Sharp Increase Research Articles

STATE BUDGET IN THE CONTEXT OF A WARTIME ECONOMY AND POST-WAR TRIGGERS FOR ITS RECOVERY

The purpose of this study is to systematically analyse the functioning of the state budget of Ukraine in the context of the war economy and identify the key triggers of its post-war recovery. The article discusses the current challenges associated with the transformation of the structure of budget expenditures, a significant budget deficit, rising public debt and the need to attract international financial assistance. The wartime period for public finances is characterised by a significant shift in priorities, with the focus on security and defence forces, social support and the restoration of critical infrastructure. This reallocation of resources is a response to the unprecedented challenges faced by the state, but it creates additional risks to financial sustainability in the medium and long term, creating uncertainty about the triggers for post-war economic recovery from the consequences of the Russian-Ukrainian war. The main methods of the study are: quantitative analysis of economic and financial indicators of Ukraine and comparative analysis with other countries that have faced military conflicts. It is determined that the key barriers are the lack of domestic financial resources, high level of external debt and significant debt service costs. The impact of international financial assistance on ensuring macroeconomic stability and prospects for improving the efficiency of its use for resource support for economic recovery are also investigated. The qualitative analysis identified the main social and economic factors that influence the structure of the State Budget in the context of the Russian-Ukrainian war, including demographic changes, internal displacement of the population, the need to support veterans, and other previously unknown or insignificant factors that were inert in the pre-war period. Based on the results of the study, the author proposes a strategic context of triggers for the modernisation of public finances, including: further optimisation of the expenditure structure, improvement of the efficiency of budgetary resources management, introduction of innovative approaches to tax policy, and ensuring transparency in the use of international financial assistance. Particular attention is paid to the adaptation of international experience to Ukrainian realities, including the introduction of mechanisms for monitoring and evaluating the effectiveness of public spending. The practical significance of the study lies in the development of recommendations for improving the efficiency of public budget management in difficult economic conditions. It is determined that post-war reconstruction requires not only financial resources, but also the consolidation of efforts of the government, international partners and civil society. The main challenge is to strike a balance between the short-term needs to finance the security and defence forces and social protection and the long-term goals of sustainable development, which is becoming more inclusive. The authors propose an approach that combines rapid response to wartime challenges with laying the foundations for inclusive economic growth in the future. It is concluded that public finances are a key instrument for economic resilience during war and the basis for its post-war recovery. In particular, strategic management of budgetary resources will minimise the impact of external shocks, promote the development of domestic markets and ensure social stability. Further research in this area will contribute to the development of effective financial planning mechanisms adapted to Ukraine's current challenges and needs. It was also found that the war economy has significantly transformed the structure of Ukraine's state budget, directing major resources to defence and social support, which has led to unprecedented spending growth and budget deficits. At the same time, a sharp increase in public debt, including external debt, has highlighted the country's dependence on international assistance. In the post-war period, economic recovery will largely depend on investments in infrastructure reconstruction, which can become a driver of economic growth and job creation. Achieving financial sustainability requires comprehensive reforms of public financial management, effective use of international support, and stimulation of long-term economic development. In addition, the article emphasises the importance of promoting the use of the frozen assets of the aggressor country, which can become a source of financing for the restoration of Ukraine's infrastructure, helping to reduce the debt burden. The authors highlight the need for further research and integration of international experience in the field of financial stabilisation and development of an inclusive economy, which will help Ukraine to cope with the challenges of post-war recovery.

The Current Research Landscape on the Machine Learning Application in Autism Spectrum Disorder: A Bibliometric Analysis From 1999 to 2023.

