Improve Energy Consumption Research Articles

Investigation to the vibration characteristics of ultrasonic transducer based on the number of piezoelectric ceramics

As the vibration actuator source, PZT applied on the ultrasonic transducers (UTs) plays a crucial role in the vibration characteristics of UTs. Most conventional research focus on the overall size and positional relationship of PZT stack, while the influence of PZT numbers on vibration characteristics of UTs is seldom reported. In this article, we present a comprehensive investigation between the PZT numbers and vibration characteristics of UTs with identical geometric configurations, specifically UTs with 2 (UT2), 4 (UT4) and 6 (UT6) PZT. The electromechanical equivalent circuit and finite element analysis (FEM) based on PZT numbers are established to investigate the impedance and resonant frequency. Furthermore, the dynamic displacement model of the UTs is proposed to study the influence of PZT numbers on amplitude, and the calculation results are consistent with harmonic response analysis. Finally, the experimental platform is established to test the vibration characteristics of the three types UTs. The results show that the resonant frequency of the transducer is not affected by the numbers of PZT, while the impedance and impedance stability can be improved by the increased PZT numbers. Moreover, the amplitude of UTs is negatively correlated with the numbers of PZT. Through experiments, it is verified that UT2 is suitable for the conditions as the load less than 1000g and amplitude less than 2.2µm, and UT4 is applicable to the other conditions. Although the UT6 exhibits excellent impedance stability, its output power is relatively high and is not suitable for the structure used in this article. The findings suggest that the number of PZTs should be designed based on the operational conditions to improve amplitude output and minimize the loss of power. The presented methods can effectively improve energy consumption and working life, making the UTs greener and more efficient.

Mining interpretable fuzzy If-Then linguistic rules from energy and economic data to forecast CO[formula omitted] emissions of regions in China

Forecasting CO2 emission is the one of most important issues for the “30⋅60” CO2 emission target in China. Due to unbalanced socio-economic developments of regions in China, exactly forecasting CO2 emissions of provinces depend on their energy consumptions and economic developments. In the paper, a novel method based on K-means clustering method and computing with words is proposed to forecast CO2 emissions of 30 provinces, which is consisted by (1) K-means clustering method is used to respectively cluster energy consumption and economic datasets of provinces and the interpretable fuzzy If-Then linguistic rules of CO2 emissions are mined from the clusters; (2) computing with words method is utilized to transform fuzzy If-Then linguistic rules into fuzzy If-Then rules with membership functions on the universe of discourse; (3) a fuzzy inference method is adopted to forecast CO2 emissions of 30 provinces. To show the usefulness and effectiveness of the novel method, energy consumptions and economic datasets of 30 provinces from 1997 to 2021 are employed to forecast CO2 emissions, metrics of MAE, MAPE, RMSE and the out-of-sample Roos2 are utilized to evaluate CO2 emission forecasting of 30 provinces, means of them reach 13.304, 15.279, 0.081 and 0.965. By comparative analysis for forecasting CO2 emissions of 30 provinces, means of MAE, MAPE, RMSE and the out-of-sample Roos2 by the novel method are more than SVM, ANFIS and MLR methods. In addition, four kinds of mechanisms influencing CO2 are discovered and analyzed by the fuzzy If-Then linguistic rules of 30 provinces, which can help to improve energy consumption and sustainable development of 30 provinces in China.

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

Improving Energy Consumption for Respond to Requests in Internet of Things by Various Optimization Algorithm, Based on the Fog Computing

Improving Energy Consumption for Respond to Requests in Internet of Things by Various Optimization Algorithm, Based on the Fog Computing

Analysis of Influential Parameters in the Dynamic Loading and Stability of the Swing Drive in Hydraulic Excavators

