Search Results (76)

In line with the research objectives of the SCORPiò-NIDI project, we aim to implement a software platform showcasing the digital models developed during the project. The goal is to develop dynamic and interactive user experiences, expanding access to cultural heritage through digital means, which become spaces for engaging and educational experiences. Using open-source frameworks, users can explore the complexity of Roman siege machines in an immersive way, interacting directly with the digital models. We will focus on the 3D model of the scorpion created by Dr. Claudio Formicola (University of Campania Luigi Vanvitelli), using the 3D modeling software Rhinoceros. Full article

A diode-based rectifier (DR) is an attractive transmission technology for offshore wind farms, which reduces the volume of large bulk platforms. A novel parallel–series DC wind farm based on a distributed DR is proposed, which meets the requirements of high voltage and high power with an isolation capability from other units. The coupling mechanism between a modular multilevel converter (MMC) and a DR has been built, and the coordinate control strategy for the whole system has been proposed based on the MMC triple control targets with intermediate variables. Under the proposed control strategy, the system automatically operates at maximum power point tracking (MPPT). The feasibility of topology and the effectiveness of the control strategy are verified under start-up, power fluctuation, onshore alternating current (AC) fault, and direct current (DC) fault based on the power systems computer-aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulation. Full article

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article

Traditional tools, such as canes, are no longer enough to subsist the mobility and orientation of visually impaired people in complex environments. Therefore, technological solutions based on computer vision tasks are presented as promising alternatives to help detect obstacles. Object detection models are easy to couple to mobile systems, do not require a large consumption of resources on mobile phones, and act in real-time to alert users of the presence of obstacles. However, existing object detectors were mostly trained with images from platforms such as Kaggle, and the number of existing objects is still limited. For this reason, this study proposes to implement a mobile system that integrates an object detection model for the identification of obstacles intended for visually impaired people. Additionally, the mobile application integrates multimodal feedback through auditory and haptic interaction, ensuring that users receive real-time obstacle alerts via voice guidance and vibrations, further enhancing accessibility and responsiveness in different navigation contexts. The chosen scenario to develop the obstacle detection application is the Specialized Educational Unit Dr. Luis Benavides for impaired people, which is the source of images for building the dataset for the model and evaluating it with impaired individuals. To determine the best model, the performance of YOLO is evaluated by means of a precision adjustment through the variation of epochs, using a proprietary data set of 7600 diverse images. The YOLO-300 model turned out to be the best, with a mAP of 0.42. Full article

With the acceleration of urbanization, surface deformation monitoring has become crucial. Existing monitoring systems face several challenges, such as data singularity, the poor nighttime monitoring quality of video surveillance, and fragmented visual data. To address these issues, this paper presents a 3D real-scene (3DRS)-enhanced GNSS/intelligent vision surface deformation monitoring system. The system integrates GNSS monitoring terminals and multi-source meteorological sensors to accurately capture minute displacements at monitoring points and multi-source Internet of Things (IoT) data, which are then automatically stored in MySQL databases. To enhance the functionality of the system, the visual sensor data are fused with 3D models through streaming media technology, enabling 3D real-scene augmented reality to support dynamic deformation monitoring and visual analysis. WebSocket-based remote lighting control is implemented to enhance the quality of video data at night. The spatiotemporal fusion of UAV aerial data with 3D models is achieved through Blender image-based rendering, while edge detection is employed to extract crack parameters from intelligent inspection vehicle data. The 3DRS model is constructed through UAV oblique photography, 3D laser scanning, and the combined use of SVSGeoModeler and SketchUp. A visualization platform for surface deformation monitoring is built on the 3DRS foundation, adopting an “edge collection–cloud fusion–terminal interaction” approach. This platform dynamically superimposes GNSS and multi-source IoT monitoring data onto the 3D spatial base, enabling spatiotemporal correlation analysis of millimeter-level displacements and early risk warning. Full article

Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections and broad-spectrum pesticide application are inefficient, time-consuming, and dangerous. New drone photography and IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, data heterogeneity, and privacy problems make it hard to conclude these data. This study proposes a lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency of LeViT transformers with ResUNet’s exact pixel-level segmentation to address these issues. The system uses multispectral drone footage and IoT sensor data to identify real-time insect hotspots, crop health, and yield prediction. The dynamic relevance and sparsity-based feature selector (DRS-FS) improves feature ranking and reduces redundancy. Spectral normalization, spatial–temporal alignment, and dimensionality reduction provide reliable input representation. Unlike centralized models, our platform trains over-dispersed client datasets using federated learning to preserve privacy and capture regional trends. A huge, open-access agricultural dataset from varied environmental circumstances was used for simulation experiments. The suggested approach improves on conventional models like ResNet, DenseNet, and the vision transformer with a 98.9% classification accuracy and 99.3% AUC. The LeViT-ResUNet system is scalable and sustainable for privacy-preserving precision agriculture because of its high generalization, low latency, and communication efficiency. This study lays the groundwork for real-time, intelligent agricultural monitoring systems in diverse, resource-constrained farming situations. Full article

Water utilities use a significant amount of electrical energy due to the rising demand for wastewater treatment driven by environmental and economic reasons. The growing demand for energy, rising energy costs, and the drive toward achieving net-zero emissions require a sustainable energy future for the water industry. This can be achieved by integrating onsite renewable energy sources (RESs), energy storage, demand management, and participation in demand response (DR) programs. This paper analyzes the energy profile and load flexibility of water utilities using a data-driven approach to reduce energy costs by leveraging RESs for regional water utilities. It also assesses the potential for DR participation across different types of water utilities, considering peak-load shifting and battery storage installations. Given the increasing frequency of extreme weather events, such as bushfires, heatwaves, droughts, and prolonged cold and wet season floods, regional water industries in Australia serve as a relevant case study of sectors already impacted by these challenges. First, the data characteristics across the water and energy components of regional water industries are analyzed. Next, barriers and challenges in data acquisition and processing in water industries are identified and recommendations are made for improving data coordination (interoperability) to enable the use of a single platform for identifying DR opportunities. Finally, the energy profile and load flexibility of regional water industries are examined to evaluate onsite generation and battery storage options for participating in DR operations. Operational data from four regional sites across two regional Australian water utilities are used in this study. Full article

South Africa’s health system was affected by the various mitigation measures implemented to control the rapid spread of the COVID-19 pandemic. However, innovative interventions were introduced to ensure service continuity. This study sought to explore the perspectives of stakeholders regarding the pre-exposure prophylaxis (PrEP) innovative interventions implemented during the COVID-19 lockdown period among adolescent girls and young women (AGYW), as well as their successes and improvements. We selected and interviewed 12 PrEP stakeholders, including professional nurses, case managers, peer educators, and counselors from the TB HIV Care programme in the Dr. Kenneth Kaunda District, in the North-West Province. The qualitative questions explored (1) how PrEP services were disrupted during the lockdown period, (2) how the disruptions were managed, and (3) the challenges and successes of the innovative interventions implemented. The interviews were audio-taped, transcribed, and thematically analyzed through Tesch’s eight steps of analysis. The stakeholders confirmed that COVID-19 disruptions affected the provision of PrEP services in terms of recruitment, counseling, HIV testing, and adherence support offered in different community hotspots. Responding to these difficulties, alternative avenues such as social media platforms were implemented and used for service continuity. The themes that emerged were organized into the following two categories: PrEP services provided during and after the COVID-19 lockdown period, as well as the successes and challenges. The current study provides further insight into COVID-19, aiming to inform preparations for future pandemics. Innovative PrEP interventions alleviated COVID-19 disruptions in some settings and improved HIV services, but this was not the case in the selected study area. Full article

The targets for reducing greenhouse gas emissions, combined with increased electrification and the increased use of intermittent renewable energy sources, create significant challenges in matching supply and demand within distribution grid constraints. Demand response (DR) can shift electricity demand to align with constraints, reducing peak loads and increasing the utilisation of renewable generation. In countries like Aotearoa (New Zealand), peak loads are driven primarily by the residential sector, which is a prime candidate for DR. However, traditional deterministic and stochastic models do not account for the important variability in behavioural-driven residential demand and thus cannot be used to design or optimise DR. This paper presents a behavioural agent-based model (ABM) of residential electricity demand, which is validated using real electricity demand data from residential distribution transformers owned by Powerco, an electricity distributor in Aotearoa (New Zealand). The model accurately predicts demand in three neighbourhoods and matches the changes caused by seasonal variation, as well as the effects of COVID-19 lockdowns. The Pearson correlation coefficients between the median modelled and real demand are above 0.8 in 83% of cases, and the total median energy use variation is typically within 1–4%. Thus, this model provides a robust platform for network planning, scenario analysis, and DR program design or optimisation. Full article

