To compare complementary interventions for improving weight gain in preterm infants via network meta-analysis (NMA). We conducted a systematic review and NMA across six databases, including randomized controlled trials(RCTs). Two reviewers independently extracted data and assessed bias. Analyses used a Bayesian framework in GeMTC software, with effects reported as standardizes mean differences(SMDs). Confidence In Network Meta-Analysisevaluated inconsistency, ranking and evidence certainty. In total, 61 RCTs (n = 5858 infants) were analysed. Tactile kinesthetic stimulation showed greatest weight gain improvement (SMD 8.9, 95% CI 0.8-17). Multisensory stimulation (SMD 7.0, 6.2-7.9) and its combination with music therapy (SMD 7.9, 6.6-9.1) were also effective. Other beneficial interventions included kinesthetic stimulation (SMD 1.3, 0.091-2.5), massage (SMD 0.70, 0.39-1.0), and massage plus tactile stimulation (SMD 0.94, 0.044-1.9). Oral stimulation reduced hospitalization duration (SMD -0.49, -0.88 to -0.098). Tactile kinesthetic, multisensory stimulation and massage combinations significantly enhance preterm infant weight gain. These interventions should be considered in neonatal care protocols to optimize growth outcomes. The PROSPERO registration number is CRD42024557428.

Artificial intelligence (AI) has advanced rapidly across clinical domains, generating both a growing evidence base and dedicated regulatory frameworks for AI-based software as a medical device (SaMD). This review provides a comprehensive assessment of clinical AI across five major domains-diagnostic imaging, gastrointestinal endoscopy, cardiology and remote patient monitoring, diagnosis of infectious diseases, and an AI-ready data infrastructure-examining Japan's regulatory framework, approved device portfolio, and research contributions in an international context. We reviewed literature published between 2019 and 2026, using Japan's regulatory trajectory, approved device portfolio, and domain-specific research output as the primary lens for international comparison and prioritizing prospective studies, multicenter trials, and real-world implementation reports. The state of evidence varies markedly across the domains examined: endoscopy AI has the strongest randomized trial base, while diagnostic imaging AI has seen a systematic decline in real-world performance despite large-scale regulatory approval. Across the three dimensions examined, Japan has a distinctive profile: its strengths are a regulatory and clinical deployment infrastructure-evince by an established program medical device pathway and among the world's highest densities of diagnostic imaging systems and endoscopy volumes-while the data infrastructure lags, constrained by limited open-access resources relative to programs such as The Cancer Imaging Archive and the European Health Data Space. Large language models and generative AI, falling largely outside existing SaMD frameworks, carry the risk of hallucinations and gaps in oversight that healthcare systems in Japan and abroad are only beginning to address. Japan's established program medical device regulatory pathway, high-volume clinical deployment infrastructure, and proven regulatory-approval-to-reimbursement pathway provide a strong foundation for clinical AI adoption; post-approval change management frameworks and clinical accountability mechanisms need to be strengthened, AI-ready data accessibility needs to be expanded, and validated tools need to be embedded within reimbursed clinical workflows to translate this foundation into internationally competitive AI development and deployment.

Minimally invasive surgery involves repetitive and physically demanding manipulation tasks that contribute to surgeon fatigue and limit ergonomic efficiency. Semi-autonomous robotic assistance offers the potential to alleviate this workload, particularly for non-destructive supporting actions, such as tissue traction and presentation. This work presents a modular robotic toolhead designed to actuate standard laparoscopic instruments on robotic platforms, providing a research platform for autonomous assistance. The developed system integrates grasping actuation with a novel capstan linear drive, axial instrument rotation, extensible electrical interfaces, and a ROS2-based software framework into a compact end-effector without requiring instrument modification. The toolhead prototype undergoes functional testing and setup evaluation in a clinical simulation. Functional verification included successful grasping and axial rotation of different laparoscopic grasper types, insertion and removal of an instrument via a dedicated interface, exchange of the complete instrument adapter and toolhead, and decoupling under excessive grasping force. The software framework enabled coordinated actuation control, system monitoring, and data acquisition for future evaluation. The integration of the system into a laparoscopic phantom setup representative of cholecystectomy and sigmoid resection procedures showed workspace compatibility and workflow integration. The results demonstrate the feasibility of the proposed modular architecture as a flexible research platform for robotic laparoscopic assistance and future investigation of force-controlled grasping and semi-autonomous manipulation strategies.