The proper design and configuration of the swing drive mechanism of a hydraulic excavator are crucial to improve energy consumption and efficiency and ensure operational stability. This paper analyzes the influence of the relationship between the parameters of a hydraulic motor and a reducer, which form the integrated transmission of a swing drive, the dynamic characteristics of a hydraulic excavator on loading, and the dynamic stability of the drive. The analysis deals with an excavator model that has the same parameters of the kinematic chain members, the same parameters of the upper structure drive mechanisms, and two variants of the swing drive that, with different integrated transmission parameters, provide the upper structure with the identical number of revolutions and equal rotating moment. One swing drive variant possesses an integrated transmission with a hydraulic motor with a low specific flow and a reducer with a high transmission ratio, while the other drive variant has the opposite parameters. Understanding this relationship is essential for optimizing the design of excavators to achieve better performance and dynamic stability under varying operational conditions. As an example, this paper provides the analysis results regarding the influence of the relationship between the parameters of the integrated transmission hydraulic motor and reducer on the loading and dynamic stability of the swing drive in a tracked hydraulic excavator of 100,000 kg in mass and 4.4 m3 in loading bucket volume, as obtained from the developed dynamic mathematical models of the excavator using the MSC ADAMS program. The results indicate that the dynamic loads on the swing drive’s axial bearing are higher in the variant with a low-specific-flow motor and high transmission ratio reducer during the acceleration and deceleration phases. However, this configuration demonstrated better dynamic stability, with lower oscillation amplitudes and shorter damping times compared to the variant with a high-flow motor and low transmission ratio. Those findings provide valuable criteria for the optimal synthesis of swing drive mechanisms in large hydraulic excavators using multi-criteria optimization methods.

Sustainable Energy Solutions in Sub-Saharan Africa: Integrating Indigenous Knowledge and Climate Resilience for Lower Carbon Emissions

Promoting sustainable energy solutions in sub-Saharan countries is crucial for addressing energy poverty, reducing carbon emissions, and fostering long-term environmental and economic sustainability. This study explored using indigenous knowledge and emerging technologies to reduce carbon footprints and GHG emissions in Africa's climate hotspots in sub-Saharan countries. The study revealed that operating institutions utilize various applications to ensure that energy management resources can mitigate the effects of carbon emissions. The study revealed that the most efficient use of natural resources for energy production requires collaboration among governments, private sectors, NGOs, and local communities. By adopting a holistic and inclusive approach, one can work toward a more sustainable and low-carbon energy future. This paper focuses on carbon footprint analysis and proposes solutions to address environmental issues in implementing sustainable energy solutions in sub-Saharan countries. A multifaceted approach involving effective strategies is needed to lower the carbon footprint. The contribution of this study is to improve energy consumption in communities in Africa by integrating climate resilience considerations into sustainable energy projects to ensure long-term viability. This will involve planning for changing climate conditions, such as extreme weather events, and designing infrastructure that can withstand and adapt to these challenges. It has been concluded that carbon footprint analysis is useful for determining the impacts of carbon particles in the world’s atmosphere. The role of energy management operations seeks to improve the assessment and analysis of carbon footprints by allowing atmospheric measurements of carbon.

An Optimization Approach for Enhancing Energy Efficiency, Reducing CO2 Emission, and Improving Lubrication Reliability in Roller Bearings using ABC Algorithm

Friction and energy waste pose significant challenges in various industrial processes. Lubrication plays a crucial role in reducing friction and optimizing energy consumption. This study focuses on analyzing, simulating and calculation the oil film thickness, friction levels, energy losses, and CO2 emissions. The objective is to optimize lubrication conditions to enhance performance, improve energy consumption, and maximize lubrication efficiency for rolling bearings in a centrifugal fan. The simulation utilizes ANSYS CFX software, MATLAB programming. The optimal oil viscosity grade is determined based on two objectives by using artificial bee colony algorithm (ABC): minimizing energy consumption (thus reducing CO2 emission) and achieving the optimal oil film thickness and viscosity ratio. The findings reveal that, under the current lubrication conditions and normal fan operation, energy losses due to oil friction amount to 36.3 MWh per year, with CO2 emissions resulting from power losses reaching 18,750 kilograms per year. By transitioning to the optimized oil grade, energy savings of 1.08 MWh per year and a corresponding reduction of 557 kilograms in CO2 emissions per year can be achieved.

Biomimetic design synthesis and digital optimization of building shading skin: A novel conceptual framework for enhanced energy efficiency

The world faces critical challenges such as climate change, an increase in energy consumption, and increased CO2 emissions. The building sector plays a significant role in exacerbating these issues. Designing sustainable buildings has grown increasingly complex, necessitating the optimization of building shading skins to enhance overall performance. Integrating inspiration from nature into design can significantly improve sustainability. Nevertheless, current frameworks for developing biomimetic adaptive building shading skins encounter limitations in efficiently translating design concepts from nature into practical applications. This study proposes a biomimetic shading skin design inspired by the Saguaro Cactus, reviewing existing literature, introducing a novel simulation framework, and exemplifying its implementation through a parametric case study. A comprehensive evaluation system assesses 1,384 simulations, utilizing Octopus and Grasshopper plug-ins for Rhino to improve energy consumption in an office building. Evaluation criteria include Energy Use Intensity (EUI), Thermal Loads, and Spatial Daylight Autonomy (sDA). The results show that the multi-angle biomimetic shading skin reduced energy consumption by 20 %, with a 36.5 % reduction in cooling load relative to the baseline model. The daylighting assessment demonstrates that up to 97 % of interior spaces receive adequate daylighting. The results underscore the superiority of biomimetic shading skin in enhancing both cooling load and energy consumption, as well as daylight performance and visual comfort. The result shows that the framework’s flexibility offers an extensive range of opportunities, appealing to designers and researchers who are motivated to harness its capabilities to enhance diverse design factors.

