Search Results (2,269)

Government agencies, energy consumers, and other societal groups have all shown concern and attention for the energy management of buildings. Relevant statistical data, however, indicate that most public buildings continue to consume large amounts of energy overall and that the issues of low energy usage and energy waste have not materially improved. As a result, this study reviewed the state of progress and potential directions for future research in the field of building energy management in public buildings using a data-driven approach. Relevant studies were obtained from three databases—Web of Science, Scopus, and China National Knowledge Infrastructure—based on certain search phrases. The text mining program VOS viewer was then used to examine the material. We provide a thorough examination of the study techniques and material, as well as a visual representation of the keywords and current state of the field. According to this study, the range of data processing outcomes; the flexibility of research system standards; and the availability of a comprehensive, unified assessment system are the main factors contributing to the practical issues facing building energy management today. Based on the geographic distribution and state of energy development, this study is the first to examine possible research avenues for building energy management in public buildings through cross-fusion research on passive energy-saving design and subjective behavioral energy-saving. It offers a foundation for developing the building energy management system best practice model in the future. Full article

Background: The hub-and-spoke model (HSM) offers a framework for efficient healthcare service delivery. This scoping review seeks to explore the implementation and effectiveness of the HSM in antimicrobial stewardship (AMS) programmes. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting guideline was followed. A systematic search was conducted in four electronic databases (PubMed, Medline, Cochrane Library, and Google Scholar) from inception until January 2024. Studies where the HSM was used for delivering any AMS activity, intervention, or action, were included. No study-specific filters were applied and all populations, study designs, and health settings were included. Data screening and selection were achieved using Rayyan. Three authors independently screened studies, with conflicts resolved by a fourth author. Data were narratively synthesised. Standard appraisal tools were impractical; however, critical evaluation of data collection and outcome reporting was ensured. Results: Out of 1438 articles, three were included in the scoping review. The primary interventions utilising the HSM in AMS involved reducing antibiotic misuse, training healthcare professionals, case-based learning, establishing AMS programmes, developing antibiograms, and formulating policies or guidelines pertinent to AMS. The studies demonstrated significant clinical improvements in AMS. Clinical outcomes from the studies include a significant reduction in antimicrobial usage and improved antibiotic management, with a notable decrease in days on antimicrobial therapy and increased antibiotic de-escalation. Key facilitators for AMS programme success were tailored education, collaborative learning, strong leadership, strategic practices, and data-driven decisions. Key barriers were leadership challenges, change resistance, knowledge gaps, inadequate data systems, resource limitations, and technological constraints. Conclusions: The review identified a literature gap in HSM use in AMS programmes. Further studies are needed to assess HSM’s effectiveness, feasibility, and cost-effectiveness in AMS contexts. Full article

This paper presents a data-driven model-free adaptive containment control (MFACC) scheme for uncertain rehabilitation exoskeleton robots, where the robotic exoskeleton dynamics are uncertain with saturation constraints. To handle uncertainties of the robotic dynamics, a model-free adaptive control (MFAC) strategy is established by linearizing the robotic exoskeleton dynamics into an equivalent data model. Considering the integral additive effect of the traditional MFAC method, an improved MFAC controller is designed in this paper. Since actuators with saturation constraints constantly affect the safety of patients during rehabilitation training, we construct a new criterion function with active constraints for the critical function of the MFAC algorithm and adopt the Hildreth quadratic programming algorithm to find the constrained optimal solution to overcome this limitation. The proposed MFACC scheme is rigorously proven by the compression mapping method to demonstrate model-free stability. Finally, the proposed control scheme is verified to be effective by simulation studies of the robotic SimMechanics model. Full article