Language deficits, restricted and repetitive interests, and social difficulties are among the characteristics of autism spectrum disorder (ASD). Machine learning and neuroimaging have also been combined to examine ASD. Utilizing bibliometric analysis, this study examines the current state and hot topics in machine learning for ASD. A research bibliometric analysis of the machine learning application in ASD trends, including research trends and the most popular topics, as well as proposed future directions for research. From 1999 to 2023, the Web of Science Core Collection (WoSCC) was searched for publications relating to machine learning and ASD. Authors, articles, journals, institutions, and countries were characterized using Microsoft Excel 2021 and VOSviewer. Analysis of knowledge networks, collaborative maps, hotspots, and trends was conducted using VOSviewer and CiteSpace. A total of 1357 papers were identified between 1999 and 2023. There was a slow growth in publications until 2016; then, between 2017 and 2023, a sharp increase was recorded. Among the most important contributors to this field were the United States, China, India, and England. Among the top major research institutions with numerous publications were Stanford University, Harvard Medical School, the University of California, the University of Pennsylvania, and the Chinese Academy of Sciences. Wall, Dennis P. was the most productive and highest-cited author. Scientific Reports, Frontiers In Neuroscience Autism Research, and Frontiers In Psychiatry were the three productive journals. "autism spectrum disorder", "machine learning", "children", "classification" and "deep learning" are the central topics in this period. Cooperation and communication between countries/regions need to be enhanced in future research. A shift is taking place in the research hotspot from "Alzheimer's Disease", "Mild Cognitive Impairment" and "cortex" to "artificial intelligence", "deep learning", "electroencephalography" and "pediatrics". Crowdsourcing machine learning applications and electroencephalography for ASD diagnosis should be the future development direction. Future research about these hot topics would promote understanding in this field.

High incidence of pneumonia cases observed in children seen in general practice consultations during the 2023–2024 season, France

BackgroundAn increase in hospitalizations for respiratory illnesses due to Mycoplasma pneumoniae was reported in France in late October 2023. Data in primary care are scarce and microbiological or radiological investigations are not routinely recommended for community-acquired pneumonia.MethodsWe computed weekly incidence rates of pneumonia and bronchiolitis cases from the electronic records of French general practitioners from January 2016 to August 2024. These weekly incidences were described in the light of the Covid-19 pandemic, overall and by age group. For better interpretation, the weekly incidences of pneumonia and bronchiolitis were compared with virological surveillance data of acute respiratory infections observed in general practice.ResultsDuring the 2016–2024 period, 108,539 cases of pneumonia and 46,411 cases of bronchiolitis were identified from 51,351,414 consultations. The incidence of pneumonia consultations in general practice during the 2022–2023 and 2023–2024 seasons is similar to that observed before the Covid-19 pandemic, after two seasons of low incidence (2020–2021 and 2021–2022). However, the 2023–2024 pneumonia epidemic is the strongest ever observed in children (0–14 years, and especially among the 5–14 years) in general practice since 2016, with an earlier onset. Regarding the incidence of bronchiolitis in children, the 2023–2024 season was in line with the 2021–2022, 2022–2023 and pre-pandemic seasons. No abnormal circulation of classical seasonal viruses was observed during the 2023–2024 season.ConclusionsThe sharp increase in pneumonia cases observed this season among children in primary care settings requires the implementation of studies to understand the cause and to confirm or refute the possible association with M. pneumoniae as observed in hospitals. Given the impact of the Covid-19 pandemic on the circulation of pathogens, it would be useful to extend, even on a temporary basis, the traditional microbiological surveillance in primary care to include common bacterial pathogens affecting the upper and lower respiratory tract, such as M. pneumoniae, S. pneumoniae or Streptococcus A.

The association between drug injection duration and hepatitis C prevalence among people who inject drugs in Iran

