This study investigated the integration of indigenous knowledge with modern Customer Relationship Management (CRM) practices in the Indian banking sector. Through a comprehensive analysis of secondary data from industry surveys, government reports, and academic studies (2018-2023), we explored how this integration impacts customer satisfaction, loyalty, and financial inclusion. Our findings reveal that banks that incorporate indigenous practices experience a 25% increase in customer satisfaction scores, 30% lower churn rates, and 20% higher cross-selling success. Financial inclusion metrics have improved significantly, with rural account ownership increasing from 70% to 85% (2018-2023). We identify five key areas of indigenous knowledge integration: personalized relationship management, community-centric banking, vernacular communication, cultural event-based products, and traditional value-aligned services. Despite the challenges in technology integration and staff training, the successful implementation demonstrates the potential of this approach. This study concludes that integrating indigenous knowledge with CRM practices leads to more culturally relevant and effective banking services, contributing to India's financial inclusion and economic development goals. This study provides a framework for banks to create inclusive strategies that are potentially applicable to other sectors in culturally diverse markets.

Objectives: We aimed to assess the relationship between circulating extracellular vesicles (EVs), hypoxia-related proteins and conventional risk factors of the life threatening coro-nary artery disease (CAD) to find more precise novel biomarkers. Methods: Patients were categorized based on coronary CT angiography (CCTA). Patients with a Segment Involvement Score (SIS) > 5 were identified as CAD patients. Individuals with a SIS < 5 were considered as control subjects. Characterization of EVs and analysis of plasma concentration of GDF15 were performed by multicolor or bead-based flow cytom-etry. Plasma protein levels of PYGM, clusterin, CPN1 were determined by ELISA. Multiple logistic regression was used to determine the association of the biomarkers with the CAD outcome after accounting for established risk factors. The analysis was built in 3 steps: first, we included the basic clinical and laboratory variables (Model 1), then we integrated the plasma protein values (Model 2), and finally complemented it with the circulating EV pattern (Model 3). To assess the discrimination value of the models, an area under the re-ceiver operating curve was calculated and compared across the three models. Results: The AUC values were 0.68, 0.77 and 0.84 in Models 1, Model 2 and Model 3, re-spectively. The variables with the greatest impact on the AUC values were Hemoglobin 0.2 (0.16-0.26) in Model 1, CPN1 0.12 (0.09-0.14) in Model 2, and circulating CD41+/CD61+ EVs 0.31 (0.15-0.5) in Model 3. Correlation analysis showed a significant impact of circu-lating CD41+/CD61+ platelet-derived EVs (p=0.03, r=-0.4176) in Model 3. Conclusion: Based on our results, the circulating EV profile can be used as a supportive biomarker along with the conventional laboratory markers of CAD, and it enables a more sensitive, non-invasive diagnostic analysis of CAD.

Simple and complex clustered DNA damage represent critical initial damage caused by radiation. In this paper a multinomial probability model of clustered damage is developed with probabilities dependent on the energy imparted to DNA and surrounding water molecules. The model consists of four probabilities: A) direct damage of sugar-phosphate moieties leading to SSB, B) OH^- radical formation with subsequent SSB and BD formation, C) direct damage to DNA bases, and D) energy imparted to histone proteins and other molecules in a volume not leading to SSB or BD. These probabilities are augmented by introducing probabilities for relative location of SSB using a <10 bp criteria for double strand break (DSB), and for the possible success of radical attack to lead to SSB or BD. Model predictions for electrons, ⁴He and ¹²C ions are compared to experimental data and show good agreement. Thus, the developed model allows an accurate and rapid computational method to predict simple and complex clustered DNA damage as a function of radiation quality and to explore the resulting challenges to DNA repair.

This study investigates the rapid growth of the e-commerce industry in Nepal while examining the associated security challenges. By analyzing factors driving e-commerce development, identifying prevalent threats, and evaluating existing security measures, this research aims to provide a comprehensive understanding of Nepal's e-commerce landscape. This research uses a combination of quantitative and qualitative data to better understand the complex relationship between e-commerce and security threats.E-commerce security is paramount for protecting online transactions. It ensures data integrity, prevents unauthorized access, and maintains confidentiality. While e-commerce offers opportunities for the banking industry, it also introduces security risks like hacking. Effective e-commerce security requires a coordinated approach, combining technical solutions with robust management practices. Key findings from this research are expected to inform the development of robust strategies to mitigate security threats and cultivate a secure e-commerce ecosystem in Nepal. By addressing the challenges and opportunities presented by e-commerce, Nepal can harness its potential for economic growth and development while ensuring a safe and secure online environment for consumers and businesses.

This study assesses the social, economic, and environmental effects of harvesting timber from the Kambui Hills forest reserve in Sierra Leone, a part of the Gola rain forest, a biodiversity hotspot along the upper Guinea forest zone that stretches through the Manor River Union basin of the Republics of Guinea, Sierra Leone and Liberia. The study area covers an area of 40 kilometers in length and five kilometers in breadth (20,348 hectares of land), with a population of 45,562 residents [1]. A ten-year time series data from 2010 to 2021 is used for this research. The data is processed using EViews 10 statistical software package in a regression analysis. The data set is collected from Food and Agriculture Organization Statistics of the United Nations (FAOSTAT); International Timber Trade Organization (ITTO); and Statistical Office of the European Union (EUROSTAT). A set of specified variables such as primary literacy Level, carbon emissions, inflation, unemployment, Gross Domestic Product (GDP), Exports, and natural resource rent are used to determine the socio-economic and environmental impacts of harvesting timber within the study area. The environmental impacts results from the regression analysis confirmed that R² or the coefficient of determination is 0.5734, which shows that 57 % of the dependent variable is significantly explained by the independent variables in the model. Similarly, the economic impact shows an R² of 0.8596, which indicates that 86 % of the dependent variable is explained by the independent variables in the model. In the same vein, the social impacts showed an R-square of 0.9667. This is statistically significant as 97 % of the dependent variable is explained by the independent variables in the model. Thus, the regression models confirmed the socio-economic effects of harvesting timber and its environmental impact on the residents of the forest area investigated. This study will conclude with recommendations to the relevant stakeholders in Sierra Leone.

Climate change remains a pressing global issue affecting sectors including agriculture and forest resources in countries such as Uganda. This research focused on exploring the viewpoints of youth in Uganda concerning adapting to climate change and their interest in getting involved in agroforestry transition. By conducting a survey involving 1138 participants from the youth demographic group aimed to gather information about their level of hopefulness towards climate adaptation efforts and how they perceive the impact of farming practices and deforestation. Furthermore, the study aimed to evaluate youth willingness to participate in agroforestry (WTP) initiatives. The study used descriptive statistics as well as Ordinary Least Squares (OLS) to analyze the collected data. We found that most participants are climate change hopeful about adapting to climate change (89%). This positive and highly significant outlook is closely related to their willingness to participate in agroforestry adaptation efforts (0.0001). Moreover, a high percentage of participants (92%) acknowledged how farming practices such as cultivation and livestock rearing can degrade land significantly. Whereas Gender, Age and Employment were found to be highly and positively significant regarding youth’s WTP in agroforestry (0.0001), Income was not. Incorporating indigenous practices and encouraging meaningful involvement from policymakers can empower the youth and strengthen community-led initiatives to address environmental decline effectively. This research highlights the capacity of youth engagement in steering successful climate resilience measures via agroforestry practices in Uganda.

Outlier detection is a critical technique across various domains, including statistics, data science, machine learning, and finance. Outliers, data points that differ significantly from the majority, can indicate errors, anomalies, or even new insights. This article provides an in-depth exploration of the primary techniques used to detect outliers, categorized into statistical methods, machine learning-based approaches, and proximity-based methods. We discuss the advantages, limitations, and specific use cases of each technique, highlighting their applicability to different types of datasets. The goal is to equip practitioners with a better understanding of how to identify and handle outliers effectively in real-world data analysis.

Rhoptry proteins of the RhopH complex (RhopH1/Clag, RhopH2 and RhopH3) participate in the formation of new permeability pathways for nutrient uptake in Plasmodium falciparum infected erythrocytes. RhopH proteins associate with the Maurer’s clefts (MC) during transport to the host cell membrane and interact with the cytoskeleton. However, the role of specific MC proteins facilitating rhoptry protein interaction and trafficking is unknown. The major interaction with MCs occurs with RhopH3. Dual roles ascribed to RhopH3 include functions in host cell invasion and formation of the plasmodial surface anion channel (PSAC). The topological orientation and organization of the RhopH3-MC membrane interaction in the host cell cytoplasm has not been described. In this study, we performed biochemical analysis to investigate the topology and trafficking of RhopH3 and Pf130 from the parasite to the erythrocyte cytoplasm using streptolysin O (SLO) permeabilization and protease treatment of infected erythrocytes. We investigated Pf130, a soluble protein previously identified by monoclonal antibodies and shown by immunoelectron microscopy (IEM) to be distributed underneath knobs and associated with the MC in infected erythrocytes. Pf130 is expressed early in the erythrocytic cycle and persists to the schizont stage. Immunofluorescence and confocal microscopy show that Pf130 is a resident MC protein due to its colocalization with the MC protein REX-1. Immunofluorescence assay (IFA) showed colocalization of Pf130 with RhopH3 in the cytoplasm of SLO permeabilized and trypsin treated infected erythrocytes. These novel findings show that both proteins were protected from protease digestion demonstrating that conformational changes are required to transport the RhopH proteins through association with the MC as integral membrane proteins.

Anomaly detection is crucial in large-scale industrial manufacturing as it helps detect and localise defective parts. Pre-training feature extractors on large-scale datasets is a popular approach for this task. Stringent data security, privacy regulations, high costs and acquisition time hinder the development of large-scale datasets for training and benchmarking. Despite recent work focusing primarily on the development of new anomaly detection methods based on such extractors, not much attention has been paid to the importance of the data used for pre-training. This study compares representative models pre-trained with fractal images against those pre-trained with ImageNet, without subsequent task-specific fine-tuning. We evaluated the performance of eleven state-of-the-art methods on MVTecAD, MVTec LOCO AD, and VisA, well-known benchmark datasets inspired by real-world industrial inspection scenarios. Further, we propose a novel method to create a dataset by combining the dynamically generated fractal images creating a ``Multi-Formula'' dataset. Even though pre-training with ImageNet leads to better results, fractals can achieve close performance to ImageNet under proper parametrisation. This opens up the possibility for a new research direction where feature extractors could be trained on synthetically generated abstract datasets mitigating the ever-increasing demand for data in machine learning while circumventing privacy and security concerns.

In plants, melatonin (MT) and abscisic acid (ABA) are conventionally treated as molecules mitigating stress responses. To understand the mechanisms of ABA–MT interplay, we examined the effects of ABA and MT treatment in loss-of-function ABA and MT mutants exposed to high light (HL) stress. ABA constantly suppressed ASMT encoding N-acetylserotonin methyltransferase in the context of differential responses of other MT biosynthesis genes in both wild type (WT) and mutants. However, this response was absent in the mutant with the disrupted ABI4. Given that the ASMT promoter region contains several potential ABI4-binding elements, these data suggest that ASMT can be a potential target gene for ABI4. A role for ABI4 in the interactions between ABA and MT is supported by the finding that ABI4 is constitutively derepressed in the MT signaling mutants cand2 and gpa1, which exhibited elevated steady state levels of ABI4 transcripts and were not regulated by either stress or melatonin. In addition, the abi4 mutant showed increased modulations in the expression of the MT catabolic genes M2H and M3H in response to ABA treatment inferring that this transcription factor is a negative regulator of ABA-dependent changes in MT content. Furthermore, all tested mutants with impaired ABA synthesis or signaling displayed elevated steady state MT levels compared to WT, while MT treatment contributed to downregulation of key ABA synthesis and signaling genes. Collectively, our results suggest that ABA and melatonin act antagonistically modulating the expression of ABA and MT signaling and metabolism genes.

In a sports hall, three different groups of young adults played the "bluegill game" (a Prisoner's Dilemma-like sports game). The present study's objective was to measure the extent of cooperative behaviour in a group of 25 team sports players. To maximise gains, a player had to adopt selfish tactics half the time. The group of team sports players came closest to this mixed-strategy Nash equilibrium (chi2=4.07 in a logistic regression, p&lt;0.05). The two control groups (23 individual sports players and 24 non-sportspeople) were found to be significantly more cooperative. The altruistic players kept the game alive while working towards their own defeat. We discuss how the "bluegill game" model can be broadened to other social situations. In these non-collaborative, non-zero-sum games, cooperation is an irrational choice on the individual level but is essential on the group level.

Deep learning methods have been widely applied to time series classification tasks. Although deep learning methods perform excellently on these tasks, they require much training data. Labeling time series data for training is very time-consuming and labor-intensive. Active learning can be used to select the most informative data for labeling in time series classification tasks to save human labeling efforts. In this paper, we propose a new active learning (AL) framework for time series data. First, a temporal classifier for pixel-level temporal classification tasks is designed. Next, We propose an effective active learning method to select informative time series samples for labeling, which combines representativeness and uncertainty. For representativeness, We use the K-shape method to cluster time series data. For uncertainty, we construct an auxiliary deep network to evaluate the uncertainty of unlabeled data. The features of the middle hidden layer with rich temporal information of the classifier will be fed into the auxiliary deep network, which is equipped with a self-attention mechanism to utilize the temporal dependencies of time series data fully. Then, we define a new loss function with the aim of improving the deep model’s performance. Finally, the proposed method in this paper was evaluated on two temporal remote-sensing image datasets. The results demonstrate a significant advantage of our method over other approaches to time series data. Code is available at https://github.com/Fighting-Golion/time_series_active_learning

Background: Menstruation is a natural physiological process experienced by 1.8 billion girls, and pre-menopausal women globally, and can be experienced about 300 times in a woman’s lifetime. Even though menstruation is a sign of normal female reproductive functions and marks an important transition into womanhood for adolescent girls, it is bounded by several taboos, myths, misbeliefs, or misconceptions in low-and middle-income countries including Ghana. Hence, the topic and cultural practices around menstruation are rarely discussed, and the experiences of young girls during menarche and early menstrual life remain poorly captured. This study explored the cultural construction of menstruation in the Ghanaian context. Methods: This study was qualitative and adopted an exploratory study design. We used purposive and convenience sampling procedures for sampling five Senior High Schools and participants in the Volta Region of Ghana. 15 Focus Group Discussions (FGD) were conducted. This comprised five FGDs for female students, five FGDs for teachers, and five FGDs for parents. For each of the FGDs, 10 to 12 participants were sampled to participate in the discussion. Data were audio-recorded, transcribed, translated, and imported into MAXQDA qualitative analysis software 2022 version for analysis. Data were coded and analyzed using a thematic analysis. Results: We found that there are a myriad of socio-cultural beliefs and practices that are associated with menstruation in Ghana. These beliefs such as menstrual blood is unclean and practices like celebration of menarche, and upholding some menstruation-related cultural/tribal taboos including household restrictions, religion, and social restrictions were identified. Our findings revealed that myths have long enveloped the facts about menstruation. Conclusion: Cultural and social practices regarding menstruation depend on girls’ education, attitude, family environment, culture, and beliefs, pointing to the need for public health officials in the Ghanaian society to provide more culturally oriented education to the general public on menstruation and the impact of some conservative socio-cultural practices on the menstrual health of adolescents.

