local community and view sustainable production as a lifestyle choice. The "Proud" cluster, consisting of young men, focus on the quality and uniqueness of their products. This research contributes to a deeper understanding of sustainable production by exploring producers' intrinsic motivations and lifestyle choices. The findings could also inform other entrepreneurial projects, such as urban initiatives.

This study investigates the use of artificial intelligence (AI) and ethnobotanical knowledge to identify natural compounds with potent antiviral activity against HPV-16. Through molecular docking simulations, we evaluated the binding affinities of various essential oil components, in-cluding Apigenin, against the key antigenic determinants of HPV-16 (E6, E7 oncoproteins, and L1 major capsid protein). Apigenin exhibited strong binding affinities, particularly to E6 Oncopro-tein. To further enhance these findings, we employed an AI-driven reverse engineering approach to predict a natural compound with superior efficacy. The AI identified Luteolin, a flavonoid with additional hydroxyl groups, as a promising candidate. Comparative docking studies demon-strated that Luteolin possesses significantly stronger binding affinities to all three HPV-16 targets, surpassing the efficacy of Apigenin. This work highlights the potential of AI in accelerating the discovery and optimization of natural antiviral agents, providing a powerful tool for pharmaco-logical and ethnopharmacological screenings. Our findings suggest that AI-guided identification of natural compounds can lead to the development of more effective antiviral therapies, warranting further experimental validation and potential synthesis in laboratory settings.

Lymphatic filariasis (LF) is a neglected tropical disease associated with poverty and poor environmental conditions. With the inclusion of vector control activities in LF surveillance actions, there is a need to develop simple methods to identify areas with higher mosquito density and consequent risk of W. bancrofti transmission. An ecological study was conducted in Igarassu, Metropolitan Region of Recife, Pernambuco, Brazil. The mosquitoes were captured in 2,060 houses distributed across 117 census tracts. The vector density index (VDI) was constructed, that measures the average number of lymphatic filariasis transmitting mosquitoes per number of houses collected in the risk stratum. Moreover, the social deprivation indicator (SDI) was constructed and was carried out through principal component factor analysis. The average number of female C. quinquefasciatus found in the high-risk stratum was 242, while the low-risk stratum had an average of 108. The overall VDI was 6.8 mosquitoes per household. The VDI for the high-risk stratum was 13.2 mosquitoes per household, while for the low/medium-risk stratum, it was 5.2. This study offers an SDI for the density of C. quinquefasciatus mosquitoes, which reduces the cost associated with data collection and allows for indicating priority areas for vector control actions.

This study primarily investigates the effect of fly ash (FA) content on the mechanical properties and hydration performance of alkali-activated ground granulated blast furnace slag cements (AAGC), and compares the related properties with ordinary Portland cement (OPC). Additionally, the study examined the hydration products, thermal analysis, MIP, SEM, chemically bound water, and pH values of AAGC. The compressive strength of AAGC showed a retrogression phenomenon from 3 to 28 days, with the 14-day and 28-day compressive strengths of AAGC being higher than those of OPC. The AAGC with 20% FA content exhibits the highest 28-day compressive strength (75 MPa). The hydration heat release rate curved of OPC and AAGC can be divided into the initial induction period, induction period, acceleration period, deceleration period and steady period. As FA content increases, the 28-day pore volume of AAGC increased while pH values and chemically bound water decrease. SEM images of AAGC with low FA content showed more microcracks.

Reproductive disorders are a common event in modern reproductive medicine, occurring both in spontaneous and assisted pregnancies. Studies of the molecular mechanisms of implantation disorders in thin endometrium, including the study of gene transcriptional activity, have shed light on the identification of potential biological markers of endometrial receptivity. Background/Objectives: to reveal significantly dysregulated selected genes expression between RIF and RPL patients with thin endometrium. Methods: Endometrial samples were collected in the implantation window days (LH+7 – LH+10) of the natural menstrual cycle from RIF patients (n=20) and RPL patients (n=19). Ten genes were chosen as target genes regarding their possible relations with the implantation process. Total RNA was purified, reverse-transcribed; gene expression was quantified by RT-PCR. Results: The expression of the IL-15, INFG and HPRT1 genes were significantly decreased in RIF with thin endometrium patients compared to the RPL patients (log2 fold change=0.92, p=0.023 for IL-15, log2 fold change=1.24, p=0.046 for INFG, and log2 fold change=0.579, p=0.046 for HPRT1). There were not significant differences in expression of CXCL8, CXCL1, MMP10, C4BPA, TNC, VEGFB and HAND2 genes between groups. Conclusions: decreased expression of IL-15, INFG and HPRT1 genes was found in patients with RIF with thin endometrium compared to the endometrium of women with RPL. This has practical significance for clinicians for differentiated prescription of immunomodulatory therapy in patients undergoing ART programs.

Companion cropping involves growing two or more species together, benefiting the conservation of natural resources and improving fertilization and nutrient cycling by balancing chemical and organic sources. Studies, as in the case of tree tomato and Hass avocado, have shown a significant increase in yield compared to monocultures, highlighting the viability of this practice. In addition to their environmental benefit, companion crops offer economic advantages to producers by obtaining multiple products in a single harvest, strengthening food security and the economy of rural families. This study evaluated three levels of fertilization and interactions between fruit trees at different altitudes, observing differential behavior in the variables evaluated. The combination of cape gooseberry and blackberry showed significantly positive results, with more leaves and fewer pests, demonstrating the benefits of companion plants. A trend towards the combined use of chemical and organic fertilizers was observed, a potential strategy to reduce costs and improve crop growth. The results were shared with producers interested in implementing companion cropping, encouraging the adoption of this more sustainable and profitable agricultural practice. Companion crop agriculture represents a viable and promising strategy for a more efficient and sustainable agriculture, with both environmental and economic benefits.

The increasing frequency and severity of phytoplankton blooms worldwide highlight the need for advancements in monitoring technologies. Optical remote sensors, such as Landsat, have proven to be a cost-effective method for large-scale, near-real-time assessments of phytoplankton biomass in lakes. However, its effectiveness is often compromised by atmospheric interferences like clouds, dust, and wildfire smoke, which can obscure the clear-sky conditions essential for accurate remote sensing. While partial atmospheric correction (removing only the Rayleigh effect) is commonly applied to address these interferences, it remains inadequate for mitigating the impact of wildfire smoke. This study investigates the potential of Landsat's coastal/aerosol band (B1) for assessing wildfire smoke interference effects on Chlorophyll-a (Chl-a) retrieval models, which serve as proxies for phytoplankton biomass. We employed cluster analysis of B1 values to create a screening system based on aerosol reflectance, categorizing smoke interference into low, moderate, and high levels. Subsequently, we applied both partial (Rayleigh-corrected reflectance) and full (Landsat 8 Level 2 surface reflectance) atmospheric corrections before developing the Chl-a retrieval models. Excluding high wildfire smoke interference (B1 > 0.07) from the Chl-a calibration dataset significantly enhanced model performance, increasing the r-squared value from approximately 0.55 to 0.80. Moderate smoke interference (0.05 < B1 < 0.07) yielded results comparable to low-interference conditions. Notably, Rayleigh-corrected reflectance, even without additional aerosol band filtering, achieved higher r-squared values for the Chl-a retrieval model than full atmospheric correction. B1 thus proves to be a valuable tool for identifying low smoke-impacted observations, offering an effective method to monitor phytoplankton biomass amid increasing wildfire activity and improving the capacity to monitor aquatic environments in a changing global landscape.

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

Despite inclusive design focusing on improving environmental accessibility for mobility aid (MA) users, it often fails to ensure true inclusivity due to mismatches between interventions and user perceptions. Therefore, understanding mobility aids users' perceptions of inclusivity is essential for advancing disability studies and design, as it highlights key interventions for more effective practices. This study aims to explore perceived inclusivity in MAs usage and identifies gaps in users' needs, classifying their needs and offering recommendations to meet them. First, the DARE-Inclusive Design Framework was used to develop interview guidelines and interpret results. Next, semi-structured, in-depth interviews were conducted with 12 experienced physiotherapists in Iran, and finally, an inductive thematic analysis was used to identify and present themes. Four themes were identified: 1. Perceived Financial Value: Assessing Worth, 2. Objective Enhancements: Optimizing Environments and MAs, 3. Subjective Enhancements: Trustworthiness, Support, and Hope, and 4. Contextual Factors: Interpretations and Representations. The four interconnected themes provide guidelines for inclusivity-oriented interventions, emphasizing financial assessment, high-tech integration, and aesthetic and symbolic considerations in mobility aid design. Physiotherapists can also mediate emotional responses and enhance inclusion during rehabilitation. Additionally, social context and disability etiology impact users' acceptance and use of MAs.

This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a U-Net framework. The U-Net architecture, commonly employed for biomedical image segmentation, is further enhanced with Depthwise Separable Convolutional Blocks to decrease computational complexity and parameter count, resulting in better efficiency and less overfitting. The Vision Transformer, renowned for its robust feature extraction capabilities utilizing self-attention processes, efficiently captures the overall context within images, surpassing the performance of conventional convolutional networks. By using a pretrained ViT as the encoder in our U-Net model, we take advantage of its extensive feature representations acquired from extensive datasets, resulting in a major enhancement in the model’s ability to generalize and train efficiently. The suggested model has exceptional performance in segmenting breast cancers from medical images, highlighting the advantages of integrating transformer-based encoders with efficient U-Net topologies. This hybrid methodology emphasizes the capabilities of transformers in the field of medical image processing and establishes a new standard for accuracy and efficiency in activities related to tumor segmentation.

Oral lichen planus (OLP) is a chronic inflammatory mucosal disease, and an apparent cause has not been identified. OLP is refractory to treatment, and malignant transformation has been reported. Therefore, discovering effective drugs with few adverse effects over long-term administration is crucial. In this study, we established an in vitro OLP model to investigate the effects of Cepharanthin®, an alkaloid mixture derived from Stephania cephalantha Hayata. Four alkaloids from Cepharanthin® suppressed the expression of cytokine genes at various degrees in activated T cells. However, the effect of Cepharanthin® was limited. On the other hand, Cepharanthin® and four alkaloids suppressed the expression of various cytokine genes in human oral epithelial cells when stimulated with IL-β. In the OLP model, activated T cells increased the inflammatory gene expression in the epithelial cells, while Cepharanthin® suppressed their gene expression. Anti-inflammatory effect of Cepharanthin® was observed by protein dot blot. The effects of Cepharanthin®️ might be expressed through two aspects: its effect of mildly suppressing the activation of T lymphocytes and strongly suppressing the response of epithelial cells to inflammatory stimuli. The in vitro model established in this study could be a tool for elucidating the mechanisms of OLP pathogenesis and disease persistence.

