Optimal upper extremity motor control and range of motion are necessary to achieve even the basic Activities of Daily Living (ADL) function. Stroke, with resulting hemiparesis, can significantly and negatively impact an individual’s ADL function. Functional Independence Measure (FIM) self-care score can provide assessment of what aspects and to what degree ADL functions are impaired. FIM self-care assessment can also track changes in ADL function during stroke recovery and rehabilitation. Recently, sensor-acquired 3D motion analysis of stroke patients’ upper extremity has shown promise as a potential alternative to assess ADL function. This observational study evaluates whether the sensor-acquired upper extremity reachable workspace (RWS) measure correlates with clinician-evaluated FIM self-care score in stroke patients. Seventeen patients with stroke were enrolled in the study. FIM self-care, NeuroQoL upper extremity, and reachable workspace outcome measures (relative surface area, RSA) were collected upon rehabilitation hospital admission, at discharge, and at 3-month visit. Pearson and Spearman’s rank correlation coefficients as well as multiple linear regression analyses were used to determine the relationships between FIM self-care, NeuroQoL, and reachable workspace RSAs. Moderately strong correlation between total reachable workspace RSA and total FIM self-care score at discharge and at 3-month were noted (r=0.619, r=0.661, p<0.05), and similarly strong correlation was also noted with the upper extremity NeuroQoL total score (r=0.690, r=0.815, p<0.05). Multiple linear regression analyses revealed a change in bilateral total RSA of 0.1 unit from admission to 3-month follow up correlated with respective change in FIM self-care score of 2.011 points (95%CI: 0.663-3.360). Longitudinal improvement in ADL function during stroke rehabilitation and recovery process is correlated with improvement in reachable workspace.

The adoption of social media metrics by small and medium-sized enterprises (SMEs) has become a critical tool for engaging customers and enhancing strategic outcomes. However, many SMEs face challenges in effectively tracking and evaluating social media investments, making it difficult to assess the effectiveness of their strategies. This systematic review identifies and analyses key social media activities and performance metrics that contribute to SME strategic success. Through an extensive review of literature from Google Scholar, Web of Science, and Scopus, we selected 138 studies published between 2014 and 2024. The analysis reveals that engagement metrics (e.g., likes, shares), brand awareness (e.g., mentions), and conversion metrics (e.g., click-through rates) significantly influence organizational performance and long-term success. SMEs leveraging detailed engagement metrics reported a 25% increase in customer retention, while brand awareness metrics led to a 30% improvement in customer acquisition. The review highlights areas requiring further research, including a need for sector-specific metric analysis and a deeper understanding of emerging social media platforms. These results offer practical insights for SMEs to enhance their strategic use of social media and adapt to rapidly changing market dynamics.

This study presents a novel predictive model to address the churn problem in the motor insurance sector using a dual-phase framework. The first phase employs a cave-degree perturbation technique for effective feature selection, while the second phase applies the Magnetic Force Perturbation Technique (MFPT) to optimize the search process and avoid local optima traps. Two metaheuristics are proposed: the Adaptive Random Forest-Assisted Large Neighborhood Feature Optimizer (ARALFO) and the Adaptive Random Forest Particle Swarm Optimizer (ARFPSO) to enhance churn prediction accuracy. The model was evaluated on two real-world motor insurance datasets, achieving a 95% accuracy rate and outperforming state-of-the-art algorithms. An ablation study confirmed the significant impact of the cave-degree and MFPT techniques in boosting predictive performance.

Aim of study: to evaluate the therapeutic impact of topically applied curcumin as Alternative or Complementary to Triamcinolone Acetonate in Treatment of Oral Lichen Planus. Background: Oral lichen planus (OLP) is a chronic inflammatory disorder affecting the mucous membranes, believed to be an immune-mediated condition. Its persisting nature can lead to extended discomfort for those affected. The primary symptoms are pain and a burning sensation, with a potential risk of malignancy if not properly managed, necessitating lifelong clinical monitoring. Presently, OLP is considered a significant challenge due to the absence of a definitive cure. Patients & patients: Thirty patients with oral lichen planus were divided into three groups, 10 patients each: Group A: ten patients with OLP were received topical triamcinolone acetonide. Group B: ten patients with OLP were received topical curcumin. Group C: ten patients with oral lichen planus were received topical triamcinolone acetonide combiend with topical curcumin. For six weeks. Each patient was examined at baseline, 1 and 3months of therapy. Pain was scored using the visual analogue scale. Salivary level of interleukin 6 were estimated at baseline, 1 and 3months evaluation periods. Results: Significant decrease in pain score was observed in all groups and there was a statistically significant difference between them at all treatment periods in favor to group C . 2 Additionally, all groups showed a decrease in the size of lesions which was greater in group A,C compared to the B group. A significant reduction of the mean interlukin6 continued down to 3 months in all groups with statistically insignificant difference between them at three months, while statistically significant difference in favor to group C at 1month was observed. Conclusion: The results proved the benefit of curcumin as a promising medication when used in combination with conventional therapy in treatment of OLP patients. However, further longterm studies with large sample size are needed.

The United States is facing an alarming surge in cyberattacks, with data breaches rising by 72% since 2021, presenting grave national security risks. Notable incidents, including Chinese hackers attempting to access high-level government information and Iranian-linked cyber groups targeting U.S. water systems, highlight the fragility of critical infrastructure. These attacks pose dangers not only to national security but also to economic stability and public health, amplified by the rapid growth of interconnected technologies and global digitalization. In response to these escalating threats, the U.S. must take a multifaceted approach. Key strategies include strengthening cybersecurity education and workforce development, enforcing more rigorous regulatory frameworks, and enhancing preparedness through incident response and recovery planning. Additionally, increased international cooperation is essential to address state-sponsored cyber threats and foster a global framework for cybersecurity defense. As cyber threats continue to evolve, future efforts will rely on technological advancements such as artificial intelligence (AI) for real-time threat detection, the adoption of zero-trust security models, and the development of adaptive, resilient cybersecurity infrastructures. Collaboration between the public and private sectors, coupled with ongoing innovation and education, will be critical to maintaining national security and promoting economic growth in an increasingly interconnected digital landscape.

This study examines the degradation of agglomerate particles in treated produced water used for hydraulic fracturing in the Bakken Formation. By analyzing d90 values and pump speeds from different manufacturers, the research quantifies particle size reduction rates, revealing how degradation contributes to pore throat blocking in tight formations. The study found that agglomerate particles in hydraulic fracturing fluids ranged from 9 to 85 microns in size, with degradation occurring most rapidly within the first five minutes of pump action. As time progresses, degradation slows, indicating that pore clogging is most likely at the beginning of the fracturing process. The research emphasizes that pump speed is a critical factor in determining the rate of particle degradation. To minimize formation damage, it is recommended to scale up particle size and use coarser particles in suspension. However, both large and small particles pose risks: larger particles can reduce porosity, while smaller particles may penetrate pore throats and form internal filter cakes. The study highlights the importance of selecting the appropriate pump speed and particle size distribution to optimize fracturing fluid design, maintain formation permeability, and reduce the risk of formation damage. Lower pump speeds and fewer coarse particles are advised to avoid pore clogging during the fracturing process.

The emergence of Artificial Intelligence (AI) has transformed the way historical data is interpreted, uncovering previously unseen trends and fundamentally altering modern management practices. This shift has significantly impacted the analysis of business history and leadership studies. However, there is still a notable gap in research regarding the effects of AI-driven reinterpretations of historical events on contemporary business strategies, leadership frameworks, and ethical considerations. This study aims to address this gap by exploring the methodologies and implications of AI in business history analysis, focusing on case studies of Walmart and JPMorgan Chase. Integrating foundational theories such as evolutionary theory, path dependency, transformational, and authentic leadership reveals how AI-driven insights can refine strategic planning, advance leadership development, and promote equitable management practices. It also highlights the ethical challenges in using AI for historical analysis, particularly the risks of perpetuating existing biases. Findings indicate that AI's capacity to analyze large volumes of historical data significantly reshapes our understanding of business evolution and leadership effectiveness. The study concludes with actionable recommendations for adopting ethical AI practices, outlining future research directions, including developing AI models for contextual analysis and exploring AI’s role in promoting diverse leadership models. This research advances academic discourse by providing a theory-driven framework for leveraging AI in business history and leadership studies. It underscores AI's transformative potential and complex implications for future business practices.

This study aimed to test the hypothesis that the use of theories in open access resources during the COVID-19 pandemic would be greater than in copyrighted resources. Using the Scopus database from August 18-28, 2023, the search terms “COVID-19,” “Coronavirus,” “SARS-CoV-2,” and “2019-nCoV” retrieved 511,920 results, of which 17,487 were selected. After filtering, 8,254 studies were analyzed. These were categorized as "Both Theory and Model," "Only Theory," and "Only Model." Using MS Excel, the researchers sorted studies based on titles, abstracts, and keywords to assess the magnitude of theory and model use. The study found that theories and models were employed across various disciplines and indexed accordingly. Further analysis included term categories, year-wise distribution, and citation patterns in relation to access categories. The study recommended future research to explore the connection between theories/models and factors such as publication trends, geography, and funding. The study concluded that, journals should encourage authors to include theories/models used in their studies in titles, especially during health emergencies, to provide early insights into handling pandemics.

Hearing-loss (HL) is a major public health issue albeit data is limited. Objectives: were to determine the frequency and factors. Design: was descriptive cross-sectional using web-based self-administered google-form questionnaire. Data analyzed by-IBM SPSS-V-24-Windows. Results: In 789 responses, 6.6 % reported HL, 62.4% from Eastern-region of which 41.2% were employed and 31.7% students. Despite 36.5% reported HL > 5-years, 21.2% sought treatments. 19.2% reported diabetes and 51.9% had family-history. Ages 11– 50 years showed female predominance (females 59.1%, males 41%). Age was significant in HL (P <0.001) irrespective of gender (males 6.8%, females 6.4%, P value =0.838) reaching peaks at 17.2% 51-60 years and 25% in 71-80 implying a potential risk (OR = 1.43, 95% CI: 0.78-2.81, p = 0.032). 34.6% indicated genetics then noise (26.9%), while infections, stroke, and fevers made 19.2%. Air or sea travels (7.7%) were insignificant (P = 0.628) while head injuries (11.5%), or low- and high-altitudes showed 21.2%. Noise-hazard (P = 0.048) and medications (P = 0.017) were significant in HL. Conclusion: thus, we show geriatric age, noise, and inheritance-history as major predispositions. However, the study was limited by small sampling. Future large cohort multicenter studies on factual clinical data would gain more insights into the HL.

Indoleamine 2,3-dioxygenase 1 (IDO1) and IDO2, originated from gene duplication before vertebrate diver-gence. While IDO1 has a well-defined role in immune regulation, the biological role of IDO2 remains unclear. Discovered in 2007, IDO2 is located near the IDO1 gene. Because of their high sequence similarity, IDO2 was initially thought to be a tryptophan (Trp)-degrading enzyme like IDO1. Differently from what expected, IDO2 displays extremely low catalytic activity toward Trp. Nevertheless, many studies, often contradictory, tried to demonstrate that IDO2 modulates immune responses by catabolizing Trp into kynurenine, a hypothesis un-convincing and, let’s say, linked to the first life of IDO2. Here, we review its “new life”, the one in which IDO2 has a functional role beyond Trp metabolism. The IDO2’s evolutionary persistence across species, despite be-ing almost inactive as an enzyme, suggests it has some relevant biological importance. IDO2 expression in human normal cells is poor, but significant in various cancers, with two prevalent SNPs. Overall, the com-parison of IDO2 to IDO1 as a Trp-degrading enzyme may have led to misunderstandings about IDO2’s true physiological and pathological roles. New insights suggest that IDO2 might function more as a signaling molecule, particularly in cancer contexts, and further studies could reveal its potential as a target for cancer therapy.

Small and Medium-scale Enterprises (SMEs) need a platform that actively enables collaboration with Research institutions and consultants since SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system for enabling, executing, and recording collaboration activities and tracking reputations among the organizations and individuals that use the platform. Blockchain provides an opportunity to build such a collaborative platform by enabling the verifiable recording of outcomes from collaborations, aggregating the resulting reputation of the collaborating parties, and offering tokenised incentives for rewarding positive contributions to the platform. Cryptocurrencies from which blockchain-based tokens are highly unstable, thereby, reducing business organisations’ interest in blockchain applications. Hence, there is a need to design a self-sustaining valuable token model that incentivises user behaviours that positively contribute to the platform. This paper explores the application of game theory in analyzing token-based economic interactions between the various groups of users in a blockchain-based collaboration platform to design and simulate a token distribution system that provides a fair reward mechanism for the users while also providing a dynamic pricing model for the utility value provided by the platform tokens. Furthermore, the paper analyzed potential risks on the token model design and propose mitigation strategies.

