Spherically symmetric Einstein-{\ae}ther (E{\AE}) theory with a Maxwell-like kinetic term is revisited. We consider a general choice of the metric and the \ae{}ther field, finding that:~(i) there is a gauge freedom allowing one always to use a diagonal metric; and~(ii) the nature of the Maxwell equation forces the \ae{}ther field to be time-like in the coordinate basis. We derive the vacuum solution and confirm that the innermost stable circular orbit (ISCO) and photon ring are enlarged relative to general relativity (GR). Buchdahl's theorem in E\AE{} theory is derived. For a uniform physical density, we find that the upper bound on compactness is always lower than in GR. Additionally, we observe that the Newtonian and E\AE{} radial acceleration relations run parallel in the low pressure limit. Our analysis of E\AE{} theory may offer novel insights into its interesting phenomenological generalization: \AE{}ther--scalar--tensor theory ({\AE}ST).

Quantization is a powerful tool for accelerating large language model (LLM) inference, but the accuracy-performance trade-offs across different formats remain unclear. In this paper, we conduct the most comprehensive empirical study to date, evaluating FP8, INT8, and INT4 quantization across academic benchmarks and real-world tasks on the entire Llama-3.1 model family. Through over 500,000 evaluations, our investigation yields several key findings: (1) FP8 (W8A8-FP) is effectively lossless across all model scales, (2) well-tuned INT8 (W8A8-INT) achieves surprisingly low (1-3\%) accuracy degradation, and (3) INT4 weight-only (W4A16-INT) is more competitive than expected, rivaling 8-bit quantization. Further, we investigate the optimal quantization format for different deployments by analyzing inference performance through the popular vLLM framework. Our analysis provides clear deployment recommendations: W4A16 is the most cost-efficient for synchronous setups, while W8A8 dominates in asynchronous continuous batching. For mixed workloads, the optimal choice depends on the specific use case. Our findings offer practical, data-driven guidelines for deploying quantized LLMs at scale -- ensuring the best balance between speed, efficiency, and accuracy.

Numerous studies suggest that common genetic and epigenetic factors such as p53, histone deacetylase (HDAC), brain-derived neurotrophic factor (BDNF), the (Ataxia Telangiectasia mutated) ATM gene, cyclin-dependent kinase 5 (CDK5), glycogen synthase kinase 3 (GSK3) and altered expression of microRNA (miRNA) play a crucial role in cancer and neurodegeneration. As there is growing evidence that epigenetic aberrations in cancer and neurological diseases lead to complex pathophysiological changes, the simultaneous targeting of epigenetic and other related pathways by dual-target inhibitors may contribute to the discovery of more effective and personalized therapeutic options. Computer-Aided Drug Design (CADD) provides comprehensive bioinformatic, chemoinformatic, and chemometric approaches for the design of novel chemotypes of epigenetic dual-target inhibitors, enabling efficient discovery of new drug candidates for innovative treatments of these multifactorial diseases. The detailed anticancer mechanisms by which the epigenetic dual-target inhibitors alter metastatic and tumorigenic properties, influence the tumor microenvironment, or regulate the immune response are also presented and discussed in the review. To improve our understanding of the pathogenesis of cancer and neurodegeneration, this review discusses novel therapeutic agents targeting different molecular mechanisms involved in these multifactorial diseases.

Biomarkers associated with the progression from gastric intestinal metaplasia (GIM) to gastric adenocarcinoma (GA), i.e., GA-related GIM, could provide valuable insights into identifying patients with increased risk for GA. The aim of this study was to utilize multi-bioinformatics to reveal potential biomarkers for the GA-related GIM and predict potential drug repurposing for GA prevention in patients. The multi-bioinformatics included gene expression matrix (GEM) by microarray gene expression (MGE), ScType (a fully automated and ultra-fast cell-type identification based solely on a given scRNA-seq data), Ingenuity Pathway Analysis, PageRank centrality, GO and MSigDB enrichments, Cytoscape, Human Protein Atlas and molecular docking analysis in combination with immunohistochemistry. To identify GA-related GIM, paired surgical biopsies were collected from 16 GIM-GA patients who underwent gastrectomy, yielding 64 samples (4 biopsies per stomach x 16 patients) for MGE. Co-analysis was performed by including scRNAseq and immunohistochemistry datasets of endoscopic biopsies of 37 patients. The results of the present study showed potential biomarkers for GA-related GIM, including GEM of individual patients, individual genes (such as RBP2 and CD44), signaling pathways, network of molecules, and network of signaling pathways with key topological nodes. Accordingly, potential treatment targets with repurposed drugs were identified including epidermal growth factor receptor, proto-oncogene tyrosine-protein kinase Src, paxillin, transcription factor Jun, breast cancer type 1 susceptibility protein, cellular tumor antigen p53, mouse double minute 2, and CD44.

