Problem Of Consumption Research Articles

Evaluation of warm mix effect in laboratory using a self-developed WMA additive Alube

In response to the problems of high mixing temperatures and energy consumption of polymer-modified asphalt mixtures in road construction, a surfactant-based warm mix additive ‘Alube’ was developed. The warm mix SBS-modified asphalt and its mixture were prepared. Their rheological performance and chemical structure were analysed by the rheological and microscopic tests. The active mixing power (P) of asphalt mixtures were examined by the variable speed mixing (VSM) test. The mixing flowability model was then established to evaluate the warm mix effect. The results indicated that the mixing flowability of asphalt mixtures obeyed the linear Bingham visco-plastic model, and the slope and intercept of the flow straight characterised the mixing viscosity and plastic limit. It is evaluated that 0.7% Alube1 and Evotherm reduced the mixing temperature of SBS-modified asphalt mixture by 14°C and 12°C, respectively. Moreover, Alube1 is suitable for a wide range of applications through warm mix asphalt technology.

Methodology and real-world applications of dynamic uncertain causality graph for clinical diagnosis with explainability and invariance

AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios, without problems of data collection, labeling, fitting, privacy, bias, generalization, high cost and high energy consumption. Through close collaboration between clinical experts and DUCG technicians, 46 DUCG models covering 54 chief complaints were constructed. Over 1,000 diseases can be diagnosed without triage. Before being applied in real-world, the 46 DUCG models were retrospectively verified by third-party hospitals. The verified diagnostic precisions were no less than 95%, in which the diagnostic precision for every disease including uncommon ones was no less than 80%. After verifications, the 46 DUCG models were applied in the real-world in China. Over one million real diagnosis cases have been performed, with only 17 incorrect diagnoses identified. Due to DUCG’s transparency, the mistakes causing the incorrect diagnoses were found and corrected. The diagnostic abilities of the clinicians who applied DUCG frequently were improved significantly. Following the introduction to the earlier presented DUCG methodology, the recommendation algorithm for potential medical checks is presented and the key idea of DUCG is extracted.

A polyhedral analysis of the synchronous management of energy production and consumption problem

A polyhedral analysis of the synchronous management of energy production and consumption problem

Sum-rate maximization for a distributed space-time block code-aided cooperative NOMA with energy harvesting

AbstractIn this paper, we exploit the spatial and transmission diversities in cooperative non-orthogonal multiple access (C-NOMA) networks to improve the system sum-rate. To achieve this, we propose a user-pairing scheme where near-field user pairs serve as relays for user pairs in the far-field region. Based on this pairing scheme, we incorporate a space-time block code transmission technique at the near-field user pairs to maximize the transmission diversity in the cooperative phase. Moreover, we consider a non-linear energy harvesting model at the near-field user pair to alleviate the problem of energy consumption during the cooperative transmission phase. Further to this, we formulate a sum-rate maximization problem that is addressed from the viewpoint of joint power allocation factor and power splitting ratio optimization. We develop a low-computational iterative algorithm based on the concepts of the Stackelberg game and the Nash bargaining solution. We benchmark our findings with different scenarios, such as energy harvesting C-NOMA with a fixed power allocation factor and power splitting ratio, energy harvesting C-NOMA without STBC, non-cooperative NOMA, and orthogonal multiple access. The results obtained via simulations outperform the benchmark schemes.

Toward intelligent food drying: Integrating artificial intelligence into drying systems

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies.

Relationship between risky alcohol consumption and academic performance in students in the health area at a Chilean University

Introduction: the phenomenon of alcohol consumption among university students, particularly in the field of health, is the subject of growing concern due to its potential impact on public health and academic performance.Objective: analyze the relationship between risky alcohol consumption and the academic performance of students studying in the health area at a Chilean university in the second semester of 2023.Methods: the study with a descriptive and transversal quantitative approach, with a sample of 361 students who responded to the AUDIT questionnaire. Academic performance was characterized using the university's grading system.Results: regarding alcohol consumption, 81,7 % (295 participants) showed low-risk consumption, 16,3 % (59 participants) high-risk consumption, and the remaining 1,9 % a possible consumption or dependence problem. Furthermore, a trend is observed in which students with problematic consumption are distributed between the categories of "good" (note 5) and "very good" (note 6), while risk and low-risk consumption are distributed between the categories of "very good", "good" and "sufficient".Conclusions: the findings of this study show that, in general, alcohol consumption does not appear to have a significant negative impact on the academic performance of health students. Although approximately 20 % of those surveyed reported problematic or risky consumption