Aging is the important risk factor for tumorigenesis. Metabolic reprogramming is hallmark of both aging and tumor initiation. However, how the crosstalk between aging and metabolic reprogramming affects the tumor microenvironment (TME) to promote tumorigenesis was poorly explored. We utilized a computational approach proposed by our previous work, termed as MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to depict age-related metabolic plasticity using pan-cancer bulk RNA-seq data. Through MMP3C analysis across 17 age-related cancer types, a high metabolic organized heterogeneity was found across those cancer types. Subsequent analysis revealed a higher degree of glycolysis and impaired oxidative phosphorylation in older patients. Similar phenomenon was also observed at the single cell resolution. Many energy generation pathways, i.e., TCA cycle, oxidative phosphorylation were weakened in activated T cells and macrophages, while it increased in exhausted T cells, immunosuppressive macrophages, and Tregs in older patients. It was suggested that aging-induced metabolic switches alter the utilization of glucose, thereby influencing immune function and resulting in the remodeling of the tumor microenvironment. This study provides new insights into the associations between tumor metabolism and TME mediated by aging, linking with novel strategies for cancer therapy.

For almost 75 years, the general solution for the Schrödinger equation was assumed to be generated by an exponential or a time-ordered exponential known as the Dyson series. We study the unitarity of this solution in case of singular Hamiltonian and provide a new methodology that is not based on the assumption that the underlying space is L2(R). Then, an alternative operator for generating the time evolution is suggested that is manifestly unitary, regardless of the choice of the Hamiltonian. The new construction involves an additional positive operator that normalizes the wave-function locally and allows us to preserve unitary even on indefinite norm spaces. Our considerations show that Schrödinger's and Liouville's equations are, in fact, two sides of the same coin, and together they become the unified description of quantum systems.

This paper presents a case study of Fulani herdsmen in Nigeria, whose traditional ethnoveteri-nary practices risk being lost as the country transitions to more intensive and enclosed livestock practices. We use a planetary health framing to make visible the value of indigenous practices that are less damaging to the environment, animal welfare and human health. Through ethnographic observation, focus group discussions (FGD) and key stakeholder interviews, we show that the Fulani use a complex system of herbal medicines and traditional herding practices to maintain herd health, manage and treat animal disease when it arises. However, their traditions often sit uncomfortably with commercial farming practices. As traditional Fulani grazing lands are eroded, dispossessed Fulani take employment from businessmen farmers but both parties’ inex-perience with shed hygiene, artificial feed and less environmentally resilient crossbreeds leads to increased incidence of infectious disease. This in turn drives higher use of antibiotics. There is a ‘causal chain’ of underlying drivers that leads, through poorer environmental, animal and hu-man health, to increased use of antibiotics. The antibiotic resistance that emerges from this chain threatens human health now and into the future. Through a planetary health framing we advocate for more respect for Fulani herdsmen and their traditional ethnoveterinary practices as an alter-native to increasing antibiotic use.

Species of the genus Kalanchoe have a long history of therapeutic use in ethnomedicine linked to their remarkable medical properties. One of them is Kalanchoe blossfeldiana succulents occurring in tropical regions. Despite the great interest in this plant, there are no reports about its therapeutic effects on the skin. In this study, the antioxidant properties and skin permeation of a topical hydrogel containing K. blossfeldiana ethanol extract was assessed. Additionally, the content of active compounds in the K. blossfeldiana extract was evaluated by UHPLC-MS and HPLC-UV. The extract was analysed with three antioxidant assays: ABTS, DPPH and FRAP. Furthermore, antielastase and antihialuronidase properties of the tested extract were assessed. The ex vivo penetration studies were performed using the Franz diffusion cells. The obtained results show that antioxidant properties of K. blossfeldiana extract were similar to ascorbic acid, while antielastase and antihialuronidase tests indicated strong antiaging and anti-inflammatory activity (IC50 was 26.8 ± 0.13 and 77.31 ± 2.44 µg/mL, respectively). Moreover, active ingredients contained in K. blossfeldiana extract penetrated through human skin and accumulate in it. In conclusion, the hydrogel containing K. blossfeldiana extract can be considered as an interesting and new alternative to dermatologic and cosmetic preparations.

With the rising awareness of environmental issues, concerns about pollution and global warming have gained importance, and electricity generation costs continue to increase. As a result, research on energy recovery using thermoelectric modules has attracted attention in recent years. Given the lack of significant breakthroughs in thermoelectric materials, improving the structure of thermoelectric modules has become a key direction for enhancing efficiency. Common thermoelectric module types include flat plate (Flat Plate TEG), annular (Annular TEG), flexible (Flexibility TEG), segmented, and cascaded designs. Factors influencing thermoelectric performance include the number, shape, height, contact area of thermoelectric elements, temperature difference, copper plate length, contact resistance, and the geometric arrangement of the elements. This study is based on the commercially available TGM1-127-1.0-0.8 thermoelectric module. By simulating the output performance of both flat plate and annular thermoelectric module structures under identical conditions—such as total volume of thermoelectric material, shape, and height of the thermoelectric legs, the total contact area with the heat source, temperature differential, and total volume of the connecting copper plate—we observed that the number of thermoelectric elements not only determines the open-circuit voltage but also influences the output power. The trend of output power remains consistent under varying temperature differences, regardless of changes in the load resistance.

This paper proposes the use of Bayesian inference techniques to search for and obtain valid instruments in dynamic panel data models where endogenous variables may exist. The use of Principal Component Analysis (PCA) allows for obtaining a reduced number of instruments in comparison to the high number of instruments commonly used in the literature, and Monte Carlo Markov Chain (MCMC) methods enable efficient exploration of the instrument space, deriving accurate point estimates of the elements of interest. The proposed methodology is illustrated in a simulated case and in an empirical application, where the partial effect of a series of determinants on the attraction of international bank flows is quantified. The results highlight the importance of promoting and developing the private sector in these economies, as well as the importance of maintaining good levels of credit worthiness

An outbreak of esophagitis and ingluvitis caused by Salmonella Typhimurium in passerines seized from illegal wildlife trafficking is described. This illegal activity causes stress and leads to lowered immunity in the birds. Additionally, inadequate hygiene conditions predispose the birds to diseases such as salmonellosis. Few studies report the occurrence of Salmonella-induced lesions in the esophagus and crop of wild birds; thus, this study aimed to describe the disease in these species, investigate its presence in the environment, and determine the antimicrobial resistance profile. Three birds of the genus Sporophila were necropsied. In the esophagus and crop, yellowish plaques corresponding to necrosis with bacterial aggregates were observed. Salmonella spp. was isolated from these lesions, with genus confirmation via MALDI-TOF. Environmental samples were collected from the enclosures and cages where the animals were quarantined, and the same bacterium was isolated. In two birds, the serotype S. Typhimurium was identified. Antibiograms performed on the strains from the birds and the environment revealed resistance to antibiotics in the classes of Penicillins, Sulfonamides, Aminoglycosides, Monobactams, Tetracyclines, and first and third-generation Cephalosporins. To the authors' knowledge, this is the first report of this agent causing death in Sporophila due to esophagitis and ingluvitis. It is also the first report of salmonellosis in three species of passerines in Brazil. The study underscores the importance of understanding the pathogens circulating in wild animals, especially within the context of One Health.

The high performance of liquid chromatography(HPLC) has long been an indispensable tool for vigilance and analysis across range disciplines such as pharmaceuticals, environmental monitoring, food safety attributes etc. We will discuss about HPLC method development and validation, types of liquid chromatography methods focusing on proper use of a mobile phase like an eluent selection, how to optimize the conditions as well take it through its various procedures required for approval from regulatory bodies. Strategies to improve method robustness, sensitivity and reproducibility are highlighted. The future breakthroughs in HPLC technology and some innovative trends have also been speculated to limit this fundamental methodology.

Dengue fever is a persistent public health issue in tropical regions, including Vietnam, where climate variability plays a crucial role in disease transmission dynamics. This study focuses on developing climate-based machine learning models to forecast dengue outbreaks in Ba Ria Vung Tau (BRVT) province, Vietnam, using meteorological data from 2003 to 2022. We utilized four predictive models—Negative Binomial Regression (NBR), Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM)—to predict weekly dengue incidence. Key climate variables, including temperature, humidity, precipitation, and wind speed, were integrated into these models, with lagged variables included to capture delayed climatic effects on dengue transmission. The NBR model demonstrated the best performance in terms of predictive accuracy, achieving the lowest Mean Absolute Error (MAE) compared to other models. The inclusion of lagged climate variables significantly enhanced the model’s ability to predict dengue cases. SARIMAX and LSTM models, although effective in capturing seasonal trends, struggled with overfitting and failed to accurately predict short-term outbreaks. XGBoost exhibited moderate predictive power but was sensitive to overfitting, particularly without fine-tuning. Our findings confirm that climate-based machine learning models, particularly the NBR model, offer valuable tools for forecasting dengue outbreaks in BRVT. However, improving the models’ ability to predict short-term peaks remains a challenge. The integration of meteorological data into early warning systems is crucial for public health authorities to plan timely and effective interventions. This research contributes to the growing body of literature on climate-based disease forecasting and underscores the need for further model refinement to address the complexities of dengue transmission in highly endemic regions.

Geothermal energy generation faces challenges in efficiency, partly due to restrictions on reinjection temperatures caused by scaling issues. Therefore, developing strategies to prevent scaling is critical. This study aims to simulate the scaling tendencies and corrosion effects of geothermal fluids on various construction materials used in scaling reactor/retention tank systems. A range of materials, including carbon steel, austenitic stainless steel, duplex stainless steel, two proprietary two-part epoxy coatings, and thermally sprayed aluminium (TSA), were tested in a simulated geothermal brine. Experiments were conducted in a laboratory vessel designed to replicate the wall shear stress conditions expected in a scaling reactor. The tests revealed varying scaling tendencies among the materials, with minimal corrosion observed. The dominant scale formed was calcium carbonate, consistent with geochemical modelling. The findings suggest that despite the high operating temperatures, the risk of corrosion remains low due to the brine's low chloride content, while the wettability of materials after immersion may serve as a useful indicator for selecting those that promote scaling.

Biofilm formation in unicellular eukaryotes, such as Candida albicans and Saccharomyces cerevisiae, represents a significant challenge in various sectors, including healthcare, agriculture, and industry. These biofilms are resistant to conventional antimicrobial treatments due to their protective extracellular matrix and complex regulatory mechanisms, such as quorum sensing (QS) and cyclic AMP (cAMP) signaling. Recent advances in the understanding of biofilm formation pathways have led to the identification of novel compounds that target these regulatory mechanisms. This review highlights breakthroughs from 2024 research, focusing on the inhibition of quorum sensing, disruption of extracellular matrix synthesis, and transcriptional/post-transcriptional regulation of biofilm-related genes. The application of these discoveries in Vietnam is particularly promising, given the country's challenges with biofilm-related infections in healthcare and productivity losses in agriculture and aquaculture. Natural product-based approaches, especially those derived from Vietnam's marine biodiversity, offer environmentally sustainable solutions for biofilm control. However, translating these laboratory findings into scalable, cost-effective applications faces challenges such as resistance development, environmental sustainability, and regulatory constraints. This review provides a comprehensive overview of compounds targeting biofilm regulatory mechanisms, evaluates their potential applications in Vietnam, and discusses future research directions, emphasizing the need for interdisciplinary collaboration to address the complexities of biofilm control.

Deuterium is a heavy nonradioactive isotope of hydrogen, having a neutron as well as a proton, making it twice as heavy. It is a natural element, present at 156 parts per million in seawater. The ATPase pumps in the mitochondria utilize proton motive force to drive ATP synthesis, and deuterons damage the pumps, producing a stutter that can cause reactive oxygen release and inefficiencies in ATP synthesis. Cellular metabolism incorporates several novel mechanisms to assure low deuterium content in the mitochondria and other organelles. Nicotinamide adenine dinucleotide (NAD) is a major carrier of deuterium depleted (deupleted) protons to supply the mitochondria. Many enzymes, especially flavoproteins, are able to use proton tunneling to fractionate out deuterium. In this paper, we argue that the amino acid proline is able to trap and sequester deuterium, and that peptidyl prolyl isomerases (PPIases) play a central role in facilitating deuterium trapping in proline-rich proteins, most notably collagen. The endothelial glycocalyx also sequesters deuterium in gelled water lining the blood vessels, creating a battery and promoting low deuterium in the circulation. Excess deuterium promotes cancer growth, and cancer cells release large quantities of lactate via aerobic glycolysis to help reverse deuterium overload, systemically.

The enormous diversity of hair fibers found in human population is striking, while the basic structure of human fibers is often the same! But why do some fibers curl? Curling is related to the machinery and processes that produce different keratin filament orientation in relation to the axis of hair growth, influenced by developmental and molecular control. It is established that a curly hair shaft is produced by a curved hair follicle that have an angulated dermal implantation, with a bulb bent in the shape of golf club, while the outer root sheath is dissymmetrical; have an outer side that is convex bulging and thin and a concave side in which the cells express alpha-smooth muscle protein reflecting internal mechanical force. Yet, the exact cause of hair curling is not known. All the previous studies were short of impacting curly hair treatment. My hypothesis is that the curling mechanical force is generated by a hypersensitive contracted Arrector Pili muscle (APM) of the hair follicle. In curly hair, each APM fiber is shortened; pulling its attachment spot upward towards the epidermis and obliquely or laterally; curling the outer root sheath to produce the curved follicle described above. This contraction has the influence at a time when the young hair root is being formed and keratin microfibrils are being oriented and packed. APM is a multi-unit muscle wherein each fiber contracts independently to create the enormous diversity of curly hair types. APM contraction can be modulated by oral administration of selective smooth muscle relaxing treatment that relax the curved follicle from within. Further studies are needed to confirm the role of APM in shaping curly hair follicle including AI studies, i.e., computer-aided target identification to demonstrate APM active site that can interact with herbal small molecules.