Green fish farming represents an integrated aquaculture approach that rears aquatic organisms in controlled environments to improve production efficiency and environmental sustainability. Although significant, current green fish farming practices are labour-intensive and expensive due to grid energy dependency resulting in operational inefficiencies and elevated fish mortality. To address these key challenges, we propose a multidisciplinary approach that involves the development of a cost-effective, solar-powered automation system that integrates computer vision and deep learning techniques for real-time monitoring of fish behaviour, water quality, feeding, and waste management. First, we design the system architecture that enables automation and ensures accurate system performance under varying conditions. Second, following the architecture, we build a complete and cost-effective smart system that works along with an intelligent software framework that leverages computer vision and deep learning techniques. Utilizing custom datasets from video frames and environmental sensors, this system utilizes convolutional neural networks (CNNs) for fish behavior analysis, real-time disease detection via camera feeds, and precise feeding control through actuators. The design also incorporates a renewable energy subsystem, employing advanced photovoltaic panels and efficient battery storage to guarantee reliable power. The major contribution lies in the seamless integration of these multidisciplinary components. Furthermore, the system architecture is modular and scalable, making it suitable for both smallholder and commercial fish farms. Cost optimization with low-cost sensors and open-source software enables economic viability for resource-constrained farmers. Extensive simulation studies confirmed significant improvements in monitoring accuracy, reduced manual intervention, and enhanced operational sustainability.

Abstract Raising awareness about air pollution and environmental conditions is increasingly important. Across Europe, affordable citizen science environmental and air quality sensors are widely accessible, and their data can be viewed by the public through various web platforms, offering insights into environmental conditions across different regions. Augmented Reality (AR) presents a powerful tool for enabling citizens to monitor local environmental conditions and understand their personal impact, thereby encouraging behavioural change. However, effectively visualising real-time environmental data within a 3D AR space presents significant challenges. To address this, innovative visualisation techniques are essential for presenting environmental data in a clear and engaging way. This paper introduces a framework, a visualisation concept, and a prototype AR application designed to overlay environmental information, such as air quality and traffic data, onto the user’s view. The software framework is capable of easily integrating additional data servers that provide new types of environmental data. These concepts were developed as part of the European project H2020 COMPAIR, which actively involved users in the development process. The application has been tested in various pilot cities and regions and is now available to the public on major app stores. This publication represents an extended version of the conference paper [1]. It contains more technical details on the proposed Air Quality (AQ) visualisation framework, the environmental data servers integrated, a section on geo-localisation and an elaborate presentation of use cases and examples.

The present study aimed to estimate the heritability as well as genotypic and phenotypic correlations of body weights at different ages, average daily gain (ADG), and Kleiber ratio (KR) in Magra sheep. A total of 5,469 growth records, spanning 26 years (1998-2023), were obtained from the Arid Region Campus of the ICAR-Central Sheep and Wool Research Institute, Bikaner, Rajasthan. Genetic parameters were estimated using a univariate animal model incorporating fixed effects and random additive genetic effects under the restricted maximum likelihood (REML) framework in WOMBAT software. The estimates of direct heritability were 0.34 ± 0.02, 0.21 ± 0.02, 0.20 ± 0.03, 0.20 ± 0.03, 0.21 ± 0.03, 0.21 ± 0.03, 0.17 ± 0.02, 0.10 ± 0.02, 0.23 ± 0.03, 0.18 ± 0.03 and 0.10 ± 0.02, respectively for birth weight (BWT), weaning weight (3MWT), 6-month weight (6MWT), 9-month weight (9MWT), 12-month weight (12MWT), Average daily gains (ADG) i.e. ADG1 (birth to weaning), ADG2 (weaning to 6 month), ADG3 (6 to 12-month), KR1 (ADG1/3MWT0.75), KR2 (ADG2/6MWT0.75) and KR3 (ADG3/12MWT0.75), respectively. Heritability in present flock was moderate in early growth traits of Magra sheep indicating potential of improvement through selection. The genetic and phenotypic correlations among the studied traits revealed potential for their simultaneous improvement, particularly in association with 3MWT. The findings indicated that early selection based on 3MWT could facilitate substantial genetic progress in the resource population.

