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Data-Driven Dynamic Optimization for Hosting Capacity Forecasting in Low-Voltage Grids
Full text linkThe sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. However, conventional HC analysis and forecasting approaches struggle to capture temporal dependencies, the impact of DOE constraints on network operation, and uncertainty in DER output. This study introduces a dynamic optimization framework that leverages the benefits of the sensitivity gate of the Sensitivity-Enhanced Recurrent Neural Network (SERNN) forecasting model, Particle Swarm Optimization (PSO), and Bayesian Optimization (BO) for HC forecasting. The PSO determines the optimal weights and biases, and BO fine-tunes hyperparameters of the SERNN forecasting model to minimize the prediction error. This approach dynamically adjusts the import/export of the DER output to the grid by integrating the DOE constraints into the SG-PSO-BO architecture. Performance evaluation on the IEEE-123 test network and a real Australian distribution network demonstrates superior HC forecasting accuracy, with an (Formula presented.) score of 0.97 and 0.98, Mean Absolute Error (MAE) of 0.21 and 0.16, and Root Mean Square Error (RMSE) of 0.38 and 0.31, respectively. The study shows that the model effectively captures the non-linear and time-sensitive interactions between network parameters, DER variables, and weather information. This study offers valuable insights into advancing dynamic HC forecasting under real-time DOE constraints in sustainable DER integration, contributing to the global transition towards net-zero emissions
Analysis of Assessment Methods for Detecting Nicotine Residue and Its Impact on Humans: A Systematic Review.
Full text linkINTRODUCTION: Thirdhand smoke (THS) was first identified by Graham and colleagues in 1953, and nicotine was detected in household dust from smokers in 1991. Thirdhand smoke (THS) consists of toxic nicotine residues that persist on surfaces long after tobacco use, posing a significant public health concern. Individuals can be exposed to thirdhand smoke through skin contact or inhalation, particularly affecting children and infants who are most vulnerable to tobacco contaminants. This review aims to assess the effectiveness of different methods for measuring nicotine THS residues to evaluate their accuracy across various age groups. METHODS: Relevant literature was sourced from databases including ProQuest (Ovid), Medline (Ovid), Embase (Ovid), Scopus, and the Cochrane Library. The timeframe for included studies ranged the last 25 years, from 1999 to 2024. Eligible participants consisted of human populations exposed to thirdhand smoke residue. For this review, the animal studies were excluded. There were no restrictions regarding age, sex, ethnicity, or nationality for participant selection. For data management and screening, the Covidence systematic tool was utilized. Data extraction was performed independently by two reviewers. This protocol was registered with PROSPERO (CRD42024574140). RESULTS: A total of 394 studies were retrieved from 5 databases for the initial screening. A total of 67 studies included in full-text screening, and ultimately, 36 studies were selected for full review. The studies were classified into four categories based on assessment methods: (1) analysis of human secretions, including salivary or urinary tests; (2) cellular analysis utilizing cellulose substrates or paper-based materials; (3) environmental assessments, which examined outdoor surfaces, vehicles, residential spaces, and fabrics; and (4) epidemiological assessments, employing surveys or questionnaires. Non-invasive matrices such as saliva and urine were frequently utilized for biomarker analysis. The studies collectively investigated nicotine and its metabolites in human biological samples, environmental surface contamination, and thirdhand smoke (THS) exposure. They employed a diverse range of assessment tools including surveys, machine learning technique, and cellulose-based substrates. CONCLUSIONS: This review identified various selective testing methods for detecting thirdhand smoke (THS) from nicotine. These assessment methods have advantages and disadvantages and underscores the need for further research. Improving these techniques for assessment of THS could significantly improve our understanding of the impact THS has on human health
Comparative study on flexural characteristics of dry ultra-high performance concrete with mono and hybrid steel fibre reinforcement
Full text linkDry ultra-high performance concrete (DUHPC) is a novel construction material that merges the properties of traditional dry concrete with UHPC, combining the advantages of both to offer superior early and long-term strength, enhanced ductility, and improved sustainability. This study investigated the flexural behaviour of DUHPC reinforced with mono and hybrid steel fibres. The evaluation focused on the load-crack mouth opening displacement (CMOD) relationship, flexural strength, initial fracture toughness, unstable fracture toughness, and fracture energy. The mono fibre contents ranged from 0.5% to 1.5%, utilizing fibres of 10 mm and 13 mm in length. For hybrid fibre reinforcement, the total fibre content maintained at 1.5%, consisting of 10 mm and 13 mm steel fibres. Experimental results showed that the addition of steel fibres significantly enhanced the flexural behaviour of DUHPC as compared to the control group. After 28 days of curing, the flexural strength demonstrated an increase of up to 401.3% relative to the control group, with fracture energy reaching 39.2 kJ/m2. Hybrid fibres exhibited varied toughening effects; the replacement of 13 mm fibres with 10 mm ones led to the reductions in flexural strength and fracture energy, while the initial fracture toughness increased by 3.9% and the unstable fracture toughness increased by 2.1%. Digital image correlation (DIC) images corroborated the superior strength and toughening effects of longer steel fibres. Moreover, scanning electron microscope (SEM) micrographs showcased the dense microstructure of DUHPC post moist/steam curing. The embedded fibres markedly improved the material's resistance to bending loads through the synergistic effects of fibre deformation, pull-out, and interfacial friction, thus enhancing the overall flexural performance of DUHPC
Machine learning-based mapping of fog water harvesting potential in Pithoragarh, Uttarakhand: Evaluating climate scenarios and geospatial influences
