SBA Research

Noise contamination is a common challenge in the analysis of time series data, where stochastic perturbations can obscure deterministic dynamics and complicate the interpretation of signals from chaotic and physiological systems. Reliable identification of noise regimes and their intensity is therefore essential for robust analysis of dynamical and biomedical signals, where incorrect attribution of stochastic perturbations can lead to misleading interpretations of system behavior. For this reason, the present study examines the role of complexity-based descriptors for identifying stochastic perturbations in time series and analyzes how these metrics respond to different noise regimes across heterogeneous dynamical systems. A supervised learning approach based on complexity descriptors was developed to analyze controlled perturbations in multiple signal types. Gaussian, pink, and low-frequency noise disturbances were injected at predefined intensity levels into the Rössler and Lorenz chaotic systems, the Hénon map, and synthetic electrocardiogram signals, while AR(1) processes were used for validation on inherently stochastic signals. From these systems, eighteen entropy-based, fractal, statistical, and singular value decomposition-based complexity metrics were extracted from either raw signals or reconstructed phase spaces. These features were used to perform three classification tasks that capture different aspects of noise characterization, including detecting the presence of noise, identifying the perturbation type, and discriminating between different noise intensities. In addition to predictive modeling, the study evaluates the complexity profiles and feature relevance of the metrics under varying perturbation regimes. The results show that no single complexity metric consistently discriminates noise regimes across all systems. Instead, system-specific relevance patterns emerge. Under given experimental constraints (data partitioning, machine learning algorithm, etc.), Approximate Entropy provides the strongest discrimination for the Lorenz system and the Hénon map, the Coefficient of Variation, Sample and Permutation Entropy dominate classification for ECG signals, and the Condition Number and Variance of first derivative together with Fisher Information are most informative for the Rössler system. Across all datasets, the proposed framework achieves an average accuracy of 99% for noise presence detection, 98.4% for noise type classification, and 98.5% for noise intensity classification. These findings demonstrate that complexity metrics capture structural and statistical signatures of stochastic perturbations across a diverse set of dynamic systems.

Web hosting platforms allow users to deploy websites easily. However many beginners face difficulties while hosting their projects. This paper presents Flip-Frame, a lightweight HTML web hosting platform that allows users to upload and host static websites quickly. The system focuses on simplicity, accessibility, and fast deployment. The proposed solution reduces technical barriers and allows beginners to host projects without complex server configuration

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language models (LLMs) have demonstrated strong performance in phishing-related classification tasks, but their operational costs and reliance on external providers limit their practical adoption in many business environments. This paper presents a detection pipeline for malicious websites and investigates the feasibility of Small Language Models (SLMs) using raw HTML code and URLs. A key advantage of these models is that they can be deployed on local infrastructure, providing organisations with greater control over data and operations. We systematically evaluate 15 commonly used SLMs, ranging from 1 billion to 70 billion parameters, benchmarking their classification accuracy, computational requirements, and cost-efficiency. Our results highlight the trade-offs between detection performance and resource consumption. While SLMs underperform compared to state-of-the-art proprietary LLMs, the gap is moderate: the best SLM achieves an F1-score of 0.893 (Llama3.3:70B), compared to 0.929 for GPT-5.2, indicating that open-source models can provide a viable and scalable alternative to external LLM services.

Antibody‐drug conjugates (ADCs) and immunotherapy have changed the treatment landscape for locally advanced and metastatic urothelial carcinoma (la/mUC). Clinical trials have demonstrated the superiority of enfortumab vedotin (EV) combined with pembrolizumab (P) over chemotherapy. This retrospective, multicentre Austrian registry analysis evaluated the effectiveness and safety of EV + P in treatment‐naïve la/mUC patients in a real‐world setting. This study included 203 first‐line EV + P treated patients from 20 Austrian centres. The majority were male (77.3%), with a median age of 70 years (range, 26–92), while 33.5% were ≥75 years. Comorbidities were common, with 25.1% having a Charlson Comorbidity Index (CCI) ≥ 5, and 46.3% moderate to severe renal impairment. Among 195 evaluable patients, the objective response rate (ORR) was 63.6% (95% CI, 56.6–70.0), including 21.5% complete responses and 42.1% partial responses. Disease control was achieved in 75.4% (95% CI, 68.9–80.9). Median progression‐free survival (PFS) and overall survival (OS) were not reached after a median follow‐up of 5.8 months. Poor outcomes were associated with ECOG PS >2 (PFS, p = .003; OS, p< .001) and CCI ≥5 (PFS, p = .011; OS, p = .08). EV + P was generally well tolerated; grade ≥3 treatment‐related adverse events occurred in 38.4% of patients and immune‐related adverse events in 19.2%. Limitations of this study include its retrospective design and short follow‐up. These findings support the clinical use of EV + P as first‐line treatment for la/mUC, demonstrating substantial effectiveness and manageable toxicity in a large real‐world population, including elderly and comorbid patients. Extended follow‐up is warranted to determine long‐term survival and predictive markers of response.