Priority rules for handling containers to improve energy consumption and terminal efficiency

AbstractThis paper addresses the optimization of the yard crane handling processes in a container terminal to reduce energy consumption and improve overall system performance. More precisely, the paper presents and evaluates different sequencing rules, based on predefined priorities, to organize the rail yard to minimize moves during the rail loading operations. The minimization of overall energy consumption and maximum tardiness are considered, simultaneously assessing these two components of the objective function to better understand how they interact and how they can be optimized together. As a novel issue in optimization, a hill climbing algorithm is implemented, searching for the yard configuration that most improves the efficiency of container handling while being able to integrate different management rules of the terminal. The reference case study is the PSA Pra terminal in Genoa, Italy. A full rail yard with known delivery times, and crane operating along a single stack, is the operative scenario. Random due time sequences are generated during test instances, while technical data of crane are used. Moreover, crane movements involve both loading and unloading along multiple axes. From the results, the best priority rules improve energy consumption and lateness of the initial configuration of the yard by up to 55%, thus allowing the terminal management to reorganize the storage areas accordingly and improve their efficiency. The proposed priority rules bridge the gap between theoretical optimization procedures and container terminal practices.

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

Route and Speed Co-optimization for Improving Energy Consumption of Connected Vehicles

Route and Speed Co-optimization for Improving Energy Consumption of Connected Vehicles

“Carbon” suppresses “energy” - How does carbon emission right trading policy alleviate the energy trilemma?

Against the backdrop of addressing the dual challenges of tightening energy constraints and carbon emission reduction, this paper, building upon the construction of an urban-level Energy Trilemma Index, employs a Difference-in-Differences (DID) model to assess the impact of carbon emission right trading policy (CERTP) on the energy trilemma (ET). Key findings include: Firstly, compared to non-pilot areas, the ET in CERTP pilot areas decreased by 1.86 %; the policy's impact persists and is even greater in the long term, reaching 2.84 %. Secondly, CERTP stimulates green technological innovation, improves energy consumption structures, and alleviates ET levels. Furthermore, the study shows that CERTP, as a market-driven energy-environmental strategy, is further enhanced by the regulatory effects of marketization and government intervention. Lastly, the policy's impact is more pronounced in eastern China, resource-based cities, regions with stringent environmental regulations, and areas with high carbon prices. Based on these findings, the paper proposes a series of policy insights, not only aiding in understanding the causal relationship between CERTP and ET but also providing valuable references for designing carbon trading mechanisms to address ET. Importantly, the conclusions offer beneficial guidance for China's energy structure optimization, ensuring energy security and sustainable development goals.

A local filtering-based energy-aware routing scheme in flying ad hoc networks

Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

Secure and efficient device‐to‐device communication in IoT: The DMBSOA‐enhanced MQTT protocol

AbstractThe Internet of Things (IoT) plays a crucial role in enhancing technology by facilitating data transfer, storage, control, and management across networks. Secure communication within IoT environments remains a significant challenge. This research aims to enhance IoT security by integrating Message Queuing Telemetry Transport (MQTT) and MQTT‐Sensor Network (MQTT‐SN) protocols with the Dual Mutation‐Based Seagull Optimization Algorithm (DMBSOA). MQTT, known for its lightweight and dependable messaging, is widely used in the IoT community but is vulnerable to cyber‐attacks, particularly concerning privacy and authentication. DMBSOA, inspired by seagulls' foraging behavior, optimizes MQTT settings to enhance security, reliability, and performance. The proposed model dynamically adjusts key parameters such as transmission frequency, Quality of service (QoS) levels, and message size to improve energy consumption, throughput, end‐to‐end delay, and packet delivery ratio. A comprehensive system model is presented, comprising publisher, subscriber, and broker nodes, with security mechanisms integrated into the broker to ensure data integrity, authentication, and encryption. MQTT operates over Transmission Control Protocol/Internet Protocol (TCP/IP), while MQTT‐SN uses User Datagram Protocol (UDP), catering to resource‐constrained devices and low‐power modes. The proposed technique attained throughput (68 kB), energy consumption (120 mJ), security level (96%), energy efficiency (98.80%), path loss (22 dB), end‐to‐end delay (35 ms), processing time (230 s) and packet delivery ratio (0.98). The DMBSOA‐optimized MQTT protocol demonstrates superior performance compared to these models, highlighting its potential to meet the evolving demands of IoT security. This research underscores the effectiveness of DMBSOA in enhancing MQTT protocol security and efficiency, providing a promising solution for secure IoT communication.

