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Much research exists on the role of textual enhancement in instructed second language (L2) development, yet little is known about how its effectiveness is influenced by multiple exposures, whether it can facilitate the acquisition of L2 derivational morphology, and how it may affect child language learning. To fill these gaps, this study employed a six-week multiple-exposure design to investigate the extent which to textual enhancement can benefit children's knowledge of L2 derivational morphemes. The study employed a pretest-posttest design, with six treatment sessions. Participants were 91 L2 learners of English in two primary school EFL contexts (Romania, Sweden). In each context, participants were randomly assigned to two groups: the +highlight group received textually enhanced texts, whereas the --highlight group read unenhanced texts during the treatment. The children read the texts through a digital reader application during their English lessons. The target constructions were the "-ion" and "-ment" morphemes. The pretest and posttest included a non-word derivational suffix choice and a nonword derivational suffix decomposition task. Results of linear mixed effects models found a small advantage of textual enhancement for the acquisition of the "-ion" morpheme by Swedish learners on the suffix choice task, but Romanian learners showed no benefits from highlighting.

Geological models are built to serve various purposes (e.g. reserve evaluation, rock stability analysis, etc.). Several software packages dedicated to geological modeling and rock stability analysis are currently available. The paper presents the workflow for rock stability analysis performed with Slide3 software from Rocscience. An important step is to build the 3D geological model both from the perspective of geometry and rock properties. Doing this in Slide3 is not an easy task and for this reason, we turned to GEOVIA Surpac. The steps were exemplified for the northern area of the Ruschita marble deposit. [ABSTRACT FROM AUTHOR]

An effective energy management system relies on the accurate prediction of electricity consumption, facilitating energy suppliers to optimise energy distribution, reduce energy waste, and avoid overloading the power system. This paper analyses different methods for the estimation of electricity consumption at the level of an urban area. A statistical model based on Trigonometric seasonality, Box-Cox transformation, Auto-Regressive Moving Average errors, Trend and Seasonal components is first presented. Then a model based on fuzzy logic is also proposed. These methods will be optimised and evaluated on a dataset collected by the electric power supply agency of Sibiu, Romania, with the goal of reducing the forecast error. The models are also compared with a Markov stochastic model and with a Long Short-Term Memory neural model. The experiments have shown that our statistical model using a history length of 200 electricity consumption values and a daily seasonality is the most efficient, with the lowest mean absolute error of 3.6 MWh, thus making it a good candidate for integration into a city-level energy management system. [ABSTRACT FROM AUTHOR]

Emerging technologies, including Blockchain, Quantum Computing, Artificial Intelligence (AI), and Internet of Things (IoT), whether utilized individually or in combinations such as AIoT and IIoT, possess a disruptive nature that is poised to fundamentally reshape the functioning of specific industries, education, and society at large. These technologies act as catalysts for digital transformation, fostering the emergence of novel business models and opportunities for development. Launched as an initiative of the Cluj IT cluster and supported by the Hasso Plattner Institute in Potsdam, Germany, Sibiu Innovation Days (SID) represented a necessity for Sibiu, for the university, for companies in the area, in the idea of bringing together of all decision makers that acts around the concepts of digitalization, innovation and knowledge transfer to a smarter society and a better life for its members. Emerging technologies can exploit Romania's connectivity and broadband capabilities, where Romania is above the European average, bringing it into line with and keeping pace with developed countries in terms of the digitalization of industry, agriculture and society as a whole. In this regard, we believe that no effort is useless to increase Romania's innovation performance, and through the activities carried out at Sibiu Innovation Days 2024, solutions to some of these challenges were addressed and proposed. [ABSTRACT FROM AUTHOR]