Incorporating sewage sludge (SS) into soils presents a cost-effective and environmentally friendly option compared to conventional farming practices. However, SS could be perceived as a double-edged sword, as it may contain a broad spectrum of contaminants, posing threats to soil, underground waters, food chains and human health. Even from an epidemiology perspective, the COVID-19 pandemic has instigated debates regarding the potential health risks associated with SS. The handling of SS is emerging as a significant issue in the European Union (EU), primarily due to its increasing utilization and the presence of contaminants such as heavy metals (HMs), microplastics (MPs), Pharmaceuticals in the Environment (PIE), and personal care products (PSPs). The Council Directive 86/278/EEC governs the limits of HMs when SS is utilized in agriculture. However, the Directive is enacted diversely throughout EU member states (MS), with their national legislations highlighting differences, gaps and various approaches in implementing the Council Directive. The Directive, which has been in force since 1986, has not undergone any significant revisions until the recent disclosure of a revised version in May 2023. The executive summary of the evaluation of the Council Directive 86/278/EEC (SWD-2023) – {final 158} specifies maximum concentrations of six HMs in SS among its regulatory measures. This article delves into capturing the current legal framework that defines and delimits the processes and obligations of EU MS regarding SS's management, treatment, and agriculture amendment. The primary objective is to explore the qualitative and quantitative differences/discrepancies between the MS National legislation and point out discontinuities and gaps, thus setting forward a cohesive step for future revisions and updates of the relevant legislation. Part of the article's results reflect the different approaches to permissible limits of HMs concentrations in the EU MS and between international organizations such as the Food and Agriculture Organization (FAO) and the World Health Organization (WHO). Moreover, the total absence of MPs and other Common Soil Pollutants (antibiotics, pharmaceuticals, etc.) limit values are evidence of the gaps in the current legislative framework.

Insects, nematodes, and aquatic animals face several biotic and abiotic stressors that can significantly affect their fitness – specifically damaging their cellular proteins' competent functional nature. In response, they have evolved sophisticated stress-responsive mechanisms. Certain endogenous proteins, known as small heat shock proteins (sHSPs), serve to maintain the stability and function of proteins under stress. Since the identification of the first sHSPs, an increasing number of sHSPs, mainly due to the new robust sequencing tools, continue to be identified and reported to play a critical role in the organism’s response to stress. This review explores and summarizes the contributions of the sHSPs implicated in stress response. Understanding their function is crucial for advancing our knowledge in agricultural pest management, climate change adaptation, and biotechnology.

The relationships between the relative abundances of chemical elements and their second ionization potentials, as well as their condensation temperatures, were analyzed using four published homogeneous abundance surveys of halo and thin disk stars. We selected 932 stars with effective temperatures ranging from 4900 K to 5900 K and used the abundances of 10 chemical elements common across all surveys: Mg, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, and Ni. The correlation coefficients between the relative abundances of these elements and their second ionization potentials and condensation temperatures were investigated in relation to metallicity and the stars' location in the Galaxy. The observed correlations may result from certain physical processes, such as the accretion of interstellar gas onto stellar atmospheres. We also analyzed the dependencies of relative abundances on second ionization potentials based on published abundance patterns in five halo stars. Evidence of charge-exchange reactions in the atmospheres of these stars was found. For more accurate determination of stellar ages from the abundance ratios of radioactive and stable chemical elements in stellar atmospheres, processes such as charge-exchange reactions and dust-gas separation should be considered in future detailed investigations. Additionally, changes in the chemical abundances in the atmospheres of halo stars after crossing the Galactic plane were found to be possible.

The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is in its 5th year and is being maintained by the inability of current spike-alone based COVID-19 vaccines to prevent transmission leading to the continuous emergence of variants and sub-variants of concern (VOCs). This underscores the critical need for next-generation broad-spectrum pan-Coronavirus vaccines (pan-CoV vaccine) to break this cycle and end the pandemic. The development of a pan-CoV vaccine offering protection against a wide array of VOCs requires two key elements: (1) identifying protective antigens that are highly conserved between passed, current and future VOCs; and (2) developing a safe and efficient antigens deliver system for induction of broad-based B- and T-cell immunity. This review will (1) present the current state of antigen delivery platforms involving a multifaceted approach, including bioinformatics, molecular and structural biology, immunology, and advanced computational methods; (2) discuss the challenges facing development of safe and effective antigen delivery platforms; and (3) highlight the potential of nucleoside-modified mRNA encapsulated in lipid nanoparticles (LNP) as the platform that is well suited to the needs of a next-generation pan-CoV vaccine, such as the ability to induce broad-based immunity and amenable to large-scale manufacturing to safely provide durable protective immunity against current and future Coronavirus threats.

Functional graded materials (FGMs) have been successfully applied in improving the insulation performance of insulators, yet there is currently no relevant literature on their application in dry-type transformers. Therefore, it is meaningful to study on the feasibility of using FGMs to improve the insulation performance of dry-type transformers through research on electric field distribution. This paper sets FGMs in the dry-type transformer spacer only or both spacer and winding end to change the relative permittivity values in the corresponding areas. By comparing the electric field distribution characteristics on the inner and outer surfaces of the high-voltage coil and the lower surface of the spacer of the dry-type transformer under each control method with the case without setting FGMs, the study found that when FGMs were only set on the spacer, the control methods set in this paper could not improve the electric field distribution of the transformer. When FGMs are employed at the spacers and end windings, two modulation strategies can mitigate the electric field intensity on the outer surface of high-voltage windings. Specifically, a continuous increase in relative permittivity can reduce the electric field intensity by 34.02%, while an initial decrease followed by an increase can result in a reduction of 13.58%. This paper provides theoretical guidance for insulation design of transformers with high voltage levels of 66 kV and above, especially as transformers with high voltage levels face greater challenges in regulating surface electric fields.

This article affords the question of methane emissions in the context of the ESG framework at the global level. The World Bank ESG-Environmental, Social, and Governance database was used. The data refers to 193 countries for a period between 2011 and 2020. The econometric models used are Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. The impact of methane on the ESG model was calculated through three equations capable of analyzing the impact of methane emissions, respectively, con-cerning the E-Environmental, S-Social, and G-Governance components of the ESG model. The re-sults show that methane emissions tend to increase even in the improvement of ESG conditions at world level.

Lithium has long been recognized for its therapeutic efficacy in the treatment of manic-depressive illness (MDI). Additionally, numerous reports highlight its protective properties against cell insults, with a remarkable majority of these effects documented in neurons. Utilizing the major mechanisms of its action, we bring insights that explain lithium's ability to preferentially promote survival in neurons. Specifically, we explore its interactions with magnesium at protein binding sites, with a particular focus on its impact on glycogen synthase kinase 3 beta (GSK3β). We propose that lower intracellular free magnesium levels in the brain, coupled with the higher expression of GSK3β, which is an enzyme whose activity is magnesium-dependent, amplify its impact on brain cells, promoting neuronal survival.

Analysis of the construction of tall buildings is important because of the changes in the microclimate in its vicinity. Ensuring wind comfort and pedestrian safety are critical requirements in urban environments. This paper reports on the wind flow field at the main campus of the Dhaka University of Engineering & Technology (DUET), Gazipur, using computational fluid dynamics (CFD) to investigate pedestrian wind comfort and safety. The simulations were conducted in ANSYS Fluent employing the Reynolds-averaged Navier-Stokes (RANS) equation. The vertical profile of the streamwise wind speed was validated using the existing observations. The results revealed that the wind speed at the building corners was higher than the air inlet velocity, whereas the wind speeds in the walkways among the buildings were found to vary. The velocity vectors surrounding the building were densely packed, with a recirculation vortex formed in the upstream flow. Furthermore, walkways can display a lower mean wind velocity coupled with high turbulence kinetic energy (TKE). The turbulent kinetic energy is seen to be as high as 1.52 m2 s-2 which exceeds the threshold limit, while wind velocity is 1.81 m/s at the same point. These findings highlight potential pedestrian safety risks and underscore the need for urban design enhancements.

Lung cancer is one of the deadliest cancers, leading to about 125,070 deaths in the U.S. each year. Survival rates depend on the type and spread of cancer, with localized non-small cell lung cancer (NSCLC) having a 5-year survival rate of 64%. High mortality is primarily due to late detection and the older age of patients, making surgery too risky. As a result, preventative measures like vaccines are crucial. This paper reviews the literature on CIMAvax-EGF, an EGF-based vaccine for lung cancer, evaluating its preventive potential, impact on survival in advanced NSCLC, and effect on lung cancer mortality in combination with other therapies. CIMAvax-EGF is a promising vaccine targeting the Epidermal Growth Factor (EGF), which promotes cancer cell growth. The vaccine prevents EGF from binding to its receptor by inducing anti-EGF antibodies, potentially halting cancer progression. It shows significant potential for treating advanced NSCLC, with vaccinated patients having a median overall survival of 12.43 months compared to 9.43 months in unvaccinated individuals. A Phase III trial showed that 78.8% of patients had a good antibody response (GAR), leading to longer survival. CIMAvax-EGF is well-tolerated, with mild to moderate side effects, and its efficacy is enhanced when combined with other therapies, such as immune checkpoint inhibitors. Given its ability to target cancer's underlying mechanisms and boost other treatments, CIMAvax-EGF represents a significant advancement in the fight against lung cancer. Administering this vaccine preventively and during treatment could be a major step in reducing lung cancer mortality.

Objective: Repetitive transcranial magnetic stimulation (rTMS) shows promise as an aphasia treatment, however its physiological mechanisms in people with aphasia (PWA) remain largely unclear. Electroencephalography (EEG) could be used to explore rTMS mechanisms. The present study investigated the acute effects of rTMS in PWA on: 1) an EEG measure of language (N400 event-related potential); and 2) picture naming. Methods: Eleven people with chronic non-fluent or anomic aphasia (64 ± 10.13 years old; 8 males; 37.27 ± 20.64 months since stroke) received one session of 1-Hz rTMS and one session of sham to the right pars triangularis at 80% resting motor threshold (RMT). N400 mean amplitude and picture naming accuracy were compared before and after each session. Results: No acute changes were observed in N400 mean amplitude or picture naming accuracy as compared to sham (p > 0.05; BF₁₀ < 0.33). Conclusions: Results suggest that language networks underlying the N400 are not altered following one session of 1-Hz rTMS in PWA. Significance: Although EEG could be useful for assessing physiological mechanisms of rTMS in PWA, the N400 does not appear to be a viable measure of acute 1-Hz rTMS effects, at least when delivered at 80% RMT in a single session.

Background/Objectives: Malnutrition significantly hinders the recovery of patients undergoing convalescent rehabilitation. Proper nutritional management can improve rehabilitation outcomes. This study aimed to establish a novel nutritional screening method (J-Method) specifically for convalescent rehabilitation patients and compare it to the widely used Mini Nutritional Assessment Short Form (MNA-SF). Methods: We developed the J-Method for convalescent rehabilitation settings and compared its results with that of the MNA-SF. The J-Method comprised six items derived from various nutritional screening methods and obtained solely from medical records, without patient interviews. Data were collected from 148 patients aged > 65 years with cerebrovascular diseases admitted to a convalescent rehabilitation ward (CRW). Nutritional status was evaluated using the J-Method and MNA-SF, and results were compared. Results: The J-Method more precisely identified patients as malnourished than the MNA-SF. The results of nutritional screening using the J-Method identified patients in need of nutritional management intervention and suggested that, to improve the rehabilitation effect, nutritional management should be initiated in an acute hospital before admission to a CRW. Conclusions: The J-Method may be more effective than the MNA-SF for nutritional screening in convalescent rehabilitation settings as it provides a more accurate assessment of malnutrition without requiring patient interviews.

Aging is a common global challenge. The World Health Organization has released the guide “Global Age-Friendly Cities: An equitable, inclusive and sustainable place” in which it is stated that age-friendly recreational landscapes are one of the important elements for cities and communities to create suitable leisure and social activities for older people. As a healthy green living room at the doorstep, residential green space is of great significance for building age-friendly recreational landscapes in residential green spaces. In this paper, based on the perspective of the demand of older adults for residential green space recreational landscapes, we use the systematic literature review method to comprehensively and deeply comb and summarize the existing research results and put forward China’s future key research directions in this field in a comparative analysis. The study found that: 1. The research heat in this direction has been high in the past five years, and the amount of literature in China and the United States is the highest. 2. The main disciplines involved in the study are landscape architecture, public health, and sociology, and the amount of literature in the direction of public health in international journals is the most, and the papers in the direction of landscape architecture in China are the most. 3. The main research methods used are quantitative research, which accounts for 58% of the literature, and questionnaire surveys, which account for 51% of the literature. 4. The main independent variables involved are green space area, walking environment, recreational and health facilities, community participation, etc., and the main dependent variables are satisfaction, frequency of green space use, physical and mental health, community cohesion, and sense of well-being. 5. The five main theories involved are demand theory, ecology theory, health promotion theory, environmental psychology, and environmental gerontology theory. International literature is mostly studied from health promotion theory and ecological theory, and Chinese literature is mostly studied from the perspective of need theory. 6. Measurement indexes are different in diverse disciplines, among which the direction of landscape architecture mainly explores the influence of green space safety, accessibility, multi-functionality, environmental aesthetics, and recreational facilities on the satisfaction, and happiness of older adults from the perspective of green space planning and design. In the direction of public health, the impact of green space on the physical, psychological, and social health of older adults is mainly explored from the perspectives of green space areas, neighborhood environments, and community participation.

