Kefir is a popular probiotic drink known for its fermented, acidic, and slightly alcoholic properties. This study aimed to determine key processing variables, namely, the ratio of kefir grains/milk, the incubation temperature, and the incubation time, that optimise the quality of pasteurised goat milk kefir. Fourteen kefir treatments built upon three grain/milk ratios (0.5%, 1%, and 1.5%), three incubation times (16 h, 20 h, and 24 h), and three incubation temperatures (15 °C, 20 °C, and 25 °C) were carried out, according to a Box–Behnken design with three central points, to determine the best triplet variables regarding the following properties: pH, acidity (% of lactic acid), syneresis (%), mesophile and lactic acid bacteria concentrations, firmness, consistency, cohesiveness, and viscosity index. Response surface analysis was applied to each of the quality attributes. It was found that all the quality properties were affected by the three factors, with the factors having, in all cases, significant effects (p<0.05) for the first-order estimation. Kefir acidity was maximised at 0.66%, 25.3 ºC, and 22.8 h, although the temperature had a much greater effect than time, and in turn, the latter had a greater effect than the grains/milk ratio. In terms of the textural properties, the firmness and viscosity index were maximised under conditions of 1.07%, 19.9 ºC, and 17.4 h and 1.28%, 18.5 ºC, and 20.4 h, respectively. At an incubation temperature of 25 ºC, syneresis was found to be between 38.5 and 39.9%; lower values (4.46 – 4.49%) were attained at 15 ºC. Lower incubation temperatures or longer incubation times can also produce kefir with the desired acidity and textural quality regarding hardness and consistency, but only if the ratio is increased (>1.0%). Thus, this study has been very valuable in understanding the effects of these three key processing variables and helping in the determination of the variables necessary to obtain goat milk kefir of good technological quality for subsequent studies.

Aerogels, with low density and high porosity, are promising materials for applications in food packaging, energy storage, thermal insulation, and water treatment [1]. This study introduces a sustainable method for producing carbon-based aerogels via hydrothermal carbonization of accessible precursors. Samples with CaCl₂ and glucose at mass ratios <2 were placed in PTFE-lined autoclaves and heated to 220°C. After cooling, the solid products were recovered and analyzed using XRD, SEM-EDX, FTIR, and UV-VIS DRS.At temperatures above 146 ºC, glucose liquefies, dissolving CaCl₂ and forming a viscous mass that decomposes above 200 ºC, releasing water vapor and CO₂, which expands the melt. The aerogel’s volume remains stable unless bubble coalescence collapses the foam. Experiments show that without CaCl₂, the carbon structure is nearly non-porous. Adding CaCl₂ creates a porous structure with a maximum pore size of ~40 micrometers at a CaCl₂/glucose ratio of 0.5. The average pore size decreases by over 50% as this ratio exceeds 1.8. XRD spectra also reveal CaCO₃ formation from CO₂ reacting with molten CaCl₂, with solid particles reducing pore coalescence by increasing viscosity. The study presents a novel method for producing carbon aerogels in a closed environment through thermal decomposition of glucose–calcium chloride mixtures. By adjusting the CaCl₂ : glucose ratio, the average pore size can be controlled, enabling formation of either non-porous amorphous carbon or closed-cell aerogels. Higher CaCl₂ concentrations result in smaller pores due to increased viscosity and enhanced surface tension of the melt. In conclusion, the development of tunable pore size aerogels through the controlled synthesis of carbon aerogels offers significant potential for a wide range of applications. These materials have the potential to revolutionize fields like energy storage, catalysis, environmental remediation, and biomedical engineering. This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CCCDI-UEFISCDI, project number PN-IV-P8-8.3-PM-RO-BE-2024-0004 within PNCDI IV.