This review explores the nuanced decision-making surrounding postoperative radiotherapy for diffuse low-grade glioma (DLGG) patients, emphasizing the need for personalized treatment to balance the benefits of tumor control with the risks of long-term side effects. Radiotherapy is a cornerstone of DLGG treatment, offering significant improvements in progression-free survival, but its potential for cognitive decline and vascular lesions necessitates careful consideration of individual patient factors. The review delves into the complexities of radiotherapy timing, revealing that while early radiotherapy enhances progression-free survival, it does not significantly impact overall survival and may lead to earlier onset of cognitive decline. Conversely, delayed radiotherapy may mitigate cognitive decline but may result in shorter progression-free survival and the onset of neurological symptoms. The review advocates for a patient-centered approach, emphasizing the importance of weighing individual risks and benefits, and highlights the potential of artificial intelligence in predicting cognitive decline and the need to revisit previous research findings in light of the 2021 WHO (World Health Organization) classification update, underscoring the evolving nature of DLGG treatment and the ongoing quest for optimal therapeutic strategies.

Childsmile is Scotland’s national child oral health improvement programme designed to reduce inequalities in oral health and ensuring universal access to NHS dental services by working closely with children and their families. Research has shown that parents with more dental concerns are less likely to bring their children for regular preventive dental check-ups. Therefore, a communication toolkit named ‘Chatterbox’ was created to help families access dental care services. This study aimed to evaluate the acceptability of Chatterbox from the perspective of parents with young children and from staff who work with them. Thirty parents of young children, volunteers, and staff affiliated with three third-sector family support organisations in Dundee participated in in-depth interviews, focus groups, and observations to assess the objectives of Chatterbox. Framework analysis was conducted to capture the themes, patterns, and findings triangulated using observations and field notes. Chatterbox was found to promote effective communication, identify barriers to dental attendance, provide a platform for reflection, convey the complexities of daily life, and boost parents' confidence to seek assistance. Chatterbox has the potential to help parents in accessing preventive dental care for their young children, when used with trained support workers.

Pro-inflammatory cytokines play a role in the failure of β-cells in type 1 and type 2 diabetes. While existing data from ‘omics’ experiments allow for some understanding of the molecular mechanisms behind cytokine-induced dysfunction in β-cells, no report thus far has provided information on the direct imaging of the β-cell landscape with nanoscale resolution following cytokine exposure. In this study, we use Airyscan-based optical super-resolution microscopy of Insulinoma 1E (INS-1E) cells to investigate the structural properties of two subcellular membranous compartments involved in the production, maturation and secretion of insulin-containing granules, the Endoplasmic Reticulum (ER) and the Golgi Apparatus (GA). Our findings reveal that exposure of INS-1E cells to IL-1β and IFN-γ for 24 hours leads to significant structural alterations of both compartments. In more detail, both ER and GA fragment and give rise to vesicle-like structures with markedly reduced characteristic area and perimeter and increased circularity with respect to the original structures. These findings complement the molecular data collected thus far on these compartments and their role in β-cell dysfunction using and lay the ground for future optical-microscopy-based ex-vivo and in-vivo investigations.

This research paper examines the transformative impact of Artificial Intelligence (AI) on the global journalism industry. The integration of AI technologies, such as machine learning and natural language processing, has drastically changed journalistic practices and media operations. The study aims to understand how AI affects news production, distribution, and ethical considerations within journalism. By providing historical context and reviewing existing literature, this paper situates its analysis within the broader academic discourse. A mixed-methods approach is used for data collection and analysis, ensuring comprehensive and reliable findings. Results indicate that AI tools enhance journalistic efficiency and capabilities but also introduce challenges related to accuracy, authenticity, and editorial judgment. The implications for employment in journalism are significant, necessitating new skills and roles. The discussion interprets these findings in the context of media evolution and ethical standards. This study concludes by advocating for a balanced approach to AI adoption in journalism, emphasizing the importance of ethical guidelines, continuous journalist training, and ongoing research to navigate this evolving landscape.

Gastrointestinal (GI) cancers, such as colorectal and gastric cancers, pose significant global health challenges due to their high rates of incidence and mortality. Even with advancements in treatment and early detection, many patients still face poor outcomes, highlighting the critical need for new biomarkers and therapeutic targets. Telomere length (TL) and telomerase activity (TA) have gained attention in this context. Telomeres, protective nucleotide sequences at chromo-some ends, shorten with each cell division, leading to cellular aging. Telomerase, a ribonucleoprotein enzyme, counteracts this shortening by adding telomeric repeats, a process tightly regulated in normal cells but often dysregulated in cancer. This review critically evaluates the role of TL and TA in the pathogenesis of GI cancers, examining their potential as diagnostic, prognostic, and predictive biomarkers. It explores how alterations in telomere biology contribute to the initiation and progression of GI tumors and assesses the therapeutic implications of targeting telomerase. By integrating findings from diverse studies, this review aims to elucidate the intricate relationship between telomere dynamics and gastrointestinal carcinogenesis, offering in-sights into how TL and TA could be leveraged to enhance the early detection, treatment, and prognosis of GI cancers.

The study objective is to evaluate the reliability of the Göttingen Mini-pig (G-MP) as a large animal model for preclinical studies addressing articular cartilage treatment procedures. Second this work compared the G-MP to the domestic pig (DP) in terms of surgical anatomy, postoperative management, and follow-up period possibilities and challenges. Six G-MP and four DP underwent a two-stage surgical procedure including cartilage harvesting using a superolateral approach followed by cartilage implantation using a medial parapatellar tendon approach. The superolateral approach exposed 11% (SD ± 5) of the entire trochlea in G-MP and 20% in the DP model. The medial parapatellar tendon approach in G-MP exposed 63% (SD ± 4) of the trochlear surface and exposed 34% (SD ± 13) of the medial femoral condyle, allowing 4 trochlear lesions of 6mm of diameter and one to the medial condyle in 4 out of 6 G-MP and in all DPs. Cartilage thickness was under 1mm in G-MP compared to more than 2mm in DP. Weight-progression was + 4 kg/week in DP and + 0.2 kg/week for G-MP. The G-MP is a feasible model for cartilage research, with adequate access to the joint via the dual approach. Thus enabling 4-5 lesions of 6mm, however a thinner cartilage in the G-MP needs to be considered.

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in-situ matchups from NB and SB (R² = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R² = 0.38, p < 0.05). In the deeper, clearer waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.

Abstract: This study aimed to assess the agronomic performance and yield stability of elite white yam (Dioscorea rotundata) genotypes across diverse Nigerian environments. Twenty-five genotypes were evaluated at three locations in two consecutive growing seasons, 2022 and 2023. The genotypes were assessed for their agronomic performance, tuber yield and related traits using various analytical tools such as AMMI (Additive main effects and multiplicative interaction) and MTSI (Multi-trait stability index). The AMMI analysis revealed significant differences among the genotypes and across the environ-ments for all traits (p

In this study a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, design of optimal neural-network based controller is done automatically. Several common reference types are studied including step, square, sine, sawtooth and trapezoid functions. Expected reference-disturbance pairs are used to train the system for finding optimal neural-network controller parameters. A separate test set is used to test the system for unexpected reference-disturbance pairs to show the generalization performance of the proposed system. Parameters of a real DC motor are used to test the proposed approach. Real DC motor’s parameters are estimated using particle swarm optimization (PSO) algorithm. Initially, a proportional-integral (PI) controller is designed using PSO algorithm for finding simple controller’s parameters optimally and automatically. Starting with neural-network equivalent of optimal PI controller, optimal neural-network controller is designed using PSO algorithm for training again. Simulations are conducted with estimated parameters for diverse set of training and test patterns. Results are compared with optimal PI controller’s performance and reported in the corresponding section. Encouraging results are obtained suggesting further research in the proposed direction.

: This study focused on assessing whether the inclusion of probiotics (B. subtilis) as feed additives during the preweaning stage can enhance the body weight and metabolic condition of neonatal calves. A total of 50 Holstein calves, all born on the same farm, were randomly divided into two homogeneous treatment groups after birth. The probiotic was administered 24 hours post-birth, signifying the start of the experimental period. The Control Group (CG), consisting of 25 calves, was fed with a milk replacer, while the Treatment Group (TG), also with 25 calves, received milk replacer supplemented with 7.5 mL/calf/day of B. subtilis probiotic, in accordance with the manufacturer's guidelines. It took one month to collect the animals. Body weight was measured at birth for all animals. A local veterinarian, working on the farm, conducted daily health checks of the calves, recording health parameters and any antibiotic treatments. Blood samples were collected from each calf at birth and again after 30 days by puncturing the jugular vein, using 10 mL evacuated serum tubes before morning feeding. Significant differences in body weight were observed at 30 days of age (p < 0.05). The CG's average body weight (BW) was 52.71 kg (±10.68), compared to 58.49 kg (±14.21) in the TG, a difference of 5.78 kg or an increase of 9.88%. Additionally, the TG exhibited significantly lower aspartate transaminase (AST) activity at 30 days of age (p < 0.001), with an average of 51.02 IU/L (±5.16) compared to 72.00 IU/L (±12.68) in the CG, a 41.12% decrease. The TG also had a significantly higher average phosphorus (P) concentration (p < 0.001) of 3.27 mmol/L (±0.22), a 9.36% increase over the CG's 2.99 mmol/L (±0.27). A significant increase in gamma-glutamyl transferase activity was observed in the TG compared to the CG (p < 0.001), with a 64.48% (or 26.29 IU/L) increase. The TG's average GGT activity was 40.64 IU/L (±22.43), while in the CG it was 14.35 IU/L (±3.45). Additionally, there was a significant 17.7% decrease (or 10.02 g/L) in total protein (TP) concentration in the TG compared to the CG, with the TG averaging 46.32 g/L (±17.43) and the CG 56.34 g/L (±9.81). Overall, dairy calves who were given conventional milk replacer along with a daily dose of 7.5 mL of B. subtilis probiotic experienced enhanced growth performance and a more favorable metabolic profile. Their body weight exhibited a 9.88% growth, but the activity of AST decreased by 41.12%, suggesting a decreased likelihood of liver damage. Additionally, their protein digestibility was 17.7% greater compared to the control group. In addition, B. subtilis had a positive impact on the overall concentration of phosphorus.

In recent years, with the development of the Internet of Things and distributed computing, the "server-edge device" architecture has been widely deployed. This study focuses on leveraging autoencoder technology to address the binary classification problem in network intrusion detection, aiming to develop a lightweight model suitable for edge devices. Traditional intrusion detection models face two main challenges when directly ported to edge devices: inadequate computational resources to support large-scale models and the need to improve the accuracy of simpler models. To tackle these issues, this research utilizes Extreme Learning Machine for its efficient training speed and compact model size to implement autoencoders. Two improvements over the latest related work are proposed: First, to improve data purity and ultimately enhance detection performance, this study partitions the data into multiple regions based on the prediction results of these autoencoders. Second, leveraging autoencoder characteristics to investigate further the data within each region. We used the public dataset NSL-KDD to test the behavior of the proposed mechanism. The experimental results show that when dealing with multi-class attacks, the model's performance was significantly improved, the accuracy and the F1-score are improved by 3.5% and 2.9%, respectively maintaining its lightweight nature.

