Hybrid Applications Research Articles

An innovative hybrid model combining informer and K‐Means clustering methods for invisible multisite solar power estimation

AbstractThe employment of behind‐the‐meter solar photovoltaic (PV) systems has gained increasing popularity in recent years as more individuals and organizations aim to reduce their reliance on conventional grid‐connected power sources and take advantage of the environmental and economic benefits of solar power. However, precisely estimating the potential output of PV systems is a challenging task, since most of the PV systems used in residential properties have been installed behind the meter. Consequently, electric power companies are limited to accessing only the recorded net electricity consumption. This article introduces an innovative approach to estimate behind‐the‐meter PV power generation within a large region, utilizing a limited representative subset. The proposed framework integrates Missforest, that is, a robust tool for missing data imputation, with a hybrid application of K‐Means, Pearson Correlation Coefficient, and Principal Component Analysis, for the precise selection of representative PV sites. Additionally, it leverages the Informer model, a cutting‐edge deep learning‐based time series model, to link the relationship between the PV power generation at representative sites and the total PV power output on the entire region. To conduct a case study, the power output of 367 PV sites and solar radiation measured at 105 weather stations in Taiwan were collected and analyzed. The application of this comprehensive methodology demonstrates a notable advancement in the estimation of “invisible” PV power generation in comparison to other established techniques.

Recent development towards the novel applications and future prospects for cellulose-metal organic framework hybrid materials: a review.

The hybrid material created by combining cellulose and MOF is highly promising and possesses a wide range of useful properties. Cellulose-based metal-organic frameworks (CelloMOFs) combine the inherent biocompatibility and sustainability of cellulose with the tunable porosity and diverse metal coordination chemistry of MOFs. Cellulose-MOF hybrids have countless applications in various fields, such as energy storage, water treatment, air filtration, gas adsorption, catalysis, and biomedicine. They are particularly remarkable as adsorbents that can eliminate pollutants from wastewater, including metals, oils, dyes, antibiotics, and drugs, and act as catalysts for oxidation and reduction reactions. Furthermore, they are highly efficient air filters, able to remove carbon dioxide, particulate matter, and volatile organic compounds. When it comes to energy storage, these hybrids have demonstrated exceptional results. They are also highly versatile in the realm of biomedicine, with applications such as antibacterial and drug delivery. This article provides an in-depth look at the fabrication methods, advanced applications of cellulose-MOF hybrids, and existing and future challenges.

Authenticity in Interactive Experiences

Building on our previous work presented at the Eurographics GCH conference, this paper further explores the characteristics of an “authentic experience”, developing a framework that can be applied to the development of XR and hybrid applications in the field of cultural heritage. While recognising the broader concept of authenticity, we have not focused on an in-depth analysis of it. Instead, we focus specifically on “authentic experiences”. Here, we have extended the definition of authenticity beyond realism or sense of presence, proposing instead a multi-dimensional approach to engaging users cognitively, emotionally, and sensorially (the “Self” dimension) and, at the same time, including two otherfundamental dimensions (the “Others” and the “World”) intrinsically connected to the “Self”. We have then further detailed these three dimensions, which are the pillars of the perception of authenticity, identifying their “components” and “elements”. Finally, we transformed the elements into “actionable” design indications, setting the ground to further develop specific design (UI and UX) strategies for digital heritage and serious game applications.

Hybrid lattice Boltzmann method for turbulent nonideal compressible fluid dynamics

The development and application of a compressible hybrid lattice Boltzmann method to high Mach number supercritical and dense gas flows are presented. Dense gases, especially in Organic Rankine Cycle turbines, exhibit nonclassical phenomena that offer the possibility of enhancing turbine efficiency by reducing friction drag and boundary layer separation. The proposed numerical framework addresses the limitations of conventional lattice Boltzmann method in handling highly compressible flows by integrating a finite-volume scheme for the total energy alongside a nonideal gas equation of state supplemented by a transport coefficient model. Validations are performed using a shock tube and a three-dimensional Taylor–Green vortex flow. The capability to capture nonclassical shock behaviors and compressible turbulence is demonstrated. Our study gives the first analysis of a turbulent Taylor–Green vortex flow in a dense Bethe–Zel'dovich–Thompson gas and provides comparisons with perfect gas flow at equivalent Mach numbers. The results highlight differences associated with dense gas effects and contribute to a broader understanding of nonideal fluid dynamics in engineering applications.