Titanium dioxide (TiO₂) is a highly versatile material extensively used due to its non-toxic nature, chemical stability and high photocatalytic activity. These features make TiO₂ particularly promising for a wide range of applications, especially as a film coating on various substrates. TiO₂ is commercially available in many forms, including pure anatase and mixed-phase products like P25, which is a mixture of anatase (80%) and rutile (approximately ≤20%), ideal for photocatalysis.

In this study, TiO₂-P25 films on FTO substrates were synthesized using the sol-gel process and studied using Variable Angle Spectroscopy Ellipsometry (VASE) to determine their optical constants and thickness. The measurements were carried out at room temperature in the wavelength range (300–900) nm at incident angles varying from 55° to 70°. The resulting thicknesses were found to be ranging from 1000 nm to 10000 nm.

Scanning Electron Microscope (SEM) measurements confirmed the porous nature of the films, suggesting an inhomogeneous structure with depth-dependent compositional gradients. The novelty of this study lies in the structural complexity of these films that presented unique challenges for VASE measurements, which are typically straightforward for thinner films under a few hundred nanometers.

A graded layer model, which allowed for an accurate representation of the depth-dependent optical variations, was employed to model the properties of these TiO₂-P25 films. This advanced modeling approach provided deeper insights into the internal structure of the films, particularly how the graded structural characteristics impact the overall optical behavior. The bottom layers of the films typically exhibited a significant increase in both refractive index and extinction coefficient, indicating greater density and higher absorbance compared to the top layers. Understanding these depth-dependent variations is essential for optimizing the use of TiO₂-P25 films in technologies such as solar cells and optical devices, where precise control over material properties is critical.

Groundwater quality analysis and potability prediction are essential for public health and sustainable environmental management. This study adopts a two-part approach, integrating statistical modeling and machine learning classification to analyze groundwater data. In the modeling phase, we introduced the Inverse Power Log-Logistic (IPLL) distribution, specifically designed to capture the unique characteristics of groundwater quality data, focusing on pH and sulfate concentrations due to their significant impact on water potability and health. pH levels are critical as they affect water acidity and the potential for heavy metal dissolution, while sulfate concentrations are commonly associated with water taste and health risks when present in excess. We derived key structural properties of the IPLL distribution, including its moments, hazard, and survival functions, with parameters estimated using maximum likelihood estimation. Compared to conventional models, the IPLL distribution shows enhanced flexibility and accuracy, as measured by metrics such as AIC, BIC, and RMSE. In the classification phase, we applied machine learning algorithms—logistic regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Random Forest—to predict groundwater potability. Performance was evaluated using accuracy, F1-score, and ROC-AUC. Random Forest achieved the highest accuracy (92.3%), F1-score (0.89), and ROC-AUC (0.94), with SVC following closely at 89.7% accuracy, 0.87 F1-score, and 0.92 ROC-AUC. Both KNN and logistic regression models also performed well, achieving accuracy scores of 87.5% and 85.2%, respectively. This study offers a comprehensive framework for groundwater analysis, combining advanced statistical modeling with effective machine learning classification. The IPLL distribution’s adaptability to environmental data and the machine learning models' predictive strength in potability assessment provide valuable insights for public health officials and environmental policymakers. This dual approach has broad potential applications in fields that require reliable data modeling and prediction.

Brain tumors significantly impact global health, posing serious challenges for accurate diagnosis due to their diverse nature and complex characteristics. Effective diagnosis and classification are essential for selecting the best treatment strategies and forecasting patient outcomes. Presently, histopathological examination of biopsy samples is the established method for brain tumor identification and classification. However, this method is invasive, time-consuming, and susceptible to human error. To address these limitations, we required a fully automated approach to classify brain tumors. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown promise in enhancing the accuracy and efficiency of brain tumor classification from magnetic resonance imaging (MRI) scans. In response, we developed a model integrating machine learning (ML) and deep learning (DL) techniques. The process started by splitting the data into training, testing, and validation sets before resizing the images and then performing cropping to enhance model quality and efficiency. Further, the relevant texture features are extracted using a modified Visual Geometry Group (VGG) architecture. These features were fed to various supervised ML models, including support vector machine (SVM), k-nearest neighbors (KNN), logistic regression (LR), stochastic gradient descent (SGD), random forest (RF), and AdaBoost, with GridSearchCV-based hyperparameter tuning. The evaluation of the model’s performance was conducted using several key metrics, including accuracy, precision, recall, F1-score, and specificity. The experimental results demonstrate that the presented approach offers a robust, automated solution for brain tumor classification, achieving the highest accuracy of 94.02% with VGG19 and 96.30% with VGG16. The proposed model can significantly assist healthcare professionals in early detection of tumors and improving diagnosis accuracy.

