Search Results (2,998)

Static mixers are commonly used for process intensification in a wide range of industrial applications. For the design and selection of a static mixer, an accurate prediction of the hydraulic performance, particularly the pressure drop, is essential. This experimental study examines the pressure drop for turbulent single-phase and gas–liquid two-phase flows through a Komax triple-action static mixer placed on a horizontal pipeline. New values of friction factor and z-factor are reported for fully turbulent liquid single-phase flow (11,700 ≤ Re_L ≤ 18,700). For two-phase flow, the pressure drop for stratified and intermittent flows (0.07 m/s ≤ U_L ≤ 0.28 m/s and 0.46 m/s ≤ U_G ≤ 3.05 m/s) is modeled using the Lockhart–Martinelli approach, with a coefficient, C, correlated to the homogenous void fraction. Conversely, the analysis of power dissipation reveals a dependence on both liquid and gas superficial velocities. For conditions corresponding to intermittent flow upstream of the mixer, flow visualization revealed the emergence of a swirling flow in the Komax static mixer. It is interesting to note that an increase in slug frequency leads to an increase, followed by stabilization of the pressure drop. The results offer valuable insights for improving the design and optimization of Komax static mixers operating under single-phase and two-phase flow conditions. In particular, the reported correlations can serve as practical tools for predicting hydraulic losses during the design and scale-up. Moreover, the observed influence of the slug frequency on the pressure drop provides guidance for selecting operating conditions that minimize energy consumption while ensuring efficient mixing. Full article

This study focuses on optimizing the machinability of Al-Fe-Cu (8000 series) alloys by developing new compositions with varying Fe and Cu contents and evaluating their mechanical, microstructural, and energy performance. For this purpose, 6061 Al alloy was melted in an induction furnace and cast into molds, and samples containing 2.5% and 5% Fe were produced. Microstructural features were analyzed using Python-based image processing, while Specific Energy Consumption (SEC) theory was applied to assess machining efficiency. An alloy with 2.5% Fe and 2.64% Cu showed superior mechanical properties and the lowest energy consumption. Increasing cutting speed and depth of cut notably decreased SEC. Machine learning (ML) analysis confirmed strong predictive capability, with R² values above 0.80 for all models. Decision Tree (DT) achieved the highest accuracy for SEC prediction (R² = 0.98634, MAE = 0.02209, MSE = 0.00104), whereas XGBoost (XGB) performed best for SCEC (R² = 0.96533, MAE = 0.25578, MSE = 0.10178). Response Surface Methodology (RSM) optimization further validated the significant influence of machining parameters on SEC and specific cutting energy consumption (SCEC). Overall, the integration of machine learning (ML), response surface methodology (RSM), and energy equations provides a comprehensive approach to improve the machinability and energy efficiency of 8000 series alloys, offering practical insights for industrial applications. Full article

This paper presents a data-driven flexible hybrid architecture which explores the use of a Specific Energy Consumption (SEC) index for predicting the Remaining Useful Lifetime (RUL) of spare mechanical parts of an industrial electric machine. The architecture carries out a hybrid process between a physics-based and data-driven deterioration model, and a similarity model based on a recursive database continuously enriched with real data on current used electrical power and the flow of raw material. The architecture enriches the production database with both synthetic and real data through continuous improvement based on the extraction of features from new incoming real data. This recursive process of database construction is carried out to improve the robustness, accuracy, and precision of estimations. The integration of the architecture aims to enhance predictive maintenance. As an example to illustrate the architecture, the case of an industrial shredder machine is analyzed from real data. The proposed architecture successfully predicts the RUL of sugarcane shredder spare parts from the recursive database and a defined threshold condition. The RUL prognosis converges toward a representative trajectory of the database after a given early time with respect to the total useful life. Full article

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively. Full article

This article presents the development of a new pneumatic device for the precise application of mineral fertilizers, designed for use in precision agriculture systems involving farming robots. The proposed device is mounted on an autonomous agricultural platform and utilizes a machine vision system to determine plant coordinates. Its operating principle is based on accumulating a single dose of fertilizer in a chamber and delivering it precisely to the plant’s root zone using a directed airflow. The study includes a theoretical investigation of fertilizer movement inside the applicator tube under the influence of airflow and rotational motion of the tube. A mathematical model has been developed to describe both the relative and translational motion of the fertilizer. The equations, which account for frictional forces, inertia, and air pressure, enable the determination of optimal structural and kinematic parameters of the device depending on operating conditions and the properties of the applied material. The use of numerical methods to solve the developed mathematical model allows for synchronization of the device’s operating time parameters with the movement of the agricultural robot along the crop rows. The obtained results and the developed device improve the accuracy and speed of fertilizer application, minimize fertilizer consumption, and reduce soil impact, making the proposed device a promising solution for precision agriculture. Full article

