Advancing Open Science

Population ageing is a growing global trend. It was estimated that by 2050, people over 60 years of age will represent 35% of the population in industrialised countries. This context demands strategies that incorporate technologies, such as fall detection systems, to facilitate remote monitoring and the automatic activation of risk alarms, thus improving quality of life. This article presents a scoping review of the leading technological solutions developed over the last decade for detecting falls in older adults, describing their principles of operation, effectiveness, advantages, limitations, and future trends in their development. The review was conducted under the PRISMA^® methodology, including articles indexed in SCOPUS, ScienceDirect, Web of Science, PubMed, IEEE Xplore and Taylor & Francis. There is a predominance in the use of inertial systems that use accelerometers and gyroscopes, valued for their low cost and wide availability. However, those approaches that combine image analysis with artificial intelligence and machine learning algorithms show superiority in terms of accuracy and robustness. Similarly, progress has been made in the development of multisensory solutions based on IoT technologies, capable of integrating information from various sources, which optimises decision-making in real time.

Flat electronic bands, characterized by a nearly dispersionless energy spectrum, have emerged as fertile ground for exploring strong correlation effects, unconventional magnetism, and topological phases. This review paper provides an overview of the theoretical basis, material realization, and emergent phenomena associated with flat bands. We begin by discussing the geometric and topological origins of flat bands in lattice systems, emphasizing mechanisms such as destructive interference and compact localized states. We will also explain the relationship between quantum metrics and flat bands, which are recent theoretical findings. We then survey various classes of materials—ranging from engineered lattices and Moiré structures to transition metal compounds—where flat bands have been theoretically predicted or experimentally observed. The interplay between flat-band physics and strong correlations is explored through recent developments in ferromagnetism, superconductivity, and various Hall effects. Finally, we outline open questions and potential directions for future research, including the quest for ideal flat-band systems, the role of spin–orbit coupling, and the impact of disorder. This review aims to bridge fundamental concepts with cutting-edge advances, highlighting the rich physics and material prospects of flat bands.

The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to improve the accuracy of forecasting environmental parameters. The research is aimed at solving key problems in environmental monitoring, particularly insufficient forecast accuracy and the complexity of processing small data with high discretization. We developed the concept of an adaptive system for predicting environmental conditions in urban agglomerations. Hybrid forecasting methods were proposed, which include the integration of quantum layers in LSTM, Transformer, ARIMA, and other models. Approaches to spatial interpolation of environmental data and the creation of an interactive air pollution simulator based on the A* algorithm and the Gaussian kernel were considered. Experimental results confirmed the effectiveness of the proposed methods. The practical significance lies in the possibility of using the developed models for operational monitoring and forecasting of environmental threats. The results of the work can be applied in environmental information systems to increase the accuracy of forecasts and adaptability to changing environmental conditions.

FeCoNiCrAl and FeCoNiCrAlScY high-entropy coatings were fabricated via electron beam physical vapor deposition. The microstructure and short-term isothermal oxidation behavior of the coatings were compared. Sc and Y inhibited coating element diffusion to the superalloy substrate and formed co-precipitated phases during coating manufacturing. The Sc/Y co-doped coating exhibited accelerated phase transformation from θ- to α-Al₂O₃ as compared to the undoped one. The effect mechanism associated with the nucleation of α-Al₂O₃ was discussed. The preferential formation of Sc/Y-rich oxides promoted the nucleation of α-Al₂O₃ beneath them, and the θ-α phase evolution process was directly skipped, which suppressed the rapid growth of θ-Al₂O₃ and the initial formation of cracks in the alumina film and provided the FeCoNiCrAl high-entropy coating with an improved oxidation property in the early oxidation stage.

SARS-CoV-2, the virus responsible for COVID-19, disrupts human cellular pathways through complex protein–protein interaction, contributing to disease progression. As the virus has evolved, emerging variants have exhibited differences in transmissibility, immune evasion, and pathogenicity, underscoring the need to investigate their distinct molecular interactions with host proteins. In this study, we constructed a comprehensive SARS–CoV–2–human protein–protein interaction network and analyzed the temporal evolution of pathway perturbations across different variants. We employed computational approaches, including network-based clustering and functional enrichment analysis, using our custom-developed Python (v3.13) pipeline, BioEnrichPy, to identify key host pathways perturbed by each SARS-CoV-2 variant. Our analyses revealed that while the early variants predominantly targeted respiratory and inflammatory pathways, later variants such as Delta and Omicron exerted more extensive systemic effects, notably impacting neurological and cardiovascular systems. Comparative analyses uncovered distinct, variant-specific molecular adaptations, underscoring the dynamic and evolving nature of SARS-CoV-2–host interactions. Furthermore, we identified host proteins and pathways that represent potential therapeutic vulnerabilities, which appear to have co-evolved with viral mutations.