The precise optical identification of aqueous solutions with different compositions is challenging for samples that have a similar refractive index at a certain temperature. However, their thermo-optic response, which describes how the refractive index varies with temperature, provides simple means for discrimination. We report on the use of a conventional fiber optic sensor based on multimodal interference to distinguish aqueous solutions of Tris and fructose based on thermo-optic effects. At room temperature, aqueous solutions of these components have indistinguishable properties. In fact, our sensor exhibited a linear dependence on concentration in both cases, with similar sensitivities of 0.2179 nm/% for Tris and 0.2264 nm/% for fructose, indicating that their discrimination is hindered based on concentration. On the other hand, by changing the temperature controllably, from 25°C to 45°C in increments of 2.5°C, we were able to change the refractive index enough to clearly discriminate between the samples at the same concentration. The thermal sensitivity measured was 0.14433 nm/°C for Tris and 0.1852 nm/°C for fructose, allowing for evident differentiation among samples in the range of35°C to 45°C. Our findings demonstrate that the proposed thermo-optic discrimination approach is effective and offers a promising practical solution; the optical fibers do not need to be prepared in a special way and the temperature control can be carried out with general-purpose laboratory equipment, like a hot plate, over a temperature range that is easily attainable. Overall, the results presented are relevant to industrial and research applications involving advanced monitoring and fine processing control.

Parkinson’s disease, described by James Parkinson, is a neurological syndrome affecting the central nervous system, leading to issues such as speech difficulties, tremors, and impaired movement. It is a prevalent neurological condition characterized by motor and cognitive impairments, affecting approximately 10 million people globally, according to WHO. Early diagnosis is critical, as delayed detection may lead to irreversible damage. Speech, being affected by motor control depletion, serves as a valuable tool for diagnosing Parkinson’s disease. This work presents a machine learning-based approach for the systematic detection of Parkinson’s disease using speech features. The dataset, obtained from the UCI Machine Learning Repository, consisting of biomedical voice measurements derived from speech recordings, and including data from 195 individuals (147 with Parkinson’s and 48 healthy controls), was analyzed, incorporating 21 features derived from speech recordings. In this work, several classification algorithms in machine learning were utilized. Specifically, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, AdaBoost, and Random Forest were implemented and evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC curves. From the experiment results, K-Nearest Neighbors (KNN) emerged as the best performer, achieving 98.31% accuracy, ideal precision for the normal class (1.00), and ideal recall for Parkinson’s cases (1.00), ensuring no missed diagnoses. Its F1-score of 0.98 highlights a strong balance between precision and recall. While AdaBoost matched KNN in accuracy, its slightly lower recall for Parkinson’s cases (0.97) makes K-Nearest Neighbors (KNN) the preferred choice. Consequently, K-Nearest Neighbors (KNN) is proposed as the most reliable model for robust and accurate Parkinson’s disease classification, showing outstanding performance compared to other models.

