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Apremilast
No full textAlcohol use disorder (AUD) affects approximately 400 million people worldwide and is frequently accompanied by chronic pain, a major yet often overlooked predictor of relapse. Emerging evidence suggests that neuroinflammatory mechanisms may contribute to both excessive alcohol consumption and pain sensitivity. Apremilast, a phosphodiesterase-4 (PDE4) inhibitor approved for inflammatory conditions such as psoriasis and psoriatic arthritis, has recently been investigated as a potential dual-action treatment for AUD. Preclinical studies demonstrate that apremilast significantly reduces alcohol intake across animal models, including genetically predisposed high-drinking strains. The drug decreases binge-like drinking and motivation for alcohol, while also lowering mechanical allodynia both during active drinking and throughout prolonged abstinence. Unlike other PDE4 inhibitors, apremilast combines anti-inflammatory and analgesic effects with reductions in alcohol-seeking behaviour, addressing two key drivers of relapse. Given the high comorbidity of AUD and chronic pain, this dual mechanism may offer a promising personalized therapeutic approach. Although early preclinical and limited clinical findings are encouraging, robust randomized controlled trials are needed to confirm efficacy and safety in humans and to evaluate potential benefits on withdrawal-related anxiety and negative affect
Impact of Ammonia (NH3) on the Energy Production in Photovoltaic Panels
No full textThe increase in energy demand, the fossil energy crisis, and the trend towards using renewable energies from sources such as the sun or wind have led to the rise in photovoltaic installations. Some of these installations are being installed on farms. While it is true that irradiation levels, location, and inclination of the panels are considered, the influence of certain gases such as ammonia (NH3), which is present in poultry, pig and dairy farms, is not considered. The present study is carried out in a poultry farm, through the implementation of two data acquisition devices that will be located in two scenarios, the first one exposed to NH3 levels and the other one free of the influence of this gas, the prototypes are equipped with a 100W panel to measure the power generated and determine if there is a difference in the energy production produced by the influence of ammonia. Data was obtained for ten consecutive days, in which it was determined that the power generated by the panel decreased in the scenario with ammonia compared to the prototype without of influence of this gas, proving that NH3 influences the decrease in power generated in the solar photovoltaic panel, obtaining average losses of 5%. It is concluded that ammonia (NH3) influences the efficiency of energy conversion in photovoltaic solar panels
Longitudinalni odnos između obiteljske otpornosti i subjektivne dobrobiti roditelja
No full textThe aim of the present study was to examine the relationship
between different family resilience dimensions (i.e., family
problem solving, utilising social and economic resources,
and family spirituality) and subjective well-being (SWB) of
mothers and fathers. The data was collected from 848 pairs
of mothers and fathers of elementary school-aged children,
as a part of the three-year longitudinal project. Using paper-
-pencil questionnaires, mothers and fathers assessed family
resilience, their life satisfaction and happiness in two study
waves. The results of structural equation modelling showed
that, among family resilience dimensions, only family
problem solving was reliably longitudinally associated with
greater SWB in both parents two years later. Other family
resilience dimensions did not contribute significantly to either
parent's SWB, after controlling for their SWB measured in the
first wave of the study. Family resilience explained only a
small portion of the variance in both mothers' and fathers'
SWB, indicating that although family resilience does play a
role in the parents' SWB, there are other individual and
family factors that should be considered.Cilj je ovog istraživanja bio ispitati odnos između raznih
dimenzija obiteljske otpornosti (obiteljskog rješavanja
problema, upotrebe socijalnih i ekonomskih resursa i
obiteljske duhovnosti) te subjektivne dobrobiti majki i očeva.
