Automated road surface classification in OpenStreetMap using MaskCNN and aerial imagery.

Introduction: OpenStreetMap (OSM) road surface data is critical for navigation, infrastructure monitoring, and urban planning but is often incomplete or inconsistent. This study addresses the need for automated validation and classification of road surfaces by leveraging high-resolution aerial imagery and deep learning techniques.

Methods: We propose a MaskCNN-based deep learning model enhanced with attention mechanisms and a hierarchical loss function to classify road surfaces into four types: asphalt, concrete, gravel, and dirt.

SpectroNet-LSTM: An interpretable deep learning approach to cardiac anomaly detection through heartbeat sound analysis.

Cardiac anomalies are severe and life-threatening, making early detection essential to reducing health risks and mortality. According to the European Society of Cardiology, over 13 million people suffer from heart valve diseases annually, often identified by heartbeat anomalies. Traditional diagnostic methods depend on specialized expertise and advanced equipment.

Regression modelling strategies for projected and sustainable kraft pulping of wheat straw.

The demand for paper and paper-based packaging has seen a massive increase in past years, resulting in accelerated deforestation to meet the rising demand, negatively impacting the environment, and there is a need to look towards different non-woody raw materials. Kraft pulping (KP) is widely used in paper making, for which the chemical dose, temperature, time, and energy required must be optimized, for which many insignificant experimental trials are performed. An effort is made to solve this problem by developing the regression equations with the help of Excel using One Factor at a Time Analysis (OFAT), followed by carrying out design of experiments (DoE) using orthogonal approach and regression analysis in Minitab software.

CoInNet: A Convolution-Involution Network With a Novel Statistical Attention for Automatic Polyp Segmentation.

Polyps are very common abnormalities in human gastrointestinal regions. Their early diagnosis may help in reducing the risk of colorectal cancer. Vision-based computer-aided diagnostic systems automatically identify polyp regions to assist surgeons in their removal.

Treating Parvovirus Triggered Refractory Hemolytic Anemia with Rituximab in Renal Transplant Recipients - A Report of Two Cases.

Parvovirus B19 is a small (26 nm), nonenveloped, single-stranded DNA (5.6-kb) virus. The only known host for parvovirus B19 is humans.

Integrable quantum many-body sensors for AC field sensing.

Quantum sensing is inevitably an elegant example of the supremacy of quantum technologies over their classical counterparts. One of the desired endeavors of quantum metrology is AC field sensing. Here, by means of analytical and numerical analysis, we show that integrable many-body systems can be exploited efficiently for detecting the amplitude of an AC field.

Driving Enhanced Quantum Sensing in Partially Accessible Many-Body Systems.

The ground-state criticality of many-body systems is a resource for quantum-enhanced sensing, namely, the Heisenberg precision limit, provided that one has access to the whole system. We show that, for partial accessibility, the sensing capabilities of a block of spins in the ground state reduces to the sub-Heisenberg limit. To compensate for this, we drive the Hamiltonian periodically and use a local steady state for quantum sensing.

Global Sensing and Its Impact for Quantum Many-Body Probes with Criticality.

Quantum sensing is one of the key areas that exemplify the superiority of quantum technologies. Nonetheless, most quantum sensing protocols operate efficiently only when the unknown parameters vary within a very narrow region, i.e.

Benford's law gives better scaling exponents in phase transitions of quantum XY models.

Benford's law is an empirical law predicting the distribution of the first significant digits of numbers obtained from natural phenomena and mathematical tables. It has been found to be applicable for numbers coming from a plethora of sources, varying from seismographic, biological, financial, to astronomical. We apply this law to analyze the data obtained from physical many-body systems described by the one-dimensional anisotropic quantum XY models in a transverse magnetic field.