Open-ended steel piles are commonly used as the foundation for offshore structures. Numerous model and field tests have demonstrated a time-dependent increase in the resistance of these piles, a phenomenon referred to as pile ageing or pile setup. Additionally, for open-ended steel piles with comparably small diameters, soil plugging enhances the resistance against axial compressive loads. Realistically predicting these effects is necessary for their reliable incorporation into design practice. This contribution presents static compression and tension pile load testing conducted in an experimental pit filled with wet, uniformly graded silica sand. In total, twelve piles (5.5 m, 325 mm) were driven into homogeneously compacted sand using a pneumatic impact hammer. Firstly, static compression pile load testing was executed at various times after installation. Subsequently, static tension pile load tests were carried out. The results of the static compression pile load tests indicate that the compressive resistance doubles over an ageing period of 64 weeks. The experimental investigations of the effect of soil plugging showed marginal soil plugging during pile installation, but a significant influence of the soil plug on the compressive resistance. Full article

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face’s Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist’s intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance. Full article

Potential multi-ship conflict situations in coastal or near-shore port areas have always been one of the important factors affecting ship navigation safety and a key target of maritime traffic regulatory authorities. In recent years, with the continuous development and integration of various emerging technologies in the maritime field, maritime traffic supervision has also shown a trend of intelligent and autonomous development. The traditional supervision method dominated by human experience is evolving towards data and model-driven practices. In order to solve the problem of ship navigation safety supervision under multi-ship conflict scenarios, it is urgent to build an intelligent conflict mitigation decision-making model. Therefore, this paper designs a novel risk mitigation decision-making model for multi-ship conflict scenarios from the perspective of maritime supervision. The model proposed in this paper first extracts high-density ship clusters based on AIS (Automatic Identification System) data and uses the MCD (Mean Core Density) and PRM (Proportion of Relative Motion) as feature indicators to further mine potential multi-ship conflict scenarios. Finally, a global optimization decision-making model is constructed to effectively mitigate conflict risks. Experimental verification shows that the intelligent decision-making model for the mitigation of maritime traffic conflict proposed in this paper can autonomously identify conflict scenarios and make reasonable decisions in real time. It can effectively ensure the navigation safety of ships in multi-ship conflict scenarios and further improve the supervision level of maritime departments. Full article

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology. Full article

Combustion involves the study of multiphysics phenomena that includes fluid and chemical kinetics, chemical reactions and complex nonlinear processes across various time and space scales. Accurate simulation of combustion is essential for designing energy conversion systems. Nonetheless, due to its multiscale, multiphysics nature, simulating these systems at full resolution is typically difficult. The massive and complex data generated from experiments and simulations, particularly in turbulent combustion, presents both a challenge and a research opportunity for advancing combustion studies. Machine learning facilitates data-driven techniques to manage the substantial amount of combustion data that is either obtained through experiments or simulations, and thereby can find the hidden patterns underlying these data. Alternatively, machine learning models can be useful to make predictions with comparable accuracy to existing models, while reducing computational costs significantly. In this era of big data, machine learning is rapidly evolving, offering promising opportunities to explore its integration with combustion research. This work provides an in-depth overview of machine learning applications in turbulent combustion modeling and presents the application of machine learning models: Decision Trees (DT) and Random Forests (RF), for the spatio-temporal prediction of plasma-assisted ignition kernels, based on the initial degree of ionization, with model validations against DNS data. The results demonstrate that properly trained machine learning models can accurately predict the spatio-temporal ignition kernel profile based on the initial energy deposition and distribution. Full article

Currently, with the increasing scale of industrial systems, multisensor monitoring data exhibit large-scale dynamic Gaussian and non-Gaussian concurrent complex characteristics. However, the traditional principal component analysis method is based on Gaussian distribution and uncorrelated assumptions, which are greatly limited in practice. Therefore, developing a new fault detection method for large-scale Gaussian and non-Gaussian concurrent dynamic systems is one of the urgent challenges to be addressed. To this end, a double-layer distributed and integrated data-driven strategy based on Laplacian score weighting and integrated Bayesian inference is proposed. Specifically, in the first layer of the distributed strategy, we design a Jarque–Bera test module to divide all multisensor monitoring variables into Gaussian and non-Gaussian blocks, successfully solving the problem of different data distributions. In the second layer of the distributed strategy, we design a dynamic augmentation module to solve dynamic problems, a K-means clustering module to mine local similarity information of variables, and a Laplace scoring module to quantitatively evaluate the structural retention ability of variables. Therefore, this double-layer distributed strategy can simultaneously combine the different distribution characteristics, dynamism, local similarity, and importance of variables, comprehensively mining the local information of the multisensor data. In addition, we develop an integrated Bayesian inference strategy based on detection performance weighting, which can emphasize the differential contribution of local models. Finally, the fault detection results for the Tennessee Eastman production system and a diesel engine working system validate the superiority of the proposed method. Full article