The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

In this paper, the task processing approach at the edge of the Internet of Things (IoT) network in improving energy consumption was investigated. First, the subject of maximizing the use of energy is defined as the main problem and then based on the state of assignment of task processing from end devices at the edge of the network to smart gateways and local servers at the edge using possible low bandwidth and low battery consumption, the problem is divided into two subject which are obtained independently. After modeling by recombining these two sub-problems, the subject of maximizing the utility gain and battery life of the end devices is solved despite the limitations of processing time and energy resources.Considering that the environment of the problem and how the terminal devices are connected with the edge devices are unknown, a repetitive reinforcement learning algorithm has been used to create an optimal solution to maximize the operational benefit of the energy consumed in edge processing. The results achieved from the simulation show the increase in network load and processing overhead with the increase in the number of active devices. The suggested method, while increasing the edge processing speed, decreases the delay and maximizes the performance gain of the IOT edge system compared to the central cloud system. It should be noted that when the number of active end devices dramatically increases, the energy consumption and bandwidth at the network edge improves, and energy consumption at the network edge decreases lonely by 12.5%.

Bond performance and constitutive model of steel bar in G-UHPC under cyclic loading

The increasing embrace of the low-carbon and environmentally sustainable construction approach has opened exciting opportunities for using environmentally friendly geopolymer based ultra-high-performance concrete (G-UHPC) in engineering structures, drawing significant attention to its performance. Seismic performance is a crucial factor in structural design, and it is greatly affected by the bond behavior between steel bar and concrete. As a result, a thorough understanding of the bond behavior of steel bars to G-UHPC provides essentially theoretical support for the structural design and nonlinear finite element analysis of G-UHPC structures, which is vital for their widespread implementation in earthquake engineering. This study tested 54 specimens from 18 groups to examine the bond performance of G-UHPC and steel bars under cyclic loading. The influence of concrete and steel bar types, protective layer thickness, bond length, and steel fiber on critical bond performance indicators were evaluated, and the bond stress-slip hysteretic and skeleton curves were analyzed. Subsequently, based on the experimental data, a constitutive model was proposed to predict the bond stress-slip relationship of steel bars and G-UHPC under cyclic loading. The results indicated that the steel bars exhibited superior bond performance with G-UHPC as compared to NSC. The bond and residual strength between steel rebars and G-UHPC was approximately 1.73 times and 3 times greater, respectively, in comparison with that between NSC and steel rebars. Increasing the strength of steel bars only resulted in a limited 2 % improvement in bond strength. The steel bar diameter, protective layer thickness, bond length, and the dosage and length of steel fibers in G-UHPC significantly influenced the bond performance. The addition of irregular-shaped steel fibers to G-UHPC has demonstrated potential in improving energy consumption, with the most remarkable enhancement observed for the inclusion of twisted steel fibers. Specifically, the incorporation of twisted steel fibers signally enhanced the energy consumption to approximately 1.3 times the energy dissipation as compared to straight steel fibers with the same dosage and aspect ratio. In addition, the accuracy of the proposed constitutive model has been validated.

Prediction of pollutant emission characteristics in ISO50001 energy management in the Americas: Uni and multivariate machine learning approach

The American continent is experiencing significant economic and industrial development driven by sustainability principles. In this context, discussions on improving energy consumption have become increasingly frequent and dynamic across various sectors of civil society, including the implementation of energy efficiency measures as advocated by the ISO50001 energy management standard. However, there is a pressing need to investigate which socioeconomic aspects are responsible for the issuance of this certification in the Americas and how these factors relate to characteristic industrial emissions, especially particulate matter. This study aims to evaluate the socioeconomic factors influencing ISO50001 standard issuance and how these adjusted factors correlate with particulate matter of 2.5 μm and 10 μm dimensions. To achieve this, machine learning techniques were employed, considering the complex nature and risk of data overfitting. Model fitting was performed through multiple lasso regression, and the relationship between the adjusted factors was examined through cross-correlation analysis. The analyses indicate a strong correlation of adjusted macroeconomic indicators, especially with PM2.5, suggesting an association with cardiorespiratory problems and methane-related origins. This work is of great relevance to academia as it proposes new concepts regarding the interaction between energy efficiency standards and particulate matter. For the industrial sector, the adjusted factors provide guidance for standard implementation while also helping to mitigate health issues. Additionally, for the government, these results can assist in formulating policies to address specific health problems related to this area.