Leveraging recent advancements in machine learning-based flavor tagging, we develop an optimal analysis for measuring the hadronic cross-section ratios R_bRb, R_cRc, and R_sRs at the FCC-ee during its WWWW, ZhZh, and t\bar{t}tt‾ runs. Our results indicate up to a two-order-of-magnitude improvement in precision, providing an unprecedented test of the SM. Using these observables, along with R_\ellRℓ and R_tRt, we project sensitivity to flavor non-universal four-fermion (4F) interactions within the SMEFT, contributing both at the tree-level and through the renormalization group (RG). We highlight a subtle complementarity with RG-induced effects at the FCC-ee’s ZZ-pole. Our analysis demonstrates significant improvements over the current LEP-II and LHC bounds in probing flavor-conserving 4F operators involving heavy quark flavors and all lepton flavors. As an application, we explore simplified models addressing current BB-meson anomalies, demonstrating that FCC-ee can effectively probe the relevant parameter space. Finally, we design optimized search strategies for quark flavor-violating 4F interactions.

This paper introduces a control system for Doubly Fed Induction Generator (DFIG) based on a Disturbance Observer (DOB) for island mode operation. The proposed control system is validated through experiments, confirming its effectiveness in maintaining stable operation during island mode. The system responded efficiently to variations in wind speed and load conditions, demonstrating the efficacy of the implemented control scheme. The proposed control system unifies the design approach for both the inner and outer loops of the cascaded control system structure, simplifying implementation and parameter tuning.

(1) Background: This case study analyzed the successful performances of female gymnasts in the finals of the 39th and 40th World Cup in Maribor (SLO). The aim was to identify variations in their execution of the Clear Hip Circle to Handstand (CHCH) on uneven bars based on kinematic parameters. (2) Methods: This study involved elite female gymnasts from the 39th (n = 5, age: 17 ± 6 months) and 40th (n = 8, age: 17.5 ± 6 months) World Cups, totaling 13 gymnasts. Kinematic analysis was performed on 15 successful routines using the Ariel Performance Analysis System (Ariel Dynamics Inc., San Diego, CA). The analysis focused on 16 anthropometric reference points and 8 body segments, including the body mass center of gravity (CG). The main reference points analyzed were the hip joint, the shoulder joint, and the CG along the xy-axes. Trajectory, velocity, angle, and angular velocity of the hips and shoulders were calculated. Pearson correlation analysis was employed to assess the relationships between the kinematic variables. (3) Results: High intercorrelations between the reference points along the xy-axes (0.81–0.99) and optimal movement velocity were found. Dispersed results were observed for kinematic parameters of angle (0.10–0.16) and angular velocity of the hip joints (0.60–1.00), with similar dispersions for shoulder joints (0.51–1.00). Three distinct techniques were identified: (1) stretched body with minimal hip joint flexion throughout; (2) extended body with a short, quick hip joint extension during shoulder movement; and (3) hyperextension in the hip joint. (4) Conclusions: The kinematic analysis revealed three different performance styles of the CHCH among finalists. These variations in technique do not affect the success of the performance. This research contributes to a better understanding of the technique but does not prefer one style over another.