Reliable vehicle localization remains challenging in GNSS-limited and GNSS-denied environments. This challenge becomes particularly severe under harsh Nordic winter conditions, where road markings, traffic signs, and visual landmarks are often obscured by snow. This paper presents SnowPole-GeoLoc, an open-source software framework for snow pole geo-localization using GNSS and LiDAR data fusion. In this framework, snow poles are treated as stable and machine-perceivable roadside infrastructure landmarks. The framework integrates deep learning-based snow pole detection from LiDAR-derived images with GNSS-assisted geolocalization. This combination enables the estimation of absolute pole locations in global map coordinates. The software provides modules for ROS bag processing, visualization, coordinate transformation, and quantitative evaluation against ground-truth pole locations. SnowPole-GeoLoc is evaluated using real-world data collected along Norwegian highways with a 128-channel LiDAR sensor and continuous GNSS measurements. The software is modular, reproducible, and publicly released with pretrained models, datasets, and environment specifications. It can be used as a standalone snow pole geo-localization tool or as a core sub-module within end-to-end vehicle localization pipelines designed for winter-degraded sensing conditions.

Molecular dynamics (MD) simulations are a widely applied tool to investigate systems of varying complexity, from isolated molecules to biomolecules consisting of many thousands of atoms. Extracting mechanistic insights from the high-dimensional data sets frequently presents a challenge. Here, we introduce the publicly available MOlecules aNd Internal Cluster Analysis of Molecular Dynamics simulations (MonicaMD) program package, a versatile and efficient tool that targets the analysis of molecules and molecular clusters in atomistic classical and semiclassical trajectories. MonicaMD provides modular access to structural information, with a focus on internal and collective variables. A further key functionality is the extraction of electrostatic information. MonicaMD offers a user-friendly workflow including dimensionality reduction, automatic feature-space generation, and a templating functionality for generated grids in order to be readily used in conjunction with quantum chemical software and machine learning frameworks. The functionality of MonicaMD offers the user a convenient and efficient bridge between classical MD and higher-accuracy quantum mechanics simulations. This synergy enabled by MonicaMD is demonstrated by the investigation of conformational analysis in a protein-ligand complex, structural and electrostatic effects of DNA intercalation, and the excited-state isomerization of a photoswitch. Additional examples include reactive coordinates of a transition-metal-catalyzed C-N coupling reaction and of the light-initiated generation of free diazoalkane, as well as an analysis of chlorophyll binding sites in a photosynthetic complex.

Biomedical time-series processing systems are relevant to a vast variety of application domains in healthcare. Such software often focuses on a single purpose, limiting reusability and increasing development efforts in research. This article introduces a modular software framework to facilitate the development of reusable multi-purpose research applications for time-series processing.

Advances in the theory and methods of computational oncology have enabled accurate characterization and prediction of tumor growth and treatment response on a patient-specific basis. This capability can be integrated into a digital twin framework in which bi-directional data-flow between the physical tumor and the digital tumor facilitate dynamic model re-calibration, uncertainty quantification, and clinical decision-support via recommendation of optimal therapeutic interventions. However, many digital twin frameworks rely on bespoke implementations tailored to each disease site, modeling choice, and algorithmic implementation. We present TumorTwin, a modular and differentiable software framework for initializing, updating, and leveraging patient-specific cancer tumor digital twins. TumorTwin is publicly available as a Python package, with associated documentation, datasets, and tutorials. Novel contributions include the development of a patient-data structure adaptable to different disease sites, a modular architecture to enable the composition of different data, model, solver, and optimization objects, and CPU or GPU parallelized implementations of forward model solves and gradient computations. We demonstrate the functionality of TumorTwin via an in silico dataset of high-grade glioma growth and response to radiation therapy. The TumorTwin framework enables rapid prototyping and testing of image-guided oncology digital twins. This allows researchers to systematically investigate different models, algorithms, disease sites, or treatment decisions while leveraging robust numerical and computational infrastructure.