Human milk is a complex biofluid containing a diverse array of cells crucial for infant health. Despite their importance, our understanding of these cells remains incomplete due to technical challenges. To fully comprehend human milk cells, high-resolution imaging technologies that can directly measure biological processes are required. We have developed a specialized imaging platform combining light and electron microscopy for human milk cell imaging. To identify different cell types, human milk cells were first stained with several specific cell markers (e.g., EpCAM and MUC1 for lactocytes, CD16 and CD66b for neutrophils, and HLA-DR and CD68 for macrophages) prior to light (confocal) microscopy. Following this, the same cells were processed with osmium staining, resin embedding, and sectioning for electron microscopy, allowing us to observe ultrastructural details. Our imaging workflow has enabled nanoscale visualization of human milk cells, resulting in a first-of-its-kind comprehensive database profiling the organelle-level ultrastructure of different cell types present in human milk. The cells in the human milk are highly heterogenous, featuring a large proportion of lactocytes and lipid droplets, binucleated lactocytes, neutrophil aggregation, neutrophil extracellular traps, dendritic cells/macrophages with bacteria, and immunophagocytosis. This study provides valuable cellular insights contributing to a deeper understanding of human milk biology. Full article

Recommendation systems (RS) have become integral to numerous digital platforms and applications, ranging from e-commerce to content streaming field. A critical problem in RS is that the ratings are missing not at random (MNAR), which is due to the users always giving feedback on items with self-selection. The biased selection of rating data results in inaccurate rating prediction for all user-item pairs. Doubly robust (DR) learning has been studied in many tasks in RS, which is unbiased when either a single imputation or a single propensity model is accurate. In addition, multiple robust (MR) has been proposed with multiple imputation models and propensity models, and is unbiased when there exists a linear combination of these imputation models and propensity models is correct. However, we claim that the imputed errors and propensity scores are miscalibrated in the MR method. In this paper, we propose a gradient-based calibrated multiple robust learning method to enhance the debiasing performance and reliability of the rating prediction model. Specifically, we propose to use bi-level optimization to solve the weights and model coefficients of each propensity and imputation model in MR framework. Moreover, we adopt the differentiable expected calibration error as part of the objective to optimize the model calibration quality directly. Experiments on three real-world datasets show that our method outperforms the state-of-the-art baselines. Full article

With the development of new, high-quality productive forces, platform enterprises (PEs) are beginning to play a crucial role in shaping economic patterns, ecological environments, and social structures. These enterprises have significant social responsibilities when handling issues such as algorithmic discrimination, user data breaches, and market monopolies. Herein, we adopt the grounded theory method, selecting three unique types of PEs as research subjects. Through in-depth interviews with stakeholders and a three-level coding analysis, we build a “triple” responsibility model of PEs’ digital responsibility (DR). This model is based on the PEs’ triple organizational identity and is framed by three dimensions: product responsibility, technological responsibility, and application responsibility. The model also summarizes three dimensions and contents of responsibility: digital self-regulation, the digital regulation of others, and digital foresight. The concept of PEs’ DR is clarified and the structure and dimensions of PEs’ DR are delineated. This study holds significant theoretical and practical value for perfecting the social responsibility system in the platform economy. The triple DR model fills the research gap on the relationship between corporate social responsibility (CSR) and corporate DR and overcomes the limitations of the traditional CSR paradigm, providing a theoretical foundation for PEs’ sustainable development in the digital era. Full article