The vaccines have to undergo independent testing at National Control laboratory (NCL) for various quality control tests to ensure safety, purity, and potency before market authorization and surveillance testing. In this study, five different inactivated cell culture rabies vaccine preparations (Aluminum adjuvanted and non-adjuvanted) marketed in India were evaluated using compendial safety test of mouse inoculation test (MIT) for residual live virus. Investigations were done to see the interference of aluminum adjuvant in rabies vaccine preparation to ensure safety as per the in-vivo compendial test for virus inactivation. After intracerebral injections, mice received the aluminum phosphate adjuvanted inactivated rabies vaccine (ARV) had neurological complications that resulted in their deaths between days 4 and 10. Further investigations were carried out to find out the root cause for mice death including fluorescent antibody test (FAT), rabies virus amplification test. Additionally, alamar blue based cell cytoxicity assay was carried out using BHK-21 cells to estimate the cell toxicity of aluminum phosphate adjuvanted inactivated rabies vaccine. The present study highlighted the challenges in ensuring the safety of adjuvated inactivated rabies vaccine at level of NCL. Further, study demonstrated the toxicity of aluminum under both in vitro and in vivo settings, sparking controversy regarding the safety of aluminum adjuvants in vaccines at the suggested compendial limit of 1.25 mg/single human dosage.

Various strategies have been employed to improve the reliability of 2D, 3D, and co-culture in vitro models of nonalcoholic fatty liver disease, including using extracellular matrix proteins, such as collagen I, to promote cell adhesion. While studies have demonstrated the significant benefits of culturing cells on collagen I, its effects on HepG2 and HepaRG cell lines after exposure to palmitate (PA) have not been investigated. Therefore, this study aimed to assess the effects of PA-induced lipotoxicity in HepG2 and HepaRG cultured in the absence or presence of collagen I. HepG2 and HepaRG cultured in the absence or presence of collagen I were exposed to PA, followed by analyses that assessed cell proliferation, viability, adhesion, cell death, mitochondrial respiration, reactive oxygen species production, gene and protein expressions, and triacylglycerol accumulation. Culturing HepG2 on collagen I was associated with increased cell proliferation, adhesion, expression of integrin receptors, and improved cellular spreading compared to culturing them in the absence of collagen I. PA-induced lipotoxicity was greater in collagen I-cultured HepG2 than those cultured in the absence of collagen I. In summary, the present study demonstrated for the first time that collagen I-cultured HepG2 exhibited exacerbated cell death following exposure to PA through integrin-mediated death. The findings from this study may serve as a caution to those using 2D models or 3D scaffold-based models of HepG2 in the presence of collagen I.

Background and objectives: Chronic Kidney Disease and Mineral Bone Disorders (CKD-MBD) are frequently associated with an increased risk of both Vascular Calcifications (VCs) and bone fractures (BFs). The complex pathogenesis of VCs and BFs involves various factors such as calcium overload, phosphate imbalance, and secondary hyperparathyroidism. Key players, such as the vitamin K-dependent proteins (VKDPs) matrix Gla protein (MGP) and osteocalcin (BGP), have pivotal roles both for VCs and BFs. The Vitamin K Italian (VIKI) dialysis study, highlighted that hemodialysis patients treated with calcimimetics had higher levels of total BGP and MGP as compared to those untreated, suggesting a potential protective effect of these drugs on BFs and VCs, beyond the beneficial effect to reduce PTH levels. Methods: ETERNITY-ITA is a multi-center, comparative effectiveness, observational, longitudinal study that will enroll 160 hemodialysis patients (80 patients treated with Etelcalcetide and 80 age and sex-matched patients treated with Calcitriol or vitamin D analogs). Nephrologists will tailor the target dose of Etelcalcetide on individual level to achieve the KDIGO PTH target. In the Etelcalcetide-treated group, the addition of calcitriol will be allowed when required by clinical practice (for correction of hypocalcemia). Conclusion: The study will evaluate the real-world effect of Etelcalcetide on VKDP levels, such as BGP and MGP, at 3, 9, and 18 months from baseline. The resulting preservation of vascular and bone health will be assessed for the first time by examining aortic and iliac artery calcifications and vertebral fractures, respectively. Keywords: bone fractures; etecalcetide; hemodialysis; vascular calcification; vitamin K

Multiple Myeloma (MM), is a hematological malignancy and poses significant therapeutic challenges. This review synthesizes evidence from pivotal clinical trials to guide induction treatment for transplant-eligible (TE), newly diagnosed MM (NDMM) patients. Emphasizing the evolution from three-drug to four-drug induction therapies, we highlight the integration of monoclonal antibodies, particularly CD38 recombinant monoclonal antibody agents, into treatment regimens. Our analysis includes a comprehensive literature review of research from major databases and conferences conducted between 2010 and 2024, culminating in the detailed evaluation of 47 studies. The findings underscore the superiority of quadruple regimens in TE NDMM, notably those incorporating daratumumab, in achieving superior responses including progression-free survival (PFS), minimal residual disease (MRD) negativity, overall response rate (ORR), and overall survival (OS) when compared to triple-drug regimens. As treatment regimens evolve with additional agents, the improved outcomes with treatment-related adverse events should be carefully balanced. This review advocates for a paradigm shift towards quadruple induction therapies for TE NDMM, offers a detailed insight into the current landscape of MM treatment, and reinforces a new standard of care.

Background: Ovarian mesonephric-like adenocarcinoma (MLA) is a newly described histological type known for its aggressive behavior. This study aims to determine the frequency of ovarian MLA, review existing literature, and elucidate its clinicopathological characteristics, including potential therapeutic targets. Methods: We retrospectively reviewed the pathological diagnoses of 516 primary ovarian cancer surgical cases at our institution from 2010 to 2023. MLAs exhibiting typical morphological and immunohistochemical features were included. The frequency and clinicopathological characteristics of these cases were summarized. Additionally, we conducted a literature search using PubMed to collect and summarize previously reported cases of ovarian MLA. Results: Among the 516 primary ovarian cancer cases, we identified 3 cases (0.6%) of MLA. The patients were 52-76 years old, and the initial FIGO stages were IC1 (2 cases) and IIIB (1 case). All cases exhibited HRP, pMMR, PD-L1 negativity (CPS<1), and HER2-low expression. Two cases experienced metastatic recurrence. A literature review identified 97 cases of MLA. MLA frequently exhibited KRAS mutations (90%, 38/42), with a recurrence rate of 39% (26/67). Conclusion: MLA accounted for 0.6% of malignant ovarian tumors at our institution, all of which were advanced or recurrent cases. These cases showed HRP, pMMR, and PD-L1 negativity, indicating a lack of current therapeutic targets. Literature also reports a high incidence of advanced and recurrent cases, highlighting the need for accurate diagnosis and the development of new treatments. The frequent KRAS mutations suggest a potential therapeutic target for recurrent or metastatic MLA.

The use of braces equipped with dissipative devices for the seismic strengthening of seismically prone reinforced concrete frames is among the most widespread methods, as it allows for high reductions in seismic vulnerability with inexpensive, quickly executed interventions, which can often be carried out mainly by the exterior, resulting in interruptions of use limited both in time and to only small portions of the building. The design methods of dissipative devices are based on the extensive use of pushover analyses capable of highlighting the structural deficiencies of the building and comparing the performances achievable by developing designs according to different methods and sizing criteria. In the present work, with reference to a case study represented by a four-story spatial frame having characteristics representative of design and construction common practice of the 1970s in Southern European countries, the performance of three different design methods is compared. The examined procedures differ for: methods for estimating the peak displacement response of the nonlinear systems, namely the well-known Equal Displacement Rule and the Equivalent (secant) stiffness and Damping rule; and criteria for distributing stiffness and strength of the bracing along the height, namely the distribution of stiffness and strength proportionally to those of the frame, and methods that vary the stiffness and strength along the height in order to minimize the eventual irregularity in elevation of the bare frame. The effectiveness of the procedures is checked by static pushover analysis and nonlinear response history analysis, performed by unidirectional and bidirectional input. Pros and cons of each procedure are summarized, all of them able to provide bracing designs that meet the performance requirements set during the design phase.

Abstract: The future of transportation is being reshaped by autonomous vehicles (AVs), which promise to revolutionize road safety, accessibility, and environmental sustainability. Central to their evolution are advancements in artificial intelligence (AI), which enable real-time perception, decision-making, and control. This paper explores the future trajectory of AI in AVs, highlighting innovative AI paradigms such as explainable AI, federated learning, and adaptive regulatory frameworks. It delves into the development of sophisticated perception algorithms for environmental understanding, advanced decision-making systems employing reinforcement learning and deep neural networks, and precise control mechanisms through predictive modeling and optimization. By addressing current challenges, including regulatory hurdles and the complexity of AI algorithms, this paper provides a comprehensive outlook on the potential and implications of AV technology. It envisions a future where AI-driven AVs are seamlessly integrated into society, offering profound insights into their impact on transportation and societal dynamics.

This research aims to explore the integration method of deep learning and large language models in speech recognition to improve the system’s recognition accuracy and ability to handle complex contexts. Deep neural network (DNN), convolutional neural network (CNN), long short-term memory network (LSTM) and Transformer-based large language model are used to build an integrated acoustic and language model framework. Experiments on TIMIT, LibriSpeech and Common Voice datasets show that the ensemble model shows significant improvements in both word error rate (WER) and real-time factor (RTF) compared to traditional models. Especially in terms of adaptability to multiple languages and accent changes, the model shows superior performance. The conclusion shows that through technology integration, the performance of the speech recognition system in complex environments can be effectively improved, providing a new direction for the future development of speech recognition technology.

Biochar is a porous material produced by pyrolisis of biomass and can be modified using metal oxide to improve its adsorption performance, especially toward organics substances. The activated biochar was synthesized from patchouli biomass to study effect of calcination temperature on both structural properties and paracetamol drug adsorption of the composite was studied. Some process routes were conducted, including the patchouli biomass pyrolysis using CoCl2 activator, the activated biochar impregnation using 0.1 M FeCl3 solutions, biochar-FeCl3 calcination at various temperatures (400, 600, 800 oC) in the almost closed porcelain crucible, product characterizations using X-ray diffraction and FTIR spectrometry, and paracetamol adsorption test at various concentrations (10, 20, 30, 40, and 50 ppm). The paracetamol concentrations were analyzed using UV-Vis spectrophotometry at 243 nm. The adsorption data was treated using Langmuir, Freundlich, and Dubinin-Radushkevich (DR) models. The product diffractograms indicated α-Fe2O3, γ-Fe2O3, FeFe2O4 beside the carbon turbostratic structure. The metal oxide peak heights increased by increasing of the temperature. FTIR spectra significantly started to change at 600 oC. Adsorption test of paracetamol gave the highest adsorption capacity of 56.37 mg/g at 800 oC (Langmuir model) with correlation coefficient of 0.964. The highest adsorption energy was achieved at 800 oC, i.e 530.33 J/mol (physical adsorption) with a dimensionless factor, the RL value of 0.86 (favorable adsorption), and the n constant of 1.58 (favorable adsorption).

