Search Results (612)

The primary functions of an energy harvesting system include the harvesting, transformation, management, and storage of energy. Until now, various types of energy, with different power levels, have been harvested and stored by the energy harvesting system. In low-power scenarios, such as microwaves, sound, friction, and pressure, a specific low-power energy harvesting system is required. Due to the absence of an external power supply in such systems, cold-start circuits play a crucial role in igniting the low-power energy harvesting system, ensuring a reliable start-up from the initial state. This paper reviews the categorization and characteristics of energy harvesting systems, with a focus on the design and performance parameters of cold-start circuits. A tabular comparison of existing cold-start strategies is presented herein. The study demonstrates that resonance-based integrated cold-start methods offer significant advantages in terms of conversion efficiency and dynamic range, while ring oscillator-based integrated cold-start methods achieve the lowest start-up voltage. Additionally, the paper discusses the challenges of self-starting and future research directions, highlighting the potential role of emerging technologies, such as artificial intelligence (AI) and neural networks, in optimizing the design of energy harvesting systems. Full article

Cold start causes a high amount of unburned hydrocarbon and particulate matter emissions in gasoline direct injection (GDI) engines. Therefore, it is necessary to understand the dynamics of spray during a cold start and develop a predictive model to form a better air-fuel mixture in the combustion chamber. In this study, an Artificial Neural Network (ANN) was designed to predict quantitative 3D liquid volume fraction, liquid penetration, and liquid width under different operating conditions. The model was trained with data derived from high-speed and Schlieren imaging experiments with a gasoline surrogate fuel, conducted in a constant volume spray vessel. A coolant circulator was used to simulate the low-temperature conditions (−7 °C) typical of cold starts. The results showed good agreement between machine learning predictions and experimental data, with an overall accuracy $R^2$ of 0.99 for predicting liquid penetration and liquid width. In addition, the developed ANN model was able to predict detailed dynamics of spray plumes. This confirms the robustness of the ANN in predicting spray characteristics and offers a promising tool to enhance GDI engine technologies. Full article

The increasing frequency of extreme climate events may significantly alter the species composition, structure, and functionality of ecosystems, thereby diminishing their stability and resilience. This study draws on temperature and precipitation data from 53 meteorological stations across Mongolia, covering the period from 1983 to 2016, along with MODIS normalized difference vegetation index (NDVI) data from 2001 to 2016. The climate anomaly method and the curvature method of cumulative NDVI logistic curves were employed to identify years of extreme climate events and to extract the start of the growing season (SOS) in Mongolia. Furthermore, the study assessed the impact of extreme climate events on the SOS across different vegetation types and evaluated the sensitivity of the SOS to extreme climate indices. The study results show that, compared to the multi-year average green-up period from 2001 to 2016, extreme climate events significantly impact the SOS. Extreme dryness advanced the SOS by 6.9 days, extreme wetness by 2.5 days, and extreme warmth by 13.2 days, while extreme cold delayed the SOS by 1.2 days. During extreme drought events, the sensitivity of SOS to TN90p (warm nights) was the highest; in extremely wet years, the sensitivity of SOS to TX10p (cool days) was the strongest; in extreme warm events, SOS was most sensitive to TX90p (warm days); and during extreme cold events, SOS was most sensitive to TNx (maximum night temperature). Overall, the SOS was most sensitive to extreme temperature indices during extreme climate events, with a predominantly negative sensitivity. The response and sensitivity of SOS to extreme climate events varied across different vegetation types. This is crucial for understanding the dynamic changes of ecosystems and assessing potential ecological risks. Full article

Platform-based businesses in the logistics market are evolving under the influence of digital transformation. Transforming the freight market into an environment where various types of freight can be traded across multiple markets and locations. Freight brokerage platforms have revolutionized the trading relationship between freight owners and vehicle owners. However, this type of system has also introduced inefficiencies, such as unestablished contracts, leading to unnecessary costs and delays. To address this issue, a freight recommendation system can assist users in finding what they are looking for while aiming to reduce failed contracts. With current advances in deep learning, complex patterns based on users’ past behaviors and preferences can be learned, enabling more accurate and personalized recommendations. This study proposes a deep learning-based freight recommendation system to provide personalized services and reduce failed contracts on freight brokerage platforms. The system is built by creating a freight transaction dataset, classifying freight categories through natural language processing and text mining techniques, and incorporating externally derived data on transportation distances. The deep learning model is trained using Autoencoder, Word2Vec, and Graph Neural Networks (GNN), with recommendation logic implemented to suggest suitable freight matches for vehicle owners. This system is expected to increase the market efficiency of the freight logistics industry and is a key step toward improving the long-term profit structure. Full article