Coordinated Control of Proton Exchange Membrane Electrolyzers and Alkaline Electrolyzers for a Wind-to-Hydrogen Islanded Microgrid

In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development of wind-to-hydrogen (W2H) systems. However, the high proportion of wind power and electrolyzers in a large-scale W2H system will bring about the problem of renewable energy consumption and frequency stability reduction. This paper analyzes the operational characteristics and economic feasibility of mainstream electrolyzers, leading to the proposal of a coordinated hydrogen production scheme involving both a proton exchange membrane (PEM) electrolyzer and an alkaline (ALK) electrolyzer. Subsequently, a coordinated control based on Model Predictive Control (MPC) is proposed for system frequency regulation in a large-scale W2H islanded microgrid. Finally, simulation results demonstrate that the system under PEM/ALK electrolyzers coordinated control not only flexibly accommodates fluctuating wind power but also maintains frequency stability in the face of large disturbances. Compared with the traditional system with all ALK electrolyzers, the frequency deviation of this system is reduced by 25%, the regulation time is shortened by 80%, and the demand for an energy storage system (ESS) is reduced. The result validates the effectiveness of MPC and the benefits of the PEM/ALK electrolyzers coordinated hydrogen production scheme.

A dynamic pricing scheme for secure offloading and resource allocation based on the internet of vehicles

With the development of sixth-generation (6G) wireless communication technology, billions of vehicles will access the network in the future, and the number of vehicle applications and user data will also increase dramatically. Traditional cloud computing faces serious problems of latency and energy consumption in handling massive data. Mobile edge computing (MEC) has emerged to dramatically improve computing efficiency and reduce energy consumption by placing computing servers at network edge locations close to vehicles. However, the service range of MEC servers is limited and cannot fully satisfy user requirements. In addition, the task offloading process has security risks. The current research focuses on how to reduce the energy consumption and latency overhead of task offloading and neglects the economic cost or data transmission security of vehicles. To solve the above problems, we propose a cooperative security offloading (CSO) scheme for auxiliary vehicles and MEC servers. Firstly, we propose a dynamic pricing mechanism for computing resources by considering the credibility of MEC servers and auxiliary vehicles, the urgency of the task, and the number of users competing for auxiliary vehicles. Secondly, to prevent malicious MEC servers and eavesdroppers from attacking, we employ homomorphic encryption to protect user privacy. Meanwhile, efficient and secure computing services are achieved by optimizing user selection decisions, offloading decisions, and resource allocation decisions. Finally, the optimal decisions are obtained by the dueling DQN-based resource allocation and pricing strategy (DDRP) and the cost-minimizing security offloading (CMSO) algorithm, which minimizes the economic cost of users while maximizing security. Simulation results show that, compared with some existing schemes, the CSO scheme effectively reduces the economic cost of users while ensuring the security of data transmission.

Reducing the environmental impact of lithium-ion battery recycling through co-processing of NCM and LFP

Due to its potential environmental hazards and the importance of valuable metal supply, the recycling of spent lithium-ion batteries (LIBs) has attracted widespread attention. The hydrometallurgical process for recycling spent lithium-ion batteries faces the problem of excessive acid consumption and the need for different redox additives to improve the leaching rate of major metals which will result in issues such as resource waste and secondary pollution. In this study, using LFP and NCM as raw materials, the inherent oxidation/reduction characteristics between them were utilized to achieve leaching rates of over 99 % for Ni, Mn, Li, and over 95 % for Co, without the addition of any redox additives under near stoichiometric sulfuric acid. The leaching kinetics and reaction mechanism of different metals indicate that LFP rapidly dissolves in the sulfuric acid solution, with the released Fe2+ acting as a reducing agent. The subsequent slower reaction between Fe2+ and NCM becomes the rate-controlling step. Afterwards, the leachate regulated by solution control can be further used to prepare Ni-Co-Mn hydroxide precursors and lithium carbonate after the formation of FePO4. This method effectively reduces chemical consumption, mitigates environmental footprint, and achieves highly economical recovery of metals from spent lithium-ion batteries.