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

Machine-learning force fields (MLFFs) are reshaping computational chemistry and biology by delivering near-quantum mechanical accuracy at a computational cost comparable to conventional force fields, enabling applications in biomolecular simulation, catalysis, and materials science. However, despite these advances, a unified and automated computational platform enabling the broader application of MLFFs is still lacking. Here, we present MAPLE (MAchine learning Potential for Landscape Exploration), a computational toolkit specially developed for MLFF-based molecular modeling, featuring a tailored software framework and parallelized algorithms for large-scale and versatile molecular modeling tasks. We demonstrated the robustness and usability of MAPLE through systematic benchmarking of state-of-the-art reactive MLFFs and applications to multiple biocatalytic scenarios, highlighting its capability for fast yet accurate simulation of catalytic reactions. By integrating accurate and efficient MLFFs with parallelized algorithms in a highly optimized and flexible software framework, MAPLE serves as a next-generation, physically informed, machine-learning-driven molecular modeling platform with broad applicability to rational catalyst design and drug discovery.

Abstract We present a method for constructing dynamical models of stellar systems described by distribution functions and constrained by discrete-kinematic data. We implement various improvements compared to earlier applications of this approach, demonstrating with several examples that it can deliver meaningful constraints on the mass distribution even in situations where the density profile of tracers and the selection function of the kinematic catalog are unknown. We then apply this method to the Milky Way nuclear star cluster, using kinematic data (line-of-sight velocities and proper motions) for a few thousand stars within 10 pc from the central black hole, accounting for the contributions of the nuclear stellar disk and the Galactic bar. We measure the mass of the black hole to be 4 × 10 6 M ⊙ with a 10% uncertainty, which agrees with the more precise value obtained by the GRAVITY instrument. The inferred stellar mass profile depends on the choice of kinematic data, but the total mass within 10 pc is well constrained in all models to be (2.0–2.3) × 10 7 M ⊙ . We make our models publicly available as part of the Agama software framework for galactic dynamics.

The commercial and scientific momentum behind quantum computing is accelerating rapidly, yet a critical gap remains between theoretical promise and practical implementation. This talk bridges that gap through some of the hands-on projects spanning quantum simulation and combinatorial optimization, implemented on real quantum software frameworks including Intel Quantum SDK, Eviden myQLM, PennyLane, and Qiskit.We begin by motivating the two most tractable near-term application domains - simulation and optimization, through current industry trends, before examining concrete implementations: a GAN-accelerated surrogate simulator for neutrino interactions in the INO-ICAL particle physics detector; a quantum-accelerated Quantum Monte Carlo workflow for correlated electron systems, developed in collaboration with Intel and presented at ISC 2025; a QAOA-based solution to the flight-gate assignment problem, developed in industry collaboration with Atos-Eviden; and a hardware-aware Variational Quantum Eigensolver (VQE).The talk concludes with honest lessons from implementing these algorithms on real platforms, the theory-hardware gap, and why hybrid classical-quantum workflows remain the practical frontier. Attendees will leave with a grounded understanding of where quantum advantage is being engineered today, and what it actually takes to get there