Mixed plastic packaging waste sorting residues (MPO323) were treated by thermal pyrolysis to utilize pyrolysis oil and char. The pyrolysis oil was found to contain aromatic and aliphatic hydrocarbons. Chlorine and bromine contents were up to 40,000 mg/kg and 200 mg/kg, respectively. Additionally, other elements like sulfur, phosphorous, iron, aluminum, and lead were detected, which can be interpreted as impurities regarding the utilization of the oils by means of chemical recycling. The pyrolysis char showed high contents of potentially active species like silicon, calcium, aluminum, iron, and others. To enhance the content of aromatic hydrocarbons and to reduce the level of contaminants, the pyrolysis oil was reformed with the corresponding pyrolysis char to act as an active material in a fixed bed. The temperature of the reactor and the flow rate of the pyrolysis oil feed were varied to gain insights on cracking and reforming reactions as well as on the performance with regard to decontamination.

Chronic wounds are a major health problem because of delayed healing and cause hardships for the patient. The infection present in these wounds plays a role in delayed wound healing. Silver wound dressings have been used for decades, beginning in the 1960s with silver sulfadiazine for infection prevention for burn wounds. Since that time, there has been a large number of commercial silver dressings that have cleared FDA approval. In this review, we examine the literature involving in vitro, in vivo, and clinical studies with commercial silver dressings and attempt to glean the important characteristics of these dressings in treating infected wounds. The primary presentation of the literature is in the form of detailed tables. The narrative part of the review focuses on the different types of silver dressings, including the supporting matrix, the release characteristics of the silver into the surroundings, and their toxicity. Though there are many clinical studies of chronic and burn wounds using silver dressings that we discuss, it is difficult to compare the performances of the dressings directly because of the differences in the study protocols. We conclude that silver dressings can assist in wound healing, though it is difficult to provide general treatment guidelines.

Men of African ancestry suffer disproportionately from prostate cancer (PCa) than other racial groups in South Africa. Equally concerning is that black South African men generally present later with higher stage and grade of the disease than their non-black counterparts. Despite this, South African black men continue to have little uptake in PCa screening behaviors. The objective of the study was to explore knowledge and beliefs of PCa among black South African survivors. A sample of 20 prostate cancer survivors, with ages ranging from 67 to 85 years (meanage = 76yrs; SD = 5.3) were selected through purposive sampling and requested to participate in the study. Data were collected through in-depth, semi-structured individual interviews, and analysed using interpretative phenomenological analysis (IPA). The findings demonstrated that black South African men had poor knowledge of PCa and that this may create an unfortunate system that preclude this population group from taking part in life-saving PCa screening services. The results highlight a need to elevate knowledge and awareness of PCa among black South African men and ultimately enhance screening practices.

A multi-environment evaluation of sorghum genotypes was conducted across six environments in the 2021 main growing season in a randomized complete block design with three replications. The objectives of the study were to estimate the magnitude of genotypes by environment interaction (GEI) and grain yield stability of drought-tolerant sorghum genotypes across different environments. Data were subjected to analysis of variance, Additive Main Effects and Multiplicative Interaction (AMMI), and GGE biplot analysis. Combined analysis of variance revealed significant variations among genotypes, environments, and GEI for yield and yield-related traits, indicating that these factors significantly affected grain yield. The maximum mean grain yield value of genotypes due to the mean effect of the environment was obtained from G1 (5119.93kg ha-1), followed by G14 (4834.57 kg ha-1), and G18 (4801.20 ha-1), while the least mean grain yield was obtained from G3 (3314.50 kg ha-1). The multiplicative variance of the treatment sum of squares due to GEI was partitioned into four principal component axes (PCA). Sum squares of the first and second interaction principal component axis (IPCA) explained 71.07% and 17.50% of the GEI variation, respectively. The IPCA1&2 mean squares were highly significant (P≤0.01), indicating the adequacy of the AMMI model with the first two IPCAs for cross-validation of grain yield variation. The magnitude of the GEI sum squares was 3.9 times that of the genotype sum squares for grain yield, indicating the presence of substantial differences in genotypic responses across environments. The results of cultivar superiority measure (Pi), yield stability index (YSI), AMMI stability value (ASV), regression coefficient (bi), and deviation from regression (S2di) depicted that genotypes G18, G22, G31, and 32 were the most stable genotypes for grain yield and biomass yield, respectively. AMMI2 biplot showed Jinka, Alduba, and Kako were the most discriminating environments as indicated by the long distance from the origin; whereas testing locations Meioso and Gato with short vector length indicated that these locations had less discriminating power on the genotypes' performance. The study has provided precious information on the yield stability status of the sorghum genotypes and the best environments for future improvement programs in Ethiopia.

(1) Background The SKT is a short cognitive performance test designed to assess impairments in memory and cognitive abilities such as attention and speed of information processing. In 2019, new regression-based norms for the English version of the SKT were calculated applying data from older cognitively healthy adults living in the US, Ireland and Australia (the norming sample) The study has two aims: to establish valid cut-offs for distinguishing between no cognitive impairment, mild cognitive impairment (MCI) and dementia (1), and to cross-validate the new norms for detecting MCI and dementia (2) in a community sample of older adults with clinical diagnoses; (2)Methods: The validation sample included 142 older adults (mean age=87.7, SD=3.55) from the Sydney Memory and Aging Study (MAS Study). Participants were classified as having normal cognition, MCI, or dementia through a consensus diagnosis as well as by the SKT; (3)Results: The sensitivity of the SKT for the differentiation of cognitive impairment (MCI or dementia) from normal cognition was 80.6%. The convergence between the SKT and the consensus diagnoses was 60.1% for all three diagnostic groups. The correlation between the SKT and the Addenbrooke’s Cognitive Examination III (ACE-III), a well validated measure of cognitive ability in older adults, was r = 0.3;. (4) Conclusions: In sum, it can be stated that the SKT is a valid tool for detecting early stages of cognitive impairment, performing very well in discriminating between no cognitive impairment and cognitive impairment (MCI or dementia).

Background: Knee osteoarthritis (OA) significantly affects quality of life and imposes economic burdens due to its prevalence and the disability it causes. The efficacy of current treatments is limited to alleviate the symptoms and not for regenerative purposes. This study aims to evaluate the efficacy and safety of combining hyaluronic acid (HA), human umbilical cord mesenchymal stem cells (hUC-MSCs), and synthetic human growth hormone (somatotropin) in the treatment of knee OA, assessing pain relief, functional improvement, and cartilage regeneration. Methods: A four-arm, double-blind randomized trial was conducted with 51 knees from 28 subjects, aged ≥ 50 with primary knee OA. Treatments involved are HA alone, HA with hUC-MSCs, HA with somatotropin, and a combination of all three. Efficacy was measured through the International Knee Documentation Committee (IKDC) score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and visual analog score (VAS) along with MRI T2 mapping of cartilage were taken on pre-implantation, 6th and 12th month. Results: All treatment arms showed improvement in VAS and WOMAC scores over 12 months, suggesting some pain relief and functional improvement. However, MRI T2 mapping showed no significant cartilage regeneration across the groups. Conclusion: While combined use of HA, hUC-MSCs, and somatotropin improved symptoms of knee OA, it did not enhance cartilage regeneration significantly. The study highlights the potential of these combinations for symptom management but underscores the need for further research to optimize these therapies for regenerative outcomes.

In this paper, we develop an effective iterative algorithm to solve a generalized Sylvester tensor equation over quaternions which includes several well-studied matrix/tensor equations as special cases. We discuss the convergence of this algorithm within a finite number of iterations, assuming negligible round-off errors for any initial tensor. Moreover, we demonstrate the unique minimal Frobenius norm solution achievable by selecting specific types of initial tensors. Additionally, numerical examples are presented to illustrate the practicality and validity of our proposed algorithm. These examples include demonstrating the algorithm’s effectiveness in addressing three-dimensional microscopic heat transport and color video restoration problems.

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. By analyzing the relationship between elevation frequency and range, the proposed method compensates for quadratic range dependence. Through the construction of an elevation oblique subspace projection technique, an elevation adaptive filter is used to separate clutter from ambiguous regions. Residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments.

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

False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news using machine learning and natural language processing techniques and to determine which techniques work the most effectively. This article first studies what constitutes false news, and how it differs from other types of misleading information. We also study the results achieved by other researchers on the same topic. After building a foundation to understand false news, and the various ways of automatically detecting it, this article provides its own experiments. These experiments were done on four different datasets, one that was made just for this article, and using 10 different machine learning methods. The results of this article were satisfactory and provided answers to the original research questions set up at the beginning of this article. This article could determine from the experiments that passive-aggressive algorithms, support vector machines, and random forests are the most efficient methods for automatic false news detection. This article also concluded that more complex experiments, such as using multiple levels of identifying false news or detecting computer-generated false news, require more complex machine learning models.

Synthetic biology uses engineering principles to redesign organisms and construct biological systems with specific functions, which is an important way to develop drugs, vaccines, biofuels, etc. In the process of transforming synthetic organisms, researchers need to conduct large-scale experiments for a long time, and many of these processes are manual and repetitive tasks, which results in the problems such as low throughput, low efficiency, and slow iteration. In addition, there are many experimental instruments and equipments required in the synthetic biology experiment process. The communication protocols between different instruments and equipments are not uniform, which seriously restricts the automation upgrade process of synthetic biology. To solve these problems, this paper put forward an integration technology based on the Object Linking and Embedding for Process Control Unified Architecture (OPC UA) to realize the interconnection of the synthetic biology automated casting platform and related instruments needed in synthetic biology process. The stability of the system is further designed and improved by using the concept of Triple Modular Redundancy. This helps the development of an efficient, stable and scalable automated casting platform for synthetic biology, which greatly improves the efficiency of synthetic biology experiments.