Hierarchical Coordinated Control Strategy for Active Power in Distribution Networks with Scaled Distributed Generation Access

Promoting scaled distributed wind turbines and photovoltaic, and other distributed generations access to the distribution network is an important measure for realize the goal of carbon peaking and carbon neutrality and to build new-type power systems. However, as the installed capacity of wind turbine, photovoltaic and other intermittent renewable energy sources climbs dramatically, the security and consumption problems of the distribution network are becoming increasingly prominent, and there is an urgent requirement to improve the active power control capability of distributed generation. To optimize the operation stability and consumption economy of the distribution network, a hierarchical coordinated control strategy for active power of scaled distributed generation is proposed based on the principle of unified control and hierarchical management, including peaking control, frequency control, mesh control and market trading control, to coordinate and optimize the system operation safety and consumption economy. The proposed strategy optimizes active power control instructions in the layer of distribution network layer, mesh layer, and functional area layer, thus fully expanding the depth and breadth of the consumption for distributed generation. The effectiveness of the proposed strategy is proved by the actual operation of Liaoning power grid.

2bit Nonlinear Diffractive Deep Neural Network (2bit ND2NN): A quantized nonlinear all-optical diffractive deep neural network implementation

To alleviate the problems of long training time and computational resource consumption in diffractive deep neural networks (D2NNs), we present the 2bit nonlinear diffractive deep neural network (2bit ND2NN) model, which employs the concept of quantized neural networks. 2bit ND2NN converts the phase of each diffraction layer pixel from continuous to discrete values, i.e., the five regions of 0, π/2, π, 3π/2 and 2π. We use the formulas for phase and relative thickness to determine the thickness values of the pixels. Furthermore, the phase and amplitude of neurons in the 2bit ND2NN model are both considered as learnable parameters. We use a revised formula to determine the size of neurons in the model, and we determine the diffraction grating spacing, the number of grating layers, the pixel size and the pixel counts through ablation experiments. Experimental results for image classification indicate that the highest accuracy achieved by 2bit ND2NN on the MNIST and Fashion-MNIST datasets is 97.88 % and 89.28 %, respectively.

Changes in alcohol consumption and alcohol problems before and after the COVID-19 pandemic: a prospective study in heavy drinking young adults

Changes in alcohol consumption and alcohol problems before and after the COVID-19 pandemic: a prospective study in heavy drinking young adults

The design of the insulation layer and energy consumption optimization of the injection mould for the motor commutator

The injection molding of the motor commutator requires a long-term high-temperature operation, which poses a problem of high energy consumption. The heating zone is located behind the mold, and there is thermal radiation and convection during heating, resulting in uneven temperature distribution. To solve the above problems, an automatic lifting and insulation mechanism is proposed. An insulation frame is built with an aerogel insulation layer inside. It is connected up to the upper table of the machine, moving up and down during processing, and connected down to the crank slider mechanism. Simulation software analyzes its insulation and temperature distribution and, through experimental verification, compares the temperature distribution and energy consumption of injection molds before and after the installation of insulation mechanisms. The experimental and simulation results show that using the automatic lifting insulation layer design scheme can effectively reduce energy consumption, improve temperature distribution unevenness, and increase the yield rate.

Partial discharge fault identification method for GIS equipment based on improved deep learning

AbstractAiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.

Hybrid non-technical-loss detection in fog-enabled smart grids

Electricity theft is one of the major factors contributing to non-technical-losses (NTLs) in power distribution networks. NTL fraud includes frauds in which consumers profit unlawfully by manipulating smart meters (SMs), intruding networks, and so forth. This unlawful act not only undermines people’s efforts to conserve energy but also disrupts the regular billing cycle for power utilities, causing financial losses. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, two NTL Detection schemes are proposed for NTL fraud prediction. Both schemes employed the auto-regressive integrated moving average (ARIMA) and the machine learning technique to predict the consumer behavior fraud pattern efficiently. Furthermore, extensive simulations are conducted on real-world electricity consumption data sets, which show that the proposed schemes outperformed state-of-the-art solutions and achieved an accuracy of 98%, a precision of 98.6%, a recall of 98.2%, an AUC of 97.9%, and an F1 score of 98.4%.