This paper presents the novel development of a shape memory polymer foam based on polymer-polymer nanocomposites. Herein, polylactide (PLA)/biosourced polyamide (PA) foams are fabricated by in-situ fibrillation of polymer blends and subsequent supercritical CO2 foaming technique. In this system, PLA serves as a shape memory polymer to endow this foam with a shape memory effect (SME), and in-situ generated PA nanofibers are employed to reinforce the PLA cell walls and provide an additional permanent phase. Concentration of PA, 5 wt.%, is chosen to form an entangled nanofibrillar network. Foams of PLA/PA nanoblends with the same content of constituents are fabricated to reveal the effect of minor phase morphology on the cell structure and shape memory behavior of polymer foams. Profiting from the reinforcing effect of PA nanofibers, the PLA/PA nanocomposite foam exhibits smaller foam cells, a narrower cell size distribution and a comparable cell concentration than the PLA/PA nanoblend foam. In addition, PA nanofibers, unlike PA nanodroplets, favor the shape fixation ratio and recovery ratio and shorten the shape recovery time.

The evaluation of landslide hazard in seismic areas is based on a deterministic analysis to define the stability of the sites. However, deterministic analysis is not able to consider various uncertainties in the analysis process. This paper focuses on the results established by the probabilistic seismic hazard analysis and extends the probabilistic landslide hazard analysis to consider the uncertainties of opposing ground deformations and strength. The work studies the areas between Nibbiano and Sant'Erasmo inhabited centres in the Camerino municipality located in central Italy. The current state of the areas shows dangerous conditions for the building structures. In fact, all constructions present evidence of damage caused by both the 2016 earthquake and slope instability. The studies, carried out through careful geological survey and detailed geophysical investigations, are performed to define the geological, geomorphological and hydrogeological characteristics of the areas and also to run a new analysis aimed to define the material stress-strain behavior. Both inhabited Centers are located on debris deposits with highly variable thicknesses. The key point regards the definition of the depth of unstable mass, its position and its extension. Therefore, this contribution aims to summarize the knowledge of the landslide phenomenon that affected the area, trying to interpret its evolution with new field investigations. Indeed, during the first step, related to the geological and geomorphological detailed surveys, a remarkable geological model was obtained to better interpret the slope dynamics in relation to the structures of this section of the ridge. This model was also confirmed by the geophysical investigations. The study involves a more detailed definition of the landslide perimeter. Indeed, to analyze the phenomenon, it was necessary to study both the local seismic amplification with finite element 2D simulations and the slope stability conditions with finite 3D difference simulation.

Tsetlin machine (TM) is a novel machine learning approach that implements propositional logic to perform different tasks like classification and regression. The TM can not only achieves competitive accuracy in these tasks but it is also able to provide results that are explainable and easy to implement using simple hardware. The TM learns using clauses based off the features of the data. Final classification is done using a combination of the clauses. In this paper, we propose the novel idea of adding regularizers to TM, called as Regularized TM (RegTM), to improve generalization. Regularizers have been widely used in machine learning to improve accuracy. We explore different regularization strategies and how they would influence performance. We show the feasibility of our methodology by different experiments on benchmark datasets.

The insuperable obstacle between gravity and quantum mechanics is their spacetimes. Introduction of a (quantum) quadratic form on a state Hilbert space and a (quantum) metric tensor in (3+1)D quantum spacetime solves the problem by providing a common spacetime for both gravity and quantum mechanics which helps further to develop an accurate and generalized form of quantum gravity, i.e., General Quantum Gravity. This formalism yields a (3+1)D pseudo-gravity first order quantum equation, which is technically more accurate than Schr\"odinger equation. A (quantum) Lorentz-like transformation is also possible to be developed in (3+1)D quantum spacetime. Instead of one common Einstein field equation to explain gravitational effects, this formalism yields two individual (quantum) Einstein field equations, namely, one for bosons and another for fermions. In this work, (quantum) metric tensor as well as quantum Riemann tensor, quantum Ricci tensor and quantum Ricci scalar all are found to be the objects of quantization. Apparently, these quantum Riemann tensor, quantum Ricci tensor and quantum Ricci scalar are also found to be multiplicatively renormalizable while all divergences of their higher orders vanish in (3+1)D quantum spacetime. Ultimately, General Quantum Gravity yields that: Quantum Mechanics and Theory of Special and General Relativity are inseparable and naturally inter-expressible for the (quantum) quadratic form and the (quantum) metric tensor in (3+1)D quantum spacetime.

Renewable energy is essential to environmental sustainability and ranks high among the United Nations strategic goals. In this context, as opposed to conventional biomass gasification, co-gasification emerges as a promising technological pathway to effectively combine the distinct benefits of various gasification feedstocks in order to generate hydrogen-rich syngas. The goal of achieving net-zero emissions has further raised the demand for biomass co-gasification. However, the complicated reactions involved in co-gasification process pose challenge in optimizing the process parameters for increased productivity and performance. To the author’s knowledge, there is no consensus regarding the most suitable machine learning (ML) based regression models for optimizing biomass-plastics co-gasification process, as no prior research has examined the effectiveness of different regression models for this process. Further, the practical application of ML models is adversely affected by their black box nature and lack of interpretability. To address these gaps, the objective of the currect research is two-fold: Firstly, to model the co-gasification process using seven different ML algorithms. Secondly, to develop an evaluation framework and assess model interpretability to select an ideal model for co-gasification interpretation and optimization. The support vector regression(SVR) model demonstrated the most reliable predictive performance. The study employed learning curve analysis and cross validation analysis to substantiate the model predictive performance and exhibit its potential to generalize. Beyond performance analysis, Shapely additive explanation, (SHAP) is first explored in the realm of cogasification to interpret the prediction of the studied models from global and local perspective. The research findings are expected to provide valuable insights to better understand and optimize biomass-plastics cogasification process for H2-rich syngas production.

In this paper we have investigated the driven quintic Duffing equa- tion. Using some analysis technique, we are able to predict the number of limit cycles around the equilibrium and to develop a theoretical ap- proach to chaos transition in damped driven systems.

A web application – IMERG Precipitation Extractor (IPE) was developed that relies on the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) data available at a global coverage. The IPE allows users to query, visualize, and download time series satellite precipitation data for various locations, including points, watersheds, country extents, and digitized areas of interest. It supports different temporal resolutions ranging from 30 minutes to 1 week. Additionally, the IPE facilitates advanced analyses such as storm tracking and anomaly detection, which can be used to monitor climate change through variations in precipitation frequency and intensity. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2,360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region (e.g., Nevada), the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of -22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash-Sutcliffe Efficiency (NSE) of 0.80, and a Kling-Gupta Efficiency (KGE) of 0.52. In the wet region (e.g., Louisiana), the average CC was 0.93, the PRB was 24.82%, the RMSE was 0.96, the MBR was 0.79, the NSE was 0.80, and the KGE was 0.18. Median RAI indices from both IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS, advocating for further global climate change studies.

Recently, the availability of many omics data source has given the rise of modelling biological networks for each individual or patient. Such networks are able to represent individual-specific characteristics, providing insights into the condition of each person. Given a set of networks of individuals, a network representing a particular condition (e.g., an individual with a specific disease) may be seen as an anomaly network. Consequently, the use of Graph Anomaly Detection techniques may support such analysis. Among the others, Generative Adversarial Networks present optimal per- formances in anomaly detection. This paper presents ADIN (Anomaly Detection in Individual Networks), a framework based on Generative Adversarial Attributed Networks (GAANs) for anomaly detection in convergence/divergence patients at- tributed networks. Preliminary results on networks generated from computational biology gene expression data demonstrate the effectiveness of our approach in detecting and explaining bladder cancer patients.

Although flow mixing and cooling can be greatly enhanced when considering the use of Fluidic Oscillators (FO), they are more commonly employed in Active Flow Control (AFC) applications where the injected pulsating flow interacts with the boundary layer in order to usually delay its separation. In fact, prior to any FO implementation in a given application, it is essential to study the range of frequencies and amplitudes it can generate as a function of the incoming mass flow and its dimensions. This point is what is being enlightened in the present manuscript for a rather novel FO configuration. We are numerically studding a standard FO configuration in three dimensions 3D and also using a two dimensional 2D approach. After comparing the 3D and the 2D results and analyzing the main differences, we modified some of the internal dimensions of the FO in order to evaluate the variation of its dynamic performance. The present results clarify which internal dimensional modifications are more effective in generating larger output frequencies and velocity field variations. Care is taken to analyze the origin of the self sustained oscillations. In fact, the present research needs to be seen as the continuation of a former one recently published , where the effect of several Reynolds numbers as well as some different internal dimensions was considered.

Numerical simulations of the sub-critical flow past a circular cylinder at the Reynolds number, Re=130,000 are performed using two numerical platforms, commercial, Ansys Fluent and open source, OpenFoam (finite volume method and large eddy simulation based on a differential equation for the sub-grid kinetic energy). An overview of the available experimental data and similar large eddy simulation studies is presented. A detailed analysis of all accumulated data demonstrates satisfactory agreement between them with a dispersion of the order of 10% (for the main integral flow parameters). A detailed comparison of the results obtained by single- and double-precision numerical methods using Ansys Fluent did not reveal any noticeable discrepancies for the integral and local flow parameters, as well as spectral characteristics. It is shown that the behavior of the dynamic system of the fluid dynamic equations computed with single precision is stable by Lyapunov and does not lead to any loss of accuracy. The reconstructed attractors of the dynamic systems in the phase space are limited by an ellipsoid.

This study evaluates the impact of a Project-Based Learning (PBL) intervention on post-pandemic educational recovery in rural Colombia, focusing on student competencies in mathematics, language, science, and 21st-century skills. Conducted in rural schools, the intervention aimed to address significant learning gaps exacerbated by the COVID-19 pandemic by providing teacher training and direct student support. A pretest-posttest single-group design was used to assess the effectiveness of the intervention, with standardized tests measuring academic competencies and an analytical rubric evaluating 21st-century skills. The results indicate significant improvements in math, language, and science test scores, with notable gains in problem-solving, collaborative work, communication, and critical thinking. However, a decline in creativity scores highlights the need for a stronger emphasis on fostering creativity within the PBL framework. Gender differences were observed, with female students generally outperforming males, suggesting the need for tailored instructional approaches. The study's limitations, including the absence of a control group, non-random sampling, and the use of subjective assessment methods, are acknowledged, with recommendations for future research to address these issues. Despite these limitations, the findings underscore the potential of PBL to enhance student learning outcomes in rural settings, offering valuable insights for educators and policymakers aiming to support educational recovery and development in similar contexts. Further research is recommended to explore the long-term effects of PBL and to refine the intervention for broader implementation.

Introduction: Clostridium difficile is a gram-positive, spore-forming anaerobe and a leading cause of healthcare-associated diarrhea globally, primarily linked to antibiotic use. Its enzyme, Glutamate Dehydrogenase (GDH), plays a critical role in the metabolism of glutamate, and its detection has become a key diagnostic marker for C. difficile infection (CDI). This meta-analysis evaluates various diagnostic modalities used for GDH detection, focusing on their sensitivity, specificity, and clinical utility. Methods: A systematic search of PubMed, Google Scholar, and Cochrane Library was conducted. Studies comparing GDH testing with gold-standard methods (PCR, toxigenic culture) for CDI diagnosis were included. Data were extracted from eligible studies, and statistical analyses were performed to calculate pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Results: The pooled sensitivity and specificity of GDH testing were 93% and 92%, respectively. The DOR was calculated to be 30.98, and the AUC was 0.9794, indicating high diagnostic accuracy. GDH testing proved effective in identifying both infected and non-infected individuals, reducing false positives and negatives. Conclusion: GDH testing is a highly sensitive and specific diagnostic tool for CDI, supporting its role in clinical settings for early detection and intervention. Despite its strengths, variability in test performance suggests the need for further research to refine GDH-based protocols and ensure consistent outcomes across diverse healthcare environments.

Drug-resistant tuberculosis (DR-TB) and HIV-coinfection present a conundrum to public health globally, and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients diagnosed with DR-TB who received treatment was conducted. Student’s t-test was performed to assess differences between two means and ANOVA between groups. Either the Chi-square test with and without trend or Fischer’s exact test was used to test the degree of association of categorical variables. Logistic regression was used to determine predictors of DR-TB treatment outcomes. Also, a decision tree classifier, which is a supervised machine learning algorithm was used. Python version 3.8. and R version 4.1.1 software were used for data analysis. A p-value of 0.05 with a 95% confidence interval (CI) was used to determine statistical significance. A total of 456 DR-TB patients were included in the study with more male patients (n = 256, 56.1%) than female patients (n = 200, 43.9%). The overall treatment success rate was 61.4%. There was a significant decrease in the % of patients cured during the Covid-19 pandemic compared to the pre-pandemic period. Our findings showed that machine learning can be used to predict TB patients' treatment outcomes.