Circles, semicircles and its derived circular sectors are ubiquitous both in human creations and in the natural realm. However, mathematically speaking they have always represented a certain level of enigma or even mysticism since ancient times for being such a perfect shape that enclosed the irrational value that we now call π. Such figure was relatively unknown until the seventeenth century; polymaths like Guarini even equated it, after so many centuries, to the Archimedes fraction of 22/7 or sometimes 25/8 which, giving a value 3.125 introduces a considerable degree of error; Eventually, Lambert had to demonstrate, well advanced the eighteenth century that π was actually an irrational number. In recent years, the author has worked with sections of spheres and other curved bodies as related to radiative heat transfer and applied to the finding of form factors pertaining to such special geometries, to the point of defining new postulates. The main theorems so far enunciated refer to the radiative exchange between circles and half disks, but recently the possibility to treat circular sectors has arrived, thanks to the research already conducted in which, by virtue of Cabeza-Lainez first postulate, we are able to know the form factor of any spherical segment over itself, discarding in this way, the former uncertainties that appeared when dealing with the inner side of spherical surfaces. As it is known, to find the exact expression of the configuration factor by integration alone is mostly a tedious task and the results are not readily available as it has been frequently proved. In the said problem of the circular sectors, the author could integrate the first two steps of the basic formulation for radiant exchange. Subsequently, the novelty of the procedure lies in introducing a finite differences approximation for the third and fourth integrals which still remain unsolved, once that we have been able to find the first and second integrals of the form factor. This possibility had not been identified by former research. This sole output provides us with an ample variety of scenarios previously unforeseen. As a consequence, we would be able to analyze with more precision but freely, the spatial transference of radiant heat for figures composed of circular sectors such as spherical sectors like quarters or octaves of spheres. Many architectural, industrial or aircraft modules and objects fall into this category. We already know that spherical shapes cannot be discretized with any accuracy as there are no spherical tiles of the same size, therefore we hope to overcome with our finding a persistent extent of error. In this situation we are able to reduce a considerable amount of hindrances in the progress of thermal radiation science, due to the advances hereby presented. As the new formulations found can be adapted for algorithms, they will be integrated in simulation procedures with significant avail. Our achievement may be considered timely in the effort invested to square the elements of mathematics for radiative transfer. Important sequels are already being derived for radiation in the entrance to tunnels, aircraft design, low-energy building construction and also lighting and HVAC industries to cite just a few.

Intercellular adhesion molecule 1 (ICAM-1/CD54), a transmembrane glycoprotein, has been considered as one of the most important adhesion molecules during leukocyte recruitment. It is encoded by the ICAM1 gene and plays a central role in inflammation. It’s crucial role in many inflammatory diseases such as ulcerative colitis and rheumatoid arthritis are well established. Given that neuroinflammation, underscored by microglial activation, is a key element in neurodegenerative diseases such as Parkinson’s disease (PD), we investigated whether ICAM-1 has a role in this progressive neurological condition and if so, elucidate the underpinning mechanisms. Specifically, we were interested in potential interaction between ICAM-1, glial cells and ferroptosis, an iron-dependent form of cell death that has recently been implicated in PD. We conclude that there exist direct and indirect (via glial cells and T cells) influences of ICAM-1 on ferroptosis and that further elucidation of these interactions can suggest novel intervention for this devastating disease.

Sclerotinia minor Jagger is a phytopathogenic fungus that causes lettuce drop, a serious problem of lettuce (Lactuca sativa L.) production. The control of this pathogen is challenging because of the presence of resistance of sclerotia, which can survive in the soil under favourable conditions. In Bulgaria, the management of lettuce drop relies primarily on the strategic application of synthetic fungicides. To find alternative methods for disease management, four bacterial isolates were screened for antagonism against S. minor. This study reports the in vitro evaluation of the antifungal activity of Bacillus subtilis, Priestia megaterium, Bacillus safensis, and Bacillus mojavensis against S. minor. The molecular identification of the isolates involved in the activity was examined by 16s rRNA sequencing. Isolated bacterial strains produced indole-3-acetic acid (IAA) in a medium supplemented with 0.1% L-tryptophan. The ability of those strains to increase the mobility of phosphorus and zinc has been elucidated. The production of siderophores has been confirmed on CAS (Chrom azurol S) medium. The inhibitory action showed by the filtrates of the bacterial growth broths against S. minor and points out the nature of the molecules involved. The evaluation of the antifungal activity was carried out in vitro and pot experiments. This study determined the effect of growth-promoting rhizobacteria on the development of lettuce. This research focuses mainly on the development of biocontrol strategies for the management of lettuce drop in greenhouses.

Tamil Nadu, a state in southern India, is bereft of any native milch or dual-purpose cattle breed and the state depends chiefly on crossbred cattle for milk production. As most (90 percent) of the milk production in the state was contributed by Jersey crossbred cattle, the genetic evaluation study was intended to assess the milk production traits viz., test-day milk yields (TDMYs), total milk yield (TMY) and 305-day milk yield (305MY) in these crossbred cattle reared under farmers’ rearing environment. Data on monthly TDMYs (TDMY1 to TDMY10) of Jersey crossbred cows (n=81,653) reared by the farmers over 24 years (1999-2022) were collected for evaluation. The influence of non-genetic factors viz., location (agroclimatic zones), period and season of calving, and parity on TDMYs, TMY, and 305MY were assessed, and the variance and covariance components for genetic parameters (heritability and genetic correlation) of the traits were estimated by the method of restricted maximum likelihood (REML) under multivariate animal model and by random regression model (RRM). The overall means of various TDMYs ranged from 4.98 (TDMY10) to 9.95 kg (TDMY2) and, for TMY and 305MY the means were 2480.33 and 2393.71 kg respectively. The heritabilities estimated by animal model for TDMYs were high and ranged from 0.39 ± 0.01 to 0.47 ± 0.01 and were fairly less variable throughout the lactation while the estimates obtained by RRM were medium to high and ranged between 0.28 ± 0.01 and 0.65 ± 0.02 for different days in milk (DIMs). The heritability estimated for TMY and 305MY were 0.48 ± 0.01 and 0.49 ± 0.01 respectively. The estimates of heritability in mid-lactation yields were comparatively little less than those at the beginning and the end of lactation. The high estimates of heritabilities for TDMYs, TMY, and 305MY obtained in the present study offered an ample scope for improving milk production through selective breeding. Genetic correlations between TDMYs estimated by animal model were positive and moderate to high, with a range between 0.47 ± 0.01 and 0.99 ± 0.00. Considerably higher estimates (0.97-0.99) were observed between adjacent TDMYs showing strong genetic associations. By RRM, genetic correlations estimated between DIMs were positive, except for a few estimates and the magnitude of genetic correlation decreased with an increase in the interval between the DIMs. The high genetic correlation observed between the TDMYs in early and mid stages of lactation with TMY and 305MY suggested that these test day yields could be used favorably for the early selection of cows for milk production which facilitates reduction in the generation interval and consequently increase the annual genetic gain for the milk production traits.

BACKGROUNDStudies concerning multisystem inflammatory syndrome in children (MIS-C) and how those patients present to the Emergency Department (ED) are scarce. Our study sought to investigate whether abnormal laboratory values were associated with worse outcomes. METHODSThis was a retrospective case-control study from March 2020 to June 2020. Patients with MIS-C were compared to a control group. Laboratory values were compared whether admitted to PICU or not. The Pearson’s Chi-square test and Fisher’s exact test were conducted for associations. RESULTSA total of 80 patients were included in the study (41MISC, 39 control). There were significant associations between patients admitted to the PICU and neutrophilia (p = 0.03) and elevated troponin (p = 0.01). There were no significant associations between having a non-good outcome and obesity, neutrophilia, lymphopenia, or elevated troponin. When MIS-C patients were compared to the control group MISC-C patients had more obesity and elevated troponin. CONCLUSIONSPatients admitted to the PICU had more neutrophilia and elevated troponin yet had a fairly good outcome. When compared to control patients, there was a significant association with obesity and elevated troponin in the MIS-C group.

This paper investigates the impact of adding exclusive two-wheeled vehicle lane based on motorcycle data and using reinforcement learning at one of Bandung’s heterogeneous intersections which uses data collected from ATCS Bandung. The Kiaracondong-Ibrahim Adjie intersection in Bandung is notorious for severe congestion, especially during rush hours. Traditional traffic management methods often fall short, necessitating innovative solutions to mitigate long wait times and extensive queue lengths. This study leverages Reinforcement Learning (RL), specifically Deep Q-learning Network algorithm, combined with introduction of additional two-wheeled vehicle lanes to optimize traffic flow at this busy intersection. The research involved configuring the SUMO platform to accurately simulate the intersection’s traffic conditions, including parameters such as lane widths and vehicle dynamics. The RL model was trained over 100 episodes using quantitative real traffic data from ATCS Bandung city. The training process minimize vehicle waiting time and queue length by adjusting traffic light phases dynamically. Results from the simulations indicated significant improvements in traffic management. Queue lengths in regular lanes decreased by 64.89% under RL control, while two-wheeled vehicle lanes saw a 26.39% reduction. Waiting times in regular lanes dropped by 80.49%, and in two-wheeled vehicle lanes by 39.96%. The study demonstrated that integrating DQN with dedicated two-wheeled vehicle lanes could substantially enhance traffic flow and reduce congestion at critical urban intersections. The findings underscore the potential of advanced RL techniques in urban traffic management. However, the study acknowledges the need for further research with more extensive resources and time to develop even more efficient traffic control systems. Future work should focus on refining these methods for broader application and exploring other innovative technologies to sustainably address urban traffic challenges.

Background:Advanced heart failure is characterized by repeated hospital admissions due to recurring need for intravenous inotropes. Among the most commonly used medications used in this context, is milrinone, which increases the myocardial relaxation velocity, what is known as lusitropy. This effect is mediated by increasing cyclic AMP, which in turn switches on phospholamban activity, an enzyme promoting calcium reuptake by sarcoplasmic reticulum. The aim of this review is to shed light on oral medications that can play a similar role by alternative pathways. Main Body:Phospholamban can be as well stimulated by cGMP, the latter is known to be increased by medications which inhibit phosphodiesterase 5. Two well-established drugs can perform this action and are currently exclusively used as vasodilators, namely sildenafil and tadalafil. Another emerging hope is vericiguat, a medication that can directly stimulates guanylyl cyclase, leading to increase in cGMP. Conclusion:We speculate that the possible introduction of these oral medications is thought to replace the need for repeated hospital and intensive care admissions. This also might mean, delaying the necessity for mechanical support or transplantations, which can change the face of advanced or end-stage heart failure.

In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible’s powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our methodology, which involves managing numerous components, dependencies, and configurations, requires high expertise and precision. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packages and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI).

Since the turn of the millennium, many individuals and groups, including Diane Moore (2007), Stephen Prothero (2007), the American Academy of Religion (2010), and The National Council for the Social Studies (2014, 2017, 2021), have advocated for better religious literacy education (RLE) in K-12 curriculum and public schools. Despite these calls, RLE has yet to make significant inroads in K-12 curriculum and public schools. This paper aims to define religious literacy and RLE, provide current rationales for its inclusion in K-12 curriculum and public schools, identify barriers to RLE, and explore the potential of global competence in promoting RLE.