In this investigation, four distinct by-products of natural materials were used to compose a mix for rammed earth construction. The mix of these materials, which have various granulometry, affords many potential combinations, making the analysis of different combinations highly complex. The Taguchi method, a statistical approach renowned for significantly reducing the number of ex-periments in complex systems, was employed to address this challenge. Each of the four ma-terials was assessed across four different levels, with moisture content also factored in as a pa-rameter within the statistical analysis. Two separate studies were conducted to obtain two optimal com-binations of the materials to enhance the dry density and the Unconfined Compressive Strength (UCS). Taguchi method demonstrated its efficacy in predicting the optimal combination of mix-tures for enhancing both objective parameters. A natural by-product mix demonstrated its po-tential for use as a building material. The enhancement of the dry density due to a modification of the mix’s Particle Size Distribution (PSD) did not produce a direct improvement in the mix UCS. The factor to be prioritized should be the mechanical strength, rather than the dry density of the sample.

Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis and treatment. This study uses metabolomics data from plasma samples to detect metabolite biomarkers that could distinguish ER-positive from ER-negative breast cancers in a non-invasive manner. The dataset includes demographic information, ER status, and metabolite levels from 188 breast cancer patients and 73 healthy controls. Supervised, unsupervised, and ensemble machine learning (ML) algorithms were applied to identify key metabolites associated with ER status, including Support Vector Machines (SVM), Multidimensional Scaling (MS), Logistic Regression (LR), and Ensemble learning. The most informative feature set, containing 28 biomarkers and two demographic factors, achieved an impressive 96% accuracy and an area under the curve (AUC) of 93% using the Logistic Regression model. These results suggest that ML has great promise for identifying specific metabolites linked to ER expression, paving the development of a novel analytical tool that can minimize current lab challenges such as analytical sample handling, subjective results interpretation, biological heterogeneity of the tumor in a non-invasive and efficient manner aiding in more precise diagnosis of breast cancer.

Pancreatic cancer, one of the deadliest cancers worldwide, necessitates improved therapeutics due to its low survival rate (12% in 5 years) and typically late-stage diagnosis [1]. This malignancy tends to present asymptomatically until advanced stages, leading to high rates of metastasis and limiting surgical options [2]. Genetic mutations in key genes like KRAS, CDKN2A, TP53, and SMAD4 are central to pancreatic cancer pathogenesis, driving uncontrolled cell proliferation and making treatment difficult [3]. Current treatment modalities include surgery, chemotherapy, radiation, and emerging immunotherapies. Surgery, particularly the Whipple procedure, offers the only potential cure but is applicable in just 15-20% of cases due to the late diagnosis [4]. Chemotherapy, though commonly used, offers limited long-term survival benefits [5]. Radiation therapy has seen advancements with techniques like stereotactic body radiation therapy (SBRT), which shows promise in reducing toxicity and improving outcomes [6] . Immunotherapy, particularly mRNA vaccines, has demonstrated potential in prolonging recurrence-free survival (>18 months), especially when combined with traditional therapies (13.4 months with surgical resection alone) [7] . Combinations of different treatments have been found to be effective. The challenge of early pancreatic cancer diagnosis mirrors the difficulty of disease management. Prophylactic immunotherapies, like vaccines, could offer a crucial breakthrough for prevention and recurrence reduction. As pancreatic cancer remains difficult to treat effectively, continued research and development in preventative and therapeutic strategies are essential to improving outcomes.

Objective:This study aims to evaluate the impact of drug-eluting bead transarterial chemoembolization (DEB-TACE) compared to conventional transarterial chemoembolization (cTACE) in patients with hepatocellular carcinoma (HCC), with a specific focus on liver fibrosis progression. By utilizing propensity score matching (PSM), the study seeks to examine the effects of both treatments on liver fibrosis and survival rates, providing evidence to inform clinical decision-making. Methods:This retrospective analysis included HCC patients who underwent DEB-TACE or cTACE at Xuzhou Cancer Hospital between October 2020 and September 2023. Propensity score matching (PSM) was employed to ensure comparability between the two groups based on baseline characteristics, with a 1:1 matching ratio. The primary endpoints included liver fibrosis markers (such as hyaluronic acid, type III procollagen, type IV collagen, and laminin), liver function trends (ALT, AST, TBIL, ALB), and adverse events. Kaplan-Meier methods were used to evaluate progression-free survival (PFS) and overall survival (OS). Results:After PSM, 92 patients were included (46 in the DEB-TACE group and 46 in the cTACE group). Results demonstrated that the DEB-TACE group showed superior outcomes in reducing liver fibrosis markers (e.g., PC-III, IV-C, and LN) compared to the cTACE group. Additionally, the incidence of nausea, vomiting, and postoperative pain was significantly lower in the DEB-TACE group (p < 0.001). Kaplan-Meier analysis revealed that PFS and OS were significantly longer in the DEB-TACE group compared to the cTACE group (p < 0.05). Multivariate logistic regression analysis further confirmed the independent protective effect of DEB-TACE in reducing liver fibrosis progression (OR = 2.39, p = 0.044). Conclusion:This study confirms the advantages of DEB-TACE in treating patients with intermediate to advanced HCC, particularly in slowing liver fibrosis progression and improving survival outcomes. The use of propensity score matching enhances the reliability of these findings. Future studies should continue to explore the potential of DEB-TACE in broader patient populations to optimize treatment strategies for HCC.

CO₂-soluble surfactant foam systems have garnered significant attention in the fields of enhanced oil and gas recovery, environmental protection, and greenhouse gas emission reduction. This review provides a comprehensive explanation of the basic theory of CO₂-soluble surfactant foam, the mechanism of enhanced oil recovery, and the classification and application of various CO₂-soluble surfactants.The mechanism of enhanced oil recovery by CO₂-soluble surfactant foam involves the effective reduction of CO₂ fluidity, the decrease in oil-air flow ratio, and the stabilization of the displacement front. Foam plays a vital role in mitigating the issues of channeling and gravity separation often caused by simple CO₂ injection. The reduction in gas fluidity can be attributed to the increase in apparent viscosity and trapped gas fraction.Future research should prioritize the development of more efficient and environmentally-friendly CO₂-soluble surfactants. It is essential to further explore the advantages and challenges associated with their practical applications in order to maximize their potential impact.

The Influenza A Virus (IAV) represents an enveloped, positive-sense and single-stranded RNA-based virus that infects mammals mainly via the respiratory system, although other bodily systems are also infected and undergo various extents of inflammatory pathogenesis. There are two well-known strains of IAV that cause life-threatening disease in mammals; H1N1 and H5N1, and the first strain caused the 1918 IAV H1N1 pandemic that claimed between 30 and 50 million human lives. Due to the significant ability of IAV to evade important immune recognition, the virus was observed to favour the onset of secondary microbial infections (i.e. bacterial or fungal), as the overall performance of the immune system became transiently weakened during the viral infection. During the IAV H1N1 pandemic, many patients died as a result of bacterial pneumonia, as pathogenic bacteria, such as Streptococcus pneumoniae and Haemophilus influenzae, gained a wider opportunity to colonise and infect vital areas of the lower respiratory tract, and such a phenomenon led to the excessive, prophylactic usage of antibiotics due to the increased levels of panic, which in turn favoured the natural selection of bacteria with genes that became resistant to such antibiotics. Antibiotics might be required for usage solely when bacteria are known to be colonising vital areas of the human body, and this aspect is tricky, as microbial colonisation as such is asymptomatic and screening is consequently rare. Recently, new variants of the avian IAV H5N1 strain were transmitted from live, infected birds to mammals, including humans in some isolated cases, and given that there have already been several zoonotic spillover events overall since the beginning of 2023, we are rapidly approaching the time when a zoonotic spillover into humans will mark the first epidemic outbreak of the avian flu in humans. A lethality rate of approximately 60% was projected by the World Health Organization, as the virus was shown to favour the development of life-threatening hyper-inflammatory responses at the levels of alveolar tissues constituted by Type II pneumocytes. There are hints that novel variants of H5N1 are capable of infecting the intestinal layer, as recently, two dolphins died as a result of ingesting infected birds within the area of the British Isles. IAV is known to suppress the production and transmission of Type I Interferons by expressing various non-structural proteins (NSPs), such as NSP1, which was found to be also packaged into exosomes and transmitted to neighbouring uninfected cells, thereby preventing them from responding to the virus in the first place. Additionally, it was determined that NSP1 induces the apoptosis of the cells it is produced in and the neighbouring ones it enters via induced paracrine signalling, and such an aspect may only emphasise upon the critical role that early innate immune stimulation plays in preventing the development of severe respiratory illness afterward, given the context of increased viral ability of suppressing the production of first-line and second-line signals. A more pronounced rate of innate immune evasion would probably be observed in H5N1 IAV infection than in the infection caused by recent variants of H1N1 IAV. The H5N1 strain of IAV was also found to secrete a higher concentration of NSP1 than SARS-CoV-2, indicating the existence of an association to the greater mortality rate of H5N1 IAV infection. A direct, prophylactic stimulation of the interferon system using a reduced oral or nasal dosage of recombinant anti-inflammatory and anti-viral interferon glycoproteins may represent the most viable approach to prevent an emergence of a life-threatening H5N1 IAV pandemic. A similar non-invasive approach could be developed for a Marburg Virus (MARV) and a Nipah Virus (NiV) infection of humans, as risks of the emergence of a Marburg epidemic and also of a Nipah epidemic may be substantial at this stage as well. Clinical testing of clinical approaches as such could be of critical importance at the moment. Animals could also benefit from related clinical approaches. Somatic natural and adaptive lymphocytes treated with IFN I and III could also constitute a substantial approach of immunisation and heavily favour an indefinite shift in the evolutionary battle between the host organism and microbes of public health concern, as IFN I and III could improve the immunity of T-lymphocytes against HIV infection and even improve the performance of the mentioned types of immune cells during other types of infections. Likewise, a combination of all intellectual and clinical efforts implicating a shift of scientific and medical focus toward natural immunity in vaccine-related research could be a wise method in making the best efforts to prevent or alleviate the effects of a possible future pandemic, given the highly significant and diverse abilities of microbial “intelligence” to effectively hijack the natural immune system, many times causing severe implications upon the quality of the overall immune response against such microbes.

Rapid weight loss strategies (including fasting and dehydration) and weight cycling (losing and gaining weight repeatedly) are acknowledged problems in sports with weight categories, including judo. While extreme fasting, especially if combined with dehydration and intense physical activity, may harm health and development, particularly of children and teenagers engaging in judo competitions, there is substantial evidence for positive health effects of other forms of fasting. Here we aim to provide an updated overview on potential health risks associated with extreme dietary strategies in judo and put them in context with health-promoting fasting approaches. We conclude that the literature on long-term health consequences of fasting in judo are scarce. This applies specifically to the potential association with eating disorders. Many reports highlight short-term risks of rapid weight loss, however, vulnerability and protection factors remain poorly understood. Rigorous scientific studies are needed to disentangle the factors that render fasting in judo healthy or hazardous, considering long-term effects both for somatic and mental health. We argue that better understanding of weight management in judo may be used to prevent health risks for athletes and possibly also to propose novel weight management strategies to overweight individuals.

Text-to-image tasks have gained significant progress, where vivid images are generated from detailed text descriptions. However, existing studies overlook scenarios where the provided text is sparse, neglect the investigation of human preferences, and fail to acknowledge the diversity of aesthetic opinions. To address those issues, we develop a methodology called personalized short-text-to-image generation through aesthetic assessment and human insights. We develop a Personality Encoder (PE) to extract personal information and establish the Big-Five personality traits-based Image Aesthetic Assessment model (BFIAA) for predicting specific human aesthetic preferences. Utilizing the BFIAA model, we fine-tune the Stable Diffusion model to align it more closely with human preferences. Our experiments demonstrate that our BFIAA model can truly reflect human aesthetic preference and the adapted generation model can generate personalized images more preferred by humans.

This research aimed to explore the effects of Silicon (Si) on managing hydrogen peroxide levels, photosynthetic pigment content, nutrient accumulation, and the production of root and shoot dry biomass in salt-stressed sunflower (Helianthus annuus L.) plants. Conducted in a glasshouse, the experiment followed a completely randomized block design in a factorial arrangement (2 × 2) and five replicates. Pots were divided into two groups: non-salt stressed, and salt stressed (100 mM NaCl), both with Si (2 mM as stabilized sodium and potassium silicate) and without Si. The study found that Si effectively reduced hydrogen peroxide concentration and enhanced photosynthetic pigments, nutrient status, and plant growth. The results showed that Si alleviated salt stress effects in sunflower plants by significantly lowering hydrogen peroxide levels and increasing photosynthetic pigments and nutrient accumulation, leading to greater root and shoot dry biomass. These findings indicate that adding Si to the growth medium is crucial for enhancing resistance to salt-induced ionic and osmotic stress, making it a promising strategy for improving crop growth and management in saline conditions.

Wideband signal detection and identification are crucial for military and civilian fields such as communication reconnaissance and spectrum security. This paper systematically sorts out the mainstream technologies of wideband signal detection and identification, and expounds the main current short-wave wideband signal detection and identification work from three perspectives: traditional expert feature methods, combination of expert features and machine learning, and wideband signal detection and identification based on deep learning. Particularly, this paper focuses on the processing methods of current key signals such as short burst signals and frequency hopping signals, as well as the latest deep learning methods in the past five years. Finally, the shortcomings of the existing broadband signal detection and identification methods are summarized, and the future trend is predicted.

Dental restoration success relies on the physical properties of luting cements. Luting cements fill the space between teeth and restoration, provide retention and protection from occlusal forces, and act as a barrier to microleakage in the oral environment. This review aims to evaluate and compare the solubility of the three most used dental luting cements: glass ionomer (GI), resin-modified glass ionomer (RMGI), and resin cement (RC). Studies selected for review compared solubilities of combination of GI, RMGI and RC in solutions with different pH levels to replicate acidic oral pH. Review of the studies concluded that resin cement had the overall lowest degree of solubility in all pH values and all ranges of storage periods, followed by RMGI and GI cement. Success of restoration is dependent upon luting cements. Results of the studies reviewed show that all dental luting cements showed some degree of dissolution. Resin cement overall demonstrated the least amount of solubility, followed by RMGI and GI cement.