This study investigates the interactions between human serum albumin (HSA) and two detergents, propylene carbonate (PC) and decyl glucoside (DG), using a combination of experimental and computational approaches. Albumin, a highly abundant and multifunctional non-glycosylated protein, plays a crucial role in various physiological processes and exhibits significant changes in plasma levels in response to inflammation. To understand the binding affinity and stability of these detergents with HSA, we employed molecular docking, molecular dynamics (MD) simulations, UV–visible spectroscopy, and preliminary single-crystal X-ray diffraction (SCXRD) analysis. Molecular docking studies revealed that DG binds more effectively to HSA than PC, as indicated by lower free energy of binding (FEB) values. MD simulations provided insights into the stability of the HSA–detergent complexes, showing that while the ligand remained relatively stable, a slight repositioning occurred during the simulation. UV–visible absorption spectroscopy confirmed the interaction of both PC and DG with HSA, evidenced by changes in the absorption spectrum, particularly around 280 nm. Preliminary SCXRD analysis of HSA crystals indicated successful crystallization, though attempts to crystallize the HSA-PC complex were unsuccessful. Overall, this study highlights the binding affinities and interaction dynamics of PC and DG with HSA, emphasizing the impact of these interactions on albumin's functionality. The integration of experimental data and computational modeling offers a comprehensive understanding of these molecular interactions, potentially guiding future research and applications in biological and industrial contexts.

This paper explores the evolving role of a user within the context of a smart space, focusing on the concepts, challenges, and emerging trends in this rapidly advancing field. As technology continues to surrounding us, transforming traditional spaces into intelligent environments, it becomes crucial to understand the dynamics between users and these smart spaces. This work delves into the foundational concepts of a smart space, which encompasses an environment enriched with sensors, actuators, and interconnected devices capable of perceiving, analyzing, and responding to user needs and preferences. Smart spaces are designed to enhance user experiences, optimize resource utilization, and improve overall efficiency. However, the introduction of smart spaces brings forth diverse challenges that impact the user's. These challenges include privacy concerns, security risks, ethical considerations, and the potential for information overload. Understanding and addressing these challenges are vital to ensuring the successful integration and acceptance of smart spaces in different domains, such as homes, offices, healthcare facilities, and cities. This paper also explores emerging trends that shape the role of users in smart spaces. These trends encompass novel interaction paradigms, personalized experiences, context-awareness, adaptive automation, and the integration of artificial intelligence and machine learning techniques. Additionally, we examine the influence of user-centered design principles, emphasizing the importance of involving users in the development and evaluation of smart space technologies. By studying the concepts, challenges, and trends in the user's role within smart spaces, this work aims to shed light on the transformative potential of these environments. Understanding how users interact with and adapt to smart spaces can guide the design, implementation, and future development of intelligent systems that prioritize user needs, preferences, and well-being. Also we have defined a strategy to evaluate the performance of this concept, namely by accessing the user feedback on the different moments.

Sugarcane, a member of the Poaceae family, is a significant cash crop globally, valued for its refreshing taste and nutritional benefits. However, its commercialization faces challenges due to rapid decay and a short shelf life caused by harmful microorganisms. This study aimed to develop safe, long-lasting sugarcane juice suitable for human consumption during off-season periods. It focused on enhancing the storage stability of bottled sugarcane juice through various treatments (T₀–T₅), including microwave pasteurization at temperatures of 80ºC and 90ºC for 15-20 min and the addition of the preservatives sodium benzoate and citric acid at a ratio of 1:2 g/L, as well as their combination. Sugarcane juice samples were stored in 120 mL PET bottles at refrigeration (5 ± 1ºC) and analyzed every 10 days over a 40-day storage period for physicochemical, color, microbial, antioxidant, and sensory attributes. The results revealed a decrease in titratable acidity, total soluble solids, L* value, and microbial content during storage. However, pH, color (a* and b* values), and total phenolic content increased significantly. The sensory attributes notably changed in later storage stages. Sugarcane juice treated with sodium benzoate, citric acid, and heat treatment (referred to as treatments T₄ and T₅) exhibited minimal sensory changes during storage. Furthermore, the study successfully produced high-quality, ready-to-drink bottled sugarcane juice with satisfactory storage stability for 40 days, ensuring its quality and safety for consumption.