Big Epidemiology is an innovative framework that extends the interdisciplinary approach of Big History to understand disease patterns, causes, and effects across human history on a global scale. This comprehensive methodology integrates epidemiology, genetics, environmental science, sociology, history, and data science to address contemporary and future public health challenges through a broad historical and societal lens. The foundational research agenda involves mapping the historical occurrence of diseases and their impacts on societies over time, utilizing archaeological findings, biological data, and historical records. By analyzing skeletal remains, ancient DNA, and artifacts, researchers can trace the origins and spread of diseases, such as Yersinia pestis in the Black Death. Historical documents, including chronicles and medical treatises, provide contextual narratives and quantitative data on past disease outbreaks, societal responses, and disruptions. Modern genetic studies reveal the evolution and migration patterns of pathogens and human adaptations to diseases, offering insights into co-evolutionary dynamics. This integrative approach allows for temporal and spatial mapping of disease patterns, linking them to social upheavals, population changes, and economic transformations. Big Epidemiology also examines the roles of environmental changes and socioeconomic factors in disease emergence and re-emergence, incorporating climate science, urban development, and economic history to inform public health strategies. The framework reviews historical and contemporary policy responses to pandemics, aiming to enhance future global health governance. By addressing ethical, legal, and social implications, Big Epidemiology seeks to ensure responsible and effective epidemiological research and interventions. This approach aims to profoundly impact how we understand, prevent, and respond to diseases, leveraging historical perspectives to enrich modern scientific inquiry and global public health strategies.

Traceability has been applied in the fisheries, for example in tuna, as a means of verification and validation of the quality of fish meat and its origin. Traceability as a platform in the fisheries remains elusive in many parts of the country due to slow adoption. This study reviewed the existing literature in relation to, “agri-fishery traceability system,” “fishery traceability,” and “food safety” from year 2000-2023. It aimed to elucidate the common drivers and barriers to the adoption of fisheries traceability system. The authors used PRISMA to analyse articles obtained from SCOPUS and WOS (Web of Science), DOAJ (Directory of Open Access Journal), Academia, and PubMed central databases. The results showed that 125 articles were included after the inclusion and exclusion process. There were nine articles included in the year 2000 to 2005, followed by 19 in 2006 to 2010, and 26 from the year 2011 to 2015. A total of 40 articles were recorded from the year 2016 to 2020 and 31 from the year 2021 to 2023. In terms of text data from the authors, “fish”, “supply chain”, “traceability system”, and “food safety” have the highest total link strength. Moreover, food safety, market compliance, consumer protection, product origin, quality, and seafood fraud are driving factors in the implementation of traceability system. Costs, inadequate practices, lack of support from the government, infrastructure, information, and laws and regulations are foreseen barriers to the adoption of a traceability system. Leading countries in fisheries traceability studies include, China, USA, Italy, Indonesia, UK, and Taiwan where the first and corresponding authors originated. While this review endeavored to find fish traceability studies in the Philippines, the lack of comprehensive and relevant literature published in this topic shifted our focus to finding barriers and drivers to adopting a traceability system. We recommend that government agencies that oversee various commodities, should link up with academics and non-government organizations in the implementation and monitoring of their traceability systems.

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The Pencil Lead Break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection.

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

This paper addresses the pressing concern of corporate tax evasion recognized by several nations, employing insights from public choice theory and tax compliance theory. The research aims to evaluate the ramifications of corporate tax evasion on public revenue, public expenditures, tax rates, and the prevailing budget deficit. Methodologically, the study utilizes questionnaire data and employs structural equation modeling for analysis. The findings indicate that tax evasion is significantly influenced by multiple factors, encompassing tax income, public expenditures, tax rates, and the budget deficit. The article offers a comprehensive exploration of both theoretical frameworks and practical implications, contributing valuable insights to the ongoing discourse on corporate tax evasion and its multifaceted impact on fiscal dynamics.

Nose mask is a fomite and its usage was mandatory during the COVID-19 outbreak. Its effectiveness has been established; however, as a fomite, it can harbor opportunistic respiratory pathogens such as Streptococcus spp., which could complicate COVID-19 infections. This study profiled Streptococcus species resident on nose masks obtained from consenting participants. Phenotypic assays and 16S rRNA sequencing were used to identify Streptococcus spp. resident on the interior and exterior of 100 nose masks analyzed. Antibiotic resistance (AMR) was determined with disc diffusion and broth microdilution assays. AMR and virulence makers were profiled with primer-specific PCR amplification. 23% of the analyzed samples had Streptococcus isolates. The isolates were highly resistant to the antimicrobials, including conventional, last-resort antibiotics, disinfectants and antiseptics. All the isolates are multidrug resistant with 80-100% levels of AMR to the 16 antibiotics tested with a Multiple-Antibiotic Resistance index greater than 0.2, indicative of potential pathogens. The isolates harbored tetB, ermB, pbp2B, pbp2b1 (resistance) and lytA, lytA1, ply1, cbpA, lytA, pavA (virulence) markers, which might constitute a serious public health risk. Overall, the study emphasizes the need to assess mask usage and the risks associated with opportunistic pathogens that are resident on frequently used nose masks in the COVID-19 era.

An analogy between magneto-fluid dynamics (MFD/MHD) and geostrophic flow in a rotating frame of reference including the existence of electromagnetic columns identical to Taylor-Proudman columns is identified and demonstrated theoretically. The latter occurs in the limit of large values of a dimensionless group representing the magnetic field number. Such conditions are shown to be easily satisfied in reality. Consequently, the electromagnetic fluid flow subject to these conditions is two dimensional and the streamlines are being shown to be identical to the pressure lines in complete analogy to rotating geostrophic flows. An experimental setup is suggested to confirm the theoretical results experimentally.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impacts from over-height vehicles. Impacts can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts to railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data is transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events like impact detection that require rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

Theoretical physics addresses the fundamental principles of nature and the mysteries of the universe. In the last century, the theory of relativity challenged people's understanding of time and space, while quantum mechanics revealed various forms of energy. However, the concept of gravity remains elusive. All matter in the universe is composed of energy that can be converted into other forms, yet it is always conserved. However, achieving a state of equilibrium, represented by the equal sign, can be challenging to achieve on both macro and quantum scales. A phenomenon that can transition from one state to infinity is called collision and is essential for understanding natural phenomena and physical processes throughout the universe. In the study of general relativity cannot be three-dimensional problems to gain mathematical inspiration, understand the application of new dimensions. Here, we elucidate the nature of gravity, which inspires the solution of the Riemann hypothesis. Our results show that the conduction mode of dimensional energy is defined by Euler's formula. And a special mode of collision is derived which translates into vibration.

Background: Historically, in the United States and globally, women have supported other women before, during, and after childbirth. Birth doulas are considered an important component to eliminating racial disparities in maternal morbidity and mortality. To address disparities in birth outcomes, Healthy Women Healthy Futures (HWHF) provides free birth and postpartum doula services to low-income women and communities of color in New York City. Methods: Secondary analysis of program data (n=364) collected between 2020 and 2021 was conducted. Univariate and multivariable analyses focused on: method of delivery, preterm birth, low-birthweight birth, breastfeeding, maternal-infant skin contact. The key exposure variable was total doula hours. Results: Clients who received prenatal doula support had low rates of preterm birth (8.8%) and low birthweight (7.1%), and high rates of vaginal delivery (69.1%), breastfeeding in the hospital (90.9%), and maternal-infant skin contact (78.2%). Multivariable analysis did not identify an association between number of doula hours and any of the five outcomes examined. Conclusion: While descriptive results point to positive birth outcomes, these effects did not persist in multivariable analyses. Data quality concerns from this intervention likely hindered the ability to detect a program impact, emphasizing the importance of funding to support data collection and management activities.

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including "artificial intelligence," "applications," "mental health," "anxiety," "LLMs" and "depression". Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for over-reliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.

Out-of-hospital cardiac arrest (OHCA) represents a significant global public health burden, characterized by low survival and few established diagnostic tools to guide intervention. OHCA presents with a wide variety of etiologies in a heterogeneous population, posing a clinical challenge to care teams. In this review, we describe evolving research focused on diagnostic approaches to OHCA following resuscitation, including electrocardiography, coronary angiography, computed tomography, ultrasonography, and serologic biomarker assessment. These diagnostic tools have been employed in post-resuscitative efforts for diagnosing ischemic and non-ischemic cardiac, respiratory, neurologic, vascular, traumatic, and metabolic causes of arrest.

Paper presents proposition of possible extension of The Standard Model Classification of Particles and Fields in way to include gravitation into it.

Background: To evaluate the effectiveness of Defocus Incorporated Multiple Segments (DIMS) in slowing myopia progression in pediatric patients as a function of age. Methods: This was a non-randomized experimenter-masked retrospective controlled observational study of European individuals aged 6 -16 years with progressive myopia but no ocular pathology. We retrospectively reviewed the charts of the participants allocated to receive DIMS spectacles (Hoya® MiyoSmart®) or single vision spectacle lenses (control group). Cycloplegic spherical equivalent (SE) and axial length (AL), were measured at baseline and at 36 month follow up. The results were stratified by age into four groups: patients that wore DIMS spectacles oldest or youngest than 10 years of age (respectively, group A, 20 patients mean age 13.6±2.2 and group C, 20 patients mean age 9.0±1.2) and age-matched control groups (group B, 18 patients mean age 13.2±2.5 and group D, 22 patients mean age 8.5±0.9). Results: At 36 months, SE and AL increase resulted significantly reduced in groups A and C, respectively compared to groups B and D (p

The awareness of the vulnerability of existing structures under both seismic and energy viewpoints highlight the need to develop integrated retrofit solutions combining the structural and thermal performances. In this perspective, the study explores the efficacy of the Resisto 5.9 Tube system, a seismic coat that improves the seismic behaviour of masonry and reinforced concrete (RC) structures, also enhancing their energy performances, thanks to the thermal coat integrated in the basic steel system. The research involves the application of a design procedure specifically developed for this retrofit system from the seismic perspective. Once all the system elements are designed, non-linear static analyses are performed with a Finite Element software comparing the building’s seismic performances before and after the application of Resisto 5.9 Tube. The results demonstrate a significant increase in seismic safety coefficient, proving that this intervention type can be useful to obtain the seismic upgrading of existing RC buildings.

Developing short-term forecasting model for global atmospheric temperature and wind in the near space is crucial for understanding atmospheric dynamics and supporting human activities in this region. While numerical models have been extensively developed, deep learning techniques have recently shown promise in improving atmospheric forecasting accuracy. In this study, convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) neural networks were applied to build for short-term global-scale forecasting model of atmospheric temperature and wind in the `near space based on the MERRA-2 reanalysis dataset from 2010-2022. The model results showed that the ConvGRU model outperforms the ConvLSTM model in the short-term forecast results. The ConvGRU model achieved a root mean square error in the first three hours of approximately 1.8 K for temperature predictions, and errors of 4.2 m/s and 3.8 m/s for eastward and northward wind predictions, respectively

Electrolyte disturbances are common in patients with traumatic brain injury (TBI), particularly affecting sodium, potassium, chloride and calcium levels. This study aims to provide insights into these disturbances within the first 24 hours post-injury. We conducted a cross-sectional analysis of 50 TBI patients, excluding those with conditions affecting electrolyte balance. Electrolyte levels were measured, and correlations with demographic data, trauma mechanisms, imaging findings, and Glasgow Coma Scale (GCS) scores were analyzed. Results indicated that chloride levels inversely correlated with GCS scores (ρ = -0.515; p = 0.002), suggesting that elevated chloride may indicate severe neurological impairment. Potassium levels were significantly associated with subdural hematoma (p = 0.032) and subarachnoid hemorrhage (p = 0.043), highlighting their potential as markers for severe brain injuries. No significant associations were found between sodium or calcium levels and the studied variables. These findings underscore the importance of early monitoring of chloride and potassium levels in TBI patients to improve management and outcomes. Future research should focus on larger, multi-center studies to validate these findings and develop comprehensive guidelines for managing electrolyte imbalances in TBI patients.