The integration of Customer Relationship Management (CRM) systems within Small and Medium Enterprises (SMEs) is increasingly recognized for its potential to enhance business performance. While CRM adoption is on the rise, the specific impacts on operational efficiency, customer satisfaction, and financial outcomes in SMEs require further investigation. This systematic review evaluates the impact of CRM systems on SME performance, with a focus on operational improvements, sales growth, and customer retention. The study aims to identify key determinants of successful CRM implementation and assess the long-term effects on SMEs' competitive advantage. The review encompasses studies published between 2014 and 2024, sourced from Google Scholar, Web of Science, and SCOPUS. A total of 46 studies were included after screening 18,306 records. The mixed-methods approach involves quantitative analysis of survey data from SME owners and managers, along with qualitative synthesis of case studies. Metrics evaluated include customer acquisition, retention rates, revenue growth, and organizational efficiency. The analysis indicates that CRM adoption leads to a 25-40% improvement in customer retention and a 15-30% increase in sales across SMEs. Operational efficiency gains range from 20-35%, primarily driven by process automation and enhanced data management capabilities. Successful CRM implementation is strongly linked to managerial support, system customization, and user training. However, barriers such as limited financial resources and technical expertise hinder the full realization of CRM benefits. CRM systems significantly contribute to SME growth by optimizing customer relationships and enabling data-driven decision-making. The review underscores the importance of strategic CRM implementation tailored to the specific needs of SMEs to maximize its impact. Future research should focus on overcoming adoption barriers and refining CRM strategies for sustained performance improvements.

Parametric quantile regression is illustrated for the one parameter new unit Rayleigh distribution called Median Based Unit Rayleigh distribution (MBUR) distribution. The estimation process using re-parameterized maximum likelihood function is highlighted with real dataset example. The inference and goodness of fit is also explored.

This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Karst areas are one of the world's major ecological fragile zones, are prone to influences from human activities and climate change. Although the vegetation cover in karst area of southwest China increased significantly in recent decades, there are obvious spatial differences in vegetation greening. To better elevate the relative importance of the anthropogenic factors and climatic factors to the vegetation greening, and explore the factors leading to the spatial heterogeneity of vegetation greening in karst desertification area where vegetation was seriously degraded, linear regression analysis, correlation analysis and residual analysis were used to evaluate the contribution of anthropic factors and climatic factors to the growth of normalized difference vegetation index (NDVI) from 1998 to 2020, and revealed the correlations between natural factors (proportion of karst areas, elevation and landscape pattern) anthropic factors (change in land use, population density and gross domestic product), climatic factors (annual temperature and annual precipitation) and the spatial-temporal variation of county NDVI in the paper. The results showed that (1) generally, the NDVI value increased by 23.59% from 1998 to 2020 in total area, and the anthropic factor dominated the process of vegetation greening (87.02%). The rapid increase of vegetation coverage occurred from 2011 to 2020, which was jointly promoted by the urbanization, economic development, increasing mean temperature and precipitation, and climate change were the main contributor (67.65%). (2) there were significant spatial heterogeneity in the NDVI, which was mainly caused by the site conditions (e.g. the proportion of karst area, landscapes pattern, mean annual temperature and precipitation) and anthropic factors (e.g. the proportion of construction land, population density and gross domestic product); the county NDVI value was negatively correlated with the proportion of karst area, cropland, grassland and construction land, population density, and gross domestic product, while it was positively correlated with the proportion of woodland, mean annual temperature and mean annual precipitation. (3) Generally, vegetation greening was faster in regions with warm and humid climate or more forestland, and lower in regions with higher average elevation and economic growth. Nevertheless, the effects of environmental factors on the vegetation greening rate varied in the different periods. The land cover pattern, population density, GDP in 2000, and the rate of urbanization and economic growth influenced vegetation growth from 2000 to 2010, while the impact of mean annual temperature and mean annual precipitation became more pronounced since 2010. The results of this study contributed to understand the importance of anthropic factors and climatic factors that affect veg-etation greening in karst rocky desertification areas in different periods, which provided deci-sion-making basis for land managers.

The increasing demand for fresh and convenient foods has led to the production of Minimally Processed Products (MPP), making them more suitable for culinary and industrial uses. However, cutting potatoes can trigger browning, negatively impacting their sensory and nutritional quality. This study aimed to evaluate different anti-browning methods on Colombian potato varieties (Diacol Capiro, Pastusa, Nevada, and Sabanera) as raw materials for the development of MPP. The physicochemical properties evaluated included moisture, pH, acidity, enzymatic activity, total phenolic content, vitamin C, and browning index. The anti-browning treatments involved immersing the potatoes in citric acid (1%), ascorbic acid (1%), and garlic extract (0.5%). Evaluations were carried out over a 15-day storage period at refrigerated conditions. The results indicated that the Sabanera variety had the highest browning index, followed by Nevada, while Diacol Capiro and Pastusa showed lower browning indices even without the use of technological coadjuvants. Among the treatments, citric acid proved to be the most effective across all varieties, with Diacol Capiro exhibiting the least browning after 15 days. Garlic extract was the least effective against browning. Sensory analysis indicated that the acidic descriptor was noted for both treated (with citric acid) and untreated (control) Diacol Capiro potatoes at similar intensities, suggesting that citric acid did not negatively affect sensory quality and/or was masked during cooking, enhancing overall quality. In conclusion, the Diacol Capiro variety treated with 1% citric acid is the most appropriate choice for use as raw material for MPP without compromising sensory quality.

Community-based tourism (CBT) is recognised as a tool for sustainable development that empowers local communities to manage tourism initiatives while protecting their environment and culture. This paper explores and compares two Ecotourism models: Chambok Ecotourism Site in Cambodia and Kerujuk Village Ecotourism in North Lombok, Indonesia. Through a comparative analysis, we examine how each community integrates tourism with local resources, empowers residents, and addresses environmental and social challenges. The study highlights the benefits of CBT in poverty alleviation and environmental conservation while also discussing the unique challenges each location faces.

Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) are persistent environmental pollutants, raising concerns due to their widespread presence and disruptive biological effects. These compounds are highly stable, allowing them to bioaccumulate in the environment and living organisms, potentially impacting critical physiological functions such as hormonal balance, immune response, and increasing cancer risk. Despite regulatory restrictions, their pervasive nature necessitates further research into their potential effects on cellular and neuronal function. This study first evaluated the cytotoxic effects of PFOS and PFOA on S1 neuroblastoma cells, revealing a dose-dependent reduction in cell viability for PFOS, while PFOA exhibited minimal toxicity until millimolar concentrations. We further investigated their potential to modulate GABAergic neurotransmission using patch-clamp electrophysiology. Both PFOS and PFOA caused a significant, but reversible, reduction in GABA receptor-mediated currents following one-minute pre-treatment. These findings suggest that PFOS and PFOA can interfere with both cellular viability and GABAergic signaling, providing critical insights into their functional impacts and highlighting the need for further investigation into the long-term consequences of PFAS exposure on nervous system health.

Introduction: Lumbar radiculopathy, frequently triggered by disc herniation, affects around one in 50 young adults (30-55 ages), leading to significant impacts on quality of life. When conservative treatments fail to alleviate symptoms, surgical intervention such as discectomy is often pursued. Technological advancements have led to various minimally invasive techniques, promising improved outcomes. Our aim was to compare clinical outcomes of different techniques. Methods: We conducted a systematic search in PubMed using "lumbar disc hernia" and the meta-analysis filter. Inclusion criteria comprised meta-analyses focusing on lumbar disc herniation treatments, published in English. A rigorous selection process adhering to PRISMA guidelines ensured high-quality evidence extraction. Results: From 28,171 initial records, 31 meta-analyses involving 87,852 patients were included. Heterogeneity across the literature presented challenges, notably in study populations, surgical techniques, and research designs. Among the techniques evaluated, full-endoscopic lumbar discectomy (FELD) emerged as superior in various perioperative parameters, including Visual Analog Scale (VAS) scores, Oswestry Disability Index (ODI), operation time, and complications. Conversely, automated percutaneous discectomy (AUTD) consistently exhibited poorer outcomes, suggesting limited effectiveness compared to other techniques. Conclusion: Despite advancements, the heterogeneity observed in the literature underscores the need for standardized approaches in minimally invasive spinal surgery research. FELD stands out as a preferred technique, offering favorable outcomes across multiple parameters. Conversely, techniques like AUTD raise concerns regarding efficacy. Future research should aim for uniformity in methodologies to facilitate accurate comparisons and guide clinical decision-making.

This study presents a novel approach to automatic generation of cultural and context-specific science assessments for K-12 education using generative AI (GenAI). We first developed a GenAI Culturally Responsive Science Assessment (GenAI-CRSciA) framework that establishes the relationship between CRSciA and GenAI, by incorporating key cultural tenets such as indigenous language, Indigenous knowledge, ethnicity/race, and religion. The CRSciA framework along with dynamic prompt strategies were used to develop the CRSciA-Generator model within the OpenAI platform. The CRSciA-Generator allows users to automatically generate assessments tailored to students’ cultural and contextual needs. In a pilot comparison test between the CRSciA-Generator and the base GPT 4o (with standard prompt), the models were tasked with generating CRSciAs that aligned with the Next Generation Science Standard on predator and prey relationship for students from Ghana, the USA, and China. The results showed that the CRSciA-Generator output assessments incorporated more tailored culturally and context assessment items for each specific group with examples, such as traditional stories of lions and antelopes in Ghana, Native American views on wolves in the USA, and Taoist or Buddhist teachings on the Amur tiger in China than the standard prompt out within the base GPT 4o. However, due to the background information provided, the CRSciA-Generator overgeneralized its output focusing on broad national contexts, treating entire countries as culturally homogenous and neglecting the subcultures. Therefore, we recommend that teachers provide detailed background information about their students when using the CRSciA-Generator. Additionally, we believe the pilot test did not fully validate the model’s efficacy, and future studies involving human experts’ review are recommended to evaluate the cultural and contextual validity of the generated assessments. We also suggest empirical studies in diverse contexts to further test and validate the model’s overall effectiveness.

Artificial intelligence AI is a sophisticated model or algorithm that has transformed a number of industries, including the medical field. In generative artificial intelligence, content is created using models or algorithms. Generative AI in healthcare revealed numerous new application domains for illness diagnosis. It has given models new outputs that they may use to create unique and fresh content. These can be used to learn new patterns that closely resemble the original data. The models covered in this review article serve as diagnostic tools in a variety of medical imaging applications. Medical specialists use generative AI in medical imaging analysis as a preliminary tool to generate large amounts of data that they can then correlate and extract useful information from. It is widely used in medical imaging, such as x-rays, magnetic resonance imaging, CTscan, COVID, cancer areas are utilized as AI diagnostic tools. Artificial intelligence (AI) has undergone a revolution because of to deep learning, which has made it possible for machines to learn from vast amounts of data and carry out complicated tasks based on that data. One of the most exciting uses of deep learning is in the creation of generative models, which are deep learning models that can produce realistic images, videos, and audio. These models have a variety of uses in the healthcare industry; including imaging. Medical imaging now has a plethora of options because to generative AI, a subfield of AI that focuses on producing original information. With improved patient outcomes, tailored treatment regimens, and greater diagnostic capabilities, it gives healthcare providers more control. Generative AI has transformed medical picture processing, interpretation, and application in clinical practice by harnessing the power of deep learning algorithms. Better diagnosis, New medicine discovery, Personalized medicine Improved medical imaging: The quality of medical images can be raised with the application of generative AI. With the aid of this technology, physicians may be able to diagnose and treat patients early by seeing more detail in images, More effective surgery: Virtual patient models can be produced using generative artificial intelligence, Surgeons can be trained and surgery plans can be made using these models, enhanced rehabilitation ,better mental health care: Chatbots that can provide patients therapy can be developed using generative AI. Medical imaging analysis is a field of healthcare that has revolutionized the way we approach patient care. The ability to examine medical images such as X-rays, CT scans, and MRI scans has allowed doctors and healthcare professionals to gain a better understanding of a patient's condition, monitor their illness, and plan their treatment accordingly. However, with the vast amounts of data produced by medical imaging technology, it is essential to have specialist analysis to retrieve useful information. One of the most significant advancements in medical imaging analysis has been the development of generative image AI models. These models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Auto encoders, have enabled healthcare professionals to analyze medical images more effectively and efficiently. The GAN model is one of the most popular generative picture AI models. It comprises two neural networks - a generator network and a discriminator network. The discriminator network's primary role is to discern between artificial and real visuals, while the generator network creates synthetic images. In an adversarial training process, the discriminator and generator work together to accurately categorize artificial and real images, with the generator trying to trick the discriminator. This technique has proved useful in radiology, where GANs are used for tasks such as image synthesis, image de-noising, and image segmentation. Image segmentation is a technique used in medical imaging analysis to split an image into several areas or segments. This has proved in identifying specific structures or regions in medical images, such as tumors, blood vessels, or organs. Generative deep models such as VAEs, GANs, and diffusion models have been applied to the augmentation of medical data, providing healthcare professionals with a more comprehensive understanding of a patient's condition. The development of generative image AI models has revolutionized medical imaging analysis, providing healthcare professionals with a more efficient and effective way to analyze medical images. Medical imaging has been a vital tool for healthcare professionals in diagnosing and treating various medical conditions. However, the interpretation of medical images is often challenging due to the complexity of the human body and the variations in anatomical structures. Therefore, image processing techniques are utilised to enhance the quality of medical images and extract relevant information. In this regard, fundamental image processing operations such as image segmentation, registration, and feature extraction are commonly utilised. However, the application of these operations might not always be appropriate due to the intricate structures of medical images, which include various anatomical variations and irregular tumour shapes. In the case of brain imaging, the presence of different tissues and structures, such as grey matter, white matter, and cerebrospinal fluid, can make it challenging to distinguish between healthy and diseased areas. More advanced image processing techniques such as machine learning algorithms are becoming increasingly popular for medical image analysis. The creation of irrelevant images that disturb the logical structure of the image can also occur due to the complexity of medical images. This adversely affect the accuracy of the interpretation of the image, leading to incorrect diagnoses and treatment plans. Hence, there is a need for sophisticated image processing techniques that can remove unwanted features and highlight the crucial information in the image. The performance of the model employed for image analysis might also be adversely affected by aberrant data and image deformations. Pre-processing techniques like noise reduction and picture normalization are essential for guaranteeing that the image is free of aberrations that could affect the interpretation's correctness. In conclusion, more sophisticated methods are needed to overcome the difficulties posed by the complexity of the images, fundamental image processing processes can be useful in improving the quality of medical images. In the end, these methods may improve patient outcomes by diagnosis.