Pomegranate juice possesses therapeutic properties against metabolic syndrome. Microencapsulation of pomegranate juice (MPJ) may extend its shelf life; however, further research is necessary to ascertain its comparative efficacy with fresh juice. This work evaluated the effect of (MPJ) in an animal model with induced metabolic syndrome (MetS). Male Wistar rats were induced-MetS by administering 30% sucrose orally for 22 weeks. Once MetS was verified in the animals, 7 groups were formed as follows: control (Ctrl); MetS, MetS+Placebo; Ctrl+MPJ; MetS+MPJ1 600, MetS+MPJ2 1800, and MetS+MPJ3 2900 mg/kg. Systolic and diastolic blood pressure, glucose, triglycerides, and total HDL-c levels in the MetS+MPJ groups improved significantly (p < 0.05) vs. the MetS group at the end of the study. Microencapsulation maintained the anti-metabolic syndrome properties of pomegranate juice and could be used to control metabolic syndrome.

The current practices of the technology transfer profession seem to reflect the belief that the chances for success are greatly improved when faculty inventors employ their social capital to facilitate the process. However, there is a gap in the university technology transfer literature regarding our understanding of faculty inventor social capital in the technology transfer process. The aim of this study was to understand whether the use of faculty inventor social capital is a causal condition for successful university technology transfer. This question was examined using a multiple case study approach and the qualitative comparative analysis (QCA) method. The results of the study suggest that the use of faculty inventor social capital is not a necessary, sufficient, or INUS condition for the occurrence of university technology transfer.

Variable retention harvesting (VRH) was designed for timber purposes and biodiversity conservation in natural forests. This system was globally tested, but few studies are related to soil microbial components. The objective was to evaluate different retention types (aggregated and dispersed retention) considering different years-after-harvesting (6, 9, 16 YAH) on soil microbial community attributes compared with unmanaged primary forests (PF) in Nothofagus pumilio forests of Tierra del Fuego (Argentina). We also evaluated the influence of climate, soil and understory vegetation. Results showed that aggregated retention increased microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and soil basal respiration (SBR) compared to dispersed retention, but with similar values than PF. However, harvested areas decreased MBC:MBN values compared with PF. We found an overall decreasing of microbial biomass and activity in 9 YAH stands, with a positive recovery at 16 YAH. Soil pH, mean annual temperature and understory vegetation cover showed a positive relationship with MBC, MBN, and SBR. The recovery after 16 YAH reaching to a different microbial community. Therefore, maintenance of retention components in managed stands for longer periods is needed. Our results highlight some advantages of VRH as a tool for conservation of forest-dwelling soil microorganisms (microbial biomass and activity).

Estimating Forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly, however integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and Airborne Laser Scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia's geographical regions, RF consistently delivered the best performance. In the northwest (NW), RF achieved an R² of 0.63 and an RMSE of 125.39 m³/plot. In the southwest (SW), it achieved an R² of 0.73 and an RMSE of 128.86 m³/plot. In the northeast (NE), RF achieved an R² of 0.64 and an RMSE of 133.77 m³/plot, and an R² of 0.70 and an RMSE of 120.72 m³/plot was achieved in the southeast (SE). These results underscore RF's precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy.

In the evolving digital landscape, the pervasive influence of Artificial Intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human imperfections. Such biases prompt rigorous contemplation on matters of fairness, equity, and societal ramifications, and penetrate the foundational essence of AI. Within this intricate context, the research ventures into novel domains by examining the potential of quantum computing as a viable remedy for bias in artificial intelligence. The conceptual framework of the Quantum Sentinel is presented - an innovative approach that employs quantum principles for the detection and scrutiny of biases in AI algorithms. Furthermore, the study poses and investigates the question of whether the integration of advanced quantum computing to address AI bias is seen as an excessive measure or a requisite advancement commensurate with the intricacy of the issue. By intertwining quantum mechanics, AI bias, and the philosophical considerations they induce, this research fosters a discourse on the journey toward ethical AI, thus establishing a foundation for an ethically conscious and balanced digital environment.

This paper investigates the flexural behavior of unfired and fired one-way slabs constructed from reinforced ferronickel slag-based alkali-activated concrete. The study aims to expand the limited knowledge of large-scale testing of alkali-activated concrete structural elements, with a particular emphasis on their post-fire performance. Four slabs, each measuring 2.1 × 0.9 × 0.18 m³, were produced—two made of alkali-activated concrete and two of conventional concrete incorporating ordinary Portland cement. Both types of concrete exhibited similar compressive strengths. In each group, one slab served as a reference (control specimen—tested under monotonic four-point bending in its original state), while the other was subjected to a standard fire curve on the tensioned side before mechanical testing. The fired slabs were then tested in a cooled condition under ambient conditions. The effect of fire on the alkali-activated concrete was further examined using mercury intrusion porosity and scanning electron microscopy. The results reveal that the load capacity of structural members cast with alkali-activated concrete is only slightly lower than that of their conventional concrete counterparts (6% lower). However, the alkaliactivated slabs retain more of their stiffness and ductility after fire exposure.

Background: To prevent excessive compression of the cortical layer, which can lead to marginal bone loss, various companies have introduced specialized drills. However, these drills often lack the necessary precision, as the operator’s hand may not be stable enough to prevent ovalization and over-widening, nor precise enough to maintain coaxial alignment. Therefore, the aim of this study was to develop a device capable of achieving calibrated cortical preparation in terms of both dimension and coaxiality. Methods: A machining technology based on drilling principles was employed to create the device. Results: Nine blades were incorporated between the transmucosal neck and the implant threads, enabling the blades to cut the cortical bone coaxially during the implant insertion process. Conclusions: The implant with integrated blades was engineered to facilitate precise cortical bone preparation, ensuring both accurate dimensions and coaxial alignment. This ensures that the radial compressive stresses transmitted from the implant to the peri-implant bone during surgery can be precisely controlled. The experimental study outcomes discussed in this article validate the effectiveness of the implant design.

The simultaneous progress of integrated op- tical frequency comb (OFC) and radio frequency (RF) photonic signal processing technique have promoted the rapid development of real-time signal processing. Integrated optical frequency comb offer multiple wave- lengths as a powerful source for RF photonic sig- nal transversal filter. Here, we review development of real-time signal processing system consisting of inte- grated OFC and RF photonic signal transversal filter in chronological order, and focus on the applications of this system such as differentiator, integrator, Hilbert transformer, and image processor. We also discuss and present our outlook on more parallel functions and fur- ther integration of real-time signal processing system.

Background: Oral hypoglycemic agents are typically the first-line treatment for type 2 diabetes mellitus (T2DM). This study examines the benefits of initiating insulin as the first-line therapy for newly diagnosed T2DM patients. Methods: A PubMed search using keywords ‘Type 2 Diabetes Mellitus’ and ‘Early STIIT’ included RCTs assessing beta cell function, insulin resistance, glycemic control, and lipid profiles. Revman 5.4.1 software was used for meta-analysis, with outcomes represented as mean difference (MD) and 95% confidence intervals (CI). Results: Significant reductions in fasting blood glucose (FBG: MD -4.75, 95% CI -7.07, -2.43), HbA1c (MD -1.84, 95% CI -3.01, -0.68), and postprandial glucose (PPG: MD -9.02, 95% CI -10.48, -7.56) were observed with early STIIT therapy compared to baseline. Beta cell function improved (HOMA-B: MD 53.70, 95% CI 27.78, 79.63) and insulin resistance decreased (HOMA-IR: MD -1.84, 95% CI -3.65, -0.04). Lipid profile improvements included reductions in total cholesterol (TC), triglycerides (TG), and LDL, with no change in HDL. BMI remained stable (MD -0.15, 95% CI -0.82, 0.51). Most patients achieved long-term remission and glycemic control without oral hypoglycemic agents. Conclusion: Early STIIT in newly diagnosed T2DM patients significantly reduces glycemic markers and maintains long-term glycemic control, preserving beta cell function. The therapy also improves lipid profiles, reducing TC, TG, and LDL. Initiating insulin as a first-line treatment shows promising results for glycemic control and overall metabolic health.

Background and Objectives: Fifty percent of individuals with borderline personality disorder (BPD) experience Non-Suicidal Self-Injury (NSSI). One of the crucial factors related to NSSI is the feeling of emptiness. Although some inner strengths, such as five precepts, meditation, and equanimity, have been identified as potential buffers against negative mental health outcomes in BPD, their role in mediating the relationship between emptiness and NSSI is not well-documented. This study aimed to explore how these inner strengths mediate the relationship between feelings of emptiness and NSSI in individuals with BPD symptoms. Materials and Methods: A total of 302 participants with BPD symptoms completed several assessments: the SCID-II Personality Disorder (Borderline PD) to assess feelings of emptiness and NSSI, the Inner-Strength-Based Inventory (i-SBI) to evaluate five precepts, meditation, and equanimity, and the Outcome Inventory Depression (OI-Dep) to assess depression. Mediation analysis was performed using IBM SPSS and AMOS, with NSSI as the outcome variable, feelings of emptiness as the predictor, and the inner strengths as mediators. Results: The participants had a mean age of 36.56, with 65.4% being female. The analysis showed that five precepts, meditation, and equanimity significantly mediated the relationship between feelings of emptiness and NSSI, with a standardized coefficient of β = 0.534 (95% CI = 0.417 to 0.647, p < 0.001). The indirect effect of emptiness through these inner strengths was significant (β = 0.034, 95% CI = 0.009 to 0.075, p = 0.005). The mediation model explained 38% of the variance in NSSI with a 3% increase, albeit small but significant. Conclusions: The study highlights a negative mediating role of inner strengths in the relationship between feelings of emptiness and NSSI meaning that as these inner strengths increase, the direct impact of feelings of emptiness on NSSI decreases. This suggests that these inner strengths act as protective factors, reducing the likelihood that someone with feelings of emptiness will resort to NSSI as a coping mechanism. These findings suggest that targeting these inner strengths could be a valuable strategy in developing interventions aimed at reducing NSSI by addressing the underlying emotional challenges associated with BPD.

The management of user data in the cloud is easily poised to become a giant issue for any business, with so much digital information floating around these days that open it up as an easy target when companies aren’t vigilant. In this paper, a novel cloud-based service is proposed that employs various advanced NLP and encryption methods with the use of blockchain. These techniques are combined to provide a solid solution to secure fundamental data such as credit card numbers, passports or any government identity cards etc. Using a hybrid NLP model, integrating Transformer Models and Named Entity Recognition (NER), to automatically catagorzing data as critical vs non-critical. Only the most important data is encrypted by a user’s cryptographic wallet before being divided into multiple chunks and stored on an exclusive cloud cluster; metadata then takes turns managing securely through blockchain to provide traceable means of retaining integrity. Smart contracts provide strict access control measures and change the cryptographic nonce if need be to prevent illegal entrance into a specific zone, thus create security. This all-inclusive strategy maintains well-known high security standards for protecting the confidentiality, availability and integrity of your sensitive data on a global scale—delivering you simple yet scalable secure world-class cloud-based data management. A proposed framework developed to fulfil the security requirements for current cloud services, which is a beneficial contribution in context of data protection and cloud Security.

: 'Xinomavro' (V. vinifera L.) is an important native red wine grape variety in Northern Greece, particularly in PDO regions. Despite its significance, there is limited research on the effects of pruning type and severity on 'Xinomavro' vine physiology, yield, and berry quality across diverse environmental conditions. This study aims to address this knowledge gap and provide growers with crucial information for optimizing vineyard management practices. The study was conducted over two consecutive years (2016 and 2017) in a vineyard in Thessaloniki, Northern Greece. Four treatments ( B12: 12 buds on 6 spurs; B24: 24 buds on 12 spurs; M12: 12 buds on 2 canes; M24: 24 buds on 4 canes) combining two bud load levels (12 or 24 count nodes) and two pruning types (short spurs or long canes) were applied to 'Xinomavro' vines in complete block randomized design. Measurements included vine water status, gas exchange, canopy characteristics, yield components, and berry composition. Bud load and pruning type significantly influenced vine canopy development, microclimate, and yield components. Short pruning with high bud load (B24) resulted in denser canopies and higher yields, while cane pruning (M12 and M24) led to more open canopies and improved berry quality indicators. Treatment effects on berry composition were inconsistent across years but showed a tendency for higher anthocyanin and total phenol content in cane-pruned vines. The study demonstrates that pruning type (short or long fruiting units) may have a greater impact on vine growth, yield, and berry composition than bud load alone in 'Xinomavro' vines. Cane pruning appears to be a more effective strategy for achieving vine balance and potentially improving grape quality under the given experimental conditions.