Podaci su prikupljeni od 848 parova majki i očeva djece
osnovnoškolske dobi u okviru trogodišnjega longitudinalnog
projekta. Upotrebom upitnika u obliku papir-olovka majke i
očevi procijenili su otpornost svoje obitelji te vlastito
zadovoljstvo životom i sreću u dva vala istraživanja. Rezultati
strukturalnoga modeliranja pokazali su da je među
dimenzijama obiteljske otpornosti samo obiteljsko rješavanje
problema longitudinalno povezano s većom subjektivnom
dobrobiti obaju roditelja dvije godine kasnije. Druge
dimenzije obiteljske otpornosti nisu značajno pridonijele
dobrobiti roditelja nakon kontrole roditeljske dobrobiti iz
prvoga vala istraživanja. Obiteljska otpornost objasnila je
samo malen dio varijance dobrobiti majki i očeva, što
pokazuje da, iako obiteljska otpornost ima ulogu u
subjektivnoj dobrobiti roditelja, postoje i drugi individualni i
obiteljski faktori koje treba uzeti u obzir
Secondary metabolites of the culturable endophytic fungus Penicillium chrysogenum and their plant growth regulator activity
No full textThis study reports the isolation of two natural compounds, zearalenone and hyrtiosulawesine, from the endophytic fungus Penicillium chrysogenum isolate SR192 which is associated with the invasive plant species Solanum rostratum. In general, both isolated compounds exhibited a dose-dependent stimulatory effect on the growth of four weed species when concentrations ranging from 5 to 100 µg mL-1 were tested. However, suppressive effects were observed when the concentration was increased to 500 µg mL-1, suggesting a biphasic response. Our findings indicate that these compounds have the potential to be further investigated as environment-friendly plant growth regulatory agents
Melamine: a brief journey from animal feed to cow's milk
No full textMelamine is a triazine compound containing 66.6% nitrogen. It is utilised in various industrial applications, including the production of plastics, adhesives, and fertilisers. However, melamine can also contaminate food and animal feed, as it is sometimes added to artificially enhance protein content, which can be toxic to both animals and humans. Notable food contamination incidents involving melamine include the 2007 pet food scandal and the 2008 melamine-contaminated milk scandal in China, which resulted in serious kidney damage and fatalities, particularly among children. The primary health risks associated with melamine arise from its ability to form crystals in the kidneys, potentially leading to renal failure. The body rapidly absorbs and excretes melamine, primarily through urine, as metabolism is minimal. Although acute doses are relatively low in toxicity, long-term exposure, especially through contaminated food, can pose significant health threats. In response to the melamine-related health crises in China, the WHO and EFSA established safe intake levels of 0.2 mg/kg/day for melamine and 1.5 mg/kg/day for cyanuric acid. Various countries have set maximum residue limits for melamine in food, with the United States and China permitting 1.0 mg/kg for infant formula and 2.5 mg/kg for other dairy products. In Europe, the limits are stricter, at 1.0 mg/kg for powdered infant formula, 0.1 mg/kg for liquid formula, and 2.5 mg/kg for foods. The determination of melamine and cyanuric acid in food and feed presents significant challenges. Advanced techniques such as high-performance liquid chromatography and tandem mass spectrometry are the most commonly employed methods
Low-Dose X-Ray Visual Weld Defect Feature Extraction and Classification
No full textThis study proposes a low-dose X-ray-based nondestructive testing method for weld defect automatic detection, focusing on resistance spot welds and butt joint laser welds. By exploiting the high penetration capability and resolution of X-ray imaging, the proposed method detects internal defects such as porosity, cracks and lack of fusion through contrast analysis. Image preprocessing techniques including median filtering, Fourier and wavelet transforms, adaptive histogram equalization and Hough Transform were applied to suppress noises and enhance defect features. Also, Contrast Limited Adaptive Histogram Equalization effectively improves image contrast and reveals subtle defect patterns. For classification, the Random Forest algorithm was adopted to extract relevant features and perform defect recognition. Experimental results show that the proposed method achieves high accuracy in classifying weld defects, with strong generalization ability and minimal misclassification. The classification accuracy reached for resistance spot welds and laser welds confirms the method robustness. This approach enhances the automation and reliability of welding quality control by providing an efficient and accurate solution for internal defect identification. The contributions of this work lie in integrating advanced image processing with machine learning techniques for precise defect detection in complex weld structures using low-dose imaging, thus supporting intelligent inspection systems in industrial applications
HFTN-SR: High-Frequency Feature Transfer Network for MR Image Super-Resolution