Background/Objectives: The importance of fixed-dose combinations (FDCs) for the treatment of hypertension is well established. However, from a stability perspective, FDCs present a challenge since the degradation of one active pharmaceutical ingredient (API) can be affected by the presence of another API. The aim of this study was to compare the degradation behaviors and evaluate the degradation kinetics of three antihypertensive drugs, perindopril tert-butylamine (PER), amlodipine besylate (AML), and indapamide (IND). Methods: The degradation processes were studied using the previously developed reverse phase high-performance liquid chromatographic (RP-HPLC) method after exposing each drug individually, as well as the combinations of two/three drugs, to different stress factors, such as light, oxidation, acidic, basic, or neutral pH values at different temperatures. Results: The results show that PER is most unstable under basic conditions and that AML displays a negative, while IND displays a positive effect, on PER stability when combined. AML is most affected by basic conditions and oxidation, and its stability is affected by both drugs positively; IND undergoes extreme photolysis, which is positively affected by AML but negatively by PER. Conclusions: Great care must be taken when formulating FDCs with these three drugs, as well as solutions or oral suspensions adjusted for geriatric or pediatric populations, since the stability of all three drugs is greatly affected by pH conditions, as well as light or oxidation factors and their interactions.

The development of point-of-care wearable devices capable of measuring insulin concentration has the potential to significantly improve diabetes management and life quality of diabetic patients. However, the lack of a suitable point-of-care device for personal use makes regular insulin level measurements challenging, in stark contrast to glucose monitoring. Herein, we report an electrochemical transdermal biosensor that utilizes a high-density polymeric microneedle array (MNA) to detect insulin in interstitial fluid (ISF). The biosensor consists of gold-coated polymeric MNA modified with an insulin-selective aptamer, which was used for extraction and electrochemical quantification of the insulin in ISF. In vitro testing of biosensor, performed in artificial ISF (aISF), showed high selectivity for insulin with a linear response between 0.01 nM and 4 nM (sensitivity of ∼65 Ω nM-1), a range that covers both the physiological and the pathological concentration range. Furthermore, ex vivo extraction and quantification of insulin from mouse skin showed no impact on the biosensor's linear response. As a proof of concept, an MNA-based biosensing platform was utilized for the extraction and quantification of insulin on live mouse skin. In vivo application showed the ability of MNs to reach ISF, extract insulin from ISF, and perform electrochemical measurements sufficient for determining insulin levels in blood and ISF. We believe that our MNA-based biosensing platform based on extraction and quantification of the biomarkers will help move insulin assays from traditional laboratory approaches to personalized point-of-care settings.

The goal of this abstract is to present available artificial intelligence (AI) software and tools for the development, assessment, and implementation of artificial intelligence/machine learning in cardio - vascular research and clinical care, ensuring they are safe, reliable

Background Determining human identity has always been important in forensic investigations. Forensic dentistry has developed significantly having a key role in determining gender and age. One of the methods that is important in forensic dentistry is the analysis of orthopantomograms, which are X-rays of the complete upper and lower jaw, including the surrounding anatomical structures. The uniqueness of the dental features recorded in orthopantomograms makes them useful for individual identification, more specifically for the assessment of gender and age. This study was conducted to evaluate the application of convolutional neural networks in automating the process of gender and age estimation based on orthopantomograms, to improve accuracy and efficiency in forensic dentistry. Methodology Convolutional neural networks are powerful tools in the field of artificial intelligence for image processing and analysis because their convolutional layers extract specific features that are characteristic of a certain class. A total of 3716 orthopantomograms collected from the database of the University of Sarajevo - Faculty of Dentistry with the Dental Clinical Center were used to create convolutional neural network models for predicting gender and age. The orthopantomograms were taken in the period from January to December 2022 for the needs of doctors and providing services to patients at four polyclinics: Clinic for Dental Diseases and Endodontics, Clinic for Oral Diseases and Periodontology, Clinic for Oral Surgery, and Clinic for Pediatric and Preventive Dentistry. Results The results derived from three developed models confirm that the developed convolutional neural networks have high accuracy. The first model estimated gender, while the second and the third models estimated age within certain age ranges, the second from 12 to 24 years, and the third from 20 to 70 years. After training on the training dataset, all models achieved high accuracy on the validation dataset. The models demonstrated high accuracy without signs of overfitting, with the first model achieving 95.98%, the second model achieving 97.90%, and the third model achieving 96.12% accuracy. Conclusion This research concluded that the developed convolutional neural networks for gender and age estimation from orthopantomograms showed high accuracy. Models' predictions of gender and two age groups exceeded 95% accuracy. Therefore, convolutional neural networks can be considered useful tools for gender and age determination in forensic dentistry and can facilitate and speed up the processes of assessment and determination of essential characteristics.