People who inject drugs (PWID) are at higher risk of hepatitis C virus (HCV) due to their behaviors such as shared injection. Employing appropriate modeling approaches is crucial for accurately evaluating the impact of other variables on outcomes, in this case, HCV seropositivity. This study aimed to assess the non-linear effect of injection duration on HCV seropositivity. From July 2019 to March 2020, 2,684 PWID in Iran were recruited. The binary outcome variable was HCV serostatus (positive vs. negative), determined by detecting HCV antibodies. The non-linear effect of injection duration on HCV seropositivity was assessed using a multilevel Generalized Additive Model in R software, adjusting the effects of other variables in the analysis. We found a non-linear effect of injection duration on HCV seropositivity status (p-value < 0.001). The probability of HCV seropositivity increased with injection duration, though this relationship was non-linear. Initially, the probability rises faster; however, this effect diminishes as the injection duration extends. An initial sharp increase in HCV risk was seen during the first 20 years of injection. HCV seropositivity was notably associated with ever HIV seropositivity (OR [Odds Ratio] = 10.54, 95% CI [Confidence Interval]: 5.39, 20.61, p-value < 0.001), ever having injected methamphetamine (OR = 1.72, 95% CI: 1.33, 2.22, p-value < 0.001), being currently married (OR = 0.67, 95% CI: 0.48, 0.93, p-value = 0.018), ever shared needle/syringe with others (OR = 2.63, 95% CI: 1.32, 5.22, p-value = 0.006), and ever being incarcerated (OR = 1.97, 95% CI: 1.50, 2.58, p-value < 0.001). Our study contributes to the field by demonstrating that a non-linear approach can reveal patterns of risk that linear models might fail to capture. These findings indicate that the relationship between injection duration and HCV seropositivity can be more complex than previously understood, underscoring the importance of employing more advanced modeling techniques in future research.

Investigating mortality trends and disparities in tricuspid valve disorder: a U.S. nationwide study from 1999 to 2023

Tricuspid valve disorder (TVD), a critical aspect of valvular heart disease (VHD), significantly impacts cardiovascular health, yet its mortality trends are not well understood. This study aimed to investigate demographic and geographic disparities in TVD-related mortality across the United States from 1999 to 2023. Using data from the CDC WONDER database, death certificates were analyzed to identify TVD-related fatalities, and age-adjusted mortality rates (AAMRs) were calculated per 1,000,000 individuals. Joinpoint regression analysis was conducted to assess annual percent changes (APCs) in mortality rates. A total of 72,805 deaths were attributed to TVD. An initial steep increase in mortality rate from 1999 to 2003 (APC: 7.9%; 95% CI: 3.9 to 14.1) followed by a stable period from 2003 to 2014 (APC: 0.1%; 95% CI: -2.7 to 1.0) and a sharp increase in AAMR from 2014 to 2023 (APC: 6.5%; 95% CI: 5.2 to 8.4). Females consistently had higher mortality rates than males, with a sharper increase after 2012. Racial and ethnic disparities were evident, with American Indian and white populations experiencing higher mortality rates than black populations. Geographic disparities were also noted, with states like Oregon, Minnesota, and Vermont, as well as the West census region, showing significantly higher mortality rates. Rural areas had higher mortality rates compared to urban areas. TVD-related mortality trends have followed a complex trajectory, with marked disparities across demographic and geographic factors. Further research is required to fully understand the factors driving these trends and their public health implications.

Mortality Patterns of Coronary Artery Diseases and Atrial Fibrillation in Adults in the United States from 1999 to 2022: An Analysis Using CDC Wonder.

Mortality Patterns of Coronary Artery Diseases and Atrial Fibrillation in Adults in the United States from 1999 to 2022: An Analysis Using CDC Wonder.

Real-world data on behavioral practices for dogs in primary care veterinary hospitals in the United States (2010-2020).