Purpose: Trauma informed care as an organisational intervention has gained increasing attention in recent years. Substance use settings may be overrepresented with service users who have experienced trauma and adversity. This systematic review will examine the effectiveness of trauma informed care as an organisational intervention from an implementation perspective. Methods: The preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines were followed. Three databases were searched; Academic Search Complete, Embase, and Scopus, supplemented with a Google Scholar search. Articles were included if they were peer reviewed in the English language from inception to February 2024 and reported on trauma informed care in substance use settings with an implementation context. Quality appraisal was conducted with the Mixed Method Appraisal Tool. Results: This systematic review of trauma informed care with implementation domains in substance use settings included (N=15) studies reporting on 12 datasets. Studies were classified as low quality in (n=5), moderate in (n=1) and high in (n=6). Studies reported positive findings on reductions in substance use, and reductions on mental health and trauma symptoms, and treatment retention across community and residential settings. While satisfaction with services provided was also highlighted for service users and employees. The results further highlight the importance of the role of leadership across implementation domains, and the possibility of using the 10 trauma informed implementation domains in different treatment contexts as a framework to support implementation. Findings in this review are mapped onto these 10-trauma informed care implementation domains and reported as a narrative synthesis. Conclusion: Trauma informed care is a promising organisational wide intervention with the potential to improve outcomes for service users and employees. Implementation can be supported by using the 10 trauma informed care implementation domains described in this review. However, most of the studies were qualitative and quantitative descriptive meaning drawing casual inferences is difficult. As such, further research should be undertaken.

The objective of this study was to evaluate the impact of incorporating fermented feed into the daily diet on the slaughter performance, meat quality, and flavor compounds of cage-raised chicken. The control group (CK) of the Guangxi Partridge chicken was provided a standard base diet (CK), which was supplemented with 10% fermented banana peel (Pe-10), 20% fermented banana peel (Pe-20), 10% fermented banana pulp residue (Pu-10) and 20% fermented banana pulp residue (Pu-20). The slaughter performance, meat quality, and flavor compounds in the meat samples of the chicken in these groups were evaluated. The results demonstrated that the chicken in Pe-10, Pe-20, Pu-10, and Pu-20 had pectoral muscle percentages, thigh muscle percentages, and total fatty acid content of the chest meat in the Pe-10, Pe-20, Pu-10, and Pu-20 groups were higher than that observed in the CK group. The moisture content, meat color, carcass weight, total net weight, and abdominal fat percentage of the meat samples in these experimental groups exhibited no notable differences. The flavor compounds in the meat samples of the chicken fed with the two concentrations of fermented banana peel and banana pulp residue exhibited notable differences from those in the control group. An increase in the quantity of fermented banana peel incorporated into the daily diet from 10% to 20% resulted in a notable alteration in the flavor profile of the chest meat samples. In conclusion, the inclusion of varying proportions of fermented banana peels and dregs in the diet has been demonstrated to enhance both the slaughter performance and the quality of the chicken, while also influencing the flavor profile.

This report describes the pathological findings of acute gastric dilatation syndrome in New World nonhuman primates that died suddenly. The animals were kept in a Zoo and were received proper nutritional and sanitary management. The animals were submitted for necropsy, and macroscopic findings included gastric and duodenal dilation with marked mesenteric vascular congestion. Gastric and intestinal contents were sent for microbiological examination, and bacterial colonies were isolated and identified as Escherichia coli, Streptococcus sp., Salmonella sp., Staphylococcus sp., and Cronobacter sakazakii. The antibiotic sensitivity test revealed that these bacteria had different re-sistance patterns to several antibiotics. Cronobacter sakazakii was susceptible to Ampicillin and Norfloxacin and resistant to Penicillin and Clindamycin. Cronobacter sakazakii is an opportunistic pathogen in animals, frequently reported in cases of human’s enterocolitis. Acute gastric dilata-tion, previously described in Old World nonhuman primates, is a cause of sudden death in Neo-tropical primates and should be considered as a differential diagnosis in cases of sudden mortality in these species. Although its pathogenicity in primates has not been elucidated, the isolation of Cronobacter sakazakii from samples suggests its presence in the environment, emphasizing the need for reinforced hygiene measures, especially of food supplied for these animals.

Adverse medical events not only harm patients and families, but also cause significant negative impact on healthcare providers, with potential to compromise future professional functioning. These “second victims” may need organizational support and rehabilitation to return to functionality. We analyzed the validity of an adapted tool, the Second Victim Experience and Support Tool (SVEST), to a population in Israel, H-SVEST. The H-SVEST was completed by 172 nurse participants working in a variety of patient care settings. The H-SVEST was assessed for content validity, internal consistency, and construct validity with confirmatory factor analysis (CFA). The CFA run on the initial model with 10 factors and 36 items did not meet criteria for goodness of fit. After removing five items based on their low factor loadings and the correlation, The model fit significantly improved with acceptable CFI, TLI, RMSEA, and SRMR; χ2(389) =626.939, p<.001; χ2/df=1.61; TLI=0.901; CFI=0.917; RMSEA [90% CI]=0.060 [0.051, 0.068]; SRMR=0.064. The final version included 31 items and 10 factors with Cronbach α values ranging from 0.66 to 0.94. The H-SVEST demonstrates robust psychometric properties and valuable insights into the second victim experience in the Israeli context. Comparative analysis with other versions highlights potential cultural influences and areas for further investigation. Implementing this tool and developing evidence-based interventions based on its results can significantly improve well-being and resilience of healthcare providers in Israel and other countries with diverse cultural populations. This study was not registered.

A theory for the micelle formation of nonionic head-tail amphiphiles (detergents) in aqueous solutions is derived based on the traditional molecular thermodynamic modeling approach and a variant of Flory-Huggins theory that goes beyond lattice models. The theory is used to analyze experimental values for the critical micelle concentration of n-alkyl-ß-D-maltosides within a mass action model. To correlate those parts of the micellization free energy, that depend on the transfer of hydrophobic molecule parts into the aqueous phase, with molecular surfaces, known data for the solubility of alkanes in water are reanalyzed. The correct surface tension to be used in connection with the solvent excluded surface of the alky tail is 30 mN/m. This value is smaller than the measured surface tension of a macroscopic alkane-water interface, because the transfer free energy contains a contribution from the incorporation of the alkane or alkyl chain into water representing the change in free volume of the aqueous phase. Flory-Huggins theory works well, if one takes into account the difference in liberation free energy between micelles and monomers, which can be described in terms of the aggregation number as well as the thermal deBroglie wavelength and the free volume of the detergent monomer.

This paper introduces the novel concept of Time-Traveling Turing Machines (TTTMs) — Turing Machines with the ability to read symbols that have been explicitly sent from their future computational state. We delve into their properties and behavior, particularly emphasizing the importance of maintaining a self-consistent computational timeline. We present theorems demonstrating the universality of these machines and the challenges in achieving faster computations than traditional Turing Machines.

Viral hepatitis caused by hepatitis B virus (HBV) and hepatitis C virus (HCV) pose a significant global health challenge, with an increasing number of deaths and a substantial burden on healthcare systems, among prisoners in particular. Nigeria ranks fifth in Africa for prison population, with 98.2% being males and are more likely than females to have higher rates of viral hepatitis and other risky behaviours. This literature review explores the impact of social 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 overcrowding, inadequate healthcare access, and food insecurity. This review reports the updated data on HBV and HCV infection rates among inmates in different regions in Nigeria, with a special focus on related risk factors, highlighting how these determinants contribute to the high rates of hepatitis infections in this category. 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 among this population group.

With the escalating proliferation of wind power plants, the imperative focus on system robustness and stability intensifies. Doubly fed induction machines (DFIMs) are extensively employed as generating units in land-based wind power plants due to their performance advantages. While the stator windings of DFIMs are directly connected to the power system, the three-phase rotor windings are connected via power converters, rendering these units highly vulnerable to voltage disturbances. During faults, voltage drops at the stator terminals lead to elevated voltages and currents on the rotor side due to electromagnetic coupling between stator and rotor, potentially damaging rotor insulation and costly power electronics. Historically, to safeguard equipment, wind power plants employing distributed energy resources such as DFIMs were disconnected from the grid during faults—an unsatisfactory solution given the burgeoning number of these installations. Consequently, grid operators and IEEE standard 2800 mandate fault ride-through or low voltage ride-through capabilities to maintain supply adequacy during recovery from disturbances and voltage fluctuations. This paper provides an overview of diverse techniques devised to enable fault ride-through for DFIMs. Additionally, it scrutinizes existing approaches in modeling DFIM behavior during faults and introduces an approach tailored specifically for DFIMs, detailing its comparative advantages and limitations vis-à-vis established models. A conclusion about the necessity for more precise models in comparison to the existing ones is provided.

While the financial technology (FinTech) sector has revolutionized conventional banking services with technology-driven solutions, it also faces considerable cybersecurity hurdles to guard against data protection and sustain the overall financial performance. The main purpose of this paper is to identify the key cybersecurity challenges encountered by FinTech companies, such as data confidentiality concerns, complex cyber-attacks, difficulties with regulatory compliance and third-party risks or technological innovations. This also assesses the regulatory responses to those challenges, such as data privacy regulations, macroprudential regimes, industry-specific guidance and international regulatory cooperation. Through this analysis, the paper attempts to synthesize current state of cybersecurity domain within FinTech and provides suggestions for strengthening data shelter and financial stability by implementing best practices with regulatory strategies.

Patients with Acute lymphoblastic leukemia (ALL) commonly present with tachycardia and or hypertension. There is little focus in the literature on the pathogenesis of this duet. It is currently established that ALL are catecholamine-secreting cells, and this means that ALL might induce hypertension and tachycardia via catecholamine hypersecretion. Proving the latter hypothesis can help in better treatment of ALL associated inappropriate tachycardia and systemic hypertension using combined alpha and beta blockers.

Haug and Tatum have recently solved the Hubble tension within a type of R_h = ct cosmology using an intuitive, smart trial-and-error search algorithm. The trial-and-error algorithm demonstrates that one can start with the measured CMB temperature and a rough estimate of H0. Based on the algorithm, one ends up matching the entire distance ladder of the observed supernovas by finding a value for H_0. However, this is a numerical search procedure, even though it can be completed in a fraction of a second on a standard computer. Here, we will demonstrate that the trial-and-error numerical method is not needed and that the Hubble tension can be resolved using the same Haug and Tatum model through a closed- form solution. This means one simply solves an equation to find the correct H0 value. This is possible because an exact mathematical relation between H_0 and the CMB temperature has recently been established, in combination with the linearity in an R_h = ct model.

Background: Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases worldwide, and its incidence has been rising in parallel with the prevalence of obesity and diabetes. A critical marker for the progression of NAFLD is advanced hepatic fibrosis (AHF), which can result in cirrhosis and hepatocellular carcinoma. This study sought to investigate the potential associations between five nutrition-related indices—the geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), controlled nutritional score (CONUT), triglyceride-total cholesterol-body mass index (TCBI), and albumin/globulin ratio (AGR)—and the progression of NAFLD and AHF. Methods: This study analyzed the association between five nutrition-related indicators and the occurrence of NAFLD and AHF by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020.03. After adjusting for demographic, lifestyle, clinical, and laboratory-related factors, the associations were examined employing various statistical techniques, including multivariate logistic regression, subgroup analysis, and smooth curve fitting analysis. Results: This study included 5,514 subjects, of whom 2,088 were diagnosed with NAFLD and 359 with AHF. After adjusting for potential confounding factors using multivariate logistic regression analysis, the results revealed that GNRI, PNI, and TCBI were positively associated with the incidence of NAFLD, while CONUT and AGR demonstrated negative associations with NAFLD. Additionally, GNRI, CONUT, and TCBI were positively associated with the incidence of AHF, while AGR demonstrated a negative association with AHF.Conclusions: The study demonstrates that nutritional status plays a dual role in the progression of NAFLD and AHF, serving as both a risk and protective factor. These findings offer a scientific foundation for the early identification of high-risk populations and the development of individualized nutritional intervention strategies.

A pink-pigmented, ovoid-rod-shaped, gram-negative bacterial strain ML10T was isolated from a meromictic lake in British Columbia, Canada. It produces bacteriochlorophyll a, which is incorporated into the reaction center and light harvesting I complexes. This alongside no anaerobic or photoautotrophic growth supports the designation of the strain as an aerobic anoxygenic phototroph. The cells produce wavy polar flagellum and accumulate clear, refractive granules, presumed to be polyhydroxyalkanoate. Sequence of the 16S rRNA gene identified close relatedness to Salinarimonas rosea (97.85 %), Salinarimonas ramus (97.92 %) and Saliniramus fredricksonii (94.61 %). The DNA G + C content was 72.06 mol %. Differences in cellular fatty acids and some physiological tests compared to Salinarimonadaceae members, as well as average nucleotide identity and digital DNA-DNA hybridization define the strain as a new species in Salinarimonas. Therefore, we propose that ML10T (= NCIMB 15586T = DSM 118510T) be classified as the type strain of a new species in the genus with the name Salinarimonas chemoclinalis sp. nov.

Fine-needle aspiration cytology (FNAC) is a pivotal diagnostic tool in oncology, utilized for evaluating suspicious lesions and stratifying tumors. The incorporation of machine learning (ML) into FNAC has revolutionized accuracy, efficiency, and diagnostic precision. This comprehensive review explores recent advances in FNAC, emphasizing the transformative role of ML in image-guided sample collection and tumor stratification. Leveraging deep learning and other ML algorithms, researchers have improved diagnostic accuracy, minimized unnecessary biopsies, and optimized treatment selection. This article highlights the transformative applications of machine learning in FNAC while addressing its current limitations and future potential.