Abstract Biofeedback is a method that supports people in gaining control over certain bodily processes by using real-time feedback on a screen. It involves monitoring physiological functions like heart rate, muscle tension, breath rhythm, skin conductance, or peripheral skin temperature that are usually involuntary. Providing that information to the recipient supports their learning to influence those functions consciously. This case study proposes a setting in which Biofeedback sessions can be supported by the calming presence of a licensed therapy dog. Welfare considerations to protect the dog include adequate preparation of the room and setting, pre-instructions for the recipient, limited session duration, providing the dog access to water, and a safe place to rest. Moreover, a case scenario based on sessions with a 10-year-old girl who initially presented with neurological symptoms and received canine-assisted Biofeedback in addition to medical and psychological treatment is described. Information © The Author 2025

Grassland biomass yield reflects a complex interaction of management intensity and environmental factors, yet quantifying the relative role of practices such as mowing and fertilization remains challenging. In this study, we introduce a multi-class machine learning framework to predict above-ground biomass on 150 permanent grassland plots across eight years (2009–2016) in Germany’s Biodiversity Exploratories and to evaluate the influence of key management variables. Following rigorous data cleaning, imputation of missing nitrogen values, feature standardization, and encoding of categorical practices, we trained CatBoost classifiers optimized via Bayesian hyperparameter search and mitigated class imbalance with ADASYN oversampling. We assessed model performance under binary, three-class, four-class, and five-class quantile-based categorizations, achieving test accuracies of 0.76, 0.57, 0.42, and 0.38, respectively. Across all schemes, mowing frequency and mineral nitrogen input emerged as the dominant predictors, while secondary variables such as drainage and conditioner use contributed as well. These results demonstrate that broad biomass categories can be forecast reliably from standardized management records, whereas finer distinctions necessitate additional environmental information or automated sensing to capture nonlinear effects and reduce reporting bias. This work shows both the potential and the limits of machine learning for informing sustainable grassland management and explainability thereof. Frequent mowing and higher mineral nitrogen inputs explained most of the predictable variation, enabling a 76% accurate separation of low and high biomass categories. Predictive accuracy fell below 60% for finer class resolutions, indicating that management records alone are insufficient for detailed yield forecasts without complementary environmental data.

Penelitian ini bertujuan untuk menganalisis strategi mitigasi fraud yang dilakukan oleh Komisi Pemilihan Umum (KPU) Provinsi Nusa Tenggara Timur dalam pengelolaan dana kampanye Pemilu 2024, dengan menggunakan pendekatan Fraud Triangle dan akuntansi forensik. Fraud Triangle yang terdiri dari tekanan (pressure), peluang (opportunity), dan rasionalisasi (rationalization) digunakan untuk mengidentifikasi faktor-faktor yang mendorong terjadinya kecurangan. Penelitian ini menggunakan pendekatan deskriptif kualitatif dengan teknik pengumpulan data melalui wawancara mendalam dan analisis dokumen. Hasil penelitian menunjukkan bahwa tekanan politik dan finansial, lemahnya pengawasan internal, serta budaya politik transaksional menjadi pemicu utama terjadinya fraud dalam pelaporan dana kampanye. KPU Provinsi NTT telah melakukan upaya mitigasi melalui sosialisasi, pemanfaatan teknologi seperti SIKADEKA, dan pelatihan sumber daya manusia. Namun demikian, tantangan geografis dan keterbatasan sumber daya masih menjadi kendala dalam pengawasan optimal. Penelitian ini menyarankan penguatan sistem audit forensik dan penegakan hukum yang lebih tegas guna meningkatkan transparansi dan akuntabilitas dana kampanye

Researchers of all disciplines produce, share, and reuse data as part of everyday research. Most funders require them to manage and document their data using data management plans (DMPs). DMPs are often static documents that researchers create by answering questions in predefined templates at the beginning of the research and, therefore, may become outdated and obsolete as the project progresses. It is essential to keep the DMP up to date at all stages of the research lifecycle, since numerous stakeholders and various services participate in data management that depend on information from them. In this article, we propose a conceptual service architecture that uses machine-actionable data management plans to automate the exchange and synchronization of information between different semi-automated research data management services acting on behalf of different stakeholders. To solve the stated problem, we analyze typical use cases in which the DMPs change and formulate requirements based on which we developed the conceptual architecture. We depict the designed architecture through a set of views, namely physical, development, logical, and process, using Unified Modeling Language and Business Process Models and Notation representation that describe the processes required to synchronize DMP information among multiple services. We instantiate it by implementing a service that connects a data repository and a DMP tool. Thus, we evaluate to what extent the defined processes help in keeping DMP contents up to date and which criteria must be fulfilled to keep them highly automated. The result of the article feeds into a larger discussion on streamlining interconnectivity and machine-actionability across planning, tracking, and assessing research phases. It also facilitates consensus building on enhancing the Research Data Alliance’s recommendation for machine-actionable DMPs.