Full text linkFog is crucial to the hydrological processes in Pithoragarh district, Uttarakhand, significantly affecting local water systems and ecosystems. This study employed five machine learning (ML) models, Gradient Boosting Machine (GBM), AdaBoost (ADB), Model Averaged Neural Network (avNNet), Naive Bayes (NB), and Shrinkage Discriminant Analysis (SDA) to map fog water potential in the area. Using data from 100 foggy locations and 23 variables (hydro-climatic, topographical, and terrain), the models were rigorously evaluated. Results showed that GBM, ADB, avNNet, NB, and SDA models identified high fog water potentiality classes in 43.91 %, 43.84 %, 44.79 %, 42.97 %, and 34.16 % of the area, respectively. ADB and GBM performed best (AUC = 0.999), followed by avNNet (AUC = 0.967), SDA (AUC = 0.967), and NB (AUC = 0.941). Key factors influencing fog occurrence included elevation, wind speed, wind exposition index, relative humidity, and mean power density, identified using the ordinary least square (OLS), various nature-inspired algorithms (such as GA, PSO, GO, DFO, HHO, and GWO), Pearson correlation, and Boruta sensitivity analysis. In this investigation, the linear regression approach was also applied to utilize ensembles of EC-Earth3, NorESM2-LM, and MIROC6 CMIP6 models for fog water forecasting climate conditions spanning from 2025 to 2055. Approximately 22.81 % and 21.52 % of the study area consistently exhibit very high potential for fog water harvesting (FWH) under the ssp245 and ssp585 scenarios, respectively. This research lays a foundation for addressing environmental concerns related to FWH and represents a significant step towards mitigating water scarcity, contributing to water security in the eastern Himalayas in line with Sustainable Development Goal 6 (SDG 6)
Nonlinear consolidation model for stratified soils with vertical drains based on spectral method
No full textMany soft soil foundations with vertical drains are stratified, and the permeability and compressibility of soils change nonlinearly during the consolidation process. A nonlinear consolidation model that can consider both vertical and radial drainage is proposed based on the void-ratio dependent compressibility and permeability (e-lgσ' and e-lgk), and a nonlinear consolidation model for the stratified soils with vertical drains is obtained based on the spectral method. The validation of the solution is verified by the degradation study and the comparative analysis with the existing nonlinear consolidation analytical solutions and laboratory tests. The results show that the average relative error between the calculated value and the analytical solution and the measured value is lower than 0.7% and 2.0%, respectively. A field case is also studied and analyzed. The results show that the calculated values of settlement and pore pressure at different depths are in good agreement with the measured data, which can predict the development of settlement and the dissipation of pore pressure in different soil layers during the consolidation process. The case study further illustrates the feasibility and applicability of the proposed model in the consolidation calculation of stratified soils with vertical drains
DeepChainIoT: Exploring the Mutual Enhancement of Blockchain and Deep Neural Networks (DNNs) in the Internet of Things (IoT)
Full text linkThe Internet of Things (IoT) is widely used across domains such as smart homes, healthcare, and grids. As billions of devices become connected, strong privacy and security measures are essential to protect sensitive information and prevent cyber-attacks. However, IoT devices often have limited computing power and storage, making it difficult to implement robust security and manage large volumes of data. Existing studies have explored integrating blockchain and Deep Neural Networks (DNNs) to address security, storage, and data dissemination in IoT networks, but they often fail to fully leverage the mutual enhancement between them. This paper proposes DeepChainIoT, a blockchain–DNN integrated framework designed to address centralization, latency, throughput, storage, and privacy challenges in generic IoT networks. It integrates smart contracts with a Long Short-Term Memory (LSTM) autoencoder for anomaly detection and secure transaction encoding, along with an optimized Practical Byzantine Fault Tolerance (PBFT) consensus mechanism featuring transaction prioritization and node rating. On a public pump sensor dataset, our LSTM autoencoder achieved 99.6% accuracy, 100% recall, 97.95% precision, and a 98.97% F1-score, demonstrating balanced performance, along with a 23.9× compression ratio. Overall, DeepChainIoT enhances IoT security, reduces latency, improves throughput, and optimizes storage while opening new directions for research in trustworthy computing
Fisetin-loaded Nanoemulsion and Fecal Microbiome Extract Enhance In Vitro Inhibition of Non-Small Cell Lung Cancer Progression
Full text linkExtracellular vesicles in pulmonary diseases: roles and therapeutic potential.
Full text linkExtracellular vesicles (EVs) are small lipid bilayer packages responsible for cellular communication. Increasing clinical and experimental evidence strongly links EVs to homeostasis and the pathogenesis of disease. In this review, we provide a brief overview of EVs and their biological significance in pulmonary disease. We outline the current challenges in diagnosis and treatment of lung diseases and discuss the rationale for exploring EVs as a novel therapeutic avenue. Beyond their biomarker potential, we outline the role and potential for therapeutic targeting of EVs in the pathogenesis of asthma, chronic obstructive pulmonary disease (COPD), lung cancer, and infectious diseases. We also explore the current literature on the use of stem cell derived EVs to drive lung repair and regeneration in inflammatory diseases. Lastly, we highlight challenges and limitations of the study of EVs in pulmonary disease and provide future perspectives with exciting opportunities for translation into therapy
Never say never: Optimal exclusion and reserve prices with expectations-based loss-averse buyers
Full text linkWe analyze reserve prices in auctions with independent private values when bidders are expectations-based loss averse. We find that the optimal public reserve price excludes fewer bidder types than under risk neutrality. Moreover, we show that public reserve prices are not optimal as the seller can earn a higher revenue with mechanisms that better leverage the “attachment effect”. We discuss two such mechanisms: i) an auction with a secret and random reserve price, and ii) a mechanism where an auction with a public reserve price is followed by a negotiation if the reserve price is not met. Both of these mechanisms expose more bidder types to the attachment effect, thereby increasing bids and ultimately revenue
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