To tackle the challenges of cold start and data sparsity in recommendation systems, an increasing number of researchers are integrating item features, resulting in the emergence of multimodal recommendation systems. Although graph convolutional network-based approaches have achieved significant success, they still face two limitations: (1) Users have different preferences for various types of features, but existing methods often treat these preferences equally or fail to specifically address this issue. (2) They do not effectively distinguish the similarity between different modality item features, overlook the unique characteristics of each type, and fail to fully exploit their complementarity. To solve these issues, we propose the user perception-guided graph convolutional network for multimodal recommendation (UPGCN). This model consists of two main parts: the user perception-guided representation enhancement module (UPEM) and the multimodal two-step enhanced fusion method, which are designed to capture user preferences for different modalities to enhance user representation. At the same time, by distinguishing the similarity between different modalities, the model filters out noise and fully leverages their complementarity to achieve more accurate item representations. We performed comprehensive experiments on the proposed model, and the results indicate that it outperforms other baseline models in recommendation performance, strongly demonstrating its effectiveness. Full article

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy. Full article

Over 50% of an engine’s energy dissipates via the exhaust and cooling systems, leading to considerable energy loss. Effectively harnessing the waste heat generated by the engine is a critical avenue for enhancing energy efficiency. Traditional exhaust heat recovery systems are limited to real-time recovery of exhaust heat primarily for engine warm-up and fail to fully optimize exhaust heat utilization. This paper introduces a novel exhaust heat recovery system leveraging thermal battery technology, which utilizes phase change materials for both heat storage and reutilization. This innovation significantly minimizes the engine’s cold start duration and provides necessary heating for the cabin during start-up. Dynamic models and thermal management system models were constructed. Parameter optimization and calculations for essential components were conducted, and the fidelity of the simulation model was confirmed through experiments conducted under idle warm-up conditions. Four distinct operational modes for engine warm-up are proposed, and strategies for transitioning between these heating modes are established. A simulation analysis was performed across four varying operational scenarios: WLTC, NEDC, 40 km/h, and 80 km/h. The results indicated that the thermal battery-based exhaust heat recovery system notably reduces warm-up time and fuel consumption. In comparison to the cold start mode, the constant speed condition at 40 km/h showcased the most significant reduction in warm-up time, achieving an impressive 22.52% saving; the highest cumulative fuel consumption reduction was observed at a constant speed of 80 km/h, totaling 24.7%. This study offers theoretical foundations for further exploration of thermal management systems in new energy vehicles that incorporate heat storage and reutilization strategies utilizing thermal batteries. Full article

Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and the resilience of these fragile ecosystems to global change. Hence, we used satellite data acquired by Sentinel-2 to perform a comparative assessment of the Normalized Difference Vegetation Index (NDVI) and the Plant Phenology Index (PPI) in mountainous regions (canton of Valais-Switzerland in the European Alps) for monitoring vegetation dynamics of four types: deciduous trees, coniferous trees, grasslands, and shrublands. Results indicate that the NDVI is particularly noisy in the seasonal cycle at the beginning/end of the snow season and for coniferous trees, which is consistent with its known snow sensitivity issue and difficulties in retrieving signal variation in dense and evergreen vegetation. The PPI seems to deal with these problems but tends to overestimate peak values, which could be attributed to its logarithmic formula and derived high sensitivity to variations in near-infrared (NIR) and red reflectance during the peak growing season. Concerning seasonal parameters retrieval, we find close concordance in the results for the start of season (SOS) and end of season (EOS) between indices, except for coniferous trees. Peak of season (POS) results exhibit important differences between the indices. Our findings suggest that PPI is a robust remote sensed index for vegetation monitoring in seasonal snow-covered and complex mountain environments. Full article

In recent years, interest in Lonicera caerulea production has grown significantly because of its nutritional and pharmaceutical benefits, leading to rapid expansion in its cultivation. L caerulea var. emphyllocalyx is a lesser-known botanical variety. Due to differences between plants of the Lonicera genus and the lack of scientific reports on micropropagation, it is necessary to determine the possibilities of in vitro propagation. The aim of this study was to elaborate a micropropagation protocol of two new breeding clones of Lonicera caerulea var. emphyllocalyx: ‘21–17’ and ‘139–24’. The experiments were carried out on in vitro cultures grown on MS medium supplemented with 1 mg·dm⁻³ BA or 1 mg·dm⁻³ mT. Two types of explants were used during the experiment: nodal fragments (NFs) and shoot-tips (STs). Before acclimatisation, some rooted microshoots were subjected to cooling at 4 °C for 4 weeks. Significantly more ST explants than NF explants started to grow at the proliferation stage. The application of BA resulted in much better proliferation and health of cultures. Cold storage of micropropagated ‘139–24’ plantlets significantly increased their survival in acclimatisation in contrast to ‘21–17’ plantlets but weakened further growth of the plants. In future in vitro studies on L. caerulea var. emphyllocalyx, BA can be used as the primary growth regulator due to its effectiveness and low cost. Nodal fragments should be considered as the main propagation material since they promote better growth rates. Additionally, further research is required to explore the effects of low-temperature storage on the growth and physiology of these plants. The results obtained in this research may contribute to the development of micropropagation technology in the future for L. caerulea var. emphyllocalyx. Full article