This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP)—were developed and evaluated using historical failure data from Malatya, Türkiye. The primary predictive variables identified were pipe diameter, pipe type, pipe age, and seasonal average ambient air temperature. The MLP demonstrated superior performance compared to the other models, attaining the lowest RMSE (1.48) and the highest R² (0.993) with respect to the training data, effectively capturing the nonlinear characteristics and failure patterns. The MLP was validated using two datasets from 24 District Metered Areas (DMAs) in Sakarya and Kayseri, Türkiye. The model’s anticipated failure frequencies exhibited strong concordance with the observed failure frequencies, even in regions of elevated failure density, indicating the model’s proficiency in identifying high-risk locations and facilitating the prioritization of maintenance activities. The work demonstrates the potential of machine learning in water infrastructure management. It emphasizes the importance of employing a hybrid method with Geographic Information Systems (GISs) in future research to enhance forecast accuracy and spatial analysis.

Background: The juvenile-pubertal period is a critical window for linear growth and bone mass accumulation. This study investigated the joint effects of folic acid (FA) and colostrum basic protein (CBP)-fortified milk powder on growth, bone health, and metabolic safety in juvenile mice. Methods: Three-week-old C57BL/6J mice (n = 120) were acclimatized for 1 week and then randomly assigned to three isocaloric diet groups for an 8-week intervention starting at 4 weeks of age: Control (AIN-93M), Milk (AIN-93M + FA/CBP-fortified milk powder), and Positive Control (AIN-93G). Body length and weight were measured twice weekly. Bone microarchitecture was assessed by micro-computed tomography, and bone remodeling was evaluated through histology and serum biomarkers. The GH–IGF-1 axis and related metabolic parameters were also assessed. Results: FA–CBP–fortified milk powder significantly accelerated linear growth at intervention week 2, with body length higher in the Milk group than in the Control group (p < 0.01). After 8 weeks, the Milk group showed improved trabecular bone mass and microarchitecture compared with Control, especially in males (p < 0.01). Bone remodeling was transiently elevated at intervention week 4, as indicated by higher serum osteocalcin and CTX-I, and by increased osteoclast and cartilage matrix formation versus Control (p < 0.05). The GH–IGF-1 axis was also temporarily activated at week 4, with elevated serum GH and IGF-1/IGFBP-3 ratio compared with Control (p < 0.05). These skeletal benefits occurred without excess weight gain or adverse metabolic effects compared with Control (all p > 0.05). Conclusions: FA-CBP-fortified milk significantly enhanced linear growth during puberty and improved bone mass and microstructure in early adulthood. These skeletal benefits are consistent with the transient activation of the GH–IGF-1 axis. Importantly, no adverse metabolic effects were detected from early intervention through adulthood, supporting its potential application in growth-promoting nutritional strategies.

Background: Diabetes mellitus is a multifactorial disease characterized by complex metabolic dysfunctions and chronic complications induced by hyperglycaemia. The design of multitarget ligands, capable of simultaneously controlling different pathogenic processes, was proposed as a promising approach to identify novel antidiabetic drugs endowed with improved efficacy. Methods: (5-Arylidene-4-oxothiazolidin-3-yl)alkanoic acid derivatives 1a–g and 2a–g were synthesized as potential multitarget antidiabetic agents. They were tested in vitro as inhibitors of both human recombinant AKR1B1 and PTP1B, and kinetic studies and molecular docking simulations for both enzymes were performed. Their effects on cellular glucose uptake, insulin signalling, and mitochondrial potential were assayed in cultures of murine C2C12 myocytes. A lipid accumulation assay was performed in HepG2 liver cells. The effects on high glucose-induced sorbitol accumulation were evaluated in lens HLE and retinal MIO-M1 cells. Results: All compounds displayed excellent AKR1B1 inhibitory activity (IC₅₀ 0.03–0.46 μM 1a–g; IC₅₀ 0.48–6.30 μM 2a–g); 1g and 2e–g also appreciably inhibited PTP1B at micromolar concentrations. Propanoic derivatives 2e–g significantly stimulated glucose uptake in C2C12 myocytes, in an insulin-independent way, reduced lipid accumulation in HepG2 liver cells, and caused hyperpolarization of C2C12 mitochondria at 10 μM concentration. Derivative 2e significantly reduced sorbitol accumulation in both HLE and MIO-M1 cells at a 5 μM concentration. Conclusions: The results reported here provided new insights into the mechanisms of action and structure/activity relationships of 4-thiazolidinone derivatives, underscoring the capability of compounds 2e–g of eliciting insulin-mimetic effects independent of hormone signalling. Among them, compound 2e also proved to inhibit AKR1B1-dependent sorbitol accumulation and, thus, emerged as a promising multitarget agent that can be considered for further investigations.