Driven by environmental and economic considerations, this study explores the viability of utilizing coconut juice residues (CJRs), a byproduct from coconut milk production, as a carbon source for bacterial cellulose (BC) synthesis in the form of a versatile bio-membrane. This work investigates the use of optimization modeling as a tool to find the optimal conditions for BC cultivation in consideration of waste minimization and resource sustainability. Optimization efforts focused on three parameters, including pH (4–6), cultivation temperature (20–30 °C), and time (6–10 days) using Design Expert (DE) V.13. The maximum yield of 9.31% (g/g) was achieved when the cultivation took place at the optimal conditions (pH 6, 30 °C, and 8 days). This approach aligns with circular economy principles, contributing to sustainable resource management and environmental impact reduction. The experimental and predicted optimal conditions from DE V.13 were in good agreement, validating the study’s outcomes. The predictive model gave the correlations of the optimal conditions in response to the highest yield and maximum eco-efficiency. The use of prediction modeling resulted in a useful tool for forecasting and obtaining guidelines that can assist other researchers in calculating optimal conditions for a desired yield. Acetylation of the BC resulted in cellulose acetate (CA) membranes. The CA membrane exhibited the potential to separate CO₂ from a CH₄/CO₂ mixed gas with a CO₂ selectivity of 1.315 in a membrane separation. The promising gas separation results could be further explored to be utilized in biogas purification applications. Full article

Traditional polyimides have highly conjugated structures, causing significant coloration under visible light. Fluorinated colorless polyimides, known for their light weight and excellent optical properties, are considered ideal for future aerospace optical lenses. However, their lifespan in low Earth orbit is severely limited by high-density atomic oxygen (AO) erosion, and the degradation behavior of fluorinated polyimides under AO exposure is not well understood. This study uses reactive molecular dynamics simulations to model two fluorinated polyimides, PMDA-TFMB and 6FDA-TFMB, with different fluorine contents, to explore their degradation mechanisms under varying AO concentrations. The results indicate that 6FDA-TFMB has slightly better resistance to erosion than PMDA-TFMB, mainly due to the enhanced chemical stability from its -CF₃ groups. As AO concentration increases, widespread degradation of the polyimides occurs, with AO-induced cleavage and temperature-driven pyrolysis happening simultaneously, producing CO and OH as the main degradation products. This study uncovers the molecular-level degradation mechanisms of fluorinated polyimides, offering new insights for the design of AO erosion protection systems. Full article

The growing problem of unsolicited text messages (smishing) and data irregularities necessitates stronger spam detection solutions. This paper explores the development of a sophisticated model designed to identify smishing messages by understanding the complex relationships among words, images, and context-specific factors, areas that remain underexplored in existing research. To address this, we merge a UCI spam dataset of regular text messages with real-world spam data, leveraging OCR technology for comprehensive analysis. The study employs a combination of traditional machine learning models, including K-means, Non-Negative Matrix Factorization, and Gaussian Mixture Models, along with feature extraction techniques such as TF-IDF and PCA. Additionally, deep learning models like RNN-Flatten, LSTM, and Bi-LSTM are utilized. The selection of these models is driven by their complementary strengths in capturing both the linear and non-linear relationships inherent in smishing messages. Machine learning models are chosen for their efficiency in handling structured text data, while deep learning models are selected for their superior ability to capture sequential dependencies and contextual nuances. The performance of these models is rigorously evaluated using metrics like accuracy, precision, recall, and F1 score, enabling a comparative analysis between the machine learning and deep learning approaches. Notably, the K-means feature extraction with vectorizer achieved 91.01% accuracy, and the KNN-Flatten model reached 94.13% accuracy, emerging as the top performer. The rationale behind highlighting these models is their potential to significantly improve smishing detection rates. For instance, the high accuracy of the KNN-Flatten model suggests its applicability in real-time spam detection systems, but its computational complexity might limit scalability in large-scale deployments. Similarly, while K-means with vectorizer excels in accuracy, it may struggle with the dynamic and evolving nature of smishing attacks, necessitating continual retraining. Full article