Information security policy compliance plays an important role in reducing the incidence of security breaches in organizations. Recently, information security practices in firms have been widely examined. Therefore, inadequate efforts have been made to assess and systematically review the present study status to offer insights into prior study findings. This systematic literature review investigated the impact of ISPC on reducing the incidence of security breaches in organizations. After inclusion and exclusion criteria process conducted, 42 research publications from Google scholar, Sage, IEEE, ACM, ScienceDirect, Springer, Jstor, Wiley, Taylor & Francis databases (2009 – 2024) included, most of the studies utilized quantitative research methods to explore factors influencing compliance with information security policies, such as employee demographics, organizational strategies, and deterrence mechanisms, A few studies adopted qualitative methods to assess the effectiveness of security programs or to propose measures to enhance compliance. The findings suggested that not only information security policy compliance reduce incidences of security breaches in organizations but also other factors such as employee training, awareness, management support, regular audits, and the integration of advanced security technologies are identified as crucial for reducing security breaches. The implications of these findings for organizational practices and future research directions are discussed, providing valuable insights for policymakers, IT professionals, and researchers aiming to strengthen information security frameworks and reduce the incidence of security breaches.

: In this paper, we present an improved 100CMM regenerative thermal oxidizer (RTO) designed for low-emission combustion, capable of processing over 95% of volatile organic compound (VOC) gas emissions at generating sites. The existing RTO system features a cylindrical drum structure that cyclically introduces and discharges VOC gas into a rotating disk, achieving excellent energy efficiency with a heat recovery rate exceeding 95%. However, the driving force exerted on the rotating disk in the RTO combustion chamber causes increased wear around the rotating shaft, leading to untreated gas being released through the outlet due to the channeling phenomenon. Additionally, prolonged use can result in operational issues such as rotation stoppages or explosions caused by pollutant discharge, dust accumulation, and thermal expansion at high temperatures of up to 800°C. To address these challenges, we designed an enhanced rotating plate drive unit while ensuring the stable operation of the RTO combustion chamber. Considering that the combustion chamber can reach temperatures of up to 920°C when processing high-concentration VOC gas, the gas burner has been designed with a diameter of 125 mm, and the outlet dimensions are set to 650 mm x 650 mm to effectively discharge high-temperature waste heat. The heat storage material in the combustion chamber is configured with a ceramic block thickness, and the outer diameter and height of the combustion chamber are set at 250 mm, 2,530 mm, and 1,875 mm, respectively, to optimize the gas residence time and the insulation material thickness. Empirical experiments were conducted to analyze changes in VOC removal efficiency, nitrogen oxide emission concentration, and total hydrocarbon (THC) concentration, ultimately evaluating the economic performance of the 100CMM RTO.

Two extruded diets isoenergetic, isonitrogenous, and isolipidic were formulated with poultry meal (CTR) as the source of animal-origin proteins (160 g/kg of feed) or with 90 g/kg of poultry meal and 70 g/kg of HFM (TRT) and were fed to eight dogs (four adult female and four adult male English Setters). Over 45 days, body weight, body condition, muscle condition, and fecal consistency scores were monitored at the beginning of the trial and after 3, 7, 15, and 45 days. No significant differences (P > 0.05) were observed for these parameters between diets and between sex. Fecal samples, collected at the same time points, were analyzed for microbiota composition. There was no significant difference in Shannon alpha diversity between the control (CTR) and experimental (TRT) diets or for diet × sampling time interaction and sex. However, beta diversity differed between diets (PERMANOVA p=0.001) and for diet × sampling interaction (p=0.009), with specific significant time points, and also differed by sex (p=0.047). LEfSe analysis showed Clostridiales, Coprococcus, Bacteroides plebeius, and others were more abundant in CTR, while Fusobacterium, Enterobacteriaceae, and others were more abundant in TRT. Temporal shifts in taxa like Prevotella copri and Fusobacterium were observed, with 25 taxa differing by sex. The study highlights diet and sex effects on gut microbiota in dogs.

This article examines the complex interactions of the multiplier effect within the realm of tourism, with a specific emphasis on national parks. The main objective is to evaluate the multiplier effect within national parks, specifically focusing on Buiratau State National Nature Park in Kazakh-stan as a case study. The multiplier effect is a fundamental principle in the field of economic analysis. Economic multiplier effect is the term used to describe the occurrence when an initial infusion of spending in a certain industry leads to subsequent rounds of extra and sustainable economic activity. In the context of national parks, this phenomenon arises when visitor expendi-tures result in several favorable consequences, including increased employment, infrastructure advancement, and general economic expansion. The article seeks to measure the multiplier effect of tourism activities in Buiratau State National Nature Park through a thorough assessment. This entails examining several variables and datasets pertaining to visitor expenditure and its influ-ence. The study contributes to the existing literature by examining a recently constructed national park in a developing country. It compares the findings of this park with well-established national parks in other areas, emphasizing the distinct problems and opportunities it presents. Further-more, the research presents a methodological framework for evaluating the multiplier effect of national parks, which may be utilized in comparable contexts. This research ultimately enhances our comprehension of the economic implications of national parks and emphasizes their function as drivers for local and regional growth.

Dark energy and dark matter are described as different manifestations of vacuum energy in the cyclic universe framework of Extra Dimensional Symmetry (EDS). Motivated by the cosmological constant problem, EDS doubles large spatial dimensions with microscopic partners of opposite chirality and dimension number (gravitational charge). In this framework, the bulk of vacuum energy, constrained by a Planck density constraint, exists in a gravitationally inert state where actual gravitational constant is zero G. This is in contrast with the gravitationally active state where actual gravitational constant is two Gs with each of these two states corresponding to opposite particle chirality. Dark energy is described as a small repulsive component of vacuum energy constrained to the active state by an asymptotically evolving asymmetry. Dark matter is described as the attractive component of vacuum energy, enabled by neutrino induced gravitational constant which also serves as neutrino mass generation mechanism. While ordinary neutrino interaction with dark energy yields light neutrino mass, chiraly inverted neutrino interaction with dense vacuum energy yields heavy neutrino mass. Transition to sterile neutrino phase can be activated by the gravitational effect of shear stress on the microscopic partners of the visible spatial dimensions. Observational and experimental tests are briefly discussed.

Historical maps possess significant theoretical and practical value in fields such as historical geography and urban planning, making them essential tools for studying urban spatial structure. This study explores the evolution of Xi'an's urban spatial structure from the Five Dynasties (909 CE) to the late PRC period (1949 CE), using historical maps and Historical GIS (HGIS) methodologies. By integrating Space Syntax and Kernel Density Estimation (KDE) with data from The Historical Atlas of Xi'an, this research identifies key trends in the city's spatial transformation. Two distinct phases of development were observed: the simpler, centralized structure during the Five Dynasties, Northern Song, and Yuan periods, and a more complex, decentralized structure during the Ming, Qing, and PRC periods. These changes were influenced by a dynamic interplay of political, economic, cultural, and environmental factors, as well as the city's need to adapt to external military threats and internal socio-economic shifts. The study emphasizes the value of HGIS in analyzing urban historical geography and the potential it offers for both understanding past urban dynamics and informing contemporary urban planning and cultural heritage preservation efforts. The findings demonstrate how Xi'an's spatial evolution reflects broader trends in Chinese history and provides insights for future sustainable development.

Background/Objectives: Lung carcinoma is the leading cause of cancer-related deaths globally, with lung adenocarcinoma being the most prevalent subtype. This study aims to review the clinical data and survival outcomes of patients diagnosed with lung adenocarcinoma who underwent surgical treatment. Methods: We conducted a retrospective analysis of 471 patients who had surgery for lung adenocarcinoma at our institution. Their medical histories, including previous neoplasms, comorbidities, tumor characteristics, and symptoms, were thoroughly reviewed. We calculated the overall survival rate and evaluated the impact of tumor grading and spread through air spaces (STAS) on patient outcomes. Results: The survival rate for the entire cohort was 76.23%. There was no significant difference in survival rates between patients classified as G1 and G2, suggesting that a simplified two-tier grading system may be more effective. Patients with STAS had a lower survival rate than those without STAS. Conclusions: Our findings indicate that a simplified grading system may improve prognostic evaluations for lung adenocarcinoma patients. Furthermore, STAS is a crucial factor affecting survival rates and should be considered in future treatment strategies. Expanding research in this area is essential to enhance treatment approaches for lung adenocarcinoma patients.

A novel method has been developed based on the amplitude data of the EM waves measured by Digisondes to calculate and investigate the relative ionospheric absorption changes. The effect of 13 solar flares (> C4.8) that occurred between 06:00 and 16:30 UT from 04 to 10 September 2017 have been studied at three European Digisonde stations (Juliusruh (54.63° N, 13.37° E), Průhonice (49.98° N, 14.55° E) and San Vito (40.6° N, 17.8° E)). Present study compares the results of the amplitude method with the absorption changes measured by the Finnish Riometer Network and determined by the NOAA D-RAP model during the same events. The X-class flares caused 1.5–2.5 dB attenuation at 30–32.5 MHz based on riometer data, while the absorption changes were between 10 and 15 dB in the 2.5–4.5 MHz frequency range according to the amplitude data. The impact caused by the energetic particles after the solar flares are clearly seen in the riometer data, while it can be observed only at Juliusruh (~55°) at some certain cases among the Digisonde stations. Comparing the results of the amplitude method with the D-RAP model it seems evident that the observed values exceed the values given by the model both at 2.5 MHz and at 4 MHz almost always during the investigated period. According to the comparison between the riometer data with the D-RAP, the model underestimates the values obtained from the riometers during the X-class solar flares, while it overestimates the caused impact during the particle events.

Bioactive like resveratrol, epigallocatechin gallate, curcumin, and other polyphenols often target various signalling pathways, including NFκB, TGFβ, and Wnt/β-catenin in osteoarthritis (OA) pathogenesis, by executing epigenetic-modifying activities. Epigenetic modulation by bioactive is gaining traction with the onset of nutrigenomics. Such modulations can target genes by histone modifications, promoter DNA methylation, and non-coding RNA expression, some of which are directly involved in disease pathophysiology but less explored in OA. OA patients often explore options that can improve the quality of their life in addition to existing treatment with nonsteroidal anti-inflammatory drugs (NSAIDs). Although bioactive and natural compounds exhibited therapeutic potential against OA, several disadvantages loom over them, like insolubility and poor bioavailability. Nanoformulated bioactives promise a better way to alleviate OA since they also control systemic events, including metabolic, immunological, and inflammatory responses, by modulating host gut microbiota that can regulate OA pathogenesis. Recent data suggest gut dysbiosis in OA. However, limited evidence is available on the role of bioactive as epigenetic and gut modulators in ameliorating OA. Moreover, the question of whether polyphenolic bioactive modulates gut microbial response that results in epigenetic-controlling activities of OA pathogenesis is not known. This narrative review explored the interplay of nanoformulated bioactive and host gut microbiota on chondrocyte growth, metabolism, and epigenetic alteration linked with OA progression and pathogenesis.

Diatraea saccharalis (Fabricius) is one of the main pests of rice crops and its early detection, that is, before the plants show damage, is essential to avoid yield losses and define effective and rational control. This work aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels. Between 2019 and 2020, two experiments were conducted in a protected environment with the cultivars IR 40 and BR IRGA 409, which presented, in a previous study, different resistance reactions. Rice plants grown in pots were manually infested with first-instar larvae of D. saccharalis, from 0 to 10 caterpillars/plant, with the plants kept in cages covered with voile fabric throughout the test. With the adjustment of regression models, it was noticed that the leaf surface temperature is related to the level of infestation and could be used to detect which IR 40 is susceptible.

This study describes the energy-momentum density tensor of matter. The main focus is on discussing the conservation laws of the general theory of relativity

This study aimed to examine the effects of the input factor allocation on tangerine production in Selayar Islands Regency, South Sulawesi Province. Binary logistic regression was the data analysis method utilized to achieve the research objective. Based on the findings, it was found that the factors of length of education, farmer group membership, farm distance, labor, pesticides, herbicides, urea fertilizer, manure, and NPK fertilizer all had a significant impact on the output of tangerines in farming. Factors such as education duration, participation in farmer groups, labor availability, and the use of herbicides, urea fertilizer, NPK fertilizer, and manure all positively and significantly impact tangerine farm production. However, the distance between farms and insecticide application imposes a substantial negative effect on tangerine yields. These results suggest that the tangerine farmers at the research site should increase their production by learning farming management, joining farmer groups, cultivating additional land, and using more urea, NPK, and manure. Pesticide application optimization can also boost tangerine production. In addition, farmers need a mix of conventional and organic fertilizers and limited pesticide use to stay productive.

The emergence of antimicrobial resistance (AMR) in pathogens transmitted through food poses a significant threat to global public health, complicating infection treatment and increasing mortality rates. This review explores the role of resistome mapping as a crucial tool for understanding the transmission dynamics of antimicrobial resistance genes (ARGs) in foodborne pathogens, such as Salmonella, Escherichia coli, Listeria monocytogenes, and Campylobacter spp., as well as various techniques for resistome mapping, such as metagenomic sequencing, PCR-based methods, and whole-genome sequencing (WGS), highlighting the significance of horizontal gene transfer (HGT) as a key mechanism for ARG dissemination in foodborne pathogens. Additionally, we investigated the influence of agricultural practices and environmental factors on AMR development, highlighting the critical need for improved surveillance, antibiotic stewardship, and global collaboration to mitigate the spread of resistant ARGs through the food chain. The perceptions gained from resistome mapping play an essential role in developing effective approaches to address AMR and to ensure food safety.