Newcastle disease, which affects poultry and is endemic in many nations across the world, is caused by Avian Paramyxovirus-1 (APMV-1). This experimental study was conducted from January to June 2021 at the National Veterinary Institute (NVI) to evaluate the virus viability and antibody titer of Newcastle disease vaccines (Hichner’s B1, Lasota, and ThermostableI2) stored at different temperature storage conditions. Chickens (12 treatment groups and 1 control group) were vaccinated and challenged with the virulent ND virus (0.5 × 10^6.5 embryonic lethal dose fifty (ELD₅₀)). The immune responses (antibody titers) of chickens were evaluated using hemagglutination (HA) and hemagglutination inhibition (HI) assays. The Newcastle disease vaccines (Hachiner’s B1 (ND-HB1), ND-Lasota, and ND-Thermostable I2) stored at +4 °C HI-induced antibody titers of 151 (±103.3), 136 (±53.4), and 145 (±91) on day 14, respectively, whereas on day 21, they increased to 160 (±82) for ND-HB1 and 144 (±74.5) for ND-Lasota. ND-Thermostable I2 showed a decrement to 133 (±44.8). All three vaccines stored at different temperature storage conditions (+4, +23, and +30 °C) used in this experiment induced antibody titers greater than 128 on day 28 post-vaccination, except the Newcastle disease vaccine Thermostable I2 stored at +30 °C. The vaccines collected from private veterinary drugstores (customer vaccines Hachiner’s B1 and ND-Thermostable I2) used in this experiment induced very low antibody titers, less than 128 antibody titers, from days 14 to 21. Statistically significant induced mean antibody titers were observed for chickens that received vaccines stored at different temperature storage conditions for 72 h (p < 0.05), except for the ND-HB1 mean HI-induced antibody titer at days 7 and 28. Further, vaccine protection was confirmed by inoculation of both the vaccinated (treatment groups) and control groups by the virulent ND virus, where the control group started dying three days post-challenge but all chicks that received the vaccines survived. Overall, this study showed the impact of temperature storage conditions on the antibody titer and their effect on the titer of the viable virus in the vaccine, and thereby its protective capacity, warranting appropriate cold chain management of the vaccines along the value chain. Full article

Integration of heat pumps combined with thermal energy storage provides a key pathway to decarbonizing the energy supply in the industry when the processes are not operated continuously. Yet, this integration of such novel systems introduces control challenges due to added dependencies between different process streams. This work investigates the control problem of heat pumps coupled to stratified thermal energy storage that is integrated into non-continuous industrial processes. A two-layer control strategy is proposed, where, in the higher level, a model predictive controller is developed for energy management using a linear model of the non-linear process. The resulting optimization problem is a mixed integer quadratic program. The low-level control layer is defined with standard industry controllers. The overall system is tested using a dynamic simulation model for the entire process, demonstrating its performance in three different cases. The control strategy optimizes heat recovery while ensuring system operability. The study demonstrates successful disturbance rejection and cold starts, wherein 100% of the targeted heat recovery can be confirmed under nominal conditions. Further evaluation in laboratory or field trials is recommended, and alternative, yet-to-be-defined, control concepts may be compared to the proposed approach. Full article