Rapid growth and domestic migration to capital city, Kathmandu Metropolitan City (KMC), Nepal have amplified land-use transition to unmanaged and unplanned urbanization have exacerbated problems of Urban Heat Island (UHI) Effect. Increasing built up surfaces, means of transport and industrial activities are major results for increasing temperature in the city area as compared to other areas. This study focuses on forecasting Land Surface Temperature (LST) using both univariate and multivariate time series models. The primary goal is to predict the impact of climatic factors on the Urban Heat Island (UHI) effect, utilizing data from 1981 to 2019. We analyzed key factors including - (i) relative humidity, (ii) surface pressure, (iii) specific humidity, and (iv) wind speed sourced from the MEERA-2 dataset. A univariate model using exponential smoothing achieved a Mean Absolute Error (MAE) of 3.17°C and an R² score of 0.94, highlighting strong predictive accuracy. Additionally, the multivariate Prophet model, which incorporates trend and seasonal components, further refined the forecasting by integrating multiple climatic factors. This study aims provides valuable information via prediction for sustainable urban planning and landscape design in KMC and other 61 districts.

Introduction: Based on clinical practice guidelines, the application of corticosteroids as first-line therapy is common. Although SSHL's etiology is primarily idiopathic, hearing loss can result from perilymphatic fistula (PLF). Recent findings show evidence of a spe-cific rate of PLF based on a cochlin- tomoprotein (CTP) detection test. Based on this rate of PLF treatment, the concepts of SSNHL need to be re-evaluated. The present study aimed to evaluate CTP in SSNHL patients and compare pre-surgical and post-surgical pure tone hearing thresholds after round and oval window sealing as first-line treatment. Material and Methods: 30 patients with unilateral SSNHL with pure tone average (PTA) (4 Freq.) of 60 dB or more were enrolled in a prospective study. All patients underwent tympanoscopy for middle ear exploration as first-line treatment. After intraoperative obser-vation of possible PLF all patients obtained middle ear lavage to gain CTP samples for following ELISA-based CTP detection tests. All patients received round window and oval window sealing with fascia. PTA hearing thresholds were analyzed post-surgical 3 weeks after treatment based on 4-frequency bone conduction (BC). Results: Average preoperative pure-tone BC threshold was 97,7 dB compared with 69 dB postoperative BC threshold. Mean BC improved by 20,3 dB after middle ear explora-tion and window sealing. 56% (17 of 30) of patients recovered at least 10 dB. Middle ear cochlin-tomoprotein detection rate was in 70% positive. Conclusion: The combination of early tympanoscopy and inner ear-specific cochlin-tomoprotein as a detection tool for suspected perilymphatic fistula shows evidence of PLF as a key causative in SSNHL.

Cerebral palsy is a group of nonprogressive conditions that affect movement and posture caused by damage to the central nervous system at an early stage of a child's development. Among this group of patients, there is a higher risk of the appearance and progression of neurogenic scoliosis, which unlike idiopathic scoliosis, progresses after skeletal maturity. The aim of the study was to analyze the association of comorbid factors with the risk of development and progression of neurogenic scoliosis. The study included 133 patients with cerebral palsy. The relationship between comorbidities in children with cerebral palsy and the risk of developing neurogenic scoliosis was demonstrated and proven.

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

Background/Objectives: The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in Physiologically-Based Pharmacokinetic (PBPK) modeling, its regulatory implications, and its role in achieving virtual bioequivalence, particularly for complex drug formulations. Methods: We conducted a systematic review of clinical trials using computational methods, particularly PBPK modeling, for bioequivalence assessments. Eligibility criteria are emphasized in silico modeling and pharmacokinetic simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, and the Cochrane Library. A search strategy using key terms and Boolean operators ensured extensive coverage. We adhered to PRISMA guidelines for study selection, data extraction, and quality assessment, focusing on key characteristics, methodologies, outcomes, and regulatory perspectives from the FDA and EMA. Results: Our findings indicate that PBPK modeling significantly enhances the prediction of pharmacokinetic profiles, optimizing dosing regimens while minimizing the need for extensive clinical trials. Regulatory agencies have recognized this utility, with the FDA and EMA developing frameworks to integrate in silico methods into drug evaluations. However, challenges such as study heterogeneity and publication bias may limit the generalizability of results. Conclusions: This review highlights the critical need for standardized protocols and robust regulatory guidelines to facilitate the integration of virtual bioequivalence methodologies into pharmaceutical practices. By embracing these advancements, the industry can improve drug development efficiency and patient outcomes, paving the way for innovative therapeutic solutions. Continued research and adaptive regulatory frameworks will be essential in navigating this evolving field.

Retrospective information about plant ecophysiology and the climate system are key inputs in Earth system and vegetation models. Dendrochronology provides such information with large spatiotemporal coverage, and carbon isotope analysis across tree-ring series is among the most advanced dendrochronological tools. For the past seventy years, this analysis was performed on whole molecules and, to this day, ¹³C discrimination during carbon assimilation is invoked to explain isotope variation and associated climate signals. Recently, however, it was reported that tree-ring glucose exhibits multiple isotope signals at the intramolecular level (see companion paper). Here, I estimated the signals’ contribution to whole-molecule isotope variation and found that downstream processes in leaf and stem metabolism each introduce more variation than carbon assimilation. Moreover, downstream processes introduce most of the climate information. These findings are inconsistent with the classical concepts/practices of carbon-isotope dendrochronology. More importantly, intramolecular tree-ring isotope analysis promises novel insights into forest metabolism and the climate of the past.

Soil acidity is a major limiting factor for potato production in Peru's high Andean region. Predicting the spatial variability of acidity is essential for recommending site-specific liming treatments. A total of 90 soil samples, taken from a depth of 0-30 cm, were collected from five locations in the province of Pachitea, Huánuco. Descriptive statistics, principal component analysis (PCA), and Pearson correlation analysis were used to identify the soil properties contributing most to total variance and those most strongly correlated with soil acidity. The ordinary geostatistical kriging method evaluated the predictive accuracy for 23 soil properties and liming requirements over a 28,463-ha area. Results showed that the Plaza Punta and Buenos Aires locations had more de-graded acidic soils, with frequencies between 55% and 100% above the general mean (30.94 ± 24.87%) and the critical threshold (25%) for potato cultivation. Variables such as exchangeable calcium percentage (ECP), Ca²⁺, Mg²⁺, sand content, and organic matter strongly correlated with soil acidity, while exchangeable H⁺ and ECP were the main contributors to the total variance. Ge-ostatistical analysis revealed that Mg²⁺ and Ca²⁺ had the highest R² values (0.87 and 0.76, respec-tively), indicating a strong fit between observed and predicted values in the spatial analysis of soil acidity. The spatial analysis predicted a liming requirement for a 28,463-ha area, ranging from 2 to 14 t∙ha⁻¹ of dolomite.

By combining a lattice subgroup diagram of Z_m with a weighted poset metric, we introduce a new weighted coordinates poset metric for codes over Z_m , called LS-poset metric. When I is an ideal in a poset, the concept of I-perfect codes with LS-poset metric is investigated. We obtain a Singleton bound for codes with LS-poset metric and define MDS codes. When the poset in poset metric is a chain, we provide sufficient conditions for a code with LS-poset metric to be r-perfect for some r ∈ N .

This work aims to evaluate the by-products obtained by thermal and catalytic pyroly-sis of the fraction (organic matter and paper) of municipal solid waste (MSW), in dif-ferent socioeconomic regions, through the yields of reaction products (bio-oil, bio-char, H2O and gas), acid value and chemical composition of bio-oils, and characteriza-tion of biochar, on a laboratory scale. The organic matter and paper segregated from the gravimetric composition of the total waste sample were subjected to drying, crushing and sieving pre-treatment. The experiments were carried out at 450 °C and 1.0 atmosphere, and at 400 °C and 475 °C and 1.0 atmosphere, using 10.0% (by mass) of the basic catalyst Ca(OH)2, in discontinuous mode. The bio-oil was characterized by acidity value and the chemical functions present in the bio-oil identified by FT-IR, NMR and composition by GC-MS. The biochar was characterized by SEM/EDS and XRD. The bio-oil yield increased with the addition of catalyst and the pyrolysis tem-perature. For catalytic pyrolysis, bio-char and gas yields increased slightly with Ca(OH)2 content, while bio-oil and H2O phase remained constant. GC-MS of the liquid reaction products identified the presence of hydrocarbons and oxygenates, as well as nitrogen-containing compounds, including amides and amines. The acidity of the bio-oil decreased with the addition of the basic catalyst in the process. The concentration of hydrocarbons in the bio-oil appeared with the addition of the catalyst in the catalyt-ic pyrolysis process as catalytic deoxygenation of fatty acid molecules occurs, through decarboxylation/decarbonylation, producing aliphatic and aromatic hydrocarbons, in-troducing the basic catalyst into the thermal process.

The alkoxycarbonylation of styrene by palladium chloride is studied employing the density functional theory (DFT). Initially, [PdCl3]– reacts with methanol to form the methoxy-bound intermediate, which undergoes β-hydride elimination to form the key intermediate [PdCl2H]–. Then, a 1,2-insertion reaction to styrene takes place to form linear and branched alkyl coordinated with the PdII. Then CO coordination followed by a 1,1-insertion reaction leads to the formation of acyl intermediate. Next, the methanolysis leads to the formation of esters. Previous reports with other catalysts suggested the intermolecular/intramolecular transition state (TS) formation with a high activation barrier, and this step was the bottleneck. To the best of our knowledge, it is the first time we have considered a two-step mechanism for the alcoholysis of the ester formation mechanism. After coordination with the metal, the methanol undergoes oxidative addition to form the PdIV square pyramidal intermediate, followed by reductive elimination to form the ester with regeneration of the metal hydride active intermediate. Deeper insight into the nature of bonding at the TSs are obtained through energy decomposition with natural orbital for chemical valence (EDA-NOCV) and quantum theory of atoms in molecules (QTAIM).

Background: the red palm weevil, Rhynchophorus ferrugineus (F.) (Col.: Curculionidae: Dryophthorinae) is a pest species that infests many palm and coconut tree species across 50 countries. Larval feeding damages the palm heart and/or the palm stipe meristem, often causing the tree’s death. The damage caused by the larvae also allows phytopathogens and other insect pests to enter the tree, which can prove lethal. Methods: two active ingredients, azadirachtin and boric acid, were incorporated into an artificial diet fed to the weevils. The efficacy of these compounds was evaluated through bioassays conducted under laboratory conditions. Results: the most effective concentration for azadirachtin (95.0±5.0 %) was observed at 50.0 ppm on day 20; for boric acid (100 %), it was most efficacious at 2,500.0 ppm on day 12. Conclusions: boric acid shows significant potential in controlling the larvae of this weevil pest species. It does so in two ways - firstly, through its direct action against the red palm weevil larvae, and secondly, by its interaction with the micro-organisms associated with the larvae, which may result in more rapid mortality amongst the pest larvae themselves.

The rapid expansion of the Internet of Things (IoT) is revolutionising industries by enhancing connectivity among people, systems, and objects. With IoT device numbers predicted to rise from 9.7 billion in 2020 to 29 billion by 2030, its integration into supply chain management is increasingly critical, improving transparency, efficiency, and decision-making. This study focuses on evaluating the impact of IoT on supply chain efficiency within five Nigerian companies, a region where IoT's potential remains largely untapped due to infrastructural and technological challenges.Methods: The research employed a quantitative methodology, with purposive sampling used to select 50 participants across the 5 selected companies, ensuring a comprehensive assessment of IoT's influence across diverse sectors. A structured questionnaire was administered online to 10 senior managers from each selected company, collecting data on IoT adoption levels, applications, and impacts on supply chain performance. Data analysis was conducted using IBM SPSS version 27, employing descriptive analysis and regression and correlation tests to examine the relationship between IoT adoption and supply chain metrics.Results: The analysis revealed a significant adoption rate of 74.4% across the companies, with notable enhancements in supply chain metrics. Specifically, a negative correlation coefficient of -0.345 was observed between IoT adoption and lead time, although this was not statistically significant (p=0.053). Inventory turnover showed marked improvement, with 60% of companies reporting increased efficiency in inventory management. Order accuracy saw substantial improvements, with 17 out of 43 respondents (39.5%) rating it as significantly enhanced. Despite these positive trends, challenges such as inadequate infrastructure and high implementation costs were significant barriers, affecting the full realization of IoT benefits.Conclusion and Recommendations: The study concludes that while IoT adoption shows promise in enhancing supply chain operations in Nigeria, the benefits are unevenly realized, largely influenced by existing infrastructural deficits and high initial costs. Recommendations include enhancing digital infrastructure, developing supportive regulatory frameworks, and implementing pilot projects to better integrate IoT solutions tailored to specific operational needs. Investing in local expertise and fostering public-private partnerships are also critical for overcoming barriers to IoT adoption and leveraging its full potential for supply chain enhancement in Nigeria.

Objectives: The aim of the present preclinical study, using rabbits as an experimental model, investigated the effects of a new implant design that incorporates specific changes in the cervical portion, using a conventional implant design as a control group. Materials and Methods: Ten rabbits and 40 dental implants with two different macrogeometries were used. Two groups were formed (n = 20 per group): Collo group, where implants with a new cervical design were used, which present a concavity (reduction in diameter) in the first 3.5 mm and with this portion of the implant without surface treatment; Control group, where conical implants with conventional design were used, which feature surface treatment throughout the body. All implants present dimensions of 4 mm in diameter and 10 mm in length. The initial implant stability quotient (ISQ) was measured immediately after the implant insertion (T1) and immediately after removing the pieces with the implants. The animals (n=5 animals/time) were euthanized at 3 weeks (T1) and 4 weeks (T2). Histological sections were prepared and, the percentage of bone-implant contact (BIC%) and the tissue area fraction occupancy (TAFO%) were analyzed in the pre-determined cervical area of the first 4 mm from the implant platform. Results: The ISQ values showed no statistical differences in T1 and T2 (p = 0.9458 and p = 0.1103, respectively). However, in T3 superior values were found for the Collo group (p = 0.0475). Higher BIC% values were presented in samples from the Collo group compared to the Control group, with statistical differences (3-weeks p = 0.0009 and 4-weeks p = 0.0007). TAFO% (new bone, medullary spaces, and collagen matrix) had statistical differences between the groups at each evaluation time. Conclusion: Within the limitations of the present pre-clinical study, the results of this study suggest that the new implant design with changes in the cervical portion (Collo group) presents significant advantages concerning the conventional implant (Control group), regarding stability in the initial times and osseointegration.

