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Аннотация. В статье анализируется влияние моделей кредитного скоринга на основе больших данных и машинного обучения на выручку финтех-компаний. Автор рассматривает механизмы роста дохода через уровень…

Аннотация. В статье анализируется влияние моделей кредитного скоринга на основе больших данных и машинного обучения на выручку финтех-компаний. Автор рассматривает механизмы роста дохода через уровень одобрения, уровень дефолта и операционные издержки, а также сопоставляет типы скоринговых моделей по их экономической отдаче. Ключевые слова: кредитный скоринг, большие данные, машинное обучение, финтех, рост выручки, уровень дефолта, уровень одобрения, альтернативные данные, финансовая инклюзия, кредитный риск, операционные издержки. Annotatsiya. Maqolada katta ma’lumotlar va mashinali o‘qitish asosidagi kredit skoringi modellarining fintex kompaniyalari daromadiga ta’siri tahlil qilinadi. Muallif tasdiqlash darajasi, defolt darajasi va operatsion xarajatlar orqali daromad o‘sishi mexanizmlarini ko‘rib chiqadi hamda skoring modellari turlarini qiyoslaydi. Kalit so‘zlar: kredit skoringi, katta ma’lumotlar, mashinali o‘qitish, fintex, daromad o‘sishi, defolt darajasi, tasdiqlash darajasi, muqobil ma’lumotlar, moliyaviy inklyuziya, kredit riski, operatsion xarajatlar. Abstract. This article analyzes the impact of Big Data- and machine-learning-based credit scoring models on the revenue of fintech companies. The author examines revenue-growth mechanisms through approval rates, default rates and operating costs, and compares scoring model types by their economic returns. Keywords: credit scoring, big data, machine learning, fintech, revenue growth, default rate, approval rate, alternative data, financial inclusion, credit risk, operating costs.

This paper presents MyEvac, a real-time flood evacuation  alert architecture that integrates citizen-submitted images and videos, analysed by Qwen2.5-VL-72B, into a three-source weighted confiden…

This paper presents MyEvac, a real-time flood evacuation  alert architecture that integrates citizen-submitted images and videos, analysed by Qwen2.5-VL-72B, into a three-source weighted confidence engine alongside official JPS river sensor data and Open-Meteo GloFAS v4 probabilistic forecasts. The system operationalises a distinctive integration pattern: VLM-derived structured flood variables - water depth, severity classification, visual indicators, Malay OCR-derived location, and temporal water rise rate - enter the alert confidence formula as a calibrated weighted term, controlled by an explicit two-of-three source consensus gate before geofenced alert dispatch. The system was verified through 150 automated tests and initially validated through four real-world Malaysian flood media inputs and live external API integration. Prototype deployment targets the Taman Sri Muda flood zone in Shah Alam, Selangor, Malaysia.

This article analyzes the emergence and development stages of the automatic transmission, the operational characteristics of mechanical transmission synchronizers, and their impact on driver performan…

This article analyzes the emergence and development stages of the automatic transmission, the operational characteristics of mechanical transmission synchronizers, and their impact on driver performance. The first automatic transmission developed by General Motors and its evolution from a hydraulic clutch to a hydraulic transformer are highlighted. Additionally, the structural design of the hydraulic transformer, the principle of torque transmission, and the advantages of automatic torque control in accordance with the vehicle's driving conditions have been scientifically substantiated.  

Snapshot of data of the Tool Registry for Digital Humanities from Wikidata. Data is being downloaded through a combination of SPARQL queries and API calls and serialised as Turtle and JSON-LD.

LLM-based agents are often criticized for lacking spatial understanding and mainly exploiting statistical text patterns. We investigate their spatial comprehension through an architecture combining ge…

LLM-based agents are often criticized for lacking spatial understanding and mainly exploiting statistical text patterns. We investigate their spatial comprehension through an architecture combining geometrical tools with a large language model serving as a high-level orchestrator in grid-world environments. The agent first collects geodesic trajectories, which are then vector‑quantized to extract a representative subset. Each resulting trajectory defines a reusable tool. The LLM is used offline to interpret the learned skills by projecting them into natural language descriptions of the underlying behavioral patterns. Online, the LLM chooses the appropriate tool conditioned on the current state and goal. Low-level control is handled by primitive actions that execute the selected skill. From an agentic AI perspective, this approach separates learning into two levels. Skill discovery is handled through unsupervised quantization of trajectories, while reasoning and decision-making are handled by the LLM. 

Reproduce all our experiments or Run VHSeek locally with the Dataset and the Source Code below. Source Code: VHSeek: https://github.com/maovshao/VHSeek

The rapid integration of artificial intelligence (AI) in higher education has created a fragmented governance landscape fraught with strategic risks. To remedy this uncertainty, this article proposes …

The rapid integration of artificial intelligence (AI) in higher education has created a fragmented governance landscape fraught with strategic risks. To remedy this uncertainty, this article proposes an evidence-based scenario planning framework that converts foresight from intuitive speculation to empirical analysis. In this study, we operationalize ‘impact’ and ‘societal uncertainty’ as computational metrics (e.g., discourse volume, sentiment polarization, network centrality) to identify the most critical driving forces shaping academic AI policy. These drivers create the axes of a 2×2 scenario matrix, from which four plausible future worlds emerge that are grounded in observed institutional tensions. The populated scenarios demonstrate significant strategic divergences, ranging from passive acquiescence to external corporate directives to strong, local algorithmic governance within Triple-Helix dynamics. Moreover, the stories highlight the need to redirect institutional resources from physical infrastructure to pedagogical agility, particularly through continuous faculty upskilling and the reconfiguration of process-oriented assessments. By anchoring future narratives in empirical data, this study offers university leaders and policy makers a transparent, contextually adaptive road map. Ultimately, these evidence-based scenarios enable higher education institutions to be the primary regulators of ethical AI use, evolving from technology consumers to proactive institutions.  

