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Design, development and test of a low cost, fast-response, multi-gas tracer gas measurement apparatus
No full textTracer gas measurements are a family of well know and widely applied experimental techniques adopted to test and verify the performance of ventilation and air conditioning systems. They are aimed at measuring either the ventilation air flow rates and or the performance of the air distribution. They make use of nontoxic, non-flammable, odourless and easy to detect gasses that are injected in the air and whose concentration is then monitored over a period of time. For this sake quite sophisticated, costly and delicate measuring apparatuses are typically used. The main disadvantages of the existing apparatuses are represented by their cost, size and the impossibility of leaving the system in the field for medium/long term monitoring campaigns. Besides, the time response is usually rather long and this hinder their use in all those cases where the ventilation air flow rates are high or when many measuring points need to be monitored simultaneously. For these reasons a new measuring system (TEBE-SENSE) was designed and built. It adopts a number of small, wireless measuring devices that can communicate with a central unit and can measure, store and elaborate the time histories of the tracer gas. The measurement apparatus is developed, so far, to work with three different tracer gases simultaneously (e.g. CO2, SF6, Propane), allowing also the analysis of inter-zonal air exchanges. The response time is in the order of a fraction of a second, a feature that allows to properly follow fast transient ventilation phenomena and to analyse strongly ventilated environments. In this paper the main features of this new system will be presented. A validation was done by comparing the results obtained with the newly proposed system with those provided by a traditional photoacoustic measurement apparatus
A review on AI-driven drowsiness detection systems using deep-learning
No full textOne important area of artificial intelligence (AI) research that directly affects healthcare, traffic safety, and human-machine interaction is the detection of drowsiness. One of the primary causes of traffic accidents, fatigue-related impairments are responsible for over 20% of serious collisions worldwide. Traditional detection methods that rely on steering changes, yawning occurrence, or eye aspect ratio limits have problems with reliability, sensitivity to lighting, and generalization. Convolutional, recurrent, and attention-driven neural networks are used in recent deep-learning (DL) techniques to effectively capture spatiotemporal features. This review summarizes current developments in AI-based drowsiness detection, including visual, physiological, and hybrid approaches. Architectures (CNN, LSTM, Transformer), evaluation standards, and research challenges are also described. A comparative analysis and outlook are offered for creating real-time, interpretable, and effective driver monitoring systems designed for smart transportation
Alpha-Music Entrainment Combined with Physiotherapy Improves Inflammatory and Functional Outcomes in Osteoarthritis of Knee: A Randomized Trial
No full textBackground: Osteoarthritis of the knee is a chronic degenerative condition characterized by pain, inflammation, stiffness, and functional limitations. Conventional physiotherapy plays a central role in its management; however, complementary neurosensory approaches such as alpha-music entrainment may enhance therapeutic outcomes by influencing pain perception, relaxation, and neurophysiological processes that regulate inflammation. Objective: To evaluate the effects of alpha-music entrainment, when combined with physiotherapy, on inflammatory markers and functional outcomes among individuals with knee osteoarthritis. Methods: A randomized controlled trial was conducted on 40 participants diagnosed with chronic osteoarthritis of the knee (Kellgren-Lawrence grade 2-3). Participants were randomly assigned to Group I (alpha-music entrainment + physiotherapy + electrical modalities) or Group II (physiotherapy + electrical modalities). Interventions were delivered five days a week for eight weeks. Outcome measures included FLIR infrared thermography, Visual Analogue Scale (VAS), Manual Muscle Testing (quadriceps and hamstrings), girth measurement, knee flexion range of motion, and Musculoskeletal Health Questionnaire (MSK-HQ). Assessments were recorded at baseline, 2 weeks, 4 weeks, and 8 weeks. Results: Both groups demonstrated statistically significant improvements over eight weeks (p < 0.05). However, Group I showed markedly greater reductions in surface temperature (mean difference 5.61°C), VAS (mean difference 8.95), swelling (mean difference 8.81 cm), and significantly higher gains in knee flexion, quadriceps and hamstring strength, and MSK-HQ scores compared to the control group (p < 0.001). Conclusion: Alpha-music entrainment combined with physiotherapy produced superior improvements in inflammation, pain reduction, muscle strength, and functional capacity than physiotherapy alone. Incorporating alpha-rhythm auditory stimulation may provide an effective adjunctive approach in the rehabilitation of knee osteoarthritis
A three-dimensional reconstruction of the interstellar magnetic field toward a star-forming region
No full textContext. The polarized thermal emission from interstellar dust offers a valuable tool for probing both the dust and the magnetic field in the interstellar medium (ISM). However, existing observations only yield the total amount of dust emission along the line of sight (LoS), with no information on its LoS distribution.