Time is what we measure with clocks. With clocks, we measure the duration of material change tuning in space. Material change runs in space only, time, as duration enters existence when measured. Time is an emergent physical reality that enters existence when measured by the observer. No measurement means no time. The motion does not require time. Motion requires only space and the physical object that moves in space, which is time-invariant. There is no physical past; there is no physical future. Past and future exist only in the human mind. Humans, we experience time-invariant space as NOW. The entire universe exists and develops into NOW.

The current study explores for the first time an eco-friendly solution casting method using a green solvent, Ethyl Acetate, to prepare feedstock/filaments from Polylactic Acid (PLA) biopolymer reinforced with carbon nanotubes (CNTs), followed by 3D printing and surface activation for biosensing applications. Comprehensive thermal, electrical, rheological, microstructural, and mechanical properties of developed feedstock and 3D printed parts were performed and analyzed. Herein, adding 2 wt.% CNTs to the PLA matrix marked the electrical percolation, achieving conductivity of 8.3×10-3 S.m-1, thanks to the uniform distribution of CNTs within the PLA matrix facilitated by the solution casting method. Rheological assessments paralleled these findings; the addition of 2 wt.% CNTs transitioned the nanocomposite from liquid-like to a solid-like behavior with a percolated network structure, significantly elevating rheological properties compared to composite with 1 wt.% CNTs. Mechanical evaluations of the printed samples revealed improvement in tensile strength and modulus compared to virgin PLA by a uniform distribution of 2 wt.% CNTs into PLA, with an increase of 14.5% and 10.3%, respectively. To further enhance the electrical conductivity and sensing capabilities of developed samples, an electrochemical surface activation treatment was applied to as-printed nanocomposite samples. Field-emission scanning electron microscopy (FE-SEM) analysis confirmed that this surface activation effectively exposed the CNTs to the surface of 3D-printed parts by removing a thin layer of polymer from the surface, thereby optimizing the composite's electroconductivity performance. The findings of this study underscore the potential of the proposed eco-friendly method in developing advanced 3D printed bio nanocomposites based on carbon nanotubes and biopolymers, using a green solution casting and cost-effective material extrusion 3D printing method, for electrochemical sensing applications.

Federated Learning (FL), as a distributed machine learning method, is particularly suitable for training models that require large amounts of data while meeting increasingly strict data privacy and security requirements. Although FL effectively protects the privacy of participants by avoiding the sharing of raw data, balancing the risks of privacy leakage with model performance remains a significant challenge. To address this, this paper proposes a new algorithm—FL-APB (Federated Learning with Adversarial Privacy-Performance Balancing). This algorithm combines adversarial training with privacy protection mechanisms to dynamically adjust privacy and performance budgets, optimizing the balance between the two while enhancing and ensuring performance. Experimental results demonstrate that the FL-APB algorithm significantly improves model performance across various adversarial training scenarios, while effectively protecting the privacy of participants through adversarial training of privacy data.

Bathymetric data is crucial for benthic monitoring in coastal areas but is traditionally obtained through costly and geographically limited acoustic methods. This study uses Satellite-Derived Bathymetry (SDB) in the Eastern Mediterranean, focusing on the Cretan Sea in Greece. It explores how variations in water column optical properties affect SDB models over four years (2019-2022) using Sentinel-2 satellite data. The research covers two areas with contrasting features: Chania Gulf and the open waters around Chrissi Island. Three methodologies were tested: band-ratio, linear-logarithmic, and an inherent optical properties linear model. Significant spatio-temporal variations in SDB models were found due to seasonal changes in water column properties, such as temperature and suspended organic materials. Linear optical properties-based methods performed best, achieving a mean RMSE close to 1 meter, slightly outperforming the ratio-based method. The logarithmic method was less effective, with RMSE values ranging from 1.3 to 1.5 meters. A preliminary Kalman Filter (KF) analysis increased RMSE to the decimeter level. This study demonstrates the impact of water column optical properties on SDB models. It highlights the value of SDB for cost-effective, high-resolution coastal mapping in complex coastlines like those in Greece.

Background and Objectives: A degenerative joint disease that primarily affects the elderly, osteoarthritis (OA) causes pain, decreased mobility, and a lower quality of life. The effectiveness of GerovitalH3, a biotrophic medication created by Ana Aslan, in reducing pain and enhancing function in older people with osteoarthritis in the knee and hip is assessed in this study. Materials and Methods: Two groups of 201 patients aged 65 and older participated in a randomized, case-control clinical trial. One group received physical therapy and GerovitalH3 periarticular injections, while the other group received just physical therapy. In addition to functional evaluations using the Lequesne Index, Activities of Daily Living (ADL), and Instrumental ADL (IADL) scores, pain was assessed using a Visual Analogue Scale (VAS). Results: The GerovitalH3 group showed a substantial decrease in pain (p<0.001) and an improvement in daily activities (p<0.001) when compared to the control group. However, there was no significant difference in walking scores (p = 0.171). There were no significant adverse effects noted. GerovitalH3 did not outperform physical therapy for reducing depression, even though both groups displayed some improvement in this area. Conclusions: This study opens a new possibility for periarticular injections of Gerovital H3 treatment combined with physical therapy, which may represent a safe and effective non-surgical option for elderly individuals struggling with OA pain and functional limitations. The findings of this study reinforce the need for further research, particularly randomized controlled trials with larger sample sizes and longer follow-up periods.

Quorum Sensing (QS) and Machine Learning (ML) hybrid systems represent a groundbreaking innovation in synthetic biology, offering unprecedented control and adaptability in microbial gene regulation and metabolic processes. QS, a microbial communication mechanism, is crucial for coordinating gene expression in response to population density, impacting behaviors such as biofilm formation, virulence, and resource optimization. However, traditional QS systems are constrained by their reliance on static, pre-programmed feedback loops, limiting their flexibility in dynamic, complex environments. This review highlights how integrating advanced ML algorithms—such as reinforcement learning and deep learning—into QS systems can overcome these limitations by enabling real-time data processing, predictive modeling, and dynamic feedback control. Through these innovations, QS-ML systems can autonomously adjust gene expression and metabolic outputs, making them more efficient and scalable in applications ranging from pathogen control to precision medicine and industrial biomanufacturing. Key case studies illustrate the successful deployment of QS-ML systems to combat antimicrobial resistance, optimize bio-production, and enhance therapeutic precision in cancer and immune modulation. Despite the clear advantages, challenges remain in data integration, system robustness, and regulatory oversight. Addressing these hurdles through interdisciplinary collaboration and developing scalable, multi-omics data platforms will be critical for advancing QS-ML systems from experimental settings to real-world applications. This review underscores the transformative potential of QS-ML systems in revolutionizing synthetic biology, with profound implications for personalized medicine, sustainable biomanufacturing, and environmental health.Quorum Sensing (QS) and Machine Learning (ML) hybrid systems represent a groundbreaking innovation in synthetic biology, offering unprecedented control and adaptability in microbial gene regulation and metabolic processes. QS, a microbial communication mechanism, is crucial for coordinating gene expression in response to population density, impacting behaviors such as biofilm formation, virulence, and resource optimization. However, traditional QS systems are constrained by their reliance on static, pre-programmed feedback loops, limiting their flexibility in dynamic, complex environments. This review highlights how integrating advanced ML algorithms—such as reinforcement learning and deep learning—into QS systems can overcome these limitations by enabling real-time data processing, predictive modeling, and dynamic feedback control. Through these innovations, QS-ML systems can autonomously adjust gene expression and metabolic outputs, making them more efficient and scalable in applications ranging from pathogen control to precision medicine and industrial biomanufacturing. Key case studies illustrate the successful deployment of QS-ML systems to combat antimicrobial resistance, optimize bio-production, and enhance therapeutic precision in cancer and immune modulation. Despite the clear advantages, challenges remain in data integration, system robustness, and regulatory oversight. Addressing these hurdles through interdisciplinary collaboration and developing scalable, multi-omics data platforms will be critical for advancing QS-ML systems from experimental settings to real-world applications. This review underscores the transformative potential of QS-ML systems in revolutionizing synthetic biology, with profound implications for personalized medicine, sustainable biomanufacturing, and environmental health.

Non-Destructive Testing (NDT) is an appealing technique for confirming the welding quality and ensuring structural integrity without causing damage. It assists in regulatory compliance, risk mitigation, and process optimization for improved industrial reliability. This study reviews potential NDT approaches. Through a rigorous analysis of existing surveys, we aim to decipher the current landscape and highlight the significant advancements in the field. Because of the potential of artificial intelligence (AI) assistive X-ray imaging-based NDT, we particularly examine the integration of AI algorithms and X-ray imaging in the NDT of welds. This convergence represents a paradigm shift, redefining traditional methods and ushering in a new era of precision, automation, and efficiency. As we navigate through potential X-ray imaging datasets, delve into crucial image processing techniques, examine feature extraction methods, and explore AI algorithms, our survey reveals the intricate interplay of technologies that drive automated weld defect categorization. The review broadens its focus by including various practical applications, highlighting the adaptable utility of AI-assistive X-ray imaging for weld defect detection in potential industrial applications. Moreover, the promising opportunities and nuanced challenges associated with integrating X-ray imaging and AI in weld integrity assessment are highlighted. It provides a comprehensive perspective on this rapidly evolving field.

Melanoma is a malignant, highly metastatic neoplasm showing increasing morbidity and mortality. Tumor invasion and angiogenesis are based on remodeling of the extracellular matrix (ECM). Selective inhibition of functional components of cell-ECM interaction, such as hyaluronic acid (HA), matrix metalloproteinases (MMPs), and integrins, may inhibit tumor progression and enhance the efficacy of combination treatment with checkpoint inhibitors (ICBs), chemotherapy, or immunotherapy. In this review, we have combined the results of different approaches targeting extracellular matrix elements in melanoma in preclinical and clinical studies. The identified limitations of many approaches, including side effects, low selectivity and toxicity, indicate the need for further studies to optimize therapy. Nevertheless, significant progress in expanding our understanding of tumor biology and the development of targeted therapies holds great promise for the early approaches developed several decades ago to inhibit metastasis through ECM targeting.

Renewable energy (RE) is one of the keys to achieving the global goal of net-zero carbon emissions by 2050. Understanding the factors influencing public participation behavior and social acceptance in RE management (REM) is thus essential to a sustainable energy transition embraced by communities and stakeholders. This study aims to assess nine indicators in REM across three dimensions of social acceptance through the public lens, employing an importance–performance analysis. Our findings reveal a significant gap between the perceived importance and performance of various REM components, suggesting dissatisfaction among respondents. Furthermore, the highest priority for improvement was given to the following strategies: "strengthening communication with residents”, "reducing air pollution emissions from power plants", "establishing supervision and management mechanisms", and "building smart grid and power storage equipment" to ensure successful implementation of REM policies. We also found that respondents who were under 50 years of age, lived in Northern Taiwan, had a higher monthly income, and had a better awareness of social acceptance were more likely to participate in the energy transition. The insights gained from this study outline policy implications for improving the public’s social acceptance and advancing the development of RE in Taiwan, contributing to the world’s carbon-neutral mission.

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

The integration of Artificial Intelligence (AI) into architecture is reshaping the industry by enhancing design processes, project management, and sustainability efforts. This study provides a comprehensive analysis of AI software tools, focusing on their functionalities in the market, automated site analysis, building performance analysis, architectural design development, and additional features. Key findings reveal that while tools like Spacio.ai, Swapp, and Autodesk Forma offer extensive features across various design phases, their effectiveness is influenced by design capabilities, sustainability and viability assessments, overall architectural design procedures, and third-party interactions. The study underscores the need for tailored AI tools that can effectively follow the architectural process from conceptualization and design to engineering and building permits, all while ensuring full regulatory compliance and adaptability in real-time. By identifying current trends and features, this study contributes to understanding the role of AI in architecture, offering valuable insights for software developers and professionals aiming to integrate AI capabilities into a robust platform. The goal is to elevate architecture to higher levels of human engagement and construction excellence, harnessing the full potential of AI.

In a recent paper [B. Michon et al., Nat. Commun. (2023) 14:3033] optical conductivity experiments in cuprate superconductors were shown to display scaling properties consistent with the Marginal Fermi Liquid theory. Here we argue, that the temperature regime studied in these experiments does not allow to distinguish between Marginal Fermi Liquid and a Shrinking Fermi Liquid. In the latter scenario, which we recently proposed and which applies near a quantum critical point, dynamical fluctuations of the order parameter with short correlation length mediate a nearly isotropic scattering among the quasiparticles over the entire Fermi surface leading to strange-metal behavior. If the damping of these nearly local fluctuations increases by decreasing the temperature, the Fermi liquid regime shrinks and the strange-metal behavior is extended to the lowest temperatures. This Shrinking Fermi liquid scenario has many similarities and some differences with respect to the Marginal Fermi liquid theory. In particular, we show that the approximate scaling properties of the optical conductivity in some high-frequency regime predicted by the Shrinking Fermi liquid scenario account to a very good extend for the experimental data.

Poverty elimination is an essential and unavoidable step in human development. Predicting poverty is the first crucial step in addressing this issue, especially in developing countries where it remains a significant concern. The main objectives of this paper are: (i) to explore and analyze the multidimensional poverty data in Pattani province, and (ii) to apply feature selection techniques (Chi-Square, Mutual Information, and Gini Index) to enhance the prediction process. These techniques help in reducing irrelevant and redundant features, leading to more efficient models and better insights. This paper presents the development of a predictive model aimed at classifying poverty and providing actionable recommendations for policymakers. Machine learning models, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), were employed to assess the impact of feature selection methods on model performance. The effectiveness of each model was evaluated through various metrics, including accuracy. The experimental results show that the success of these models in predicting poverty, with DT, RF, and SVM obtaining 93%, 95%, 94% of accuracy, respectively. The findings underline the importance of feature selection in improving the performance of machine learning models.