In inflationary cosmology the form of the potential is still an open problem. In this work, second-order effects of the inflationary potential are evaluated and related to the known formula for the primordial perturbations in a wide range of scales. We found effects that may help to unravel the unknown inflationary potential form and impose new constraints on the parameters that define this potential. In particular, we demonstrate here that even slight deviations in the inflaton potential can lead to significant differences in the calculated spectra if inflation persists for sufficiently long and the normal modes of perturbations are affected by these variations.

Background: Front-of-pack-labels (FOPLs) on packaged foods provide consumers with essential information about the nutritional content of food products, enabling them to make informed choices and align their dietary habits with their health goals. However, evidence on the comprehensibility, use, and effectiveness of FOPLs remains limited, particularly in low- and middle-income countries like Kenya. Objective: In this study, we assessed the effectiveness of three FOPLs in helping consumers identify excess nutrients of concern in packaged food products and their potential influence on food purchases in Kenya. Methods: A total of 2198 shoppers with diverse backgrounds were recruited from selected supermarkets in three Kenyan main cities (Nairobi, Mombasa, Kisumu) and a small town (Garissa). Participants were randomized to three FOPLs: Red and green Octagon label (RG), Red and Green Octagon with icons (RGI) and a black Octagon Warning label (WL). In the control phase, participants were shown unlabeled images of packaged food products and they responded to a set of questions. In the experimental phase, the same product images were presented with one of the three front-of-pack labels they were randomized to, and participants answered the same questions again. Differences in the proportions of participants correctly identifying the nutrients of concern and the changes in intention to purchase unhealthy foods before and after exposure to FOPLs were analyzed using frequency tables and Chi-Square tests. The effectiveness of the three FOPLs was further assessed using a modified Poisson regression. Results: Results showed that exposure to FOPLs significantly improvement participants' ability to correctly identify nutrients of concern across various food categories. Additionally, FOPLs reduced consumers’ intentions to purchase unhealthy foods, with the Warning Labels (WL) emerging as particularly effective in enhancing consumers' understanding of product healthiness and reducing the intention to purchase unhealthy foods compared to the other two FOPLs. Conclusion: These findings underscore the potential role of FOPLs, particularly the WL as an effective regulatory tool for promoting healthier food choices in Kenya.

Côte d’Ivoire has the main cashew tree culture area worldwide, but the cashew apple produced is still underutilized although its potential for industry. In the present study, the aim was to isolate and identify acetic acid bacterial strains, and to assess their potential use for cashew apple-based vinegar production. Vinegar mother from fermented juice was used to isolate acetic acid bacteria on a standard glucose-based medium. Physiological and biochemical tests followed by 16S rRNA gene analysis and phylogeny were used for isolate characterization. Moreover, their acetic acid production capacity was assessed. As results, five (05) strains of the Acetobacter genus were isolated. Phenotypic and phylogenetic analysis revealed that four of them, namely OYA2, OYA6, OYA9, and OYA10, belong to A. tropicalis / A. senegalensis species complex with 99,7% or 100% similarity. The last strain, OYA7, match to A. syzygii (99,7%). All the isolates were resistant to 15° alcohol (v/v) and grew well between pH 5.0 and 6.5. Their optimal growth temperatures varied between 27°C and 37°C, and only isolate OYA6 grows up to 40°C. They produced vinegar with yield (Yp/s , g/g) varying from 0.82 to 0.92, and acetic acid content (g/L) of 80.67 ± 2.1, 70.26 ± 0.9, 70.11 ± 1.7, 68.70 ± 1.5 and 67.22 ± 0.40 were obtained with OYA6, OYA7, OYA2, OYA10, and OYA9, respectively. Thus, the isolate OYA6 appeared as the best candidate for vinegar production, owing to its superior yield and thermotolerance ability that need to be further explored for industry use purpose.

We assessed machine learning techniques (MLTs) for downscaling global climate model’s precipitation to local level in Bolivia. The skill, assumptions and uncertainty of the process are analyzed. Additionally, future projections are delivered. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machines (SVM), is used on four zones (highlands, Andean slopes, Amazon lowlands and Chaco lowlands). The downscaled series’ skill is evaluated in terms of relative errors. The uncertainty is analyzed through variance decomposition. MLTs’ skill is adequate in most cases, especially for the highlands and the Andean slopes. Moreover, RF tends to outperform SVM. Stationary assumptions are robust in the highlands and Andean slopes. Perfect prognosis assumption is poor in the highlands and Andean slopes; topographical complexity appears to be the reason. Regarding uncertainties, MLTs are the dominant source. In future projections, the highlands show shorter dry spell lengths with more frequent but less intense high rainfall events and higher annual rainfall. The Andean slopes exhibit an increased annual rainfall with a reduction in the high precipitation intensities but an increased frequency. The Amazon lowlands present a decrease in annual rainfall, and the Chaco lowlands an increase.

Surface defect detection based on machine vision and convolutional neural networks (CNNs) is an important and necessary process that enables rubber ring manufacturers to improve production quality and efficiency. However, such automatic detection always consumes substantial computer resources to guarantee detection accuracy. To solve this problem, in this paper, we present a CNNs optimization algorithm based on the Ghost module. First, we replace the convolutional layer with the Ghost module in CNNs so that feature maps can be generated using cheaper linear operations. Second, we use an optimization method to obtain the best replacement of the Ghost module to achieve a balance between computer resource consumption and detection accuracy. Finally, we use an image preprocessing method that includes inverting colors. We integrated this algorithm into YOLOv5, which we trained on a dataset with 122 images of rubber ring surface defects. Compared with the original network, the network size decreased by 30.5% and the computational cost decreased by 23.1% whereas average precision only decreased by 1.8%. Additionally, the network's training time decreased by 16.1% as a result of preprocessing. These results show that the proposed approach greatly helped practical rubber ring surface defect detection.

Background: Skin- (SSM) and nipple-sparing (NSM) mastectomies are frequently performed surgeries with a considerable risk for post-operative hematoma or seroma. Tranexamic acid (TXA) is a potent antifibrinolytic drug commonly used in many surgical fields but rather novel in plastic and specifically breast surgery. This study investigates the influence of TXA in patients undergoing SSM or NSM with expander-based reconstruction (EbR) on post-operative outcomes. Methodology: A retrospective study was conducted on 132 patients undergoing uni- or bilateral SSM or NSM with EbR between May 2015 and March 2022. Patients receiving systemic TXA treatment for 48 hours following a standardized protocol were compared to those who received no treatment. Multivariable linear regression was performed to identify influencing factors and quantify their effect on drainage volume, duration of drain placement, length of hospital stay, post-operative bleeding, and seroma formation. Results: 132 patients underwent a total of 155 mastectomies (72 in the TXA group, 83 in the control group). TXA significantly reduced drainage volume (-22.3ml, P=0.011). Duration of drain placement and length of hospital stay were significantly shorter in the TXA group (P&lt;0.001 and P=0.001). No significant side effects were reported. Conclusion: TXA is a safe drug if administered respecting the well-defined contraindications. Systemic TXA administration significantly reduces drainage volume in patients undergoing SSM or NSM and should encourage surgeons to reconsider using drains in post-operative protocols. Duration of drain placement and length of hospital stay were significantly reduced in the TXA group but other factors like resection weight might have a more substantial impact.

Cirsium japonicum, a traditional herb, exhibits significant anti-inflammatory activity; however, the main components and potential mechanisms of C. japonicum remain unclear. The aim of this study is to investigate the anti-inflammatory mechanism of Cirsium japonicum through network pharmacology and cellular experiments. The effective components and potential targets for anti-inflammatory activity of C. japonicum were identified using traditional Chinese medicine systematic pharmacology database, TCMSP analysis platform, and GeneCards database. The drug-component-target-disease network diagram was constructed using Cytoscape3.8.0 software, while the protein interaction network diagram was created using STRING database and Cytoscape3.8.0 software. Gene ontology (GO) enrichment and KEGG pathway enrichment analysis were carried out using DAVID database. Molecular docking between key targets and active components was constructed with AutoDock software to determine the best binding target. Results revealed that 14 active components of C. japonicum targeted 171 anti-inflammatory proteins. GO function enrichment analysis yielded 173 items, while KEGG pathway enrichment analysis identified 48 signaling pathways related to inflammation regulation. Molecular docking showed that strong affinity between sitosterol, stigmasterol, and other components with key targets such as peroxisome proliferator-activated receptor α recombinant protein (PPARA) and cyclooxygenase-2 (PTGS2). Vanillin, one active ingredient of C. japonicum, inhibited the release of lipopolysaccharide (LPS)-induced inflammatory factors in RAW264.7 cells. These findings suggest that C. japonicum may exert its anti-inflammatory effects by modulating the phosphatidylinositol 3-kinase (PI3K)/ protein kinase B (Akt) signal pathway (PI3K-Akt) and apoptin signal pathway, highlighting the multi-component, multi-target, and multi-channel molecular mechanism underlying its anti-inflammatory properties. Finally, the anti-inflammatory effect of vanillin, an effective component in C. japonicum, was verified by cell experiments. This study provides a new understanding of the pharmacological mechanisms of C. japonicum in treatment of inflammatory conditions.

Programmed cell death 4 (Pdcd4) is a tumor suppressor, which has been demonstrated to efficiently suppress tumorigenesis. Biochemically, Pdcd4 binds with translation initiation factor 4A and represses protein translation. Beyond its role in tumor suppression, increasing evidence indicates that Pdcd4 enhances the chemosensitivity of several chemotherapeutic drugs. To date, several translational targets of Pdcd4 have been identified. These targets govern important signal transduction pathways, and their attenuation may enhance chemosensitivity or overcome drug resistance. This review will discuss the signal transduction pathways regulated by Pdcd4 and the potential mechanisms through which Pdcd4 enhances chemosensitivity or counteracts drug resistance.

Objectives: This study investigated the efficacy and safety of performing phacoemulsification using the eight-chop technique with iris hooks in patients with small pupils. Methods: The iris hooks and control groups each included 65 eyes. Cataract surgeries were performed using the eight-chop technique. The operative time, phaco time, aspiration time, cumulative dissipated energy, and volume of fluid used were measured. Best-corrected visual acuity, corneal endothelial cell density (CECD), and intraocular pressure (IOP) were measured preoperatively and postoperatively. Results: In total, 130 eyes of 107 patients (mean age, 75.9 ± 7.1 years; 58 men, 72 women) with cataracts were evaluated. The mean operative time, phaco time, aspiration time, cumulative dissipated energy, and volume of fluid used were 10.6 min, 20.7 s, 101.1 s, 7.8, and 38.0 mL, respectively, in the iris hooks group and 4.6 min, 16.2 s, 72.1 s, 7.0, and 28.9 mL, respectively, in the control group. The decrease in CECD at 19 weeks postoperatively was 2.1% and 1.2% for the iris hooks and control groups, respectively. In both groups, IOP decreased significantly (all p &lt; .01) at 7 and 19 weeks postoperatively. No intraoperative complications were observed in either group. Conclusions: The eight-chop technique using iris hooks resulted in a small postoperative reduction in CECD and excellent values for intraoperative outcome measures. In addition, those cataract surgeries were very short, efficient, and safe, without complications. The eight-chop technique using iris hooks could provide an ideal solution for patients with small pupils.

This paper presents the theory, design, and application of a dual-branch series-diode analog pre-distortion (APD) linearizer to improve the linearity and efficiency of a K-band high power amplifier (HPA). A first-of-its-kind, frequency-dependent large-signal APD model is presented. This model is used to evaluate different phase relationships between the linear and nonlinear branches, suggesting independent gain, and phase expansion characteristics with this topology. This model is used to assess the impact of diode resistance, capacitance, and ideality factor on the APD characteristics. This feature is showcased with two similar GaAs diodes to find the best fit for the considered HPA. The selected diode is characterized and modeled between 1-26.5 GHz. A comprehensive APD design and simulation workflow is reported. Before fabrication, the simulated APD is evaluated with the measured HPA to verify linearity improvements. The APD prototype achieves a large signal bandwidth of 6 GHz with 3 dB gain expansion and 8∘ phase rotation. This linearizer is demonstrated with a 17-21 GHz GaN HPA with 41 dBm output power and 35 % efficiency. Using a wideband 750 MHz signal, this APD improves the noise power ratio (NPR) by 6.5-8.2 dB over the whole HPA bandwidth. Next, the HPA output power is swept to compare APD vs. power back off for the same NPR. APD improves the HPA output power by 1-2 W and efficiency by approximately 5-9 % at 19 GHz. This efficiency improvement decreases by only 1-2 % when including the APD post-amplifier consumption, thus suggesting overall efficiency and output power improvements with APD at K-band frequencies.

