Despite the many advantages of renewable energy sources, the stochastic nature of their generation creates a mismatch between the timing of electricity production and demand. Without appropriate storage solutions, surplus energy may remain unused. Therefore, the development of energy complexes based on solar power plants with the integration of battery energy storage systems, as well as the development of corresponding computational models for them, becomes critical for ensuring the stability, flexibility, reliability, and efficiency of power systems. Battery energy storage systems have become widely used due to their availability, high response speed, significant energy density, and sufficient power capacity. However, their cost remains relatively high. This paper proposes a methodology and a calculation model for determining the optimal forecasted capacity and the rational storage requirements of an energy complex consisting of a solar power plant and a battery energy storage system operating in parallel with the grid at constant power under short-term forecasting conditions (day-ahead or longer). The proposed approach makes it possible to minimise the costs of energy companies associated with the short-term lease of part of a battery energy storage system when they do not own one, or, if such a system is available, to lease out its unused capacity and obtain corresponding profits. The validation of the computational model was carried out using a dataset of hourly daily power outputs of solar power plants in the Integrated Power System of Ukraine for 2018. Statistical analysis of the obtained results showed that the probability of occurrence of maximum deviations for the optimal capacity of the energy complex (5.4%), as well as for the power and capacity of the battery energy storage system (13% and 18%, respectively), does not exceed 0.05 during the year. Although the proposed methodology was applied using solar power plant generation data for the national power system as a whole, it can also be used for individual solar power plants located in different regions and countries with different climatic conditions. Certainly, the calculated coefficients will differ, but the methodology itself and the sequence of its application will remain the same.

Folate receptor-alpha (FRα) transports folate to germline cells and across the placental and blood-brain barrier to the brain. FRα autoantibodies (FRαAb) occur in ~70% of autistic children devoid of genetic defects, and ~1/3 of their asymptomatic parents. Folinic acid circumvents blocked folate transport. In children positive for FRαAb whose parents tested negative for FRαAb, high dose folinic acid treatment before 3 ½ years, results in improved outcome (88%). High autism prevalence with poor outcome (29%) is observed when mothers or both parents are positive for FRαAb. We postulated that folinic acid treatment of parents before and during pregnancy may reduce the risk of autism in further offspring. In a prospective open label pilot study, parents with positive FRαAb wishing to have further children, took daily levo-folinic acid (3.75 mg) or an equivalent dose of dl-folinic acid (7.5 mg) three months before conception and throughout pregnancy. Among five families where all mothers and three fathers tested positive for FRαAb, folinic acid prevented autism in all 7 children born. This contrasts significantly (Fisher’s exact test p=0.011) with the high autism prevalence of 54 % in offspring from 28 untreated families where either only mothers (n=15) or both parents (n=13) had FRαAb. Therefore, folinic acid intervention before and throughout pregnancy, in parents positive for FRαAb, may reduce the risk of autism in their offspring.

Background: Current surveillance strategies after treatment of endometrial cancer remain largely based on clinical stage and histological grade, reflecting a pre-molecular understanding of disease behavior. However, molecular classification has revealed profound biological heterogeneity across endometrial cancer subtypes, including differences in recurrence patterns, prognosis, and treatment responsiveness. Despite this progress, surveillance strategies have not yet been systematically adapted to molecular risk stratification. Objective: To propose a biology-driven surveillance framework for endometrial cancer that integrates molecular subtype, clinicopathological risk factors, and recurrence phenotype. Methods: This narrative review and conceptual framework synthesizes evidence from cohort studies, molecular classification analyses, international guidelines, and literature addressing recurrence patterns and treatment responsiveness across molecular subtypes of endometrial cancer. Results: We propose a three-tier surveillance model stratifying patients into low-, intermediate-, and high-risk groups. The framework integrates molecular subtype with clinicopathological modifiers and expected recurrence phenotype. Within the no specific molecular profile (NSMP) subtype, CTNNB1 mutation status is incorporated as a primary modifier, assigning CTNNB1-mutated tumors to the intermediate-risk group regardless of estrogen receptor (ER) status. In CTNNB1 wild-type NSMP tumors, ER expression functions as a secondary modifier, allowing identification of a biologically low-risk subgroup. L1CAM expression is considered a high-risk modifier within NSMP. The framework also accounts for differences in therapeutic modifiability of recurrence, including the role of immunotherapy in mismatch repair-deficient tumors. Conclusions: Uniform post-treatment surveillance does not reflect the biological diversity of endometrial cancer. The proposed framework provides a biologically grounded approach to surveillance that aligns follow-up intensity with recurrence phenotype and therapeutic opportunities. This model may serve as a conceptual basis for prospective studies evaluating personalized surveillance strategies in endometrial cancer.