Xyleborus beetles, a diverse group of ambrosia beetles, present significant challenges to forestry and agriculture due to their damaging burrowing behavior and symbiotic relationships with fungi. This review synthesizes current knowledge on the biology, ecology, and management of Xyleborus. We explore the beetles' life cycle, reproductive strategies, habitat preferences, and feeding habits, emphasizing their ecological and economic impacts. Control and management strategies, including preventive measures, chemical and biological control, and integrated pest management (IPM), are critically evaluated. Recent advances in molecular genetics and behavioral studies offer insights into genetic diversity, population structure, and host selection mechanisms. Despite progress, managing Xyleborus effectively remains challenging. This review identifies future research needs and highlights innovative control methods, such as biopesticides and pheromone-based trapping systems.

Background/Objectives: Bacterial infections (BI) are a major cause of mortality in patients with alcohol-associated hepatitis (AH); however, only a few studies have investigated BI in AH in the last decade. Therefore, we aimed to assess the features and outcomes of BI in patients with AH. Methods: This observational descriptive study included patients with AH admitted to a tertiary academic hospital between 2016–2021. Clinical and complete microbiological data were recorded and complications, including acute-on-chronic liver failure (ACLF), and mortality over 90-days were compared between infected and non-infected patients. Results: Overall, 115 patients with AH were recruited and 75 had severe AH; among them, 66 started corticosteroid treatment. We identified 69 cases of BI in 44 patients; the incidence of BI at hospital discharge was 32.2%, which reached 38.2% at 90 days. The predominant infection site was the chest (35%). Among the identified bacteria (52.1%), half were gram-positive and half gram-negative. A low rate of multidrug-resistant bacteria (14%) was also noted. Infected patients during hospitalization (n=37) exhibited higher rates of hepatic decompensation and ACLF p=0.001) and lower survival (81.8% vs. 95.8%, p=0.015) than did non-infected patients (n=78). In-hospital infected patients (n=22) exhibited worse survival (72.7%) than did those infected upon admission (93.3%) or non-infected patients (94.9%) (p=0.009). Corticosteroid-treated patients displayed a nonsignificant increase in the total number of BI; however, without greater mortality. Conclusions: BI were common in our cohort of patients with AH. Patients with in-hospital infections commonly experienced serious complications, including high ACLF and death rates. Infections diagnosed upon admission were treated without affecting survival.

Background: The different disease forms of cutaneous leishmaniasis (CL) are influenced by parasite and host factors. Malnutrition impairs immunity. It has been found to disrupt immune protection by favoring the production of anti-inflammatory prostaglandins over pro-inflammatory cytokines. In Ethiopia, data on CL patients' nutritional status remains scarce. This study aims to assess clinical polymorphism and its association with the nutritional status of the patient. Methods: A retrospective study was conducted from June to July 2024, and patient’s data was analyzed from those treated for CL. Nutritional status was assessed through MUAC, Z-score, and BMI. Results: A total of 217 CL patient’s statuses were nutritionally defined and 22% of them were found malnourished. Most of the CL problems concentrate on age

The External Occipital Protuberance (EOP) is a notable anatomical landmark connected to neck musculature. With the rise of tech neck syndrome due to prolonged smartphone use, concerns have emerged regarding its influence on EOP morphology. This study compares EEOP characteristics in contemporary individuals (aged 18-30 years) from Abruzzo with ancient skulls from the Opi necropolis (VII-Vth century BC) Methods: Four null hypotheses were tested concerning EEOP prevalence and severity: (1) no gender differences, (2) no differences between modern and ancient populations, (3) no gender-based variations in severity, and (4) no temporal differences in severity. Statistical analyses were conducted to assess the significance of these hypotheses. Results: The study found a higher prevalence of EEOP in modern individuals compared to ancient populations, with no significant gender differences in prevalence. However, severity of EEOP showed significant variation by gender and historical period, with modern individuals exhibiting greater severity. Conclusions: The observed increase in EEOP prevalence and severity in modern populations suggests a potential influence of tech neck posture associated with increased screen time. These findings underscore the need for further research into the effects of contemporary lifestyle factors on cranial morphology.

This paper proposes an optimal path type classification scheme for the three-point Dubins problem. It allows to directly extract the shortest path type from a Dubins set, evaluating only the relative initial and final configurations with the via point position using a suitable partition of the Cartesian plane. Two alternative approaches are proposed to address the problem: an analytical approach and a heuristic one. The latter was revealed to be much faster from a computational point of view. The proposed classification logic makes the path planning for the three-point Dubins problem much more effective and suitable for real-time applications. Numerical examples are provided to show the efficiency of the proposed strategy.

A major reason for failure of the immune system to detect tumor antigens (TA) is the insufficient uptake, processing, and presentation of TA by antigen-presenting-cells (APC). Immunogenicity of public and private TA of the individual patient can be markedly increased by in situ targeting of tumor cells for uptake by APC, without the need to identify and characterize the TA. This is feasible by intra-tumoral administration of alpha-gal micelles comprised of glycolipids presenting the carbohydrate-antigen “alpha-gal epitope” (Galalpha1-3Gal beta1-4GlcNAc-R). Humans produce a natural antibody called “anti-Gal” (constituting ~1% of immunoglobulins) which binds alpha-gal epitopes. Tumor injected alpha-gal micelles spontaneously insert into tumor cell membranes, so that multiple alpha-gal epitopes present on tumor cells. Anti-Gal binding to these epitopes activates the complement system, resulting in killing of tumor cells, and recruitment of multiple APC (dendritic cells and macrophages) into treated tumors by chemotactic complement cleavage peptides C5a and C3a. In this process of converting the treated tumor into personalized TA vaccine, recruited APC internalize anti-Gal opsonized tumor cells and cell membranes, process the internalized TA and transport them to regional lymph-nodes. TA peptides presented on APC activate TA specific T and B cells to proliferate and destroy metastatic tumor cells presenting the TA. Studies in anti-Gal producing mice demonstrated induction of effective protection against distant metastases of the highly tumorigenic B16 melanoma following injection of natural and synthetic alpha-gal micelles into primary tumors. This treatment was further found to synergize with anti-PD1 antibody. Phase-1 clinical-trials indicated that this immunotherapy is safe and can induce infiltration of CD4+ and CD8+ T cells into untreated distant metastases. It is suggested that in addition to converting treated metastases into autologous TA vaccine, this treatment should be considered as neo-adjuvant therapy administering alpha-gal micelles into primary tumors immediately following their detection. Such immunotherapy will convert tumors into personalized anti-TA vaccine for the period prior to their resection.

This study focuses on calibrating the linear Drucker-Prager yield criterion for polypropylene under multiaxial loading conditions, using the Arcana machine to achieve a wide range of triaxiality states. The Drucker-Prager criterion has been adapted for polymers, composites, and metals due to its ability to account for the influence of hydrostatic pressure on yield stress. This study employs the Arcana machine, which allows for testing flat plate samples under various angles, thereby simulating different stress states from pure shear to biaxial tension. Additionally, uniaxial compression tests were conducted to extend the range of triaxiality. The material used in this study is Sabic 83MF10, a polypropylene polymer. Samples were prepared by injection molding and cut to specific dimensions. Tests were performed using a universal testing machine with the Arcana handle, and the results were analyzed to determine the yield strength, pressure, and triaxiality for each sample. The results showed a linear relationship between von Mises stress and hydrostatic pressure, with a friction angle (β) of 20.65° and material cohesion (d) of 27.81. The numerical simulations in Abaqus confirmed the validity of the Drucker-Prager model, accurately reflecting the moment of yielding for the tested samples.

Ming-style round-backed armchairs as a classic Chinese chair design are renowned for the blend of ergonomics, practicality, and elegance. Although the Chinese armchair furniture industry has made strides in sustainability, there is still a need for further improvements in material selection, manufacturing processes, and supply chain management to fully support the development of sustainable furniture. Laminated bamboo lumber as new sustainable materials are becoming a popular option for furniture designers in the era of "sustainable design". This research aims to investigate the feasibility and application of laminated bamboo lumber in the design of Chinese armchair furniture and proposes innovative ideas for optimizing the armchair's design. First, an empirical study was conducted to comprehensively understand the classification of Ming-style round-backed armchairs’ structure to develop a 3D model. Secondly, an experimental research method was then employed to explore the parameters of laminated bamboo lumber for future studies. Additionally, a validation experiment was conducted to compare real-world scenarios with simulations using finite element analysis in Ansys. Finally, finite element analysis was used to test the feasibility and application of laminated bamboo lumber in furniture design, focusing on the material’s mechanical properties. Results indicated that laminated bamboo lumber has excellent mechanical properties as furniture design materials, subsequently, using the optimized results, the use of 0.6319 kg of steel was successfully reduced by 23.68% to ensure the armchair’s stability. This provides a reference for the future compter-assist and standardized innovative design of Chinese armchair. By incorporating compter-assist design and lightweight optimization, it is possible to save materials and make more efficient use of resources, thereby contributing to sustainable development goals in the field of furniture design.

Honeypots are vital security tools that enable cybersecurity specialists to scrutinize the tactics employed by attackers. By doing so, they can get valuable insights into existing and upcoming internet dangers. Cyber-attacks are in a perpetual evolution, increasingly sophisticated and tailored to exploit specific vulnerabilities. Hence, it is imperative for both organizations and individuals not only to consider but also implement methods to safeguard themselves against cyberattacks and malware. This paper assesses a specific group of honeypot systems by considering detection range, emulation accuracy, data quality, dependability, scalability, performance, extensibility, installation options, setup difficulty, and maintenance requirements. Furthermore, it provides conclusions regarding the relative effectiveness of diverse honeypot systems in different situations. As a part of the research, it analyzes the latest progress and innovations in honeypot development, including enhancements in machine learning, heightened automation, integration with other security tools, cloud-based honeypots, and deception technology by using simulated assaults to showcase the logging and recording capabilities of these honeypots in capturing attack information. After carefully analyzing each honeypot, each has unique advantages, disadvantages, and special characteristics.