Polygonatum cyrtonema Hua, a plant with a long history of consumption in China, serves both medicinal and edible purposes, and it exhibits numerous pharmacological effects, including promoting kidney health and enhancing immune function. However, the effect and molecular mechanism of Polygonatum cyrtonema polysaccharides (PCPs) on hyperuricemia have not yet been reported. The hyperuricemic mice model was induced by the intraperitoneal injection of potassium oxonate (PO, 300 mg/kg), combined with the intragastric administration of hypoxanthine (HX, 300 mg/kg). Biochemical assays in mice revealed that PCPs markedly lowered high serum uric acid levels, suppressed xanthine oxidase (XOD) activity, and reduced the expression of inflammatory cytokines, including interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). Western blot analysis demonstrated that PCPs downregulated urate transporter 1 (URAT1), while H&E staining showed that PCPs effectively restored renal histological integrity. Here, we isolated and identified the PCPs, which consist mainly of rhamnose, glucuronic acid, galacturonic acid, glucose, galactose, and arabinose, with a molar mass ratio of 0.5:2.15:0.47:16.58:3.66:1.09. Furthermore, the galactose residue that docked with both XOD and URAT1 molecules forms more hydrogen bonds and exhibits a lower binding energy, which enables the improved regulation of both targets. We have demonstrated for the first time the improving effect of PCPs on hyperuricemia, and revealed their regulatory mechanisms by modulating xanthine oxidase, inflammatory factors, and uric acid transporters. This study not only provides new insights into the anti-hyperuricemic activity of PCPs in mice, but also lays a foundation for its potential application in the functional foods of anti-hyperuricemia. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

With the development of embedded electronic devices, energy consumption has become a significant design issue in modern systems-on-a-chip. Conventional SRAMs cannot maintain data after powering turned off, limiting their use in applications such as battery-powered smartphone devices that require non-volatility and no leakage current. RRAM devices are recently used extensively in applications such as self-charging wireless sensor networks and storage elements, owing to their intrinsic non-volatility and multi-bit capabilities, making them a potential candidate for mitigating the von Neumann bottleneck. We propose a new RRAM-based hybrid memristor model incorporated with a permanent magnet. The proposed design (1T2R) was simulated in Cadence Virtuoso with a 1.5 V power supply, and the finite-element approach was adopted to simulate magnetization. This model can retain the data after the power is off and provides fast power on/off transitions. It is possible to charge a smartphone battery without an external power source by utilizing a portable charger that uses magnetic induction to convert mechanical energy into electrical energy. In an embedded smartphone self-charging device this addresses eco-friendly concerns and lowers environmental effects. It would lead to the development of magnetic field-assisted embedded portable electronic devices and open the door to new types of energy harvesting for RRAM devices. Our proposed design and simulation results reveal that, under usual conditions, the magnet-based device provide a high voltage to charge a smartphone battery. Full article

Forecasting electricity consumption is one of the most important scientific and practical tasks in the field of electric power engineering. The forecast accuracy directly impacts the operational efficiency of the entire power system and the performance of electricity markets. This paper proposes algorithms for source data preprocessing and tuning XGBoost models to obtain the most accurate forecast profiles. The initial data included hourly electricity consumption volumes and meteorological conditions in the power system of the Republic of Tatarstan for the period from 2013 to 2025. The novelty of the study lies in defining and justifying the optimal model training period and developing a new evaluation metric for assessing model efficiency—financial losses in Balancing Energy Market operations. It was shown that the optimal depth of the training dataset is 10 years. It was also demonstrated that the use of traditional metrics (MAE, MAPE, MSE, etc.) as loss functions during training does not always yield the most effective model for market conditions. The MAPE, MAE, and financial loss values for the most accurate model, evaluated on validation data from the first 5 months of 2025, were 1.411%, 38.487 MWh, and 16,726,062 RUR, respectively. Meanwhile, the metrics for the most commercially effective model were 1.464%, 39.912 MWh, and 15,961,596 RUR, respectively. Full article

This study examines the causal impact of China’s New Energy Demonstration City construction policy on corporate energy consumption. The results demonstrate that this policy effectively reduces corporate energy consumption. The policy significantly decreases the consumption of coal, natural gas, and diesel. Although the policy significantly reduces energy consumption in both local state-owned enterprises (SOEs) and non-SOEs, its effect does not show statistically significant variation across different types of controlling shareholders. The energy-saving effect is particularly pronounced in the following industries: Manufacturing, Electricity, Heat, Gas, and Water Production & Supply, Wholesale & Retail Trade, Information Technology Services, Leasing & Business Services, and Water Conservancy, Environment, and Public Infrastructure Management. The policy operates through multiple channels: internal mechanisms including direct innovation effect, accelerated green M&As effect as well as digital empowerment effect, and external moderators including marketization level and green finance environment. The findings yield important insights for scholars, policymakers and corporate stakeholders. Full article