No full textMagnetic resonance imaging plays a crucial role in clinical diagnosis due to its ability to provide information on soft tissue structure. At present, multi-contrast super-resolution (SR) methods for magnetic resonance (MR) images have been widely studied and have achieved good results. However, most studies overlook the impact of modal differences between reference and low-resolution (LR) image features on feature reconstruction, which may result in inaccurate reconstruction of detail features due to improper alignment of structural features. To address this issue, we propose a high-frequency feature transfer network for MR image SR task (HFTN-SR), which consists of two feature extraction branches and one high-frequency feature transfer branch. Considering the modal differences between the target and reference images, a high-low frequency decomposition method is used to decompose the reference image into high-frequency and low-frequency components, where the low-frequency components are used in the subsequent network to match LR target image features. A feature extraction block (FEB) is constructed to extract and integrate high-frequency and low-frequency features of the reference image, as well as features of the LR target image. In response to the modal differences between two image features, a feature transfer block (FTB) is designed to establish the correlation between the low-frequency features of the reference image and the target image features, and use the correlation matrix to transfer the high-frequency features of the reference image to the target image features. To further reduce the loss of shallow features caused by the increase in network depth, a standardized combined residual feature module (SCRFM) is constructed to supplement the shallow features of the target image into the final reconstructed features. Experiments on the public dataset FastMRI and the self-built dataset AXA show that the performance of HFTN-SR is superior to some state-of-the-art (SOTA) methods. Notably, HFTN-SR achieves the highest PSNR and SSIM scores across all tested datasets, with significant improvements in visual quality and detail reconstruction
A MapReduce Approach to Model Big Data with Fuzzy Functions Identified Based on Fuzzy C-Means Algorithm
No full textRecently, big data has become increasingly important in the fields of scientific research and application. However, due to the characteristic features of big data such as high volume, velocity, variety, variability, value, and complexity, processing it with traditional analysis methods is quite a challenging process. In this context, frameworks like MapReduce are commonly used in the modeling of big data and in parallel and distributed data processing techniques. In this study, it is aimed to use fuzzy functions based on the fuzzy c-means (FCM) algorithm under the MapReduce architecture for modeling systems based on large data sets. In the study, it is explained in detail how the FCM algorithm is parallelized in the mapping phase; subsequently, it is demonstrated how the data is reduced in the reduce phase and how the fuzzy functions are derived. The proposed approach demonstrates the effectiveness of fuzzy functions within the MapReduce framework in modeling systems based on various large datasets. Additionally, the success of the methodology has been thoroughly discussed through the evaluation of the obtained fuzzy functions and performance analysis
Intelligent Simulation System of Human-Computer Interaction Motion Based on Bullet Physics Engine
No full textWith the rapid advancement in computer technology, human motion simulation has gained substantial attention across various domains; however, existing simulation models frequently encounter challenges such as poor controllability, inadequate stability, and limited anti-interference capability. To address these limitations, this study introduces an intelligent human-computer interaction (HCI) motion simulation system based on the Bullet physics engine and reinforcement learning. The proposed system leverages the proximal policy optimization (PPO) algorithm, enhanced with a multi-objective reward function designed to precisely constrain policy updates through the comparative analysis of new and old strategies. Experimental evaluations reveal significant performance improvements, with the PPO algorithm achieving maximum reward values notably higher (by 43.5 and 307.2) than comparative algorithms, and demonstrating a minimum loss error reduction of up to 0.059. The model's stability and anti-interference capabilities were further validated under challenging scenarios involving external forces and obstacles, showing quicker recovery and superior resilience. Additionally, the model demonstrated a high correlation with real human joint movements, with fitting accuracies of 91.4% for knee joints and 93.2% for hip joints, highlighting its effectiveness and practical applicability. As a result, the study effectively improves the stability and anti-interference of the human simulation model and realizes the accurate control of the model
A Bayesian Network-Based Risk Assessment Model for Gas Pipeline Intelligent Management Systems
No full textTo enhance the accuracy and efficiency of gas pipeline risk assessment within intelligent management systems, this study proposes a Bayesian network-based data modeling and risk assessment framework. Traditional risk assessment methods rely heavily on statistical analysis and expert judgment, often struggling with uncertainty and interdependencies between risk factors. In contrast, Bayesian networks effectively model complex probabilistic relationships, providing a more dynamic and adaptive risk evaluation approach. This study integrates a cloud-based uncertainty processing model with Bayesian inference to improve risk prediction. Experimental validation using real-world gas pipeline monitoring data demonstrates that the proposed method achieves a 98.8% accuracy rate, significantly outperforming conventional techniques. Additionally, the assessment period is reduced to approximately two months, enhancing real-time decision-making capabilities. These findings highlight the potential of Bayesian networks to transform gas pipeline safety management by offering precise, efficient, and adaptive risk assessment models. Future research will explore integrating real-time IoT sensor networks and machine learning-based anomaly detection to further optimize predictive capabilities