To quantify the behavior problems and prescription of psychoactive medications in dogs seen at primary care veterinary hospitals. Medical records of canine patients in a nationwide network of primary care veterinary hospitals in the US obtained between January 1, 2010, and December 31, 2020, were retrospectively analyzed. Information retrieved included prescription of clomipramine, fluoxetine, or trazodone; patient signalment; and common behavior problem labels (aggression, separation anxiety, and fear or anxiety unrelated to separation anxiety). Associations between behavior problem labels and drug prescriptions were assessed with Pearson χ2 tests. A total of 32,468,046 canine medical records were retrieved. The overall prescription percentage was 0.03% for clomipramine, 0.02% for fluoxetine, and 1.33% for trazodone during the study period. Prescription of trazodone started in 2013, with a sharp increase after 2015. The frequency of behavior problem labels increased 10-fold from 1.0% in 2010 to 10.2% in 2020, with an overall frequency of 5%. Among dogs with behavior problem labels, 0.15% were prescribed clomipramine, 0.14% were prescribed fluoxetine, and 8.4% were prescribed trazodone. Notably, about 70% of dogs receiving psychoactive drugs had no associated behavior problem labels used in the study. Frequencies of behavior problem labels and prescriptions in primary care settings were both low, with notable variations among psychoactive drugs. Furthermore, about 70% of cases prescribed psychoactive medications lacked target behavior problem labels. These findings highlight a critical need to bridge the knowledge gap between identifying behavior problems and effectively addressing them in behavioral practice within primary care veterinary settings.

J-shaped association between blood urea nitrogen-to-creatinine ratio and mortality in critically ill patients with acute pancreatitis: a retrospective cohort study using the MIMIC-IV database

BackgroundBoth blood urea nitrogen (BUN) and creatinine (Cr) are indicators of kidney function, and the BUN/Cr ratio has been identified as an independent prognostic marker for adverse outcomes in critically ill patients with various conditions. However, the relationship between the BUN/Cr ratio and long-term mortality in critically ill patients with acute pancreatitis (AP) remains unclear. Hence, the primary objective of this study was to determine the prognostic value of the BUN/Cr ratio in patients with AP.MethodsWe conducted a retrospective cohort study using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The primary exposure variable was the BUN/Cr ratio at intensive care unit (ICU) admission, and the primary outcome was 365-day all-cause mortality. Kaplan–Meier analyses and multivariate Cox proportional hazards models were used to assess this relationship, while restricted cubic spline (RCS) was used to explore potential non-linear associations. In addition, subgroup analyses were conducted to assess consistency between groups.ResultsA total of 850 critically ill patients with AP were included, with a mean age of 59.61 years, 58.59% male, and an overall 365-day mortality rate of 20.94%. Patients in the highest BUN/Cr quartile had significantly higher mortality rates compared to those in lower quartiles. Multivariate Cox regression analysis demonstrated that, even after adjusting for potential confounders, an elevated BUN/Cr ratio remained an independent predictor of increased 28-day and 365-day mortality. RCS analysis confirmed a J-shaped relationship between the BUN/Cr ratio and 28-day and 365-day mortality, with a sharp increase in the risk of death above the 16.80 threshold. Subgroup analysis indicated that this association was consistent across various patient characteristics.ConclusionThis study identified a non-linear relationship between the BUN/Cr ratio and 365-day mortality in critically ill patients with AP, suggesting that the BUN/Cr ratio may serve as an easily accessible, cost-effective, and accurate prognostic marker for this population.

Prediction rotary drilling penetration rate in lateritic soils using machine learning models

The city’s population growth in developing countries over the past two decades led to a sharp increase in water supply needs. This generates proliferation of drilling companies and a highly competitive environment, in Cameroon in particular. A regular optimization of drilling operations in geological formations appears to be crucial and urgent, so as to reduce drilling costs since the drilling equipment used is too expensive and sometimes scarce. The present paper investigated an accurate machine learning model for the penetration rates (ROP) prediction in lateritic soil covers layers. The present study investigates various machine learning techniques including the linear regression, K-Nearest Neighbors, ridge regression and Random Forest, to predict the penetration rate. Data from four (04) defined parameters: the percussion pressure, Pp (MPa); the blowing pressure, Ps (MPa); the pressure of compressor, Pc (MPa); the rotation speed, Vr (tr/min) have been used to build the dataset, for training and validation tests, with an 70/30 ratio. The drilling time and drilling depth ranges from 0.3 to 2.8 h, and from 0.85 to 4.6 m respectively, for a constant rotation speed of 1350 tr/min while values of pressures range between 2.5 and 294.3 MPa. The key performance metrics including correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. Results show that the Random Forest model exhibited the best accuracy, with a R2 value of 0.999, with RMSE and MAE values of 0.0225 and 0.0121 respectively. A relatively high accuracy has been obtained for the K-nearest neighbors method with R2, RMSE and MAE values of 0.933, 0.1547 and 0.0802 respectively. Relatively low to values of metrics have also been obtained for linear regression and ridge regression methods. Related R2, RMSE, MAE values obtained are respectively 0.8612, 0.2232 and 0.1724 for the linear regression, and 0.8447, 0.2361 and 0.1894 for the ridge regression method. The discussion of the obtained results shows that, the Rain Forest model predicts the ROP value with a highly good accuracy, and can thus greatly contribute in reducing the costs and time related to drilling operations in lateritic soil covers context.