The impact of green innovation on the carbon performance of Chinese manufacturing enterprises: Moderating role of internal governance

With the increasing problems of global warming, ecological damage, and excessive consumption of resources, a greater number of countries are focusing on corporate carbon performance and carbon emissions. Enterprises can save energy and reduce emissions through green innovation, which will improve the efficiency of carbon use and contribute to higher levels of corporate carbon performance. Meanwhile, effective corporate governance can improve enterprise performance, while internal governance systems can affect enterprise carbon performance. This study aims to reveal how the green innovation affect carbon performance of enterprises and the role of internal governance. So this study utilizes the data of China's A-share listed manufacturing companies from 2010 to 2020 to examine the impact of green innovation on corporate carbon performance through a fixed-effects model, and to examine the impact of corporate internal governance structure on green innovation and carbon performance through a moderating-effects model. The data used in this study are mainly from China Securities Market and Accounting Research Database (CSMAR) and China Research Data Service Platform (CNRDS), and the robustness test is conducted by replacing the independent variables using the 2sls method. The findings indicate that green innovation has a significant contribution to enterprise performance on carbon. Ownership concentration and executive political connections negatively moderated the relationship between green innovation and carbon performance. Director compensation positively moderates the relationship between green innovation and carbon performance. This research provides a theoretical groundwork for future studies on green innovation and carbon performance, and its findings are instructive for both the introduction of government policies and the restructuring of internal corporate governance.

Evaluating Two Brief Motivational Interventions for Excessive-Drinking University Students.

Two brief computerized motivational interventions for excessive-drinking university students were evaluated. Participants (N = 88, females = 61.5%, mean age = 21.05 years) were randomly assigned to a control group or one of two experimental groups: Computerized Brief Intervention (CBI) or Computerized Brief Intervention-Enhanced (CBI-E). CBI followed the principles of Motivational Interviewing to motivate participants to change their drinking behavior. CBI-E additionally used the principles of Systematic Motivational Counseling to identify and discuss with participants their dysfunctional motivational patterns that were interfering with their attainment of emotional satisfaction. At baseline and a three-month follow-up, the participants completed a battery of measures of alcohol consumption and related problems. At baseline, the participants were confirmed to be heavy drinkers with many drink-related negative consequences. Males and females responded differently to the interventions. During follow-up, males' alcohol use was ordered: CBI-E < CBI < Controls. The females in all three groups reduced their alcohol use, but there were no significant group differences. Males responded to the interventions as expected. For females, the assessment itself seemed to serve as an effective intervention, and there were no post-intervention differences among the three groups. Suggestions for future research using CBI and CBI-E are discussed.

An optimal energy efficient routing in WSN using adaptive entropy bald eagle search optimization and density based adaptive soft clustering

Wireless Sensor Network (WSN) uses soft computing techniques to reduce task time consuming and unsolvable energy consumption problems. This study used soft-computing-based methods to demonstrate the best data transfer in WSN. Nodes in a network are initially clustered using density-based Adaptive Soft (DAS) clustering. Afterward, the cluster head (CH) is selected using a modified beetle swarm optimization technique. Distance, energy, trust, and throughput are all considered when deciding on the ideal CH. The node with the highest entropy for data transmission is then determined by calculating each node’s entropy weight values based on these factors. The CH carries out the data aggregation after the data collection from the sensor nodes. Finally, entropy value based bald eagle search (EBES) optimization with an adaptive entropy value is used to perform the finest energy efficient routing, a strategy for the best possible data transmission. The proposed approach attains improved performance than the compared existing approaches in terms of delay (6.5 ms), throughput (320.1 kbps), energy (1.92j), and packet delivery ratio (218.7%), the work provided is contrasted to the various current methods. The performance of the proposed approach is compared to existing approaches to prove its effectiveness, and it has been proven to perform better than the existing routing approaches.

AMPLIFY: attention-based mixup for performance improvement and label smoothing in transformer

Mixup is an effective data augmentation method that generates new augmented samples by aggregating linear combinations of different original samples. However, if there are noises or aberrant features in the original samples, mixup may propagate them to the augmented samples, leading to over-sensitivity of the model to these outliers. To solve this problem, this paper proposes a new mixup method called AMPLIFY. This method uses the attention mechanism of Transformer itself to reduce the influence of noises and aberrant values in the original samples on the prediction results, without increasing additional trainable parameters, and the computational cost is very low, thereby avoiding the problem of high resource consumption in common mixup methods such as Sentence Mixup. The experimental results show that, under a smaller computational resource cost, AMPLIFY outperforms other mixup methods in text classification tasks on seven benchmark datasets, providing new ideas and new ways to further improve the performance of pre-trained models based on the attention mechanism, such as BERT, ALBERT, RoBERTa, and GPT. Our code can be obtained at https://github.com/kiwi-lilo/AMPLIFY.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.