Artificial General Intelligence (AGI) represents a pivotal advancement in AI with far- reaching implications across technological, ethical, and societal domains. This paper addresses the following: (1) an in-depth assessment of AGI’s transformative potential across different sectors and its multifaceted implications, including significant financial impacts like workforce disruption, income inequality, productivity gains, and potential systemic risks; (2) an examination of critical ethical considerations, including transparency and accountability, complex ethical dilemmas and societal impact; (3) a detailed analysis of privacy, legal and policy implications, particularly in intellectual property and liability, and (4) a proposed governance framework to ensure responsible AGI development and deployment. Additionally, the paper explores and addresses AGI's political implications, including national security and potential misuse. By analyzing and considering computer science, philosophy, economics, and policy perspectives, we offer a multidisciplinary view of AGI’s challenges and opportunities, advocating for proactive measures to align AGI development with human values and societal interests.

Aging is a gradual and irreversible physiological process that leads to declines in tissue and cell functions, along with an increased risk of developing various age-related diseases. The primary driving force associated with aging is the accumulation of damaged genetic material in the cell, such as DNA. DNA damage can be caused by endogenous and exogenous factors, which leads to genome instability, mitochondrial dysfunction, epigenetic modifications, and proteostatic disturb. Another driving force associated with aging is the disruption of cellular metabolism. This disruption is closely linked to alterations in the function of metabolic pathways, including insulin/IGF-1 and mTOR, which regulate crucial cellular processes like cell growth, cell proliferation, and apoptosis. The activation of the insulin/IGF-1 signaling pathway highly promotes cell growth and proliferation, while also inhibits autophagy and increasing ROS production. This ultimately leads to accelerated aging. Another crucial signaling pathway is the mTOR signaling pathway. It is responsible for detecting nutrient availability and controlling cell growth and metabolism. The dysregulation of mTOR function can lead to the development of neurodegenerative diseases, which are characterized by the aggregation of protein. Activation of transposable elements is the other driving force of aging, caused by changes in DNA methylation and the loss of heterochromatin. As a result, this leads to DNA damage, genomic instability, and inflammation. The aim of this review is to elucidate the consequence of DNA damage and other associated factors drive aging.

The study explores the potential of Cell-Penetrating Peptides (CPPs) for targeted drug delivery, particularly in anticancer applications. Despite certain drawbacks, such as short duration of action and poor stability in vivo, CPPs show promise when optimized through chemical modification or clever design. Advancements in targeted drug delivery using CPPs, especially for transporting anticancer drugs and imaging reagents, highlight their evolving efficacy. Four different CPP chains (7 alpha, 7 beta, 12 alpha, and 12 beta) and four in-house anticancer lead molecules were considered for the study. The CPPs were modeled and validated for in silico interaction analysis. Targeting proteins crucial in cell growth and signaling pathways, such as ADRM1, PSMD4, CDK1, CDK2, CDK4, CDK6, P53, and EGFR, presents a significant therapeutic opportunity. The in silico molecular interaction analysis involving CPP-conjugated lead molecules and these targets indicates favorable binding affinities, particularly with beta-confirmation 12 and 7 peptide chains. Notably, CPP conjugation enhances interactions without compromising ligand potentiality. This delicate balance emphasizes the role of CPPs in augmenting protein-ligand interactions, suggesting their potential benefits in targeted drug delivery, pending further optimization and experimental validation.

Diabetic kidney disease (DKD) is regarded as a silent assassin due to its delayed signs. It is a progressive illness that can appear in various ways, from mild to severe. The severity of DKD must be determined in order to make educated treatment decisions and understand the hazards that the patient may encounter. DKD is one of the primary causes for death and morbidity among noncommunicable illnesses, affecting 10% to 15% of the global population. Timely prediction of DKD is expected to improve patients' health and reduce mortality. This study aims to predict DKD using machine learning models like Decision Trees, Random Forest, Logistic Regression, Bagging Tree Model and Support Vector Machine, to help in timely detection and cure of this deadly disease. Clinical data required for model training were taken from Kaggle's public repository and additional samples of 50 individuals were collected from local hospital, Coimbatore, Tamil Nadu. Meta-heuristic algorithms based on Swarm Intelligence, such as Particle Swarm Optimization, Ant Colony Optimization, and Firefly Optimization, were employed to optimize the selected models. The main aim of this study is to compare the predictions of different models and identify models that best agree with the given scenario.

Requirements elicitation from multiple human sources involves information validation and conflicts resolution to deal with uncertainty inherent in the process. Most requirements analysis methods focus on expressing the requirements and ignore the uncertainty inherent in the process of requirements elicitation. This paper build on the work presented by the author in [1,10], where a method for requirements elicitation from multiple sources was presented. This paper gives an overview of the method and presents detailed case studies. The cases studies were designed to illustrate different aspects of the Source Control Method and to determine its feasibility and its practical utility as a problem investigation and information validation technique.

This paper provides a brief overview of how the Haug and Tatum Cosmological Model (HTC) outperforms the Λ-CDM model in numerous categories. In particular, we show why the HTC model is a strong competitor to the Λ-CDM model. For example, HTC appears to resolve the Hubble tension by making use of newly-discovered fundamental relationships between the CMB temperature, the Hubble parameter, and cosmological redshift. Moreover, HTC appears to better explain certain early universe telescopic observations than the Λ-CDM model.

Lactate plays a critical role in cell metabolism and disease development. Under conditions of hypoxia or high-intensity exercise, cells use the glycolytic pathway to convert glucose into pyruvate, which is then reduced to lactate to quickly obtain energy. The traditional role of lactate is being redefined, as it is not only a provider of energy but also an important signaling molecule that regulates cell physiological functions, including histone lactylation modification, which affects gene expression. However, excessive accumulation of lactate is associated with the development of various diseases, such as liver disease, cancer, and cardiovascular disease. Research has also found that small-molecule drugs can regulate lactate levels, providing new possibilities for treating related diseases.

This scoping review seeks to map the landscape of how Artificial Intelligence (AI) is leveraged within educational environments to support students with disabilities and inclusive strategies and experiences. The research question concerns the role and impact of AI across diverse educational settings and in particular: “How is Artificial Intelligence (AI) being utilized within educational settings to support individuals with disabilities and promote inclusive education?”. The review explores this question under four pivotal dimensions: Educational Context, Disabilities and Special Needs, Artificial Intelligence Technologies, and Inclusivity and Inclusive Practice. Each contributes to a comprehensive understanding of the interdisciplinary nature of this inquiry. To ensure a comprehensive analysis, four major research databases have been used: Scopus, EBSCO, ERIC, and Web of Science (WoS). This robust search strategy enabled us to capture a wide array of relevant literature. The review also addresses ethical considerations essential for the responsible integration of AI in education, such as privacy, accessibility, and bias. By mapping existing research and identifying gaps, this scoping review lays the groundwork for future advancements in AI-driven inclusive educational practices.

The rapid advancement of 3D printing technology has revolutionized the fabrication of microneedle arrays (MNAs), which hold great promise in biomedical applications such as drug delivery, diagnostics, and therapeutic interventions. This review provides an exploration of the diverse materials used in the production of 3D-printed MNAs, including photopolymer resins, biocompatible materials, composite resins, and cutting-edge materials designed to improve mechanical properties, biocompatibility, and functional performance. By analyzing recent research and technological innovations, this paper presents an assessment of material characteristics, fabrication techniques, and their alignment with application-specific requirements. Additionally, it discusses future directions and addresses potential challenges in advancing the field of 3D-printed MNAs.

Techniques for monitoring calcium carbonate and silica deposits (scale) in geothermal power plants and hot spring facilities using fiber optic sensors have already been reported. These sensors continuously measure changes in light transmittance with a detector, and when applied to field tests, require the installation of a power supply and sensor monitoring equipment. However, in some sites, a power supply may not be available, or a specialist skilled in handling scale sensors is required. To overcome this problem, we have developed a method for evaluating scale formation based on a batch process that can be used by anyone. In other words, this method involves depositing scale on a section of the optical fiber sensor, then fusing this section to the optical fiber and measuring it. Using this sensor, a technician in the field can simply place the sensor at the desired location, collect the samples at any given time, and send them to the laboratory to measure the transmittance. This simple and easy method were achieved by using a hetero-core type of fiber optic. This evaluation method can measure with the same sensitivity as conventional real-time methods, while the transmittance response corresponds to the saturation index (SI) changes of scale components in the solution due to increases in temperature and concentration. In the field of carbon dioxide capture and storage (CCS), this evaluation method can be used to quantitatively measure the formation of carbonate minerals, and it can also be used as an indicator for determining the conditions for CO2 mineral fixation, as well as in experiments using batch-type autoclaves in the laboratory testing. It is also expected to be used in geothermal power plants as an evaluation method for scale formation such as amorphous silica and to prevent agents that hinder stable operation.

To address the issue of GNSS receivers being unable to function properly in tunnels due to the loss of Global Navigation Satellite System(GNSS) signals, this paper proposes a two-dimensional differential positioning system for tunnel environments, based on dual leaky coaxial(LCX) cables with GNSS signal frequency relay forwarding. The system receives mixed GNSS signals from open environments and utilizes the frequency selection capabilities of the MAX2769E chip to separate and generate radio frequency signals at different frequencies corresponding to GPS, BDS, and GLONASS. These signals are then used to drive three ports of the LCX cables, which are laid in parallel within the tunnel. By leveraging the uniform radiation characteristics of the LCX cables, stable GNSS signal coverage is achieved throughout the tunnel. On the receiving end, the GNSS receiver achieves two-dimensional positioning by utilizing inter-satellite pseudorange differences and reference point error correction. Simulation results indicate that the dual T-shaped radiating LCX cables configuration offers excellent positioning accuracy and noise resistance, achieving meter-level positioning accuracy in tunnel environments.

This paper provides an in-depth comparative analysis of three prominent machine learning techniques: decision trees, neural networks, and Bayesian networks. Each method is explored in terms of its theoretical foundations, algorithmic structure, strengths, limitations, and real-world applications. Decision trees are celebrated for their simplicity and interpretability, making them ideal for decision-making systems, but they often struggle with overfitting and poor performance on high-dimensional data. Neural networks, while capable of achieving high accuracy and effectively handling complex, non-linear patterns, are criticized for their "black-box" nature and computational intensity. Bayesian networks distinguish themselves through their ability to model uncertainty and incorporate prior knowledge, making them highly applicable in scenarios requiring probabilistic reasoning, yet they are challenging to scale for complex, high-dimensional data sets. This comparative analysis highlights the distinctive advantages of each method, their performance across various domains such as healthcare, finance, and risk assessment, and the growing potential of hybrid models that combine the strengths of these techniques. The paper concludes by discussing future research opportunities, particularly in enhancing model interpretability and scalability while addressing domain-specific challenges.

Social media platforms have become pivotal tools for small and medium-sized enterprises (SMEs), offering vast opportunities for enhanced organizational engagement. However, these platforms also present challenges such as data privacy concerns, feedback management, and content saturation. This systematic review critically evaluates the existing literature on the dual role of social media in fostering organizational engagement while addressing key barriers faced by SMEs. We systematically assessed 104 peer-reviewed articles sourced from Scopus, Web of Science, and Google Scholar, focusing on the impact of social media on marketing strategies and organizational outcomes in SMEs. The Newcastle-Ottawa Scale was used for quality assessment, and effect measures, including mean difference and odds ratio, were employed to evaluate performance metrics such as customer engagement, business performance, and long-term organizational impact. Key findings indicate that 74% of SMEs reported improvements in brand visibility, with customer engagement increasing by 65%. However, significant concerns were identified, with 45% of studies highlighting privacy issues and 52% addressing challenges in managing negative feedback. The review emphasizes that while social media can enhance market reach and customer interaction, its effectiveness largely depends on strategic content management and planning. This review provides actionable insights for SMEs aiming to optimize social media use, highlighting the need for future research to address privacy management and feedback strategies for sustained success.

Background Laparoscopic cholecystectomy constitutes the current “gold standard” treatment of symptomatic gallstone disease. In order to avoid intraoperative vasculobiliary injuries it is mandatory to establish “critical view of safety”. In case of poor identification of the cystic duct and artery leading to a missed intraoperative injury, patients present with elevated liver function tests (LFTs) or increased bilirubin postoperatively. The aim of this study is to present a series of patients of our institute with elevated liver enzymes and bilirubin after laparoscopic cholecystectomy in the absence of intraoperative injury or any other obvious etiology and to provide a possible explanation of this finding. Methods From 2019 to 2023, 200 patients underwent elective laparoscopic cholecystectomy at the Papageorgiou General Hospital and at the European Interbalcan Medical Center of Thessaloniki utilizing the “critical view of safety” method. We retrospectively collected the intraoperative reports and the pre and postoperative imaging and laboratory studies of the patients included in this series. Postoperative LFTs and bilirubin levels were extracted and the reason for their transient elevation was examined. Results From 200 cases of laparoscopic cholecystectomy, in 6 patients elevated LFTs and bilirubin were found on the first postoperative day, which is suggestive of a missed intraoperative injury. All patients were asymptomatic. During the investigatory workup, a triple-phase CT of the liver and/or an MRCP were ordered, but no pathological findings, such as biliary injury, hepatic artery injury or choledocholithiasis, were found. On postoperative day 3, LFTs and bilirubin levels decreased or normalized without any intervention. No postoperative complications were reported. Conclusion In select cases, a transient increase in LFTs and/or bilirubin may be observed in the early postoperative period after elective laparoscopic cholecystectomy in the absence of an obvious etiology. A possible interpretation of these findings could involve the pneumoperitoneum or the anesthesia regimens used intra- or perioperatively. The specific cause however remains undetermined and yet to be examined by future studies.

The Blue Economy is a recent development paradigm, created in response to a refocusing of sustainable development during preparations for the UN Conference on Environment and Development in 2012 in which the ‘green economy’ was proposed. Coastal and island States called for a ‘blue’ equivalent. In the years since the ‘blue economy’ has been enthusiastically received by many, but its exact nature remains uncertain and contested. In this paper I examine in more detail the practices, the technologies, the materialities, of the BE ‘dispositif’ to address the question of ‘place’, as it is only in the context of place, I argue, that we can really understand how the Blue Economy is enacted. In doing so, I make the argument that the Blue Economy is a ‘security dispositif’ (referencing Michel Foucault) and that to govern Blue Economy places well, we need to pay attention to the emergent space-time relations of the dispositif ‘in place’. Finally, I argue for a rethinking of economy and of blue economy governance, drawing on relational analysis of empirical cases in Kenya to call for a blue economy that is more sensitive to communities and the places they inhabit – a blue œconomy.