Mazkur maqolada musobaqa oldi tayyorgarlik bosqichida ayol bokschilarning manyovr harakatlari (distansiyani boshqarish, aylanma va siljima harakatlar, ring bo‘ylab moslashuvchan harakatlanish) s…

Mazkur maqolada musobaqa oldi tayyorgarlik bosqichida ayol bokschilarning manyovr harakatlari (distansiyani boshqarish, aylanma va siljima harakatlar, ring bo‘ylab moslashuvchan harakatlanish) samaradorligini oshirish metodikasi tadqiq etilgan. Ayollar boksida manyovr harakatlarining o‘ziga xos jihatlari tana tuzilishi, reaksiya tezligi va taktik fikrlash xususiyatlari asosida ishlab chiqilgan maxsus mashqlar majmuasi taklif etiladi. Tadqiqot natijalari musobaqa oldi bosqichida maqsadli manyovr mashqlarini qo'llash texnik-taktik tayyorgarlik darajasini sezilarli darajada oshirishini ko‘rsatadi.

Background: Situs inversus totalis, also known as dextrocardia with situs inversus, is a rare congenital condition characterized by complete mirror image reversal of normal positioning of thoraci…

Background: Situs inversus totalis, also known as dextrocardia with situs inversus, is a rare congenital condition characterized by complete mirror image reversal of normal positioning of thoracic & abdominal organs. While individuals are often asymptomatic, its association with female infertility is uncommon and sparsely reported. Recognition of this condition is important during infertility evaluation to avoid diagnostic and procedural challenges.  Case Presentation: We report the case of a woman presenting with primary infertility who was incidentally diagnosed with Dextrocardia & Situs inversus during routine clinical and imaging evaluation. The patient had regular menstrual cycles and no significant past medical history. A chest X-ray, transabdominal & transvaginal ultrasonography revealed complete situs inversus, with a right-sided cardiac apex and transposition of abdominal organs. Detailed gynecological evaluation showed normal uterine morphology and ovarian reserve parameters. Further infertility work-up did not reveal additional contributory factors. The patient was counseled about the condition and its implications for reproductive management.  Discussion: Recognition of dextrocardia with situs inversus is essential for accurate interpretation of imaging findings and appropriate planning of diagnostic and therapeutic procedures. Awareness of reversed anatomy is particularly important for safe surgical interventions and assisted reproductive techniques when needed.  Conclusion: This case highlights the importance of considering rare congenital anomalies like dextrocardia with situs inversus during infertility evaluation. Early identification allows individualized counseling and tailored reproductive management, thereby improving fertility outcomes.

Overview This repository contains the Python machine learning code, preprocessed laboratory dataset, and interactive 3D visualisations for predicting the fracture energy (Gc) of silica-nanoparticle-mo…

Overview This repository contains the Python machine learning code, preprocessed laboratory dataset, and interactive 3D visualisations for predicting the fracture energy (Gc) of silica-nanoparticle-modified epoxy polymers across ambient and low-temperature conditions. Operating within a Green AI framework, this computational pipeline demonstrates how explainable non-linear regression models can deliver accurate mechanical predictions and physical insights, reducing the reliance on resource-intensive and non-recyclable laboratory testing. Key System Variables Predictor 1: Particle Loading (wt%, range: 0.0 to 20.3 wt%, 20 nm silica nanoparticles) Predictor 2: Environmental Temperature (°C, range: -80°C to 23°C) Target Variable: Fracture Energy Gc (J/m²) Preprocessing & Algorithmic Benchmarking Data Preprocessing: Micro-scale Core-Shell Rubber (CSR) modified epoxy samples were filtered out to eliminate high variance resulting from fundamental differences in micro-scale toughening mechanisms (such as micro-cavitation and shear-banding). Model Benchmarking: Four regression architectures were evaluated using 10-fold cross-validation (CV) and test split performance metrics: Optimised Random Forest (Selected Model): Demonstrated the strongest generalisation stability with a Test Split R² of 0.688, 10-Fold CV R² of 0.755, MAE of 13.97 J/m², and RMSE of 18.29 J/m². Gradient Boosting: Achieved high cross-validation fidelity (R² = 0.847) but exhibited overfitting on unseen data (Test Split R² = 0.440). Support Vector Regression & Linear Regression: Support Vector Regression (R² = -0.094) and Linear Regression (Test Split R² = -0.220) failed to model the non-linear toughening behaviour. Physical Validation via SHAP SHapley Additive exPlanations (SHAP) were implemented to ensure the machine learning model aligns with physical material behaviour: Low-Temperature Threshold: A distinct slope change in feature attribution occurs below -40°C, identifying a critical sensitivity threshold in brittle fracture conditions. Toughening Plateau: SHAP dependence analysis identified a saturation point at approximately 15 wt% silica content, matching physical observations of nanoparticle debonding followed by plastic void growth. Repository Files XAI_Fracture_Prediction_Pipeline.ipynb: Executable Python notebook containing data ingestion, preprocessing routines, model benchmarking, hyperparameter optimisation, and SHAP interpretability scripts. Silica_Epoxy_Dataset.csv: Cleaned experimental dataset of nano-silica modified thermosetting epoxy tested under ambient and low temperatures. Interactive_3D_Fracture_Surface for Gredient Boosting: Standalone Plotly 3D response surface mapping temperature and particle content against predicted Gc, overlaid with actual laboratory measurements. Interactive_3D_Fracture_Surface for Optimsed Random Forest: Standalone Plotly 3D response surface mapping temperature and particle content against predicted Gc, overlaid with actual laboratory measurements.