This study aimed to optimize modified starch from Mangifera indica (mango) fruit using acid hydrolysis and pre-gelatinization via computer-assisted techniques as a substituent for pharmaceutical tableting excipients. The hydrolysis and microwave-assisted pre-gelatinization time and temperature were optimized using a three-level factorial design. The modified starches were characterized for flowability, compressibility, and swelling properties. It was found that all parameters fit a quadratic model, which can be used to predict the properties of the modified starch. The optimized hydrolysis reaction was 3.8 h at 56.4 °C, while the pre-gelatinization reaction was 3 min at 150 °C. Structural changes were found, ascertaining that starch modification was successful. The optimized hydrolyzed starch showed superior properties in relative to unmodified M. indica fruit starch and comparable characteristics to conventional excipients. The optimized pre-gelatinized starch presented an excellent enhancement in the flow and compression properties, with %swelling greatly augmented 3.95-fold and 1.24-fold compared to unmodified starch and SSG, respectively. Additionally, the pre-gelatinized starch presented comparable binding effect, while the hydrolyzed powder had reduced binding capacity due to shorter chains. The findings revealed that the use of software-assisted design of experiment facilitated a data-driven approach to optimize the modifications. The optimized modified mango starch demonstrated potential as a multifunctional excipient, capable of functioning as binder, disintegrant, and diluent. Full article

Introduction: Accurately detecting arousal events during sleep is essential for evaluating sleep quality and diagnosing sleep disorders, such as sleep apnea/hypopnea syndrome. While the American Academy of Sleep Medicine guidelines associate arousal events with electroencephalogram (EEG) signal variations, EEGs are often not recorded during home sleep testing (HST) using wearable devices or smartphone applications. Objectives: The primary objective of this study was to explore the potential of alternatively relying on combinations of easily measurable physiological signals during HST for arousal detection where EEGs are not recorded. Methods: We conducted a data-driven retrospective study following an incremental device-agnostic analysis approach, where we simulated a limited-channel setting using polysomnography data and used deep learning to automate the detection task. During the analysis, we tested multiple signal combinations to evaluate their potential effectiveness. We trained and evaluated the model on the Multi-Ethnic Study of Atherosclerosis dataset. Results: The results demonstrated that combining multiple signals significantly improved performance compared with single-input signal models. Notably, combining thoracic effort, heart rate, and a wake/sleep indicator signal achieved competitive performance compared with the state-of-the-art DeepCAD model using electrocardiogram as input with an average precision of 61.59% and an average recall of 56.46% across the test records. Conclusions: This study demonstrated the potential of combining easy-to-record HST signals to characterize the autonomic markers of arousal better. It provides valuable insights to HST device designers on signals that improve EEG-free arousal detection. Full article

The wind-driven aerodynamic noise of super-high-rise building facades not only affects the experience of use inside the building but also reduces the life cycle of building facade materials to some extent. In this paper, we are inspired by the micro-groove structure of shark skin with damping and noise reduction properties and apply bionic skin to reduce the aerodynamic noise impact of super-high-rise buildings. The aerodynamic noise performance of smooth and super-high-rise building models with bionic grooves is simulated via CFD to investigate the noise reduction performance of different bionic groove patterns, such as I-shape, ∪-shape, V-shape, and ∩-shape patterns, and their corresponding acoustic noise reduction mechanisms. This study showed that the bionic shark groove skin has a certain noise reduction effect, and the I-shaped groove has the best noise reduction effect. By applying bionic skin, the aerodynamic noise of super-high-rise buildings can be effectively reduced to improve the use experience and environmental quality of the buildings and provide a new research idea and application direction for the aerodynamic noise reduction design of building facades. Full article