The study investigates using a recommender system to improve the accuracy of depth of defect (DoD) estimation in immersive steel samples. The proposed method can enhance prediction performance and be applied across various industries like water, oil, and gas. The recommender system is trained on experimental data from an immersive wedge sample and then evaluated on test data from a different sample to demonstrate its potential for precise and non-invasive DoD detection.

Aim: To determine the clinical performance association of periodontal disease with diabetes mellitus, using an umbrella review. Material and Methods: An information search was carried out up to August 2023 in the following electronic databases: PubMed, Cochrane database, Scopus, SciELO, Google Scholar and OpenGrey. We included studies that were systematic reviews (SR) with or without meta-analysis, without time and language restrictions, that evaluated primary studies that evaluated the association between periodontal disease with diabetes mellitus. Literature or narrative reviews, rapid reviews, intervention studies, observational studies, preclinical and basic research, abstracts, comments, case reports, protocols, personal opinions, letters, and posters were excluded. The AMSTAR-2 tool was used to determine the methodological quality of the included studies. Results: The preliminary search yielded a total of 577 articles, discarding those that did not meet the selection criteria, leaving only 17 articles. An association was found between periodontal disease and diabetes mellitus (type 1 and type 2 diabetes mellitus and gestational diabetes mellitus). Conclusion: Based on the results and conclusions of the SR with a high overall confidence, periodontal disease is associated with the onset of type 1 and type 2 diabetes mellitus and gestational diabetes.

Influenza behavior depends on several characteristics: the agent itself, the host response, and environmental changes that determine seasonality, presentation, disease severity, lethality, and intensity of contagion. Vulnerable populations, such as the elderly, people with underlying diseases, and incomplete vaccination status, can influence disease presentation and epidemiology. These information changes are necessary for existing surveillance systems. Special surveillance of human and animal diseases is necessary, considering recent outbreaks and epidemic waves of viral diseases such as avian influenza. This study analyzed the impact of the COVID-19 pandemic on influenza vaccination coverage in Mexico and Central America. We used existing data and information in the countries and what was reported to WHO and UNICEF on administrative coverage of influenza vaccination reported by the countries, and the PAHO surveillance databases for influenza and other respiratory viruses. Influenza vaccine application varies in Central American countries and Mexico. Vaccination schedules have been adapted following WHO and PAHO recommendations, and considering the epidemiological, organizational conditions and financial realities of each country. Surveillance systems must be prepared for a possible “tridemic” in humans involving influenza, SARS-CoV-2 and respiratory syncytial virus (RSV). Influenza-associated disease was considerably affected during the COVID-19 pandemic. Given the preventive measures implemented, cases were unusually low during 2021-22, but cases increased sharply in 2022-23 caused by influenza A and influenza A H1N1 pdm09, with some isolated cases of influenza B.

New data, including Raman spectroscopy, characterize unusual mineral assemblages from rocks of the Naylga and Khamaryn–Khyral–Khiid combustion metamorphic complexes in Mongolia. Several samples of melilite–nepheline paralava and other thermally altered (metamorphosed) sedimentary rocks contain troilite (FeS), metallic iron (Fe0), kamacite (Ni-bearing Fe0), taenite (Ni-rich Fe0), barringerite or allobogdanite (Fe2P), schreibersite (Fe3P), steadite (Fe4P = eutectic α-Fe+Fe3P), wüstite (FeO), and cohenite (Fe3C). The paralava matrix includes a fragment composed of magnesiowüstite–ferropericlase (FeO–MgO solid solution), as well as of spinel (Mg,Fe)Al2O4 and forsterite. The highest-temperature mineral assemblage belongs to a xenolithic remnant, possibly Fe-rich sinter: molten ash left after underground combustion of coal seams. The crystallization temperatures of the observed iron phases were estimated using phase diagrams for the respective systems: Fe–S for iron sulfides and Fe–P for iron phosphides. Iron monosulfide (high-temperature pyrrhotite) with inclusions of Fe0 underwent solid-state conversion into troilite at 140 °C. Iron phosphides in inclusions from the early growth zone of anorthite–bytownite in melilite–nepheline paralava crystallized from < 1370 to 1165 °C (Fe2P), 1165–1048 °C (Fe3P), and <1048 °C (Fe4P). Phase relations in zoned spherules consisting of troilite + Fe0 (or kamacite, taenite) + Fe3P ± (Fe3C, Fe4P) reveal potential presence of a homogeneous Fe–S–P–C melt at T ~1350 °С, which separated into two immiscible melts in the 1350–1250 °С range, namely, a dense Fe–P–C melt in the core and a less dense Fe–S melt in the rim. The melts evolved in accordance with cooling paths in the Fe–S and Fe–P–C phase diagrams. Cohenite and schreibersite in the spherules crystallized between 988 °C and 952 °C. The crystallization temperatures of minerals were used to reconstruct redox patterns with respect to the CCO, IW, IM, and MW buffer equilibria during melting of marly limestone and subsequent crystallization and cooling of melilite–nepheline paralava melts. The origin of the analyzed CM rocks was explained in a model implying thermal alteration of low-permeable overburden domains in reducing conditions during wild subsurface coal fires, while heating was transferred conductively from adjacent parts of ignited coal seams. The gas regime in the zones of combustion was controlled by the CCO buffer at excess atomic carbon. Paralava melts exposed to high-temperature extremely reducing conditions contained droplets of immiscible Fe–P–S–C, Fe–S, Fe–P, and Fe–P–C liquids which then crystallized to reduced mineral assemblages.

We present a simple proof of the well-known axiomatic characterization of state-salient decision rules on the domain of all strict preference profiles, using Pareto property and Global Independence of Irrelevant Alternatives. Our proof appeals to Sen’s Impossibility Theorem and an extract from a proof of the same theorem, that is available in an earlier paper of this author.

October 17, 2024 Dear Prof. Dr. Rizzo and Dr. Oshiro, Prof. Dr. Rizzo: Editor-in-Chief of the Endocrinology section. Dr. Oshiro: Guest Editor of the “Advances in Clinical Diabetes, Obesity, and Metabolic Diseases” special Issue. I would like to submitt the manuscript entitled “Hyperornithinemia-Hyperammonemia-Homocitrullinuria Syndrome in Vietnamese Patients” by Khanh Ngoc Nguyen et al. to be considered for publication as an article in the “Advances in Clinical Diabetes, Obesity, and Metabolic Diseases” of the “Medicina” journal. This study included retrospective and prospective analyses of four unrelated Vietnamese children diagnosed with Hyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome. HHH syndrome is one of the rarest urea cycle disorders and is characterized by failure of mitochondrial ornithine uptake, hyperammonemia, and accumulation of ornithine and lysine in the cytoplasm. Genetic analysis is key for diagnosis. In this study, we report the phenotype, genotype, treatment, and outcome of the four patients. To our best knowledge, this study is the first report of HHH patients in Vietnam. A diverse onset age and delayed diagnosis were observed. All four cases showed hyperornithinemia and prolonged prothrombin time. Three out of four cases presented with hyperammonemia, elevated transaminases, and uraciluria. No homocitrulline was detected in the urine. Genetic analyses identified three pathogenic variants in the SLC25A15 gene. The c.535C>T (p.Arg179*) variant is a common variant in Vietnamese patients. The c.562_564del (p.Phe188del) and c.408del (p.Met137Cysfs*10) variants were found in one case. The latter variant has yet been reported in the HHH patients in the literature. After intervention with a protein-restricted diet, ammonia-reducing therapy, and L-carnitine supplementation, no case had hyperammonemia and liver enzyme levels returned to normal range. Our results high-lighted clinical and biochemical heterogeneity of HHH syndrome and suggested that HHH syndrome should be considered when individuals present with hyperammonemia, elevated transaminase, and decreased prothrombin time. We believe these findings will be of interest to the readers of Medicina. We confirm that neither the manuscript nor any parts of its content are currently under consideration for publication with or published in another journal. All authors approved the final version of the manuscript and agree with its submission to Medicina. Thank you for your time and effort. We look forward to hearing from you regarding our submission. Yours sincerely, Chi Dung Vu (PhD., Assoc. Prof.) Center of Endocrinology, Metabolism, Genetic/Genomics and Molecular Therapy, Vietnam National Children’s Hospital, 18/879 La Thanh, Dong Da, Hanoi, Vietnam Email: dungvu@nch.gov.vn. Tel: +84-246-2747643

Perinatal asphyxia is a leading cause of neonatal morbidity and mortality, often resulting in hypoxic-ischemic encephalopathy (HIE) with lasting neurodevelopmental impairments. Therapeutic hypothermia has been proposed as a treatment to mitigate brain damage, but its effects on specific brain structures remain under investigation. This study explores the relationship between cerebral volumetry, cortical thickness, and neurodevelopment in term neonates with perinatal asphyxia, with or without therapeutic hypothermia. A prospective cohort of term neonates with perinatal asphyxia was divided into two groups: one group exposed to therapeutic hypothermia and another group from a different institution that did not receive therapeutic hypothermia (non-exposed). Brain MRI data were analyzed using Infant-FreeSurfer and machine learning algorithms to quantify volumes of the basal ganglia, gray matter, white matter, cortical gyri, cerebellum, and cortical thickness. Neurodevelopment was assessed using the Bayley Scale III, focusing on motor, cognitive, and language domains, with additional genetic analyses of neurodevelopmental markers. Statistical comparisons adjusted for therapeutic hypothermia revealed significant differences in brain volumes. Hypothermia-treated infants demonstrated increased gray and white matter volumes and greater cortical thickness in specific regions, correlating with improved neurodevelopmental outcomes. These findings suggest that therapeutic hypothermia has a neuroprotective effect on specific brain structures, contributing to better cognitive and motor development. The study underscores the potential of therapeutic hypothermia as a critical intervention for perinatal asphyxia. It highlights the need for further research on its long-term benefits, incorporating advanced neuroimaging techniques and genetic insights to optimize outcomes.

Background/Objectives: This study aimed to compare the diagnostic accuracy of the Wells and Geneva scores, used with a 500 ng/mL D-dimer cutoff, along with the age-adjusted D-dimer (AADD), YEARS, and PEGeD algorithms in patients with and without COVID-19. The performance of various D-dimer thresholds was also evaluated. Methods: This retrospective study included patients who presented to the emergency department and underwent computed tomography pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). The diagnostic performance of clinical prediction algorithms was compared between two groups. Results: We analyzed data from 1423 patients; PE diagnosis and COVID-19 positivity rates were 7.3% and 69.9%, respectively. In patients with COVID-19, the Wells score with a 500 ng/mL D-dimer threshold exhibited 97.22% sensitivity (95% CI: 80.53–100.00) and 4.99% specificity (95% CI: 3.58–6.39). Using AADD increased the specificity to 7.81% (95% CI: 6.08–9.54) while maintaining sensitivity at 97.22% (95% CI: 93.43–100.00); similar results were observed for the Geneva score. The YEARS algorithm exhibited 86.11% sensitivity (95% CI: 78.12–94.10) and 32.75% specificity (95% CI: 29.73–35.78), whereas the PEGeD algorithm exhibited 86.11% sensitivity (95% CI: 78.12–94.10) and 34.06% specificity (95% CI: 31.00–37.12). The YEARS and PEGeD algorithms demonstrated better performance in patients with COVID-19 in terms of specificity and accuracy. Conclusion: COVID-19 infection did not significantly affect the diagnostic performances of the clinical algorithms for PE. YEARS and PEGeD performed better in patients with COVID-19, making them preferable. Higher D-dimer thresholds improved specificity but increased the risk of missed PE diagnoses.

In today’s complex decision-making landscape, multi-criteria decision-making (MCDM) frameworks play a crucial role in addressing conflicting criteria. This paper introduces a novel framework that combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with random hypergraphs to enhance decision processes. Traditional MCDM methods often face challenges due to uncertainty and interdependencies among criteria. Our approach leverages random hypergraphs to better capture the relationships between criteria, offering a refined representation of decision problems. We delve into the theoretical foundations of this framework, detailing its algorithmic implementation and methodologies for evaluating alternatives under uncertainty. Performance comparisons illustrate the advantages of the proposed TOPSIS framework, emphasizing how random hypergraphs enrich TOPSIS’s analytical capabilities. This research advances the theoretical understanding of MCDM frameworks while providing practical insights for practitioners seeking robust solutions in complex and uncertain decision-making environments.

Recurrent laryngeal nerve palsy remains a significant complication following minimally invasive esophagectomy for esophageal cancer. Despite advancements in surgical techniques and lymphadenectomy precision, the incidence of recurrent laryngeal nerve palsy has not been improved. Recurrent laryngeal nerve palsy predominantly affects the left side and may lead to unilateral or bilateral vocal cord paralysis, resulting in hoarseness, dysphagia, and an increased risk of aspiration pneumonia. While most cases of recurrent laryngeal nerve palsy are temporary and resolve within 6 to 12 months, some patients may result in permanent nerve dysfunction, severely impacting patient quality of life. Prevention strategies, such as nerve integrity monitoring, robotic-assisted minimally invasive esophagectomy, and advanced dissection techniques, aim to minimize nerve injury, though their effectiveness varies. Management of recurrent laryngeal nerve palsy includes voice and swallowing rehabilitation, reinnervation techniques, and, in severe cases, surgical interventions such as thyroplasty and intracordal injection. As recurrent laryngeal nerve palsy can lead to significant postoperative respiratory complications, a multidisciplinary approach involving surgical precision, early detection, and comprehensive rehabilitation is crucial to improving patient outcomes and minimizing long-term morbidity in minimally invasive esophagectomy.

Background: Objective training load indexes (TL) used in resistance training lack of physiological significance. This study aimed to provide a muscle physiology-based approach for quantifying TL in resistance exercises (RE). Methods: Following individual torque-velocity profiling, fifteen participants performed isokinetic leg extension exercise sessions at low (C1), moderate (C2), and high (C3) intensities. Systemic and local physiological responses were measured, and sessions were volume-equated according to the "volume-load" (VL) method. Results: Significant differences were found between sessions in terms of mechanical work (

This study introduces a novel seatbelt-integrated, non-invasive beam-focusing metamaterial sensing system characterized by its thinness and flexibility. The system comprises a flexible transmitarray lens and an FMCW radar sensor, enabling accurate detection and analysis of seatbelt usage and positioning through human tissue. The metasurface design remains effective even when subjected to different bending angles. Our system closely tracks heart rate and respiration, validated against standard reference methods, highlighting its potential for enhancing in-car healthcare monitoring. Experimental results demonstrate the system’s reliability in monitoring physiological signals within dynamic vehicular environments.