Oxidative stress and chronic inflammation create a perpetual cycle in the elderly, where impaired immune function amplifies susceptibility to oxidative damage, and oxidative stress further weakens the immune response. This cycle is particularly detrimental to the respiratory system of the elderly, which is an easy target for constant exogenous harmful attacks during cold/flu season or under heavy air pollution. Herbal medicines that protect respiratory function are seen as safer alternatives to conventional therapies; however, there is limited availability of scientifically validated, safe, and effective natural supplements for these conditions. In this study, we evaluated a standardized bioflavonoid composition, UP446, that contains bioactives from the roots of Scutellaria baicalensis and the heartwoods of Acacia catechu as a natural and nutritional supplement for its antioxidative and immunoregulatory effects in oxidative stress-accelerated aging and chemically induced immune suppression mouse models. Immunosenescence was induced through the repeated subcutaneous inoculation of D-galactose (D-Gal) at a dose of 500 mg/kg/day in CD-1 mice. UP446 was administered orally at doses of 100 mg/kg and 200 mg/kg starting in the fifth week of immunosenescence induction. This study lasted a total of ten weeks. All mice received a quadrivalent influenza vaccine 2 weeks before termination. Whole blood, serum, spleen homogenate, and thymus tissues were processed for analysis. Cyclophosphamide (Cy)-induced immunosuppression was triggered by three consecutive injections of cyclophosphamide at 80 mg/kg/day, followed by the oral administration of UP446 for 18 days at doses of 100 mg/kg and 200 mg/kg. Blood was collected from each animal at necropsy, and serum was isolated for IgA and IgG ELISA analysis. UP446 was found to improve immune response, as evidenced by the stimulation of innate (NK cells) and adaptive immune responses (T cells and cytotoxic T cells), an increase in antioxidant capacity (glutathione peroxidase), the preservation of vital immune organs (the thymus), and a reduction in NFκB. UP446 also increased serum levels of IgA and IgG. The findings presented in this report demonstrate the antioxidative, anti-inflammatory, and immune-regulatory activities of UP446, suggesting its potential use in respiratory conditions involving immune stress due to aging, oxidative stress, and/or pathogenic challenges. Full article

This study investigates the enhancement of vehicle warm-up performance using phase-change materials (PCMs) and various thermal storage methods. The primary objective is to utilize the thermal energy lost during engine cooling to improve the cold-start performance, thereby reducing fuel consumption and emissions. Thermal storage devices incorporating PCMs were developed and tested by measuring temperature changes and energy transfer over soaking periods of 4, 8, 16, and 24 h. The results show energy transfers of 591, 489, 446, and 315 kJ at 4, 8, 16, and 24 h, respectively. In terms of the warm-up time, the use of thermal storage devices reduced the time required to reach 70 °C by up to 24.45%, with significant reductions observed across all soaking periods. This reduction in the warm-up time directly contributes to faster engine stabilization, leading to proportional improvements in fuel efficiency and a corresponding decrease in exhaust emissions, including CO₂. The findings highlight the effectiveness of PCMs in improving the engine warm-up performance and emphasize the importance of optimizing thermal storage systems to balance energy efficiency and practical application considerations. Additionally, the experimental data provide useful benchmark information for computational simulation validation, enabling the further optimization of automotive thermal management systems. Integrating a PCM-based thermal storage device can significantly enhance a vehicle’s warm-up performance, leading to reduced fuel consumption and lower emissions. Full article

Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation methods still suffer from insufficiently exploiting sparse information and the mismatch between personalized interests and general knowledge. This paper proposes a model named Adaptive Knowledge Contrastive Learning with Dynamic Attention (AKCL-DA) to address the above challenges. Specifically, instead of building contrastive views by randomly discarding information, in this study, an adaptive data augmentation method was designed to leverage sparse information effectively. Furthermore, a personalized dynamic attention network was proposed to capture knowledge-aware personalized behaviors by dynamically adjusting user attention, therefore alleviating the mismatch between personalized behavior and general knowledge. Extensive experiments on Yelp2018, LastFM, and MovieLens datasets show that AKCL-DA achieves a strong performance, improving the NDCG by 4.82%, 13.66%, and 4.41% compared to state-of-the-art models, respectively. Full article

Serverless computing is a new cloud computing model suitable for providing services in both large cloud and edge clusters. In edge clusters, the autoscaling functions play a key role on serverless platforms as the dynamic scaling of function instances can lead to reduced latency and efficient resource usage, both typical requirements of edge-hosted services. However, a badly configured scaling function can introduce unexpected latency due to so-called “cold start” events or service request losses. In this work, we focus on the optimization of resource-based autoscaling on OpenFaaS, the most-adopted open-source Kubernetes-based serverless platform, leveraging real-world serverless traffic traces. We resort to the reinforcement learning algorithm named Proximal Policy Optimization to dynamically configure the value of the Kubernetes Horizontal Pod Autoscaler, trained on real traffic. This was accomplished via a state space model able to take into account resource consumption, performance values, and time of day. In addition, the reward function definition promotes Service-Level Agreement (SLA) compliance. We evaluate the proposed agent, comparing its performance in terms of average latency, CPU usage, memory usage, and loss percentage with respect to the baseline system. The experimental results show the benefits provided by the proposed agent, obtaining a service time within the SLA while limiting resource consumption and service loss. Full article