Short‐term energy forecasting using deep neural networks: Prospects and challenges

AbstractThis study presents an in‐depth overview of deep neural networks (DNN) and their hybrid applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from three perspectives: basics, challenges, and prospects. The study compares recent literature using metrics like mean absolute error (MAE), mean average percentage error (MAPE), and root mean square error (RMSE). Findings indicate that combining automated data‐driven models with enhanced DNNs effectively addresses forecasting challenges. It also highlights the role of DNNs in integrating energy prosumers, renewable energy systems, microgrids, big data, and smart grids to improve STEF.

Voltage Deviation Improvement in Microgrid Operation through Demand Response Using Imperialist Competitive and Genetic Algorithms

In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject of extensive research in the field of microgrid operational optimization. The energy demand is considered a crucial element for energy management due to its fluctuating nature over the day. The use of demand response strategies for energy management is one of the most important factors in dealing with renewables. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) have gained great attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper, both ICA and GA, as well as their hybrid application, are used to significantly enhance the voltage regulation in microgrids. The integration of optimization techniques with demand response strategies improves the overall system efficiency and stability. The results proved that the hybrid method provides valuable insights for optimizing energy management systems.

Chemical enhanced oil recovery using carbonized ZIF-67 MOFs and sulfonated copolymers at high reservoir temperatures

Traditional nanoparticle and polymer-based enhanced oil recovery methods face challenges such as nanoparticle agglomeration and polymer adsorption, particularly at elevated reservoir temperatures. This study introduces hybrid polymeric nanofluids, combining carbonized Zeolitic Imidazolate Framework-67 (ZIF-67) with sulfonated copolymers to address these issues. Viscosity tests were conducted at 25 °C, 50 °C, 75 °C, and 90 °C, revealing a 39.67 % increase in viscosity at 90 °C, attributed to electrostatic and hydrogen bond interactions. For subsequent analyses—stability, adsorption, and core flooding—75 °C was selected as the representative high reservoir temperature. Stability assessments, confirmed via UV–Vis spectroscopy, demonstrated nanofluid durability over at least seven days. Carbonized ZIF-67 reduced polymer adsorption on rock surfaces, enhancing oil recovery and reducing environmental impact. Dynamic core flooding experiments showed a 45.8 % improvement in oil recovery by the hybrid nanofluid on carbonate rock samples. The hybrid nanofluid increased RF and RRF, improving the mobility ratio and redirecting flow to lower-permeability zones, reducing unswept oil and enhancing macroscopic sweep efficiency. Winsor Type III emulsification during the post-flooding phase of polymer-nanoparticle injection significantly improved trapped oil mobilization. Reduced contact angles from 170° to 100° altered the wettability, enhancing oil recovery. Carbonized ZIF-67 nanoparticles increased viscous forces, reduced capillary forces, and minimized polymer adsorption. The polymeric nanofluid achieved a capillary number of 1.0 × 10−3, two orders higher than that of polymer alone, resulting in superior performance. Injectivity optimization showed shear rates of 15.9 s−1 in 1700 mD cores and 79.8 s−1 in 80 mD cores, causing pressure drops of 15 psi and 1200 psi, respectively. This highlights injectivity challenges in 80 mD cores, while cores ≥ 290 mD are ideal for our polymer-nanoparticle hybrid applications.

The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.

Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids. This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted. We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Improvements to EXSIM in Ground Motion Simulation for Earthquakes Explained by Double‐Corner‐Frequency Source Model

AbstractThe stochastic EXtended finite‐fault ground‐motion SIMulation algorithm (EXSIM) has been widely applied in simulating and predicting broadband strong ground‐motion. However, an increasingly number of researchers have found that EXSIM may overestimate ground‐motions at low frequencies for some large‐magnitude earthquakes and/or thrust earthquakes, for which the far‐field source model has been explained by a double‐corner‐frequency model. Despite controversy, the double‐corner‐frequency model is now being accepted as one of the main categories of the far‐field source model. This study demonstrated the limited applicability of EXSIM to earthquakes explained by the double‐corner‐frequency source model, by presenting the equivalence between motions generated by EXSIM and those generated by EXSIM's point‐source version, SMSIM, which adopts the ω‐square single‐corner‐frequency model. Furthermore, two improvements to EXSIM have been proposed: (a) the incorporation of the asperity‐distributed stress‐drop compound faults model and (b) the hybrid application of EXSIM with the proposed model. The effects of the two improvements have been verified by comparing EXSIM‐generating motions with recorded ground‐motions for the 2013 Mw 6.7 Lushan thrust earthquake. Significantly, consistent simulation accuracy has been achieved across high‐ and low‐frequency bands as well as in far‐ and near‐fields. The consistent accuracy of the improved EXSIM in simulating high‐ and low‐frequency ground motions enables its direct and independent application to broadband ground motion simulations. Moreover, the first validation of this consistent accuracy in both near‐ and far‐field scenarios further enhances its application in earthquake engineering practices.