Aims. We present a new method designed to give access to the LoS distribution of the dust emission, both in terms of intensity and polarization.
Methods. We relied on three kinematic gas tracers (HI, 12CO, and 13CO emission lines) to identify the different clouds present along the LoS. We decomposed the measured intensity of the dust emission, Id, into separate contributions from these clouds. We performed a similar decomposition of the measured Stokes parameters for linear polarization, Qd and Ud, to derive the polarization parameters of the different clouds, and from this we inferred the clouds’ magnetic field orientations.
Results. We applied our method to a 3 deg2 region of the sky, centered on (l, b) = (139°30′, −3°16′) and exhibiting signs of star formation activity. We found this region to be dominated by an extended and bright cloud with nearly horizontal magnetic field, as expected from the nearly vertical polarization angles measured by Planck. More importantly, we detected the presence of two smaller, depolarizing molecular clouds with very different magnetic field orientations in the plane of the sky (≃65° and ≃45° from the horizontal). This is a novel and viable result, which cannot be directly read off the Planck polarization maps.
Conclusions. The application of our method to the G139 region convincingly demonstrates the need to complement 2D polarization maps with 3D kinematic information when looking for reliable estimates of magnetic field orientations
Integrated analysis of the effects of urbanization on surface climate, runoff and net carbon assimilation: Literature review and bibliometrics
No full textRapid urbanization is profoundly modifying terrestrial ecosystems, in particular by influencing surface climate, water runoff and net carbon assimilation. This article proposes an integrated analysis combining an exhaustive bibliographical review and systematic bibliometric analysis, based on a corpus of 392 publications extracted from the Scopus and Web of Science databases, covering the period 1975-2025. Three main themes are studied: the effects of urbanization on the urban heat island and surface climate, changes in surface runoff, and impacts on carbon sequestration. Detailed analyses by environmental subfield highlight the evolution of publications, the geographical distribution of research, thematic networks, scientific collaborations and major editorial sources. The results reveal an exponential growth in scientific production, with a marked domination by China, followed by the USA and India. The research presents a clear thematic structuring around direct impacts, remote sensing methods and advanced modelling, while highlighting important gaps, notably the under-representation of arid and tropical zones and the lack of longitudinal studies on the long-term effects of urban planning policies. The study offers a synthetic and critical overview of current knowledge, identifying the main dynamics, methodologies and issues to be addressed in order to better support the sustainable management of urban environments in the face of climatic and environmental challenges
LandSure: Blockchain-based Property Verification and Tokenized Ownership Transfer System
No full textThere are still forged documents, no clear records of past history of ownership, and the absence of verification systems that cannot be tampered with which plague land verification and the property ownership transfer in India. The current literature shows that blockchain is used to register land in a safe manner, but most studies fail to incorporate automated document verification via OCR and discuss the possibility of tokenizing fractions of ownership in a single system. The purpose of this study is to fill this gap and suggest Landsure a blockchain-based property verification and token transfer model that will guarantee transparency, immutability, and reduction of fraud. The system has automated document extraction based on Tesseract OCR, backend validation based on Node.js and MongoDB, and tokenizing ownership based on Solidity smart contracts on a private Ethereum network (Hardhat). Experimental results indicate that OCR-based verification is effective at extracting structured fields of scanned documents and smart contracts are effective at keeping ownership records which do not change, eliminating 38% of manual verification in simulated workflows. The findings reveal that OCR and blockchain integration enhance the document authenticity checks and tokenization provides transfer of ownership in a secure and traceable manner. The research incriminates the high possibility of practical implementation with further extensions to AI-based checking and publicly implemented blockchains
Limestone–Calcined Clay: An Alternative Binder for 3D Concrete Printing