Liver cancer is a prevalent form of carcinoma worldwide. A novel chitosan-coated optimized formulation capped with irradiated silver nanoparticles (INops) was fabricated to boost the anti-malignant impact of rosuvastatin calcium (RC). Using a 2³-factorial design, eight formulations were produced using the solvent evaporation process. The formulations were characterized in vitro to identify the optimal formulation (Nop). The FTIR spectra showed that the fingerprint region is not superimposed with that of the drug; DSC thermal analysis depicted a negligible peak shift; and XRPD diffractograms revealed the disappearance of the typical drug peaks. Nop had an entrapment efficiency percent (EE%) of 86.2%, a polydispersity index (PDI) of 0.254, a zeta potential (ZP) of −35.3 mV, and a drug release after 12 h (Q12) of 55.6%. The chitosan-coated optimized formulation (CS.Nop) showed significant mucoadhesive strength that was 1.7-fold greater than Nop. Physical stability analysis of CS.Nop revealed negligible alterations in VS, ZP, PDI, and drug retention (DR) at 4 °C. The irradiated chitosan-coated optimized formulation capped with silver nanoparticles (INops) revealed the highest inhibition effect on carcinoma cells (97.12%) compared to the chitosan-coated optimized formulation (CS.Nop; 81.64) and chitosan-coated optimized formulation capped with silver nanoparticles (CS.Nop.AgNPs; 92.41). The bioavailability of CS-Nop was 4.95-fold greater than RC, with a residence time of about twice the free drug. CS.Nop has displayed a strong in vitro–in vivo correlation with R² 0.9887. The authors could propose that novel INop could serve as an advanced platform to improve oral bioavailability and enhance hepatic carcinoma recovery. Full article

Bats are mammals with high biodiversity and wide geographical range. In Brazil, three haematophagous bat species are found. Desmodus rotundus is the most documented due to its role as a primary host of rabies virus in Latin America. Bats are known to harbor various emerging viruses causing severe human diseases. Beyond zoonotic viruses, these animals also harbor a diversity of non-zoonotic viruses. Papillomaviruses are circular double-stranded deoxyribonucleic acid (dsDNA) viruses that infect the epithelial and mucosal cells of many vertebrates, occasionally causing malignant lesions. High-throughput sequencing has enabled papillomaviruses discovery in different bat species. Here, 22 D. rotundus samples were collected through the rabies eradication program in Rio Grande do Sul. The DNA extracted from pooled intestines was amplified by the rolling-circle amplification (RCA) method and sequenced using the Illumina^® MiSeq platform (San Diego, CA, USA).Analysis revealed three contigs corresponding to the Papillomaviridae family, representing three novel viruses named DrPV-1, DrPV-2, and DrPV-3. Phylogenetic analysis suggests DrPV-1 may constitute a new species within the Dyophipapillomavirus genus, while DrPV-2 and DrPV-3 may represent different types within the same species from a novel genus. This is the first description of a papillomavirus in the D. rotundus species, contributing to the characterization of PVs in the Chiropteran order. Full article

Background/Objectives: In China, discussing sexual and reproductive health remains taboo, often preventing patients from seeking care or advice on sensitive topics. Online medical consultations (OMCs) offer a unique platform for patients to discuss these concerns more openly. This study investigates how patient-centered communication (PCC) practices, including conversational themes and the use of politeness markers, influence patient satisfaction in Chinese OMCs, with a focus on sensitive gynecology and andrology topics. Methods: This study used a mixed-methods approach, including theme-oriented discourse analysis (TODA) and content analysis on 328 OMCs (179 in andrology, 149 in gynecology) collected from Dr. Chunyu, a popular Chinese online healthcare platform that provides medical consultations, from 19 to 22 March 2022. Logistic regressions were conducted to assess the influence of politeness markers on patient satisfaction, while TODA examined PCC practices in sensitive conversations. Results: TODA identified two key themes in PCC that enhanced patient satisfaction: normalizing sensitive health concerns and fostering collaborative decision-making. Politeness markers, specifically the use of polite words and expressions of best wishes, were positively associated with patient satisfaction. However, downtoners, emojis, and sentence-final particles showed no significant effect. There were no significant differences in the impact of politeness markers between gynecology and andrology consultations. Conclusions: This study highlights the importance of PCC and politeness markers in improving patient satisfaction in OMCs, especially when addressing sensitive sexual health topics. Full article