Background/Objectives: The rapidly increasing rate of obesity has become an extremely important public health problem, particularly in developed countries. Obesity is associated with a range of health problems, often referred to as the metabolic syndrome. Adipose tissue is now regarded as an endocrine organ responsible for the hormonal secretion of adipokines, which are cytokines involved in various physiological processes. It has been established that adipokines play a key role in the regulation of many processes in the human body. The aim of the current study was to use an animal model to investigate the possible influence of obesity and adipokines on the gestational period, on the development of offspring, and to assess whether these changes are influenced by the administration of antioxidant agents and flavonoids. Methods: The present study was performed using 5 groups of 7 female Wistar albino rats. A control group was used to which a 5% lipid diet was administered, and the other 4 groups were fed an obesogenic 65% lipid diet. From the 4 groups that received obesogenic diet one group received no supplement, and the rest of 3 received Detralex, Sel-E-Vit and Rutin (antioxidants and flavonoids). Study times for both pregnant groups and offsprings: on day 15 of gestation, venous blood was drawn to determine adipokine (leptin and visfatin) levels; on days 18–22 ultrasound examination was performed to measure the thickness of adipose tissue in the abdominal wall; for each batch a number of 10 offspring were selected for the measurements (pup weight, brain weight, head length, head width, spine length, width between shoulder blades, coxal bone length), adipokine levels in the offspring (from brain tissue) were also determined, as well as the existence of changes in the brain tissue of the offspring identified by electron microscopy. Results: The results of the study showed that the high-fat diet (HFD) led to a significant increase in body weight and abdominal wall thickness in pregnant females compared to the control group. The levels of leptin and visfatin were also affected by the HFD, with leptin levels being significantly higher in the HFD group and visfatin levels being lower. In the offspring, the HFD group had a significantly higher body mass and brain weight compared to the control group. The anthropometric measurements of the offspring were also affected by the maternal diet, with the HFD group having larger dimensions overall. Interestingly, the offspring of the groups that received flavonoids in addition to the HFD had significantly smaller dimensions compared to both the HFD group and the control group. Conclusions: The results of this experimental study reinforce what is already known about the effects of obesity on the gestation period and offspring and at the same time, the current study highlights the existence of possible adverse effects of flavonoid compounds on the development of pregnancy and offspring, opening the way for future studies on the benefits and risks of using these compounds during gestational period. [ABSTRACT FROM AUTHOR]

Livezeni is one of the few active remaining collieries from Jiu Valley, in the Meridional Carpathian Mountains. Mining waste facilities classification is introduced according to Romanian technical prescription PT-C 39. Preliminary geotechnical studies on the basement rocks and the waste material are necessary to define the maximum height and the slope angle for ensuring the stability of the waste dump. The first step supposes modeling the morphology of the area based on various information sources. The decision on the shape and geometry of the dump could be taken according to various geotechnical, technological, legal and economic aspects involved, based on analysis done on the digital terrain model. A computerized design process allows the possibility to analyze several variants in less time and ensure results that are more accurate and useful in the later stage of building and remediation of the area. This process is illustrated on the waste dump Livezeni.

The restructuring of the mining sector in the Jiu Valley basin in the context of decarbonization has as its main goal the reduction of atmospheric pollution. Despite the fact that previous studies suggest an overall improvement in air quality as a result of this process, most ignore the lack of an immediate alternative to the use of coal as a primary energy resource both locally and regionally. This lack of alternatives has been found to have a negative influence on air quality through the noxious gases and Particulate Matter released into the atmosphere following the burning of some fuels, cloth and footwear waste, especially in the historical residential areas of the cities. In this paper, some of the results obtained from the instrumental determinations carried out in different areas of the Petroșani Municipality are presented, with special regard to the air quality in the Historical Center of this city.

Gold nanoparticles (Au-NPs) have been explored as potential vectors for enhancing the antitumor efficacy of doxorubicin (DOX) while minimizing its cardiotoxic effects. However, the impacts of DOX Au-NPs on cardiac function and oxidative stress remain inadequately understood. This study aimed to explore the effects of DOX Au-NPs in comparison to free DOX, focusing on oxidative stress markers, inflammation, ultrastructural changes, and cardiac function. Male rats were divided into the following four groups: control, citrate Au-NPs, DOX, and DOX Au-NPs. Cardiac function was assessed using echocardiography, and oxidative stress was evaluated through Nrf2, malondialdehyde (MDA) and superoxide dismutase (SOD) levels, and the GSH/GSSG ratio. The ultrastructure of cardiac tissue was assessed by transmission electron microscopy (TEM). Rats treated with DOX Au-NPs exhibited significant cardiac dysfunction, as indicated by a reduction in fractional shortening and ejection fraction. Oxidative stress markers, including elevated MDA levels and a reduced GSH/GSSG ratio, were significantly worse in the DOX Au-NP group. SOD levels decreased, indicating compromised antioxidant defenses. Citrate Au-NPs also caused some alterations in cardiac function and ultrastructure but without other molecular alterations. DOX Au-NPs failed to mitigate cardiotoxicity, instead exacerbating oxidative stress and cardiac dysfunction. DOX Au-NPs possess cardiotoxic effects, necessitating further investigation into alternative nanoparticle formulations or therapeutic combinations to ensure both efficacy and safety in cancer treatment. [ABSTRACT FROM AUTHOR]