Solid-State Synthesis and Thermoelectric Properties of CuFeSe2-CuFeS2 Solid Solutions.

Thermoelectric technology, which converts heat and electricity into each other, has been attracting attention from the perspective of efficient energy utilization. Recently, eco-friendly and cost-effective Cu-based thermoelectric materials have been actively studied. In particular, efforts have been made to improve thermoelectric properties and enhance performance through the formation of solid solutions. This study examines the formation and thermoelectric properties of Cu-chalcogenide solid solutions between eskebornite (tetragonal CuFeSe2) and chalcopyrite (tetragonal CuFeS2), synthesized as CuFeSe2-ySy (y = 0-2) using solid-state synthesis. These compounds share similar crystal structures, which enable the formation of solid solutions that enhance phonon scattering and may potentially improve thermoelectric performance. As the S content (y) increased, the lattice parameters a and c decreased, attributed to the smaller ionic radius of S2- compared to Se2-, as X-ray diffraction analysis identified single-phase regions for 0 ≤ y ≤ 0.4 and 1.6 ≤ y ≤ 2, respectively. However, for 0.8 ≤ y ≤ 1.2, a composite phase of eskebornite and chalcopyrite formed, indicating incomplete solid solution behavior in the intermediate range. Thermoelectric measurements showed a sharp increase in electrical conductivity with increasing S content, alongside a transition in the Seebeck coefficient from positive (p-type) to negative (n-type), attributed to the intrinsic semiconducting nature of the end-member compounds. Eskebornite behaves as a p-type semiconductor, whereas chalcopyrite is n-type, and their combination affects the carrier type and concentration. Despite these changes, the power factor did not show significant improvement due to the inverse relationship between electrical conductivity and the Seebeck coefficient. The thermal conductivity decreased significantly with solid solution formation, with CuFeSe0.4S1.6 exhibiting the lowest value of 0.97 Wm-1K-1 at 623 K, a result of enhanced phonon scattering at lattice imperfections and the mass fluctuation effect. This value is lower than the thermal conductivity values of single-phase eskebornite or chalcopyrite. However, the reduction in thermal conductivity was insufficient to compensate for the modest power factor, resulting in no substantial enhancement in the thermoelectric figure of merit.

Study on associated effects of extreme drought and heatwave on air quality in South Africa during october 2022

This study investigates the impact of combined drought and extreme heatwave events on air quality in South Africa during October 2022 (study period). An analysis of meteorological data revealed a sharp increase in surface air temperature and a significant decrease in precipitation, resulting in an extreme heatwave and drought period. The Standardized Precipitation Evaporation Index (SPEI) for October 2022 showed an extreme drought value of -2.01, reflecting increased dryness over the past two decades. Meteorological analysis highlighted the influence of the intensified stationary Angola heat low and Botswana upper ridge subtropical high, facilitating dry and warm air advection into the region, exacerbating an extreme climatic situation with heatwave conditions. Ozone levels exceeded 80 ppb (unhealthy) in northern, central, and eastern regions, particularly on October 13, with concentrations ranging between 88.2 and 93 ppb. Long-term data (1980–2022) showed rising trends in PM2.5 and black carbon concentrations, peaking at 1.48 × 10⁻⁸ kg/m³ and 8 × 10⁻¹⁰ kg/m³ in October 2022, with the highest values in Free State Province. Additionally, the UV Aerosol Index (UVAI) showed significant growth over the last decade, with high values in October 2022, signaling worsening air quality and environmental health concerns. The findings emphasize the important need for mitigation measures to address the compounding effects of extreme weather on air quality and public health.