This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multi-frequency dither, and dictionary-based waveform recognition for target detection and classification. Rather than forming conventional spatial images or relying primarily on Doppler processing, RaDICAL encodes target geometry directly into a composite receiver waveform and performs hypothesis testing by matching measured signals to a library of predicted responses. The paper develops the SUCA-based signal model for point and extended targets and formulates recognition as a waveform-domain dictio-nary matching problem using normalized complex correlation and QR-domain process-ing. A reproducible MATLAB-based study evaluates waveform separability, probability of detection versus dictionary SNR, physical power balance, receiver operating char-acteristic (ROC) behavior, and detection performance versus illuminator EIRP. The results show that deterministic frequency dither produces distinctive composite wave-forms with strong hypothesis separability. ROC simulations demonstrate reliable detec-tion at physical SNR levels below those typical of classical single-pulse matched-filter detection, while EIRP-based analysis indicates feasible detection for targets ranging from large aircraft to small drones and pedestrians. These results support the feasibil-ity of waveform-domain passive sensing using deterministic spatial–frequency encoding and dictionary-based recognition.

Large-scale population surveys provide valuable information for studying child well-being, yet their structure often limits direct application of machine learning methods. The National Survey of Children’s Health (NSCH) is one of the most comprehensive datasets for monitoring children’s health and development in the United States, but the raw survey files contain skip patterns, categorical variables, and complex survey design elements that require substantial preprocessing before predictive analysis can be performed. This study presents a curated machine-learning-ready dataset derived from the 2023 NSCH survey together with a fully reproducible computational pipeline for studying child flourishing. The pipeline constructs a binary flourishing indicator based on four survey items capturing curiosity, persistence, emotional regulation, and engagement in learning. After removing skip codes and missing responses, 1,978 valid observations were retained from the original dataset of more than 55,000 records. Feature selection using mutual information was applied to produce a reduced set of interpretable predictors suitable for benchmarking and educational use. Baseline experiments using logistic regression and random forest models show moderate predictive performance, suggesting that child flourishing cannot be accurately predicted using demographic and household variables alone. A methodological comparison between weighted and unweighted models further shows that incorporating survey weights consistently reduces predictive performance. By releasing both the curated dataset and the reproducible pipeline, this study provides a reusable resource for machine learning research on child well-being.

This paper evaluates the practical use of the Three-Dimensional Discrete Cosine Transform (3D-DCT) for video and volumetric image compression. While one- and two-dimensional DCT transforms are widely used in modern multimedia standards, their three-dimensional extension has received limited attention in real coding systems. In this work, a complete 3D-DCT–based encoder is developed by extending a JPEG-like pipeline to operate on three-dimensional data blocks. The proposed approach processes groups of video frames as 3D cubes and applies a separable 3D-DCT followed by quantization, coefficient serialization, and entropy coding. Unlike conventional video codecs that rely on motion estimation and compensation, the proposed system exploits temporal redundancy directly through the transform domain, resulting in a simpler coding structure with reduced algorithmic complexity. Different configurations are evaluated, including alternative methods for computing the 3D quantization matrix, serialization schemes, and variable block depth along the temporal dimension.Experimental results obtained from multiple test videos and volumetric medical datasets, including CT and MRI studies, demonstrate that the proposed method achieves competitive compression ratios while maintaining good reconstruction quality measured in terms of Peak Signal-to-Noise Ratio (PSNR). The results indicate that 3D-DCT provides a flexible and computationally simple solution for both video compression and three-dimensional medical image coding, particularly in applications where implementation simplicity and frame-level accessibility are important.