A Study on the Hybrid Application of Classifiers and Predictors for the Predictive Maintenance of Cooling System

A Study on the Hybrid Application of Classifiers and Predictors for the Predictive Maintenance of Cooling System

Hot Topics at the 18th SDEWES Conference in 2023: A Conference Report

The present paper reviews the hot topics at the 18th Conference on Sustainable Development of Energy, Water, and Environment Systems (SDEWES) held from 24 to 29 September 2023 in Dubrovnik, Croatia. The selected papers, including aspects of biomass energy application, energy saving in building, low-carbon development and climate change adaptation, hybrid application of sustainable energy, energy storage system, and other measures in sustainable development, are published in this Special Issue of Energies. The 12 selected papers in Energies and corresponding literature that relates to the above topics and has been published in the most recent year are reviewed. In particular, diversifying energy applications are trending towards comprehensive integration with more efficient clean energy capacities for global energy procurement, which can also offer increased resilience and sustainability, reduce reliance on fossil fuels, mitigate environmental impacts, and enhance energy security through a mix of renewable sources and innovative technologies. Meanwhile, energy storage has become increasingly important, which can manage the intermittent of renewable energy, stabilize the grid, and improve energy security and resilience against disruptions. In addition to conventional solar energy storage, biomass energy storage, building energy storage, water storage, etc., can also reduce the overall energy costs, deferring investments in additional transmission infrastructure.

The Research Advances in Distant Hybridization and Gynogenesis in Fish

ABSTRACTDistant hybridization and gynogenesis are two prevalent breeding techniques for fishes. Drawing from the research achievements of our team and the existing literature, we summarize the reproductive traits and genetic features of fishes derived from distant hybridizations and gynogenesis, and we deduce the fundamental mechanisms of these two methods and compare them, discerning their common and different characteristics. Both distant hybridization and gynogenesis techniques can alter genotypes and phenotypes, thus establishing them as significant breeding methods. Additionally, the genetic principles and the basic biological characteristics of distant hybridization and gynogenesis in fish have been inferred. We propose the concepts of macro‐hybrid and micro‐hybrid based on extensive experimental findings from fish distant hybridizations and gynogenesis. The term “macro‐hybrid” refers to offspring from distant hybridization that possess two distinct subgenomes, each inherited from one of the two parental species, such as allodiploid and allotetraploid lineages. The concept of “micro‐hybrid” refers to offspring, including autodiploid and autotetraploid lineages, as well as those resulting from artificial gynogenesis, whose genome almost originates solely from the maternal parent but in which certain DNA fragments derived from the paternal parent insert. Distant hybridization and gynogenesis are vital techniques in fish genetics, breeding, and evolution. We highlight the prospective paths for research and application of distant hybridization and gynogenesis in fishes.

Comprehensive study on building chiller fault feature and diagnosis using hybrid CNN

Chillers are one of the biggest energy consumption devices in HVAC systems. Abnormal operation may undermine the performance, energy efficiency, and environment. This study comprehensively explores the hybrid applications of deep convolutional neural network (CNN) in chiller fault diagnosis and fault feature extraction. Unlike the computer vision where the feature locations are fixed, for chiller fault, it can be changed. The effect of feature sequence on diagnostic performance is carefully investigated, and found that it depends on the size and number of the convolution kernels. Small and large number of kernels may extract fine and enough features for the model to counter the location change and maintain the basic characteristics of the faults. 1-D CNN is further studied as a hierarchical feature extractor combined with the traditional machine learning, like k-nearest neighbor (KNN), decision tree (DT), and random forest (RF), to build a hybrid strategy. It is found that the highest accuracy of 99.85% is achieved by RF plus 1-D CNN with an accuracy of 100% for refrigerant leakage. Fine and clear features from deeper structure are most favorable for the weak learner like DT, but may harm the information diversity of RF and lower its accuracy.