Trends in Necrotizing Fasciitis-Associated Mortality in the United States 2003-2020: A CDC WONDER Database Population-Based Study.

Necrotizing fasciitis (NF) is a severe and rapidly progressing soft tissue infection with high mortality rates. Despite the urgency of this condition, there is limited research on long-term NF-related mortality trends in the United States. This study aims to analyze NF-related mortality trends in adults aged 25 and older in the United States from 2003 to 2020, focusing on variations by sex, race/ethnicity, and geographic region. NF-related deaths were identified using the CDC WONDER database through the ICD-10 code M72.6. Crude and age-adjusted mortality rates (AAMRs) were calculated across demographic groups and regions. Temporal trends were assessed using the joinpoint regression, providing annual percent change (APC) in mortality rates. From 2003 to 2020, a total of 19,158 NF-related deaths were recorded, marking a 120.6% increase, rising from 824 deaths in 2003 to 1842 in 2020. The overall AAMR increased from 0.44 per 100,000 in 2003 to 0.71 per 100,000 in 2020. Males consistently had higher mortality rates than females and both sexes saw a sharp rise in AAMR after 2015. By race/ethnicity, American Indian or Alaska Native populations exhibited the highest mortality rates, followed by Black or African American individuals. Regional trends revealed that the West had the highest AAMR, whereas the Northeast recorded the lowest. Asignificant rise in mortality rates was observed across all regions after 2014. Additionally, urban-rural analysis indicated that large central metropolitan areas had consistently elevated mortality rates, whereas smaller metropolitan and noncore areas experienced sharper increases. NF-related mortality has significantly risen in the United States since 2014, with distinct disparities based on sex, race, and geographic region. Contributing factors may include chronic conditions, healthcare access issues, and climate-related events. Public health interventions focusing on early diagnosis, timely treatment, and addressing healthcare inequities are essential for improving outcomes (highlighted shows corrections).

Artificial intelligence revolutionizing type 2 diabetes mellitus self management (DSM): A digital breakthrough

Dear Madam, Diabetes Mellitus (DM) is a chronic metabolic disorder characterised by persistently elevated plasma glucose levels and is one of the leading causes of global chronic health problems globally¹. International Diabetes Federation (IDF) reports that 537 million people live with DM, With projections of 643 million by 2030 and 783 million by 2045². Pakistan ranks among the top 10 countries with the highest Diabetes cases, with Type 2 Diabetes Mellitus (T2DM) caused by endogenous resistance of the body cells to insulin leading to chronic hyperglycaemia being a significant public health issue due to increased deaths, especially among the 40-60 age group¹. Given the sharp increase in cases, it is pertinent to understand the factors contributing to its increased incidence, such as genetics, dietary habits of complex protein intake, birth weight, sedentary lifestyle, lack of physical activity, high-calorie intake, cigarette smoking, and obesity¹. Thus, early identification is crucial for preventing disability and death in high-risk individuals. This involves patient education, screening, and ongoing medical care to prevent acute complications like ketoacidosis and reduce long-term risks which impact practically every organ in the body, including retinopathy, nephropathy, neuropathy, diabetic foot, stroke, and cardiovascular complications³. Diabetes management necessitates commitment to a variety of self-care practices that are frequently quite taxing for patients: meal planning, carb counting, exercise, blood glucose monitoring, and daily activity adjustment. Therefore, Diabetes self-Management (DSM) has always been challenging to both patients and Health care professionals. But over the past decade, the diabetes management paradigm has been significantly transformed by the integration of new technologies, with AI playing a crucial role in recognizing Diabetes Self-Management (DSM) apps as essential therapeutic aids for patients4. Diabetes management apps offer documentation and analysis of various parameters like blood glucose, eating habits, physical activity, and medical therapy4,5. These apps also track disease progression and facilitate communication between patients and caregivers4,5. Compared to patients in the control group, patients who got coaching and decision support via the Well Doc app showed higher reductions in haemoglobin A1c (HbA1c) levels5. The FareWell app was used in cohort research that found adults with type 2 diabetes to have the same effect, and those users reported feeling more confident about controlling their condition5. To prevent Diabetes Mellitus Type 2, a recent study by Esrat Jahan et al. Shows the potential advantages of smartphone applications by lowering obesity6. ---Continue