Tiny Machine Learning (TinyML) enables Machine Learning (ML) models to run on resource-constrained devices, which is critical for Industrial Internet of Things (IIoT) systems requiring low latency, energy efficiency, and local decision-making. Nevertheless, deploying TinyML in IIoT remains challenging due to diverse applications, hardware, frameworks, and deployment methodologies, highlighting the need for a structured and focused review. Existing review articles mainly address general IoT or edge AI, leaving a critical gap in a unified and systematic understanding of TinyML applications, system components, and methodologies within IIoT contexts. Consequently, this systematic literature review (SLR) addresses this gap by analyzing 35 peer-reviewed studies published between 2018 and 2026, offering a comprehensive and structured synthesis of TinyML-enabled IIoT systems. The selected works are synthesized across three major dimensions: applications, system components, and methodologies. In terms of applications, TinyML is primarily used for predictive maintenance, equipment monitoring, anomaly detection, energy management, and general-purpose applications. The general category captures cross-domain solutions that do not fit into a single industrial application. A comparative analysis of all application categories is conducted in terms of accuracy, latency, memory, and energy. For system components, a structured comparison shows how hardware, software, and sensing choices shape performance and applicability. Hardware platforms are grouped by microcontroller families, highlighting dominant types. Software frameworks are summarized, showing the widespread use of lightweight toolchains for on-device inference. Sensor types are categorized, with vibration sensing most common. They are supported by other sensing methods such as vision, sound (acoustic), and environmental sensors. Finally, the methodologies examined in this SLR provide a comprehensive view of the data foundations, model selection, and optimization strategies. In short, this SLR converges diverse TinyML-IIoT applications, microcontroller-based hardware, lightweight software frameworks, sensing modalities, varied datasets, and optimization strategies, while also identifying challenges and future research directions.

Many mitigation strategies for programming plagiarism and collusion focus solely on either penalising students involved in such misbehaviour or manually educating students regarding the matter. This paper combines both strategies within a software framework and automates the education strategy. It informs students about plagiarism and collusion based on code similarity and instructors' expectations through an assessment submission system with three variants. Highly similar submissions are alerted, while original submissions have their similarities simulated. In addition, the quality of the submissions is also reported through static analysis. On the due date, student submissions are checked using a similarity detector that provides human-language explanations, which has two variants. Students using our framework show greater awareness of programming plagiarism and collusion, and are less likely to engage in such misbehaviours.

The Internet of Things (IoT) has emerged as a transformative force in residential automation, enabling smart home systems that interconnect sensors, actuators, and computing infrastructure to deliver improved convenience, energy efficiency, and security. This paper presents a structured review of IoT-based smart home automation systems, synthesizing findings from five open-access research works covering system architectures, wireless communication protocols, hardware platforms, software frameworks, and cybersecurity. Key findings indicate that Wi-Fi and ZigBee dominate current deployments due to their complementary power and bandwidth profiles; fog computing is reshaping gateway architectures toward lower latency and greater resilience; ESP8266/ESP8285 microcontrollers and Raspberry Pi boards constitute the most widely adopted hardware platforms for cost-sensitive deployments; and cybersecurity particularly authentication, encryption, and device vulnerability management, remains the most critical unresolved challenge. Future directions including AI-driven automation, standardized interoperability, and privacy-preserving architectures are discussed. Keywords— Internet of Things; smart home automation; Wi-Fi; wireless sensor networks.

The ICARUS experiment is part of the Short-Baseline Neutrino program at Fermilab. Its primary objective is to explore the possible existence of sterile neutrinos in the O(1 eV) mass range and to clarify the anomalies observed in the Liquid Scintillator Neutrino Detector and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber, capable of producing high-resolution 3D images and precise calorimetric measurements of ionizing particles. This technology allows for a detailed study of neutrino interactions across a broad energy range, from a few keV to several hundred GeV. The track reconstruction is achieved through a software framework that applies a series of pattern recognition algorithms, transforming raw detector signals into fully reconstructed event topologies. This process involves identifying interaction vertices, particle tracks, and electromagnetic showers within the TPC. However, in certain cases, these algorithms may mistakenly break a single particle track into several shorter segments, interpreting each as a distinct particle. Since track length is used to estimate the particle's energy, such fragmentation can result in an energy underestimation of several hundred MeV. Furthermore, when a track is split into multiple segments, the particle identification (which relies on analyzing the energy loss as a function of the residual range) may fail, potentially leading to the loss of the entire event. To mitigate this problem, we have developed a dedicated algorithm designed to identify and reconnect (“stitch”) the tracks that were erroneously divided into multiple segments.

An Integrated Software Framework for Aging Life Prediction of High-Speed Railway Vibration-Damping Rubber Components

Helix 1.0: An open-source framework for reproducible and interpretable machine learning on tabular scientific data