No full textAdditive manufacturing in construction has emerged as a promising alternative to conventional form-work-based building processes. However, most printable mixtures are rich in ordinary Portland cement OPC, which is associated with high embodied carbon and energy consumption. Active 3D concrete printing researchers are exploring limestone-based binders, specifically Limestone–Calcined Clay Cement LC3, as substitutes to OPC for a low carbon footprint. This literature review summarises recent research on LC3 as a low-carbon binder for 3D printing. Primary sources show calcined clay improves static and dynamic yield stress and buildability but reduces flowability, while limestone filler acts as a fine filler, improving particle packing and early-age hydration. Optimised LC3 mixtures achieve high yield stress and thixotropy that enable the printing of up to 23 layers while keeping good pumpability and extrudability. Fibre reinforcement increases 28-day compressive and flexural strength without clogging the nozzle. Studies report that LC3-based concretes can attain 28-day compressive strengths of 30–50 MPa while achieving up to 30–50 % reductions in CO2 emissions relative to OPC. This review discusses rheological behaviour, mechanical properties, sustainability benefits, and mixture-optimization strategies of LC3, also pointing out research gaps and future directions for climate-positive 3D printing
Grievance Redressal System: A Web-Based Public Grievance Portal
No full textThis study addresses the need for efficient, transparent, and citizen centric grievance handling in modern e governance. Existing manual and semi digital systems often suffer from delays, lack of real time tracking, missing evidence support, and limited accountability. A review of current literature reveals gaps such as absence of unified platforms, inadequate automation, and minimal analytical insights for administrators. The objective of this study is to design and implement a web-based Grievance Redressal System that automates complaint submission, categorization, monitoring, and resolution. The system is developed using HTML, CSS, JavaScript, ReactJs, and MySQL, following a secure three tier architecture. Experimental evaluation shows a 40% reduction in resolution time, a 1.2 second average response time, and an 87% user satisfaction rate. Findings confirm that automated tracking, structured workflows, and digital records significantly improve transparency, accountability, and administrative efficiency. The system has strong implications for smart city governance, enabling data driven decision making, public trust building, and scalable digital transformation
Enhancing video face recognition through Illumination and pose compensation models
No full textThis work envisions a strong framework for improving video-based face recognition by addressing simultaneously the issues of pose and illumination changes. Since even exhaustive efforts in face recognition research have not eliminated the impact of dynamic environments in real-world applications on recognition accuracy caused by nonlinear facial distortions and lighting differences, this work is timely. To circumvent these challenges, a hybrid deep learning model is constructed that combines SENet and channel attention mechanisms for efficient spatial feature extraction and a Transformer network and cross-attention for temporal dependency modeling. The new system begins with face detection via SENResNet, feature refinement via Transformers, and stable tracking via the Regression Network-based Face Tracking (RNFT) model. This end-to-end system enables effective learning of invariant representations over diverse poses and lighting conditions. Testing on benchmark datasets shows substantial improvement in recognition accuracy and robustness, confirming the effectiveness of the proposed system in real-world applications of video surveillance and humancomputer interaction
Understanding Biometric-based Systems for Detecting and Preventing Cyber-attacks: Current Trends, Emerging Technologies, and Synthetic Biometrics
No full textToday, everyone is heavily dependent on computers, mobile devices, and digital systems/applications to store, access, and transmit their data and personal information. On the other hand, cybersecurity threats, exploitation of digital systems, and new, complex cyberattacks are evolving daily. This requires a growing need for innovative approaches to protecting data beyond traditional methods. The study explores the use of biometric systems in cybersecurity for the prevention and detection of cyberattacks by integrating them for authenticating and authorizing individuals. Biometric authentication is used to verify an individual's identity and grant them access based on their roles or authorizations. The paper focuses on understanding current trends and emerging technologies in biometric systems, while recognizing that these systems are not immune to cyberattacks. Can synthetic biometric data that is generated using virtual identities be an option to be considered to minimize the risk of exposing user identity in the event of a data breach, and as a means to preserve privacy, be explored as part of this research? A qualitative study is carried out using existing literature and analyzed based on the generated themes. The outcome of the study resulted in a multi-layered conceptual framework integrating the modalities of biometric systems with synthetic data and a model offering a feedback loop that can enhance operational efficiency and cultivate user trust and resilience. The study also provides insights relevant to businesses and researchers to build their systems and enhance research from a user perspective