A new modular neuroprosthesis suitable for hybrid FES-robot applications and tailored assistance

BackgroundTo overcome the application limitations of functional electrical stimulation (FES), such as fatigue or nonlinear muscle response, the combination of neuroprosthetic systems with robotic devices has been evaluated, resulting in hybrid systems that have promising potential. However, current technology shows a lack of flexibility to adapt to the needs of any application, context or individual. The main objective of this study is the development of a new modular neuroprosthetic system suitable for hybrid FES-robot applications to meet these needs.MethodsIn this study, we conducted an analysis of the requirements for developing hybrid FES-robot systems and reviewed existing literature on similar systems. Building upon these insights, we developed a novel modular neuroprosthetic system tailored for hybrid applications. The system was specifically adapted for gait assistance, and a technological personalization process based on clinical criteria was devised. This process was used to generate different system configurations adjusted to four individuals with spinal cord injury or stroke. The effect of each system configuration on gait kinematic metrics was analyzed by using repeated measures ANOVA or Friedman’s test.ResultsA modular NP system has been developed that is distinguished by its flexibility, scalability and personalization capabilities. With excellent connection characteristics, it can be effectively integrated with robotic devices. Its 3D design facilitates fitting both as a stand-alone system and in combination with other robotic devices. In addition, it meets rigorous requirements for safe use by incorporating appropriate safety protocols, and features appropriate battery autonomy, weight and dimensions. Different technological configurations adapted to the needs of each patient were obtained, which demonstrated an impact on the kinematic gait pattern comparable to that of other devices reported in the literature.ConclusionsThe system met the identified technical requirements, showcasing advancements compared to systems reported in the literature. In addition, it demonstrated its versatility and capacity to be combined with robotic devices forming hybrids, adapting well to the gait application. Moreover, the personalization procedure proved to be useful in obtaining various system configurations tailored to the diverse needs of individuals.

Advancing Element Identification for Web and Mobile Automation

Element identification is a cornerstone of automation testing, directly influencing the reliability, scalability, and efficiency of test scripts. With the rapid evolution of web and mobile applications, traditional element identification methods—such as XPath, CSS Selectors, and static attributes—are increasingly challenged by dynamic DOM structures, shadow DOMs, virtual DOMs, and hybrid frameworks. These challenges are further amplified in mobile environments, where gestures, native elements, and platform-specific attributes add layers of complexity. This paper explores the advancements in element identification techniques that address these challenges for both web and mobile automation testing. It presents a comparative analysis of traditional methods and emerging solutions, highlighting their limitations in modern application scenarios. The study introduces hybrid strategies, including context-aware locators, heuristic-based identification, and early implementations of adaptive and self-healing locators. It also examines the role of cross-platform tools like Selenium, Appium, and Cypress in tackling identification issues. Furthermore, the paper emphasizes the importance of unified approaches that bridge web and mobile testing, particularly in hybrid applications with embedded web views. Future trends, such as collaborative tagging standards between development and testing teams, are discussed to provide a forward-looking perspective on overcoming element identification bottlenecks. By addressing key pain points and proposing innovative techniques, this paper aims to guide testers, developers, and tool creators in adopting resilient, scalable, and adaptable element identification strategies that meet the demands of modern software applications.

Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation

With the galloping progress of the changing climates all around the world, Machine Learning (ML) approaches have been prevalently studied in many types of research in this area. ML is a robust tool for acquiring perspectives from data. In this paper, we elaborate on climate change mitigation issues and ML approaches leveraged to solve these issues and aid in the improvement and function of sustainable energy systems. ML has been employed in multiple applications and many scopes of climate subjects such as ecosystems, agriculture, buildings and cities, industry, and transportation. So, a Systematic Literature Review (SLR) is applied to explore and evaluate findings from related research. In this paper, we propose a novel taxonomy of Deep Learning (DL) method applications for climate change mitigation, a comprehensive analysis that has not been conducted before. We evaluated these methods based on critical parameters such as accuracy, scalability, and interpretability and quantitatively compared their results. This analysis provides new insights into the effectiveness and reliability of DL methods in addressing climate change challenges. We classified climate change ML methods into six key customizable groups: ecosystems, industry, buildings and cities, transportation, agriculture, and hybrid applications. Afterward, state-of-the-art research on ML mechanisms and applications for climate change mitigation issues has been highlighted. In addition, many problems and issues related to ML implementation for climate change have been mapped, which are predicted to stimulate more researchers to manage the future disastrous effects of climate change. Based on the findings, most of the papers utilized Python as the most common simulation environment 38.5 % of the time. In addition, most of the methods were analyzed and evaluated in terms of some parameters, namely accuracy, latency, adaptability, and scalability, respectively. Lastly, classification is the most frequent ML task within climate change mitigation, accounting for 40 % of the total. Furthermore, Convolutional Neural Networks (CNNs) are the most widely utilized approach for a variety of applications.