The gut microbiome known as the “hidden organ” in humans has received wide attention due to its pivotal role in human health, influencing various physiological functions and metabolic processes. Multiple factors influence the gut microbiome and diet emerging as a prominent determinant. Increasing evidence verifies different dietary patterns modulate gut microbial composition and its functionality. Asia, home to the world's largest population, showcases immense diversity, reflected in its complex dietary patterns influenced by tradition and modernization. A plethora of research evidence proves Asian diet confers beneficial effects on maintaining and improving health through favorable alteration of gut microbiome. Yet up to date, no comprehensive review exists on the influence of the Asian diet on gut microbiome. This review critically examines published data on dietary components from Eastern, Southeastern, Southern, Western and Central Asian diet and their interaction with the human gut microbiome across healthy and diseased states. It identifies strengths and limitations in Asian gut microbiome research. Asia experiences rapid dietary transitions demanding further investigation into their effects on the gut microbiota. However, evidence proves traditional Asian diets hold promise for developing personalized dietary interventions and therapeutics to promote gut health and prevent chronic diseases by modulating gut microbiome.

The emission of toxic gases from industrial production have intensified issues related to atmospheric pollution and human health. Consequently, the effective real-time monitoring and removal of these harmful industrial gases have emerged as significant challenges. In this work, the density functional theory (DFT) method was utilized to examine the adsorption behaviors and electrical characteristics of Ni-decorated InSe (Ni-InSe) monolayer when interacting with twelve gases (CO, NO, NO2, NH3, SO2, H2S, H2O, CO2, CH4, H2, O2 and N2). A comparative assessment of adsorption strength and sensing properties was performed through analyses of electronic structure, work function and recovery time. The results show that Ni doping enhances the electrical conductivity of the InSe monolayer and improves the adsorption capabilities for six toxic gases (CO, NO, NO2, NH3, SO2, H2S). Furthermore, the adsorption of these gases on the Ni-InSe surface is characterized as chemisorption, as indicated by the analysis of adsorption energy, density of states, and charge density difference. Additionally, the adsorption of CO, NO, NO2, and SO2 results in significant alterations to the bandgap of Ni-InSe, with changes of 18.65%, 11.37%, 10.62%, and –31.77%, respectively, underscoring its exceptional sensitivity. Moreover, the Ni-InSe monolayer exhibit moderate recovery times of 3.24 s and 22.72 s at 298 K for the SO2 and H2S, respectively. Consequently, the Ni-InSe is regarded as a promising gas sensor for detecting SO2 and H2S at room temperature. This research establishes a foundation for developing Ni-InSe based gas sensor in detecting and mitigating harmful gas emissions.

The Cyber Security Incident Response Team executes its critical duties in the Centralized Security Operation Centers. Its primary target is to protect organizational resources from all types of Advanced Persistent Threats (APT) and attacks while making the correct judgments at the right time. Despite the great efforts and the efficient tools, the incident response mechanism faces two significant challenges. The generation of large amounts of alerts with huge rates of false positives per unit of time and the time-consuming manual expert engagement in the post-alert decision phase. The main aim of this research study is to investigate the potential of Machine Learning techniques in solving the above challenges. This study proposes six event detection modules to identify 14 different types of malicious behavior using Splunk. The available APT dataset is extended by adding more alert types. Then, the machine learning techniques i.e., Random Forest, and XGBoost, are applied to improve the decision process and reduce the false positive alerts of the Security Information and Event Management system. The proposed model outperforms the state-of-the-art techniques by predicting an accuracy of 99.6%.

Background/Objectives: In case of myocarditis, electrocardiogram (ECG) may suggest the pattern of ST segment elevation myocardial infarction (STEMI) or non-ST segment elevation myocardial infarction (NSTEMI). NSTEMI pattern is less frequent in case of myocarditis, but it remains unclear if presence of ST segment elevation in case of myocarditis is related to more severe condition and more damage in myocardium. Methods: This is a retrospective study, involving 38 patients, admitted with myocarditis. Patients were divided into two groups – patients with ST segment elevation (STE) pattern in ECG (25) and patients without ST segment elevation (non-STE) pattern (13). The data compared were epidemiology, laboratory and instrumental tests results. Data was analysed using IBM SPSS Statistics v26.0. A p value

Identification of nutritious rice variety through non-destructive detection technology is necessary for high quality seed production. With the development in technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining scientific community attention. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain-filling stage. Different models (Stepwise Multiple Linear Regression, SMLR and the Back Propagation Neural Network, BPNN) for estimating rice nutritional quality indicators based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R2 =0.9516 and RMSE=0.3492, indicating high accuracy and stability of the model. Overall, hyperspectral imaging technology combined with various model could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner.

Structural Health Monitoring (SHM) plays a vital role in ensuring the health status of a wide range of structures, such as bridges, buildings and large infrastructure in general. The advantages of this process can be further enhanced by incorporating more numerical and statistical approaches into traditional methods, such as Finite Element Analysis and Machine Learning. In this study a bridge structure is examined, and neural networks are trained with data derived from finite element analyses under static loads and dynamic excitations. Initially, a binary classification problem is addressed, where numerically trained classifiers are tasked with identifying whether the structure is in a healthy state or not. This category is further divided into two subcategories, depending on the extent of the damage present in the structure. Subsequently, a multi-class classification problem is defined, where three different damage classes of the same extent are considered, and the trained network is required to distinguish between them. Finally, conclusions are drawn from the results of the study regarding the model error parameter, the impact of the damage size, as well as the types of neural networks and training data used.

This article presents the results of isolating and identifying EPF species and screening thermotolerant and highly virulent EPF strains that are promising for controlling Colorado potato beetle populations. Soil samples were collected from southern Kazakhstan for the isolation of fungi used in the experiments. Using the insect bait method, 41 isolates of EPF were obtained from the soils of southern Kazakhstan. PCR analysis based on ITS and TEF loci (elongation factor Ef1a) showed that all the isolates belong to the species B. bassiana. However, analysis of the ITS locus revealed two cryptic species: B. bassiana and B. pseudobassiana. No other fungal species were detected, likely due to the insufficient informativeness of the ITS and TEF loci in distinguishing species. Further studies with other loci are required. As a result of selecting natural isolates for virulence and thermotolerance, the following strains were identified: B13, B14, B15, B22, B23, B24, B25, B23-23, Bc4, Bc7, and Bc8. These strains showed high virulence (100% on the 11th day after treatment) and tolerance to suboptimal temperatures (+15°C and +30°C). Therefore, they are considered the most promising for use in the context of climate change. The data suggest that, despite the source of the isolates, EPF have great potential for controlling Colorado potato beetle populations, as the majority of isolates lead to fatal outcomes. Their geographical origin primarily influences their temperature preferences, which negatively affect their effectiveness in field conditions.

Reeling from the global supply chain crisis created by the COVID-19 pandemic, the maritime industry still faces multifaceted challenges for its sustainable survival. Those challenges include shifts in trade routes, congestion at major seaports, container shortages, volatility in fuel prices, the scarcity of skilled maritime labor, and potential technical glitches stemming from digitized maritime operations. To help the maritime industry handle these challenges, this paper identifies major drivers and deterrents that affect the efficiency of ocean carriers’ performances over time using two different versions of data envelopment analysis (DEA) models and the Malmquist Productivity Index (MPI). After evaluating the operating efficiencies of multinational ocean carriers across the globe over extended periods (pre- and post-pandemic periods), we developed a benchmark standard that can guide best-in-class sustainable maritime practices.

Graph unlearning has emerged as a crucial technique in privacy-preserving applications, particularly in scenarios where sensitive data must be erased from graph-based systems. This paper explores the fundamental concepts, challenges, and methodologies associated with graph unlearning, including its application to domains such as social networks, financial transaction systems, and healthcare networks. By delving into various algorithms, including local and global unlearning methods, we analyze the trade-offs between accuracy, privacy, and computational efficiency. The paper further investigates the security risks associated with graph unlearning, including adversarial and inference attacks, and proposes mitigation strategies to safeguard unlearning processes. We also examine the unique challenges posed by federated learning systems, where unlearning requires coordination across decentralized clients. Finally, the paper evaluates graph unlearning techniques using performance metrics such as time complexity, accuracy, and privacy guarantees, supported by real-world examples from diverse applications. The findings emphasize the importance of developing robust, scalable, and secure unlearning mechanisms to ensure data privacy and compliance with regulations. Evaluation and metrics for Graph Unlearning will also be discussed in this paper.

Recent advancements have been observed in the global legal framework, particularly in the recognition of Indigenous peoples' rights, among which the right to self-determination stands out. This right encompasses the ability to define their political status and to pursue economic, social, and cultural development autonomously. The Inter-American Commission on Human Rights (IACHR) has identified six criteria to evaluate adherence to these rights. This study aims to assess the progress made by Chile, Colombia, and Mexico in meeting these criteria. Employing a non-experimental, cross-sectional, and exploratory research design, the study utilized a questionnaire based on a Likert scale to gather data from residents of Indigenous communities within the three countries under consideration. Findings suggest that the criterion most adequately met is that of cultural identity and non-discrimination, whereas the criterion pertaining to the right to prior, free, and informed consultation and consent is the least fulfilled. Through principal component analysis, it was noted that Colombia exhibits a higher level of compliance with the right to self-determination, followed by Mexico, with Chile demonstrating a divergence and lagging in progress. The study concludes that a significant challenge for these countries lies in continuing efforts to effectively meet these standards and ensure the right to self-determination for Indigenous and tribal peoples.

Quorum sensing (QS) is a sophisticated bacterial cell-to-cell communication mechanism that allows bacteria to sense population density through the secretion and detection of diffusible small molecular signals. This process leads to the coordinated expression of specific genes at the transcriptional level. Over time, continuous research has elucidated the genetic elements and regulatory principles of QS. Recently, synthetic biology has leveraged these insights to construct genetic circuits incorporating QS components, enabling both intra-species and inter-species artificial communication. These QS-based genetic circuits hold significant potential for applications in biotechnology and biomedicine. This paper reviews several well-characterized microbial QS systems and their functional roles, while also introducing the application of QS-based genetic circuits in cellular communication across species. The paper further discusses the role of QS in the development of biological computing tools, population density regulation, and metabolic flux control, offering a forward-looking perspective on future advancements. For intra-species communication, the focus is on the use of QS systems in constructing biological computing tools, including toggle switches, biosensors, and logic gates in synthetic biology. These tools, designed on the QS mechanism, can more precisely coordinate cellular behavior by integrating biological control circuits to achieve spatial, temporal, and population-level regulation. In the context of inter-species communication, the introduction of QS systems plays a pivotal role in population density control and metabolic flow regulation. By recombining metabolic networks, QS enables the redistribution of metabolic flux in desired pathways, facilitating the regulation of population density and the co-culture of mixed strains. Moreover, combining QS with oscillator models has shown great potential in synchronizing microbial communities. In summary, in-depth research into QS mechanisms and their applications not only lays a solid foundation for understanding microbial ecological competition and dynamic balance, but also offers promising avenues for regulating pathogenic bacteria and developing innovative disease control strategies.

These counterexamples have shown the relationships between the power of a Pythagorean equation and Pythagorean’s triples. There are three positive integers that satisfy Fermat’s equation when the power n is equal to 2 in the case of the Pythagorean theorem and it’s applicable relationships in right triangles. On the other hand, as the power varies from positive to negative integers or any integers other than 2, such as in the case of Fermat’s last theorem, the hypotenuse z of a right triangle tends to be equal to one of the sides x or y of the triangle and the other side tends to be equal to zero. And the application of this idea describes the behavior of a mass object in motion. Since the total energy of the object is the sum of its energy in motion and its rest energy, at a particular power n, the total energy of the object tends to be equal to either the rest energy or the photon energy.

A broadband 28 nm complementary metal-oxide-semiconductor (CMOS) power amplifier was implemented using a distributed amplification design. To develop a model library for high-frequency design, various test patterns for active and passive elements were fabricated and compared through measurements. As a result, an n-channel field-effect transistor (NFET) with a symmetrical layout was adopted as the active device, and a co-planar waveguide (CPW) structure with floating first and second metal layers at the bottom was selected as the transmission line for the passive element. These choices demonstrated superior radio frequency (RF) characteristics at high frequencies compared to other device candidates. Furthermore, to address the low breakdown voltage of CMOS, a triple-stacked FET structure was designed as the gain cell of the distributed power amplifier (DPA). The fabricated DPA showed a maximum small-signal gain of 22 dB and a minimum of 10 dB from DC to 56 GHz, with a maximum saturated Pout of 20 dBm and a minimum of 13 dBm from 1 to 39 GHz. Notably, these results were achieved on the first attempt by designing solely based on measurement data from the test patterns.

This study presents the development and implementation of a remote monitoring system for a dual-axis solar tracker, integrating Industry 4.0 technologies such as IoT, SCADA, and MQTT. The system, which combines a Siemens S7-1200 PLC, an IoT gateway, and a SCADA platform based on Ignition, demonstrated significant increases in solar capture efficiency, with production gains of 25-30% compared to fixed systems, reaching up to 67.65% under ideal conditions. In addition to improving operational management through predictive monitoring and early fault detection, the study identified areas for improvement, such as the need for real-world testing and the integration of weather predictions. In conclusion, the research validates the feasibility and effectiveness of automated IoT and SCADA-based systems to optimize solar trackers, significantly contributing to improved energy efficiency in photovoltaic installations.