Carbon Nano-Onions-Based Matrix Nanocomposite as Sensing Film for Resistive Humidity Sensor

This paper reports several preliminary investigations concerning the relative humidity (RH) detection response of a chemiresistive sensor that uses a novel sensing film based on a matrix nanocomposite comprising pristine carbon nano-onions and polyvinylpyrrolidone polymer at 1/1 w/w. The sensing device, including a polyimide substrate and gold electrodes, is obtained by depositing by drop casting the sensing layer on the sensing structure. The sensing layer’s morphology and composition are analyzed by Scanning Electron Microscopy, Atomic Force Microscopy, Fourier Transform Infrared Spectroscopy, X-Ray diffraction, and Raman spectroscopy. The experimental measurements show that the resistance of the tested nanocomposite slightly increases with RH for RH less than 50% and has a sharp increase with RH for larger RH. Several types of possible RH sensing mechanisms are identified and discussed. The decrease of the hole concentration in the CNOs in interaction with water molecules, which act as electron donors, and the rapid swelling of the hydrophilic polyvinylpyrrolidone polymer at high RH levels are the RH sensing mechanisms that best explain the measured RH detection behavior. The hard–soft acid–base principle also supports the experimental data. The hysteresis characteristic of the sensor is improved after the first operating cycle.

Production and Mechanical Characterization by Compression Tests of Al Alloys with Weaire-Phelan Cells Manufactured by the Lost-PLA Technique.

In this study, the mechanical behavior of AA6082 foams with Weaire-Phelan (WP) cell structures under compressive loading was analyzed. The foams were produced using the lost-PLA replication method, a cost-effective and straightforward manufacturing technique. A total of six aluminum alloy samples were fabricated and subjected to compression tests to assess both their mechanical performance and the repeatability of the results. The produced foams demonstrated a well-defined morphology and high-quality surface finish, accurately replicating the geometries of the original PLA 3D-printed templates. The experimental density of the foams closely matched theoretical values, confirming the consistency of the replication process. The compressive stress-strain response of the Weaire-Phelan cell foams displayed an initial linear elastic region, followed by three distinct plateau regions with increasing stress levels. The final densification phase occurred when the structure could no longer accommodate further plastic deformation, leading to a sharp increase in the compression load. From the stress-strain data, the specific energy absorption of the foams was calculated. The average specific energy absorption was measured to be 4 J/cm3, with a standard deviation of 0.49 J/cm3 across the six tested samples. These results indicate reliable mechanical performance and reproducibility of the manufacturing process, making these foams suitable for applications requiring energy absorption and lightweight structural components.

A study on the growth and challenges of India’s cut flower export

This study examines the growth trajectory and challenges of India’s cut flower export sector, focusing on its development, market trends, and competitive potential on the global stage. India is becoming a major player in the global floriculture sector after seeing a sharp increase in cut flower exports. The study assesses the factors that have contributed to this increase, including as improvements in farming practices, the development of export infrastructure, government assistance, and the growing demand for floral goods around the world. The sector does, however, also confront significant obstacles, including poor infrastructure, uneven quality standards, climate risks, and fierce rivalry from other top exporters. The research also emphasises the sector's socioeconomic effects, especially its contribution to the creation of jobs in rural areas. This report offers insights and suggestions to improve India's position in the global cut flower export market by examining the potential and challenges, guaranteeing long-term growth and a larger market share. This study intends to offer suggestions for improving India's standing in the international floriculture market by identifying these growth factors and obstacles. According to the findings, the sector's performance might be greatly improved in the upcoming years by overcoming current obstacles, continuing innovation, and implementing strategic policies. Keywords: Cut flower, Export, Floriculture, India and Market.