Efficacy of gaseous ozone and UVC radiation against Candida auris biofilms on polystyrene surfaces

The growth and attachment of highly resistant biofilms on/to polymer-based surfaces, including reusable medical devices is a significant health concern in infection control. The increasing prevalence of the multidrug-resistant yeast, Candida auris, has recently garnered immense interest as a contributor to healthcare-associated infections. In this study, we investigate the potential of disinfection processes (ozone, UVC, and ozone + UVC) to inactivate C. auris biofilms (NCPF 8971), grown on polystyrene surfaces. A bespoke decontamination chamber is used to expose the substrate for 20–60 mins, corresponding to ozone doses between 1000 and 3000 ppm.min and UVC doses between 2864 and 11,592 mJ/cm2, respectively. The performance of these 3 treatment methods against the biofilms and vegetative/planktonic cells of the organism is comparatively evaluated. While complete inactivation (> 8 log10 reduction, CFU/mL) of the vegetative cells is observed within 40 mins by all methods, the biofilms proved considerably difficult to inactivate. Only 3.3 log10 reduction was achieved using ozone for 40 min, whereas 7.2 log10 reduction was obtained using UVC for 40 min. A hybrid application of ozone and UVC improved the decontamination efficacy compared to their independent applications (i.e., ozone only and UVC only). Scanning electron microscopy results yielded new insights into the inactivation mechanisms; thus providing the needed foundation for newer developments in oxidation-based processes for biofilm mitigation and control.

Perspectives on 2D materials for hybrid and beyond-Si image sensor applications

Abstract The complementary metal–oxide–semiconductor (CMOS) image sensor has become essential and ubiquitous in our daily lives as it is present in almost every pocket. As demand for compact, multifunction, and high-efficiency Internet of Things applications continues to rise, novel configuration designs and manufacturing methods, such as neural network integration and 3D stacking have been implemented to enhance the CMOS image sensor’s (CIS) performance. However, the progress of image sensors based on silicon CMOS technology would eventually be limited by the intrinsic optical, electrical, and mechanical properties of silicon material. This has led to the exploration of two-dimensional materials (2DMs) and the emergence of 2DMs as promising candidates for the next generation of optoelectronic devices. In this article, we discuss the current advancements and challenges associated with silicon CISs and the potential benefits of incorporating 2DMs in the image sensor. We highlight three critical opportunities for 2DMs, including Si CMOS/2DMs hybrid structure and direct growth techniques of 2DMs on Si for back-end-of-line integration, 2DMs-based neuromorphic photodetectors (PDs) and optical neural networks for in-image-sensor-processing, and curved image sensor based on 2DMs PDs for bionic detection. With the growing maturity of 2DM technologies, we anticipate that the device scaling and the increase of integration density of 2DM electronics in the image sensor will continue, leading to the development of highly efficient, compact, intelligent, and versatile 2DM image sensors in the near future.

Material engineering and application of hybrid biomimetic-de novo designed elastin-like polypeptides

The global concern over environmental consequences of petrochemical-derived plastics underscores the urgent need for sustainable and biodegradable polymers. In this context, elastin-like polypeptides emerge as a promising solution, offering multiple advantages, including remarkable mechanical properties, biocompatibility, customizable functionalities, and renewable sourcing through biosynthetic production in microbes, making them a compelling choice for various applications. We previously demonstrated accelerated engineering of a new class of elastin-like polypeptide‐based materials through hybrid biomimetic‐de novo predictive molecular design. The resulting variants exhibited enhanced molecular stability compared to their natural counterparts, catering to a range of technical applications that involve harsh downstream processing conditions. Here, we showcase the use of some of these previously discovered hybrid variants and illustrate the effective translation of the predicted molecular designs in structural and functional materials in several high-added-value applications. This includes multiscale drug-encapsulating vehicles with controlled release, multifunctional wound coverings, and all-aqueous-based biobased photoresists for creating 2D/3D microstructures.

Hybrid E-Learning Model and Applications

Hybrid E-Learning Model and Applications