The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the 'adam' and 'RMSprop' optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources. [ABSTRACT FROM AUTHOR]

The research paper aims to build a composite index of the financial performance of companies, to find if the impact of the COVID-19 crisis was significantly positive for most manufacturing companies listed on Bucharest Stock Exchange, and to look if the manufacturing companies were resilient being prepared with savings that could have mitigate the effects of this pandemic crisis. The results of the FE model selected show that 31.67% of the company's equity variation is justified by the two independent variables, the stronger correlation of equity being with reserves. Based on the composite index of financial performance built, the manufacturing companies were grouped in three clusters: a cluster with low financial performance companies (z < 4), a cluster with good financial performance companies (4 = z = 8) and a cluster with high financial performance companies (z > 8). The third cluster groups the most analysed companies, on which the pandemic crisis had a positive impact, which achieved the highest financial performance; they are those companies that take advantage from the COVID-19 crisis, adapting their business strategy to the market conditions imposed. The article adds value to the specialty literature by building the financial performance's composite indicator, clustering the manufacturing companies by financial performance' Z-score. [ABSTRACT FROM AUTHOR]

Doxorubicin (DOX) is an effective anticancer drug, but its use is limited by dose-dependent heart toxicity. Quercetin is a natural antioxidant frequently studied for its beneficial properties. Moreover, a wide range of dietary supplements are available for human use. This in vivo study aimed to explore the potential cardioprotective effects of quercetin in chronic DOX treatment. A total of 32 Wistar rats were randomly divided into four groups: control, DOX, DOX/Q-50, and DOX/Q-100, treated with saline, 2.5 mg/kg body-weight DOX, 2.5 mg/kg body-weight DOX + 50 mg quercetin, and 2.5 mg/kg body-weight DOX + 100 mg quercetin, respectively, for two weeks. Rats were monitored using cardiac ultrasound (US) and markers for cardiac injury. Oxidative damage and ultrastructural changes in the heart were investigated. Chronic DOX treatment led to a decline in cardiac function and elevated values of NT pro-BNP, troponin I, and CK-MB. Quercetin treatment slightly improved certain US parameters, and normalized serum NT pro-BNP levels. Furthermore, DOX-induced SOD1 depletion with consequent Nrf2 activation and DNA damage as shown by an increase in γH2AX and 8HOdG. Quercetin treatment alleviated these alterations. Oral administration of quercetin alleviated serum markers associated with DOX-induced cardiotoxicity. Furthermore, it exhibited a favorable impact on the cardiac US parameters. This suggests that quercetin may have potential cardioprotective properties. [ABSTRACT FROM AUTHOR]

Ensuring a balance between economic growth and environmental sustainability is crucial to reduce the impact of CO2 emissions and ensure sustainable economic development for future generations. The goal of this study is to investigate the influences of the adjusted gross dispensable income (GDI) per capita, the gross domestic product (GDP) per capita, energy consumption per capita, economic inequality in the population (GINI), and the median age on the carbon emissions in 27 European countries. In the dynamic panel analysis, CO2 emissions represent the dependent variable, while adjusted GDI/capita, GDP/capita, energy use/capita, median age, and GINI are the independent variables. The valid results of the model show us that only the past values of CO2 emissions, GDP/capita, and median age significantly influence the carbon emissions in the European countries analyzed. The median age and the GDP/capita have inversely proportional impacts on the carbon emissions in Europe. A 1% increase in GDP/capita produced a 0.006% decrease in carbon emissions in Europe. The younger the population is, the higher the carbon emissions. In contrast, the adjusted gross disposable income/capita, the energy consumption/capita, and economic inequality do not significantly influence CO2 emissions/capita in the European countries and period analyzed. [ABSTRACT FROM AUTHOR]