Piezo-dependent surveillance of matrix stiffness generates transient cells that repair the basement membrane.

Piezo-dependent surveillance of matrix stiffness generates transient cells that repair the basement membrane.

THE PRACTICE OF USING TRADITIONAL MEDICINAL PLANTS IN MANAGING DIABETES MELLITUS - A JUSTIFIED REVIEW IN CURRENT SCENARIO

Currently, the universe is experiencing a rapid systemic modification in the disease patterns resulting in a sharp increase in the non-infectious diseases and a marked drop in infectious diseases. Central obesity, hyperglycemia, hypertension, and hyperlipidemia are the diseases included in metabolic syndrome. Herbal medicines may be considered effective agents for the treatment of metabolic syndrome since these plants consist of various bioactive compounds with a synergistic effect when compared with the monotherapy of chemical agents. These complex diseases in metabolic syndrome can be managed by treatment or prophylactic therapy with these bioactive compounds. Therefore, the present study reviews various medicinal plants beneficial effects in treating diabetes mellitus, which is one among the metabolic syndromes. KEY WORDS: Metabolic syndrome, Diabetes mellitus, Bioactive compounds, Oral hypoglycemic agents

Increased Atmospheric Aridity and Reduced Precipitation Drive the 2023 Extreme Wildfire Season in Canada

AbstractCanada experienced an unprecedented wildfire season in 2023. Here, we analyzed the exceptional scale, dominant driving factors, and potential impacts on permafrost of these wildfires using Moderate Resolution Imaging Spectroradiometer burned area (BA) observations and machine learning methods. We found that the 2023 coast‐to‐coast wildfires raged a staggering area of 13.02 Mha, more than seven times the historical average (2001–2022). The extreme wildfires were predominantly driven by record‐breaking drought, characterized by heightened atmospheric aridity and reduced precipitation, with specific water deficit thresholds triggering sharp increases in BA. Over 80% of the 2023 wildfires occurred in Canada's permafrost regions, with burned areas increasing more than six‐fold across extensive permafrost, including Arctic regions. These wildfires are expected to accelerate permafrost degradation and considerable carbon release from thawing, presenting previously overlooked risks. Our results emphasize the urgent need for immediate climatic action to mitigate climate change and pressures from wildfire and permafrost degradation.

Antidepressant treatment of depression in children and adolescents: a systematic review and dose-response meta-analysis.

Pediatric major depressive disorder (MDD) often leads to recurrent depression in adulthood. The efficacy, safety and dose dependency of pharmacological effect is unclear. We conducted a systematic review and dose-response meta-analysis comprising of 22 double-blind randomized controlled trials, the majority of which had short trial durations ranging from 6 to 12 weeks. Studies were identified from PubMed, Ovid Embase, Ovid Medline, Ovid PsycInfo, Wanfang, ClinicalTrial.gov and CENTRAL until July 31, 2023. Doses of all antidepressants were converted to fluoxetine equivalents. Outcomes including treatment response, remission, suicidality, tolerability and acceptability were assessed. Sensitivity analysis, funnel plot and the trim-and fill method are used to assess and adjust for publication bias. Findings revealed that antidepressants were marginally more effective than placebos in terms of treatment response, but significantly increased the risk of adverse effects. No significant differences were observed in remission, suicidality, or overall dropout rates. Dose-response analysis indicated a relatively flat increase in response probability with higher fluoxetine equivalent doses, but also a sharp increase risk of discontinuation due to side effects. This study suggests that antidepressants for pediatric MDD may be less effective in adults, emphasizing the need to balance treatment benefits with potential adverse effects when considering interventions for this population.