Trehalose, a disaccharide made of two D-glucose molecules, is found in various organisms like bacteria, yeast, fungi, insects, and plants. It serves as an energy and carbon source in plants and has signaling functions in yeast and plants, affecting metabolic pathways and growth regulation. It also plays a role in protecting proteins and cell membranes from damage caused by stress. This study aims to determine the best level of trehalose supplementation in layer diets between 34 to 49 weeks of age, due to limited literature on its impact on laying hen productivity. Experimental diets were based on nutritional recommendations and included six trehalose levels. The study was conducted over five periods of 21 days each, evaluating various parameters such as feed intake, egg production, egg quality, and bone characteristics. The results showed that trehalose supplementation influenced the performance of laying hens, as well as some aspects of egg and bone quality.

To investigate the effects of predation of the paddle crab Charybdis japonica on the culture and survival of scallops (Mizuhopecten yessoensis) during bottom culture, we investigated the behavioral characteristics of three sizes (small, medium, and large) of scallops in response to exposure to crabs. We also measured the activities of superoxide dismutase, catalase, arginine kinase, and octopine dehydrogenase in the gill, adductor muscle, and mantle of scallops before and after exposure to predation. Tissues that showed significant differences between control and test specimens were selected for deep sequencing of the transcriptome to identify and validate the key genes that were sensitive to predation. We found when M. yessoensis is stimulated by the presence of predators, its behavioral characteristics and related physiological and ecological indexes undergo significant changes. The results are relevant for developing specifications for M. yessoensis seedling casting during bottom culture.

Detrital zircons from two samples of metaterrigenous rocks from the Lykhmanivka Syncline, Middle Dnieper Domain of the Ukrainian Shield (Skelevate Formation of the Kryvyi Rih Group), have been dated by the LA-ICP-MS U-Pb method. Metasandstones from the northern part of the Syncline yield zircons belonging to four age groups: 3205 ± 20 Ma, 3089 ± 11 Ma, 2939 ± 8 Ma, and 2059 ± 4 Ma. In contrast, zircon from metasediments from the southern end of the Lykhmanivka Syncline fall within two age groups: 3174 ± 13 Ma, and 2038 ± 9 Ma. The source area was dominated by rocks of the Auly Group (3.27–3.18 Ga) and the Sura Complex (3.17–2.94 Ga). The proportion of zircons dated at 2.07–2.03 Ga, which reflects the timing of metamorphism, is 5%. The metamorphic nature of the Paleoproterozoic zircon allows us to define the maximum depositional age of the metaterrigenous rocks of the Lykhmanivka Syncline at ca. 2.9 Ga, which is in good agreement with the earlier results from the metaterrigenous rocks of the Kryvyi Rih – Kremenchuk basin. Our data also indicate the local nature of sedimentation and the absence of significant transport and mixing of detrital material within the basin.

The combination of micro- or nanofluidics and strong light–matter coupling has gained much interest in the past decade, which has led to the development of advanced systems and devices with numerous potential applications in different fields, such as chemistry, biosensing, and material science. Strong light–matter coupling is achieved by placing a dipole (e.g. an atom or a molecule) into a confined electromagnetic field, with molecular transitions being in resonance with the field and the coupling strength exceeding the average dissipation rate. Despite intense research and encouraging results in this field, some challenges still need to be overcome, related to the fabrication of nano- and microscale optical cavities, stability, scaling up and production, sensitivity, signal-to-noise ratio, and real-time control and monitoring. The goal of this paper is to summarize recent developments of micro- and nanofluidic systems employing strong light–matter coupling. An overview of various methods and techniques used to achieve strong light–matter coupling in micro- or nanofluidic systems is presented, preceded by a brief outline of the fundamentals of strong light–matter coupling and optofluidics operating in the strong coupling regime. The potential applications of these integrated systems in sensing, optofluidics, and quantum technologies are explored. The challenges and prospects in this rapidly developing field are discussed.

Fungi inhabiting deep subseafloor sediments have been shown to possess anaerobic methane (CH₄) production capabilities under atmospheric conditions. However, their ability to produce CH₄ under in situ conditions with high hydrostatic pressure (HHP) remains unclear. Here, Schizophyllum commune 20R-7-F01, isolated from ~ 2 km below the seafloor, was cultured in Seawater Medium (SM) in culture bottles fitted with sterile syringes for pressure equilibration. Subsequently, these culture bottles were transferred into 1-liter stainless steel pressure vessels at 30 °C for 5 days to simulate in situ HHP and anaerobic environments. Our comprehensive analysis of bioactivity, biomass, transcriptomics revealed that S. commune not only survived but significantly enhanced CH₄ production, reaching approximately 2.5 times higher levels under 35 MPa HHP compared to 0.1 MPa standard atmospheric pressure. Pathways associated with carbohydrate metabolism, methylation, hydrolase activity, cysteine and methionine metabolism, and oxidoreductase activity were notably activated under HHP. Specifically, key genes involved in fungal anaerobic CH₄ synthesis, including methyltransferase mct1 and dehalogenase dh3, were upregulated 7.9- and 12.5-fold, respectively, under HHP. Enhanced CH₄ production under HHP was primarily attributed to oxidative stress induced by pressure, supported by intracellular Reactive Oxygen Species (ROS) levels and comparative treatments with cadmium chloride and hydrogen peroxide. These findings suggest that fungi persisting in deep-sea sediments over millions of years have the potential to significantly contribute to global CH₄ flux.

Electronic waste generation has been following a continuously rising trend. With electronics containing a myriad of materials some of which are hazardous, toxic, extremely rare or precious combined with more stringent legislative laws which encourage the reuse and optimised recycling of the materials included in e-waste, the need for a holistic approach is inevitable. Such an approach should not only identify the materials found in the various types of e-waste and their respective quantities but also needs to highlight the possible handling techniques and their respective impacts and associated hazards whether to the environment or human health. Performing such a task manually would be exasperating and costly while requiring extensive resources which might not be met with a justifiable economic gain; thus, the use of the advancement in computational sustainability to draw a complete picture and aid in an informed decision-making process is crucial. The e-waste ontology developed in this paper is one way of addressing the problem since it covers the treatment domain, the hazard and the materials contained within the different types of e-waste. The ontology developed can be used directly from the protégé software or by embedding it into a developed Decision Support System.

Pulmonary arterial hypertension (PAH) is a complex and devastating disease that the underlying cellular and molecular mechanisms are largely remains unknown. This study aimed to elucidate the key hub genes and pathways in PAH by bioinformatics analysis. In the current study, we performed WGCNA analysis to systematically identify the hub genes for PAH using transcriptome microarray data. From the Gene Expression Omnibus (GEO) database, one microarray dataset (GSE113439) was downloaded for this study. There were 26 samples in this data set, including 15 PHA samples and 11 normal controls. Based on weighted correlation network analysis, 11 modules were identified and the MEgreenyellow module showed a significantly positive correlation with PAH (r = 0.93, P = 1e−06). The genes in greenyellow module were mainly significantly enriched in mitophagy related pathways by KEGG analyses. Combined with the protein–protein interaction (PPI) and co-expression networks, ten hub genes were identified as candidate biomarkers for PAH in the greenyellow module, including CTNNB1, NIPBL, ROCK2, ROCK1, SCAF11, JAK1, BIRC6, KIF5B, PPP1R12A and XRN1. These hub genes might be potential targets for clinical therapy against PAH. The molecular mechanisms involved in these genes that affected the prognosis of PAH should be further validated through biological and basic studies.

An optical fiber scale sensor based on the detection principle of surface plasmon resonance (SPR) was developed for the rapid, high-sensitivity real-time evaluation of scale precipitation from geothermal fluids. The optical fiber SPR scale sensor was fabricated by depositing a gold thin film on the surface of an optical fiber with an exposed core. The optimal gold film thickness of the sensor was determined to be 30 nm, which achieved a refractive index sensitivity of 2140 nm per refractive index unit. A field test was conducted using geothermal brine from the Obama Binary Geothermal Power Plant in Unzen, Nagasaki Prefecture. A conventional optical fiber scale sensor and the SPR sensor were simultaneously assessed using raw and pH-adjusted brines. For the SPR sensor, a peak shift of 0.27 nm/min was observed at a response time of 1 min, whereas no change in transmittance was observed for the conventional sensor until 180 min. After the experiments, scanning electron microscopy–energy-dispersive spectroscopy analysis was conducted on the sensors, and findings showed that the deposition of Mg–SiO2 scale did not significantly differ between the two sensors. The developed SPR sensor achieved faster scale precipitation detection (tens of minutes to hours) than the conventional sensor.

Background: Anxiety disorders can be both an independent risk factor of many somatic conditions and the cause of complications of various organic disorders leading to poor prognosis. Objectives: The purpose of this study is to compare the heart rate variability (HRV) recorded both during the day and at night in patients with anxiety disorders and healthy individuals. Methods: The study was conducted on fifty consecutive outpatients with anxiety disorders who participated in intensive group psychotherapy. The diagnosis was made according to DSM-IV-TR criteria using the PSE-10 questionnaire. Out of 50 patients under study, 17 were diagnosed with panic disorder (PD) and 21 with generalized anxiety disorder (GAD). The control group consisted of 40 healthy people. The resulting three groups were compared in terms of circadian HRV using the Oxford Medilog Suprima Holter System. Results: The HRV analysis showed the nocturnal falls in the time and frequency parameters related to parasympathetic activity (rMSSD, pNN50, HF). The most unfavourable alterations were observed in the PD group, which comprised the disappearance of day vs. night amplitude of the majority of HRV parameters. Hence, the PD group was particularly susceptible to the arrhythmic events. It was noticeable that nocturnal vagotonia in the PD group was significantly lower as compared with the GAD and control group. Conclusions: The innovative aspect of the present study was to find a distinct profile of HRV with regard to the types of anxiety disorders because of the existing considerable differences between the nature of PD and GAD.

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

Viral hepatitis caused by hepatitis B virus (HBV) and hepatitis C virus (HCV) poses a significant global health challenge, with an increasing number of deaths and a substantial burden on healthcare systems. This literature review explores the impact of socioeconomic determinants of health (SDoH) on the prevalence and outcomes of HBV and HCV infections among male prison inmates in Nigeria. By examining factors such as risky behaviors, overcrowding, inadequate healthcare access, and food insecurity, this review highlights how these determinants contribute to the high rates of hepatitis infections in Nigerian prisons. The review underscores the urgent need for a multifaceted approach involving policy changes, improved healthcare access, and enhanced preventive measures to address these health disparities. The findings aim to inform public health strategies and interventions to reduce the transmission and impact of viral hepatitis in prison settings, aligning with global health goals and promoting health equity.

Recommender systems in education are becoming more widespread, typically focusing on recommending courses or study materials. This study proposes a machine learning approach to recommend a university degree based on high school and university standardised test results, incorporating students' socioeconomic information as input variables. The objective is to develop a tool for students’ decision-making, supporting the sustainable development goal of Quality Education by providing a data schema to maximise the likelihood of a successful match between the student's profile and the academic program. With its focus on equity in education, this study provides a data-driven approach to assist students in selecting suitable university degrees, aiming to improve educational outcomes and inspire a more equitable education system.