Multicultural education remains a foundational yet contested area of teacher preparation in increasingly diverse, unequal, and politically polarized educational contexts. This article offers a conceptual review of multicultural education in teacher preparation, focusing on culturally responsive, critical, and democratic approaches. Drawing on foundational and contemporary scholarship, including examples from research on culturally responsive pedagogy, critical curriculum analysis, intercultural competence, Indigenous education, and democratic teaching, the review synthesizes major conceptual traditions, pedagogical applications, and ongoing implementation challenges. The article argues that multicultural education is most effective when positioned as a foundational and integrative orientation across teacher preparation rather than as a stand-alone course or symbolic diversity requirement. It proposes a conceptual framework organized around three interrelated pedagogical orientations culturally responsive, critical, and democratic enacted through reflective practice, curriculum transformation, community and contextual engagement, and pedagogical translation. The review concludes by highlighting persistent tensions related to institutionalization, faculty preparedness, policy climates, and assessment, while identifying future directions for research and program development in diverse national, sociocultural, and decolonizing educational contexts. Overall, the article positions multicultural education as an ongoing project of pedagogical, curricular, and democratic renewal in teacher preparation.

The Hodgkin-Huxley (HH) model has dominated quantitative neuroscience since 1952. Its authors explicitly acknowledged its phenomenological character and called for a deeper mechanistic account. We propose that this account is the IMH model of nerve conduction. The model is based on three biophysical foundations: (1) the Ling polyelectrolyte gel framework, in which intracellular K+ is adsorbed at protein sites and the resting ionic distribution is a Donnan equilibrium thermodynamically stable that does not require a metabolic pump; (2) the Hofmeister ion series, which governs differential adsorption of K+ versus Na+; and (3) the hydraulic wave equation for a fluid-filled elastic tube, which predicts conduction velocity from myelin elastic modulus rather than sodium channel density. In this framework, the action potential is a coupled ionic-hydraulic phase transition propagating as a pressure wave in the periaxonal space. Electrical events are causally secondary – the electromagnetic shadow of the hydraulic wave, not its cause. We demonstrate that the model resolves a 75-year-old anomaly identified but left unexplained by Huxley and Stämpfli in 1949 : positive current enters a node before the membrane potential reaches its maximum, a relation the authors themselves described as "impossible in a system of resistances and capacities.'' We present nine falsifiable predictions distinguishing the IMH model from HH, covering myelin mechanics, mechanoreceptor adaptation, terminal arborisation geometry as the physical substrate of the Umwelt, motor tremor as hydraulic interference, the temporal basis of conscious perception, and the coupled physical constraints that explain why large-diameter unmyelinated fibres do not exist in nature.

Pavements are essential elements of transportation networks and are instrumental to the growth and development of a country. To harness the full potential of this infrastructure, it is necessary to maintain it in proper structural and functional conditions. Conventional techniques, such as manual inspection can be used to determine the functional conditions of the pavement. However, these techniques have different shortcomings, including high monitoring costs, time consumption and requirement of traffic control during pavement surveying. These shortcomings can be mitigated by utilizing various Artificial Intelligence (AI) techniques such as machine learning and deep learning, for pavement surface inspection. This study aims to systematically review state-of-the-art deep learning techniques such as You Only Look Once (YOLO), Convolutional Neural Networks (CNNs), and vision transformer architectures for pavement distress detection. Deep learning techniques can autonomously detect various types of pavement distress including longitudinal and transverse cracks, rutting, faulting, patching, shoving, raveling and potholes from the pavement surface. The findings from the review indicate that YOLO and CNN were extensively employed by researchers, however in recent times, vision transformers gained popularity among researchers and pavement engineers. Overall, this study highlights the critical role played by different deep learning techniques in transforming pavement monitoring, leading to safer, more resilient, and sustainable transportation infrastructure.

Congenital atlantoaxial instability (AAI) occurs more often in young, small-breed dogs. It usually results from malformations in the craniocervical junction. These malformations cause atlantoaxial subluxation or dislocation and compress the cervical spinal cord. As a result, dogs can have varying levels of neurological problems. Surgery is commonly used to realign the vertebrae. However, outcomes after surgery vary widely, and complications are common. Magnetic resonance imaging (MRI) helps identify abnormalities in the vertebral canal, spinal cord damage, and other brain diseases. These findings can affect the dog’s prognosis. This retrospective study assessed MRI findings and their association with surgical outcomes in 20 dogs with AAI. Older age and certain MRI results—such as severe ventral spinal cord compression and syringomyelia—were linked to worse recovery. Many MRI abnormalities were found, but not all predicted poor outcomes. These results show the importance of a thorough MRI before surgery. They also suggest that younger age and not having syringomyelia are good signs for recovery after AAI surgery.