The burgeoning Internet of Medical Things (IoMT) offers unprecedented opportunities for real-time patient monitoring and predictive diagnostics, yet the current systems struggle with scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence. This paper introduces Med-Q Ledger, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger’s efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments. Full article

The interest in analyzing alternative fuels and new propulsion technologies for shipping decarbonization is growing rapidly. This paper aims to evaluate the performance of high-temperature polymeric exchange membrane fuel cells (HT-PEMFCs) fed by reformed methanol and their potential application as a propulsion system for vessels. The proposed system is intended to be installed on board a 10 m long ship, designed for commercial use in the marine area of Capri Island. Numerical and experimental analyses were performed to estimate the system’s performance, and a feasibility assessment was carried out to verify its real applicability on board the reference case study. From the numerical perspective, a CFD model of the ship hull, as well as a thermochemical model of the propulsion system, was developed. From the experimental point of view, the system behavior was tested by means of a dedicated test bench. The results of the numerical models allowed for the sizing of the propulsion system and the calculation of the fuel consumption. In particular, to satisfy the ship’s power demand, two 5 kW HT-PEMFCs were needed, with a total fuel consumption of 12.7 kg over a typical daily cruise, with a methanol consumption of 1.88 kg/h during cruising at 7 knots. The feasibility analysis highlighted that the propulsion system fits the vessel’s requirements, both in terms of volume and weight. Full article

With the rapid advancement of the electric vehicle (EV) industry, the ownership of EVs and their charging power have increased significantly, gradually exerting a greater impact on the power grid. To meet the diverse charging needs of different EV users, the coordinated planning of fast- and slow-charging stations can reduce the influence of charging loads on the power grid while fulfilling user demands and increasing the number of EVs that can be served. This paper establishes a collaborative planning model for multi-type charging stations (CSs), considering the comprehensive satisfaction of EV users. Firstly, a comprehensive satisfaction model of multi-type EV users considering their behavioral characteristics is established to characterize the impact of fast- and slow-charging CSs on the satisfaction of different types of users. Secondly, a two-layer cooperative planning model of multi-type CSs considering comprehensive satisfaction of EV users is established to determine the location of CSs and the number of fast- and slow-charging configurations to satisfy the users’ demand for different types of charging piles. Thirdly, a solution algorithm for the two-layer planning model based on the greedy theory algorithm is proposed, which transforms the upper layer charging pile planning model into a charging pile multi-round expansion problem to speed up the model solving. Finally, the validity of the proposed models is verified through case studies, and the results show that the planning scheme obtained can take into account the user’s charging satisfaction while guaranteeing the economy, and at the same time, the scheme has a positive significance in the promotion of new energy consumption, reduction in network loss, and alleviation of traffic congestion. Full article

Predicting building energy consumption is an essential part of demand-side management since it enables cost-effective building operation under limited resources. Recent prediction models have adopted deep learning networks due to their high capabilities in extracting occupants’ energy use patterns from historical data. Additionally, augmenting historical data by decomposing existing input times-series into several temporal components can enhance prediction performance, particularly for new buildings. However, it still remains unclear how newly created predictors, through the decomposition of existing time-series, affect the performance of building energy use prediction. Therefore, to address this gap, this study proposed a deep learning-based energy use prediction framework that employs the Unobserved Component Model to create new input time-series based on existing ones. Then, the performance of the proposed prediction framework was evaluated using two years of historical data collected from a case building. The main findings are threefold. First, deep learning networks achieved a higher prediction performance during training than during testing. Second, testing performance was generally better when using the augmented dataset than the raw dataset. Third, the proposed data augmentation method contributes to a 1.26% decrease in MAPE and a 3.42% decrease in CvRMSE. This suggests that the proposed prediction framework can be applied to simulations of buildings with limited time series dataset to more accurately predict energy consumption at the building level. Full article

In recent years, anomaly detection in digital environments has become a critical research area due to issues such as spam messages and fake news, which can lead to privacy breaches, social disruption, and undermined information reliability. Traditional anomaly detection models often require specific training for each task, resulting in significant time and resource consumption and limited flexibility. This study explores the use of Prompt Engineering with Transformer-based Large Language Models (LLMs) to address these challenges more efficiently. By comparing techniques such as Zero-shot, Few-shot, Chain-of-Thought (CoT), Self-Consistency (SC), and Tree-of-Thought (ToT) prompting, the study identifies CoT and SC as particularly effective, achieving up to 0.96 accuracy in spam detection without the need for task-specific training. However, ToT exhibited limitations due to biases and misinterpretation. The findings emphasize the importance of selecting appropriate prompting strategies to optimize LLM performance across various tasks, highlighting the potential of Prompt Engineering to reduce costs and improve the adaptability of anomaly detection systems. Future research is needed to explore the broader applicability and scalability of these methods. Additionally, this study includes a survey of Prompt Engineering techniques applicable to anomaly detection, examining strategies such as Self-Refine and Retrieval-Augmented Generation to further enhance detection accuracy and adaptability. Full article