Improving energy consumption for dynamic intake and exhaust FAÇADES: A new setup design: Part B

Dynamic insulation technology adjusts thermal resistance to reduce building energy consumption in response to climate changes. Sustainable building practices prioritize optimizing the building envelope to enhance energy efficiency and regulate the interaction between indoor and outdoor environments. The previous part of this study (Part A) provided an overview of dynamic insulation technology and its potential effectiveness in Iraqi structures with intermittent heating and cooling. The successful implementation of dynamic thermal insulation necessitates careful planning and design solutions that balance performance and efficiency. This paper (Part B) three wall models (static, dynamic, modified dynamic) to evaluate their thermal performance during the heating season. The results demonstrate that the intake facade achieves a preheating efficiency of 77% for the dynamic wall and 81.8% for the modified dynamic wall at the maximum permissible fresh air limit. This improvement in efficiency enhances the quality and conditions of fresh air intake. Additionally, the modified dynamic wall exhibits a notable 50% reduction in pressure loss compared to the dynamic wall, resulting in lower intake or exhaust fan power and thus energy consumption. The results of this paper will guide the presentation of cooling season results and the experimental aspect of future work, offering valuable insights for further exploration. Practical applications This study aims to enhance the efficiency of dynamic facades by investigating the physical phenomena within wall layers. The paper delves into the determination of optimal airflow patterns within facades, the assessment of airflow path modifications, and the evaluation of energy efficiency achieved by implementing dynamic insulation at the intake and exhaust facades during the heating season. The findings contribute to streamlining design complexities and expanding design options for specific climates, thereby ensuring the pragmatic implementation of this technology.

Practical Sustainable Software Development in Architectural Flexibility for Energy Efficiency Using the Extended Agile Framework

Many regular business operations are transforming into digital services, increasing advanced multi-platforms, rapid operational alignment, flexibility, and environmental impact through energy consumption, hardware waste, and technology investments. Flexible and sustainable system development models emphasizing energy efficiency can help innovate software development as digital servicing applications shift. This research is motivated by the need to improve energy consumption in early software design and development due to rising technological efficiency and sustainability demands. Although effective in iterative development and stakeholder engagement, traditional Agile methodologies often struggle with long-term sustainability and energy efficiency. Extended Agile, combining Agile, layered architecture, and aspect-oriented frameworks (ALAI), promises to improve system modularity, flexibility, maintainability, and sustainability. This study’s findings are not just theoretical, but also practically relevant, as they explore the energy efficiency of ALAI software development methodologies, using graduate admission information system services (GAISS) as an example. GAISS is a complex system that handles the entire process of graduate admissions, from application submission to final decision. The study quantifies the energy usage of a student-list webpage by analyzing Microsoft IIS server logs from February 2022 to May 2024. Directly applicable findings show that the GAISS based on the ALAI framework reduces energy consumption by 10.7914% compared to traditional Agile software developments. ALAI used 892.80 kWh versus Agile’s 1000.80 kWh during operations, saving energy. These findings demonstrate the benefits of integrating aspect-oriented frameworks and layering approaches into Agile methodologies, contributing to sustainable software development discourse. The study emphasizes the importance of energy-efficient frameworks such as ALAI to reduce software systems’ environmental impact and promote software development sustainability. The findings of this study, with their practical relevance, assist software developers and organizations in choosing software design and development methods that maximize operational efficiency and environmental sustainability.

Improving energy consumption with ADAS in autonomous driving communication system

The article analyses the trends in the development of energy saving and recovery technologies in electric motor vehicles. Promising directions for the development of energy saving technologies, in particular with the use of driver assistance systems, are considered and identified. Increasing the occupancy of cars with electronic driver assistance systems increases the overall energy consumption. Recommendations for choosing the optimal technologies and methods of energy saving for motor vehicles are given. For some methods, saving energy is a complement to the main area of work. The combination of intelligent vehicles and appropriate traffic management tools can help to further realise the transport benefits of intelligent vehicles.